An additional issue to address is the endogeneity of lockdowns and other NPI measures. Countries that experienced more deaths may have introduced stricter NPI measures, which may have been driving economic losses. To better disentangle the roles played by the spread of Covid-19 itself (as captured by the monthly number of reported deaths), and both NPI and economic support measures in driving the evolution of nighttime lights, panel data methods are employed. In particular, the following regression is estimated: where for city i, in country c in month t, ln(SOLict) is the (natural) log-transformed sum of nighttime lights level, Xct includes a measure of deaths (as ln(deaths + 1)), and Ict is a vector of policy variables in the form of both NPI and economic support measures. Estimates are obtained by using monthly data from January 2015 to March 2021.<sup>15</sup> The specification also includes city fixed effects (αi) to control for unobserved time-invariant city characteristics that may be correlated with both the outcome and Xct, and allows for differential trends of lights across countries (τct). The countryspecific month-of-year effects (δcmontht) control for potential seasonal effects at the national level, including those associated with atmospheric distortions of the lights data and any predictable monthly variation in cloud coverage.

Results from the baseline regression are shown in Table 1. Column 1 shows that disease progression, as measured by the natural log of number of deaths, has a significant negative relationship with a city’s nighttime light intensity, as expected. Likewise, the estimated coefficients on the stringency and economic support indices have the _a priori_ expected negative and positive signs, respectively. The estimates suggest that a doubling of Covid-19-related deaths reduces a city’s nighttime lights by 0.85 percent, while a jump in the stringency of NPIs from 0 to 100 – i.e., going from no NPIs whatsoever to a stringent national lockdown – reduces them by 3 percent. In relative terms, the negative impact of deaths on nighttime lights is large compared to the negative impact of NPIs. A one standard deviation change in ln(deaths) (S.D. = 3.4) and the stringency index (S.D. = 28.5) are associated with a reduction in ln(SOL) of 2.9 and 1.1, respectively.

Columns 3 and 5 illustrate important differences between cities in high- and lower-income countries. The estimated negative coefficient on Covid-19-related deaths is driven mainly by the lower-income country subsample. The estimated coefficient on NPI stringency is found to be more negative in highincome countries, likely reflecting their greater capacity to enforce lockdown measures. On the other hand, for high-income countries, the estimated coefficient on deaths is puzzlingly positive. Similarly, the estimated coefficient on economic support is counter-intuitively negative for the lower-income country sample. As discussed further below, this could reflect reverse causality, where the continuation of economic activity resulted in higher death rates or a Covid-19 crisis induced recession led to the provision of economic support.

> 15 Starting with January 2015 helps us to examine interactions with characteristics measured in 2015 in the next section.

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