It is worth noting that a sizeable proportion of people in some countries do not have internet access, which might limit the ability of some predictors—e.g., Google search trends—to track the crisis. We address this concern in two ways. First, we link the absolute values of the OLS residuals to the data on the internet users per capita to see whether the accuracy of our prediction depends upon the internet penetration.<sup>9</sup> As shown in Figure 13, our regressions with LASSO-selected features perform better in high-income countries with high internet penetration—as manifested by the negative relationship between the absolute values of the OLS residuals and the internet users per capita. However, we cannot say that LASSO-selected features have a greater capability to track the Stringency Index in countries with higher shares of internet users—if anything, the actual values of the index in question have smaller deviations from their predicted counterparts in countries with lower internet penetration.

Second, we look at the within-country dynamics of the economic performance in a panel of states with comparable levels of income that mainly consists of OECD members. To get a sense of the temporal heterogeneity of economic growth across quarters in 2020, Figure 14 plots quarter-overquarter, seasonally adjusted GDP growth rates for the 41 countries for which we have data. There are two striking patterns. First, every country recorded a lower growth rate in the second quarter than in the first, consistent with the peak of stringency measures shown earlier in Figure 1. Second, every country recorded a higher growth rate in the third quarter than in the second, though the overall pattern is negative growth across the second half of the year.

Table 4 presents the regression results for the quarterly-level data. Column one presents results with all variables and quarter dummies, but without country fixed effects. Column two includes country dummies to ascertain whether these features can predict changes within countries during the crisis. The first mobility factor seems to be the only consistent predictor of GDP growth according to the first two columns of Table 4. The inclusion of country dummies seems to improve the precision of some coefficients and increases r-squared and adjusted r-squared quite substantially.

Column three presents the group lasso-selected features and their coefficients. The group lasso selects both sets of fixed effects and all other candidate features except for the religion index and the first labor index. The overall predictive power as measured by r-squared decreases only slightly, and the adjusted r-squared goes up slightly.

At this stage, it might be useful to compare the relative strength of the real-time indicators’ predictive power across the two samples of countries used in our analysis. The first is a sample of 61 countries with different income status. The sample in question includes 34 high-income, 18 upper-middle-income, eight lower-middle-income and one low-income countries. The second sample consists of 39 countries with relatively more high-income states (29 high-income countries).

> 9 We have obtained the information on the internet users per capita (most recent year available) from the World Telecommunication/ICT Indicators Database. See https://www.itu.int/en/ITUD/Statistics/Pages/publications/wtid.aspx.f

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