declines in nighttime lights in China (Elvidge et al., 2020), India (Beyer, Franco-Bedoya and Galdo, 2021), and Morocco (Roberts, 2021). By the same token, some scholars have taken advantage of real-time information on air pollution. Improvements in air quality during the onset of the COVID-19 pandemic were particularly noticeable in China and the Republic of Korea, two countries with generally poor air quality (Liu et al., 2020; Koo et al., 2020; Seo et al., 2020; Zhang et al., 2020; Ju, Oh, and Choi, 2021), but were also apparent in many cities in Sub-Saharan Africa (Masaki et al, 2021).

Finally, several studies investigate the ability of Google data and food prices to track the COVID19 crisis. For example, Woloszko (2020) uses Google Trends data to track economic activity in 46 OECD and G20 countries. Abay et al. (2020) utilize Google search terms to investigate changes in demand for selected services across 182 countries during the COVID-19 pandemic. Sampi and Jooste (2020) employ Google mobility data to nowcast monthly industrial production dynamics in Latin America and the Caribbean in roughly the same period. Dietrich et al. (2021) document increases in food prices following the introduction of more stringent social distancing policies.

In this paper, we contribute to the latter strand of studies, seeking to answer the following question: how well did real-time indicators track the COVID-19 crisis across a broad range of countries? To answer this question, we collected information on nitrogen dioxide (NO2) emissions and nighttime light intensity at the subnational level for 142 countries with a population of more than 500,000 people. Our findings reveal substantial heterogeneity in the ability of remote sensing indicators to track the COVID-19 crisis, across regions of the world and country income groups. Following the beginning of the crisis in the first quarter of 2020, NO2 emissions showed the steepest drops in the Middle East and North Africa (when grouped by regions) and upper-middle-income countries (when grouped by income level). Nighttime lights showed the steepest decline in East Asia and Pacific (when grouped by regions) and lower-middle-income countries (when grouped by income level). Although not every indicator seems to capture the beginning of the COVID-19 crisis in all corners of the world, both NO2 and nighttime lights show recovery starting from the second quarter of 2020.

In addition to these satellite-derived indicators, we compiled a set of indicators that have the potential to reflect the COVID-19 crisis, namely Google mobility data, Google search data, and data on food prices. Of these additional real-time indicators, Google mobility, religious Google search terms, and food prices appear to reflect the onset of the crisis best. Following a sharp change in Google search for religious terms, food prices, and mobility trends at the end of the first quarter of 2020, all these indicators show recovery starting from the second quarter of 2020.

Finally, we also examine whether these real-time indicators predict deviations from projected GDP in a subset of countries, most from the OECD, in which quarterly GDP estimates are available. Selected features from grouped lasso explain around 88 percent of the total variation from predicted GDP in the subsample. Around half of this is explained by real-time indicators, with the remaining half due to quarter and country fixed effects. Two indicators – a price indicator and a mobility indicator – alone explain more than 30 percent of the total r-squared, or just short of 30 percent of the total variation in the dependent variable. The results suggest that real-time indicators can complement existing tools to monitor the economic impacts of a crisis like COVID-19, in which mobility was sharply restricted.

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