of infections and deaths amount to 19 and 29 percent of the world’s cases.<sup>15</sup> Still, the low rates of infections, and especially death rates, in low-income countries relative to richer countries are arguably attributed to the younger age structure of the population in the region.

The age structure of the population plays a role in the health consequences of COVID-19. Nations with a large share of older people have typically registered high rates of COVID-19 infections and deaths. Figure 1 plots the percentage of the population 65 years or older vis-à-vis the COVID-19 confirmed cases per million people, while Figure 2 depicts the relationship between the percentage of the population 65 years or older and the COVID-19 deaths per million people. The scatterplots clearly illustrate the incidence of COVID-19 cases and fatalities across the different stages of the demographic dividend —as captured by the cross-country distribution of the population 65+ years for industrial countries, Sub-Saharan Africa (SSA), and non-SSA developing countries. The figures show that developing countries with lower shares of older population tend to have a lower incidence of infections and, particularly, deaths. Consequently, countries with greater shares of older populations tend to have higher infection rates and death rates (Figures 1 and 2).

# **4. Data Description**

This section describes the sources of variables used in the regression analysis to assess the growth impact of: (a) the different channels of health and economic transmission of COVID-19, and (b) the policy responses to combat the pandemic. We collected data for a cross-section of 135 developing countries —of which 47 countries are in Sub-Saharan Africa. Given that the real effects of the pandemic are still evolving, we approximate the dependent variable as the differences between 2020 GDP growth forecasts conducted by the World Bank‘s Global Economic Prospects before and after the pandemic which is January 2020 and July 2020, respectively.<sup>16</sup> We also conduct our regression analysis with alternative measures of our dependent variable: (i) the differences between 2020 GDP growth forecasts conducted by the IMF in October 2020 vis-à-vis October 2019; and (ii) the differences between 2020 GDP growth forecasts between October 2020 and October 2019 as undertaken by the World Bank. The explanatory variables have been classified in five different groups: (a) international trade, (b) international financing flows, (c) health and human capital, (d) macroeconomic space, and (e) policy responses.

_International Trade._ Exposure to foreign trade is measured by the value of exports and imports as percentage of GDP. In our prior, the countries that are more exposed to international trade experience a greater decline. The impact of trade on growth is further explored to the trade structure by product and by partner. When looking at trade by product, we collect data on natural

> 15 Note that the relatively low incidence of COVID-19 cases (as well as fatalities) in poorer countries—and, notably, Sub-Saharan Africa—should be taken with caution because the spread of the pandemic in the region may be understated due to the low rates of testing—as cases could exist undetected. Some low-income countries have been unable to acquire the much-needed number of testing kits. Testing in these countries is low—especially when normalized by their entire population.

> 16 In a similar spirit, Berkmen, Gelos, Rennhack and Walsh (2012) use the difference between 2009 actual growth and growth forecasts made before the crisis to provide an initial exploration into the growth effects of the 2008-09 global financial crisis. The authors argue that the use of these forecasting errors enables them to avoid issues such as controlling for variations in growth rates that are due to differences in levels of development or cyclical positions, or other factors unrelated to the impact of the crisis.

9