# **_3.4 Event-study analysis: Do high-density cities fare better?_**

In our final analysis, we deploy a difference-in-difference approach to estimate whether the impacts of the Covid-19 crisis differed between high- and low-density cities, based on a comparison of their differences before and after the onset of the crisis. First, we define a city as high-density if it has a population density that was in the top quartile of city population densities _within the country in which it is located_ , while all remaining cities are defined as low-density.<sup>22</sup> A comparison of the TLL for these two groups of cities shows that low-density cities have experienced larger TLL, even though the aggregate GDP generated by the two groups in 2015 was roughly the same (Figure 7). Second, we interact the density classification with a dummy that equals 1 for all months from March 2020 onwards (i.e., the post-Covid-19 onset period) and 0 for all prior months (i.e., the pre-Covid-19 onset period), noting that the WHO declared Covid-19 to be a global pandemic on March 11, 2020. The following equation is then estimated: ln (SOLict) = γCovict + θ(Covict × Hidensict) + ηXct + βIct + αi + τct+ δcmontht + εict [4] where: ln(SOLict) is the (natural) log-transformed nighttime sum of lights level; Covict represents the post-Covid-19 dummy, which is equal to 1 if time is March 2020 onwards (and zero otherwise); Hidensict is a dummy for high-density cities (top quartile within its country); Xct is a measure of Covid-19 deaths (natural log-transformed); and Ict are policies in the form of NPIs and economic support measures. Following the same notation as in equations 1 and 2, the specification also includes city fixed effects (to control for time-invariant city characteristics) and allows for differential trends of lights across countries as well as country-specific month-of-year effects to control for potential seasonal effects (possibly associated with atmospheric and other distortions of the lights data, including any predictable monthly variation in cloud coverage at the national level). In robustness checks, country-specific post-Covid-19 dummies are introduced to allow for national impacts of Covid-19 to vary by country (leaving between-year variation within each country after the onset of Covid-19).

The results are reported in Table 3 for the full global sample of cities, as well as for cities in just lowerincome countries and cities in just high-income countries. The results suggest that while, on average, the Covid-19 crisis has negatively impacted lights in cities, the effect has been smaller for highdensity cities (see results in columns 1 and 2).<sup>23</sup> This result is robust to using country-specific postCovid-19 dummies (column 3). Comparing the results in columns 4 and 5 with those in columns 7 and 8, respectively, this time, the estimated coefficients are similar across cities in high- and lowerincome countries. They are not, however, statistically significant for cities in high-income countries, likely due, at least in part, to the smaller sample.

The estimates suggest that after the onset of the pandemic, the loss in light intensity in high-density cities was around 2 percentage points less than for lower density cities. For comparison, the estimates in column 4 suggest that less dense cities in lower-income countries experienced a loss of 10 percentage points in their nighttime lights intensity following the emergence of Covid-19. Lastly, some differences in the estimated coefficient on the post-Covid-19 dummy are observed for the lower-income and high-income country subsamples. For the lower-income country subsample, the inclusion of Covid-19-related deaths and NPIs in column 5 absorbs the negative effect of the postCovid-19 dummy in column 4. By contrast, for cities in high-income countries, the magnitude and

> 22 Hence, we define a city as being high density if its population is dense _relative_ to other cities in the same country. The classification of cities into high- and low-density is based on their 2015 populations.

> 23 Some care of interpretation is required here, however, given that the estimated coefficient on the interaction dummy (i.e., the post-Covid-19 onset dummy  high-density dummy) is only significant at the 10 percent level.

18