in nighttime light growth to assess the effect of the 2016 Indian banknote demonetization. Second, using nighttime lights is particularly appropriate for this study since this measure is available at high spatial granularity at monthly frequency.<sup>2</sup> This allows us to match nighttime lights to the relevant district-level zone classification to determine economic activity in the pre-period in March and April, and to compare it to activity in the post-period in May, June, and July. In contrast, official quarterly GDP estimates and other measures of overall economic activity like electricity consumption are not disaggregated at the district level. Finally, nighttime lights from satellites represent an objective measure of economic activity, which is also immune to biases due to survey non-response which might be correlated with lockdown policies.

We find that nighttime light intensity in India dimmed during the strict national lockdown in March and April and recovered in May and subsequent months. In May, districts with the most severe restrictions witnessed a 12.4% (0.06 standard deviation) lower recovery in nighttime light intensity compared to those with the least restrictions. The recovery for districts with intermediate restrictions was 1.7% (0.01 standard deviation) lower. While remaining in the same direction, the impact of the zone classification tapered off in June and July. Our findings are robust to several logically orthogonal robustness checks. These include a test of the impact of the zonal classification on the pre-period, where we reassuringly find no effect. We restrict the sample to only those districts that border a differently classified zone, to non-metropolitan districts, and those that were not subsequently reclassified in mid-May and find no qualitative change in our results. Finally, a test with placebo zone classifications yields coefficients that are close to zero and insignificant. Facebook mobility data from cellphone locations indicates that restricted mobility is a plausible channel. Monthly household data point to lower household income but not consumption as an important channel for these findings. More developed districts with above median population density, share of employment in services, credit per capita, and mean age experienced larger impacts of the restrictions.

Our study contributes to the growing literature on the economic impacts of government interventions to mitigate pandemics. Deb et al. (2020) find large effects of containment measures to slow the spread of COVID-19 on economic activity across countries. Goolsbee and Syverson (2020) examine the drivers of pandemic-related economic decline in the United States and compare consumer behavior across commuting zones to distinguish between government restrictions and the role of fear – both of which matter. Kong and Prinz (2020) quantify the employment impact of different state-level containment measures in the United States and Petroulakis (2020) shows that high non-routine jobs reduce the probability of job losses. While

> 2While data from the United States Air Force Defense Meteorological Satellite Program (DMSP) using the Operational Linescan System (OLS) are only available at annual frequency, recent data from the Suomi National Polar Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are monthly.

3