The remainder of the paper proceeds as follows. Section 2 describes our data. Section 3 presents our descriptive graphical findings, while Section 4 presents our empirical results. Section 5 concludes.

# 2. Data and Methodology

# 2.1 Data

We consider a variety of real-time indicators, including data on nitrogen dioxide (NO2), nighttime lights, Google mobility indicators, Google search data, and food price data. NO2 is a pollutant produced by everyday human activity, such as burning fossil fuels. As such, it is attractive as a potential measure of the changes brought about by the COVID-19 crisis, as changes in patterns of human behavior can directly affect NO2 emissions. We use the TROPONOMI Level 2 Nitrogen Dioxide product obtained initially by the Sentinel-5 Precursor, an Earth observation satellite launched as a part of the Copernicus Programme in October 2017. We obtained data from January 2019 to December 2020, with the resulting series containing monthly concentrations of NO2 observed at the subnational level.

We also collected information on monthly nighttime lights intensity from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the Soumi-NPP Earth observation satellite processed by Elvidge et al. (2017). Nightlights have been found to correlate well with some measures of economic growth and development (see, e.g., Henderson et al., 2012) and may therefore provide a window into possible changes in economic activity brought about by the COVID-19 crisis. We use the Annual VNL Version 1 product that corrects data influenced by stray lights, excludes observations impacted by cloud cover, and has greater spatial coverage (especially in the northern part of the globe; for additional information, see Mills et al., 2013). As in the case of the NO2 series, the resulting data set contains monthly time series on nighttime light intensity observed at the subnational level from January 2019 to December 2020. To account for seasonal fluctuations in NO2 and nighttime lights series, we construct year-over-year deviations in 2020 relative to the same month in 2019, expressed in percentage changes.

A potential problem with these satellite-derived variables is the possibility of capturing noise, and its consequences are exacerbated given that we work with large-area zonal statistics. For example, Mills et al. (2013) point out that the Annual VNL Version 1 product has not been filtered out from aurora lights, fires, boats, biomass burning, and glass fares. Also, a failure to account for cloud pressure may artificially elevate the air pollution level observed in the data. To remove the influence of extreme outliers—whose values are more likely to be driven by noise—we look at the median values of nighttime lights/NO2 concentration in a given group of subnational regions when analyzing the data. For example, when aggregating data by region/income group, we look at the median subnational region in a given month. Likewise, we pick the median subnational region in a given country when constructing the country-level data.

In response to the crisis, Google has published trends in the movement of people across different categories of places, like workplaces, transit stations, and residences, using data derived from cell 4