populous subnational regions within a given country. We obtained subnational estimates of the 2018 population counts from WorldPop.<sup>5</sup>

# 2.2 Methodology

First, we look at broad trends in the real-time indicators. Using our current knowledge of the evolution of the crisis throughout 2020, we identify key patterns in trends using purely descriptive graphical representations of those indicators that we believe to be the most reflective of the COVID-19 crisis in most parts of the world. We start with using both satellite-derived variables– nighttime lights and NO2–to track economic activity. We continue with the Google mobility indices and the Google Search data set. Finally, we use food prices restricted to the following six food items commonly consumed in many countries: bread, chicken, eggs, milk, onions, and rice.

Second, we estimate simple cross-section regressions with three separate dependent variables: GDP growth, deviation of actual GDP growth from the pre-pandemic expected growth, and the stringency index presented in Hale et al. (2021), which we refer to as the “Oxford Stringency Index.” We obtained country-level GDP – as well as values for predicted GDP from prior to the crisis – for a large group of countries for the entire year of 2020 from the IMF. We then constructed deviations of actual GDP from predicted GDP to analyze how well the real-time indicators predict these deviations. There are 62 countries for which we have information on the 2020 growth rates plus the complete list of real-time indicators described above. Table A1 lists the countries included in this analysis. We first present regressions including all candidate predictors. Then, to create a more parsimonious model, we also employ lasso to select features. We use the cross-validation (CV) method to select the value of that minimizes the mean squared error (MSE) in the validation sample and present the resulting estimates. We include information on 2019 GDP per capita, total population, and primary school enrollment rate as candidate covariates taken from the World λλ Development Indicators database.

Third, we obtained more detailed quarterly GDP data for a subset of countries that mainly consists of the OECD members.<sup>6</sup> In addition, since the quarterly data form a panel of countries, we employ group lasso to select features, grouping quarter fixed effects and country fixed effects.<sup>7</sup> The quarterly GDP growth data primarily come from high-income states, whereas the cross-sectional data set consists of countries with varying income statuses. One might expect that the predictive power of lasso-selected covariates is different across the samples, so we focus on these differences throughout the results below. There are 39 countries with non-missing data for quarterly GDP and the candidate features; a list is available in Table A2 of the appendix.

Some candidate features are likely to be strongly collinear. We thus use factor analysis to group the data as follows:

1. Two factors for food prices: apples, bananas, bread, cheese, eggs, lettuce, meat, chicken, onions, oranges, potatoes, milk, and rice (all relative to January of 2020)

> 5 https://www.worldpop.org/

> 6 These GDP statistics come from the OECD (2021).

> 7 In other words, lasso will either select all quarterly dummies or none of them (and the same for country dummies). We implement this using _gglasso_ in R.

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