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data/part_2/0027704783.json

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+{"metadata":{"gardian_id":"28c376631a4c0df79993be95aaaec266","source":"gardian_index","url":"https://dataverse.harvard.edu/api/access/datafile/:persistentId/?persistentId=doi:10.7910/DVN/HJRIYU/ME3EBE","description":"This Social Accounting Matrix (SAM) was built for the year 2007–2008 for Pakistan. The proposed approach to estimating SAMs is motivated by an information theoretic approach to estimation (Judge & Mittelhammer, 2012) that takes a Bayesian perspective on the efficient use of information: “Use all the information you have, but do not assume any information you do not have.\"  The methodology used to develop this SAM ensures that it is perfectly consistent with the National Accounts. The SAM includes 51 sectors of activity, 27 factors of production, and 18 household groups, allowing tracing direct and indirect effects of potential scenarios through production and consumption linkages and capture distributional effects. The use of this SAM is illustrated using a semi input-output multiplier model. Output multipliers in Pakistan, accounting for supply constraints, range between 1.1 and 1.4, and shocks to livestock and industry have the largest spillover effects.","id":"843832596"},"keywords":[],"sieverID":"03d35c16-a1b0-443b-badf-5a9d0d5ab460","pagecount":"30","content":"Ltd. (IDS), and other development partners to provide information relevant for the design and implementation of Pakistan's agricultural and rural development strategies. For more info, please visit pssp.ifpri.infoA Social Accounting Matrix (SAM) is a single-entry internally consistent accounting system that documents all the economic transactions within an economy. It supports the continuing need to use recent and consistent multisectoral economic data for policy analysis and the development of economy-wide models (Robinson, Cattaneo, & El-Said, 2001). It is an extended set of national accounts that disaggregates value-added in each production activity into payments to various factors such as land, labor, and capital, and disaggregates household incomes and expenditures according to various household types. Mathematically, a SAM is a square matrix in which each account is represented by a row and a column. Each cell shows the payment from the account of its column to the account of its row. Thus, the incomes of an account appear along its row and its expenditures along its column. The underlying principle of double-entry accounting requires that, for each account in the SAM, total revenue (row total) equals total expenditure (column total). A limited number of Social Accounting Matrixes have been constructed for Pakistan in the past (Table 1.1). The first countrywide social accounting matrix (SAM) for Pakistan dates to 1979 and was built by the Pakistan Institute of Development Economics (PIDE) in 1985. This was followed by a SAM for the year 1984-85, created by the Federal Bureau of Statistics (FBS), with collaboration with the Dutch government under the Improvement of National Accounting System (INAS) project. Since this SAM had a single household group, it was not suited for analyzing distributional effects across households. Siddiqui and Iqbal (1999) generated a new SAM for 1989-90 and disaggregated data with eight household groups. It aggregated the Input-Output (IO) table industry classifications into five production accounts, namely agriculture, industry, health, education and other sectors. In 2004, Dorosh, Niazi, and Nazli (2004) produced a SAM of Pakistan for the year 2001-02. It contained 19 household groups and 34 production accounts. Since the households were disaggregated by province and the number of commodities were larger than Siddiqui and Iqbal (1999), it was more suitable for analyzing effects of shocks in specific industries on different socioeconomic groups. Later, Waheed and Ezaki (2008) created a financial SAM for the year 1999-2000. While the previous SAMs were mainly built on the real economy, growing importance of capital flows and availability of associated data allowed the authors to disaggregate the workings of the loanable funds market into disaggregated payments related to physical and financial flows among institutions. 1Production activities were aggregated into six accounts: i) agriculture, ii) mining and quarrying, iii) manufacturing, iv) electricity, water, and gas, v) construction, and vi) other sectors.We build a SAM for Pakistan for financial year 2007-08 that relies on contemporaneous National Accounts and household data, as well as information present in the SAM built by Dorosh, Niazi, and Nazli (2004) 2 . This work is part of the Pakistan Strategy Support Program, which supports the Government of Pakistan with evidence-based policy reform for pro-poor economic growth and enhanced food security. The SAM has started and is expected to be used with Computable General Equilibrium (CGE) modeling to analyze the macro and distributional impact of policy changes throughout the economy. 3   Compared to the disaggregation in Dorosh, Niazi, and Nazli (2004), the textile industry has been further disaggregated into knitwear, garments, and other textiles. The cotton lint-yarn activity has been disaggregated between ginning, spinning, and weaving. Chemicals account has also been disaggregated as fertilizers and other chemicals. As in most developing countries, Pakistan's services sector has been growing in importance so its disaggregation is crucial for policy relevant analysis. Reflecting this, trade has been divided between wholesale, retail, and other trade, while the transport sector now has separate accounts for road, rail, air, water, and other transport. Housing has been divided between rented and owned, while private sector service is disaggregated into education, health care, business services, personal services, and other private services.We have explicitly included four types of economic agents in our SAM, namely producers, households, government, and the rest of the world. Households are disaggregated according to province and agricultural households are further divided by farm ownership and size. 4Non-agriculture households are divided by whether they are urban or rural and by expenditure quintiles (1, 2, and others). Out of the 18 household groups, 12 represent agricultural households. This enables an in depth analysis of the agricultural sector and its linkages with other industries.The paper is organized in the following way. Section 2 explains how the SAM was generated. Section 3 briefly analyzes the structure of the Pakistan economy in light of the SAM. Section 4 demonstrates the use of the SAM with income multiplier analysis and Section 5 concludes. Our proposed approach to estimating SAMs is motivated by an information theoretic approach to estimation (Judge & Mittelhammer, 2012) that takes a Bayesian perspective on the efficient use of information: \"Use all the information you have, but do not assume any information you do not have.\" Previous work on SAM estimation using this approach includes: Judge and Mittelhammer (2012), Golan, Judge et al. (1994), Robinson, Cattaneo et al. (2001), Golan, Judge et al. (1996), Debowicz (2010), andZellner (2004).To generate the SAM following this approach, we followed a series of major steps that are explained in Figure 2.1. The steps, which are explained in detail below, start from a schematic SAM (Figure 2.2) and, using a variety of data sources, and balancing the accounts of the SAM with the use of a 'cross-entropy' technique, lead to a macro-consistent and disaggregated SAM.  The next step was to split domestic value added into 51 sectors of activity in the SAM (listed in Figure 2.5, together with the rest of the SAM accounts). For this purpose, we started from value added by major sector, as in 2007-2008 Gross National Product at current factor cost in National Accounts, which sums to the value added in the Macro-SAM. To split the major sectors present in this classification into those present in the SAM, as illustrated in Figure 6, we conducted the following steps: a. Major and minor crops were disaggregated into wheat, rice, cotton, sugarcane, fruits and vegetables, and other field crops using the 2007-08 data from Agricultural Statistics of Pakistan ( 2009), \"Gross value addition of Major Crops at Current Factor Cost\" and \"Gross value addition of Minor Crops at Current Factor Cost\".b. Rice and wheat were further split using production of rice varieties and irrigated and non-irrigated wheat from Agriculture Statistics of Pakistan.c. Livestock was split into poultry and other livestock using the value of their output as informed also by the Agriculture Statistics of Pakistan. f. Transport was split into road, rail, water, air, and other such as transport by tubes, using \"National Accounts of Pakistan: Rebasing from 1980-81 to 1999-2000\", Federal Bureau of Statistics (2004).g. Housing was split into owned and non-owned using the \"Survey on Community and Personal Services,\" Federal Bureau of Statistics (2001).h. \"Social, community, and personal\" services was split into business, education, health, personal, and other services using the same source.Once the value added of each sector in the SAM was estimated, we split the values into payments to land, capital, and labor. For the crops in the SAM, this was done using the factor shares at activity level in the cost of production of Pakistan (2003) informed by the Agriculture Policy Institute (formerly known as Agricultural Prices Commission). Wheat factor shares were updated using cost of production data for 2008-09 from the Agriculture Policy Institute (2009). For the remaining activities, the shares of labor and capital were informed by Dorosh, Niazi, and Nazli (2004), IO91, and the map from sectors in IO to those in SAM (Appendix 2).Then  Same as activities except Wheat irrigated and Wheat non-irrigated activities aggregated as one commodity (Wheat).Labor ( 10  After a series of adjustments that reduced the imbalances at the commodity level to be less than 30% of the average between supply and use, we arrived at a new proto-SAM (PSAM 1B). We then generated a consistent proto-SAM based on it, seeking to minimize the crossentropy distance between the proto-SAM and the SAM imposing the series of controls present in the Macro-SAM (PSAM 1C). When balancing the SAM at this stage, and following the approach described in Golan, Judge, and Robinson (1994), we treated every cell in the SAM as being specified with an error support set whose weights are estimated to minimize a cross-entropy distance between the prior and the solution SAM. This treatment is strongly related to the one described in Robinson, Cattaneo, and El-Said (2001), with key differences. In the previous approach, the column coefficients in the SAM were treated analogously to probabilities and included directly in the cross-entropy minimand, generating the need for special treatment of negative cells and accounts with zero sums in the SAM. In the approach we apply, developed by Sherman Robinson and Scott McDonald -starting in turn from Robinson, Cattaneo, and El-Said ( 2001) -, the cross-entropy minimand only includes probability weights for a selected error support set, such that the SAM coefficients are no longer treated as analogous to probabilities and negative entries and accounts with zero sums do not require any special treatment.8 The present approach allows specification of a prior estimate of the mean and standard error of selected cell entries (expressed either as values or column coefficients), column sums, and macro aggregates. These errors can be specified as additive or multiplicative-exponential. For the aggregates present in the Pakistan Macro-SAM, we set a zero standard error. This allowed us to arrive at a SAM that is perfectly consistent with the Macro-SAM, such that the sum of the value added in the solution SAM was exactly equal to the GDP at factor cost in the Macro-SAM; the private final consumption in the solution SAM summed exactly the private final consumption in the Macro-SAM, etc.To fully disaggregate the single household group and the three factors (labor, capital, and land) present in PSAM 1C into the complete set of 27 factors and 18 households in the SAM, we conducted the following steps. The value added of the specific activities was split among the 27 factors using the shares present in the 2000-2001 SAM for Pakistan, in turn informed by the PRHS (Pakistan Rural Household Survey 2001). Then, regarding payments from factors to institutions, after assigning the payments from factor income to government and non-residents as informed by the Macro-SAM to formal capital, the household income matrix was generated in the following way (Figure 7).Labor, agricultural capital, and non-agricultural formal capital were split following the Household Income and Expenditure Survey (HIES) 2007-08 incomes. Livestock was split following the value of the livestock capital stock owned by households in HIES 2007-08. Land and water income was split following the 2000-01 SAM, which in turn is based on the PRHS. For land, all returns to land of large farms (defined in terms of cultivated area) are paid to large landowners in proportion of farm area of respective regions. For medium and small farms in each region, returns to land are allocated to the four types of farmers (large, medium, small, and landless) according to the shares of each group in total land revenues of small and medium farms, derived from data from the PRHS 2001-02. Specifically, returns of small and medium-sized farms for each region (Punjab, Sindh, and Other Pakistan) were allocated to households according to the following formula: LandShareh = (CultAreah -rr * Land_Inh + rr * Land_Outh) / Total Cultivated Area, where LandShareh is the share of household h in total land revenues, CultAreah is cultivated area of household h, rr is the rental cost of land (assumed to be 50%), Land_Inh is net land rented in of household h, and Land_Outh is net land rented out of household h.Returns to informal non-agricultural capital (which includes returns to self-employed labor in informal sector activities) are split between rural and urban households using as proxy the share of rural population in total population as informed by HIES (67%). The split across rural households is made using shares of each household in reported incomes from nonfarm enterprises, calculated using per capita earnings from the PRHS 2001-02 and household population totals from HIES. The remaining 33 % of non-agricultural wage incomes are allocated between urban non-poor and poor households using an 85:15 ratio.9 Returns to agricultural capital are split among households in proportion to their land income.In the absence of detailed information, public transfers and remittances to households informed in the Macro-SAM were allocated among households in proportion to their total expenditures.Finally, regarding the uses of funds by households, final private consumption of each commodity was split among the 18 households using HIES 2007-08 to provide a prior. A relatively high (15%) saving rate was used as a prior for medium/large farms and non-farm (quintiles 3 to 5) and a relatively low (7%) saving rate was used as a prior for the remaining households except urban other (quintiles 3 to 5). Then, the prior saving rate of the urban other (quintiles 3 to 5, which also captures enterprise savings) was determined residually from the domestic private saving figure in the Macro-SAM, generating a rate for this household group of 37.5%.After a series of adjustments that reduced the imbalances at the household level to be less than 30% of the average between income and expenditure, we re-ran the software to generate a new SAM that minimizes the cross-entropy distance between the proto-SAM and the SAM imposing the series of controls present in the Macro-SAM, allowing the generation of a balanced SAM that is perfectly consistent with the Macro-SAM. The structure of value added (Table 3.1) is characteristic of a semi-industrialized economy, with a relatively low share of agriculture (20%), and large shares of industry and services (27% and 53%, respectively). Livestock accounts for more than half of value added of the agricultural sector. Much of the industrial production is strongly linked to agriculture, including wheat, rice and sugar milling and textile production (linked to cotton).10 Trade (wholesale and retail) and transport generate more than half of the value added in services.Exports are a relatively low share of total output (6.5%); imports are concentrated in the industrial sector (including petroleum products, part of the mining sector) and in private services (particularly, business services).   Large and medium farmers of Pakistan earn a large share of their income from land (Table 3.3). However, small and landless farmers rely on labor, livestock, and other capital for most of their income. Rural non-farm and urban households mostly rely on their labor and other capital as the sources of income. The importance of agricultural income by household group is generally lower in the recent SAM than in the Permanent Rural Household Survey (PRHS) of 2001-02, suggesting that households have more diversified income sources than as suggested by PRHS data (Table 3.3). The SAM shows that agricultural income accounts for a large share of income for all farmers, especially for the medium and large farms (66 % of their total income), consistent with the PRHS data. To illustrate the use of the SAM, we use income multiplier analysis. A survey of income multiplier analysis methods and findings can be found in Haggblade, Hazell, and Reardon (2007). To capture the production and consumption linkages, taking into account the supplyrigidities present in Pakistan, we use a semi-input-output model, with constrained linear relationships among quantities in the model and fixed prices. In this approach, sectors are classified into two groups: those that are supply constrained and those that are supplyresponsive. Output responses are permitted only in supply-responsive sectors. For these models to produce a suitable approximation of reality, the supply-constrained sectors must correspond to tradable goods whose domestic supply remains fixed at the prevailing output price. Therefore, we follow this approach. In the supply-constrained sectors, imbalances between supply and demand are equilibrated via changes in net exports.The starting point is the sector-specific equilibrium conditions, i.e.(1 + ) = + + + + , where is pre-commodity-tax gross output, is commodity tax rate, is intermediate demand of good by sector , is household consumption of good by household , is public consumption of good , is investment (fixed and change in stock) demand for good , and is net export of good . Intermediate and factor demand are assumed to be proportional to output production, i.e. = and = , where and are the requirements of intermediate input and factor to produce a unit of . Household consumption is given by = (1 ) , where is pre-tax income of household , is the corresponding tax rate, and is the share of post-tax income of household spent on commodity . Finally, pre-tax household income is the sum of factor income and transfers received by the household from other agents, i.e. = + , with = , and being the share of household in the income of factor .Replacing the intermediate and factor demand and household demand function into the equilibrium condition, we find thatwhich can be solved either for (demand-constrained sector) or for (supply-constrained sector), fixing either (demand-constrained sector) or (supply-constrained sector).We conduct a series of simulations where a constant injection is applied to the economy (100 billion rupees during the year), either to supply (supply-constrained sector) or to net export demand (remaining sectors). We run a simulation focusing the injection only in crops (SIMC), where each crop receives a proportion of total injection given by its share in the total value added of crops. We then do the same for livestock (SIML), for industry (SIMI), for services (SIMS), and for all sectors (SIMA). Finally, we divided the absolute changes in output values by the injection, getting to the following output multipliers (Table 4.1). All aggregate output multipliers are in the 1.1-1.4 range, with livestock and industry having the highest output multipliers. These multipliers are significantly below the ones found for India by Pal, Pohit, and Roy (2012), probably reflecting that the mentioned analysis assumes the absence of supply rigidities, which we seek to capture here, but aligned with the 1.5 value added-multiplier reported in Dorosh, Niazi, and Nazli (2003), Haggblade, Hammer, andHazell (1991), andMellor (1995). As expected, the output multipliers are largest in the sector in which the injection takes place (main diagonal of the table). We also see that the direct effects are larger than the indirect, and that most of the indirect effects are concentrated into the services sectors.12 Finally, the injection into the services sector has the lowest output multiplier for the entire economy. Source: Authors' elaboration.This paper presented the Social Accounting Matrix (SAM) of Pakistan for the year 2007-08, which seeks to support the continuing need to use recent and consistent multi-sectoral economic data for policy analysis and the development of economy-wide models (Robinson et al., 2001). In particular, it is expected to become a vital part of the Pakistan Strategy Support Program (PSSP) run by the International Food Policy Research Institute (IFPRI), which supports the Government of Pakistan with evidence-based policy reform for pro-poor economic growth and enhanced food security. The presented approach to estimating this SAM is motivated by an information theoretic approach to estimation (Judge & Mittelhammer, 2012) that takes a Bayesian perspective on the efficient use of information: \"Use all the information you have, but do not assume any information you do not have.\" The presented SAM will be used with Computable General Equilibrium (CGE) models to analyze the impact of policy changes throughout the economy of Pakistan. It combines both inputoutput and national income and product accounts, supplemented by other information from a variety of sources and uses a \"cross-entropy\" approach to balance the accounts. This SAM allows specification of a prior estimate of the mean and standard error of selected cell entries (expressed either as values or column coefficients), column sums, and macro aggregates, providing an updated and consistent database that is fully consistent with macroeconomic-level data and that is highly disaggregated, allowing for detailed macroeconomic and distributional analysis of relevant events.The SAM highlights a series of relevant characteristics of the Pakistan economy. The livestock (10.5% of the economy) and trade sectors (18.4% of the economy) are shown to be significant contributors to the total domestic value added. For agricultural products, land is, unsurprisingly, the biggest component of value added. Manufacturing activities depend heavily on formal capital, while labor and other capital are important for most services. Large and medium farmers of Pakistan earn a large share of their income from land. However, small and landless farmers rely on labor, livestock, and other capital for most of their income. Rural non-farm and urban households mostly rely on their labor and other capital as income sources. To illustrate the use of the SAM, we conduct income multiplier analysis. In particular, to capture the production and consumption linkages, taking into account the supply-rigidities present in Pakistan, we use the semi-input-output model. All aggregate output multipliers turn out to be in the 1.1-1.4 range, with livestock and industry having the highest output multipliers. These multipliers are significantly below the ones found for India by Pal et al. (2012), probably reflecting that the mentioned analysis assumes the absence of supply rigidities, which we seek to capture here. The multipliers are, however, aligned with the 1.5 value added-multiplier reported in Dorosh et al. (2003) and others. Results suggest that the direct effects are larger than the indirect and that most of the indirect effects are concentrated into the services sectors. 13 Finally, the injection into the services sector has the lowest output multiplier for the entire economy.  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data/part_2/0031088324.json

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+{"metadata":{"gardian_id":"71c639c775a48babb392792810bcfedd","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/bf52dc09-656c-4117-a2f4-24481f34e656/retrieve","description":"This country factsheet presents key agricultural R&D indicators in a highly accessible visual display. The publication also features a more in-depth analysis of some of the key challenges that the country’s agricultural R&D system is facing, and the policy options to address these challenges.","id":"-1202772624"},"keywords":[],"sieverID":"6e52eee9-028a-4cd8-84bd-d8bb8cfdaab2","pagecount":"4","content":"Agricultural R&D spending in Eswatini fell significantly between 2009 and 2012 but increased somewhat thereafter, in inflation-adjusted terms.Eswatini invested 0.70 percent of its AgGDP in agricultural research, which is less that the 1 percent minimum level recommended by the African Union and the United Nations. As a small country, unable to take advantage of economies of scale, Eswatini actually requires higher levels of investment than average in order to establish and maintain basic research infrastructure and staffing.Eswatini's national research system is one of the smallest in Africa. It is also unique in that its higher education agency, UNESWA, employs more FTE researchers than its only government research agency, DARSS. UNESWA also employs a comparatively high number of PhD-qualified researchers, whereas DARSS only employed one during 2009-2014.The Eswatini National Agricultural Research Authority Bill, proposing the establishment of an agricultural research authority, has passed both houses of Parliament and is currently awaiting endorsement by the King. The bill calls for improved priority setting and recommends that research platforms be established at both national and regional levels. It is intended that such platforms will facilitate interactions among stakeholders to determine a strategic research agenda.  In 2016, 67 percent of UNESWA-FA's PhD-qualified researchers were in their 50s and 60s. Researchers qualified to the BSc and MSc levels and those employed at DARSS were considerably younger.  ","tokenCount":"221","images":["-1202772624_1_4.png","-1202772624_4_2.png","-1202772624_4_3.png","-1202772624_4_4.png","-1202772624_4_5.png","-1202772624_4_6.png"],"tables":["-1202772624_1_1.json","-1202772624_2_1.json","-1202772624_3_1.json","-1202772624_4_1.json"]}

data/part_2/0054244217.json

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+{"metadata":{"gardian_id":"deac1cb51987541e223e3cb648bef56c","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/c7d5c1e3-bb0a-4c22-8f0d-36e2cf44b7a1/retrieve","description":"People often think of property rights in a narrow sense as ownership - the right to completely and exclusively control a resource. However, property rights are better understood as overlapping “bundles” of rights.","id":"-853786518"},"keywords":[],"sieverID":"4d6d45c9-9f92-4ec4-829c-d378ca3f6d8b","pagecount":"4","content":"People often think of property rights in a narrow sense as ownership -the right to completely and exclusively control a resource. However, property rights are better understood as overlapping \"bundles\" of rights. There are many combinations of such rights, but they can often be grouped as:• Use rights -such as the right to access the resource (for example, to walk across a field), withdraw from a resource (pick wild plants), or exploit a resource for economic benefit (commercial fishing); and• Control or decision-making rights -such as the rights to management (plant a crop), exclusion (prevent others from accessing the field), or alienation (rent out, sell or give away the rights).These rights may also be conditioned by the amount, timing, and other aspects of resource use and management. Several individuals or groups may have different kinds of rights over the same resource. For example, all members of a community may be allowed to bathe in a river or collect drinking water, but only certain farmers may be permitted to draw water to irrigate a field or to decide how to distribute that water in the dry season.At the same time, the state may claim ultimate \"ownership\" of the water, including the right to reassign it to others. Even on land declared as state forest land, individuals from a community may have the right to collect medicinal plants or fallen branches for firewood (use), local groups may have the right to plant trees (management) and guard them (exclusion), but the state may retain the right to approve any felling of trees and to collect revenue from users.To recognize property rights in practice, we need to look beyond state-issued titles to the resource. As illustrated in Figure 1, there are multiple sources of property rights, including:• International treaties and law;• State (or statutory) law;• Religious law and accepted religious practices;• Customary law, which may be formal written custom or living interpretations of custom;• Project (or donor) law, including project or program regulations; and• Organizational law, such as rules made by user groups.The co-existence of these laws does not mean that all laws are equal, or equally powerful. Each is only as strong as the institution that stands behind it. Often, state law is more powerful and used by government officials, for example, to declare and enforce forests as state property.Statutory law is also used by powerful outsiders, such as logging companies with concessions in customary lands, to claim resources in ways that are not locally recognized as legitimate. On the other hand, actions of local communities, such as petitioning, demonstrations, and roadblocks, are ways of claiming locally recognized rights as well as seeking recognition of their rights by the state.In some cases, state law is not as relevant as the village, ethnic community, or user group in determining property rights on the ground. For example, state laws on inheritance are often ignored in favor of religious laws or local custom. Research has shown that state titling programs do not always provide stronger security than customary rights and may even be a source of insecurity for women and households with less information or fewer connections to obtain government land registration.While legal pluralism can create uncertainty because rival claimants can use a large legal repertoire to claim a resource, multiple legal frameworks also provide flexibility for people to maneuver in their use of natural resources.Often, the more variable the resource, the more flexible are the property rights that develop over it. Water rights are particularly fluid, changing by season and year, depending on the availability of the resource and demands for water. Similarly, many customary rangeland management systems negotiate access rights depending on factors like weather and the social relations between the Local/ customary and organizational groups. This flexibility provides a measure of security in times of drought or other disasters, by creating reciprocal expectations of resource sharing between groups.Another source of change in property rights comes from interaction between types of law. These different legal frameworks do not exist in isolation, but influence each other. Changes in state law can influence local custom, and changes in customary practices can also lead to changes in state law.For state law to be effective on the ground, effective implementation is required. Legal literacy programs may be needed to inform the public -and even government officials -about changes in the laws.How exactly these different legal orders influence each other depends on power relationships between the \"bearers\" of different laws. Power relationships also determine the distribution of rights and whether people can effectively claim their rights. Actual rights on natural resources are therefore a product of locality, history, changes in resource condition and use, ecology, and social relationships and are subject to negotiation. Thus, in practice, property rights are not cast in stone or in title deeds, but negotiated.Effective resource management entails balancing benefit entitlements and the responsibilities that come with property rights. After failing to effectively manage natural resource systems centrally, many governments are now undertaking decentralization and devolution programs to transfer responsibility for resource management to local governments and user groups. Unfortunately, many such programs emphasize the transfer of responsibilities without transferring the corresponding rights. As a result, user groups may lack the incentive, and even the authority, to manage the resource.When devolution programs do transfer rights over resources to user groups or local government, that particular institution becomes the gatekeeper that determines individuals' rights over the resource. An effective voice in those organizations becomes essential in exercising any decisionmaking rights over the resource. This situation can be especially problematic for women when formal rules limit membership to the \"head of household\", or where social norms make it unacceptable for women to speak up in public. Because strengthening the control rights of some means restricting the use rights of others, those who are not members of the groups in question may have less access to the resource. Thus, while effective transfers of rights and responsibilities from centralized government agencies to local organizations can lead to more sustainable resource management, authorities must give due attention to the equity outcomes, especially noting who loses access to resources.Although property rights have a powerful influence on human welfare and natural resource management, this institution is complex. Property rights do change over time, but legislative reform alone is unlikely to alter the manifestation of property rights on the ground. Rather, change occurs through social and power relations and negotiations between different groups, who may appeal to a variety of legal bases for claiming property rights. Instead of looking for simple \"solutions\" to property rights issues, it is more useful to try to understand the complexity. This approach involves looking at the claims and the bases of the claims made by individuals, groups, or government entities to different bundles of rights over the resource, and at the different types of laws that pertain to the use or management of the resource. Security of tenure is important, but so is flexibility to respond to changing conditions that affect resource use and property rights.","tokenCount":"1169","images":["-853786518_1_1.png","-853786518_3_1.png"],"tables":["-853786518_1_1.json","-853786518_2_1.json","-853786518_3_1.json","-853786518_4_1.json"]}

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+{"metadata":{"gardian_id":"ff83c13400a9f68de1d8f6f694892c8a","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/283a5e53-4c71-48da-8509-1066ef8baf18/retrieve","description":"EC financial support has helped to ensure that IFPRI’s research remains responsive, innovative, and effective in tackling the obstacles that stand in the way of achieving food security and poverty alleviation. Over the years, the EC’s support to IFPRI provided the necessary foundation for cuttingedge research in areas such as climate change; biofuels; and the nexus of agriculture, nutrition, and health. This support has helped IFPRI partner with strategic European partners in civil society, higher education, and public sector areas, with a focus on making food security research accessible through the development of global public goods, capacity-building networks, and technological platforms. In June 2015, IFPRI, the EC, and the German Ministry for Economic Cooperation and Development (BMZ) joined together to bring experts and stakeholders to discuss the latest thinking on sustainably improving livelihoods and welfare at a Brussels workshop, “Improving Food Systems for Better Lives.” The workshop highlighted the need for greater convergence among diverse actors, sectors, and stakeholders for the development and implementation of innovative solutions that will improve food systems and enhance food and nutrition security around the world.","id":"-1133983083"},"keywords":["Cover-Hugo De Groot/CIMMYT","p. 4-Erly Tatontos/World Bank","p. 6-EC","p. 8-David Spielman/IFPRI","p. 9-Neil Palmer/CIAT","p. 10-Liangzhi You/IFPRI","p. 11-IRRI","p. 12-Milo Mitchell/IFPRI","p. 13-Divya Pandey/IFPRI","p. 14-Milo Mitchell/IFPRI"],"sieverID":"65e47479-832c-4c02-ac02-87bd3b672565","pagecount":"16","content":"In the wake of the food crises of the early 1970s and the resulting World Food Conference of 1974, a group of innovators realized that food security depends not only on crop production, but also on the policies that affect food systems, from farm to table. In 1975, the International Food Policy Research Institute (IFPRI) was founded to provide solid research and evidence-based policy options to sustainably reduce poverty and end hunger and malnutrition. The European Commission (EC) has long championed food security, nutrition, and sustainable agriculture. Since 2006, the EC has committed €1 billion of its annual budget to strengthen and support global food security and sustainable agriculture. In fact, with the 2008 adoption of the €1 billion Food Facility program, the EC was the first donor to proactively confront the global food price crisis of 2007-2008, allowing for rapid responses to food price volatility. The EC's Food Facility program helped reduce the negative impact of food price fluctuations for more than 59 million people.EC financial support has helped to ensure that IFPRI's research remains responsive, innovative, and effective in tackling the obstacles that stand in the way of achieving food security and poverty alleviation. Over the years, the EC's support to IFPRI provided the necessary foundation for cuttingedge research in areas such as climate change; biofuels; and the nexus of agriculture, nutrition, and health. This support has helped IFPRI partner with strategic European partners in civil society, higher education, and public sector areas, with a focus on making food security research accessible through the development of global public goods, capacity-building networks, and technological platforms.In June 2015, IFPRI, the EC, and the German Ministry for Economic Cooperation and Development (BMZ) joined together to bring experts and stakeholders to discuss the latest thinking on sustainably improving livelihoods and welfare at a Brussels workshop, \"Improving Food Systems for Better Lives.\"The workshop highlighted the need for greater convergence among diverse actors, sectors, and stakeholders for the development and implementation of innovative solutions that will improve food systems and enhance food and nutrition security around the world.Much of IFPRI's work is framed within the two CGIAR Research Programs that IFPRI leads: Policies, Institutions, and Markets (PIM) and Agriculture for Nutrition and Health (A4NH). This brochure highlights key collaborations between IFPRI and the EC.The 2014 report, launched with assistance from A4NH at the Second International Conference on Nutrition (ICN2), provided a comprehensive account of the global state of malnutrition. Undeniable evidence revealed that the world was off-course for meeting the World Health Assembly (WHA) targets for 2025.The measures and evidence presented by the report helped spark increased commitment to reduce malnutrition and build accountability. The 2015 report updated the inaugural report and examined the factors contributing to the notable progress made in reducing undernutrition. The project developed a toolbox to analyze the effects of short-and medium-term policies, thereby supporting EU policy makers and stakeholders in the design   Since its inception, the FSP has been equipping policy makers with evidence and analytical tools to address food security challenges. Expansions and updates to the FSP continue. New country-specific portals (Central America and the Caribbean, India, and Africa south of the Sahara) were subsequently developed, and a \"RoadTo\" feature on global trade negotiations was added.The portal also provides policy makers with alerts on daily price volatility through IFPRI's Excessive Food PriceVariability Early Warning System.X By serving as a source of reliable, evidence-based information on global and local food prices for both decision makers and the general public, the portal has contributed to a broader understanding of events and possible policy responses. IFPRI produced 30 publications and disseminated findings through high-level presentations and briefings, resulting in more than 700 media citations. In addition to conducting surveys of consumers, farmers, and traders, the research teams developed a model to simulate regional grain markets and how they interact. IFPRI's research in Ethiopia dispelled the commonly believed causes of cereal price hikes: cross-border trade, increased demand for consumption, diversification into high-value crops, and speculative hoarding. IFPRI found that high prices were instead caused by a combination of overestimation of yields and macroeconomic policies.X Some stakeholders believed Ethiopia's new Productive Safety Net Program had injected too much cash into the rural market, increasing rural cereal demand and driving up prices. IFPRI's research showed that safety net payments did not significantly raise grain prices. Ultimately the government increased the payments so recipients could afford the more expensive grain.X When prices spiked, the Ethiopian government forbade the World Food Programme (WFP) from buying grain from local markets for its relief programs, believing its involvement in the market would only exacerbate the problem. Eventually, after the research results were released, the WFP was once again allowed to buy from local markets.X IFPRI's research revealed that the government's crop-forecasting procedures-which produced the data used to devise food security policy-could be strengthened. This led the EC and others to provide grants to Ethiopia's Central Statistical Agency to improve its production forecasts. In  The EC-funded project updated the economic models used for policy analysis and decision making with the latest data, including data on the impacts of conflict. In addition, the models were improved by incorporating poverty and nutrition impacts in order to ensure the well-being of the most vulnerable. Final reports for both evaluations are expected to be released in the next two years. This research also aims to improve the efficiency of fertilizer markets and to remove market distortions at both the global and local levels. The study will initially, for cross-country and cross-regional learning purposes, focus on two key countries in Africa and two countries in Latin America but could eventually be extended to other countries in Africa south of the Sahara.A4NH is built on the premise that agriculture can contribute more toward improving nutrition and health.A4NH's research is designed to support agricultural researchers, value-chain actors, program implementers, and policy makers in reshaping their actions to better contribute to nutrition and health outcomes.The program also aims to enhance synergies between agriculture and the nutrition and health sectors to maximize the benefits and minimize the risks of agricultural actions on human nutrition and health. For more information, see www.pim.cgiar.org.Hunger and malnutrition are persistent problems that demand multifaceted solutions. By serving as a trusted voice on food policy issues, and through its role as the lead center for the CGIAR Research Programs on Agriculture for Nutrition and Health (A4NH) and","tokenCount":"1063","images":[],"tables":["-1133983083_1_1.json","-1133983083_2_1.json","-1133983083_3_1.json","-1133983083_4_1.json","-1133983083_5_1.json","-1133983083_6_1.json","-1133983083_7_1.json","-1133983083_8_1.json","-1133983083_9_1.json","-1133983083_10_1.json","-1133983083_11_1.json","-1133983083_12_1.json","-1133983083_13_1.json","-1133983083_14_1.json","-1133983083_15_1.json","-1133983083_16_1.json"]}

data/part_2/0081235132.json

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+{"metadata":{"gardian_id":"234d4db0777b67c45160fc95db84b01d","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/91ed1b75-3631-4dd8-be30-f97e009ddd0f/retrieve","description":"Large-scale government interventions in cereal markets supported by public stocks have been a central part of food in the Indian sub-content since the days of British colonial India. Following the Great Bengal Famine of 1943, during which an estimated three million people died of hungerrelated causes, the colonial government instituted a system of government sales and distribution of cereals designed to help ensure minimum food consumption for poor households (Sen 1982). Public distribution systems continued in both India and Pakistan following independence in 1947. The famine conditions in 1972-74 that followed the liberation war and Bangladesh’s independence in December 1971 strongly reinforced the perceived need for major public interventions to ensure food security (see Ahmed, Haggblade, and Chowdhury 2000).","id":"-1587823884"},"keywords":[],"sieverID":"2c75774b-98ef-4818-a0c9-69e697161a5e","pagecount":"20","content":"Large-scale government interventions in cereal markets supported by public stocks have been a central part of food policy in the Indian sub-content since the days of British colonial India. Following the Great Bengal Famine of 1943, during which an estimated three million people died of hungerrelated causes, the colonial government instituted a system of government sales and distribution of cereals designed to help ensure minimum food consumption for poor households (Sen 1982). Public distribution systems continued in both India and Pakistan following independence in 1947. The famine conditions in 1972-74 that followed the liberation war and Bangladesh's independence in December 1971 strongly reinforced the perceived need for major public interventions to ensure food security (see Ahmed, Haggblade, and Chowdhury 2000).Over the past five decades, this public food grain distribution system (PFDS) has played a major role in addressing chronic food insecurity in Bangladesh. The heart of PFDS, at least in terms of the volume of cereals, has been annual distribution programmes involving in-kind transfers to poor households to reduce chronic undernutrition (such as, food-for-works and food-for-education programmes). Open market sales from public cereal stocks have also been used, particularly in the 1970s and 1980s, to prevent or mitigate price surges. Large-scale emergency relief operations have addressed food security needs following natural disasters, including the extraordinary floods that caused major production shortfalls in 1987, 1988 and 1998. Domestic procurement of rice and wheat, generally at a fixed procurement price, has been a part of the government strategy to increase incentives for production through stable, remunerative prices for farmers.Nonetheless, there have been major changes in PFDS over time as donors have reduced food aid and the Bangladesh government has improved targeting of its distribution programmes. There has been an increased reliance on market mechanisms since major liberalization in the early 1990s as well; although the spike in prices of rice in international and domestic markets in 2007 and 2008 led to pressures to reverse this trend. Increasing demand for and volume of higher quality rice in the last decade has muted the impact of interventions in the coarse rice market. Technical change in drying of paddy and an ongoing major expansion of government storage facilities promise further changes in the role of public stocks and distribution in the Bangladesh food system.This chapter is designed to provide a broad assessment of the impact of public cereal stocks and market interventions in Bangladesh over time, as well as highlight future policy options. Section 8.2 describes the structure and evolution of PFDS in Bangladesh over the three major periods: (i) an initial period of heavy government intervention in markets, substantial food aid and relatively high stocks in the 1970s and 1980s; (ii) transition to lower volumes of food aid and distribution, accompanied with a significant role for private imports, from the 1990s through 2006; and (iii) aftermath of the 2007-08 world price spike with increasing calls for larger public stocks and more intervention in domestic markets. Section 8.3 then discusses the current PFDS and factors influencing the levels, timing and composition (rice versus wheat) of stocks, procurement and distribution. Section 8.4 covers the response to major production shortfalls and price shocks, as well as estimates of the impacts of current PFDS interventions. Section 8.5 summarizes and suggests implications for the current debate in Bangladesh on the role of large stocks and government interventions. Deficits and Major Government Interventions: 1971-1972to 1991-1992 In the first few years after its independence in 1971, 2 Bangladesh was faced with massive food security problems. Poor weather, combined with an 1 For overviews of the history of Bangladesh PFDS programmes and market interventions, see Ahmed, Haggblade, and Chowdhury (2000) and Ali, Ali, Jahan, Ahmed, and Rashid (2008).2 Bangladesh declared independence on 26 March 1971, but the ensuing war with (West)Pakistan lasted until 16 December 1971.infrastructure greatly damaged by the war with West Pakistan, contributed to a sharp decline in the main monsoon season (aman) rice crop. Rice production fell sharply in 1971-1972and 1972-1973, averaging 16.5 percent less than in 1969-1970. Moreover, constrained . Moreover, constrained  The Bangladesh government, with support from international donors, responded with sharp increases in public investments in infrastructure, agricultural research and extension, as well as an expansion in public distribution supplied largely by food aid. Major gains were achieved in the 1980s and 1990s due largely to increased production of the winter season (boro) rice crop, made possible by an expansion of shallow tube well irrigation and increases in fertilizer use and planting of improved high yielding variety (HYV) seeds. Wheat production also increased from 102 thousand tons in 1971-1972 to 1,075 million tons in 1980-81 (and ultimately reached a peak of 1.9 million tons in 1999-2000). 4 , 5 Total rice and wheat production rose from 9.89 million tons in 1971-1972 to 24.91 million tons in 1999-2000, enabling Bangladesh to eliminate its \"food gap\", defined as the difference between the amount of food grain required to meet the consumption target of 454 grams of food grain per person per day and net domestic production (Figure 8.1).The distribution of food aid (almost exclusively in the form of wheat) through PFDS was also a key component of government food security strategy. Food aid flows jumped from 502 thousand tons in 1969-1970 (the year before independence) to 1.56 million tons in 1972-1973. From 1972-1973to 1991-1992, food aid averaged 1.26 million tons per year, supplying the grain for 60 percent of PFDS distribution during this period (Figure 8.2).3 See Alamgir (1980) and Sobhan (1979).4 Over the nearly 30 year period, wheat area harvested rose seven-fold and wheat yields doubled.5 Rice is the major food commodity in Bangladesh in terms of both production and consumption, accounting for 69.8 percent of the 2,450 calories/person/day consumed in Bangladesh in 2013. Wheat is a relatively minor food commodity, accounting for 6.1 percent of calories in the same year. Per capita rice and wheat consumption in Bangladesh were 171.7 and 17.5 kg/person/year, respectively (FAO Food Balance Sheet 2013).  8.1: Bangladesh: Net Production and Food Gap (1980-1981to 2014-2015)    8.2: Food Aid and PFDS Distribution, 1976-1977to 2014-2015 (3-year  The green revolution technology (fertilizer, improved seeds and irrigation) that spurred increased rice and wheat production directly benefits the farmers who adopted the technology. Increased production also benefited consumers as real rice prices (i.e. rice prices adjusted for overall inflation) declined from the late 1970s to the early 1990s.Public food grain stocks in the early 1970s were very low, reflecting low levels of distribution and very tight government budgets. Stocks averaged only 276 thousand tons (48 thousand tons of rice and 228 thousand tons of wheat) from 1972-1973through 1974-1975 (Table 8.1) (Table 8.1). Thereafter, stocks were built up to an average of 865 thousand tons (374 thousand tons of rice and 491 thousand tons of wheat) from 1975-1976 through 1991-1992 as food aid and government domestic procurement increased (Figure 8.3). Given that total PFDS distribution changed little between these two periods, the ratio of stocks to distribution remained approximately constant at about 42 percent. Aid and Lower Public Stocks: 1992-1993to 2006-2007 Several major changes in policy in the early 1990s had far-reaching effects on the Bangladesh food system: liberalization of private sector wheat and rice imports; elimination of major ration (sales) channels in the PFDS combined with a sharp reduction in domestic procurement; and donor decisions to reduce food aid, essentially linked to the success of Bangladesh in increasing cereal production. Liberalization of wheat and then rice imports in the early 1990s provided substantial stability to cereal prices. In spite of increased wheat production, domestic demand for wheat far outpaced the supply. From 1992From -1993From through 1999From -2000, commercial , commercial wheat imports averaged 402 thousand tons per year even with large-scale food aid; and wheat inflows averaged 780 thousand tons per year in this period. Commercial wheat imports subsequently increased to an average of 1,510 million tons per year from 2001-2002through 2007-2008. In contrast, until 2003, when India began to subsidize exports, private sector rice imports were profitable only in years of relatively poor harvests, including both the 1997 and 1998 aman (monsoon season) harvests (discussed in detail below).While private sector imports of wheat increased, food aid flows declined in the 1990s, in part because the steady increase in total domestic production of rice and wheat eliminated the food gap-a major rationale for food aid imports. After the European Union phased out its food program, food aid flows fell to only 242 thousand tons in 2002-2003. However, unlike the years just after independence, growth in foreign exchange resources (from textile exports and workers' remittances) and government revenues (linked to overall economic growth) reduced the importance of food aid resources as a share of the value of imports and government expenditures to only 1.3 and 1.7 percent, respectively. 6  The shift from rationed sales and open market operations to targeted cereal distribution brought about major gains in efficiency in terms of benefits and costs reaching poor households. Prior to these reforms, the rural rationing and urban statutory rationing channels accounted for about one-fourth of total food grain distribution (which averaged 2,294 million tons). 7 Total sales channels, including open market sales and other programmes, accounted for 63.5 percent of distribution, with relief and food-for-works channels accounting for the other 36.5 percent of distribution in these years (Dorosh 2001).  8.3: Bangladesh: Public Rice andWheat Stocks, 1996-1997  Reforms in 1991-1992 and 1992-1993 closed the rural rationing and statutory rationing channels, in an effort to improve the targeting of food grain distribution, as well as to reduce fiscal costs (Ahmed, Haggblade, and Chowdhury 2000). 8 As a result, both the percentage and total amount of 6 By comparison, food aid averaged BDT 18.3 billion in real terms at 2000 prices from 1980 to 1984, equal to 22.1 percent of total aid, 11.6 percent of government expenditures, and 10.9 percent of total imports (del Ninno, Dorosh, and Islam 2002).7 Ration sales data are for 1988-1989 to 1990-1991. 8 For an analysis of political economy issues related to the food subsidy reforms, see Ahmed, Haggblade, and Chowdhury (2000).food grain distributed through targeted and relief channels increased in the mid-to late-1990s, averaging 1,166 million tons per year from 1995-1996 to 1997-1998, amounting to 72.8 percent of the 1,603 million tons of total annual average distribution during these three years.By the early 2000s, PFDS had shrunk to only 1.30 million tons per year (average from 2002-2003 to 2006-2007) and stocks fell accordingly to an average of 720 thousand tons per year (531 thousand tons of rice and 189 thousand tons of wheat). For the 1992-1993 to 2007-2008 period as a whole, however, average stocks were somewhat higher (856 thousand tons). The distribution for the period, however, was only about two-thirds that of the 1975-1976 to 1991-1992 period and stocks as a share of distribution rose to 59.6 percent (Table 8.1).In spite of the reduced role of PFDS, market prices of rice were low and stable in the early 2000s because of private sector imports of relatively lowquality Indian rice supplied directly or indirectly from India's own Public Distribution System (Figures 8.4 and 8.5). 9   • 1988-1989to 1995-1996from Dorosh, Shahabuddin and Farid (2004), 74.• Bangladesh Ministry of Food, Food Planning and Monitoring Unit (FPMU).The period of relative stability of rice and wheat prices of the early 2000s ended with the 2007-2008 world price shock. For Bangladesh, the most serious aspect of the crisis was the decision by the Government of India to ban exports of ordinary (i.e., non-aromatic) rice in late 2007 because of their concerns about relatively poor wheat harvest (and relatively low domestic procurement) earlier in the year. Moreover, this export ban helped trigger a surge in prices and shortfalls in quantities in the international rice market. Ultimately, Bangladesh was able to negotiate a limited amount of imports from India, but not enough to prevent a surge in prices in Bangladesh, as well.In the aftermath of the 2007-2008 price shock, the Government of Bangladesh again built-up rice (and subsequently wheat) stocks. Public rice stocks, which averaged 531 thousand tons over the 2002-2003 to 2006-2007 period, were increased rapidly to an average of 1,032 million tons in the July 2008 to December 2009 period, mainly through increased domestic procurement (Table 8.2). Public wheat stocks also rose from 145 thousand tons in 2007-2008 to 429 thousand tons in the July 2012 to February 2016 period, mainly sourced from public sector imports. Overall, public food grain stocks nearly tripled from 617 thousand tons in 2007-2008 to 1.69 million in the July 2012 to February 2016 period. insect attack. Stock rotation through a first in-first out stock management plan mitigated losses to some extent, but in years when procurement was unusually high relative to distribution, rice stocks accumulated rapidly and much of this grain stayed in warehouses for nine months or more, even though there is generally significant quality deterioration after six months in storage. This implies that to avoid excessive storage losses, rice stocks need to be replaced at least twice per year. Thus, for a minimum rice stock of one million tons, distribution (including sales) must be at least two million tons to maintain stock quality (Dorosh, Shahabuddin, and Farid 2004 Losses in rice quality are not included in calculations of the official government calculations of the costs of PFDS in Bangladesh unless the rice is actually discarded (or sold as animal feed). Yet, these losses can be large in some years. Assuming a 15 percent deterioration in the market value of the 706 thousand tons of rice kept in storage for over seven monthsessentially all of the rice distributed that year, estimated losses to recipients 10 A new World Bank funded project begun in mid-2016 which is designed to ease the storage constraints in Bangladesh through construction of new warehouse facilities and silos for rice. By increasing the length of time rice can be safely stored, these investments could enable Bangladesh to increase rice stocks by a factor of two or three without increasing rice distribution. Nonetheless, long periods of storage still imply high interest costs. of PFDS rice were BDT 1.02 billion (about 19 million US dollars) in 2000-2001, equivalent to 11 percent of the total net outlays (net costs) of PFDS rice distribution and 13 percent of the estimated market value of PFDS rice distribution (see Dorosh and Farid 2003; Dorosh, Shahabuddin, and Farid  2004). 11  Note that not all of grains actually reach the intended beneficiaries. Estimates of leakages (diversions of grains) vary considerably across distribution channels and over time, ranging from more than 95 percent for the urban and rural rationing channels in the early 1990s (Ahmed 2000). Subsequent reforms reduced leakages substantially-to less than 20 percent for Food For Education in 2000 (Ahmed, del Ninno and Chowdhury 2004) and only 6 percent in the Vulnerable Group Development (VGD) program in 1998 (del Ninno 2001).  8.7: Bangladesh: Public Rice andWheat Stocks, 1996-1997  Source: Bangladesh Ministry of Food data.Since the late 1980s, Bangladesh has faced two major food crises. The first occurred in 1998 when a major flood hit the country, causing a production shortfall in excess of two million tons. The second, ten years later in 2008, resulted from an export ban on rice imposed by India that coincided with a sharp rise in world cereal prices. In both cases, the shocks led to a significant drawdown of stocks, increased imports and ultimately increased food distribution.In mid-1998, domestic rice prices rose along with floodwaters that ultimately covered two-thirds of the country. As domestic prices reached import parity levels, private sector rice imports flowed across the border from India, quickly adding to total market supplies (at no cost to the government).Continuing its policy of encouraging private sector imports, the Bangladesh government enabled the private sector to import substantial quantities of rice and keep the domestic market price from rising above import parity levels. 12 According to official government estimates, more than two hundred thousand tons of rice per month were imported from August 1998 to March 1999, with private rice imports reaching 288 thousand tons in January and 345 thousand tons in February 1999. Source: Author and Bangladesh FPMU data.12 Earlier that year, following a production shortfall in late 1997, the Government of Bangladesh had removed a 2 percent import surcharge on rice (signaling support for private sector rice imports) and simplified customs procedures in order to encourage rice imports. See Dorosh (2001).In comparison with private sector rice imports, government interventions in the domestic rice market, constrained by low levels of stock, were small, only 399 thousand tons from July 1998 through April 1999. Private sector rice imports, equal to 2.42 million tons in this period, were thus 6.1 times larger than government rice distribution.  If private sector imports were unavailable (or banned) from any source then, with no change in government imports, total supply would have been 12.1 percent less (apart from private stock changes) and rice prices could have risen by 40 to 60 percent, to an average of between BDT 18.7/ kg and BDT 21.3/kg. 13 Such an increase in the rice price level would likely have been unacceptable to the Government of Bangladesh and public sector imports would have been increased. But public sector imports of a magnitude equal to private sector flows would not have been feasible.During the 1998 calendar year alone, private sector imports, mainly from India, reached 2.26 million tons. Had the Government of Bangladesh 13 In the absence of private sector imports, domestic supply would have been 14.839 million tons, a 12.1 percent reduction in per capita supplies relative to the actual estimated levels. Assuming an elasticity of demand of -0.2 to -0.3, prices would need to rise by 12.1/0.3 (40 percent) to 12.1/0.2 (60 percent) to equilibrate market supply and demand. See del Ninno, Dorosh and Smith (2003).Source: Bangladesh Ministry of Food, Food Planning and Monitoring Unit (FPMU) data. Bangladesh had also experienced major production shortfalls at the time of the 1974 famine after the 1988 floods. In 1974, floods caused an 8.1 percent reduction in the aman rice harvest relative to trend (a 600-thousand-ton reduction in rice production for the calendar year, equal to 5 percent of production in the previous calendar year). Rice prices rose by 58.2 percent between May-July 1974 andAugust-November 1974 (Table 8.3). Low public stocks (208 thousand tons, only 2.7 kg/person) and the inability to rapidly acquire more grains in international market during the critical August-November period heavily constrained public distribution options and the government's ability to calm food markets.  1974-1975 1984-1985 1988-1999 1998-1999 Food  Dorosh, and Islam (2002).The floods in 1988-1989 led to an 18.1 percent reduction in the aman harvest relative to trend. Public stocks (which averaged 1,167 million tons from August to November 1988) were nearly four times larger in per capita terms than in 1974 (2.7 and 10.9 kg/person in 1974 and 1988, respectively). Grain from these stocks combined with public sector imports enabled the government to use public distribution channels and stabilize markets and reach flood-affected households.In 1998, average public stocks in August-November (669 thousand tons, 5.5 kg/person) were only half the per capita levels of ten years earlier (although double those of 1974). Nonetheless, the large inflow of private sector imports more than compensated for relatively small increase in public distribution. For the nine-month period from July 1998 through April 1999, public distribution of rice was only 399 thousand tons; private sector imports (banned in 1974 and 1988) were 6.1 times larger (2.42 million tons).Although Bangladesh did not experience a major production shortfall in 2007 or 2008, the 2007-2008 world price shock had major adverse effects on Bangladeshi consumers. In October 2007, as world prices of rice and other cereals rose, India cut off rice exports due to relatively low public wheat stocks (Dorosh 2009;Headey and Fan 2008;Slayton 2009;Timmer 2010). Average wholesale rice prices in Bangladesh rose by 45 percent in real terms between November 2007 and April 2008, even though India agreed to allow fixed quantities of rice exports to Bangladesh at a price higher than BPL prices. Ultimately, total rice imports by Bangladesh reached 1.7 million tons in 2007-2008. Model simulations of the changes in net rice supply (production, imports and net market injections by the government) and consumption demand (as determined by real per capita income and population growth) account for only 9 percent of the actual 45 percent increase in real rice prices actually observed (November 2007 to April 2008, as compared to one year earlier (Dorosh and Rashid 2013). However, simulations of an increase in private stockholding of about 900 thousand tons (equivalent to about two weeks of consumption) result in a simulated real price increase approximating the historically observed increase.These simulations suggest that private stock changes could have been a major factor in explaining the price increases of 2007-2008. In the absence of an increase in private stockholding, a total of about one million tons of net market injections (i.e., an extra 300 thousand tons of rice in addition to the approximately 700 thousand tons of net rice distribution that actually occurred in the period from November 2007 to April 2008) would have been sufficient to stabilize rice prices. These results suggest that ready availability of approximately one million tons of rice through drawdown of public stocks or imports would enable Bangladesh to handle similar disruptions in future, provided that private imports could supply an amount similar to that in 2008 (1.25 million tons).Bangladesh has enjoyed considerable success in increasing food security both in terms of availability of food and access to food. Rice production more than doubled from independence in 1971 to 1999-2000 when Bangladesh achieved its target levels of domestic cereal production (454 grams per person per day). This increase in production has not only reduced the country's dependence on cereal imports, but has contributed to raising rural incomes and reducing the real price of rice from the early 1980s to the late 1990s that benefited all poor net consumers of rice. The trade liberalization in the early 1990s that permitted private rice and wheat imports has also enhanced national food security as private sector imports have added to total cereal supply, especially after major domestic production shortfalls.The PFDS has also played an important role through targeted distribution that has increased access to food by poor households. Management of this system has involved complex policies related to the choice of commodities (rice versus wheat), domestic versus international procurement, maintaining incentives for private sector imports, and managing public stocks. In the 1970s and 1980s, food aid and government imports channeled through PFDS were major means of increasing the supply of food. During these years, much of this food was distributed through rationed sales at subsidized prices, although food for work programmes were also large. Large-scale domestic procurement at fixed prices during these periods was designed to spur production. Following liberalization of private imports of rice and wheat in the early 1990s that coincided with a shift from rationed sales to targeted distribution, PFDS functioned mainly as a safety net. Private trade in rice stabilized prices and supplies following the 1998 flood, as well as during the early 2000s.Since the 2007-2008 world food price shock and temporary disruption of rice imports from India, the government policy has shifted towards lesser reliance on international markets. Public cereal stocks have been increased, along with domestic procurement and public distribution (including a return to rationed sales). There are plans for investments in expanded grain storage and drying facilities that would enable storage of rice for longer periods without major quality deterioration, as well. Nonetheless, private sector imports of rice and wheat continue on a large scale, side by side with government purchases and sales in the domestic market, and large public stocks.Food security in Bangladesh has been greatly enhanced over the past two decades by policies that have allowed a major public food grain distribution to co-exist with private sector trade. Increasing the efficiency of the public distribution system while maintaining incentives for private sector trade can help ensure that food security continues to improve in the coming decades as well.","tokenCount":"4011","images":[],"tables":["-1587823884_1_1.json","-1587823884_2_1.json","-1587823884_3_1.json","-1587823884_4_1.json","-1587823884_5_1.json","-1587823884_6_1.json","-1587823884_7_1.json","-1587823884_8_1.json","-1587823884_9_1.json","-1587823884_10_1.json","-1587823884_11_1.json","-1587823884_12_1.json","-1587823884_13_1.json","-1587823884_14_1.json","-1587823884_15_1.json","-1587823884_16_1.json","-1587823884_17_1.json","-1587823884_18_1.json","-1587823884_19_1.json","-1587823884_20_1.json"]}

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+{"metadata":{"gardian_id":"028591a9a372ea05a78cadceefd0e541","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/b4f1d267-3fe5-4689-8cfe-7237a7873375/retrieve","description":"The role of school quality in determining educational outcomes has received much research attention in the United States. However, in developing countries, where a significant part of the school age population never attends school, policymakers must consider both quality and quantity when deciding how to maximize the impact of scarce investments. Acknowledging this difference in the policy environment in developing countries, this paper provides comparative estimates of the impact of quality versus quantity investments in school supply in rural Mozambique, one of the world’s poorest countries. Policy simulations show that improving school quality (through the pupil-teacher ratio) increases grade attainment and efficiency by approximately 9 percent with no impact on overall enrollment rates. However, these same results can be generated by increasing starting enrollment probabilities through the establishment of new schools in all rural villages that currently do not have schools. Furthermore, similar rates of increase in school achievement indicators can be achieved by building schools in only 56 percent of all villages currently without schools, provided these schools are placed in those villages that also do not have a school nearby. When cost information is considered, the main policy implication is that the expansion of school quantity through well targeted placement of new schools will provide the greatest increase in educational outcomes for Mozambique at this time.","id":"1948099595"},"keywords":[],"sieverID":"bd6f0df0-d7c1-441b-9e81-c297f412cf27","pagecount":"46","content":"will eventually be published in some other form, and that their content may also be revised.The importance of raising educational levels in developing countries is undisputed.The question is how: should budget-constrained governments focus on quantity by increasing the number of schools, or should the focus be on improving the quality of those schools already open? This question is not only of policy interest in developing countries. The importance of school quality for schooling outcomes has been studied extensively in the United States, and the results are far from straightforward, leading to a recent debate on whether school resources matter at all for educational performance (see Hedges, Lane, and Greenwald 1994 and the reply by Hanushek 1994). The apparent lack of consensus on the role of school inputs is attributable in part to differences in measures of school quality, as well as differences in the unit of observation. For example, in two relatively recent studies on the impact of \"school quality\" on subsequent wages, Card and Kruger (1992) and Betts (1995) present contrasting results, but measure school quality at different levels (state averages versus individual school level). In many ways these two studies are typical of this literature, making it difficult to provide a generalization that might be useful for educational policy.In comparison to the U.S. literature, the literature on the role of school quality in poorer countries is not as developed, not because the issue is any less important, but because of the lack of sophisticated data sets that link details about school supply with Presumably, efficiency in the allocation of resources to education is only one objective of 1 policymakers; other likely objectives are equity in educational opportunities, or universal access to primary education. demand side information. A number of recent articles have exploited data sets designed explicitly to study the role of school quality factors in determining student achievement.These studies go beyond the standard analysis of school access (measured by distance or travel time to the nearest school), and try to discover what specific school characteristics seem to determine children's schooling outcomes. For example, Tan, Lane, and Coustère (1997) show that in the Philippines, classroom furniture and workbooks provide the highest payoff in terms of school achievement in the first grade, while Glewwe and Jacoby (1994) also find that classroom quality (measured by blackboards and nonleaky roofs) is more important than teacher training in determining student achievement in Ghana. The limited evidence available shows that school inputs seem to matter more in developing countries than they do in the United States, but probably because the overall level of inputs is so low.However, one important difference between industrialized countries and poor countries is that the latter must consider school quantity as well as school quality, as there is often a significant portion of the school-age population that is not served by the educational system. In these settings, it is not sufficient to establish that school quality matters. Rather, governments in poor countries must consider the cost effectiveness of investments that improve school quality relative to investments that expand the school network, especially given the extremely limited resources available. 1 Some economists have argued for labor market wages as the appropriate metric to gauge school 2 effectiveness, but these data are not available in our survey.In this paper, we explicitly consider the trade-offs between quality and quantity in the allocation of school supply resources in a poor rural economy, recognizing that even in an environment with low rates of schooling and poor infrastructure, improving the quality of existing schools might be more effective at improving educational outcomes than simply opening more schools. The benefits of the two different types of investments are likely to differ by schooling outcome, and so we examine the impact on the initial decision to send a child to school, and two measures of subsequent performance, school efficiency (defined below) and highest grade attained. We hypothesize that the main impact of increasing the 2 quantity of schools is to increase enrollment, whereas enhancing school quality is likely to be more important for other dimensions of schooling outcomes. In doing so, we do not ignore the strong possibility that improving school quality is also likely to positively influence school enrollment, or that expanding the number of schools may improve school achievement (for example, by reducing the amount of time a child spends traveling to and from school each day).supplemented with information on schools provided by the Planning Directorate of theThe household survey, titled Inquérito Nacional Aos Agregados Familiares Sobre As Condições de Vida (IAF), was conducted in 1996/97 by the Mozambican Instituto Nacional de Estatística. The survey was designed as a three-stage, stratified-cluster sample, and covered 43,000 individuals living in 8,250 households. The survey contains information on children's schooling, adult education, and other household characteristics, including consumption expenditures and landholdings, which are used as measures of household well-being or access to resources. In this paper, the analysis is limited to rural areas, where 80 percent of the Mozambican population resides.The school infrastructure data comes from an impressive database maintained by the National Planning Directorate of the MOE. This database contains basic information on schools throughout the country, collected through a survey administered to all schools at the beginning and end of each school year. Coverage is excellent, with over 90 percent of schools returning questionnaires containing information on, among other things, pass rates, teacher training, pupil-teacher ratios, and building characteristics.We aggregate (lower) primary school information to the administrative post level, and merge this with the household survey data to obtain measures of school supply and quality available to the households in the IAF. There are 10 provinces in Mozambique.There are two administrative units below the administrative post in rural areas: the localidade 3 (locality) and the village.Two other studies of a similar flavor to this one use older samples of adults who have already finished 4 school. This of course assumes that community school supply characteristics did not change, or that there was no migration. Given Mozambique's history, these assumptions are not tenable; hence, the sample is restricted to children who are currently of school-going age.further divided, depending on size, into two or more administrative posts. This low level 3 of aggregation reduces the measurement error in our supply-side variables, allowing us to capture with some degree of confidence the supply-side constraints faced by the households. Note that the same school infrastructure information is attributed to all households in the same administrative post.There are 6,385 rural children aged 7-14 in the IAF sample, coming from 3,333 households and 606 villages, distributed among 173 administrative posts. The MOE 4 database has information on 2,982 rural lower primary schools, distributed among 174 administrative posts.Mozambique's recent history is dominated by war, first for independence from Portugal (gained in 1975), and then a civil war between the government and Renamo, a guerilla organization sponsored first by Rhodesia and later by apartheid-era South Africa.The \"new\" Mozambique emerged in 1992 with a cease-fire between Renamo and the government and the first multiparty elections in 1994, but according to the World Bank's This stands for escola primária de primeiro grau.World Development Report (1998), Mozambique still has one of the lowest GDP levels per capita in the world.Since its rebirth, Mozambique has realized high rates of economic growth, especially in the rural economy, as refugees and other displaced people return to their homes and fields. However, infrastructure, especially in the rural areas, is poor or nonexistent. For example, during the rainy season it is not uncommon for the northern provinces to be cut off by land from the central and southern parts of the country.Information gathered from the community questionnaire of the IAF indicates that only 19 percent of the rural population have access to a health post in their village, while 67 percent of households have a primary school in their village.The schooling system consists of lower primary (grades 1-5, known as EP1 ) and 5 upper primary (grades 6-7), and two cycles of secondary education (grades 8-10 and grades 11-12, respectively). Upper primary education is supply constrained, and admission is based on performance and age, with younger children receiving priority. This penalizes children who have repeated or who entered school late. Secondary education is rare, with fewer than 5 percent of current adults having completed even the first cycle of secondary education. There is an annual primary school matriculation fee of $5, but material, where available, is supplied by the school.We model the schooling decision of the household via a reduced form equation,where E is the school outcome for child i in household h, X are characteristics of the  Behrman and Deolalikar (1988) and Strauss and Thomas (1995). Empirical applications can be found in Case and Deaton (1999), Alderman et al. (1996), andHanda (1996).We use three schooling indicators or outcomes as our dependent variable in equation ( 1). The first is whether a child ever enrolled in school. This dichotomous variable is modeled using probit, and measures the propensity of the household to send its children to primary school. Table 1, which provides summary statistics of the variables used in the regression analysis and policy simulations, shows that only 53 percent of rural children aged 7-14 have ever enrolled in primary school.For those children who once enrolled in school, we model their highest grade attained and their schooling efficiency. These are our \"achievement\" variables in that they measure the level of schooling the child achieved and the amount of time it took to attain that level. Schooling efficiency is constructed as the highest grade completed divided by the grade the child should have completed, given his age and assuming no repetition or delay in starting school. For children who were once enrolled but are no longer in school, we are able to construct efficiency because we know when they completed their highest grade. Children who started school on time and never repeated will have an efficiency score of 1 (or 100 percent).These two achievement measures create some potential econometric challenges, and therefore we have experimented with several different specifications of the error term. The essential problem is the nonnormality of the error term for the two variables. First, the data are truncated because we only observe (or use) them for children who once attended school. For this selection problem, we face the same constraint as Alderman et al. (1996), in that there is no theoretical basis for including information in the probit equation (for ever having attended school) and not in the two other outcome equations, since all three are reduced forms from the same underlying structural model. We are thus relegated to identifying the sample selection rule through functional form alone, using the inverse Mills' ratio derived from the probit model.Second, the efficiency variable we have constructed has two modes at 0.5 and 1, largely because children aged 7 or 8 who have attended school can mathematically onlyThe estimating sample for grade attainment is children 7-14 years of age who ever attended school.6For efficiency, it is children 9-14 who ever attended school.have efficiency scores of 0.5 or 1. This problem arises by construction and not from any fault of the data. We obtain a distribution for this variable that more closely approximates normal by simply dropping 7-and 8-year-old children from our sample when estimating the model for school efficiency. However, for both highest grade attained and efficiency, we have estimated models (and policy simulations) using Tobit and ordered probit.Estimates of our policy variables are remarkably robust to choice of model, as are the main results of the policy simulations. Results using these other distributional assumptions (and the relevant simulations) are available from the authors upon request. The distribution of grade attainment and efficiency for the children in the sample used for estimation are shown in Figures 1 and2. 6   Using the estimated models described above, we then simulate the impact on schooling outcome of a set of independent interventions aimed at increasing school access and quality. In a variation on the approach of Alderman et al. (1996), we do this in two stages, first by simulating the impact on the probability that a child will be sent to school, and then multiplying the simulated probability of being sent to school by the simulated highest grade attained and school efficiency. For example, the simulated impact of policy p on the highest grade attained is estimated as where E(oe ) is the mean predicted years of schooling completed (across the entire 7-14 p year-old population) following implementation of the policy, E(Ê ) is the mean predicted p probability of a child in that age group ever enrolling in school following implementation of the policy, and the final term is the mean predicted years of school completed conditional upon the child ever enrolling in school. A similar exercise is carried out for schooling efficiency, for policy interventions aimed at increasing quantity, quality, and both. We also compare these simulated benefits to costs to give an idea of the cost effectiveness of the different types of interventions considered in the simulations.We use three key variables as our proxies for school \"quality\" and school \"quantity.\" School quality is measured by the pupil-teacher ratio (PTR) in the administrative post, while quantity is measured by the number of (lower) primary schools in the administrative post, and a dummy variable indicating whether the household has a school within 60 minutes. This latter variable comes directly from the community module of the IAF, while the former two indicators are taken from the MOE database. We use the PTR in the same spirit as Case and Deaton (1999), that is, as a general measure of school quality, recognizing that it is likely to be correlated with other aspects of school quality such as building conditions, access to books, etc. Hence the results on the PTR that we present below should not be interpreted as the impact on educational outcomes of the PTR per se, but as the impact of school quality in general.The first-stage regression predicting log of per capita expenditures was estimated separately for the 7 north, center, and south of the country. Results are available upon request.The control variables in the regression models are the age and sex of the child; age, sex, and literacy of the household head; whether any adult in the household has completed upper primary school (grade 7); whether an adult female in the household has completed lower primary school; and a set of provincial dummy variables. Household access to resources are controlled for by using the log of consumption expenditures per capita and landholdings. The former variable is instrumented using the cluster (or village) median per capita consumption as the key identifying variable. 7In this section we address briefly two data issues that may influence the interpretation of our econometric analysis. The first issue is that of endogenous program placement effects, or the idea that the placement of infrastructure may be correlated with household characteristics, leading to biased estimates of the impact of this infrastructure on household schooling decisions. For example, if richer communities, or communities with more educated parents, demand better school services and are in a position to influence government resource allocation decisions, there will be a positive correlation between household income or education, children's schooling outcomes, and school infrastructure. We have discussed the program placement issue in the National Planning Directorate of the MOE. Decisions about the allocation of resources to villages areWe were able to speak to urban teachers only, but believe that households probably have even less 8 say in the much poorer and spread out rural areas of the country.decentralized to the provincial level. However, there was considerable skepticism about the ability of villages to influence resource allocation decisions at the provincial directorates of education. The same sentiment was expressed by teachers we spoke to, who believed that parents had very little say in how resources were allocated among different schools. 8The flip side of the problem of endogenous program placement is the possibility that better-off households, more educated parents, or parents who value education more highly will choose to live in an area with higher quality schools. This is one of the prime methodological concerns of the literature on school quality effects in industrialized countries. However, even though the population of Mozambique is more mobile than the norm for Sub-Saharan Africa (largely because of the war and resettlement), this is not considered a problem. Land tenure, community and familial ties, and the poor state of infrastructure (including schools) suggest that households do not move, particularly into rural areas, in search of good quality schools.These arguments are borne out by an analysis of the community-level determinants of the placement of \"recent\" schools in rural Mozambique, reported in Handa (1999).Nearly 50 percent of the schools reported in the IAF survey were built after the peace accords of 1991. Using only these recently built schools, ordered probit regressions of \"exposure time\" (defined as years since school was built) on community-level This is consistent with a policy that gives trained teachers larger classes. 9 characteristics such as median consumption and the proportion of adults with various levels of schooling show no significant association between village-level socioeconomic characteristics and exposure time to school. Identical results are also found when school quality indicators are regressed on the same village-level socioeconomic indicators.The second issue is the extent to which the PTR represents school quality in general. We assess whether our school resources \"follow\" teachers by presenting regression estimates of the correlates of the PTR. For each lower primary school in the MOE data, we construct several other indicators of school quality and regress them against the PTR and a set of provincial dummy variables. These estimates, presented in Table 2, show that resources do follow teachers in rural Mozambique. In column (1) of Table 2, schools with higher PTRs also tend to have more than one shift, fewer cement classrooms as a proportion of all classrooms, larger class sizes, and more students per number of classrooms in the school. Schools with high PTRs also tend not to have a secondary school in the same administrative post. The one exception is the proportion of teachers in the school who are trained, which is positively correlated with the PTR. 9Columns 2 and 3 of Table 2 provide results with the PTR in log form (column 2), and then the PTR, class size, and pupils per classroom entered in log form. These two models improve the overall fit of the regressions by 10 percentage points, and the individual coefficients and t-statistics tell the same general story-the PTR is negatively correlated with other dimensions of school quality in lower primary schools in rural Mozambique.EVER ATTENDED SCHOOL Probit coefficient estimates (and respective mean probability derivatives) of the probability of ever having attended school are shown in Table 3. In this model we correct for the potential endogeneity of per capita consumption expenditures by including as an independent regressor the residual from the first-stage regression predicting log per capita expenditure, as recommended by Rivers and Vuong (1988).The results indicate that family background, as measured by per capita consumption and the education of adult household members, are significant determinants of the probability that a child has ever been enrolled in school. For example, living in a household where the head is literate raises the probability of ever attending school by 16 percentage points, as does living in a household with a female adult who has at least fifth grade education. Enrollment chances increase steadily with age, and are 13 percentage points lower for girls relative to boys.The school supply results show that \"quantity\" is more important than school \"quality\" in determining a child's chances of ever attending school. Enrollment chances do not vary according to the mean PTR in the administrative post, but do vary significantly according to the number of schools in the post, as well as the distance to the nearest school. Living within a one-hour walk of a school raises the chance of going to school by Alderman et al. (1996) also report school quality indicators to be negatively correlated with student 10 achievement in their \"select\" sample of children who attend school in Pakistan.29 percentage points, while adding 10 more schools to the administrative post raises the probability of ever having attended by 2 percentage points.HIGHEST GRADE ATTAINED When the model in column (1) is estimated over all children, the coefficients of the two \"quantity\" variables become positive and statistically significant. Note also that if all 10 children who entered school did so at the appropriate starting age, and moved to the next grade each year, the difference in the coefficients of adjacent age dummies would be 1. In column (1) this difference is 0.1 between age 8 and 9, and is largest between ages 12 and 13 (0.33).In column (2) we explore whether the impact of the PTR varies by age of the child.Following Case and Deaton (1999), we enter age linearly and interact it with the PTR; the positive coefficient of the interaction term indicates that the negative impact of a high PTR is reduced as the child gets older. We also checked for gender differences in the determinants of grade attainment by interacting all variables with the female child dummy.The only significant interaction was with literacy of the head of household, and so in column (3) we show a form of the model that includes this interaction. The positive and significant coefficient indicates that having a head that is literate raises girls' grade attainment by 0.204 relative to boys.Table 5 presents OLS regression estimates of the determinants of schooling efficiency (multiplied by 100). For this outcome as well, school quality matters. The coefficient of PTR is negative and highly significant, and the value of its coefficient implies that a one standard deviation decline in the PTR will raise schooling efficiency by about 5 percentage points (9 percent at the mean). Note that efficiency declines steadily with age at a rate of 2 percentage points, on average, per year, which is mostly driven by the larger proportion of children who started school late, or who repeated a year, in the older age groups. In column (2) we show the specification where age is treated linearly and interacted with PTR. As in Table 4, this interaction is positive and significant, once again implying that the negative impact of a high PTR is less pronounced for older children. The policy implication of this coefficient is clear. If a choice must be made concerning where to emphasize school quality improvements, Mozambique should reduce the PTR at lower grades first. Finally, column (3) presents the specification with female child interacted with head´s literacy. As with the other school achievement measure, the highly significant and positive coefficient (4.2) wipes out the negative direct effect of being a female child (-.2).Using these econometric results as a basis, we now explore the expected outcomes from a set of plausible actions that the government can take to increase school enrollment and improve school achievement. In particular, we focus on interventions on the supply side of the equation, namely, increasing the quantity of schools (thus increasing access to schools) and improvements in school quality, as proxied by the PTR. The preferred models for the simulations are the probit estimates presented in Table 3 and the OLS estimates presented in column (3) of Tables 4 and5. Table 6 shows the results of these simulations, presented as the percentage change in the mean value of the three outcome variables: the probability of ever attending school, the number of years of school successfully completed, and schooling efficiency. Recall from equation ( 2) that for the latter two measures, there are two mechanisms for improvement. One is through improved schooling efficiency and more years completed by those already in the school system, while the other is through increases in school enrollment. Table 6 presents two sets of results for the mean change in school achievement. One is the global (unconditional) estimated mean change for the entire population ages 7-14, and the other is the (conditional) estimated mean change, which considers only that portion of the age group who have ever attended school.The first striking finding is that improving school quality alone (Simulation 1) has almost no impact on the probability of attending school, but a significant positive impactThese simulations also imply hiring new teachers, as the PTR ratio is assumed to be held constant 11 in simulations 2-4.on school achievement for those who do enroll, raising the average number of years successfully completed by 9 percent and average schooling efficiency by 8 percent. In contrast, the three quantity-oriented interventions (Simulations 2-4) have a much larger 11 impact on the probability of a child ever enrolling in school. The increases in enrollmentshown in Table 6 correspond to increasing the enrollment rate from the current 53 percent to between 58 percent and 60 percent. This large increase in enrollment leads to significant increases in average school achievement for the 7-14 year-old age group, even though the impact on mean achievement among school attendees is slightly negative. In Simulations 2-4, the unconditional percentage increases in schooling efficiency are greater than the increase seen in the quality-only intervention (Simulation 1), whereas the increases in the average number of years completed in Simulations 2-4 are in the same neighborhood as the increase seen in Simulation 1. Not surprisingly, the combined quality-plus-quantity simulation (Simulation 5) yields results that are approximately equal to the sum of the results of its two components (Simulations 1 and 3).Simulations 2-4 reveal the critical role of targeting in the placement of new schools.Building schools in the villages that are currently the most poorly served (Simulation 3) yields improvements in school achievement that are almost as strong as building a school in every village that does not have a school (Simulation 2), even though the latter implies building, equipping, and staffing nearly twice as many schools. Likewise, targeting school placement to poorly served areas has a much larger impact on school enrollment and achievement than random allocation of an equal number of schools among all villages without schools (Simulation 4).One of the three key elements that determine the size of the changes shown in Table 6 is the magnitude of the change in the independent variables in the simulation (the other two are the size and sign of the estimated regression coefficient and the proportion of the population affected by the simulation). Simulation 5 clearly yields the best outcomes, but at the cost of what additional resources, relative to the other simulations presented? In this section we estimate and compare the cost of achieving the results in the five policy simulations presented here.Following discussions with MOE officials and nongovernmental organizations working in primary education in Mozambique, we estimate that it costs $50,000 to build and furnish a standard concrete school with three classrooms. From these sources we also estimate that the annual salary and administrative costs for a primary school teacher in Mozambique are in the neighborhood of $960; for the present analysis we do not consider the cost of training additional teachers. To make the two different types of school inputs considered reasonably comparable, we use the (nondiscounted) cost of a teacher over 20 years in the calculations. With this information we estimate the cost of implementing each of the simulations, specifying that the new schools built, and the additional students Simulations 2-4 all indicate that the growth in enrollment will be slower than the growth in numbers 12 of schools. Therefore, assuming a constant current PTR implies an increase in the ratio of teachers per school.attracted by these new schools, will be matched by enough additional teachers to preserve the current PTR. The cost calculations are shown in Table 8.12 As the simulations typically involve varying more than one variable at a time (e.g., building new schools will change both the number of schools in the administrative post and the proportion of households who have a school within a one-hour's walk), we cannot simply divide the intervention cost by the relevant regression coefficient, as in Tan, Lane, and Coustère (1997). Instead, the total cost estimates in the final column of Table 8 are divided by the percentage increases in school enrollment and achievement shown in Table 6 to arrive at the estimated cost per unit of benefit, which is shown in Table 7. These results show that in the context of present-day Mozambique, investing in school quantity (access) is a much more cost-effective mechanism for increasing school enrollment than is investing in school quality. For school achievement, investments in school quantity are also slightly more cost-effective in raising average schooling efficiency, whereas investments that focus on school quality appear to be marginally more cost-effective in improving the average number of years successfully completed. If the cost of training the new teachers required for these interventions were included, the advantages of the quantity-oriented approach would be even greater than is shown in Table 7. Similarly, lengthening the time horizon for teacher costs, or increasing teacher salaries (which might be required to attract additional teachers) also reinforce the finding that, at this juncture, focusing on school quantity is more cost-effective than focusing on school quality.The role of school quality in determining educational outcomes has received much research attention in the United States. However in developing countries, where a significant part of the school-aged population never attends school (and most of those who do attend drop out after a few years), and where physical access to schools is a serious problem, policymakers must consider both quality and quantity when deciding how to maximize the impact of scarce investments. The current study explicitly acknowledges this fundamental difference in the policy environment in developing countries, and provides comparative estimates of the impact of quality versus quantity investments in school supply in rural Mozambique, one of the world's poorest countries, where the net primary school enrollment rate is only 50 percent.The regression estimates presented in Section 4 indicate that school quantity (or access) matters for primary school enrollment, while school quality (proxied by the PTR)is an important determinant of schooling efficiency and highest grade attained. Based on these regressions we provide a set of simulations designed to provide quantitative estimates of the benefits of various policy alternatives, and which allow for the \"total effect\" of each intervention via increased enrollment rates and longer years in school.Results from these simulations show that improving school quality (by reducing the PTR) increases grade attainment and efficiency by approximately 9 percent with no impact on overall enrollment rates. However, these same results for average school achievement can be generated by increasing starting enrollment probabilities through the establishment of new schools (increasing school quantity) in all rural villages that currently do not have schools. Furthermore, similar rates of increase in school achievement indicators can be achieved by building schools in only 56 percent of all villages currently without schools, provided these schools are placed in those villages that also do not have a school in a nearby village.These results are based on the PTR as the measure of school quality. There are of course other dimensions of school quality, such as curriculum reform and management innovation, which are unrelated to the PTR, and which may have important positive implications for both school enrolment and achievement.When one considers the relative cost of the quantity and quality interventionsproposed, the results for school achievement are the same. If increasing the net enrollment rate is also considered as a policy goal, then increasing school quantity is clearly the preferred direction for investments in educational supply. Furthermore, in a cost-benefit sense, geographically targeting the expansion of the school network is the best policy option for Mozambique, given present conditions.The political economy of the quality-versus-quantity decision also deserves mention.Our economic analysis leads to the conclusion that the targeted building of new schools should be a priority in Mozambique, and this policy is consistent with a political philosophy that stresses equity of educational opportunity, including reaching into remote areas that are typically at the fringes of government service provision. More specifically, it is consistent with the current strategy in the Mozambican MOE, which cites universal primary education as its highest priority (MOE 1998). Expansion of the school network alone will not guarantee that every child in Mozambique will attend school, but it is both a necessary and cost-effective first step in that direction.    Assumes that there are no villages with more than one EP1 school. Number of additional teachers based upon the a predicted increase in the number of students, maintaining the same pupil / teacher ratio. Teacher costs reflect cost of one teacher over a period of 20 years.","tokenCount":"5453","images":["1948099595_1_1.png"],"tables":["1948099595_1_1.json","1948099595_2_1.json","1948099595_3_1.json","1948099595_4_1.json","1948099595_5_1.json","1948099595_6_1.json","1948099595_7_1.json","1948099595_8_1.json","1948099595_9_1.json","1948099595_10_1.json","1948099595_11_1.json","1948099595_12_1.json","1948099595_13_1.json","1948099595_14_1.json","1948099595_15_1.json","1948099595_16_1.json","1948099595_17_1.json","1948099595_18_1.json","1948099595_19_1.json","1948099595_20_1.json","1948099595_21_1.json","1948099595_22_1.json","1948099595_23_1.json","1948099595_24_1.json","1948099595_25_1.json","1948099595_26_1.json","1948099595_27_1.json","1948099595_28_1.json","1948099595_29_1.json","1948099595_30_1.json","1948099595_31_1.json","1948099595_32_1.json","1948099595_33_1.json","1948099595_34_1.json","1948099595_35_1.json","1948099595_36_1.json","1948099595_37_1.json","1948099595_38_1.json","1948099595_39_1.json","1948099595_40_1.json","1948099595_41_1.json","1948099595_42_1.json","1948099595_43_1.json","1948099595_44_1.json","1948099595_45_1.json","1948099595_46_1.json"]}

data/part_2/0086882710.json

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+{"metadata":{"gardian_id":"2e102b07050a1993889c209d549c8de6","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/d136db42-119b-43aa-8742-2b57af31bdf9/retrieve","description":"In 2014, African heads of state reaffirmed their commitment to the Comprehensive African Agricultural Development Program (CAADP) through the adoption of the Malabo Declaration (AU 2014). The declaration included commitments to reduce hunger and poverty, boost intra-regional trade, enhance resilience to climate variability, and, in line with the Maputo Declaration a decade earlier, to continue allocating to agriculture at least 10 percent of government expenditure. Despite this long-standing spending commitment, Ghana’s agricultural budget share has remained well below 10 percent during the last decade. Depending on accounting principles followed, estimates range from 1 to 2 percent (CAGD 2016), 2 to 4 percent (FAO 2014) or 6 to 8 percent (MoFA 2017a). Given strong evidence that agricultural spending in developing countries yields significant returns (Mogues et al. 2015), Ghana’s relatively weak agricultural performance during the period from 2007 to 2017 may be linked to low levels of spending. At 4.3 percent per annum, agricultural GDP growth has only been half that realized in the non-agricultural sectors (MoF 2018). This weak agricultural growth has also not benefited the poor. Rural poverty has increased in recent years, especially in northern Ghana (GSS 2018). While budgetary allocations to agriculture matter, the quality of spending is as important (Akroyd and Smith 2007). In this regard, concerns have been raised about the decline in allocations to agricultural research, knowledge transfer, and infrastructure in favor of spending on routine operations (FAO 2014; World Bank 2017). Ideally, sector budgets should maintain a healthy balance between investments in a sector’s capacity to grow, e.g., infrastructure or farmers’ knowledge, and expenditures that are fully consumed in the same period, e.g., operational expenses or subsidies (Benin & Tiburcio 2018; Mogues et al. 2015). Following Ghana’s unfavorable assessment in the African Agricultural Transformation Scorecard (AATS), which was launched by the African Union (AU) in 2018, and in light of policy developments, budgetary trends, and socioeconomic outcomes, Ghana’s development partners called for an increase in funding allocated to the agriculture sector at the national Joint Sector Review for Agriculture in June 2019. They further called for improvements in the effectiveness of agricultural spending, with the distortionary effects of large-scale subsidy programs highlighted as a specific concern. A recent study led by IFPRI’s Ghana Strategy Support Program (GSSP) and FAO’s Monitoring and Analyzing Food and Agricultural Policies (MAFAP) project considers these issues further (Aragie et al. 2019). Specifically, using an economywide model of the Ghanaian economy, the research-ers examined how changes in the level and composition of public agricultural expenditure affect socioeconomic outcomes in the short and medium term in Ghana. This note highlights selected key study findings.","id":"918609392"},"keywords":[],"sieverID":"b39ef749-90a5-4aa9-b36f-1cb4e8852286","pagecount":"4","content":"This publication has been prepared as an output of GSSP and has not been independently peer reviewed. Any opinions expressed here belong to the author(s) and do not necessarily reflect those of the institutions they represent or the development partners associated with this work.In 2014, African heads of state reaffirmed their commitment to the Comprehensive African Agricultural Development Program (CAADP) through the adoption of the Malabo Declaration (AU 2014). The declaration included commitments to reduce hunger and poverty, boost intra-regional trade, enhance resilience to climate variability, and, in line with the Maputo Declaration a decade earlier, to continue allocating to agriculture at least 10 percent of government expenditure.Despite this long-standing spending commitment, Ghana's agricultural budget share has remained well below 10 percent during the last decade. Depending on accounting principles followed, estimates range from 1 to 2 percent (CAGD 2016), 2 to 4 percent (FAO 2014) or 6 to 8 percent (MoFA 2017a). Given strong evidence that agricultural spending in developing countries yields significant returns (Mogues et al. 2015), Ghana's relatively weak agricultural performance during the period from 2007 to 2017 may be linked to low levels of spending. At 4.3 percent per annum, agricultural GDP growth has only been half that realized in the nonagricultural sectors (MoF 2018). This weak agricultural growth has also not benefited the poor. Rural poverty has increased in recent years, especially in northern Ghana (GSS 2018).While budgetary allocations to agriculture matter, the quality of spending is as important (Akroyd and Smith 2007). In this regard, concerns have been raised about the decline in allocations to agricultural research, knowledge transfer, and infrastructure in favor of spending on routine operations (FAO 2014;World Bank 2017). Ideally, sector budgets should maintain a healthy balance between investments in a sector's capacity to grow, e.g., infrastructure or farmers' knowledge, and expenditures that are fully consumed in the same period, e.g., operational expenses or subsidies (Benin & Tiburcio 2018;Mogues et al. 2015).Following Ghana's unfavorable assessment in the African Agricultural Transformation Scorecard (AATS), addresses the infrastructure investment deficitestimated at around US$30 billion (MoF 2018)-in areas such as transport, water and sanitation, and energy.Alongside other initiatives such as One-District-One-Factory (1D1F), Planting for Export and Rural Development (PERD), and Rearing for Food and Jobs (RFJ), the current portfolio of agriculture-specific or agriculture-supportive policies present a holistic, broadbased, decentralized, and agriculture-led development vision for Ghana. However, these are ambitious programs in terms of their scope and funding requirements. Budget constraints may ultimately require prioritization of some activities or investments over others, which may have important implications for outcomes. Aragie et al. demonstrate this using extension services and input subsidies as case studies.The research involved calibrating a recursive-dynamic Computable General Equilibrium (CGE) model customized for rural investment and policy analysis to a 2015 Social Accounting Matrix (SAM) for Ghana. The model includes a separate investment module which translates public agricultural expenditure into agricultural productivity changes. This transforms the generic CGE framework from one where agricultural productivity changes are modeled as exogenous shocks to one where productivity changes are endogenously linked to the level and composition of the budget.The investment module is calibrated with program evaluation data and information on government program costs. Importantly, the module assumes that different types of spending, e.g., on rural roads, research, extension services, or input subsidies, affect agricultural subsectors differently and have different returns to investments. This permits evaluation of complementarities and trade-offs between different levels and types of spending. The general equilibrium framework further allows for the analysis of both direct and indirect (spillover) effects, as well as financing considerations for alternative spending regimes.Several scenarios are explored. A baseline scenario assumes a \"business-as-usual\" development trajectory under the existing set of policies and spending trajectories. This means the agricultural budget share remains constant at 3.6 percent over the 2015 to 2025 simulation period (Figure 1) and there is almost no change in the budget allocation across extension services, input subsidies, and other areas of investment (Figure 2). Since the agricultural budget continues to grow as it has in recent years, it almost doubles from around GHC 590 million to GHC 1.1 billion by 2025.Two hypothetical \"accelerated agricultural spending scenarios\" are then modeled for comparison against the baseline. In these, additional funding is allocated to the agricultural sector and financed through government borrowing, such that the agricultural budget share doubles to 7.2 percent within five years and remains constant thereafter (Figure 1). Under an \"extension scenario\", half of the increase in the agricultural budget is allocated to extension services, while the other half is allocated to remaining expenditure categories using baseline shares. Similarly, under a \"subsidy scenario,\" half of the increase in the agricultural budget is allocated to input subsidies.By design, the two accelerated spending scenarios are similar in terms of overall budget envelope-the budget grows to GHC 2.2 billion by 2025-but distinct in terms of their evolving budget compositions and coverage rates. Under the extension scenario, 28 percent of the budget is allocated to extension services by 2025, compared to only 3 percent in the baseline scenario (Figure 2). This leads to an almost sixfold increase in the share of households receiving extension services during the 2015 to 2025 period. Likewise, under the subsidy scenario, the allocation to input subsidies reaches 43 percent by 2025 compared to 23 percent in the baseline. This is associated with a four-fold increase in the cropped area that benefits from the supply of subsidized farm inputs.Agricultural GDP in the baseline scenario continues to grow as it has in recent years, averaging 3.5 percent per annum between 2015 and 2025. In contrast, accelerated agricultural spending raises agricultural GDP growth to 8.6 percent in the extension scenario and 7.6 percent in the subsidy scenario over the same period. Thus, for a given budget, the extension-oriented scenario outperforms the input subsidy-oriented scenario. However, this result varies by sub-period (Figure 4). Between 2015 and 2020 when the agricultural budget expands rapidly, the subsidy scenario is associated with higher agricultural GDP and average annual growth compared to the extension scenario (12.4 versus 10.5 percent). However, thereafter, agricultural growth under the subsidy scenario slows significantly as budget constraints prevent continued rapid expansion in the subsidy coverage rate. In the later years of the study period, the extension scenario outperforms the subsidy scenario (6.7 versus 3.1 percent). The extension scenario starts yielding superior outcomes in terms of agricultural GDP roughly 6 years into the program.National GDP (at market prices) expands at 5.6 percent per annum in the baseline scenario. This accelerates to 6.4 and 6.3 percent in the extension and subsidy scenarios, respectively, thus mirroring the results for agricultural GDP. Structurally, growth in the components of GDP, e.g., consumption, investment, or government expenditure, are not markedly different in the two sub-periods in the baseline. However, there are differences in the level and structure of growth in the two sub-periods under the accelerated growth scenarios. For example, private consumption growth under the subsidy scenario exceeds that under the extension scenario during the 2015 to 2020 period (7.1 versus 6.6 percent), but the extension scenario performs better during the 2021 to 2025 period (private consumption growth of 6.1 versus 5.0 percent).As direct beneficiaries of agricultural policies, rural farm households gain more from accelerated spending in agriculture than do rural non-farm households, with income gains of 4.2 percent in the extension scenario and 4.0 percent in the subsidy scenario. However, even greater income gains are enjoyed by poor urban households, who see their real incomes rise 4.6 and 4.2 percent in the extension and subsidy scenarios, respectively. This is because food prices decline substantially when agricultural productivity increases. Whereas lower prices may have mixed effects for farm households who are both producers and consumers of food, it holds significant benefits to urban poor households who spend much of their income on food. As with earlier results, income gains among poor rural and (especially) urban households are higher under the subsidy scenario compared to the extension scenario initially (2015 to 2020), but not later (2021 to 2025).The household income results suggest that targeted agricultural interventions remain an effective measure for reducing poverty in both rural and urban settings. Figure 5 plots changes in rural poverty. Even under the baseline scenario, where GDP grows at 5.6 percent against a population growth rate of 2.3 percent, Ghana can anticipate rural poverty to decline from 38 percent in 2015 to 23 percent in 2025. However, the pace at which poverty falls increases under the accelerated spending scenarios, with rural poverty rates falling to 17 and 18 percent in the extension and subsidy scenarios, respectively. The rate of poverty reduction is higher under the subsidy scenario during the 2015 to 2020 period, but not during the 2021 to 2025 period.The results highlight a common sustainability challenge of subsidy programs. Output growth under smallholder input subsidy programs is primarily associated with new beneficiaries being brought into the program and being converted from low-yield producers using traditional inputs to high-yield producers using modern (subsidized) inputs. When budget constraints prevent continued expansion of subsidy coverage, existing beneficiaries will still be able to maintain high crop yields through continued access to modern inputs, but there will be no further growth in their yields or output. Since no new beneficiaries are added, there will be no further growth originating from the conversion of additional low-yield farmers into high-yield ones.Under the extension scenario, farming knowledge does not deteriorate quickly and the cost of maintaining or building on existing knowledge is relatively low. This leaves a greater share of public resources under this scenario to expand coverage and reach new beneficiaries, even when the budget is constrained. This results in the extension scenario being more sustainable than the subsidy scenario in the longer run.These results do not suggest that subsidy programs should be abandoned; on the contrary, there are many arguments in their favor. They represent a useful tool for rapidly raising yields and boosting growth, especially in settings where input use is low, and households are resource-constrained. However, more emphasis is needed on increasing the sustainability of these programs. For example, returns to fertilizer subsidies can be increased by helping farmers raise fertilizer use efficiency, e.g., through extension services focused on improving soil fertility or promotion of modern seed use; or through efforts made to ensure that farmers continue using fertilizer when they graduate from the program. However, subsidy programs should also not come at the expense of other important investments, such as in extension, research, or rural infrastructure, all of which tend to provide more sustainable returns in the longer term. The ideal is to have a balanced public agricultural investment portfolio.In summary, Aragie et al. demonstrate how economywide modeling frameworks can provide a complete and consistent method for planning and analyzing complex policy interventions. Their results show that it is not only the level of agricultural spending that matters, but also the composition of spending. Policymakers, when faced with budget constraints, must acknowledge trade-offs and be aware of the implications of their policy prioritization decisions. More generally, policies and investment plans should move from long \"wish lists\" to strategic documents that identify priorities within a reasonable budget envelope.The analysis by Aragie et al. can certainly be improved. Better information from agricultural budgets, program costs, and impact evaluations will help refine the investment module. Also, assumptions about policy design and impact channels can be refined. In this regard, additional modeling of input subsidies provided through the Planting for Food and Jobs (PFJ) initiative will be particularly useful to these debates.Economywide models are suited to analysis of complex policy interventions. This research demonstrates that both the level and composition of agricultural spending matter.","tokenCount":"1937","images":["918609392_1_1.png","918609392_2_1.png","918609392_2_2.png","918609392_3_1.png","918609392_3_2.png"],"tables":["918609392_1_1.json","918609392_2_1.json","918609392_3_1.json","918609392_4_1.json"]}

data/part_2/0087983638.json

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+{"metadata":{"gardian_id":"40cf49b2e6838e4ee3cecb6a2728a6bb","source":"gardian_index","url":"https://dataverse.harvard.edu/api/access/datafile/:persistentId/?persistentId=doi:10.7910/DVN/ESTIZZ/XUBH58","description":"This survey was conducted to identify the current capacity within Nigeria for providing evidence for policymaking and for creating this capacity for future generations. This is part of the program to support the designing and implementing of evidence-based, pro-poor, gender sensitive and environmentally sustainable agricultural and rural development policies and strategies in Nigeria. The data presented originates from the survey conducted in 2008. A total of 25 institutions and 184 individuals were sampled. Topics covered for institutions include statements seeking to establish an understanding of the agricultural and rural policy environment and process, institution and institutional environment, financial resource management, human resources management, staff performance and performance appraisal, organizational management, autonomy in personnel and budgetary issues, and technical capacity. Topics covered for individuals include background information about respondent, experience with the agricultural and rural development policy process, gender in the policy process, environmental issues in the agricultural and rural development policy process, job satisfaction and institutional incentives.","id":"-2061307681"},"keywords":[],"sieverID":"4444353d-6e2a-4f76-90dc-5144fc0b6dbc","pagecount":"1","content":"While all information, which would allow individuals to be identified, has been deleted from the files, all other information remains in the data files. The decision not to alter the contents of the data files means that the user of these files will need to take care in handling missing observations, outlier values, and violations of logical consistency. The authorized use of these data is limited to government, academic, and research institutions (or individuals associated with these institutions) to be used for informing and improving government policy or for educational purposes. The data is not authorized to be used for commercial purposes. The data are provided 'as is' and in no event shall IFPRI be liable for any damages resulting from use of the data. While great effort was taken to obtain high quality data, the accuracy or reliability of the data is not guaranteed or warranted in any way.","tokenCount":"150","images":[],"tables":["-2061307681_1_1.json"]}

data/part_2/0090227179.json

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+{"metadata":{"gardian_id":"da7e63e1ce5f7b108895dccf1ddd346c","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/c7434420-0029-4467-b471-7f0cd7998e17/retrieve","description":"Sub-Saharan Africa (SSA) faces great challenges in development, including the highest poverty rate in the world, food insecurity, and malnutrition. Given that agriculture is the single most important source of rural livelihood in Africa, an agricultural growth strategy will go a long way to reducing hunger and poverty on the subcontinent. Among the numerous challenges to enhancing agricultural production in SSA is the large spatial and temporal variability and availability of water resources. Currently, agriculture in SSA is predominantly rainfed. The limited access to water in arid areas or during dry seasons and drought spells often presents restrictions to improving agricultural productivity. Therefore, enhanced agricultural water management has been regarded as a promising solution to boost levels of agricultural productivity in SSA.","id":"1809500120"},"keywords":[],"sieverID":"44c96c41-c51d-4fac-a628-cd78381dbeb0","pagecount":"8","content":"Sub-Saharan Africa (SSA) faces great challenges in development, including the highest poverty rate in the world, food insecurity, and malnutrition. Given that agriculture is the single most important source of rural livelihood in Africa, an agricultural growth strategy will go a long way to reducing hunger and poverty on the subcontinent. Among the numerous challenges to enhancing agricultural production in SSA is the large spatial and temporal variability and availability of water resources. Currently, agriculture in SSA is predominantly rainfed. The limited access to water in arid areas or during dry seasons and drought spells often presents restrictions to improving agricultural productivity. Therefore, enhanced agricultural water management has been regarded as a promising solution to boost levels of agricultural productivity in SSA. Motor pumps are one example of a promising agricultural water management technology. These pumps generally consist of either diesel or electric engines coupled with a lowlift centrifugal pump. The major advantage of motorized pumps is their considerable capacity relative to traditional water-lifting means, making it possible to expand irrigated surface areas. Their exibility to move among di erent water sources and many farmers is another advantage. Disadvantages include high capital costs, high recurrent costs, high maintenance levels, and the emission of greenhouse gases.This brief is based on a study that uses an integrated modeling system that combines geographic (GIS) data analysis, biophysical and economic predictive modeling, and crop mix optimization tools to assess the regional potential for smallholder agricultural water management in SSA and South Asia (SA). It focuses on the potential for the expansion of motor pumps throughout SSA.The assessment process includes two components: ex-ante GIS and predictive modeling analyses. The ex-ante analysis uses a set of suitability criteria to identify areas where the technology could potentially be applied, pixel by pixel, across the region. The formulation of assessment criteria and the scoring scheme were developed through expert consultations and validation and re ect the best available expert knowledge. For motor pumps, the environmental suitability criteria for ex-ante GIS analysis are shown in Table 1.A pixel with a score greater than 55 is considered to have irrigation potential. The application areas derived from the suitability analysis were also compared with the laborconstrained application areas obtained from rural population analysis at the basin level; the minimum of the two application areas in a river basin was selected as the nal exante estimate.The results derived from ex-ante GIS analysis are further re ned in an analysis that involves the application of two biophysical and economic predictive modeling tools: the Soil and Water Assessment Tool (SWAT) and the model of Dynamic Research Evaluation for Management (DREAM). Currently, agriculture in SSA is predominantly rainfed and farming activities concentrate in the rainy seasons. This analysis assumes that motor pumps would enable producers to extend crop production into the dry season, when the irrigation demand is highest. Under this assumption, the SWAT and DREAM models were run to simulate the hydrology, estimate crop water demand and agricultural productivity in the added dry growing season, and forecast price shifts in agricultural commodities as a result of increased supply. The results produced from the SWAT-DREAM predictive analysis allow for quantitative water balance and cost-bene t analysis of irrigation activities. This further constrains the potential for irrigation expansion compared to the ex-ante analysis, based on physical scarcity and economic viability.  Other key assumptions in the predictive modeling assessment include the following:• Water Availability. Both groundwater and surface water can be used for irrigation. The groundwater abstraction rate is restricted so as not to exceed the recharge rate. Moreover, 20 percent of runo is reserved for environmental ows.• Cultivation of Particular Crops. The assessment assumes that motor pumps are used for the cultivation of a series of crops based on evidence from eld studies including: tomatoes, onions, peppers, cabbages, beans, peas, potatoes, sweet potatoes, sugarcane, ground nuts, maize, wheat, and rice.• Fertilizer Input. Agricultural production in SSA is characterized by the wide presence of low-input farming systems. However, because there exists strong synergy between water and nutrient management-that is, farmers need to provide an appropriate amount of nutrients to the soil, especially nitrogen, to ensure irrigation is e ective in improving crop yields-medium rates of nitrogen fertilizer applications were assumed in the crop simulation. The assumed amount of nitrogen fertilizer applied to each crop type is shown in Table 2.The estimated yields of selected crops cultivated under irrigation and assumed nitrogen fertilizer applications (as opposed to the estimated yields in low-input farming systems in SSA) are shown in Table 3.• Production and Irrigation Costs. Assumed costs of production for the selected crops are shown in Table 2.A cost for irrigation of US$263 per hectare per year was also assumed, with average amortized capital investment costs of $78/ha-yr (original capital investment: $269 and a reinvestment timeframe of eight years) and operating costs of $185/ha-yr. The costbene t results are very sensitive to these cost assumptions. A sensitivity analysis in which irrigation costs were increased or decreased by 50 percent was, therefore, conducted.It is expected that irrigation will boost agricultural productivity and increase the supply of agricultural commodities, while also lowering their prices. To account for the e ect of price changes on the economic pro tability of irrigation development, the DREAM model is used to forecast price shifts. Baseline data for the model were obtained from FAOSTAT Food Balance sheets, FAO PriceSTAT, and the IFPRI IMPACT model.It was found that the estimated irrigation potential is also sensitive to changes in initial crop prices. A 30 percent increase and a 30 percent decrease in initial crop prices were implemented as additional sensitivity analyses.The ex-ante assessment shows that the potential for the expansion of motor pumps is 48 million ha, potentially reaching a rural population of 309 million people (Figure 1 and Table 4). The potential for motor pump expansion is highest in the Gulf of Guinea region, with a potential expansion of over 12 million ha reaching 74 million people, driven primarily by the large potential in Nigeria. The Eastern and Indian Ocean countries and the Sudano-Sahelian region also show considerable potential for expansion of the technology, with 67 and 79 million people potentially reached in each of these regions, respectively. Compared to the Gulf of Guinea, the potential is spread more evenly across countries in the Eastern and Sudano-Sahelian regions, with signi cant potential found in Ethiopia, Kenya, Madagascar, Tanzania, and Uganda in the Eastern region and Burkina Faso, Chad, Mali, Niger, and Sudan in the Sudano-Sahelian region.awm-solutions.iwmi.org Taking river basin hydrology, environmental constraints, yield improvements, costs of the investment, and price impacts of expanding crop production into account results in lower potential for adoption of motor pumps in the region compared to the ex-ante assessment (Figure 2). The results of the SWAT-DREAM assessment for motor pumps are summarized in Table 5 for the baseline scenario. The results indicate a potential area expansion of 29.6 million ha, which is less than two thirds of the area potential shown in the exante analysis.The total number of people reached is reduced to 185 million, compared to 309 million in the ex-ante assessment.Total net revenues as a result of the expansion of motor pumps throughout the region would be $22 billion per year, with revenues highest in the Eastern and Indian Ocean countries, Southern Africa, and the Gulf of Guinea. This expansion would be accompanied by a signi cant increase in water consumption. The total increase in water consumption as a result of the expansion of motor pumps in SSA is estimated at 67 billion m 3 /yr, which amounts to a 98percent increase.   The results of sensitivity analysis (Table 6) show that estimated application areas, net revenues, and rural population reached increase with decreasing irrigation costs and higher food prices, and vice versa.With a 50 percent reduction in the cost of irrigation, the application area would increase by 1.6 million ha, net revenues would increase by $4 billion per year, and the rural population reached would increase by 10 million, compared to the baseline. Conversely, application area decreases by 4 million ha, net revenues decline by $4 billion, and the number of people reached is reduced by 27 million when irrigation costs increase by 50 percent.The potential for motor pumps is even more sensitive to changes in the initial crop price. Under the di erent crop price scenarios, a 30 percent increase in initial crop price results in an additional potential application area of 1.5 million ha, an increase in net revenues of $17 billion, and an additional 9 million people reached.A decrease in the initial crop price results in a lower application area (by 11.5 million ha), a reduction in net revenues (by $15 billion), and a decline in rural population reached (by 71 million), compared to the baseline. Water consumption increases signi cantly under scenarios resulting in an increase in motor pump expansion. A 50 percent decrease in irrigation costs or a 30 percent increase in initial crop price would increase water use by an additional 1 and 3 billion m 3 /yr compared to the baseline, respectively.Groundwater resources are important to the expansion of motor pumps. The baseline scenario assumed that groundwater was available to support the expansion of motor pumps up to the groundwater recharge level. However, when groundwater is excluded as a water source, area potential for motor pumps drops substantially, to 22 million ha for all of SSA. This result also suggests that the potential estimates obtained after SWAT-DREAM modeling is heavily constrained by the physical availability of water resources, with variations across countries and basins.The impacts of climate change on the application potentials of motor pumps, the application areas, rural population reached, net revenue, and water use across SSA were also estimated under two climate scenarios projected by the CSIRO-Mk3.0 model (Csia) and the CNRM-CM3 model (Cnra) (Table 7). In a preliminary analysis, the two scenarios were identi ed as the \"driest\" and \"wettest\" scenarios, respectively, among 12 future climate change scenarios projected by general circulation models for SSA. We use the A2 SRES scenario, which is considered moderate.The results in Table 7 show that changes in the estimated application area due to climate change range from -10 percent to +6 percent.Motor pumps have a very large potential for expansion in SSA in terms of application area and rural population reached, although the estimated numerical values are highly sensitive to the assumptions we made in the assessment.The expansion of motor pumps may be constrained by various factors, including the physical scarcity of water in the dry season. Both \"hard\" and \"soft\" constraints on water availability were imposed in the assessment, with groundwater withdrawals limited to recharge levels and a reserve of 20 percent of runo for environmental ows. Under these constraints, the expansion of motor pumps in SSA would consume an additional 67 billion m 3 of water per year, a 98-percent increase over current water use.Unregulated adoption of motor pumps will likely result in larger expansion, which could create signi cant tension over water allocation and undermine the sustainability of aquatic environments. To address the concerns associated with motor pump expansion, regulations and policies that help to internalize externalities of irrigation development should be developed hand-in-hand with investments in this area.Motor pumps have a very large potential for expansion in SSA, but unregulated adoption could undermine the sustainability of the water reserves.","tokenCount":"1890","images":["1809500120_1_1.png","1809500120_2_1.png","1809500120_5_1.png","1809500120_8_1.png"],"tables":["1809500120_1_1.json","1809500120_2_1.json","1809500120_3_1.json","1809500120_4_1.json","1809500120_5_1.json","1809500120_6_1.json","1809500120_7_1.json","1809500120_8_1.json"]}

data/part_2/0093615558.json

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+{"metadata":{"gardian_id":"46fa91708a094310ff1f4f0ccd526b29","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/94aeb919-5b68-48eb-a952-86bc2ac6567c/retrieve","description":"There are concerns that increasing women’s engagement in agriculture could have a negative effect on nutrition because it limits the time available for nutrition-improving reproductive work. However, very few empirical studies have been able to analyze whether these concerns are well-founded. This paper examines whether an increase in women’s time in agriculture adversely affects maternal and child nutrition, and whether the lack of women’s time in reproductive work leads to poorer nutrition. Using data from Bangladesh, Cambodia, Ghana, Mozambique, and Nepal, we find that on the whole, in poor households, reductions in women’s reproductive work time are detrimental to nutrition, especially for children. In contrast, women’s and children’s nutrition in nonpoor households is less sensitive to reductions in time on reproductive work. Working long hours in agriculture reduces women’s dietary diversity score in Ghana and nonpoor women’s in Mozambique. However, for poor women and children in Mozambique, and children in Nepal, working in agriculture in fact increases dietary diversity. This suggests that agriculture as a source of food and income is particularly important for the poor. Our results illustrate that women’s time allocation and nutrition responses to agricultural interventions are likely to vary according to socioeconomic status and local context.","id":"1220282230"},"keywords":["time use","gender","agriculture","nutrition","poverty"],"sieverID":"a732840f-827d-49d9-bca0-212704c6c5a9","pagecount":"52","content":"established in 1975, provides evidence-based policy solutions to sustainably end hunger and malnutrition and reduce poverty. The Institute conducts research, communicates results, optimizes partnerships, and builds capacity to ensure sustainable food production, promote healthy food systems, improve markets and trade, transform agriculture, build resilience, and strengthen institutions and governance. Gender is considered in all of the Institute's work. IFPRI collaborates with partners around the world, including development implementers, public institutions, the private sector, and farmers' organizations, to ensure that local, national, regional, and global food policies are based on evidence. IFPRI is a member of the CGIAR Consortium.Researchers have paid increasing attention to the links between agriculture and nutrition to identify the conditions in which agricultural and nonagricultural programs could improve nutrition. Kadiyala and others (2014) outlined six pathways through which agriculture and nutrition are connected. Two of the six pathways specifically address the potential effects of women's time use on nutrition.One of the agriculture-nutrition pathways proposes that increasing women's engagement in agriculture can negatively impact child nutrition by reducing women's time for nutrition-enhancing activities (Headey, Chiu, and Kadiyala 2012;Kadiyala et al. 2014). A range of practices contribute to the underlying determinants of nutrition-care, diets, and health (UNICEF 1990). These practices include food preparation; feeding; breastfeeding; child hygiene; collecting clean water and cooking fuel; good hygiene and sanitation practices; and accessing health services, such as antenatal care, regular health checkups, child vaccinations, health and nutrition information, and government food and nutrition programs (Glick 2002;Smith et al. 2003;Bhalotra 2010;Rani and Rao 1995;Lamontagne, Engle, and Zeitlin 1998). Given social norms dictating that women provide the bulk of reproductive work,1 women under a time constraint may carry out such practices with reduced frequency or reduced quality, or forego them altogether.In some circumstances, other members of the household may substitute in these activities, but the quality of their care could be worse than maternal care, especially when the child is an infant or the caregiver an older child (Glick 2002;Headey, Chiu, and Kadiyala 2012;Engle, Menon, and Haddad 1999). This suggests that sufficient time spent on reproductive work is important, but that other factors, like knowledge and access to assets, may mediate the effects of this time on nutrition.However, achieving access to knowledge or assets often requires time. Maternal time, shifted from reproductive work to agricultural or other productive work, can increase access to food and income. This may especially benefit nutrition if women control the earnings from their own work or if working increases their decisionmaking power, given evidence that women are more likely to spend earnings on nutrition-enhancing purchases (Gillespie, Harris, and Kadiyala 2012;Smith et al. 2003;Malapit and Quisumbing 2015). Whether maternal time is more beneficial for nutrition in productive or reproductive work is also contingent on the care needs of children, which vary by age (Lamontagne, Engle, and Zeitlin 1998).The second agriculture-nutrition pathway that relies on women's time use suggests that women's employment in agriculture requires them to intensify work effort, lengthen working hours, and increase their overall work burden in productive and reproductive activities combined (Kadiyala et al. 2014). Agricultural activities that require long working hours are likely to have a negative effect on women's nutritional status (Higgins and Alderman 1997;Headey, Chiu, and Kadiyala 2012) and are especially risky during pregnancy (Rao et al. 2003).However, there is little evidence to corroborate the conditions under which an increase in women's time in agriculture and a reduction in time in reproductive work influence child and maternal nutrition, most likely because datasets containing both maternal and child nutrition and women's time use are scarce (Kadiyala et al. 2014;Headey, Chiu, and Kadiyala 2012).This study aims to examine, theoretically and empirically, whether the lack of women's time in reproductive work leads to poorer maternal and child nutrition, and whether an increase in women's time in agriculture, or productive work, has a detrimental effect on maternal and child nutrition. The paper makes a theoretical contribution by developing a framework to establish how agricultural investments impact women's time and nutrition. By doing so, we identify two additional agriculture-nutrition pathways by which agricultural investments can impact nutrition. The paper also makes an empirical contribution by studying the evidence on the effect of women's time allocation on their dietary diversity score, minimum acceptable diet, children's dietary diversity score, minimum dietary diversity, and minimum meal frequency in different country contexts, namely Bangladesh, Cambodia, Ghana, Mozambique, and Nepal.Our analysis disaggregates the impact of women's time on maternal and child nutrition by poverty category, with poor households defined as those belonging to the poorest asset quintile. Poor households not only represent low socioeconomic status, but they also lack access to or ownership of time-saving assets.We develop the theoretical framework on agriculture, time use, and nutrition based on Singh, Squire, and Strauss's (1986) agricultural household model by introducing women's time in domestic work and a nutrition production function. The advantage of the agricultural household model is that it recognizes the role of households as both producers and consumers of agricultural produce, and it can easily incorporate women's time and nutrition. Given that many agricultural projects involve public capital investments in agriculture and rural infrastructure, we examine the impact of public capital investments on women's time allocation and nutrition. We assume that the household is composed of a mother, a father, and a child, but the father's labor supply is inelastic and unresponsive to price changes. The household maximizes a welfare function that allocates the mother's time between farm work (f), leisure (l), and nutrition-improving domestic work (t). The latter involves activities such as cooking, caring, domestic work, or accessing health care. Hence, the mother faces a time constraint as follows:where T is the mother's total time available net of self-care such as sleeping and eating. We assume that prices and wages are exogenously given. The household produces only one crop, and we exclude the possibility of child labor. Total farm labor (L), which is the sum of the mother's time in farm work (f) and that of hired labor, and capital (K) are used to produce an agricultural crop following the production function(2)The production function exhibits diminishing marginal productivity to labor and capital, Q LL < 0, QKK < 0. We assume that the level of capital is fixed and exogenous. The justification for the exogeneity of capital is that agricultural and rural infrastructure projects, rather than a decision within the household itself, often determine the household's level of and access to capital.Nutritional status (N), denoted by a nutrition production function, is generated by the household consumption of agricultural staples (Xa), mother's time in domestic work (t), and level of capital (K). Capital increases the marginal product of nutrition because it includes time-saving technology, such as access to electricity, piped water, or transportation:2 N = N (Xa, t, K), (3) where the marginal product of each input is positive, and Nxx < 0, Ntt < 0, and NKK < 0. We assume that capital can reduce the mother's time in domestic work, and therefore the mother's time and capital are substitutes (NtK < 0). However, we also assume that consumption of agricultural crops does not impact women's time, and hence Ntx = 0. 3The household maximizes the following welfare function, which consists of utility derived from mother's leisure time (l) and the household nutritional status (N), which are additively separable, as shown below:where the marginal utility of each argument is positive, and Ull < 0.The budget constraint, similar to the formulation by Singh and others (1986), is given as follows:(5)The household produces its crop at a level Q, and (Q -Xa) is the agricultural surplus that it can sell in the market at price pa. The term (L -f) is hired labor if (L -f) > 0, and it is the mother's time spent in wage work outside her farm if (L -f) < 0, and w is the market wage. It is assumed that the mother's labor and hired labor are perfect substitutes, and that the wage rate the mother can receive by engaging in wage work is the same as the hired laborers' wage.By substituting the production function (2) and time constraint (1) into the budget constraint (5), we can rewrite equation ( 5) asThe terms on the left-hand side are costs to the household, while the terms on the right-hand side are considered full income (Singh, Squire, and Strauss 1986).By substituting the nutrition function into the welfare function, the household maximizes the welfare function subject to (6). Following the agricultural household model, we first maximize agricultural profits by differentiating the Lagrangian equation with respect to total farm labor L. This results in the household's producing agricultural outputs until the marginal product of labor is equal to its wage rate. As noted by Singh and others (1986), total farm labor demand (L) is a function of the wage rate and pa, and is determined independent of the welfare function. This means that production decisions are made first, and consumption decisions are made subsequently. Once the level of agricultural output Q* is chosen, full income can be derived by substituting L* and Q* into the right-hand side of (6). Given the full income, the household can maximize welfare by differentiating the Lagrangian equation with respect to t, l, and Xa. Once optimal time in reproductive work d* and leisure l* is identified, the mother's time in own-farm labor (f) is calculated from equation (1). If the total farm labor L* is greater than f*, then the household will hire labor, and if it is less, the mother will engage in wage work.We examine the effect of an exogenous increase in capital (K) on the mother's time allocation and nutrition because it increases the productivities of agriculture and nutrition. The comparative statistics are derived by totally differentiating the first-order conditions. Doing so gives the following Hessian determinant:4 |H| = Pa QLL{ w 2 Nxx (Ull + Ntt ) + Pa 2 Ull Ntt } < 0.(7) Using Cramer's rule, the effect of capital on women's domestic time is given byWe cannot sign expression (8) without making further assumptions. Capital investments such as better access to piped water and sources of fuel or electricity can reduce women's time in domestic work, which is the second term of (8) that begins with Ntk. We refer to the effect of capital investment on reducing domestic work as the \"reproductive time displacement effect.\" The investments can also increase agricultural productivity and output, and hence full income, which we refer to as the \"agricultural productivity effect.\" An increase in production and income allows the mother to spend more time in nutrition-improving domestic work (shown by the first term of [8]). If the capital investments' agricultural productivity effect is large enough to outweigh the reproductive time displacement effect, then capital investments increase domestic work; that is, dt/dK > 0. If, on the other hand, the reproductive time displacement effect is greater than the agricultural productivity effect, then capital investment reduces the domestic work burden.In order to examine the effect of a capital increase on women's farm work (f), we have to first assess its impact on leisure. Using Cramer's rule, we find that capital has a positive effect on leisure time, as shown below:Capital investments reduce women's total work burden, which is the sum of domestic and agricultural work. This equation also predicts that women in capital-poor households will have less leisure time and be more time constrained than women in nonpoor households.The effect of an investment on the mother's farm work (f) is derived by differentiating the mother's time constraint (1) with respect to capital:Substituting ( 8) and ( 9) into (10), and rearranging givesThe sign of the expression depends on the relative size of the reproductive time displacement effect (the first term in [11]) and the agricultural productivity effect (the second term in [11]). If the productivity effect is larger than the reproductive time displacement effect, then capital investment reduces the mother's farm work: she would spend more time in domestic work. In contrast, if the reproductive time displacement effect is greater than the productivity effect, capital investment increases farm work, and she would spend less time in domestic work. Therefore, the mother shifts her time allocation to the activity that experiences a lower productivity increase, while her overall work burden falls.Capital investment increases consumption of agricultural crops (Xa) as shown below:There are several ways in which greater capital stocks can impact nutrition. First, higher levels of capital stock raise consumption of agricultural products, which in turn has a positive effect on nutrition through the nutrition production function. Second, more capital increases agricultural production, and hence the household's full income, thereby improving nutrition through an income effect.5 Third, capital investment raises the marginal productivity of nutrition (NK > 0), which directly affects nutrition. For example, certain investments can improve nutrition without having any implications on women's time. Installing improved sanitation facilities may not affect time, but it directly leads to a better nutritional outcome. Fourth, if the agricultural productivity effect is greater than the reproductive time displacement effect, it can increase the mother's domestic work and reduce her farm work. This in turn has a positive effect on nutrition through the nutrition production function. In contrast, if the reproductive time displacement effect is greater than the agricultural productivity effect, it can reduce the mother's domestic work and increase her farm work, which would worsen nutrition. The net effect of capital investment on nutrition would be positive if the consumption, income, nutrition productivity, and agricultural productivity effects outweigh the reproductive time displacement effect. The relative size of the five effects on nutrition depends on the type of capital investments. Some may have a greater impact on reducing women's domestic time than they do on increasing agricultural output or nutrition.Table 2.1 summarizes the expected signs of capital investments on women's time allocation and nutrition. We compare the predictions of the theoretical framework with the agriculture-nutrition pathways from Kadiyala and others (2014), shown in the fourth column of Table 2.1. It should be noted that Kadiyala and colleagues (2014) considered agriculture in a more comprehensive manner, including price changes and input support, while we look only at the effect of an increase in capital stock, and hence the nutrition implications of our model are not complete. 6 Our analysis also includes capital investments that are not directly related to agricultural investments. Therefore, although the results are not directly comparable, it is nevertheless useful to compare them. The first and second pathways in Kadiyala and others (2014) refer to agriculture as a source of food and of income for food and nonfood expenditures. These are similar to the consumption and income effects of capital investment, respectively, in our theoretical framework. The fifth and sixth pathways focus on the detrimental effects of maternal employment in agriculture on child and maternal nutrition, which is similar to our prediction when the time displacement effect exceeds the agricultural productivity effect.Our theoretical framework identifies two more pathways through which agricultural investments can impact child and maternal nutrition: the direct nutrition productivity effect and the agricultural productivity effect. In particular, the latter effect, namely that investments can actually reduce women's farm work, increase domestic work, and improve nutrition, has not been recognized in the literature so far.We can see that the impact of capital investment on women's time allocation and nutrition is far from clear-cut. There are several reasons why the predictions are ambiguous. We do not know, a priori, the shape of the utility and nutrition production functions; the relative size of capital investment's income effect, nutrition productivity effect, agricultural productivity effect, and reproductive time displacement effect on women's domestic work; and whether capital and labor are substitutes or complements. The shape of the production functions can be assessed only with empirical evidence. 6  Kadiyala and others (2014) included two pathways that we do not explore here. These relate to price changes and policy, and intrahousehold allocations. In our theoretical model, it is possible to include other inputs (such as fertilizers, seeds, or pesticides), costs related to accessing markets, and production of multiple crops (Singh, Squire, and Strauss 1986). We can then assess the nutrition impact of price changes of one or more crops; costs of fertilizers, seeds, or pesticides; and improving access to markets.The study uses 2012 data from the Bangladesh Integrated Household Survey (BIHS); population-based surveys from the US Agency for International Development (USAID) Feed the Future initiative in USAID's zones of influence (ZOIs) in Cambodia, Ghana, and Mozambique; and a baseline survey of a USAID-funded nutrition program called Suaahara in Nepal. 7  Table 3.1 provides the gross national income per capita of the five countries, which range from the lowest, Mozambique, at $1,140 8 per capita, followed by Nepal. The highest is Ghana, with $3,910 per capita. All are classified by the World Bank as low-income countries, except for Bangladesh and Ghana, which are lower-middle-income countries. The datasets collected information on household and individual characteristics, food security, and maternal and child nutrition. For the surveys in Bangladesh, Cambodia, Ghana, and Mozambique, primary female and male household members, usually the household head and spouse, responded to questions about their time use, degree of participation in decisionmaking in key economic activities, and group participation, which are part of the Women's Empowerment in Agriculture Index questionnaire (Alkire et al. 2013). In Nepal, the respondents were mothers of children younger than five, and their husbands, if available (Cunningham et al. 2013).Two-stage probability sampling methodology identified the enumeration areas (EAs) and then randomly selected the households within the EAs. In Nepal, multistage cluster sampling was used, with the final-stage sampling unit being rural households with children younger than five (Cunningham et al. 2013). A mother and an index child were surveyed in each household, with the index child randomly selected in each household from among the household's children younger than five.Both time use and nutrition indicators vary throughout the year depending on seasonal fluctuations in labor demand and food security. The period in the year in which survey data were collected is important to note to contextualize these findings. Data collection in all countries overlapped at least partially with a lean season.Bangladesh data collection occurred between October 26 and November 30, 2011. Data collection coincided with a transition out of a lean period and the beginning of the harvest of the aman rice crop, the major crop for smallholder households. October and November are considered a lean season; preceding the harvest of aman rice in mid-November, agricultural employment is relatively scarce. As the harvest begins, labor demand is high, increasing workloads especially for men (GIEWS and FAO 2015).Cambodia data collection occurred in September 2012, which is considered the lean season that precedes the main rice harvest in December (Tickner 1996).In Ghana, data collection, from July 1 to August 17, 2012, coincided with the rainy season and the major farming season in the region. Workloads in this period are particularly high relative to the rest of the year, and the work intensity was thought to have raised the rate of nonresponse. This period also coincided with Ramadan, the Muslim period of mandatory fasting, beginning July 20 and lasting for 7 Feed the Future data for Haiti, Uganda, and Zambia were analyzed too. However, for Uganda and Zambia, a significant proportion of observations on the time use data exceeded 24 hours in a day or totaled less than 1,000 minutes. For Haiti, more data cleaning is needed before further analysis can be conducted.8 All dollar amounts are in international dollars.about a month. Although children are not required to fast, some families encourage their children to practice fasting. Women's dietary diversity and children's minimum acceptable diet indicators may be affected by household observance of Ramadan (Zereyesus et al. 2013).In Mozambique, data were collected from February 5 to May 6, 2013, in Manica, Nampula, and Zambezia. When the Feed the Future ZOI was expanded to include three districts in the province of Tete, an additional round of data collection was undertaken from November 22, 2013, to January 3, 2014. Though the two rounds of data collection occurred six months apart, both rounds were primarily conducted during the lean season in the respective region: October-February in southern and central Mozambique, and December-early March in the north (Feed the Future FEEDBACK 2014), corresponding with the rainy and cyclone season.Nepal data collection occurred from June 13 to October 6, 2012. July to August is considered the lean season in Nepal (WFP 2012), with the start of maize and paddy harvest occurring from September to October (WFP 2013). Food insecurity is expected to be higher during the lean season and attenuate in September, as agricultural workloads rise.Nonresponse rates are relevant to this study because they could indicate the most timeconstrained respondents, those that faced too great a time burden to respond to the survey. In Ghana, nominal sample sizes based on stunting and underweight indicators were adjusted to account for households without children in the age group of 0-59 months and further inflated by 10 percent to adjust for nonresponse. In Mozambique, nonresponse rates were 2.25 percent for male and female adults together (Feed the Future FEEDBACK 2014). Sampling weights corrected for unequal probabilities, noncoverage of the population, and nonresponse. 9The sample includes households engaged in either agricultural or nonagricultural activities, or both. The geographic areas cover rural households, except for Ghana and Mozambique, where 20 percent and 15 percent of households are located in urban areas, respectively. In order to assess the impact of time allocation on women's dietary diversity, the sample is restricted to women in the reproductive age of 15 to 49, following standard practice for women's dietary diversity (Arimond et al. 2010).The BIHS, conducted in 2011-2012, is representative of the rural areas of the seven administrative divisions (Sraboni et al. 2014). It used a two-stage sampling design to first allocate and select 275 primary sampling units (PSUs) among the seven divisions, and select 5,503 households within each PSU (Sraboni et al. 2014). We restrict our analysis to women between the ages of 15 and 49, yielding a sample of 4,248 women.Cambodia's data were collected for the baseline of the Helping Address Rural Vulnerabilities and Ecosystem Stability (HARVEST) program impact evaluation in the USAID ZOI. The study used a cluster sampling approach to select 1,500 treatment households in 60 village clusters and 600 control households in 24 village clusters. (Vuthy et al. 2013), for a total of 2,100 households interviewed in Pursat, Battambang, Siem Reap, and Kampong Thom provinces. The sample that we work with is 1,494 women, ages 15-49.In Ghana, the survey interviewed 4,410 households in the four northern regions of Brong Ahafo, Northern, Upper East, and Upper West.10 It included 2,465 women aged 15-49, with time use information and women's dietary diversity. We focus our analysis on female respondents whose total time recorded per day was less than or equal to 1,440 minutes (that is, 24 hours) and greater than 1,000 minutes. About 30 percent of the sample had a recorded total time of more than 1,440 minutes or less than 1,000 minutes, making the size of our final sample 1,735 women.In Mozambique, 2,864 households were surveyed, and 1,804 female respondents provided time use and dietary diversity information. Our sample includes 1,741 women between the ages of 15 and 49.The baseline survey of a USAID Nepal project, Suaahara, was conducted in June-October 2012 and was administered to 4,080 mothers with children younger than five, and their husbands, if available (Cunningham et al. 2013). The areas covered include 16 districts in three agroecological zones-mountains, hills, and Terai. Our sample consists of 4,003 women between the ages of 15 and 49, all of whom were mothers of children younger than five.The time use module was administered to respondents using a 24-hour recall period in 15-minute intervals. The surveys ask how the respondent spent the last 24 hours in the following categories: sleeping; eating and drinking; personal care; school; work as employee; work in own business; farming/livestock/fishing; shopping / getting services (including access to health services); weaving, sewing, and textile care; cooking; domestic work (including fetching water and wood); caregiving for children/adults/elderly; traveling and commuting; watching TV / listening to radio / reading; exercising; social activities and hobbies; religious activities; and others. The respondents also indicated whether the activities were primary or secondary activities.We define reproductive work to be the sum of activities related to cooking, domestic work (including fetching water and firewood), caring for a child or adult, shopping, and getting services. Activities classified as agriculture include farming, fishing, and livestock care, while nonagriculture includes working as employee or owner of a business, and weaving, textile, or sewing work. Productive work is defined as the sum of time in agricultural and nonagricultural work.The categorization of agricultural activities for Bangladesh deviates from this definition in that even though all farm-related activities (including home gardening) and fishing fall under agriculture, offfarm activities (including off-farm postharvest activities and drying paddy from the harvest) are classified as domestic work. Further, livestock rearing falls under nonagricultural work. These factors explain why Bangladesh's average time recorded for agriculture (4 minutes) is low and the mean time in domestic work is higher than in other countries, as shown in Table 3.2. In Nepal, time spent on cooking and domestic work is combined, and this category also includes weaving and textile care.Table 3.2 presents the average time men and women spent on productive and reproductive activities as a primary activity in the past 24 hours, with an indication that all of the differences between men's and women's activity times are highly significant (with nonagricultural work in Cambodia the single exception). In all countries but Bangladesh, women's overall work burden (reproductive and productive workload) is greater than men's. The difference in workload is greatest in Nepal, with women working 2.7 hours more than men per day, and least in Bangladesh, where men reported spending about 45 minutes more time working than women per day. In all countries, women spend more time in domestic work, cooking, and caregiving than men, with women spending between 3.2 (Cambodia) and 5 (Bangladesh) more hours per day on reproductive work than men. Men spend more time on agricultural and nonagricultural work, though women spend at least 3 hours in agricultural work in all countries except Bangladesh, due to the categorization explained above.Because men's time in reproductive work was low, we did not model the effects of men's time on nutrition in this study. However, the possibility of any shifts in gender norms could eventually lead to a greater impact of men's reproductive time on child and maternal nutrition outcomes.Table 3.3 presents the average time women spend in each activity, disaggregated by poverty category. Because secondary activities were not often recorded, the average time includes only primary activities. Poor households are defined as those that belong to the poorest quintile of that country's asset index. We constructed an asset index using principal component analysis with variables indicating whether the household owns a house or structures, nonagricultural land, large consumer goods, nonfarm business equipment, bike, motorcycle, or car, and has electricity; a flush toilet; and piped, a tube well, or a protected well as the main source of drinking water.11 Not only are these asset variables a proxy for socioeconomic status, but assets also have implications on women's time because many assets save time. *** Sources: Authors' calculations using data from Bangladesh Integrated Household Survey (2011) for Bangladesh; Feed the Future surveys for Cambodia (2012), Ghana (2012), and Mozambique (2012-2013); and baseline survey of Suaahara project for Nepal (2012). Notes: *** 1 percent significant, ** 5 percent significant, * 10 percent significant. a For Nepal, domestic work includes cooking and shopping. For Bangladesh, domestic work includes off-farm agricultural activities. b For Bangladesh, nonagricultural work includes livestock raising. n/a indicates that the data were not available.  (2012)(2013); and baseline survey of Suaahara project for Nepal (2012). Notes: *** 1 percent significant, ** 5 percent significant, * 10 percent significant. a For Nepal, domestic work includes cooking and shopping. For Bangladesh, domestic work includes off-farm agricultural activities. b For Bangladesh, nonagricultural work includes livestock raising. n/a indicates that the data were not available.Women in Nepal have the heaviest work burdens among all five countries, spending about 11 hours a day in total on productive and reproductive work. Women in Mozambique spend the least, at about 7.5 hours. These differences are likely to depend on the technology available and the agricultural season. The poor face greater workloads than the nonpoor in Bangladesh, Ghana, and Nepal because the average time in total reproductive and productive work is much greater for the poor than the nonpoor, and the difference is statistically significant. This confirms the prediction from our theoretical framework that the poor have a greater work burden and have less leisure than the nonpoor.Since we define poor households as those in the lowest asset quintile, they have a lower capital endowment than nonpoor households. The theoretical framework predicts that if the agricultural productivity effect of a capital investment were to exceed the time displacement effect, then women's domestic work burden would be greater and women would spend less time on farm work. This suggests that poor women would spend more time in agriculture, while the nonpoor would allocate more time to reproductive work. We generally find this holds true. In Ghana and Nepal, poor women spend close to 5 hours in agriculture, while the figures for nonpoor women are 3 hours in Ghana and 4 hours in Nepal. In Bangladesh, poor women spend 284 minutes in domestic work (which includes off-farm agricultural activities) versus 269 minutes for nonpoor women.12 However, in Cambodia and Mozambique, there is no difference in agricultural work by poverty category.Women also spend a large portion of their day in reproductive work. In Cambodia and Ghana, women on average allocate close to 5 hours to reproductive work, while in Nepal, the figure is greater than 6 hours. The impact of being poor on women's reproductive time has two competing effects, as suggested by the theoretical framework. On the one hand, it can increase the time for reproductive work or cooking because poor women lack time-saving assets. 13 On the other hand, it can reduce time for reproductive work because women have to spend more time in productive work or agriculture to compensate for the lack of income. In Mozambique and Nepal, nonpoor women spend more time than poor women in reproductive work, and the average time in reproductive work is particularly high for Nepal, 6.4 hours by poor women and 6.1 hours by nonpoor women. The reverse is true in Cambodia, and there is no statistical difference in mean time spent by poverty category in Ghana.In most countries, there is no difference in caregiving by poverty category, although there is likely to be underreporting of such work, given the low reporting of secondary activities. The difference in average time for cooking is pronounced in Bangladesh, Mozambique, and Nepal (the latter based on domestic work, which includes cooking), with nonpoor women spending more time than poor women. This suggests that poor women may not have enough time to prepare nutritious meals. In Cambodia, cooking time is greater for the poor than the nonpoor, but the difference is significant only at 10 percent. For nonagricultural work, the nonpoor spend more time than the poor in Cambodia and Ghana, but the reverse is true in Bangladesh and Nepal.Taken together, these results reveal that poor women face heavier workloads in Bangladesh, Ghana, and Nepal, particularly due to their substantial involvement in agricultural activities. Nonpoor women allocate more time for reproductive work than poor women in Mozambique and Nepal. This pattern is particularly pronounced in cooking time in Bangladesh, Mozambique, and Nepal. We explore its influence on child and maternal nutrition in Section 4.Understanding the type of activities in which female respondents were involved in the last 12 months is essential for analyzing time use data (Table 3.4). Most women in Mozambique (97 percent) engaged in cash crop farming in the last 12 months, but only 25 percent participated in food crop farming.14 In contrast, 67 percent of men were involved in food crop farming, while only 20 percent participated in producing cash crops. In other countries, women were more likely to have been involved in food crop than in cash crop farming. In Nepal, for example, half the women were involved in cash crop farming, while most (91 percent) participated in food crop farming. Almost 90 percent of women engaged in livestock raising in Nepal, which is not surprising given the high percentage of households who owned small or large livestock. This is followed by Cambodia, Bangladesh, and Ghana, at around 50 percent participation for women in raising livestock. In Mozambique, only 24 percent of women tended to livestock, and only 30 percent of households owned small livestock. Women's participation in nonfarm economic work (such as self-employment or small businesses) was highest in Ghana, at close to 40 percent, and most limited in Nepal, at 9 percent. Weights used.Table 3.5 presents the summary statistics for the five countries.   (2012)(2013); and baseline survey of Suaahara project for Nepal (2012). Notes: # = 1, 0 if otherwise. ^= 1 if achieved, 0 otherwise. Weights used. n/a indicates that the data were not available. Table 3.5 reveals that women in Cambodia have the highest dietary diversity of the countries studied, at 4.6 (out of 9 food groups), while women in Mozambique have the lowest, at 3.3. Women's dietary diversity is close to 4 for Bangladesh and Ghana, and 3.8 for Nepal.Children in Nepal have the highest dietary diversity, at 3.6 (out of 7 food groups). However, this could be partly attributable to the inclusion of older children (0-5 years old) in the Nepal sample, whereas the other country samples include only younger children, up to 23 months. Children's dietary diversity in Cambodia is 3.3, followed by Bangladesh (3.1) and Ghana (2.6). Children in Mozambique have the least dietary diversity, at 2.4.More than one-third of children aged 6-23 months in Cambodia and Nepal achieved a minimum acceptable diet, while about 17 percent achieved it in Bangladesh and Ghana. Again, Mozambique has the worst child nutrition by this measure, with only 9.5 percent of children achieving a minimum acceptable diet.Cambodia and Nepal have the highest percentage of children 6-23 months who achieved minimum dietary diversity, 46 percent, and one-third of children achieved it in Bangladesh and Ghana. In Mozambique, only 19 percent achieved minimum dietary diversity.Cambodia and Nepal have the highest proportion of children achieving minimum meal frequency (71 and 75 percent, respectively), while on the other end, less than 40 percent of children in Mozambique receive minimum meal frequency. Bangladesh and Ghana are not much better, with about 44 percent of children receiving minimum meal frequency.The challenge in examining the impact of women's time use on nutrition is that there could be unobserved characteristics, such as a woman's preference or ability to cook or care for her child, that influence her time allocation and also impact nutrition (Glick 2002). For example, a woman who likes to cook may spend more time cooking, thereby providing her family with more varied meals. Assessing the impact of cooking time on dietary diversity without taking into account the endogeneity of time would overstate the impact of time. Alternatively, a woman who is an efficient cook might be able to produce a diverse meal in less time. This would lead to an underestimation of the impact of time on dietary diversity. Hence, in the case of cooking time, the direction of the bias is not clear. Therefore, we use an instrumental variables technique to take into account the endogeneity of time use. The instruments are discussed later in this section.The first outcome we study is women's dietary diversity. Given the possible endogeneity of time, we use a two-stage least squares (2SLS) model, first estimating the log of women's time spent in activity j, and in the second stage assessing the impact of women's time use in activity j on their dietary diversity. The results from the 2SLS model will be compared with estimates from an ordinary least squares (OLS) model. We estimate the equationwhere N is women's dietary diversity in the previous day, βi are parameters to be estimated, log(time use +1)j is the log of time spent plus 1 minute in activity j in the last 24 hours (in minutes), X is a vector of individual and household characteristics, and u is the error term. In order to examine how the effect of time on women's dietary diversity changes with poverty status, we include an interaction term: β2 is the interaction effect of time use and poverty. Time allocation is aggregated into seven activity groups for reproductive and productive work, as discussed in Section 3.The other outcomes we study are whether a child aged 6-23 months achieved the minimum acceptable diet, minimum dietary diversity, minimum meal frequency, and child dietary diversity (out of seven food groups) in the past 24 hours. These are population-level infant and young child feeding (IYCF) indicators that measure food-related aspects of child feeding that are critical for child nutritional status and, ultimately, child survival. These indicators are intended to be assessed together to capture the multidimensional nature of appropriate child feeding (WHO 2008). Because minimum acceptable diet, minimum dietary diversity, and minimum meal frequency are binary outcomes, we estimate the average marginal effects from a probit model, and for child dietary diversity score, we use an OLS regression. Instrumental variable (IV) models were not used because the models did not converge.Our data are particularly well suited to studying the relationship between women's time use and these outcome variables because the time frame is consistent. It is possible to estimate the effect of women's time use in the last 24 hours on the women's and children's nutrition outcomes in the past 24 hours precisely because the reference periods are comparable. However, there are several limitations of this methodology. First, while we are able to assess the quantity of caregiving in terms of time, we are not able to evaluate the quality of care adults or children receive. Second, the activities are classified in broad terms. For example, it is not possible to assess the kind of agricultural activity the respondent engaged in, even though the implications on dietary diversity may be different if the respondent engaged in subsistence agriculture rather than agricultural wage work. Our data do not permit us to make these distinctions. Third, the data collection took place in only one season in a given country and may not fully reflect seasonality in time use. Fourth, given the low response rate on secondary activities in the datasets, the methodology is likely to underestimate time spent on certain activities, particularly caregiving, that usually occur concurrently with others. Last, even though maternal and child anthropometric measures were collected in the datasets, we do not assess the impact of time use on these outcomes because the causality is likely to be reversed. Anthropometric outcomes are a result of activities that took place before the surveys were conducted, while the time use information is collected in reference to the past 24 hours.This section defines the outcomes used in the analysis.Women's dietary diversity is defined by the number of food groups, out of nine, that female respondents consumed from in the past 24 hours. The food groups include (1) starchy staples, (2) green leafy vegetables, (3) other vitamin A-rich fruits and vegetables, (4) other fruits and vegetables, (5) organ meat, (6) meat and fish, (7) eggs, (8) legumes and nuts, and (9) milk and milk products (Kennedy, Ballard, and Dop 2011). Individual-level dietary diversity scores for women and children have been demonstrated to positively correlate with micronutrient adequacy of the diet (Arimond et al. 2010).Minimum dietary diversity is achieved when a child aged 6-23 months has consumed from at least four food groups during the previous day (WHO 2008). The seven food groups used for calculating this indicator are (1) grains, roots, and tubers; (2) legumes and nuts; (3) dairy products (milk, yogurt, cheese); (4) flesh foods (meat, fish, poultry, and organ meats); (5) eggs; (6) vitamin A-rich fruits and vegetables; and (7) other fruits and vegetables. Breastmilk is not included in the seven food groups because the indicator is intended to measure the quality of the complementary food diet (WHO 2008).Minimum meal frequency is the proportion of breastfed and nonbreastfed children 6-23 months old who receive solid, semisolid, or soft foods a minimum number of times per day. Meals do not include breastmilk but do include milk feedings for nonbreastfed children. For nonbreastfed children, the minimum number of feedings per day is four. For breastfed children, the minimum is two feedings at ages 6-8 months, and three for 9-23 months.Minimum acceptable diet is the summary IYCF indicator that is achieved when a breastfed child 6-23 months old has consumed the minimum dietary diversity and the minimum meal frequency in the previous 24 hours. To meet this indicator, in the past 24 hours a nonbreastfed child 6-23 months old must have received at least two breastmilk feedings, and also achieved minimum dietary diversity (not including breastmilk) and minimum meal frequency.Children's dietary diversity is measured as the number of food groups a child aged 6-23 months consumed from in the past 24 hours. This indicator uses the same seven food groups as minimum dietary diversity, and similarly, breastmilk is not included in order to reflect the quality of a complementary food diet.Time use activities are classified into seven categories, as discussed in Section 3. For Bangladesh, we exclude the regression analysis for time spent in domestic work and agriculture because off-farm postharvest activities and drying paddy from the harvest are classified as domestic work, and livestock rearing falls under nonagricultural work. In addition, for Bangladesh, Nepal, and Mozambique, we exclude women's nonagricultural activities because more than 87 percent of respondents recorded zero minutes for these activities.A woman's time allocation could be influenced by the social norms in her village. Studies have shown that gender norms dictate the types of economic activities women engage in, or their level of involvement (Kevane and Wydick 2001;Balagamwala, Gazdar, and Mallah 2015). Social norms also affect the degree of involvement in reproductive work by women and men (Akerlof and Kranton 2000;Bittman et al. 2003). For example, a woman may feel compelled to spend more time in agricultural work if she sees other women in the village engaged in these activities. Similarly, a woman may be obliged to spend a significant amount of time in domestic tasks because women in the village are equally burdened with these chores.In this study, social norms can be proxied by the leave-out means of women's time spent in productive work or reproductive work in the village. The leave-out mean of woman i's productive work is derived by taking the average of the female villagers' productive work time, minus woman i's productive work time. It is a good candidate for an instrument to identify equation ( 11) because the leave-out mean for woman i is exogenous to woman i's time allocation, since it excludes her time in the calculation, and the leave-out mean does not directly affect nutrition in her household. The leave-out means for women's time in reproductive work (or cooking or caregiving) are included as instruments in the first stage of regressions measuring reproductive work (or cooking or caregiving), while leave-out means for time in productive work are used as instruments in the first-stage regressions estimating productive work. If women feel pressure to follow social norms in their communities, we expect the leave-out means for a particular activity to have a positive effect on women's time allocated to that activity.Individual characteristics contained in vector X in equation ( 11) to estimate women's dietary diversity include the respondent's age and years of education,15 and whether the respondent is pregnant or lactating. For household characteristics, we control for the household size and the household head's age, schooling, and occupation. 16 When estimating children's diet, we include the mother's age and education, age in months and sex of the child, and whether there is a younger child present in the household. Owning or having access to land is important for agricultural livelihoods; thus the size of cultivable land is included in the Bangladesh regressions, and a dummy variable for owning agricultural land is included for Ghana and Nepal.17 Ownership of livestock is likely to improve nutrition by allowing households access to meat, eggs, and milk. Therefore, dummy variables for owning large or small livestock are included among the control variables. Using wood, dung, or agricultural residue as the main source of cooking fuel is included because this type of fuel can prolong women's domestic work and cooking due to gender-assigned roles in collecting fuel.Household composition has implications on women's reproductive work because young children require more of their time, while older children, especially girls, can substitute for women in household responsibilities. Hence, the numbers of people living in the household in age categories 0-4, 5-10, 11-18, 19-59, and 60 and up, disaggregated by sex, are included as regressors. We also include the household dependency ratio, defined as the ratio of household members aged 0-14 and 65 and up, to those between the ages of 15 and 64.Local dummy variables for divisions in Bangladesh, provinces in Cambodia and Mozambique, regions in Ghana, and areas in Nepal (mountains, hills, and Terai) are included to account for unobservable location-specific characteristics. We also control for the household's religion by including a dummy variable for whether a household is Hindu for Bangladesh, and whether a household is Muslim for Ghana. For Nepal, we include dummy variables for low-caste and mid-caste households.Table 5.1 summarizes the first stage of the 2SLS, estimating women's time allocation by activity, showing only the coefficients on being poor, the household composition, and the instruments. 18 The proxies for social norms, namely the leave-out means for time in the respective activities, are positive and significant at the 1 percent level in most activities by country, confirming that social norms influence the way women spend their time. However, the instruments in Cambodia do not perform well because they are not significant for reproductive work, cooking, caregiving, productive work, and nonagricultural work. For domestic work and caregiving in Mozambique, and cooking in Ghana, the instruments are weakly significant or not significant. In these cases, the OLS results are preferred over the 2SLS.       (2012)(2013); and baseline survey of Suaahara project for Nepal (2012). Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors in parentheses. Other independent variables include the respondent's age and years of education; whether the respondent is pregnant or lactating; the household size; the household head's age, schooling, and occupation; the size of cultivable land for Bangladesh; a dummy for owning agricultural land for Ghana and Nepal; dummy variables for owning large livestock or small livestock; the number of people in the household in age categories 0-4, 5-10, 11-18, 19-59, and 60 and up, disaggregated by sex; the dependency ratio; region/division/province/urban dummy variables; religion dummy variables for Bangladesh and Ghana; and caste dummy variables for Nepal. n/a indicates that the data were not available.Having other women (aged 19-59) in the household reduces women's reproductive work burdens, especially for cooking and caregiving. Younger girls (aged 11-18) and older women (aged 60 and up) can also substitute for women's caregiving in Cambodia, Ghana, and Mozambique. Interestingly, for Cambodia, Ghana, and Mozambique, the presence of younger boys reduces women's caregiving time, suggesting that boys can substitute in these activities, too. Women's time in agricultural and nonagricultural work is not affected by household composition, except in Cambodia, where women spend less time in agriculture when they have young children.Table 5.2 presents the estimations of the effects of women's time use on their dietary diversity. OLS coefficients are shown unless 2SLS coefficients are preferred. OLS estimates are preferred when the IV diagnostic tests reveal that the endogenous variables are not endogenous, when the Kleibergen-Paap Wald F-statistic shows that the instruments may be weak, or when the underidentification test suggests that the system may be underidentified.  Tables 5. 3, 5.4, and 5.5 show the average marginal effects from probit estimates of the impact of women's time use on achieving minimum acceptable diet, minimum dietary diversity, and minimum meal frequency, respectively. Table 5.6 provides the OLS coefficients of the impact of women's time use on children's dietary diversity (out of seven food groups).        Women's time in domestic work increases their dietary diversity in Cambodia and Nepal; the same is true for poor women in Ghana. Further, women who spend more time cooking have improved diets in Bangladesh, Cambodia, Ghana, and Nepal,19 suggesting that those close to the pot have better diet quality. For children's dietary quality outcomes, women's domestic work is positively correlated with achieving a minimum acceptable diet and minimum dietary diversity in Ghana; the correlation also holds true for children in poor households in Cambodia. Furthermore, women's cooking time has a positive effect on the likelihood that poor children consume a minimum acceptable diet in Bangladesh, Ghana, and Mozambique (as shown in Figure 5.1), but the effect is insignificant for nonpoor children. Women's cooking time also improves poor children's dietary diversity scores in Cambodia and Ghana, as presented in Figure 5.2, but its effect is insignificant for nonpoor children. We find that women's cooking time does not impact minimum dietary diversity and meal frequency except in one case. In Mozambique, cooking time increases the chances that children attain minimum meal frequency. Unlike the results on minimum acceptable diet, the effect of cooking time on meal frequency in Mozambique does not vary by poverty status. Not having enough time to cook affects poor and nonpoor children equally in attaining minimum meal frequency because meal frequency does not measure the quality of diet, as the number of food groups consumed does.These results suggest that lack of cooking time has a detrimental effect on women's dietary diversity and children's dietary diversity in poor households but does not have any impact on children in nonpoor households. Therefore children in nonpoor households-households with more assets-are less sensitive to reductions in cooking time; women can spend less time cooking but their children can still achieve more diverse diets than poor children.Children in Muslim households in Ghana are less likely to consume a minimum acceptable diet and achieve minimum dietary diversity, which may be because the data collection took place during Ramadan. However, women in Muslim households do not consume less diverse diets than other women. Children in the middle and low castes face a lower probability of achieving minimum dietary diversity in Nepal. In Bangladesh, women in Hindu households have less diverse diets, but residing in Hindu households does not affect children's diets. Educated women consume more diverse diets in Cambodia and Nepal, and they are more likely to have children with higher dietary diversity scores in Mozambique and Nepal.Last, we find no evidence that household composition has any effect on maternal and child nutrition, with a single exception in Nepal. Having a greater number of women aged 11-18 in the household improves the likelihood that a child will attain minimum meal frequency, suggesting that the presence of other caregivers can improve IYCF practices and child nutrition.Using data from five countries, we examined whether the lack of women's time in reproductive work leads to poorer maternal and child nutrition, and whether an increase in women's time in agriculture or productive work has a detrimental effect on maternal and child nutrition.We found evidence that poor and nonpoor households experience different impacts on nutrition from women's time use on reproductive and agricultural work. For reproductive work, our results show that lack of time to cook negatively impacts women's dietary diversity and poor children's diet quality but does not impact nonpoor children.On the whole, women's and children's nutrition in nonpoor households appears to be less sensitive to reductions in time on reproductive work. In part, this is because they start from different places, with nonpoor households more likely to achieve adequate nutrition than poor households, and nonpoor households less time-burdened than poor households. In contrast, in poor households, reductions in women's reproductive work time tend to be more detrimental to nutrition, especially for children.Nonpoor households-households with more assets-achieve better nutrition outcomes with less time in reproductive work than the poor. Assets enhance the overall impact of reproductive work time on nutrition. From this evidence, it is clear that agricultural development interventions need to be particularly careful to not encroach on the time of women in poor households, especially because these women already face greater time constraints than women in nonpoor households.There are several ways women can reduce their time burden in reproductive work without detracting from nutrition. We found evidence of substitution by girls and older women in the household, especially in cooking and caregiving. There was also some substitution by boys in caregiving. Transformation of gender norms, with men taking on greater responsibility in domestic work, can help reduce women's work burdens.Another way to reduce time burdens in reproductive work is to increase access to time-saving assets or technologies, as suggested by Johnston and others (2015). However, our theoretical framework predicts that the nutrition effect of time-saving technologies in domestic work is not so clear-cut because women may shift toward farm work. Agricultural interventions that introduce capital that saves time in domestic work should therefore be careful to ensure that the other effects of capital offset the potential reduction in nutrition-improving activities. The shift itself away from domestic work is not necessarily detrimental, because the overall effect on nutrition depends on the type of capital investments, which in turn determine whether the consumption, income, nutrition productivity, and agricultural productivity effects on nutrition outweigh the reproductive time displacement effect. Consistent with the predictions of the theoretical model, we see from the empirical evidence that the net effect of capital on nutrition is often positive. However, we do not know the relative strengths of the five effects posited in the theoretical model because the data do not allow for income measurements.Furthermore, the data show that additional time in reproductive work is not always beneficial for nutrition. Women's time in caregiving can be compatible with improving maternal and child nutrition, but in the case of Nepal, where women are most time burdened, caregiving time negatively impacts women's dietary diversity and poor children's dietary diversity and meal frequency. In situations where women face extreme time constraints, caregiving may come at the expense of other activities that would enhance diets for women and children.Women's time in agricultural work also has differential impact on poor versus nonpoor households. Working long hours in agriculture has a detrimental effect on women's dietary diversity in Ghana and that of nonpoor women in Mozambique, confirming the prediction of the agriculture-nutrition pathways and the theoretical model. However, for poor women and children in Mozambique, and children in Nepal, women's working in agriculture in fact increases dietary diversity. This suggests that agriculture as a source of food and income is particularly important for the poor (Kadiyala et al. 2014;Ruel and Alderman 2013).Our empirical findings suggest that the impact of policy interventions on women's time allocation and nutrition outcomes are likely to vary by socioeconomic status and local context. They also imply that assets are important mediators of the nutrition impact of time use, often enhancing it. Poor women are extremely time constrained and would have to spend much more time in nutrition-enhancing activities in order to catch up with nonpoor households' nutrition levels-time that they do not have. However, certain assets can reduce the time necessary to achieve adequate nutrition. The theoretical model could provide a useful tool to identify what kinds of investments are appropriate for women's and children's nutrition and where the trade-offs of agricultural investments can be in affecting women's time and nutrition.","tokenCount":"9314","images":["1220282230_1_1.png","1220282230_39_1.png","1220282230_39_2.png","1220282230_40_1.png","1220282230_40_2.png","1220282230_40_3.png","1220282230_41_1.png","1220282230_42_1.png","1220282230_42_2.png"],"tables":["1220282230_1_1.json","1220282230_2_1.json","1220282230_3_1.json","1220282230_4_1.json","1220282230_5_1.json","1220282230_6_1.json","1220282230_7_1.json","1220282230_8_1.json","1220282230_9_1.json","1220282230_10_1.json","1220282230_11_1.json","1220282230_12_1.json","1220282230_13_1.json","1220282230_14_1.json","1220282230_15_1.json","1220282230_16_1.json","1220282230_17_1.json","1220282230_18_1.json","1220282230_19_1.json","1220282230_20_1.json","1220282230_21_1.json","1220282230_22_1.json","1220282230_23_1.json","1220282230_24_1.json","1220282230_25_1.json","1220282230_26_1.json","1220282230_27_1.json","1220282230_28_1.json","1220282230_29_1.json","1220282230_30_1.json","1220282230_31_1.json","1220282230_32_1.json","1220282230_33_1.json","1220282230_34_1.json","1220282230_35_1.json","1220282230_36_1.json","1220282230_37_1.json","1220282230_38_1.json","1220282230_39_1.json","1220282230_40_1.json","1220282230_41_1.json","1220282230_42_1.json","1220282230_43_1.json","1220282230_44_1.json","1220282230_45_1.json","1220282230_46_1.json","1220282230_47_1.json","1220282230_48_1.json","1220282230_49_1.json","1220282230_50_1.json","1220282230_51_1.json","1220282230_52_1.json"]}

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+{"metadata":{"gardian_id":"e3c8b884a75cbe1a36efa3d16138199e","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/e754b6ce-2c9f-4597-959c-80dc5248100e/retrieve","description":"In August 2022, we surveyed 467 active rice millers from 13 states and regions across Myanmar to learn more about the impacts of the current political and COVID-19 crises. This report presents the key results and analysis from those interviews. Key findings  Electricity and fuel disruptions were cited as the most significant disruption by 91 percent of millers in August 2022. Rising fuel prices and access issues afflicted smaller, local mills while larger mills were more affected by interruptions in electricity supplies.  Continuing a trend from March 2022, banking and credit disruptions were less prevalent in August. Lending and borrowing show only minor changes relative to 2021.  Average milling throughput declined by more than 20 percent compared to a year prior, and stored volumes of both paddy and rice showed similar declines.  Rice prices and milling margins increased sharply by about 40 percent compared to last year, driven by rising global prices, and, most importantly, by a rapid devaluation of Myanmar kyat. In USD terms, the price increases are more modest and closer to global changes. At the parallel (unofficial) exchange rate, prices have declined.  Prices of byproducts (in kyats) have also increased sharply from the last year, especially for rice bran which is important to the animal feed industry. Looking forward  Looking forward to the 2022 monsoon harvest and marketing season, over half of all millers expect a decline in paddy production of at least 10 percent in their townships and an additional 22 percent of millers expect a smaller decline. Just 3 percent expect their local paddy production to be higher in 2022 monsoon than in 2021.  Changes in input use (e.g., a decline in fertilizer application) are far and away the most cited reason for lower expected paddy production. Half of millers said that less favorable rainfall patterns compared to 2021 are also a factor in lower paddy production.  On top of lower reported throughput in August 2022 and lower storage volumes, a decline in monsoon paddy production would have large implications for both rural and urban households. Lower supply coupled with the continued and widespread disruptions to utilities and transport, could drive prices even higher. At the same time, unpredictable foreign exchange and export policies could make it difficult for value chain actors to anticipate supply and demand conditions, resulting in higher price volatility.","id":"-888432719"},"keywords":[],"sieverID":"a7afe75b-0889-4ee0-9a9a-b48dd7e3514f","pagecount":"9","content":"In August 2022, we surveyed 467 active rice millers from 13 states and regions across Myanmar to learn more about the impacts of the current political and COVID-19 crises. This report presents the key results and analysis from those interviews.Rice mills are the most important link between farmers and consumers in Myanmar's rice value chain. Mills buy paddy from farmers and process it into rice, and hence, any severe disruptions to rice mills will affect both rural rice-producing households and urban consumers.Since June 2020, we have monitored rice millers in Myanmar, and this is the 12 th Research Note in the series. In this Research Note, we present evidence from interviews conducted in August 2022 with 587 rice millers in 138 townships from 13 states. We examine (i) disruptions caused by the current political and COVID-19 crises; (ii) impacts resulting from transportation restrictions and recent changes in foreign currency regulations; (iii) changes in business operations including throughput, employment, paddy stocks, and credit offered/borrowed; (iv) paddy, rice, and byproduct price changes relative to one year prior; and (vi) details on transportation disruptions.From August 15 to August 25, 587 mills were interviewed via telephone, of which 467 (80 percent) were active in the 30 days prior to the interviews and 120 (20 percent) were inactive (Table 1). The number of inactive mills has increased compared to March by 10 percent mainly due to normal seasonality (Table 1), but inactive millers also cited difficulty in purchasing paddy and safety issues during crisis. COVID-19 disruptions did not have a large impact on mill closures in August. Similar to March, we interviewed traditional small and micro-mills locally known as Halar Sat and Ngar Pone Sat (16 percent of the sample). These mills play an important role in remote rural communities providing milling services on commissions mostly for household consumption despite having much lower milling capacity. The subsample of medium/large millers (84 percent of sample) is more urban, better educated, more experienced, more likely to keep written records and more engaged in paddy purchasing and rice selling in a week (Table 1). Consistent with prior survey rounds, we asked rice millers what type of disruptions they have experienced in the last 30 days. Millers were greatly affected by high fuel prices and high transportation costs (81 percent and 58 percent, respectively) as well difficulties accessing electricity (75 percent) and fuel (55 percent) (Figure 2). In general, utilities and transport disruptions in August were similar or worse than in March. However, banking disruptions have eased somewhat, with especially large declines in reported disruptions in making and receiving payments. Credit access has apparently also improved somewhat since March. Most credit taken in is from private banks, though mills located in Naypyitaw received loans from the Myanmar Agricultural Development Bank. Thirty-seven percent of mills reported difficulties finding paddy to buy, though we do not have a comparison data point from March. With most millers not directly involved in exporting, only 6 percent cited the unpredictable currency regulations as a disruption, though the policies certainly have indirect effects on the domestic rice sector.When further probed on what type of disruption they perceived as most challenging. Electricity and fuel access and prices remained the largest issues for mills in August (91 percent, up from 82 percent in March; Figure 3). For modern mills which rely on electrical power to operate machinery, electricity supply disruptions were the largest issue, while the small mills which mostly use dieselpowered equipment reported rising fuel costs as the main issue. Reported fuel costs more than doubled from one year prior. Rising fuel prices also affect transportation costs which increased by an average of 63 percent. Figure 3 shows that other sources of disruption declined in relative importance between March and September 2022. Just 1 percent of millers felt that banking disruptions were the most significant in August, down from 7 percent in March. Similarly, mobility restrictions and curfews were not a leading issue for millers in August, despite 28 percent reporting those disruptions (Figure 2). Among the mills reporting movement restrictions, 44 percent of small mills reported potential safety issues during transport, compared to just 11 percent of modern mills. A series of questions on milling operations were asked to understand how rice millers have responded to these challenges. The data reveal troubling declines in milling throughput and storage for both small/micro mills and medium/large mills (Table 2). For medium and large mills, average throughput for the monsoon season declined by about 16 percent relative to 2021, while August throughput was 22 percent lower, perhaps reflecting more acute issues in electricity access in the month. Miller storage of both paddy and rice also declined relative to 2021, perhaps suggesting lower quantities produced in the pre-monsoon season.Credit -both taken in and lent out to farmers -is mostly stable for both mill types. An increase in working capital of 16 percent even with a decline of throughput suggests that more capital is required for the same quantity of paddy. Daily wages increased by about 10 percent while fees for milling on commission also rose relative to one year prior, but by a smaller percentage for small/micro mills (4 percent increase) compared to medium and large mills (13 percent). In each survey, we collect mill-level price data for paddy, rice, and milling byproducts at the time of interview with recall data back to one year prior. In this note, we report findings for the two rice varieties: Emata, the predominant variety for local consumption and exports, and Pawsan, a more expensive type preferred locally by affluent urban consumers but with negligible exports (Figure 4). In our sample, Emata varieties are more common (306 millers sold Emata in August 2022 survey while only 64 sold Pawsan).Paddy and rice prices soared in August 2022 compared to five months ago and the same time last year (Figure 4) primarily due to increases in global prices driven by the Ukraine war 1 and rapid depreciation of the Myanmar kyat. Year-on-year changes in Emata and Pawsan purchase prices increased by 46 percent and 40 percent respectively in August while rice selling prices increased by 37 percent and 40 percent respectively. Emata prices increased by more than triple and Pawsan doubled compared to year-on-year changes seen in March. Paddy-to-rice milling margins have also grown for both groups compared to March and last year. In the March round that was conducted right before the release of foreign currency restriction by Central Bank of Myanmar (CBM), margins were stable and year-level margin changes were almost zero. In contrast, year-on-year margin changes in this round have jumped by approximately 26 percent for Emata and 40 percent for Pawsan. Given large global market disruptions and the large gap between official and parallel market exchange rates 2 , we compare the price and margin changes in our miller data in USD terms to similar rice price changes in Thailand and global markets (Figure 5). At the official exchange rate, the yearon-year price increases for Emata and Pawsan outpace prices in the global and Thai markets and, with rising transport costs, consumer price changes are likely higher still. However, at the informal exchange rates (the more accurate market value of the kyat), USD rice prices have declined yearon-year. The discrepancy between the CBM rate and the informal market rate and frequent changes in foreign currency regulations create enormous uncertainty in the market which may have led to price volatility. Unpredictability in the export market could have a cascading impact on domestic market pricing as well, particularly for Emata, which is heavily exported. Milling margins for Emata are stable in USD at the official rate, signaling continued competition and access to exports (Figure 5). Further, at unofficial rates, margins have declined since 2021, implying that millers are not extracting exorbitant profits and may be suffering during the heightened volatility.2 Exchange rates in August 2022 and August 2021 show that MMK lost 25 percent of its value against the USD and 10 percent of its value against the Thai Baht at the CBM official rates, but MMK lost 51 percent against the USD at the informal parallel exchange rate. In addition to global price increases and MMK depreciation, it is possible that other, more local disruptions to paddy supply or milling volumes could contribute to the price increases. To evaluate millers' perspectives on price changes this year, we report their perceived reasons for the changes (Figure 6). Overall, political instability is the most cited reason (54 percent reporting) and it is particularly acute with millers in the Dry Zone (73 percent) and the Delta (51 percent). Higher transport costs (37 percent) and fuel prices (27 percent) are also perceived as contributing to higher prices. Millers also perceive a decline in paddy availability to buy (36 percent) while 17 percent cited a decline in paddy area planted during the pre-monsoon season, which was most common in the Dry Zone where conflict was more widespread. In addition to the main milling output of rice, byproduct prices have also increased markedly (Table 3). Sales of milling byproducts such as broken rice and rice bran are an important source of mill revenue and profits, particularly for modern mills. Millers were less likely to sell byproducts in August 2022 compared to one year earlier, despite rising prices. Large grade broken rice, which is exported in high volumes, show the smallest price increase at 40 percent, perhaps anchored by the official exchange rate. In contrast, small grade broken rice prices increased by 59 percent and bran prices increased by percent on average compared to August 2021. These products are sold domestically, and rice bran is an important input in fish and poultry feeds. Thus, the large price increase could reflect a substitution by fish and poultry farmers away from expensive imported feeds, as well as domestic feed manufacturers substituting feed ingredients. The monsoon growing season is essential for Myanmar's food security, accounting for about 80 percent of all paddy rice production annually. In the August survey, we asked millers about their forward-looking expectations of the monsoon paddy harvest in their townships in 2022 compared to 2021. Over half of all millers expect a decline of at least 10 percent and an additional 22 percent of millers expect a smaller decline. Just 3 percent expect their local paddy production to be higher in the 2022 monsoon than in 2021. The outlook is bleak across the three agro-ecological zones in our survey, including the Delta region which serves as Myanmar's rice basket. The Hills region shows the greatest pessimism though we note that we have a small sample of millers there. Much smaller (at least 10%) Smaller (0-10%) About the same Larger (0-10%)Much larger (at least 10%)Changes in input use (e.g., a decline in fertilizer application) are far and away the most cited reason for lower expected paddy production (Figure 8). Half of millers said that less favorable rainfall patterns compared to 2021 are also a factor in lower paddy production, while 32 percent of millers reported that lower acreage of paddy planted by farmers was another important factor (especially in the Dry Zone). On top of lower reported throughput in August 2022, and lower storage volumes, a decline in monsoon paddy production would have large implications for both rural and urban households. Lower supply coupled with continued and widespread disruptions to utilities and transport, could drive prices even higher. At the same time, unpredictable foreign exchange and export policies could create additional uncertainty for value chain actors, and greater price volatility.","tokenCount":"1921","images":["-888432719_1_1.png","-888432719_1_2.png","-888432719_1_3.png","-888432719_2_1.png","-888432719_9_1.png"],"tables":["-888432719_1_1.json","-888432719_2_1.json","-888432719_3_1.json","-888432719_4_1.json","-888432719_5_1.json","-888432719_6_1.json","-888432719_7_1.json","-888432719_8_1.json","-888432719_9_1.json"]}

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+{"metadata":{"gardian_id":"3940ea47ae6f9daa69c8da6e370b1c9e","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/d2d7a4c5-cf23-44fd-81c3-4c5b3db17023/retrieve","description":"Despite a considerable increase in agricultural research spending in recent years, Myanmar is still seriously underinvesting. At just 0.06 percent in 2017, the country’s agricultural research intensity ratio (that is, spending as a share of AgGDP) is one of the lowest in the world. The number of agricultural researchers has grown steadily over time, as has the average qualification level of researchers. The majority of researchers are crop scientists, however, leaving other important areas (notably livestock and fisheries) severely underresearched. ADS was launched in 2018 to address many of the challenges that Myanmar’s national agricultural research system is facing, including severe underinvestment, organizational fragmentation, limited geographic dispersion of research, neglected research domains, and an ineffective extension system.","id":"-1695404622"},"keywords":[],"sieverID":"2b2ed1c5-c0b0-408e-9c1c-59854fa02c96","pagecount":"8","content":"Until 2011, Myanmar was one of the world' s most isolated economies. This situation severely affected overseas postgraduate training opportunities for agricultural scientists. Since sanctions against the country were eased, greater donor-funded training opportunities became available, and the average qualification levels of agricultural scientists steadily improved. In 2017, DAR, DOA, and YAU employed 24, 16, and 19 FTE researchers with PhD degrees, respectively. The number of MSc-qualified staff employed at these three agencies has also increased considerably since 2000. Many recent MSc-and PhD-qualified graduates were trained in Australia, China, Germany, Japan, Malaysia, the Netherlands, Singapore, and South Korea. In addition, three scientists completed PhD training locally at YAU in 2017, and many more are set to follow in the coming years. Many of DOA' s PhD-qualified staff are only conducting limited research, however, and they are often assigned to management tasks or to international projects because of the advanced English language skills they obtained during overseas training. In Myanmar, a young scientist with a BSc degree in agriculture can earn a higher salary in an entry-level position with an NGO or donor-funded initiative than a PhD-qualified senior scientist can at a government institution. The fact that the country's public-sector salaries are so low makes attracting and retaining highly qualified research staff extremely challenging. Moreover, promotional opportunities at government agencies tend to be based on seniority rather than merit, making government agencies unattractive employers for young, ambitious scientists. Those who succeed in getting promoted often move into administrative positions, leaving research altogether. Despite the influx of a large number of young, qualified scientists in recent years, motivating and retaining them will be challenging given the low salary levels, lack of promotional opportunities, and reality that the skills they gained overseas cannot always be applied back home because of outdated research infrastructure or financial constraints. Nowhere in the world is the share of female agricultural researchers higher than in Myanmar. While in many countries in South Asia, women represent around one-fifth of the pool of agricultural researchers, in Myanmar, more than 70 percent of agricultural researchers are female. In addition, female researchers in Myanmar do not hold considerably lower average degree levels than their male colleagues, as is the case in most low-and middle-income countries around the world. Nonetheless, women are severely underrepresented in research management positions in Myanmar. And while the high rate of participation of women in agricultural research may appear positive, the underlying reason is a cause for concern. Civil servant salaries are so low in Myanmar that they act as a disincentive to predominantly male household heads because they are insufficient to support a family. As a result, research positions mainly attract female applicants. After decades of neglect, Myanmar's agricultural research system is being rebuilt. Despite a considerable increase in recruitment in recent years, the country's human research capacity remains inadequate in terms of the number of researchers, their qualifications and experience, and their ability to address the diversity of Myanmar's agroecological zones. Moreover, researcher salary levels are too low to attract and retain highly qualified scientists. Almost all of DAR's PhD-qualified researchers and the vast majority of researchers with MSc degrees are stationed at headquarters in Yezin, just outside the capital of Naypyidaw. YAU, the University of Veterinary Science, and FRI also operate from Yezin, whereas UY and the largest research unit under DOA-the Horticulture and Plant Biotechnology Division-are Yangonbased. With 90 percent of the country's PhD-qualified agricultural researchers located in either Yezin or Yangon, Myanmar's agricultural research system is extremely centralized geographically.The development and adoption of improved varieties occurs more rapidly and effectively if undertaken through a more decentralized approach, where farmers and agribusinesses interact closely with research and extension. DAR's current network of satellite farms already provides the basic infrastructure for such an approach, but several factors impede the satellite farms from participating in research prioritization and design, including the very low number of researchers with PhD and MSc degrees at these farms, limited exposure of Yezin-based researchers to farmers due to transport and budget constraints, and a general tradition of hierarchical decisionmaking. In addition, the satellite farms tend to focus more on seed production than on research because they are expected to generate revenues from the production and sale of seed. Nonetheless, with appropriate mandates for (multidisciplinary) staffing and cropping systems, the satellite farms could play a vital role in identifying research priorities for the major cropping systems in their respective areas, and engaging more closely with extension workers to design and implement onfarm trials and demonstrations in response to the needs and priorities of local farmers. This kind of decentralized research model is currently being piloted in the Sagaing region, involving DAR and DOA researchers, extension staff, and local farmers (Boughton and Win 2019).Accounting for just 0.06 percent of AgGDP and less than 1 percent of MOALI's total budget, agricultural research is grossly underfunded in Myanmar. Moreover, the research system is highly fragmented and coordination among the various research entities is minimal, often leading to costly duplication of research activities.The recent launch of ADS and the development of an agricultural research masterplan are clear signs of the government's prioritization of agricultural research. However, much higher investment levels are needed for research to effectively respond the agricultural sector's many challenges. Diversification of funding should be promoted through a more enabling policy environment that stimulates private funding.Research governance would need to be reorganized under a national structure to eliminate duplication of effort and enhance linkages among research agencies and between researchers and farmers.Myanmar stands out from most low-and middle-income countries when it comes to the composition of its agricultural research spending. Whereas in most countries salaries typically represent 50-70 percent of a national agricultural research institute's investments, DAR spent just 19 percent of its total budget on salaries during 2013-2017, and DOA spent only 38 percent. The main reason for these low shares is the country's extremely low civil service salary levels. Although a nationwide pay rise in 2015 remedied the situation somewhat, salaries remain too low to enable DAR and DOA to attract and retain highly qualified research staff.Another factor that makes DAR stand out from its international counterparts is its relatively high share of capital investment. In fact, most of the growth in research expenditure in recent years was driven by increased capital investment. A large influx of donor funding, following the country's first ever donor conference in 2013, coupled with an increase in the government's prioritization of agricultural research, has initiated the much-needed upgrade of research infrastructure and equipment. However, much more funding for the upgrade of laboratories, office space, research equipment, vehicles, and information technology is needed in the coming years, particularly in the regions, to overcome decades of neglect.MOALI temporarily reduced its funding to DAR and DOA in 2017. Spending levels were forecast to rise thereafter based on the launch of ADS and a number of large donor-funded projects.The new Agricultural Development Strategy (ADS) and investment plan for MOALI were launched in 2018 to address many of the challenges the country's national agricultural research system is facing, including severe underinvestment, organizational fragmentation, limited geographic dispersion of research, severely underresearched areas (including livestock, fisheries, and socioeconomics), and an ineffective extension system. ADS proposes a unified national agricultural research and extension system and the establishment of a National Agricultural Research Council mandated to coordinate and guide all research on crops, livestock, fisheries, postharvest issues, and socioeconomics. This new entity would conduct a thorough review of the country's current system of agricultural research stations and extension units under different departments, with a view to rationalizing and integrating their activities to ensure that priority research areas in all of Myanmar's major agroecological zones receive sufficient coverage.In LIFT is a multi-donor fund (currently consisting of the United Kingdom, the European Union, Australia, Switzerland, the United States, Canada, and Ireland) established in 2009 to address food insecurity and poverty. LIFT funds a variety of research projects focused on rural livelihoods, as well as aquaculture, pulses, maize, oilseeds, and rubber value chains. Capacity strengthening plays an important role in all LIFT-funded projects.For more than 30 years, JICA has supported DAR's seed bank for germplasm. It also plays an important role in strengthening YAU's capacity and research infrastructure through the Technical Cooperation Project. Currently, JICA is supporting a five-year project (2018-2023) to strengthen rainfed lowland and upland rice breeding based on genomic technology. This project will introduce paddy-based genetic breeding to Myanmar in order to develop high-yielding and pest-and disease-tolerant rice varieties.KOICA (South Korea) funds a number of projects related to agricultural mechanization and seed multiplication.The government of Myanmar is currently in negotiation with the World Bank for a US$80 million loan for the Myanmar National Food and Agriculture Systems Project (2020-2024). The proposed project will focus on increasing the productivity of selected high-value commodities, enhancing agricultural diversification and competitiveness, and diversifying diets in selected agroecological zones. Other important components of the project will be upgrading the infrastructure of research stations and laboratories, as well as building capacity and providing policy support within the national agricultural research and extension system.Negotiations are currently ongoing with the Asian Development Bank for a Climate-Friendly Agribusiness Value Chains Sector Project. The project will consist of a US$40.5 million loan from the Asian Development Bank and a US$22 million grant from the multilateral Global Agriculture and Food Security Program. A portion of the funding will be allocated to DOA and DAR to assist with the development of climate-resilient varieties of rice, beans, pulses, and oilseeds, along with their subsequent commercialization. The program also includes financing to upgrade laboratories focusing on food products, insecticides, and fruit quality. Most of MOALI's services focus on the production of rice paddy and represent a disconnect from Myanmar's highly diversified farming system. The heavy focus on paddy is also reflected in the very high number of new rice varieties released by DAR, compared with other crops. Moreover, DAR's research tends to be disproportionately focused on maximizing yields, and as such neglects other critical research areas, such as pest and disease control, water management, and soil science. Given these constraints, the uptake of many of DAR's improved varieties and technologies has been limited to date.The unified national agricultural research and extension system proposed under ADS is certainly a step in the right direction to enhance the adoption of improved varieties. The involvement of extension agencies, farmers, and agribusiness companies in the design of the research agenda and the development of improved varieties will make Myanmar's research system more demand-driven.As previously mentioned, strengthening DAR's network of satellite farms (and better coordination with DOA farms) is essential to making this decentralized approach a success.Increasing the efficiency of agricultural production-that is, getting more output from the same amount of resourcesis critical for improving food security. TFP is an indicator of how efficiently agricultural land, labor, capital, and other inputs (seed, fertilizer, and so on) are used to produce a country's agricultural outputs (crops and livestock). TFP is calculated as the ratio of total agricultural outputs to total production inputs, so when more output is produced from a constant amount of resources, TFP increases. R&D activities produce new technologies and innovations are a crucial factor driving TFP, but technological spillovers from abroad, higher numbers of skilled workers, investments that favor the development of input and output markets (such as roads and communications), and government policies and institutions that promote market development and competition are major drivers as well.Growth in agricultural output stagnated during the 1980s but has accelerated since (although it remains low compared with most Asian countries). Input growth has been low, reflecting Myanmar's relatively low labor productivity and limited tractor and fertilizer use. In fact, TFP growth has been the main driver of the country's output growth over time. Between 1981 and1988, TFP growth  Conventional recommendations of agricultural research intensity levels, such as the 1 percent target set by the United Nations, assume that national investments should be proportional to the size of the agricultural sector. In reality, a country's capacity to invest in agricultural research depends on a range of variables, including the size of the economy, a country's income level, its level of diversification of agricultural production, and the availability of relevant technology spillovers from other countries. In efforts to address these nuances, ASTI developed a multifactor indicator of research intensity that comprises a range of weighted criteria (for further details, see Nin-Pratt 2016). Under this approach, countries with similar characteristics (income, size of the economy, and size of the agricultural sector) are deemed to require similar minimum levels of research investment, and investment below that level is interpreted as an indication of potential underinvesting compared with similar countries.ASTI's weighted indicator of research intensity shows that Myanmar is, indeed, grossly underinvesting in agricultural research. Based on the structural characteristics of the economy and the agricultural sector, the country should be able to invest 0.61 percent of its AgGDP in agricultural research. To have reached this target, Myanmar would need to have spent 141,720 million kyat in 2017, instead of the 13,705 million kyat it actually spent (both in current prices).What would it take for Myanmar to close the investment gap by 2030, and how would increased agricultural R&D investment affect future productivity growth? In an effort to answer these questions, ASTI ran long-term projections on the impact of historical agricultural research investment on the country's agricultural output and productivity, and on the investments that would be needed to reach future targets. Results indicate that to reach the 0.61 percent investment target by 2030, Myanmar would need to increase research investment by a yearly rate of 20 percent during 2017-2030, which seems infeasible given its much more modest historical investment growth rates. However, spending growth at half this rate-that is, at 10 percent per year-is thought to be more realistic and would still yield a considerable TFP response. ASTI projections indicate that with 10 percent yearly agricultural R&D investment growth between 2017 and 2050, the productivity of Myanmar's agricultural sector would be 70 percent higher in 2050 than it is today.It is not only the quantity, but also the quality of agricultural R&D investment that is important. Myanmar has set the goal of diversifying its agricultural production and increasing its exports. It will therefore need to invest proportionally less in research on cereals and more on high-value commodities, such as fruit and vegetables, cash crops, livestock, and fisheries. Neighboring Thailand successfully diversified its agricultural sector decades ago, allowing it to become highly profitable and competitive today. Of its agricultural researchers' time, Thailand currently allocates just 10 percent to cereals but nearly a quarter to livestock and fisheries. In contrast, Myanmar still allocates about one-third of its research effort to cereals (mostly rice and maize) and just 4 percent to livestock, while aquaculture and fisheries remain virtually unresearched.ASTI carried out detailed analysis of the optimal allocation of research resources across commodities, along with the effects on future TFP growth. Results showed that productivity growth for pulses and high-value commodities could roughly double by 2050, and that rice productivity could increase by 60 percent. For this to occur, research investment in livestock, fruit and vegetables, and pulses would need to increase at rates of 15, 14, and 13 percent per year, respectively, during 2017-2050, whereas investment in all other commodities would need to grow at 6.5 percent per year. Total sectoral TFP growth under this \"high-value\" scenario would be the same as when investment increases were distributed more evenly across commodities. Nevertheless, if Myanmar is to successfully diversify agricultural production and make its agricultural sector more profitable and competitive on global markets, it will be important to accelerate research investment in high-value commodities.Actual research spending and attainable targets, 2017 ","tokenCount":"2617","images":["-1695404622_1_4.png","-1695404622_1_5.png","-1695404622_8_2.png"],"tables":["-1695404622_1_1.json","-1695404622_2_1.json","-1695404622_3_1.json","-1695404622_4_1.json","-1695404622_5_1.json","-1695404622_6_1.json","-1695404622_7_1.json","-1695404622_8_1.json"]}

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+{"metadata":{"gardian_id":"a7210d3b20a588e0e6790d65d2c2a862","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/1ec9dde6-ff4a-4e79-8784-857a537a074b/retrieve","description":"The food and agriculture sector is pivotal not only to addressing undernutrition but also to containing and preventing the spread of diet-related noncommunicable disease. This context requires action throughout the food system, from sustainably managing natural resources and input supplies to enabling consumption of healthy diets and promoting gender equity. Political commitment is growing, but much remains to be done in terms of strengthening the information base to support strategic decision making, and developing capacities for implementation at scale. In April 2016, the UN General Assembly enacted a Decade of Action for Nutrition, and nutrition is directly or indirectly related to all of the Sustainable Development Goals. This enabling environment at the global level should foster further progress in the region, and conversely, African countries can inspire other regions of the world by pursuing innovative approaches for unleashing the latent potential of the agrifood sector to drive positive change in nutrition.","id":"691508565"},"keywords":[],"sieverID":"4ad75cd7-2ae4-48aa-8739-efc62ed95810","pagecount":"16","content":"mproving nutrition is a complex challenge that requires contributions from many sectors at many levels, from policy to grassroots action. Direct (nutrition-specific) interventions, usually delivered by the health sector, and indirect (nutrition-sensitive) 15 programs, implemented by a variety of sectors, are needed, both underpinned by enabling policy environments (Black et al. 2013). Even if the recommended package of nutrition-specific interventions put forward by the Lancet Maternal and Child Nutrition Series (2013) were scaled up to 90 percent population coverage in the 34 countries with the highest burden of undernutrition, child stunting would fall by only 20 percent (Bhutta et al. 2013). This means that efforts to scale up nutrition-specific interventions need to be paired with investments in nutrition-sensitive development programs and policies that address the underlying drivers of malnutrition.In Africa south of the Sahara, progress in reducing undernutrition has been lagging behind that of other regions over the last decade (IFPRI 2015).In Africa, the majority of the nutritionally vulnerable population is dependent in some way upon agriculture as a primary source of livelihood-for food, for employment, and for income. Agriculture has close links to both the direct causes of undernutrition (diets, feeding practices, and health) and the underlying factors (such as income; food security; education; access to water, sanitation, and hygiene; access to health services; and gender equity). The sector has huge potential to drive down rates of malnutrition (Kadiyala et al. 2014;Pinstrup-Andersen 2012). Yet, as in many low-and middle-income countries with a high dependence on agriculture-based livelihoods and a high burden of undernutrition, this potential for agriculture is currently not being realized (Ruel and Alderman 2013;Gillespie et al. 2013;Balagamwala and Gazdar 2013;Kadiyala et al. 2014). Agricultural growth may generate more gains for nutrition than gross domestic product (GDP) growth per se (Webb and Block 2012), but nutrition has historically not been a primary concern for agricultural policy makers-for whom aggregate staple crop production is the primary target (Ecker, Breisinger, and Pauw 2011;Headey, Chiu, and Kadiyala 2012). There is also a marked paucity of evidence that agricultural interventions are benefiting nutrition (Ruel and Alderman 2013), related to the following factors:• Failings in terms of the design and implementation of interventions, which are not as nutrition enhancing as they could be.• Limitations in terms of targeting (relatively few interventions are targeted to the 1,000-day window 16 within the human life cycle).• Poor design of evaluations, which are seldom rigorous enough (in terms of sample size, valid comparison groups, and so on) to demonstrate impact (Ruel and Alderman 2013).Agricultural interventions are rarely designed to have impacts on nutrition, and evaluations are rarely empowered to detect such impacts. In 15 Nutrition-sensitive programs draw on complementary sectors such as agriculture, health, social protection, early child development, education, and water and sanitation to affect the underlying determinants of nutrition, including poverty; food insecurity; and scarcity of access to adequate care resources and to health, water, and sanitation services. Key features that make programs in these sectors potentially nutrition sensitive are that they address crucial underlying determinants of nutrition, they are often implemented at large scale and can be effective at reaching poor populations who have high malnutrition rates, and they can be leveraged to serve as delivery platforms for nutrition-specific interventions (Ruel and Alderman 2013). 16 The \"1,000-day window\" refers to a crucial period (starting with a child's conception and continuing through nine months of pregnancy and the first two years of life) when nutrition is of critical importance for a child's developing brain and body, after which most growth and development deficits are largely irreversible. short, in terms of both policy and programs, there is an apparent disconnect between agriculture and nutrition. This disconnect represents a challenge-but also an opportunity. The many links between agriculture and nutrition (Figure 5.1) suggest that agricultural policies, interventions, and practices can be better designed to enhance nutrition and health benefits. We need to understand why the disconnect persists and, more importantly, how we can turn agriculture into a powerful lever for raising people's health and nutritional status, while at    • Pathway 1: Agriculture as a source of food for household consumption. This is the most direct pathway by which household agricultural production translates into consumption (via crops cultivated by the household). In the context of various market failures, farmers may make production decisions with the objective of directly shaping their diets through consumption of their own farm produce.• Pathway 2: Agriculture as a source of income for food and nonfood expenditures. Like other productive sectors, agriculture is a source of household income (raised through wages earned by agricultural workers or through the marketed sale of food produced) and expenditure on nutrition-enhancing goods and services (including health, education, and social services). But agriculture is known to be a more important source of income for the poor and undernourished than other sectors.• Pathway 3: Effects of agriculture policy and food prices on food consumption. The link between agricultural policy and food prices involves a range of supply-and-demand factors that affect the prices of various marketed food and nonfood crops. These prices, in turn, affect the incomes of net sellers as well as the ability of net buyers to ensure household food and nutrition security (including diet quality).• Pathway 4: Effects of women's employment in agriculture on intrahousehold decision making and resource allocation. Agricultural labor conditions can influence the empowerment of women and thus their control over nutrition-relevant resources and decision making, particularly regarding food and healthcare.• Pathway 5: Effects of women's employment in agriculture on childcare and child feeding. This pathway relates to the challenges that heavy and prolonged female workloads in agriculture present to ensuring adequate care for young children.• Pathway 6: Effects of women's employment in agriculture on their own nutritional and health status. This pathway relates to the possibility that the often arduous and hazardous conditions of agricultural labor pose substantial risks for maternal nutritional and health status (when their work-related energy expenditure exceeds their energy intake, their dietary diversity is compromised, or they fall sick because of the conditions in which they work).Agriculture can influence nutrition outcomes through effects on the ability of households and individuals (especially women) to grow, consume, and sell food, and to generate income. Since nonfarm activities do not possess an intrinsic linkage to nutrition, Pathway 1 potentially makes agriculture a special sector, but it also opens up complex dynamic policy tradeoffs (Kadiyala et al. 2014). Pathway 3 also makes agriculture a special sector due to its influence on the composition of diets through macroeconomic linkages. Pathways 4-6, focusing on the conditions under which women engage in agricultural labor and their ability to control and use resources (including time and earned income), have unfortunately been neglected in the past, as we will see in the next section.The pathways described above clearly illustrate how agriculture can contribute to improved nutrition. However, experience and research findings, as also highlighted above, show that the potential positive nutritional impacts of agriculture are seldom fully unleashed and that advances in agriculture can even lead to negative impacts (for example by increasing women's workloads or leading to a decrease in crop and thus dietary diversity). Recognizing that \"business as usual\" is insufficient for agriculture to improve nutrition, FAO and the Agriculture-Nutrition Community of Practice 18 facilitated a consultation process between 2011 and 2013 to develop a fact sheet titled \"Key Recommendations for Improving Nutrition through Agriculture and Food Systems\" (FAO 2015a; Box 5.1).These recommendations are principles that can be applied to the design of agriculture programs to enhance their nutritional impact. They It is interesting to note that while the initial focus was on nutrition-sensitive agriculture, in recent years the discourse has shifted to a focus on nutrition-sensitive food systems (CGIAR 2015). Indeed, an analysis of nutritional problems in the 21st century-as populations become increasingly urbanized and markets globalized-makes it obvious that action is required not only at the level of production but in all stages of the food value chain: from natural resource management and input supply to production, transportation, processing, retailing, and consumption.Delivering and promoting the consumption of safe food that is affordable and of good nutritional quality on a year-round basis thus requires working with a broad range of stakeholders-governments, farmers, agribusiness, retailers, and consumers. 3. Target the vulnerable and improve equity through participation, access to resources, and decent employment.4. Collaborate with other sectors and programs.5. Maintain or improve the natural resource base.6. Empower women.7. Facilitate production diversification, and increase production of nutrient-dense crops and small-scale livestock.8. Improve processing, storage, and preservation to retain nutritional value and food safety, to reduce seasonality and postharvest losses, and to make healthy foods convenient to prepare. 9. Expand market access for vulnerable groups, particularly for marketing nutritious foods.10. Incorporate nutrition promotion and education.Source: FAO (2015a).With a view to shedding light on the policy and institutional challenges to and the opportunities for enhancing the nutrition sensitivity of agriculture in Africa, IFPRI and the FAO collaborated on the LANEA initiative19 in Ethiopia, Kenya, and Uganda in 2013-2014. LANEA had the following objectives:• To review the evidence base on linkages between agriculture and nutrition in the region .• To describe the policy and institutional landscape surrounding the agriculture-nutrition nexus.• To elicit the perceptions of stakeholders on the challenges and opportunities of leveraging agriculture for nutrition. In each of the LANEA countries, agriculture continues to play an important role in the overall economy, employing a large percentage of the work force. In all three countries, the majority of the population relies on agriculture for its livelihood: 80 percent in Ethiopia, 75 percent in Kenya, and 73 percent in Uganda (FAO 2011). In Ethiopia, agriculture accounts for more than 46 percent of GDP, and nearly 40 percent of rural farmers (about 5 million households) cultivate land of less than half a hectare, from which they produce only half of their annual food needs (FAO and CAADP 2013a). In Kenya, the sector directly contributes 24 percent of GDP and indirectly contributes 27 percent through linkages with manufacturing, distribution, and other service-related sectors (KARI 2012). Agriculture is one of the primary growth sectors in Uganda, accounting for 24 percent of GDP in 2011-2012 (FAO and CAADP 2013b).Study participants in each country identified a number of similar challenges and opportunities in relation to the enabling environment for agriculture to impact nutrition. Respondents in each country shared similar perspectives on how these environments can be shaped and sustained.Knowledge of the linkages between agriculture and nutrition was perceived as being low in all three countries. Table 5.1 shows the breakdown of studies that emerged from the evidence review in 2014, mapping evidence to the six-pathway structure shown in Figure 5.1.When asked for their perspectives on how agriculture can be leveraged for nutrition, study participants shared a number of ideas indicating a growing awareness of the pathways from agriculture to nutrition. In all three countries, interviewees mentioned Pathway 1-agriculture as a source of food for household consumption-by far the most frequently.Most of the studies (26 of 51) identified in the evidence review mapped to this pathway as well. Stakeholders talked about the role of agriculture in providing food and income for diverse diets, and participants in Uganda and Ethiopia perceived potential negative consequences of agriculture when it is used solely for cash crops and market production at the expense of nutritious foods for local consumption.Study participants in each country also highlighted the role of gender, with stakeholders in both Uganda and Ethiopia pointing to the importance of land tenure for women, and a Ugandan participant describing the need to have a gender-sensitive lens for integrating nutrition within agriculture.Participants often suggested that when women have control over resources, they are more likely to use the resources on food and care for their children, thus impacting nutrition. However, stakeholders also felt there was insufficient evidence to understand how agriculture can impact nutrition, with further research on the pathways required-especially Pathways 5 and 6, which relate to women's employment in agriculture and its impact on childcare and women's own nutritional status. Only 5 of 51 studies related to these two pathways.Although research and data are seen as key, stakeholders described these areas as weak. Research on agriculture-nutrition linkages remains low in all three countries, as seen in the evidence reviews. Interviews indicated that even when research knowledge exists, it is often not communicated effectively to policy and program decision makers. Stakeholders stressed the need for more funding for research that is practical and actionable, and that demonstrates \"what works\" for nutrition-agriculture integration.Informants felt that capacity to collect timely and accurate data on nutrition and agriculture at the national and regional levels was needed, as well as capacity to analyze and communicate such data in a meaningful manner.This theme of communication was evident in each country, in terms of not only communicating evidence to policy makers but also communicating nutrition messages to households. Participants from all three countries strongly emphasized the need to contextualize messages within social and cultural values that may differ by region and livelihood zone. In Uganda, participants suggested using social marketing for communication, and in Ethiopia, participants stressed the need for different nutrition messaging depending on the audience. Stakeholders also stated that research is needed to understand regional and cultural differences related to nutrition in order to better develop targeted programs. They also suggested learning from other successful cross-sector initiatives such as those related to HIV/AIDS.In each country, there is growing momentum to address nutrition, with policies and platforms that either have potential to address or are currently addressing nutrition multisectorally (Table 5.2). All three of the countries have joined the SUN movement and are taking part in other initiatives such as CAADP, which have the potential to support efforts to leverage agriculture for improved nutrition. in 12 countries, has proven instrumental in convincing decision makers, in particular ministries of finance, of the need to act. Carrying out similar studies around the continent will contribute to raising political and financial commitments in favor of nutrition. In addition, advocacy efforts should be oriented toward holding governments to account for food and nutrition security-related promises they have made by signing recent declarations (for example, the Sustainable Development Goals and the Second International Conference on Nutrition, SUN, and CAADP commitments).A key message concerns the need to integrate clear nutrition objectives, indicators, activities, and investments in agriculture investment plans and to align these plans with multisectoral nutrition plans. These steps will require strong dialogue across departments within the ministry or ministries responsible for agriculture, livestock, fisheries, and natural resources, as well as continuous dialogue with other ministries, in particular health. This dialogue needs to occur at a sufficiently high level of decision making to ensure that policy decisions can be made and acted upon.Advocating for action is not enough. Decision makers in both the public and private sectors need information on what exactly can be done and at what cost, and they need to be held to account for doing it.Currently, most national information systems are not equipped to provide such information. Three types of information are key-on outcomes, on policies, and on financing. We discuss the first two here, with the financing discussion in the final section.First we consider information on outcomes. Agriculture contributes to improved nutrition primarily by improving diets. But currently, very few governments collect information on individual food consumption, especially for women and young children. 20 Without knowing what people eat, it is difficult to design programs that can address dietary gaps and to monitor whether these gaps are effectively addressed. A new methodology for measuring the minimum dietary diversity of women (MDD-W) has recently been developed by various stakeholders (FAO and IRD 2015) and is being taken up by several countries and promoted by development partners. The MDD-W is also included in the CAADP Results Framework, and several countries (including Niger, Nigeria, and Ethiopia) are working to include the indicator in national surveys. Supporting countries in developing the capacity to collect and analyze this information to inform food system and agricultural policy and program design, and to monitor their impacts, is key. Linked to consumption is the critical issue of access to a healthy diet, which also needs better tracking (Herforth 2015; Global Panel on Agriculture and Food Systems for Nutrition 2015).Second, we look at information on policies for nutrition-sensitive agriculture and food systems. Improving existing policies and programs requires an understanding of what is already going on, and whether and how it is working. Unfortunately, few countries have effective policy monitoring and analysis systems, particularly with regard to food security and nutrition. With the rising focus on nutrition, several policy mapping exercises have taken place-for example in the context of the SUN movement-but these are often one-time exercises, led by development partners and conducted with donor funds. There is thus a need to strengthen national systems of policy mapping and analysis to ensure 20 Individual food consumption provides a measure of dietary adequacy; household food consumption indicators are a measure of household access to food. that they are closely tied to policy formulation and decision making. This is not an easy enterprise, given methodological difficulties (for example, deciding what to map) and institutional difficulties (collating and analyzing information from various sectors and ministries). Nevertheless, positive examples are emerging, such as that of Zimbabwe (Box 5.3).Zimbabwe's national integrated Food and Nutrition Security Information System (FNSIS) is managed by the Food and Nutrition Council (FNC), the central coordinating body for all food and nutritionrelated issues. The FNSIS monitors and reports on the food and nutrition security situation and program implementation. A greater emphasis is now being placed on using policy information and analyses to better plan programs and improve monitoring on a subnational level. Thirty district-level food and nutrition security committees (FNSCs) have been created to support action at a decentralized level. The FNSCs facilitate data collection and analysis, as well as informed decision making and effective knowledge transfer, to all stakeholders, from the local to the provincial and national levels. At the heart of the FNSIS is a repository that brings together food security and nutrition data, information, and knowledge from various national and subnational information systems in the country. In addition to gathering food and nutrition security data, the FNC is starting to monitor food and nutrition security policies, programs, and legal frameworks in terms of content, objectives, and level of implementation. While this process is still in an early stage, these efforts receive strong political support and have been extended continuously.Source: Authors.Policy mapping and analysis is also key to informing regional planning processes and supporting accountability on countries' commitments such as those embodied in the Maputo Declarations. Countries are also interested in learning from one another and building on success stories implemented by their neighbors. The need for regional information sharing on relevant policies clearly emerged as a follow-up to the workshops conducted through The objective of this platform is to stimulate sharing of information and experiences across sectors and countries and to improve the strategic use of data and information on food and nutrition security at both the country and regional levels. The New Partnership for Africa's Development (NEPAD) knowledge portal is expected to foster evidence-based dialogue and a multisectoral approach among countries and regional stakeholders. This process was initiated through consultations in the South African Development Community region, and the aim is to scale it up to the rest of the continent. This work is inspired by the Plataforma de Seguridad Alimentaria e Nutricional, a which plays a central role in stimulating learning and accountability at the regional and country levels in the Latin America Without Hunger 2025 initiative.Source: Authors. Note: a www.plataformacelac.orgOverall, in terms of policy and governance, the need for cross-sectoral (horizontal) coherence is evident. For agriculture to be accountable for nutrition, high-level coordination, clear indicators, and mechanisms to share and foster dialogue at all levels are needed.While undernutrition remains a priority problem for Africa, most countries are also faced with a growing prevalence of diet-related noncommunicable diseases, such as diabetes, cardiovascular disease, and obesity. These are tied to evolutions in consumption patterns-that is, greater consumption of fat-and carbohydrate-rich foods and processed foods-that go hand in hand with urbanization and integration into global markets. The response to such trends lies only partly in national agricultural production policies. There is a large role for policies related to marketing and labeling of foods, consumer awareness, retail, and trade. The challenge is to ensure that such policies generate incentives for the private sector (from production to retail) to provide for healthier diets, and for consumers to consume healthier diets. Unfortunately, the evidence of what works and how to simultaneously address nutrition concerns and economic objectives is scarce, inasmuch as the world is collectively facing this new challenge (Hoddinott, Gillespie, and Yosef 2015;Dangour et al. 2013).Capacity-building efforts aimed at strengthening knowledge and skills are important not only at the policy level but also in terms of building human resources for integrating nutrition across sectors. Capacity development, knowledge building, and coordination efforts all require extra funding.In terms of capacity at the grassroots, there is considerable interest in integrating nutrition into agricultural extension systems, including participatory approaches such as farmer field schools and pastoral field schools. This integration can lend great value toward ensuring that various sectoral institutions with complementary messages reach families in communities with information and skills regarding nutrition.This field is fraught with several challenges, including understaffing and underresourcing of agricultural extension systems, leading to poor coverage and difficulty in reaching households; the great variety of topics to be covered by extension agents, making it difficult for them to have knowledge With regard to financing improvements in the nutrition sensitivity of agricultural and food systems, better information is needed on both costs and public expenditures. Few countries have a proper tracking system for public expenditure on agriculture in general, making it all the more difficult to track what part of that investment contributes to nutrition. Key questions that planners are struggling to address include how much it costs to make agriculture nutrition sensitive and what variables should be tracked. The answers to these questions are not straightforward. The types of interventions required to enhance the nutrition sensitivity of agriculture will differ from one context to the next (that is, in one context the priority may be agricultural diversification and in another, access to safe drinking water for agricultural workers), and thus the cost also differs. Moreover, in some cases, there may not be a direct cost (for example, the total investment may be the same for nutritious crops as it is for a common staple crop with limited nutritional value), but there may be an opportunity cost (for example, if the market returns on the staple crop are higher). It may be possible to identify \"win-win\" opportunities-for example, investing in diversified cropping systems (including crop rotation and reduced pesticide use) that generate benefits both for the environment (in terms of climate change adaptation) and for nutrition. Another win-win emerges when farmers choose to grow crops that have good nutritional value as well as high market value. The food and agriculture sector is pivotal not only to addressing undernutrition but also to containing and preventing the spread of diet-related noncommunicable disease. This context requires action throughout the food system, from sustainably managing natural resources and input supplies to enabling consumption of healthy diets and promoting gender equity.Political commitment is growing, but much remains to be done in terms of strengthening the information base to support strategic decision making, and developing capacities for implementation at scale. In April 2016, the UN General Assembly enacted a Decade of Action for Nutrition, and nutrition is directly or indirectly related to all of the Sustainable Development Goals.This enabling environment at the global level should foster further progress in the region, and conversely, African countries can inspire other regions of the world by pursuing innovative approaches for unleashing the latent potential of the agrifood sector to drive positive change in nutrition.","tokenCount":"3989","images":["691508565_4_1.png","691508565_4_2.png","691508565_4_3.png","691508565_4_4.png","691508565_4_5.png","691508565_4_6.png","691508565_4_7.png","691508565_4_8.png","691508565_4_9.png","691508565_4_10.png","691508565_4_11.png","691508565_4_12.png","691508565_4_13.png","691508565_4_14.png","691508565_4_15.png","691508565_4_16.png","691508565_4_17.png","691508565_4_18.png","691508565_4_19.png","691508565_4_20.png","691508565_4_21.png","691508565_4_22.png","691508565_4_23.png","691508565_4_24.png","691508565_4_25.png","691508565_4_26.png","691508565_4_27.png","691508565_4_28.png","691508565_4_29.png","691508565_4_30.png","691508565_4_31.png","691508565_4_32.png","691508565_4_33.png"],"tables":["691508565_1_1.json","691508565_2_1.json","691508565_3_1.json","691508565_4_1.json","691508565_5_1.json","691508565_6_1.json","691508565_7_1.json","691508565_8_1.json","691508565_9_1.json","691508565_10_1.json","691508565_11_1.json","691508565_12_1.json","691508565_13_1.json","691508565_14_1.json","691508565_15_1.json","691508565_16_1.json"]}

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+{"metadata":{"gardian_id":"6d32a097dd9ad6b6b9b62667aa948480","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/20b5beb7-dd19-4674-a7da-270ad5dffc24/retrieve","description":"Floodplain wetlands are the major common pool natural resource in Bangladesh. Mostly men fish, and both men and women collect aquatic plants and snails. Case studies contrast a women-only, men-only, and mixed community based organization (CBO), each of which manages a seasonal floodplain wetland. The two CBOs in which women hold key positions are in Hindu communities where more women use aquatic resources, work for an income, and belong to other local institutions. In the oldest of these CBOs, more women have gradually become office bearers as their recognition in the community has grown. In the Muslim community, only a few women collect aquatic resources and in this community most women do not perceive floodplain natural resource constraints to be very important to them. These women have no role in the CBO and feel that they have no say in decisions about the fishery, unlike many women in the other two sites. The fishery management activities in all three sites are similar and catches and biodiversity appear to have improved, demonstrating that women can play an effective role in community organizations for fishery management. Those who are represented in the CBOs reported significant increases in their participation and influence. Men and women in all three sites recognized that decisionmaking and management of their fisheries had improved, but community support and compliance were higher where both men and women had an active role in this process. Women had a more diverse set of criteria for effective CBOs than men. The men-only CBO saw itself as more of a membership based organization than as representing all of the community.","id":"-1536520726"},"keywords":["floodplains","fisheries","Community","Bangladesh"],"sieverID":"e17565c6-9f53-4e71-ad19-f465c526bb17","pagecount":"68","content":"The CGIAR Systemwide Program on Collective Action and Property Rights (CAPRi) is an initiative of the 15 centers that belong to the Consultative Group on International Agricultural Research. The initiative promotes comparative research on the role played by property rights and collective action institutions in shaping the efficiency, sustainability, and equity of natural resource systems. CAPRi's Secretariat is hosted by the International Food Policy Research Institute's (IFPRI) Environment and Production Technology Division (www.ifpri.org).CAPRi Working Papers contain preliminary material and research results and are circulated prior to a full peer review in order to stimulate discussion and critical comment. It is expected that most Working Papers will eventually be published in some other form, and that their content may also be revised.Bangladesh is traversed by numerous rivers and creeks as it is the delta of the Ganges-Brahmaputra-Meghna rivers. Only 7.5 percent of the 1.5 million km 2 catchment area of these rivers lies in Bangladesh (Huda 1989), and the water draining from China, Nepal and India produces a combined peak flow in Bangladesh of about 100,000 cumecs, five times the peak flow of the Mississippi (Coleman 1968), and it may exceed 160,000 cumecs in a 1-in-100 year flood (FAP 4 1993). More than two-thirds of Bangladesh is floodplains and may be classified as wetlands according to the Ramsar Convention's definition 3 . About six to seven percent of Bangladesh is always under water, seasonally 21 percent is deeply (>90cm) flooded and around 35 percent experiences shallow inundation (FAO 1988). The wetlands of Bangladesh include mangrove forests, natural lakes, freshwater marshes, baors (oxbow lakes), 1 Parvin Sultana, Flood Hazard Research Centre, Middlesex University, Queensway, Enfield, EN3 4SA, UK ([email protected]) 2 Paul Thompson, Flood Hazard Research Centre, Middlesex University, Queensway, Enfield, EN3 4SA, UK ([email protected]) 3 \"Areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static, flowing, fresh, brackish or salt, including areas of marine water, the depth of which at low tide does not exceed six meters\" beels (floodplain depressions), fish ponds and tanks, one large reservoir, estuarine areas and extensive seasonally inundated floodplains.Fishing is traditionally and culturally the preserve of men; fishing by women is limited to their own household ponds or floodwaters near the homestead in the monsoon season. In the past, fish caught by women were seldom sold and any fishing they did was only for family consumption. Access to and control over natural resources by women was virtually unknown.Men believe that fishing is a male activity and women have no role in catching fish.Therefore, for building fishery management institutions men prefer that only men be included in decisionmaking.This paper investigates the development of institutions for community management of floodplain and fishery resources vis-à-vis the different roles of women and men in these community-based organizations (CBOs), and the outcomes of the organizations in terms of resource management actions, changes in livelihoods, and changes in assets. The paper focuses on three community-based organizations established mainly for management of capture fisheries; in addition in all three sites smaller groups of poor women were formed for micro-credit, but these were only represented in two of the community-based organizations.Despite similar facilitation from a local NGO (Banchte Shekha) which normally only works with poor women, the three case study sites differ greatly in the extent to which women are involved in resource management decisions and activities.The four million hectares of inland water bodies and floodplains in Bangladesh are among the word's richest and most complex fisheries. These rivers, beels (lakes), baors (oxbow lakes), haors (large deeply flooded depressions), and floodplains support some 260 fish species (Rahman 1989). About 80 percent of rural households catch fish for food or to sell (FAP 16 1995), and about 60 percent of animal protein consumption comes from fish, and of this 80 percent is from freshwater fish (BBS 1997). However, fish consumption has declined between 1995-96 and 2000 by 14 percent to 11.1 kg/person/year (Bangladesh Bureau of Statistics household expenditure survey data quoted in Muir 2003).Since the advent of the green revolution, Bangladesh has made tremendous strides in increasing rice production. This success has occurred through many changes in the management of land and water. More areas have been brought under rice production, irrigation has expanded greatly, and areas have been drained and protected by flood control embankments. However, these changes have been at the expense of fish; the area of inland water bodies and the duration of inundation in some areas have fallen, and thereby there has been a reduction in the habitat for fish.In addition to embankments, drainage and flood control; natural siltation along with over fishing are commonly cited as causes of the deterioration of the country's fishery resources (Hughes et al. 1994;Ali 1997). Yet fisheries remain key floodplain resources, and the restoration of floodplain fisheries through community-based management has the potential to be a major strategy to improve and make more sustainable the livelihoods and quality of food consumed by poor people. The National Water Policy has recently emphasized reserving wetlands for fish in a reversal of past trends (MWR 1999). Previous fisheries policies have discouraged development of local institutions for fisheries protection and management, but this may now be reversed.In addition to fisheries, Bangladesh wetlands support a wide diversity of both cultivated and wild food plants. For example 2,929 local varieties of rice have been reportedly used in different regions of the country (NCS 1991). About 13 species of wild wetland plants are eaten (Karim 1993); the grains are used as a substitute for rice, fruits and root stocks; the seeds are eaten raw, roasted or as puffed grain and are also used to make flour; and the stems and leaves are used as vegetables. In addition to almost all species of fish, shrimp and crabs are used as human food, and mollusks are used both as feed for domestic ducks and in freshwater prawn culture. Wetland plants are also used as fodder and medicine, for mat making and fuelwood, and to protect homesteads against wave erosion.The majority of rural women in Bangladesh are not only poor but are also caught between two very different domains: one determined by culture and tradition that confines their activities inside homesteads and the other shaped by increasing landlessness and poverty that forces them outside into wage employment. Women from poor and female-headed households by necessity take culturally unaccepted work as laborers in garment industries in the urban areas, fish processing, brick breaking, earthwork for road construction and road maintenance.The role of women in society is seen as subsidiary to that of men and as having its principal concern with the household, reproduction, childcare and family management. The distortions show particularly in:• average literacy -38 percent for women, 52 percent for men (BBS 1998);• age at first marriage -20 for women and 28 for men (World Bank 1998);• education enrollment rates -women compose only about 30 percent of the secondary and higher roll (BBS 1998)  and; • labor force-only 18 percent of women participate in the labor force compared with 43 percent of men (United Nations 2000), and have significantly lower wages when they do, but contribute 80 percent of the unpaid family work.Since the 1980s, the status of women and the amelioration of their disadvantaged position in Bangladesh has been a major concern of the NGO movement. Whatever the limitations, there have been impressive strides in the empowerment and economic emancipation of women under the programs of the major national NGOs, which have raised the economic role and voice of women in rural society. Only over the last two decades have policy-makers, planners, researchers and society in general begun to consider and value women's economic contribution to food production and income generation.In Bangladesh, fishing is the second most important occupation in the non-farm sector.Traditionally, only men in the fishing communities were engaged in catching fish, although some old and widowed Hindu women did catch fish for their household consumption as well as for sale in the southern part of the country. Now not only do old and widowed women fish, but all poor women irrespective of religion, age and marital status are found to catch shrimp fry in the coastal areas of Bangladesh. About 80 percent of the work force in shrimp fry collection is women and children. This change has happened due to extreme poverty and the growth of shrimp farming which has created a low cost way of earning money. In 2000, the price of each shrimp fry was around Tk. 1-2 and on average each woman could earn about Tk. 5,000 (approximately US$ 95) in a fry catching season (January to March).Although fry catching by women is quite accepted in the coastal areas, fishing by women in inland water is not yet a common site. Some Hindu women catch fish in the canals and water bodies near their houses with rods and hooks, rarely with cast nets. Women also catch fish by hand in shallow water and paddy fields, particularly in the coastal areas.In shrimp processing plants, 80 percent of the work (such as deheading, sorting, peeling of small shrimps, and packing) is done by women while men break ice slabs for preservation. More generally in inland fisheries most of the post-harvest work such as drying fish is done by the women. Women also are responsible for storing processed fish. Gears such as nets and traps are made mostly by women and other family members. When the men sit idle or do not go out fishing they help in net making. Mending and cleaning nets are mostly done by men, but tanning is solely done by the women.Women also collect snails and aquatic plants. They sell snails to the duck and prawn farmers. Sometimes traders buy snails and they engage women as paid laborers to break the snails. This snail trade has become a very popular business in the southwest of Bangladesh where there has been a rapid expansion of shrimp and prawn farming. While this provides an additional income source for women who are able to access snails freely, it is increasingly thought by local people (men and women) that there is now an overexploitation of snails.In most of Bangladesh, men make fishing related decisions, such as when to fish for income and food, whether to preserve any fish, what to purchase with the money earned, and even what to purchase from women's income, as they are mostly fishing and earning from it.Beels are natural depressions where water stands during the monsoon, and in the monsoon there is open access for fishing for members of the surrounding communities. Rain water and daily tidal influences are the main sources of seasonal flooding. All three of the sites covered by this study are protected by flood control embankments constructed along the rivers by the Bangladesh Water Development Board (BWDB).Goakhola-Hatiara Beel is a seasonal floodplain beel covering at its maximum extent around 250 ha. The beel is connected by Goakhola Khal (a natural canal) via a sluice gate to Afra Khal (a secondary river), which connects to Bhairab river some 3 km downstream of the beel, but local rainfall is the main source of water in the beel. All of the lands within the beel are privately owned and are cultivated mainly with paddy in the dry season. The area is under approximately 1.2-1.8 m of water for five to six months of the monsoon each year. During the monsoon, paddy is also grown on much of the area (and very recently has changed from traditional mixed aus and aman paddy to early monsoon (aus) paddy). Land owners have shallow ditches (locally called kua) in their land where no crop is grown but where they trap water and fish at the end of the monsoon and by the end of the dry season they drain out all the water and catch the fish. The five villages around the beel (Hatiara, Goakhola, Bakri, Mandiarchor and Debbhog) are entirely Hindu communities. In December 1996 there were 355 households living around the beel, of which 89 were already NGO (Banchte Sekha) group members.As all the land is private, farmers dominate in the area and as this is a floodplain and the community is a Hindu farming community, the number of professional fishers is very negligible. Access to aquatic resources during the monsoon is free for all from the surrounding villages owning land in the beel. Anyone can fish anywhere in the monsoon, but in the post monsoon period nobody is allowed to fish near the private kuas. In the nearby Bhairab river, high competition for fishing exists and the Hindu community does not feel comfortable fishing there throughout the year. Thus, the poor, including the landless poor, do not depend always on fishing. Most of the households catch fish at some point in the year, over a third sell fish, and the remainder fish only for their own consumption.Maliate Beel covers 100 ha of private land just east of Goakhola-Hatiara Beel, and the two beels are interconnected with another three seasonal beels in the monsoon. Water stays permanently in only 3 percent of the area. One channel from the beel area is connected to the river. During the dry season 70 percent of the low-lying land is cultivated with irrigated high yielding varieties of paddy, while the rest of the land is cultivated with other winter (rabi) crops. The few high lands are occupied by homesteads. The four villages around the beel are inhabited by 591 households. They are all lower caste Hindus.Shuluar Beel is a seasonal beel (flooded in the monsoon season), and is larger than the other two beels, covering at its maximum extent around 1,000 ha. It is located in Narail district in southwest Bangladesh. The beel is connected by a canal to the rivers Chitra and Nabaganga (secondary rivers), but rainfall is the main source of water in the beel. All of the land in the beel is private and is cultivated mainly with paddy. There are around 967 households living in five villages around the beel. Approximately 90 percent of households are Muslim. The beel is seasonal and in the monsoon there is open access for fishing for members of the surrounding communities. Almost all of the households catch fish at some point in the year. Half of the households that depend on fishing and other aquatic flora and fauna for income are very poor; the other half of the households just fish for their own consumption.The community of Goakhola-Hatiara Beel has since November 1996 been supported by projects to establish community based management of the fishery. An NGO, Banchte Shekha, from the region that only works with poor women has facilitated this with support from the government and WorldFish Center, and the focus has been on conserving fish in the dry season (Thompson et al. 2003). In late 2001, Maliate and Shuluar Beels were added to the same program in a second phase of the Community Based Fisheries Management (CBFM) Project (WorldFish Center 2003). The general CBFM model adopted in the three sites is to include representatives from all types of stakeholders in the Beel Management Committees (BMC). The institutions themselves were formed through selection by the community members, NGO staff and the local fishery department.The approach in two beels -Goakhola and Shuluar -included: stakeholder analysis; informal grouping according to livelihood characteristics; developing consensus on the livelihood categories and among all stakeholders on problems, constraints and possible solutions; and analysis of social, economic and environmental impacts of the solutions. In both cases the local community formed a BMC with all types of stakeholders in the floodplain, but gave priority to the fishers, although the number of full time fishers in these beels is very few.The approach adopted in Maliate Beel involved all stakeholders but identified women as the main stakeholders interested in taking action based on past experience in Goakhola (Figure 1).The NGO formed groups with the women for income generating activities. There, other stakeholders in the community participate as members of the advisory committee.In addition, from mid-2003 Integrated Floodplain Management (IFM) has been promoted in Goakhola-Hatiara Beel through a research project involving the same agencies (Sultana et al. 2005), with implications for the local institutions. The focus of the IFM approach has been to improve overall floodplain productivity by better understanding the Greater access to and control over the use of aquatic resources by women and poor people links between private and common pool resources and decisions of individual farmers and collective action. For example, it has facilitated farmers (who also catch fish for food) in testing and then adopting alternative dry season crops that do not require irrigation and thereby reduce abstraction of surface water for irrigation, resulting in more water in the dry season which is a critical habitat for fish that is now protected by the community.Studies undertaken by the CBFM-1 and 2 projects since 1996 to understand the fishery As part of a study of institutional issues for integrated floodplain management, focus groups were held with all of the BMCs in 2003. As part of the project to promote uptake of IFM approaches, participatory planning was undertaken in Goakhola-Hatiara and Maliate Beels, and data were collected on agricultural changes, water levels and fish catches. In addition, as part of that study household impact surveys were undertaken for all three sites in August 2005. Moreover at different times, participatory assessments and learning sessions with focus groups comprising representatives of each stakeholder group were held.In general, women of ethnic and other minority groups are more liberated and more outspoken than the rural Muslim women in Bangladesh. Two of the case study sites -Goakhala-Hatiara and Maliate Beels -are Hindu communities, where about 90 percent of women fish seasonally for food and income. About 60 percent of women and children catch snails for household use or for income, and about 10 percent of women are employed as snail breakers. However, the scenario is different in Shuluar Beel, where the majority of the people in the community are Muslim and conservative. Men take all the decisions and women remain within the house. Men do not want their women to join in any group or organization.The data from monitoring household activities in 2003 and 2004 has been summarizedfor the main natural resources (Figures 2a-2e). Fishing was a major activity for menaveraging about 80 days a year in all three beels (slightly less in Shuluar). On average at least one woman (including girls) from a household spent about 40 days a year fishing in both Goakhola and Maliate (Hindu communities), but no women were involved in fishing in Shuluar (Figure 2a). The patterns were similar to this for day laboring -no women did this work in Shuluar, but in the other two beels women were just as likely as men to do daily laboring work in both 2003 and in 2004 (Figure 2d).Although in Shuluar Beel women are not involved in fishing or day laboring, they collect aquatic plants and snails and break snails for selling or work as snail breakers for traders. These women are from very poor families who have no men in the family to provide an income. Snail collection only happens in the early morning and when snails can easily be caught as they float on the water surface; this is also when fewer men are around. Women break snails at home and sell to traders who come to their homes. In contrast, in the other two beels women from all categories of households catch snails whenever they have time, including when they are fishing (but they are also busy in their farm or working on others' farms and they do post harvesting work too).The Beel Management Committees involved in CBFM activities start with the representatives from NGO (Banchte Shekha) primary groups. Each primary group has 10-15 members, all female. The female group members save regularly and have their own income generating activities (IGAs) and all the members are not necessarily involved in fishery activities. The BMC is a selected body comprised of group representatives, representatives of other stakeholder categories and local leaders whom the community and NGO select to be in the committee. BMC members meet every month but if there is an emergency they meet any As the NGO has no male groups, there is no direct way of supporting households dependent on men who fish for an income to divert from fishing during the closed season (fish breeding season when fishing is prohibited by the committee in order to conserve fish).But credit is disbursed through the female groups to women from those poor fisher households.The Beel Management Committee (BMC) was formed in 1997 with representatives from a mixture of professions in the community. Most of them are farmers and fishing is their seasonal activity. The committee has always contained several women, all of whom are members and representatives of the groups formed by Banchte Shekha. Table 1 shows how the committee has evolved since 1999. Representatives of two villages, Goakhola and Hatiara, dominate in the committee. The main activity of the BMC has been to take up fish conservation measures and it tried unsuccessfully to extend to water control The BMC is also responsible for coordination with other stakeholder groups as well as different organizations. It takes decisions through participatory discussion with the primary groups. The women members of Banchte Sekha guard kuas which they have protected as dry season fish sanctuaries in the day time while men in the BMC and husbands of the women guard at night. The BMC members, aided by public announcements, inform the general community not to poach in these kuas.To coordinate between villages, there was a male advisory committee composed of elderly people and local elites until 2003. The advisory committee was responsible for providing necessary support to the BMC and to liaise with the local government for back-up support.The BMC has succeeded in implementing the local rules that it sets through guarding by women and men and the support of men and women including local leaders, and claims that only 10 percent of the community breaks the rules. Some people who were fishing illegally during the closed season were subject to punishment of different levels when caught by the BMC members.The BMC has a bank account jointly operated with the NGO staff member supporting their activities. Each member makes contributions to the fund. The CBFM project provided some revolving fund and grants, and the entire fund was deposited in the account. Moreover, the BMC successfully appealed to the Union Parishad (local council) chairman and got the lease to the khal (canal) without any fees imposed for making it into a fish sanctuary. The BMC has a small community center located next to the beel. The land was donated by one of the BMC members, and the structure was built through a CBFM-2 grant. For proper identity and formal recognition, the BMC should be registered with the government; however this has not been done yet as the Social Welfare Department ended new registrations in 2005.This arrangement was modified in 2002 when representatives from the BMC, farmers, fishers, farmer field school and sluice gate operators formed an integrated floodplain management committee. This committee is working as an apex body and coordinates the activities of all the local institutions. In this 15-member committee six women are also included from the BMC and from the farmer field school.The institutional arrangement for CBFM in Maliate Beel is similar to that for Goakhola-Hatiara Beel, with an important difference being that, given the strength of its primary groups in this area, Banchte Shekha helped them to form a BMC that comprises only women from its primary groups. Women here observed that fishery resources are continuously depleted and there was no conservation for the future generation. They first discussed this with the men, but men were not interested in forming any institutions to improve fishery management. However, these women sought the help of respected men from the community as an advisory committee, since they could more easily persuade men to follow the BMC rules in a male dominated society.Thus, women have taken a lead in fishery conservation and management in the beel. Not everyone in the community though has accepted the leadership of women in fishery management. Some men raised questions about the competence of women in future management. A few started to catch fish in order to see how women ensured compliance with the rules the BMC set on fishing. Although women were guarding the sanctuaries during the day time, at night it is not physically safe for women to be in the beel so the women successfully asked their husbands to guard. As shown in Table 2, the women felt the need to involve some men at least in an advisory committee. This advisory committee included locally respected people who have substantial influence on the community. The advisory committee members talked to anyone who broke the rules in order to make them aware about the future impacts of not protecting fish, and subsequently the BMC reported nobody from the community broke the rules. This committee also negotiates with the local government to support water retention and fish sanctuaries, and helps the women of the BMC to make linkages with local experts and officials. Maliate BMC is registered with the social welfare department, giving it a legal identity.They have group savings, a rolling credit fund for income generation activities for women, and a fund for the BMC. The chairperson has been selected to be the vice president of the District Committee Against Women's Repression and also secretary of the beel Cluster Committee that coordinates management of five connected beels including Goakhola and Maliate Beels.Because it is adjacent to Goakhola-Hatiara Beel and links with it in the monsoon, IFM has effectively been extended from Goakhola to Maliate. The BMC members and farmers have been invited to IFM activities such as field days, participatory assessments and exchange visits.After seeing the IFM committee in Goakhola, the community in Maliate also formed a similar 15 member IFM committee, but most (nine) of its members are women and come from the BMC and most of the men come from its advisory committee.Before the CBFM project this beel never had any local institution for resource management or any development work. The community comprises mostly of Muslims and women's voices are not heard. In this area, NGOs were not allowed to work freely with women.Banchte Shekha only works with women and when they started the CBFM-2 project they faced problems for forming women's groups. The men did not allow women to take part in the BMC and no women were included in any committee (Table 3). Even during the Participatory Action Plan Development (PAPD) workshop, women were not allowed to come to the plenary for discussion. After forming the BMC the committee needed funds for establishing sanctuaries, and men wanted credit for alternative occupations during the closed season. Banchte Shekha refused to lend money to the men and they kept motivating BMC members to allow women to be part of the fishery development work. After one year, the men allowed women to form a few groups.Women are now receiving credit and the men have become used to it. After several meetings, the BMC felt that women could be a good publicity link as they talk with other women during leisure time or visits to their kin. They decided to add two women to the committee. However, the original BMC was large and members were not attending meetings regularly, so in 2003 they reduced the number in the general body and formed a nine-member executive committee of the active people but did not include any women; and the members of the general body (including the two women there) do not have a role in decisionmaking. There has been no change in the committee membership or numbers since 2003. The BMC reported that about 20% of the community still breaks their resource management rules. The roles of women and men in Goakhola-Hatiara Beel have changed over time. This site has the longest history of CBFM and has always had men and women in its CBO.Between 1999 and 2002 about 30 percent of the committee members were women; in 2003, the number increased to 52 percent, but in addition from 1999 to 2002 there was a male advisory committee to help with liaison activities and convincing people to observe the BMC rules. Moreover, in 1999 all four office bearers were men so the level of women's involvement in decisionmaking was limited, but then in 2000-2003 two out of five office bearers were women. In 2004 the advisory committee was dropped, an executive committee was formed with 52 percent of its members being women, and half of the eight office bearers were women. Thus over time women have become accepted by men as playing a more active role in decisionmaking and now they have a roughly equal role to men.There have been no effective changes in the last three years in the other two sites:Maliate has only women in the committee, but has a male advisory committee which the women wanted as it helps them for linking with local institutions and obtaining help for night time guarding. Shuluar has throughout had all male decisionmaking committees; although in 2003 women's groups were formed for savings and credit they are not represented in decisions on fishery and floodplain resource management.These differences between sites are also reflected in the establishment of community centers: in both beels where women are involved, it was women office bearers who donated land to build a community center, whereas the men-only CBO negotiated with a male landowner who was not active in the CBO to temporarily make land available, and thus the CBO's tenure is less secure. Under CBFM-2, the BMCs from the adjacent beels formed a cluster committee (Figure 3) in 2003. The cluster committee is composed of seven members, one from each beel plus a member from DoF. The cluster committee was formed to strengthen all the individual BMCs and to help them develop a unified action plan so that all the water bodies in the same connected cluster benefit from one another's management activities equally. It acts as a local conflict resolution body. This committee also works as a pressure group for any fisheries policy implications. In this section we review impacts that may be associated with the CBFM institutions developed in the three case study sites, wherever possible distinguishing men and women's opinions of possible impacts, but also considering overall changes and differences between the sites since each represents a different extent of women's and men's involvement in the resource management CBOs (BMCs and additionally the IFM committees in Goakhola and Maliate). We consider here: men and women's perceived problems, outcomes and trends in the fishery, and participants' assessments of institutional arrangements and their effectiveness.There is some evidence that the problems and issues prioritized by men and women differ and this could have a bearing on collective action. However, problem censuses conducted separtely with men and women at different times and then consolidated indicate that the differences are greater between sites than between men and women (Table 4). In both Goakhola-Hatiara and Maliate, 70-90 percent of the main problems identified related to common pool natural resources -fish, surface water, floods and other aquatic resources, while the remaining problems identified were mainly related to private natural resources (low crop prices, for example). In Shuluar, only 25 percent of women's priority problems related to common pool natural resources, and 60 percent were not natural resource related, while for the men 44 percent of their priority problems were common pool natural resource related and 31 percent were not natural resource related.The differences appear to directly relate to the extent to which women and men actively collect common pool resources to support their livelihoods. For example, in the Muslim community of Shuluar mostly men fish and collect other aquatic resources and ranked these areas as high priorities but few women use wetland resources and they ranked poor communications and public services as their main problems). The differences also reflect the extent to which the local societies are concerned for the commons. The Hindu communities appear to have a greater concern for common resource problems even though the aquatic environments and status of natural resources were similar in all three sites). The lesser concern of the men over the aquatic resources appeared to be evident from the record of their attendance in monthly meetings and immediate decisionmaking. Despite this apparent difference in local priorities between men and women, the CBO in Shuluar has adopted some of the same interventions, such as fish sanctuaries, as in the other beels. In addition to differences in types of problems identified, there is a difference in the number of problems identified (10 for women, 16 for men) in Shuluar. The reason may be that women participants have limited knowledge about problems outside of their own sphere. Their exposure to issues outside the home is non-existent.  In each focus group discussion about 15-16 persons were in the session. Separate sessions were held with each stakeholder category, but only men and women from the landless/poor category are shown here for comparability. In Shuluar this was immediately before forming the BMC, in Maliate this was two years after forming the BMC, and in Goakhola this was immediately before forming the IFM committee but six years after forming the BMC under CBFM.The general resource management activities and actions in all three case study sites are similar. The BMCs protect fish in the dry season in some deep ditches (small sanctuaries), and they declared the early monsoon season closed for fishing. As a result some scarce fish species have been restored. However, the impact and the processes are different. In Shuluar, only men benefit economically from fisheries management, but in the other two beels both men and women fish and collect other aquatic resources and now earn more than before.In Goakhola-Hatiara Beel, from the dry season of 1997-98 to the dry season of 2001-02 usually five kuas were rented and protected as sanctuaries each year. The individual kuas differed between years, as the BMC chose those whose owners were willing to rent them and which were thought to have a good fish population. No fishing was allowed in those kuas.The average kua is about 7.8 decimals in area, indicating a total sanctuary area of about 0.16 ha out of a total area of kuas of about 2.9 ha. In 2003 to 2005 no kuas were rented as sanctuaries. The BMC designated the whole of the khal as a dry season sanctuary up to and including the early monsoon, but allowed fishing there in the monsoon and post monsoon.The area of the khal in the dry season is about 1-1.5 ha. In the 2004-05 dry season the BMC excavated some plots that were bought by CBFM-2 project to create permanent sanctuary kuas, but these will not have any impact on fish catches until 2006 since they were dry for excavation in the dry season of 2004-2005.In Maliate and Shuluar Beels, the same strategy was adopted: from the dry season of 2002-03 some kuas were rented as sanctuaries and were protected, and in 2004-05 some permanent sanctuary kuas were created. Similarly in all three sites each year the first three months of the Bangla year (Baishak, Jaistha and Ashar) -mid April to mid-July -have been declared by the BMC as a closed season with no fishing permitted in the beel or khal.The data on fish catches comprises two parts: catches from various gear (mostly gill nets, traps, hook and line, and cast nets in years of higher water levels, plus a few lift nets located in the khals); and the catch from the kuas. Data are only available for different gears for a series of years for Goakhola, which indicate higher catches from 1998 onwards (a year after the start of conservation measures), but also shows exceptionally high catches in 2001-02 (mostly from lift nets) that were not continued (Figure 4). Overall, there do appear to be gains from improved fishery management, (at least in Goakhola-Hatiara Beel, which has a longer series of data), which translate into higher fish catches, although the catch has fluctuated between years (Figure 4). This benefit reaches both men and women there, since women also catch fish and can show a return from their involvement in fishery management through better fish consumption and a supplementary source of income.A major part of the fish catch, usually about a quarter of the total catch, comes from the many kuas (ditches) in the floodplain of Goakhola (and also in Maliate) Beel. In Goakhola before the introduction of IFM, kua catches fluctuated around 50 kg per kua (water area of just over seven decimals). Kua catches increased in 2002 in line with the increase in fish population and catches experienced from 2001 (the kua harvest takes place in the first months of the year and involves fish left over in the ditches from the previous monsoon). This increase continued up to 2004; in 2005 to conserve some fish kuas were harvested only one or two times and a few were left un-fished, but the catch remained high (Table 5). The trend was similar in Maliate, but in Shuluar there was a notable gain in kua harvests in 2005, suggesting that conservation measures there have been effective, but that the benefits may go more to owners of ditches who tend to be better off than many of the other households involved in open water fishing.  The fish species count in Maliate Beel demonstrates that women are just as capable as men in protecting fish. In both of the beels where women are involved in the CBOs and in resource management (Goakhola Hatiara and Maliate), they have maintained sanctuaries and guarded them in the day time, and have been helped by men (husbands) to guard the sanctuaries at night. Moreover, much of the pressure to ensure community compliance with sanctuaries and fishing rules comes from women in the homestead who control what is cooked, discuss the issue in group meetings, and (in the same two beels) decide to catch or not catch fish by their own hands.A household survey was undertaken in August 2005 in which men and women from the same households were interviewed separately, mainly to assess their opinions about the institutional arrangements for resource management and perceived changes over the last three years. The sample covered the same households (30 each in Goakhola and Maliate and 50 in Shuluar) that had been surveyed earlier. However, an additional sample of farming households was surveyed and, where appropriate, data from this larger sample are reported.Education levels differ between men and women and between women in all the beels (Table 6). The women in Maliate Beel are more educated than the women in Goakhola and Shuluar and even better educated than men in the same beel. This may be one of the reasons for women assuming the lead positions in all the floodplain resource management institutions in that area. One reason for, and component of, the subordinate position of the women in Shuluar Beel is perhaps lack of education and awareness. However, for the last few years girls have received grants and wheat from the government for attending school up to the secondary level so their status may change over time as more parents send their daughters to school. Respondents were heads of household (mostly men) and spouse/senior person of the opposite gender in the household None of the sample respondents are professional fishers in Goakhola-Hatiara, and there are no known full time fishers in this community (Table 7). Virtually all households there have some farmland, and the fishing period is short, with a lack of other sources of fish during the rest of the year. The river near Goakhola-Hatiara and Maliate beels does not hold a large population of fish. The men are mostly involved in part-time fishing. They use traps and gill nets after the monsoon and fish for both food and income. The women are involved in fishing mostly for food; some widows and women from poor households sell fish to make money. This picture is very different from other parts of Bangladesh where women never fish in open water. In Shuluar Beel women do not fish except for a few women from very poor families who fish by hand when water recedes in November-December. In all the beels women only use rod and line or hand to fish. The income in all three beels from harvesting different aquatic resources was quite substantial considering that these common pool resources are only available during the monsoon and provide an extra income (Table 8). It was reported that due to conservation of fish during the dry season, in the wet season the amount and value of fish harvested in open water and in private ditches increased. Benefits are not distributed evenly in Shuluar Beel where landowners are now preventing other people from fishing in their lands. However, they are not harvesting fish by dewatering. The findings are consistent with the labor use in collecting aquatic resources discussed earlier: men mainly fish, while women in Goakhola and Maliate obtain over half of the value of aquatic resources they collect from plants and snails.Moreover, women contribute almost half of total household income derived from floodplain common pool resources in those two beels, but very little in Shuluar. Separate data for 2004-05 (Table 9) showed similar average household incomes from aquatic resources in Goakhola and Maliate Beels to the figures in Table 8, but rather higher average incomes from fish in Shuluar. Average household incomes in Goakhola in 2004-05 were double those in Shuluar, and 75 percent higher than in Maliate. However, the main source of income for Goakhola-Hatiara is government service and business, and not from the beel itself. Only about 25-30 percent of average household income comes from own-farm cultivation in all three beels. Daily wage income is low in Goakhola compared to other sources, but a substantial amount comes from daily sources in Maliate. Aquatic common pool resources contributed 16 percent of household income in Maliate and Shuluar, but only 6 percent in Goakhola due to the high non-beel related incomes there. As might be expected, given their dominance over income earning activities, men borrowed and sold assets more than women in 2004-05. But it is notable that even in Shuluar 21 percent of borrowing and asset sales were by women (Table 10), as in Shuluar they receive some loans from the NGO. Men in general in all three sites had wider sources for borrowing, such as banks and money lenders. In Maliate Beel, in addition to belonging to the NGO groups, women have their own revolving loan fund from which they can borrow money which may help explain the higher percentage (48 percent) of total loans and asset sales taken by women, and the relatively higher ratio of borrowing and asset sales to income. These women manage the amount by themselves. In Goakhola the IFM committee also has a fund but the amount is too small to use as revolving loan fund. However, in the 2004-05 rabi (dry) season they requested and received seasonal loans from Banchte Shekha for rabi crop cultivation.This was a big help to them.Source: household impact survey US$ 1=Tk.62 in early 2005 In Goakhola-Hatiara and Maliate Beels, women's involvement in local organizations is higher than in Shuluar Beel (Table 11). In Shuluar the sample women are only involved in NGO groups. The apparently low membership of women in different organizations in MaliateBeel is because few of the women from the BMC were included in the sample. By comparison the sample from Goakhola includes women who are active in the CBM and IFM committees as well as school committees and NGO groups. The results are consistent with information from focus groups -that women in Goakhola (but also Maliate) are more involved in local institutions outside of those created for fishery and floodplain management. When separate focus groups were held to assess the level of social capital in their community using five indicators and scales, the scores differed between men and women and between sites (Table 12). In Goakhola and Maliate Beels, all indicators were much higher than in Shuluar except for conflict, indicating a much higher general level of trust and cooperation in those beels. Since this assessment was made when the BMCs in Maliate and Shuluar were being formed, this difference in levels of social capital helps to explain differences in the effectiveness of the BMCs, including greater problems in Shuluar. The respondents thought that there was scope for improvement and mentioned that difficulties over access to water bodies for the poor was one reason that social capital needs to be Respondents were head of household (mostly men) and spouse/senior person of opposite gender in household improved. In general, men scored all of the indicators lower or the same as women in all three sites, indicating that women may see their communities as more harmonious than do men. In the household survey in August 2005, opinions were taken in response to a range of statements related to collective action, fishery and floodplain resource management issues (Appendix 1). The responses indicate high levels of agreement that people could participate now in managing common resources, and that poorer households were benefiting. Notably, less than half of the women think that their voice is heard in beel management decisions in the beels with mixed men and women in the BMCs such as Goakhola-Hatiara, but in Shuluar only 8 percent of women think their voice is heard. Similarly, knowledge of women in Shuluar regarding improved floodplain management is less. However, all respondents accept fishing related rules. In addition, some impacts of the IFM project are apparent in Goakhola where there has been less increase in groundwater irrigation through shallow tubewells Source: PRA focus groups held in 2002(STW), and more respondents recognize the scope to limit water quality problems from jute retting (which have been addressed by the IFM project through training and demonstrations there).Men and women from the households were asked separately to score the present situation and that of three years before for a range of indicators for community based management of these floodplains. A self-weighting ladder scale was used ranging from 1 (worst imaginable case) to 10 (best imaginable case). The results (Appendix 2) indicate that in Goakhola participation and influence on decisions both at community level and regarding the fishery has increased significantly for men and women, but was scored significantly higher by men than their spouses. By comparison in Maliate, with the all-women BMC, only women reported significant increases in participation and influence and mainly with regard to the fishery and IFM. In Shuluar, only men reported significant increases in participation, and they also have significantly higher scores for general participation and influence then their wives, unlike in Maliate.Respondents believe that decisionmaking on fishing rules, access and resource management have all in general improved significantly. In Goakhola-Hatiara, despite having the longest established CBFM institutions and activities, both men and women reported similar significant improvements and the scores did not differ much from the other two beels.In Maliate, where women take the beel management decisions, they perceived more significant improvements than men, and reported an increase in fair access that was significantly greater then for men, which presumably reflects their increasing role in beel management since 2002 and the formation of their BMC, and their voluntary formation of a committee for IFM. However, in Shuluar Beel the changes in scores were contradictory: men and women gave significantly higher scores in 2005 for rule making, active fishery management and compliance, yet men reported a decline in fair access and both men and women regard the overall condition of the floodplain to have become worse. The reasons for this are not clear, but considering the timing of the survey in August 2005 when relatively more jute had been grown and there were problems with the quality of water in the beels and fish kills, the opinions may have been influenced by this. Although slightly more jute was also grown in Goakhola and Maliate, the increase was less and there the IFM project facilitated training and piloting of less harmful retting techniques and farmers avoided retting so much within the beel.The most revealing evidence of differences that may affect the way that the CBOs function came from discussing with the committee members (i.e. women and men in Goakhola, women only in Maliate, and men only in Shuluar) what their criteria were for successful integrated floodplain resource management. The committees that included women identified more criteria (16 for Goakhola, 20 for Maliate), compared with just 10 in Shuluar, and the criteria differed (Appendix 3). All three agreed that strong leadership was the most important factor for success, but after that the CBOs with women members rated establishing the authority (legitimacy) of the CBO for resource management next (and that they had achieved this), while the men-only CBO emphasized establishing a fund for future activities (which they had yet to achieve).The women-only CBO placed as 3 rd , 4 th and 5 th participatory decisionmaking, representation of different stakeholders in decisions and having a management plan (and said they had achieved all of these). The mixed CBO emphasized social responsibility in the community, awareness among all community members and timely implementation of activities (and was partially satisfied it had achieved these). The all male CBO emphasized cooperation and respect among members of the committee, establishing community rules, and compliance with the rules (and was also partially satisfied). Thus the women only CBO places greater value on participatory processes leading to its plans, the mixed CBO on whole community action and norms, and the male only CBO on setting rules that it sees as in the interests of the community.Although measuring impacts on fisheries and livelihoods from community based management initiatives is not easy and is compounded by variability between years, in all three communities both men and women recognize gains and improvements in the health of the resource, even where women did not have a role in decisionmaking. Consequently, the BMCs reported high acceptance and compliance with limits they set on resource use, although compliance was higher in the sites where women had a role in decisionmaking and men also were active decision makers (Goakhola) or where men advised and endorsed decisions (Maliate), than in the site where women played no role (Shuluar). In each case, the number of conflicts decreased over time and the BMCs have been recognized, and their plans accepted, by the communities which now follow rules set by the BMCs. The number of rules introduced by the committee increased during the study for those involving women -Goakhola-Hatiara and Maliate Beels. The Maliate BMC has been more adaptable, slowly introducing rules and adjusting the rules between years. For example, if the members see small sized fish or new species in the closed season they have prolonged the closed period through motivational work with the community. They tell the community that the fish price will be higher after a month when fish size increases. The women usually take the initiative to tell each family and they convince family members to wait to catch fish. These initiatives are spontaneous and the community appreciates these initiatives.Ability to establish community based organizations where women play an active or leading role is influenced by local community norms and culture and the acceptance of women's involvement in economic activities outside the home. In the study area, this is greater among Hindu communities than in the Muslim dominated area where women do not normally have much, if any, say in public affairs. This is also affected by education levels -in Shuluar few women have attended school whereas the average education level of women and men in the other two beels is almost equal. There appears to be a compounding effect of education, social norms, economic activity and mobility which constrain or permit women to have equal roles with men for natural resource management.The status and recognition given to women by the local community and leaders reflects this experience and although hard to quantify, was highlighted by women in focus group discussions. In Goakhola and Maliate, women reported increasing recognition of their voices and willingness to listen to their opinions, which in turn led to increased willingness of the women to join local institutions and greater acceptance by men of their decision to do so.For example, the female BMC members reported also belonging to several other local committees and institutions, and this was also shown by sample surveys. By comparison, in Shuluar Beel women have not been given any place in the BMC by the men, who do not recognize the fact that some women do actually depend on using non-fish aquatic resources.Consequently, women have no power or role in decisionmaking in Shuluar Beel, and although these women now recognize the value to the community of fishery related rules, the BMC has not addressed many of their concerns.It is also evident that facilitation by an NGO that focuses solely on women's development, as is the case in all three case studies, is not sufficient to ensure women's participation in decisionmaking and community institutions because their participation is also affected by cultural norms and the extent to which women and men directly use the resources.Hence it is important for those planning to support and facilitate community based management of natural resources to follow processes that include women and help both men and women recognize the uses, opinions and relevance of those resources as they relate to women (as in participatory planning in the cases considered here). Where local social norms and culture limit the public voice of women, women cannot be expected to take a lead in resource management and will need a long term plan for developing their capacity and changing men's opinions. However, it is clear that at least in the context of Bangladesh floodplains, women-led community organizations can improve fishery management, and involving women in fishery management appears to be associated with greater community wide acceptance of management rules and reduced conflict. Policy should aim for community-wide participation including an active role for women. ","tokenCount":"9071","images":["-1536520726_1_1.png","-1536520726_1_2.png"],"tables":["-1536520726_1_1.json","-1536520726_2_1.json","-1536520726_3_1.json","-1536520726_4_1.json","-1536520726_5_1.json","-1536520726_6_1.json","-1536520726_7_1.json","-1536520726_8_1.json","-1536520726_9_1.json","-1536520726_10_1.json","-1536520726_11_1.json","-1536520726_12_1.json","-1536520726_13_1.json","-1536520726_14_1.json","-1536520726_15_1.json","-1536520726_16_1.json","-1536520726_17_1.json","-1536520726_18_1.json","-1536520726_19_1.json","-1536520726_20_1.json","-1536520726_21_1.json","-1536520726_22_1.json","-1536520726_23_1.json","-1536520726_24_1.json","-1536520726_25_1.json","-1536520726_26_1.json","-1536520726_27_1.json","-1536520726_28_1.json","-1536520726_29_1.json","-1536520726_30_1.json","-1536520726_31_1.json","-1536520726_32_1.json","-1536520726_33_1.json","-1536520726_34_1.json","-1536520726_35_1.json","-1536520726_36_1.json","-1536520726_37_1.json","-1536520726_38_1.json","-1536520726_39_1.json","-1536520726_40_1.json","-1536520726_41_1.json","-1536520726_42_1.json","-1536520726_43_1.json","-1536520726_44_1.json","-1536520726_45_1.json","-1536520726_46_1.json","-1536520726_47_1.json","-1536520726_48_1.json","-1536520726_49_1.json","-1536520726_50_1.json","-1536520726_51_1.json","-1536520726_52_1.json","-1536520726_53_1.json","-1536520726_54_1.json","-1536520726_55_1.json","-1536520726_56_1.json","-1536520726_57_1.json","-1536520726_58_1.json","-1536520726_59_1.json","-1536520726_60_1.json","-1536520726_61_1.json","-1536520726_62_1.json","-1536520726_63_1.json","-1536520726_64_1.json","-1536520726_65_1.json","-1536520726_66_1.json","-1536520726_67_1.json","-1536520726_68_1.json"]}

data/part_2/0168831468.json

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+{"metadata":{"gardian_id":"49d96716a9ac5f1d0a55e85809a17a42","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/038b68b2-4eff-4e00-b40d-f068d56f0993/retrieve","description":"The supplies of fish in the world’s vast oceans once seemed inexhaustible. Not any more. In the past three decades, production and consumption of fish have risen so dramatically that the world’s wild fisheries may fall victim to their own success. Meanwhile, the growing aquaculture industry has attempted to fill the gap between supply and demand. But as the global appetite for fish continues to increase, current trends in the fish sector pose serious risks to the environment, to the well-being of poor people, and to the viability of the fish sector itself.\n\nWhat is the outlook for fish in a globalizing food economy, and how will trends in the fish sector affect the poor and the environment during the next two decades? A new book from IFPRI and the WorldFish Center, Fish to 2020: Supply and Demand in Changing Global Markets, and an accompanying food policy report, Outlook for Fish to 2020: Meeting Global Demand, examine changes in the fish sector; the forces driving these changes; and the implications of the changes for fish consumption, production, prices, trade, the environment, and the world’s poor.","id":"-1142505354"},"keywords":[],"sieverID":"d8016770-f175-4ab8-a81b-152e4d5a9195","pagecount":"6","content":"G lobal consumption of fish has doubled since 1973, and the developing world has been responsible for nearly all of this growth. Countries with rapid population growth, rapid income growth, and urbanization tend to have the greatest increases in consumption of animal products, including fish products, and the developing world has experienced all three trends. China, where income growth and urbanization have been major factors, dominates consumption of fish products. It accounted for about 36 percent of global consumption in 1997, compared with only 11 percent in 1973 (Figure 1). India and Southeast Asia together accounted for another 17 percent in 1997, with total consumption doubling since 1973. Although total fish consumption declined somewhat in the developed countries, this decline was dwarfed by the increases in the developing world.Besides being used as food, fish is also increasingly demanded for use as feed. Nearly one-third of the world's wild-caught fish are \"reduced\" to fishmeal and fish oil, which are then used in feeds for livestock like poultry and pigs and in feeds for farmed carnivorous fish. Because aquaculture is likely to grow quickly over the next 20 years, some experts are concerned that rising demand for fishmeal and fish oil could place heavier fishing pressure on already threatened stocks of fish used for feed.To meet the burgeoning demand for fish, production has soared.The growth in production, like that in consumption, comes almost entirely from developing countries (Figure 2), which now produce nearly three times as much fish as developed countries.Exploitation of wild fish stocks rose rapidly during the 1970s and 1980s, thanks to expanded fishing fleets, new fishing technologies, and increased investments in the fishing sector. Global capture of fish for food jumped from 44 million tons in 1973 to 65 million tons in 1997. By the late 1980s, however, the stocks fished by many wild-fishing operations were fully exploited and even overexploited. Since then, despite increases in investment and fishing capacity, fish production from wild fisheries has slowed or stagnated.  Developing countries have taken the lead in producing fish from wild fisheries since the 1980s, partly because of the establishment of 200-mile exclusive economic zones (EEZs) around coastal nations.Whereas developed-country production from wild fisheries exceeded developing-country production by 6.6 million tons in 1973, by 1997 the developing countries were producing twice as much as the developed countries.Because most wild fisheries are near their maximum sustainable exploitation levels, production from these fisheries will likely grow only slowly to 2020. Although fishers could probably produce more by targeting underexploited species that have been in lower demand, it is not clear that consumers will accept these species. More important, such a change could cause large shifts in species composition and indirectly harm predator species, with severe consequences for the environment.With wild fish production stagnating, growth in overall fish production has come almost entirely from the global boom in aquaculture, especially in developing countries. Aquaculture now represents more than 30 percent of total food fish production, and Asia accounts for 87 percent of global aquaculture production by weight. In the coming decades aquaculture will likely be the greatest source of increased fish production as fish farmers expand the water surface area under cultivation and increase yields per unit of area cultivated. But the sector must overcome several major challenges if it is to sustain the rapid growth of the past 20 years. It will face competition from other users for land and water. Disease and the scarcity of fishmeal and fish oil derived from wild-caught fish may also constrain aquaculture production. Growth in aquaculture production will also depend heavily on the level of public and private investment in the sector. Because of the slow growth in wild fisheries, the level of aquaculture production will play a large role in determining the relative prices of fish commodities.Fish products are a heavily traded commodityroughly 40 percent of global fish output by value in 1998 was traded across international borders-and the enormous rise in fish production in developing countries has caused an about-face in the direction of trade in fish products since the early 1970s. In 1973 the developed world was a net exporter of 818,000 tons of food fish, but by 1997 these countries were net importers of 4,045,000 tons of food fish. By the late 1990s more than 50 percent of fish exports came from developing countries.As a consequence of rising demand and slowergrowing production, the real prices of most fresh and frozen fish have risen since World War II, in contrast to prices of most animal-origin foods, which have declined steeply over the past several decades. Exceptions to the general rise in fish prices are canned finfish, which have declined in popularity in developed countries since the One of the most striking trends in the capture of fish for food has been China's emergence as the largest producer and the simultaneous decline of Japan's production. Whereas Japan's production fell from 18 percent of world production in 1973 to 7 percent in 1997, China increased its share from 9 percent to 21 percent. But China's astonishing growth during the 1990s in fish production, and the contrast between reported production data and household consumption survey data, has raised suspicions about the accuracy of reported totals. One study concluded that Chinese fishery production-including aquaculture-was overestimated by 43 percent in 1995. If China has indeed overreported its fish production (possibly because of institutional incentives), global fish production trends are much less rosy than they otherwise appear.early 1970s, and some individual commodities like shrimp and salmon, which have seen large gains in production owing to aquaculture.T o help clarify the consequences of different policy and environmental scenarios for the fish sector, IFPRI researchers drew on a tool called the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT).This model projects fish (and other food items) supply, demand, and trade not only for a baseline (or most likely) scenario in 2020, but also for alternative scenarios, such as slower or faster aquaculture expansion, lower Chinese production, more efficient use of fish feed, and ecological collapse of wild fisheries.Fish are highly likely to continue becoming more expensive to consumers compared with other food products over the next two decades, according to the model. Prices for food fish, fishmeal, and fish oil are likely to rise under nearly all scenarios. Faster aquaculture expansion is the only scenario leading to a drop in the projected real prices of low-value food fish, though it would also cause a significant rise in the price of fishmeal.The one scenario that leads to slightly lower real fishmeal prices is the one that improves efficiency in fishmeal and fish oil conversion through rapid technological progress.People in the developing world will increase their total consumption of food fish, whereas total consumption will remain static in the developed world. Even under the ecological collapse scenario, global per capita consumption declines only a small amount-from 17.1 kilograms per year under the baseline scenario to 14.2 kilograms.This is largely because producers respond to resulting major price increases for fish products by pursuing greater aquaculture production.The rapid growth in fish production is also likely to continue, with developing countries producing an ever-increasing share. More and more fish production will come from aquaculture, whose share in worldwide fish production is projected to increase from 31 to 41 percent in 2020 in the baseline scenario.Assumptions about investment in aquaculture are crucial for production results in other scenarios. For instance, faster aquaculture expansion would produce 25 million metric tons more fish than slower aquaculture expansion.Technology also matters greatly. Making fishmeal more efficient in its effects on the growth of farmed fish reduces fishmeal production by 1 million tons compared with the baseline, a result that would reduce fishing pressure on fish used as feed.Net exports from the developing world are projected to continue through 2020, though at a lower level than presently.This is mainly because of rising domestic demand within developing countries for fish because of population growth, income growth, and urbanization.A healthy natural environment is essential to main- taining fish harvest levels in the face of increasing demand. Unfortunately, fishing activities around the world often cause large-scale damage to the aquatic environment.Most environmental damage stems from wild fisheries, where overfishing poses by far the greatest environmental threat. Overinvestment in fishing and the resulting overcapacity have led to excessive exploitation of fish stocks, especially by developed-country fleets. During the 1970s and 1980s fleet size increased twice as fast as fish harvests. Most stocks of wild fish today are classified as fully exploited, and an increasing number are overexploited, in decline, or in recovery. Moreover, wild-fishing operations capture, kill, and discard a massive quantity of bycatch-fish that are the wrong size, the wrong species, or otherwise undesirable. Global discarded bycatch of fish and other marine organisms is currently estimated at more than 20 million tons a year, nearly one-quarter of the world fish catch. Some fishing practices-like bottom trawling, blast fishing, and poison fishing-destroy marine habitats. Fishing itself can also harm ecosystems by removing massive quantities of a species and leading to wholesale changes in the food web dynamics of those systems.Many people hope aquaculture can sustainably ease pressure on threatened wild stocks, but it has environmental problems of its own. As aquaculture production has become more widespread and intensive, the movement of live aquatic animals and products has increased, making the accidental spread of disease more likely. Effluent from aquaculture ponds and pens, like fertilizer, undigested feed, and biological waste, is often released directly into surrounding waterways. And rapidly increasing demand for fishmeal and fish oil may place pressure on the wild stocks from which these products are derived.Over the past few decades coastal aquaculture development, especially shrimp farming, has caused the destruction of hundreds of thousands of hectares of mangrove forests, which are crucial for filtering nutrients, cleansing water, and protecting ecosystems from floods and storms. In addition, farmed fish that escape into the wild can threaten native species by acting as predators, competing for food and habitat, or interbreeding and changing the genetic pools of wild organisms. Concern over escaped species is likely to intensify in coming years as genetically modified fish are developed for aquaculture.P oor people are facing new barriers in both their production and consumption of fish. Even by the standards of developing countries, landless fish workers and artisanal fishers are often among the poorest of people, and they generally operate at a small scale and use traditional fishing practices.Yet new technologies and environmental requirements may favor large-scale, capital-intensive operations at the expense of traditional and small-scale commercial fishers.The rising importance of fish trade also raises barriers to poor producers. Developed countries have erected nontariff barriers in response to consumers' concerns about the food safety of fish. Meeting the new requirements for documenting the safe handling, processing, and origin of fish products requires considerable experience, skill, and investment. Developing countries that can address new hygiene and food safety requirements, fair labor practices, and environmental needs will have the opportunity to capture more of the lucrative export market. But if the poor are to benefit from this potentially profitable activity, policymakers will need to find ways of including smaller-scale producers in these arrangements.In addition, the rising cost of low-value food fish to the poor is a real policy concern. Even a small amount of fish is an important dietary supplement for poor peo-ple who cannot easily afford animal protein and who rely mainly on starch diets. But over the past 30 years fish has become more expensive relative to other food items because fish demand, primarily from relatively wealthy consumers in developing countries themselves, is outstripping supply.A s demand for fisheries products grows during the next several years, technology must play a crucial role in helping suppliers keep pace in a sustainable way.Nearly one-third of the world's wild-caught fish are not consumed directly by humans but rather are \"reduced\" to fishmeal and fish oil and consumed in feed by farmraised animals, such as chickens, pigs, and other fish.This situation has raised concerns that demand for fishmeal and fish oil from the burgeoning aquaculture sector will raise prices for these commodities and place increasingly heavy pressure on wild fisheries to produce fish for feed.Technology can reduce the risks of higher prices and overfishing by providing alternatives to fishmeal and fish oil in aquafeeds, such as protein-rich oilseed and grain byproduct meals. For a variety of reasons, vegetable meals are not ideal substitutes for fishmeal in aquafeeds, so research is needed to help overcome this problem.Technological advances that improve information and management methods are now needed more than advances to increase fishing capacity. Satellite remote sensing and other information technologies can help provide better information about wild fish stocks as well as help monitor fishing activity and improve consumer information about the condition and origin of fish products. But successful management of the world's wild-fishing operations will depend on the coordination of technology and policy. One example is a vessel monitoring system, which employs satellite tracking to allow onshore tracking of vessel movements, thereby enhancing the enforcement of regulations.Technology is also crucial to avoiding the environmental damage and waste caused by certain fishing practices. Although some types of fishing gear may be banned altogether, others may be modified. Bycatch reduction devices, or BRDs, are increasingly used in fish-Copyright © 2003 International Food Policy Research Institute and WorldFish Center. All rights reserved. Sections of this book may be reproduced without the express permission of, but with acknowledgment to, the International Food Policy Research Institute and WorldFish Center.Contact [email protected] for permission to reprint.Cover photo © World Bank/Edwin Huffman Page 2 © WorldFish Center/Modadugu V. Gupta Page 4 © WorldFish Center/Dominyk Lever ing operations to lower the amount of unintended catch. But without policy incentives to encourage their use, along with training and extension, BRDs will remain unused or ineffectively used.Breeding technology in aquaculture is in its relative infancy. Breeders have significantly raised productivity for a few commercial species such as salmon, trout, and tilapia, but the successful cultivation and breeding of other species such as cod and bluefin tuna would be a tremendous boost to high-value aquaculture.Genetic modification and biotechnology also hold tremendous potential to improve the quality and quantity of fish reared in aquaculture, although not without significant controversy and risk. Biotechnology has the potential to enhance reproduction and the early developmental success of cultured organisms.The possible environmental effects of genetically modified aquatic organisms are not well understood, however, and concerns exist over possible human health risks.The documented escapes of farmed salmon and their threat to native wild populations demonstrate that caution should be employed when considering the introduction of a new species into an ecosystem.Although intensification of aquaculture can potentially generate high levels of environmental problems, capitalintensive production systems often give producers more control over problems like effluent pollution and the spread of disease.Technology may in fact present economies of scale in the control of environmental problems.Intensification can raise the risk of disease. Management techniques such as rotation of cultured species and lower-density stocking of organisms can partially address this risk, but antibiotics and water control technologies like aerators and water recirculation sys-tems can also mitigate the stress caused by high concentrations of organisms.Technologies based on local knowledge systems and different political and cultural contexts can also help develop aquaculture in underexploited water bodies, such as rice paddies, irrigation canals, reservoirs, and seasonal or perennial ponds in developing countries. Some technologies long employed in traditional aquaculture systems can also be useful in addressing concerns raised by water management, effluent control, disease control, and land use in intensified aquaculture.I n both developing and developed countries, policy- makers can establish policies and promote institutions that will lead to more sustainable management of fish resources while also ensuring the survival of small-scale producers. One basic step is simply seeing to it that the sector gets the policy attention it deserves.To improve policy outcomes in the developing countries, policymakers in the developed countries should rationalize their food safety systems for seafood imports, harmonize and modernize tariff classifications, and offer technical assistance in eco-labeling and food safety to small-scale, developing-country fish exporters.Finally, the focus of demand-side policies in developing countries should be to facilitate South-South trade, to provide public goods to assure domestic food safety, and to help ensure that fish products reach those in developing countries who need them the most from a nutritional standpoint.By taking account of the major shifts taking place in the fish sector and combining forward-looking policies with useful new technologies, policymakers can help ensure that the fish sector remains environmentally sustainable as well as beneficial for the world's poor people.","tokenCount":"2769","images":["-1142505354_1_1.png","-1142505354_1_2.png","-1142505354_1_3.png","-1142505354_1_4.png","-1142505354_2_1.png","-1142505354_4_1.png"],"tables":["-1142505354_1_1.json","-1142505354_2_1.json","-1142505354_3_1.json","-1142505354_4_1.json","-1142505354_5_1.json","-1142505354_6_1.json"]}

data/part_2/0185054088.json

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+{"metadata":{"gardian_id":"1a5d753c81d821d030aacb7b1dc234a8","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/7bdba10e-0831-47ec-a29b-7bb11839e1df/retrieve","description":"","id":"-1819142053"},"keywords":[],"sieverID":"51a8df08-b5c4-4906-972f-b82de1d6362d","pagecount":"4","content":"District Nutrition Profiles (DNPs) are available for 707 districts in India. They present trends for key nutrition and health outcomes and their cross-sectoral determinants in a district. The DNPs are based on data from the National Family Health Survey NFHS-4 (2015NFHS-4 ( -2016) ) and NFHS-5 (2019NFHS-5 ( -2021)). They are aimed primarily at district administrators, state functionaries, local leaders, and development actors working at the district-level.  Note: NA refers to data unavailable for a given round of NFHS/Census.• What are the trends in infant and young child feeding (early initiation of breastfeeding, exclusive breastfeeding, timely initiation of complementary feeding, and adequate diet)? What can be done to improve infant and young child feeding? • What are the trends in IFA consumption among pregnant women in the district? How can the consumption be improved?• What additional data are needed to understand diets and/or other determinants?  Note: NA refers to data unavailable for a given round of NFHS/Census.• How can the district increase women's literacy, and reduce early marriage, if needed?• How does the district perform on providing drinking water and sanitation to its residents? Since sanitation and hygiene play an important role in improving nutrition outcomes, how can all aspects of sanitation be improved? • How can programs that address underlying and basic determinants (education, poverty, gender) be strengthened?• What additional data are needed on food systems, poverty or other underlying determinants? Note: NA refers to data unavailable for a given round of NFHS/Census.• How does the district perform on health and nutrition interventions along the continuum of care? Does it adequately provide both prenatal and postnatal services to women of reproductive age, pregnant women, new mothers and newborns? • How has access to health and ICDS services changed over time (food supplementation, health and nutrition education and health checkups)?","tokenCount":"300","images":["-1819142053_1_1.png","-1819142053_1_2.png","-1819142053_1_3.png","-1819142053_1_4.png","-1819142053_1_5.png","-1819142053_1_6.png","-1819142053_1_7.png","-1819142053_1_8.png","-1819142053_1_9.png","-1819142053_1_10.png","-1819142053_1_11.png","-1819142053_1_12.png","-1819142053_2_1.png","-1819142053_2_2.png","-1819142053_2_3.png","-1819142053_2_4.png","-1819142053_2_5.png","-1819142053_2_6.png","-1819142053_2_7.png","-1819142053_2_8.png","-1819142053_2_9.png","-1819142053_2_10.png","-1819142053_2_11.png","-1819142053_2_12.png","-1819142053_3_1.png","-1819142053_3_2.png","-1819142053_3_3.png","-1819142053_3_4.png","-1819142053_3_5.png","-1819142053_3_6.png","-1819142053_3_7.png","-1819142053_3_8.png","-1819142053_3_9.png","-1819142053_3_10.png","-1819142053_3_11.png","-1819142053_3_12.png","-1819142053_4_1.png","-1819142053_4_2.png","-1819142053_4_3.png","-1819142053_4_4.png","-1819142053_4_5.png","-1819142053_4_6.png","-1819142053_4_7.png","-1819142053_4_8.png","-1819142053_4_9.png","-1819142053_4_10.png","-1819142053_4_11.png","-1819142053_4_12.png"],"tables":["-1819142053_1_1.json","-1819142053_2_1.json","-1819142053_3_1.json","-1819142053_4_1.json"]}

data/part_2/0231987251.json

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+{"metadata":{"gardian_id":"4ebed614ec448741f00a845fdc08f7fa","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/67e834fe-5785-461f-b95e-83267979d7d4/retrieve","description":"The importance of reducing food loss and food waste has captured the public imagination since it became one of the targets of the United Nations Sustainable Development Goals. The urgency of this issue and the awareness of its significance to the development community has been growing steadily. Even so, policies to address food insecurity or the increasing pressure on the world’s available land that is being caused by growing populations and changing diets have aimed mainly at increasing agricultural yields and productivity. These efforts are often cost- and time-intensive and do not consider food loss and waste reduction as a tool to help meet growing food demand; nor do they consider food loss reduction as a means to ease pressure on land. Food loss also entails unnecessary greenhouse gas emissions and excessive use of scarce resources including land (FAO 2019); thus, policies to reduce food loss will also benefit the environment. Finally, cutting food loss can help disadvantaged segments of the population, as the loss of marketable food can reduce producers’ incomes and increase consumers’ expenses. Most of the literature uses the terms postharvest losses (PHL), food loss (FL), food waste (FW), and food loss and waste (FLW) interchangeably, but they rarely refer consistently to the same concept. Recent publications (FAO 2014, 2019; HLPE 2014; Lipinski et al. 2013) have tried to clarify this by defining FL as unintentional reductions in food quantity or quality before consumption, that is, from the producer to the wholesale market, inclusive. These losses usually occur in the earlier stages of the food value chain—between production and distribution. This definition, however, does not include crops that are lost before harvesting or are left in the field; nor does it include crops that are lost due to poor harvesting techniques or sharp price drops; nor crops that are not produced because of a lack of adequate agricultural inputs, such as fertilizer, or because of a shortage of available labor. In 2019, the FAO developed the Food Loss Index (FLI), following the definition of food loss mentioned above. According to the FLI, an estimated 14 percent of food produced is lost every year. The major losses are in Central Asia and Southern Asia (20.7 percent), as compared to sub-Saharan Africa, which experiences a 14 percent food loss (FAO 2019), and Latin American and the Caribbean where 11.6 percent is lost. When examining losses in terms of food groups, the highest level of loss is reported in roots, tubers, and oil-bearing crops, followed by fruits and vegetables. It is not surprising that fruits and vegetables incur high levels of loss (more than 20 percent) given their highly perishable nature.","id":"-1771903692"},"keywords":[],"sieverID":"06d99e61-fff2-404f-970d-12c72f54d029","pagecount":"5","content":"The importance of reducing food loss and food waste has captured the public imagination since it became one of the targets of the United Nations Sustainable Development Goals. The urgency of this issue and the awareness of its significance to the development community has been growing steadily. Even so, policies to address food insecurity or the increasing pressure on the world's available land that is being caused by growing populations and changing diets have aimed mainly at increasing agricultural yields and productivity. These efforts are often cost-and time-intensive and do not consider food loss and waste reduction as a tool to help meet growing food demand; nor do they consider food loss reduction as a means to ease pressure on land. Food loss also entails unnecessary greenhouse gas emissions and excessive use of scarce resources including land (FAO 2019); thus, policies to reduce food loss will also benefit the environment. Finally, cutting food loss can help disadvantaged segments of the population, as the loss of marketable food can reduce producers' incomes and increase consumers' expenses.Most of the literature uses the terms postharvest losses (PHL), food loss (FL), food waste (FW), and food loss and waste (FLW) interchangeably, but they rarely refer consistently to the same concept. Recent publications (FAO 2014(FAO , 2019;;HLPE 2014;Lipinski et al. 2013) have tried to clarify this by defining FL as unintentional reductions in food quantity or quality before consumption, that is, from the producer to the wholesale market, inclusive. These losses usually occur in the earlier stages of the food value chain-between production and distribution. This definition, however, does not include crops that are lost before harvesting or are left in the field; nor does it include crops that are lost due to poor harvesting techniques or sharp price drops; nor crops that are not produced because of a lack of adequate agricultural inputs, such as fertilizer, or because of a shortage of available labor.In 2019, the FAO developed the Food Loss Index (FLI), following the definition of food loss mentioned above. According to the FLI, an estimated 14 percent of food produced is lost every year. The major losses are in Central Asia and Southern Asia (20.7 percent), as compared to sub-Saharan Africa, which experiences a 14 percent food loss (FAO 2019), and Latin American and the Caribbean where 11.6 percent is lost. When examining losses in terms of food groups, the highest level of loss is reported inDECEMBER 2021 roots, tubers, and oil-bearing crops, followed by fruits and vegetables. It is not surprising that fruits and vegetables incur high levels of loss (more than 20 percent) given their highly perishable nature.To create better strategies for reducing food loss and waste, it is essential to measure losses accurately and consistently. It is important to identify where in the value chain the losses occur and what determinants are behind those losses. Researchers supported by the CGIAR Research Program on Policies, Institutions, and Markets (PIM) developed innovative methodologies relative to the traditional methodology that only measures self-reported quantity of losses as an aggregate for the full value chain. The two new methodologies-one based on commodity attributes and the other based on the quality categories recognized by the market-quantify food loss both in terms of quantity and quality and identify how and where food loss occurs for different commodities and value chain nodes.The methodology incorporates components of the Food Loss Index for SDG 12.3.1 (Figure 1), that is, on-farm postharvest, transport, storage and distribution, processing, and packaging. It also includes losses identified at the preharvest and harvest levels, thus capturing the entire value chain before retail. All three methodologies can measure losses at different stages of the value chain and can be applied across crops and regions. The \"aggregate self-reported method\" is based on reporting by the producers, intermediaries, and processors regarding the food losses they each incurred; the \"category method\" is based on the evaluation of a crop and the classification of that crop into quality categories; and the \"attribute method\" is based on the evaluation of a crop according to inferior visual, tactile, and olfactory product characteristics.This methodology was implemented in nine countries: Ecuador (potatoes), Peru (potatoes), Guatemala (maize and beans), Honduras (maize and beans), Ethiopia (teff), China (wheat), Ghana (groundnuts and yams), Mozambique (maize) and Tanzania (maize). Figure 2 shows level of losses (in value) separately at the producer, intermediary, and processor levels; it does so for the three estimation methodologies listed above, that is, the aggregated self-reported (S), the category (C), and the attribute (A) methods. As shown in Figure 2, loss figures are consistently largest at the producer level and smallest at the intermediary level. Across the different estimation methodologies, loss at the producer level represents between 60 and 80 percent of the total value-chain loss, while the average losses at the intermediary and processor levels are around 7 and 19 percent, respectively. When analyzing the causes of food loss, Figure 3 presents the major reasons for losses reported by the producers at preharvest, crops left in the field, and postharvest. The most commonly reported preharvest factors were pest infestation, disease, and drought; the main reasons for crops being left in the field were inadequate harvesting techniques, lack of laborers, and weather; and the major causes for postharvest losses were plagues, rodents and other animals, and damage caused by workers during harvesting or sorting. Addressing food loss across the value chain requires all actors to have a common understanding of the concept. A collaborative effort is also needed to collect better data across the value chain and across different commodities and contexts. As stated earlier, food loss has been defined in many ways and there is disagreement over the proper terminology and methodology for measuring it.Policymakers need to work with value chain actors to translate these insights into action. They should focus on collecting evidence-based and consistent information across the value chain and ensuring that public and private sector investments facilitate food loss reduction, with hotspots being specifically targeted.Understanding definitions is the first step, but it is also important to know how much, where, and why food is lost and wasted. As a second step, we need to be clear about the underlying objectives for reducing food loss and waste related to efficiency, food security, or the environment. Third, we need to understand the cost-effectiveness of food loss and waste interventions and to know how much can be recycled back into the food system. Fourth, we need to know the extent to which food loss and waste and the measures to reduce them affect the pursued objectives: is there evidence on interventions and incentives that can help?","tokenCount":"1105","images":["-1771903692_1_1.png","-1771903692_2_1.png","-1771903692_3_1.png","-1771903692_4_1.png"],"tables":["-1771903692_1_1.json","-1771903692_2_1.json","-1771903692_3_1.json","-1771903692_4_1.json","-1771903692_5_1.json"]}

data/part_2/0242200012.json

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+{"metadata":{"gardian_id":"00d7f7f3200dd5d299fa9893aabc3ca7","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/8110d884-e83e-4e9d-8181-7ee22a9ccf5a/retrieve","description":"This paper examines macro-economic developments in Ethiopia between 2004/05 and 2008/09, focusing on the external accounts and the real exchange rate. Simulations using a Computable General Equilibrium (CGE) model of Ethiopia's economy show that, compared to a policy of foreign exchange rationing, a policy of real exchange rate depreciation and no rationing improves economic efficiency and welfare of all households except those who receive the rents (excess profits) arising from rationing.","id":"636466095"},"keywords":[],"sieverID":"dc0c5f4c-f2fa-47dc-8e86-50ae3320a8a5","pagecount":"2","content":"This paper examines macro-economic developments in Ethiopia between 2004/05 and 2008/09, focusing on the external accounts and the real exchange rate. Simulations using a Computable General Equilibrium (CGE) model of Ethiopia's economy show that, compared to a policy of foreign exchange rationing, a policy of real exchange rate depreciation and no rationing improves economic efficiency and welfare of all households except those who receive the rents (excess profits) arising from rationing.Between 2004/05 and 2007/08, Ethiopia successfully accelerated its economic growth through a deliberate policy of expanded domestic credit to finance private investment and increased foreign borrowing to finance public investment.Increased investment implied increased demand for imports of capital and intermediate goods, and thus for foreign exchange, by private (and public) sector investors. At the same time, workers' remittances and private transfers were increasing, supplying resources for private investment in residential housing and other domestic consumption and investment.As a result, merchandise imports surged by 87 percent ($3.2 billion) between 2004/05 and 2007/08. Half of this increase in merchandise imports was financed by a 195 percent increase in private transfers (including workers' remittances); increases in merchandise exports and capital inflows each financed 16-19 percent of increases in merchandise imports (Figure 1).the US dollar, the real exchange rate appreciated by 13.8 percent between July 2004 and January 2008, and by a total of 33.8 percent through July 2008 (Table 1). By early 2008, with foreign exchange reserves sharply reduced and import demand in excess of supply of foreign exchange, there were two broad options: Devalue the currency so as to reverse the real exchange rate appreciation of the previous few years, reducing demand for imports, increasing supply of exports and restoring equilibrium in the market for foreign exchange; or Control imports by imposing foreign exchange controls and allow the exchange rate to remain overvalued (and in fact become increasingly overvalued)Nominal depreciation from 9.83 to 11.39 Birr/US$ between July 2008 and March 2009 helped reduce real appreciation of the birr to 29.9 percent, but incentives for production of tradables (export goods and import substitutes) were still substantially below July 2004 levels (Figure 2). Moreover, Ethiopia was increasingly financing its current account deficit through drawdown of official foreign exchange reserves. From June 2007 to March 2008, foreign exchange reserves fell by $381 million (equivalent to 13 percent of the value of merchandise imports in that period). For 2007/08 as a  Compared with the rationing scenario, the real exchange rate depreciates by 12 percent and incomes of the rural non-poor rise by 5.7 percent, in large part because of the improved performance of the export crop sector. The real incomes of the rural and urban poor also improve by 2.2 and 4.0 percent respectively, reflecting increased economic activity (a 0.7 percent increase in real GDP). Although real household incomes of the urban non-poor fall by only 3.4 percent relative to the base scenario, the fall relative to the foreign exchange rationing simulation is very large (-17.4 percent), because these households no longer receive rents (excess profits).Nominal Ex Rate CPI World Price Index (Birr)Source: EDRI and authors' calculations. Note: In this figures an appreciation of the real exchange rate is denoted as a decrease in the index.To assess, the economy-wide implications of rationing of foreign exchange, including implications for income and consumption of poor households, we utilized a CGE model of the Ethiopian economy to examine two broad policies:1) the surge in investment, public transfers and foreign capital inflows between 2004/05 and March 2008, and 2) strategies in response to the subsequent decline in foreign capital inflows: import rationing (the policy actually adopted) and an alternative strategy (a significant depreciation of the real exchange rate).  (2001).The Ethiopia CGE model simulations suggest that there are substantial adverse efficiency and distributional effects of foreign exchange rationing. Foreign exchange controls result in the creation of large rents that likely accrue mainly to non-poor households. At the same time, foreign exchange controls reduce economic efficiency so that realThe modeling results presented here are not meant as definitive estimates, but rather as indicators of the broad magnitudes of the effect of the policies simulated. ","tokenCount":"682","images":["636466095_1_1.png","636466095_1_2.png","636466095_1_3.png","636466095_1_4.png","636466095_1_5.png","636466095_1_6.png","636466095_2_1.png","636466095_2_2.png","636466095_2_3.png"],"tables":["636466095_1_1.json","636466095_2_1.json"]}

data/part_2/0260775819.json

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+{"metadata":{"gardian_id":"661f2708db98b1306148d32134231207","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/4a15f15b-a147-4cce-8453-33e3acde7762/retrieve","description":"Over the last decade, land governance has become a major priority for the development community.1 A particular focus has been on sub-Saharan Africa due to the recognized paradox of high levels of land availability and low productivity in the region (see Deininger et al. 2012). While poor land governance systems have long been identified as a key reason for this disjuncture, the relatively recent large-scale impetus to improve land governance emerged from the inclusion of land management in 2009 as one of the four pillars under the African Union’s Comprehensive Africa Agriculture Develop-ment Program (CAADP). Subsequently, in the wake of the G-8’s launch of the New Alliance for Food Security and Nutri-tion in 2012, many international initiatives have emerged to promote better land governance. These include the African Union’s Land Policy Initiative (AULPI) and the World Bank’s Land Governance Assessment Framework (LGAF). At the national level in Africa, land registration and land titling are the most common approaches to reform (Sikor and Müller 2009), with governments selecting among a broad spectrum of modalities to pilot. These include rural land use plans in some francophone countries (e.g., Benin, Burkina Faso, and Côte d’Ivoire), systematic land tenure regularization (Ethio-pia, Madagascar, Rwanda), and communal land demarcation and registration (e.g., Ghana, Mozambique, Tanzania) (see Byamugisha 2013).","id":"1052079429"},"keywords":[],"sieverID":"529f728a-f717-49a0-b050-f1016546a5ce","pagecount":"18","content":"Over the last decade, land governance has become a major priority for the development community. 1 A particular focus has been on sub-Saharan Africa due to the recognized paradox of high levels of land availability and low productivity in the region (see Deininger et al. 2012). While poor land governance systems have long been identified as a key reason for this disjuncture, the relatively recent large-scale impetus to improve land governance emerged from the inclusion of land management in 2009 as one of the four pillars under the African Union's Comprehensive Africa Agriculture Development Program (CAADP). Subsequently, in the wake of the G-8's launch of the New Alliance for Food Security and Nutrition in 2012, many international initiatives have emerged to promote better land governance. These include the African Union's Land Policy Initiative (AULPI) and the World Bank's Land Governance Assessment Framework (LGAF). At the national level in Africa, land registration and land titling are the most common approaches to reform (Sikor and Müller 2009), with governments selecting among a broad spectrum of modalities to pilot. These include rural land use plans in some francophone countries (e.g., Benin, Burkina Faso, and Côte d'Ivoire), systematic land tenure regularization (Ethiopia, Madagascar, Rwanda), and communal land demarcation and registration (e.g., Ghana, Mozambique, Tanzania) (see Byamugisha 2013).Despite the broad recognition of the importance of such reforms, there continues to be wide variations in the degree of progress towards improved land governance across the region. The political economy of this contested process, and the institutional factors that underlie such dynamics, is one of the key reasons why efforts at land governance can stall and why reform modalities borrowed from other countries may not be successfully implemented. In order to understand these factors, this paper focuses specifically on the case of land governance in Nigeria.Nigeria is a critical case for regional efforts in Africa to improve land governance for a number of reasons. First, only three percent of Nigeria's vast land resources are formally registered (see Adenjyi 2013; Birner and Okumo 2012). Absence of clear title to land can inhibit farmers from using land as collateral or to motivate investments to improve land quality, leading many to depend on depleted lands and to be unwilling to combat land degradation (see Phillip et al. 2009). This can have large implications for improving agricultural productivity, especially since Nigeria is now heavily dependent on food imports. Furthermore, Deininger and Xia (2014) note that fear of expropriation by the state is one of the key sources of rural tenure insecurity in Nigeria. Secondly, a lack of information and clear title has implications for land valuation and therefore inhibits raising tax revenue that could support government expenditures on broader public goods and services, a diversification away from oil as the primary source for raising domestic revenue, and less dependence for states on intergovernmental transfers (see Adenjyi 2013; Byamugisha 2013; Suberu 2015a).Thirdly, Nigeria continues to be one of Africa's most rapidly urbanizing countries based on population size and urbanization levels. Between 2014 and 2050, the urban population in Nigeria is expected to grow by 212 million people, with the urban population increasing from 47 to 67 percent of the total population during the same time period (UN 2014). Under Nigeria's Land Use Act, titles are only given to recognize the right of occupancy, rather than title to buy or rent land. Consequently, there is widespread squatting and the spread of informal settlements in urban areas (USAID 2010). For instance, in the Federal Capital Territory of Abuja, LeVan and Olubowale (2014) observe that this pushes many into the informal market as property use agreements depend on negotiation between migrants to the city and \"indigenous landlords.\" Since 2003, almost 800,000 homes have been destroyed in the capital city by government bulldozers destroying informal settlements (USAID 2010). Poor land governance also contributes to other forms of violence and geographically-concentrated conflicts in the country. These include longstanding disputes in the Niger Delta linked to environmental degradation caused by the oil industry and conflicts over grazing rights in the Middle Belt of Nigeria between farmers and pastoralists.Yet, despite a notable and widely recognized need among a broad range of stakeholders regarding the need to improve land governance in Nigeria, progress remains slow. To address this puzzle, this paper applies the Kaleidoscope Model (KM) of policy reform. As detailed elsewhere (see Resnick et al. 2015), the KM provides a framework to address the question of why a policy change occurs in one geographic locale and not another, in one policy arena but not another, or at one time period but not another. Drawing on other influential studies of policymaking in developing countries (see Fox and Reich 2013;Kaufman and Nelson 2004), the framework focuses on five key elements of the policy cycle: agenda setting, design, adoption, implementation, and evaluation and reform. This allows for tracing why a policy fails to be implemented by taking into account where gaps may have existed during other stages of the policy cycle. As Hall (1993) highlights, policy change is rarely one overarching outcome, but, rather, consists of smaller policy changes related to design, adoption, and implementation along the way. By looking at all elements of the policy cycle, the KM offers more nuanced understandings of when and why smaller changes sometimes cumulate and result in larger outcomes, while other changes do not. In doing so, the KM can help pinpoint bottlenecks to policy change and identify whether improved policies are hindered by low capacity, insufficient political will, or both.In doing so, the paper highlights two key challenges for land governance reform in Nigeria-policy inertia surrounding the legislative framework and ongoing struggles to implement the systematic land titling and registration (SLTR) across states. In both cases, a key problem has been the wavering commitment of high level policy actors and lukewarm support by governors at the state level. Methodologically, these findings are uncovered through intensive process tracing and semi-structured interviews conducted with knowledgeable stakeholders in Abuja, Nigeria during June 2016. Process tracing focuses on patterns of change and causation through thick description and attention to the sequence of independent variables and observed outcomes (see Collier 2011). The interviewed stakeholders encompassed representatives from the Office of the Vice President; Federal Ministry of Power, Housing and Works; the Office of the Surveyor General; the National Land Transparency Initiative; Abuja GIS; Presidential Technical Committee on Land Reform (PTCLR); Surveyors Council of Nigeria (SURCON); and the Growth and Employment in States (GEMS3) project that is supported by the United Kingdom's Department for International Development (DfID). For the purposes of anonymity, interviewees are identified throughout by their institutional affiliation rather than by name whenever they are directly quoted.The main implications of the present paper are twofold. First, although land governance is well-recognized as intensely political (see Boone 2013;Cotula et al. 2006;Deininger et al. 2012;Palmer 2007), many recent initiatives have sidestepped this fact to promote more technical interventions. Such interventions, including the SLTR, can be hindered by not more explicitly embracing political realities. Secondly, a variety of fragile and/or post-conflict countries, such as Kenya, Nepal, and Pakistan, are pursuing greater devolution and federalism while grappling with ongoing land governance challenges. Understanding how this dynamic has unfolded in a democratic federal setting such as Nigeria can therefore be illustrative in these other contexts.In terms of organization, the following section reviews the literature on drivers of policy reform and specifically the main variables underlying the Kaleidoscope Model. The next section considers the issue of land governance and specifically the Nigerian context. Subsequently, we discuss how land governance re-appeared on the policy agenda in the late 2000s, the choice of the SLTR as the design mechanism for land registration, and the various challenges tied to the implementation of deeper land governance reforms. The final section concludes by discussing the key factors from the KM that are critical in the Nigerian case and some policy considerations.Scholarship on the political economy of policy reform is extremely vast but often quite disaggregated according to a particular element of the policy process. By contrast, the KM attempts to provide a comprehensive framework that integrates the insights from cumulative scholarship. As seen in Figure 1, the inner circle of the KM illustrates 16 key variables, labelled \"key determinants of policy change,\" that the political economy and public policy literatures suggest are significant (see Resnick et al. 2015 for a review of these literatures). The 16 key variables identified in Figure 1 may not be uniformly relevant to all policy domains and country contexts, but a subset of these variables should help provide analytical leverage for understanding blockages in, or facilitators of, the policy process.To understand how a policy first emerges on the policy agenda, the KM emphasizes the importance of a recognized and relevant problem, focusing events, and powerful advocates. A recognized, relevant problem is equivalent to the \"problem stream\" in Kingdon's (1984) multiple streams approach. The relevance criterion narrows the range of policy issues that could potentially emerge on the agenda because certain issues will have greater or less resonance with decision makers. In turn, a country's socioeconomic, demographic, and geographic context shapes the resonance of specific issues. For instance, generating smallholder incomes or addressing food insecurity is more important in low-income, more agrarian countries.The importance of focusing events is well-documented in the literature (see Birkland 1997) and have also been referred to as \"critical junctures\" (Collier and Collier 1991), \"punctuated equilibria\" (Pierson 2004;Thelen 2003), or \"windows of opportunity\" (Kingdon 1984;1995). In all cases, they refer to exogenous shocks or events that have the potential to shift the policy trajectory. The focusing event may be a major crisis, such as a food deficit or price crisis, an economic collapse, regime change, or a natural disaster. Yet, focusing events and a relevant problem are rarely sufficient on their own to propel an issue on the policy agenda. Instead, there needs to be powerful advocates who push for action on that issue (see Sabatier 2007). These advocates can come from a range of sources, including high-level government officials, political parties, civil society, the private sector, the research community, foreign investors, or donor agencies. Policy design involves grappling with the much more technical elements and modalities of addressing the relevant problem identified at the agenda setting stage. One factor is empirical research and knowledge disseminated through epistemic communities (Haas 1992) of researchers and experts, as well as donors, policy entrepreneurs, and technocrats. At the same time, these communities may have stark divisions among themselves in terms of an appropriate policy design, and institutionally entrenched technical perspectives can also cause some solutions to be prioritized and others marginalized (see Freeland 2013). A second distinct but related factor driving design issues involves norms, biases, and ideologies. Sabatier's (1988) notion of different types of policy beliefs is relevant in this regard. While there may be secondary beliefs about the narrow design features of a policy, these may be informed by deep beliefs about human nature shaped by norms and socialization (see also Sabatier and Jenkins-Smith 1993). Ideas and beliefs, however, intersect with a third important factor, which is the cost-benefit calculations of advocates and decision makers. Policy designs shape the type of interest group dynamics that emerge and that may subsequently influence policy adoption. These calculations may involve political goals, such as winning votes or gaining influence, or economic motivations, including the affordability of a particular design.Policy inertia is often more common than change (see Pierson 2004), so even after a set of reform designs has been proposed, it cannot be assumed that a policy reform will be adopted by government policymakers. Tsebelis (1995: 293) in particular highlights the importance of veto players, which he defines as \"an individual or collective actor whose agreement is required for a policy decision\" (see also Tsebelis 2002). Institutional factors that underlie different political regimes determine the constellation of veto players (see Lijphart 1999;Shugart and Carey 1992;Thelen and Steinmo 1992). Federalism, for instance, theoretically creates more veto players than do unitary systems of government by not only adding an extra legislative chamber but also powerful state or provincial governors. As we will see in the Nigerian context, this is more likely to constrain policy change, and in turn promote the status quo policy, because a broader coalition in favor of reform is needed. The position of government veto players may be influenced by the relative power of advocates versus opponents, whether financial, electoral, or political. Opponents to adoption may not have existed at the agenda setting stage but emerge after a policy design is solidified. Different regimes typically derive their support and legitimacy from different sets of stakeholders, with democracies often needing to cater to a broader range of stakeholders than is required in authoritarian settings (see Bueno de Mesquita et al. 2003). When and how quickly adoption occurs often involves a degree of propitious timing, which in turn is shaped by the nature of the policy and the motivations of the policy advocates. Such timing may be related to the occurrence of elections, the budget calendar, or the legislative calendar.Many policies, however, can be adopted but not effectively implemented. Policy implementation refers here to administrative changes, public expenditure outlays, or the delivery of the actual goods and services promised by the policy. Implementation clearly requires a degree of technical capacity within the government bureaucracy, the private sector, or civil society to roll out or scale up policy reform. This encompasses not only technical capacity, which includes education, skills, and relevant infrastructure, but also administrative capacity, such the availability of decentralized institutions or inter-sectoral coordinating mechanisms. Capacity can intersect quite strongly with requisite budgetary allocations, which are essential for most reforms. Delays in resource disbursements may trigger delays in implementation. In cases where decision makers delegate policy implementation to the private sector, civil society, or sub-national government agencies, discretionary application by these agents can lead implementation to deviate from the designers' intent or even stymie implementation altogether. These are identified as implementing stage veto players in the KM. Finally, implementation requires continued commitment of policy champions, who are typically high-level level bureaucrats or political leaders that sustain momentum even when others' attention to the issue might fade. As Pelletier et al. (2012: 28) observes, \"high-level policy champions may be the only actors capable of generating system-wide commitment on the part of midlevel ministry officials and staff, and the managers and implementers at regional, municipal, and local levels.\"When policies are implemented, the evaluation and reform stage is absolutely critical to determine whether small refinements or a complete overhaul is needed. One impetus for reform at this stage is the changing knowledge and beliefs of existing policy champions about the effectiveness of a policy or the best way to achieve the original policy goal. Hall's (1993) seminal work on policy paradigms notes that this might occur at three levels: making routine amendments to existing policy instruments, adopting new policy instruments to address existing policy goals, or shifting the goals themselves as policymakers learn from past policy mistakes and become influenced by new ideas and debates. As in the policy design stage, the drivers of belief changes may come from many sources, including media reports, parliamentary inquiries, and advocacy groups that may, for example, uncover misuse of resources or unintended policy consequences. A second factor is a change in material resources available for a policy, which may be linked to new policy crisis or a shift in donor funding. A final factor identified in the KM are shifts in the institutional setting, which can affect policy priorities and preferences. Institutional changes can upend the entire policymaking machinery and include the arrival of a new cabinet minister or president, the passing of a new constitution that re-assigns powers over functions, or the reshuffling of parliamentary committees.As is well recognized, not all policies are equivalent. Some policies are characterized as being \"stroke of the pen,\" such as exchange rate devaluation or deregulation (see Doner 2009;Grindle 2004). More broadly, Lowi (1972) delineates three types of policies: distributive, redistributive, and regulatory. Distributive policies involve the distribution of \"new resources\" and can be \"disaggregated and dispensed unit by small unit\" (Lowi 1964: 690), and include input or food subsidies that are targeted to specific groups. Regulatory policies, such as those affecting food and seed safety, differ from distributive ones because the short-term winners and losers are much more obvious and such policies tend to occur along sectoral lines: \"the impact of regulatory decisions is clearly one of directly raising costs and/or reducing or expanding the alternatives of private individuals\" (Lowi 1964: 690). Redistributive policies involve changing the distribution of existing resources, and the impacts of these policies may not be solely on individuals or households but on larger social classes, defined in terms of socioeconomics, ethnicity, race, religion, or even livelihoods. Among others, land governance reforms are quintessential redistributive policies in that they involve reallocating existing resources in a way that may have considerable effects on the welfare and social relations of different classes or societal groups. By applying the KM to the issue of land governance, we can better uncover how \"policy determines politics\" (Lowi 1972) and in turn, affects prospects for reform.Land governance is a multi-faceted concept referring to a broad range of factors integral to efficiently and effectively managing land issues. Palmer et al. (2009: 9) define it as \"the rules, processes, and structures through which decisions are made implemented and enforced, the way that competing interests in land are managed.\" Based on Dale and McLaughlin's (2000) seminal work, land administration encompasses four key functions, including juridical (e.g., registering, allocating, delimiting, demarcating), regulatory (e.g., developing and enforcing restrictions on land use and transfer), fiscal (e.g., valuating land and collecting taxes), and information management (e.g., maintaining information systems that help with implementing and enforcing regulations and resolving disputes). These functions are more or less integrated into the five dimensions of the World Bank's Land Governance Assessment Framework (LGAF) encompassing the legal and institutional framework, land use planning and taxation, public land management, public provision of land information, and dispute resolution mechanisms (see Deininger et al. 2012 andDeininger et al. 2014 for more details). As Borras and Franco (2010) note, the term is increasingly used to refer to \"technical and administrative governance, rather than a matter of democratizing access to and control over wealth and power.\"Much of the more overtly political economy literature recognizes the high level of conflict and contestation associated with land policy. This scholarship has tended to concentrate in two different broad domains. One includes a focus on the role of the state vis-à-vis external actors, particularly investors, and has emerged in the wake of increased attention to land grabs (e.g., Borras et al. 2010;Nolte 2014;German et al. 2013;Wolford et al. 2013). A second and much more expansive literature focuses on sub-national dynamics. These include analyses centered on the conflicts between statutory and communal tenure regimes and the attendant impact of land policies on the authority and legitimacy of customary authorities (Berry 2009;Bruce and Knox 2009;Ribot 2003). Other work in the same vein highlights the close ties between land ownership and citizenship rights, with particular attention to the implications for migrants versus indigenous communities (e.g., Boone 2007;Boone and Duku 2012;Lund and Boone 2013) and even for electoral politics (Boone 2009;Boone 2014;Klaus and Mitchell 2015).Yet, the specific contours of land reform in federal systems is often missing from the literature on political economy and sub-national actors. In the Nigerian context, however, this is a critical factor since it shapes the number of veto players and their interests in land governance reform. Indeed, Nigeria is one of Africa's few democratic federal systems and the only one in the region where management of surface land is constitutionally deemed a non-concurrent power overseen by the states. While Ethiopia is a federal system, land is a shared competence between the national and regional governments (see Bruce and Knox 2009) and, importantly, land legislation remains the reserve of the federal government (Deininger et al. 2012). South Africa has some characteristics of federalism, but constitutionally promotes a system of \"cooperative governance\" whereby intergovernmental relations are governed by recognition of three spheres of government (national, provincial, and local) and their functions are distinct but interdependent (see de Villiers and Sindane 2011). Most responsibilities are shared concurrently between the national and provincial levels. Consequently, the federal dimension of land governance in Nigeria can create additional barriers to policy reform than are witnessed in other African countries.More specifically, Nigeria consists of 36 states and 774 local governance areas (LGAs). Its structure is classified as a \"coming together\" federalism (Stepan 1999) that emerged at independence in 1960 to promote a more multi-ethnic and integrated country and thereby mitigate against destabilizing demands for autonomy (Suberu 2009). The 1999 Constitution, particularly Part I (sections 2, 3, 6) and Part II (section 7), delineate the contours of the federation. Specifically, the bicameral federal National Assembly consists of 109 Senators and 360 members of the House of Representatives.2 Each state in turn is overseen by an elected governor and the House of Assembly. Other important executive bodies include the National Council of States (NCS) and the National Economic Council (NEC). The NCS consists of the president, vice-president, all former presidents, all former chief justices, the president of the senate, speaker of the House of Representatives, all governors, and the attorney general. The NEC includes a subset of these actors, including all the state governors and the vice president, as well as the governor of the central bank (Federal Republic of Nigeria 1999).There are therefore at least three potential axes of political tension: between governors and the president, between governors and state assemblies, and between the president and federal legislators. Indeed, this dimension can be most pronounced when there is a disjuncture between the political parties in control at these different levels. For instance, when the People's Democratic Party (PDP) was in control from 1999 to 2015, governors from the then opposition Action Congress of Nigeria (ACN) party often faced trouble in accessing inter-governmental transfers, or they could be impeached by their state assemblies through pressure from the president (Fashagba 2015). 3 Similarly, since governors often have a high degree of control over the career paths of legislators, there is a high level of party defection to the governor's affiliation. Consequently, a high degree of partisan congruence between the executive and legislative branch is found in almost all of the states. This then allows the governor to use the state legislature as a rubber stamp on executive initiatives (Baba 2015).For amending any element of the 1999 Constitution, which is known as an \"alteration,\" supermajority approval is needed. Specifically, it needs to be approved by both two-thirds members in the bicameral National Assembly and the House of Assembly in at least two-thirds of the 36 states in order to pass (Suberu 2015b). 4 Presidential assent is then required. The large number of veto players that need to be involved therefore implies that constitutional alterations are relatively rare. 5   This overview of the constitutional setup is relevant for land governance for at least two reasons. First, residual powers, which are the exclusive domain of the states, include control over surface land even as other functions intimately linked to land, such as housing and mining, are on the concurrent and exclusive federal lists of the 1999 Constitution, respectively. 6 Secondly, Section 315 (5) of the 1999 Constitution notes that the Land Use Act, which is the overarching land legislation in Nigeria, cannot be altered or repealed except through the constitutional amendment process described above (Federal Government of Nigeria 1999).Introduced in 1978 under the military government headed by Olusegun Obasanjo, the Land Use Decree was announced as a way for land to be \"held in trust and administered for the use and common benefit all Nigerians\" (cited in Francis 1984: 5). It was intended to standardize control over land tenure between the North and South of the country. Prior to 1978, land in the North was under the Land Tenure Law of 1962, which in turn reflected the Land and Native Rights Ordinance from 1910. The latter stated that all land in Northern Nigeria was public and under the control of the governor who could grant and take away rights of occupancy for the common benefit (Francis 1984). However, in Southern Nigeria, where indirect rule was more common under British colonialism, the land tenure system was much more heterogeneous. Communal and family lands were recognized but lacked identifiable boundaries, and they were subject to abuse by traditional rulers who increasingly sold large tracts of these lands to land speculators in the wake of Nigeria's independence and increasing urbanization (Adeniyi 2013). This resulted in a large number of land conflicts and litigation (Mabogunje 2010).The Land Use Decree essentially extended the northern system to the whole country and effectively nationalized land. State governors manage urban lands and issue rights of statutory occupancy (rather than ownership), while LGAs manage rural lands designated for agricultural use and can issue rights of customary occupancy. The maximum amount of undeveloped land in urban areas that could be held is 0.5 hectares, while the equivalent in rural areas is 500 hectares. Land Use Allocation Committees were intended to advise the governors on urban land, while Land Allocation Advisory Committees were supposed to play the same function for LGAs on rural land. Furthermore, the Decree stated that a governor's consent is required to transfer a statutory certificate of occupancy (CfO) through either mortgage or assignment, while the same is required from the LGA for the transfer of the customary right of occupancy (Adeniyi 2011). The Decree effectively resulted in state appropriation of communal and family lands in the south without compensation. The military and civilian governments incorporated the Decree as an Act into the 1979 and 1999 constitutions, respectively, as a way of \"institutional lock-in\" to ensure that it would not be easily revoked or altered. To amend the Land Use Act, a draft bill would first need to be sent to the NEC and NCS, which together will serve in an advisory role. In turn, if these bodies feel that it can be tabled, it will be shared with the legislative branch for supermajority approval both at the federal and state levels.While the resulting Land Use Act facilitated appropriation of land for public purposes and reduced land-related litigation, Adeniyi (2013) and Mabogunje (2010) elaborate on how the Act is both abused and neglected at the same time by governors. On the one hand, governors can take advantage of the need for their consent by charging high fees for this service as a means of raising additional state revenue. Moreover, the ability of governors to claim \"overriding public interest\" as a justification for revoking a right of occupancy can be abused and increase the vulnerability of tenants. As Adeniyi (2013: 12) notes, \"governors can allocate property at their discretion. With absolute arbitrary economic and political power in their hands, governors can easily dispossess their political opponents and farmers. Since the procedures for tenure individualization are neither affordable nor clear, people often do not bother applying.\" In turn, banks have become increasingly reluctant to accept CfOs as adequate collateral for obtaining a loan. In any case, CfOs only allow for a 4 There are some exceptions to this. Provisions on human rights and boundary adjustments require a four-fifths approval (see Suberu 2015b).5 Some constitutional alterations occurred in 2010-2011 with respect to guaranteeing budget autonomy for the Independent National Electoral Commission, streamlined provisions for internal party democracy, established an industrial court, and provided clearer timelines for conducting elections (see Suberu 2015b). In 2015, the requisite legislative supermajorities were obtained to alter the strength of presidential powers outlined in the Constitution, but the outgoing president at the time, Goodluck Jonathan, withheld his assent. 6 Interviews with officials from Office of the Vice President and Federal Ministry of Power, Housing, and Works, Abuja, Nigeria. maximum of 99 years on the land and therefore many do not apply for the CfOs since it otherwise limits their rights to land, thereby contributing to a massive informal land market (see Adeniyi 2013, annex 7.5A).On the other hand, the need for the governor's consent to assign or mortgage land hampers the development of the land market since obtaining his or her signature for CfOs can take a great deal of time if the governor is traveling or has prioritized other responsibilities. In addition, only a few state governments have actually established the required Land Use Allocation Committees, and most governors lack accurate information about the distribution of lands within their states and the owners of those lands. This neglect has prompted many citizens to simply reject the assumption in the Land Use Act that all land is vested in the government and continue to advertise familial or communal ownership to ward off buyers.The legislative framework established by the Land Use Act certainly casts a shadow on efforts to improve land governance in Nigeria, which are sorely needed. Indeed, the LGAF process resulted in Nigeria receiving a score of C or D on 69 of the 88 dimensions that underlie the Framework (Adeniyi 2013).7 Moreover, the World Bank's Doing Business rankings classify Nigeria as the worst in Africa after Togo for registering a property (World Bank 2015).The challenges with the Land Use Act have been widely recognized, and opposition to the Act has been relatively high among lawyers, farmers, land professionals, and traditional authorities since its inception (see Adeniyi 2013;Francis 1984). Yet, as noted, the Act was re-inserted into Nigeria's Constitution when it transitioned from a military dictatorship to civilian rule. Notably, the first democratically elected president of the Fourth Republic was Obasanjo, who had overseen the articulation of the Decree in 1978. This occurred despite widespread calls for its removal, including by the Constitutional Debate Coordinating Committee that examined contentious issues in the 1999 constitution (Ojo 1999). In many ways, it is seen as a vestige of Nigeria's military past and not compatible with a democratic regime. In 2005, Obasanjo noted that the Decree would not be abolished, but that it should be removed from the Constitution and enacted as a new Act of Parliament: \"\"The Land Use Decree is not being abrogated. If I may say this, some of the developments that we have carried out in this country would not have been possible without the Land Use Decree. What I am trying to do is to take it out of the Constitution and let it be an act by itself, because in that case it is easier to amend than when it is part and parcel of the Constitution\" (cited in Ojedokun 2005). However, no progress was made.In January 2007, with the approach of the presidential elections, the candidate for the PDP, Umaru Musa Yar'Adua, rolled out his Seven Point Agenda. At the top of this manifesto was a commitment to review the 1999 Constitution and specifically to repeal the Land Use Act in order to attract greater private investment (Ekpunobi 2007). After ultimately winning the elections, he formally launched the Agenda in May of that year. Point two of the Agenda focused on enhancing food security, while point five explicitly targeted land reforms, including again changing land laws. In November of that year, the then-named Federal Ministry of Environment, Housing, and Urban Development organized a series of stakeholder forums on the topic that were overseen by Professor Akin Mabogunje.In April 2009, Yar'Adua's government submitted the Land Use Act (Amendment) Bill to the National Assembly, which contained 14 amendment clauses specific to sections 5, 7, 15, 21, 22, 23, and 28 of the existing Act. The main aim of the clauses were to restrict the need for governors' consent to land sales only and render it unnecessary for mortgages, leases, and other land transfers. In addition, the Bill intended to recognize customary rights over land ownership and for all farmers to use land as collateral for loans intended to support commercial farming (Ghebru et al. 2014). Yar'Adua also sought the support of the NCS and the Governor's Forum to ensure that the state assemblies would endorse the Bill if and when it was passed at the federal level (This Day 2009). In an interview, Yar'Adua articulated why the land was so important for his administration:The bottom-line in terms of whatever we do is this: we want to grow the economy so that people will have jobs. So, agriculture and land reform come in here. Land administration because, I always said that one of the things that we failed to do in this country is to bring land to play its part in the development of the national economy as a capital asset. And this is why land reform and modernisation of the land administration form part of the Seven Point Agenda, because it will have a great impact on the modernisation of the national economy (cited in Ogunlesi 2009).The Bill received support from key professional bodies, including representatives of the Nigerian Institute of Town Planners and the Nigerian Institution of Estate Surveyors and Valuers (This Day 2009). Hansards from the National Assembly reveal that the Bill received rhetorical support from a number of legislators. However, it was then sent to the Constitution Review Committee (CRC) of the Senate and the House before any further deliberations occurred. In March 2010, the House CRC still failed to present its report and the amendment proposals were tabled for a later date for consideration of other legislation proposed by Yar'Adua on electoral reforms. At the same time, however, the House rejected the president's bill to establish a National Land Reform Commission (NLRC), which would have created a depository for land title holders. House Representatives rejected the bill at the second reading, claiming that it encroached on state control over land as specified in the residual list of the Constitution and noted that it would have been more appropriate if only limited to the Federal Capital Territory (Oham 2010).Even prior to submitting the Bill to the National Assembly, Yar'Adua established a nine-person technical committee, known as the Presidential Technical Committee on Land Reform (PTCLR), and appointed Mabogunje as the head of it. The PTCLR was given seven major terms of reference for its work. Among other responsibilities, it was intended to advise the government on a plan for registering landholdings, creating a national cadastre, and developing mechanisms for land valuation and conflict resolution (Ghebru et al. 2014;USAID 2010). Institutionally, the PTCLR was located within the Office of the Secretary to the Government of the Federation, which was intended to give it high-level visibility and access.The president was actually working at two levels by trying to first expunge the Land Use Act from the Constitution and amending clauses and secondly, trying to find technical options to reduce the uncertainty surrounding most Nigerians' possessory rights (see Mabogunje 2010).As noted above, one of the main mandates of the PTCLR was to help guide the government on how to register landholders. The main approach that was proposed was systematic land titling and registration (SLTR).Sporadic titling has long been the major approach in Nigeria, whereby someone would only request a title if they needed one. By contrast, SLTR involves verifying parcel use and boundary demarcations in a systematic manner in order to build a registry database of the distribution of ownership, rights, and boundaries of land parcels for the country. Dispute mechanisms are built-in ex-ante, because owners are confirmed via engagement with neighbors and community leaders are brought in to resolve problems when they emerge. 8 The intention is to ensure that no parcel is left without an identified owner (Ukaejiofo and Nnaemeka 2014). Though the Continuously Operating Reference Stations, coordinates of the land are confirmed, then titled and registered. The approach requires using general boundaries, defined by natural features, rather than fixed boundaries that are measured more precisely by land surveyors.Research generally suggests that the approach is less expensive than a more sporadic approach (e.g., Hanstad 1998;Zevenbergen 2004). Policy diffusion from other countries rather than commissioned feasibility studies within Nigeria appears to have motivated the choice of approach. As one member of the PTCLR noted, \"The committee was new and stumbled on this approach.\" 9 The PTCLR became convinced of the approach after visiting other countries in 2013 that had successfully implemented the approach, including Indonesia, Rwanda, South Africa, and Thailand (Bello 2014).Initially, the program was intended to be applied in one state in each of the country's six geopolitical zones (i.e. South-East, South-South, South-West, North-Central, North-East, North-West). 10 However, due to a lack of sufficient funding, PTCLR decided to concentrate on just two states, Kano in North-West and Ondo in South-West. One reason that these states were selected was the demand from their respective governors. In the case of Ondo state, for example, the governor (Olusegun Mimiko) had formerly been the Federal Minister of Housing and Urban Development and therefore recognized the salience of reform for enhancing tenure security. Resources for the two pilot states were forthcoming from a variety of major donors, including the World Bank, the United Nations' Food and Agricultural Organization (FAO), and the DfID/Growth and Employment in States (GEMS3) project. In the case of GEMS3, for instance, there were already ongoing efforts in Kano to enhance land tilting and registration so the pilot was a natural complement to those activities. 11   The major hurdle for acceptance of the SLTR was from the professional community of land surveyors. Many felt that the switch to general boundaries that is implicit in the SLTR undermined their profession and perhaps even their future job prospects. More specifically, members of the National Institute of Surveyors (NIS), which is a non-governmental 8 Representative of the National Land Transparency Initiative, Abuja, Nigeria. 9 Interview with member of the PTCLR, Abuja, Nigeria.10 Interview with member of the PTCLR, Abuja, Nigeria.11 Interview with GEMS3 representative, Abuja, Nigeria.professional organization, initially opposed the use of general boundaries because the method has a large margin of error in measurement (i.e. plus or minus three meters in area). This lack of precision seemed to contradict the surveyors' training and expertise and the status quo method for delineating parcel boundaries. It also made some surveyors wary that they might be liable if there was ever litigation and a surveyor was asked to defend his or her coordinates. 12 Moreover, some also feared that providing title documents to all occupiers would limit demand for the services of surveyors in the future (Ukaejiofo and Nnaeimeka 2014).In order to overcome this resistance, the Surveyors Council of Nigeria (SURCON), which is an autonomous government body that helps certify surveyors and regulates the profession, engaged in extensive sensitization with members of the profession. This was done through meeting members of the NIS in small sections that correspond to their geographical locations. While there remains some lingering resistance, most surveyors better understand the initiative and all Surveyors-General at the state level have agreed to the approach. 13   Consequently, SLTR is gaining recognition and demand beyond the initial two states of Kano and Ondo that were selected by the PTCLR. For instance, there has been momentum to adopt the approach in states such as Anambra, Cross Rivers, Jigawa, Katsina, Kogi, and Zamfara. In some cases, the PTCLR approached the state governor to provide sensitization about the program while in others, e.g., Cross Rivers, there was homegrown demand for the program. Yet, in all cases, the critical component has been having the support of the respective state governors who are the key veto players for this registration program to be adopted and implemented.While professional concerns over the SLTR design have been increasingly addressed, the larger issue is whether the institutional capacity exists to implement the SLTR at scale. For example, there are only 2,300 registered land surveyors in Nigeria and, therefore, significant capacity constraints. Moreover, while many states are now computerizing their land records, the software used is at the discretion of the state government. In addition, the technological resources to roll out the SLTR remain constrained. While the SLTR program adopted the FAO's SOLA software, migrating to this software has been a challenge in those states that were accustomed to a different software (Ukaejiofo and Nnaemeka 2014). Furthermore, in the pilot states, the Federal Government paid for the installation of Continuously Operating Reference Stations and found suitable officers to read and use the data. However, whether the resources will be available to do this more widely across the country is questionable. Other concerns are that, except for in Ondo, SLTR has not really been integrated into the state land ministries where it has been piloted. To be operational and sustainable as a long-term approach for registration, a department within the state ministries needs to oversee SLTR and to be in a position to fund this function even when donor support has ended.This is particularly true since the GEMS3 program is entering its final year (at the time of writing). The Nigeria Land Transparency Initiative (NLTI), which is located within the current Federal Ministry of Housing, Power, and Works, but jointly chaired with the Federal Ministry of Agriculture, was intended to provide some long-term institutionalization of GEMS3 interventions. For example, it was intended to provide a public registry of surveyors from SURCON and NIS as well as help to develop a manual to implement SLTR. They have also been trying to integrate a lot of information about property registration into the public domain and assist with sensitizing the public. 14 Funding, however, now threatens the future of the NLTI, and the merger of the Ministry of Lands and Housing with Power and Works in 2015 has caused other priorities to emerge on the ministerial policy agenda, with NLTI struggling to find a champion to advance its cause. A proposal submitted by the NLTI in 2015 for funding was rejected by the National Assembly. 15   Budget constraints are even more pronounced for the PTCLR. For instance, members of the PTCLR note that their budget from the federal government has been increasingly reduced. 16 Another challenge is late disbursement of funding, with the PTCLR only receiving its 2016 operating budget in June rather than January, which severely hampers its ability to engage in workshops and other essential sensitization activities. 17 There are three views on why federal resources for the PTCLR have dwindled over time. One view is that the PTCLR disproportionately invests much of its efforts into sensitization activities and sees its role as a \"silent enabler.\" Since the impact of sensitization is difficult to 12 Interviews with SURCON members, Abuja, Nigeria. 13 Interview with SURCON member and surveyor, Abuja, Nigeria.14 Interview with GEMS3 and with NLTI, Abuja, Nigeria. 15 Interview with the NLTI, Abuja, Nigeria. 16 Interview with member of the PTCLR, Abuja, Nigeria. 17 Interview with representative of the Federal Ministry of Power, Works, and Housing, Abuja, Nigeria. measure, the National Assembly has been skeptical of the PTCLR's role: \"people want to see value for money.\" 18 A different view is that the PTCLR's unique mandate is not clear given that there are many other institutions involved in land governance in Nigeria, and, in the absence of performance indicators, there is no way to monitor its effectiveness. 19 The third perspective is that the PTCLR has lost the commitment of policy champions to support it. Most notably, Yar'Adua passed away in 2010 and his successor, Goodluck Jonathan, launched the Agricultural Transformation Agenda, which pushed aside the Seven Point Agenda and the prominence that had been accorded to land reform. 20 When Jonathan was prevented from serving a second term after losing the 2015 elections to Muhammadu Buhari, this again hampered the PTCLR's activities. Since Buhari was from the former opposition party, the APC, his election represented a shift away from the PDP regime, and the PTCLR was left unsure for almost a year as to whether it would still be a relevant entity in the new government. 21 In September 2015, the Federal Ministry of Lands and Housing was merged with Power, Works and Housing and a new minister, Babatunde Fashola, was appointed. One casualty of the merger is that land issues receive less prominence, since power and electricity are viewed as more pressing. Ensuring that land stays on the policy agenda has proved difficult, with a key member of the PTCLR recognizing, \"We need support from the top.\" 22   These challenges are compounded by suspicions at the state level that the PTCLR is a federal government entity that intends to undermine states' control over surface land. Fashola's record of being a strong supporter of states' rights, reinforced through his tenure as Lagos State governor, is also suspected as a barrier to obtaining more ministerial support and prominence. 23   Furthermore, the PTCLR's ability to maintain momentum with SLTR is also complicated by the fact that the 2015 elections resulted in a new crop of governors in most states, which required renewed investments in sensitization and building relationships with these executives. 24   Beyond the SLTR, the PTCLR and its other partners, including the donors, continue to also struggle to gain momentum on the legal framework. A new Draft Regulation for the Land Use Act was prepared in January 2013 that gives high prominence to the SLTR approach. In particular, it takes its point of references from sections 34(2) and 36(2) of the Act, which emphasizes that \"where the land is developed, the land shall continue to be held by the person in whom it was vested immediately before the commencement of this Act as if the holder of the land was the holder of a statutory right of occupancy issued by the Governor under this Act.\" Section 36(2) expresses similar language for rural land. The italicized language was targeted and the Draft suggests that those holding developed land before the Land Use Act began should be entitled to the issuance of a certificate of occupancy and be registered under the process of SLTR (PTCLR 2013). Yet, more than three years later, the draft regulation has stalled at the level of the NES, and there has been difficulty in getting the attention of the ministry that oversees land. One suspected reason for the draft regulation stalling is that governors continue to fear that the federal government is trying to take away their powers. As one member of the PTCLR observes, \"We need to explain that no government agency is taking your land away, but we're trying to make your land more viable.\" 25 To push the process forward, the PTCLR and its partners increasingly have attempted to work with the Office of the Vice President, since he oversees the NEC and could mobilize both the minister and the governors.26 As observed by Adeniyi (2013), the overriding challenge in Nigeria is to determine \"how can those benefiting from the very weak system of land administration allow a change in the system?\" More broadly, others have observed that technical interventions, such as land titling and registration, are most successful when there is the political will to pursue them (see Byamugisha 2013;Ukaejiofo and Nnaemeka 2014). By applying the Kaleidoscope Model to the difficult case of land reform in Nigeria, we can gain a clear understanding of where the sources of political will are located as well as which elements of the Model are more or less relevant for understanding what is driving or hampering this particular case of policy reform.18 Interview with member of the PTCLR, Abuja, Nigeria.19 Interview with representatives from the Office of the Surveyor General. 20 Interview with official from the Federal Ministry of Power, Housing, and Works.21 Interview with member of the PTCLR.22 Ibid. 23 Interview with official from the Federal Ministry of Power, Housing, and Works.24 Interview with member of the PTCLR.25 Interview with PTCLR Member, Abuja, Nigeria.Specifically, reform of the Land Use Act, and broader improvements of land governance, was a recognized relevant problem for decades. Although no particular focusing event occurred that increased the salience of reforming the Act, the election to the presidency of Yar'Adua and the transition out of office of Obasanjo, who had originally instituted the Act, resulted in a high level advocate for reform. Yar'Adua pursued a two pronged approach that involved both pushing for legislative reform and creating a visible committee, PTCLR, within the president's office to look at technical solutions. His interest in land governance coincided with donors' growing focus on the issue, motivated by the New Alliance and CAADP commitments, which resulted in an expansion of the advocacy coalition to propel the policy reform agenda forward.When considering which design to follow, the PTCLR larger drew on knowledge and research about the benefits of SLTR that emerged from the diffusion of policy experiences from other countries. Donors provided some resources to help initiate pilot interventions in selected states where individual governors were enthusiastic. This generated evidence indicating that the benefits of SLTR outweighed some of its expected costs. Moreover, while there were biases against the general boundaries approach by some of the land surveyors, intensive efforts at sensitization increasingly helped them to become proponents of the intervention, rather than opponents.The multiple veto players in the Nigerian system hampered legislative reform-as noted earlier, changing the Land Use Act is tantamount to changing the Constitution and requires the consent of a majority of governors. However, the adoption of SLTR was more decentralized and individual governors were given autonomy to choose SLTR for their respective states. In other words, fewer veto players were involved in SLTR adoption, which made adoption easier where there was a willingness to do so.Yet, to proceed with implementing SLTR at scale, a variety of capacity constraints and institutional challenges will still need to be overcome and renewed enthusiasm will be needed among a new set of governors. Requisite budgetary resources are currently not adequate for the intervention. This largely reflects the loss of a high level policy champion in the wake of Yar'Adua's death in office in 2010. Since SLTR implementation is still in various stages across states, it is difficult to make firm conclusions about evaluating and reforming the modality. However, findings in states such as Jigawa reveal that land owners have low demand for collecting their CfOs, reflecting that policymakers may have overestimated the importance of land titling for some populations. 27 In addition, most state administrations did not budget sufficient resources for the intervention even as donor support is coming to a conclusion. In other words, the changing material conditions listed in the Model may affect SLTR's further expansion. Key institutional shifts have also occurred since SLTR was first proposed by the PTCLR. These include the change in governors during the 2015 elections in some states, the takeover of the federal administration by the erstwhile opposition, and the merging of the federal ministry that historically has overseen land issues.At least two broader lessons emerge from the case study. First, if political will is lukewarm and threatens resource allocations for policy implementation, then donors need to stay committed for the long term. Many of the donor programs related to land governance in Nigeria operate on a five-year cycle, including GEMS3. Yet, implementing SLTR at scale is known from other contexts to take between 10 and 15 years (see Ali et al. 2015). Aligning donor support with realistic timeframes for policy implementation is therefore critical, as is prioritizing capacity building.Secondly, reforms with respect to both land governance and in other policy domains need to be sequenced appropriately and correspond with local political realities. While Rwanda is oft-cited as a successful example of SLTR implementation (see Ali et al. 2015;Byamugisha 2013), it has a unitary system and an authoritarian regime that can commit to reforms that require long time horizons (see Poulton 2014). Nigeria, by contrast, has a multi-party democracy where elections actually have shifted government administrations and therefore policy priorities and the availability of policy champions. In addition, it has a federal structure and a constitution that accords states with oversight over surface land as a residual power. While working at the legal and technical levels to improve land governance has some advantages in creating momentum around land reform in Nigeria, the two domains are not mutually exclusive and each require buy-in from governors. 28   More optimistically, however, while the federal system creates more points of veto power that may exacerbate policy inertia, it also offers the opportunity for greater policy experimentation at the sub-national level. For instance, there have been ongoing efforts by the Federal Government to establish grazing reserves in Nigeria. In July 2012, the \"National Grazing Route and Reserve Bill\" was deliberated by the Nigerian Senate. Initially, the Senate was divided over whether the Government had the constitutional backing to create grazing reserves in any state and failed to promulgate a new law (see Muhammed et al. 2015). Yet, by mid-2016, the Ministry of Agriculture announced that 11 states donated 27 Interview with Director of Lands, Jigawa State. 28 The case of Liberia is instructive whereby legal reform and capacity constraints have been prioritized to ensure a more gradual implementation of systematic registration (see Byamugisha 2013).5,000 hectares of land each for grazing reserves (Joseph 2016). Similarly, despite all the hurdles, a few Nigerian governors do continue to be committed, at least rhetorically, to SLTR implementation. 29 This optimistically suggests that, in the long run, state level efforts with SLTR may generate a sizeable coalition for change that enables legislative reform at the federal level.","tokenCount":"8898","images":["1052079429_1_1.png","1052079429_5_1.png"],"tables":["1052079429_1_1.json","1052079429_2_1.json","1052079429_3_1.json","1052079429_4_1.json","1052079429_5_1.json","1052079429_6_1.json","1052079429_7_1.json","1052079429_8_1.json","1052079429_9_1.json","1052079429_10_1.json","1052079429_11_1.json","1052079429_12_1.json","1052079429_13_1.json","1052079429_14_1.json","1052079429_15_1.json","1052079429_16_1.json","1052079429_17_1.json","1052079429_18_1.json"]}

data/part_2/0267206824.json

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+{"metadata":{"gardian_id":"5bc51d19148c5eda73db31e066e74c1e","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/b51ea42a-83cd-467b-b812-a6cf6775300f/retrieve","description":"Farmers in mixed crop-livestock systems produce about half of the world’s food. In small holdings around the world, livestock are reared mostly on grass, browse, and nonfood biomass from maize, millet, rice, and sorghum crops and in their turn supply manure and traction for future crops. Animals act as insurance against hard times and supply farmers with a source of regular income from sales of milk, eggs, and other products. Thus, faced with population growth and climate change, small-holder farmers should be the first target for policies to intensify production by carefully managed inputs of fertilizer, water, and feed to minimize waste and environmental impact, supported by improved access to markets, new varieties, and technologies.","id":"1473308626"},"keywords":[],"sieverID":"0a49d97a-4f87-4253-a1c3-12ae2da6a2f9","pagecount":"5","content":"crop improvement programs (23,24) (Box 1). It is important, therefore, that we expand the scope of and access to new marker platforms to provide efficient, cost-effective screening services to the breeders. Communication and mechanisms for delivery of material to breeders must be developed. There is an urgent need to expand the capacity of breeding programs to adopt new strategies. The clearly documented high rate of return on such investments in the past should be kept in mind (25).The concerns about food security and the likely impact of environmental change on food production have injected a new urgency into accelerating the rates of genetic gain in breeding programs. Further technological developments are essential, and a major challenge will be to also ensure that the technological advances already achieved are effectively deployed.Smart Investments in Sustainable Food Production: Revisiting Mixed Crop-Livestock Systems M. Herrero, 1 * P. K. Thornton, 1 A. M. Notenbaert, 1 S. Wood, 2 S. Msangi, 2 H. A. Freeman, 3 D. Bossio, 4 J. Dixon,5 M. Peters, 6 J. van de Steeg, 1 J. Lynam, 7 P. Parthasarathy Rao,8 S. Macmillan, 1 B. Gerard,9 J. McDermott, 1 C. Seré, 1 M. Rosegrant 2   Farmers in mixed crop-livestock systems produce about half of the world's food. In small holdings around the world, livestock are reared mostly on grass, browse, and nonfood biomass from maize, millet, rice, and sorghum crops and in their turn supply manure and traction for future crops. Animals act as insurance against hard times, and supply farmers with a source of regular income from sales of milk, eggs, and other products. Thus, faced with population growth and climate change, small-holder farmers should be the first target for policies to intensify production by carefully managed inputs of fertilizer, water, and feed to minimize waste and environmental impact, supported by improved access to markets, new varieties, and technologies.usiness as usual\" investments in agriculture, although necessary (1,2), are unlikely to deliver sustainable solutions as the world rapidly changes (3,4). At the recent G8 summit in Italy, the leaders of the world's wealthiest countries promised to invest U.S.$20 billion to improve global food security. Most of that money is likely to flow to the developing world, where over the next few decades agricultural systems, already facing a va-riety of stresses, will be expected to accommodate a massive population surge. Even an investment of this magnitude could fail to generate food security if its deployment is not well planned and based on sound science.The usual culprits, such as inefficient aid delivery, government corruption, and political unrest, are a barrier to progress but are not the most important problem. Rather, it involves a fundamental failure to appreciate the range of dif-ferent agricultural systems that are expected to feed our planet in the coming decades and their policy needs. The diverse pressures that are acting on agricultural systems in various parts of the world include population increase, rising incomes and urbanization, a rapidly rising demand for animal products in many developing countries, and a fierce competition for land and water (3,5,6), all of which will have profound effects on food security (1). Croppers and livestock keepers the world over have steadily accumulated local experience and knowledge that will help them to adapt in the future, but the rapid rates of change seen in many agricultural systems in developing countries may simply outstrip their capacity.Yet, recent scientific assessments (1, 2, 7-10) and the technical and policy recommendations that flow from them have not fully captured the complex biological, social, and economic dynamics of the variety of chal-1 International Livestock Research Institute (ILRI), Post Office Box 30709, Nairobi, Kenya. 2 International Food Policy Research Institute (IFPRI), 2033 K Street NW, Washington, DC 20006, USA. 3 International Finance Corporation, The World Bank Group, Washington, DC 20433, USA. 4 International Water Management Institute (IWMI), Colombo, Sri Lanka. lenges likely to confront future crop and livestock production (5).Recently, the Consultative Group on International Agriculture Research (CGIAR) considered the issues facing mixed crop and livestock production, one of the predominant forms of agriculture in the developing world (3). Mixed systems enable the farmer to integrate different enterprises on the farm; in such systems, livestock provide draft power to cultivate the land and manure to fertilize the soil, and crop residues feed livestock (Fig. 1). Moreover, income from livestock may be able to buffer low crop yields in dry years. These mixed systems may be used intensively close to urban markets, as well as in less productive areas with limited market access.The synergies between cropping and livestock husbandry offer many opportunities for the sustainably increasing production (11) by raising productivity and increasing resource use efficiency both for households and regions. This, in turn, can increase incomes and secure availability and access to food for people while maintaining environmental services. However, during the next 20 years mixed croplivestock farmers may not be able to stay abreast of population growth, environmental change, and the increasing demand for animal products (1,3).According to the CGIAR analysis, the world's one billion poor people (those living on less than $1 a day) are fed primarily by hundreds of millions of small-holder farmers (most with less than 2 ha of land, several crops, and perhaps a cow or two) and herders (most with fewer than five large animals) in Africa and Asia (3). Furthermore, mixed crop-livestock systems could be the key to future food security; two-thirds of the global population already live in these sys-tems, and much of the future population growth will occur there. Already, mixed systems produce close to 50% of the world's cereals and most of the staples consumed by poor people: 41% of maize, 86% of rice, 66% of sorghum, and 74% of millet production (3). They also generate the bulk of livestock products in the developing world, that is, 75% of the milk and 60% of the meat, and employ many millions of people in farms, formal and informal markets, processing plants, and other parts of long value chains (3).The pressures currently acting on the so-called \"high-potential,\" intensively farmed lands of developing countries are large enough to slow and possibly end the substantial increases in growth rates of crop production seen during recent decades. For example, diminishing water resources are becoming a huge constraint to rice and wheat production in South Asia (1). There, livestock numbers are projected to increase significantly: cattle and buffalo from 150 to 200 million animals by 2030 and pigs and poultry by 40% or more in the same period (1,3). Pressures on biomass to feed these animals are already high, with trade-offs in the use of resources (land, water, and nutrients) becoming increasingly hard to balance in these systems, especially as competition for biomass for food, feed, fertilizer, and fuel increases (3,12,13). Similar caps on natural resources in the East African highlands and other high-potential agricultural areas of Africa are appearing in the form of infertile soils, degraded lands (13,14), depleted water sources, carbon losses, shrinking farm sizes, and decreasing farm productivity (14,15). Recent research suggests that some of these areas may not respond to increased fertilizer inputs and will need a closer integration of livestock and crop production to improve productivity (14,15).The key will be to develop sustainable intensification methods that improve efficiency gains to produce more food without using more land, water, and other inputs (3,16,17). For example, in parts of Asia there is considerable scope to produce more meat and milk in mixed systems through more efficient production systems (Box 1). Over the past 30 years, researchers have doubled the efficiency with which chickens and pigs convert grain into meat (6,16), and this has resulted in less grain consumption per unit of poultry and pig meat produced. Although global poultry and pork prices have decreased significantly, this has been at the expense of increasing the price of cereals available for human consumption (1) and has promoted deforestation in the neotropics (16,18).In some regions, farmers will have to change the species of livestock they keep to use their resources more efficiently, and policies to promote livestock specialization will be needed. A measurable shift is already taking place in South Asia's intensive mixed crop-livestock systems, In developing countries, some crops like maize, wheat, sorghum, and millet are dual purpose: Their grain provides food for humans and their residues are used as feed for livestock. Traditionally these crops have been bred to improve grain yield and drought and pest resistance. However, in the past decade it has been recognized that farmers in mixed crop-livestock systems value the crop residues sometimes as much as the grain owing to their importance as a feed for livestock, particularly in the dry season (29). Breeding programs for these crops are increasingly being adapted to include breeding for residue quality without compromising the original objectives associated with increasing grain yield.In India, where the demand for crop residues as feed is very high, improved dual-purpose varieties of sorghum and millet have had significant impacts on the productivity and efficiency of crop-dairy systems. Small-holders have been able to increase the milk production of buffalos and cows by up to 50% while at the same time obtaining the same grain output from their crops. This has increased the demand for dual-purpose crops with relatively high-quality crop residues, and burgeoning fodder markets have developed around cities like Hyderabad (29).from ruminant and crop production to intensive industrial poultry. Here, rates of growth in poultry production are projected to exceed 7% per year by 2030, which is two-to threefold higher than rates of growth for ruminants or crop production (3). Specialization and intensive industrial livestock production will in turn require environmental and trade regulations. For example, in parts of Asia, large numbers of pigs in unregulated intensive industrial systems pollute water sources in peri-urban areas (19) (Fig. 2). Concentration of animals can also increase the risk of outbreaks of emerging infectious diseases, afflicting livestock and people alike (20) (Box 2).Extensive Crop-Livestock Systems Significant contributions to future food security could be made in the more extensive mixed crop-livestock systems used in developing countries, where there is less pressure on the land and the crop productivity is far from optimal (21). For example, yields of dryland crops such as sorghum, millet, groundnut, and cowpea could easily be increased by a factor of three with appropriate land preparation, timing of planting, and use of fertilizers and pesticides (21). In specific circumstances, genetically modified (GM) crops can be an important contribution to improving crop productivity by increasing water use efficiency or reducing the impacts of pests and diseases. Policies and public investments in infrastructure and market development will be essential to create systems of incentives, reduce transaction costs, and improve risk management (10,22). Integration of production in these systems to supply agro-ecosystems services to the more-intensive systems will also be needed to ensure sustainability (3).Investing in extensive mixed systems will require considerable changes in public investments. Instead of allocating most resources to highly populated areas or those with high agricultural potential, developing-country governments will have to begin investing in infrastructure and services for more extensive areas (22), many of which are likely to be affected by climate change in the future (2). With better roads, markets, health facilities, and other infrastructure and services, the rural-to-urban migration rates in the extensive mixed areas could be reduced (10), thus nurturing the next generation of food producers.In developing regulatory frameworks for sustainable food production, we need to define the limits to agricultural intensification (11,16). Lessons can be learned from the developed world in terms of matching efficiency gains and environmental regulation. For example, the European livestock sector grew slightly in the past decade while reducing greenhouse gas emissions by 9% (23). Particularly in the developing world, we need to determine criteria for defining intensification thresholds at local levels before irreversible environmental degradation occurs (16).Any agricultural investment portfolio funded by the G8 should be sufficiently diverse to include payments for protecting water, carbon, biodiversity, and other global goods and ecosystem services where rangeland and other systems are under significant pressures and need to deintensify or stop growing altogether (3,24,25). For example, implementing schemes to pay farmers for protecting water towers in the Himalayas could be key for food production in large parts of South Asia. Such schemes could be aimed at sustaining stream flow early in the growing season, when water inputs are critical for crop production (26).Relatively modest extra investments could halve child malnourishment rates in developing countries (currently 27%), in spite of projected population growth to 2050 (27).Nevertheless, to reduce poverty while increasing food supplies and maintaining functional ecosystems will require well-regulated and differential growth in crop and livestock production (1,3,6). It will require public and private investments in the more-extensive mixed agricultural systems neglected in the past (22). It will require higher public and donor funding for research and development in the livestock sector, which historically has been lower than those for food crops, often by a factor of 10 or more (28). It will require differentiated and nuanced policies able to assess the trade-offs between agro-ecosystem services and human well-being (5). And it will require that governments and donors, together with scientists and other stakeholders, precisely target technological, investment, and policy options to suit different farming systems and regions (3).There is no doubt that agriculture as an engine for growth is regaining recognition by governments in developing countries (10). Together with the commendable and significant financial commitments of G8 countries to developingcountry agriculture, they now need to match it with an intellectual commitment-one that embraces a new agricultural frontier and new efficiencies, incentives, and regulations in the food systems of developing countries.  Recent outbreaks of avian influenza in both domestic poultry and the human population have been a source of considerable concern. The disease, caused by the highly pathogenic H5N1 virus, appears to move between poultry and wild birds and to people. The virus was identified in domesticated geese in southern China in 1996 and in humans in Hong Kong in 1997. H5N1 avian influenza then spread rapidly in 2002, with outbreaks in poultry, wild birds, and other mammalian species in more than 60 countries. By the end of 2009, 467 human cases and 282 deaths had been reported to the World Health Organization (30). In response, more than 200 million poultry have been killed by the virus or culled to prevent its spread.The epidemiology of the disease is not well understood because there are many vectors, including wild birds and other wildlife. However, large concentrations of birds in both backyard and intensive systems, coupled with poor disease control or underfunded veterinary services in some developing countries, could be significant risks for the spread of the disease. The risks of livestock diseases, including those associated with intensifying systems, will need to be addressed through developments in disease surveillance and early warning systems.Christopher B. Barrett* Food security is a growing concern worldwide. More than 1 billion people are estimated to lack sufficient dietary energy availability, and at least twice that number suffer micronutrient deficiencies. Because indicators inform action, much current research focuses on improving food insecurity measurement. Yet estimated prevalence rates and patterns remain tenuous because measuring food security, an elusive concept, remains difficult.T he 2008 global food price crisis, which sparked riots in more than two dozen countries, rekindled political and scientific interest in food security. In their July 2009 joint statement, the G8 heads of state agreed \"to act with the scale and urgency needed to achieve sustainable global food security\" (1). To direct scarce resources to where they can do the greatest good, actions must be guided by reliable information as to who is food insecure, where, when, and why. This requires improved measurement of food insecurity and its causes and greater attention to key institutional and policy lessons learned.Among the various definitions currently in use, the prevailing definition, agreed upon at the 1996 World Food Summit, holds that food security represents \"a situation that exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life.\" This high standard encompasses more than just current nutritional status, capturing as well vulnerability to future disruptions in access to adequate and appropriate food (2,3).Food security is commonly conceptualized as resting on three pillars: availability, access, and utilization. These concepts are inherently hierarchical, with availability necessary but not sufficient to ensure access, which is, in turn, necessary but not sufficient for effective utilization (4). For most of human history, lives were short and unhealthy due in large measure to insufficient macronutrient (carbohydrate, fat, and protein) intake. Beginning in the 18th century, however, a succession of countries broke free of the nutritional poverty trap (5,6), thanks largely to increased food availability made possible by advances in agricultural production; hence, the common association of food security with supply-side indicators, typically measured in daily calories per person.Adequate availability is necessary, but does not ensure universal access to \"sufficient, safe and nutritious food.\" Access is most closely related to social science concepts of individual or household well-being: What is the range of food choices open to the person(s), given their income, prevailing prices, and formal or informal safety net arrangements through which they can access food? As Nobel Laureate Amartya Sen wrote, \"starvation is the characteristic of some people not having enough food to eat. It is not the characteristic of there being not enough food to eat. While the latter can be a cause of the former, it is but one of many possible causes\" (7). Access reflects the demand side of food security, as manifest in uneven inter-and intrahousehold food distribution and in the sociocultural limits on what foods are consistent with prevailing tastes and values within a community. Access also accentuates problems in responding to adverse shocks such as unemployment spells, price spikes, or the loss of livelihood-producing assets. Through the access lens, food security's close relationship to poverty and to social, economic, and political disenfranchisement comes into clearer focus. But because access is an inherently multidimensional concept, measurement becomes more difficult than with availability (4).Utilization reflects concerns about whether individuals and households make good use of the food to which they have access. Do they consume nutritionally essential foods they can afford, or do they choose a nutritionally inferior diet? Are the foods safe and properly prepared, under sanitary conditions, so as to deliver their full nutritional value? Is their health such that they absorb and metabolize essential nutrients? Utilization concerns foster greater attention to dietary quality, especially micronutrient deficiencies associated with inadequate intake of essential minerals and vitamins. *To whom correspondence should be addressed. E-mail: [email protected]","tokenCount":"3114","images":["1473308626_1_1.png","1473308626_2_1.png","1473308626_3_1.png","1473308626_3_2.png","1473308626_3_3.png","1473308626_3_4.png","1473308626_3_5.png","1473308626_3_6.png","1473308626_3_7.png","1473308626_3_8.png","1473308626_4_1.png","1473308626_4_2.png"],"tables":["1473308626_1_1.json","1473308626_2_1.json","1473308626_3_1.json","1473308626_4_1.json","1473308626_5_1.json"]}

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+{"metadata":{"gardian_id":"bb4f111ab69c48204960a7adc88c74e2","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/33b4d642-34af-4e00-8356-b07314c8f546/retrieve","description":"Justifications of foreign aid may be classified into two basic categories. The first claims that aid is a form of progressive international taxation in which income is redistributed from rich to poor countries in much the same way as among income classes within nations. Recipients of aid preferthis justification, but it has not been broadly accepted in donor countries, and in recent years may have lost ground. Public opinion surveys in countries belonging to the Organization for Economic Cooperation and Development (OECD) indicate that while 79 percent of those interviewed in the United States approved of emergency aid, only 49 percent supported development assistance (OECD 1984a). The second justification is that aid is needed to correct international market failures.","id":"-392315461"},"keywords":[],"sieverID":"a79ef396-98a3-4880-9d95-f7e1fd59015a","pagecount":"22","content":"Justifications of foreign aid may be classified into two basic categories. The first claims that aid is a form of progressive international taxation in which income is redistributed from rich to poor countries in much the same way as among income classes within nations. Recipients of aid preferthis justification, but it has not been broadly accepted in donor countries, and in recent years may have lost ground. Public opinion surveys in countries belonging to the Organization for Economic Cooperation and Development (OECD) indicate that while 79 percent of those interviewed in the United States approved of emergency aid, only 49 percent supported development assistance (OECD 1984a). The second justification is that aid is needed to correct international market failures.With respect to the latter, it is recognized that private capital may not flow easily to developing countries due to perceptions of political risks and that rates of return in these countries are likely to be lower due to their lack of the physical, human, and institutional infrastructure necessary for capital to be highly productive. Studies of the pulp and paper industry, for instance, show that even with the necessary natural resource base, direct foreign investments in countries such as Indonesia are few compared to those in the United States and Canada due to inadequate electrical power, transport, and communications systems. Also, returns to investors may be delayed, or they may be realized in the form of locally traded goods and services that cannot be easily converted into foreign exchange. Transfer of technology, infrastructure, and institutional and manpower capacity for further development does not occur effectively via the market. Their benefits are broadly distributed, leading to a free-rider problem; and the resources invested in them are not easily recoverable. These problems are especially serious in the agricultural sector. International capital 1. ODA is defined as those resource flows to developing countries by multilateral institutions and government aid agencies with a grant element of at least 25 percent and with the objective of promoting economic development.transfers through the market are therefore likely to be particularly difficult to achieve for the development of agriculture, and governments must play an active role to create the necessary infrastructural, institutional, and manpower base to foster the growth of private capital formation through the market in due course.African governments are, however, unable to respond adequately to these problems chiefly because of severe constraints on trained manpower and administration. At independence, many countries had only a few national university graduates and no institutions of higher learning. In 1958, less than 10 thousand African students were attending universities at home or abroad (1 per 20 thousand population), some 6,500 of whom were from Ghana and Nigeria. In Kenya and Tanzania, fewer than 20 percent of high-level, civil service posts were held by Africans in the early 1960s (World Bank 198 la). Most positions in government were held by Europeans. The trade sector and the small, modern industrial sector were dominated by ethnic minorities from Asia and the Middle East. Local enterprise, administration, and trained manpower were more developed in West than in East and southern Africa. Until their trained manpower and institutional capacity increase to perform critical development functions effectively, African gevernments need assistance to formulate and implement development strategies.Foreign assistance can play an important role in developing the capacity of African governments to fulfill their legitimate developmental role through what Krueger (1984) calls the \"knowledge transfer.\" Such a transfer can improve the productivity of not only capital from abroad but also capital mobilized domestically. Donors can help governments evaluate the relative costs and benefits of alternative development strategies. They can help governments deal with problems of instability associated with agriculture arising from substantial year-toyear weather-induced fluctuations in production, externally prompted instability in international primary commodity markets, and the consequent uncertainty in export earnings and revenues. In addition, donors can assist in developing appropriate institutions for agricultural research, dissemination of information, and other structural problems of African agriculture.Donors have indeed been heavily involved in African economic development. Following the 1973-74 drought, commitments for official development assistance (ODA) to Africa increased from $3.9 billion to a peak of $10.9 billion in 1980 and then declined slightly to $10.3 billion in 1982. 1 Net ODA disburse-ments to sub-Saharan Africa (representing about 80 percent of total ODA commitments) more than doubled in current prices, from $3.7 billion in 1975 to $7.7 billion in 1982. Overall per capita ODA disbursements in Africa were $21 in 1982, compared to $5 in South Asia, $2 in Far East Asia and Pacific, and $9 in Latin America (OECD 1984b). The higher ODA levels in Africa mean that donors play a greater role in resource allocation and in the policies of African countries, albeit inadvertently. I will illustrate later how this large role has reinforced the distortionary resource allocation and poor policies of governments.According to the Development Assistance Committee (DAC), about 25 agencies give ODA to Africa. A plethora of private voluntary agencies are involved in relief aid and have been reorienting their assistance toward long-term development. In addition, the World Bank, the African Development Bank (Afos), and some OPEC-sponsored agencies provide concessionary loans to African countries.Substantial competition prevailed among these donors in the 1970s. Not only was their number large but, despite aid tying of various kinds, donor resources were frequently larger than the investment opportunities provided by well-conceived projects accompanied by the necessary technological and implementing capacity to insure an adequate rate of return. Information available to donors about these opportunities is generally poor in Africa. As a rule, recipients have been careful to keep donors uninformed about the activities of other donors.2 This is partly because of their fear of the donors \"ganging up,\" but also because dealing with donors individually has allowed recipient countries to channel donor resources to meet their own priorities.I demonstrate later in this paper that the donors' project-by-project approach lacked the overall sectoral and macroeconomic view necessary to project selection. Donors generally supported the development objective of newly independent governments of increasing the presence of government in various spheres of economic activity. They did not much question the feasibility of achieving this objective within their limited institutional, financial, and technical resources. Indeed, donors frequently themselves encouraged the overcommitment and misallocation of resources through the projects they chose to finance. Of course, a substantial increase in economic services of governments occurred through donor assistance that would not have been possible otherwise. However, these projects not only diverted resources but especially policy atten-tion away from the highest priority issues of short-term economic management and long-term economic development.This chapter also demonstrates that changes in development fashions in the donor community added to the problem of poor expenditure choices, thereby reinforcing poor government policies. The recent African \"crisis\" has led the international development community to recognize and respond to some of these lessons from the 1970s. However, these lessons do not yet go far enough to increase the effectiveness of future aid to Africa.To support these contentions, I first consider the financial, technical, and food-related components of donor aid that are relatively easy to quantify. In addition, I also examine the nature of knowledge transfers that may have occurred through donors' participation in the selection, design, and implementation of projects, through policy advice, and through conditions attached to project and program assistance, including balance-of-payments support.This task is made difficult by a variety of factors. There are differences in behavior among donors and over time. For instance, while the World Bank has followed the practice of evaluating the recipients' macroeconomic, sector, and project-level policies in the context in which project lending is done, the EEC, USAID, French FAC and CCCE, the Scandinavian agencies, and the International Fund for Agricultural Development (IFAD) do not have the analytical capacity to evaluate macroeconomic or sector-level policies but provide project-level assistance alone. IFAD even depends on other donors to appraise the projects it finances.Other international agencies such as the United Nations Development Program (UNDP) and the Food and Agriculture Organization (FAO) provide policy advice and technical assistance but do not finance development projects. The International Labor Organization (ILO), for instance, has had a significant influence on development policies through its once much-celebrated employment missions. The Consultative Group on International Agricultural Research (CGIAR) has been active in the development of agricultural technology. In addition, individuals or consortia of \"private\" Western consultants, such as the professionals in the Institute of Development Studies at Sussex, England, have been advising various African governments. The diversity of the advice given by numerous donors causes much confusion in recipient countries, which do not have the capacity to evaluate the advice.While aid volumes increased sharply, per capita African food production declined in many African countries in the last decade, in contrast to increases achieved in most other parts of the developing world. The African continent also lost its share in most agricultural exports. Some of the poor African agricultural performance in the 1970s and early 1980s is attributed to adverse weather and unfavorable international terms of trade. Gestation lags in achieving benefits from donor-funded projects may also be long at early stages of development. Nevertheless, troubling questions arise about the nature of donor assistance, given the large aid volumes. In what form was aid given, and for what purpose? What does the experience of the recent past tell us about the priorities of governments and donors? For instance, what importance was attached by donors to ensuring a policy framework conducive to long-term growth and to developing the domestic capacity of recipients to ensure high returns to investment? In short, should the aid community share the blame for the poor African performance in the 1970s as it has shared the glory of the green revolution in Asia?The sources of Africa's production crisis are, of course, controversial. The World Bank in its first major report on sub-Saharan Africa places the primary blame on inappropriate domestic policies of African governments (World Bank 198 la). The governments in turn argue that the role of external shocks-in the form of droughts and adverse changes in international terms of trade-was underrated by the Bank. A number of Western advisers to African governments added inappropriate policy advice and poor selection of projects by donors to the list of external factors (IDS 1983;Bauer 1971;Lappe et al. 1980). Subsequent reports on Africa prepared in the World Bank give greater weight to the adverse role played by donors (World Bank 1983a, 1984a). However, the full implications of this past experience for future donor policies has yet to be explored.Evaluating aid impact is fraught with many difficulties. Generalizations are not easy, given the peculiarities of the 40-odd African countries and the large number of donors. \"Without aid\" situations are difficult to construct. Not only do the recipients' overall economic development policies need to be evaluated, but judgments need to be made as to what policy decisions, including marginal investments, took place as a result of aid. Due to fungibility, projects financed by donors are not necessarily those undertaken by recipients (Singer 1965). Time lags in realizing benefits also compound the problem of aid evaluation. Even the acceptance of donor advice by governments may involve considerable lags, making it difficult to attribute changes in government policies to donors rather than to other factors, such as external shocks. Wholehearted acceptance of advice by governments may not mean effective implementation, and a variety of factors may offset the impact of good advice, properly implemented.Despite these difficulties, systematic evaluation of aid is long overdue. Donors have accorded Africa the highest priority, and there is a growing shift in their aid from project to conditional program lending. Much of the existing literature, on the other hand, is either highly aggregative and general (Grinols and Bhagwati 1976;Marsden and Roe 1983) or project specific (de Wilde 1967;Lele 1975). There has been little examination of the type of advice donors have given on macroeconomic and sector policies or the extent to which their own assessment of the recipient countries' constraints has been either accurate or reflected in the content of their assistance. Thus the extent to which they have helped to increase domestic capacity to address problems of economic development has not been systematically researched.In this section I first examine the pattern of resource allocation of donor-funded projects in Africa and then explore their likely distortionary effects. The World Bank's activities are given particular attention, partly because its share of total multilateral aid grew substantially. Also, more information was available on the Bank's activities at the time of writing.The changing pattern of World Bank allocations to Africa are noticeable from the 1960s to the 1970s. During the 1960s, support for the transportation sector accounted for half of total Bank loans and credits to Africa (World Bank 1970-84). In the 1970s, the share of transportation declined to 24.7 percent and in the early 1980s to 15.6 percent, although absolute amounts increased in nominal terms. Bank lending to industry became important around the mid-1970s, reaching a high of 17 percent of the total in 1976 but falling to a low of 3 percent in 1981. However, it recovered to about 9 percent in 1982. Lending to the crucial educational sector increased absolutely, in nominal terms, but decreased from 10.6 percent of the total in the 1960s, to 7.5 percent during the 1970s and 4.1 percent in 1980-84. Given the trained manpower constraint, it is important to note that support for education has never acquired the glamour that rural development enjoyed in the 1970s.The relative shift in the Bank's assistance from upstream activities, such as investment in human capital and infrastructure, to the more downstream efforts to immediately influence development outcomes is evident from allocations to agricultural and rural development projects. These projects occupied center stage in the 1970s; their share of Bank funds in that decade rose to 31.6 percent, from 11.3 percent in the 1960s. However, it dropped to 26.1 percent in the early 1980s, as Africa's growing macroeconomic problems exacerbated the difficulties of project implementation. Structural adjustment efforts made up some of the difference. These structural adjustment loans (SALs) accounted for 15 percent of total lending to Africa in 1980-84. Given the crucial role of agriculture, many SALs focused on the agricultural sector.Donor assistance of course cannot be evaluated in isolation from overall domestic policies and expenditures of recipient governments. An examination of the latter is clearly beyond the scope of this paper. However, given that donors finance a substantial portion of national expenditures in Africa, the projects they finance can provide a suggestion as to the types of expenditures they have considered appropriate to support. It is evident from the preceding data on World Bank activities in the 1960s that its assistance had been confined largely to the provision of public goods. In the 1970s, however, it shifted to supporting a much broader range of public sector services.The balance of resources allocated to national and international agricultural research are similarly of interest in Africa. The experience of the agriculturally more advanced Asian countries suggests that international agricultural research institutions are best able to contribute to the development of appropriate location-specific agricultural technologies when national agricultural research systems are well established and have the capacity to \"borrow\" technologies.Expenditures by the four international agricultural research institutions of the CGIAR in Africa were $42 million in 1980 and $58 million in 1983. Other CGIAR institutions also operate in or concentrate on issues related to Africa. FAO'S data on the percentage distribution of ODA capital commitments to agriculture, however, indicate that during the 1974-83 period, of the total donor resources spent on agriculture and rural development in Africa, 3 percent were spent on the development of national research systems and training, compared to 5.4 percent in Asia (FAO 1984). The unit cost of employing research scientists in Africa, however, is three times that in Asia (Oram 1978). Thus, even though relatively low, the percentage of funds allocated to research in Africa must overstate the real value of these resources. It is noteworthy, however, that national governments of some of the African countries increased substantially their own expenditures on agricultural research (Oram 1981).The downstream nature of donor assistance in Africa is also evident from the resources allocated to crop production relative to those allocated to the development of the capacity of national governments to adopt crop production technologies. During 1974-79,21.6 percent of ODA in Africa went to support crop production efforts (meaning purchase of farm machinery, tractor hire services, and direct production on state and cooperative farms) compared to only 5.3 percent in Asia. When expenditures on administration and management are added, donor expenditures on crop production in Africa amount to far larger shares of total ODA. Many crop production efforts supported the tendency of African governments to increase government services for crop production. There was a decline in donor support to crop production efforts from 21.6 percent in 1974-79 to 11.2 percent in 1980-83, which probably reflects macroeconomic difficulties that created problems in project implementation.The complexity of donor projects was also greater in Africa relative to Asia-paradoxically, since Africa has poorer capacity to implement complex projects. In 1980-81, multisectoral \"rural development projects\" received up to 24 percent of capital assistance to agriculture in Africa, compared to only 13.8 percent in Asia. The multisectoralness of these projects has added enormously to the difficulty of their administration, diverting attention from the most basic agricultural development problems, such as the inadequacy of profitable technical packages or the unavailability of the right inputs.Poor choice of projects by donors in Africa is also suggested by other data. For instance, despite the land-surplus nature of African agriculture, 41.5 percent of the African Development Fund's (ADF) assistance in 1974-75, went to land intensification (in the form of \"land and water development\"), compared to 26.7 percent of assistance by the Inter-American Development Bank (IADB) in Latin America and 3.1 percent of assistance by the Asian Development Bank (ADB) in Asia, the most densely populated region. In 1980-81, aid to land and water development by the ADB rose to 24.0 percent of its lending, while IADB'S aid dropped to 6.3 percent. During the entire period 1974-83, however, land and water development in Africa was 8.8 percent of total assistance, compared to 26.8 percent in Asia, reflecting the relatively small allocations to land intensification by donors such as the World Bank and the European Economic Community.The much larger share of the AfDB's resources going to land and water development in Africa appears to be a response to the African government's policy of aiming for food self-sufficiency through investment in irrigation projects. Such projects are frequently large scale and capital intensive. Evidence suggests not only that returns to large-scale irrigation have been low or negative in Africa (Pearson 1981) but that such projects also lead many African countries to tie up a large portion of their inadequate budgetary resources for agriculture in costly irrigation schemes. According to various World Bank reports, \"federal allocations [for irrigation in Nigeria] exceed those to any other [agricultural] category,\" and while \"virtually all other allocations were being cut ... irrigation [commitments] were increased from N562 million in 1982 to N755 in 1983.\" 3 Kenya's 25-year irrigation program is forecast to cost $3.4 billion; \"the Bura Irrigation Project alone currently accounts for over 25 percent of [the Ministry of Agriculture's] development budget.\" Mauritania, Mali, and Senegal are engaged \"in a massive effort to dam the Senegal River and bring into production some 300-400 ha of new irrigated agriculture, at a cost of $3,4 billion.\" Once again, in their downstream approach to assistance, donors have placed relatively little emphasis on land surveys and the development of small-scale low-cost irrigation in Africa, in contrast to the resources allocated to large scale irrigation.Agroprocessing is frequently carried out in the public or the parapublic sector in Africa and tends to be more capital intensive relative to Asia, where it is usually small scale and more likely to be carried out in the private sector. It is noteworthy that in 1980-81, as much as 48 percent of the Arab Bank for Economic Development's (ABADEA) lending to Africa went to agroprocessing. In that same year in Asia, no ADB funding was allocated to agroprocessing. Overall, agroindustrial projects constituted 8.7 percent of the total ODA allocations in Africa, compared to 2 percent in Asia.The effectiveness with which technical assistance is used is of considerable importance, given its large share of aid in Africa. Of particular interest are the resources allocated to technical assistance to fill specific gaps in trained manpower as distinct from those devoted to the development of existing human capital. A recent World Bank study suggests that in the early 1980s some 80 thousand resident nonnationals funded by donors were providing technical assistance to public and parastatal institutions in sub-Saharan African countries, at an estimated annual cost of around $4 billion, or $10 per capita (World Bank 1983a).The role of technical assistance in total aid programing, however, varies greatly by donors and recipients. The technical assistance component of the World Bank's loans and credits to agriculture rose from about 4 percent of loans and credits in 1974-76 to over 8 percent in 1980-81. Self-standing technical assistance projects funded by the World Bank for improving economic and sector management also increased-from 0.09 percent in the 1960s, to 0.6 percent during the 1970s, to 2.8 percent in the early 1980s. FAO'S technical assistance seems to have remained about the same the world over. 4 Much of the technical assistance provided by the World Bank and FAO are to fill in specific gaps in trained manpower. Bilateral donors provide some of their technical assistance explicitly for education and training. France is the largest provider of such technical assistance, notable in Ivory Coast and Cameroon. Over a quarter of total French ODA was allocated to education in 1980-82 (OECD 1984b). The annual amount of $4 billion noted above does not include expertise through voluntary agencies such as the Peace Corps.There seems to be little correlation between the rhetorical emphasis placed by African governments on self-reliance in domestic management and the number of technical assistance personnel they have accepted. Tanzanian rhetoric, for instance, has been highly pro-self-reliance, while Malawi's president has been controversial in Africa due to his tardiness in Africanization. Kenya's announced policy has perhaps been more neutral. The World Bank study on technical assistance indicates, however, that the number of technical assistance personnel in the public and parastatal sector per unit of agricultural GDP in 4. No regional breakdown of FAO'S technical assistance was available at the time of writing.Tanzania, Malawi, and Kenya were roughly the same. Equal acceptance of technical assistance does not mean, however, that it is used equally effectively among countries, and here Tanzania's rhetoric may convey more clearly its official attitude to the use of technical assistance to fill gaps. The question of human capital development and self-reliance in Africa is addressed later.Food aid can be a means to alleviate short-term food shortages but can also have a disincentive effect on domestic production if given in large quantities, continued indefinitely, and unaccompanied by an effort to institute effective longrun domestic policies. The volume of cereal imports by low-income African countries soared from 789 thousand metric tons in 1961-63 to 3,677 thousand in 1980-82 (Gusten 1984)). Data from the International Food Policy Research Institute (IFPRI) show that imports of cereals by sub-Saharan countries increased from an average of 1.9 million tons in 1961-63, to3.1 million in 1969-71, and 4.6 million in 1976-78 (Huddleston 1984;;FAO 1983). African food imports have been increasing even more sharply in the early 1980s. Food aid's share in total cereal imports by sub-Saharan Africa increased from 8 percent in 1961-63, to 18 percent in 1976-78, and 23 percent in 1981. This is in contrast to Asia, where the share of food aid in total cereal imports decreased from a high of 33 percent in 1961-63 to only 7 percent in 1981. Per capita food aid for sub-Saharan Africa also increased from 0.62 kilograms in 1961-63 to 6.02 kilograms in 1981; it decreased for all other regions. As high as 87 percent of total food aid to sub-Saharan Africa in 1976-78 was in the form of grants. However, commercial food imports still constituted three-quarters of total food imports during the 1980-84 period in Africa.I have presented evidence above to illustrate that in a variety of ways the choice of donor projects in the 1970s increased the demand for scarce human, institutional, and technological resources without helping to increase their supply commensurately. Further, donor activities encouraged undue growth of the public sector.I will demonstrate below that aid was also more susceptible to changing trends in development thinking in the international donor community than was the case in Asia. For instance, emphasis by donors shifted in the 1970s from export to food crop production, from autonomous to government administered projects, from discrete functional projects to multisectoral programs, and from established institutions to creation of parastatal marketing and agroprocessing agencies. Some of these shifts, such as increased support of parastatal agro-processing and marketing institutions, reflect policy preferences of African governments. Others, such as the emphasis on complex, integrated, rural development projects, reflect changing trends in the donor community. To the extent that they involved inappropriate \"solutions\" to the problems confronted, donor-funded projects helped to make the initial problems worse. In contrast to Krueger's concept of aid based on achieving knowledge transfer, the initial low knowledge base of the African governments and their poor capacity to screen knowledge offered to them by donors made them more vulnerable than their counterparts in Asia to changing fashions in the donor community. These arguments are supported by the examples of the following four types of projects: food crop, national, multisectoral, and marketing-agroprocessing.The first important shift in donor attention, rhetoric, and perhaps funding in the 1970s was from export to food crop production in support of the African resolve to achieve food security. Although precise data are not readily available, research on aid currently under way makes such a shift in relative emphasis readily evident. And yet these same food self-sufficiency policies of governments have been under attack in the 1980s by the donor community.The second shift was from the earlier independent, discrete projects administered by relatively autonomous project entities to those operated by the main line of government ministries and departments. The shift was more noticeable in East Africa and southern Africa than in West Africa. In West Africa, not only did autonomous projects continue to be operated, but they included a substantial number of external technical assistance personnel in managerial positions.The shift to government administration for project implementation was the result of learning by doing. The experience gained from earlier projects had suggested that independent project entities cannot avoid indefinitely the problems of trained manpower and recurrent funding faced by government administration. On the contrary, they posed difficulties for their integration into the mainstream of administration. A consensus therefore emerged in donor circles that externally assisted projects should address more directly the basic government constraints of funding, staffing, and procedures (Lele 1975). Indeed, donors' initial investments in these discrete projects were frequently the springboard for their establishing major, national, rural development programs to address these constraints. And yet national projects have frequently tended to contribute to the overextension of government's limited planning and implementing capacity in the rural sector, rather than to help establish the necessary capacity.Donor attention also shifted from earlier functional projects, such as national agricultural credit or extension projects, to multisectoral area development projects, involving promotion of agricultural production as well as social services. Such a direct \"assault on poverty\" (World Bank 1975, 1980c, 1982), through a combination of directly productive services and social services, was in response to the growing disenchantment with the trickle down approach wrongly associated with the green revolution. Concerns about ensuring basic needs (Griffin 1972;Frankel 1971) articulated strongly in the early 1970s must also have played a part. Program expansion to low-income producers and regions became popular with African governments. 5  Health and rural water supply are, of course, scarce in Africa, and their provision can make an important contribution to social welfare and long-run rural development. However, administratively complex multisectoral projects have not been the most cost-effective means of providing rural social services. Many projects lead to expansion of government staff in the social sectors without the necessary operating and technical support to make them effective. In Tanzania, for instance, less than half of the village water systems provided through rural development projects are functional (World Bank 1983b).Parastatal marketing and agroprocessing entities proliferated throughout the 1970s. Revenue generation has been a well-established justification of marketing boards for export crops (Helleiner 1964), but parastatal marketing also has been extended to food crops, especially in eastern and southern Africa.Extensive donor support for parastatals during the 1970s enabled them to expand on a scale that otherwise would not have been possible. Donors supplied funds to establish agroprocessing parastatals, for technical support to improve their efficiency, and for subsequent reorganization. \"Development\" parastatals, designed to provide credit and extension, proliferated in Senegal and Cameroon. The concept of such parastatals was either inherited from the colonial administrations or received support from the technical assistance provided by donors (Waterbury 1983). This does not mean, however, that it did not have a strong appeal among African governments for reasons of ideology and political control.The project-by-project approach to assistance followed by donors led them to focus on improving individual parastatal entities. Investment analysis, by giving the true economic cost of resources, would have allowed donors instead to question the macroeconomic feasibility of improving a large number of such individual parastatals, given the trained manpower and other resources needed for their management. Expansion of parastatal activities, encouraged by donors, was frequently associated with overvalued exchange rates, uniform producer pricing, growth of parastatal employment, and capital-intensive agroprocessing techniques. These problems of poor policies were sometimes recognized at the technical level in donor agencies but did not receive the attention of the donor agencies' higher management or of governments during the period of rapid growth in aid resources. Intense donor competition for projects and generally favorable donor assessment of the recipient countries' macroeconomic management contributed to this problem. Donors did not begin to address the basic consequences of the growth of parastatals until macroeconomic difficulties in the late 1970s became apparent.We now turn to the question of the impact of the donor-funded projects. Among all the donors, the World Bank is by far the most important source of systematic impact evaluations of \"completed\" projects. Its Operations Evaluation Department audits all completed projects and occasionally prepares sector-specific audit reports on the lessons learned. The Bank's experience suggests that projects designed to be implemented in four to five years typically take six to seven to complete. This is not surprising in view of the limited implementing capacity, nor is it confined only to Africa. Since evaluations take a year or so, there is a lag of at least five to seven years before lessons learned from given programs are incorporated into later projects. The experience gained from completed projects thus does not necessarily reflect the current state of knowledge. However, it does provide useful insights.Comparison of performance of African projects with those in Asia can also provide additional insights. According to the Bank's evaluations, projects completed by the late 1970s and early 1980s in Africa have had significantly lower economic rates of return-around 15 percent in 1979-83-compared to South Asia-about 30 percent (World Bank 1984a). Many World Bank projects in Africa completed in the recessionary 1979-82 period had economic rates of return of far less than 10 percent, and several had close to zero or negative rates. Asian projects have not experienced a similar decline in rates over time. Lower rates for Africa are perhaps to be expected, given the earlier stage of Africa's institutional and technological development and consequently poorer and frequently deteriorating institutional and physical infrastructure. In addition, much of the Bank's agricultural lending in Africa has been for rainfed agriculture, while in South Asia it was mainly for irrigated agriculture. Inadequate support of national agricultural research systems compound the technical problems in projects based on rainfed agriculture. In South Asia, not only did water control ease the development of new technologies, but the bilateral U.S. as-sistance that preceded World Bank projects had already played an important role in the development of technical packages (India, Government of, 1976). The sequencing of agricultural services has thus been more nearly correct in Asia.Donor involvement in the development of appropriate agricultural policies and sequencing of public investments in activities seems, generally, to have been of higher quality in Asia. Research currently under way in the World Bank indicates that the breadth and the depth of the agricultural policy issues discussed among donor experts and their national policy counterparts in the 1950s and 1960s in India showed a far more substantial awareness of the limitations of human and institutional capital on the part of the government and the donors. Indeed, the Indian government sought to channel aid directly to developing these dimensions. Donors responded by providing some of the most qualified experts in the field. India's size and importance, of course, helped make this possible. Nevertheless, the lack of trained manpower and institutional capacity, while judged to be a constraint by experts, has not received adequate attention in aid programs in Africa either by governments or donors. It is also important to note that knowledge of effective agricultural development policies was transferred from Japan and Taiwan, which were relatively more advanced, to other developing countries in South Asia in the 1960s and 1970s. Such transfers have not occurred, in turn, from Asia to the developing countries of Africa.Evidence on the sources of the realized rates of return in Asia and Africa suggests that donor interventions have not even addressed the most important constraints to agricultural development in Africa, as they did in Asia. For instance, Asian projects were aimed at intensification strategies, which raise, productivity of land-the most scarce resource in Asian agriculture. However, in Africa, where land is frequently in excess supply and labor is the binding constraint, yield-increasing technologies should not always be the major goal of projects. And yet most donor-funded projects in Africa have not addressed the problem of low labor productivity. This may explain the lower-thanexpected yields, which contributed to the relatively low rates of return from African projects completed in the late 1970s. Indeed, even labor scarcity frequently has not been recognized as a constraint, even though time and again this has been identified as the most basic technological problem in Africa since the early 1960s.Many agroprocessing entities in which donors invested suffer from underutilized capacity because of export crop production shortages. Since governments have been criticized for export crop production failure, the policies of donors toward African export crop production need further investigation.Policy varies greatly among donors. The World Bank supported the production of export crops such as cotton, groundnuts, and palm oil, which have a relatively elastic international demand. For beverages such as tea and coffee, for which projected world demand is limited, the Bank's policy was to support only the \"rehabilitation\" of existing production, except in low-income countries with few economically viable alternatives. However, under the Foreign Assistance Appropriations Act of 1981, the U.S. representative on the Bank's board of directors was instructed \"to oppose any assistance (by the Bank) ... for the production of any commodity for export, if it is in surplus on world markets and if the assistance will cause substantial injury to United States producers of the same, similar or competing commodity.\"USAID is prohibited from providing any loans that would create or increase competition with U.S. producers of the same or competing goods, unless agreement can be reached on limiting exports into U.S. markets to less than 20 percent of the enterprise's annual output. USAID has also reduced support for export crop production because of its policy that loans were not to be made to developing countries for the purpose of increasing production of nonfood crops \"where world surpluses exist and are expected to continue\" (USAID 1984). The 1973-74 drought was partly responsible for the USAID staff's concern about the need to foster food security in Africa. However, the need to appease U.S. producer lobbies must also have played a part in the adoption of this policy.The International Fund for Agricultural Development (IFAD) was established in 1977 to address the issue of domestic food production and thus does not support export crop production. The Consultative Group on International Agricultural Research (CGIAR), a major international source of biotechnology, similarly has focused its research exclusively on food crops, such as wheat and rice (Oram and Paulino 1978). This is because establishment of the CGIAR in 1971 was prompted by concern about world hunger. Also, breakthroughs in agricultural research through international \"public\" efforts have been centered on food crops and primarily on wheat and rice. FAO and much of the donorfinanced social science research on African agriculture has been directed almost exclusively to food crops. Some bilateral European agencies and the EEC have supported export crop production under the Lome Agreement.One consequence of the emphasis on food production in the 1970s has been that relatively little is known about Africa's ability to compete effectively in the production of export crops in which it has traditionally had a comparative advantage. Technological advances in production of some of these crops have been rapid in other developing regions. Africa's competitors have also pursued aggressive export policies in international markets, which are frequently less than competitive. In the meantime, the relative profitability of food and export crops has changed radically at the farm level in many African countries due to growing domestic food shortages.The rapid shift in Africa's factor proportions caused by population growth and changing balances between domestic food supply and demand has led to substantial diversity among regions and even within individual countries with regard to comparative advantage. Detailed economic research is urgently needed in different parts of Africa on this issue.Food crops not only occupied a special place in donor assistance strategies but also in the agricultural policies of African governments. While African governments attach immense importance to addressing the problem of domestic food supply stability for domestic political and social reasons, there are great differences of opinion as to the means of achieving it among donors. Many European donors have strongly endorsed food security concerns and supported domestic stocks, whereas the United States, the World Bank, and others have been more concerned about the effects on efficiency of stabilization policy and have encouraged greater reliance on trade. The concept of food security is, however, ill defined by governments. They frequently use the term to mean food self-sufficiency. The growing share of food imports in domestic consumption makes food self-sufficiency appealing on sociopolitical grounds. Operation Feed the Nation in Nigeria in the mid-1970s and several other such \"operations\" were based on on inadequate assessment of the factors constraining food production-the policies, time, and institutional capacity required for effective implementation and the cost of alternative strategies.Of late, donors have placed particular emphasis on raising producer prices of food crops as a means of stimulating production, and many African governments have already taken substantial measures to raise not just producer prices but also food product prices for consumers. The question, however, remains as to the relative role of official pricing policy reform vis-a-vis other aspects of agricultural policy, especially measures to improve the marketing environment. Furthermore, questions arise as to the types of changes in pricing policies such reform should entail. Whether to increase producer prices for food crops relative to export crops in order to improve the relative incentives for food crop production is such a question. Relative price shifts help to bring about shifts in production in favor of specific food and export crops. However, the aggregate agricultural production elasticity is believed to be between only 0.1 and 0.2.Increasingly, incentives for food production are derived in the short run through the informal market, in which prices are determined by the weather and the level of carryover stocks, rather than the level of official prices. Donors have generally not shown much direct concern about interyear price and supply instability caused by this situation. They have recently begun to advocate that many East African governments that exercise monopoly control of food crop marketing should abandon this policy and become buyers and sellers of last resort. Experience elsewhere in the developing world indicates that this is the most appropriate policy. Guaranteeing minimum prices not only may reduce producer risks but also may provide consumers with some protection through supply stabilization. However, if governments are to be given practical guidance in liberalizing food markets, empirical analysis is needed on a country-bycountry basis to determine appropriate mechanisms for establishing (1) ranges of producer and consumer prices for traded and nontraded crops; (2) the size, location, and content of operating food stocks; (3) the manner in which governments should go about liberalizing; and (4) the role public institutions should play in purchasing, stocking, and selling grain. Little such analysis now exists.The long-run solution to the marketing issue is to rely on the private sector for direct marketing functions and to increase government investment in transportation, communications, and information networks. These are precisely the public goods that would improve market performance. However, donors have taken relatively little interest in improving the functioning of commodity markets. For instance, few answers are available to questions such as; Do market failures occur? And if so, what are their sources? How free is entry? Are ethnic minorities in a position to exercise oligopolistic control of markets due to their better access to information, working capital, transport, and so on? As a general rule, donors have not accorded high priority to funding empirical research in these areas. Few resources have been devoted to the development of markets or to understanding how well they work.An exception to the lack of research into these questions is the policy analysis carried out in Tanzania's Marketing Development Bureau (MDB), which was initially supported by UNDP, with technical assistance from FAO. The World Bank took over its funding by the late 1970s, when UNDP financing ran out. MDB'S national staff, with external assistance, have become an effective source of pricing policy analysis in Tanzania's otherwise desolate policymaking environment. MDB'S analysis shows that in some years and in some parts of the country unofficial market prices of maize since the early 1980s have been as high as five times the official prices. The government's share in marketed maize had fallen by 60 percent in the three years from 1978-79 to 1981-82 due to these price differences. Informal market prices have thus indicated the extent to which official producer prices would need to be raised to obtain greater control of supplies by the public sector. The Tanzanian example indicates that donor support for data collection and analysis is fundamental in improving policy. It is, however, by no means sufficient without the political will for reform.The low emphasis donors gave to agricultural technology development in the 1970s is both noteworthy and unfortunate. Improved agricultural technology is an important source of production incentives in its own right. Besides, the incentives brought about by technical change are, in principle, continuous, whereas price reform is generally a once-and-for-all phenomenon. However, donor-funded projects have by and large been predicated on the mistaken assumption that appropriate technology exists. Even when an attempt has been made to develop it, it has been largely through individual projects rather than by building national research systems. Frequently, the rapid expansion of donorfinanced production projects have increased demand for trained manpower, thereby robbing national agricultural research systems of their limited staff.The small size of African countries and the diversity of their crop production conditions complicate the problems of developing technological capacity in Africa. Regional cooperation is urgently needed among countries for establishment of clear priorities for individual national research institutions, especially given the limited scientific and management resources available. Donors have begun to address these questions only recently. Resources allocated to national research projects increased substantially in the 1980s. Nevertheless, their approach is still fragmented; they have established, on a piecemeal basis, a large number of separately funded research projects. There is little coordination between the research organizations regarding the type of training being given to the national staff and the scientific approach adopted toward research.As we have seen previously, food aid plays a significant welfare role. Although much of food aid goes to urban consumption, it helps to reduce the funneling of scarce rural food supplies into higher-income urban centers in periods of critical shortages. Food aid also helps governments avoid the disruptive effects of crop failures on food prices and thus on urban wages and prices. Whether the growing amounts of food aid has depressed producer prices is not a question to which there is a clear answer. This suggests that, in overall terms, food aid may not have reduced production incentives. However, in individual years, the timing of arrival of food aid is frequently not well synchronized with domestic shortages.Also the ease with which African governments have been able to mobilize food aid may have encouraged a tendency on the part of many African policymakers to postpone the critical long-term policy decisions needed to increase domestic production. Food aid is readily available, because the amount of food needed is small compared to the amount of food available from grants or through international trade. Furthermore, the competition among some OECD countries to dispose of food surpluses is intense and in all likelihood will increase in the future, as surpluses increase. In contrast, domestic food crop failures in India in the late 1950s and the early 1960s led to more direct pressure on the Indian, government from the donor community to improve domestic food production performance. The size of India's requirements may have prompted this response; the lack of surpluses in the United States-the major foodsurplus nation at that time-was also a factor.Food aid has reinforced the shift in consumer tastes toward wheat and rice, a shift that began with urbanization. This shift has increased the pressure on governments to invest in irrigation schemes in order to increase domestic production of these preferred grains.Properly administered, long-term guarantees of food aid have the potential to increase both domestic employment and food consumption and to improve policies supportive of long-term agricultural development. This would require tying food aid guarantees to a clear timetable for the improvement of agricultural policies in recipient countries.Guaranteeing long-term food aid that supports domestic policy reforms should not be economically difficult for donors, since Africa receives only a quarter of total food aid, and donors have been generating large domestic surpluses. However, food aid is not coordinated among donors. The emergence of the EEC and Japan as surplus regions has increased competition in food surplus disposal. Also, many African governments are not strongly oriented toward long-term reforms in formulating domestic agricultural policy. Their aid requests and other policy initiatives tend to be ad hoc, with short time horizons. Also, governments find political risks of reform too great without long-term guarantees of aid. Donor's domestic food aid programing, on the other hand, does not allow them to give long-term guarantees of supplies. USAID has recently considered three-year food aid agreements instead of traditional annual arrangements. But in many countries, even this is too short a time to allow the necessary overhaul of policies.Donor attention has shifted to input distribution only recently. The focus of a series of major loans by the World Bank, for instance, is on fertilizer distribution and pricing policies in Nigeria. However, only some aspects of the difficult input distribution problem can be addressed by a single donor. Disparate shortterm aid by individual donors can actually compound problems of procurement and distribution. For instance, assistance by bilateral donors may determine fertilizer imports for a particular year but may lead to substantial fluctuations in types and quantities of fertilizers donated over time. Unreliable supplies discourage the use of fertilizers and the cultivation of higher-yielding fertilizerintensive crops. To avoid this, donors must either revise their policy of tying aid to specific commodities or provide three-to five-year guarantees for supplying the most appropriate nutrients. On the other hand, African governments must formulate input policies to utilize aid more effectively. The absence of both conditions may explain the ever-changing patterns of donor support and government allocations for importation of inputs noted in our ongoing research on Africa. Trade and aid policies affecting input supply have, however, not received much attention in donor analysis and dialogue. Donor-financed agricultural development projects have tended to be predicated on the assumption either that trade restrictions will not obstruct fertilizer imports and supply, or that the governments will give special preference to the provision of inputs to donor-funded projects. While specific donor projects may indeed be exempted in this manner, for the agricultural sector as a whole, trade restrictions tend to pose a serious problem. Commercial agricultural imports are particularly hard hit in periods of foreign exchange scarcity, as they usually have low priority. Also, a noncompetitive domestic fertilizer industry, frequently established with export credits provided by donor countries, adds to the distortionary prices and unstable supplies of inputs.To the extent that changing patterns of donor assistance are a result of learning by doing, such changes are of course desirable. However, we have illustrated in this chapter that many shifts are a result of factors other than learning by doing and that they have major costs. They tend to deter donors from taking a long-term view of agricultural policy and from assisting recipients in establishing the necessary capacity. Policy-based lending has allowed donors to address some of these issues. But useful advice from donors is still lacking, compared with the magnitude of the task. Besides, it is not yet clear that their view is as long term as it needs to be.Despite large volumes of aid and changes in strategies, donor assistance has focused on direct-impact activities rather than on developing self-sustaining capacity. Only recently have donors begun to pay attention to establishing foreign exchange, capital and recurrent expenditure budgets, agricultural research institutions, systems for maintenance and operation of transportation networks, input delivery, output marketing, and collection and dissemination of agricultural data and information. The recent macroeconomic crisis has not only made donors more inclined to address these issues, but governments have been more willing to accept advice in return for urgently needed, rapiddisbursing loans. Whether this form of assistance will be effective in the long run will depend on the cohesiveness and quality of donors' advice. An equally important precondition is the governments' recognition that domestic policy reform and development of national capacity in a variety of areas is critical for improving long-term development prospects.Even if donors and recipients respond, however, improvement of manpower and institutional capability in Africa will not be a short-term task. First, there has been rapid growth of government and increasing Africanization. Government staffs have grown substantially because of national agricultural research and extension systems; agricultural price-formulating bodies; planning, budgeting, and monitoring systems in the ministries of agriculture; and large parastatal marketing services. The circumstances in which technical assistance staff are deployed is different from the colonial period in several respects. Instead of being in managerial positions, a great majority of technical assistance staff are used by governments either in \"advisory\" roles or to respond to the various initiatives of donors. Involvement of the technical assistance staff in policymaking, including in the provision of analytical support, is usually too weak to be effective. The demand by recipient governments for technical assistance as a resource that can \"process\" donor projects has increased, however, as the emphasis of donors has shifted to policy-based lending.Constraints on supply of qualified technical assistance personnel also condition the attitudes of recipients. A free market does not exist in technical assistance. Responsibility for provision of technical assistance is divided between the World Bank, which mainly finances it, and agencies such as UNDP and FAO, which principally administer it. Bilaterals prefer to use their own nationals, irrespective of their qualifications. The high turnover of technical assistance staff means not only that the recipient country does not gain from \"learning by doing\" but also that such personnel cannot compensate for the turnover of national staff. Indeed, the cost of learning by doing by technical assistance staff is frequently quite high.Emphasis in the donor community on training high-level African policymakers has increased in recent years. However, much of this growth is in short-term training. There is still relatively little focus on the long-term issues of education and training to develop a skilled work force in Africa. Given the high cost of technical assistance personnel-estimated to be well over $100 thousand per person annually in 1984-and the loss of their experience through high turnover, the returns to investment in education and training of Africans are bound to be competitive. However, there have been neither systematic attempts to measure the benefits of alternative forms of assistance for improving human capital nor emphasis on improved formal education and on-the-job training of nationals. Ironically, attention by donors to the long-term development of human capital in Africa may have declined, as the data on World Bank lending testify, since the macroeconomic difficulties have led to a crisis mentality in the donor community. Only a long-term approach by the donor community to the problem of human capital and institutional development on a priority basis is likely to lead to an effective solution.The development of primary data on many aspects of African agriculture is similarly crucial to improving policy. Despite substantial financial and technical assistance by donors, routine collection of reliable primary data is still conspicuously absent. The prospects for an effective implementation of reformed policy are poor indeed if information systems are not improved to ascertain consequences of those policies and to refine their content.It is evident that donor assistance has encouraged misallocation of capital. This has been encouraged by at least four factors. The first is the direct intellectual support provided by many Western advisors to independent African governments in developing plans of import substitution industrialization-for example Tanzania's Second Plan. Second, the lack of aid coordination has allowed small recipient countries to successfully play one donor against the other and to avoid donor review of their overall investment programs. Third, competition among donors for the limited number of viable projects in a period of rapid growth in real aid commitments has led donors to divide projects among themselves and subsequently to protect their \"own\" projects. Finally, protectionist tendencies in donor countries have been reflected in aid policies. This has reinforced African scepticism about the virtues of export orientation and expert advice.The prolonged economic crisis in Africa and the drying up of aid has led to some significant changes in donor behavior in recent years. Aid coordination has become more active. Medium-term targets for government expenditures are reviewed and discussed routinely before more aid is pledged by donors. Project aid has given way to increased sector and program aid in support of the maintenance and operations of current domestic capacity. And policy has come to the forefront of the government-donor dialogue. And yet, a sense of deja vu accompanies much of this change. The recent donor response is not yet based on an adequate analysis of the African agricultural \"problem.\" For instance, increased support of imported inputs is hardly an effective use of aid if the imports are used in projects that are inappropriate to begin with, such as capitalintensive agroprocessing plants or irrigation schemes. Second, a significant portion of national investments supported by many donors continues to be made in the public sector, without much regard for economic criteria either by the donors or the recipients. Emphasis on getting prices right can improve allocational decisions in the public sector only when nonprice factors do not overwhelm government and donor decisions.A major reorientation of foreign aid is thus essential to improve Africa's growth prospects. A systematic analysis of donor's policies and government policies is required if yet another swing in donor fashions is to be avoided.","tokenCount":"9360","images":["-392315461_1_1.png","-392315461_2_1.png","-392315461_3_1.png","-392315461_4_1.png","-392315461_5_1.png","-392315461_6_1.png","-392315461_7_1.png","-392315461_8_1.png","-392315461_9_1.png","-392315461_10_1.png","-392315461_11_1.png","-392315461_12_1.png","-392315461_13_1.png","-392315461_14_1.png","-392315461_15_1.png","-392315461_16_1.png","-392315461_17_1.png","-392315461_18_1.png","-392315461_19_1.png","-392315461_20_1.png","-392315461_21_1.png","-392315461_22_1.png"],"tables":["-392315461_1_1.json","-392315461_2_1.json","-392315461_3_1.json","-392315461_4_1.json","-392315461_5_1.json","-392315461_6_1.json","-392315461_7_1.json","-392315461_8_1.json","-392315461_9_1.json","-392315461_10_1.json","-392315461_11_1.json","-392315461_12_1.json","-392315461_13_1.json","-392315461_14_1.json","-392315461_15_1.json","-392315461_16_1.json","-392315461_17_1.json","-392315461_18_1.json","-392315461_19_1.json","-392315461_20_1.json","-392315461_21_1.json","-392315461_22_1.json"]}

data/part_2/0272451390.json

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+{"metadata":{"gardian_id":"02bc39d87dc998299fdc051cb0232ef3","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/0edecfec-8502-4878-9a9f-f79eddb8ff02/retrieve","description":"Shalini Roy POLICY SEMINAR Virtual Event - COVID-19: Emerging problems and potential country-level responses APR 30, 2020 - 09:00 AM TO 10:15 AM EDT","id":"-1335029374"},"keywords":[],"sieverID":"d128fa4f-de40-4bdf-ab89-9315e0340c04","pagecount":"5","content":"why-gender-sensitive-socialprotection-critical-covid-19-response-low-and-middle-incomeWhat type of assistance should be and how?• Relax existing conditions for receiving assistance• Provide cash benefits (e-payments if possible)• Consider additional in-kind benefits if possible to safely provideWho should be targeted?• Provide geographically-targeted universal benefits• Consider women as the main recipients ","tokenCount":"42","images":["-1335029374_1_1.png","-1335029374_1_2.png","-1335029374_1_3.png","-1335029374_1_4.png","-1335029374_2_1.png","-1335029374_2_2.png","-1335029374_3_1.png","-1335029374_3_2.png","-1335029374_4_1.png","-1335029374_4_2.png","-1335029374_5_1.png","-1335029374_5_2.png"],"tables":["-1335029374_1_1.json","-1335029374_2_1.json","-1335029374_3_1.json","-1335029374_4_1.json","-1335029374_5_1.json"]}

data/part_2/0286150395.json

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+{"metadata":{"gardian_id":"4b19d3dcfb09d27129c5ba018803815d","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/32b9bb89-7c70-42e5-98f4-5f3bddcddac1/retrieve","description":"This profile provides an assessment of a country’s potential to report on national-level coverage1 for a set of 16 nutrition interventions and recommends key actions to strengthen coverage measurement. Intervention coverage data is essential for tracking implementation and impact of strategies and investments to reduce malnutrition.","id":"814912768"},"keywords":[],"sieverID":"219a740a-c5d8-4af8-bac8-49ceff370143","pagecount":"2","content":"Data on coverage collected? b (yes/no) Indicator reported in report? C (yes/no) Data source Year Availability of datasets Coverage indicator d (yes/no)• Continue to collect and report data on coverage of IFA supplementation during pregnancy, counseling for exclusive and continued breastfeeding, and vitamin A supplementation through populationbased surveys.• Ensure all available coverage data are reported. Data elements for two intervention coverage indicators (counseling for exclusive and continued breastfeeding; vitamin A supplementation) were collected in recent national surveys but not reported in survey reports.• Make the reports, registers and reporting forms, and datasets for administrative data publicly available, especially to allow reporting on coverage of interventions implemented at the national level.• Add indicators on coverage of one pregnancy intervention (nutrition counseling during pregnancy) and three child interventions (counseling for complementary feeding; support for exclusive and continued breastfeeding; and management for severe acute malnutrition) to future population-based surveys. • Consider including new data on interventions that are country priorities and implemented through the health system in the administrative data system.T his profile provides an assessment of a country's potential to report on national-level coverage 1 for a set of 16 nutrition interventions and recommends key actions to strengthen coverage measurement. Intervention coverage data is essential for tracking implementation and impact of strategies and investments to reduce malnutrition.We reviewed the availability of coverage data for 16 nutrition interventions grouped along the maternal, newborn, child, adolescent continuum of care. Interventions were prioritized by regional stakeholders. 2 The table summarizes which national data sources report coverage data by intervention. A detailed database is available for download here; a technical note describing the assessment methodology is available here.1 Coverage is defined as the proportion of individuals in need of a service or intervention who receive that service (Marsh et al. 2020). Coverage indicators are calculated by dividing the number of people receiving a defined intervention by the population eligible for or in need of the intervention. 2 Regional Nutrition Working Group in West Africa including various implementing and technical partners and donors, UNICEF, and DataDENT.","tokenCount":"339","images":["814912768_1_3.png"],"tables":["814912768_1_1.json","814912768_2_1.json"]}

data/part_2/0306666175.json

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+{"metadata":{"gardian_id":"f0238a516d22fbddda9ea785c5a4c043","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/1d3b350a-61d6-4b95-86fc-38d721ded8b7/retrieve","description":"Although India possesses 4.9 percent of the total average annual runoff in the rivers of the world, the per capita water availability from surface as, well as groundwater sources is assessed at 3,200 cubic meters Jm3) per year, compared with the availability of more than 17,500 m3 in the Soviet Union, 6,500 m3 in Japan, and 6,200 m3 in the United States. The population that India had at the beginning of the twentieth century is likely to have increased 50 percent by the end of the century. This would further reduce the per capita availability of water. Surface water would constitute about 80 percent of this; the remainder would be from groundwater.","id":"-936571641"},"keywords":[],"sieverID":"d631ec73-15cf-4808-a91e-0b81b86e6797","pagecount":"15","content":"Although India possesses 4.9 percent of the total average annual runoff in the rivers of the world, the per capita water availability from surface as, well as groundwater sources is assessed at 3,200 cubic meters Jm 3 ) per year, compared with the availability of more than 17,500 m 3 in the Soviet Union, 6,500 m 3 in Japan, and 6,200 m 3 in the United States. The population that India had at the beginning of the twentieth century is likely to have increased 50 percent by the end of the century. This would further reduce the per capita availability of water.Surface water would constitute about 80 percent of this; the remainder would be from groundwater.A broad-based assessment of the potential of surface water resources was attempted prior to the era of planning in India. The methodology adopted by different agencies varied from empirical approach to statistical analysis, depending on the data available. During 1902/03 the First Irrigation Commission estimated 144.3 million hectare meters (million ha m), based on runoff coefficient. Dr. A. N. Khosla, an experienced engineer, evolved a formula correlating runoff as a function of rainfall and temperature using studies of yields and rainfall data for Sutlej, Mahanadi, and other Indian river systems. According to his 1940 assessment, the average annual flow of all the river systems of India was about 167.3 million ha m. Based on observed runoff data, Dr. K. L. Rao, an eminent engineer and ex-union minister for irrigation and power, estimated the potential to be 164.5 million ha m in 1975. The Central Water and Power Commission, an official technical body of the Ministry of Water Resources, worked out the surface water potential of different regions during 1954/56. This estimate was based on the statistical analysis of available flow data on rivers and suitable rainfall runoff relationships. According to their estimate, the annual flow of all the river systems in India was 188.1 million ha m, which was revised to 181.1 million ha m in 1987 by the Central Water Commission. The assessment figure of surface water potential that is used for present planning is 180 million ha m. Table 4.1 gives surface water potential, as indicated by average annual flow in the river systems, as 180 million ha m. This figure is assessed on the basis of India's annual precipitation, which is estimated to be 400 million ha m, including 392 million ha m from rainfall and 8 million from snowfall. The country's surface water potential of 180 million ha m takes into account immediate runoff within India, some basin runoff outside India, regeneration of effluent streams, and seepage and leakage from irrigation canals.Figure 4.1 presents a flow chart of India's water resources.A broad study of India's groundwater potential in 1972 estimated 60 million ha m from annual replenishable resources of groundwater. This was based on an assumed percentage of groundwater recharge from rainfall, supplemented by assumed levels of surface water inflows from storage works, canals, and other contributing factors. The available data could not precisely estimate recharge coefficients in hard rock areas, which constitute about 80 percent of the terrain. This estimate therefore was uncertain.Even in alluvial areas, there was scope for more precise estimating.However, this was considered to be the first broad-based assessment, which would be reviewed and revised in the future to match the demands of groundwater development.Research schemes have been undertaken and encouraged to establish field values for groundwater recharge in different topographic and soil conditions. This research has provided some data at the micro-level to support macro-level planning.Since the above figures indicate the overall water resources potential of the country, an exercise was carried out to assess the usable surface water resources, taking into account physiographic, sociopolitical, environmental, legal, constitutional, financial, and technological limitations at a particular time. The assessment ofPlanning water resources development in any country requires first and foremost a precise assessment of its availability. The need to assess both surface water and groundwater potential is particularly critical because the investment costs of irrigation projects using scarce water resources are accelerating. Any error in the assessment would cause avoidable waste in the construction of canal networks, block huge investments, and ultimately result in recurring financial losses. Five year plan investments of Rs 150 billion from 1951 to 1985 for major and medium irrigation schemes were seriously criticized by economists trying to evaluate performance of this sector with the usual yardsticks of efficiency, economic return, or input-output ratio. Similarly, overestimating groundwater resources is likely to block institutional funds by encouraging high default rates on loans to farmers. The precise, systematic assessment of available surface water and groundwater resources is, therefore, considered essential for planning and developing Indian water resources.The first stage of systematic planning began in 1951 and has been periodically reviewed thereafter.In some important states like Uttar Pradesh, groundwater development has reached the level targeted earlier as ultimate potential, but considerable potential remains for further groundwater development.Revising earlier targets in this and similar cases is necessary before taking up further programs on a firm technical and financial footing.   None of these assessments, however, include the contribution of water flows from snow, ice, and glaciers. Specific details about their contribution will be finalized after completion of an ongoing pilot study of these factors. Based on the above figures from various agencies, the present figure of usable surface water is considered to be 70 million ha m.A scientific assessment of the groundwater potential of the country was not undertaken until a few years ago.Groundwater mapping and exploration were sporadic and limited to specific areas. The Geological Survey of India (GSI) was responsible for systematically investigating and mapping groundwater in the country as a whole.As a result of the increased emphasis on groundwater development in the past few years, extensive exploration work was done in the desert areas of Rajasthan, the foothills of the Himalayas, the coastal alluvial tracts of Orissa, Andhra Pradesh, and Tamil Nadu, and the basins of Narmada, Tapi, and Purnea.The Central Groundwater Board roughly assessed the annual usable groundwater resources to be 42 million ha m per year.Given the water requirements of various crops and prevailing irrigation practices, the ultimate irrigation potential of India is estimated to be 113 million ha; 58 million ha from major and medium irrigation projects and 55 million ha from minor schemes, which include 40 million ha from groundwater.The total surface and groundwater irrigation potential created by the end of the Sixth Five-Year Plan (1984/85) was 68 million ha: 30.5 million ha from major and medium irrigation schemes and 37.5 million ha from minor irrigation schemes, which include more than 80 percent groundwater schemes like tubewells, dugwells, and filter points.During the first two years of the Seventh Five-Year Plan, irrigation potential on the order of 4.4 million ha was added, bringing the total irrigation potential created by 1986/87 to 72.30 million ha.The target was 113 million ha, that is, full development of irrigation potential by the end of century, but India is now expected to achieve this level early in the next century.The growth of irrigated area in India from plan to plan is indicated in Table 4.2, which shows irrigation potential created and its use under major, medium, and minor irrigation schemes. A graphical representation in Figure 4.2 reveals the widening gap between potential created and actual use. One of the reasons for this wide gap is the higher estimates of potential adopted for certain projects.The actual performance of the reservoirs showed that the water resources potential had been overestimated. This meant that irrigation targets had to be revised.As investment in water resources development increased practically in geometrical progression from plan to plan, ancillary activities supporting systematic, precise assessment of surface and groundwater availability were expanded.A wide network of hydrological observations of river gauges, supported by an expanded hydrometeorological network of the Indian Meteorological Department and its manual and automatic rain gauge recorders, has made valuable data available for establishing better defined rainfall-runoff relationships and unit hydrographs. The advent of modern technology   It will be necessary to assess precisely the availability of surface water and groundwater by that time to meet the ultimate demand.Exploitation of groundwater in states like Uttar Pradesh and Gujarat has reached the level of ultimate groundwater potential according to past assessment, and the irrigation potential of existing and ongoing irrigation projects in Gujarat and Andhra Pradesh has reached the ultimate surface water potential.It is imperative to know the available water balance before taking up any more projects in these states. A reassessment exercise of both surface and groundwater is already on the agenda of these states and of the central government.The present figure The generalized partial differential equation of groundwater flow used in aquifer simulation is also a water balance equation representing all the above terms. Whereas in a real basin resource estimation, equation (4.1) can be used considering the system as a single control volume in order to avoid computational complexity, the simulation model can provide a solution of equation ( 4.1) at any point in space and time. In fact, almost all the versatile simulation models contain programmed schemes to compute mass balance, which accompanies the system's response information.Combining the terms R p , R c , Ri, I , I , and S_ provides an assessment of recharge over the time for which equation (4.1) is solved.Over a long period it provides normal recharge to the aquifer and indicates the quantum of groundwater that can be withdrawn on a continuing basis. This is referred to as the safe yield of the basin.The earlier assessment of 42 million ha m of usable groundwater is already being revised by various states and the Central Groundwater Board. An expert group was set up in which representatives of select states provided realistic input for all the above parameters in order to revise the groundwater potential of the country.The revised groundwater potential is now assessed at 45.66 million ha m, of which hardly 13 million are being used at present. This is, however, being discussed and examined by the state authorities. Since the ultimate groundwater potentials are worked out in terms of the area to be irrigated, a close and careful view has to be taken of the crop water requirement of an average irrigated hectare of land before revising the original figure of 40 million hectares.The assessment of surface water potential and its usable quantum is based on hydrological computations correlated with available hydrological and meteorological data for a certain number of years. However, apart from the direct surface runoff from precipitation, other factors also need precise consideration because they contribute at one stage or another to building up the available quantum of water that could be used for irrigation and other purposes. Water released through canals on to fields filters partly into the ground, where it passes through root zones and recharges the groundwater.The losses in the canals due to leakage and seepage add to the flow of surface water as well as groundwater.Ground strata permitting, the water that enters leaves in the form of affluent streams that add to the surface flow.In the absence, however, of this basic data on the parameters for assessing water resources, a preliminary exercise was undertaken based on empirical norms decided after observing the performance of canals and streams in operation.Studies were also initiated for scientific evaluation of canal losses, crop water requirements, studies of aquifer characteristics, and other parameters to get improved data.Although a number of studies were carried out to determine the basic parameters, no final view was taken for the country as a whole.A regional approach was adopted for assuming values for recharge to groundwater, losses in canals, evapotranspiration, and crop water requirements in consultation with the concerned departments. A provisional consolidated picture based on studies of basins was then adopted for larger geographic units.The targets set for ultimate irrigation potential in terms of hectarage were based on studies of reservoir performance, suitable crop patterns and their water requirements, and irrigation efficiencies.The precise water requirements for specific crops and commands are still to be finalized, taking into account the relationship between water used for irrigation and crop water requirements .The statistical picture of the country as a whole is compiled from irrigation figures on the extent of crop area being irrigated in each state.The gross cropped area was 180 million ha, and the gross irrigated area was 60.4 million ha (with an irrigation potential of 68 million ha) by the end of the Sixth Five-Year Plan (1984/85).That is, an average of 33.6 percent of the total crop area is under irrigation.Although the broad breakdown of gross irrigated area under surface water (major, medium, and minor schemes) and groundwater is available, a precise split of irrigated area on a crop-by-crop basis is not (see Table 4.3). The design of canal systems is based on assumptions of specified percentage losses in the conveyance systems, but empirical figures, which can be obtained by studies to quantify losses from headworks to the outlets and from outlets to the water courses, .are also needed. The figures of 8 cusecs per million ft (2.44 m per million m ) of wetted perimeter for unlined canals and 2 cusecs per million ft (0.61 m 3 per million nr) for lined canals are being revised with more refined data from the performance evaluation studies of completed irrigation projects in India.The figures for irrigation development reported here need to be corrected somewhat because tubewells and dugwells operating in the commands of irrigation channels create duplicate accounting. The precise figures are, however, not available.The government of India is aware of this problem and has begun to administer an irrigation census to obtain more precise irrigation statistics for the Seventh Five-Year Plan.The lack of volumetric information on the supply of water for irrigation means that precise information on the actual field supply of water for different crops is also missing. This is one of the major reasons for the lacuna in the statistics on the achievement of irrigation targets and crop water requirements  1985-1990(New Delhi: Government of India, 1985). n.a. Not available.for the country as a whole, although research studies have revealed some broad information.Because developing surface water and groundwater in isolation has deleterious effects, they are now being developed in combination, which optimizes resource use. The difference between combined or supplemental use and conjunctive use is important. Conjunctive use does not mean that surface water resources and groundwater resources are independently optimized to serve the same general objectives in a basin.Rather, it means that the natural subresources of both systems are used in a complementary manner to allow more cost-effective development than could be obtained even with the optimal plans for independent development.With this definition, many of the so-called conjunctive use projects planned and implemented in India are, instead, joint or supplemental use projects.The problems of planning conjunctive use are formulated using an optimization model of the water resources system.The decision or control variables of the model are the groundwater and surface water allocations.The optimal decision maximizes the objectives of development, while satisfying the hydraulic response equations of the surface water and groundwater systems and any constraints limiting the head variation and the surface water availability.The implementation of conjunctive use needs to be worked out in phases covering various aspects of survey, investigation, design, and planning, including monitoring and evaluation through PERT and CPM charts.Systematic conjunctive use is part of the guidelines for formulating irrigation projects in India.Too little attention was, however, given to this important aspect of improved irrigated agriculture in the past.In future planning, this aspect was given priority in the thrust of the Seventh Five-Year Plan.The present objective of planning for irrigated agriculture in India is to increase agricultural production to 180 million tons of foodgrains by the end of the Seventh Five-Year Plan (1990). What is important is the actual use of irrigation, which lags behind the potential created.The gap between actual use and created potential has been steadily widening from plan to plan, as shown in Table 4.2 and Figure 4.2.The gap of 1.25 million ha that existed at the end of the first plan gradually increased to 4.7 million ha for major and medium irrigation projects alone at the end of the sixth plan.This does not include the gap of 2.3 million ha from minor irrigation, which can be covered by increasing power supply and other administrative measures.These gaps are expected to reach about 9 million ha by the end of the Seventh Five-Year Plan.Timely remedial measures to close this gap gradually are being contemplated.These include improvements in existing programs in the irrigation sector.As long as the rate at which additional irrigation potential is created is more than the rate at which additional use increases, the gap will be difficult to close unless concerted efforts are made to improve existing schemes to accelerate the pace of use. The main thrust of the seventh plan is to fulfill this basic objective by improving water management, modernizing existing techniques of operation and maintenance of irrigation networks, using surface water and groundwater conjunctively, supplementing inadequate surface water supply created by mismanagement with groundwater from tubewells or dugwells in command, improving agricultural practices to increase yields, developing command areas to take care of integrated development of irrigated agriculture through on-farm development works (constructing field channels, regulatory structures, and land leveling), enforcing warabandhi (rotational irrigation), and last but not least, evaluating the performance of these improvement measures periodically so that timely corrections can be made if required. Some of these measures were identified and initiated during the past two plans, and they have improved and increased agricultural production.Ensuring farmers delivery of the right amount of water in the right place and at the right time will be the slogan of future irrigation engineers in the field of water management. All efforts are being made to fulfill this slogan in the shortest time possible by combining the efforts of all the agencies involved in operating and maintaining irrigation systems. This includes government agencies as well as private and cooperative agencies such as farmers' cooperative groups, village-level water committees, and irrigation cooperative societies.Conjunctive planning is an ideal solution for ensuring full use of the irrigation assets created in a reasonable period of time. Conjunctive planning is different from conjunctive use.It plans the commands of major and medium irrigation projects along with all minor irrigation works possible in such commands, including surface water and groundwater like dugwells, tubewells, river lifts, village tanks, and small surface water schemes.Minor irrigation works should be completed in advance as segments of the command of major and medium irrigation projects. Because of their limited size, they can be developed easily within a period of two to five years. By the time the water from the canal system of major and medium irrigation projects is ready to be delivered at the head of the commands of minor schemes, the water will be flowing in an already developed command.This would save a considerable amount of the time and effort presently being used to develop commands and establish irrigation under major and medium irrigation projects. After ensuring its availability, the water from major and medium canals can be substituted for the source that feeds minor irrigation systems.This concept of planning requires considerable advance action from all concerned with developing irrigation in India. It is essential, however, to close the gap in use and shorten the time lag between implementing the project and accruing benefits from huge investments on irrigation projects.Reducing this lag would make the projects yield quick results in an economical manner.Improving the existing program will help ensure that the investments in the irrigation sector earn maximum economic returns. This is essential to improve agricultural production and alleviate poverty in this country, which is the ultimate goal of planning.","tokenCount":"3294","images":["-936571641_1_1.png","-936571641_2_1.png","-936571641_3_1.png","-936571641_4_1.png","-936571641_5_1.png","-936571641_6_1.png","-936571641_7_1.png","-936571641_8_1.png","-936571641_9_1.png","-936571641_10_1.png","-936571641_11_1.png","-936571641_12_1.png","-936571641_13_1.png","-936571641_14_1.png","-936571641_15_1.png"],"tables":["-936571641_1_1.json","-936571641_2_1.json","-936571641_3_1.json","-936571641_4_1.json","-936571641_5_1.json","-936571641_6_1.json","-936571641_7_1.json","-936571641_8_1.json","-936571641_9_1.json","-936571641_10_1.json","-936571641_11_1.json","-936571641_12_1.json","-936571641_13_1.json","-936571641_14_1.json","-936571641_15_1.json"]}

data/part_2/0321728672.json

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+{"metadata":{"gardian_id":"0ae7766257249b2dc5677ae314f1ac8b","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/4fe86e36-a5cc-417b-9032-8a09631bd0b3/retrieve","description":"","id":"-1766376563"},"keywords":[],"sieverID":"005cf7f0-17ad-423e-a7ba-5a0e385299f0","pagecount":"30","content":"The paper proposes a framework to understand the complex linkages food insecurity and HIV and AIDS. It distinguishes four types of interventions to address these linkages and hopes the model will help to structure research, policy and programming. Interventions distinguished in this paper are aimed at both promoting food security and include antiretroviral treatment and nutrition support. The four types of interventions include: containing HIV and preventing AIDS through comprehensive treatment regimes which include nutritional support; mitigating the effects of AIDS through support; providing HIV-sensitive, but not HIV-exclusive, safety nets at the individual, household and community levels; as well as limiting the exposure to risk through HIV prevention activities. The authors note that adequate responses to HIV and AIDS and food insecurity must be tailored to specific epidemic settings.1. Many of the structural drivers of HIV risk and vulnerability-including income and gender inequalities, mobility, food insecurity and malnutrition -are affected by seasonality. 2. The seasonal coincidence of household food stress and increased incidence of certain diseases can affect the health and nutritional status of individuals living with HIV as well as their ability to access and adhere to treatment. 3. In the context of HIV and AIDS impacts as the determinants of resilience become eroded, seasonal fluctuations become harder to manage. In southern Africa, for instance, rainfall patterns permit only one major harvest a year. For families struggling with HIV, missing this harvest could be disastrous. As the hunger season ends and young crops become available, vulnerable families often consume them \"green\" before their full nutritional value is reached. In terms of policy responses to the effects of seasonality, there is little evidence of agencies and governments proactively responding at large-scale to the HIV-hunger nexus itself, let alone the seasonal dimensions. AIDS-sensitive, seasonally appropriate social protection systems are needed to protect families. Agriculture and health sectors need to capture synergies and work together more. All of this requires an understanding of the different wavelengths of shocks and stresses, and the way they intertwine.Paper presented at the Global Environmental Change and Human Security (GECHS) Conference, Oslo, June 2009. Gillespie, S. and S. Drimie [email protected] Paper tracks the evolution of the theory of vulnerability in the context of AIDS, food and nutrition and its practical application over this last decade with reference to the conceptual work of RENEWAL. The paper also explores approaches to understanding and responding to the complex web of interactions, as well as the types, sources, levels and stages of vulnerability. The paper finds that although there was a growing body of knowledge on the links between poverty, inequality and the spread of HIV, there were still large gaps in understanding how and why the interaction of forces dissolve some households while others survive, adapt and may even prosper. In the face of the challenges posed by the interactions between HIV, AIDS, food and nutrition security, there is no convenient magic bullet intervention and no blueprint. As such no single conceptual tool or framework can capture that complexity. Continual reflection and engagement are required to bring us closer to finding lasting solutions to such dynamic vulnerability.HIV and Mobility in the Lake Victoria Basin Agricultural Sector. 2009 Drimie, S., Weinand, J., Gillespie S. and Wagah, M.The Lake Victoria region has the highest HIV prevalence in the East African Community. This region also has a significant concentration of commercial agricultural plantations, which rely on mobile workers, an extensive system of out-grower schemes, and linkages with neighboring communities and transportation routes. Reviewing the relationships between the various components of the plantation system and the spread of HIV, which is a complex and dynamic process, it is revealed that there has been relatively little research on these interactions, and the relevant policies and programs are generally silent on mobility-induced vulnerability to HIV.1. High food prices could affect HIV prevention: Food insecurity is associated with increased mobility-a marker of enhanced risk of HIV exposure-and likelihood of engaging in transactional sex. Food insecurity at the household level is likely to translate to malnutrition with possible detrimental effects on the immune system. 2. High food prices could affect care and treatment: Adults living with HIV require 10-30% more energy than before they were infected, and children may need up to 100% more. Inadequate dietary quantity and quality exacerbated by the current spike in food prices, may, therefore, lead to more frequent, more severe opportunistic infections and a more rapid progression to AIDS. For people living with HIV who are on treatment, nutrition is important for treatment adherence. First, some of the negative side-effects of antiretroviral therapy are reduced if medicines are taken with food. 3. Higher food prices could affect mitigation of AIDS impacts: Chronic food insecurity constrains resilience and forecloses options to adapt to any stress. For instance, children may be taken out of school to work for cash for food; increasing chronic food insecurity constrains resilience and forecloses options to adapt to any stress. Costs of supporting an orphan may result in fewer extended families being able to care for additional orphans. Assessment, monitoring and tracking of vulnerability, food insecurity and the interactions between HIV and hunger needs to be strengthened. There is also a need to Going beyond short-term assistance, to build bridges between agriculture and health sectors to ensure longer term support to livelihoods where HIV and hunger coexist.The impact of the global human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS) epidemic is most severe in sub-Saharan African countries already affected by undernutrition and food insecurity. There is a two-way relationship between HIV and undernutrition and food insecurity, which is mainly synergistic and operating at different levels. HIV infection increases energy and nutrient requirements, yet it reduces food security. The result is nutritional deficiencies, which increases progression of HIV infection. Both undernutrition and food insecurity may also lead to increased risk of transmission. Nutritional intake and status may affect metabolism of antiretroviral drugs, some of which may affect body composition, and increase risk of metabolic syndrome. In addition, HIV is transmitted through breast-feeding, thus making it a double-edged sword in low-income populations.October 2007 AIDS. 21 Suppl 7: S5-S16. Gillespie, S., S. Kadiyala and R. Greener [email protected] At the macro level there is a weak positive relationship between national wealth and HIV prevalence across countries in sub-Saharan Africa, where higher prevalence is seen in the wealthier countries of southern Africa. Strong urban-rural economic linkages, good transport links and high professional mobility may translate into both higher incomes and higher HIV incidence. National poverty rates on the other do not show a strong association with HIV prevalence, but income inequality does. Countries with greater inequality have higher HIV prevalence, especially in sub-Saharan Africa but also to a lesser extent in Asia and Latin America. Micro level evidence that poverty is a major driver of the epidemic is rather mixed. Several studies that adopt ethnographic methodologies suggest that material poverty increases the risks of contracting HIV mainly through the channel of high risk behaviour adoption.AIDS cannot be termed a \"disease of poverty.\" Although it is true that poor individuals and households are likely to be hit harder by the downstream impacts of AIDS, their chances of being exposed to HIV in the first place are not necessarily greater than wealthier individuals or households. Relative wealth appears to have a mixed influence on HIV risk depending on context and an array of mediating factors. Gender inequality appears to be particularly important. Education in general appears to be protective with regard to HIV risk, and the interaction effects between education and wealth could be very positive-when people have resources, and the ability to use those resources, they can act on safeguarding their sexual health. Sustained efforts to improve education levels as well as targeted and tailored messages on HIV prevention efforts can yield positive results. Approaches to HIV prevention need to cut across all socioeconomic strata of society and tailored to the specific drivers of transmission within different groups-with particular attention to the vulnerabilities faced by youth and women, and to the dynamic and contextual nature of the relationship between socioeconomic status and HIV.February 2007 Crush, J. B. Frayne & M. Grant [email protected] The high urbanization rates and increasing rural-urban linkages in the region, both domestic and cross-border, and demonstrates that rural and urban food security is highly interdependent. While migration itself fuels the rapid spread of HIV in the region, the disease may be undermining this new social economy and urban food security through its impacts on rural production for the towns. In addition, HIV/AIDS may be diminishing the capacity of migrants to pursue other food security strategies in town too, including urban agriculture.In terms of policy and further research, it is important to develop typologies (of: reciprocity, risk, coping, and intervention) in order to scale up interventions by understanding contextual needs and allowing intervention replication to take place on an understanding of place specific typologies. The outcomes of the research may be useful international public goods for adaptation other contex ts experiencing the triple threat of migration, HIV/AIDS and food insecurity.This summary report sheds light on the particular vulnerability of children in the context of what HIV and AIDS is doing to families and communities in sub-Saharan Africa. Some aspects of vulnerability have been clarified, while others remain a little blurred. In some cases, the context-specificity of interactions and impacts generates what may be construed as \"contradictory results\", which are not immediately policy-friendly. This is hardly surprising when considering the myriad factors and processes that determine the nature and degree of the multiple impacts that occur. Impacts and responses are determined by the dynamics in several contexts (demographic, epidemiological, socio-economic, cultural, psychosocial, organizational), as are the impacts and responses to other stressors beyond HIV and AIDS. More detailed research is thus needed to distinguish the various dynamics of interaction in different socio-economic contexts, and at different stages of the epidemic. Such a diversity of impacts needs to be matched by diversity among researchers working collaboratively. Bridges need to be built between social scientists, epidemiologists, public health specialists, nutritionists, agricultural economists and other professionals. But one aspect of emerging evidence is clear: households and communities have demonstrated extraordinary capacity to respond to stresses imposed by AIDS. This capacity, however, may now be on the verge of being overwhelmed in many places. So, although more and better research is clearly needed, there is also an immediate need for concerted and largescale action. A useful approach for most stakeholders is thus to adopt a structured \"learning-by-doing\" mode and progressively build a library of operationally-relevant research from various contexts while developing tools and processes to turn evolving local understanding into appropriate local responses. Strategically, the principle of capacity strengthening from the ground-up, viewed through the eyes of a vulnerable child, is central. The HIV/AIDs pandemic is a global crisis with consequences that will be felt for decades to come. Thirty-nine million people are currently infected with the virus, including more than 25 million from Sub-Saharan Africa. Many millions are affected in different ways. The ability of households and communities to ensure their own food and nutrition security is increasingly being threatened. With the most detailed evidence base yet assembled, this review systematically maps our growing knowledge of the interactions between HIV/AIDS and food and nutrition security, pointing to where and how future policy needs to change to remain relevant and effective.HIV/AIDS and Food Crises: RENEWAL in Africa 2004 Loevinsohn, M. & S. Gillespie [email protected] Provides the rationale for the RENEWAL network in Africa which was formed to effectively address the interactions between HIV/AIDS and food insecurity and to fill knowledge gaps and strengthen capacity and learn-by-doing in partnership with people who are directly affected by the disease. The following are key factors identified as key to addressing challenges that limit appropriate responses to the AIDS pandemic in Africa and provide a rationale for the RENEWAL network. Knowledge gaps: existing research on the interrelationship between HIV/AIDS and food security, and actions derived from it is limited in several ways. Though there is increasing understanding that the interactions are two-way, much attention remains focused on AIDS' impact on food security rather than on the other direction, i.e. how food systems, policy, and practice may contribute to the spread. Lack of evidence on \"what works\": There is little empirical basis to guide responses. Where organizations have launched actions that address HIV/AIDS-food security links, they have rarely been monitored. Clear operational hypotheses and indicators are seldom stated. Limited action: Many actors find difficulty in identifying their particular role in the response to the pandemic. This is because the dynamics in their particular sector are poorly understood. National actors and donors alike have largely not mainstreamed the implications of AIDS into their policy processes.Livelihoods: Understanding and Responding September 2003 FCN Discussion Paper 157, IFPRI Loevinsohn, M., and S.Gillespie [email protected] This paper describes the kinds of understanding and responding that are needed for agriculture, food and nutrition-relevant organizations to effectively confront HIV/AIDS. The paper starts by outlining some underlying principles that need to be grasped in order to understand the variable and changing nature of the epidemics, focusing mainly on the variance among epidemics and the notions of susceptibility, vulnerability, resistance and resilience. These concepts are illustrated through describing the particular interactions between food and nutrition insecurity and HIV/AIDS and their implications for response strategies. The paper also examines the implications of this understanding for the ways in which different people in affected households, communities and in affected sectors, may best respond. This review reveals how HIV/AIDS is enmeshed in the social and economic fabric of countries. Responding to the disease requires responses that are not only multi-sectoral but multi-level-from the rural farmer adopting and adapting to livelihoods to reduce risk to national policy makers embarking on a comprehensive review of the AIDS-relevance of existing development policy.Rethinking Food Aid to Fight AIDS, 2003 FCN Discussion Paper 158, IFPRI Kadiyala, S. and Gillespie, S. [email protected] AIDS is changing the development landscape in Sub-Saharan Africa. Can and should food assistance be used to combat HIV/AIDS? The answer to both questions is an unequivocal \"yes.\" As people struggle to cope, food usually becomes their main concern. As evidence mounts of the ways in which food and nutrition insecurity may increase both susceptibility to HIV and vulnerability to AIDS' impacts, and how AIDS in turn exacerbates food and nutrition insecurity, the involvement of food assistance organizations becomes a moral imperative. This paper, draws upon the findings of a WFP mission to eastern and southern Africa in March 2002 and a review of relevant literature, to highlight implications of the AIDS epidemic for food assistance strategy and programming. For food assistance programs to reduce both HIV susceptibility and AIDS-related vulnerability, a new strategic perspective must be adopted-one that places communities and people's livelihoods at the center of analysis and uses an \"HIV/AIDS lens\" to refocus current programs. The impact of human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) on people's lives and on development is staggering. Millions have died and livelihoods have been devastated, particularly in Sub-Saharan Africa. Agriculture and natural resources are important components of such livelihoods. And the nutritional status of those infected and affected plays a large part in determining their current welfare and their ability to further develop their livelihoods towards activities that help to mitigate the impacts of AIDS and prevent the spread of HIV. This paper first reviews the potential pathways through which HIV/AIDS affects assets and institutions generally and then the specific impacts on agriculture, natural resource management, food security, and nutrition. The review addresses the question of how the public sector can and should respond to these challenges. The focus is primarily on mitigation, though the authors note that effective mitigation can also serve as a very cost-effective form of prevention. As labor becomes depleted, new cultivation technologies and varieties need to be developed that do not rely so much on labor, yet allow crops to remain drought resistant and nutritious. The analysis demonstrated significant human capital loss due to AIDS-related mortality and morbidity in Zambia's agricultural extension service in the period 2002 -2007. Nearly 13% staff died, and an estimated additional 9% of human capital was lost due to illness. Importantly, experienced workers were likely to die during the period. In other words, human capital loss was concentrated among workers who accumulated specific human capital in the agricultural extension system. Our simulation also showed that the loss of human capital led to about 3.3% decline in maize production. If the presence of agricultural extension services is important to mitigating negative effects of adverse external conditions such as erratic climate and/or adopting new technologies in agricultural production, this could be an underestimate. The study finds that at the contextual level, after taking into account women's decision making autonomy and household food security: 1) women living in districts characterized by women's land tenure security are less vulnerable to HIV infection. Landholding sizes were also associated with decreased vulnerability to HIV, and this was particularly the case for single women, indicating that for these women, size land holdings may be particularly important because access to land largely depends on availability of land: 2) Women were also less vulnerable to HIV in districts with easy access to women's organizations; 3) Women in living in districts characterized by dominance of cash crop production were also less vulnerable to HIV Viability of small-scale cash crop production may imply a lower likelihood of household members engaging in migrant labor reducing the HIV risk associated with Agriculture.migration ; 4) by contrast, in districts characterized by a high contribution of wage employment to average household income, women were more vulnerable to HIV infection, which may imply greater economic dependency of on men where wage labor predominates or that female labor increases risk possibly related to mobility. In terms of policy, efforts to safeguard women's rights to property such as land may be an important component not only for poverty alleviation, but also for HIV/AIDS prevention strategies. The study also points to the need for policy-makers to recognize that one of the critical factors in enhancing HIV/AIDS mitigation strategies is to ensure that the agricultural sector remains an effective source of livelihoods. The study demonstrates that the relationship between land, food security and HIV is complex. While contracting HIV may bring about loss of property, the extent to which this happens and the outcomes of the loss are influenced by household personal characteristics such as education and type of marriage as well as accessibility and effectiveness of support and intervention programs. Nevertheless, respondents living with HIV were less food secure than the control group, an assessment made using present use of land. Those living with HIV had a higher tendency than the control group to regard the quality of their food crop and livestock production as poor.In terms of policy, there is need to accelerate government policy on the transformation of customary land, including the squatter system into freehold where the individuals are provided with registered title to customary land. It is important to note that giving HIV and AIDS a special status in policymaking may attract skepticism in public; a lot can be achieved with the existing policies if they are effectively implemented. Food security is lower among households affected by prime-age adult mortality, as reflected by their more frequent worrying about food, running out of food, and/or recent experience with hunger. Mortality-affected households are also more likely to have had to eat less desirable food due to shortage. Whiles there is a high level of reliance on wild foods in the Agincourt field site, for example use of wild spinach at least weekly, households that make dietary use of wild foods more often are not necessarily more food secure. Also households affected by adult mortality are not more likely to use wild vegetables as compared to their non-mortality counterparts. Combined ethnography with repeated questionnaire surveys (1986,1990,1997) and final study conducted in 2006 50% of the sampled households had at least one death due (certainly to likely) to AIDS and 29% were taking care of orphans during 2006. There was considerable heterogeneity across households in terms of their ability to deal with the epidemic and very low rate of household dissolution. The better-off households (with higher levels of resources and income) have on average been more able to absorb the effects of AIDS illness and death. The immediate impact of the death of heads of households is often acute with effects of reduced cultivation time, harvests, and loss of other resources. There is also evidence of change: while the level of voluntary testing is still extremely low, there have been increases in the availability of testing in the rural areas; some people are paying more attention to the known behavior and holding each other to stricter standards than before.The study highlights the importance of stronger linkages between responses to the HIV/AIDS epidemic and social and economic policy designed to increase household income and food security and improve access to basic services of education and health. This paper summarizes evidence from three RENEWAL (Regional Network on AIDS, Livelihoods, and Food Security) research studies and one policy review on the interactions between AIDS and agriculture in Zambia, and its implications for future policy and programming.The unit of analysis adopted for each study varies, spanning the individual, household, cluster, and community-levels, drawing attention to the wider socioeconomic landscape within which households operate.As agriculture is the livelihood base for the majority of people affected by AIDS in sub-Saharan Africa, the interactions between AIDS and agriculture, and their implications for policy and programming, are of fundamental importance. This paper identifies the ways in which livelihood activities, within the prevailing norms of gender, sexuality and perceptions of risk in rural Zambia, can influence susceptibility to HIV, and how the nature and severity of the subsequent impacts of AIDS is modified by the specific characteristics and initial conditions of households, clusters and communities.The findings demonstrate the importance of studying the risks, vulnerabilities and impacts of the AIDS epidemic in the context of multiple resource flows and relationships between and within households -and in the context of other drivers of vulnerability, some of which interact with HIV and AIDS. The paper addresses several factors that enable or hinder access to formal support programs, and concludes by highlighting the particular importance of engaging communities proactively in the response to HIV and AIDS, to ensure relevance, sustainability and scale. Cluster analysis is a methodology that clarifies overlapping connections between households and the different roles and positions of all individuals within a cluster. In this case a cluster is defined as a group of producers between which there are multiple resource exchanges, usually based on the factor of kinship, labor and food exchange or common access to draught power.HIV/AIDS has a greater impact on livelihoods in the Mpongwe area, where it is now a fullfledged epidemic. In Teta, the disease has remained peripheral though people largely with external contacts have died. While the disease has undoubtedly exacerbated vulnerability to food insecurity, the authors note that the effects of the disease are not straightforward. For instance surprising resilience has been displayed within the context of the predominantly matrilineal social system. Uncertainty, however, prevails with factors such as livestock disease contributing to the impacts. The authors contend that uniform strategies for HIV prevention, treatment or social protection are not appropriate. Strategies need to be modified to take into account different cultural contexts, rural and urban.HIV 1. Among beneficiary respondents, there was a common perception that land reform projects were at risk from HIV and AIDS in the same manner that a private company might be, for instance because of the impact on the labor force as well as its 'management' or leadership. These perceptions did not differ significantly between affected and nonaffected households. The impact of HIV/AIDS on land rights thus seems to be muted, in contrast to earlier work that attributes tenure insecurity to HIV/AIDS. This may suggest that this earlier study only examined HIV/AIDS affected households thus over-attributing tenure insecurity to HIV/AIDS. 2. There was compelling evidence that on redistribution and restitution projects, AIDSaffected households were less food secure than non-affected households. Land reform in particular tended to benefit affected households as there were indications that affected households were more apt to have sourced one or more ingredients for the previous day's meal from the land acquired via land reform, as though consciously mindful of the need to secure a diverse and healthy diet.The main policy implication of the research was that HIV/AIDS as a threat to land reform was less significant than land reform as a means of mitigating the household-level (and perhaps community-level) impact of AIDS. 1. A rise in community mortality rates from 0 to 24% of all 393 communities was associated with a 6% decline in the land area cultivated at the community level. There is little evidence that communities are shifting their cropped area toward labor-saving crops such as cassava, as is sometimes contended. 2. There is some evidence of increasing marginal impacts on mean income or income per capita at the community level as community mortality rates rise. 3. The analysis shows that the effects of AIDS-related mortality on rural livelihoods are complex in that they depend significantly on initial community conditions such as level of mean education, wealth, farm size, population density, connectedness with markets and infrastructure and dependency ratios. Both qualitative and quantitative research methods were used in three village sites in Lilongwe rural district 1. Farming households' earnings from agricultural exports and remittances declined during the 1990s, engendering rural income diversification, deagrarianization and depeasantization. The famine of 2001-02 and the ongoing AIDS epidemic have been intricately embedded in these processes. 2. During the famine and its aftermath, ganyu (casual labor) has gained in importance as a source of income for all economically active household members, particularly women and youth. 3. Fatalism prevailed in which people felt they were not able to control the disease and identified village extra marital sex, women's increasing transactional sex and men's drinking and womanizing leisure time activities as contributing to the spread of the disease. The study revealed similar stressors on household welfare and livelihoods in diverse locations. Findings illustrated the differences in the way these stressors intersected and interacted and also revealed common 'symptoms' of vulnerability, despite the contextspecific nature of the lived experience of vulnerability. The study therefore suggests it is possible to identify region-wide symptoms of vulnerability at the level of households and livelihoods, which can complement the Human Development Indicators for assessments of poverty. We cannot conclude that the findings are definitive and applicable throughout southern Africa but we can say that the study pointed out probable region-wide symptoms of vulnerability; in other words, appropriate foci for larger studies seeking to assess vulnerability in southern Africa. Furthermore, it affirms the importance of study designs that take into account people's experiences. Finally, since it is hard to focus on vulnerability to one stressor, given the multitude of regional challenges in southern Africa, the SAVI model proved to be a useful device to differentiate the locus of different symptoms of vulnerability: in its terms, as function of context, response and outcome. There is cause for concern about the future well being of children in Southern Africa. Families are often unable to recover sufficiently from the many entwined stressors they are exposed to representing their external vulnerability (structural dimensions of vulnerability and risk). HIV/AIDS, for instance, exacerbates the impacts of other stressors and intensifies the insecurity of many communities. These entwined vulnerabilities mean that families struggle to adequately plan and act to provide their children with stability. Integrated responses are needed -these are often best provided by local community-based organizations that operate in an enabling environment facilitated by the state.Food Prices and the HIV Response: Findings from rapid regional assessments in eastern and southern Africa July 2009 Food Security, Vol 1, (3), 261-269 Gillespie, S; Drimie, S; Jere, P and Msuya, P http://www.springerlink.com/cont ent/9557g3121t5x202r/ [email protected] Two regional assessments involving key informant interviews and consultation meetings with National AIDS Commissions and partner organizations including the government and UN; focus group discussions with selected support groups for people living with HIV as well as a review of available literature on the subject. Countries visited included Malawi, Zambia and South Africa (southern) and Uganda, Kenya and Tanzania (eastern Africa) with additional interviews providing information on Zimbabwe and Swaziland.The studies found that food prices affected HIV prevention. Sudden increases in food insecurity often lead to distress migration which is a marker of enhanced risk of HIV exposure, both for the person moving, and for other adults who may remain at home. Higher food prices also affected care and treatment. Adults living with HIV require 10-30% more energy than before they were infected, and children may need up to 100% more. The rising cost of food seriously constrains the ability to ensure an adequate nutritional intake. For PLHIV who are on treatment, nutrition is important for adherence. Higher food prices affected mitigation of AIDS impacts. Evidence clearly shows that it is the poor and food insecure who suffer greater and more enduring livelihood impacts from concurrent health and economic shocks. Chronic food insecurity constrains resilience and forecloses options to adapt to any stress. In terms of responses, The food price crisis strengthens the multi-pronged rationale for linking food and nutrition security with AIDS programming. It also makes it much harder to achieve and sustain such integration. To stimulate better understanding and response, there is need to establish a platform for regular public discussions on these issues at national and regional levels as has been done on prevention and antiretroviral therapy issues in the past. Findings from all three sites show that people are aware of the threats to their welfare and of their limited options to sustain their families and livelihoods. It appears families are hardly \"coping\" in that they are not able to improve their living conditions and are living with the constant threat of things getting worse. In some cases families were able to invest into children's education and houses. However, this study suggests that these investments are not enough to provide children with the means and skills to achieve a stable existence.Investigating the empirical evidence for understanding vulnerability and the associations between poverty, HIV infection and AIDS impact. 2007. AIDS. 21 Suppl 7: S1-S4. Gillespie, S.; Greener, R.; Whitworth, J., (eds.) [email protected] focus of this special supplement is on bringing together and understanding the data on the socioeconomic dimensions of the epidemic. Its origins derived from a meeting sponsored by UNAIDS and hosted by the Health Economics and HIV/AIDS Research Division of the University of KwaZulu-Natal in Durban from 16 to 18 October 2006. The aim of the symposium was to bring together researchers to share knowledge and experience and to address What do the papers tell us? Put simply, the causes and consequences of the epidemic are complex and policy needs to take this into account. Although poor individuals and households are likely to be hit harder by the downstream impacts of AIDS than their less poor counterparts, their chances of being exposed to HIV in the first place are not necessarily greater than wealthier individuals or households. It is too simplistic to refer to AIDS as a 'disease of poverty'. As an infectious disease, it is appropriate that the primary core response to HIV focuses on public health prevention strategies and on medical treatment and care. But if we are to make further strides in combating the epidemic we need broadbased prevention, that is, prevention that deals with the contextual environment and the underlying socioeconomic, behavioural and psychological drivers of the epidemic. Like the virus, these strategies need to cut across all socioeconomic strata of society. On the downstream side, gaps in our understanding of the spread of HIV and impact of AIDS. Outputs of this meeting were a review of the main longitudinal socioeconomic data collections in Africa with a bearing on HIV. Of the ten papers, four focus on drivers, four on impacts and two on both.although AIDS impoverishes households, its effects are not uniform. Again, appropriate responses need to take account of the context-specificity and dynamic nature of the stresses, shocks and local responses brought by AIDS, so that mitigation measures are appropriately designed. The results of the study have demonstrated that in armed conflict, displacement, and food insecurity situations, women and girls are more vulnerable and at risk of contracting HIV/AIDS than their male counterparts because they have limited access and control over the much needed resources, especially food/nutrition, as a result of failing support systems. Yet, paradoxically, they shoulder more responsibility for meeting the food needs of their households. While the study established that there are high levels of awareness about HIV and AIDS among IDPs, motivation to act on this information and adapt safer sexual behavior is low. The conditions under which they live and the values that they have adopted as a result of staying in camps for a long time tend to compromise their resilience in avoiding risky sexual behavior.August 2006 Byron, E., S. Gillespie & P. Hamazakaza [email protected] Qualitative study involving in-depth interviews in four rural communities in Southern ZambiaThe findings suggest the need for key community and institution stakeholders to improve and alter the structural and environmental context-both push and pull factors underlying risk of HIV infection. Also evident is that inadequate attention is paid to individual agency, or the lack of agency, as an obstacle to implementing prevention strategies. The perceptions and beliefs about susceptibility to HIV infection and prevention in these communities suggest that both women and men need to be directly involved, with male roles in risk behavior and prevention receiving greater attention. In other words the gender inequities that underlie vulnerability and agency must be addressed within prevention strategies. The major challenge is the need to maximize scale while simultaneously addressing the local drivers of risk in thousands of different communities. A literature review focused on the social determinants of urban health was undertaken and distilled into a number of key issues. These were then used to synthesis the findings from a cross sectional household survey with migrant and non migrant groups in Johannesburg.The social determinants of urban health (SDUH) differ within cities, perpetuating urban inequalities. In Johannesburg, key differences were found between the SDUH in informal settlement and the central city. Migration status is shown to be a key determinant of urban health as internal South African migrants are significantly more likely to enter the city and locate in a peripheral urban informal settlement. Internal migrants residing in the peripheral informal settlements are worse-off than cross-border migrants residing in the central-city; internal migrants experience a range of challenges associated with residing in the periphery of the city. The findings highlighted variation in migration histories, access to services, perception of risk of HIV between central-city and the peripheral informal settlement, which clearly shows that 'place matters' and that the context of HIV presents an additional challenge programmers as they must engage with the continuum of HIV related needs, including prevention, testing, support, and access to treatment. Place matters when considering the impact of HIV and AIDS on households that are concentrated in peripheral informal settlements, where access to basic services, healthcare and ART is inadequate. Human Capital Accumulation, Migration, and the Transition from Urban Poverty: Evidence from Nairobi Slums September 2009 Yamauchi, F., O. Faye & E. Zulu [email protected] Baseline survey targeting 2000 households randomly selected from the migration theme in the APHRC's Urbanization, Poverty and Health Dynamics program. Tracking of individuals was implemented in 3 distinct waves. Wave 1 identified first batch of outmigrants from the baseline survey; Wave 2 updated information collected in waver while also recruiting new out-migrations; and Wave 3 updated information for migrants interviewed in wave 1 and 2.Five key findings were established: 1. Returns to schooling are relatively low or insignificant among slum residents. 2. Returns to experience measured by the length of stay in Nairobi are positive which indicates that agent accumulated destination experience over time helps increase their income. 3. Schooling and experience are complementary in determining the transition probability of moving out of slums to non-slum urban areas. 4. Schooling increases income change among out-migrants who moved to non-slum urban areas. Longer duration in slum areas, however, decreases the above education effect. 5. Orphans are significantly less likely to move to the urban formal sector. The findings imply that those who accumulate human capital in urban areas have higher probabilities of getting out of slum poverty; those who lose capital (e.g. children who lose household human capital in their parents) are likely to be trapped in slum poverty.Security: Exploring the Linkages in Johannesburg, South Africa May 2009 Vearey, J., L. Nunez & I. Palmary A cross-sectional household survey with migrant and non-migrant groups in Johannesburg. The sample survey was divided between one purposively selected urban informal settlement and three Cross-border migrants in the inner city (most from Zimbabwe) are most likely to report that their access to food improved since moving to Johannesburg, whereas internal migrants in the informal settlement are more likely to report that food access has worsened. Almost 70% of respondents in informal settlements also reported to have experienced a food shortage in the past 12 months whilst 55% reported this from urban formal areas. Similarly, [email protected] purposively selected urban formal areas (three suburbs in the dense inner city). A total of 195 households were interviewed in the informal settlement and 292 households in urban formal areas of the inner city. 60% (n=292) were internal South African migrants, 31% (n=150) were cross-border migrants and 9% (n=44) a control group of non-migrants. A range of qualitative methods was undertaken including focus group discussions and key informant interviews within each area were undertaken.approximately 65% of urban informal respondents had a \"deficient dietary score\", depicting the nutritional inadequacy of their diet, as opposed to 60% of urban formal respondents that had a \"sufficient dietary score\". In terms of testing and perceived risk of HIV, residents of the informal settlement were significantly more likely to report ever having tested for HIV; 64% (n = 123) reported ever having tested for HIV compared to 44% (n = 128) of residents of the inner-city. Residents of the informal settlement were significantly more likely to report that they felt at risk of HIV (58%; n = 106) compared to residents of the inner-city (40%; n = 112). Overall, female respondents are significantly more likely to report that they feel at risk of HIV (54%; n = 138) than male respondents (39%; n = 78).The key implication of this was that HIV is increasingly associated with urban areasparticularly urban informal areas where HIV prevalence is double that of urban formal areas; 25.6% compared to 13.9% for adults aged 15-49 years. Overlaying this, people living in urban informal areas are likely be to food insecure, an exacerbating factor to risk to HIV and vulnerability from AIDS. The data was generated using mixed methods that included observations in the city and rural villages of Gurage zone, formal and informal surveys.Migration is generally considered to contribute positively to the achievement of secure livelihoods. Money is the most important remittance that links rural to urban areas. Biodirectional transfer of money, food and goods is apparent and the reciprocity is more visible in the case of good transfers from and to rural areas and from urban areas, showing the need to consider two-way and multi-faceted flow of remittances. Generally low dietary diversity among respondents, however, migrants from rural to urban areas more likely to be food secure. 3. Migrants' perceived risk of contracting HIV and hence the inclination to testing is lower compared to non-migrants. The lower perceived risk and lower tendency to test may be due to the likelihoods of migrants being cautious. Availability and accessibility of HIV testing centers and support institutions for those infected is crucially important. 73% of the respondents were aware that HIV+ people were receiving different kinds of support, including access to ARVs. 81% of the respondents also affirmed that movement of people increases incidence and transmission of diseases. This is an important perception because it clearly influences the attitudes towards migrants. The study adopted a descriptive exploratory research design using existing quantitative data from the cross-sectional survey described above (under Vearey et al) in which Zimbabwean migrants were the prominent international migrant group (n=118). Second, follow-up qualitative indepth interviews with four respondents, were conducted to explore in detail specific cases where respondents used a public healthcare facility or where they had to make a difficult decision due to illness in a foreign country.The majority of Zimbabwean migrants, who were relatively newly arrived, did not seek healthcare in South Africa neither did they report \"ever falling ill\". Out of 118 respondents only 25 reported an illness incidence of which 17 sought help from different health service providers, 11 of them at a government health facility. None of them was denied on the basis of their legal status. Some of the users of healthcare services were satisfied with the treatment they received. There is little evidence in the findings to support the hypothesis that legal status is a deterrent factor among migrants to seek treatment at government hospitals. Instead factors such as proximity of the healthcare facility to the respondent's place of residence were the more important reasons in choosing a certain healthcare provider. Also the generally low utilization tendencies could be attributed to the \"healthy migrant hypothesis\". A survey with a larger sample size could establish more diverse patterns of health care utilization among Zimbabwean migrants in South Africa.January 2009 Matzopoulos, R., J. Corrigall & B. Bowman A health impact assessment was conducted and informed by: 1) a brief literature review of scientific papers and international guidelines and policies on migration, health and humanitarian disasters; 2) publicly available media reports describing the status of migrants following the xenophobic attacks; 3) analyses of unpublished data collected by humanitarian aid agencies; 4) key informant input collected via questionnaire from key actors in government and NGOs. Key systems that informed the assessment recommendations comprise: a) socio-demographic assessment, b) health determinants assessment, c) health status assessment, and d) health systems assessment.Key findings were clustered around nutrition, sanitation, infectious diseases, mental health and health systems. Nutrition: Many of the migrants force into the camps originated from informal settlements and many would have suffered nutritional duress prior to migration. The immediate response following the xenophobic attacks-food donations-had an immediate positive effect on nutrition; these improvements were, however, temporary and eroded by food irregularity in the long-term. Sanitation: Gastroenteritis was commonly reported to the media by camp residents. Infectious Diseases: Authors were unable to obtain accurate data on the HIV sero-prevalence amongst migrants displaced by xenophobic attacks. Based on the high prevalence of HIV in the communities the migrants originated from, it was not unreasonable to assume that at least 11% were HIV+. There was also a high incidence of TB in these communities and expected to be the case in the camps. Mental health: Widespread emotional and physical trauma manifested by feelings of low self esteem and hopelessness. Health systems: Rapid health assessment systems to inform health system responses were lacking leading to fragmented and uncoordinated health system response The analysis draws on findings from wider anthropological fieldwork on the converging impact of TB, HIV and food insecurity, focusing for the purpose of this paper on ethnographic case-studies of seven newly diagnosed TB patients coinfected with HIV and their households. In South Africa and Zambia inequities increased both vulnerability to infection and disease and likelihood of delayed diagnosis, delayed treatment and care for TB and HIV. In Zambia, those in treatment for TB fell deeper into poverty, were in debt and experienced food insecurity. In South Africa, affected households managed better in the short-term because of the disability grant and other welfare initiatives, but in the long term were unable to resume their previous livelihoods.In the context of poverty, food aid and transport costs should be made available to TB patients and PLHIV on ART. The effectiveness of the disability grant in SA in buffering absolute poverty speaks to the need for similar social protection during TB treatment in Zambia and other countries. The converging impact of TB, HIV and food insecurity exposes the need to tackle inequities in these settings. Government health systems also need to try and diagnose TB more promptly and emerging therapeutic responses within HIV services need to be replicated within TB services.Tuberculosis, HIV, Food Insecurity, and Poverty in rural Zambia: An ethnographic account of the Southern province 2008 M. Chileshe [email protected] thesis is based on fieldwork conducted in Pemba/Batoka in the Southern part of Zambia between September 2006 and July 2007. The core approach of fieldwork was case studies of nine people (four women and five men) who were suffering from TB, and their households; and a comparative sample of seven households that did not have a TB patient.The participatory methods included timelines, seasonal calendars, observation and semi-structured interviews. The main aim of all methods was to find out how the nine TB-patients experienced life in a wider social context, the problems they faced within their households in terms of food security and accessing both TB and HIV treatment.Addressing the emergent phenomena of rural tuberculosis and the advanced HIV epidemic, this thesis explores issues of economic crisis, food security and emotional burden from the perspective of rural people impacted by TB and HIV/AIDS. The main argument is that the poor population has a limited capacity to cope with the trajectory of TB illness in the context of food insecurity and at this stage of the HIV epidemic, without external welfare support.The study also shows that access to care in rural areas can be very costly. Accessing ART in the rural area involved repeated visits to the hospital and substantial costs. Due to these accumulated costs, some participants and their households were tipped into deeper poverty. In addition, TB tipped households into emotional turmoil -precipitating divorce, splitting up households and straining key family relationships. From their experiences, the study reveals that it is indeed very difficult for the poor to cope with TB and HIV without external support. Multiple-orphan households and multiple orphan households that cared for at least one foster child were 2.42 and 6.87 times more likely to be food insecure, respectively, than nonorphan households. No other category of orphan household was at elevated risk.The food security impact of caring for orphans varied significantly among orphan households, requiring food security planners to focus resources on the households most heavily impacted by HIV/AIDS, including multiple-orphan households, rather than focusing on conventional designations of vulnerability, such as orphans and vulnerable children. Excess prime-age adult mortality arising from AIDS in the local marriage market (district) lower the marriage age for females and reduces their premarital sexual activities in Malawi. Marriage is still customary in Malawi, therefore it is not common to refuse marriage, however, in areas experiencing a high HIV prevalence, women tend to marry younger, in order to find a safer spouse.These findings have further implications on human capital formation among women and for the next generations. First, early marriage means less schooling among young women, which may weaken their bargaining power in the household and consequently have negative outcomes on children. Second, a longer period of marriage may also imply an increase in fertility, which may also have a negative impact on child schooling.There is need to incorporate these endogenous changes in human behavior into policy frameworks. Deaths of prime-age working adults significantly increase both the female and male adolescent labor supply, stopping adolescent schooling. Deaths of prime-age adults result in decreased female enrollments, suggesting that girls shift activity, possibly staying home to take care of the sick and of the household in general. For male adolescents, enrollment decreases prior to the death of a working adult, suggesting that their response is different and possibly associated with compensating an income loss.The study also found that female adults tend to join the labor force after the death of prime-age adult males.This study used data from the Demographic and Health Surveys (DHS) that were conducted in the 2000s from 14 Sub-Saharan African Educational attainment is negatively correlated with HIV infection in countries where the AIDS epidemic has spread but such a relationship is not found in countries with low prevalence rates. These results suggest that the association between education attainment and HIV infection is weakly positive or insignificant at the beginning of the epidemic, but education has a significant effect of reducing HIV risks as the epidemic becomes severe. This relationship is especially significant for women. The results also indicate that the prevention effect of educational attainment on HIV infection is particularly strong for younger generations. This result is sensitive to gender and the stage of the AIDS epidemic, with the effect being strongest for women in countries where the epidemic has matured. While these empirical results show a negative relationship between educational levels and HIV infection in the countries where the epidemic has spread across the general population, education may play an important role in HIV prevention even in the countries where the epidemic is just emerging through the spread of knowledge and awareness of HIV and AIDS. The Impacts of Adult Death on Child Growth and Nutrition: Evidence from Five Southern African Countries July 2008 Brunelli, C., E. Kenefick & F. Yamauchi [email protected] Data were purposively collected to assess the effect of WFPs food aid program. In each intervention site a cross-sectional community household survey that permitted assessment of the difference in impact of adult death on child growth between beneficiary and nonbeneficiary households was conducted.The impact of adult death on child weight is significantly negative among non-food aid households in Lesotho, Zambia and Namibia, negative but insignificant in Swaziland, and positive and significant in Malawi. In Lesotho, Zambia, Namibia and Swaziland, the impact is insignificant in food-aid households. Except for Swaziland all countries show smaller impacts of adult death on child weight in the food-aid group, which suggest that food aid mitigates the shock on children nutrition. Policymakers and international agencies should be encouraged to actively use food transfers to mitigate the negative impacts of AIDS in the adult population. It is important for governments to institutionalize social protection mechanism, such as food transfers to the poor and vulnerable, in order to prevent the negative impacts of AIDS on children. Impact of orphanhood on underweight prevalence in sub-Saharan Africa. 2008 Food and Nutrition Bulletin 29 (1), 32-42. Rivers, J., Mason, J., Silvestre, E., Gillespie, S., Mahy, M., Monasch, R. [email protected] This paper assesses the nutritional impact of orphanhood, taking account of various potentially confounding factors. Child anthropometry and orphan status were examined in 23 Multiple Indicator Cluster Surveys and Demographic and Health Surveys throughout sub-Saharan Africa, which were subsequently merged into larger, regionspecific datasets (East, West, and Southern Africa). To compare orphans and nonorphans, linear regression and probit models were developed, taking account of orphan status and type, presence of a surviving parent in the household, household structure, child age and sex, urban versus rural residence, and current wealth status.Few differences emerged between orphans and nonorphans in controlled and uncontrolled comparisons, regardless of orphan type, presence of surviving parent, or household structure. Age differentials did confound nutritional comparisons, although in the counterintuitive direction, with orphans (who were 8 months older on average) becoming less malnourished when age differences were taken into account. Wealth did appear to be associated with orphanhood status, although it did not significantly confound nutritional comparisons. Orphans were not consistently more malnourished than nonorphans, even when potential confounding variables were examined. Since household wealth status is likely to change after becoming affected by HIV ruling out wealth as a potential confounder would require more detailed, prospective studies. The impacts of HIV and AIDS on children are mediated by families, as are the prospects for providing sustainable assistance for the long term. The support for affected children, thus, has been left largely to families, extended kin and communities The capacity of families to protect children and to compensate for the loss of caregivers and security, however, is highly dependent on the social context. With initiatives such as JLICA and the vigorous advocacy of a number of child-oriented agencies, the spotlight is slowly moving to children. The current response, however, is composed of small, localized, largely serendipitously located projects reaching at most a few thousand children with services of uncertain effectiveness.To have bigger impact, it requires larger and more systemic responses on which local initiatives can build. Support garnered for children also needs to be directed to families. In the highest prevalence countries, HIV and AIDS cluster in families. It is through worsening household conditions that children are adversely affected. Given the long-time scale of HIV and AIDS, unless we adopt a family oriented approach, we will not be in a position to interrupt the cycle of infection, provide treatment and ensure family care to all who need it.August 2009 AIDS Care. 21(S1): 83-97 Sherr, L., Varall, R., Mueller, J [email protected] objective was to systematically review measures and effects of HIV on neurocognitive outcomes for children. Published studies were identified through the use of electronic databases supplemented by hand searching and coverage of the gray literature. All studies including children with HIV infection, which utilized at least one systematic measure of cognitive functioning, reported on place, sample size, age, and outcome measures, and included a control group, were eligible for inclusion.The data are highly North American biased (63%) with European studies accounting for 13% and only two from South America and seven from Africa, where the vast majority of HIV-infected children are found. Eighty-one percent of studies reported a detrimental effect on neurocognitive development, however measured, whilst three reported no differences and four had mixed findings. Thirty-three percent provided data on child gender, but only 8% went on to analyze data according to gender. The numbers are too small for definitive findings, but it's notable that three quarters found no gender differences. There seems to be some evidence of detrimental effects of HIV infection and exposure on cognitive development, but the lack of systematic measures, controlled trials and age-specific investigations render the literature inadequate. There is an urgent need for internationally agreed and validated measures to be incorporated and for these to record data by age and gender. Review of over 300 documents on cash transfer programs for poor families and related issues around the world. Impact assessments of 20 cash transfer programs, 10 unconditional and 10 conditional were reviewed based on their reporting of quantitative impacts through a reasonably strong research design.Cash transfers have demonstrated significant impacts on the human capital of children in many countries in Latin America and Asia and evidence is building for such programs in sub-Saharan Africa. If cash transfers succeed in increasing children's presence in school, their benefits may multiply by reducing HIV risk and increasing children's access to additional services. In addition to promoting school enrolment and attendance, and preventative health-care activities, options under discussion or underway include early childhood development, after-school programs, child protection and other social welfare services, information, education and communication activities, savings schemes, skills training, voluntary testing and counseling, ART counseling and services, home-based care, micronutrient and food supplementation and nutrition counseling. The central policy debates with respect to transfer design center on who to target; who most needs benefits-AIDS affected households or very poor families and how to reach both. What criteria should be used to process those being targeted? It is important that multiple criteria are used in combination to prevent mistargeting. For those on ARVs food may be a better transfer. Important to also convene social protection around orphans and other vulnerable children, such as street children and those in child-headed households.A Systematic review on the meaning of the concept 'AIDS Orphan' -confusion over definitions and implications for care May 2008 AIDS Care. 20(5):527-36. Richter, L., Desmond, C, Wakhweya, A., Adato, M., Belsey, M., Chandan, U., Drimie, S., Haour-Knipe, M., Hosegood, V, Kimou, J., Madhavan. S & Mathambo, V [email protected] Draws on work conducted in the JLICA's Learning Group 1: Strengthening Families, as well as published data and empirical literature to provide a comprehensive literature review around the term \"AIDS orphan\". The papers were systematically coded and reviewed to understand when and how a child is labeled an orphan, and to summarize the effect of orphanhood on outcome measures, most notably psychologically and physically. All controlled studies published prior to 2006 were reviewed.This study provides a systematic review to examine the use, overuse and misuse of the term \"orphan\" and explores the benefits and limitations of this approach. It then summarizes the knowledge on orphans to date. Using a search strategy of published studies and recent conference abstracts, 383 papers were identified where the concept of AIDS and Orphan was raised. A consistent picture of negative effects of parental death (however defined) on a wide range of physical, socioeconomic and psychological outcomes were recorded. Seventeen studies met criteria for in-depth review (empirical, fully published, control group). The majority of studies are cross-sectional (two are longitudinal) and employ a wide array of measures -both standardized and study specific. This detailed analysis shows a mixed picture on outcome. Although most studies report some negative effects, there are often no differences and some evidence of protective effects from quality of subsequent care and economic assistance. The lack of consistent measures and the blurring of definitions are stumbling blocks in this area. The results although based on a small sample showed that participant households appeared to have very weak social networks, an indication of their vulnerability. Cash transfers appeared to strengthen the social networks and social capital of participant households. Additional resources enabled recipients to participate in community events, share food and borrow when in need because they had a capacity to repay. The studies in the Western and Eastern Cape show that HIV positive parents, their children, and other caregivers face challenges along the continuum of morbidity, mortality and orphanhood. The study results emphasize the role of already established patterns of childcare arrangements as primary safety nets in the context of AIDS in South Africa. Mothers who were aware of their HIV positive status were actively planning for their children's future, within their limited resources, with plans ranging from organizing future care giving arrangements to preparing wills for inheritance. In KwaZulu Natal where interviews were conducted with household fostering children, the study found that decisions to take in children were seldom contested, but where conflicts occurred, they were related to tensions between the patrilocal residence ideal and the matrilocal status quo, or to efforts to obtain the deceased's property or to access social grants. HIV positive people in the community with higher rates of disclosure had greater access formal institutional support through local NGOs and government social services and greater opportunities to take a positive leadership role as HIV positive individuals in the community. The creation of an enabling, resource-rich environment for HIV disclosure holds the potential to form a virtuous cycle whereby individuals are more likely to disclose, thus offering personal and community benefits and further perpetuating disclosure at all levels within society. The results of the study have demonstrated that in armed conflict, displacement, and food insecurity situations, women and girls are more vulnerable and at risk of contracting HIV/AIDS than their male counterparts because they have limited access and control over the much needed resources, especially food/nutrition, as a result of failing support systems. Yet, paradoxically, they shoulder more responsibility for meeting the food needs of their households. While the study established that there are high levels of awareness about HIV and AIDS among IDPs, motivation to act on this information and adapt safer sexual behavior is low.The conditions under which they live and the values that they have adopted as a result of staying in camps for a long time tend to compromise their resilience in avoiding risky sexual behavior.Although there is need to systematically review relevant policies relating to land tenure and rights of access to resources, proper implementation of the existing ones would make a big difference. The nutrition intervention provides an important source of food support to the most vulnerable patients, with supplemental food contributing to increased dietary diversity and quantity for patients and their households. Collected foods were shared among the household members with preferential allocation to PLHIV. Individuals enrolled in the program while already on ARV treatment self-report greater adherence to their medication, fewer food-related side effects, and a greater ability to satisfy increased appetites. The regional workshop was a key step in preparing Uganda's participation in and joint ownership of a regional network that seeks to enable agricultural R&D organizations in Uganda and Malawi (Tanzania and Zambia likely to join in a year or so) to respond effectively to HIV/AIDS in collaboration with organizations in social development and public health. The workshop established the following as key action priority research: Short-term: a. assessing existing programs and policies and testing modified versions; b. identifying and supporting innovation in AIDS-affected farm households. Medium Term: a. developing new options for and with HIV/AIDS-affected communities; b. identifying agricultural systems that make people particularly vulnerable or resilient to AIDS/ susceptible or resistant to HIV; c. assessing impacts-access to management resources, long-term and aggregate effects of AIDS on rural society and the agricultural economy, impacts on agricultural knowledge of the young and other vulnerable groups: Methods: a. Longitudinal studies drawing on social, agricultural, or epidemiological information and if possible triangulating information obtained be different methods; b. cross-sectional studies using quantitative or qualitative methodologies including nutrition surveys, prevalence surveys, behavioral surveys, social mapping, focus group interviews, and other participatory methods; c. retrospective or historical studies to establish events and trends in a community and establish spatial and temporal resolution on patterns of illness ; d. field trials that develop and assess specific interventions, usually through comparison among communities; e. evaluative research; and d. socio-economic and gender analysis and environmental impact analysis.HIV/AIDS, Agriculture and Food Security in Malawi: Background to Action 2001 Ngwira, N., S, Bota & M. Loevinsohn [email protected]/ Background on Linkages: Rural poverty, food insecurity and lack of livelihood opportunities contribute to susceptibility to HIV infection of rural people, especially young adults and women. Poor women with few other subsistence options may resort to transactional sex. Furthermore rural women who are poor and economically dependent have limited influence on the conditions under which sex occurs. Young adults, both men and women, often find few opportunities to make a living and are forced to migrate in search of work. Organizational and Institutional Responses: 1. In many cases there is little reliable evidence to guide agricultural-linked actions that seek to prevent HIV's spread or to mitigate AIDS' impacts. In particular as relates to agricultural sector institution, the impact of AIDS is still not well quantified, but has been severe. 2. There is a tendency for governments to leave grassroots activities to NGOs. These are the activities that can really make an impact on the quality of lives of families at risk of HIV and affected by AIDS. 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+{"metadata":{"gardian_id":"6b159d3f78be36bbb8e3a20ae0ce07e6","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/ced5977e-2861-4ea7-8d00-7f1f3cd61887/retrieve","description":"The Cereal Systems Initiative for South Asia (CSISA) works to reduce hunger and increase food and income security of resourcepoor farm families in South Asia through the development and inclusive adoption of new cereal varieties, sustainable agricultural technologies and policies.","id":"-313998694"},"keywords":[],"sieverID":"3330f996-6c7c-410e-ba0c-46ff6f91cbbf","pagecount":"19","content":"early Sowing of WheatTraditionally, farmers in eastern India have planted wheat in late November or early December, making the crop more vulnerable to damage from late-season heat, called terminal heat, when it exceeds 35 o C. CSISA research shows that productivity progressively declines from more than 5 to less than 2.5 tons per hectare when sowing is delayed from the first half of November to the last half of December.In 2009, CSISA, through the International Maize and Wheat Improvement Center, launched a campaign in Bihar and eastern Uttar Pradesh to promote early wheat sowing, between 1 and 15 November, to help combat the negative effects of terminal heat. Early wheat sowing, in combination with zero tillage (ZT), can increase yields as the crop is more likely to avoid damaging heat during the grain-filling stage. Early sowing also allows farmers to adopt high-yielding, longduration wheat varieties, which can further improve productivity.Zero tillage is the way of growing crops from year to year without disturbing the soil through tillage, thus saving irrigation water, increasing organic matter retention and nutrient cycling and suppressing weeds. In the heat-and stress-prone eastern Indo-Gangetic Plains, early sowing of wheat and ZT are important adaptations for coping with the present and projected climate extremes.CSISA increased adoption of early sowing and ZT through sharing relevant data with policy makers, demonstrating the benefits in farmers' fields, training service providers in ZT technology and educating extension staff, input dealers and distributors. Most E arly wheat sowing is a non-cash input that even smallholder farmers can benefit from and is one of the most important adaptations to climate change in the eastern Indo-Gangetic Plains.importantly, CSISA joined the Bihar Department of Agriculture's seasonal planning meetings, sharing relevant research findings and providing evidence of the benefits of early sowing and ZT. Influenced by CSISA's findings, in 2013 the Bihar Department of Agriculture modified its highly influential advisory to farmers, officially recommending that farmers sow wheat before November 15.CSISA surveys show that 340,000 farmers in Bihar and 280,000 farmers in eastern Uttar Pradesh now practice timely sowing of wheat.CSISA's research demonstrates that by combining crop diversification with conservation agriculture and precision resource management, crop yields and land productivity can increase by 11percent, irrigation requirement can decrease by 71 percent and overall profitability can increase by 32 percent.More than 120,000 hectares of wheat now benefit from timely planting across Bihar and eastern Uttar Pradesh hub districts due to the combined efforts of CSISA and the state agriculture departments.Early sowing of wheat resulted in a grain harvest of nearly 1.5 times the Indian average yield of 3 tons per hectare. Under best conditions, early sowing has given a grain harvest of up to 7.3 tons per hectare.120,000average wheat yield hectares producingSabriti Nayak, a tribal farmer from Badjod village in Odisha, required 10 to 12 farmhands for two days to manually transplant 0.4 hectare of rice seedlings. However, she would often face labor shortages at peak rice-sowing times. After working for long hours in wet fields for nursery preparation, uprooting and transplanting seedlings, Nayak would develop skin problems and other ailments. Faced with the risk of scarce and expensive labor, last year she adopted mechanical transplanting of rice. By doing so, she could not only sow the same area in just one hour without any laborers, she didn't have to worry about skin infections either.The mechanical transplanter plants rice seedlings at precise depths and spacing, decreasing the time and drudgery involved in a task that is mostly done by women farmers in India.In collaboration with the Odisha Department of Agriculture, CSISA worked over the past three years to popularize the mechanical transplanting of rice in puddled and non-puddled conditions. From a very low base of 40 farmers adopting machine transplanting of rice in 2013, CSISA, through the International Rice Research Institute, has facilitated more than 2,000 farmers, covering nearly 2,200 hectares across the districts of Puri, Bhadrak and Mayurbhanj, to adopt mechanical transplanting.CSISA facilitates farmers to become service providers by providing them hands-on technical training, linking them with machinery dealers and helping them evaluate their profitability. CSISA also provides business development support to service providers so they can create mat-type rice nurseries and sell rice mats for mechanical transplanting.F armers in Odisha have begun adopting mechanical transplanting to address labor scarcity and the high cost of labor. To increase adoption and access to machines, CSISA links service providers with machinery dealers and helps them evaluate the profitability and 'business case' for mechanical transplanting.It used to take 15 laborers one full day to transplant rice seedlings onto farmer Anam Behera's field in Puri district's Malikpali village. \"When I first heard about mechanical transplanting I was immediately impressed by the fact that my labor requirement would be reduced to nearly zero,\" said Behera. As an added bonus, he now saves approximately 13 kg of seeds as well besides also reducing his tractor's fuel consumption.In Odisha, the number of farmers practicing mechanical transplanting of rice increased from 40 in 2013 to 2,000 in 2015.Mechanical transplanting saves farmers about Us$ 100 per hectare when services are procured through service providers. Half of the potentially arable land in the plateau areas of Mayurbhanj district of Odisha is considered unsuitable for growing rice and remains fallow for most of the year. With high poverty and low literacy rates, these uplands are home to four indigenous tribes: Santal, Ganda, Bathudi and Lohar. Some tribal families grow local varieties of maize in home gardens for household consumption and sell the little surplus as green cobs in the local market. However, yields are often low because farmers use old varieties and traditional sowing methods and lack information about modern agronomic practices to control weeds and manage inputs.In collaboration with Odisha Department of Agriculture, CSISA started working with women's self-help groups (SHGs) in this area to improve maize yields through collective maize farming. One such women's SHG, Johar Jaher Ayo, earned net profit of US$ 240 in 2014 by selling surplus green maize and maize grain, in addition to harvesting a ton of green maize for use by their families and relatives.In this year, five more SHGs practiced collective maize farming in Badbil village in the plateau area. Saraswati SHG earned a profit of US$ 1,200 and Subhapatni SHG earned US$ 1,000 by selling green cobs and dry grain.All these groups have adopted a package of improved practices introduced by CSISA, which include use of hybrids, appropriate plant population using seed drill machine and judicious nutrient management.N on-accessibility of a maize grain market leads to distress sales of green cobs by farmers in local markets. CSISA is working with partners to help farmers build forward market linkages with maize mills as an enabling factor for intensification and income generation.More than financial profits, the women farmers were pleased that they could provide nutritious food for their children during the lean season from August to October, when grain stores from the previous cropping season have usually been used up.While the sale of green cobs can be remunerative for these communities, it is a perishable commodity that cannot be stored and local markets often become saturated, leading to lower prices and distress sales. At the regional scale,The results of five years of research at CSISA's Karnal Research Platform demonstrate that Kharif maize is a suitable and profitable alternative to rice in the rainy season in northwest India to address issues of scarcity of water, labor and energy in the region.there are large markets for dry grain among feed and food millers, who source maize in bulk and not from individual farmers.CSISA is helping by linking these tribal farmers with new and established markets for maize grain through organizing community consultations with representatives of poultry feed mills in the predominantly tribal belt of Mayurbhanj.Maize yield increased by nearly 75 percent with the use of hybrids, under line sowing and improved agronomy in Mayurbhanj, Odisha.\"Conservation agriculture is all about savings,\" said Sunderaj, a farmer in Thiruvaiyaru, a village in Thanjavur district in Tamil Nadu. Sunderaj was thinking about quitting agriculture before he learned about practices such as direct seeded rice and laser land levelling. In 2014, using these practices with support from CSISA, Sunderaj's yields went up from 4 to 6 tons per hectare, even though he used 50 percent less water and was able to save 45 percent on fertilizer, seeds, tillage and other costs. His confidence in the profitability of agriculture has been restored.To expand conservation farming methods among more farmers like Sunderaj, CSISA has developed strong strategic partnerships and built the capacity of key partners including the Department of Agriculture, Tamil Nadu Agricultural University, MS Swaminathan Research Foundation and Reliance Foundation. These partnerships have helped generate awareness and increase the adoption of improved agricultural practices in Tamil Nadu. As a result, the Tamil Nadu Department of Agriculture has now recommended that farmers use machine drill seeding for rice in the Cauvery Delta region and distributed a direct seeded rice manual to farmers to support its expansion.partnerships support expansion of conservation agriculture 75% increase in maize yields fostering rural entrepreneurs bIG buSIneSS In meCHAnIzInG SmAll FARmS Barsaprasad Hembram, a maize farmer from Mayurbhanj district, Odisha, purchased a variety of modern farm machines last year through a government scheme that gives farmers a 50 percent subsidy on tractors and auxiliary implements such as the seed drill. Today, Hembram uses his new farm equipment to provide agricultural custom-hire services to other farmers, charging US$ 14.35 per hour for the tiller. Not only does this service give Hembram additional income, it helps other smallholder farmers who cannot afford to buy machines on their own to reap the benefits of modern farming technologies.India has a large number of smallholder farmers with less than 2 hectares, especially across eastern India, where the average landholding size is decreasing and ownership of machines by smallholder farmers is often not economically viable. To help bridge the gap between the demand for new technologies and the supply of those services, CSISA focuses on strengthening networks of service providers (SPs).The concept of custom-hire service began evolving as farmers purchased conservation agriculture machines including zero-till seed drills, laser land levelers, rice transplanters, bed planters and threshing machines.During the last three years, CSISA has facilitated more than 1,900 progressive farmers in Bihar, eastern Uttar Pradesh and Odisha to become SPs and has been building their capacities through trainings on relevant knowledge and skills, such as conservation agriculture, small-scale machinery, business development services and financial B y empowering service providers with technologies that have a strong business case at every step along the value chain, CSISA works to facilitate the uptake of sustainable technologies that would otherwise be beyond the reach of most smallholder farmers. Orissa University of Agriculture and Technology (OUAT) has endorsed the Rice Crop Manager and will be extending its recommendations to the farmers through their network.The tool includes both a web-based and mobile Android application with a simple, user-friendly interface providing personalized fertilizer guidance (optimum amounts of nitrogen, phosphorus and potassium) for critical growth stages of the plant in order to increase yield and profit.smart Tools for Farmers help Increase Yield CSISA supports a network of more than 1,900 mechanized service providers across India.Across Bihar and eastern Uttar Pradesh hub districts, over 50,000 hectares of ZT wheat were sown by CSISA-supported service providers in 2013-14, reflecting an area increase of 42 percent over the previous year.Initiative for South Asia 2012-2015Advancing Sustainable Innovations for a Food Secure Future in India direct Seeded riceFARmeRS' PRoFItS R esearch indicates that DSR is effective in reducing emission of methane, a potent greenhouse gas that contributes to global warming. For each ton of rice production with conservation agriculture practices, on average 400 kg CO 2 equivalent was reduced compared to conventional puddled transplanted rice.The dry direct seeded rice (DSR) method is gaining popularity in Bihar, Haryana, Odisha and Tamil Nadu, thanks to the researchers, agricultural departments and enterprising farmers who have tested and implemented this new technique. Faced with threats of depleting groundwater, shortages of farm labor, rising production costs and climate variability, more farmers are adopting DSR, which can be both environmentally friendly and cost efficient. DSR involves sowing seeds directly into the soil using a machine called a seed drill and provides an alternative to the traditional practices of germinating seeds in a nursery and then transplanting seedlings into the field, or broadcasting, in which rice seeds are tossed by hand onto the soil surface. DSR with line sowing can substantially improve the productivity of rice. It brings many benefits to farmers -reduces cultivation costs by US$ 90 per hectare and reduces water consumption by 25 percent.To increase DSR adoption among farmers, CSISA has worked with the state agriculture departments, state agriculture universities and other partners to conduct technical trainings on DSR for farmers and service providers. CSISA, in partnership with Tamil Nadu Agriculture University, organized a season-long direct seeded rice course over a period of five-and-a-half months to provide a comprehensive training for the state extension system of Tamil Nadu, covering all aspects of growing drill-seeded rice, from crop planning to milling and processing.CSISA researchers continue to improve access to effective weed management and affordable herbicides for direct seeded rice to help achieve increased adoption of this technology at a larger scale.Direct seeded rice with line sowing improved the rice yield by over 25 percent per hectare in Odisha.Higher yields with reduced production cost increased the net profit for farmers by up to Us$ 72 per hectare.increase in yield/ hectare Jha bought a crossbred cow for US$ 377, which initially yielded about 12 liters of milk per day. After receiving training from CSISA, he started giving a balanced concentrate feed prepared using locally available materials, which increased the cow's milk yield to 18 liters per day. Motivated by his success, Jha bought three additional cows and today, he owns more than 20 crossbred dairy cattle. With steadily rising demand for milk in Bihar, Jha is able to tap into this expanding market for augmenting the household farm income.CSISA is helping farmers like Jha become more aware of improved feeding practices, livestock health management and animal nutrition to improve the yield and quality of milk. Traditionally, farmers were feeding their animals unchopped paddy straw, as well as maize leaves, which are difficult for the animals to digest.Over the past three years, CSISA, through the International Livestock Research Institute, has addressed major livestock feed constraints by introducing practices that improve the efficiency of rice and wheat straw feeding through chopping, increase the use of inexpensive, locally available and nutritionally rich supplementary feeds such as maize grains and by promoting underutilized cereal residues such as maize stover. The field-based work has been complemented by laboratory-based research, which has identified more than 24 improved cereal crop varieties that are good for both grain and fodder quantity and quality.T he development of local service providers for straw chopping and concentrate feed production provided a new business opportunity and improved availability and access to better feed for dairy farmers.Milk yield increased by at least 10 percent when farmers increased the use of inexpensive, locally available and nutritionally rich supplementary feeds.Use of chopped fodder to feed livestock increased the milk yield by 14 percent for dairy farmers in Odisha and Bihar.Major Impacts of the Cereal Systems Initiative for South Asia 2012-2015Advancing Sustainable Innovations for a Food Secure Future in IndiaOn a hot summer day in Muzaffarpur district of Bihar, around 60 women farmers gathered to learn about the handheld mechanical maize sheller that would liberate them from the painful and tedious practice of shelling maize by hand. The women were enthusiastic and spoke confidently. Bholi Devi, one of the group members, said, \"Learning new ideas and people approaching us with new knowledge on agriculture has increased our confidence.\"CSISA works to empower women in agriculture by ensuring their access and exposure to modern and improved technological innovations, knowledge and entrepreneurial skills that can help them become informed and recognized decision-makers in agriculture.In Bihar, CSISA started Kisan Sakhi (roughly translates to farmer friend), jointly with Bihar Mahila Samakhya, an Indian Government program on rural women's empowerment. Thanks to the Kisan Sakhi initiative, many women farmers like Bholi Devi were introduced to new practices such as improved weed management, maize intercropping, intensification of cropping systems with summer green gram, machine transplanting of rice under non-puddled conditions and nursery management. Notably, CSISA also helped a women's group in Muzaffarpur become mechanized service providers for rice planting -the first entrepreneurs of their kind in Bihar.In Odisha, CSISA has engaged with women's self-help groups (SHGs) and their federations to undertake participatory technologyIn Odisha and Bihar, CSISA has leveraged the social capital of the many women's self-help groups that have been formed by the government and other civil society partners. These groups have provided readymade entry points for training and social mobilization, while also providing other antecedents for innovation including access to credit.  Well-structured public policies can incentivize smallholder farmers, rural entrepreneurs and consumers toward choices that improve welfare, enhance yield and are environmentally sustainable. CSISA, through the International Food Policy Research Institute, developed a critical mass of research needed to promote an actionable and evidence-based agenda for improving public policies to address South Asia's cereal systems.CSISA's policy work has strengthened the quality of the debate around seed systems development in the region through analysis of rules and regulations governing the seed market and the roles for public and private investment. Similarly, it has tackled the question of input subsidies and their impact on sustainable intensification. For example, it looked at the economic and environmental trade-offs associated with poorly targeted subsidies for laser land leveling in eastern Uttar Pradesh and provided the state government with alternative strategies for improving the efficiency of these subsidies. CSISA has also worked to improve extension models to ensure that they are site-specific, tailored to different groups, such as women farmers and include opportunities to reach smallholders at a large scale.CSISA has focused its strategic research on sustainable intensification to inform and improve labor-, energy-and water-saving conservation agriculture practices and crop diversification. Conducted at two platform sites in India representing distinct agro-ecologies: Karnal, Haryana and Patna, Bihar, these on-farm and on-station research results have significantly influenced CSISA's outreach to farmers over the past three years.Kharif Maize has been demonstrated to be a more suitable and profitable alternative to rice in the rainy season in northwest India to address the issues of rising scarcity of water, labor and energy in the region.Mustard is a viable third crop option for diversification in the Rabi season in eastern India, which also allows 300 percent cropping intensity as spring season maize can be planted after the mustard harvest.As part of the sustainable intensification effort, short duration rice hybrids followed by long duration wheat led to higher system productivity and also acts as a guard against climate change. It enables system intensification of 200% (in rice-wheat) and 300% (in rice-mustard-spring maize or mung bean).Influencing policies for Improved agricultural Growth strategic Research Builds evidence Base In India, farmers with large landholdings from prosperous agricultural states like Punjab can buy expensive and sophisticated machines for farm operations. However, resource-poor farmers with smaller landholdings from states such as Bihar may not have funds to buy these machines.CSISA has been working to ensure that farmers all along the spectrum of landholdings have access to differently priced and scale-appropriate machinery based on their specific requirements. CSISA has also helped improve existing designs of harvest and postharvest machinery to better meet local needs.For rice, mechanized threshing offers many advantages over manual threshing in terms of increased efficiency, reduced drudgery, cost and labor savings. Until recently, farmers in Bihar only had two options to choose from -the very large and expensive axial flow thresher or the compact pedal-powered open drum thresher that has low capacity and is difficult to operate for long periods by women farmers, who are responsible for most threshing activities in India. The only mediumsized option was an electric motor powered open drum thresher available from other states, which was not effective as many farms in Bihar do not have reliable access to electricity. Farmers needed a medium-sized, affordable, efficient and portable mechanical paddy thresher. CSISA worked with a local fabricator to modify the existing design and created the diesel engine powered open drum thresher, which farmers could easily buy and use.M arginal farmers usually harvest and thresh manually to get full-length straw for fodder. Manual threshing involves significant drudgery, creates bottlenecks for planting the next crop and generates losses from delayed processing. CSISA worked to modify threshers to meet the farmers' needs resulting in an increased customer base for service providers.Similarly, in collaboration with local dealers, CSISA worked to modify the existing mechanized maize sheller to an electric motor-powered double cob maize sheller, which can shell 150 kg maize per hour and consumes only 2-4 units of electricity. Priced at US$ 126, the machine is also fairly affordable. CSISA worked with partners to bring down machinery costs through diversifying the number of manufacturers and encouraging competition.postharvest MechanizationMechanical paddy threshing in eastern Uttar Pradesh reduced postharvest losses by 3 percent compared to manual threshing, helping advance wheat sowing by 9 days.Asia faces multiple challenges to ensuring food security, including dwindling water supplies for irrigation, changes in urbanization patterns and a growing threat of increased virulent diseases. Improved rice and wheat varieties can play a major role in ensuring food security and combating these challenges by developing superior cultivars with good quality traits and genetic resistance to biotic and abiotic stresses.CSISA-supported rice and wheat breeding work from 2012 to 2015 helped to produce new varieties and hybrids with high yield potential, region-specific grain quality traits, biotic and abiotic stress-tolerance and suitability for different cropping systems. These have been shared with the national agriculture research and extension systems (NARES) and many of these elite breeding lines are currently in advanced stages of testing in multi-location trials at state and national levels.CSISA is also working to improve faster dissemination of superior, disease-resistant and stress-tolerant varieties to farmers. In the last year alone, 12 wheat varieties were released, as part of CSISA's breeding work, for different environments and management conditions of South Asia. Out of 60 rice entries tested during the 2014 dry season under machine-sown dry direct-seeded rice, 15 entries recorded more than 7.5 tons per hectare. These improved varieties are often more profitable for resource-poor farmers and are one of the most effective adaptive strategies for events associated with climate change in South Asia. 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data/part_2/0351812382.json

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+{"metadata":{"gardian_id":"b92ffcd24f58548610f45d1261b7c2a8","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/a7c42ccb-c6dd-46e7-a5a7-798b9b44d252/retrieve","description":"","id":"-1419527887"},"keywords":[],"sieverID":"97454ed8-a303-47a6-9fb1-3d903c191045","pagecount":"4","content":"District Nutrition Profiles (DNPs) are available for 707 districts in India. They present trends for key nutrition and health outcomes and their cross-sectoral determinants in a district. The DNPs are based on data from the National Family Health Survey NFHS-4 (2015NFHS-4 ( -2016) ) and NFHS-5 (2019NFHS-5 ( -2021)). They are aimed primarily at district administrators, state functionaries, local leaders, and development actors working at the district-level.  Note: NA refers to data unavailable for a given round of NFHS/Census.• • What are the trends in underweight and anemia among women (15-49 yrs) in the district?• What are the trends in overweight/obesity and other nutrition-related non-communicable diseases in the district? • What are the trends in infant and young child feeding (early initiation of breastfeeding, exclusive breastfeeding, timely initiation of complementary feeding, and adequate diet)? What can be done to improve infant and young child feeding?  Note: NA refers to data unavailable for a given round of NFHS/Census.• How can the district increase women's literacy, and reduce early marriage, if needed?• How does the district perform on providing drinking water and sanitation to its residents? Since sanitation and hygiene play an important role in improving nutrition outcomes, how can all aspects of sanitation be improved? • How can programs that address underlying and basic determinants (education, poverty, gender) be strengthened?• What additional data are needed on food systems, poverty or other underlying determinants? Note: NA refers to data unavailable for a given round of NFHS/Census.• How does the district perform on health and nutrition interventions along the continuum of care? Does it adequately provide both prenatal and postnatal services to women of reproductive age, pregnant women, new mothers and newborns? • How has access to health and ICDS services changed over time (food supplementation, health and nutrition education and health checkups)?","tokenCount":"300","images":["-1419527887_1_1.png","-1419527887_1_2.png","-1419527887_1_3.png","-1419527887_1_4.png","-1419527887_1_5.png","-1419527887_1_6.png","-1419527887_1_7.png","-1419527887_1_8.png","-1419527887_1_9.png","-1419527887_1_10.png","-1419527887_1_11.png","-1419527887_1_12.png","-1419527887_2_1.png","-1419527887_2_2.png","-1419527887_2_3.png","-1419527887_2_4.png","-1419527887_2_5.png","-1419527887_2_6.png","-1419527887_2_7.png","-1419527887_2_8.png","-1419527887_2_9.png","-1419527887_2_10.png","-1419527887_2_11.png","-1419527887_2_12.png","-1419527887_3_1.png","-1419527887_3_2.png","-1419527887_3_3.png","-1419527887_3_4.png","-1419527887_3_5.png","-1419527887_3_6.png","-1419527887_3_7.png","-1419527887_3_8.png","-1419527887_3_9.png","-1419527887_3_10.png","-1419527887_3_11.png","-1419527887_3_12.png","-1419527887_4_1.png","-1419527887_4_2.png","-1419527887_4_3.png","-1419527887_4_4.png","-1419527887_4_5.png","-1419527887_4_6.png","-1419527887_4_7.png","-1419527887_4_8.png","-1419527887_4_9.png","-1419527887_4_10.png","-1419527887_4_11.png","-1419527887_4_12.png"],"tables":["-1419527887_1_1.json","-1419527887_2_1.json","-1419527887_3_1.json","-1419527887_4_1.json"]}

data/part_2/0357885050.json

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+{"metadata":{"gardian_id":"54525cb6cddee9dac9dfe1648a4f6359","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/9b175a3d-0433-437e-94eb-4fadf0f69007/retrieve","description":"Concerns about harmful environmental impacts are frequently raised in research and policy debates about population growth in the hills and mountains of developing countries. Although establishing wildlife corridors and biosphere reserves is important for preserving selected biodiverse habitats, for the vast majority of hilly-mountainous lands, the major ecological concerns are for the sustainability of local production systems and for watershed integrity. What matters for sustained use of those lands not only is the number of producers but also what, where and how they produce. Evidence from empirical research indicates that population growth in hills and mountains can lead to land enhancement, degradation, or aspects of both. This can be explained by extending induced innovation theory to address environmental impacts of intensification. Increases in the labor-land endowment ratios of households and in local land demand and labor supply make the opportunity cost of land relative to labor increase. As a result, people use hilly-mountainous land resources more intensively for production and consumption, thus tending to deplete resources and significantly alter habitats. But, at the same time, capital- and labor-intensive methods of replenishing or improving soil productivity may become economically more attractive, production systems that enhance the land if the expected discounted returns are greater than those of systems that degrade the land. Users will choose production systems that enhance the land if the expected discounted returns are greater than those of systems that degrade the land. In addition to population change, other factors—market conditions, local institutions and organizations, information and technology about resource management, and local ecological conditions—determine the returns from various production systems.","id":"-2116696491"},"keywords":[],"sieverID":"584babd6-d507-4d56-a28e-f4dda67013b2","pagecount":"81","content":"Discussion Papers will eventually be published in some other form, and that their content may also be revised.Human populations are growing in many hilly-mountainous areas in developing countries. This growth is expected to continue for at least the next few decades. There is considerable historical evidence to suggest that intensification of farming systems is likely to occur with population growth, sufficient to ensure stable or increasing agricultural production (if not always per capita income) particularly in areas of \"higher-potential\" where densities are not yet already very high (for example, Ruthenberg 1980;Turner, Hyden, and Kates 1993). This proposition, however, hinges on the assumption that land degradation does not constrain the process, and it is evident that in hilly and mountainous areas, intensification poses greater potential ecological risks both for local production and for the broader environmental services provided by upper watersheds. Indeed, many researchers, policymakers, environmentalists, and other members of society argue that greater production in these areas due to population pressure degrades forests, farms, and grazing lands and creates significant, negative off-site environmental impacts (FAO 1994;Harrison 1992; Lele and Stone 1989;Repetto 1986;Southgate 1988;World Bank 1992).A number of microeconomic arguments underlie this pessimistic assessment. As population density increases, the supply of people who clear trees increases (Kang and Wilson 1987;Southgate 1988;World Bank 1992) and the demand for products from forests or forest land grows too. As a result, a larger area is deforested and biodiversity diminishes. In addition to these forest changes, both the number of producers who use degrading agricultural or grazing practices (for example, Repetto 1986) and the demand for crops and livestock produced with degrading practices increase (Brown and Wolf 1984). To increase production and because the marginal product of labor decreases, farmers move onto and cultivate lower quality land (FAO 1994;Gillis, Perkins, Roemer, and Snodgrass 1987;UNFPA 1991). Lower quality land, by definition, tends to degrade more and faster. Finally, people become poorer as population grows. As such, they are less able (UNFPA 1991) and willing (World Bank 1992) to conserve soil and make other resource-enhancing investments.A contrasting view-more optimistic, but also grounded in microeconomic theory-is that population growth may lead to land enhancement (for example, Simon 1981). This view suggests that people plant larger areas with trees or plant more trees in a given area as the demand for products from trees and the number of tree farmers increase. Similarly, farmers use land-enhancing agricultural or grazing practices either more frequently in a given area or on larger areas as the demand for agricultural and livestock products increases and as the number of producers increase. People change their production methods to overcome declines in the marginal product of labor, change property institutions to better protect land, and make landscape investments (Boserup 1965;Hyden, Kates, and Turner 1993;Ruttan and Hayami 1991;Kates and Turner 1993). The poorer are people who rely on land for their sustenance and have no better alternative, the greater the incentive they have to protect the source of their sustenance (Pagiola 1994).Other scholars argue that rural depopulation or labor shortages threaten land degradation. For example, throughout rural Africa, local population decline or diminished growth due to greater access of people to non-local economies will make many existing resource management systems unsustainable due to labor shortages (Goldman 1995).The issue of how per capita incomes or other measures of economic welfare change 1 with population growth is also critical to the policy debate, but not addressed directly in this review. In this paper we maintain a focus on key relationships between population growth, production and productivity, and environmental conditions. in local labor supply and loss of farming knowledge and experience could threaten the viability of the complex multistoried homegardens of the Chagga (Fernandes, Oktingati, and Maghembe 1984). In Nepal, the major shift in the population from the middle hills to the lowlands, or terai, could lead to a substantial reduction of labor inputs into the intensive farming and land-management systems of these hills and, thereby, could be ecologically devastating (Blaikie and Brookfield 1987).These divergent assessments of the effects of population growth and decline on land and other natural resources suggest different answers to important policy questions: How can policymakers promote land enhancement in hilly-mountainous areas? Under what conditions should policymakers encourage out-migration and depopulation? Under what conditions should policymakers accept or even encourage population growth by promoting infrastructural development and private property rights for local land users? In this paper, we argue that neither the pessimistic nor optimistic assessments can adequately answer these questions. What is needed is a comprehensive framework to understand not only the ecological potential of specific areas to sustain intensive or extensive production systems but also the microeconomic factors that increase the likelihood that people will develop or adopt production systems that can be ecologically sustained under hilly-mountainous conditions. This paper has two related purposes. The first is to examine and critique existing empirical evidence about how population change affects land quality in hilly-mountainous areas. In section 2, we review literature linking demographic change with changes in forestry, agriculture, and livestock production and in land quality. 1   The second purpose is to develop a microeconomic conceptual framework to understand changes in land management in these areas. In section 3, the empirical results of section 2 are put into perspective with an analysis of the microeconomic basis for choices about production systems. Emphasis is given to how population growth, institutionalorganizational factors, information and technology for resource management, market forces, and intrinsic ecological conditions can create microeconomic incentives for resourceconserving investments, products, inputs, and technologies. The implications of the evidence and analysis for research and policy are explored in the final section.The evidence analyzed in this paper comes from our review of English-language literature from economics, sociology and geography that pertains primarily, but not exclusively, to the hilly-mountainous areas of the Himalayas, Andes, Southeast Asia, East Africa, and Central America. Sources of literature include key-word searches on computer indices, direct contacts with social and soil science experts, and our own bibliographies. The type of evidence (data sources and analyses)-for example, aerial photographs over time, researcher assessment or statistical correlation of variables from case studies, or estimated coefficients of multivariate econometric models-is presented in various tables and discussed in the text.Intensification of land use and management in the hills and mountains of developing countries could potentially have three types of negative effect on land quality. The degradation of soil, water and vegetation resources can reduce the long-term productive potential of the land, leading to declining production and impoverishment of producers.Expansion of land area under agricultural production into new regions, or into niches of existing farm landscapes that were previously uncultivated, threatens habitats needed to support unique or valued biodiversity. (Indeed, mountain areas in the tropics and sub-tropics contain some of the world's richest biodiversity reserves.) Finally, increased populations and intensification could affect populations and economic activity downstream from hill and mountain settlements, by interfering with critical watershed functions. Possible threats include water pollution, water supply reductions, flooding due to inadequate water absorption or vegetative cover to slow water movement during storms, and sedimentation clogging water channels or building up behind dams.The most critical land management factors affecting environmental outcomes are land use, vegetative cover, landscape modifications and management practices. This section looks, in turn, at empirical evidence relating population growth with forest, agricultural and pastoral land management and quality.A number of statistical analyses document an inverse relation between population and forest cover. For example, according to multivariate statistical analysis of FAO data for 98 low-and middle-income countries, a one percent increase in population during 1975-1980 led to small and statistically weak reductions in forest cover of 0.12 percent during 1980-1985 (Deacon 1994). An estimation of Spearman's rank correlation coefficient (r ) between change s in forest and woodland area and population growth rates for 99 more and less developed countries between 1975 and 1981 was -0.683 (Mather 1987). The correlation coefficient (r ) 2 between population density and forest cover was -0.8 for Latin America and moist Africa, and -0.96 for Asia (Palo and Mery, 18th IUFRO Congress Report; Ljubljana 1986 as cited by Harrison 1992). Population density at the municipio (district) level of Guatemala between 1950 and 1981 was strongly inversely correlated with the percent of the municipio's land area in forests (Mendez 1988 as cited by Bilsborrow and DeLargy 1991).Some cross-sectional data imply a positive correlation between deforestation and population pressure. For example, Cameroon and Tanzania have more arable land per capita and smaller percentages of total forest area being deforested than Malawi, Nigeria, Senegal, and Kenya have (Lele and Stone 1989). Case studies for Meru district, Kenya (Bernard 1993), an eastern hill area of Nepal (Kumar and Hotchkiss 1988), and the middle hills of Nepal (Blaikie and Brookfield 1987) indicate that population growth leads to a reduction in forest cover. One longitudinal case study of a village near Ranomafana, Madagascar (Harrison 1992) illustrates this population growth-deforestation process well:The land in what is now the village of Ambodiaviavy was dense forest at the start of the 1940s. Then, in 1947, eight families, 32 people in all, came to the area after French colonials burned down their old village. These families farmed only the valley bottoms, which were easily irrigated by the stream running down from the hilltops. Each family took as much land as they were capable of working; no land shortage existed. The valley bottom lands had filled up completely by the 1950s. New couples started to clear forest on the sloping valley sides. They moved gradually uphill. As a result of natural growth and immigration from an overcrowded area nearby, the village population had increased tenfold, to 320, and the number of families had grown to 36 by 1990. By that time the people had cleared forest two thirds of the way to the hilltops.Evidence that population growth induces a decrease in forest cover is presented in Table 1.However, caution is required in interpreting findings of a positive correlation between forest cover (or change in forest area) and population density (or population growth rate).Such findings are consistent both with the argument attributing deforestation to population increase, and the argument that increases in population density followed initial deforestation due other factors, such as logging or ranching More case histories, such as that of Ambodiaviavy, Madagascar, or time-series data are needed to determine whether population growth preceded deforestation. Moreover, negative correlation between population density and forest cover at a national or regional level can, as we will illustrate below, mask positive correlation between population density and planted tree density in specific areas or in exceptional national or regional cases.In many cases, plantation agriculture, logging, and ranching are clearly more proximate causes of reductions in forest cover than is local population growth. For example, the rainforest on the lower slopes of Malaysia was cleared for rubber and palm oil plantations (Gupta 1988). In particular, conversion of rainforests for plantation crops was responsible for 89 percent of total deforestation from 1976 to 1981 in peninsular Malaysia, according to the FAO (Gillis, Perkins, Roemer, and Snodgrass 1987). Excessive commercial logging of various hardwood species has caused a substantial portion of forest disturbance in the Philippines (Cruz, Meyer, Repetto, and Woodward 1992).After commercial loggers convert the primary forest into degraded secondary forest, smallscale agriculturalists convert the degraded secondary forest into farmland (Cruz, Meyer, Repetto, and Woodward 1992;Kummer 1992) forest stands instead of primary forest. Thus, hillside population pressure in the Philippines has not been the chief cause of primary forest degradation and its role in the conversion of secondary forest into farmland, to the extent that degradation has occurred, is debatable.In Central America, cattle ranching is the major factor in forest destruction (Myers 1981). Even after the elimination of subsidized public credit and fiscal incentives for livestock, deforestation continues (at a lower pace) due to attempts to claim public lands, public road construction, colonization programs and the market characteristics of cattle-not to population increase. In Costa Rica, although the demand for pasture land has historically been the principal cause of deforestation, timber extraction and the expansion of banana and other plantations have now become the leading causes of deforestation (Cruz, Meyer, Repetto, and Woodward 1992;Kaimowitz 1995).To summarize, most of the empirical evidence suggests that population growth is indeed associated with deforestation of natural forests, which would frequently have negative effects on natural forest biodiversity.However, other empirical research indicates that increases in population density may be associated with increases in planted-tree density. For example, in two districts near Lake Victoria, Kenya managed-tree cover in agricultural areas was significantly greater in the late 1980s than earlier in this century, when population densities were lower (Scherr 1993).Machakos, Kenya became more densely covered, by larger trees, as the number of people Forest area has not significantly changed since at least 1900 in a densely peopled area 2 at Thokarpa, east of Kathmandu, despite large population increases (Mahat 1985 as cited in Blaikie andBrookfield 1987).increased fivefold between 1930and 1990(Tiffen, Mortimore, and Gichuki 1994a). In Ruhengeri, Rwanda, although the population growth rate between 1978 and the mid-1980s was 2.9 percent and the average population density was 367 persons/km in 1984, the total 2 area reforested nearly doubled from 5,487 hectares in 1980 to 10,354 hectares in 1985, out of a total of 168,470 hectares (Ford 1993). In Algeria, where most people live in or near the Atlas mountains, population growth was very high-32 percent-and the percent increase in forest and woodland area was one of the largest of 127 countries during the 1970s (Heilig 1994).Average population density on Java is 760 persons per square kilometer (Fujisaka 1989b). On this densely populated island, the area devoted to intensive multistory home gardens increased with population density, occupying anywhere from 15 to 75 percent of the cultivated land, producing more than 20 percent of household income and 40 percent of household caloric requirements, and providing one of the highest returns to labor of all available employment opportunities (Stoler 1978).In four village panchayats in two central hill districts of densely populated Nepal, the number of trees per hectare grew two-to three-fold on rain-fed terraced farms during the period 1964-1988 and substantial increases in tree densities also occurred on non-cultivated segments of farm land. Similar increases in tree cover on rain-fed terraces were detected over most areas of these two districts and in several other hill. The population in these hills probably almost doubled during 1960-1990(Carter and Gilmour 1989). 2The chief reason for this positive correlation is that people tend to consciously allocate parts of their farms for tree growing or preserve existing trees on their farms at all population densities beyond the levels associated with forest or bush fallow. For example, at the time they entered into pulpwood growing contracts with the Paper Industries Corporation of the Philippines (PICOP), farmers were engaged in low-density extensive agriculture; the average land holdings were 11 hectares per farm household (Arnold 1987, 177). Woodlots have been common in western Kenya for more than 50 years and today cover about 60,000 hectares ofThe increase in the area of small woodlots accounts for 83 percent of the total 3 increase in tree cover during 1980-1985 in Ruhengeri, Rwanda (Ford 1993).the landscape, some of which is the most densely populated in the area (Spears 1987, 55 and 59). In many locations on Cebu, one of the most densely populated and hilly islands in the 3 Philippines, farmers have sustainably cultivated and harvested fuelwood and produced charcoal for decades (Bensel and Remedio 1993;Kummer, Concepcion, and Canizares, forthcoming, 16). Finally, farmers in about 50 percent of the area of Nepal deliberately retain or plant strips of trees and shrubs across and along the perimeter of steep-40 percent-70 percent slope-fields and in terraces on more gently sloping fields to control soil erosion and to have as sources of fodder, firewood, and fence posts (Fonzen and Oberholzer 1984).Similarly, case studies indicate that as population density increases, people in some instances transform native forests, swidden land, or recently logged areas into agroforests that are more economically beneficial but nonetheless ecologically viable. For example, on the southern and eastern slopes of Mt. Kilimanjaro, one of the most densely populated areas in Tanzania, the Chagga people gradually replaced the natural forest with home gardens (Fernandes, Oktingati, and Maghembe 1984). This multistoried agroforestry system involves integration of several multipurpose trees and shrubs with food and cash crops and livestock simultaneously on the same unit of land. With an average size of 0.68 ha, homegardens are labor-intensive and human capital-intensive, that is, require intimate knowledge of various trees, crops, and plants and their ecological requirements (Fernandes, Oktingati, and Maghembe 1984). In another instance, as a result of increases in relatively low population densities on the islands of Roti and Savu in eastern Indonesia, people replaced shifting cultivation with a more sedentary production system based on nearly total exploitation of the multipurpose lontar palm, which had dominated their degraded fallows. This tree-based economy is both economically and ecologically superior to the degraded swidden systems on nearby islands (Fox 1977). Table 2 summarizes the evidence relating population growth with increases in tree density.The watershed effects of replacing natural forest with planted trees depends upon their configuration (in relation to annual crops or other land uses), associated ground cover, water  in 1937in , 1948in , 1960in -61, 1978in , research Ford (1993, 155-156, 163-, 155-156, 163-  (1984,73,76) surveys or informants agroforest Mt. Kilimanjaro, Tanzania Carter and Gilmour (1989, 381-382, Two-to  phrase means that all the other factors that could influence soil productivity, such as fertilizer use, are held constant so that they do not influence this outcome.management practices, and geographic features. According to recent evidence from the tropics and sub-tropics, most watershed functions can, in principle, be provided in many farming landscapes by these features (Jackson and Scherr 1996).Evidence from around the world indicates that length of fallow and population density are negatively correlated (Kumar 1973;Turner, Hanham, and Portararo 1977). Similarly, a table of frequencies between population density and cropping frequency in 52 cases in sub-Saharan Africa implies that cropping frequency and population density are positively correlated (Pingali, Bigot, and Binswanger 1987). Case histories and quantitative data from specific developing countries or areas around the world also indicate that increases in population density lead to decreases in fallow periods or increases in cropping frequency (Blaikie and Brookfield 1987;Cruz, Meyer, Repetto, and Woodward 1992;Harrison 1992;Hyden, Kates, and Turner 1993;Nwafor 1979;Perkins 1969 as cited by Bilsborrow 1987).(See Table 3.) As the length of fallow decreases, ceteris paribus, soil productivity decreases in most cases (Boserup 1990;Brady 1990;Fujisaka and Sajise 1986;Kang and Wilson 1987). 4Expansion of agricultural land is a special case of an increase in cropping frequency.According to Harrison (1992), population growth was allegedly responsible for 72 percent of arable land expansion in developing countries between 1961 and 1985. However, he assumes that the only three factors responsible for changes in arable land are changes in population, agricultural production per capita, and farm area per unit of production (the inverse of yield). Multiplied together these factors are identical to the expansion in agricultural land. So an increase in any of these factors will 'cause' expansion, even if the -14-With some notable exceptions (Geertz 1963) and (Soerjani, Eussen, and Titrosudirdjo 5 1983), most observers consider the conversion of forest into Imperata cylindrica grassland an example of land degradation. However, farmers in South Kalimantan tend to be more divided about the desirability of this grassland (Blaikie and Brookfield 1987, 164-176).Awrajas range in size from 3,400 to 16,000 sq. km. (Grepperud 1996).factor has not created an incentive for the change. Moreover, Harrison interprets this increase in farm land as evidence of environmental degradation. But, if expansion of farm land occurs in previously degraded forests, shrub land, Imperata grassland, or pastures, that expansion should not necessarily be considered evidence of degradation. 5Econometric analysis of the effects of population pressure on agricultural land quality are rare. An effort to use existing secondary data for the Ethiopian highlands found that as the ratio of the population-supporting capacity to the actual rural population of awrajas (districts) decreases below one, the likelihood that these indigenous land areas are classified into more severe categories of soil erosion increased significantly and substantially (Grepperud 1996). 6   Longitudinal case studies indicate that increases in population densities have led to greater soil erosion, declining soil fertility, or slope failures in several areas: the volcanic slopes of Meru District, Kenya (Bernard 1993), the formerly fertile highlands of Ethiopia (Hurni 1990 as cited in UNFPA 1991, 93), Rwanda's fertile northwestern region (Nyamulinda 1988 as cited in Clay, Byringiro, Kangasniemi, Reardon, Sibomana, and Uwamariya 1995), the Awka-Nnewi Region, Nigeria (Okafor 1993), a watershed in West Java (Repetto 1986), and the hills of eastern Zambia (Robinson 1978). Expansion of agricultural production in the highlands of Kenya, Tanzania, Rwanda, and Burundi has led to increases in soil loss and sediment load in streams (Berry, Lewis, and Williams 1990). The extent of soil erosion worsened as population densities increased in communal land areas of Zimbabwe (Tagwira 1992). (See Table 4.) Soil erosion, soil-fertility decline, and use of rocky or density areas Hurni (1990) Ethiopian Highlands Cited by UNFPA (1991, 93), unclear Estimate of annual soil loss in area and statistic on primary data increase in national population from 1950 to 1970 Nyamulinda (1988) Northwestern Rwanda growth induced expansion of agriculture onto Cited by Clay et al. (1995, 68), unclear primary data Slumps and landslides occurred after populationmarginal lands Okafor (1993, 328-330, 341) Awka-Nnewi, Nigeria Extrapolation of population census Soil erosion dominates heavily-populated landscape in (not entirely hilly) data, unclear data on soil erosion this region Repetto (1986, 14) Watershed in West Java USAID, population data for entire and 6 mm/yr in 1980s while population grew 1.9% Thus, while we generally accept the empirical evidence of a positive correlation between population density and expansion of agricultural land or other forms of increases in cropping frequency, the evidence that these changes in land use inexorably lead to degradation is still debatable. Increases in population density eventually lead to decreases in soil fertility and increases in soil erosion as a result of increases in cropping frequency if the cropping pattern and all other methods of agricultural production do not change. In many instances, however, land users not only increase cropping frequency but also substitute other means of replenishing soil fertility for fallowing and make land improvements that conserve soil, water, and fertilizer and that enable a more productive soil-air-water relationship (Boserup 1965;Boserup 1990). These landscape investments and changes in crops and technologies improve land quality or, at least, help to maintain land capabilities for future use (see Table 5).Numerous historical cases from the Americas and Africa support this more optimistic view of the environmental impacts of population growth (Binswanger and Pingali 1988).Archeological research indicates that ancient terraces, walled fields, and intensive agricultural techniques were in use in southern Mesoamerica during the peak period of population density, toward the end of the first millennium A.D. (Boserup 1981). The Mayas instituted terracing of sloping fields to make continuous cropping possible during the Classic period (A.D. 300-800), a period of relatively high population density (Boserup 1990;Rice 1991).Before the colonial period, farmers in the densely populated Jos plateau in Nigeria, the Mandara mountains in Cameroon, the Kikuyu highlands in Kenya, and the steep highlands of Rwanda used ridging, tie-ridging, silt traps, and elaborate systems of stone-walled terraces (Pingali, Bigot, and Binswanger 1987). As early as the nineteenth century on Ukara Island, a small island in Lake Victoria where population density had increased since the fifteenth century and land ownership had become individualized, the Wakara people's agricultural practices included manuring, production of fodder crops, erosion control measures, such as terraces, and irrigated rice farming of the lowlands and lakeshore regions (Pingali, Bigot, and Binswanger 1987). In those highlands of sub-Saharan Africa that served as refuges from slavery and warfare, or where groups were unable to expand their domain into neighboring  (1987, 36, 42fn) Mandara mountains, Cameroon; Jos plateau, Ridging, tie-ridging, using silt traps, and Nigeria; Kikuyu highlands, Kenya; and Unclear primary data stone terracing in these densely Rwanda populated, pre-colonial refuges Pingali, Bigot, and Binswanger Ukara Island, Lake Victoria in 1800s and Ludwig (1968) and Iliffe (1979), Fodder cropping, manuring, and(1987, 42fn, 43, and45, 49) Kainam, Great Rift valley before 1890 whose primary data are unclear terracing in concentrated settlements Pingali, Bigot, and Binswanger Kigezi District, Uganda Purseglove (1946), whose primary Intercropping and terracing in area with (1987,46,70) data are unclear 144 people/km in 1944 2 Tiffen, Mortimore, and Gichuki Machakos, Kenya Photographs in 1937, 1948, 1960-61, More terracing, manuring, and (1994a, 3-12, 67-76, 194-200, 239-1978, and 1991, farmer   Roche (1987Roche ( , 1988)), whose primary Increased use of inorganic fertilizers and Fujisaka (1989b, 144-145) highlands of Java data are unclear, official statistics, manures, and bench terracing farmer interviews, researcher observation areas controlled by more powerful tribes, people frequently cultivated small plots of land with extremely high inputs of labor for investments such as terracing and for maintaining soil fertility (Pingali, Bigot, and Binswanger 1987).More contemporary evidence also indicates that increases in population density lead not only to increases in cropping frequency but also to increases in the incidence of land improvements and the use of soil fertility enhancers. A frequency table of farming intensity and technologies used in 57 specific locations of Asia, Africa, and Latin America indicates that more land investments are made when farmers crop land multiple times in a year than when farmers cultivate only once a year; no land improvements are made when farmers engage in forest, bush or grass fallow (Pingali and Binswanger 1987). Since increases in population density are positively correlated with higher intensities of land use, and these are in turn positively correlated with more land improvements, increases in population density are implicitly correlated with more land improvements.indicate an implicit positive correlation between population density and both land improvements and higher use of fertilizer and other inputs (Hyden, Kates, and Turner 1993).For example, in Machakos District, Kenya, as population density increased over time ) and space, not only did the percentage of cultivated land increase but the percentage of terraced cultivated land increased too and manuring became widespread. In general, less soil erosion occurred and agricultural land quality had improved by the end of the sixty-year period (Tiffen, Mortimore, and Gichuki 1994a). Pingali and Binswanger (1987) illustrate the argument with this case:The population density of the Kigezi district was about 144 people per square kilometer in 1944 and 280 in the early 1980s. As population density increased, cultivation proceeded upward from the middle altitudes of the Birunga mountain range, toward the summits of the hills, and then toward the valley. By 1946 almost all the summits were being cultivated and cultivation of the bottomland had begun. In this area of very steep slopes farmers rely exclusively on labor for power and intercrop field and root crops on small terraced plots.  Vogel (1987) Man and the Biosphere Programme productivity associated with migration Yemen and Oman Steffen (1979)  demographic decline occurred, terraced hill land was abandoned, irrigation ditches decayed, terrace walls became destroyed, the landscape became desiccated, and catastrophic soil erosion and siltation occurred (Naveh and Dan 1973).Whenever East African societies of refugees from tribal warfare and slavery spread out of their concentrated settlements, they invariably abandoned terracing and other laborintensive practices and reverted to shifting cultivation. For example, after European colonial powers succeeded in controlling East Africa, many of the Wakara migrated from Ukara Island to the regions bordering the southeastern part of Lake Victoria. There they abandoned the advanced methods of crop husbandry and resumed shifting cultivation. In another instance, the Iraqw people expanded northward and settled below the Rift wall during the early 1890sas Masai domination subsided. They quickly increased their herds and abandoned the sophisticated agriculture of Kainam (Pingali, Bigot, and Binswanger 1987).A similar process of disintensification and landscape disinvestment occurred in the distant and recent past in parts of the Americas. The reduction of the Indian population in the urbanized societies of Latin America after the arrival of the Spanish and imported African slaves brought on ruralization and a return to extensive subsistence systems. In particular, the Mayas in southern Mesoamerica, abandoned their elevated fields and terraced land and returned to long-fallow techniques (Boserup 1981). As people in La Alta Mixteca of Oaxaca, Mexico abandoned the pre-Hispanic terrace systems and various agricultural environments, land and water were rapidly degraded, and severe erosion extended over the region (Garcia-Barrios and Garcia-Barrios 1990).More recently, a reduction in rural population density and the concomitant increase in the amount of land managed per household between 1955 and 1985 also led to a reduction in the time that farmers from San Andres Lagunas, Oaxaca, Mexico spent on maintenance of terraces and land containers (Garcia-Barrios and Garcia-Barrios 1990). This conscious neglect of old terraces and furrows has caused accelerated erosion of hilly lands and siltation of reservoirs used to irrigate once fertile land in the valley (de Janvry and Garcia 1988).Recent decreases in population densities in hillsides in parts of the Mediterranean, Arabian peninsula, and Africa have been associated with similar negative results. For example, serious depopulation of the hills around the Jos plateau, Nigeria in recent times has led to visible scars on the landscape; unrepaired terrace walls are probably causing more soil erosion (Netting, Stone, and Stone 1993). Migration from and abandonment of terraced fields in Malta and neighboring Gozo have led to neglect of fertile slopes, soil erosion, and soil impoverishment (Busutti 1981). Previously terraced land in Yemen and Oman continues to be abandoned due to massive migration out of rural areas. As a result, soil erosion on and below these abandoned terraces is severe. Eventually entire hillsides are stripped of any productive capacity (Dregne 1992;Speece and Wilkinson 1982;Vogel 1987).Relationships between human and animal population growth, production methods, and land quality are complex (see Table 7). In developing countries between 1961 and 1985, the annual population growth rates of humans and livestock were 2.3 and 1.3 percent, respectively (Harrison 1992). These growth rates indicate that as human population grows in developing countries as a whole, animal population grows but generally at a slower rate.Moreover, these growth rates probably reflect a more specific trend. Namely, as human populations grow from low to medium-high (>75-100/km ) densities, animal populations 2 grow and animal densities on ranges and pastures increase too.But this trend is reversed over time and space with more population growth. That is, as human population densities increase beyond medium-high densities, grazing areas become smaller and, eventually, animal population densities decline too. Thus, increases and decreases in animal populations and densities can occur in contiguous areas where the land frontier is open in some parts but closed in others. For example, total human and animal populations both increased between the 1950s and the 1980s for Machakos, Kenya as a whole. However, the livestock population decreased in two of the five agro-ecological zones of this District-the highlands-as the corresponding human population and persons per square kilometer grew during this period. As of 1990, these zones had the highest number of people per square kilometer but the lowest livestock density among all zones in the District (Tiffen, Mortimore, and Ackello-Ogutu 1993). observation, traditional grazing areas decreased as the number of humans in the area increased after the land frontier was closed in Meru District, Kenya (Bernard 1993), West Usambara mountains (Feierman 1993), Ruhengeri, Rwanda (Ford 1993), and Kisii District, Kenya (Okoth-Ogendo and Oucho 1993).At densities beyond medium-high levels, people do one or more of the following:substitute crop residues for pasture, gather forest fodder, cultivate fodder grasses on separate plots or on erosion-control bunds, eventually restrict livestock to stalls, and switch to smaller animals. Livestock production in the middle hills of Nepal (Blaikie and Brookfield 1987),Ruhengri, Rwanda (Ford 1993) and Machakos (Tiffen, Mortimore, and Ackello-Ogutu 1993) illustrate this evolution of feeding methods and animal sizes. Some historical and contemporary case studies suggest the reverse: as population decreases from high or medium levels, grazing areas and thus livestock densities increase. For example, one consequence of the 'serious' depopulation of the once densely-populated hills around the Jos plateau, Nigeria, is an increase in the amount of land used for grazing. Some men take advantage of the increased natural grazing possibilities by keeping larger herds of cattle; livestock densities increase, by implication, in these cases (Netting, Stone, and Stone 1993). The authors do not indicate, however, whether these larger herds and the increase in grazing area have contributed to the concurrent land degradation in the area.Evidence from all the case studies reviewed is broadly consistent with this conclusion (McIntire, Bourzat, and Pingali 1992): similar livestock population densities are possible at a wide range of human population densities in all agro-ecological zones. But, the feeding methods are land-intensive at low levels and labor-intensive at high ones.Some case study and anecdotal evidence do suggest a link between population growth in hills and mountains and actual degradation of these areas due to livestock production. For example, in the Ethiopian highlands, uncontrolled grazing is a major cause of soil degradation, along with encroachment of cropping into forested areas and continuous cropping (Ehui, Williams, and Swallow 1995). The human population densities and growth -25-The population growth rate between 1950 and 1990 was 2.3 percent for Ethiopia, 7 2.0 percent for Burundi, and 3.0 percent for Rwanda (United Nations 1993). The highland population density (people per square kilometer) in 1994 was 98 in Ethiopia, 164 in Kenya, 230 in Burundi, and 311 in Rwanda (Hoekstra and Corbett 1994).rates in this area are moderate relative to densities and growth rates in other East African Increasing human and livestock pressure since 1948 has caused loss of vegetative cover and soil erosion in various parts of Israel-Palestine (Naveh and Dan 1973).Degradation of rangeland vegetation, in terms of composition and the extent of cover, is considered a serious problem in the Indian state of Himachal Pradesh, the trans-Himalayas, and west Asia (Scherr and Yadav 1996). Over the last few decades, population in most Himalayan regions has grown more than 2.5 percent per year, which implies a doubling of human numbers in less than 28 years (Denniston 1995).In densely populated Nepal, livestock is a very important element in farming systems.The number of livestock per human inhabitant is among the highest in the developing world portions into terraced arable fields with fodder grasses planted besides terrace walls, along drainage ways, and in gullies (Tiffen, Mortimore, and Ackello-Ogutu 1993).Palestine, nomadic Arab tribes began to replace settled, Mediterranean agriculture with pastoral nomadism. As depopulation occurred from the time of this conquest until the end of the 1800s, pastoral land use became more and more dominant and, by implication, the amount of land used for grazing increased (Naveh and Dan 1973). Allowing livestock to graze on land previously used for agricultural production is one likely reason for the land degradation that occurred during this period of prolonged depopulation.Similarly, the people who survived the epidemics and social disruptions of Spanish colonizers in La Alta Mixteca, Mexico began to raise goats and allowed them to graze on the abandoned hilly land with terraces. This implied increase in goat density and their grazing on abandoned terraced land created additional soil erosion and ecological disruption (Garcia-Barrios and Garcia-Barrios 1990).Case evidence also suggests that if population growth induces degradation of hilly rangelands, then this degradation most likely occurs when population densities are at relatively low or moderate levels but not at high levels. For example, the rangelands that are currently being degraded in the greater Himalayan region generally have low to moderate population densities for this region (Scherr and Yadav 1995).Population growth can also induce improvements in land used for livestock production. For example, the same population growth in the 1930s that induced degradation of some grazing lands in Machakos, Kenya also sparked public and private efforts to rehabilitate some of the degraded grazing lands in the area. Although these efforts continuedBecause people change crops, use different methods of replenishing soil nutrients and 9 make land improvements, the Ricardian assumption of fixed land quality, or soil productivity, does not hold.The empirical evidence reviewed provides a basis for the following generalizations about the relationship between population growth, production systems, and land qualities in the hills and mountains of developing countries:1) Changes in farming systems associated with population growth can lead to either land degradation or land enhancement, or aspects of both.2) As population grows, people use land more frequently to increase production. That is, as their numbers increase, people tend to convert 'unused' forests or grasslands into crop land, grazing land, or planted tree land, to utilize 'unused' spaces for these purposes, to 'harvest' products from forests or tree farms sooner, to reduce the length of fallow, to cultivate annuals for a second or third time within a year, to graze a pasture more frequently, or to increase the stocking rate on a given piece of land. If land investments or changes in products or methods of production do not occur, these increases in production frequency deplete various biological and physical resources or degrade biophysical characteristics of hills and mountains.3) However, as population grows, people tend to produce more labor-intensive crops and substitute labor-intensive and capital-intensive inputs and production techniques for land-intensive ones. Some of these products, inputs, and production techniques also enhance certain aspects of the land, for example, vegetative cover or soil nutrient levels. Moreover, population growth commonly induces people to make landscape investments, such as trees, terraces, vegetative barriers, and fodder banks, which improve the land's capacity for intensive and sustainable use, for example, tree cover, soil water-holding capacity, or fodder availability. 9Because people can and sometimes do invest and change crops, inputs, and techniques, production systems that enhance landscapes, minimize degradation, or mimic many of the ecological functions of 'original' vegetation in watersheds exist at all levels of population density.However, population growth from low levels of population density often leads to decreases in the abundance and diversity of certain natural resources in the hills and mountains. In particular, this often leads to conversion of 'natural' grazing land into agricultural land or of 'natural' forests into grazing land or farms, or the fragmentation of such areas. Some of these conversions can cause irreversible damage to certain aspects of hilly-mountainous ecosystems, such as loss of unique habitats and biodiversity.Population decline from medium or high levels of population density often leads to disintensification of production systems and land degradation. As their numbers decrease, people tend to neglect or cease maintenance of hilly-mountainous landscapes previously transformed by abundant human labor, to overgraze livestock, to not produce other land-intensive crops that protect the landscape, and to not make traditional labor-saving investments, such as tree planting, that also enhance the local environment. On the other hand, depopulation which leads to large-scale land abandonment or reversion to natural vegetation may have positive environmental effects; this pattern is found mainly where population densities were initially low or in peri-urban areas with high non-farm employment opportunities.Although examples exist in which people make landscape investments at all levels of population density, the type of investment changes as population density increases.For example, people may protect naturally-growing trees or maintain dry season grazing reserves in low population density systems, while construction of contourhedgerow terraces or intensively-managed fodder banks would usually be found in systems with high population densities. More labor-or capital-intensive land improvements often represent the end of transitional phases from relatively degrading to relatively enhancing land use systems, following earlier adaptations in technologies, variable input use or management practices.We conclude from the above that population growth in hilly-mountainous areas of developing countries can lead either to land degradation or land enhancement. To predict which outcome is more likely requires an understanding of how population growth affects microeconomic incentives for land management. In general, most of the environmental impacts of production increases in these areas depend on whether sufficient microeconomic incentives exist for people to choose production systems-products, inputs, land use intensities, technologies, and landscape investments-that enhance land characteristics or, at least, retard their degradation.   Low off-season real wages are an important incentive for more frequent cropping 10 by cultivators who rely on hired farm workers or produce for the market (Boserup 1965;Boserup 1990).An increase in the number of local people who work, due to immigration or natural increase, implies an increase in the supply of labor. This increase, in turn, puts downward pressure on agricultural wages. As labor costs fall, labor-intensive products and production methods become more profitable relative to others.Meanwhile, the demand for land increases, due to the increased number of local producers and increased local demand for land-using products; these raise the opportunity cost of not using land for production. An increase in the number of homesteads, paths, etc., also makes land for production more scarce. As land costs rise, land-intensive products and production methods become relatively less profitable.A case study from a hilly area of Sumatra, Indonesia illustrates the importance of the relative cost of land and labor on the type of production system that prevails. In this area, where lands are still abundant and not degraded, farmers strongly prefer cinnamon-based relay agroforestry systems over bench terracing because cinnamon requires little labor once seedlings are established and can remain in the ground until the market offers a high price (Belsky 1994).In particular, decreases in the opportunity cost of labor relative to land induce producers to increase cropping frequency.For the same reason, producers tend to 10 substitute labor-intensive means of providing nutrients to crops, such as weeding or collecting and applying manure, for land-intensive means, such as fallowing.People also tend to substitute labor for land to provide nutrients to livestock by improving grazing land, as the opportunity cost of fodder from 'natural' grazing land increases relative to the opportunity cost of fodder from labor-enhanced pastures. For example, some methods of range improvement-such as hedging, fencing, bush and indigenous tree management, scratch ploughing, and reseeding or replanting-became more attractive in Machakos, Kenya as the number of people increased from the low population density levels of the 1930s (Tiffen, Mortimore, and Gichuki 1994b).As land becomes still scarcer relative to labor, people substitute even more laborintensive means of providing nutrients to livestock-such as planting, cutting, and carrying fodder to animals in stalls-for the land-intensive means of open grazing on improved pastures. For example, cattle are stall-fed much of the time in west-central Nepal and the manure, together with leaf material from animal bedding, is then applied to fields (Blaikie and Brookfield 1987).Increases in the cost of labor relative to land induce opposite changes in crop choice and livestock production. Higher labor costs due to demographic decline explain why Muslim conquerors of Israel-Palestine engaged in pastoral rather than intensive agricultural production and why people in La Alta Mixteca, Mexico at the start of the colonial period began not only to raise goats but also to cultivate wheat, two low-labor demanding products (Garcia and Garcia 1990).As agricultural wages decrease due to increases in labor supply, labor-intensive improvements to agricultural land also become more profitable (Pingali and Binswanger 1987). For example, in Mindanao, Cebu, Batangas, and Bicol-areas that represent a wide variety of soils and rainfall patterns in the Philippines-the probability that farmers construct and maintain grass strips, tree-grass strips, contour hedgerows, and rockwalls increases as real agricultural wages decline (Templeton 1994b). In Rwanda, by contrast, farmers maintain longer grass strips, anti-erosion ditches, hedgerows, and radical terraces as the agricultural wage increases (Clay, Byiringiro, Kangasniemi, Reardon, Sibomana, and Uwamariya 1995).This result might mean that higher farm wages enable farmers who were net suppliers of labor to self-finance these investments.The same changes in the supply of labor and demand for land also induce changes in the credit market. As the supply of labor increases, the demand for credit to finance wages rises. Moreover, as the demand for land increases, the collateral value of land increases and, as a result, the supply of credit increases as well (Binswanger, McIntire, and Udry 1989).Consequently, land improvements, particularly those with high start-up costs, become more economically feasible as population grows. For example, people tend not to terrace agricultural land until population densities are high because these investments tend to be not only labor-intensive but also cash-intensive initially. In contrast, the improvements in grazing land that people began to make at the early stages of population growth in Machakos were attractive because farmers could make them with their own household labor and, thus, did not need cash to pay hired laborers (Tiffen, Mortimore, and Gichuki 1994b). (See Table 8.)Larger Families, Fewer Land Holdings, and Smaller FarmsIf labor, land, credit, and insurance markets were perfectively competitive and farmers shared the same technology and goals, then farm sizes and total labor allocated to production on farms would not vary with the land-labor endowments of households (Kevane 1996). But, since market imperfections are widespread in hilly-mountainous areas, production systems are typically a function of the land-labor endowments of farm households.Higher population densities can imply larger household size. As the number of nonworking family members increases, working family members may increase cropping frequency and work harder in other ways to produce enough food (Boserup 1990). As the number of working family members increases, a farmer is more able to do one of more of the following:expand the area under annual cultivation (Boserup 1990), make land improvements (Boserup 1965), cultivate labor-demanding crops, or use labor-intensive production methods. Farmers can do these things because they can 'borrow' the labor of other family members and because household labor is less costly than hired labor (Binswanger, McIntire, and Udry 1989).Holding family size constant, higher population densities imply more families and lower average land holdings (Clay, Byiringiro, Kangasniemi, Reardon, Sibomana, and Uwamariya 1995;Pingali and Binswanger 1987). Because family labor is cheaper than hired labor and because the incremental collateral and insurance value of land increases as land holdings decrease, smaller land-labor endowments imply smaller farms. As farm size decreases, the amount harvested per cropping decreases, merely as a result of a reduction in cultivable space. To prevent a decline in output per year and to meet subsistence needs over the course of a year, farmers reduce the length of fallow or the share of land under fallow (Boserup 1990;Boserup 1965;Clay, Byiringiro, Kangasniemi, Reardon, Sibomana, and Uwamariya 1995). But farmers also are more likely to make land improvements or undertake more of these investments as farm size decreases to enable these desired increases in cropping frequency (Boserup 1990) and to prevent decreases in yields per cropping that otherwise In an area of the Kakamega district of Kenya, households that have very small farms 11 (0.6 ha. on average) and that need to devote a substantial part of their labor to non-farm employment grow trees (Arnold 1987). In Mbiuni, an area of Machakos, Kenya with both sub-humid and semi-arid rainfall, the number of fruit trees per hectare increases as farm size decreases (Tiffen, Mortimore, and Gichuki 1994a).occur as cropping frequency increases (Boserup 1990;Clay, Byiringiro, Kangasniemi, Reardon, Sibomana, and Uwamariya 1995).National land use data for Costa Rica indicate that, as farm size decreases, the shares of farm land in annual and perennial crop production become larger and the shares of farm land as forest and pasture become smaller (Cruz, Meyer, Repetto, and Woodward 1992).Similarly, according to national survey data of households and parcels in Rwanda, the share of land under cultivation increases as the amount of cultivable land per adult equivalent decreases (Clay, Byiringiro, Kangasniemi, Reardon, Sibomana, and Uwamariya 1995). In the upper Huallaga Valley of Peru, where average farm sizes range from 18-52 hectares, the shares of land in perennial crop production become larger and shares of fallow land and forests become smaller as farm size decreases (Bedoya 1987).Econometric evidence from Rwanda, selected areas of Thailand, the Philippines, and the Dominican Republic indicates that decreases in farm size induce agricultural land investments, such as bunds, grass strips, hedgerows, and rock walls (Clay, Byiringiro, Kangasniemi, Reardon, Sibomana, and Uwamariya 1995;de la Brière 1996;Feder, Onchan, Chalamwong, and Hongladarom 1988;Segura-de los Angeles 1986;Templeton 1994a).Case study evidence suggests that tree densities initially decrease but, in some cases, eventually increase as farm sizes increase. Among participants in the Agroforestry Extension Project (AEP) in Siaya and South Nyanza Districts in Kenya, tree density decreases as farm size increases from less than 0.7 ha. to 7.3 ha. (Scherr 1995). In land-scarce Rwanda, small 11 farmers grow more trees per hectare (Kangasniemi and Reardon 1994 as cited by Clay, Byiringiro, Kangasniemei, Reardon, Sibomana, and Uwamariya 1995) and the percent of cultivated land with 'many' trees increases as cultivable land per adult equivalent decreases (Clay, Byiringiro, Kangasniemi, Reardon, Sibomana, and Uwamariya 1995). But in an area of the Philippines farmers manage woodlots because their land-holdings considerably exceed the area on which they can cultivate food and other crops with family labor (Arnold 1987).One case study suggests a similar negative correlation between livestock density and farm size. In Mwala, a semi-arid area of Machakos, the number of livestock per hectare of an individual farm increases as farm size decreases because people with smaller farms are more likely to plant fodder and practice stall-feeding and use more cash to purchase non-farm inputs, such as feed (Tiffen, Mortimore, and Ackello-Ogutu 1993). (See Table 8 for summary.)Governments regulate or claim de jure ownership of much larger portions of hills and mountains than flat areas in developing countries, typically for watershed or forest protection, or for public parks or reserves. Although occasionally successful, government land-use regulations and natural resource policies have generally failed to control deforestation (Repetto 1988;Scherr, Jackson, and Templeton) and have interfered with indigenous resource management. For example, commercial loggers, ranchers, and their agents have deforested areas in the Philippines, Costa Rica, and Sabah because short-term timber leases, lack of titles for uncleared or underutilized land, and titles for land cleared of trees did not create secure, long-term interests in forests (Cruz, Meyer, Repetto, and Woodward 1992;Repetto 1988). In most countries in Central America and the Caribbean, harvesting laws and regulations created to protect forests have been a major disincentive to farm tree-planting activities (Current and Scherr 1995).As population densities increase over time or space, de facto land tenure systems become less general and more specific, less communal and more individualized (Binswanger, McIntire, and Udry 1989;Boserup 1965) Ability to transfer parcels of land does not increase the likelihood that a farmer 14 makes long-term improvements in Ruhengeri, Rwanda; in Ejura, Ghana; or in Madzu, Kenya (Place and Hazell 1993).Since population growth induces greater precision in the definition and allocation 15 of property rights (Ruttan and Hayami 1991), cultivators who are non-owners, communal owners, or owners without title in hilly-mountainous areas are more likely to have secure, long-term rights of access to land and be able to sell or bequeath their usufruct rights in areas with high population densities.affect the likelihood that farmers bund and has a smaller impact on the likelihood that they clear stumps from land than in two other provinces with negligible informal credit (Feder and Onchan 1987).Insecure or short-term rights of access usually have the opposite effect. For example, female cultivators resisted building permanent erosion-control ridges on rent-free land used for subsistence cropping in the West Usambara mountains in Tanzania during the 1950s because they expected to be evicted by male owners once improvements were made (Feierman 1993).'Insecure' property rights can also mean that owners are uncertain about their ability to restrict access. Thus, in certain areas of the Philippines owners forbid tenants to plant perennials for fear of losing their ownership claim (Sajise 1987as cited in Fujisaka 1994). Forthe same reason, however, non-owners or settlers plant trees, create fences, or terrace to gain more secure rights in various countries (Place and Hazell 1993;Sellers 1988;Fujisaka and Wollenberg 1991). Hence, land improvements can also create more secure property rights.More importantly, cultivators do not necessarily need to have titles or even be private owners to have secure, long-term access rights to land. For example, lack of title has not 13 prevented farmers, most of whom are private owners, from improving land in Kitui-Machakos, Kenya (Pagiola 1993) or in the Tatumbla area of Honduras (Lutz, Pagiola, and Reiche 1994). Non-ownership of land by cultivators has no discernible effect on the 14 likelihood that they cooperatively build check dams in small watersheds in Haiti (White and Runge 1994). (See Table 9 for a summary.)15 In the past and present, labor exchange groups and other local mutual self-help organizations have enabled farmers to, among other activities, terrace and make other land improvements. For example, women's labor-exchange groups, called mwethya, have helped poorer farmers to construct indigenous bench terraces, or fanya juu, in Machakos (Tiffen, Mortimer, and Gichuki 1994a). People who already belong to peasants groups that engage in collective social and economic activities, called groupman, are six times more likely to join a cooperative effort to build check dams in Maissade, Haiti (White and Runge 1994).Similarly, small, work-sharing groups and their promotion by credible extension agents effectively promoted non-paddy terracing in the Philippines (Fujisaka 1989b);Queblatin 1985;Templeton 1994a). 16   Obversely, a key reason for current degradation of hilly landscapes in a rural community in Mexico is the breakdown of local institutions that mobilized familial and collective labor for steep slope management and erosion control (Garcia-Barrios and Garcia-Barrios 1990). Similarly, a breakdown in cooperation among women, who do much of the agricultural work, is a secondary reason why they have abandoned many terraces and poorly maintain the remaining ones on Rusinga Island, Kenya (Conelly 1994).  Technical assistance programs have been widely used in hilly and mountain areas to promote resource conservation. But some forms of extension are more effective than others.For example, local people as paratechnicians successfully promoted agroforestry in nine out of eleven projects in Central America and the Caribbean and small, in-kind, material inputs generally provided by these and other extension agents effectively persuaded farmers in these areas to try related practices (Current, Lutz, and Scherr 1995). Farmer-instructors and other credible extension agents who provide free or below-cost plant materials that are locally appropriate are more effective than one-shot classes, demonstration farms, radio messages, or a lack of extension in inducing farmers in the Philippines to invest in contour hedgerows and rockwalls in the Philippines (Fujisaka 1989a;Templeton 1994a). Similarly, the 'community-based' approach of CARE International's Agroforestry Extension Project (AEP)in Siaya and South Nyanza Districts, Kenya was more appropriate for local conditions than 'commodity-based,' 'training-and-visit,' 'farming systems,' or 'media-based' extension (Scherr 1992).Extension programs do not always provide effective incentives. In some cases, the subsidized crop, technique, or investment is economically or ecologically inferior to nonsubsidized ones (Belsky 1994;Fujisaka 1994). Moreover, farmers who have rock walls, trees, or hedgerows installed for free by others or who receive food or cash in exchange for this work are not likely on their own, unpaid initiative to make new landscape improvements, to maintain the existing investments, or to inspire non-beneficiaries to follow suit (Belsky 1994;Current and Scherr;Fujisaka 1989b;Hudson 1991;Lutz, Pagiola, and Reiche 1994).(See Table 11.)Widespread diffusion of land-improving innovations, either from indigenous or introduced sources, is essential to sustainable management of hillsides and mountains. The quality and penetration of local information systems can influence the transaction costs of information exchange, and hence the degree of diffusion of appropriate practices in a particular hillside region (Röling 1988 ). The degree of access by local people to external  Fujisaka (1989b, 152), Queblatin (1985, 73), Templeton (1994a, 111, 121-122, 155-156, 169, 224-229) Cebu Lack of maintenance of subsidized land without subsidies or spontaneous imitation sources of information, through extension programs, participation in associations, government contacts, informal networks (ethnic groups, family, employment), mass media, etc., can also affect the cost and availability of relevant information for land management. Finally, diffusion is strongly affected by the economic attractiveness of the market conditions described in the section below.In closed, subsistence-oriented economies, the processes described above are largely responsible for changes in land management systems. In open economies, however-which characterize most hilly-mountainous regions-broader market factors will influence farmers' incentives in significant ways, particularly affecting transport, input and labor costs.Population growth enables lower transport costs (Boserup 1990) and improvements in transport infrastructure encourage immigration (Pingali and Binswanger 1987). Transport costs are one measure of the degree of access that producers in hilly-mountainous areas have to buyers in markets in other areas or countries. Declines in costs of transporting to and from hilly-mountainous areas make production more profitable due to higher product prices and lower prices of fertilizers and other purchased factors of production at the farmgate.Increases in the price of a product induce an intensification of production of that good similar to what occurs with local population growth. That is, as the price of a farm product increases, people use land more frequently to produce the good and allocate more land, labor, and purchased inputs to its production. But, because some inputs, such as manure, enhance land and because payoffs from land-improving investments increase too, the effects of these reallocations on land quality is indeterminate.Price Changes for Annual Crops. As a rule, annual crops provide less vegetative cover than perennial crops or trees for timber. Moreover, the soil is exposed to rainfall from harvest These products include perennial field crops (for example, bananas), shrub crops (for 19 example, coffee or tea), and tree crops (for example, rubber or oil palm) (Ruthenberg 1976).until new crops grow to provide some vegetative cover. Thus, the greater the area that a farmer devotes to production of annual crops at the expense of perennial crops or timber, the less likely her production system enhances land quality. For the same reason, increases in the frequency of annual cropping without any change in production methods or amounts of landenhancing inputs usually cause soil degradation.Case studies from Indonesia (Belsky 1994) and from Eastern Nigeria, West Africa, Kenya, Tanzania, and Uganda (Pingali 1989) indicate that cropping frequency and the evolution from shifting cultivation to sedentary cultivation to multiple croppings per year is determined in part by increases in farmgate prices and better access to markets. But farmers are more likely to terrace or make other land improvements in response to larger wholesale markets in the Philippines (Templeton 1994b), to better access to vegetables markets in Java (Fujisaka 1989b), and to greater profitability of agricultural production in Rwanda (Clay, Byiringiro, Kangasniemi, Reardon, Sibomana, and Uwamariya 1995). Moreover, increases in annual crop prices can induce greater use of inputs that replenish soil productivity. Hence, the net effect on land quality of increases in annual crop prices is indeterminate. Results of theoretical analyses of a permanent increase in annual crop prices echo this indeterminacy (Ardila 1991;Barbier 1990;LaFrance 1992;Pagiola 1993). (See Table 12.)Price Changes for Perennial Crops. Products that are harvested from trees, shrubs, and other perennial plants that stay rooted generally provide the best protection against rainfall erosion on sloping lands, because these plants provide some vegetative cover of the soil at all times. As a result, increases in perennial crop production in hilly-mountainous areas tend 19 to reduce erosion from rainfall and, thereby, improve soil quality. However, the full effect on soil quality and other natural resources of any price increase for perennial products also depends on the extent to which, if any, the price increase encourages people to convert nearby open-access forests or natural grazing land to perennial crops and the extent to which this conversion creates transitional degradation or represents loss of habitat of important species.  Belsky (1994, 432-433) and Eastern Nigeria, West Pingali (1989, 3) Africa, Kenya, Tanzania, Sumatra, New Guinea, and UgandaBetter market access Case studies More frequent cropping Fujisaka (1989b, 144-145, 150) Diang Highlands, Java Case study Better access to Widespread terracing and use of vegetable markets manure and inorganic fertilizer Templeton (1994b, 9, 20, 37  (1995( , 82, 89, 97) Templeton (1994b, 11-12, 19-20) , 11-12, 19-20) Philippines Higher fertilizer costs Econometric More likely to terrace -48-Hence, an increase in the price of perennial crops will probably have the strongest positive impact on soil quality in high population density areas where open-access resources no longer exist.Some case studies examine farmer responses to perennial price increases. For example, farmers in the middle volcanic slopes of Central-East Java created widespread change from annual crops to cash perennials, planted coffee, cloves, apples, grapes, and other perennial crops because the markets for these crops were strong (Fujisaka 1994;Fujisaka 1989b). Farmers on the Philippine island of Palawan, where the population density is 200/km , converted swidden plots into fruit orchards in part because of their proximity to a 2 'good' market (Eder 1981 as cited by Raintree and Warner 1986). Low coffee prices almost induced Chagga farmers on Mt. Kilimanjaro to remove coffee bushes from their homegardens (Fernandes, Oktingati, and Maghembe 1984). Subsidies for rice-an alternative land use-and declining market demand for damar threatened ecologically protective damar agroforest systems in Sumatra (Mary and Michon 1987). (See Table 12.)Meanwhile, if non-timber forest products increase in value, this may induce farmers to reduce forest-clearing. However, this result is dependent on a complex of other factors, in particular tree tenure, the potential for domestication of the valued products, and market access (Dewees and Scherr 1996).Price Changes for Timber Products. The effects of timber price changes depend on whether the changes are expected. In response to an unexpected but permanent increase in timber prices, farmers harvest more wood from a given area of privately-managed, mixed-age stand of trees in the short-term because the opportunity costs of not harvesting the more valuable trees and of not utilizing the land to plant new ones increase unexpectedly. But, since trees are not allowed to grow as long, farmers harvest less wood (that is, smaller trees with smaller canopies) and replant at shorter intervals in the long-term (Clark 1976). Since timber harvests typically expose the soil for a significant period and timber transport may cause further damage, unexpected price increases probably harm land quality. However, expected future price increases, similar to unexpected current price decreases, enhance land quality because they induce people decrease current harvest rates on private lands (Clark 1976) and, thus, to This area is not hilly. But the case study result is important and relevant to hilly 20 areas.increase tree biomass densities in the form of older trees with larger canopies, higher tree densities in younger-age stands, or new tree plantings.Empirical evidence is consistent with the argument about the effects of anticipated price increases on timber management. For example, farmers manage woodlots and smallscale forest farms because of rising farmgate prices for wood products, as is the case in Kenya (Dewees and Saxena 1995;Scherr 1995), or expectations of favorable prices in the future, as was the case with contract tree growers in the Philippines (Spears 1987). Although photographs from the 1930s show a Rwandan landscape almost bare of trees, by 1989 every small farm had its own woodlot, because of the scarcity of wood and the emergence of wood markets (Harrison 1992). Increases in tree densities in Nepal have been greatest in those areas where forest products are in short supply and, thus, where the opportunity cost of not using land for economic trees is high (Carter and Gilmour 1989). Dewees and Saxena (1995) and Scherr (1995) show similar findings for Central and Western Kenya.The effects of price increases on tree management are stronger the more that property rights enable residents to (expect to) capture future benefits of new trees. For example, population-induced increases in demand for fuelwood between 1952 and 1983 not only led large-scale entrepreneurs to extend the open-access fuelwood hinterland of metropolitan Kano City, Nigeria by cutting and transporting wood from greater distances from the metropolitan area, but also led farmers in the close-settled zone of the metropolitan area to increase the tree density on their private farms (Mortimore 1993). (See Table 12.) 20Since land in general becomes scarcer as population density in hilly-mountainous areas increases, the opportunity cost of nutrients for plant growth increases. Fertilizer and other nutrient supplements become cheaper relative to fallowing. But whether decreases in fertilizer prices make landscape-improving investments and practices more attractive depends on whether those investments are intended for, or dependent on, nutrient conservation.Increasing amounts of fertilizer may be needed to substitute for the erosion-induced loss of soil nutrients; terraces may be constructed to protect the investment in fertilizers. On the other hand, fertilizer price declines may deter farmers from investing in longer-term soil improvements (which improve organic matter content or texture), such as composting systems or green manures.Empirical investigations of the effect of fertilizer prices on landscape investments or the choice of production techniques are rare. Farmers in well-settled hillsides of the Philippines are less likely to use grass strips, contour hedgerows, and rock wall terraces as the farmgate price of fertilizer decreases (Templeton 1994b). Farmers in Ambodiaviavy,Madagascar do not use fertilizer on hillsides because fallowing and opening new hill forest for production are still possible, no local fertilizer market exists, and production credit is not available (Harrison 1992). Purchased fertilizers will not usually be used in non-marketed crops, due to cash constraints, hence price changes will have little impact on such systems.In open economies, farm wages do not necessarily decrease as population density increases, because the demand for non-farm labor might increase. As a result, farmers may not make labor-intensive investments in land or maintain labor-intensive production systems as population grows. For example, in spite of increases in population density on Rusinga Island, Kenya since the 1930s, farmers, who are usually women, have abandoned most agricultural stone terraces and poorly maintain the remainders on steep hillsides of the island because a growing number of people, particularly young ones, allocate their labor away from farming into more remunerative commercial fishing or migrant employment (Conelly 1994).Similarly, in spite of increases in population density in a Bolivian watershed between 1953-1991, farm households in these areas have ceased using various soil conservation techniques and reduced their participation in reciprocal labor exchange because members of these households allocate their labor time to more lucrative commercial (that is, off-farm) activities or migrant employment (Zimmerer 1993).Numerous other case studies indicate a similar phenomenon. The better are off-farm income opportunities relative to on-farm ones, the less time that people devote to farm production and the less soil conservation that they practice in Latin America (de Janvry and Garcia 1988) and in specific hilly-mountainous areas of Mexico (Garcia and Garcia 1990),Ecuador (Southgate 1988), and the Dominican Republic (Murray 1992). In one area of the Philippines, farmers with off-farm and non-farm work opportunities were less likely to establish and maintain contour hedgerows and grassy strips (Fujisaka 1993). Similarly, better job opportunities in the oil, tourist, maritime freight handling, and other non-agricultural industries in urban areas were the primary reason for abandonment or neglect of terraced hilly land and consequent soil erosion in Yemen (Vogel 1987), Oman, Malta and Gozo (Busutti 1981), Israel and Palestine (Bunyard 1980), and the Canary Islands (Bunyard 1980). (See Table 13.)However, increases in the opportunity cost of farm labor induced by better off-farm income opportunities do not automatically induce land users to make more environmentally destructive production choices. For example, rising agricultural wages in Gujarat and other states in India have encouraged farmers to have woodlots because growing trees is less laborintensive than production of many other agricultural crops and spreads labor requirements over the year (Noronha 1982;Swaminathan 1987). Similarly, the steady outmigration of much of the active work-force from two hill districts of central Nepal and the higher labor costs implied by this out migration were one of the main reasons why residents of these areas planted more trees on their farms. As a result, people spent less time, effort, and money collecting fuelwood and fodder from their farms than from more distant communally-owned forests (Carter and Gilmour 1989). Dewees (1995) reports a similar result for woodlots in central Kenya.Better nonfarm income opportunities can also enable a household to self-finance land investments, particularly those with high start-up costs. For example, in Machakos farmers sometimes use off-farm earnings and livestock sales to pay laborers to terrace (Tiffen, Mortimore, and Gichuki 1994). In Rwanda farmers maintained more soil conservation infrastructure as nonfarm income and the value of livestock holdings increased (Clay,  and Turner (1993), \"the quality of an agricultural environment is as much as product of its use, including landscape transformations, as of raw nature.\"Hills and mountains in developing countries generally have greater variation in rainfall, sunlight, elevation, topography, soil, and other ecological characteristics than flat areas, not only in aggregate, but also among and within fields. Thus, cropping patterns and production techniques exhibit greater variety in hilly-mountainous areas than in flat areas. For ecological reasons (and not simply reasons related to risk aversion, labor-spreading or unreliable food markets) production of monocultures is not likely to be as economically attractive in these sloping areas as elsewhere. This greater ecological diversity implies that production is more human-capital intensive, that is, requires more farmer knowledge and research information, and intra-regional trade is more likely to occur in hilly-mountainous areas than in flat areas with less natural diversity.Differences in ecological conditions affect both the benefits and the costs of producing various goods and, thereby, affect the types of production. Climate broadly constrains the amount and types of plants and animals that either grow naturally or humans produce. For example, lower slopes and depressions are the only lands cultivated in arid regions because only these places have sufficient water-holding capacity to allow any cropping at all (Pingali, Bigot, and Binswanger 1987). Thus, hilly-mountainous land in arid regions is typically used for grazing animals since the low rainfall and water-holding capacity of soil are enough for 'Tropical' and 'temperate' areas differ according to temperature. 'Humid,' 21 'subhumid,' 'semiarid,' and 'arid' areas differ according to rainfall.growth of perennial grasses but usually make production of annual or tree crops either ecologically or economically unviable.On the opposite end of the rainfall spectrum, humid and sub-humid areas with soils that are relatively susceptible to leaching and acidification are more ecologically conducive to perennial cropping than to annual cropping (Pingali, Bigot, and Binswanger 1987;Ruthenberg 1976). For example, coffee, tea, rubber, other perennial crops, natural forests, 21 degraded forests, and homesteads with perennial crops dominate the hilly landscape in many upland humid zones, for example, Ratnapura district, Sri Lanka (van der Bliek 1991) and the east African highlands (Hoekstra and Corbett 1994). Cereal and tomato cropping were neither profitable nor sustainable in two humid and once densely-forested areas in the southern Sierra Madre mountains of the Philippines but production of multistory perennial crops intercropped with roots crops was both (Fujisaka and Wollenberg 1991).Evidence from sub-Saharan Africa (Pingali, Bigot, and Binswanger 1987) suggests that as rainfall increases from arid to semiarid to sub-humid levels, cropping frequency increases; the effect of a longer production period dominates any possible detrimental effects of leaching or acidification on soil. But as rainfall increases from sub-humid to humid levels, cropping frequency usually decreases because leaching and greater acidity become severe enough to offset the benefit of longer growing seasons.Increases in rainfall might have a similar effect on the intensity of livestock production.In general, as rainfall increases from arid towards sub-humid conditions, the amount of household-produced and purchased inputs used for livestock increases because fodder grows better with more rainfall. But excessive heat and humidity can hurt livestock growth by upsetting animal physiology and making conservation of fodder for the dry season very difficult (MacArthur 1976).The greater the rainfall, the greater the potential erosion of soil nutrients, rooting 22 depth, and commercial fertilizer. Thus, the benefits of controlling erosion of soil nutrients, rooting depth, and commercial fertilizer with landscape structures increase as rainfall increases. On the other hand, the benefits of conserving water and soil moisture with these land improvements decrease as rainfall increases.In addition to affecting product choice, annual cropping frequency, and the intensity of livestock production, rainfall also affects the profitability of soil conservation. Case 22 studies from Machakos, Kenya (Tiffen, Mortimore, and Gichuki 1994a), Burkina Faso (World Bank 1992), and Haiti (White and Jickling 1994) indicate that bench terraces, rockwalls, and other soil conservation structures have a larger positive impact on crop yields in dry years or dry areas. Econometric evidence is equivocal: farmers are more likely to terrace as rainfall decreases from humid to sub-humid levels in hilly-mountainous areas of the Philippines (Templeton 1994a) but they maintain longer conservation structures per hectare as rainfall increases in Rwanda (Clay, Byiringiro, Kangasniemi, Reardon, Sibomana, and Uwamariya 1995).Location on hills and mountains also affects the economic attractiveness of production and landscape investments. As farmers move down a hill or mountain, they trade off higher yields with higher labor requirements for land preparation, for lower risk of drought (Pingali and Binswanger 1987). As a result of these tradeoffs, people tend to produce annual crops on mid slopes at low population densities (Pingali and Binswanger 1987). The inherent risk of drought does not change as population grows but tillage of heavier soils on the lower slopes becomes cheaper as the opportunity cost of labor decreases. Thus, cultivation in the upper slopes is usually the least desirable option at high population densities because yields from annual cropping are usually lowest and drought risk greatest in these areas (Pingali and Binswanger 1987). Various case studies illustrate the relative disadvantage of production of annual crops in these areas: people use upper slopes in Gambia, Tanzania, Zambia, and Botswana (Pingali and Binswanger 1987) and ridgetops in Nepal (Blaikie and Brookfield 1987) primarily for forestry or grazing.In addition to location on a hill or mountain, the steepness of that location also matters for choices of production activity and technique. In Rwanda, the steepest areas were traditionally reserved for pasture, woodlots, and perennial crops; frequent fallows were common practice. Yet as a result of population growth in recent decades, some farmers now cultivate slopes once thought to be too steep and fragile for annual cropping (Clay, Byiringiro, Kangasniemi, Reardon, Sibomana, and Uwamariya 1995). In densely populatedBurundi and the Kigezi district of Uganda, people also cultivate very steep fields by hand (Pingali, Bigot, and Binswanger 1987). In Nepal, the steepest slopes and the ridgetops are unterraced and used primarily for pasture and occasionally for annual crop cultivation (Blaikie and Brookfield 1987).These case studies suggest that, except in cases of extreme land scarcity, people use the steepest portions of land for grazing or wood production because the susceptibility to erosion and costs of annual crop production are highest on these portions and, if waterholding capacity and soil depth vary inversely with slope, the yields are lowest there as well.However, if population growth and wage levels make hand hoeing sufficiently cheap relative to land, annual crop production replaces livestock or perennial crop production even on the steepest areas.Steepness of fields, or slope, also affects the benefits and costs of terraces and other landscape investments. Econometric evidence from Rwanda and the Philippines indicates that the net benefits of terraces and other soil conservation structures are highest on fields of medium steepness (Clay, Byiringiro, Kangasniemi, Reardon, Sibomana, and Uwamariya 1995;Templeton 1994a). This evidence suggests that as slope increases from low to medium levels, the benefits of conserving soil, water, and fertilizer with terraces or other terrace-creating landscape structures increase faster than the costs of these investments. But net benefits, while positive, decrease as slope increases from medium to high levels, since the costs of these investments-particularly the space and maintenance required for the structures, the difficulty of maneuvering draft animals, and the likelihood of terrace walls collapsing-grow faster than the benefits.The microeconomy of land management depends on local population growth, property rights and resource management institutions, systems for information access and technological innovation, market integration, and local ecological conditions. Local population growth leads to increases in the supply of labor and demand for land, larger families, fewer land holdings, more individualized and specific property rights, and other microeconomic changes. How can population growth lead to land degradation or enhancement? An increase in the demand for local goods leads to an increase in their production. But larger families, smaller farms, and an increase in the local supply of labor create incentives for farmers not only to use land more frequently for production but also to switch to crops and production techniques that use more labor and other 'cheaper' inputs per production cycle and to make labor-intensive and cash-intensive land improvements. The expected returns on land improvements increase as local property rights become more individualized and specific. The net benefit of collective management of hard to privatize resources can improve with predicting resource quality outcomes. Most studies did not compare evidence across sites, time, and space and, thus, did not disentangle the relative importance of site, household, community, market, and other institutional and policy factors.Still, the evidence reviewed and our microeconomic interpretation of it do provide a basis for identifying eight substantive research and policy challenges about the environmental impacts of production systems in hilly-mountainous areas and the microeconomic configurations that give rise to them.First, 'land degradation' needs to be better defined. The term can refer to depletion in renewable components of hilly-mountainous land (for example, vegetative cover or nitrogen content), to depletion of non-renewable resources (for example, soil depth), to reductions in land productivity that result from depletion, or to possible extinction of plants or animals and other irreversible changes. Future research and policy need to define different types of land degradation so as to distinguish private and social costs and benefits, and clarify the objective and value of policy intervention. For example, a policy objective to protect certain habitats might require creating biodiversity reserves, while the objective of protecting tree biomass (for watershed protection or long-term forest production) might only require creating secure property rights or expectations of favorable timber prices.Second, 'carrying capacity' is not a useful absolute concept for analysis of the environmental impact of human population and production over time or space. In any instance or place, carrying capacity for humans depends on the extent of landscape transformations and the nature of production systems (Kates, Hyden, and Turner 1993).Since changes in market conditions, institutions, and local population induce changes in products, input use, technologies, land-transforming investments, and institutions, carrying capacity is endogenous. Moreover, as evidence about depopulation and some deforestation experience suggest, certain landscape transformations are irreversible. Hence, carrying capacity is also path dependent.Third, high rural population levels are not directly associated with degradation.However, the slower the rate of demographic growth or decline, the more time that people have to innovate and adopt products, technologies, individual property rules, and local Pingali and Binswanger (1987) and Paarlberg (1994) make similar arguments about Sixth, the microeconomic basis for the empirical relationships between farm size and production frequency, tree density, agricultural investments, and livestock density needs to be more thoroughly investigated. That is, the research challenge is to identify specific market failures that form the basis of these relationships and examine their effects on land quality through their impacts on choices of products, inputs, technologies, and investments. For example, labor heterogeneity and credit market imperfections might account for the effect of","tokenCount":"13145","images":[],"tables":["-2116696491_1_1.json","-2116696491_2_1.json","-2116696491_3_1.json","-2116696491_4_1.json","-2116696491_5_1.json","-2116696491_6_1.json","-2116696491_7_1.json","-2116696491_8_1.json","-2116696491_9_1.json","-2116696491_10_1.json","-2116696491_11_1.json","-2116696491_12_1.json","-2116696491_13_1.json","-2116696491_14_1.json","-2116696491_15_1.json","-2116696491_16_1.json","-2116696491_17_1.json","-2116696491_18_1.json","-2116696491_19_1.json","-2116696491_20_1.json","-2116696491_21_1.json","-2116696491_22_1.json","-2116696491_23_1.json","-2116696491_24_1.json","-2116696491_25_1.json","-2116696491_26_1.json","-2116696491_27_1.json","-2116696491_28_1.json","-2116696491_29_1.json","-2116696491_30_1.json","-2116696491_31_1.json","-2116696491_32_1.json","-2116696491_33_1.json","-2116696491_34_1.json","-2116696491_35_1.json","-2116696491_36_1.json","-2116696491_37_1.json","-2116696491_38_1.json","-2116696491_39_1.json","-2116696491_40_1.json","-2116696491_41_1.json","-2116696491_42_1.json","-2116696491_43_1.json","-2116696491_44_1.json","-2116696491_45_1.json","-2116696491_46_1.json","-2116696491_47_1.json","-2116696491_48_1.json","-2116696491_49_1.json","-2116696491_50_1.json","-2116696491_51_1.json","-2116696491_52_1.json","-2116696491_53_1.json","-2116696491_54_1.json","-2116696491_55_1.json","-2116696491_56_1.json","-2116696491_57_1.json","-2116696491_58_1.json","-2116696491_59_1.json","-2116696491_60_1.json","-2116696491_61_1.json","-2116696491_62_1.json","-2116696491_63_1.json","-2116696491_64_1.json","-2116696491_65_1.json","-2116696491_66_1.json","-2116696491_67_1.json","-2116696491_68_1.json","-2116696491_69_1.json","-2116696491_70_1.json","-2116696491_71_1.json","-2116696491_72_1.json","-2116696491_73_1.json","-2116696491_74_1.json","-2116696491_75_1.json","-2116696491_76_1.json","-2116696491_77_1.json","-2116696491_78_1.json","-2116696491_79_1.json","-2116696491_80_1.json","-2116696491_81_1.json"]}

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+{"metadata":{"gardian_id":"e24479714f164705801419d9951b1ca2","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/edfd4db0-91f4-4d31-96d1-725b905ecd00/retrieve","description":"The May 2008 draft agricultural modalities (WTO 2008) are the result of seven years of hard negotiations. Their structure, if not every detail, seems likely to be the basis for final proposals that must either be ratified or rejected by governments. The modalities cover the three pillars of domestic support, market access, and export competition. In this paper we examine their implications for the United States. The imposition of additional WTO disciplines on domestic support is a major issue for the United States. Higher world prices for major export commodities have reduced the amount of support provided to U.S. farmers in recent years but the long drawn-out process of concluding a new Farm Bill reflects the continued political importance of farm programs. Analysis of the most ambitious provisions of the draft modalities suggests that if a relatively high price environment continues the United States will be able to adapt to the new WTO domestic support commitments by making modest adjustments in its domestic policies. There are issues with a limited number of commodities. Cotton poses problems for meeting product specific bindings on AMS and blue box support and sugar pose problems for meeting product specific AMS commitments. These could be addressed by changing support programs in order to reduce notified support. Changes in the definition of the dairy support program in the 2008 Farm Act, which do not imply any fundamental change in the way the program actually operates, will reduce notified support for dairy. It is possible that other procedural changes could be made to reduce notified support for other commodities, for example, sugar. The strengthened disciplines on domestic support would have the effect of squeezing out a lot of the “water” in the amount of support that the United States can provide to U.S. farmers and stay within its WTO commitments. The United States would still have the option of changing the composition of support – expanding the use of the green box and making use of non product-specific support up to the limit imposed by de minimis and the overall OTDS binding. Nevertheless, significant reductions in the OTDS and the total AMS severely constrain the room for manoeuvre for support that is most closely linked to prices. However, if the optimistic price environment assumed by the U.S. Department of Agriculture does not materialize, limits on the total AMS and some product-specific AMS limits could well be exceeded unless alternatives to current support policies were found With respect to market access, while U.S. agricultural tariffs are relatively low on average there are some high tariffs on products such as sugar, meats, dairy products and beverages and tobacco. The relatively low tariff average means that around 90 percent of the tariff lines fall in the first band of the proposed tariff-cutting formula in the draft modalities, and hence are subject to the lowest proposed reductions. Despite this, the cuts in agricultural tariffs resulting from application of the formula are relatively substantial, with the trade weighted average MFN applied rate falling from 8 percent to 3.5 percent. Application of the proposed tariff escalation formula has virtually no impact on the average tariff, while application of the tropical products formula would reduce the post-round tariff from 3.5 percent to 3.2 percent with the largest impact on sugar, dairy products and tobacco. The sensitive product option is likely to have a relatively small impact on U.S. tariffs. With respect to market access for the United States, we find that the proposed tariff formulas in the modalities would sharply reduce average tariffs facing U.S. exports – from 14 percent to 8.7 percent. Most of this reduction comes from a sharp fall in the tariffs applied by other industrial countries. The provisions of the tariff formula for developing countries; the higher binding overhang; and lower initial rates of applied protection imply a much smaller reduction in the tariffs facing U.S. exports in developing countries. The sensitive product provisions reduce by half the reduction in the average tariff facing US exporters in other developed markets and, combined with special products, mean that applied tariffs faced by US exporters in developing countries decline very little. The elimination of export subsidies used by other countries (particularly, the European Union) has been a major U.S. objective in the current round of WTO negotiations. This would require the elimination of the one remaining export subsidy program for dairy products (DEIP) that has not been active in recent years. The draft modalities would also require changes in U.S. export credit programs, but these have already been modified to bring interest rates in line with those charged by commercial lenders. In addition, the intermediate export credit program (GSM 103) was eliminated in the 2008 Farm Act. Some additional modifications in financing terms for the remaining program (GSM 102) would probably be required to ensure full cost recovery Activity under U.S. food aid programs has been declining in recent years. The draft modalities foresee reduced emphasis on the provision of in-kind aid. The 2008 Farm Act contains some limited provisions for sourcing aid through local purchases of food in developing countries. However, a more general move in that direction can be expected to result in less support for food aid programs among farm groups and increasing difficulties in obtaining Congressional appropriations for food aid programs.","id":"1103530998"},"keywords":[],"sieverID":"d3e49ecf-9716-44d6-89c5-103db3ef9b94","pagecount":"52","content":"The May 2008 draft agricultural modalities (WTO 2008) are the result of seven years of hard negotiations. Their structure, if not every detail, seems likely to be the basis for final proposals that must either be ratified or rejected by governments. The modalities cover the three pillars of domestic support, market access, and export competition. In this paper we examine their implications for the United States.The imposition of additional WTO disciplines on domestic support is a major issue for the United States. Higher world prices for major export commodities have reduced the amount of support provided to U.S. farmers in recent years but the long drawn-out process of concluding a new Farm Bill reflects the continued political importance of farm programs. Analysis of the most ambitious provisions of the draft modalities suggests that if a relatively high price environment continues the United States will be able to adapt to the new WTO domestic support commitments by making modest adjustments in its domestic policies. There are issues with a limited number of commodities. Cotton poses problems for meeting productspecific bindings on AMS and blue box support and sugar pose problems for meeting productspecific AMS commitments. These could be addressed by changing support programs in order to reduce notified support. Changes in the definition of the dairy support program in the 2008Farm Act, which do not imply any fundamental change in the way the program actually operates, will reduce notified support for dairy. It is possible that other procedural changes could be made to reduce notified support for other commodities, for example, sugar.The strengthened disciplines on domestic support would have the effect of squeezing out a lot of the \"water\" in the amount of support that the United States can provide to U.S.farmers and stay within its WTO commitments. The United States would still have the option of changing the composition of support -expanding the use of the green box and making use of non product-specific support up to the limit imposed by de minimis and the overall OTDS binding. Nevertheless, significant reductions in the OTDS and the total AMS severely constrain the room for manoeuvre for support that is most closely linked to prices. However, if the optimistic price environment assumed by the U.S. Department of Agriculture does not materialize, limits on the total AMS and some product-specific AMS limits could well be exceeded unless alternatives to current support policies were found With respect to market access, while U.S. agricultural tariffs are relatively low on average there are some high tariffs on products such as sugar, meats, dairy products and beverages and tobacco. The relatively low tariff average means that around 90 percent of the tariff lines fall in the first band of the proposed tariff-cutting formula in the draft modalities, and hence are subject to the lowest proposed reductions. Despite this, the cuts in agricultural tariffs resulting from application of the formula are relatively substantial, with the trade-weighted average MFN applied rate falling from 8 percent to 3.5 percent. Application of the proposed tariff escalation formula has virtually no impact on the average tariff, while application of the tropical products formula would reduce the post-round tariff from 3.5 percent to 3.2 percent with the largest impact on sugar, dairy products and tobacco. The sensitive product option is likely to have a relatively small impact on U.S. tariffs.With respect to market access for the United States, we find that the proposed tariff formulas in the modalities would sharply reduce average tariffs facing U.S. exports -from 14 percent to 8.7 percent. Most of this reduction comes from a sharp fall in the tariffs applied by other industrial countries. The provisions of the tariff formula for developing countries; the higher binding overhang; and lower initial rates of applied protection imply a much smaller reduction in the tariffs facing U.S. exports in developing countries. The sensitive product provisions reduce by half the reduction in the average tariff facing US exporters in other developed markets and, combined with special products, mean that applied tariffs faced by US exporters in developing countries decline very little.The elimination of export subsidies used by other countries (particularly, the European Union) has been a major U.S. objective in the current round of WTO negotiations. This would require the elimination of the one remaining export subsidy program for dairy products (DEIP) that has not been active in recent years. The draft modalities would also require changes in U.S.export credit programs, but these have already been modified to bring interest rates in line with those charged by commercial lenders. In addition, the intermediate export creditprogram (GSM 103) was eliminated in the 2008 Farm Act. Some additional modifications in financing terms for the remaining program (GSM 102) would probably be required to ensure full cost recovery Activity under U.S. food aid programs has been declining in recent years. The draft modalities foresee reduced emphasis on the provision of in-kind aid. The 2008 Farm Act contains some limited provisions for sourcing aid through local purchases of food in developing countries.However, a more general move in that direction can be expected to result in less support for food aid programs among farm groups and increasing difficulties in obtaining Congressional appropriations for food aid programs.The May 2008 agricultural modalities (WTO 2008) are the result of seven years1 of hard negotiations, and their structure, if not every detail, seems likely to be the basis for final proposals that must be ratified or rejected by governments. In this paper we examine the implications of the draft modalities for the United States.As a major exporter of agricultural products, the UnitedStates has a substantial offensive interest in obtaining significant reductions in applied tariffs through a new WTO agreement on agriculture. With the exception of a few products, such as dairy and sugar, U.S. agricultural tariff protection is relatively low. On the defensive side, market access is frequently seen as less sensitive than domestic support in the United States for several reasons. The first is the considerable U.S. comparative advantage in many key agricultural products. The second, related to the first, is the fact that tariffs are ineffective in providing support to export-oriented industries. A third is the apparent willingness of policy makers to make budget transfers to a sector that is small relative to the total economy and the total States in recent years. However, export credit guarantees are used for some commodities, and these may be subject to tighter disciplines. Food aid, particularly aid in kind, has political support among commodity groups in the United States, and this will also be affected by a new agreement. Again, the offensive objective of eliminating direct export subsidies by other countries might be a prize worth pursuing, even if this will limit future U.S. export assistance options.Whether a Doha agreement is ultimately acceptable to the United States will depend in large measure on whether market access provisions perform the needed balancing act of reducing tariffs enough in other countries, while allowing enough residual flexibility to permit smaller reductions in U.S. tariffs on commodities with politically strong domestic constituencies.Furthermore, a key determinant of the acceptability of a Doha Agreement in the United States will be whether it provides sufficient agricultural market access to compensate for potential political pain in other areas, especially reductions in agricultural domestic support and some non-agricultural tariffs.The imposition of additional WTO disciplines on domestic support is a major issue for the United States.Tariff protection for much of U.S. agriculture is relatively low in comparison to some other major countries, but substantial support can be provided through a range of government programs for a range of products that have limited tariff protection, particularly when domestic prices fall. Green box support now accounts for roughly 80 percent of total support (figure 2 and table 1).Table 2 shows that the most rapidly growing category of green box expenditure is for domestic food aid, which accounted for 70 percent of the green box total in 2005.In addition to direct income support, whose status is being questioned in the WTO, disaster relief and environmental payments have been important components of this support, totalling roughly $10 billion annually in recent notifications.The de minimis allowances have proved to be important for the United States. Table 1 shows that de minimis  -a larger reduction in the AMS limit for cotton than for other commodities, with an accelerated reduction schedule.The implications of the proposed modalities for the United States are summarized in tables 6-9.Table 6 contains the base values for the value of production, overall trade distorting support (OTDS) and the total and cotton AMS. The U.S. total AMS was just over 5 percent of the value of production, so the United States is not subject to the additional effort (additional reduction) requirement of paragraph 14 of the draft modalities.  (1995-2000). In that case, the 2004-05 average can be used as a base (paragraph 25). In addition, if support for a commodity was below de minimis throughout the period 1995-2000, the de minimis value for that period can be used (paragraph 26). These rather complex conditions are very significant for the United States. The modalities (table 5) imply the following:1.-Only one of the 47 commodity categories for which an AMS was notified by the United States for at least one year since 1995 would have a zero AMS binding. This is avocados for which a small amount of trade adjustment assistance payments was notified in 2005, and thus falls outside of the base period. Overall, the application of the rules specified in the modalities creates a considerable amount of \"policy space\" by providing product-specific AMS limits for virtually all important and minor US agricultural commodities, even if the actual notified support for some of these has been small in the past.2.-Five of the commodities would be subject to the three-year phase-in for the reduction specified by paragraph 26 (barley, corn, dairy, sorghum and sugar);all but dairy and sugar would be subject to an additional reduction in their AMS limit implied by the 130 percent restriction in paragraph 26.3.-The cotton binding would be superseded by additional provisions (table 5). In the absence of these provisions the cotton AMS binding would be reduced from a base value of roughly $1.5 billion to a bound value of roughly $1.1 billion over a period of 3 years.The additional provisions result in a reduction from a base value of $800 million to a bound value of $142 million over a period of 20 months.4.-If the methodology specified in the modalities is applied strictly some commodities could be eligible for multiple caps. One important case concerns the livestock category and the cattle and calves category.The US has notified both of these categories in the past. There are some technical questions associated with how the calculation would be made, but our estimate is that the change in methodology would reduce notified market price support for dairy by roughly 65 percent.The application of the revised approach results in a projected notification of $1.9 billion in 2014, compared to $5.5 billion under the previous method. If it were not for this change, we project that the US would exceed its Total AMS binding in 2014 by roughly $0.2 billion, rather than being $3.4 billion below the binding.We are able to analyze the implications of the productspecific AMS and blue box bindings for some of the most important commodities (dairy, sugar, barley, corn, cotton, peanuts, rice, soybeans and wheat). Our indicate that there are significant issues to be faced for a limited number of commodities, two of which (cotton and sugar) have proved to be highly politically sensitive in the United States.If the economic environment that is foreseen in current USDA projections materializes, it seems that the UnitedStates will be able to adapt to the new WTO domestic support modalities by making some modest adjustments in its domestic policies. Although that does not imply that such changes would be politically easy to make. A more attractive option from a domestic political perspective might be to make changes in the way that support is notified to the WTO.The However, by doing this the support estimates would ignore the potential impact of trade barriers on domestic prices, when these are above world market prices. 9Despite this issue of \"strategic\" behaviour in notifications, we should acknowledge that changes in market access (tariffs and tariff-rate quotas) could imply that existing support programs for some commodities (such as sugar) would have to be modified in any case. 10 However, the question of whether the United States would be required to make substantive or merely cosmetic changes to dairy and sugar programs in order to satisfy future domestic support commitments is an issue which is not explored further in this paper.It seems clear that the stringent modalities for cotton create issues for that particular commodity in terms of meeting future WTO commitments. Moreover, large reductions in the OTDS and the reduction in the total AMS severely constrain the room for manoeuvre for support that is most closely linked to prices. The optimistic price environment used in the projections may not materialize. In that case, limits on the total AMS and some product-specific AMS limits could well be exceeded unless some other alternative to current support policies was found.One option for U.S. policymakers might be to use the for support to be included in the NPS. However, the potential for box shifting is limited by the total OTDS binding which would be roughly $13 billion under the higher OTDS reduction percentage of 73 percent, and $16 billion under the 66 percent reduction. Figure 6 illustrates the situation implied by our projections under the lower OTDS binding. If nothing were done to change the methods used to notify support under the 2008 Farm Act (and there is no significant increase in the PS AMS due to the ACRE program), roughly $6 billion would be available for additional NPS support. If the higher OTDS binding were applied in the DDA agreement this would only increase the amount of NPS AMS that could be utilized (by $2 billion) if the higher de minimis were also to apply. Otherwise the 2 percent de minimis would act as a constraint on the amount of NPS AMS that could be provided.In conclusion, the strengthened disciplines on domestic support in the proposed modalities would have the effect of squeezing out a lot of the \"water\" in the amount of support that the United States can provide to U.S. farmers and still meet its WTO commitments.Several commodities, most notably cotton, but also sugar, pose problems in meeting product-specific commitments. There may be room for manoeuvre by changing the nature of the support program for sugar.The option of moving support into the green box or non product-specific AMS could also provide some policy space for meeting future WTO commitments. However, it should be stressed that our projections assume a relatively high price environment for major U.S. crops.If prices were to fall substantially, so that major pricesupport payments were triggered, the likelihood of meeting WTO commitments on domestic support under a continuation of existing programs would be substantially different from the assessment presented here.The Simplification of non ad valorem tariffs-as proposed in the modalities-is highly desirable, but needs great care if \"dirty tariffication\" of the type seen in the UruguayRound (Hathaway and Ingco 1996) is not to result in higher bound tariffs than would otherwise be justified.One particularly important factor is the effect of different weighting schemes on summary measures of tariffs. The widely used simple average is easy to compute, but fails to reflect the fact that some goods are much more important in trade than others. The trade-weighted average tariff corrects this problem, but suffers from potential bias in that high tariffs tend to reduce the volume of imports, and hence to assign low weights to tariffs on highly protected products. The MacMApHS6 reference group methodology (Bouët et al   2008) deals with the problem by using weights for a reference group of countries, potentially at the cost of using weights that are less relevant to a country's own potential imports. If the reference group as a whole has a large share of imports in the country's highly protected goods, then the reference group average is likely to be higher than the trade weighted average. The monitoring of TRQs proposed in the draft modalities appears to be very important for ensuring that real market access is provided by an expansion of tariff-rate quotas.The tariff on ethanol raises some special issues. The bound tariff is low -between 1.9 percent and 2.5 percent. The main source of protection comes from an additional duty and charge of 14.27 cents per litre (the AVE ranges between 30 and 50 percent). Since this is not a tariff, it would not be cut under a tariff reduction formula agreed in the WTO negotiations. This may be an important issue for ethanol exporters such as Brazil.In order to analyze agricultural reform world-wide, it is necessary to make some simplifying assumptions about the extent of protection and the effects of liberalization. One of these is to use ad valorem equivalents (AVEs) to assess the effect of non ad valorem tariffs. A second is the standard set of assumptions in our database (MAcMapHS6) regardingTRQs-that the out-of-quota tariff of a TRQ regime imposes an effective constraint on imports when they are more than 98 percent filled; that the in-quota tariff is effective when they are less than 90 percent filled;and that the average is relevant for TRQ commodities where the quota is between 90 and 98 percent filled.The modalities for agricultural market access liberalization are complex, and the details of the draft modalities are extremely important in determining the outcome. We therefore spell out in some detail the nature of the proposal, and the way that we have examined its implications.The centrepiece of the market access modalities is a tiered reduction formula, which provides for larger proportional cuts on higher tariffs. Many key issues that were undetermined in the earlier framework (WTO   2004) have now been resolved. There is agreement on four bands in each case, and on the boundaries between these. While there are ranges associated with the depth of cut in each band, the ranges are now sufficiently narrow that it seems reasonable to focus analysis on the centre of each range. the gap between the processed and unprocessed product tariff is less than 5 percentage points, then the tariff escalation procedure is not applied, so that the tariff cutting rule does not bring the tariff on the processed product below that on intermediates. A list of tropical products will be subject to deeper-thanformula cuts, while developed countries will provide duty-free access on cotton.All countries are permitted to make some smaller cuts on \"sensitive\" products. The modalities include a limit on the number of such products, and provisions for increases in market access under TRQs for products where smaller-than-formula cuts are made. The amount of tariff lines allowed to be treated as sensitive is to be between 4 and 6 percent for industrial countries, except if they have more than 30 percent of their tariffs in the top band, or have scheduled their tariffs at the six digit level, in which case it will be between 6 and 8 percent. If the formula cut is reduced by two thirds, then market access must be increased by 4-6 percent of domestic consumption; if the reduction is by one third, then the increase in market access will be between 3 and 5 percent. 11 Part of the resulting increase in market access will derive from reductions in tariffs, while part reflects the fact that securing an increase in market access for a TRQ commodity requires an increase in TRQs as well as a reduction in tariffs. Developing countries have the right to declare one third more products as \"sensitive\" than developed countries.A key issue in assessing the impact of the draft modalities is how sensitive products will be chosen. One approach assumes that these products will be the ones with the highest bound tariffs (Sharma 2006); a second assumes that they will be those with the highest applied tariffs (Vanzetti and Peters 2008); and a third uses a tariff-revenue-loss criterion under which products are selected on the basis of the value of imports times the reduction in the tariff rate (Jean, Laborde and Martin   2006).The three approaches produce very different results.The use of the highest bound tariff approach suggests that sensitive products will have a modest impact on the reduction in average tariffs; the highest-applied tariff criterion suggests a slightly larger impact; and the tariff-revenue-loss criterion suggests that even small numbers of sensitive products can greatly diminish potential reductions in applied tariffs. In our view, an approach based on the height of the tariff alone (bound or applied) results in the selection of many products as \"sensitive\" that are inherently unimportant 12 , and are unlikely to be strong candidates for inclusion on the sensitive list. Conversely, much more important products are excluded. Unfortunately, all of the procedures are ad hoc, making it difficult to distinguish among them, despite the policy importance of their different impacts.Jean, Laborde and Martin ( 2008) use an approach grounded in the willingness of policy makers to retain higher and hence more costly tariffs on some products.They take into account the fact that high protection on an important product such as sugar is more costly than high protection on a minor commodity. They use the estimated cost of these tariffs to work out the extent of the policy makers' preference for protecting industries.With this information, they develop selection approaches to help identify which products are likely to be chosen as sensitive. While this information is not needed by policy makers at home-who know which products they plan to choose as sensitive-it is likely to be particularly valuable to foreign policy makers, who must guess the implications of sensitive products for their market access opportunities abroad.Use of this simplified criterion implies a tendency to select products that have significant shares of total imports (at domestic prices); have high initial applied tariffs; and would face large cuts in applied rates if the reduction formula were applied without exceptions.While decisions about sensitive products should, in principle, be made for all products simultaneously, the authors show that examining tariff lines one at a time provides a good approximation to the results obtained by considering all possibilities together. They also show that the consequences for product selection are likely to be similar to those of the tariff-revenue loss rulethat is, even the exclusion of small numbers of tariff lines is likely to cause a large reduction in the average tariff cut. 13 We base our computations on 2004 tariff and trade data. We use the WTO approach to computing the ad- products is still to be determined. In the light of this we use the tropical product list from the Uruguay Round. 15 A key determinant of the effects of the tiered formula is where individual tariff lines lie in the bands. From table 16, it is clear that the vast majority of U.S. tariff lines fall in the lowest band of the tiered formula. It is also clear that this result holds whether attention focuses on the share of tariff lines cut; the tradeweighted average; or tariffs weighted by a reference group. This means that these tariffs are subject to the lowest reductions of around 50 percent. It also implies that the 54 percent minimum average cut is more likely to be binding for the US than for economies with higher agricultural tariffs, such as the EU or Japan, particularly if a significant number of products are classified as sensitive and hence subjected to smaller tariff cuts.Table 17 shows the implications for average tariffs by chapter, and for all agricultural products, of applying the formula and the additional provisions that do not involve discretion on the part of the importing countrythe tariff escalation and tropical product provisions.From the table, it is clear that application of the formula is likely to result in substantial reductions in trade-weighted average applied tariffs. The tiered formula alone would reduce average tariffs from 8 to 3.5 percent. The proposal for additional cuts to mitigate tariff escalation has relatively little impact at the aggregate level, or for any chapter as a whole.Additional liberalization of tropical products would, however, have a noticeable impact on the overall average tariff and on chapters for such products as edible fruit, cereals, oilseeds, gums, and tobacco. The complete package of formula cuts would reduce average applied rates from 8 percent to 3.1 percent, a cut of 61 percent. The corresponding average cut on dutiable lines would be 57 percent.The first two columns of table 18 show the implications of the tariff-cutting formula, including the provisions for tariff escalation and tropical products, for effective applied tariff rates-that is tariff rates that take into account the tariff preferences received by some exporters to the United States. The third and fourth columns show the implications of these tariff cuts for preference margins. As is clear from the table, the average preference margin on U.S. agricultural products is relatively small, at 1.6 percent, even prior to cutting the tariffs. This is less than half the margin applying in the European Union (Jean and Laborde 2008).The tariff reductions by chapter in table 18 show some interesting patterns. The tiered formula would bring about substantial reductions in tariffs on the product groups with the highest tariffs, such as sugar, dairy products, tobacco and meat. In sugar and tobacco, the tariff reduction would be almost two-thirds, while tariffs on dairy and meat products would be reduced by over half. The reductions in preference margins on most of these highly protected categories would be relatively small, except for sugar and tobacco.In the upper part of the table, we present figures by groups of countries. Developing countries and LDCs currently benefit from an average preferential margin of 2.9 percentage points. However, because of product specialization, they face higher average barriers in the U.S. than developed country exporters. This is particularly true for LDCs, who face a 14.5 percent tariff compared to 6.2 percent for developed countries.We do not incorporate a duty free-quota free (DFQF) initiative in the core formula, but we do include the cotton initiative. The formula has a similar effect for developed and developing country exporters and a stronger effect for LDCs, for whom the applied tariff is cut from 14.5 percent to 4.9 percent. However, preference erosion takes place and the preferential margin is reduced from 2.9 percent to 0.8 percent. The final tariff faced by LDCs is 4.9 percent, suggesting that a full DFQF package in agriculture could be of significant value to these exporters.As indicated above, sensitive products were selected using the formula suggested by Jean, Laborde and Martin ( 2008), which takes into account the importance of the good in domestic demand, the cut in prices implied by the formula, and the extent to which treatment as a sensitive product reduces the size of that cut. In addition, we took into account the requirement that tariffs be reduced by an average cut of 54% in industrial countries. 16 If this constraint is binding, reductions in each category need to be scaled up proportionately until the minimum average-cut of 54 percent is achieved.We assume that sensitive products would be subject to a reduction of two-thirds from the formula cut. This is because the change in the degree of TRQ expansion required is very slight when a smaller (one half or onethird) deviation is taken. However, we specify the reduction from the formula cut as one half, rather than two-thirds, to allow for the potential market access improvements resulting from TRQ expansion. Under this assumption, the 54 percent average-cut constraint is not binding for the United States when we assume 5% of sensitive products. Allowing more sensitive products or using a two-third deviation from the formula would result in this constraint being violated. This result is consistent with the initial U.S. proposals to allow a limited number of sensitive products (1 percent).The result of this procedure is the list of 33 six-digit sensitive products shown grouped by 4 digit positions in table 19. The first column of the table shows the HS four-digit code for each product. The second gives a brief description of the product and the third the number of HS6 products concerned. The fourth shows the applied MFN tariff for the sensitive products in the 4 digit position after application of the formula. The fifth shows the tariff after applying half of the formula cut.The sixth column shows the volume of imports under the TRQ category of which this sensitive product is a part. And the final column shows the size of the TRQ.The TRQ categories are frequently broader than the tariff line, but it is the degree of quota fill or over-fill that determines whether the out-of-quota tariff, the inquota tariff, or the quota itself determines the level of protection.A key feature of the table is that, for many of the selected products, even the one half deviation from the formula considered here results in a post-round tariff that is much higher than would otherwise be the case.The figures on quota fill suggest that, for most of these products, out-of-quota imports take place, suggesting that these out-of-quota tariffs are the key determinants of protection. Finally, the low official quota fill rates suggest that the monitoring TRQ provisions of the modalities are likely to be important in ensuring that enlargement of the TRQs for sensitive products will actually increase market access.We do not expect that the products actually selected as sensitive by the United States will correspond exactly to the list in table 19, since that will depend on the outcome of domestic political negotiations. Some of the factors taken into account in the political process will go beyond those incorporated in our simple selection rule. If, for instance, one industry has become better organized politically since the last revision of the tariff schedule, it may be able to prevail over industries whose political influence has declined. Industries that are more concerned than others about the potential use of adjustable tariffs to reduce the variability of domestic prices might similarly prevail over some on our list. However, we think it likely that the factors incorporated in our criterion for sensitive products-the importance of imports at domestic prices; the height of the initial tariff; the depth of the formula cut; and the extent to which sensitive product treatment allows tariff cuts to be reduced-will be important in product selection.One other potentially important interaction is that between the choice of sensitive products and the Table 20 shows the importance of allowing sensitive products for the MFN tariffs applied by the UnitedStates. The first column of the table shows the initial MFN tariffs; the second gives the tariffs after application of the formula, including treatment for tariff escalation and tropical products; the third shows the tariff after the application of the formula and sensitive product treatment.Table 20 shows that the provision for sensitive products reduces the size of the reduction in U.S. agricultural tariffs. Without sensitive products, the tariff average would be reduced from 8 to 3.1 percent, a cut of 4.9 The second and third rows of table 20 show that the initial tariffs and the tariff cuts differ enormously between the products likely to be chosen as sensitive and those not on the sensitive list. The weighted average tariff on sensitive products is more than ten times as high as that on non-sensitive products, at 29 percent. For these products, sensitive product treatment has a large impact on the required tariff cut.The reduction in the average tariff on sensitive products falls from 19.1percentage points to 11.8 percentage points. By contrast, the cut in average tariffs on nonsensitive products is 1.5 percentage points.Turning to the individual chapters, we find very large differences in the tariff cuts with and without sensitive products. For dairy products, we find that sensitive product treatment causes a reduction in the tariff cut from 15.7 percentage points to 11.6 percentage points.For sugar and products, the reduction is from 34.1 to 21.3 percentage points. For tobacco and products, the reduction is from 29.2 to 20 percent.The impact of the modalities on the tariffs facing U.S.exports is extremely important for policy makers seeking to overcome political resistance to reductions in U.S. tariffs and the imposition of additional constraints on domestic support. is highly regulated, it appears that the authorities ensure the TRQs are expanded sufficiently to meet the demand from their livestock sector and crushing industries. 18 For this reason, we adjust the MAcMapHS6 methodology so that maize and soybeans imported for feed use face only the in-quota tariff.In preparing the estimates presented in table 21, we used the same approach in partner markets as in the United States. That is, we first apply the tariff-cutting formula for that group of countries together with any adjustments (WTO 2008): such as the minimum averagecut for industrial countries and the maximum cut for developing countries; the increased cuts for tariff escalation and tropical products; the 10 percent smaller cuts allowed in small and vulnerable economies; and the 7.5 percentage points smaller cuts in recently acceded members. 19 We also allow for the impact of the provisions for sensitive and special products. Sensitive products are selected using the methodology discussed previously; assuming that the formula cut is reduced by 33 percent. The draft modalities allow for a range of alternatives for special products (e.g., percentage of special products between 13 percent and 20 percent, degree of shielding from liberalization to be defined and adjusted to the scope of coverage). We use an approach similar to that for sensitive products 20 with 5.6 percent of products having no cut and 8.4 percent of products having a 15% cut.The second column of table 21 shows that the tariffcutting formula would reduce the average tariff facing U.S. agricultural exports from 14.0 percent to 8.7 percent in the absence of provisions for sensitive and special products, a reduction of around 40 percent from initial levels. Once sensitive and special products are incorporated, however, the reduction becomes much smaller. In that case, the tariff facing U.S. exports declines from 14 percent to 11.4 percent, a reduction of 3.6 percent or 20 percent from the initial level.By applying the tariff cutting formula without the flexibilities for sensitive and special products, the tariff facing U.S. exports to developed country markets would be cut by more than half, from 18.7 to 9.1 percent. By contrast, the tariff reduction in developing country markets (including Korea as a developing member for agriculture) would be from 10. percent to 7.9 percent, a reduction of 20 percent. The reduction of 9.6 percentage points in the industrial country markets would cut prices of imports from the United States by substantially more than the reduction in developing country markets.As previously noted, the treatment of the Republic of Korea has an enormous impact on the aggregate figures.If we exclude Korea from the set of developing countries, the average tariff rate facing the United States is 7 percent prior to application of the formula and 5.8 percent afterwards. These figures make clear that the provisions of the tariff formula for developing countries; the higher binding overhang in developing countries; and the lower initial rates of applied protection in the developing countries imply a much smaller reduction in the tariffs facing U.S. exports in developing countries than in industrial countries. The smaller effect of the developing country formula on their applied rates means that sensitive and special products have much less impact on developing country tariffs than tariffs in industrial countries.The sensitive and special product provisions appear, on the face of it, to provide much greater flexibility for developing countries. As noted previously, we assume that developing countries are allowed to classify 14 percent of their tariffs as special products, and an additional 6.65 percent as sensitive products. 21 In addition, much smaller cuts in tariffs on special products are assumed. Despite these differences in the treatments of industrial and developing country tariffs, it is clear from table 21 that much of the impact of the flexibilities on postround tariffs facing U.S. exports comes from sensitive products in the industrial countries, rather than from sensitive and special products in developing countries.When sensitive products are allowed in developed countries, the average post-Round tariff facing U.S.agricultural exporters rises from 9.1 percent to 13.2 percent. This is an increase of about 50 percent, or 4.1 percentage points in the average tariff. By contrast, allowing for sensitive and special products in the developing countries, excluding South Korea; allows them to maintain a tariff of 7 percent, rather than 5.8 percent (an increase of 20%), against U.S. imports. Our analysis shows that U.S. agricultural tariffs are relatively low on average, with average bound rates of 8 percent, and applied preferential rates of 6.3 percent.However, there are some high tariffs on products of interest to developing countries, such as sugar, meats, country group, we find that the formula would cut developing country tariffs on U.S. agricultural exports from 7.3 to 5.9 percent. Adding sensitive and special products would allow only an increase from 5.9 percent to 6.8 percent. Since the cuts in tariffs resulting from the formula are low, the sensitive and special product exceptions have a relatively small impact on developing country tariffs.  The draft modalities for export subsidies, credit guarantees and related measures and food aid are summarized in table 25. The modalities imply the elimination of direct export subsidies, tighter conditions on the use of export credits, and a general shift towards a cash-basis for food aid, rather than the use of donated commodities.As noted, the modalities for export subsidies would require the elimination of DEIP. Dairy farm groups still view DEIP to be an important complement to domestic price support programs. However, many believe that the U.S. dairy industry would gain if market access were to increase, particularly in developing countries, so the elimination of DEIP might be acceptable to dairy farmers if expanded market access resulted from a new WTO agreement.The draft modalities require that the maximum repayment term for export financing be no more that 180 days. Since the United States has already eliminated its intermediate financing program, this would require that the current minimum financing period for GSM-102 of six months would become the maximum. As noted above, interest rates have already been modified to reflect the degree of risk involved, i.e. to bring the rates more in line with those that would be charged by commercial lenders, but this may not be sufficient to meet the requirement that in future schemes should be \"selffinancing\". Although the draft modalities are unclear on this point, this could require that export credit operations cover all their costs, includingImplications for the United States of the May 2008 Draft Agricultural Modalities administrative overhead. It is difficult to calculate the total costs of U.S. export financing schemes from currently available data, so the extent to which interest charges or fees might have to be increased to meet the self-financing requirement can not be determined. It seems likely, however, that some additional modifications in the terms of existing programs would be required to ensure full cost recovery.As noted earlier, activity under U.S. food aid programs has been declining in recent years.Nevertheless, farm groups are willing to support these programs providing that they are linked to the provision of U.S. commodities. The 2008 Farm Act expanded the authorization for appropriations to $2.5 billion per year, whereas pervious legislation had not specified a specific authorized amount. However, this does not mean that this amount of aid will actually be appropriated by the Congress.The draft modalities foresee reduced emphasis on the provision of in-kind aid. There was pressure for a change in this direction during the debate on the 2008 Farm Act, but the outcome was strictly limited.The legislation establishes a pilot program for purchasing food aid in developing countries, but with only has $60 million dollars in mandatory spending. A more general push away from a focus on in-kind aid can be expected to result in even less support for food aid programs among farm groups and increasing difficulties in obtaining Congressional appropriations for food aid programs.In conclusion, the elimination of the potential use of export subsidies by other countries (particularly the European Union) has been a major U.S. objective in the current round of WTO negotiations. If the achievement of that objective requires further changes in export credit and food aid programs, the price to be paid might be politically acceptable domestically.where ti is the initial tariff; si is the share of the import at domestic prices in domestic spending; fi is the tariff cut implied by the formula; and ci is the reduction in the formula cut permitted for sensitive products.14 See appendix table A.1 for information on the implications of using different methods for calculating AVEs.15 A new list (the Cairns list) that has been the focus of discussion includes fewer tariff lines than the Uruguay Round list since products that face zero MFN tariffs in the developed markets have been deleted.For this reason, even if the number of products is smaller, the scope of liberalization is not reduced.Moreover, the Cairns list adds other products that are not tropical (e.g., onions) but are considered as diversification products.Implications for the United States of the May 2008 Draft Agricultural Modalities 16 We assume that this criterion concerns only dutiable tariff lines.17 Measured by the tariff rate times the value of imports. 18 We thank Ed Allan, Lyn Hofmann and John Dyck of USDA-ERS for their advice on the operation of these policies.19 Except the very recently acceded members who are not required to make any tariff cuts. 20 It is obvious than with the broad categories of guidelines for selecting special products (from poverty reduction to tariff revenue losses and sector competitiveness), it will be easy for countries to select freely the products they want.  1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005   Green BlueTotal AMS (1) direct payments only count against the Total AMS limit if their inclusion in the NPS causes this to exceed the de minimis level Source: Based on WTO notifications  Note: Qualifications apply if product-specific AMS amounts above de minimis levels have been introduced since the base period (para 24) or the product-specific AMS was below the de minimis level during each year of the base period (para 25). In the former case, an average of the two most recent notified AMS values can be used as the base; in the latter case, the de minimis level for the base period may be used.Source: Based on WTO (2008)      WTO commitments, 1995  TO commitments, 1995  TO commitments, 1995  TO commitments, 1995----2002 2002 2002  2002   Value 2. Export credit guarantee, insurance and other risk coverage programs to be selffinancing (a test of coverage of operating costs and losses for a rolling 4 or 5 year average will be used to determine this).Food Aid 1. Needs-driven and in grant form, not linked to commercial exports of agricultural or other products and not linked to market development objectives 2. Cash or in-kind aid protected in emergency situations (the so-called safe box) with monetization restricted to aid to least-developed countries for transport and delivery only.3. Non-emergency in-kind aid to be subject to needs assessment by international or regional intergovernmental organizations, targeted to food insecure groups, and to have minimal displacement effect.Monetization either to be prohibited or permissible only to fund the transportation or delivery of aid, or procurement of agricultural inputs for low-income or resource-poor producers, Appendix Table Table  ","tokenCount":"7290","images":["1103530998_1_1.png","1103530998_1_2.png","1103530998_1_3.png"],"tables":["1103530998_1_1.json","1103530998_2_1.json","1103530998_3_1.json","1103530998_4_1.json","1103530998_5_1.json","1103530998_6_1.json","1103530998_7_1.json","1103530998_8_1.json","1103530998_9_1.json","1103530998_10_1.json","1103530998_11_1.json","1103530998_12_1.json","1103530998_13_1.json","1103530998_14_1.json","1103530998_15_1.json","1103530998_16_1.json","1103530998_17_1.json","1103530998_18_1.json","1103530998_19_1.json","1103530998_20_1.json","1103530998_21_1.json","1103530998_22_1.json","1103530998_23_1.json","1103530998_24_1.json","1103530998_25_1.json","1103530998_26_1.json","1103530998_27_1.json","1103530998_28_1.json","1103530998_29_1.json","1103530998_30_1.json","1103530998_31_1.json","1103530998_32_1.json","1103530998_33_1.json","1103530998_34_1.json","1103530998_35_1.json","1103530998_36_1.json","1103530998_37_1.json","1103530998_38_1.json","1103530998_39_1.json","1103530998_40_1.json","1103530998_41_1.json","1103530998_42_1.json","1103530998_43_1.json","1103530998_44_1.json","1103530998_45_1.json","1103530998_46_1.json","1103530998_47_1.json","1103530998_48_1.json","1103530998_49_1.json","1103530998_50_1.json","1103530998_51_1.json","1103530998_52_1.json"]}

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+{"metadata":{"gardian_id":"d94bdd747e9a55816d6e2894a8f23cc3","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/40ae8a96-21a5-41e5-bb98-ed5affdcd5d8/retrieve","description":"In this paper, we study the transformation process Indian agriculture exhibited in the recent past, studying its policy implications. Between the years 2005-06 and 2015-16, more than 52 million workers left agriculture, which did not have any effect on agricultural output due to productivity improvements. We estimate the contribution of productivity growth and structural change in agriculture to national productivity growth during 1981-2016. We estimate differentials in agricultural productivity and in their ability to contribute to the structural change process for 21 major states of India. Using revised employment estimates, we trace major changes during the pre-reforms (before 1991) and post-reforms periods. Results show that in the pre-reforms period, the impact of productivity improvements in agriculture on agricultural output was equated by the new workforce entering into this sector, leading to a stagnant labor productivity trend. The labor-shift from agriculture during the early years of the post-reforms period, which increased further in the next decade, has led to a consistent rise in agricultural productivity. In the absence of reforms and the associated labor shift, the productivity growth in Indian agriculture would have been much lower. A similar labor shift during the last decade has not affected agricultural output, which has risen more rapidly. This result holds true for almost all states studied. There exists a positive relation between labor-shift and agricultural output in a cluster of states. Decomposition results indicate ‘within-sector’ productivity growth is the major source of overall growth, with a rising contribution of ‘structural change’. Studying the sources of growth across states offers new scope to achieve inter-sectoral productivity convergence.","id":"177006351"},"keywords":["structural change","agricultural growth","labor productivity","decomposition","India v"],"sieverID":"1ffdb49e-a371-4427-a92c-2abb9b7110e8","pagecount":"26","content":"The International Food Policy Research Institute (IFPRI), a CGIAR Research Center established in 1975, provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition. IFPRI's strategic research aims to foster a climate-resilient and sustainable food supply; promote healthy diets and nutrition for all; build inclusive and efficient markets, trade systems, and food industries; transform agricultural and rural economies; and strengthen institutions and governance. Gender is integrated in all the Institute's work. Partnerships, communications, capacity strengthening, and data and knowledge management are essential components to translate IFPRI's research from action to impact. The Institute's regional and country programs play a critical role in responding to demand for food policy research and in delivering holistic support for country-led development. IFPRI collaborates with partners around the world.Contribution of agriculture in productivity growth and structural change (1982-83 to 1993-94) ...... 13 Table 2. Contribution of agriculture in productivity growth and structural change (1994-95 to 2004-05) ...... 14 Table 3. Contribution of agriculture in productivity growth and structural change (2005-06 to 2015-16) ...... 15 Successful economic transformation is accompanied by a declining contribution of low-productive sectors like agriculture and an increasing share of manufacturing and services in national output resulting from a corresponding shift of labor from the former to the latter sectors (Fisher 1939;Clark, 1940;Lewis, 1954;Ranis, & Fei, 1961;Kuznets, 1973). While the historical trends of developed countries such as the United Kingdom, United States of America, Italy, Spain, Japan, and most of the Asian economies follow a definite path of structural change, there had been differences in patterns among many developing and emerging economies, especially among the Latin American nations (Dennis and Iscan, 2009;Neuss, 2019). Even in the developed economies, there is substantial heterogeneity in various activities within the high-productive services sector (Jorgenson & Timmer, 2011).In India, the pattern of structural change is rather unique. While output-based transformation-a consistent decline in agricultural gross domestic product (GDP) share in the national GDP with a declining or stagnant industry share and rising services sector share-is clearly visible, rigidity is observed in the labor market. Even on the output side, the growth path observed is atypical, with an exceedingly high output growth in service sector (Goel & Echavarria, 2015) and a stagnant share in industry sector. On the employment side, the share of agricultural labor has declined over time, but agriculture still employs around half of the workforce (47%) (ILO, 2017). The industrial labor share is rising. It is yet to attain the peak, following which one would expect a decline. The labor share in services has dominated its industrial counterpart since independence. Thus, there is considerable deviation in the Indian structural change, especially in the labor market, and the pace of transformation has been relatively slower in the labor market than in the output market.The employment trend indicates that India has maintained 'Jobless Growth' for more than two decades (Datt, 1994;Himanshu, 2011;Tejani, 2016;Abraham, 2017). The International Labour Organization's (ILO) statistics show that the low employment rate is believed to persist at least until 2022, and the annual number of jobs to be created to sustain the present employment rate is estimated to be around 8 million (World Bank, 2018). This pattern of faster transformation in output market and slower pace of change in labor market along with a 'no jobs' trend has caused huge productivity differentials across sectors. Our estimates based on RBI (2018) show that the labor productivity, measured in real terms as the ratio of Gross Value Added (GVA) to the number of persons employed, was 11.5 times more in finance sector, 3.4 times more in trade, and 2.6 times more in manufacturing than it was in agriculture during 1981. During 2016, these ratios increased to 18.5 times, 5.2 times and 4.7 times, respectively. Leaving aside construction sector, employment generation has either been slower or stagnant in all sectors.Though the employment growth lagged behind the output growth in most of the sectors in the past, total workforce was rising gradually over time in absolute terms, indicating a slower pace of labor absorption. This was common in agriculture sector as well, but the trend persisted only until the mid-2000s. Since then, the sector witnessed a drastic reduction in the workforce, which includes both farmers and agricultural laborers. According to the National Sample Survey Office (NSSO), between 2004-05 and 2011-12, 21 million farmers left agriculture (Mehrotra et al., 2014). A recent estimate shows about 55 million laborers left agriculture sector between 2004-05 and 2015-16 (RBI, 2018)).The above trend raises three major questions. First, why did the labor force withdraw from agriculture? The possibility of declining 'real' agricultural wages could be seen as the 'push' factor behind this shift. However, studies indicate that the real wages were rising in agriculture over the years, and sometimes, they have surpassed the average nonagricultural wages. Growth in real wages was 1.5% between 1999-00 and 2004-05, which rose to 5.0% during 2004-05 to 2007-08 and doubled further to 11.8% during 2007-08 to 2011-12. The growth in nonagricultural wages during these periods was 1.5%, 3.9% and 8.4%, respectively (Himanshu & Kundu, 2016). Thus, as wages grew more rapidly in agriculture than nonagriculture, one could not argue that relative wage gains in nonagricultural sectors was the factor behind labor-shift from agriculture.Second, does a fall in agricultural productivity growth, and hence a profitability-decline, cause farmers to quit agriculture? This seems to be not the case. Productivity growth in agriculture was steadily rising over the years, and it grew more rapidly since the mid-2000s (Chand & Parappurathu, 2012) when we witnessed farmers leaving agriculture more intensively. During 1995-96 to 2004-05, average growth in agriculture, forestry and fishery subsectors were 2.3%, 2.1% and 3.3%, respectively. Within the crop sector, growth in cereals, pulses, oilseeds, fruits and vegetables and fibers were 0.5%, 0.2%, -1.1%, 3.8% and -1.2%, respectively. During 2004-05 to 2011-12, growth improved in all sub-sectors and crop-groups mentioned above. Growth in agriculture rose to 3.4%, forestry rose to 2.3% and fishery rose to 4.4%. In crop subsector, the growth in cereals improved to 2.6%, it was 1.3% in pulses, oilseeds recovered from a negative growth to 1.4%, fruits and vegetables growth increased further to 5.0% and in fibers it was 8.0%.After a decade long decline in growth, the period after the mid-2000s was known as 'growth recovery period' in agriculture (World Bank, 2014). Further, the terms-of-trade were in favor of agriculture (India, MoAFW 2016), the expenditure on agriculture and irrigation by the public sector increased across states (Bathla, 2017), and the Government provided support in the form of increased allocations for research (Singh & Pal, 2015). Growth turned more inclusive across states, leading towards convergence in productivity (Balaji & Pal, 2014). In short, as agriculture sector holistically recovered from the decelerating growth trend in the mid-1990s and entered into a higher growth trajectory, productivity growth was clearly not the factor behind farmers' withdrawal.Third, what are the alternative sectors where the famers and laborers are moving? Existing evidence indicates construction sector rather than any other sectors. For example, Mehrotra et al. (2014) observed a decline of around 21 million farmers and 15 million laborers from agriculture between 2004-05 and 2011-12. This decline was accompanied by an increase of 25 million construction sector workers during the same period. Though one would not strictly assume the entire addition in construction came from agriculture, it is highly likely since there exists a limited possibility for workers flowing into construction from other nonagricultural enterprises where real wages are relatively high. Also, there exists demand for human capital factors like literacy and skill that restrict farmers and unskilled agricultural workers from moving into sectors like finance and communication. Our own estimates show that some farm households turned into agricultural laborers, others carried out petty trading, and some turned towards sectors like manufacturing and service-related nonfarm industries (Figure 1). Out of all households who carried out farming in 2004-05 as their primary occupation, only 64% remained in agriculture during 2011-12. About 9% of them turned into agricultural laborers, 11% entered as wage laborers into nonfarm enterprises, and 4% started carrying out petty trade activities. In short, the discussions above clearly indicate agriculture has performed relatively well even in the phase of massive shift of farmers and laborers from agriculture. Such sustenance in production despite labor-shift has been the result of productivity improvements in agriculture resulting from increased use of inputs such as quality seeds and fertilizers, expansion in irrigated cropping due to higher allocations, higher research expenditures and favorable terms of trade. To the other end, the impact of inter-sectoral shift on overall economic growth had varied among nations with time. While in many cases it has shown larger positive impacts, there are nations in which it had little or no, or even negative, impact on growth.For example, analyzing the case of developed nations, Jorgenson & Timmer (2011) show the effect of structural change on productivity growth was 'zero' in the United States, less than 5% in the European Union and less than 6% in Japan. In contrast, Diao et al. (2018) report as much as 80% of growth in Tanzania had been due to structural change process. In general, structural change was growth-enhancing in African nations during the early 1960s and 1970s, which was led primarily by improvements in manufacturing productivity. McMillan et al. (2014) shows it turned growth-reducing since 1990s, including in Latin American nations. In their study, de Vries et al. (2015) note the reallocation process has created static gains with dynamic losses in African economies. Diao et al. (2017) show that this labor-shift process has not only helped to attain higher growth but also assisted in poverty reduction in these nations. Among emerging economies, in India, structural change contributed around one-fourth (23%) of growth during 1990-2005(McMillan & Rodrik, 2011).In this paper we study the transformation process in Indian agriculture, estimate the contribution of productivity growth in agriculture, along with the agricultural labor-shift, to the national productivity growth and compare it with its earlier trends since the 1980s. Further, given the huge productivitydifferentials in agriculture across regions and their ability-differentials to participate in the structural change process, varying contributions of these factors were also estimated for 21 major states of India for the said period, divided into different sub-periods, and their improvements have been traced.The approaches to analyze the sources of growth are several. One way is to model the indicator(s) of growth against different drivers identified by the theory and estimate the model econometrically through regression-based approaches. It helps in understanding the relative importance of each factor, their magnitude and the direction of causality (Barro and Lee, 1994;Jones, 2002). Another way is to follow growth accounting procedure and decompose the growth for a given period attributable to different factors. It provides a breakdown of observed economic growth into components associated with changes in factor inputs and a residual that reflects technological progress and other elements (Barro, 1999). While the second approach provides just the factor share(s), it is not devoid of theory. Discussions on basic growth accounting can be found in Solow (1957), Kendrick (1961), Denison (1962), and Jorgenson and Griliches (1967). Hsieh (2002) derived dual approach using the equality between output and income from factors, and Jorgenson & Timmer (2011) used cost-based measures.The level of disaggregation of growth emerging from various sources varies with methods. Traditional shift-share analysis of Fabricant (1942) decomposes economic growth into 'within effect' that explains productivity growth within the sector and 'between effect' by contributing to aggregating inputs. Timmer and de Vries ( 2009) noted that interpretation of results from this traditional shift-share method is not straightforward and developed a modified shift-share analysis method in which they adjusted the betweeneffect of an expanding sector to take into account its relative productivity level. They divided sectors into expanding and shrinking and calculated the between-effect relative to the average productivity level of the shrinking sectors and reported substantial differences among the estimates of traditional and modified method.The World Bank (2009)'s Shapley approach decomposes total growth into 8 different components in six stages. At stage-1, it decomposes the productivity growth into employment rate changes, changes in output per worker and demographic changes. At stage-2, employment changes are further decomposed into changes in employment by sectors. At stage-3, changes in output per worker is decomposed into changes linked to variations in output per worker within sectors and changes linked to sectoral relocation of workers between sectors. The fourth stage goes further in understanding the role played by each sector on the aggregate effect of employment relocation across sectors while the fifth stage looks at the role of capital and TFP as sources of changes in output per worker at the aggregate level. The sixth stage puts all the elements together, to see how each factor affected total per capita growth. Alternatively, some researchers rely on advanced modeling systems such as Computable General Equilibrium (CGE) procedure for a detailed enquiry (Benfica et al., 2019).In this paper, with an objective to observe the contribution of productivity growth and structural change in agriculture to the overall economic growth, we followed the approach developed by McMillan and Rodrik (2011) that decomposes labor productivity growth into 'within' sector component and 'structural change' component. Several studies have followed their approach covering wide geographical regions to explain the structural change process and its implications for growth (Morley et al., 2019;Bathla et al., 2019;Diao et al., 2018). They decomposed productivity growth using the equationwhere \uD835\uDC4C\uD835\uDC4C \uD835\uDC61\uD835\uDC61 and \uD835\uDC66\uD835\uDC66 \uD835\uDC56\uD835\uDC56,\uD835\uDC61\uD835\uDC61 refer to economy-wide and sectoral labor productivity levels, respectively, and \uD835\uDF03\uD835\uDF03 \uD835\uDC56\uD835\uDC56,\uD835\uDC61\uD835\uDC61 is the share of employment in sector i. The Δ operator denotes the change in productivity or employment shares between t -k and t.The first term in the decomposition is the weighted sum of productivity growth within individual sectors, where the weights are the employment share of each sector at the beginning of the time period, which they call as the 'within' component of productivity growth. The second term captures the productivity effect of labor reallocations across different sectors. It is essentially the inner product of productivity levels (at the end of the time period) with the change in employment shares across sectors. They call this as the 'structural change' term. When changes in employment shares are positively correlated with productivity levels this term will be positive, and structural change will increase economy-wide productivity growth.The decomposition above clarifies how partial analyses of productivity performance within individual sectors (for example, manufacturing) can be misleading when there are large differences in labor productivities \uD835\uDC66\uD835\uDC66 \uD835\uDC56\uD835\uDC56,\uD835\uDC61\uD835\uDC61 across economic activities. In particular, a high rate of productivity growth within an industry can have quite ambiguous implications for overall economic performance if the industry's share of employment shrinks rather than expands. If the displaced labor ends up in activities with lower productivity, economy-wide growth will suffer and may even turn negative. In the present case, the above procedure was applied separately for three different sub-periods i.e. 1982-83 to 1993-94, 1994-95 to 2004-05 and 2005-06 to 2015-16, and estimates were obtained for all states and the country as a whole.Carrying out the decomposition analysis demands statistics pertaining to GVA and work force engaged in different economic activities. Since we attempt to capture the factor contributions across states, the detailed estimates need to be calculated for all states. In the present study, we considered 21 major states of India (Figure 2). While national level estimates form the basic framework in the National Accounts Statistics (NAS) system and can easily be retrieved, complexity arises when one attempts to study the economic process for the disaggregated units when the economic activities are classified into different sub-sectors. In the present study, we divide the total economy into three major sectors i.e. agriculture, industry and services. We cover the period between 1980-81 and 2015-16. The sub-periods considered are: a  Empirical research suggests the use of GVA than GDP in the decomposition framework. The country shifted to GVA based system very recently and the statistics for detailed sub-sectors are available only since 2011-12. For example, the GVA statistics for the entire nation are given by the Central Statistics Office (CSO) for the period 1951-2012. But it divides the economy only to five sub-sectors namely: a) Agriculture, forestry & fishing, mining; b) Manufacturing, construction, electricity, gas and water supply and quarrying; c) Trade, hotels, transport & communication; d) Financing, insurance, real estate and business services and e) Community, Social & Personal services. While this classification appears to be sufficient to study the transformation process, clubbing different sub-sectors into the mentioned five groups seems inappropriate for India. While the sector agriculture requires greater attention in our study, the output of the sector is combined with mining and quarrying in the data set. The other simpler and alternative way is to limit our study for the period 2011-12 to 2016-17 using the detailed sub-sectoral GVA statistics.Since the transformation process relates to the changes in economic structure over a relatively longer span, shrinking the study to a few years would provide an incomplete picture. For the purposes of this study, alternate data sets that provide GVA statistics for India were considered. Notable among them were the Groningen Growth and Development Centre (GGDC)-10 Sector Database, United Nations Statistics Division (UNSD) Database, Penn World Table Database, World Economic Outlook Database, Total Economy Database and KLEMS Database. The GGDC database provides GVA and employment statistics for the period 1950-2012 dividing the economy into 10 sectors. The UNSD database divides the economy into 7 sectors covering the period 1970-2015 and provides GDP and GVA based statistics. While the Penn World Table Database provides statistics on a variety of macroeconomic variables, the GVA statistics is not provided and sub-sectoral classification is not available. Similar is the case with World Economic Outlook and Total Economy databases.The KLEMS database released by the Reserve Bank of India (RBI, 2018) provides GVA and workforce statistics along with several interesting variables such as labor and capital share estimates etc., dividing the total economy into 27 sectors for the period 1981-2016. In the present study, we used the KLEMS database for obtaining national estimates. As GVA estimates are not available for states for the entire period, we used the estimates of Net State Domestic Product (NSDP) given by the CSO of the Ministry of Statistics and Programme Implementation (MoSPI). The GVA and workforce estimates under different sectors were aggregated into three major sectors. The GVA estimates used are in real terms at 2011-12 prices.In principle, labor productivity should be measured based on number of hours the workers spend to produce a unit of output. In the absence of data pertaining to 'hours of work' spent in a given industry, it is more logical to follow 'major-time-use' criterion. Traditionally, the employment estimates in India based on this criterion are computed using the information collected in the Employment and Unemployment Surveys conducted quinquennially by the NSSO. The KLEMS database provides time-series of employment estimates generated using the surveys. Still, we generated our own estimates for the following reasons. The estimates of 'persons employed' provided in the KLEMS database is based on the 'principal + subsidiary' occupation status, meaning that even if a person has engaged in any occupation for a relatively shorter period, he/she is counted as employed. Since this approach to counting slightly inflates employment figures, it results in underestimated labor productivity values. To account for this bias, the number of persons employed in 'principal' occupation status in agriculture, industries, and services were computed using the household level survey data for the years 1983, 1987-88, 1993-94, 1999-00, 2004-05 and 2011-12. Since NSSO estimates are still unavailable for the year 2015-16, the KLEMS estimates for the year 2015-16 was adjusted to obtain the desired values. To explain, the size of workforce for the year 2015-16 according to 'principal' status was computed using differences in workforce shares using 'principal' and 'principal + subsidiary' estimates provided in the Labor Bureau Survey at stage 1. At stage 2, using the differences in workforce and labor force shares of Labor Bureau, the size of workforce was estimated. The sectoral employment shares provided in KLEMS for the year 2015-16 was then used to derive the new sectoral workforce estimates. Labor Bureau provides annual employment estimates for the past few years but these data are not comparable with the NSS estimates.The NSSO survey underestimates the population by design even though the sampling strategy it follows is based on the projected population estimates of the Population Census, and hence the workforce across sectors and states. Using these unadjusted figures at the national level would provide one slightly different labor productivity estimates. When such exercise is carried out for different states at a disaggregated level, may provide a biased productivity estimate. To avoid the bias, the employment statistics obtained using survey estimates were appropriated with the census estimates. The following corrections were carried out. First, the decadal census estimates were interpolated or extrapolated to obtain a long-time series for the period 1981-2016. Second, using the worker-population-ratios given by the NSSO, the time-series of workers were obtained. This series is adjusted for the reference survey period followed by the NSSO and a new series was obtained. Third, the differences in workforce in census and survey-based estimates were then distributed among different sectors. The resulting sectoral employment series provides the new estimates adjusted to the census estimates and are used in the present study.The long-run trends in sectoral output shares clearly indicate the pattern of shift into an industrial economy since the mid-1970s until the early 1990s, and the dominance of services-led growth thereafter (Figure 3). In agriculture, the share of GVA has declined consistently from 58% during 1960 to 15% during 2016, with no major aberrations over time. Industrial and services sector output shares show notable changes. The industrial GVA has stagnated for about a decade since the mid-1960s and has risen consistently since then. But this rising trend has not persisted for more than two decades. Since the mid-1990s, the industrial share turned back to stagnate, the period which one would mind for realizing the benefits of economic reforms in the country. Since then, the services sector had dominated in generating higher output. This transformation in output components noted by a consistent decline in agricultural output and the rise in industries and services had rather failed to record corresponding labor-adjustments in respective sectors. The statistics for the year 2015-16 show that while 29% workers produced 53% of national output through services, 46% workers produce mere 15% output in agriculture sector. This reflects the inability to absorb labor along growth and had led to huge productivity differentials between these sectors. Our estimates show that the average labor productivity was highest in manufacturing during 1960, amounting Rs. 68,000 (US$ 903) at 2011-12 prices. The productivity levels were Rs. 31,000 (US$ 405) and 49,000 (US$ 651) in agriculture and services, respectively, during this period. By 2016, while productivity level rose only by 2.4 times in agriculture, it raised by 3.9 times in industries and 8.2 times in services.We observe that labor productivity was almost stagnant in agriculture for more than three decades since 1960s. Much of the increase was recorded since early 1990s when the structural reforms were introduced. The period following reforms in the country is noted for 'growth deceleration' in agriculture (Figure 4). Almost all the commodities registered a negative growth (Figure 5), and except for few states, growth deceleration was felt almost in all states. Between 1993-94 and 2004-05, growth was just 0.4% in cereals, 0.1% in pulses, -0.7% in oilseeds and -0.2% in fibers. Even the output in allied sector commodities like milk, meat, egg and fish declined during this period. This contrasting picture -that a deceleration in growth but an increase in labor productivity in agriculture -has essentially been the result of the phenomenon of structural change, explained by the changing composition of employment shares among sectors. The rate of labor absorption started to decline in agriculture since reforms. On an average, agricultural sector absorbed 4.4 million workers between 1970s and 1990s, which dropped by half to 2.2 million during the decade following reforms. This decline has led to the raising labor productivity trends in agriculture. More interesting is the pattern in successive periods. Since the mid-2000s, the agricultural workforce registered a negative growth, falling in absolute size. The employment growth was -2.2% 2005-06 and 2015-16. One would note that this was the period when the country's largest employment guarantee program -The Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) -was introduced. The result of such policy choice on agricultural labor demand and subsequent effect on farm wages are well documented in the literature (Gulati et al., 2014). Given that labor cost roughly constitutes more than one-third total operational cost in agriculture, one would expect a negative impact on agriculture. Rather, the output growth was 3.4% during this period, registering improvements in growth in almost all major commodity groups (Figure 4). As stated earlier, this period is noted for 'growth recovery' in agriculture. Among various factors, a rise in public investment, improvements in terms of trade and improvements in input use are responsible for this recovery. This resulted in a shift in labor productivity to a higher trajectory than the earlier phase, combined together with the faster structural change process.Observing the performance of output growth against labor-shift across states shows interesting picture.Plotting the change in share of NSDP against the share of agricultural labor-shift in each state, it is shown that the structural change process has not interfered with the output generation in most of the states (Figure 6). Though the degree of relation varied among them, except for the state of Kerala, none of the states registered negative output growth. Further, Himachal Pradesh was the only state where the workforce increased, that too marginally. In states like Madhya Pradesh and Rajasthan, the NSDP has raised by more than 40% against a reduction in labor share by 30%. In Bihar, Tamil Nadu and Andhra Pradesh, NSDP has risen between 30% and 40%. While the size of labor-share decline was 36% in Tamil Nadu, in Bihar and Andhra Pradesh, it was 14% and 11%, respectively. Assam, Haryana and Gujarat are the other states with notable increase in output. The rise in output share in these states was between 25% and 30%. Beyond this 'no effect' impact, to some extent, one could observe a positive correlation between NSDP and the agricultural labor-shift among cluster of states. In these states, when higher the share of labor shifting into nonfarm industries and services, higher is the agricultural output growth. The relative position of states like Madhya Pradesh, Rajasthan and Tamil Nadu at the one end and West Bengal, Gujarat and Haryana at the other end leads to such conclusion. Still, such results do not hold always. There are cluster of states with opposite trend. Punjab, Odisha, Jammu & Kashmir, Maharashtra and Uttar Pradesh on the one hand characterized by low changes in output share despite of higher share of labor-shift, and on the other, Bihar and Andhra Pradesh achieving higher output growth with smaller level of labor shift. In some sense, one could link such relations with the extent of diversification within agriculture and the capacity of nonfarm sectors in these states to absorb agricultural labor.Following observations emerge from the above discussion. While in pre-reform period the impact of productivity improvements in agriculture were equated by the new workforce entered in this sector leading to constant labor productivity, structural change during the post-reform period led to a drastic shift towards a rising productivity. In the absence of change, productivity growth in agriculture today would have been much lesser. More importantly, the role of construction sector in absorbing agricultural workforce is worth mentioning. Estimates indicate that between 2005-06 and 2015-16, about 52 million workers left agriculture. During the same time, about 42 million new entrants joined in construction, signaling a major shift of agricultural workers into construction. Thus, the contribution of agriculture to national productivity growth is not constant and would shift with the shifting role of productivity and structural change.It is evident from above discussion that the contribution of within-sector productivity growth and intersectoral shift in agriculture has varied in different periods. The results of growth decomposition are displayed in Figure 7, and detailed estimates are presented in Tables 123. National level estimates consistently affirm 'within-sector' component was the major source of growth than structural change throughout the period. With the rising level of labor productivity over time, the contribution of productivity growth within the sectors as well has improved. During the pre-reforms period 1982-83 to 1993-94, the labor productivity growth was 3.3% a year. This rose to 4.2% during the post-reform period 1994-95 to 2004-05 and to 8.8% during 2005-06 to 2015-16. Accordingly, the contribution of within-sector growth as well rose to 2.6%, 3.2% and 6.4%, respectively during these periods. While the estimates across states also proved 'within-sector' component as the major source, relative shares of productivity growth had greatly varied across space and time.    When compared with services and industries, the share of agricultural sector to productivity growth was relatively less. Looking at the national estimates, agriculture contributed only 26% to the national withinsector productivity component during 1982-83 to 1993-94. In the forthcoming period, it declined further to 12% between 1994-95 and 2004-05. When growth in agriculture recovered since the mid-2000s, as we noted in earlier discussions, agriculture sector's contribution raised back to the earlier level. The share of agriculture improved to 24% during 2005-06 to 2015-16. But as observed in industries and services sectors, agriculture sector's contribution differed in different states over time. The sector's contribution was relatively high in Himachal Pradesh, Punjab, Haryana and West Bengal in the pre-reforms period. The share was more than 80% in former two states and between 70% and 80% in the latter two. During the 1994-95 to 2004-05, while agriculture sector's share in total productivity growth of Himachal Pradesh reduced to less than 40%, the share was above 60% in Punjab's productivity growth. In Uttar Pradesh, agriculture emerged as the new source to contribute, raising its share from 15% in pre-reforms period to 58% in the post-reform decade. Since the mid-2000s, a new set of states witnessed high contribution from agriculture i.e. Jammu & Kashmir, Assam, Madhya Pradesh and Rajasthan. While the contribution of agriculture was more than 60% in the former two states, it was between 40% and 50% in latter two states.In terms of structural change, the contribution of agriculture was negative throughout the study period. This negative contribution has risen in the post-reforms period. The contribution was a negative by 30% between 1982-83 and 1993-94. It turned further to negative 36% in the next decade and remained at the same level of negative 36% during 2005-06 to 2015-16. Similar was the case when we look across states. Among all states, Punjab and Haryana were the only states where agriculture's contribution was positive to the states' overall structural change process in the pre-reforms period; but we note that the contribution of structural change to the state's productivity growth was negative by 8%. In early post-reform decade, it remained positive in Punjab, turned negative in Haryana. During the latest decade, except Punjab, there were no states in which a labor-shift from agriculture contributed positively to the state's overall structural change process.The results of the analysis presented above have several policy implications for Indian agriculture. The trends in sectoral output and employment components over long-run clearly indicate the country's notable success in reducing the share of agricultural output in national output, but failure to relocate low-productive agricultural workers towards industries and services. The success factors had primarily been the expansion of industries sector during the mid-1970s to the mid-1990s, and the dominance of services sector since then.Turning our attention towards labor market, the factors behind failure of labor relocation could be attributed to a major extent to the dominance of capital component over labor in the growth process.In other words, since the economic reforms in the early 1990s, the growth process has turned increasingly capital intensive than it was earlier, replacing more labor (Figure 8). This would be more evident while observing the pace of growth in capital formation in the country. While the level of capital formation in agriculture has increased only by 2.6 times between the years 1991 and 2017, the increase is 6.5 times in non-agriculture sector. Deeper analysis among different subsectors shows that this increase is as high as 19 times in the finance sector, 15 times in the construction sector and 5 times in the manufacturing sector.Corresponding decline in labor share had been 15.1%, 3.3% and 8.5%, respectively in these sectors. This was also reflected in the slowdown in employment growth in this period. Despite a major contribution of 'within sector' component to growth, the trends in different sub-periods clearly indicate increasing share of 'structural change' component in labor productivity growth. This has increased from 20% in the pre-reform period to 25% in the early decade of post-reform period and further to 28% in the recent decade. In some way, this signals an improvement as it helps to slowdown the intersectoral divergence in labor productivity. This has helped to shift agriculture sector, on which the present paper concentrates, from a stagnant level to a rising level of productivity with important welfare consequences. The association of negative employment growth with accelerated output growth reminds us the need for diversifying cereal based monoculture towards cash crops, fruits and vegetables and high-value allied sector commodities like milk, meat, egg and fish, . Such a switch would lead to more output per unit of labor. We believe this was the major reason behind the non-negative output growth in most of the states in recent decade.Further, we believe the reason behind 'no effect' of labor withdrawal on agricultural output is in part the left-out labor could be the \"surplus\", as described in the structural change theories. As elimination of labor surplus leaves the output untouched, the states could have observed a non-negative growth. The varying contributions of components of growth sources among states provide an opportunity to focus the regions where structural change can be encouraged. The continued positive contribution of agriculture in total structural change in Punjab indicates the flow of more productive labor from agriculture to less productive sectors and hence warrants discouraging such shift through appropriate policies. On the other hand, supporting labor-shift from agriculture in states with negative contribution signals that enabling further shift could lead to inter-sectoral productivity convergence. Investing in technologies that raise output per unit of labor, promoting practices such as farm mechanization and enhancing the farmers' and laborers' skills, not just in agricultural operations but also for industries and services sectors could further augment this convergence process. A set of carefully calibrated labor sector policies that improve both laborers' earnings in different sectors and encourage inter-sectoral productivity convergence is needed.In this paper we studied the transformation process in Indian agriculture by estimating the contribution of productivity growth and structural change in agriculture to the national productivity growth during 1981-2016. Given the huge productivity-differentials in agriculture across regions and their ability-differentials to participate in the structural change process, the varying contributions of these factors were also estimated for 21 major states of India for different sub-periods and major changes have been traced. In doing so, new employment estimates were used to address the differences in survey and census-based estimates.Results show that in the pre-reforms period the impact of productivity improvements in agriculture was equated by the new workforce entering into this sector, leading to a stagnant labor productivity trend. Laborshift from agriculture during the early years of the post-reforms period, which increased further in the next decade, led to a rise in productivity. In the absence of reforms and the associated labor shift, the productivity growth in Indian agriculture would have been much lower. A similar labor shift during the last decade has not affected agricultural output, which has risen more rapidly. This result holds true for almost all states studied. There exists a positive relation between labor-shift and agricultural output in a cluster of states.Decomposition results indicate 'within-sector' productivity growth is the major source of overall growth, with a rising contribution of 'structural change.' 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+{"metadata":{"gardian_id":"f89191a15b032794eea130568ffb7afb","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/66feccc1-64a0-411f-b315-f7e110cf2436/retrieve","description":"The 193 individual country profiles capture the status and progress of all UN Member States, and the 80+ indicators include a wealth of information on child, adolescent and adult anthropometry and nutritional status, in addition to intervention coverage, food supply, economics, and demography. This tool is particularly useful for nutrition champions at the country-level, as it presents a wide range of evidence needed to assess country progress in improving nutrition and nutrition-related outcomes.","id":"-187557602"},"keywords":[],"sieverID":"2b70e049-7b2b-4fe2-b492-3e5398a46315","pagecount":"2","content":"Under-5 stunting, 2014 a Under-5 wasting, 2014 b Under-5 overweight, 2014 a WRA anemia, 2011 b EBF, 2014 a On course, good progress Off course On course, at risk Off course Off course, no progress Sources: a Definitions of progress developed by GNR's Independent Expert Group with guidance from WHO/UNICEF; b WHO 2014. Notes: Currently it is only possible to determine whether a country is on or off course for five of the six WHA targets. The year refers to the most recent data available; on/off-course calculation is based on trend data. WRA = women of reproductive age. EBF = exclusive breastfeeding.INCOME INEQUALITY Gini index score* Gini index rank † Year 32 28 2011 Source: World Bank 2015. Notes: *0 = perfect equality, 100 = perfect inequality. † The countries with a Gini index are ranked from most equal (#1) to most unequal (#145).","tokenCount":"144","images":[],"tables":["-187557602_1_1.json","-187557602_2_1.json"]}

data/part_2/0498629188.json

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+{"metadata":{"gardian_id":"99cb92448496123074dbb605f2cace49","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/d12fa8f8-dc6d-4132-b4ff-2d9e756e88ae/retrieve","description":"","id":"-1232482647"},"keywords":[],"sieverID":"75d39c69-ea6a-4502-bffc-e195ef9f2fb6","pagecount":"8","content":"• Two consecutive projects, funded by World Bank loans and bilateral funding, have been a major influence on the development of research infrastructure in Tanzania in the 1990s and beyond. • DRD is the main agricultural research agency in Tanzania and accounted for close to two-thirds of the country's total spending and research staff in 2000.• During the 1990s, DRD was reorganized with a view to reducing operational costs and increasing efficiency. This process included the privatization of coffee, tea, and tobacco research, which resulted in three newly established nonprofit institutions.• Like many African (and other) countries, DRD has become increasingly dependent on donor funding. More than half of total 2000 revenue came from the World Bank and other donors. • Some private companies and NGOs conduct limited adaptive research, but they apparently do not have appropriate research facilities and hence rely on DRD facilities and researchers. However, several of the commodity boards fund research at DRD on the nonprofit institutions.Tanzanian public agricultural research since the early 1990s using new survey data collected under the Agricultural Science and Technology Indicators (ASTI) initiative (IFPRI-ISNAR-ASARECA 2001-02). 1Thirteen agencies were engaged in agricultural research in Tanzania in the 1990s, 12 of which are included in our sample. 2 These 12 agencies employed a total of 542 fulltime equivalent (fte) researchers and spent a combined 10 billion 1999 Tanzanian shillings on agricultural research and development (R&D)-equivalent to $26 million at 1993 international prices (Table 1). 3 The Department of Research and Development (DRD) under the Ministry of Agriculture and Food Security (MAFS) is the principal agricultural research agency accounting for close to two-thirds of total research spending and fte researchers. DRD is headquartered in Dar es Salaam. It has 22 agricultural research institutes and livestock centers led by a central institute in each of the seven agroecological zones (the zonal center in Uyole leads the Southern Highlands zone, for example).In February 2001, the government of Tanzania was restructured. At that time, DRD's livestock research institutes were relocated under the newly established Ministry of Water and Livestock Development. Despite this, to-date, livestock research at the field level remains under DRD responsibility largely because it is part of the Tanzania Agricultural Research Project (TARP-II), which is funded through a World Bank loan.The Agricultural Science and Technology Indicators (ASTI) Initiative consists of a network of national, regional, and international agricultural R&D agencies managed by IFPRI and ISNAR. The initiative compiles, processes, and makes available internationally comparable data on institutional developments and investments in public and private agricultural R&D worldwide, and analyses and reports on these trends in the form of occasional policy digests for research policy formulation and priority setting purposes.Primary funding for the ASTI initiative was provided by the CGIAR Finance Committee/World Bank with additional support from the Australian Center for International Agricultural Research (ACIAR), the European Union, and the U.S. Agency for International Development (USAID).  e The 327 faculty staff employed in the five higher-education agencies spent between 10 and 30 percent of their time on research, resulting in 90.3 fte researchers.For the past 10 years the Tanzanian national agricultural research system has been guided by the National Agricultural and Livestock Research Masterplan (NALRM), which was formulated in 1990/91. NALRM established the framework for \"rightsizing\" the DRD research network, whereby research resources were to be streamlined to increase efficiency and effectiveness. Given the national drive for decentralization and the need to be more responsive to the demands of the farming community, a zonal prioritization exercise was conducted in 1993/94. While some decisionmaking responsibilities shifted to the zonal level as a result, envisaged zonal autonomy has not been fully realized. Human resource management is centralized under the Civil Service Department (CSD) of the president's office and funds are still disbursed through DRD headquarters, which also handles the procurement of goods and services.During the 1990s significant changes in the global and national economic environment affected agricultural research. In response, DRD management recognized the need to conduct research in a more holistic and integrated way by involving the farming community and other stakeholders in participatory technology development and transfer. In October 2001, MAFS completed its Agricultural Sector Development Strategy (ASDS)-the conclusion of a participatory process involving a wide range of stakeholders. ASDS became the basis for publicand private-sector action in support of agricultural growth and rural poverty reduction in Tanzania, and as of early 2003 the Agricultural Sector Development Programme (ASDP) was in preparation to put ASDS into effect at subsector levels, including research.In addition to DRD, five other government institutes are involved in agricultural research in Tanzania. MAFS has administrative responsibility for the Tropical Pesticides Research Institute (TPRI) and the Tse-Tse and TrypanosiomasisResearch Institute (TTRI), 4 while the Tanzania Fisheries Research Institute (TAFIRI), the Tanzania Forestry Research Institute (TAFORI), and the Tanzania Wildlife Research Institute (TAWIRI) fall under the Ministry of Natural Resources and Tourism. Except for TTRI, the institutes have semiautonomous status, allowing them to set their own research programs and seek nongovernment funding, at the same time maintaining secure government funding for staffing and basic facilities. 5 We were unable to obtain data on TAWIRI so it is excluded from our 12-agency sample; the remaining four government agencies accounted for 17 percent of the sample's total agricultural R&D spending in 2000.Tanzania's two nonprofit research institutions, the Tanzania Coffee Research Institute (TACRI) and the Tea Research Institute of Tanzania (TRIT), accounted for 7 percent of total agricultural R&D spending in 2000. Both were only recently established-TRIT in 1997 and TACRI in 2000-through privatization of research activities that had been the responsibility of DRD (and are included in DRD data in the preprivatization years). Both TACRI and TRIT are funded by a cess on tea and coffee production as well as government and donor contributions. Tobacco research at DRD's Tumbi Research Institute was terminated in 1995. A third nonprofit institution-the Tobacco Research Institute of Tanzania (TORITA)-was established in 2000, but as of December 2002, the institute has yet to initiate its own research activities, and uses DRD staff, who submit project proposals to TORITA's board for their approval and funding.The five higher-education agencies involved in agricultural research in Tanzania accounted for about 16 percent total expenditures in agricultural research in 2000. The Sokoine University of Agriculture (SUA) was responsible for most of these activities, employing 243 faculty staff or-adjusted toAgricultural research in Tanzania (then Tanganyika) was initiated as early as the late nineteenth century. German colonial powers at that time established laboratory facilities within the botanical garden and trial farms across the region to study crop plants and husbandry. In the 1920s, under British rule, agricultural R&D was virtually abandoned, but in the next few decades research stations were established as part of the Departments of Agriculture and Veterinary Sciences. Agricultural research was largely the domain of the local colonial government until World War II, during which time the British government sought a more active role in the promotion of science and technology in its colonies. This led to the creation of several regional agricultural research organizations in East Africa that complemented or partially replaced existing facilities. Two of these, the East African Marine Fisheries Research Organization (EAMFRO) and the Tropical Pesticides Research Institute of East Africa (TPRI), were located in Tanzania.With independence in 1961, the Tanzanian Ministry of Agriculture inherited relatively well-developed research infrastructure, but activities depended heavily on British researchers and favored export commodities-like cotton, coffee, and sisal-over food crops. In the two decades following independence, investment in agricultural R&D rapidly developed resulting in the establishment of several research stations and an expansion of the research focus to include food crops and natural resources. reflect time spent on research-73 fte research staff. Research is undertaken at each of the university's 4 faculties and 2 institutes, and falls predominantly into the category of applied research; basic research only accounts for 2 percent of all SUA research activities (SUA 2002). The four institutes and departments of the University of Dar es Salaam (UDSM), by comparison, played only a small research role in 2000, employing a combined total of 17 fte agricultural researchers. Gavian et al. (2002) report that some private bodies and nongovernmental organizations (NGOs) conduct adaptive research, but mainly in collaboration with DRD using the government's research facilities.As part of the of the restructuring of national agricultural research, the National Agricultural Research Council (NARC) was established in 1990 to oversee the coordination of agricultural research at both public and private agencies and to ensure that the research agenda meets national agricultural development objectives. NARC could not perform its duties as it lacked statutory powers and had no budget provisions for its operations and in reality the coordination of agricultural research is carried out by to the Research and Development Committee on Agriculture and Natural Resources under the Commission for Science and Technology (COSTECH).Notable levels of collaboration occur among the various Tanzanian agricultural research agencies, as well as with regional and international agencies. Nationally, DRD collaborates closely with a large number of government and nonprofit agencies such as TAFORI, COSTECH, TPRI, SUA, TACRI, and TRIT. International linkages include collaboration with ASARECA and other regional networks, as well as many of the international centers. Collaboration is basically via bilateral agreements. Donor agencies like the World Bank and African Development Bank (ADB) have also provided substantial support to national agricultural R&D in Tanzania.The total number of fte agricultural researchers remained fairly constant during the 1990s (Figure 1a). DRD's total research staff numbers declined slightly (in absolute and relative terms), but this was primarily the result of privatization of coffee and tea research. Total fte researchers at the other government agencies and at the higher-education agencies increased during 1991-2000. The share of expatriate research staff further declined to negligible levels.Public R&D spending data were only available for the period 1996-2000, during which time expenditure doubled to 26 million 1993 international dollars or 10 billion 1999 Tanzanian shillings (Figure 1b). This growth was the result of World Bank loans to DRD and SUA through the National Agricultural and Livestock Research Project (NARP I) and the TARP-II project along with increased research activities at the other government and higher-education agencies. DRD's increased 1998 spending resulted from termination payments to retrenched staff of the previous two to three years.Spending per scientist almost doubled from $25,000 in 1995 to $48,000 in 2000 but was still very low level compared with spending in surrounding countries. Underlying data are available on the ASTI website (www.asti.cgiar.org). TAWIRI is excluded from our 12-agency sample (and hence the category \"other government\") because data were unavailable. Expenditures for TAFIRI, TTRI, and the higher-education sector are estimates based on average expenditures per researcher for the government sector.Although the total number of researchers remained fairly constant in the 1990s, staff qualifications-in terms of the share of researchers with PhD and MSc degrees-increased considerably. Excluding expatriate staff, 78 percent of the 538 fte researchers in our 12-agency sample had postgraduate level training in 2000 compared with a corresponding share of 57 percent in 1991 (Figure 2). A higher proportion of the university staff held postgraduate degrees compared with staff at other agencies, which is consistent with other African countries and regions (Pardey et al. 1997 andBeintema andPardey 2001). Of note, however, is the high share of university staff with doctorate degrees at 72 percent-much higher than in many other African countries. For the 10-agency sample for which data were available, 19 percent of the total fte researchers were female, ranging from 13 percent of those holding doctorate degrees to 25 percent of all researchers trained to the BSc level. The higher-education agencies employed relatively fewer female researchers. Onequarter of the researchers at the other government agencies were female, but most held lower degree qualifications than the Tanzanian average. In 2000, the average number of support staff per scientist in the 10-agency sample for which data were available was 2.5, made up of 0.9 technicians, 0.9 administrative personnel, and 0.7 other support staff such as laborers, guards, and drivers (Figure 4). DRD had the highest ratio of support staff per scientist in 2000 (3.2), although this was still only about half the corresponding 1991 number, reflecting the retrenchment of (mainly) nontechnical and administrative support staff during 1995-98. Total public spending as a percentage of agricultural output (AgGDP) is a commonly used research investment indicator that enables a nation's agricultural R&D spending to be viewed in an international context. In 2000, Tanzania invested $0.38 for every $100 of agricultural output-less than half the average of $0.85 for Africa as a whole in 1995 (Figure 5). Despite the two consecutive World Bank loans, the salary share of total DRD spending remained fairly high during 1996-98, but in recent years DRD spending has shifted somewhat toward physical infrastructure, equipment, and staff training. In 2000, total salaries and operational costs each accounted for about a quarter of DRD's total spending (Figure 6). The high level of salaries in total spending in 1998 stems from the retrenchment of support staff mentioned above, along with a raise in the minimum wage for government employees.Sources: Tanzania data are from Table 1; AgGDP data are from World Bank (2002); other intensity ratio data are from Pardey and Beintema (2001).As in many countries in the surrounding region, one of the most serious problems is low public salary levels for researchers compared with salaries at similar nongovernment agencies. The  Notes: Annual data are taken the respective budget year (1996 data from budget year 1995/96 and so on). Data include estimated salaries for expatriate staff.  (World Bank 1999 andNORAD 1999). This sub-project under TARP-II was completed in December 2002. Information on funding sources was only available for one other government agency. Most of TAFORI's funding was provided by the government with donor funding contributing about 10 percent over this time, although this share has apparently increased during this period. Agricultural research at SUA is almost completely funded by external donors and half of those projects are funded by the Norwegian Agency for Development Cooperation (NORAD). Only 2 percent of the University's total agricultural research projects are funded by in-country sources (SUA 2002).Government and donor funding to DRD between 1996 and 2000 can best be described as erratic (Figure 7). Government contributions are determined by the parliament and, to a small degree, by the local districts. One of DRD's problems has been inconsistencies between the budget allocations and actual disbursements (allocations have even exceeded approved budgets in some years). About two-thirds of total government funding underwrites the recurrent budget, 90 percent of which is earmarked for salaries and benefits. This seems high, but as previously mentioned, researcher salaries are low compared with other African countries. The development budget represents about one-third of the total allocation, drawn almost completely from World Bank contributions, which are given in the form of loans and are therefore treated as part of the total government allocation (Gavian et al 2001). The government is obligated to disburse 10 percent of its development budget as local counterpart funding, though this does not necessarily happen in practice. Notes: Annual data are taken the respective budget year (1996 data from budget year 1995/96 and so on).The government allows its agencies to retain internally generated revenues. Hence DRD research institutes have incentive to generate these so-called self-help funds, and do so via sales of produce, secretarial services, printing units, and institutional fees from contract research. These sources accounted for 4 percent of total DRD funding in 1996-2000. Most of these funds are used to maintain equipment and infrastructure and for purchasing inputs. In addition, DRD receives also funding from commodity levies. In 1996-2000, these sources accounted for 12 percent of total funding; more than half this amount was generated from cashews. Research funding through commodity levies is relatively high in Tanzania compared with other African countries. 6 The government set up various commodity levies, but the share of revenues that are allocated to research as well as the collection mechanisms differ:• One-third of the 3 percent levy on the total export value of raw or processed cashews is earmarked to research.7 Most of these funds are channeled to DRD's agricultural research institute in Naliendele. Since the mid 1990s commodity-based funding accounted for more than twothirds of Naliendele's total budget. This funding is used for cashew as well as other crops research in the region. The first competitive fund for agricultural research was established in 1991 and became operational in 1994. The fund, called the National Agricultural Research Fund (NARF), was devised to support research activities under the National Masterplans separate from those of NARLP-I. The fund was also intended to improve linkages among agricultural research agencies in Tanzania, between the public and private sectors, and with agencies outside of Tanzania. As of June 30, 2002, over US$750,000 had been allocated to NARF, but only about half this amount had actually been disbursed.NARF has succeeded in exposing scientists to collaborative research, thus improving overall linkages, especially with SUA. Some setbacks have arisen, however, resulting from unexpected delays in approval of proposals from lengthy review procedures and the failure of anticipated funding agreements. Nevertheless, NARF remains a promising funding source for cross-cutting zonal research issues.In 1997, the Zonal Agricultural Research Fund (ZARF) was established to address what were perceived as inherent problems with NARF, specifically that it operated in isolation of farmlevel clients. ZARF is more decentralized, empowers local stakeholders and zonal institutes, and is seemingly more financially sustainable with contributions from district councils, nongovernmental organizations, the governments of Sweden and the Netherlands, and other donors. These contributions are matched equally by the World Bank through TARP-II. As of June 30, 2002, stakeholder contributions to ZARF totaled over US$280,000, with World Bank matching funds totaling US$216,000. ZARF only funds operational costs.ZARF is operational in the lake, central, northern, and southern zones and is being implemented in the remaining three zones. Overall, funding levels are far below demand though the initiative holds significant potential for sustainable research.The allocation of resources across various lines of research is a significant policy decision; hence detailed survey information was collected on the number of fte-researchers working in specific commodity and thematic areas.In 2000, close to half the 379 fte researchers in the 25-unit sample (for which we were able to obtain data) conducted crop research. Livestock accounted for 17 percent of the total, while fisheries research for 9 percent, natural resources for 7 percent, and forestry for 6 percent (Figure 8a). This allocation would differ slightly were the higher-education sector included: SUA's website indicates that close to a quarter of the university's research projects focused on livestock, 19 percent on forestry and natural resources, and while only 11 percent on crop production and horticulture (SUA 2002).Two thirds of the DRD researchers in our sample were involved in crop research. Rice, maize, coconut, cassava, tea, and coffee accounted for 6-9 percent each, while the remaining crops researchers (60 percent) focused on a wide variety of other crops (Figure 8b). Most livestock researchers were conducting research on dairy, beef, or pastures (Figure 8c). In 2000, 19 percent of DRD's researchers were working on crop genetic improvement, 13 percent on crop pest and disease control, and 10 percent on livestock pest and disease control (Table 2). The remainder of DRD's researchers focused on other crop and livestock research with only a small portion working on natural resources or other thematic areas. A large share of the researchers at the other five agencies conduct research on other thematic areas such as postharvest, food safety, and socioeconomics. Agricultural research in Tanzania follows the regional pattern of high dependency on donor funding. Yet even with the high donor support, agricultural research investments per researcher and as a share of AgGDP remain very low, in part because government employees earn very low salaries relative to their colleagues at nongovernmental organizations or in other countries.Recent institutional developments focused on increasing DRD's efficiency, which (among other measures) included downsizing, privatizing tea and coffee research, and instituting new funding sources and allocation mechanisms such as the ZARFS.1. The authors are grateful to Olympia Icochea for her assistance with the data processing; numerous colleagues in Tanzania for their time and assistance with data collection; and Jeremiah Haki, Barnabas Kapange, Mary Lutkamu, Gaudence Mitawa, and Han Roseboom for useful comments on drafts of this brief. -The one government agency excluded from our sample is the Tanzania Wildlife Research Institute (TAWIRI), for which we were unable to obtain data. 3. Unless otherwise stated, all data on research expenditures are reported in 1999 Tanzanian shillings or in 1993 international dollars. 4. TTRI is funded separately from DRD and hence is not officially classified as a DRD unit; however, operationally the DRD's Animal Disease Research Institute (ADRI) coordinates TTRI's research activities and the two entities work in close collaboration.5. In accordance with the Frascati Manual (see Methodology box on page 8), these parastatals are classified as government agencies because they are largely administered by the government and receive more than half of their annual funding from government sources.6. This section draws largely on Gavian et al (2001), who elaborate on the various producer levies.7. The research share of the cashew levy was recently doubled from 0.5 to 1 percent.8 At that time, 92 research proposals had been processed-19 for approval, 29 for revision, and 36 for peer review. The remaining 8 were rejected. ","tokenCount":"3555","images":["-1232482647_1_1.png","-1232482647_1_2.png"],"tables":["-1232482647_1_1.json","-1232482647_2_1.json","-1232482647_3_1.json","-1232482647_4_1.json","-1232482647_5_1.json","-1232482647_6_1.json","-1232482647_7_1.json","-1232482647_8_1.json"]}

data/part_2/0509620882.json

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+{"metadata":{"gardian_id":"6ee1d1b8e27195a0ea691d4735af5dd4","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/f20e8cc2-73ce-4c4d-9661-562439dda228/retrieve","description":"This country factsheet presents key agricultural R&D indicators in a highly accessible visual display. The publication also features a more in-depth analysis of some of the key challenges that the country’s agricultural R&D system is facing, and the policy options to address these challenges.","id":"-620310789"},"keywords":[],"sieverID":"5a3d7ca1-86f5-48a1-9281-97a04e3d854e","pagecount":"4","content":"In terms of agricultural research investment and capacity levels, as of 2014 South Africa ranked second from a regional perspective, after Nigeria. Investment in agricultural research rebounded between 2008 and 2013 in inflation adjusted terms, after a period of decline. In 2014, South Africa's agricultural research spending as a share of AgGDP was unusually high by African standards (2.78 percent), but levels appear to have contracted since then. Capacity strengthening at ARC Compared with agricultural research agencies elsewhere in Africa, human resource capacities within ARC's institutes tend to be more balanced in terms of degree levels, gender balance, and age distribution. The number of PhD-qualified researchers increased over time, but an issue remains with replacing an aging pool of senior scientists. An effective succession plan is needed, including strategies for recruitment, training, and mentoring. Funding diversification at ARC The 2009 governmental restructuring that led to the transfer of some functions from the Department of Agriculture to a newly formed Department of Rural Development and Land Reform benefited ARC through funding for a number of new projects. During this time, however, funding from the country's commodity boards decreased significantly.ARC's Biotechnology Platform was established in 2010 as a research and service-driven mechanism targeting the development of agricultural biotechnology. Through the platform, resources are developed for the application of advanced genomics, molecular breeding, and bioinformatics for dissemination within ARC, to the Council's collaborators, and to the private sector and other research agencies throughout Africa. The platform also provides an environment in which the next generation of highly skilled young researchers can be developed. The highest proportion of researchers employed at both PhD and MSc levels are mainly in the plant and crop science fields. As of 2014 there were no researchers specializing in poultry, dairy sciences, or fisheries and aquaculture. The total number of researchers employed by ARC fell during 2000-2014, resulting in a decline in the Council's share of the national total from 66 to 56 percent. In contrast, strong growth in the number of researchers employed in the higher education sector caused its share to increase from 12 percent in 2000 to 20 percent in 2014. Notes: Data for the higher education sector in 2014 are from DHET-HEMIS. \"Other\" includes other government and nonprofit agencies, the data for which were estimated based on expenditure data from IHSRC-CeSTII and spending per researcher data from ARC and the higher education sector.Agricultural researchers employed at ARC and in the higher education sector by qualification levelAlthough the higher education sector employs far fewer agricultural researchers than ARC (in FTEs), its share of researchers with PhD degrees rose considerably. ARC's share of PhDqualified researchers also rose over time, and numbers and shares of researchers qualified to the BSc-degree level declined at both ARC and in the higher education sector. As of 2014, about 40 percent of the PhD-qualified researchers employed at ARC and in the higher education sector were in their 50s and 60s. Although substantial, this share is lower than corresponding shares in many other African countries. During 2009-2014, salaries accounted for close to 60 percent of ARC's expenditures. Recent growth in spending was partly driven by increased operating and program costs, as well as increased investment in capital infrastructure. During 2012-2014, ARC received about two-thirds of its funding from the government. Changes in the funding allocation of the country' s commodity trusts caused funding generated through commodity taxes to decline significantly during this timeframe. In contrast, during 2010-2014, revenues generated through the sale of goods and services more than doubled due to an increase in project-based funding stemming from government restructuring. In 2014, more than one-third of ARC's researchers were conducting crop research, 22 percent were undertaking livestock research, and 25 percent were focused on research related to natural resources. That year, 38 percent of all crop researchers were focused on issues related to fruit. Other important crops being researched were wheat, vegetables, and legumes and pulses.Livestock, 22%Natural resources, 25%  ARC published an average of 187 journal articles per year during 2012-2014, primarily in international journals. Publications per researcher averaged 0.5 per year. ","tokenCount":"679","images":["-620310789_1_4.png","-620310789_4_2.png","-620310789_4_3.png","-620310789_4_4.png","-620310789_4_5.png","-620310789_4_6.png"],"tables":["-620310789_1_1.json","-620310789_2_1.json","-620310789_3_1.json","-620310789_4_1.json"]}

data/part_2/0514250358.json

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+{"metadata":{"gardian_id":"07121dfe0a7dee3cc4516ac0c50a146d","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/f8ccafa7-1a44-4b24-896f-1b0d1fb51759/retrieve","description":"This study explores the effects of fluctuations in household income on health and nutrition outcomes from birth to adulthood. We analyze data from a nationally representative, 13 year rolling panel dataset from Kyrgyzstan spanning 2004–2016. We address the endogeneity of income by instrumenting for income with predicted income, obtained using the household’s initial period share of income from different sources and aggregate growth rates over time in each source. Young children (age 0-5) exposed to reductions in income experienced reductions in height that were largest for girls and those under age two—groups that additionally experienced increases in stunting. Reduced consumption of healthy foods, reduced dietary diversity, and less expenditure on health may help explain the results. A channel possibly offsetting negative impacts is a decrease in fertility. At the same time, older children and adults saw decreases in BMI and—for adults—decreases in the incidence of overweight.","id":"-1603011476"},"keywords":["child health","nutrition","income fluctuations","overweight","Central Asia"],"sieverID":"e009da55-b9b8-4758-9b8a-606bc6d633d3","pagecount":"71","content":"established in 1975, provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition. IFPRI's strategic research aims to foster a climate-resilient and sustainable food supply; promote healthy diets and nutrition for all; build inclusive and efficient markets, trade systems, and food industries; transform agricultural and rural economies; and strengthen institutions and governance. Gender is integrated in all the Institute's work. Partnerships, communications, capacity strengthening, and data and knowledge management are essential components to translate IFPRI's research from action to impact. The Institute's regional and country programs play a critical role in responding to demand for food policy research and in delivering holistic support for country-led development. IFPRI collaborates with partners around the world.How do income fluctuations affect health and nutrition outcomes, and how do these effects vary by gender and across the life cycle? Studies looking both across and within countries reveal strong correlations between income and health (Cutler et al. ity. Our focus on a low-income, developing country is motivated by research showing that poor households tend to under-insure against reductions in income (Townsend Townsend, 19941994, 19951995; Jalan andRavallion Jalan andRavallion, 1999 1999;Dercon Dercon, 20022002;Yang Yang, 20082008), making them more vulnerable to such fluctuations. The poor also face a higher arrival rate of health shocks (Currie andStabile Currie andStabile, 2003 2003), and their negative health impacts accumulate over time (Case et al. Case et al., 20022002). As poorer households' inability to smooth their consumption over time has been shown to disproportionately affect women (Dercon andKrishnan Dercon andKrishnan, 2000 2000), we estimate health impacts by gender.We address the endogeneity of income to health and consumption using an instrumental variables approach; we instrument for income with predicted income, obtained using the household's initial period share of income from six different revenue sources, and agricultural production costs from two different sources (crop and livestock), and aggregate growth rates of each of these eight revenues and costs over time.Our paper extends existing literature in several ways. First, we focus on the impacts of recurring fluctuations in income-in our case due to price shifts-as opposed to either extreme shocks that form natural experiments or targeted cash transfer programs. This helps isolate the health and nutrition consequences of income fluctuations from the trauma of extreme shocks, and increases the external validity of the findings beyond individuals targeted by transfer programs. Second, by exploiting not only time but also spatial variation in exposure to macroeconomic shocks to income and shifts in the costs of agricultural inputs, we are able to separate the effects of reductions in household income from other countrywide or region-wide shocks. These include everything from the quality of public services to the relative prices of different foods and essential nutrients. Third, we address identification challenges related to the simultaneity of income and health using an instrumental variables approach following Bartik Bartik (19911991). Showing causal relationships is broadly challenging. Some efforts to achieve identification have included instrumenting for income with five-year changes in terms of trade (Pritchett andSummers Pritchett andSummers, 1996 1996) and past rainfall (Bengtsson Bengtsson, 20102010); we build on these efforts. Fourth, we add to a scant literature on the impacts of fluctuations in income on the incidence of overweight and obesity-two phenomena currently experiencing a rapid (Popkin et al. Popkin et al., 20122012) and costly (Thorpe et al. Thorpe et al., 20042004) rise worldwide.4 4 Fifth, relatively little attention has been paid to the differential impacts of fluctuations in income across the life cycle. The focus has generally been on young children-often disaggregated by gender-while ignoring potentially important impacts on older children and adult women and men. When studies do consider adults, they often consider the impacts of negative shocks in utero or during early childhood on subsequent adult health, rather than the impacts of contemporaneous fluctuations in income. Finally, we explore potential causal channels related to consumption, dietary diversity, expenditure on healthcare, and fertility.We find that young children (age 0-5) exposed to reductions in household income experienced reductions in height and height-for-age z-scores that were largest for girls and those under age two-groups that additionally experienced increases in stunting. Both girls and boys experienced reductions in weight, weight-for-age z-scores, and weight-for-height z-scores. Reduced consumption of healthy foods, reduced dietary diversity, and less expenditure on healthcare may help explain the results. A channel possibly offsetting negative impacts is a decrease in fertility. At the same time, older children and adults saw decreases in BMI following reductions in income and-for adults-decreases in the incidence of overweight. The effects of reductions in income on the BMIs of older children and youth appear to be mostly driven by men; we find no evidence that fluctuations in income affect the BMI or incidence of overweight in older female children (age 5-18) or female youth (age 18-35).In contrast, reductions in income among older adults (age 35 and older) lower BMIs and reduce the incidence of overweight among both men and women.This paper adds to a growing literature employing shift-share instruments, following Bartik Bartik (19911991). For example, Card Card (20012001) studies the effects of immigrant inflows on labor market outcomes by interacting initial immigrant composition of a place with immigration flows from origin countries. Acemoglu and Linn Acemoglu andLinn (2004 2004) investigate the effect of potential mar-ket size on entry of new drugs and pharmaceutical innovations by constructing an instrumental variable that is the interaction of age-group spending patterns with demographic changes.Dube and Vargas Dube andVargas (2013 2013) consider how income shocks affect armed conflict by instrumenting for the product of hectares of coffee grown in a base year and internal coffee prices in the current year with the product of hectares grown in the base year and current year international prices; they do a similar calculation involving oil production. Acemoglu et al. Acemoglu et al. (2013Acemoglu et al. ( 2013) ) examine the effects of income on health expenditures by instrumenting for local area income with time series variation in oil prices interacted with local oil reserves. Nunn and Qian Nunn andQian (2014 2014) study the effects of US food aid on conflict in recipient countries by instrumenting for US food aid shipments with the interaction of lagged US wheat production and a country's average tendency to receive US food aid over a 36-year period. Erten et al. Erten et al. (2019Erten et al. ( 2019) ) investigate the impact of tariff reductions on labor market outcomes using the predicted exposure to tariff reduction, constructed from the average tariffs across all industries, weighted by baseline industry employment shares. Erten andLeight Erten andLeight (2019 2019) study how China's accession to the World Trade Organization affects local structural transformation using a measure of tariff uncertainty constructed using the gap between tariff rates for countries with NormalTrade Relations (NTR) status and those with non-NTR tariffs in each industry, weighted by the baseline composition of employment by industry prior to WTO accession. And Leight Leight (20162016) estimates the impacts of shocks to labor demand in the secondary (industrial and mining) sector in China on local economic outcomes using a shock constructed from the baseline composition of county employment and national employment fluctuations.The paper is organized as follows. Section 2 2 provides background on Kyrgyzstan's economy, healthcare system, and levels of health. Section 3 3 describes our data, empirical strategy, and method for identifying the causal effects of fluctuations in household income. Section 4 4 presents results, while Section 5 5 explores potential causal mechanisms driving them. Section 6 6 shows the robustness of our findings to several alternative specifications and provides evidence to support the assumption of parallel trends. Finally, Section 7 7 concludes.Kyrgyzstan declared its independence from the Soviet Union in August 1991, and joined the Commonwealth of Independent States in May 1993. It is a land-locked, mountainous, low-income country in Central Asia. The period of our study spans 2004-2016, a time during which income per capita grew only modestly; in 2004, the Kyrgyz Republic had a GDP per capita of $757 (in constant 2010 USD) which grew to a still modest $1,042 by 2016. In 2016, 25.4 percent of people lived below the national poverty line, with even higher rates of poverty in rural areas (World Bank World Bank, 2019a2019a,b b). In total, about three quarters of the poor and four fifths of the extreme poor live in rural areas (FAO FAO, 20152015).As in many developing countries, agricultural production is a central part of the economy of Kyrgyzstan. Throughout 2004-2016, over 64 percent of the population has lived in rural areas (World Bank World Bank, 2019b2019b). Agriculture's share in GDP was 33 percent in 2004, but it declined to 13 percent by 2016 (World Bank World Bank, 2019b2019b). Nonetheless, 39 percent of employment in 2004 and 27 percent in 2016 was in agriculture, despite the fact that only 7 percent of the land is arable. The vast majority of agricultural production occurs on small individual farms (FAO FAO, 20152015). In our sample for analysis, about 28 percent of agricultural income comes from livestock and meat production and about 67 percent comes from harvested crops, with the remainder coming from meat production and food processing.Kyrgyzstan has achieved significant progress over the last two decades on an array of measures of child health and nutrition. prior to that, it was pooled at the oblast level (Ibraimova et al. Ibraimova et al., 20112011). As a result of these reforms, the country experienced a significant increase in access to healthcare during the 2000s and a reduction in reports of unofficial, informal payments for healthcare. Nonetheless, informal payments for some services (e.g., anesthesia) reportedly persist in some places, making affording healthcare still problematic for the poorest (Falkingham et al. Falkingham et al., 20102010). As of 2016, domestic private health expenditure was about 58 percent of total health expenditure (World Bank World Bank, 2019b2019b). Obtaining high-quality healthcare is also a challenge-particularly in rural areas. Knowledge levels of healthcare providers are also generally low. For example, on a 52-question test related to maternal care, rural physicians scored an average of 55.2 percent, and nurses and midwives scored under 50 percent (Wiegers et al. Wiegers et al., 20102010).We hypothesize that fluctuations in household income will influence households' consumption and various other decisions-with implications for health and nutrition outcomes. To test this, we estimate the following fixed effects model:where i indexes individuals, j indexes households, k indexes the oblast (i.e. region) in which the household resides, t indexes years, and s is the chosen lag structure in years (either 1 or 2). O ijkt is a health or nutrition outcome in our main analysis, which is measured in the first quarter of the year (January-March). We consider several other outcomes, described in Section 3.4 3.4 and analyzed in Section 5 5, when we explore the mechanisms driving our main results; these relate to consumption, dietary diversity, healthcare expenditure, and fertility.H jkt is total annual household income; it includes non-agricultural income (paid employment, self employment, one-time work, pensions and other benefits, and capital income) and five types of agricultural incomes (crop production, livestock sales, meat production, hunting/ gathering, and production of processed food)-all net of the costs of agricultural production (crop production and livestock rearing). We log the total annual household income, but also present robustness checks showing similar results when using the level of income, described in Section 6.3 6.3. X j,k,t=0 is a vector of household-level controls, taken from the first year the household j entered the sample, and Y ijkt is a vector of individual-level controls, both described in Section 3.5 3.5. α k are oblast fixed effects while µ t are year fixed effects. If all variation in H jkt were random, β 1 would provide the causal effect of an increase in total household income on health and nutrition-related outcomes. In Section 3.2 3.2, we explain how we account for the likely endogeneity of this variable.We consider both specifications in which we use lagged household income, H jk,t-1 as well as specifications in which we instead consider a two year lagged value, H jk,t-2 . The former examines impacts in the first quarter (January-March) following the calendar year over which annual income is measured. The latter allows an additional year for impacts to materialize. The speed with which impacts materialize may depend on the particular outcome in question and age group, motivating analysis of both. We do not consider longer lags given the short number of years (four at the median) that households appear in our sample; the sample and it selection are described in detail in Sub-section 3.1 3.1.Our data source is the Kyrgyzstan Integrated Household Survey (KIHS), a nationally- We include in our sample for analysis households earning at least some income from agriculture during their first year in the sample. Households not initially earning income from agriculture that subsequently earn it enter the sample. 61.8 percent of household-year observations from the KIHS sample are thus included in our main sample. As this is a non-restrictive definition of dependence on agriculture, Table A11 A11 explores the robustness of the results to omitting urban households, and to only considering households earning a substantial share of income from agriculture (specifically, those earning over 10, 20, 30, 40, and 50 percent of income from agriculture during their first year in the sample). In total, we have 35,961 unique individuals from 8,845 households in our main sample for analysis.6 6Reverse causality and omitted variables may bias ordinary least squares (OLS) estimates of the health and nutrition effects of income fluctuations. gender roles may grow incomes by spurring women's greater involvement in the labor force, and this may increase health through increased food consumption-thus leading to upward biased OLS estimates. But they may also lead to reductions in investments in children by increasing the opportunity cost of such investments by women-accordingly downward biasing OLS estimates. This makes it difficult to sign the bias in OLS estimates. It also makes it important to empirically examine the mechanisms driving our results.We follow a large literature based on Bartik Bartik (19911991) to identify the causal effects of fluctuations in income. Specifically, we predict logged total household income in year t = n by taking the baseline (year t = 0) values of six sources of household revenue (income from the non-agricultural sector, crop production, livestock sales, meat production, hunting/ gathering, and production of processed food) and two costs (crop production costs and livestock others who happen to be born on the same year, month, and day as another household member, this problem is likely to be minimal. In our raw data, for example, only 0.45 % observations (where an observation is an individual-year) are duplicates in terms of year, household identifier, gender, and exact birth date-on par with the 2015 twin birth rate in the U.S. (Martin et al. Martin et al., 20172017).production costs) that jointly sum to total income, and multiplying each by the oblast × area type (rural or urban) aggregate growth rate in this revenue (or cost) source between t and t + n.8 8 Formally:where g j,k,r,t and g j,k,c,t are the average growth rates of revenue source r and cost source c, respectively, between year t = 0 and year t in the oblast × area type. We then use this predicted (i.e. \"projected\") household income variable as an instrument for actual total household income.Using this instrumental variables strategy, our first and second stage equations are:where γ k and δ k (η t and σ t ) are oblast (year) fixed effects in the second and first stages, respectively. Our year fixed effects absorb the impacts of nation-wide movements in revenues from different sectors over time, while our oblast fixed effects capture regional differences in the composition of income. Importantly, identification in no way comes from endogenous household decisions to change the household's relative reliance on different sectors (e.g., the non-agricultural sector vs. crop agriculture) over time. We further control for the logged value of initial (year t = 0) revenue, revenue j,r,t=0 that the household earned from each of the six revenue sources, r = {1, . . . 6}, and logged value of the initial year costs, cost j,c,t=0that the household incurred from each of the two cost sources, c = {1, . . . 2}. And we also control for the interaction of each logged income or cost with a linear time trend, to allow households to be on different trends according to their initial reliance on different sources of revenue, or exposure to different costs. Finally, in case households are also on different secular trends according to their initial year total income, H j,k,t=0 , we additionally control for logged initial total income and its interaction with a linear time trend.Our IV strategy thus exploits that part of household income that is due to exogenous changes in the profitability of different forms of earning income, and the costs of different ways of earning income. Our key identifying assumption is that predicted (i.e. projected) total income only affects health-related outcomes through its effects on household income.As for all IV estimates, our estimates reflect average effects for observations that comply with the instrument-that is, we estimate a local average treatment effect (Imbens andAngrist Imbens andAngrist, 1994 1994). Compliers are observations that experience higher total household incomes following increases in the average earnings in sectors on which they are especially reliant and/or that experience higher total household incomes following decreases in the costs of types of production on which they are heavily reliant. In other words, our IV estimates are not driven by the effect of having higher total household income for households and individuals whose total household income is unaffected by changes in the average regional profitability of sectors in which they are heavily engaged.It is useful to consider the size of typical fluctuations in annual income that sample households experience from one year to the next. In Figure 2 2, we compute, for each income observation, the absolute value of the percentage change relative to the previous year. For example, if income is 100 in year t = 0 and becomes 70 in year t = 1, or if it becomes 130 in year t = 1, we in either case assign 30 percent as the absolute value of the fluctuation. On average, households experienced an income fluctuation of roughly 36 percent, year-over-year.Following the logic of Abadie et al. Abadie et al. (2017Abadie et al. ( 2017)), we cluster standard errors at the household level; fluctuations in household income due to shifts in the profitability of different sectors which we exploit vary at the household level as they depend on both the unique sources of revenues and costs that characterized what the household chose in its initial period in our sample and the timing (year) of the household's entry into the sample.As Table 2 2 shows, total household income is indeed strongly correlated with predicted income from agriculture. Whether or not we use our full control set, and regardless of which sample we use-young children, older children, youths, older adults, or a household-level sample-the coefficient on predicted income is between 0.62 and 0.73, and our first stage F statistic is always above 360-far from suggesting any problems of weak instruments.As we lack data on the precise procedure employed for dropping households from the rolling panel each year, we are unable to distinguish households that exit the sample due to planned exit versus more problematic attrition-such as that due to household refusal to participate or inability to locate the household or its members. To assess whether attrition is likely to be non-random as opposed to random (i.e. planned attrition due to the rolling nature of the panel), we used our household-level sample to analyze the extent to which household income, or lagged income, predict the household leaving the sample in the following year.In Table A3 A3, we find little evidence that household income significantly influences household attrition. While higher income in the current year predicts lower attrition in column 1 (which includes only our basic control set), the coefficient is incredibly small in magnitude: a 36 percent reduction in income (corresponding to the sample mean year-over-year change in income, as shown in Figure 2 2) leads to a 0.024×ln(1.36) = 0.007 percentage point increase in the likelihood of attriting from the sample in the following year. Thus, we effectively obtain a precisely estimated zero attrition rate. Additionally, that significance of the coefficient on income disappears when we include our full control set (column 2). Further, we find no evidence that income lagged one year, or lagged two years, has any impact on attrition-either with our limited control set (columns 3 and 5) or our full control set (columns 4 and 6). We conclude that non-random attrition is unlikely to impact our interpretation of the results.Our primary outcomes consist of several measures of the nutritional status of young children (ages 1-5), older children and adolescents (ages 5-18), youth (ages 18-35), and older adults (ages 35 and over). These outcomes are measured in the first quarter of the year (i.e.between January and March), during the initial visit with the household in which roster data (including on gender and exact age) were also collected; the median date of data collection is February 15. Fluctuations in income naturally will take time to influence measures of long-term health and nutrition. As such, we are interested in the impacts of lagged income.When we use a single lag of income (H t-1 ), we are considering income over the calendar year that began (ended) approximately 13.5 months (1.5 months) prior to measurement of our health and nutrition outcomes. And when we use a two year lag of income (H t-2 ), we are considering the calendar year that began (ended) approximately 25.5 months (13.5 months)prior. This is depicted visually in Figure 3 3.We omit children under 12 months from our main analysis as none of them would have been in utero by the start of the year over which H t-2 is measured, and over 40 percentwould not yet have been in utero by the start of the year over which we measure H t-1 . Table A4 A4 illustrates the ages of children (e.g., A months pre-pregnancy, B months in utero, or C months old) at the start and end of each year over which we measure income. Excluded children (ages 0-11 months) are shown in grey shade. For example, the table shows that if we had included children aged 4 months old, they would be between 0.5 months pre-pregnancy (i.e. not yet conceived) and 2.5 months old during the year over which we measure income when we lag income by one year, and they would be 12.5 months pre-pregnancy to 0.5 months pre-pregnancy during the year over which we measure income when we lag income two years. We show that results are robust to including all children.Nutritional status in young children is generally assessed using height-for-age Z-scores (HAZs), weight-for-age Z-scores (WAZs), and weight-for-height Z-scores (WHZs). In addition to considering height and weight themselves as outcomes, we thus construct each of these measures, which also utilize information on child gender, exact age (in years, months, and days), and global child growth standards from the World Health Organization World Health Organization (2006Organization ( 2006)).Z-scores measure deviation from the WHO (2006) reference population's mean; a Z-score of 0 means that the individual has the mean score. Having a HAZ < -2 is known as stunting.For older children and adolescents (ages 5-18) as well as adults, we additionally compute the individual's body mass index (BMI) and consider whether they are overweight (BMI ≥ 25 kg/m 2 ) or obese (BMI ≥ 30 kg/m 2 ). We complement these objective measures of health with household head reports about the subjective well-being of members-specifically, we code a dummy for whether each member is in good health.10 10 Subjective impressions of the main respondent are likely to be noisy, so we interpret findings with this caveat in mind. We employ several measures of household consumption and dietary diversity, all summarized in Table 1 1. Consumption data come from a two week recall and do not indicate which household members consumed the food. However, they were collected at quarterly intervals during the calendar year (i.e. we have four observations per household in a given year) as opposed to once, helping us achieve greater between-household variation in consumption. 13 13   We code dummies for the household consuming each of 11 mutually-exclusive categories of food (cereals, eggs, fruits, meat and poultry, pulses/ legumes/ nuts, roots and tubers, fresh vegetables, fish/ seafood, dairy products, oils, and sugar); they take on a one only if the food category was consumed during all four quarters. These comprise our measures of the extensive margin of consumption. We additionally computed the logged average amount consumed, across all four quarters, of each food category-capturing the intensive margin of consumption. Each food category is measured either in liters or in kilos (kg), as appropriate. 14 14 To better understand the dairy products category, we further sub-divide it into milk products (such as milk, cream, or kefir, all measured in liters) and cheese products (such as cheese, curds, butter, sour cream, or yogurt, all measured in kg) and code both a dummy and the logged amount consumed of each. There is a twelfth, \"other\" category for which we can code a dummy for consumption but cannot compute the logged amount given it contains a diverse mix of foods varyingly measured in liters or kilos. 15 15Beyond total amounts of food consumed, existing research shows that there is a strong association between child dietary diversity and nutritional status, and that dietary diversity reflects diet quality (Arimond andRuel Arimond andRuel, 2004 2004;Moursi et al. Moursi et al., 20082008;Rah et al. Rah et al., 20102010). We thus code two dietary diversity indices. The first is the household dietary diversity score (HDDS); to construct it, we count the total number of our 12 categories of food for which the13 24 hour or 7 day recalls are more commonly used in studies of dietary diversity (Arimond andRuel Arimond andRuel, 2004 2004). This was an additional motivation to use our four observations per household per year in this way.14 A number of studies similarly measure consumption in terms of quantity (e.g., liters or kilos) consumed (Ali andTsou Ali andTsou, 1997 1997;Ives Ives, 20022002;Suryanarayana andSilva Suryanarayana andSilva, 2007 2007). 15 While we use this as a food category in computing our dietary diversity index, we do not analyze the dummy or the logged amount of its consumption independently.) household head reported its consumption during each of the four visits.16 16 Our choice of these 12 categories and our method of combining them into a HDDS follows Swindale and Bilinsky Swindale andBilinsky (2006 2006).17 17 The second is a \"healthy\" HDDS, which we code similarly but considering only a subset of four relatively healthy food categories: fruits, pulses/ legumes/ nuts, vegetables, and fish/ seafood. This is similar to an index created by Imamura et al. Imamura et al. (2015Imamura et al. ( 2015)).18 18We also measure whether or not women are pregnant as well as two associated measures:whether or not the woman normally practices contraception (this outcome is missing for pregnant women), and whether or not she wants more children. We include in this analysis all women of reproductive age (15-49) who have had their first period and are married or otherwise sexually active. In these regressions, we control for the number of children a woman already has (a continuous variable); on average, sample women already have 2.8 children.We present results with and without our full set of controls. In general, results are not sensitive to inclusion of controls and our first stage is always strong. We include in all specifications geographic and time fixed effects, a quadratic in age, and a male dummy. We further include in all specifications controls for the initial period income for the household from each of the six income sources, the initial period costs faced by the household from each of the two cost sources, as well as the initial period value of total household incomeall logged-plus a linear time trend interacted with each of these nine variables.Our full set of controls additionally includes individual-level dummies for relationship with the household head, being married, and having a general secondary degree or higher.19 19It also includes several household-level controls, summarized in Table 1 1. These include a dummy for residing in an urban area, logged land area farmed, 20 20 the number of unique agricultural goods the household produces annually, dummies for household size, a quadratic in age for the household head, and dummies for the head having a general secondary degree or higher, being married, and being male. All household-level controls are taken from the year in which the household enters the sample.A few features of our sample are noteworthy. As Table 1 1 is in keeping with the fact that we have disproportionately rural sample that depends on agriculture. About 79,000 soms of total income-or about 60 percent-comes from nonagricultural income, with the other 40 percent (agricultural income) coming mostly from harvested crops and income from livestock and meat production. these are growing through 2009, then declining, and then experiencing another period of growth before declining again during 2013-2016. However, through this period, there is stable income from meat production. Panels B, C, and D of Table 1 1 summarize additional household-level outcomes. On average, households consumed only 1.9 of the four relatively healthy food categories used to construct our \"healthy\" HDDS (fruits, pulses/ legumes/ nuts, vegetables, and fish/ seafood) and 8.7 of the 12 food groups used to construct our HDDS.20 Zero land was imputed to 0.1 square meters of land.Table 3 3 presents regression results where our outcomes are the height (columns 1-2), HAZ (columns 3-4), and a dummy for stunting (columns 5-6) of young children (aged 1-5). PanelsA and B show ordinary least squares (OLS) results when total household income is measured during the calendar year immediately preceding the quarter 1 (Q1) measurement of health and nutrition outcomes (t -1), and when it is measured two calendar years prior (t -2), respectively. Panels C and D present the instrumental variables (IV) second stage analogues of panels A and B, respectively. We present specifications with both our basic (odd-numbered columns) and full (even-numbered columns) control sets. Focusing on our preferred, full controls specifications, a reduction in household income predicts significantly lower child height and HAZ regardless of lag structure employed and method of estimation (OLS or IV)-though IV coefficients are modestly larger. While OLS results suggest that reductions in income increase stunting in young children, this finding does not hold in the IV results, where coefficients are smaller in magnitude and statistically insignificant. The negative effects of income on height and HAZ in the specifications in which we measure income in year t-1 become larger and generally more significant in the specifications measuring income in year t -2, despite the lower sample size (and thus statistical power) of this further lagged specification; this is consistent with the impacts taking time to materialize.Our IV specification with the full set of controls reveals that a 36 percent reduction As Table A6 A6 reveals, these impacts are driven by girls. For neither lag structure, and for neither height nor HAZ, do we find impacts on boys. Compared to the full sample, the same 36 percent reduction in income reduces girls' HAZ scores a year later by an even higher 0.086 standard deviations, which grows to 0.093 standard deviations after two years. Girls further experience a statistically significant increase in stunting not seen among boys in response to reductions in income; the magnitude is non-trivial, contributing to a 0.113×ln(1.36) = 0.035 percentage point increase in stunting after one year, and a 0.123×ln(1.36) = 0.038 percentage point increase after two. Boys may be protected from reductions in households income in ways that girls are not-with long-term impacts on their health and nutrition.Effects of income on height, HAZ, and stunting are concentrated on children under age 2(Table A7 A7). Compared to the overall effects, effects on 2-5 year olds are smaller in magnitude and statistically insignificant. However, effects are larger and more statistically significant compared to overall effects when we restrict attention to 1-2 year olds, in columns 2, 5, and 8. Table A8 A8 similarly shows that statistically significant effects of income on height, HAZ, and stunting of 0-1 year olds and 0-2 year olds are in all cases larger than comparable effects on all 0-5 year olds. This is consistent with very early childhood being a critical period.In addition to impacts on height, Table 4 4 shows that we also find statistically significant reductions in the weight, weight-for-age (WAZ) Z-scores, and weight-for-height Z-scores (WHZ) In contrast to our findings for young child height, we find no evidence that the weight, WAZ, or WHZ scores of girls and boys are differently affected by reductions in income (Table A9 A9). Nor do we find that the weight, HAZ, and WHZ of 1-2 year olds are generally more heavily affected by reductions in income than are those of 2-5 year olds (Table A10 A10).Negative effects of reductions in income on weight and related measures for young children appear to matter across genders and ages.We next consider the impacts of income fluctuations on the height, weight, and BMI of children and adolescents (ages 5-18) in Table 5 5. We do not consider as outcomes dummies for these individuals being overweight or obese as these rates are incredibly low in this population (   A2 A2). We interpret this as effectively a precisely estimated zero effect. More notably, however, youth also experience a decrease in the incidence of overweight after one year that is sustained when we instead measure income with a two year lag. Specifically, a 36 percent reduction in income leads to a 2.1 percentage point reduction in the incidence of overweight after one year (0.067×ln(1.36)), which declines to 1.5 percentage points after two years (0.049 × ln(1.36)). These compare to a mean (standard deviation) of the overweight dummy among youth of about 0.18 (0.39). We find no impacts of fluctuations in income on the incidence of obesity in youths, possibly due to the small mean of this variable in the youth population (only 0.02) (Table A2 A2).Among those over age 35, we obtain similar results (Table 7 7), though coefficients are larger in magnitude and statistical significance compared to the youth sample. We also estimate statistically significant impacts on the weights of older adults that are not found in the youth sample. A 36 percent reduction in income leads to a 0.28 unit reduction in older adults' BMIs after one year that is roughly sustained after two years (0.911 × ln(1.36))relative to a standard deviation of the older adult BMI of 3.9. Also, older adults subjected to an average-sized decline in income of 36 percent tend to experience a 0.141×ln(1.36) = 0.043 percentage point decline in the incidence of overweight after one year that is roughly sustained for a second year. We interpret the null effects of income on adult heights as a useful placebo analysis. It suggests that the results do not reflect some form of systematic measurement error as opposed to real reductions in height following negative economic shocks.Among young children, girls' anthropometric outcomes were most susceptible to reductions in income. Among adults, however, we find distinct patterns, shown in Table A13 A13; for youth, it is men whose BMI and incidence of overweight are most sensitive to the level of income; we find no significant impacts on women for either outcome. Among older adults (age 35 and older), in contrast, income affects both men's and women's BMIs and incidence of overweight-with impacts generally larger in magnitude for women compared to men.Overall, the results suggest that economic downturns may be good on some metrics for the health of youth and older adults. To the extent that high BMIs and overweight pose public health concerns, downturns in household income may reduce them. Importantly, though, we identify very modestly-sized impacts. While our results suggest that high BMI and the incidence of overweight are likely to become growing problems as income situations improve in the Kyrgyz Republic, they do not reflect a looming public health epidemic.We next turn to subjective impressions of health-specifically, a dummy for being in good health (Table 8 8). These findings come with the caveat that they are reports from the main respondent rather than the individual whose health status we measure, but we argue that this should if anything introduce noise that makes it more difficult to identify statistically significant effects. Despite reductions in objective measures of health following reductions in household income, we only find robust evidence of deteriorations in subjective impressions of health among 5-18 year olds following reductions in income. OLS results suggest positive impacts on all groups, but these are not robust to IV, where we find the coefficients generally lower in magnitude and statistically insignificant. This may reflect that adults' subjective health is not vulnerable to such shocks, and that respondents almost uniformly reported 1-5 year olds to be in good health (97.6 percent are reported to be in good health). Among 5-18 year olds, effects are very small; a 36 percent reduction in income yields about a 1 percentage point improvement in subjective impressions of health in the specification with a one year lag that is sustained and slightly larger after two years. This provides some evidence that survey respondents-most typically household heads-do not feel that household members' health suffers drastically following a reduction in household income.Overall, we have observed that reductions in household income have statistically significant and meaningful effects on not only young children but also on older children as well as adults. These findings hold even after accounting for the endogeneity of income to health outcomes using our instrumental variables strategy. Young children face significant reductions in height, HAZs, and WAZs-critical measures of long-term health and nutrition. They also experience reductions in WHZ, where a low value indicates acute malnutrition. Older children and adolescents experience reductions in weight though not in height. However, youth and older adults benefit from decreases in the incidence of overweight.To better understand the potential causal mechanisms driving these results, we consider three sets of outcomes: those related to household consumption and dietary diversity, healthcare expenditure, and fertility. These help capture whether nutrition and health impacts are due to changing diets, other investments in health, and/or selection into child bearing. As we noted earlier, while the measures we employ have important caveats, the results can at least provide suggestive evidence on the mechanisms at work.We first explore the food security implications of reductions in household income by considering whether they influence what food groups households consume, the quantities consumed, and dietary diversity. Here, we consider the effects of contemporaneous income and income lagged one year rather than considering longer lags as we expect income to have immediate impacts on consumption-in contrast to its effect on health and nutrition outcomes, which are further down the causal chain. The effects of income on our consumption and dietary diversity variables indeed decline when we go from measuring income in the current period to measuring it with a one year lag ( relative to its standard deviation of 0.616, this is a 0.07 standard deviation decline.Examining consumption of specific foods, Panel B provides evidence on the extensive margin (outcomes are dummy variables for consumption) while Panel C shows evidence on the intensive margin (outcomes are logged quantities consumed). From both panels, we see that following a decrease in household income, individuals are less likely to consume fruits, fresh vegetables, roots and tubers, and dairy products-possibly explaining declines in young children's health. However, consumption of sugar also goes down with declines in incomepossibly helping explain declines in BMI and the incidence of overweight among adults.Consumption of meat and poultry is interestingly counter-cyclical-possibly indicating that households are more likely to slaughter and consume their animals in times of economic downturn as a coping strategy. However, effects of income on meat and poultry consumption are found only on the extensive margin (whether meat and poultry are consumed) and not on the intensive margin (logged amount of consumption).In Table 10 10 we consider another important health input: expenditure on both outpatient and inpatient healthcare. We identify statistically significant and large declines in healthcare expenditures following declines in income. Specifically, a typically-sized, 36 percent reduction in income leads to about a 42 percent reduction in expenditure on outpatient costs a year later (1.36 1.134 = 1.42). And after a two year lag, we find statistically significant and even larger impacts on both types of medical expenditures: in particular, a 70 percent decrease in outpatient expenditures (1.36 1.722 = 1.70) and a 22 percent decline in inpatient expenditures(1.36 0.639 = 1.22). Thus, reductions in investments in healthcare may also explain declines in health-with outpatient care, which may be of a less \"urgent\" nature compared to inpatient care, being most sensitive to declines in income.Next, we consider whether reductions in household income influence decisions on fertility in women aged 15-49; these results are reported in Table 11 11, and importantly reflect selfreports. Column 1 reveals that declines in income predict a greater likelihood of practicing contraception21 21 -though these results are not statistically significant at conventional levels.As women's discomfort with talking about contraception with an enumerator may tend to introduce noise, we consult a related outcome measure that is slightly less personal: whether a woman wants to bear additional children (column 3). When we consider a one year lag of income, we find that a typical, 36 percent reduction in income decrease the reported desire to have additional children by 0.09 × ln(1.36) = 0.028 percentage points-a sizeable reduction compared to the variable's mean of 0.43. This is largely sustained a year later, when it declines only slightly to a 0.078 × ln(1.36) = 0.024 percentage point reduction.Next, we find a small but precisely estimated decrease in the incidence of pregnancy following a reduction in income. We estimate a 0.023 × ln(1.36) = 0.007 percentage point reduction in the probability of being pregnant when using income lagged by one year, which grows to a larger 0.040 × ln(1.36) = 0.012 percentage points when lagging by two years.As the mean (standard deviation) of our pregnancy dummy is only 0.065 (0.247), this is a sizeable decrease. The results suggest that women avoid pregnancy in response to bad economic conditions. With fewer children and without the demands of a pregnancy, parents can conceivably invest more in the children they have. Thus, changes in fertility patterns may help blunt the negative effects of reductions in household income on very young children.We carry out a number of robustness checks to assess the validity and sensitivity of those results which were statistically significant for at least one lag structure. These include a check for pre-trends, tightening the requirements to be considered an agriculture-dependent household (thus reducing the sample size), and omitting various observations or trimming variables. In this section, we describe each of these in turn.A within-sample check for pre-trends, shown in Table 12 12, helps us validate our identification strategy. For each individual, we split their observations, ordered in chronological order, into two halves. For the first half, we measure the average level of each outcome variable. For the second half, we measure the average level of income. 22 22 We then observe whether income in the later period predicts significantly different health outcomes in the earlier period. If current income predicts past outcomes, conditional on controls, we would worry that health outcomes measured today are correlated with income merely due to these prior trends. We measure income in three ways and check for pre-trends with each: logged predicted income (i.e. the value of our IV), fitted values of logged predicted income (i.e. our stage two 22 If there are an odd number of years, we always assign one more year to the first period.right-hand-side income variable), and logged actual total income. 23 23 In these cross-sectional regressions, we control for the full set of controls in our 2SLS regressions.24 24 We find few statistically significant coefficients-no more than we would expect by random chance. We conclude that our results are not likely due to pre-trends.In order to appear in our sample, we require only that households have at least some of their total household income coming from agriculture. Our aim is to capture households that are at least somewhat reliant on the rural sector-and who likely rely on a diverse array of income sources. As this is an arbitrary definition of dependence on agriculture, Table A11 A11 explores the robustness of the results to omitting urban households, and to only considering households earning a substantial share of income from agriculture (specifically, over 10, 20, 30, 40, and 50 percent during their first year in the sample). While sample size and thus statistical power decline with each of these restrictions, we show that the results are remarkably stable in magnitude and statistical significance across all six specifications.Next, Table A12 A12 considers the sensitivity of our results to four separate specifications: a)omitting 2004-2008 data, b) omitting Bishkek and Osh, which contain the two largest cities, c) trimming the top 1 percent of observations of both income and our outcome variables, and d) using the level (instead of log) form of income. All of these analyses, except d), yield smaller datasets and thus less statistical power to detect effects. However, to the extent that our results hold up, they can increase our confidence in the validity of our findings. Omitting early years is motivated by the fact that anthropometric data collection improved over time and exhibits measurably less variance by 2009. Dropping Bishkek and Osh is motivated in part by the fact that households in two largest cities, Bishkek and Osh city, are likely atypical compared to households more dependent on agriculture-possibly making only a few soms of income from a very small garden. Further, they may have substantially greater access to services such as healthcare, or other social safety nets, possibly blunting negative effects of downturns in income. Trimming the top 1 percent of observations allows us to assess whether findings are driven by outliers. Finally, using the level of income and of the predicted income instrument helps address potential concerns about our choice of functional form. Overall, our results are highly robust to these various alternative specifications. While point estimates change slightly and there are isolated cases of statistical significance being lost or gained, we generally find that all of our main conclusions are intact.This study provides causal evidence from agriculture-dependent households in Kyrgyzstan that fluctuations in household income can have modest but statistically significant effects on children's long-term health and nutrition status and the BMIs and incidence of overweight in adults. It also provides evidence of several channels possibly explaining these impacts.Our evidence comes from a study of agriculture-dependent households in a nationally representative, 13 year rolling panel dataset spanning 2004-2016. We address the endogeneity of income to child health, consumption, and other decisions using an instrumental variables approach; specifically, we instrument for household income with predicted income, obtained using the household's initial period share of income from different sources and aggregate growth rates over time in each source. We find that young children (age 0-5) exposed to negative income shocks experienced reductions in height and height-for-age Z-scores that were largest for girls and children under age two. These groups additionally experienced increases in stunting. Reduced consumption of healthy foods, reduced dietary diversity, and less expenditure on health may help explain the results. A channel possibly offsetting negative impacts is a decrease in fertility. At the same time, older children and adults saw decreases in BMI and-for adults-decreases in the incidence of overweight.Our consumption data were household-level and thus mask important intra-household decision-making regarding how to respond to reductions in household income. More analysis is needed that uses better quality health and nutrition data, such as that captured by 24 hour food diaries, to understand changes in consumption patterns within the home following reductions in income. While our analysis of sex-and age-disaggregated data on anthropometric outcomes and subjective health reports are helpful in assessing the impacts of this process of intra-household decision-making on different members, such data would be helpful to better understand the precise changes in consumption taking place within the household.Another channel worth further exploring is how these fluctuations in income influence migration patterns for women and men, as well as the household structure itself. Departure of some members and shifts in the time and labor burdens of other members may themselves have profound impacts on child health, and may also be spurred by income fluctuations.Our findings provide both good news and bad news for the double burden of malnutrition.While reductions in income, which are ubiquitous in developing country settings and against which households generally under-insure, contribute to under-nutrition in young children, they also reduced over-nutrition in older children and adults. They do so both by decreasing the diversity of diets, leading households to consume less of healthy foods, and reducing overall food consumption. While overall reductions in consumption may be helpful for the problem of over-nutrition in older children and adults, poorer-quality diets combined with lower consumption appear to be contributing to under-nutrition in young children. This suggests the need for public health officials and practitioners in development to respond to fluctuations in household income with tailored solutions that can reduce under-nutrition without simultaneously increasing over-nutrition.     Source: Authors' calculations based on KIHS 2004-2016.  Notes: We include in our sample only those households earning at least some income from agriculture. For individuals, we show the first stage for the height outcome. For households, we show the first stage for the household dietary diversity score outcome. The dependent variable is logged total income. Income is measured in 2010 Som. Our instrumental variable is logged predicted total income. Predicted total income is constructed from eight sources of income and cost by multiplying the household's initial period level with the growth rate of the average level in an oblast-rural/ urban combo for each income/ cost component, excluding one's own household. The basic control set includes year, oblast, and urban fixed effects, initial period level of income or cost (logged), the initial period value of household income (logged), and interactions of each of the latter two with a linear time trend. They also include several very basic individual-level controls (present in all regression specifications that include individual-level outcomes): a quadratic in age and a dummy for being male. Our full control set additionally includes several individual level controls: dummies for relationship with the household head, being married, and having a general secondary degree or higher (these latter two controls are omitted when we consider children age 1 to 5). It also includes several household-level controls: the number of unique agricultural goods produced, logged land farmed (with zero land imputed to 0.1 square meters of land), dummies for household size, a quadratic in age for the household head, and dummies for whether the head has a general secondary degree or higher, is married, and is male. All household-level controls are taken from the year in which the household enters the sample.   7,191 7,191 7,191 7,191 7,191 7,191 Source: Authors' calculations based on KIHS 2004-2016.Notes: We include in our sample only those households earning at least some income from agriculture. Income (annual) is measured in 2010 Som. We use the logged form of income and its instrument in the regressions. HAZ is the child's height-for-age Z-score computed using WHO 2006standards (World Health Organization World Health Organization, 20062006). The instrumental variable and the full set of controls are described in Table 2 2   7,191 7,191 7,191 7,191 7,191 7,191 Source: Authors' calculations based on KIHS 2004-2016.Notes: We include in our sample only those households earning at least some income from agriculture. Income (annual) is measured in 2010 Som. We use the logged form of income and its instrument in the regressions. WAZ and WHZ are the child's weight-for-age and weight-for-height Z-scores respectively, computed using WHO 2006standards (World Health Organization World Health Organization, 20062006). The instrumental variable and the full set of controls are described in Table 2 2   Notes: We include in our sample only those households earning at least some income from agriculture. Overweight is defined as having a BMI of at least 25. Obesity is defined as having a BMI of at least 30. Income (annual) is measured in 2010 Som. We use the logged form of income and its instrument in the regressions. The instrumental variable and the full set of controls are described in Table 2 2  Notes: We include in our sample only those households earning at least some income from agriculture. Overweight is defined as having a BMI of at least 25. Obesity is defined as having a BMI of at least 30. Income (annual) is measured in 2010 Som. We use the logged form of income and its instrument in the regressions. The instrumental variable and the full set of controls are described in Table 2 2   Notes: We include in our sample only those households earning at least some income from agriculture. Data in our regressions are annual, but based on quarterly observations. Consumption dummies (Panel B) takes on a value of 1 only if the food category is consumed in all four quarters. Logged consumption (Panel C) is averaged over the four quarters to get annual outcomes. The household dietary diversity score (HDDS) is constructed by counting the number of the 12 total food categories that have been consumed in the last 2 weeks during each of the four quarterly visits. A \"healthy\" HDDS is constructed similarly by counting the number of categories a household consumes from: fruits, pulses/ legumes/ nuts, vegetables, and fish/ seafood. In Panel B, the category of dairy products is further sub-divided into milk products (such as milk, cream, or kefir, measured in liters) and cheese products (such as cheese, curds, butter, sour cream, or yogurt, measured in kg) for the purposes of understanding the dairy category better, but these two subcategories are not considered when constructing the HDDS. Panel A and B outcomes are missing in 2007 because 2007 data were annual while other years were quarterly. This does not affect the consumption level used in the panel C outcomes, but it does make it impossible to accurately compare 2007 with other years for the outcomes of panels A and B. Income (annual) is measured in 2010 Som. We use the logged form of income and its instrument in the regressions. The instrumental variable is described in Table 2 2. We include all control variables described in    Notes: We divide all years an individual/a household in our sample into two halves of roughly equal length. We calculate average level of health-related variables in the first period . We take the first year of the second half as the initial year and re-construct the instrumental variable and predict the income used as the second stage right-hand-side income variable. We compute the average income using three variants of income measures, income instrument, predicted income (second stage right-hand-side variable), and the endogenous income in Panels A, B, and C. All regressions in pre-trends analysis include the full set of controls in individual-level regressions in Tables 2 2 except for interactions with time trends. Time variant individual controls are averaged across all years in first period. In addition, we include dummies for \"base\" and \"end\" year of the first period for each individual.    Age at end of year t -1 0.001 16.5 months pre-pregnancy 4.5 months pre-pregnancy 7.5 months in utero 1 15.5 months pre-pregnancy 3.5 months pre-pregnancy 8.5 months in utero 2 14.5 months pre-pregnancy 2.5 months pre-pregnancy 0.5 months old 3 13.5 months pre-pregnancy 1.5 months pre-pregnancy 1.5 months old 4 12.5 months pre-pregnancy 0.5 months pre-pregnancy 2.5 months old 5 11.5 months pre-pregnancy 0.5 months in utero 3.5 months old 6 10.5 months pre-pregnancy 1.5 months in utero 4.5 months old 7 9.5 months pre-pregnancy 2.5 months in utero 5.5 months old 8 8.5 months pre-pregnancy 3.5 months in utero 6.5 months old 9 7.5 months pre-pregnancy 4.5 months in utero 7.5 months old 10 6.5 months pre-pregnancy 5.5 months in utero 8.5 months old 11 5.5 months pre-pregnancy 6.5 months in utero 9.5 months old 12 4.5 months pre-pregnancy 7.5 months in utero 10.5 months old 13 3.        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Emphasis is placed on supporting the Government's Growth and Development Strategy III (MGDS III) -an agenda designed to reduce poverty through sustained economic growth and infrastructure development.One of our recent research outputs was an in-depth study of Malawi's commodity exchange and warehouse receipts landscape. Current research is looking at contract farming, the profitability of warehouse receipts for smallholder farmers, and crowdsourcing of farmgate prices.While Malawi has made progress in recent years, it still ranks among the world's least developed countries. According to official estimates, poverty decreased from 52.4 percent in 2004/05 to 51.5 percent in 2016/17 but rose from 55.9 percent to 59.5 percent in rural areas. With a Human Development Index of 0.485 in 2019, Malawi ranks 172 out of 189 countries. Its annualized economic growth has also decelerated from 7.4 percent in 2006-2010 to 3.7 percent in 2014-2018. Furthermore, undernutrition remains a challenge with a stunting prevalence of 37 percent among children under five in 2015/16.The country remains one of the 20 countries in the world whose economies are most dependent on agriculture. In 2018, 84 percent of Malawi's rapidly growing population lived in rural areas, while 88 percent of its labor force worked in agriculture in 2016/17. Thus, growth in agriculture, particularly in smallholder-based agriculture, along with rural development is critical to meet the country's food security and poverty reduction goals. Using existing datasets such as the Integrated Household Surveys, this theme analyzes key issues affecting the operation of social safety nets and humanitarian programs in Malawi. IFPRI has investigated the coverage of different social safety nets and input subsidies in Malawi to shed more light on the performance of these programs.Our research on integrating nutrition and agriculture into community-based childcare centers informed the design and scale-up of the Government's early childhood development program and interventions in nutrition-sensitive agriculture. IFPRI researchers have also examined how Malawi can better leverage its smallholder agriculture sector to improve nutrition.We have designed the impact evaluation and baseline data collection for DFID's Building Resilience and Adapting to Climate Change (BRACC) program, which is being implemented in four districts in southern Malawi.We also continue to work on poverty and inequality mapping.Finally, Malawi is a priority country under IFPRI's Compact2025 initiative, which brings together stakeholders to set priorities, innovate, and synthesize lessons learnt to accelerate progress in ending hunger and undernutrition by 2025.Since agricultural budgets are limited, financial resources must be invested wisely to maximize their impact. IFPRI research under this theme aims to help policymakers understand the trade-off and complementarities of different agricultural investments.The Farm Input Subsidy Program (FISP) is an important area of investment for the Government of Malawi.Previous IFPRI research has looked at the economywide impacts and risks of FISP, its targeting to different types of households, and the costs of implementing a Universal Fertilizer Subsidy.IFPRI Malawi has also investigated priorities for irrigation investment in Malawi, along with advising on the issues connected with seed sector reform. With nationally representative data from 3,000 households collected over the course of three years, IFPRI researchers have analyzed the demand for and supply of agricultural extension.Malawi is a country with significant agro-ecological diversity. All our research in Malawi is made available to the public and we use several formats to disseminate our findings. Key publications include:• Working Papers that provide in-depth analysis of research topics• Policy Notes as condensed research summaries aimed to inform decision-makers• Key Facts Sheets to present data relevant to key policy issues for agriculture, food systems, and other development topics• Monthly Maize Market Reports to provide clear and accurate information on the variation of daily maize prices in selected markets throughout Malawi• Quarterly newsletters as brief overviews of our research activities, events, publications, and outputsMaSSP regularly organizes research seminars, where IFPRI or external speakers present on their current research topics relevant to food and nutrition security in Malawi.For more information, visit www.massp.ifpri.info or follow us on Twitter (@IFPRIMalawi).To subscribe to our newsletter, please visit: http://massp.ifpri.info/massp-mailing-list/.","tokenCount":"676","images":[],"tables":["-760008950_1_1.json","-760008950_2_1.json"]}

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+{"metadata":{"gardian_id":"391dd49d5b5932d0bcd45ef0d379afd2","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/5f9b818d-3ccc-43c1-bfbd-e9e8f0f608c8/retrieve","description":"Christophe Béné POLICY SEMINAR UNFSS Science Days Side Event: COVID-19, food systems, and One Health in an urbanizing world: Research responses at a national level Co-Organized by CGIAR and RUAF JUL 6, 2021 - 09:30 AM TO 11:00 AM EDT","id":"-1704955733"},"keywords":[],"sieverID":"0e0576cf-6618-47e6-a497-8682763054ca","pagecount":"8","content":"• Global assessment of the impacts of COVID-19 on food systems and their actors • Focus on the food security and nutrition • Preliminary elements of a food system resilience research agenda to build back better.Framework 2• Food Security (FAO 1996) • Food Environment (Herforth and Ahmed 2015)• Food systems wastes and losses diversity of food items    ","tokenCount":"57","images":["-1704955733_1_1.png","-1704955733_1_2.png","-1704955733_1_3.png","-1704955733_1_4.png","-1704955733_2_1.png","-1704955733_2_2.png","-1704955733_3_1.png","-1704955733_3_3.png","-1704955733_4_1.png","-1704955733_4_2.png","-1704955733_4_3.png","-1704955733_5_1.png","-1704955733_5_2.png","-1704955733_5_3.png","-1704955733_6_1.png","-1704955733_6_2.png","-1704955733_6_3.png","-1704955733_7_1.png","-1704955733_7_2.png","-1704955733_7_3.png","-1704955733_8_1.png","-1704955733_8_2.png","-1704955733_8_3.png"],"tables":["-1704955733_1_1.json","-1704955733_2_1.json","-1704955733_3_1.json","-1704955733_4_1.json","-1704955733_5_1.json","-1704955733_6_1.json","-1704955733_7_1.json","-1704955733_8_1.json"]}

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+{"metadata":{"gardian_id":"6a352fbed0cc73ea02cd270b660f727a","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/2dbf66c4-1e52-484b-96f1-81e74c503dc5/retrieve","description":"","id":"1565081351"},"keywords":[],"sieverID":"6410b9b4-f99e-4ceb-a168-566b61094db8","pagecount":"12","content":"In the wake of the food crises of the early 1970s and the resulting World Food Conference of 1974, a group of innovators realized that food security depends not only on crop production, but also on the policies that affect food systems, from farm to table. In 1975, the International Food Policy Research Institute (IFPRI) was founded. IFPRI was fortunate to have as its first board chairman world-renowned Australian economist Sir John Crawford, who was a passionate advocate for international agricultural research and an architect of the Consultative Group on International Agricultural Research (now the CGIAR Consortium), of which IFPRI is a member. The Institute provides solid research andevidence-based policy options to partners in donor and recipient countries. The issues have changed and expanded over time, ranging from food subsidies and commercialization of agriculture in the early years to promotion of public-private partnerships, investment in agricultural research, provision of safety nets to strengthen resilience, prioritization of nutrition intervention for women and children, strategies for climate change adaptation and mitigation, support for country-led agricultural growth strategies, and partnerships with other stakeholders in global movements such as Scaling Up Nutrition in recent years. Working with many longstanding partners, including the government of Australia and its Australian Centre for International Agricultural Research (ACIAR), IFPRI's food policy research has contributed to reducing poverty and improving food security for the world's poor. This brochure highlights key collaborations between IFPRI and the Australian government, often in partnership with other institutions.Where should a country invest its money? How policymakers answer this complex question can have an impact on the welfare of their constituents-especially those living in poverty-for generations. In the mid-1990s, China had a significant number of poor and undernourished people. Where the government chose to invest its money had the potential to change the country's label from \"low income\" to \"middle income.\" China's large rural population needed more effective government policies to stimulate agricultural growth and ensure that smallholders had avenues to prosperity, either through increasing agricultural productivity or moving into new sectors when farming was not The program, funded in part by ACIAR, produced a series of policy research reports used by national and regional policymakers in China to evaluate the impact of public investment in different sectors and set future investment priorities. Research results demonstrated that investments in agricultural research, rural roads, and education do the most to promote rural economic growth and reduce poverty. Other investments, particularly in irrigation and some welfare programs, proved much less effective on both scores. Results also showed that building low-cost roads, such as basic rural feeder roads, yielded returns four times greater than building higher-quality roads. Investment in low-cost roads in both rural and urban sectors also led to greater poverty reduction. Regional analysis suggested that investments in less developed areas of the country not only offer the largest poverty reduction per unit of spending, but also produce the highest economic returns.The research results, which provided practical information and recommendations, informed and influenced Chinese and other policymakers in a number of ways. The research results showed that market opportunities for small-scale pig production will continue to exist The project, to be completed in December 2012, is already yielding outcomes.X Two Indonesian research institutes received training from IFPRI in how to use the latest survey analysis software and now use that software to process questionnaires.X Six Indonesian PhD students at the University of Adelaide are using datasets from IFPRI's project in their research.HarvestChoice ","tokenCount":"575","images":["1565081351_1_7.png","1565081351_3_2.png","1565081351_4_4.png","1565081351_4_5.png","1565081351_5_3.png","1565081351_6_3.png","1565081351_6_4.png","1565081351_7_3.png","1565081351_8_3.png","1565081351_9_3.png","1565081351_10_2.png","1565081351_11_4.png","1565081351_11_5.png"],"tables":["1565081351_1_1.json","1565081351_2_1.json","1565081351_3_1.json","1565081351_4_1.json","1565081351_5_1.json","1565081351_6_1.json","1565081351_7_1.json","1565081351_8_1.json","1565081351_9_1.json","1565081351_10_1.json","1565081351_11_1.json","1565081351_12_1.json"]}

data/part_2/0569120713.json

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+{"metadata":{"gardian_id":"29eb1eee1934a2d7feb8e50e9b9ce49f","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/8f0bd37a-ea77-4337-b080-305ded80badc/retrieve","description":"Gloria Folson and Futoshi Yamauchi Side Event: How Japan’s know-how can help address food and nutrition challenges in the developing world Tokyo Nutrition for Growth (N4G) Summit 2021 NOV 30, 2021","id":"-1032769372"},"keywords":[],"sieverID":"55023885-5df1-429c-a25a-284b472939eb","pagecount":"16","content":"To test the effectiveness of KOKOPlus over a period of 6 months to improve nutritional status and child development in HIV exposed children 6-18 months of age in Accra.Objectives: To measure and compare the ff between intervention and control arms 1) Nutritional status 2) Micronutrient status 3) Morbidity rates 4) Achievement of child development goals using a standardized tool, the Caregiver Reported Early Development Instrument (CREDI).  • KOKOPlus has been found to be microbially safe and its shelf life estimated to be more than 12 months.• Ghosh et al ( 2019) found a potential positive dose-response effect of KOKOPlus on length-for age, weight-for-age and weight-for-length z scores • KOKOPlus is produced by the Ghana Nutrition Improvement Project (GNIP), supported by the Ajinimoto Foundation.   • Participants in the intervention arm receive KOKOPlus and nutrition education.• Participants in the control arm receive nutrition education, no KOKOPlus.• Both arms receive no placebo during the study period.• Nutrition education is done monthly on individual basis with reference to the child's age.    • The mean weight for age z-score (WAZ) for the intervention arm was -0.716 ± 1.07 and that for the control arm was -0.729 ± 1.27 (p=0.314). (<5s, Accra= -0.6, MICS, 2017/18) • The mean height for age z scores (HAZ) for intervention and control arms were 0.044 ± 3.0 and 0.057 ± 3.0 respectively (p=0.346). (<5s, Accra = -0.6, MICS, 2017/18) • The mean weight for height z scores (WHZ) for intervention and control arms were -0.840 ± 1.87 and -0.874 ± 2.01 respectively. (<5s, Accra = -0.4 MICS, 2017/18) • The two study arms did not differ with respect to nutritional indicies WAZ, HAZ and WHZ.• 1 child was found to be severely underweight and have been duly excluded from the study  • Males had mean WAZ of -0.967 ± 1.37 and females had mean WAZ of -0.755 ± 1.15 (p=0.025).• Also, males had mean WHZ of -0.874 ± 2.09 and females had mean WHZ of -0.839 ± 1.78 (p=0.009).• Males and females did not differ with respect to HAZ • Mean Hb for males was 9.78g/dL ± 1.7 and that for females was 10.11g/dL ± 1.53 (p=0.009).• Baseline data however shows that male and female participants differed significantly with respect to WAZ.  ","tokenCount":"372","images":["-1032769372_1_1.png","-1032769372_1_2.png","-1032769372_1_3.png","-1032769372_1_4.png","-1032769372_1_5.png","-1032769372_2_1.png","-1032769372_2_2.png","-1032769372_2_3.png","-1032769372_2_4.png","-1032769372_2_5.png","-1032769372_2_6.png","-1032769372_2_7.png","-1032769372_2_8.png","-1032769372_3_1.png","-1032769372_4_1.png","-1032769372_4_2.png","-1032769372_4_3.png","-1032769372_5_1.png","-1032769372_5_2.png","-1032769372_5_3.png","-1032769372_5_4.png","-1032769372_6_1.png","-1032769372_6_2.png","-1032769372_6_3.png","-1032769372_6_4.png","-1032769372_6_5.png","-1032769372_8_1.png","-1032769372_8_2.png","-1032769372_8_3.png","-1032769372_8_4.png","-1032769372_8_5.png","-1032769372_8_6.png","-1032769372_8_7.png","-1032769372_8_8.png","-1032769372_8_9.png","-1032769372_8_10.png","-1032769372_8_11.png","-1032769372_8_12.png","-1032769372_8_13.png","-1032769372_8_14.png","-1032769372_8_15.png","-1032769372_8_16.png","-1032769372_8_17.png","-1032769372_8_18.png","-1032769372_8_19.png","-1032769372_9_1.png","-1032769372_9_2.png","-1032769372_9_3.png","-1032769372_9_4.png","-1032769372_9_5.png","-1032769372_10_1.png","-1032769372_10_2.png","-1032769372_10_3.png","-1032769372_10_4.png","-1032769372_10_5.png","-1032769372_11_1.png","-1032769372_11_2.png","-1032769372_11_3.png","-1032769372_12_1.png","-1032769372_12_2.png","-1032769372_12_3.png","-1032769372_12_4.png","-1032769372_12_5.png","-1032769372_12_6.png","-1032769372_13_1.png","-1032769372_13_2.png","-1032769372_13_3.png","-1032769372_13_4.png","-1032769372_13_5.png","-1032769372_14_1.png","-1032769372_14_2.png","-1032769372_14_3.png","-1032769372_14_4.png","-1032769372_15_1.png","-1032769372_15_2.png","-1032769372_15_3.png","-1032769372_15_4.png","-1032769372_16_1.png","-1032769372_16_2.png","-1032769372_16_3.png"],"tables":["-1032769372_1_1.json","-1032769372_2_1.json","-1032769372_3_1.json","-1032769372_4_1.json","-1032769372_5_1.json","-1032769372_6_1.json","-1032769372_7_1.json","-1032769372_8_1.json","-1032769372_9_1.json","-1032769372_10_1.json","-1032769372_11_1.json","-1032769372_12_1.json","-1032769372_13_1.json","-1032769372_14_1.json","-1032769372_15_1.json","-1032769372_16_1.json"]}

data/part_2/0584916552.json

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+{"metadata":{"gardian_id":"22dd940e34a040b55e11417eed93946e","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/60346884-b71c-4bb0-b212-db57e0a9cf5f/retrieve","description":"This paper provides a quantitative impact assessment of the community-based integrated natural resources management project (CBINReMP) in the Lake Tana region in Ethiopia during 2011-2019. By promoting greater community participation, the CBINReMP provided support to watershed communities for the restoration of degraded soils and water sources, rehabilitation of forests, as well as in obtaining access to secure land titles and practices for climate change adaptation. The project further provided support towards diversification of incomes in off-farm activities and incentives for women’s empowerment and youth employment. This way the project aimed to support rural livelihoods through improvements in household incomes, dietary diversity, agricultural productivity, and resilience to climatic shocks, among other livelihood objectives. To assess the project’s impacts, the study had to deal with numerous methodological complications owing to as the project’s nature and design. The lack of a proper baseline survey, incomplete information about targeted watershed communities and often lack of clear distinction lines between the project’s interventions and support provided to communities through other mechanisms made it hard to identify the true impact of the CBINReMP. Four additional challenges had to be faced: possible selection biases because of non-random placement (targeting) of the project; self-selection of beneficiaries into receiving the project; possible spatial spill-over effects of project benefits to non-treatment communities, and the project’s phased rollout. A propensity-score matching procedure was adopted to assess the CBINReMP’s impacts by comparing treatment (beneficiary) and control groups outcomes related to the livelihood indicators listed above. This paper discusses how the mentioned complications were addressed to provide a sound assessments of the project’s true impacts. While certain limitations remain, the key finding that can be drawn with confidence is that the CBINReMP had only very limited, quantitatively verifiable impact on rural livelihoods. It seems to have contributed to higher household incomes and some greater dietary diversity, but only where the project managed greater community participation. However, even for those beneficiaries, livelihood conditions had not become significantly more productive, diversified, resilient, or sustainable than those of the comparison group. The paper ends with recommendations on how to avoid methodological obstacles through better design of the M&E framework for multi-intervention, community-based projects.","id":"-301972245"},"keywords":[],"sieverID":"066547be-2262-49ee-8c8a-d87f38bf83ca","pagecount":"52","content":"The International Food Policy Research Institute (IFPRI), a CGIAR Research Center established in 1975, provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition. IFPRI's strategic research aims to foster a climate-resilient and sustainable food supply; promote healthy diets and nutrition for all; build inclusive and efficient markets, trade systems, and food industries; transform agricultural and rural economies; and strengthen institutions and governance. Gender is integrated in all the Institute's work. Partnerships, communications, capacity strengthening, and data and knowledge management are essential components to translate IFPRI's research from action to impact. The Institute's regional and country programs play a critical role in responding to demand for food policy research and in delivering holistic support for country-led development. IFPRI collaborates with partners around the world.This paper assesses the quantitative impact of the community-based integrated natural resources management project (CBINReMP) implemented by the Amhara Region Bureau of Agriculture and Natural Resources (BoANR), IFAD, and partners in the Lake Tana Watersheds (LTWs) in Ethiopia during 2011-2019. 1 By promoting greater community participation, the project provided support to watershed communities for the restoration of degraded soils and water sources, rehabilitation of forests, as well as in obtaining access to secure land titles and practices for climate change adaptation. The project further provided means to communities and households for diversifying incomes through off-farm activities, as well as incentives for women's empowerment and job creation for youth. This way the project aimed to improve livelihoods through improvements in household incomes, dietary diversity, agricultural productivity, and resilience to climatic shocks, among other livelihood objectives.Several aspects in the design and implementation of the project complicated rigorous, quantitative impact assessment. First, the project involved multiple interventions dependent on community needs and participation complicating the identification of the project's contribution (as a whole and its components) to observed outcomes. Second, the project's targeting of beneficiary communities and households was not clearly defined from the outset, complicating the identification of proper control group to assess \"treatment effects\". Third, furthermore, not all beneficiary communities received support through the project for the entire range of interventions, while many non-beneficiary communities in the same area received, again in varying degree, support from other agencies for similar interventions, thereby further complicating the identification of control groups and benefits attributable to the CBINReM project. Fourth, many of the beneficiary communities not only already had received support in watershed management prior to the project, but also several project interventions underwent change during the course of its eight-year implementation. For instance, the climate change adaptation component was introduced after a mid-term evaluation. Lacking a baseline and intermediate survey data makes it hard to assess how those changes and their differential implementation weigh on the outcomes. Lastly, the impact assessment also meant to use geo-spatial data to analyse changes in vegetation coverage and other agro-ecological and landscape variables. The use of this type of data was hampered by absence of shapefiles for both beneficiary and control-group watersheds. As a result, the impact assessment for these dimensions could only be undertaken by approximation, as explained further in Section 3.The impact assessment method was chosen to account as much as possible for these attribution problems. Admittedly, not all of these confounders of actual impacts could be fully addressed, as explained in Section 3. Primary survey data were collected among representative samples of beneficiary and control-group households2 and watershed community leaders after the end of the project (in March 2020).We find that, overall, the CBINReM project had only very limited quantitatively verifiable impact on rural livelihoods. It seemed to have contributed to higher household incomes and some greater dietary diversity, but only where the project managed greater community participation. However, even for those beneficiaries, livelihood conditions had not become significantly more productive, diversified, resilient, or sustainable than those of the comparison group. Furthermore, it is not clear what factors would have led to greater community participation than in other participant communities, and the contributions noted above might be due to those underlying factors rather than the CBINReM project. Despite the limited observed impacts and also despite the limitations and attribution problems underlying that assessment, the present analysis should serve policy makers and development partners in Ethiopia and elsewhere regarding how to improve on the design of community-based interventions for sustainable rural livelihoods and poverty reduction, as well as for the design of monitoring and evaluation (M&E) frameworks to facilitate improved impact assessment.The remainder of this report is organized as follows. Section 2 provides a description of the project's objectives, design and implementation, as well as of its theory of change from which we derive the key questions for the impact assessment. Section 3 describes the community and household survey design, the geo-spatial data used for assessing impacts on vegetation and water retention, and the methodology followed for assessing the impacts on rural livelihoods. This section also explains the matching procedure for comparing treatment and control groups. Section 4 provides a summary of key household and community characteristics of treatment and control groups. Section 5 presents the main findings of the impact assessment, while section 6 concludes with a brief summing up of the key findings, the limitations of this assessments and recommendations for improving project design and monitoring such that it will allow for better impact assessments of future community-based watershed management and rural livelihoods projects.Despite relatively high economic growth over the past decade, the agricultural sector in Ethiopia is still characterized by its subsistence nature and low productivity. Agricultural systems are highly dependent on climate and are vulnerable to extreme climate events. Environmental degradation, as exhibited in significant land and water resource degradation together with biodiversity loss and deforestation, remains a key challenge. Ethiopia loses some 2 billion tons of fertile soils annually to land degradation, and the siltation of water bodies is already a major threat to irrigation development (IFAD, 2009a). Recent estimates using satellite imagery show that land degradation hotspots over the last three decades cover about 23 per cent of the land area in the country (Gebreselassie, Kirui, and Mirzabaev, 2016). About one third of rural households farmed less than 0.5 ha in rain-fed agriculture, which was insufficient to produce enough food to meet the intake requirements of the average household. Most agricultural production was used to meet household consumption needs, and most households experienced a prolonged deficiency in food availability during the pre-harvest period (IFAD, 2009b).Against this background, the CBINReMP was designed and implemented by the Amhara Region BoANR, IFAD, and partners in the LTWs in Ethiopia during 2011-2019.The project area comprised the entire Lake Tana Watershed in the Amhara National Regional State (ANRS) in Ethiopia. At the start of the project, about 2.3 million people belonging to 450,000 rural households lived in the area, distributed over 24 woredas (districts) and 347 kebeles (villages). Lake Tana accounts for almost half of total water surface in the country. For decades, the project area suffered from severe land degradation caused by overgrazing, deforestation, unsustainable agricultural practices, and overexploitation of wetlands. The sustainability of livelihoods in the area further suffered from encroachment on fragile hillsides, insecurity of land tenure, growing population pressure and related increased land fragmentation, and high dependence on bio-mass energy which deprived soil organic matter (IFAD, 2009b).The project aimed to facilitate land certification benefits to all 450,000 smallholder farm households living in the LTWs area. About 312,000 households were to benefit, in addition, from pasture development, soil and water conservation, participatory forestry, and development of watershed management plans. About 25,000 unemployed youth, including women, would be trained, and assisted in engaging in off-farm income generating activities (IFAD, 2009b). For all beneficiaries, improving incomes was stated as a primary outcome of the project. According to the project design report, per capita income of the target group averaged US$80 per year in 2009, well below the national average per capita income of US$340 and the then national poverty line of US$110 per person per year (IFAD 2009b: vii). Most beneficiary households depended on agriculture for their livelihoods, facing limited access to land and agricultural inputs, severe soil erosion and high vulnerability to impacts of climate change.The project aimed to address these challenges with a set of interventions promoting sustainable natural resource management with a high level of community participation. Both the role of the community and the nature of the participation of beneficiaries added to the complexity of the impact assessment. First, to understand the project's implementation and outcomes, it is important to note that interventions were planned at the kebele level. Kebeles are the smallest administrative unit in Ethiopia, similar to wards or villages in other contexts. Kebeles typically consist of multiple microwatersheds. Interventions were targeted towards micro-watersheds but not all watersheds within a kebele were selected as beneficiary communities. This complicates the assessment of the project's impacts, since support coordinated at the kebele level might benefit both targeted and non-targeted watersheds within the kebeles. A second important aspect to note is that community participation included in-kind (labor-time) contributions from beneficiary households to implement actual interventions at the community level, such as for terracing for soil improvement, rehabilitation of watershed gullies, etc., were organized and coordinated at the kebele level. These contributions were made as part of Ethiopia's existing \"mass mobilization\" program. For assessing the impact, one would not only need to be able to identify whether the project helped leverage additional community labor being provided but also whether such labor inputs were redirected towards the implementation of the project activities. 3 We address this in Section 5. (d) improve pasture and forage management in 630 sites covering 9,450 ha of communal grazing lands; (d) rehabilitate 18,900 ha of degraded community forests; (e) establish participatory forest management covering some 10,000 ha in five sites of public forests; (f) rehabilitate 32,500 ha through off-farm soil and water conservation measures; and (g) conserve and enhance biodiversity.In addition to these targets, as mentioned, approximately 25,000 unemployed youth were expected to benefit from off-farm income-generating activities and employment opportunities. Also, at a later stage (from 2016), a component was added which aimed at mainstreaming climate change in the 3 It is relevant to point out here that, according to the qualitative project impact assessment (IFAD 2021), community leaders did consider that the participation in community works increased the community's sense of responsibility for natural resource use. At the same time, however, in many cases increased participation took the form of providing actual labor time but not necessarily associated with a sense of being able to give direction to planning of the watershed works.Among 24 communities visited for the qualitative assessment, leaders of 23 communities indicated they felt they had little (46%) to only some (50%) influence on the watershed plan, as that planning mainly took place at the kebele level.They described the planning approach as \"top down\" with government institutions taking decisions that were subsequently communicated to the communities for implementation.project activities and was articulated into two sub-components, namely: adaptation to climate change and mitigation of climate change. However, this component was implemented only in a limited number of watersheds.The targeting of beneficiary households was neither systematic nor transparent. According to the project concept report and findings from the qualitative assessment (IFAD, 2021), all residents in the targeted watersheds were considered beneficiaries. Moreover, no information was collected about beneficiaries' characteristics and their baseline performance measured by main outcome indicators, derived from the list of proposed interventions. Instead, the targeting strategy focused on watershed communities assumed to meet the criteria. All households belonging to selected communities were, in principle, considered beneficiaries, though for certain interventions ad hoc criteria were introduced to target beneficiary households within selected communities. 4The community targeting also lacked a clear approach. A review of both the project design report and project implementation manual could shed no light on the process of selection of the 650 watershed communities, which were ultimately selected as beneficiaries. The project completion report indicates that the watershed selection was based on the \"level of degradation of the watershed, the presence of gullies that are beyond the capacity of smallholding farmers to restore, and woredas with no intervention from other projects/donors\" (IFAD 2019: 5).However, no complete listing of watersheds existed or could be provided, although -according to the project implementation manual -the ANRS has been said to have identified 800 \"micro-catchment areas\" belonging to the Lake Tana Watershed (IFAD, 2009c).The project's theory of change consisted of three pathways to achieve its targets. An underlying assumption for the project's successful implementation was that government agencies of the Amhara National Regional State were committed to adopt policy, enforce regulatory frameworks promoting and facilitating landscape-scale watershed management practices (IFAD, 2009b).The first pathway (and overall outcome) is towards \"increases in household income\", resulting from all other project outcomes. The second, labeled as \"intensification and extensification of river basin management\" is premised on ANRS bureaus encouraging and raising awareness among communities of the benefits of improved and participatory resources management in the Lake Tana river basins.Local communities through their watershed management committees were to take greater responsibility for sustainable resource management, seeking both social equity and empowerment of women and youth in the process. The third pathway, labeled \"increased resilience of watershed resource users\" assumes that with more participatory watershed management improved practices for landscape conservation and climate smart agriculture would be widely adopted increasing resilience of users.Based on the theory of change, the following key questions were raised for the quantitative impact assessment:• Did the project contribute to better socio-economic conditions among project beneficiaries, in terms of incomes, assets and food security?o If incomes increased, was this due to income diversification (e.g. income generating activities) and/or improvement of agricultural productivity? o Likewise, did the project improve ownership, or security of access to land, water or productive resources?o What helped improve yields? Higher levels of technology adoption, or higher soil fertility, or adoption of suitable agricultural practices?o Did project beneficiaries have improved and more regular access to sufficient nutritious food (e.g. as measured in greater dietary diversity)?• Did the project contribute to strengthen and improve participatory community watershed management practices? o Did project beneficiaries increase participation in watershed planning and management?o Did the project contribute to empower women's in watershed and household-level decision-making? More in particular, did the land certification empower women in this regard?Regarding possible \"contamination\" or \"spillover\" effects, three important considerations had to be accounted for in this evaluation: (1) the nature of the project intervention where spillover effects may arise; (2) the mechanism through which the spillover effects may occur; and (3) possible confusion of the project intervention with other, complementary types of support organized at the kebele level.In the case of CBINReMP, the land and forest management, watershed restoration, and soil conservation interventions likely created spatial spillovers. These could be a result of ecological processes, but could also be the result from behavioral responses, such as when restricting access to resources in one area induces a rise in extractive activity elsewhere (Baylis et al, 2016;Deininger and Xia, 2016;Ostwald and Henders 2014). Spillovers not only affect net impacts but can also bias impact estimation when they influence non-target areas that were intended to serve as control observations. Both the nature and mechanism of spillover effects influence the impact assessment design and underlying identification strategy, which will be further looked at during methodology development. Regarding the third consideration, based on the qualitative assessment, beneficiaries were not always able to identify the project-specific activities as distinct from other recurrent natural resource conservation interventions. Hence, for the present assessment it had to be assumed that the treatment communities and areas only benefited from the CBINReMP's interventions.The principal aim of this evaluation is to assess the impact of the project on project beneficiaries.Impacts are assessed for four outcomes considered key to rural poverty reduction: (i) increases in household income and assets; (ii) improved human and social capital and empowerment; (iii) improved food security and agricultural productivity; and (iv) strengthened community institutions and participation.The overall impact evaluation of the CBINReMP conducted by IFAD's Independent Office of Evaluation and IFPRI employed a mixed-method approach. Both quantitative and qualitative data were collected, with the latter being collected prior to quantitative data collection to help inform the design of the quantitative survey. The qualitative data were used to inform interpretation of the quantitative results. Additionally, geo-spatial data were analysed to assess the biophysical indicators as outlined in the theory of change. Here we outline the quantitative approach and the use of geospatial data. This is an ex-post impact evaluation conducted after completion of the project activities. Lacking proper baseline survey data of beneficiary communities and households, 5 a quasi-experimental design method was used to estimate average treatment effects through comparison of beneficiaries and a \"control\" group.To evaluate the impact of the project on household income, agricultural productivity, and other social economic indicators, the impact evaluation must attempt to account for potential observable sources of selection bias, with the idea that by accounting for those observables, unobservables are also somehow balanced between the treatment and control groups.5 A baseline survey was not undertaken until after several years of the start of the project. The late undertaking of the baseline survey implies that the state of conditions that existed in the project areas prior to CBINReMP interventions cannot exactly be established. Also, as noted in the mid-term review of the project (IFAD 2014), the baseline survey that eventually was conducted in 2013 was not considered to sufficiently comprehensive in design and information coverage to facilitate proper monitoring and evaluation of the project's achievements.In doing so, the impact assessment had to face the challenges identified in the previous section:-selection bias because of non-random placement (targeting) of the project; -self-selection of beneficiaries into receiving the project; -possible spatial spill-over effects of project benefits to non-treatment communities; and -a phased rollout approach.To account for the non-random placement of the project, we control for observable community-level characteristics and geographical attributes that are exogenous to the project -i.e. most of which refer to the period before the project intervention and might be correlated with the project's targeting strategy. However, we acknowledge that the evaluation cannot account for all possible unobservable confounders. In the context of this study, all households living within the targeted watersheds are considered as beneficiaries, so the results can be considered as \"intent-to-treat\" effects. Hence, selfselection of the beneficiaries to take part in the community watershed activities is not an initial challenge.As planning of the project intervention was done at the kebele level, the interventions could have benefited both targeted and non-targeted watersheds within a treated kebele. To check for potential spatial \"spillover\" effect due to the kebele level planning of the project, we first identified whether the control watersheds belonged to a kebele which included a treated watershed or not. We then reestimated the treatment effects, comparing separately the targeted watersheds with control watersheds located either within or outside the kebeles with treated watersheds. The results of this exercise (reported in Annex Table A.1) do not show consistent pattern that would support the argument of detectable \"spillover\" effects due to the design of the project. Lastly, it was not possible to account for any influence of the phased roll out of the project interventions, possessing only after-project information of beneficiary household and community characteristics and overall benefits they received, not how or when they were phased in.An additional challenge was to identify a proper control group in light of the way beneficiary watersheds were selected. As stated above, the initial selection of watersheds gave priority to those with higher perceived resource degradation. As explained further below, we randomly selected the control group watersheds from a list of non-project watersheds. Since the non-project watersheds thus likely would face less resource degradation this could influence the assessed outcomes, given possible difference in key initial conditions. To account for this potential \"mismatch\" in conditions between treatment and control group, the household and community survey questionnaires included questions regarding the (perceived) state of natural resource degradation at the start of the project (10 years ago) and this information was used in the matching procedure minimizing such differences.The quantitative data were collected both at the household and community levels. The CBINReMP was implemented in three macro-watersheds covering four zones (i.e. West Gojjam, Central Gondar, South Gondar, and Awi) around the Lake Tana sub-basin. Specifically, the project covered 24 intervention woredas or districts. In two of these woredas, Quarit and Yilmana Densa, only one micro-watershed was targeted and, consequently, had to be dropped from the sample selection.Furthermore, in South Gondar only one component of the project (land certification) was implemented in all five woredas and no information was available for the list of watersheds covered by the project in the kebeles belonging to these woredas. Likewise, three woredas (Wogera, Gondar Ketema, Dangla Ketema) with only either treatment or control kebeles/watersheds were also excluded. Thus, the quantitative impact assessment had to be limited to the 14 woredas for which watershed level information on implementation activities was available. Within these 14 woredas, the project reportedly reached about 153 kebeles and 517 community or micro-watersheds. These kebeles and micro-watersheds constituted the sampling frame for treated or beneficiary watersheds.A three-stage sampling strategy was followed. In the first stage, three kebeles each from the nine woredas having 10 or more treated kebeles and two kebeles each from the remaining five woredas, with less than 10 treated kebeles, were selected using simple random sampling. Thus, a total of 37 treated kebeles were considered. In the second stage, two treatment watersheds were selected from each sample kebele selected in the first stage using simple random sampling. The sample of watersheds was drawn from the list of watersheds initially targeted by the project. In the third stage, based on the list of community members provided by the watershed management committee, 12 farm households were selected from each community watershed using systematic random sampling.Once the sample treatment kebeles were identified, it was decided to select control group community watersheds and households from a list of non-intervention kebeles neighbouring to the selected treatment kebeles. This decision was made on grounds of similarities in agro-ecological conditions and presumably also socio-economic conditions. While this could not be fully verified during the sampling process, it was further assumed that the control group kebeles and watershed communities not only had no part in the CBINReMP but also not from any other watershed development project by development partners (other than the periodic natural resource conservation implemented by the government through mass mobilization). 6 The attempt here was to avoid any problem of contamination of intervention benefits between treatment and control group, while having a proper control group would allow for proper estimation of treatment effects. Following the establishment of the sample frame for control group communities, the same three-stage sample selection procedure was followed for the control group sample selection.The sample size thus obtained consisted of 74 treatment watershed communities and 887 treatment households and 62 control group watershed communities and 768 control households (Tables 1 and2).  Household and community questionnaires were developed, pre-tested in the field, and modified accordingly before the actual survey data collection, which took place during March 2020. Of the1,674 households identified from the sampling frame for interview, 1,665 of them were available and willing to complete the household survey implying a response rate of 98.9 percent. Likewise, community level data was collected from 136 sample micro-watersheds. One key informant (typically head of household) was interviewed for collecting the household-level data, while several respondents were sought to provide the information relating to the community survey questionnaire (typically, two members of the community watershed committee, one or two elders from the community, and woman and youth representatives).The questionnaire of the community survey included questions regarding community organization; community's access to infrastructure, institutions, services, and markets; community-led natural resource conservation and climate adaptation practices. The household survey included modules on household composition, land use, land certification, crop and livestock production and utilization, natural resource conservation, extension services and credits, off-farm income, food security, adaptation strategies, and participation in community planning and works. Interviews were conducted in Amharic, local language of the study area.This impact assessment makes use of agro-climatic and geo-spatial data to assess the biophysical indicators as outlined in the theory of change. According to the project design report, interventions for all targeted 650 watersheds were designed using geo-spatial information. However, none of the area shapefiles needed to geographically identify micro-watersheds could be provided by the project managers or local authorities.Due to the unavailability of the shapefiles, new watershed area data were created. The total sampled watershed area was 're-created' from information provided by respondents to the community questionnaire; specifically, using the responses to the questions regarding how much time it took, in minutes, to walk from the north to the south edge, as well as from the east and west edge. This walking time was converted to distance and then projected into an estimated rectangle area of the watershed. The GIS-derived centroid was then applied to centre of the rectangle. On this basis, we estimated that the mean of the sampled watershed area was 7.7 km 2 with a median of 5.2 km 2 . Given the application of a uniform walking time, imposed boundary form and typical variations in respondent estimation, these estimates should be taken with a fair degree of possible error. For instance, although watersheds should be discrete objects, many watersheds had overlapping boundaries or centroids that did not seem to conform to topography. This has implications for treatment and control groups since they were subsequently modelled, in some instances, as overlapping. Regardless of these limitations, remote-sensed data was derived from these rectangles and consists of four major variables.To capture changes in the landscapes due to interventions, we utilize satellite remote-sensing images from MODIS, LandSat, and a derived dataset called Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Spatial datasets are derived from three primary sources both of which were available near the year of the start of project interventions. We use MOD13Q1 and MYD13Q1MODIS products to construct an interpolated 8-day equivalent Normalized Difference Vegetation Index (NDVI) time series with a 250m resolution.7 Landsat 8 collection tier 1 was used to generate annual cloud-free median NDVI. The NDVI is generated from the Near-infrared (NIR) and Red bands of each scene as (NIR -Red) / (NIR + Red), and ranges in value from -1.0 to 1.0. NDVI is sensitive to the presence of chlorophyll and is regularly used as a proxy for plant health and productivity. From the same source, we also calculate annual Normalized Difference Water Index (NDWI) which is sensitive to changes in water content of vegetation, with values ranging from -1 to 1.8 Both LandSat products are annual but have a significantly higher spatial resolution than MODIS products (30m versus 250m, respectively) The time series properties of rainfall are measured by the CHIRPS dataset. CHIRPS incorporate 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. In this case, we resample the rain data to 75m spatial resolution to ensure that each enumeration area has an observation associated with it. Precipitation is collected by dekad (Funk et al. 2014). There are three dekads in a month, the first two being 10 days long, and the third being the remaining days in the month.All data is summarized over time to help differentiate changes within treatment and control watersheds. For instance, we might look at whether NDVI or \"greenness\" is higher in intervention areas than in the control group. The challenge then is to create a set of indicators that meaningfully describes differences between the watersheds for the seven years for which we have data.A large number of potentially important time-series features were derived from the remotely sensed imagery. For the sake of brevity, we only describe those features that were used in the final analysis.Note, that most time-series indicators will be more robust for the MODIS and CHIRPS because of their significantly higher temporal frequency. In Table 3, we describe the set of metrics extracted and a brief description of each. Each time-series metric described below is then summarized by their mean value for all land within each of the treatment and control watersheds. As the data captures the entire watershed and does not allow for spatial heterogeneity within the watershed (i.e., individual plots), our statistical analysis is restricted to statistical differences contrasting treatment and control watersheds. Owing to these limitations, the geo-spatial data are used to provide complementary, contextual information to interpret the results of the quantitative impact assessment based on the household survey data but could not be directly used for the estimation of the treatment effects.The propensity-score matching procedure controlled first for initial heterogeneity between watersheds and households, based on the probability of a watershed and household participating in CBINReMP conditional on its observable co-variates. Subsequently, to estimate the treatment effects, a doubly robust estimation method was applied, which combines propensity-score estimation and regression-based methods (PSM Weighted Regression) (Wooldridge, 2007). The doubly robust estimation method allowed the evaluation to better account for the observable community characteristics that are correlated with program participation and the outcomes, while assuming that unobservables are also balanced between the participants and control group on average.Matching was conducted at two levels. The first step consisted of matching treatment and control groups at the watershed/community level. Since each kebele was assumed to include a pool of qualified micro-watersheds and households possessing similar characteristics as those of project communities and households, the community-level propensity score was adopted to find counterfactual communities outside the project area but either within the same kebele or a control watershed from neighbouring kebele. A restriction was applied to the communities within the same district to assure geographical similarity and spatial proximity between project watersheds and potential control watersheds. Matching parameters were derived from the community-level data.Selection of the matching variables was done with due caution, because if the project's objectives were met, some of the variables might have changed because of the project. For example, even the household demographics may also not be valid matching parameters, like marriage or migration.Since CBINReMP was a nine-year project, the project might have affected virtually any variable one could think of at the household level, including variables that are often used in matching models such as household demographic characteristics, asset holdings, or production variables. Therefore, it was decided instead to use variables measured in the community survey that largely reflected pretreatment variables that could be measured. Since the community or watershed level was the targeted unit of intervention, it made most sense to also develop propensity scores at that level. Ideally, those variables should reflect the type of characteristics used for the selection of beneficiary watersheds for the CBINReMP in the first place. After controlling for these variables, the remaining variation in characteristics of watersheds should be considered to be approximately random, rather than due to unobservable differences between selected and control watersheds.The variables for the matching of treatment and control group cases were subsequently selected using the LASSO regression model (e.g., Tibshirani, 1996). The LASSO model is a method for selecting variables to be included in a regression in a way that it maximizes predictive value.Intuitively, it is not very different from a standard regression, but with the main difference being that it includes a penalty function for inclusion of variables that do not help explain the outcome. For measuring propensity scores, we combine the LASSO regression with a logit model, in which we use a cross-validation algorithm to choose variables to include in propensity-score estimation. The list of potential variables included community variables that were arguably exogenous, as well as interactions between variables that were continuous or discrete and continuous. The LASSO is increasingly used in studies requiring estimation of propensity scores, particularly in epidemiology.In that literature, Franklin et al. (2015) find that the LASSO outperforms other estimators.The second step was to use the propensity scores to estimate the predicted probability of inclusion for each watershed. For each household in a watershed, the propensity score indicates the predicted probability that the household belongs to a treated watershed community rather than to a comparison group of non-treated watersheds. The propensity scores p are then used as weights for the comparison observations, that is, while each treatment observation receives a weight of one, the control-group observations receive a weight of The project implemented wide range of activities focusing on participatory watershed management, pasture and forage development, soil and water conservation, and biodiversity and ecosystem protection. However, evidence from the qualitative assessment shows that the degree of participation in the various project activities varied considerably across targeted watershed communities. A descriptive analysis of the participation variables of the household and community surveys also confirmed this was clearly the case. This leads us to make a distinction between \"high\" and \"low\"community project participation and assess potential impact heterogeneities. The distinction was made based on close examination of responses to 18 survey questions related to household and community participation in the planned activities of the project (Appendix Table A.3). A \"participation score\" (ranging from 0 to 18) was created to rank communities from low to high level of participation. To ensure a comparable counterfactual, two of the control-group watershed communities with a participation score of more than 12 were dropped from the sample. The high project participation score in these cases could reflect that, despite being identified as non-treatment, these were nonetheless direct or indirect beneficiaries and hence cannot be considered part of the control group.In the analysis of the treatment effects (section 5) we make this distinction between \"high-\" and \"low-participation\" treatment groups based on the degree of project-related activity participation. Since community participation was both a means to the outcomes and an (intermediate) objective of the project, the distinction made could confound the actual impacts of the project. Based further on the information provided by communities during the qualitative focus group discussions, we interpret higher participation as synonymous to the intensity of the project's effort (i.e. participation level in the treatment) and that more treatment would more likely help generate the targeted outcomes. We return to this issue in Section 5.The quantitative impact assessment will be based on an estimation of the average treatment effect on the treated (ATT) for the projects targeted outcomes. The ATT is estimated as the difference between the outcome variable for the households among which the treatment was administered, and among households that were not offered the treatment. The average treatment effect of CBINReM project is estimated using a doubly robust method, as indicated above when discussing the LASSO method for the matching procedure. That is, while we regress the outcome variable over the treatment status, higher weights were given to non-beneficiary observations with characteristics more like beneficiaries and lower weights otherwise.Formally, the specification of the regression model used to estimate the ATT can be formulated as follows:Where: Y is (lead and intermediate) output variable; \uD835\uDC47\uD835\uDC47\uD835\uDC47\uD835\uDC47\uD835\uDC47\uD835\uDC47\uD835\uDC47\uD835\uDC47\uD835\uDC47\uD835\uDC47 refers to the treatment status (i.e. -treated or control) which is a measure of treatment effect; Z 1 refers to different community-level co-variates selected by the LASSO model; α 1 , β 1 and β k are parameters to be estimated; subscript \uD835\uDC56\uD835\uDC56 denotes households, \uD835\uDC57\uD835\uDC57 indexes watersheds, and \uD835\uDC58\uD835\uDC58 denotes the co-variates; ε is a mean-zero error term. Here, the primary null hypothesis to be tested is whether β 1 (ATT) is equal to zero.Section 5 presents the main findings from the application of the estimation procedure. The findings are shown not only for the comparison between entire treatment group and the control group, but also for respectively, the high-participation and low-participation treatment groups, on the one hand, and the control group, on the other.By the nature of the project, treated and control groups were not allocated randomly. Hence, to evaluate the extent to which the two groups will be comparable, we executed a series of balancing tests on household and community level characteristics. Accordingly, Table 4 describes the household characteristics of the treated and control groups. The results show that, with the exception of distance to cooperatives, the two groups show neither detectable nor statistically significant differences in their demographic characteristics, asset holdings, and access to training and market centers.Source: Own computation, 2020Note: ** refers to 5-percent significance level.The balancing tests on community level characteristics of the treated and control watersheds are presented in Table 5. The two groups face similar agro-ecology conditions and degrees of access to basic infrastructure and services, such as telecommunication, electricity, and health services. The two groups are also comparable in their total population and area coverage. While, on average, the treated watersheds are located closer to both markets and cooperatives, the treatment and control group  5.Source: Own computation, 2020Note: * and ** refer to 10 and 5percent significance level, respectively.Four spatially derived variables were used to assess whether control or treated watersheds exhibited important differences regarding vegetation cover changes, soil water retention mapping (irrigation or other water management strategies) or were impacted by relative annual rainfall differences. Given that the data was not normally distributed, median tests were performed. Table 6 indicates that none of the variables were found to be statistically different, suggesting geo-spatial conditions were roughly similar on average for the watershed areas where the control and treatment groups were located. However, it should be remembered that the lack of clearly delineated, mutually exclusive, boundaries implies this conclusion has to be taken with great caution.Given this caveat, NDVI and NDWI trend lines were drawn through the data to determine if there were changes in vegetation coverage over the 7-year period of observation (2013)(2014)(2015)(2016)(2017)(2018)(2019). A positive slope would imply increase greening of the watershed over time, while a negative slope would indicate a deterioration of vegetation cover. While both the MODIS and Landsat harvested variables revealed a statistically significant positive slope for the median of the sampled watersheds, there were no statistical differences between the treatment and control groups. The potential reasons for the overall positive slope could be attributed to improved erosion techniques or common land rehabilitation undertaken in all watersheds but it may also be due to exogenous factors like increased rainfall experienced during the final years of the project's implementation. The median water index was slightly negative with no statistical differences between the two groups (it should be noted that the overall mean was slightly positive because of a few large positive values).Source: Own computation, 2020Note: No statistically significant differences were found. a The Wilcoxon rank sum test is a non-parametric test that may be used to assess whether two distributions of observations obtained between two separate groups on a dependent variable are systematically different from one another.We subsequently looked at changes in geo-spatial conditions over the 2013-2019 period by testing standard deviations for the key indicators. Again, we did not find statistically significant differences between control and treatment groups. Given that the MODIS product was collected at a higher frequency (every 8 days versus an annual aggregation for Landsat), further tests on the means and medians were performed, but also in this case no statistically significant differences could be identified. Annual variations in rainfall could suggest important variations in NDVI and NDWI indexes but, while there were some annual differences in area rainfall, co-variation suggests relatively similar impacts on both treated and control watersheds.As indicated, the analysis described above suffers from several limitations. In the final section, we provide some recommendations for a methodology that would overcome such limitations in the use of geo-spatial data for future impact evaluations of this kind.Findings of the impact of previous soil and water conservation efforts on land productivity in Ethiopia show mixed results. A study in Tigray region through a 500 households survey suggests that plots with stone terraces experience higher crop yields (Pender and Gebremedhin, 2006). Similarly, Holden et al. (2009) used nearest neighbour and kernel matching to measure the impact of stone terraces in Tigray region and found a significant and positive effect on land productivity. Kassie et al.(2007) had found similar positive findings in another evaluation. Kassie et al. (2009), in contrast, found that plots with bunds resulted in lower yields compared to non-conserved plots using matching methods and switching regression analysis on farm-level data from high rainfall areas in western Amhara. A recent impact evaluation from a watershed intervention program in Ethiopia found that sustainable land management (SLM) practices contributed to water security for both crop and livestock production, as reflected in crop yields for maize, mango, and millets using data for 561 households and 2,900 plots belonging to four watersheds (Kato et al. 2019).Existing evidence on the impact of watershed developments on livelihood and income improvements are also mixed and based on case studies of limited number of watersheds. Bearing these mixed experiences in mind, the remainder of this section presents estimated average treatment impact of the CBINReM project on lead and intermediate outcome variables. Definitions and measurement units of the outcome variables described in the Tables 7, 8 and 9, presented below, can be found in Appendix Table A.4, while more detailed information on the means and skewness in key outcome and intermediate variables can also be found in the Appendix (Tables A.5a-b and A.6ab).Table 7 shows the impact of CBINReM project on rural livelihoods in terms of the main targeted outcomes of improved household incomes, food security, asset holdings, agricultural productivity, and social capital. The first column of each table compares the average treatment effect between treated and control groups, assuming that there is no significant difference in extent of participation among beneficiaries within the treated watersheds. After relaxing this assumption, the second and third columns show the estimated treatment effects after comparing, respectively, the high-and lowparticipation treatment groups with the control group.Overall, the results in Table 7 show no detectable differences between treatment (when taken as a whole) and control groups with respect to livelihoods status, social capital, and agricultural productivity. However, we do observe some significant treatment effects when comparing the \"highparticipation\" treatment group with the control group (column 2 in Table 7). Beneficiary households with high community participation have significantly higher income and greater dietary diversity than the control group. Specifically, the incomes of high-participation treatment households were, on average, 17.8 percent higher than that of the control group.The dietary diversity score exceeded that of the control group by 0.4 units. Dietary diversity is especially important among populations with diets based on starchy staples where micronutrient deficiency is more likely, as is the case in the project area. A higher score is an indicator of increased economic access to a varied diet for household members (though the indicator does not reflect intrahousehold dietary patterns) (Swindale and Bilinksy, 2006). While this does not follow directly from the method applied here, likely the better access to more diversified food is closely associated with the higher incomes of the high-participation treatment group.Finally, we further find a significant and positive treatment effect for cow milk productivity among high-participation beneficiaries. However, we do not find discernable differences for cereal yields or any of the other livestock productivity outcomes. -0.070 (0.12) -0.065 (0.12) -0.068 (0.13)Note: ** and * refer to5 and10percent significance level, respectively. a The social cohesion index is a composite of five perceptions about belongingness of individuals in the community regarding economic opportunity, opportunity in public affairs, tolerance to conflict of interest, and adequate representation in institutions. The related questions are contained in module H (questions H2) of the community questionnaire (see Annex 1). In the questionnaire the response categories are:1 Strongly Agree, 2 Agree, 3 Disagree, and 4 Strongly Disagree. In the estimation of a reverse valuation was used, such that a higher index means higher social cohesion.In summary, project beneficiaries in communities with high degrees of participation in communitybased natural resource management activities enjoy higher incomes and this may also allow them to have better diets. However, these positive livelihood outcomes have not come with other targeted livelihood improvements (relative to the comparison group) in terms of agricultural productivity, social cohesion, or asset holdings. The higher milking cow productivity likely underpins a modest part of the estimated income impact and, while notable, the impact was not among the central targeted outcomes of the CBINReM project. Next, we turn to the assessment of the project's impacts on intermediate outcome variables.A first important intermediate outcome of the project is that it seems to have significantly increased participation of beneficiary households in providing labor time for most of the community works promoted (Table 8). The survey results show that the treatment groups spend visibly more time on communal terrace construction, cut-off drainage and tree planting, though this is not the case for gully rehabilitation. The labor participation in these types of communal works among the 'highparticipation' treatment group households is broadly the same as that for the average treatment group. However, the confidence level for all of these estimates is low, such that none of the differences between control and treatment groups were found to be statistically significant. A significant impact for labor participation would have been important in terms of the project's theory of change, which saw enhanced community participation for sustainable land and water management as key to create better and more resilient livelihoods for the beneficiary population. Given the lack of statistical significance we cannot confirm with any certainty that the project was effective or not in promoting community participation in SLM works to underpin livelihood improvements.For other participatory variables we find little difference between treatment and control groups. For instance, 68% of households of both control and treatment groups participate in the watershed planning process and almost equal shares form part of grazing groups and other forms of community participation (see Appendix Table A.7). Beneficiary communities are somewhat more likely to have a watershed plan (86%) compared with the control group (77%). As indicated in Section 3, however, we did find significant differences in degrees of participation, such that we separated the treatment group in terms of high-and low-participation treatment and participation. Thus, when assessing the project's impact, we can no longer see differences in participation as a true impact for these subgroups of beneficiaries for obvious endogeneity problems. Hence, the finding shown in Table 9 that the substantially higher shares of households in the high-participation treatment group participating in watershed planning (95% versus 77%) and forming members of shared grazing land communities (61% versus 46%) cannot be taken as intermediate outcomes of the project, but just the confirmation of the relevance of the distinction made.  Overall, we cannot take these findings regarding participation as confirmation or rejection of the effectiveness of the theory of change, since we cannot establish causal relationship between this element of community participation and the impact on livelihood improvements discussed above.We also find few discernable differences between treatment and control group household for a range of key intermediate outcome variables as targeted by the project. For the group of treatment households as a whole, we only find a significant impact on perceived increases in herd size (compared with ten years ago) and this perceived impact is even stronger for the high-participation treatment group (Table 10). While there are no other significant impacts for the treatment group as a whole, we find three other significant impacts for the high-participation treatment group.Specifically, these are benefits from \"cut and carry\" of forage from communal grazing lands, and(perceived) increased participation on soil and water conservation practices on both own and communal lands.Regarding the first benefit, since villagers sell at least part of forage from the cut-and-carry practice in the market, this may have contributed to the positive income effects for the high-participation treatment households and likewise for the perceived increase in herd size. Again, however, the estimates in Table 10 do not allow inference of such a causal relationship. Most likely though, the impact on raising average household income has been limited. Although the \"cut-and-carry\" system has a potential of ensuring sustainable forage off-take, the cutting is done only once or twice per year. Such long cutting return periods may satisfy those who have land or other means of livelihoods, but not the landless or the marginal farmers who have no other alternative means of livelihoods while they are waiting for their biennial cut-and-carry share. Those who have land could use crop residues in the meantime or they may have other ways of producing fodder at the farm.Regarding the benefits from increased soil and water conservation practices, the qualitative survey (IFAD, 2021) concluded for a sub-sample of beneficiary watershed communities that the project \"successfully promoted the construction of physical and biological soil and water conservation (SWC) structures in off-farm degraded areas and supported improved land administration and certification.\" The finding below of perceived improvements in engagement of SWC practices would be consistent with that finding. However, that assessment recognized, at the same time, that focusing only on off-farm structures did not sufficiently address the causes of land degradation while the project's support to the generation of new income-generating activities little effective in diversifying livelihood opportunities. These implementation weaknesses could explain the lack of impact on key lead and intermediate outcomes, such as crop productivity, income diversification, and resilience to climate change. The qualitative survey (IFAD, 2021) further found that the project helped rehabilitate and protect the vegetation coverage of degraded land in a sub-sample of beneficiary watersheds through the SWC practices. However, as already pointed out in section 4, our geo-spatial analysis could not find significant differences in vegetation coverage or soil water retention between the beneficiary and control group communities. Hence, while the project may have contributed to NDVI improvements, we cannot conclude it was more effective than other interventions and factors that may have helped similar improvements in non-beneficiary watersheds.For other elements of the theory of change we do not find any significant impacts of the project, such as for income diversification, off-farm income generation, women's empowerment (e.g. through participation in watershed communities), reduction in free grazing land, reducing conflict over land, or -as mentioned -perceived improvements in resilience to climate change or cereal yields.This study set out to assess whether the CBINReMP led to increases in household income (including through income diversification); improved access to land, water and productive resources; improved crop and livestock yields; and greater food security and dietary diversity. These improvements would result from the implementation of sustainable landscape, soil and water conservation practices through community participation. The related interventions were further expected to result in greater resilience among communities and households, including to climate change.Lacking a proper baseline study, no rigorous assessment could be made of the improvements over time and how much the project's interventions contributed to such interventions. Due to this limitation, the analysis was restricted to an ex-post impact assessment comparing beneficiary households and communities with a comparison group. Doing so, was constrained by further complications and limitations as noted in Section 3. Nonetheless, a few key conclusions can be drawn:(a) The project seems to have effectively promoted community participation but in an uneven way, such that a clear distinction can be made between high and low participation across communities.The reasons why participation was higher in one sub-set of communities are not immediately clear, but the important finding is that, essentially, we only find statistically significant project impacts among the high-participation treatment groups. It validates the project's premise of the importance of the chosen participatory approach but leaves open the question why this was not sufficiently pursued or did not work equally well across all beneficiary watershed communities.We can say, though, that the greater community participation in soil and water conservation practices among the high-participation sub-group was a result of the project, as perceived by those beneficiaries.9 (b) Beneficiaries with high participation have significantly higher incomes (17.8%) than the control group. It is unclear, however, which project activities have contributed, and how, to this positive impact. In comparison with the control group, it is not associated with better crop yields, greater income diversification or off-farm income opportunities, and neither with enhanced women's empowerment, nor reduced conflict over land. We do find significantly higher cow milk productivity and greater herd size among beneficiaries with high participation, as well as benefits from \"cut-and-carry\" forage collection. To a limited extent, these outcomes could partially explain the impact on incomes. The lack of impact on crop productivity or income diversification suggest that the promotion of SWC practices and income-generating activities induced no direct economic gains to beneficiary households. Part of this outcome might be explained by the fact that SWC were mostly promoted for off-farm, community resource protection (IFAD 2021),hence not directly impacting on farm productivity or household-level economic opportunities.(c) Dietary diversity is greater among beneficiaries with high levels of participation. This outcome may be associated with the higher incomes of those beneficiaries, as well as with higher productivity of milking cows. Again, we cannot say for sure based on the evidence obtained and the finding is not corroborated by commensurate improvements in food security, as measured through the food insecurity experience scale.(d) The analysis of geo-spatial data showed improvement in vegetation coverage over the 7-year period of observation (2013-2019) and of most of the project's period of implementation. This greening of the watersheds over time could be associated with improved erosion techniques or common land rehabilitation. However, such improvements were observed for all watersheds in the area and no statistical differences could be detected between the CBINReMP beneficiary watersheds and the control group. The potential reasons could be that such improvements may have taken place through different means in all watersheds as well as because of exogenous factors, such as the increased rainfall experienced in the LTW area during the final years of the project's implementation.In summary, this ex-post assessment indicates that the CBINReMP had only very limited, quantitatively verifiable impact on rural livelihoods. It seemed to have contributed to higher household incomes and some greater dietary diversity, but only where the project managed greater community participation. However, even for those beneficiaries, livelihood conditions had not become significantly more productive, diversified, resilient, or sustainable than those of the comparison group.The lack of a proper baseline survey, incomplete information about targeted watershed communities and often lack of clear distinction lines between the project's interventions and support provided to communities through other mechanisms make it sheer impossible to identify the true impact of the CBINReMP. Four additional challenges had to be faced, including possible selection biases because of non-random placement (targeting) of the project, self-selection of beneficiaries into receiving the project, possible spatial spill-over effects of project benefits to non-treatment communities, and the project's phased rollout.The last challenge could not be address having only an after-the-project survey to undertake the impact assessment. As discussed in greater detail in Section 3, the other three challenges could be addressed to a large extent. First, to account for the non-random placement of the project, we control for observable community-level characteristics and geographical attributes that are exogenous to the project. Admittedly, of the many possible unobservable confounders not all could be accounted for.Second, we had to consider all households living within the targeted watersheds as beneficiaries, so the findings as presented should be considered as \"intent-to-treat\" effects. Third, since project interventions were planned at the kebele level, they could have benefited both targeted and nontargeted watersheds within a treated kebele. We checked for the potential spatial \"spillover\" effect due to the kebele level planning, as described in Section 3. We did not find any systematic pattern that could point at significant \"spillover\" effects owing to the project's design.This said, the findings are limited to what we were able to test with the limitations of an after-theintervention survey and no comparison of rural livelihood and watershed management conditions at the start or mid-stage of the project's implementation. That is, we could only make an ex-post comparison between the treatment group and the comparison group, and, what is more, given apparent uneven implementation of the project's intervention across the target group we distinguished between communities with high and low participation levels. This distinction did allow us to detect discernable impacts in terms of some of the key outcome variables, suggesting that high community participation is a pre-condition for obtaining the observed impacts. In doing so, however, we could no longer assess, rigorously, whether such higher community participation was effectively induced by the project (for obvious endogeneity issues).There are a few basic principles related to project design that can help better prepare projects for ex post impact assessment in the future. For the purpose of this discussion, we assume random assignment of projects to a portion of eligible communities is not possible. From an evaluation perspective, conducting a first phase of a project with random assignment to learn about what works in advance of a second phase in which scale up occurs is ideal, but difficult to implement in practice due to timing issues.The basic principles are as follows. First, it is important to better track where projects will be implemented, where they will not be, and reasons that decisions are made about where to implement projects and where not to do so. Second, it is important to track what types of investments or interventions take place in specific places. Third, it is extremely helpful to collect baseline information in both communities that are to be targeted for interventions, and in similar communities that are not to be targeted. None of these principles are perhaps surprising, but it is worth emphasizing them here.Information about targeting long-term projects is really crucial in developing ex post impact evaluations. Here, the more information that can be recorded, the better. If the analyst can know why specific places are targeted whereas others are not, they can control for those differences in analysis,and the unobservable component of potential program placement bias becomes minimized. Of course, with large programs it could become more difficult to find valid comparison groups; meaning, it could become more difficult to find comparison groups that are similar to the participant groups. However, in a situation like the CBINReMP, the ex-post evaluation was forced to make uncomfortable assumptions about these unobserved differences, in part because the watersheds in which implementation occurred were only partly known, and as discussed below the borders of those watersheds remain unknown.A second somewhat basic change that can help ex post impact evaluation of complex projects like CBINReMP is to track which interventions take place in which places (in this case, in which watersheds). Complex interventions often have lots of different parts and naturally evaluators (or planners of future projects) would like to know which components are more likely to have impacts on outcomes of interest, which components might need a redesign, and which components work well together (or do not). However, answering all these questions in a convincing manner requires a substantial amount of data collection. More basically, though, the questions cannot be answered well if there are no records (or only piecemeal records) of what interventions took place where. In this case, the survey asked about it, but there was little useful variation, making questions about project components nearly impossible to answer.Third, when projects are not randomized, having baseline data becomes quite essential. Ideally, the baseline data collection can then be used later in efforts to match participants or participant communities with like members of the control group. There are two reasons such data are essential to have, and one programming implication. A first reason is that they can help build the case that changes occur in the participant group relative to the control group in a way that cannot be done in simple before-after comparisons (or after project comparisons). Before-after comparisons are notoriously inadequate at demonstrating changes. If comparisons are made just after projects end, one has to make uncomfortable assumptions about the types of variables that would not have changed or have to use recall data that are subject to well-known errors such as telescoping. In this report, such assumptions had to be made. Programmatically, then, it is important to make decisions about some places in which projects will be implemented in advance of that baseline survey (and starting any activities), so the baseline survey is not wasted on places in which the project never occurs (this issue was an issue in RIMS surveys collected in the past).Sections 3 and 4 indicated at the limited use this evaluation could make of geo-spatial data. Here, we provide some recommendations for a methodology that would overcome such limitations in the use of geo-spatial data for future impact evaluations of this kind.The single most enhancing component would be an accurate depiction of watershed boundaries, without which a significant amount of error in measurement should be expected. Delineation might take two forms, one through the digitization of existing physical watershed boundary maps, and the second, through recreating the original watershed methodology used to create the watershed boundaries which would require the exactly matching watershed pour-point coordinates, as well the digital elevation map used to create it. Beyond these suggestions to create accurate boundaries, a handful of other improvements could be implemented. Significant effort could be made to filter out non-agricultural land from imagery at a localized level. This would enhance the measurement of the crop cycle. Additionally, all variables could be de-trended using precipitation data. Although our research indicated that the sampling design, that chose neighboring watersheds as complimentary treatment and control, makes weather patterns difficult to delineate, differences between watersheds might simply be due to the erratic nature of rainfall in Ethiopia's complex topology. Finally, a larger set of time-series features could be analyzed. For instance, looking at whether treatment watersheds are less likely to experience sudden shocks to plant health, observed by a steep decline in NDVI in any given month. While additional research could be undertaken, the basic limitation of inaccurate and approximate watershed boundaries makes these efforts less productive.    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+{"metadata":{"gardian_id":"88ce81c02e7c7b3a32f8a2a88d2b8082","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/60ae99e6-1b39-4c72-9351-0989cee90b04/retrieve","description":"","id":"-873093475"},"keywords":[],"sieverID":"4d3464b4-2466-4e24-8d3f-024aeecca4b3","pagecount":"10","content":"L'effectif total des chercheurs agricoles a doublé au Burkina Faso depuis le début des années 90, mais les dépenses consacrées à la R&D agricoles ont été caractérisées par une grande irrégularité.• En 2001, l'Institut de l'Environnement et de Recherches Agricoles (INERA), principal organisme de R&D agricole, employait environ 60 % de l'effectif national de chercheurs et absorbait 60 % des dépenses nationales consacrées à la recherche agricole.• Outre l'aide financière importante fournie par les donateurs bilatéraux, l'INERA a été largement tributaire depuis 1989 de deux projets consécutifs financés en grande partie par des prêts de la Banque Mondiale. En dépit de dix ans de croissance économique continue, le Burkina Faso se classe toujours parmi les derniers pays selon le classement en Indice de Développement Humain (IDH) des Nations Unies (PNUD 2003). Ce pays ouest-africain enclavé se caractérise par une faible fertilité des sols et par des fluctuations pluviométriques qui fragilisent fortement le secteur agricole. Celui-ci joue toutefois un rôle important dans l'économie du pays, puisqu'il emploie plus de 90 % de la population active et représente plus de 38 % du produit intérieur brut (PIB) ainsi que près de la moitié des exportations (Banque Mondiale 2003 ;FAO 2004). Le gouvernement burkinabé accorde en conséquence une grande priorité à la recherche-développement (R&D) agricole. En 2001, le Burkina Faso comptait 11 organismes engagés dans la recherche agricole (huit organismes d'État et trois établissements d'enseignement supérieur) qui sont tous inclus dans notre échantillon d'enquête. 2 Ces 11 organismes employaient ensemble 261 chercheurs en équivalent temps plein (ETP) et dépensaient plus de 3 milliards de francs CFA de 1999, correspondant à 22 millions de dollars internationaux de 1993 (Tableau 1). 3  En 2001, l'Institut de l'Environnement et de Recherches Agricoles (INERA), principal organisme de recherche agricole burkinabé, employait environ 60 % de l'effectif total de chercheurs agricoles et absorbait également 60 % des dépensesL'initiative ASTI (Agricultural Science and Technology Indicators ou Indicateurs relatifs aux sciences et technologies agricoles) est un réseau d'organismes de R&D agricoles nationaux, régionaux et internationaux géré par la division ISNAR de l'IFPRI. L'initiative rassemble, traite et fournit des données mondialement comparables sur les développements institutionnels et les investissements réalisés dans la R&D agricoles dans les secteurs public et privé, et ce dans le monde entier. Elle analyse ces tendances et en fait état dans des rapports généraux d'orientation ayant pour objectif d'aider à la formulation de la politique de recherche et à la mise en place de priorités.Le financement principal de cette initiative ASTI provient du Comité financier du CGRAI/Banque Mondiale, une aide complémentaire étant fournie par l'ACIAR (Centre Australien pour la Recherche Agricole Internationale), l'Union Européenne et l'USAID (Agence américaine pour le Développement International).Par Gert-Jan Stads et Sébastien Issa Boro Les premières institutions qui ont conduit la recherche agricole au Burkina Faso (appelé la Haute Volta jusqu'en 1984) ont été fondées du temps du gouvernement colonial français. À cette époque, la majorité des activités de recherche dépendaient des stations expérimentales françaises de Bambey (au Sénégal) ou de Kankan (en Guinée). Lorsque le Burkina Faso a accédé à l'indépendance en 1960, le pays a hérité de quatre stations (Farako-Bâ, Niangoloko, Saria et Kamboinsé), mais d'aucune structure institutionnelle de recherche.À l'instar de nombreux pays ouest-africains colonisés par les Français, le Burkina Faso a signé au lendemain de l'indépendance des accords bilatéraux avec la France afin d'assurer la continuité de l'aide française apportée à la recherche agricole. Ces accords garantissaient que la France restait le principal exécuteur de la recherche agricole dans le pays. Pratiquement tous les chercheurs actifs au Burkina Faso dans les années 70 étaient des Français. Ce n'est qu'en 1978 que la Direction Générale pour la Recherche Scientifique et Technologique (DGRST) du Burkina Faso est devenue responsable de l'élaboration de la politique gouvernementale sur le plan des sciences et de la technologie. La plupart des activités de recherche conduites auparavant par les Français ont été alors transférées au DGRST. Toutefois, la part des chercheurs français au sein du système de recherche agricole burkinabé est resté important jusqu'au milieu des années 80.En Nous avons identifié trois établissements burkinabés d'enseignement supérieur engagés dans la recherche agricole. En 2001, ces établissements représentaient seulement 6 % de l'effectif des chercheurs et des dépenses liées à la recherche agricole. Le premier d'entre eux, l'Institut de Développement Rural (IDR), qui faisait partie à l'origine de l'Université de Ouagadougou (UO), a été intégré en 1995 à l'Université Polytechnique de Bobo-Dioulasso (UPB). L'IDR comptait en 2001 6,3 chercheurs ETP engagés dans des recherches portant sur les ressources naturelles, les ressources halieutiques, la nutrition animale, la parasitologie du cheptel et la pédologie. Les deux autres établissements d'enseignement supérieur participant à la recherche agricole en 2001 étaient des Unités de Formation et de Recherche rattachées à l'Université de Ouagadougou : l'UFR des Sciences de la Vie et de la Terre (UFR-SVT) et l'UFR des Sciences Économiques et de Gestion (UFR-SEG). À l'UFR-SVT, 5,6 chercheurs ETP conduisaient un petit nombre de recherches sur les ressources naturelles, la biodiversité végétale et la production animale, tandis que les 4,5 chercheurs ETP de l'UFR-SEG se concentraient essentiellement sur des questions socio-économiques.Nous n'avons recensé au Burkina Faso aucune entreprise privée à but lucratif effectuant des recherches agricoles. Durant la période 1971-2001, l'effectif total de chercheurs agricoles employés au Burkina Faso a augmenté en moyenne de 6,4 % par an (Figure 1a). 4 Ce taux de croissance annuel a été plus élevé pour l'INERA (8,3 %) que pour les autres organismes d'État (4,7 %) ou les établissements d'enseignement supérieur (2,8 %). 5 Entre 1990 et 2001, le nombre total de chercheurs agricoles ETP du Burkina Faso a doublé, passant de 131 à 261. Le support financier fourni dans les années 90 dans le cadre du PRA-I et PNDSA-II a permis aux organismes du CNRST de recruter à un rythme bien plus rapide.Jusqu'en 1985, les recherches agricoles étaient essentiellement conduites par des chercheurs français travaillant pour des instituts français actifs au Burkina Faso. Toutefois, au cours des années 70 et 80, un important soutient financier de la part des donateurs étrangers a permis à un plus grand nombre de chercheurs burkinabé de suivre une formation diplômant. Le rôle de ces derniers au sein du secteur national de la recherche agricole s'est ainsi rapidement accru à partir de la deuxième moitié des années 80 (Mazzucato 1994). En 1991, on comptait 33 chercheurs ETP expatriés travaillant au Burkina Faso, alors qu'ils n'étaient plus que 8 dix ans plus tard. Deux tiers d'entre eux travaillaient à l'IRSAT et au CNSF ; en 2001, l'INERA n'employait qu'un seul expatrié.Malgré une courbe irrégulière, le total des dépenses consacrées à la recherche agricole affiche une tendance générale à la hausse de 6,2 % en moyenne par an au cours de la période 1971-2001 (Figure 1b). De 1971 à 1989, les dépenses ont augmenté de façon continue de 7,0 % par an. Les années 90 se caractérisent par une brusque montée des dépenses due à l'aide financière accordée dans le cadre du PRA-I et du PNDSA-II, deux projets essentiellement financés par des prêts de la Banque Mondiale. Les chutes marquées des dépenses en 1996 et 2001 résultent respectivement de la fin du PRA-I en 1996 et de la suspension temporaire du PNDSA-II à la fin de l'année 2000. En 2001, au Burkina Faso, le total des dépenses consacrées à la recherche agricole (22 millions de dollars) ne correspondait plus approximativement qu'à la moitié de celles de 1993 (40 millions de dollars). La reprise du financement du PNDSA-II en janvier 2002 s'est accompagnée d'une remontée du total des dépenses.  L'effectif total de chercheurs a été en augmentation constante tout au long des années 90. Cette augmentation, combinée à la baisse marquée du total des dépenses consacrées à l'agriculture en 2001, a entraîné une baisse du montant moyen des dépenses par chercheur (Figure 2). En conséquence, le chiffre de 2001 de 83 000 dollars était bien inférieur à son équivalent de 1991 (qui était de 195 000 dollars) ou même au chiffre de 2000 (120 000 dollars). En dépit de cette baisse importante, le montant moyen des dépenses par chercheur au Burkina Faso en 2001 était comparable à la moyenne enregistrée pour l'Afrique de l'Ouest.  En 2001, sur un échantillon comprenant huit organismes, 8 % des chercheurs burkinabés étaient des femmes (Figure 4), pourcentage inférieur à celui de 12 % enregistré en 1991 et plutôt faible comparé aux chiffres relevés dans la plupart des pays ouest-africains (Beintema 2003 ;Mazzucato 1994)  En 2001, sur un échantillon comptant 11 organismes, l'effectif moyen du personnel de soutien employé par chercheur était de 2,7, comprenant 1,1 technicien, 1,0 employé administratif et 0,5 employé apportant une autre forme d'assistance (tel que manoeuvre, gardien, chauffeur, etc.) (Figure 5). Ce ratio personnel de soutien/chercheur est peu élevé comparé à celui de nombreux autres pays ouest-africains. L'INERA présentait un ratio de 3,2, chiffre légèrement supérieur aux ratios correspondants des autres organismes d'État et des établissements d'enseignement supérieur. À la fin des années 90, le PNDSA-II a fourni une aide significative pour le recrutement de techniciens et d'employés administratifs. Toutefois les aptitudes de certains membres du personnel d'assistance laissent parfois à désirer. La plupart des organismes du CNRST manquent de personnel de soutien possédant les compétences nécessaires pour réparer le matériel de laboratoire moderne acquis dans le cadre du PRA-I et du PNDSA-II (Khelfaoui 2001). Le total des dépenses publiques en tant que pourcentage du produit intérieur brut agricole (PIBA) est un indicateur courant des investissements réalisés dans la recherche qui permet de placer les dépenses consacrées à la R&D agricole d'un pays dans un contexte comparable au niveau international. En 2001, le Burkina Faso a investi 0,50 dollar pour chaque 100 dollars du produit intérieur agricole, chiffre nettement inférieur à celui de 1981 (0,70 dollar) et de 1995 (0,95 dollar) (Figure 6). Le ratio d'intensité de 1995 du Burkina Faso était supérieur aux ratios correspondants de l'Afrique (0,85) et de l'ensemble des pays en développement (0,62).Sources: Burkina Faso de la Figure 1b; PIBA par la Banque Mondiale (2003); les autres rapports d'intensité sont de Pardey et Beintema (2001).L'abondance des fonds issus du PRA-I et du PNDSA-II a permis à l'INERA de réaliser d'importants investissements dans l'infrastructure, les équipements et la formation du personnel. Cette situation a entraîné des frais de fonctionnement et des immobilisations relativement élevés au cours des années 90, les dépenses salariales totales correspondant en moyenne à 27 % du total des frais, tandis que les frais de fonctionnement et les     (Khelfaoui 2001).Comme nous l'avons mentionné plus haut, la Banque Mondiale a joué un rôle important dans le financement de la recherche agricole au Burkina Faso au cours des 15 dernières années. Le PRA-I a été mis en oeuvre de 1989 à 1996, essentiellement pour renforcer la capacité de la recherche agricole du pays tout en répondant aux besoins des agriculteurs. Le renforcement des liens entre la recherche agricole et la vulgarisation ainsi qu'une revalorisation des infrastructures matérielles de l'INERA et de l'IRBET constituaient les priorités du PRA-I. Le budget total du projet (18,8 millions de dollars américains) se composait d'un prêt de la Banque Mondiale (17,9 millions de dollars américains) et d'une contribution modeste de l'État burkinabé (0,9 million de dollars américains). En dépit d'un démarrage assez lent du projet, le coût total du PRA-I a dépassé le budget prévu. À la fin du projet (31 décembre 1996), 20 millions de dollars américains avaient été dépensés, des fonds complémentaires ayant été fournis par l'USAID, par les gouvernements des Pays-Bas et du Canada ainsi que par l'Union Européenne (Banque Mondiale 1988et 1997a). Les objectifs principaux du PRA-I ont été de façon générale largement atteints. Des progrès significatifs ont été réalisés dans le renforcement de la capacité des chercheurs agricoles locaux, dans la planification et la mise en oeuvre de programme de recherche et dans la réalisation d'un meilleur équilibre entre les programmes. Toutefois, l'amélioration des liens entre la recherche agricole et la vulgarisation avait peu avancé (Banque Mondiale 1997a).De 1991 à 2001, l'INERA a été fortement tributaire des financements des donateurs étrangers. En moyenne 34 % du total des fonds de l'institut étaient fournis par la Banque Mondiale, 36 % par d'autres bailleurs de fonds, 25 % par le gouvernement burkinabé et le reste par des organisations de producteurs, des entreprises privées et l'institut lui-même puisant dans ses propres ressources (Figure 8). Les principaux donateurs bilatéraux de l'INERA au cours de cette décennie 1991-2001 étaient les Pays-Bas et la France. 7 Au nombre des autres bailleurs de fonds, on comptait l'Union Européenne, l'USAID, le Centre de Recherche pour le Développement International (IDRC) du Canada, l'Institut du Sahel, et la Fondation Internationale des Sciences de Suède. Les ressources propres de l'INERA et les fonds provenant des entreprises privées représentaient en moyenne 2 % du financement total de INERA durant la décennie 1991-2001. Toutefois la part des ressources générées de façon interne et des fonds accordés par des entreprises privées s'est accrue depuis le début des années 90, passant de 1 % de l'ensemble du financement de l'institut en 1991 à 6 % en 2001. L'entreprise SOFITEX, avec laquelle l'INERA a signé un accord de recherche, a contribué financièrement de façon continue au programme de l'INERA portant sur le coton. De 1993 à 1997, elle a fourni chaque année  1991 1992 1993 1994 1995 1996 1997 1998 1999   L'avenir financier de l'INERA demeure très incertain. Le gouvernement burkinabé est actuellement en train de négocier avec la Banque Mondiale dans le but d'assurer un prêt pour la troisième phase du projet national de recherche agricole. Cette phase devrait s'appuyer sur les résultats du PRA-I et du PNDSA-II, afin de contribuer à un secteur de recherche agricole productif et compétitif répondant aux besoins des agriculteurs tout en améliorant leurs moyens d'existence. En attendant qu'une décision soit prise concernant la troisième phase, les organismes de recherche agricoles burkinabé resteront très dépendants du support financier de l'État et surtout des donateurs (étrangers).Axée sur les produits La ventilation des ressources entre les différents axes de recherches représentant une décision de politique générale importante, des informations tirées d'enquêtes détaillées ont été rassemblées sur le nombre de chercheurs ETP travaillant sur des produits ou des thématiques spécifiques.En 2001, le quart des 209 chercheurs ETP de notre échantillon (comptant huit organismes) effectuait des recherches sur les cultures, 22 % examinaient les ressources naturelles, 18 % la foresterie et 13 % l'élevage (Figure 9). Les chercheurs de l'INERA consacraient relativement plus de temps aux recherches axées sur les cultures que leurs homologues des autres organismes d'État et des établissements d'enseignement supérieur de notre échantillon. À l'INERA, les recherches portant sur les cultures étaient largement axées sur le riz et le sorgho, représentant chacun 26 % des recherches effectuées par les 54 chercheurs ETP de l'institut se consacrant aux cultures (Figure 10a). Les autres cultures importantes en tant que sujet de recherche de l'INERA étaient le maïs (19 %), le millet et les légumes (10 % chacun). Les principaux centres d'intérêt dans la recherche sur l'élevage à l'INERA étaient les bovins (représentant 31 % des recherches de l'institut pour les 22 chercheurs spécialisés dans l'élevage), les ovins et les caprins (23 %), et la volaille et les produits laitiers (17 % chacun) (Figure 10b).   1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 en millions de dollars internationaux 1993Ass. de producteurs Propre revenu / Entr. Privées en milliards de francs CFA 1999  -La plupart des données de ce document sont extraites d'enquêtes non publiées (IFPRI, ISNAR, et CORAF/WECARD 2002-03).-Les données ont été rassemblées sur la base de méthodes statistiques communément acceptées au plan international et de définitions mises au point par l'OCDE et l'UNESCO pour les statistiques relatives à R&D (OCDE 1994 ;UNESCO 1984). Nous avons regroupé les estimations dans trois catégories institutionnellesorganismes d'État, établissements d'enseignement supérieur et entreprises commerciales ou industrielles, ces dernières se subdivisant en entreprises privées et associations à but non lucratif. Nous avons défini la recherche agricole publique de façon à inclure les organismes d'État, les établissements d'enseignement supérieur et les associations à but non lucratif, excluant par là les entreprises privées. La recherche privée comprend les recherches effectuées par les entreprises privées à but lucratif mettant au point des technologies liées à l'agriculture concernant la production de l'exploitation agricole mais aussi les activités situées en aval et en amont de celle-ci.-Le terme « recherche agricole » englobe tant les recherches agronomiques, zootechniques, sylvicoles et halieutiques que les recherches sur les ressources naturelles en liaison avec l'agriculture, toutes les mesures reflétant des résultats effectifs et réels.-Les données financières ont été converties en dollars internationaux de l'année 1993 en exprimant les unités monétaires locales courantes en prix constants au moyen du déflateur du PIB du Burkina Faso de l'année de base de1993 et en les convertissant ensuite en dollars américains à l'aide du facteur de conversion de la parité de pouvoir d'achat (PPA) de 1993, ces deux éléments étant fournis par la Banque Mondiale (2003). Les PPA sont des taux de change synthétiques utilisés pour refléter le pouvoir d'achat de la devise d'un pays en comparant des prix en fonction d'un plus large éventail de biens et de services que dans le cadre des taux de change conventionnels.-Les salaires et frais de séjour de nombreux chercheurs expatriés qui travaillent sur des projets financés par des bailleurs de fonds étant directement payés par l'organisme donateur, ces données n'apparaissent généralement pas dans les rapports financiers des organismes de recherche et développement agricoles. Ces coûts implicites ont été estimés sur la base d'un coût moyen par chercheur évalué en 1985 à 160 000 dollars internationaux de 1993 et réajustant ce chiffre à l'aide des indicateurs de taux de variation des frais réels de personnel par chercheur ETP dans le système public américain des stations expérimentales agricoles. Cette méthode d'extrapolation part de l'hypothèse que la tendance des frais de personnel concernant les chercheurs américains est une variable remplacement plausible de la tendance des frais réels de personnel recruté au niveau international des organismes de recherche et de développement agricoles.Pour plus de détails sur la méthodologie statistique, consultez le site Internet d'ASTI (http://www.ASTI.cgiar..org).","tokenCount":"3010","images":["-873093475_1_1.png","-873093475_1_2.png","-873093475_1_3.png"],"tables":["-873093475_1_1.json","-873093475_2_1.json","-873093475_3_1.json","-873093475_4_1.json","-873093475_5_1.json","-873093475_6_1.json","-873093475_7_1.json","-873093475_8_1.json","-873093475_9_1.json","-873093475_10_1.json"]}

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+{"metadata":{"gardian_id":"e30358fb92cc045996530834efb544ce","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/02fae608-a2e3-4c51-b776-4c3b2326a254/retrieve","description":"","id":"465099806"},"keywords":[],"sieverID":"a52cc771-95f8-4d8f-9c37-d5c95bd17030","pagecount":"12","content":"ost people would say agriculture is about growing food; they are right. Agricultural performance, after all, is measured in terms of production-for example, yield or grain production. The purpose of agriculture, however, does not stop there. At a deeper level, the purpose of agriculture is not just to grow crops and livestock for food and raw materials, but to grow healthy, well-nourished people. One of farmers' most important tasks is to produce food of sufficient quantity (that is, enough calories) and quality (with the vitamins and minerals needed by the human body) to feed all of the planet's people sustainably so they can lead healthy, productive lives. This is effectively one of the goals of agriculture, although it is rarely made explicit.Could agriculture do more to meet this goal? Recently the international development community has turned its attention to the potential for the agriculture, nutrition, and health sectors to work together to enhance human well-being. In some ways, of course, agriculture, health, and nutrition are already deeply entwined. Agricultural production is an important means for most people to get the food and essential nutrients they need. And in many poor countries, where agriculture is highly labor intensive, productive agriculture requires the labor of healthy, wellnourished people. Yet, in other ways agriculture, health, and nutrition are quite separate: professionals in these three fields usually work in isolation from one another, with their efforts sometimes dovetailing in mutually beneficial ways and sometimes working at cross-purposes.In an ideal world, consumers would be fully aware of the merits of nutritious foods, and producers, processors, and marketers, in turn, would know how to produce, process, and market these high-quality, nutrient-rich foods. Market forces would provide the incentives, through product prices, to all involved in producing or consuming nutrient-rich foods. Unfortunately, our world is less than ideal, and market prices do not provide an adequate incentive for producing nutritious food. And, even if prices did reflect the nutritional value of food, they could put nutritious foods out of reach of poor people. This means public interventions are needed to correct market failures (when prices do not reflect the nutritional value of foods) or to improve affordability (for poor people).How much more could agriculture do to improve human well-being if it included specific actions and interventions to achieve health and nutrition goals? What kinds of changes would maximize agriculture's contribution to human health and nutrition, and how could human health and nutrition contribute to a productive and sustainable agricultural system?Over the past century or so, agricultural development has been based on a paradigm of increasing productivity and maximizing the production of cereals. This paradigm has produced an agricultural system that is the world's primary source of calories and employs 60-80 percent of people in low-income countries (IFC 2009). The ramping up of cereal production in the Green Revolution, for example, saved countless lives in Asia (Hazell 2009), and agricultural growth there has served as a springboard for a blistering pace of economic growth, improving the lives of millions. At the same time, agricultural intensification has led to a concentration on grain production; crowded out nutrient-dense crops like pulses, fruits, and vegetables; increased the risk of agriculture-associated diseases; led to the development of new diseases (such as the evolving forms of influenza); and exacerbated environmental degradation that can have negative consequences for human health. Moreover, millions of smallholders who produce food still suffer from poverty and hunger, and recent food price hikes have made those who are net buyers of food even more vulnerable.A look at the current global health and nutrition situation suggests agriculture can make an even greater contribution to health and nutrition. Indeed, leveraging agriculture for health and nutrition has the potential to speed progress toward meeting all eight of the Millennium Development Goals. The world's farmers already provide billions of people with diverse, healthy diets -yet more needs to be done. About one-seventh of the world's population is going hungry (FAO and WFP 2010). In developing countries, one out of four children-about 146 million in all-is underweight (UNICEF 2006). Millions of people suffer from serious vitamin and mineral deficiencies. For example, vitamin A deficiency compromises the immune systems of about 40 percent of children younger than age five in developing countries and results in the early deaths of about 1 million young children each year. Iron deficiency impairs the mental development of 40-60 percent of the developing world's children aged 6 to 24 months and leads to the deaths of about 50,000 women a year during pregnancy and childbirth (Micronutrient Initiative and UNICEF 2004). The economic cost of micronutrient deficiencies is estimated to be 2.4-10.0 percent of gross domestic product (GDP) in many developing countries (Stein and Qaim 2007). Thus the Copenhagen Consensus has ranked vitamin A and zinc supplements for children and iron and iodine fortification of food as numbers one and three, respectively, in its solutions to the most important human challenges (Copenhagen Consensus Center 2008). Hunger and malnutrition have effects that last throughout the life cycle, with poorly nourished children growing up to be less healthy and productive than they could be. Girls who do not get the nutrition they need are at great risk of becoming undernourished women who, in turn, are at increased risk of giving birth to the next generation of undernourished children (ACC/SCN 2000).While some people are getting too little food, others are getting too much of the wrong food. Diets centered on cheap, calorie-dense, nutrient-poor foods (including both \"fast foods\" and nutrient-poor staples) are deepening the emerging epidemic of obesity and chronic diseases in countries undergoing economic and nutrition transitions. Overweight affects more than 1 billion people globally, and obesity affects at least 300 million. Since 1980, obesity rates have risen threefold or more in some areas of North America, the United Kingdom, Eastern Europe, the Middle East, the Pacific Islands, Australasia, and China (WHO 2010;Nugent 2011).The chapters in this volume look at the links among agriculture, nutrition, and health and their potential to convey more benefits to poor and hungry people. The authors come at the issues from many perspectives, examining not only the overall links among the three sectors, but also the specific roles played by economic and agricultural growth, innovations in crop science and food supply chains, the health of agricultural laborers, agriculture-associated diseases, women's place at the intersection of the three sectors, and the challenges of advocacy and policymaking.In Chapter 2 of this volume, John Hoddinott describes a conceptual framework that clarifies the links among agriculture, nutrition, and health. This framework includes the physical, social, legal, governance, and economic settings in which people live and work; the resources -time and capital-at their disposal; and the processes associated with agricultural production and determinants of health and nutritional status. These elements of the framework suggest pathways through which agricultural production and markets can affect health and nutrition, including changes in incomes, crop varieties, production methods, and allocation of resources within households. A clear framework that shows the relationships among agriculture, nutrition, and health can help decisionmakers exploit the links in policies and programs.It is also possible to look beyond agriculture to the whole food system and its interaction with nutrition and health (see Chapter 3 by Per Pinstrup-Andersen). The food system includes not only agriculture but also natural resources and inputs; oveRview transport, storage, and exchange; secondary production; and consumption. Each of these food system activities can interact with health and nutrition, in both obvious and less obvious ways. Integrated actions related to, for example, zoonotic diseases, HIV/AIDS, crop protection, sustainable management of natural resources, and food safety can not only promote agricultural productivity, but also improve nutrition and health and help overcome poverty traps.It is natural to assume that economic growth has a positive impact on people's nutritional status through increased incomes and food expenditures, but the limited evidence available shows that, in a number of developing countries, economic growth has failed to result in better nutrition.Various studies show that in many agrarian countries agricultural growth is more effective than growth in other sectors in reducing undernutrition (see Chapter 4 by Shenggen Fan and Joanna Brzeska and Chapter 5 by Derek Headey). The composition of growth, the distribution of growth, and the conditions under which growth takes place all matter. Growth in agricultural subsectors where poor people are engaged, such as staple crops, contributes more to reducing poverty and increasing calorie intake than growth in, for instance, export crops. Later in the development process, growth in other sectors besides agriculture becomes more important in improving food and nutrition security. Neither agricultural nor nonagricultural growth alone, however, is sufficient to reduce child undernutrition or micronutrient malnutrition (see Chapter 6 by Olivier Ecker, Clemens Breisinger, and Karl Pauw and Chapter 7 by Karl Pauw and James Thurlow). Complementary programs in nutrition, health, water and sanitation, and behavior change communication also need to be implemented and targeted to vulnerable populations, especially women and young children. More broadly, improvements in healthcare access and female education and reductions in fertility rates and poverty will help make nutrition more responsive to growth.Despite great strides in food production, agricultural growth has not had its expected benefits for nutrition in India, which is home to one-third of the world's undernourished children (see Chapter 20 by Stuart Gillespie and Suneetha Kadiyala). One part of the solution to this \"Indian enigma\" likely involves focusing on crops and livestock that have large nutritional impacts on both farmers and consumers. Another part may involve addressing socioeconomic factors that affect the link between agriculture and nutrition, including the distribution of assets, particularly land; the role and social status of women; rural infrastructure; and rural health and sanitation. Yet another part involves addressing other drivers of undernutrition by, for example, improving education and social welfare systems.Although the agriculture, health, and nutrition sectors all seek to improve human well-being, agriculture has rarely been explicitly deployed in this way. However, opportunities exist all along the agricultural value chain to improve nutrition and reduce health risks. In Chapter 9, Corinna Hawkes and Marie T. Ruel examine how a value-chain approach to development can incorporate nutrition goals and thereby make nutritious foods more available and affordable for the poor. This approach starts by looking at every component of the food supply chain from the field to the table -including production, postharvest processing, marketing, and trade -and determining where value for nutrition can be integrated. Incentives are created in ways that do not interfere with the creation of economic value for supply-chain actors. New initiatives are emerging in several developing countries to explore the value-chain approach's potential to improve nutrition.Another innovation for leveraging agriculture to improve nutrition is biofortification-the breeding of new varieties of food crops with improved nutritional content. When people in malnourished communities receive these varieties to grow and eat, biofortified crops can contribute to the overall reduction of micronutrient deficiencies in a population. Compared with other approaches to micronutrient malnutrition, such as supplementation and fortification, biofortification offers several advantages: it targets poor people and rural areas; it is cost-effective because after the initial investment in research, the crops are available year after year; and it is sustainable because it relies on staple crops that people are already accustomed to eating. A pilot program in Mozambique and Uganda that has disseminated varieties of orange sweet potato with high levels of vitamin A has already shown increased vitamin A intakes in vulnerable groups (see Chapter 10 by Howarth Bouis and Yassir Islam). Successful results depend on high levels of bioavailability or bioconversion of the nutrients and high rates of farmer and consumer adoption.Part of the pressure on the global food system in recent years has come from rising incomes and rapid urbanization in developing countries, which have increased global food demand. IFPRI's International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) shows that rich countries' dietary shift toward healthier and less-meat-intensive diets could increase calorie availability and reduce child malnutrition in poor countries. This finding suggests governments in rich countries should consider encouraging consumers to move away from meat-intensive diets through, for example, nutrition education and government-sponsored feeding programs (see Chapter 8 by Siwa Msangi and Mark W. Rosegrant).While agriculture can improve health through improved incomes or improved nutrition, it may also increase risks for certain diseases. Additionally, the food value chain involves many hazards: microbiological hazards, such as food-borne oveRview pathogens; physical and chemical hazards, such as plant toxins and pesticides; and occupational hazards, such as accidents. Poor people face challenges in producing and consuming safe food. Policymakers are increasingly using risk analysis to help them decide on regulatory and other actions to reduce health risks along the food value chain (see Chapter 11 by Pippa Chenevix Trench, Clare Narrod, Devesh Roy, and Marites Tiongco).Another way of classifying agriculture-associated diseases is based on transmission pathways; high-burden categories include zoonoses, food-associated diseases, water-associated diseases, and occupational diseases. Such diseases sicken and kill billions of people a year and impose enormous economic costs, especially on poor countries. It is important to assess the full costs of these diseases, not only to human health but also to agricultural productivity, the food economy, and the ecosystem. Because the causes and effects of these diseases are complex, they call for interventions that integrate several sectors, including agriculture and livestock production, human medicine, veterinary medicine, and environmental science (see Chapter 12 by John McDermott and Delia Grace). Malaria, for example, is often linked to irrigation development and changes in land use associated with agriculture. It imposes heavy healthcare costs on small-farm households and impedes agricultural development by leading to declines in labor. The problem of malaria makes a clear case for coordination of health and agricultural policies (see Chapter 15 by Kwadwo Asenso-Okyere, Felix A. Asante, Jifar Tarekegn, and Kwaw S. Andam).It is clear that disease cuts the productivity of farm labor in both the short and long terms and that farm labor itself can harm people's health and nutrition status. This means that health and agriculture interventions should be designed with these two-way linkages in mind. But does it follow that health investments necessarily improve agricultural productivity? Research on this question is sparse. The available evidence suggests that some inexpensive health interventions (such as micronutrient supplements) can have large effects, that health interventions are most effective when combined with education and infrastructure investments, and that improving children's health can lead to increased adult productivity in the long term (see Chapter 13 by Paul E. McNamara, John M. Ulimwengu, and Kenneth L. Leonard and Chapter 14 by Kwadwo Asenso-Okyere, Catherine Chiang, Paul Thangata, and Kwaw S. Andam).Women are an important group linking agricultural development and human health and nutrition. They are not only responsible for food preparation and caring for young children and ill household members, but in many countries women are also the main agricultural producers. Strengthening women's position both within the agricultural sector and within the household can significantly improve households' nutrition and health. Experiences from several agricultural development strategies show much scope exists for increasing women's access to and control over resources, such as household income (see Chapter 16 by Ruth Meinzen-Dick, Julia Behrman, Purnima Menon, and Agnes Quisumbing).Making policies that leverage agriculture for nutrition and health poses particular challenges. Malnutrition and poor health are the result of many factors and require action in a whole range of sectors. Although the health and agriculture sectors have well-established institutions within government, they are not organized in ways that readily allow for cross-sectoral action. And the nutrition sector often lacks a high-profile place in government. It suffers from a lack of awareness about the consequences of and solutions to malnutrition, weak commitment from political leaders, and limited resources for public investment. Nonetheless, there are ways to promote action on nutrition and across sectors, including advocacy by civil society and community groups and the cultivation of policy champions (well-connected and well-informed people with access to the policy process). Agriculture-associated health problems require joint agriculture and health solutions. Achieving these joint solutions may involve creating incentives for intersectoral collaboration, implementing multisectoral policy reviews, carrying out health-impact studies of agricultural development projects, and promoting joint agriculture, nutrition, and health policy formulation and planning (see Chapter 17 by Todd Benson, Chapter 18 by Robert Mwadime, Chapter 19 by Brenda Shenute Namugumya, and Chapter 21 by Joachim von Braun, Marie T. Ruel, and Stuart Gillespie).The best approach to finding positive synergies among agriculture, nutrition, and health may depend on a country's position in the dietary transition, where stage one is a diet low in calories and micronutrients, stage two is a diet adequate in calories for most people but with inadequate micronutrients, and stage three is a diet that provides excessive calories, still with possible micronutrient deficiencies. In stage one countries, government's primary task is to provide public goods that contribute to improvements in agriculture, nutrition, and health, such as infrastructure, education, and health services. During stage two, the task is to deliver targeted agricultural, nutritional, and health services to people who do not experience the benefits of growth. At stage three, governments must regulate the growing private sector, including commercial farms, food manufacturers, retailers, and restaurants (see Chapter 22 by Robert Paarlberg).Breaking down the siloes between the sectors will require a change in thinking. Education in all three sectors can do more to highlight the synergies among them and develop a shared body of knowledge that will follow students into their professional lives. Professionals in the three sectors should retain their deep expertise in their subject areas, while also gaining a greater familiarity with the other sectors' oveRview main concerns and opportunities. By developing cross-disciplinary programs, educational institutions can produce graduates and professionals who -in their capacity as extension workers, healthcare providers, or nutrition counselors -can effectively translate the linkages among agriculture, health, and nutrition in the field for the benefit of all. In addition, evaluations of projects and programs in all three sectors should take the other sectors into account, to help implementers gain feedback and to create incentives for collaboration.The links between the three sectors -and consequently, potential solutions -will undoubtedly look different in different countries and regions, given the variations in agricultural systems and practices, food systems, and health and nutrition status. Initial efforts in some countries can point the way to potentially effective approaches and show what works and what does not. It is important to examine how successes can be adapted and scaled up in different regions because the lessons learned from experience to date will suggest areas for investment and policy change.In Africa, poor nutrition and health remain persistent problems. Although a new focus on agriculture in the region presents an opportunity for countries to exploit the links among agriculture, nutrition, and health as they revise their agricultural policies and direct more funding to the sector, many policymakers at the national, district, and local levels still do not see nutrition as a development issue that should play a role in agricultural planning -despite the existence of several programs linking the sectors in that region. Raising nutrition's profile in African policymaking circles will thus require strong advocacy from civil society to senior policymakers.In South Asia, malnutrition is disturbingly high. Important questions remain about why strong economic growth in the region, especially in India, has not done more to push down rates of malnutrition there. It is clear, however, that investments are needed to improve safety net systems and targeted nutrition programs; increase the production and consumption of nutritious foods; enhance gender equity; and strengthen agricultural technologies, rural infrastructure, information technology, and irrigation, water, sanitation, agricultural extension, and credit systems. In addition, programs often rely on nongovernmental organizations (NGOs) for funding and support; when NGO funding stops, so do the programs. Consequently, it is important to ensure program sustainability to improve people's nutrition and health.Although East Asia does not suffer from as much undernutrition as some other regions, problems of malnutrition remain. For a number of countries in East Asia, agriculture means rice production. Impressive gains in the productivity of rice farmers in recent decades have helped raise incomes and reduce hunger. Nonetheless, many farmers still have problems getting access to high-quality seeds, fertilizers, water, rural infrastructure, and machinery for processing. It is also important to promote more diverse diets and educate farmers in the region about the potential for growing more nutritious crops, such as fruits and vegetables. A holistic, community-based approach to linking agriculture, nutrition, and health has worked well in some countries, including Thailand. Experience there shows the importance of teaching people about nutrition at the community level, teaching agricultural skills, and making sure farmers have the land, credit, and postharvest technologies they need.Walking the line between undernutrition and overnutrition has proven difficult in many parts of the world. In Latin America, hunger overlaps with overweight and obesity, sometimes even in the same family, so efforts are needed to deal with both undernutrition and health problems related to overnutrition. Argentina, for example, has recognized that overweight is concentrated among its poor citizens. Joint public and private action is needed to help reduce sugar, salt, and saturated fat in manufactured food products. Brazil has one of the world's largest school feeding programs, which brings together agriculture, nutrition, and health, but poverty-and hunger-related social programs have not yet reached all poor and marginalized groups, so more remains to be done.Finally, in the high-income countries, overweight and obesity are reaching epidemic levels. In many of these countries, government policies are designed to maximize the export value of crops and enable low food prices at home, with deleterious effects on the health and nutrition of citizens. Unfortunately, evidence of cost-effective countrywide approaches to decreasing overweight and obesity is extremely scant. As with micronutrient interventions, overweight and obesity prevention will likely need a much more multisectoral approach. Educational programs on nutrition and health in schools and communities can build awareness, but they must also take into account the psychology of consumers and the difficulty of changing their behaviors.The world food system, where the agriculture, health, and nutrition sectors come together, faces serious challenges in the coming years. High and volatile food prices are likely to be a reality for the foreseeable future. They pose difficulties not only for food consumers, who often shift their diets to cheaper, less-nutritious foods, but also for food producers, who may reduce their investments in agriculture in the face of increased input prices and uncertain output prices. Rising populations and changing diets are putting pressure on farmers to produce more food with the same resources. And climate change creates risks for agriculture and health-and oveRview by extension, nutrition-that are only beginning to be understood. This is the context in which decisionmakers at all levels and in many sectors will need to act (see Chapter 23 by Shenggen Fan, Rajul Pandya-Lorch, and Heidi Fritschel).At the same time, attention to the agricultural sector is growing, along with an interest in leveraging agriculture for nutrition and health. Now is an ideal time to look for solutions that will not only help make the agricultural system highly productive and sustainable, but also maximize its contributions to human well-being.","tokenCount":"3854","images":[],"tables":["465099806_1_1.json","465099806_2_1.json","465099806_3_1.json","465099806_4_1.json","465099806_5_1.json","465099806_6_1.json","465099806_7_1.json","465099806_8_1.json","465099806_9_1.json","465099806_10_1.json","465099806_11_1.json","465099806_12_1.json"]}

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+{"metadata":{"gardian_id":"6f59d807e0479f1209473c15ab3b18f3","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/97ad048d-d4e0-44c8-8fa1-bd7d51f73f47/retrieve","description":"Since 2013, the Feed the Future Innovation Lab for Food Security Policy (FSP) has combined multidisciplinary research on emergent issues facing food systems with policy analysis to provide an enabling environment for improved food security. Supported by the U.S. Agency for International Development (USAID), FSP is implemented through a consortium of three research institutions: Michigan State University (MSU), the International Food Policy Research Institute (IFPRI), and the University of Pretoria. These policy research institutions, together with local institutions, have provided data and analysis that national and subnational governments and regional bodies can use to generate informed policies around food and food security. This engagement has involved supporting national governments’ and regional organizations’ agri-food system–related policy processes with evidence generated through applied research on food systems. This evidence enabled participants in those processes to consider the merits of various policy options with a stronger conceptual and applied understanding of the issues at stake. As outlined in Box 1, FSP efforts targeted five main activity areas, with a regional emphasis on Africa and Asia.","id":"-717667392"},"keywords":[],"sieverID":"6fa33cd8-b7c6-49e9-92bd-6d2eb991e994","pagecount":"4","content":"Since 2013, the Feed the Future Innovation Lab for Food Security Policy (FSP) has combined multidisciplinary research on emergent issues facing food systems with policy analysis to provide an enabling environment for improved food security. Supported by the U.S. Agency for International Development (USAID), FSP is implemented through a consortium of three research institutions: Michigan State University (MSU), the International Food Policy Research Institute (IFPRI), and the University of Pretoria. These policy research institutions, together with local institutions, have provided data and analysis that national and subnational governments and regional bodies can use to generate informed policies around food and food security. This engagement has involved supporting national governments' and regional organizations' agri-food systemrelated policy processes with evidence generated through applied research on food systems. This evidence enabled participants in those processes to consider the merits of various policy options with a stronger conceptual and applied understanding of the issues at stake. As outlined in Box 1, FSP efforts targeted five main activity areas, with a regional emphasis on Africa and Asia. This brief reviews FSP's achievements from 2013 to 2018 and discusses some of the key lessons learned. The FSP project has contributed to building more informed, effective, and sustainable policy systems for food security by focusing on frontier research related to agri-food system and nutrition transformation, long-term and responsive policy engagement with a diverse range of stakeholders, and strengthening the capacity of local researchers, policy analysts, and civil society through training events, university networks, and partnerships with local research institutes. In each of these three areas, key lessons have been identified that are discussed below.The FSP Innovation Lab is a Leader with Associates award funded under a cooperative agreement through USAID's Feed the Future Initiative. Its activities are supported with core funding from USAID's Bureau for Food Security through the Leader award, and from USAID country missions and regional offices through buy-ins and Associate Awards. To strengthen agri-food system-related policy processes and to expand knowledge and capacity for effective policy design and implementation, the initial design of FSP specified five activity areas:• Component 1: Country/regional-level collaborative research on farms, firms, and markets and formulation and analysis of policy options. • Component 2: Country/regional-level capacity building for policy formulation and implementation (data, analysis, advocacy, consultation, coordination, implementation). • Component 3: Global collaborative research on how best to strengthen policy processes and build policy capacity. • Component 4: Engagement in global policy debates on food and nutrition security based on field-level research and analysis that is done in a manner that deepens and strengthens the basis on which food policy debates take place. • Component 5: Engagement on a strategic analytical agenda and support to donor policy and strategy.In addition, FSP's research and applied policy efforts singled out four cross-cutting themes-gender, youth employment, nutrition, and climate change-for specific attention, as appropriate. Country-and regionallevel activities (i.e., Components 1 and 2) have received core funding as a precursor to, or together with, USAID Mission buy-ins or Associate Awards.This Brief is based on the Feed the Future Innovational Lab for Food Security Policy Synthesis Report 1 published under the same title and by the same authors.Over the past five years, FSP researchers have documented the transformation of agri-food systems, with a major focus on Africa and, in Asia, Myanmar. Four dimensions of agrifood system transformation received the most attention: diet changes, post-farm processing and distribution, farming, and factor markets. Specifically, rapid urbanization, coupled with steady increases in per capita income, are driving rapid change in consumer diets with mixed nutritional implications. While these changes include increased consumption of fresh and animal source products often beneficial for relatively poor consumers' nutritional status, data from eastern and southern Africa also indicate that dramatic changes have already occurred in the share of purchased food that is highly processed, even among poorer households. This creates a major policy trade-off since, on the one hand, such foods translate into growing rates of overweight, obesity, diabetes, hypertension, and cardiovascular disease. On the other hand, as urban markets grow, there are supply-side pressures for improved food packaging, preservation, and quality. Although many African cities have developed modern retailing and supermarket systems, increasing evidence points to a \"quiet revolution\" in wholesaling, processing, and logistical operations driven by emerging small and medium-sized agribusiness firms entering these supply chains (Muyanga et al. 2019).These demographic changes, and their influence over consumer demand, have accelerated adoption of more input-intensive practices, including productivity-enhancing purchased inputs, such as fertilizer and improved seed, as well as labor-saving technology, such as herbicides, mechanized land preparation, and mobile mechanical threshers. For instance, by 2016, roughly one-third of cropproducing households in Ghana used mechanical tillage for land preparation, much of it through hiring-in tillage services from tractor-owning farmers (Diao et al. 2017). In Mali, cereal farmers in the southern part of the country applied herbicides on more than half of their maize and sorghum plots by 2015. In doing so, they control weeds at half the cost of hand-weeding (Haggblade et al. 2017). With the declining share of labor used on producers' own farms, projections from eastern and southern Africa indicate that the share of post-farm employment increases steadily during agri-food system transformation (Tschirley et al. 2015).In addition to labor, land markets are also affected by these trends. The growing commercialization of land in Africa has resulted in a class of medium-scale farms, between 5 and 100 hectares, whereby land formerly allocated to local people by traditional authorities increasingly is being sold if there are buyers willing to pay the right price for it. FSP research on land tenure security stresses the growing importance of legal recognition of property rights, especially for women and youth, which can encourage long-term investments in land that contribute to agricultural productivity growth (Jayne et al. 2019).These dimensions of agri-food system transformation are increasing pressures on governments both to ensure that the opportunities associated with these changes will benefit their citizens and to mitigate disruptions that may be associated with these changes. Given the need for effective policy responses, FSP researchers have examined the policy processes through which governments determine their vision for agri-food system development, regulate transformation processes, and accordingly allocate scarce financial, human, and institutional resources.In doing so, the FSP consortium has advanced theory and practice on pathways to policy change in at least two key ways. First, FSP researchers have demonstrated that policy impact requires a deep understanding of the underlying policy processes at the regional, country, and subnational levels. Just as global dynamics have influenced the nature of agrifood system transformation in recent years, changes in developing countries with respect to communication technology, education levels of researchers and government officials, and the array of civil society actors necessitates novel thinking about how to approach capacity building. FSP provided a natural lab for experiments in building capacity for policy research, with an emphasis on three main approaches.A first approach involved networking with universities to build the human capital that will enable future policy research. This was a major objective of FSP's Nigeria project whereby research teams often consisted of an FSP consortium researcher, a Nigerian faculty member, and a Nigerian graduate student. This allows FSP researchers to ensure that their analysis is locally informed and guided by policy relevance, while the Nigerian counterparts gain exposure to new research skills and the opportunity to publish in international outlets. FSP also has established partnerships with specific universities in Tanzania and Myanmar.A Dialogues and symposia: In Tanzania, the Annual Agricultural Policy Conference enables government, the private sector, researchers, and civil society to learn about FSP-supported research, to highlight progress toward reforms, and to identify new areas of policy priority. In Malawi, FSP country project (NAPAS) initiated the Malawi Land Symposium series, which provided a platform for land and agriculture sector stakeholders to discuss issues related to land and agricultural commercialization. This platform enhanced the involvement of the Ministry of Lands and land sector stakeholders, which influenced the National Agricultural Investment Plan's (NAIP) focus on land tenure security.Embedding policy advisor in a ministry: In Malawi and Tanzania, policy advisors have been embedded within the Ministry of Agriculture, while in Malawi, a policy advisor was also located within the Ministry of Planning. In Myanmar, the country project director has an appointment as policy advisor in the Department of Planning. This proximity to policymakers increases the ability of research to respond to short-term demands of high-priority policy issues while also increasing the likelihood that policymakers will access and digest rigorous research, potentially influencing their thinking.South-South learning: A variety of forums have been held between policymakers from Africa and Asia in order to exchange experiences and promote cross-country learning on agricultural mechanization. Tours to Bangladesh by Ghanaian and Nigerian policymakers, as well as an agricultural mechanization forum hosted in Ethiopia in 2017, exposed African decision-makers to a broader \"menu\" of market-based policy options to consider, from successfully mechanizing Asian countries.Parliamentary briefings: In Kenya, Myanmar, Nigeria, and Tanzania, making presentations to parliamentarians gives research findings an audience beyond the executive branch. Moreover, since parliamentarians in such countries typically lack access to research facilities, such briefings improve their capacity to exercise oversight.Journalist briefings: The training of journalists, such as in Malawi, is critical to improve media awareness of food security issues. By providing subject-specific context for journalists, such training aimed to improve the accuracy and scientific content of their articles and identify leverage points to push policy reform. In Kenya, pre-conference meetings with journalists and their editors has allowed for in-depth coverage of research findings and increased the likelihood that findings are accurately framed. In Nigeria, there has been ongoing training with Senate Media on policy communications.Action-oriented research: Involving government officials in the research process can increase the likelihood that findings are locally owned. In Ghana, government officials were integrated into the research team looking at mechanization, giving the Ministry of Food and Agriculture a first-hand assessment of the problems with its AMSEC program. In Nigeria, agricultural policymakers from 33 state governments and the Federal Capital Territory of Abuja were exposed to a data and analysis training event hosted by FSP aimed at identifying priority crops. The resulting policy notes have been used by some state governments in negotiations with private sector actors.projections for particular commodities. A number of other trainings were facilitated by the creation of novel tools, such as an integrated framework for gender analysis and a database to track policy (in) coherence across legislation and strategies related to food security at both the national and regional levels (Babu et al. 2019).A lesson derived from the experience of FSP is that training courses are most effective when they are complemented by opportunities to apply material on the job and when they include staff across levels of seniority.Collaboration with university staff offers many benefits, but care must be taken to ensure that such collaboration does not become a distraction from faculty members' teaching and mentoring responsibilities. Research institutes are more likely than university professors to respond to short-term policy needs, but their ability to exert policy influence depends on the quality of such institutes' leadership, their ability to retain competent staff with competitive pay structures, and a sustainable fundraising strategy (Babu et al. 2019).With its three-pronged focus on research, policy, and capacity, FSP has made important advances over the past five years in both Africa and Asia. While recognizing that any engagement in the food security arena requires a degree of humility, this brief summarized some of FSP's achievements and lessons about what works, where, and when to advance global food security. The project underscores that successful policy influence will continue to require a blend of tailored human and organizational capacity building, with focused research on the knowledge frontier, to enable developing countries to resolve complex policy challenges on their journey to self-reliance.","tokenCount":"1962","images":["-717667392_1_1.png","-717667392_1_2.png","-717667392_1_3.png","-717667392_1_4.png","-717667392_1_5.png"],"tables":["-717667392_1_1.json","-717667392_2_1.json","-717667392_3_1.json","-717667392_4_1.json"]}

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+{"metadata":{"gardian_id":"61e4b5aff4f4da94e598ae94e030a753","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/403c102f-36e4-4b50-b5f8-2b73ae478dcf/retrieve","description":"A common criticism of antipoverty programs is that the high share of administrative (nontransfer) costs substantially reduces their effectiveness in alleviating poverty. Yet there is surprisingly little hard empirical evidence on such programs' costs. A recent international review of targeted poverty alleviation programs in less developed countries found cost information -- which was rarely comparable between studies -- for fewer than one-third of the programs examined. Improved information and a better understanding of the costs of such programs are crucial for effective policymaking. This study proposes and implements a methodology for a comparative analysis of the level and structure of costs of three similar poverty alleviation programs in Latin America, in order to assess their cost-efficiency. The findings underscore that any credible assessment of cost-efficiency requires a detailed analysis of program cost structures that goes well beyond simply providing aggregate cost information. -- Authors' Abstract","id":"1122257324"},"keywords":["cost-efficiency","poverty alleviation","human capital"],"sieverID":"858d1d05-6a4f-4afe-aa5c-f0c410550f9d","pagecount":"52","content":"will eventually be published in some other form, and that their content may also be revised.It is widely accepted that social safety nets have a crucial role to play both in alleviating poverty and promoting social and economic development (World Bank 1997).A common criticism of such programs, however, is that a large proportion of their budget is absorbed by administrative costs and never reaches the intended beneficiaries.1 How these resources are used affects the poverty alleviation benefits of the program and, consequently, its overall cost-effectiveness.There is very little rigorous empirical evidence on the costs and cost structures of social safety net programs in developing countries, however, that makes proper assessment of the criticism that such programs are \"expensive\" difficult. 2 For example, in their review of targeted poverty alleviation programs in developing countries, Coady,   Grosh, and Hoddinott (2002) find cost information of any sort for only 32 of the 111 programs examined, and most of these were from a single source on Latin America (Grosh 1994). Moreover, the available cost information is rarely comparable between studies, even for similar programs. Some studies refer to administrative costs, while others consider costs only in terms of theft or other losses and leakages. When the focus is on administrative costs, it is often unclear whether the figures refer to the entire life of the program or only a specific period, such as the most recent year. For programs that have high initial fixed costs, undergo extensive learning-by-doing, or are at different stages of maturity, analyses based on different time periods can lead to very different conclusions. Improved information and a better understanding of the costs of such programs are crucial for effective policymaking.This study proposes and implements a methodology for a detailed, comparative analysis of the level and structure (the various activities being carried out) of costs for three similar poverty alleviation programs in Latin America. They are the Programa Nacional de Educación, Salud y Alimentación (PROGRESA) in Mexico, the Programa de Asignación Familiar-Fase II (PRAF) in Honduras, and the pilot Red de Protección Social (RPS) in Nicaragua. The primary objective of these programs is to generate a sustained decrease in poverty in some of the most disadvantaged regions in their respective countries. The programs' underlying premise is that a major cause of the intergenerational transmission of poverty is the inability of poor households to invest in the human capital of their children. Supply-side interventions on their own, which increase the availability and quality of education and health services, are often ineffective in resolving this problem, since the resource constraints facing poor households preclude them from incurring the private costs associated with utilizing these services (e.g., travel costs or opportunity cost of women's and children's time). These innovative programs address this problem by targeting transfers to the poorest communities and households and by conditioning the transfers on attendance at school and health clinics. This conditionality effectively transforms cash transfers into human capital subsidies for poor households.Since the total program budgets are the sum of administrative costs and total (cash and in-kind) transfers, we evaluate the cost-efficiency of each program by considering the cost of making a one unit transfer to a beneficiary; this is the \"cost-transfer ratio\" or CTR (Coady, Perez, and Vera-Llamas 2004). 3 How we use and interpret the CTR depends on how it is calculated and on program characteristics. Features of the program, such as targeting and monitoring, size, type and delivery mechanism of the transfers (e.g., cash or in kind, demand-or supply-side), coverage, duration, and whether the program is expanding influence the CTR. So do whether the fixed costs of setting up the program (or just recurring costs) are included and whether the entire life of the program or a specific period is under consideration. We highlight these and other issues in the cost analysis and propose strategies for resolving them. We show how cost information can be used to assess the relative cost-efficiency of the different programs, making clear that understanding the details that go into the calculations and the design differences between programs is essential for making sensible comparisons, even for similar programs such as the three considered in this paper.While focusing on CTRs would be sufficient to evaluate a program whose sole objective was to disburse transfers, the programs considered in this paper have more ambitious goals and design features aimed at achieving them. First, transfers are targeted to poor areas and to poor households within those areas. Second, transfers are conditioned on households investing in the nutrition, health, and education of their children. The combination of targeting and conditioning makes these programs operationally and administratively complex, which can be expected to affect both the level and structure of program costs, as well as program performance. Hence, there is a potential trade-off: reducing the CTR may not be cost-effective if it comes at the expense of activities devoted to important administrative tasks, such as targeting the poor or monitoring compliance. 4 It would therefore be incorrect to interpret the CTR as a measure of overall cost-effectiveness, and we thus refer to the CTR as a measure of \"cost-efficiency.\" We use the term \"cost-effectiveness\" only when we incorporate broader program objectives into the analysis.Section 2 motivates our approach to analyzing cost-efficiency with a simple model of the welfare impact of antipoverty programs. Section 3 describes the three programs. Section 4 presents the comparison of costs between programs, showing how one can move from accounting data to detailed breakdowns of cost structures to measures of cost-efficiency. Section 5 briefly discusses the \"cost-effectiveness\" of these programs, summarizing the evidence on their targeting effectiveness and human capital impacts. Section 6 concludes.This section presents a simple model to characterize how we think about the welfare impact of these programs. The model underlies our approach to assessing costefficiency. 5 Consider a program that transfers resources to households in two forms, pure cash transfers and in-kind transfers in the form of increased expenditures on human capital services (e.g., education and health services). Maximized household (denoted h) utility is given by an indirect utility function, V h (m h , E; q), which is a function of the cash transfer received from the program, m h , the increased expenditures on human capital services, E, and commodity and factor prices, q.6 For expositional convenience, we assume that initial transfers and expenditures are zero, so that dm h = m h and dE = E. Social welfare is measured by a standard Bergson-Samuelson social welfare function:W {V 1 (m 1 , E; q),…..,V h (m h , E; q),…..V H (m H , E; q)}, defined over h = 1…H households. The welfare impact of the program is derived by differentiating W with respect to cash transfers and in-kind expenditures on services.Holding factor and commodity prices constant, this yields, which can be rewritten as, where (MV h /Mm h ) is the private marginal utility of income, β h is defined as the social value of additional lump-sum income to household h (the so-called welfare weight, which is typically larger for poorer households), m h is the lump-sum income given to the household by the program, WTP h is the household's willingness-to-pay for an extra unit of program expenditures on human capital services (i.e., (MV h /ME) divided by (MV h /Mm h )), and E is the total expenditures by the program on these services. The total welfare impact of the program is thus the sum of the social valuation of cash and in-kind transfers. This valuation depends, for example, on how many of these benefits accrue to poor households as well as on the effectiveness of in-kind transfers.Dividing each term by total cash transfers (T = 3 h m h ) and the sum of households' willingness-to-pay (WTP = 3 h WTP h ) yields, where θ h is the share of each household in the total cash transfer budget and φ h is the share of each household in the aggregate willingness-to-pay across households. For expositional convenience, we assume that the share of each household's cash transfers in the total cash transfers (θ h ) is the same as the share of each household in the aggregate willingness-to-pay (φ h ). Thus,where α can be interpreted as capturing the progressiveness of transfers. The total cost to the government of providing benefits (B) is made up of the sum of cash transfers (T), total in-kind expenditures (E), and total program operational costs (C):Multiplying and dividing by B, we can rewrite the total welfare impact of the program aswhich is the welfare impact per unit program expenditure (i.e., the benefit-cost ratio) multiplied by B, the size of the program. A full cost-benefit analysis of the program would require an evaluation of both the targeting effectiveness of the program (as captured by α) as well as the true benefits arising from in-kind expenditures (as captured by WTP). This is beyond the scope of this paper.To focus in on cost-efficiency, we make two further simplifying assumptions.First, we set α = 1; this is equivalent to setting welfare weights to β = {1,0} for {poor, nonpoor} households and assuming that all transfers accrue to poor households, i.e., perfect targeting. Second, we value in-kind expenditures at cost, i.e., the total willingness-to-pay across households for an extra dollar of in-kind expenditures is exactly one dollar, so that the total welfare impact of the program is given by the sum of cash and in-kind transfers (multiplied by the progressiveness parameter, α). This assumption is the same as that typically made in benefit-incidence analyses of public expenditures (Demery 2003). Under these assumptions, the cost-benefit ratio can be written as, where CTR is the cost-transfer ratio defined earlier, i.e., the ratio of nontransfer program costs to total program transfers. Since this measure of efficiency abstracts from important program effectiveness issues, we refer to it as a \"cost-efficiency ratio.\" Finally, since some components of program nontransfer costs (C) can affect the overall cost-effectiveness of the program, it is not necessarily desirable to minimize this ratio. For example, program expenditures arising from setting up and implementing program targeting rules will presumably have a return in terms of improved targeting effectiveness (higher α), but while the costs will be included in the CTR, the benefits will not. Similarly, expenditures associated with setting up and implementing mechanisms for monitoring adherence to program rules will presumably lead to greater effects on human capital, but will also only be reflected as a cost in the CTR. Although we do not attempt to calculate a measure of cost-effectiveness for each program, in Section 5 we discuss the existing evidence on the relative targeting effectiveness and human capital impacts of the programs, facilitating a more complete comparison of program costs.To analyze the cost structures of these complex programs, it is necessary to understand how they operate and how they have evolved. Table 1 summarizes some basic features of each program. for Grade 3) are eligible for education transfers. Transfers increase by grade and are higher for girls than for boys in middle school (Grades 7-9). In 1999, monthly benefits were 80 pesos for Grade 3.8 By Grade 9, benefits rise to 265 and 305 pesos for boys and girls, respectively. In addition to enrollment, transfers are conditioned on an 85 percent attendance record, and children are allowed to repeat a grade, at most, twice.The second component of the transfer, for food security, health, and nutrition, is 125 pesos per month for each household, conditioned on household members making regular trips to health clinics for a range of preventive health checks and attending monthly nutrition and hygiene information sessions. The education and food security transfers are independent: beneficiaries can receive one and not the other, even if they are eligible for both. In addition to the cash transfers, beneficiary households with children under age 3 receive a monthly nutritional supplement intended for the infant that contains essential micronutrients.There is a ceiling of 750 pesos per month for education and food transfers combined. On average, the transfer to beneficiary households constitutes around 20 percent of preprogram annual household expenditures. The program design of PROGRESA (as well as of PRAF and RPS) calls for the money to be given to mothersbased on evidence that resources in the hands of women often lead to better outcomes for child well-being and household food security (Strauss and Thomas 1995). Transfer amounts are indexed to inflation and adjusted every six months, something not done in the other two programs.PROGRESA was targeted in two stages. The first stage identified the most marginal rural localities, using a specially constructed \"marginality index\" constructed from the national census. The selected localities were then visited to ensure they had access to the required supporting infrastructure (schools and health clinics). The second stage targeted households within eligible localities, using specially collected census data to classify households as \"poor\" or \"nonpoor,\" based on a statistical analysis of income and other household characteristics. After beneficiary households are identified, a general assembly is held to explain the objectives of the program, to incorporate households, and to inform them of their responsibilities and rights.The expansion of the program throughout Mexico took place in several phases. states. This constitutes approximately 40 percent of all rural households, or one-ninth of all households in Mexico.PRAF (Phase II, Honduras) was implemented in the second half of 2000 and includes both demand-and supply-side transfers. 9 On the demand side, the education subsidy is 812 lempiras (L) per child per year, up to a maximum of three education transfers per household. 10 This transfer is conditioned on the enrollment and regular attendance of all children who have not yet completed Grade 4 of primary school. The food security, health, and nutrition transfer provided for pregnant women and children under age 3, is L644 per beneficiary per year, with a maximum of two transfers per household. This transfer is conditional on pregnant women and children making monthly trips to health clinics for preventive checkups and growth monitoring. Transfers are 9 The analysis for PRAF draws from Caldés and Coady (2003).10 Lempiras (L) is the Honduran currency; in 2000, the exchange rate was approximately L15 per US1$.distributed twice a year and, on average, comprise about 4 percent of preprogram total household annual expenditures (one-fifth of the equivalent percentage of PROGRESA).Unlike PROGRESA, where the supply side is left to the education and health ministries to manage, PRAF directly invests resources to ensure adequate supply-side services. For education, PRAF makes grants to school parent associations to be spent on local schools. For health and nutrition, PRAF makes grants to local health service committees to improve the quality of health care provided by the government health system, and it implements a community-based child growth and monitoring program that provides mothers with one-on-one counseling.The program was geographically targeted to poor municipalities, which were chosen by ranking all municipalities according to the average rates of stunting observed in the 1997 National Census of the Height of First-Graders. Seventy municipalities with the highest rates of stunting were considered eligible (MNPTSG 2002). Of these, 50were randomly selected, leaving the others as a control group for the program evaluation.In 40 of the chosen municipalities, all households with pregnant women, children under age 3, and/or children aged 6-12 who had not yet completed Grade 4 of primary school were eligible for benefits (the remaining 10 municipalities selected received only the supply-side transfers described below). Transfers began in November 2000 and, by the end of 2002, PRAF had 47,800 beneficiaries and was operating in 50 rural municipalities (out of a total of 298) from seven departments. Eighty-seven percent of the households in these departments are classified as poor.The third program, RPS (Nicaragua), began as a pilot in 2000 in rural areas in the northern part of the central region of Nicaragua. 11 Each participating household receives a food security, health, and nutrition transfer of 240 córdobas (C$) per month, conditional on taking children under age 5 to health clinics for scheduled appointments and attending health and nutrition information clinics. 12 To receive a monthly education transfer of C$120 per household, households with children ages 7-13 who have not completed Grade 4 of primary school have to ensure their enrollment and over 85 percent attendance at school. In addition, the household receives C$275 annually upon enrollment for each eligible child in school. The money covers school supplies (e.g., uniforms and shoes) and C$60 annually to be handed over to the teacher. Half of that amount is intended to supplement teacher salaries, and the other half to purchase school materials. Similar to PROGRESA (though much larger than PRAF), these two transfers constitute, on average, approximately C$3,800 (or US$300), which comprised 18 percent of total annual household expenditures for beneficiary households before the program.Like PRAF, RPS has supply-side components, though they differ substantially.For education, there is the incentive paid to the teachers per student beneficiary described above. For health and nutrition, to ensure adequate supply in the poor, rural communities in which it is operating, RPS trains (and pays) private providers to deliver the health-care services required by the program, as well as to assist with monitoring household compliance of program requirements. These services, provided free to beneficiary households, are focused on children under age 5 and include growth and development monitoring, vaccination, and provision of antiparasites, vitamins, and iron supplements.The materials are provided by the Ministry of Health. Children under age 2 are seen monthly, while those ages 2-5 are monitored bimonthly.The pilot program was implemented in two (out of 17) relatively poor departments in Nicaragua, chosen using a combination of poverty and operational criteria. Around 80 percent of rural households in these departments are classified as poor. The departments have easy physical access and communication, strong institutional capacity and local coordination, and reasonably good coverage of health posts and schools. Six (out of the 20) municipalities from these departments were then chosen on the basis of their participation in an existing supply-side program emphasizing local level participation. A marginality index was constructed and an index score calculated for each of the 59 rural comarcas (administrative areas comprising one to five villages) in the six municipalities, using data from the 1995 national census. Forty-two comarcas were chosen to participate in the first stage of the pilot phase in which there was to be only geographic targeting. Twenty-one were randomly excluded from the program for two years, and these constituted the control group for the program evaluation (Maluccio and Flores 2004). Nearly all of the 6,000 households in these areas were eligible to receive program benefits and received their first transfers in October 2000. In the second stage of the pilot program (begun in early 2001), 80 percent (i.e., 4,000) of households in the remaining 17 comarcas that were not part of the evaluation were selected, using household targeting based on a proxy means test (IFPRI 2002).The above descriptions make clear that while they have many similarities, the three programs also have important differences. These affect how we collect and process cost information, interpret the CTR, and the extent to which we can make sensible comparisons between programs. For example, all three programs are at different stages Thus, although the pilot phase was nearing maturity in 2002, we will need to account for the fact that they are \"contaminated\" by costs that should be attributed to the expansion.In addition to the differences in program maturity, there are also important program-design differences. While all three programs have a demand-side component, their structures and size differ. PROGRESA is solely a demand-side program, providing transfers of, on average, 20 percent of total household expenditures. RPS delivers similarly sized transfers, while those of PRAF are substantially smaller. PRAF and RPS have significant supply-side interventions. Further, the experiences of PRAF and RPS demonstrate the substantial effort that is required to simultaneously put in place both demand-and supply-side components. To the extent that the supply-side components are relatively cost-intensive, this needs to be taken into account when comparing programs.Finally, even the supply-side components of PRAF and RPS differ in the services they provide, how those services are provided, and who pays for them. PRAF uses the existing public health-care system, whereas RPS contracts private providers to deliver the services. Consequently, the two programs face very different program costs, even for components of the services that are similar, such as vaccine provision.We must bear all of these differences in mind when analyzing and comparing the cost structure of the three programs.The primary source of information on program costs is typically the program's accounting records. It is usually straightforward to obtain annual data on total program costs and transfers, ingredients for the initial estimates of the cost-transfer ratio. 13 Table 2 presents this information for each of the programs. For PROGRESA, the average CTR for the program to end-2000 (total nontransfer program costs divided by total program transfers for four years) is 0.106. That is, 10.6 pesos were spent on administrative costs for every 100 pesos transferred to households. Equivalently, 9.6 percent of the total budget was absorbed by program costs. 14 We must be careful, however, in interpreting this ratio. First, it includes costs relating to the external evaluation of the program. This was a once-off evaluation that, while influencing the redesign of these and other related programs, did not substantially affect program design or operations in real time. This type of external evaluation must be distinguished from ongoing internal monitoring and evaluation, which did feed continuously into program decisionmaking, improving current program design and operations. The external evaluation is best treated as a sunk fixed cost that would not recur in a fully developed mature program, whereas the internal monitoring and evaluation is a recurring activity. Second, in addition to the external evaluation, the costs presented include a variety of other costs plausibly treated as fixed set-up costs associated with start-up activities. As the program matures, average fixed costs will converge toward zero and the CTR will converge toward a value that reflects only variable costs.Lastly, for data spanning a number of years, adjustments to account for inflation and depreciation of capital investments can be made. Notes: PROGRESA figures are translated into U.S. dollars using a constant (1999) exchange rate of 10 pesos per US$1, and PRAF, using a constant exchange rate of L15 per US$1. RPS accounting records were provided in U.S. dollars.a PRAF accounting costs have been adjusted to include unaccounted for costs such as water, telephone, electricity, and additional staff hired for the delivery of the transfers.Since most fixed costs tend to be incurred at the start of the program, examining the annual CTR separately for each year sheds light on the relative importance of these types of costs over time and on the expected long-run CTR for a (more) mature program.As the program matures, we expect the annual CTR to decrease, since fixed costs will decline. This is what we find for PROGRESA, where the annual CTR decreases rapidly over the four years, starting at 1.342 in the first year and declining to 0.054 in 2000.Even the annual CTR of 0.054 observed in 2000 might include some fixed costs, however, and therefore still might overestimate the long-run CTR for a fully mature program. We consider this possibility and ways to control for it in the analysis below.We can use the evolution over time of the estimated CTRs to assess how much we would overestimate this measure of cost-efficiency if we base it on snapshots of the program in its early stages. The final row of Table 2 presents the cumulative average CTR for the program. Because of the sharp decline in estimated annual CTRs, basing the average CTR on only the first two or three years of data substantially overestimates the average calculated at end-2000, when all beneficiary households had been included and the program was nearing maturity. In 1998, the cumulative average is four times as large as the four-year average, and even in 1999 was more than 1.5 times as large. Had we carried out the analysis in early 2000 using only information to end-1999, the results for PROGRESA would have differed substantially. Hence, it is important to ensure that the CTR estimates are as comparable as possible before attempting comparisons between programs, or even within a program between years.Apart from declining costs (mainly due to decreasing fixed costs), a second reason the annual CTR decreases over time has been that the programs under consideration have expanded, and thus total transfers are increasing. Table 2 shows that transfers in PROGRESA increased fivefold between 1998 and 2000, from $149 to $775 million. Decreases in costs were not as pronounced, dropping from $48 to $42 million over the same period.We turn now to the other two programs. Since they contain both demand-and supply-side transfers, we use the sum of these to calculate the total transfer in the denominator of the CTR. As described in Section 2, this implicitly equates the value of a unit of transfer to households, regardless of whether it is given directly to the household in cash or indirectly via health and education (in-kind) services. For the in-kind transfers, then, we are valuing their benefit, i.e., the beneficiaries' total willingness to pay, at the cost of provision. In the case of PRAF, this includes transfers made to school parent associations and local health teams, as well as the cost of the community-based child growth program. For RPS, it includes transfers given to teachers as well as the value of payments made to the private health-care providers. Therefore, even the year 2002 is likely to yield an overestimate of the pilot program's CTR.The presence of fixed costs associated with setting-up and planning program activities, as well as activities associated with expansion or operational difficulties, makes clear that it would be misleading to use the \"unadjusted\" CTRs presented above either for within program evaluation or as the basis for comparison of the relative costefficiency of the three programs. A proper comparison requires further consideration of the details of their cost structures, in particular, the relationship between program activities and costs.To do this, we first identify key program activities and then link them to their associated costs. To the extent possible, we delineate program activities in sequential order in the life cycle of the program, according to whether they correspond to fixed or variable costs for the program, and in a manner to facilitate comparison among the programs. This will enable us to better approximate the cost structure and CTR of mature programs. It will also permit simulation of hypothetical alternative programs that do not include all the activities of the actual programs. For example, by identifying the costs associated with household targeting or with the conditioning of the program, we can simulate CTRs with and without these program features.While any such categorization of activities is necessarily somewhat subjective, there are some fairly obvious, broadly defined activities in the three programs that are common to most social safety net programs (e.g., program design and benefit delivery).Others are common to targeted conditional cash transfer programs (e.g., identification and incorporation of beneficiaries, and conditionality).The key activities we identified for the three programs are:1. Program design and planning: Designing and planning program implementation, including the selection of program areas (geographic targeting) and coordination with the education and health sectors.2. Identification of beneficiaries (household targeting): Collecting, processing, validating, and analyzing household socioeconomic data to be used in identifying eligible households and for logistical planning.Planning and convening beneficiary meetings in each community to inform participants of their responsibilities and rights under the program; collecting and processing participation forms.Calculating transfers, informing beneficiaries about scheduled transfers, and ensuring that the transfer process is carried out in a timely and orderly manner.Organizing and providing the supply-side services (e.g., organizing the health services provision and making payments to providers or to other supply-side agents).6. Conditionality: Distributing, collecting, and processing the registration, attendance, and performance forms to schools and health-care providers. The first three activities (numbers 1-3) must be undertaken at the outset, before any cash transfers are made. Program design, including the selection of localities, is a sunk fixed cost that does not vary with the total size of the program (i.e., the number of beneficiary localities or households). Therefore, this component of average fixed costs per unit of transfer (or per household) will decrease as the program expands to include more households, representing an economy of scale. Identification and incorporation of households, on the other hand, while also fixed (at least over the medium term), involve one-time costs that increase with the number of households included in the program. The next four activities (4-7) recur throughout the life of the program and are expected to increase with the number of beneficiary households. External evaluation, as discussed above, is a fixed cost that would typically end in an ongoing program.In collaboration with the local teams in each country and using program documents, we identified all the specific activities for each program, grouped them into one of the above key activities, and calculated the fraction of time spent by program personnel on each activity in each year. 15 From this information, we developed a time allocation matrix for each program (Appendix Table 6). 16 While it would be unwise to consider this methodology measurement-error free, it can identify substantive trends and patterns in the activity mix. Reassuringly, much of what we see in the matrices can be corroborated by our knowledge of the program activities and their relative intensities over time.The next step in the analysis is to associate, where possible, the various accounting costs with program activities. Some accounting line-item costs can be allocated directly to certain activities without ambiguity. For example, the fees paid to firms delivering the monetary transfers can be allocated directly to the delivery of demand-side transfers activity or the cost of collecting the baseline evaluation survey to the external evaluation activity. We refer to these as \"directly assignable costs.\" For many other costs, such as salaries of management personnel, direct assignment is not possible because they cut across program activities. These are allocated to program activities using the time-allocation matrix. By multiplying total unassigned costs by the time-allocation matrix percentages, we can distribute these shared costs across program activities. We refer to these as \"indirectly assignable costs.\"After assigning all costs to activities, we calculate the activity cost shares, i.e., the fraction of costs devoted to each activity (Table 3). For PROGRESA, over the first four years of the program, the largest cost items are identification of beneficiaries, delivery of transfers, and conditionality, accounting for 34, 22, and 18 percent of total costs (excluding transfers), respectively. The annual profile of these cost shares reflects the sequential nature of these activities. The cost share for identification of beneficiaries decreases from 61 percent in 1997 to 3 percent in 2000. In contrast, the share for conditionality activities increases from 8 percent in 1997 to 24 percent in 2000.Similarly, the cost share for delivery of transfers increases from 8 percent in 1997 to 41 percent in 2000. This shift of costs toward predominantly recurring cost items is consistent with the program nearing maturity. By 2000, recurring activities account for 85 percent of total program costs.In the case of PRAF, over the first four years of the program, activities associated with the external evaluation and the identification of beneficiaries (which included the incorporation of beneficiaries) were the most important cost items, accounting for 35 and 26 percent of total program costs, respectively. These were followed by delivery of demand-and supply-side transfers, which combined to account for 16 percent of total The identification and incorporation of beneficiaries were not separable for PRAF; the figures in the row for identification represent the sum of those two activities.costs. The high cost share for the external evaluation explains a large portion of the difference in the program average CTRs for PRAF relative to PROGRESA.17 In addition to declining fixed costs, the evolution of PRAF cost shares over time also reflects the operational difficulties encountered in the program, particularly in 2002.In program design and planning activities according to whether they were for the pilot phase or for the expansion phase begun in 2003, shows that the former declined substantially over the three years, whereas the latter increased in roughly equal proportions-the combined effect is that the total share dedicated to design activities was roughly constant over the three years (Caldés and Maluccio 2004).Delivery of demand-and supply-side transfers accounted for more than onequarter of costs in 2002, with the latter comprising the majority of those costs.Conditionality, i.e., activities related to monitoring whether households are complying with the program requirements, has grown in intensity over time, as the number of beneficiaries grew. General program monitoring, including monitoring of supply services, also increased substantially over the period. The rise in 2002 was due in part to the implementation of random spot-checks of private providers after some were discovered to have been delivering poor quality services. Presumably, these activities had an effect on the quality of services and the overall human capital impact of the  The identification and incorporation of beneficiaries were not separable for PRAF; the figures in the row for identification represent the sum of those two activities.Even with these adjustments, the above CTRs are likely to overestimate the longrun CTRs, since they still include a variety of fixed or quasi-fixed costs. Earlier, we described how one can treat the last year observed for each program as a better estimate (than the aggregate) for the program in a mature state. After excluding external evaluations, the final year (for which we have data) annual CTRs are 0.049, 0.163, and 0.331 for PROGRESA, PRAF, and RPS, respectively. Based on these numbers, the two supply-side programs still appear to cost substantially more, with the RPS pilot costing twice as much per unit of transfer as PRAF. This methodology implicitly assumes that the programs are all nearing maturity. While plausible for PROGRESA, this is less likely for the other programs. PRAF has had operational difficulties associated with updating the beneficiary lists, implementing the supply side, and monitoring conditionality. For the RPS pilot, 2002 includes fixed design costs associated with preparing for the expansion of the program. Therefore, the final year annual CTRs are still likely to overestimate long-run CTRs.Therefore, to better approximate the long-run CTR and provide a fairer comparison between programs, we further adjust the CTR by excluding the fixed costs we can identify. The activity categories are roughly sequential in nature, with the first three (numbers 1-3) representing activities that need to be carried out at the outset of the program before any transfers are distributed to households or service providers. We do not expect these activities to be important cost components for the mature program.Therefore, by subtracting these costs, we can derive better estimates of the long-run CTRs. 19 These adjusted estimates are shown in the bottom row of Table 4 and result in final-year annual CTRs of 0.041, 0.068, and 0.212 for PROGRESA, PRAF, and RPS, respectively. Based on these ratios, we get the same ranking across programs according 19 It is probable that some of these costs are recurring in the medium term, however, such as activities related to the identification of beneficiaries that may include some costs related to periodic updating of registration system. We are implicitly assuming that these are relatively small and are offset by fixed costs that exist in the other activities but we do not subtract out. Alternatively, one can think about the estimates excluding the fixed costs as representing lower bounds.to program costs, but now PRAF is closer to PROGRESA, while the RPS pilot remains relatively more costly.Apart from the relative complexity of the RPS supply-side intervention, 20 which comes with consequent monitoring and conditioning costs, another reason that the costtransfer ratio for RPS is higher than the others is related to its being a pilot. Even within the activities we treat as recurring, part of the activities for RPS during the pilot had to do with one-time costs, as new modalities were considered and the team explored how best to do things. Caldés and Maluccio (2004) disaggregate all the various activities into fixed and variable components, and find that, indeed, this further reduces the annual CTR, particularly in the earlier years.CTRs may also differ between programs because their average transfer levels differ. If two programs are identical except for the fact that the average household transfer in the first is twice that in the second, then the CTR for the first would be half that for the second, assuming the same level of operational efficiency and negligible costs directly related to the size of the transfer (such as delivery costs). When both supply-and demand-side transfers are included for RPS and PRAF, the average transfer for RPS is similar to that for PROGRESA, whereas that for PRAF is approximately one-third the size. Therefore, increasing the level of transfers in PRAF by a factor of three would decrease our estimate of the long-term CTR for the program to 0.024, even lower than PROGRESA. This is somewhat surprising, since PROGRESA involves only a demandside intervention (which, based on these programs' experiences, we believe is less costly to implement than a supply-side component), and RPS appears to be an effectively run intervention as documented in its impact evaluation (Maluccio and Flores 2004). We have already noted that the lower costs for PRAF are due, in part, to fewer resources 20 Table 2 shows that RPS has, by far, the largest relative supply-side transfers, suggesting that only for RPS would a simulation netting out the \"supply side\" of these programs make a substantial difference in the estimated CTRs. We simulate the CTR for RPS if it had no supply-side services by subtracting out all costs that we can associate with the supply side, and the corresponding transfers. The 2002 annual CTR declines from 0.211 reported in the text to 0.162, indicating that the supply-side transfers are, indeed, more cost-intensive.being devoted to conditionality and routine program monitoring and evaluation. A concern, however, is that this may have adverse implications for the effectiveness of the program.To promote their objectives of decreasing current poverty and generating a sustained decrease in poverty over time, the three programs have two key design features.First, in order to ensure that transfers reach the poorest households, the programs use varying combinations of geographic, categorical, and proxy-means targeting methods.Second, the transfers are conditioned on households undertaking certain actions intended to enhance the nutrition, health, and education outcomes of family members, particularly children. Both of these features require resources, thus increasing the share of administrative costs in the program budgets and, consequently, the CTRs.We assess the relative importance of the costs associated with these key activities by calculating their share in total program costs, after excluding the external evaluation and fixed costs described earlier. We assume that costs associated with the identification of beneficiaries are incurred only when household targeting is used-in the absence of household targeting, there is no operational need for the program to collect and analyze household information. While perhaps not entirely true, since even an untargeted program may require some sort of household registration system, we are implicitly assuming that any such related costs would be minimal. This would be the case, for example, if a reliable and recent census were already available. Similarly, if there were no conditioning, the program would not incur the costs of incorporating households or of certifying that beneficiaries are satisfying their responsibilities.Table 5 presents the share of targeting and conditioning costs in total program costs for all three programs over the periods considered. Excluding external evaluation (the first column for each program), the proportions make clear that targeting and conditioning costs are substantial. Combined, they account for 60, 49, and 31 percent for PROGRESA, PRAF, and RPS, respectively. These shares increase modestly when we also exclude costs for program design in the share calculation (second column for each program). The relatively low percentage for the RPS pilot partly reflects the fact that setting up and implementing the supply side, an activity included in the \"other\" category in the table, has proved to be very resource intensive. The absence of these activities in PROGRESA increases the relative shares of targeting and conditioning costs. Targeting costs in PRAF are higher than they otherwise would have been, due to the difficulties in maintaining the beneficiary identification system. At the same time, the resources allocated to dealing with these problems appear to have come at the expense of monitoring conditionality, suggesting that the latter are smaller than would otherwise have been the case during a normal operating year. On balance, it is possible that the sum of the two activities is about right, though there is no way for us to be certain.Nevertheless, even with these caveats, the message from this simulation is clear: costs devoted to targeting and conditioning form a substantial part of the ongoing operations of these programs. It is essential that these activities generate an adequate return; we turn now to an (admittedly crude) assessment of their cost-effectiveness. Targeting will be cost-effective if the incurred costs result in a sufficient increase in the share of transfers reaching the poorest households, thereby improving the programs' current poverty alleviation. The evidence indicates that the payoff from targeting has been high across all three programs. A comparative analysis (MNPTSG  2002) finds that the poorest 40 percent of households received 62, 79, and 80 percent of total transfers in PROGRESA, PRAF, and RPS, respectively. In other words, these relatively \"poor\" households receive from 1.5 to 2 times their population shares. To put this performance in perspective, for the more than 100 programs reviewed by Coady,   Grosh, and Hoddinott (2002), the median targeting performance was consistent with 50 percent of program benefits accruing to the poorest 40 percent of the population (i.e., the poor receiving 1.25 times their population share). The three programs discussed here all ranked in the top third of those reviewed in Coady, Grosh, and Hoddinott (2002).For two of the programs, PROGRESA and RPS, the human capital impacts have also been substantial (Skoufias 2004; Maluccio and Flores 2004). For education, the main effect of PROGRESA was to increase enrollment rates in secondary school (Schultz 2000; Behrman, Sengupta, and Todd 2001). Among those who successfully completed primary school, the program increased enrollment rates in the first year of middle school by 15 percentage points for girls and 7 percentage points for boys. In the RPS, primary enrollment rates in Grades 1-4 were about 70 percent before the program and increased a massive 18 percentage points with the program (Maluccio and Flores 2004).The effects on nutrition were also substantial. In PROGRESA, prior to the program, stunting levels for children aged 12-36 months were very high, at 44 percent.The program had a substantial effect on reducing the probability of stunting, increasing the annual mean growth rate by 16 percent (or 1 centimeter per year) for these children (Behrman and Hoddinott 2000; Gertler 2000). There is also evidence of a substantial increase in food consumption and dietary diversity (Hoddinott and Skoufias 2003). RPS has also had an enormous impact on a range of health and nutrition indicators. The percentage of children under age 3 who were weighed in the past six months increased by 30 percentage points, from around 60 percent prior to the program. This was accompanied by a decline of six percentage points in the prevalence of stunting for those under 5 (from 42 percent before the program), an unprecedented decline in such a short period of time. The results on expenditures suggest that not only have the total expenditures on food increased, but so, too, has the food budget share, by nearly four percentage points. The program has had a beneficial impact on dietary diversity; both the number of different food items consumed and the nutritional quality of the diet improved, with households eating more meat, fats, and fruits (Maluccio and Flores 2004).Preliminary evidence regarding the human capital impacts of PRAF suggests that these are smaller than for the other two programs (IFPRI 2003). For example, it appears to have had little impact on primary enrollment rates (which were already quite high),although there was an improvement in dropout rates. Visits by children to health clinics for growth monitoring and vaccinations increased in areas with the demand-side program, but the program does not appear to have improved health outcomes. Nor was there any effect on the nutritional status of children as measured by child growth indicators. These small effects are consistent with the evidence of operational difficulties in terms of implementing the supply side and monitoring conditionality. These results reinforce concerns that the low CTR of PRAF comes at the expense of the program's overall effectiveness. Possibly more important, however, we must bear in mind that these relatively small effects reflect not only the operational difficulties encountered in PRAF, but also the lower transfer level per household compared to the other programs-in PRAF, the transfer was calculated as an amount to compensate for the opportunity cost of children attending school, and was therefore much smaller than the other programs.This paper has assessed the cost-efficiency of PROGRESA, PRAF, and RPS by focusing on the cost-transfer ratio, defined as the ratio of nontransfer costs (i.e., administrative costs) to transfers. In doing so, we have demonstrated that for a meaningful assessment of cost-efficiency, it is misleading to make calculations using only the typically available raw accounting data, the approach normally taken (Coady,   Grosh, and Hoddinott 2002). Rather, one must delve into the details and activities of the program. Features of the program, and how the CTR is calculated, are important for how it is used and interpreted. This is particularly true for start-up programs, which typically have a lot of up-front fixed costs associated with design and setting up operations, and for complex programs, such as conditional cash transfer programs, that have a number of costs associated with specific design features. It is essential to keep in mind that this examination of program costs, transfers, and CTRs includes not only the costs required to transfer the money to the beneficiaries, but also costs of activities that may enhance the effectiveness of the program (e.g., targeting or monitoring of conditionality). Therefore, in addition to the level of costs, we focused on the structure of costs as reflected in the various activities involved in each program. These details must be considered to make sensible comparisons between programs, either within the same country or between countries.This paper begins to fill the large gap that exists in the empirical knowledge of the cost structures of poverty alleviation programs. In the context of three large poverty alleviation programs in Latin America, we have shown how typically available cost data,augmented by program-level information on time use, can be used to undertake an assessment of the cost-efficiency of the program. The analysis also underscores that the estimates we present, and how they should be interpreted and used for comparison between alternatives, depends sensitively on how they were calculated. Very different numbers emerge when one takes snapshots of programs at different stages or when we include or exclude up-front setup or fixed costs. This reflects the fact that fixed costs are typically a more important component of total program costs earlier in the life of the program. Over time, average fixed costs converge to zero, so that the average CTR (or, equivalently, share of administrative costs in total costs) converges to the ratio of recurring operating costs to total transfers (or to their share in total costs).How do these three programs' cost-efficiencies compare to those of other poverty alleviation programs in the region? As highlighted at the outset, such evidence is hard to come by and, where it exists, is often not comparable. Grosh (1994) finds that the share of administrative costs for programs she considered ranged from under 1 percent to 29 percent, with a median of 9 percent. For programs involving proxy-means tests, the median was slightly higher, at 10 percent. In Section 4, we calculated various CTRs for each of the programs, two of which serve as lower and upper bounds of our best estimates of the long-run CTR. These are the final year annual CTRs for the program without external evaluation and all fixed costs (Table 4, bottom row), and is the program without external evaluation and program design, but including the other fixed costs that may, to some extent, be recurring (Table 4, penultimate row). These produce a range for each of the programs of 0.041-0.047 for PROGRESA, 0.068-0.161 for PRAF, and 0.212-0.245 for the RPS pilot. The lower estimated costs for PROGRESA undoubtedly reflect, in part, economies of scale (it is a massive program in comparison to the others), as well as the fact that it does not have a supply-side component.For PROGRESA, even its upper bound CTR of 0.047 compares well with the median program reported in Grosh (1994), all the more impressive, given the relative complexity of PROGRESA's design compared to more conventional social safety net programs. Furthermore, it is very low when compared to the LICONSA (a subsidized milk program delivered through state shops in urban areas) and TORTIVALES (a tortilla subsidy program) programs in Mexico, which had program costs equivalent to 40 pesos and 14 pesos per 100 pesos transferred, respectively (Grosh 1994). If we assume that the median levels reported in Grosh (1994) adequately reflect operating costs, then the lowerbound CTR for PRAF also compares well with the median program, though this conclusion is subject to the caveats made throughout regarding our estimates for PRAF.The RPS pilot, however, which has a lower-bound CTR equal to 0.212, appears to be relatively expensive. Of course, RPS is much more complex than conventional poverty programs, and there is clear evidence that it has had large human capital impacts-much is being bought with these expenditures.In closing, we caution that it is difficult to be certain about these comparisons, since it is unclear exactly what is included in the figures quoted in Grosh (1994). 21 It may be that the variation in these numbers reflects different cost definitions rather than","tokenCount":"8368","images":["1122257324_1_1.png","1122257324_12_1.png","1122257324_12_2.png","1122257324_12_3.png","1122257324_12_4.png","1122257324_12_5.png","1122257324_12_6.png","1122257324_12_7.png","1122257324_12_8.png","1122257324_12_9.png","1122257324_12_10.png","1122257324_12_11.png","1122257324_12_12.png","1122257324_12_13.png","1122257324_12_14.png","1122257324_12_15.png","1122257324_12_16.png","1122257324_12_17.png","1122257324_12_18.png","1122257324_12_19.png","1122257324_12_20.png","1122257324_12_21.png","1122257324_12_22.png","1122257324_12_23.png","1122257324_12_24.png","1122257324_12_25.png","1122257324_12_26.png","1122257324_12_27.png","1122257324_12_28.png","1122257324_12_29.png","1122257324_12_30.png","1122257324_12_31.png","1122257324_12_32.png","1122257324_12_33.png","1122257324_12_34.png","1122257324_12_35.png","1122257324_12_36.png","1122257324_12_37.png","1122257324_12_38.png","1122257324_12_39.png","1122257324_12_40.png","1122257324_12_41.png","1122257324_12_42.png","1122257324_12_43.png","1122257324_12_44.png","1122257324_12_45.png","1122257324_12_46.png","1122257324_12_47.png","1122257324_12_48.png","1122257324_12_49.png","1122257324_12_50.png","1122257324_12_51.png","1122257324_12_52.png","1122257324_12_53.png","1122257324_12_54.png","1122257324_12_55.png","1122257324_12_56.png","1122257324_12_57.png","1122257324_12_58.png","1122257324_12_59.png","1122257324_12_60.png","1122257324_12_61.png","1122257324_12_62.png","1122257324_12_63.png","1122257324_12_64.png","1122257324_12_65.png","1122257324_12_66.png","1122257324_12_67.png","1122257324_12_68.png","1122257324_12_69.png","1122257324_12_70.png","1122257324_12_71.png","1122257324_12_72.png","1122257324_12_73.png","1122257324_12_74.png","1122257324_12_75.png"],"tables":["1122257324_1_1.json","1122257324_2_1.json","1122257324_3_1.json","1122257324_4_1.json","1122257324_5_1.json","1122257324_6_1.json","1122257324_7_1.json","1122257324_8_1.json","1122257324_9_1.json","1122257324_10_1.json","1122257324_11_1.json","1122257324_12_1.json","1122257324_13_1.json","1122257324_14_1.json","1122257324_15_1.json","1122257324_16_1.json","1122257324_17_1.json","1122257324_18_1.json","1122257324_19_1.json","1122257324_20_1.json","1122257324_21_1.json","1122257324_22_1.json","1122257324_23_1.json","1122257324_24_1.json","1122257324_25_1.json","1122257324_26_1.json","1122257324_27_1.json","1122257324_28_1.json","1122257324_29_1.json","1122257324_30_1.json","1122257324_31_1.json","1122257324_32_1.json","1122257324_33_1.json","1122257324_34_1.json","1122257324_35_1.json","1122257324_36_1.json","1122257324_37_1.json","1122257324_38_1.json","1122257324_39_1.json","1122257324_40_1.json","1122257324_41_1.json","1122257324_42_1.json","1122257324_43_1.json","1122257324_44_1.json","1122257324_45_1.json","1122257324_46_1.json","1122257324_47_1.json","1122257324_48_1.json","1122257324_49_1.json","1122257324_50_1.json","1122257324_51_1.json","1122257324_52_1.json"]}

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+{"metadata":{"gardian_id":"d7b29083a99e90c2f85d6401c963ad40","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/481be2c3-7273-46f5-9f1f-3f2d9f6b419e/retrieve","description":"The international and local Nicaraguan media have widely reported on the “coffee crisis” in Latin America and there is substantial evidence that there has been a downturn and that this has been more severe in the coffee-growing regions. Using household panel data from a randomized community-based intervention carried out in both coffee- and noncoffee-growing areas, I examine the role of a conditional cash transfer program, the Red de Protección Social (RPS), during this downturn. While not designed as a traditional safety net program in the sense of reacting or adjusting to crises or shocks, RPS has performed like one, with larger estimated program effects for those who were more severely affected by the downturn. For example, it protected households against declines in per capita expenditures and, while not significantly depressing labor supply relative to before the program, muted additional labor supply for beneficiaries in coffee-growing areas, relative to their counterparts without the program. Beneficiaries who participated in the coffee industry as laborers before the program were more likely to have exited the coffee industry, whereas those who participated as producers were less likely to have exited. The findings are consistent with the existence of credit constraints inhibiting such transitions in the absence of the program. Overall, then, RPS appears to be playing an important part in the risk-coping strategies of households..","id":"-820139282"},"keywords":[],"sieverID":"0af04fb7-a206-4298-8b08-f106a5308190","pagecount":"2","content":"s coffee prices dropped in the last few years, poverty appeared to rise in the coffee-producing countries of Central America, particularly in rural areas. Little research exists on these dynamics at the micro level, however. Even less evidence exists on the potential for social safety-net programs to protect poor households.The Nicaraguan Red de Protección Social (RPS) or Social Safety Net was designed to reduce both current and future poverty via cash transfers targeted to households living in extreme poverty in rural Nicaragua. The transfers are conditional, with households monitored to ensure that they undertake prescribed actions to improve their children's human capital development.RPS's specific objectives include (1) supplementing household income for up to three years to increase expenditures on food, (2) improving the healthcare and nutritional status of children under age five, and (3) increasing primary school enrolment for the first four grades.Data. The data come from an annual panel survey of nearly 1,500 households implemented for the evaluation in both intervention and control areas of RPS in 2000 before the program began, and in 2001 and 2002 after the program was operating. The survey was a stratified random sample of households at the comarca level. Half of the comarcas were randomly selected for the program. The areas represented comprise a relatively poor part of the rural Central Region in Nicaragua.Econometric Methodology. The empirical approach exploits two key features of the data: the randomized design of the evaluation and the panel structure, allowing the use of double-difference estimation techniques. The analysis estimates a series of reduced-form specifications that essentially estimate program effects, differentiating them for households residing in coffee or noncoffee-growing areas. The resulting measures can be interpreted as the expected effect of implementing the program in a similar population elsewhere.In comarcas without the RPS, the study found the following highlights.• Expenditures. Household and food expenditures declined 11 percent, on average, in control areas in noncoffee-growing areas. In coffee-growing areas, expenditures dropped 27 percent (see figure). • Labor supply. Nonbeneficiaries worked longer hours during the downturn, even though they apparently earned less income, as reflected in their expenditures. The labor increase was particularly sharp in coffee-growing areas. • Child labor and school enrolment. In 2000, boys 7-12 years old in coffee-growing areas were more likely to be working than those in noncoffee-growing areas. Not surprisingly, their net primary-school enrolment rates were substantially lower in coffeegrowing areas. During the study years, however, child labor for young boys declined in all areas. At the same time, primary enrolment rates improved modestly-and somewhat more so in coffee-growing areas. Effects of the RPS on Beneficiary Households. In comarcas where the RPS was operating, the study found the following highlights.• Expenditures. Household and food expenditures were significantly higher than in control comarcas.Expenditures actually rose by 6 percent in noncoffee-growing areas. It slipped by only 3 percent in coffee-growing areas (see figure). • Labor supply. Households worked approximately the same hours as before-while nonbeneficiary households were working significantly more.• Child labor and enrolment. School enrolment rates of boys and girls rose significantly more than in control areas. Child labor simultaneously declined.A major cause of the intergenerational transmission of poverty is the inability of poor households to invest in their children. Increasing the availability and quality of health and education services can be ineffective if people are too impoverished to make use of them. Programs like RPS attack the problem by targeting transfers to the poorest communities and householdsand conditioning the transfers on attendance at school and health clinics. This effectively transforms pure transfers into human capital subsidies for poor households.While not designed as a traditional safety net program, RPS has performed like one, protecting most those in greatest need. It provided a cushion for per capita expenditures and protected coffee laborers from working additional hours. It also safeguarded investment in children. Thus RPS played a significant role in helping poor, rural Nicaraguans weather the coffee crisis. ","tokenCount":"650","images":["-820139282_1_1.png","-820139282_2_1.png"],"tables":["-820139282_1_1.json","-820139282_2_1.json"]}

data/part_2/0633469713.json

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+{"metadata":{"gardian_id":"8e86ff9a8b3b099eab6a5d409526f34b","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/8f9b7432-028a-48c6-b11a-8426a3ec853c/retrieve","description":"One of the hallmark accomplishments of the Uruguay Round Agreement on Agriculture (AoA) was the inclusion of agriculture in a system of multilateral rules and disciplines, including disciplines governing domestic support. Under those provisions, domestic support was capped based on support levels in a historical base period and then reduced over the implementation period of the agreement32. The AoA also encouraged Members to reform agricultural support towards minimally production- and trade-distorting support by exempting those measures from reduction commitments according to criteria laid out in Annex 2 of the AoA (the Green Box).","id":"-1929114494"},"keywords":[],"sieverID":"479e85f5-e6bb-4d05-a566-77bc6902f92c","pagecount":"13","content":"One of the hallmark accomplishments of the Uruguay Round Agreement on Agriculture (AoA) was the inclusion of agriculture in a system of multilateral rules and disciplines, including disciplines governing domestic support. Under those provisions, domestic support was capped based on support levels in a historical base period and then reduced over the implementation period of the agreement 32 . The AoA also encouraged Members to reform agricultural support towards minimally production-and trade-distorting support by exempting those measures from reduction commitments according to criteria laid out in Annex 2 of the AoA (the Green Box).The immediate effect of the AoA was that many Members reformed their agricultural policies to comply with the new disciplines. Indeed, even prior to the conclusion of the Uruguay Round, two of the largest subsidizing Members--the United States and the Europe Union--had adopted policies that partially delinked payments from production, effectively capping payments (Josling, Tangermann and Warley 1996). By 2005, the average level of domestic support among OECD countries had declined to 26 percent of the value of production, down from 36.4 percent of the value of production at the time of the launch of the Uruguay Round in 1986 (OECD 2021). By 2010, the average level of support among OECD countries was 19 percent (Figure 1). Many have criticized the AoA for providing numerous exemptions to reduction commitments (Glauber 2019). Article 6.5 of the AoA exempts support from reduction commitments for \"production limiting programs\" if such payments are made on the basis of fixed areas and yields or a fixed number of livestock. Article 6.2 exempts direct and indirect support measures such as input subsidies if they are designed to encourage agricultural and rural development and that are an integral part of the development programme of developing countries. Under the de minimis provisions of AoA (Article 6.4), there is no requirement to reduce trade-distorting support in any year in which the aggregate value of the product specific support does not exceed 5 percent of the total value of production for that agricultural product. In addition, non-product specific support which is less than 5 percent of value of total agriculture production is also exempt from reduction. The 5-percent de minimis threshold applies for developed countries. The de minimis threshold is 10 percent of developing countries and 8.5 percent for China (WTO 2003).A number of proposals aimed at reducing and harmonizing agricultural domestic support have been introduced by WTO members since 2017. Most of the proposals would eliminate or sharply reduce AMS entitlements or cap overall support at current de minimis thresholds. In some proposals, caps would be tied to historical entitlements (for example, JOB/AG/177. Rev2 and JOB/AG/199)) while others would tie caps to the value of production (JOB/AG/112 and JOB/AG/137). Several proposals introduce product-specific caps to prevent concentrating domestic support in a handful of commodities. Most all proposals would exempt Least Developed Countries (LDCs), and in some cases, Small and Vulnerable Economies (SVEs) and Net Food Importing Developing Countries (NFIDCs) from reduction commitments.In the analysis which follows we examine three potential avenues for providing additional disciplines for agricultural support. The proposals would replace and strengthen domestic support disciplines in Article 6 of the Agreement on Agriculture with an overarching discipline on overall trade distorting support (OTDS). The new disciplines would harmonize support across Members by capping OTDS based on a percent of the total value of agricultural production (VoP). The level of ambition would be determined based on the percentage with special and differential treatment considered for developing country Members. To prevent Members from undermining the new disciplines by concentrating support on a handful of commodities, product-specific caps are also examined.Under the proposed discipline, the OTDS would include all forms of production-and trade-distorting domestic support under Article 6 of the AoA. This would include all measures that are currently notified under Article 6: amber box outlays (including de minimis); outlays notified under Article 6.5 (blue box); and outlays notified under Article 6.2 (the so-called development box). We also consider an alternative measure that would take into consideration special and differential treatment for developing countries by excluding outlays under Article 6.2 from the OTDS. As under the current AoA, such measures of assistance would be exempt from reduction commitments.Under the current AoA, the current ceiling for a Member's total AMS was based on support levels over a historical base period (for example, 1986 to 1988), reduced over the implementation period by reduction formula laid out in individual Member schedules. The approach taken in this analysis would base caps on a specified percentage a Member's total value of production. For Developed countries, a Member's OTDS in a given year could not exceed 5 percent of the value of production for that year 34 . For Developing country Members, the OTDS could not exceed 10 percent of the value of production for that year. In keeping in line with its accession requirements, China's OTDS would be capped at 8.5 percent of the value of production for that year.An overall cap based on the total of all agricultural production may leave much discretion to concentrate production-and trade-distorting support to a few key commodities. Our analysis considers a third scenario, where in addition to the overall cap on OTDS, anti-concentration measures would include product-specific caps that would cap support at a specified percent of the value of production for each commodity. Under the AoA, Members without AMS entitlements currently face an effective product-specific cap at the de minimis threshold for each commodity. In our scenario, we consider capping individual commodity support at twice the level of the overall cap. For example, for developed country Members, product specific caps would be set at 10 percent of the value of production for that commodity. Product-specific caps for developing country Members would be set at 20 percent of that commodity's value of production. China's product specific cap would be set at 17 percent of the value of production. In this section we look at trade-distorting domestic support over the next 10 years assuming a business as usual baseline and then consider the impacts of proposed new disciplines on trade-distorting domestic support as summarized in the previous section and Table 1.The modeling framework is based on IFPRI's dynamic global computable general equilibrium model, MIRAGRODEP. MIRAGRODEP has been widely utilized to study issues related to international trade and trade policy studying new agreements in the context of the WTO (Laborde, Piñeiro and Glauber, 2017), or regional negotiations (Bouet, Laborde and Traore, 2018)  35 .Figure 2 shows available support levels, expressed in 2017 constant USD in 2030, under the three scenarios. Under the business-as-usual baseline, over USD 1.4 trillion could be theoretically applied towards trade distorting domestic support (that is assuming full use of AMS and de minimis support and assuming continued use of Article 6.5 and Article 6.2 support proportionate to current usage and assuming 2030 projected production values). That estimate of policy space could be higher still if members increased the relative use of Article 6.5 and Article 6.2 in the future 36 . Those policies are currently unconstrained.Under the business-as-usual baseline, LAC countries account for just 10 percent of total support available to producers. Non-LAC developing countries are projected to account for over two-thirds of total policy space available in 2030. Developed countries account for the remaining 22 percent of the total available OTDS.Total available trade distorting domestic support is projected to decline 61 percent from baseline levels by 2030 under the OTDS scenario. Most of that decline can be attributed to the reduction in available de minimis support (which has been cut by 50 percent from the levels under the AoA). The largest declines are for developed countries which, in addition to de minimis allowances, had large AMS entitlements under the baseline. On average, available OTDS for developed countries is projected to decline by 80 percent whereas available policy space declines by 59 percent for LAC countries and 55 percent for non-LAC developing countries. Under the new constraints, policy space for developed countries and LAC countries each account for about 11 percent of the total, while the share of available OTDS for non-LAC developing countries accounts for about 78 percent of the reduced total.Inclusion of Article 6.2 measures under the OTDS (OTDS&6.2) is not projected to have much impact on policy space with only marginal effects on LAC and non-LAC developing countries. This is largely due to the large amount of policy space afforded by 10 percent of those countries' value of production (8.5 percent for China).35. More information on MIRAGRODEP is provided in Appendix 1 while information on the construction of the business-as-usual baseline is provided in Appendix 2. 36. For example, if all Members increased Article 6.5 and Article 6.2 support to equal 5 percent of the value of production, available policy space could exceed 3 trillion USD. Russian Federation, South Africa, Ukraine, and Viet Nam.able OTDS. Use of trade-distorting support under the OTDS scenario actually increases for non-LAC developing countries (up 5 percent from baseline levels). The increase in use reflects the fact that for developing countries with no AMS entitlements, the OTDS scenario would replace the implicit product-specific cap under de minimis provisions with an overall cap on all trade distorting support.Including Article 6.2 support under the OTDS (OTDS&6.2) is projected to have no additional impact on use of trade distorting support. However, capping the OTDS and imposing product-specific caps (OTDS&Cap) is expected to reduce trade distorting domestic support by 22.5 percent for high income countries and by 3 percent for non-LAC developing countries. Use of trade-distorting support among LAC countries would be largely unaffected due to the relatively minor use of support by those countries. Capping and reducing trade distorting domestic support is expected to have only small impacts on global agricultural production though there are small but significant shifts at the regional level (Figure 4). Globally, the largest impacts occur under the OTDS scenario with product-specific caps (OTDS&Cap), where the global value of production is projected to increase by 0.1 percent. Production is projected to decline among high-income countries where the projected decline in domestic support is projected to be higher than among other country groupings. Production in LAC countries is projected to increase by more than 0.2 percent under the product-specific cap scenario (OTDS&Cap) with larger than average gains in Argentina (up 0.3 percent), Brazil (up 0.4 percent) and Uruguay-Paraguay up 0.3 percent. Like production impacts, constraints on trade-distorting domestic support are expected to have small impacts on global exports (Figure 5). Total exports are projected down 0.1 percent under the OTDS scenario where amber plus blue box spending is constrained.Much of the decline is expected to occur in the more highly-supported high income countries (down 0.2 percent). With the exception of Mexico, most LAC countries are expected to increase exports, with the largest increases in the Mercosur countries.Capping the OTDS and imposing product-specific caps (OTDS&Cap) is expected to result in 0.5 percent decline in agricultural exports in the high-income countries, but much of that decline is expected to be offset by increases in exports from LAC countries such as Brazil (up 0.6 percent), Uruguay-Paraguay (up 0.4 percent), Argentina (up 0.3 percent) and Mexico (up 0.2 percent). Constraining domestic subsidies is expected to have small, but generally positive, impacts on agricultural prices (Figure 6). Prices for most agricultural products are expected to rise by less than 0.2 percent. Fibers are the exception as constraints on the OTDS are expected to result in increased global production resulting in lower fiber prices. Those im-pacts are negated by implementing constraints on fiber-specific support in the OTDS&Cap scenario. Under that scenario, support for commodities such as cotton are constrained, which reducers productions and results in higher fiber prices. We now consider the impacts of domestic support disciplines on farm income. On the one hand, subsidized producers suffer income losses due to reduced farm subsidy payments. Those losses may be offset, to some degree, by increases in farm prices and receipts. On the other hand, those producers who are less dependent on subsidies generally gain because of price increases and potential shifts in production.The impact of constraining OTDS on global farm income is negligible (Figure 7). Farm income in high income countries is projected to fall 1.4 percent from baseline levels with a cap on OTDS and almost 2.8 percent when product-specific caps are imposed (OTDS&-Cap). Those declines largely reflect the 20 percent decrease in trade-distorting support for those countries (Figure 3). Farm income in LAC countries is projected to increase 0.12 percent over baseline levels in 2030. The Mercosur countries are expected to post slightly higher gains in farm income (0.14 to 0.17 percent increase). Capping product-specific support is expected to increase farm income by almost 0.3 percent in LAC countries while farm income in non-LAC developing countries like China and India are expected to be constrained by product-specific caps. Overall, average farm income in non-LAC developing countries is expected to fall marginally (down 0.05 percent) under the product-specific cap scenario (OTDS&Cap). ","tokenCount":"2161","images":["-1929114494_1_3.png","-1929114494_2_3.png","-1929114494_6_3.png","-1929114494_7_3.png","-1929114494_8_3.png","-1929114494_9_3.png","-1929114494_10_3.png","-1929114494_11_3.png"],"tables":["-1929114494_1_1.json","-1929114494_2_1.json","-1929114494_3_1.json","-1929114494_4_1.json","-1929114494_5_1.json","-1929114494_6_1.json","-1929114494_7_1.json","-1929114494_8_1.json","-1929114494_9_1.json","-1929114494_10_1.json","-1929114494_11_1.json","-1929114494_12_1.json","-1929114494_13_1.json"]}

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+{"metadata":{"gardian_id":"b86b4e39498daf2a5825af7b2d3c729b","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/91f66733-baaf-4d65-9f69-44bb73dc53f3/retrieve","description":"Urbanization is moving fastest in Africa south of the Sahara, with major implications for food security and other governance challenges. Large urban poor populations rely heavily on the informal economy for accessible, affordable food. Most eggs, meat, fish, and milk sold to the urban poor are from informal markets. Food security policies in urban Africa face institutional, administrative, and political challenges: Lack of local mandate for food security under decentralization policies. Lack of cross-sectoral, cross-ministerial policy integration. Political contest over cities that occasionally leads to violence. Government interventions have focused on control, regulation, and often violent eradication of the urban informal food economy.","id":"1700542805"},"keywords":[],"sieverID":"c4614e78-d648-4945-b41b-501e01fa5d6e","pagecount":"8","content":"Urbanization is a global phenomenon, but in Africa south of the Sahara 1 its pace and impact are particularly notable. Africaas urban population is the fastest growing in the world. By 2030, the continent is expected to reach a tipping point, when for the first time the majority of the regionas population will live in urban areas. 2 These broad trends capture a tremendous degree of variation across urban Africa, ranging from the megacities of Kinshasa and Lagos, which are home to more than 10 million people, to secondary cities like Tema in Ghana and Ndola in Zambia, with populations of fewer than 750,000 people. 3 While these demographic shifts contribute to a number of urban policy challenges, including limited housing supplies, infrastructure bottlenecks, pressure on scarce public services, and environmental degradation, the implications for food security in urban Africa are equally significant.The urban poor are more vulnerable than their rural counterparts are to fluctuations in food prices and exchange rates. Urban residents in Africa are less likely to produce food for their own consumption and they devote a higher share of their household budgets to food purchases than rural populations. 4 This vulnerability was evident during the 2008 and 2011 global food price spikes, when Africa experienced the highest incidence of urban food price riots. 5 Africaas urban centers are characterized by both a growing middle class and growing urban poverty. 6 Significant pockets of food insecure populations can be found in even the wealthiest countries in the region. For example, food insecurity is endemic in the poorest neighborhoods of Gaborone, Botswana, and Windhoek, Namibia. 7 More broadly, diets in African cities rely heavily on starchy staples, and this lack of diversity contributes to malnutrition. 8The governance challenges to enhancing food security in urban Africa span institutional, administrative, and political dimensions. Institutionally, food security policies involve intersectoral coordination across multiple ministries, which typically occurs under the leadership of ministries of agriculture or health. When the focus is explicitly on the urban dimensions of food security, greater engagement is needed with ministries of urban and local development. National food security strategies, however, are often created parallel to, rather than in concert with, urban development strategies. This hinders full integration of urban food security into national planning. For example, Ugandaas recent national urban policy focused on water, housing, and waste management but neglected food security. 9Administratively, many African countries are pursuing varying degrees of decentralization, which implies that a growing number of government actors are engaged in different dimensions of urban food security. But food security policy formulation is rarely devolved entirely to local or municipal governments, precisely because food security commitments require sustained financing and intersectoral capacity that is often even weaker at the subnational than at the national level. Moreover, local autonomy over food security could result in uneven progress across communities within the same country. In South Africa for instance, arguably one of the regionas most decentralized countries, local governments have no clear mandate over food security. 10 Local governments are, however, often granted administrative authority to regulate urban markets, particularly when it comes to monitoring adherence to food safety regulations. Yet this authority is often shared between urban councils and national ministries, which muddles accountability.Politically, as cities become more economically important and home to a sizable share of voters, they can become a focal point for power disputes between mayors and presidents and between ruling and opposition parties. 11 At its most extreme, this culminates in political violence that disproportionately affects the urban poor, including their food security. For example, intense fighting during the 2000s in Côte daIvoireas commercial capital of Abidjan severely reduced dietary diversity among the cityas population. 12 This chapter explores the linkages between these governance dimensions and urban food security through the lens of the informal economy. 13 Oversight of the informal economy rarely falls to any one particular ministry, and its regulation is typically shared between local and national governments. As an important source of votes, the sector is also sometimes politicized by presidents and mayors, especially around elections. While most of the urban poor rely heavily on the informal sector, including street traders and marketers, for access to affordable food, adherence to food safety standards is much less constrained than for those operating in the formal food sector. Concerns over food safety partially explain the difficult relationship between African governments and the informal sector, which is characterized by alternating periods of harassment and appeasement. This chapter reviews these dynamics and highlights approaches that have enabled governments to protect the health of low-income urban consumers while allowing the informal economy to thrive and contribute to food security and nutrition.In many African cities, the informal economy has long been the linchpin of food security for the urban poor. 14 Despite the trend of supermarket expansion in the region, the urban poor continue to depend heavily on informal markets and street vendors for daily purchases and use supermarkets only periodically for bulk purchases of staples. 15 Most of the eggs, fish, meat, and milk sold to the poor in urban Africa are from informal markets. In countries such as Côte daIvoire, Kenya, Mali, and Uganda, 80 to 90 percent of raw milk is purchased from vendors or small-scale retailers. 16 More broadly, a survey of over 6,000 households in low-income neighborhoods in 11 African cities found that 70 percent of urban households regularly purchase their foods from the informal market or street vendors. 17 Notably, reliance on the informal sector varies depending on how wealthy a country is: 90 percent of households in the South African cities of Cape Town and Johannesburg buy their food from supermarkets compared with only 23 percent in Maputo, Mozambique. 18Indeed, many observers contend that supermarkets in Africa are still largely a niche element of food retail and will continue to be so in the near future. 19 A study focused on Kenya predicts that supermarket chains will continue to capture only a fraction of the urban fresh fruit and vegetable market. 20 Similarly, despite the presence of supermarkets in Zambia for more than 20 years, they still serve only a small share of the population. 21The informal economy is critical to urban food security for several reasons. First, informal markets tend to be located closer to low-income housing settlements than are supermarkets, making informal markets the main source of food for many of the urban poor. Itinerant traders offer a convenient source of foodstuffs for busy urbanites. Physical proximity is especially important because in many African cities irregular provision of electricity makes long-term refrigeration difficult, requiring almost daily food purchases.Second, the informal economy improves food affordability through both incomes and prices. Informal vendors can sell in smaller quantities, at lower prices, and on credit. 22 Moreover, the informal economy is a critical source of income for the urban poor, accounting for approximately 72 percent of nonagricultural employment in Africa. 23 Street vending and informal trade are especially important sources of livelihoods and financial independence for women, who are the primary sellers of street foods and perishable goods, such as fruits and vegetables. 24 In addition, informal trade is often the entry point into urban employment for newly arrived rural migrants. 25Third, the informal economy plays a critical role in the agricultural value chain. Many value chains have two tracks, with formal value chains serving middle-and upper-class consumers and export markets and informal ones serving low-income consumers in domestic markets. 26 Poor rural smallholders face lower barriers to entry when selling to informal traders and markets than to more formal and regulated markets. Yet even some large-scale agribusiness companies expand their markets by segmenting their consumers via the informal sector. In the dairy sector, for example, companies sell single-serving milk sachets to vendors who lack refrigeration and who in turn sell them to the poor. 27Despite the importance of informal markets to the food security of the urban poor, African governments have a difficult relationship with the sector. In fact, many African countries still retain colonial-era legislation on street vending that penalizes both sellers and buyers. 28 Unpredictable \"decongestion\" exercises by governments often involve arresting and fining informal vendors, confiscating their merchandise, and demolishing market stalls. The Accra Metropolitan Assembly in Ghana even established a Fast Track Court in the mid-2000s for trials of street hawkers who had been arrested. 29Violence toward members of the informal sector, as reported in the media, has increased in the region over the last two decades (Figure 1). These episodes include Zimbabweas Operation Restore Order (2005), Malawias Operation Order (2006,2015), Nigeriaas Zero Tolerance Campaign in Lagos ( 2009), South Africaas Operation Clean Sweep (2013), and the Keep Zambia Clean and Healthy campaign (2007,2015). This violence not only hurts a vulnerable sector of society that is already food insecure but also reduces access for others who depend on the sector for many of their fresh and nutrient-dense foods. For instance, in the wake of Operation Order in the Malawian cities of Blantyre, Lilongwe, and Notes: \"Informal\" refers here to street hawkers, vendors, marketers, and traders. \"Africa\" refers to countries south of the Sahara. The events are gathered from media reports in cities and secondary towns.Zomba, higher food insecurity was observed among the poor when vendors were forced to the citiesa outskirts. 30 While harassment of informal vendors is not unique to Africa and is also present in Southeast Asia and Latin America, the scale of the informal economy and its importance to urban livelihoods is much greater in Africa than it is in those regions. 31 Violent crackdowns on the sector can have serious consequences for Africaas urban food security.This behavior toward the informal sector reflects many of the regionas urban governance challenges. Institutionally, it is rare to find high-level government ministries that explicitly promote the interests of informal workers. In the absence of such support, a vibrant set of informal sector workersa associations emerged in the region over the last decade to address government harassment. 32 Yet many of these associations are too fragmented or underfunded to affect the policy process. 33 Administratively, authority over regulating informal sector activities can be extremely confusing. Higher levels of government may contravene the actions of lower tiers. For instance, in 2009, Zambiaas Ministry of Local Government and Housing paid the Zambian police service to remove street vendors in Lusakaas central business district, thereby directly intervening in an area of responsibility delegated to the Lusaka City Council. 34 Politically, informal markets can become infiltrated by partisanship, and informal workers heavily influenced by party politics, which can affect how both markets and vendors are treated by governments. 35 Zimbabweas Operation Restore Order, for example, was launched by the ruling party in all major urban areas after the party lost the 2005 parliamentary elections in these constituencies. The violent demolition campaign caused over 700,000 urban poor to lose their homes and informal businesses and exacerbated already insufficient food access for this population. 36 As seen in Figure 2 with reference to public opinion in Zambia, such draconian policies toward informal workers enjoy relatively high levels of support from middle-class constituents who work in the private and government sector.While politics plays an important role, governments justify their harsh treatment of informal sector workers by pointing to concerns about tax evasion, trespassing on private land, traffic congestion, and food safety. Certainly informal markets are less likely to take measures to assure food safety. Many vendors and marketers operate in settings without access to electricity, waste disposal, clean water, or appropriate sanitation practices, meaning that foods are often not handled hygienically. 37 This increases the risk of foodborne illness for the urban poor, with its own set of problems, but also contributes to micronutrient deficiencies. 38 While the informal food sector can offer consumers low prices, the trade-off is less regulation of quality control and labeling than is found in formal food value chains, leaving poor consumers more vulnerable to contaminated, adulterated, and spoiled foods. 39 Crackdowns and harassment do not necessarily improve these circumstances, though. In fact, research in developing countries such as Brazil suggests that frequent crackdowns reduce the incentives for those in the informal food economy to invest in the practices or equipment that would improve food safety. 40One common policy intervention in Africa is to upgrade or build new marketplaces with proper sanitation and lighting in order to move informal vendors off the streets while also addressing food safety concerns. Yet these efforts rarely succeed in permanently discouraging traders from returning to the streets. Rising land costs in major cities, overlapping land claims in city centers, and a dearth of suitable land under public ownership often result in new markets being built on less expensive peri-urban land-often located far from informal workersa regular customers. 41 Moreover, fees for stalls in upgraded markets are often expensive, so stalls go to more affluent vendors or foreigners rather than the poorest traders. 42 Politics also plays a role in these processes, as seen in Dakar, Senegal, where a popular opposition-party mayor attempted to raise money for a new market for street vendors through a municipal bond, an initiative ultimately thwarted by the national government. 43In addition to market improvements, governments could protect the interests and health of low-income urban consumers and still allow the informal economy to thrive by focusing more on education and training. In Kenya, where informal milk trading accounts for about 86 percent of milk sold, Kenyaas Dairy Board established a Dairy Traders Association in 2009 that provides informal traders with training on the basics of milk hygiene and simple quality tests. Upon completion of the short training course, traders receive a certificate to obtain a milk vending license and therefore avoid receiving a fine from the Dairy Board. 44 Similarly, in Nigeria, the International Livestock Research Institute designed a training course for butchersa associations in informal markets to improve hygienic behavior and develop best practices. In return, butchers can display their completion certificates to customers, and they often disseminate their learning to colleagues within their associations. 45 In Dakar, where women comprise a majority of those selling prepared food in the streets, illiteracy and poor education often contribute to a lack of awareness about sanitation standards. After a community was trained in food hygiene by a local nongovernmental organization, the participating women successfully lobbied for canteens where they could safely prepare foods. 46 Scaling up such interventions with street-vending and marketing associations, and capitalizing on mobile technology, could significantly contribute to the transfer of knowledge on hygienic food preparation practices and change behaviors accordingly (Box 1).More broadly, a variety of options exist for improving the governance of the informal economy beyond addressing food safety concerns. These include institutionalizing regular engagement between local governments and informal workers within management units of city councils and marketplaces. 47 One attempt at this is Zambiaas 2007 Markets and Bus Station Act, which aimed to place the control of markets and bus stations under management boards. In the case of markets, these boards include representatives of local authorities, vendors, and consumers who decide jointly how markets operate. This transparency and engagement in turn encourage many vendors to pay the requisite stall fees that cover investments in sanitation and other infrastructure. 48 Relatedly, improved transparency in the use of stall fees and other taxes enhances accountability between informal workers and the government. Fiscal earmarking of such payments explicitly for improved infrastructure in markets could build trust between authorities and informal workers while also increasing local government revenue. 49Approaches in other regions are instructive and feasible in the African context. In Hanoi, Viet Nam, vendors and the government arrived at a compromise approach known as \"restricted tolerance\"-street vendors can work freely during certain times of the day if they clean up any street litter at the end of their allotted time. 50 In Peru, informal workers are participating in developing a law on self-employment and working with Limaas city government to revise street-vending bylaws. 51 In Africa, participatory engagement of street vendors and marketers in reforming anachronistic legislation that legitimizes arbitrary harassment of informal workers offers a likely first step in improving governance.Urban spaces are not just characterized by demography and geography but also by a distinct set of legal, institutional, and governance dimensions that should be taken into account by any policy recommendations to tackle food security. Common policy efforts thus far to address urban food security include urban agriculture and biofortification programs. An equally important component should be more proactive incorporation of the informal economy into policy discussions on urban food security combined with less harassment of those whose livelihoods depend on the sector. This is particularly important because Africaas urban expansion has occurred largely in the context of low per capita economic growth and only negligible shifts in the economic structure of most countries toward more formal sector employment. 52 Without sufficient formal sector jobs, the informal sector will continue to be a key source of employment and food access for the urban poor. Tellingly, the particular importance of food safety within the informal economy is a major issue in East and Southeast Asia as their growing middle classes become increasingly concerned with tracing the origin of their food. 53 This further suggests that food safety and urban informality will continue to dominate the agenda of African policy makers for the foreseeable future, as the middle class is only just beginning to expand in the region.More broadly, in an era of decentralization and rapid urbanization in Africa, addressing urban food security requires horizontal cooperation across sectors and ministries as well as vertical coordination across tiers of government. Additionally, it requires novel approaches, including many of those discussed here, for addressing longstanding dilemmas for urban planners and local governments, including how to humanely manage the informal sector and harness its potential to improve food security. As policy interest grows in secondary cities and towns, which do not yet face such intense service delivery pressures and high land values, an opportunity exists to plan the design of markets to best accommodate informal workers as these cities grow. 54 Moreover, with the Sustainable Development Goal on inclusive cities (SDG 11) and the launch of the United Nationsa New Urban Agenda, the possibility is greater than ever before to better integrate a focus on food security and management of the informal economy into urban planning processes.Informal sector associations representing the urban poor have grown tremendously over the last decade, facilitated by international umbrella organizations such as Shack/Slum Dwellers International and Women in Informal Employment: Globalizing and Organizing. While growth of these associations resulted in fragmentation and competition in some cases, useful examples show where they have played a key role in advancing informal sector interests. 1For instance, Kenya's Federation of the Urban Poor (Muungano wa Wanavijiji) worked to establish a Food Vendors Association within some of Nairobi's informal settlements to map vending locations and their proximity to environmental hazards, such as flooding, sewage, and garbage heaps. Leaders of the association are all women and they undertake routine clean-ups of hazardous areas in their settlements. 2 Such practices could be replicated elsewhere with engagement of informal associations and support from local governments, donors, and the private sector. The resulting information could be communicated via text message to both consumers and food vendors so that such areas could be avoided and targeted for drainage and garbage collection by municipal authorities.\"Street vending and informal trade are especially important sources of livelihoods and financial independence for women, who are the primary sellers of street foods and perishable goods, such as fruits and vegetables.\"","tokenCount":"3264","images":[],"tables":["1700542805_1_1.json","1700542805_2_1.json","1700542805_3_1.json","1700542805_4_1.json","1700542805_5_1.json","1700542805_6_1.json","1700542805_7_1.json","1700542805_8_1.json"]}

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+{"metadata":{"gardian_id":"05dac1556a259b189830df16bf3e2b63","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/8d60b337-a511-4e8c-865e-d7523355904d/retrieve","description":"Rather than looking at the association between poverty and various household and individual characteristics on a one-to-one basis (bivariate analysis), which often oversimplifies complex relationships and can lead to erroneous conclusions, this report uses multiple regression to analyze poverty and living standards econometrically. As methodological choices can have a strong influence on the results,much of the report is given over to a detailed discussion of the methodology used to conduct the analysis and sensitivity analysis to assess the robustness of the findings to alternative methodological choices. These include the construction of region-specific poverty linesand the empirical model of poverty determinants used. Estimates of poverty levels and the results of the model are presented, followed by simulations that indicate the impact on poverty of specific policy interventions.\" -- from Text","id":"-1027925165"},"keywords":[],"sieverID":"4ebc7d91-bbb7-4f40-91b4-1c10c19222db","pagecount":"2","content":"If your order is not received within 2 weeks (USA) or 6 weeks (outside USA) please let us know.Rather than looking at the association between poverty and various household and individual characteristics on a one-to-one basis (bivariate analysis), which often oversimplifies complex relationships and can lead to erroneous conclusions, this report uses multiple regression to analyze poverty and living standards econometrically. As methodological choices can have a strong influence on the results, much of the report is given over to a detailed discussion of the methodology used to conduct the analysis and sensitivity analysis to assess the robustness of the findings to alternative methodological choices.These include the construction of region-specific poverty lines and the empirical model of poverty determinants used. Estimates of poverty levels and the results of the model are presented, followed by simulations that indicate the impact on poverty of specific policy interventions.Although the goal is to determine the extent of absolute poverty-a fixed standard of living-in the country as a whole, prices, demographics, and consumption patterns differ from one area to another.Therefore, regional poverty lines are drawn (rather than a single line) in order to approximate a uniform standard of living. By grouping together provinces with similar patterns, 13 regions and 13 food and nonfood poverty lines are devised.The 13 poverty lines reflect regional differences in the cost of attaining the same minimum standard of living.Per capita consumption (total household consumption divided by the number of household members), rather than income, is used as the basic measure of individual welfare in this report.The consumption measure includes food and nonfood goods and services, whether purchased, home-produced, or received as a gift or payment in kind. Employing a two-step approach, the authors model the determinants of household consumption and then use standard poverty indexes-such as the headcount ratio and the poverty gap-to measure poverty as a function of the household's consumption level and the relevant poverty line.When the poverty lines are applied to the 1996-97 survey data, it appears that 10.9 million peopletwo-thirds of the population at that time-lived in a state of absolute poverty, with the incidence of poverty higher in rural than in urban areas.The incidence of poverty is highest in the central part of the country, with poverty rates about the same in the north and the south. At the provincial level, poverty rates varied widely, with slightly less than one-half of the population in Maputo City below the poverty line, rising to 88 percent in Sofala province.Kenneth R. Simler, Sanjukta Mukherjee, Gabriel L. Dava, and Gaurav Datt RESEARCH REPORT ABSTRACT sustainable solutions for ending hunger and povertyThe econometric model of poverty determinants includes demographic data such as age and sex of household members, education levels, employment, landholding, use of agricultural inputs, type of crops cultivated, community characteristics and access to services, and seasonal variations in welfare. As a test of sensitivity to underlying assumptions, alternative models that allowed for different definitions of the poverty lines and the dependent and independent variables were also examined; these produced similar results.The analysis identifies five principal elements of a poverty reduction strategy for Mozambique.These are (1) increased investment in education, (2) sustained economic growth, (3) adoption of measures to raise agricultural productivity, (4) improved rural infrastructure, and (5) reduced numbers of dependents in households.The research shows that education is a key determinant of living standards. Even one person in a household with education beyond the primary level tends to boost a family out of poverty.Therefore, high priority should be given to increasing school enrollment and achievement, while also addressing the gender, urban and rural, and regional disparities that currently exist.During the prolonged period of strife and economic decline, 1987-96, per capita GDP grew at only 0.6 percent a year. With peace, the prospects for economic growth and poverty reduction are promising. A sustained annual growth rate in per capita consumption of 4 percent in real terms over the next five years could reduce the incidence of poverty by as much as 20 percent, if the growth rate is equal across all income levels.Much of this success in reducing poverty depends on increasing agricultural productivity by promoting the use of modern agricultural inputs such as improved seed varieties, fertilizer, and mechanization. At the time of the survey only a small percentage of Mozambican farmers used improved inputs. In a setting where land availability is not a binding constraint over much of the country, increasing the size of smallholders' land is not likely to reduce poverty significantly. Wider provision of roads, markets, banks, and extension and communication services to rural villages would also go a long way toward stimulating agriculture and reducing poverty.The research indicates that the larger the number of dependents supported by a working adult, the more likely the household is to fall beneath the poverty line. Family planning programs will not only alleviate poverty but also improve women's health, labor force participation, and productivity.The importance of women's education in this context cannot be overemphasized.It may not be surprising that the priority areas for development are among those that were most adversely affected by the war: roads, bridges, schools, and teachers were all frequent targets of antigovernment rebels. Nevertheless, even at the low levels found in post-war Mozambique, education, infrastructure, and agricultural technology are key factors that distinguish poorer households from richer households and also point the way to poverty reduction in the future.","tokenCount":"895","images":[],"tables":["-1027925165_1_1.json","-1027925165_2_1.json"]}