Examining the Economic Impact of COVID-19 in India through Daily Electricity Consumption and Nighttime Light Intensity

The COVID-19 pandemic has disrupted economic activity in India. Adjusting policies to contain trans- mission while mitigating the economic impact requires an assessment of the economic situation in near real-time and at high spatial granularity. Th...

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Bibliographic Details
Main Authors: Beyer, Robert C.M., Franco-Bedoya, Sebastian, Galdo, Virgilio
Language:English
Published: World Bank, Washington, DC 2020
Subjects:
Online Access:http://documents.worldbank.org/curated/en/763351592916493022/Examining-the-Economic-Impact-of-COVID-19-in-India-through-Daily-Electricity-Consumption-and-Nighttime-Light-Intensity
http://hdl.handle.net/10986/33986
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Summary:The COVID-19 pandemic has disrupted economic activity in India. Adjusting policies to contain trans- mission while mitigating the economic impact requires an assessment of the economic situation in near real-time and at high spatial granularity. This paper shows that daily electricity consumption and monthly nighttime light intensity can proxy for economic activity in India. Energy consumption is compared with the predictions of a consumption model that explains 90 percent of the variation in normal times. Energy consumption declined strongly after a national lockdown was implemented on March 25, 2020 and remained a quarter below normal levels throughout April. It recovered somewhat subsequently, but electricity consumption was on average still 13.5 percent lower than normal in May. Not all states and union territories have been affected equally. While electricity consumption halved in some, others were not affected at all. Part of the heterogeneity is explained by the prevalence of manufacturing and return migration. At the district level, higher COVID-19 infection rates were associated with larger declines in nighttime light intensity in April. Together, daily electricity consumption and nighttime light intensity allow monitoring economic activity in near real-time and high spatial granularity.