The paper ‘Lights out? COVID-19 containment policies and economic activity’, authored by researchers from World Bank, IIM Ahmedabad, and RBI, was recently published in the Journal of Asian Economics. The researchers conducted district-level analysis of night-lights, CPHS, and other datasets to study economic impact and recovery in response to COVID-19 containment policies.

Night lights captured by satellite imagery is an increasingly popular measure of economic activity used in research. Over 150 economics papers have been published using two main night lights datasets, the Defense Meteorological Satellite Program (DMSP) dataset and the newer Visible Infrared Imaging Radiometer Suite (VIIRS) dataset.

This new study used VIIRS night-lights data and found that economic recovery was 9.3 per cent lower in districts with maximum COVID-19 restrictions, as compared to districts with minimal restrictions. The authors supplement this finding with monthly household income and consumption data from CPHS. It was observed that lower household income and consumption are important channels through which the district-level impact on light-intensity occurred. Notably, their analysis of CPHS data indicated that household incomes declined more than consumption during the pandemic.

In addition to using night-lights and CPHS in conjunction for economic analysis, researchers have also used CPHS to identify and correct for biases in night lights data.

A study by Patnaik et al identifies and develops a correction for bias in VIIRS night-lights data caused by cloud cover. They conjecture that a reduction in radiance may be due to (a) cloud cover attenuating the true light radiance and (b) lower incomes during the monsoon seasons causing reduction in night lights. To identify changes in income, they study CPHS monthly household income at the HR^* level, from January 2014 to December 2019. They observe that there is no seasonal decline in income during the monsoon season, as seen in CPHS data. On this basis, they posit that that cloud cover can cause downward bias in night-lights datasets and offer a method of correction for the same.

CPHS data provides useful indicators of economic activity at high frequency and multiple geographical strata. The use of CPHS with night-lights and other datasets offer novel methods to understand India’s economy, as seen in recent studies.

*A Homogeneous Region is a cluster of neighbouring districts classified by CMIE, that share similar agro-climatic conditions, have relatively similar levels of urbanisation and female literacy compared to other neighbouring districts.