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...
| Main Authors: | , , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2020
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| 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 |
| 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. |
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