example, the price of onions increased by around 10 percent in March relative to January before dropping back to January levels in May/June. Likewise, the price of chicken is consistently higher – relative to January – in low-income countries. In fact, chicken does not appear to drop below January prices until the end of the year.

# 4. Which real-time indicators are the best predictors of the 2020 growth rate and the severity of stringency measures?

Our next aim is to understand which real-time indicators best predict 2020 growth rates across countries and the severity of stringency measures.

We begin with regressions of growth rates and the Oxford Stringency Index on the groups of variables described above, as well as our set of macro variables – log of GDP per capita (PPP, 2019), log of 2019 total population, and 2019 primary school enrollment rate. We present results from these country-level regressions in Table 1. The first column uses the actual growth rate as the outcome, while the second column reports the results for deviation from the predicted annual growth rate. The last column of Table 1 uses the Oxford Stringency Index on the left-hand side.

In the first two columns of Table 1, the coefficients’ signs are consistent across the columns. Both price factors have negative signs; Google mobility data is positively associated with growth and its deviation from the 2019 projections. Satellite-derived indicators are also positively correlated with economic activity.

The last column of Table 1 paints a slightly different picture. Both price factors have opposite signs. Higher mobility is associated with the lower value of the stringency index. Of the two remote sensing indicators, only NO2 significantly predicts the stringency index. It is worth noting that when a variable is significant in both columns two and three, it is always of the opposite sign across the two columns.

Column two of Table 2 shows the coefficients of features selected for the growth rate’s deviation from its 2019 projection. Besides three features selected in the previous column–the second price factor and two mobility indices–lasso selects the second labor search index. Together with the macro variables, these lasso-selected features explain 49.5% of the variation in the outcome. Finally, except for the education and sports search index, lasso selects all the indices for the stringency regression. The lasso-selected variables explain 68% of cross-country variation in stringency measures and a higher value of adjusted r-squared indicates a better fit compared to the unrestricted specification reported in column three of Table 1.

To assess the individual predictive power of every lasso-selected feature, we check the contribution of every variable to the r-squared in each model, expressed in percent. Table 3 reports the results of this decomposition. According to our findings, Google mobility and food prices have the largest explanatory power. The two groups of indices contribute to 72.5%, 87.2%, and 58.4% of the overall r-squared from growth rates, their deviations from projected values, and stringency index regressions, respectively. The search terms also contribute to a reasonably large share of r- squared in every regression reported in Table 2. Finally, satellite-derived data and macroeconomic variables account for the minor shares of the goodness-of-fit measures.

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