The analysis suggests that, purely on account of demography, the share of HICs could reach 31 percent, which is almost three times lower than the present estimate. Conversely, the share of UMICs could more than double to 39 percent, whereas that of LMICs and LICs could rise, respectively, to 27 percent (by a factor of 9) and 4 percent (by a factor of 22). These estimates are subject to the influence of other factors, but these in our view do not offer a convincing explanation of the stark discrepancies between rich and poor countries. In fact, collectively, they may well serve the raise the share of the developing world relative to the baseline.

The third part of the paper speculates on the likely path going forward. It concludes that the observed disconnect between the reported and estimated data entails a grim prediction for the future mortality toll of COVID-19 in the developing world. It entertains the possibility that the current skew towards HICs may be partly the result of inequality in data quality but deems that the first-order reason for the present dichotomy between rich and poor countries is because the pandemic has not yet run its full course through the age distributions of the different parts of the world. Whether this grim prediction will be realized will depend importantly on measures governments and individuals will undertake to mitigate and slow the pace of infection.

It is true that the generally younger age distributions of the developing world will offer a measure of protection against the virus as younger people are exponentially less vulnerable keeping all else constant. But we should not forget that the developing world, as a whole, counts in absolute terms many more people in the vulnerable age cohorts than the HICs combined: its 60+ population is 2.4 times larger and the 70+ population 1.8 times. The fact that these vulnerable groups in developing countries represent a smaller share in their own populations compared to HICs should not diminish our focus on mitigating the impact of pandemic in the developing world.

# **2. Context and Added Value**

We briefly describe the two main contributions in this paper, how they build on the emerging literature and add value to it. Clearly, many different efforts have been made thus far to track to the mortality burden of the COVID-19 pandemic.<sup>3</sup> Past efforts have ranged from monitoring absolute deaths, in some cases corrected for delay in registration, tracking cause-specific crude mortality rates, normalized by population size, and calculating excess death ratios, which relate current to expected all-cause deaths to pick up deviations from past patterns and correct for data quality.

The first part of the paper adds to this literature by introducing an indicator of ‘relative severity.’ The relative severity ratio expresses COVID-19 mortality relative to the pre-pandemic mortality profile of individual countries. We compare COVID-19 deaths to all-cause deaths before the pandemic as well as the top-cause-specific deaths for each country. The expression of mortality in relative terms speaks to the fact that countries may have adapted to specific patterns of mortality and that deviations from such patterns may indicate pressure points, particularly on health systems. Comparisons with previous patterns give a country-specific flavor of the severity of the COVID-19 pandemic, which could be used to corroborate realities on the ground.

> 3 See Aron and Muellbauer, 2020; Burn-Murdoch and others, 2020; Ciminelli and Garcia-Mandicó, 2020; EuroMOMO, 2020; The Economist, 2020c; Wu and others, 2020.

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