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The COVID-19 pandemic exposed limitations to economic measurement. | wb/data/pdfs/WPS10002.pdf | 0 | [] | [] | [] | Introduction | 1. | false | false | 0 | 66 | 8 | 16 | true |
Across the world, governments enacted lockdowns and other containment measures to mitigate the public health crisis, with strong implications for economic activity. | wb/data/pdfs/WPS10002.pdf | 0 | [] | [] | [] | Introduction | 1. | false | false | 1 | 164 | 22 | 30 | false |
Rational policy making requires evaluating economic impacts and assessing the economic situation in real-time. | wb/data/pdfs/WPS10002.pdf | 0 | [] | [] | [] | Introduction | 1. | false | false | 2 | 110 | 14 | 19 | false |
However, in many countries, traditional indicators of economic activity do not provide that. | wb/data/pdfs/WPS10002.pdf | 0 | [] | [] | [] | Introduction | 1. | false | false | 3 | 92 | 13 | 18 | false |
First, national accounts estimates are available with substantial lags and often only at the national level. | wb/data/pdfs/WPS10002.pdf | 0 | [] | [] | [] | Introduction | 1. | false | false | 4 | 108 | 16 | 21 | false |
In addition, national accounts estimates in many countries, including in Bangladesh, are only available at annual but not quarterly frequency. | wb/data/pdfs/WPS10002.pdf | 0 | [] | [] | [] | Introduction | 1. | false | false | 5 | 142 | 20 | 26 | false |
Second, data collection through surveys, which are fundamental for the traditional estimation of gross value added, became more difficult during the pandemic due to severe containment measures. | wb/data/pdfs/WPS10002.pdf | 0 | [] | [] | [] | Introduction | 1. | false | false | 6 | 193 | 27 | 35 | false |
In Bangladesh, for example, it took more than a year to finalize the gross value-added estimates for fiscal year 2020 (FY2020) (from July 2019 to June 2020). | wb/data/pdfs/WPS10002.pdf | 0 | [] | [] | [] | Introduction | 1. | false | false | 7 | 157 | 27 | 42 | false |
In such a situation, high-frequency indicators of economic activity can provide crucial insights. | wb/data/pdfs/WPS10002.pdf | 0 | [] | [] | [] | Introduction | 1. | false | false | 8 | 97 | 13 | 19 | false |
Many new approaches to tracking economic activity have been introduced since the outbreak of the COVID-19 pandemic. | wb/data/pdfs/WPS10002.pdf | 1 | [
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For example, high-frequency indicators measuring mobility based on phone locations provided by Google and Facebook have been used to assess the economic situation in real-time (Spelta and Pagnottoni 2021) . | wb/data/pdfs/WPS10002.pdf | 1 | [
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Beyer, Franco-Bedoya, and Galdo (2021) use daily electricity consumption and monthly nighttime light intensity to analyze subnational economic activity during the pandemic in India. | wb/data/pdfs/WPS10002.pdf | 1 | [
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Few comparable indicators for tracking the economy in real-time are available in Bangladesh. | wb/data/pdfs/WPS10002.pdf | 1 | [
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In this paper, we establish daily electricity consumption as a useful indicator for tracking economic fluctuations in Bangladesh. | wb/data/pdfs/WPS10002.pdf | 2 | [] | [] | [] | Introduction | 1. | false | false | 0 | 129 | 18 | 22 | false |
First, we confirm a meaningful relationship between electricity consumption and short-term economic fluctuations. | wb/data/pdfs/WPS10002.pdf | 2 | [] | [] | [] | Introduction | 1. | false | false | 1 | 113 | 13 | 19 | false |
Second, we estimate a daily electricity consumption model that explains over 90 percent of the daily variation in electricity consumption. | wb/data/pdfs/WPS10002.pdf | 2 | [] | [] | [] | Introduction | 1. | false | false | 2 | 138 | 20 | 24 | false |
Deviations of actual electricity consumption from the model prediction can then be interpreted as a real-time indicator of economic activity and can be used to assess economic shocks. | wb/data/pdfs/WPS10002.pdf | 2 | [] | [] | [] | Introduction | 1. | false | false | 3 | 183 | 28 | 34 | false |
We provide this measure not just at the national level, but also for the country's eight divisions separately. | wb/data/pdfs/WPS10002.pdf | 2 | [] | [] | [] | Introduction | 1. | false | false | 4 | 110 | 18 | 24 | false |
Using this measure, we show that electricity consumption in Dhaka fell over 40 percent compared with normal during the first lockdown in April 2020 and remained below normal until early 2021. | wb/data/pdfs/WPS10002.pdf | 2 | [] | [] | [] | Introduction | 1. | false | false | 5 | 191 | 31 | 36 | false |
In stark contrast, the later lockdowns had smaller impacts, suggesting strong adaptation to the COVID-19 containment measures. | wb/data/pdfs/WPS10002.pdf | 2 | [] | [] | [] | Introduction | 1. | false | false | 6 | 126 | 17 | 27 | true |
The layout of the paper proceeds as follows. | wb/data/pdfs/WPS10002.pdf | 3 | [] | [] | [] | Introduction | 1. | false | false | 0 | 44 | 8 | 11 | false |
Section 2 reviews related literature. | wb/data/pdfs/WPS10002.pdf | 3 | [] | [] | [] | Introduction | 1. | false | false | 1 | 37 | 5 | 8 | false |
Section 3 describes the data sources. | wb/data/pdfs/WPS10002.pdf | 3 | [] | [] | [] | Introduction | 1. | false | false | 2 | 37 | 6 | 9 | false |
Section 4 presents stylized facts on electricity consumption in Bangladesh. | wb/data/pdfs/WPS10002.pdf | 3 | [] | [] | [] | Introduction | 1. | false | false | 3 | 75 | 10 | 13 | false |
Section 5 analyzes the relationship between electricity consumption and economic activity in Bangladesh. | wb/data/pdfs/WPS10002.pdf | 3 | [] | [] | [] | Introduction | 1. | false | false | 4 | 104 | 13 | 17 | false |
Section 6 estimates a daily electricity consumption model. | wb/data/pdfs/WPS10002.pdf | 3 | [] | [] | [] | Introduction | 1. | false | false | 5 | 58 | 8 | 11 | false |
Section 7 analyzes deviations from the model during COVID-19 in Dhaka. | wb/data/pdfs/WPS10002.pdf | 3 | [] | [] | [] | Introduction | 1. | false | false | 6 | 70 | 11 | 19 | true |
Section 8 concludes. | wb/data/pdfs/WPS10002.pdf | 3 | [] | [] | [] | Introduction | 1. | false | true | 7 | 20 | 3 | 6 | false |
The long-term relationship between electricity consumption and economic growth has been well researched. | wb/data/pdfs/WPS10002.pdf | 4 | [
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Apergis and Payne (2011) find a causal relationship between electricity consumption and economic growth for 88 countries over 1990-2006. | wb/data/pdfs/WPS10002.pdf | 4 | [
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In high-, upper-middle-, and lower-middle-income countries, the causality runs in both directions. | wb/data/pdfs/WPS10002.pdf | 4 | [
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In low-income countries, they find only a unidirectional causality from electricity consumption to economic growth. | wb/data/pdfs/WPS10002.pdf | 4 | [
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Due to the strong association of electricity consumption and economic activity, variations in electricity consumption can be used to assess economic fluctuations. | wb/data/pdfs/WPS10002.pdf | 5 | [
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Chen et al. | wb/data/pdfs/WPS10002.pdf | 5 | [
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(2020) find that electricity usage in Europe declined between 10 and 15 percent during the acute phase of the COVID-19 pandemic. | wb/data/pdfs/WPS10002.pdf | 5 | [
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Historically, a 1 percent drop in electricity usage in Europe has been associated with a 1.3 to 1.9 percent drop in output. | wb/data/pdfs/WPS10002.pdf | 5 | [
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Cicala (2020) observes a one-to-one relationship between the variables of interest in the United States, including during the global financial crisis, and uses the relationship to assess the economic damage from COVID-19. | wb/data/pdfs/WPS10002.pdf | 5 | [
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Similarly, Beyer, Franco-Bedoya, and Galdo (2021) show that energy consumption declined strongly in India after a national lockdown was implemented on March 25, 2020, and confirm that electricity consumption can be a useful indicator in emerging market economies. | wb/data/pdfs/WPS10002.pdf | 5 | [
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Ai, Zhong, and Zhou (2022) estimate the impact of the COVID-19 pandemic in Hunan province (China) through firm-level electricity consumption data. | wb/data/pdfs/WPS10002.pdf | 5 | [
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They employ a difference-indifferences model to show that electricity consumption in Hunan province dropped by 27.8 percent during the early stage of the pandemic. | wb/data/pdfs/WPS10002.pdf | 5 | [
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For Bangladesh, empirical evidence of the relationship is ambiguous. | wb/data/pdfs/WPS10002.pdf | 6 | [
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Yıldırım, Sukruoglu, and Aslan (2014) do not find a link between energy consumption and economic growth in Bangladesh. | wb/data/pdfs/WPS10002.pdf | 6 | [
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Mozumder and Marathe (2007) find a unidirectional causality from per capita gross domestic product (GDP) to per capita electricity consumption, using annual data from 1971 to 1999. | wb/data/pdfs/WPS10002.pdf | 6 | [
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Along the same line, Asghar (2008) examines the causality between energy consumption and income from 1971 to 2003 and concludes that in Bangladesh, a unidirectional Granger causality runs from GDP to electricity consumption. | wb/data/pdfs/WPS10002.pdf | 6 | [
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In contrast, Sarker and Alam (2010) and Hossain and Saeki (2011) find a reverse causal relationship running from electricity generation to economic growth. | wb/data/pdfs/WPS10002.pdf | 6 | [
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More recently, Hossen and Hasan (2018) find unidirectional causality running from electricity consumption to GDP in Bangladesh from 1972 to 2011. | wb/data/pdfs/WPS10002.pdf | 6 | [
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Finally, Alam et al. | wb/data/pdfs/WPS10002.pdf | 6 | [
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(2012) and Ahamad and Islam (2011) find bi-directional, long-run causality between Bangladesh's economic activity and energy consumption. | wb/data/pdfs/WPS10002.pdf | 6 | [
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Our study contributes to the literature in three ways. | wb/data/pdfs/WPS10002.pdf | 7 | [] | [] | [] | Related Literature | 2. | false | false | 0 | 54 | 9 | 12 | false |
First, it reconsiders evidence on the causality between electricity consumption and economic activity in Bangladesh at annual frequency and adds monthly analysis. | wb/data/pdfs/WPS10002.pdf | 7 | [] | [] | [] | Related Literature | 2. | false | false | 1 | 162 | 22 | 30 | false |
Second, this is the first study to analyze electricity consumption as an high-frequency economic indicator for Bangladesh, with lessons for other emerging markets and developing economies. | wb/data/pdfs/WPS10002.pdf | 7 | [] | [] | [] | Related Literature | 2. | false | false | 2 | 188 | 26 | 33 | false |
Third, it uses the indicator to compare the costs of the different lockdowns that were enacted to contain the COVID-19 pandemic, to help assess them. | wb/data/pdfs/WPS10002.pdf | 7 | [] | [] | [] | Related Literature | 2. | false | false | 3 | 149 | 25 | 37 | true |
Monthly electricity consumption in Bangladesh is available from the Bangladesh Bureau of Statistics (BBS) from 1993 until 2013, when the data series was discontinued. | wb/data/pdfs/WPS10002.pdf | 8 | [] | [] | [] | Electricity Consumption | 3.1 | false | false | 0 | 166 | 24 | 31 | false |
Daily electricity consumption data can be scraped from the Bangladesh Power Development Board and is available for all eight divisions (Dhaka, Chattogram, Mymensingh, Sylhet, Khulna, Rajshahi, Barisal, and Rangpur) from 2010 until now, with only a one-day lag. | wb/data/pdfs/WPS10002.pdf | 8 | [] | [] | [] | Electricity Consumption | 3.1 | false | false | 1 | 260 | 38 | 67 | false |
For this study, daily data from February 2010 to October 2021 has been compiled. | wb/data/pdfs/WPS10002.pdf | 8 | [] | [] | [] | Electricity Consumption | 3.1 | false | false | 2 | 80 | 14 | 18 | false |
1 For 2.5 percent of all the days during this period, data are missing and were hence linearly interpolated using the day before and day after the missing one. | wb/data/pdfs/WPS10002.pdf | 8 | [] | [] | [] | Electricity Consumption | 3.1 | false | false | 3 | 159 | 29 | 38 | false |
For the analysis of the relationship with economic activity, we use annual data on GDP and industrial production (2000-21) from the National Accounts statistics published in the BBS Bluebook and the monthly Quantum Index of Industrial Production data (July 2012 to April 2021) from the BBS. | wb/data/pdfs/WPS10002.pdf | 9 | [] | [] | [] | Economic Activity | 3.2 | false | false | 0 | 290 | 46 | 59 | false |
Monthly export data (2016-20) were taken from the Export Promotion Bureau. | wb/data/pdfs/WPS10002.pdf | 9 | [] | [] | [] | Economic Activity | 3.2 | false | false | 1 | 74 | 11 | 18 | false |
We analyze the correlations between electricity consumption per capita and subnational poverty levels, the share of households with access to electricity (from the Bangladesh Household Income and Expenditure Survey), and firm density (from the Economic Census 2013). | wb/data/pdfs/WPS10002.pdf | 10 | [] | [] | [] | Other Data | 3.3 | false | false | 0 | 266 | 37 | 48 | false |
To assess the efficacy of using electricity consumption to analyze the impacts of disasters, we extract disaster data (2016-20) from the Center for Excellence in Disaster Management & Humanitarian Assistance. | wb/data/pdfs/WPS10002.pdf | 10 | [] | [] | [] | Other Data | 3.3 | false | false | 1 | 208 | 30 | 38 | false |
For estimation of the electricity consumption model, we use temperature data (2010-21) from the Bangladesh Meteorological Department and the dates of holidays and Ramadan from timeanddate. | wb/data/pdfs/WPS10002.pdf | 10 | [] | [] | [] | Other Data | 3.3 | false | false | 2 | 188 | 26 | 37 | false |
2 To measure the stringency of the COVID-19 containment measures Bangladesh adopted and their impact on mobility, we rely on the Oxford COVID-19 Government Response Tracker (OxCGRT) 3 Stringency Index (2020-21) and Google mobility data from the Google Community Mobility Reports. | wb/data/pdfs/WPS10002.pdf | 10 | [] | [] | [] | Other Data | 3.3 | false | false | 3 | 279 | 41 | 62 | true |
Finally, data on daily COVID-19 cases and division-level data on the number of cases and deaths were retrieved from the Institute of Epidemiology, Disease Control and Research and the Directorate General of Health Services websites, respectively. | wb/data/pdfs/WPS10002.pdf | 10 | [] | [] | [] | Other Data | 3.3 | false | false | 4 | 246 | 36 | 50 | true |
first, electricity consumption is increasing over time; second, there is seasonality in the data; and third, electricity consumption varies strongly from day to day. | wb/data/pdfs/WPS10002.pdf | 10 | [] | [] | [] | Other Data | 3.3 | false | false | 5 | 165 | 24 | 33 | false |
Part of the daily variation may be explained by measurement or reporting issues and may not actually reflect variation in electricity consumption. | wb/data/pdfs/WPS10002.pdf | 10 | [] | [] | [] | Other Data | 3.3 | false | false | 6 | 146 | 22 | 25 | false |
Electricity consumption can hence be at best a noisy measure of economic activity. | wb/data/pdfs/WPS10002.pdf | 10 | [] | [] | [] | Other Data | 3.3 | false | false | 7 | 82 | 13 | 16 | false |
The variation is much larger in some divisions compared with others. | wb/data/pdfs/WPS10002.pdf | 10 | [] | [] | [] | Other Data | 3.3 | false | false | 8 | 68 | 11 | 14 | false |
For example, it tends to be lowest in Dhaka and highest in Rajshahi. | wb/data/pdfs/WPS10002.pdf | 10 | [] | [] | [] | Other Data | 3.3 | false | false | 9 | 68 | 13 | 19 | false |
Map 1 shows the total electricity consumption per capita in the eight divisions before the COVID-19 pandemic in 2019. | wb/data/pdfs/WPS10002.pdf | 11 | [
{
"end": 783,
"ref_id": null,
"start": 782,
"text": "4"
}
] | [
{
"end": 459,
"ref_id": "FIGREF2",
"start": 451,
"text": "Figure 2"
}
] | [] | Electricity Consumption over Time | 4.1 | false | false | 0 | 117 | 19 | 27 | true |
There is strong variation in electricity consumption per capita across Bangladesh. | wb/data/pdfs/WPS10002.pdf | 11 | [
{
"end": 783,
"ref_id": null,
"start": 782,
"text": "4"
}
] | [
{
"end": 459,
"ref_id": "FIGREF2",
"start": 451,
"text": "Figure 2"
}
] | [] | Electricity Consumption over Time | 4.1 | false | false | 1 | 82 | 11 | 14 | false |
Dhaka has the largest per capita consumption, followed by Khulna, Chattogram, and Mymensingh. | wb/data/pdfs/WPS10002.pdf | 11 | [
{
"end": 783,
"ref_id": null,
"start": 782,
"text": "4"
}
] | [
{
"end": 459,
"ref_id": "FIGREF2",
"start": 451,
"text": "Figure 2"
}
] | [] | Electricity Consumption over Time | 4.1 | false | false | 2 | 93 | 13 | 26 | false |
These divisions are more developed than the others, showing that the level of development is positively correlated with electricity consumption per capita. | wb/data/pdfs/WPS10002.pdf | 11 | [
{
"end": 783,
"ref_id": null,
"start": 782,
"text": "4"
}
] | [
{
"end": 459,
"ref_id": "FIGREF2",
"start": 451,
"text": "Figure 2"
}
] | [] | Electricity Consumption over Time | 4.1 | false | false | 3 | 155 | 22 | 26 | false |
Figure 2 shows the correlation between per capita electricity consumption and the rate of poverty (panel a), household access to electricity (panel b), and firm density (panel c). | wb/data/pdfs/WPS10002.pdf | 11 | [
{
"end": 783,
"ref_id": null,
"start": 782,
"text": "4"
}
] | [
{
"end": 459,
"ref_id": "FIGREF2",
"start": 451,
"text": "Figure 2"
}
] | [] | Electricity Consumption over Time | 4.1 | false | false | 4 | 179 | 28 | 39 | false |
There is a strong negative association between electricity consumption and the poverty rate, implying that income poor divisions are also energy poor. | wb/data/pdfs/WPS10002.pdf | 11 | [
{
"end": 783,
"ref_id": null,
"start": 782,
"text": "4"
}
] | [
{
"end": 459,
"ref_id": "FIGREF2",
"start": 451,
"text": "Figure 2"
}
] | [] | Electricity Consumption over Time | 4.1 | false | false | 5 | 150 | 22 | 26 | false |
4 Barisal and Sylhet are outliers and have much lower electricity consumption than a linear relationship would suggest, possibly due to lagging power distribution infrastructure and labor for expanding coverage and operations. | wb/data/pdfs/WPS10002.pdf | 11 | [
{
"end": 783,
"ref_id": null,
"start": 782,
"text": "4"
}
] | [
{
"end": 459,
"ref_id": "FIGREF2",
"start": 451,
"text": "Figure 2"
}
] | [] | Electricity Consumption over Time | 4.1 | false | false | 6 | 226 | 32 | 42 | false |
As expected, per capita electricity consumption strongly increases with the number of people having access to the grid. | wb/data/pdfs/WPS10002.pdf | 11 | [
{
"end": 783,
"ref_id": null,
"start": 782,
"text": "4"
}
] | [
{
"end": 459,
"ref_id": "FIGREF2",
"start": 451,
"text": "Figure 2"
}
] | [] | Electricity Consumption over Time | 4.1 | false | false | 7 | 119 | 18 | 22 | false |
In addition, it increases with the density of firms, likely reflecting electricity consumption used for economic production. | wb/data/pdfs/WPS10002.pdf | 11 | [
{
"end": 783,
"ref_id": null,
"start": 782,
"text": "4"
}
] | [
{
"end": 459,
"ref_id": "FIGREF2",
"start": 451,
"text": "Figure 2"
}
] | [] | Electricity Consumption over Time | 4.1 | false | false | 8 | 124 | 17 | 22 | false |
Despite obvious noise in the data, electricity consumption growth is strongly correlated with other measures of economic activity. | wb/data/pdfs/WPS10002.pdf | 12 | [] | [
{
"end": 139,
"ref_id": "FIGREF3",
"start": 131,
"text": "Figure 3"
},
{
"end": 786,
"ref_id": "FIGREF5",
"start": 778,
"text": "Figure 4"
}
] | [] | Electricity Consumption Growth and Economic Indicators | 4.2 | false | false | 0 | 130 | 18 | 22 | false |
Figure 3 shows quarterly changes in electricity consumption from the first quarter of FY2016 to the last quarter of FY2021 and quarterly changes in industrial production and exports. | wb/data/pdfs/WPS10002.pdf | 12 | [] | [
{
"end": 139,
"ref_id": "FIGREF3",
"start": 131,
"text": "Figure 3"
},
{
"end": 786,
"ref_id": "FIGREF5",
"start": 778,
"text": "Figure 4"
}
] | [] | Electricity Consumption Growth and Economic Indicators | 4.2 | false | false | 1 | 182 | 28 | 37 | false |
At 0.60 for industrial production and 0.66 for exports, both correlations are strong. | wb/data/pdfs/WPS10002.pdf | 12 | [] | [
{
"end": 139,
"ref_id": "FIGREF3",
"start": 131,
"text": "Figure 3"
},
{
"end": 786,
"ref_id": "FIGREF5",
"start": 778,
"text": "Figure 4"
}
] | [] | Electricity Consumption Growth and Economic Indicators | 4.2 | false | false | 2 | 85 | 13 | 22 | false |
The fourth quarter of FY2020, when economic activity was severely disrupted due to the COVID-19 pandemic, is a clear outlier with a strong decline in exports and electricity consumption. | wb/data/pdfs/WPS10002.pdf | 12 | [] | [
{
"end": 139,
"ref_id": "FIGREF3",
"start": 131,
"text": "Figure 3"
},
{
"end": 786,
"ref_id": "FIGREF5",
"start": 778,
"text": "Figure 4"
}
] | [] | Electricity Consumption Growth and Economic Indicators | 4.2 | false | false | 3 | 186 | 29 | 43 | true |
Changes in electricity consumption can be used as a proxy to infer insights on the economic impacts of shocks, for example, the COVID-19 pandemic, associated lockdowns, or natural disasters. | wb/data/pdfs/WPS10002.pdf | 12 | [] | [
{
"end": 139,
"ref_id": "FIGREF3",
"start": 131,
"text": "Figure 3"
},
{
"end": 786,
"ref_id": "FIGREF5",
"start": 778,
"text": "Figure 4"
}
] | [] | Electricity Consumption Growth and Economic Indicators | 4.2 | false | false | 4 | 190 | 29 | 44 | true |
Figure 4 shows that electricity consumption declined sharply in the fourth quarter of FY2020, right after the first lockdown was imposed in Bangladesh at the end of March 2020. | wb/data/pdfs/WPS10002.pdf | 12 | [] | [
{
"end": 139,
"ref_id": "FIGREF3",
"start": 131,
"text": "Figure 3"
},
{
"end": 786,
"ref_id": "FIGREF5",
"start": 778,
"text": "Figure 4"
}
] | [] | Electricity Consumption Growth and Economic Indicators | 4.2 | false | false | 5 | 176 | 29 | 37 | false |
Electricity consumption recovered subsequently, but year-over-year growth remained minimal in the first quarter of FY2021. | wb/data/pdfs/WPS10002.pdf | 12 | [] | [
{
"end": 139,
"ref_id": "FIGREF3",
"start": 131,
"text": "Figure 3"
},
{
"end": 786,
"ref_id": "FIGREF5",
"start": 778,
"text": "Figure 4"
}
] | [] | Electricity Consumption Growth and Economic Indicators | 4.2 | false | false | 6 | 122 | 15 | 26 | false |
It took another two quarters before electricity growth was again close to the rates observed prior to the pandemic. | wb/data/pdfs/WPS10002.pdf | 12 | [] | [
{
"end": 139,
"ref_id": "FIGREF3",
"start": 131,
"text": "Figure 3"
},
{
"end": 786,
"ref_id": "FIGREF5",
"start": 778,
"text": "Figure 4"
}
] | [] | Electricity Consumption Growth and Economic Indicators | 4.2 | false | false | 7 | 115 | 19 | 24 | false |
By the fourth quarter of FY2021, electricity consumption growth reached the pre-pandemic level. | wb/data/pdfs/WPS10002.pdf | 12 | [] | [
{
"end": 139,
"ref_id": "FIGREF3",
"start": 131,
"text": "Figure 3"
},
{
"end": 786,
"ref_id": "FIGREF5",
"start": 778,
"text": "Figure 4"
}
] | [] | Electricity Consumption Growth and Economic Indicators | 4.2 | false | false | 8 | 95 | 13 | 24 | false |
Electricity consumption can also be used to track short-run economic activity during natural disasters. | wb/data/pdfs/WPS10002.pdf | 12 | [] | [
{
"end": 139,
"ref_id": "FIGREF3",
"start": 131,
"text": "Figure 3"
},
{
"end": 786,
"ref_id": "FIGREF5",
"start": 778,
"text": "Figure 4"
}
] | [] | Electricity Consumption Growth and Economic Indicators | 4.2 | false | false | 9 | 103 | 14 | 19 | false |
To show this, we average growth in monthly electricity consumption over the following natural disasters: Cyclone Roanu in 2016, the landslides and floods in 2017, the floods in 2018 and 2019, and Cyclone Amphan in 2020. | wb/data/pdfs/WPS10002.pdf | 12 | [] | [
{
"end": 139,
"ref_id": "FIGREF3",
"start": 131,
"text": "Figure 3"
},
{
"end": 786,
"ref_id": "FIGREF5",
"start": 778,
"text": "Figure 4"
}
] | [] | Electricity Consumption Growth and Economic Indicators | 4.2 | false | false | 10 | 219 | 36 | 47 | false |
Looking at average growth in electricity consumption (figure 5) during the month of a disaster, there is an average decline of approximately 10 percent. | wb/data/pdfs/WPS10002.pdf | 12 | [] | [
{
"end": 139,
"ref_id": "FIGREF3",
"start": 131,
"text": "Figure 3"
},
{
"end": 786,
"ref_id": "FIGREF5",
"start": 778,
"text": "Figure 4"
}
] | [] | Electricity Consumption Growth and Economic Indicators | 4.2 | false | false | 11 | 152 | 24 | 30 | false |
Electricity consumption then picks up gradually and overshoots in the second month after the disaster, presumably due to reconstruction efforts and to make up for some interrupted economic activity during the month of the disaster. | wb/data/pdfs/WPS10002.pdf | 12 | [] | [
{
"end": 139,
"ref_id": "FIGREF3",
"start": 131,
"text": "Figure 3"
},
{
"end": 786,
"ref_id": "FIGREF5",
"start": 778,
"text": "Figure 4"
}
] | [] | Electricity Consumption Growth and Economic Indicators | 4.2 | false | false | 12 | 231 | 35 | 41 | false |
First, we determine the long-run association between electricity consumption and economic activity based on annual data. | wb/data/pdfs/WPS10002.pdf | 13 | [
{
"end": 747,
"ref_id": "BIBREF15",
"start": 727,
"text": "Toda-Yamamoto (1995)"
}
] | [] | [] | Cointegration and Causality | 5.1 | false | false | 0 | 120 | 16 | 22 | false |
We perform the Johansen cointegration test on two bivariate annual models: (i) real GDP and electricity consumption, and (ii) industrial production and electricity consumption. | wb/data/pdfs/WPS10002.pdf | 13 | [
{
"end": 747,
"ref_id": "BIBREF15",
"start": 727,
"text": "Toda-Yamamoto (1995)"
}
] | [] | [] | Cointegration and Causality | 5.1 | false | false | 1 | 176 | 24 | 38 | false |
To uncover the long-run equilibrium and causal relationships between the selected variables, data from 2000 to 2020 are analyzed. | wb/data/pdfs/WPS10002.pdf | 13 | [
{
"end": 747,
"ref_id": "BIBREF15",
"start": 727,
"text": "Toda-Yamamoto (1995)"
}
] | [] | [] | Cointegration and Causality | 5.1 | false | false | 2 | 129 | 19 | 25 | false |
Each of the estimated models contains one cointegrating vector, suggesting that an equilibrium relationship holds in the long run. | wb/data/pdfs/WPS10002.pdf | 13 | [
{
"end": 747,
"ref_id": "BIBREF15",
"start": 727,
"text": "Toda-Yamamoto (1995)"
}
] | [] | [] | Cointegration and Causality | 5.1 | false | false | 3 | 130 | 19 | 25 | false |
The existence of a cointegration relationship also implies that at least one causal relationship exists among the variables. | wb/data/pdfs/WPS10002.pdf | 13 | [
{
"end": 747,
"ref_id": "BIBREF15",
"start": 727,
"text": "Toda-Yamamoto (1995)"
}
] | [] | [] | Cointegration and Causality | 5.1 | false | false | 4 | 124 | 18 | 23 | false |
To check pairwise causality, we employ the Toda-Yamamoto (1995) Granger causality test, which is robust to the identified cointegration relationship. | wb/data/pdfs/WPS10002.pdf | 13 | [
{
"end": 747,
"ref_id": "BIBREF15",
"start": 727,
"text": "Toda-Yamamoto (1995)"
}
] | [] | [] | Cointegration and Causality | 5.1 | false | false | 5 | 149 | 20 | 36 | false |
5 It uncovers unidirectional causality from GDP to electricity consumption ( 2 = 6.24, p = 0.04) and bidirectional causality between industrial production and electricity consumption. | wb/data/pdfs/WPS10002.pdf | 13 | [
{
"end": 747,
"ref_id": "BIBREF15",
"start": 727,
"text": "Toda-Yamamoto (1995)"
}
] | [] | [] | Cointegration and Causality | 5.1 | false | false | 6 | 183 | 26 | 45 | false |
6 | wb/data/pdfs/WPS10002.pdf | 13 | [
{
"end": 747,
"ref_id": "BIBREF15",
"start": 727,
"text": "Toda-Yamamoto (1995)"
}
] | [] | [] | Cointegration and Causality | 5.1 | false | true | 7 | 1 | 1 | 3 | false |
Second, we determine the short-run association between electricity consumption and economic activity based on monthly data. | wb/data/pdfs/WPS10002.pdf | 14 | [] | [] | [] | Cointegration and Causality | 5.1 | false | false | 0 | 123 | 16 | 22 | false |
Since Bangladesh publishes only annual GDP data, we can only use industrial production. | wb/data/pdfs/WPS10002.pdf | 14 | [] | [] | [] | Cointegration and Causality | 5.1 | false | false | 1 | 87 | 13 | 17 | false |
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