# **3. Empirical findings**

# **_3.1 Roles of disease progression, NPIs and recovery packages in explaining the evolution of lights_**

Panel A of Figure 4 shows the total monthly deaths for the countries in our sample, with the sum of monthly SOL residuals plotted for high-income countries and lower-income countries separately.<sup>11,</sup> 12 In the case of lower-income countries, higher levels of monthly deaths are associated with lower levels of economic activity, as measured by deviations of city lights from their trajectories (correlation coefficient = -0.35). However, this negative correlation between deaths and economic activity is less obvious in high-income countries. In fact, for high-income countries, there is a weak positive correlation (correlation coefficient = 0.05) between deaths and economic activity. This could be due to the presence of reverse causality, which we discuss further below. Some other differences between high- and lower-income countries are also worth noting. Monthly deaths on the right axes show that high-income countries had much higher levels of deaths than lower-income countries – at their peak Covid-19 deaths were three times higher in high-income countries. Two important reasons for this difference exist: (i) lower levels of reporting of Covid-19 deaths and cases in lowerincome countries; and (ii) a higher share of the elderly in the populations of high-income countries who are more at risk from the disease.<sup>13</sup> Looking at the left axis on each graph also reveals that the loss of lights experienced, for all cities in aggregate, in lower-income countries is much larger than that experienced in high-income countries. Panel B of Figure 4 plots the six-month moving average of the median of the monthly SOL residuals, and the trends confirm that the average lower-income country city experienced a larger deviation in its growth trajectory as compared to the average high-income country city. This may be a result of cities in lower-income countries having different characteristics. For instance, the average population density of cities in high-income countries is 3,100 persons per square km (n = 406), while for cities in lower-income countries it is 10,900 persons per square km (n = 2,435). The regressions below estimate the impact of the Covid-19 crisis on cities globally, while the next subsection returns to the question of the role of population density in determining the economic impacts of the crisis.

Lastly, a sharp rebound in aggregate activity in high-income countries is observed after December 2020. Though the reason for this is unclear, it may be a response of economic activity to the development of effective Covid-19 vaccines.<sup>14</sup>

> 11 Lower-income countries consist of all countries that the World Bank does not define as high-income (see https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lendinggroups).

> 12 Ideally, in Panel A of Figure 4, we would like to show total monthly deaths in cities. However, data for total monthly deaths is only available at the national level for countries globally.

> 13 Jedwab, Pereira and Roberts (2021) present data on the historical evolution of age dependency ratios, defined as the ratio of the number of people aged 65 or older to the working age population (aged 15-64), for a global sample of cities. They show that whereas, on average, age dependency ratios for cities in high-income countries stand at around 0.2 today (or around 4-5 working age adults per senior), for cities in lower-income countries they stand at below 0.1 (i.e., more than 10 working age adults per senior).

> 14 The US Food and Drug Administration (FDA) approved the Pfizer vaccine for emergency use by December 11, 2020.

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