# 1. Introduction

SARS-CoV-2, the virus that causes COVID-19, has reached nearly every corner of the world, resulting in millions of deaths (Dong et al., 2020). To decrease the spread of the virus, governments around the world have implemented lockdowns and mobility restrictions. These restrictions are apparent in Figure 1, which visualizes the Oxford Stringency Index, broken down by regions. Governments introduced peak stringency immediately following the onset of the COVID-19 pandemic in the first quarter of 2020, after which there was a mild decline in stringency. These restrictions led to massive economic disruption, as the COVID-19 pandemic has resulted in a reversal of a decade-long declining global poverty trend, pushing upwards of 100 million people into extreme poverty in 2020 (World Bank, 2020).

In response to the growing demand for accurate statistics of the economic cost of the pandemic and resulting policies, scholars have adopted three general approaches designed to shed light on the impact of measures like mobility restrictions and lockdowns. One strand of literature used past data to forecast the state of the world's economic well-being during the pandemic. In April 2020, during the peak stringency adopted in most countries, the IMF projected a 3 percent contraction of world economic output, much larger than during the 2008 global financial crisis (IMF, 2020a). The 2021 growth rate was projected to be 6.5 percentage points below the pre-pandemic forecast made in January 2020 (IMF, 2020b). Following the onset of the COVID-19 crisis, other work has used real-time forecasting. Combining pre-pandemic household surveys with the growth projections predicted that nearly 49 million people would fall below the $1.90 poverty threshold in 2020 real terms (Mahler et al., 2020a). An updated set of estimates published in June 2020 painted a gloomier picture, predicting that the number of newly poor individuals would range from 71 million to 100 million by the end of 2020 (Mahler et al., 2020b).

The second strand of literature uses interviews of representative parts of the population to understand socio-economic impacts, also as input to parameterize modeling and forecasting efforts. In the context of the pandemic, most interviews were conducted by phone<sup>2</sup> and – to a smaller extent – by using online surveys. Khamis et al. (2021), for example, use data from the high-frequency phone survey (HFPS) to estimate the early impact of COVID-19 on the labor markets of 39 countries. Their findings show that the pandemic has negatively affected labor market outcomes in these countries with respect to job and income losses, lack of payment and job changes. In a related paper, Kugler et al. (2021) focus on the distributional implications of the crisis, showing that female, less educated, and younger workers were initially more severely impacted. Bundervoet et al. (2021) demonstrate that the pandemic’s initial effects were both widespread and highly regressive, with the most vulnerable segments of the population being disproportionally affected.

The third strand of studies makes use of remote sensing indicators to monitor economic and other human activity during the COVID-19 pandemic. For example, the literature has long recognized that satellite images can be used to monitor the state of economic development in places where this information is difficult to obtain by other means (e.g., Henderson, Storeygard and Weil, 2012; Donaldson and Storeygard, 2016; Beyer et al., 2018; Kim, 2022). Studies have documented

> 2 For example, the World Bank conducted phone surveys in dozens of countries and published a harmonized dashboard available at https://www.worldbank.org/en/topic/poverty/brief/high-frequency-monitoring-surveys.

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