We note that in Table 4, the value of r-squared from the regression with lasso-selected features – 0.882 – is above the one obtained from the identical exercise reported in column one of Table 3, which is 0.486. However, this includes both quarter and country fixed effects, so the comparison is not apples-to-apples. According to the last column of Table 4, the second quarter dummy explains the most considerable fraction of r-squared from the GDP growth regression. This finding is not surprising, given the dip in 2020Q2 GDP growth shown in Figure 13. Consistent with the r- squared decomposition results from the cross-section of countries – reported earlier in Table 4 – among all the real-time indicators, Google mobility and food prices are the first and second largest contributors to the r-squared from the GDP growth model, respectively. Altogether, the lassoselected real-time indicators account for 45.7% of the r-squared obtained after fitting the model reported in column three of Table 4. In other words, the features in question explain 0.457 × 0.882 = 40.3% of the variation in growth rates in the panel of high-income countries. It might be useful to contrast this figure with that obtained from the cross-section of countries that includes states with lower income.

According to column one of Table 3, lasso-selected features contribute to (1 −0.0574) = 94.3% of the overall r-squared reported in column one of Table 2. The r-squared in question is equal to 0.486. Therefore, the lasso-selected features explain 0.486 × (1 −0.0574) = 45.8% of the variation in GDP growth in the cross-section of 61 countries. This explanatory power of real-time indicators is comparable to that observed in the sample of high-income countries. From this observation, we conclude that, among all the real-time indicators considered in our analysis, Google mobility data and staple food prices are the most robust predictors of GDP growth and that their explanatory power is highly comparable across the two samples of countries available to us. The latter conclusion speaks to the promise of the ability of the two sets of real-time indicators in question to track different crises irrespectively of the country’s income status.

# 5. Conclusion

In this paper, we examined the capability of a set of real-time indicators to track the economic impacts of the COVID-19 crisis around the world. Of five different categories of real-time indicators, Google mobility, Google search trends, and food prices seem to have the strongest potential to track the onset of the pandemic. Google mobility data showed a steep reduction in mobility trends across all six categories of places, with a steady recovery that followed a gradual relief of stringency measures worldwide. The ability of Google search terms to track the COVID19 crisis appeared to be income- and region-specific. Except for high-income countries, religious search terms lined up with the onset of the pandemic, returning to the pre-crisis level in the second half of 2020. However, the generalizability of these terms to other crises remains uncertain. Furthermore, searches for food banks, assistance programs, and online education are highly dependent on the location and level of income. Food prices also painted an informative picture of the COVID-19 crisis state throughout 2020. The data showed that prices for basic food categories dropped sharply in most countries, rising to their pre-pandemic values only at the end of 2020. While NO2 and nighttime lights do not appear to follow the beginning of the COVID-19 crisis everywhere, both indicators show recovery starting from the third quarter of 2020.

A more rigorous investigation would require longer time series of real-time indicators and a higher geographic resolution of the analysis. For example, utilizing an extended temporal coverage, future 13