in our model is computed as<sup>_T_</sup> _π_<sup>_t−_</sup> _rt_<sup>1</sup> +<sup>_<u>/I</u>_</sup> _π_<sup>_t_</sup> _dt_<sup>_−_1, where</sup><sup>_πrt_and</sup><sup>_πdt_are the population-weighted average</sup> recovery and mortality rates in period _t_ . We take the _R_ 0 at the period after the lockdown policy ends, as the counter-part of the post-lockdown reproduction number in Flaxman et al. (2020).<sup>17</sup> The resulting _ξ_ ranges between 1.9 to 3.5 among the 7 countries with an interior solution, and we take the average of 2.32 to apply to all the 85 countries in the full sample.<sup>18</sup>

# **3.3 Mortality Rates**

We look to two distinct literatures to calibrate our mortality parameters.

**COVID-19 mortality** Walker et al. (2020) projects hospitalization and mortality rates per age group that are in turn based on findings from China reported by Verity et al. (2020).<sup>19</sup> Conditional on infection, the average projected hospitalization and mortality rates in lowand middle-income countries are listed in Appendix Table B.2. In the calibration of the model, we use country-specific rates as countries have different age distributions within the broader age groups defined in our model.

For severe cases of COVID-19 infection, hospitalization offers treatments such as oxygen

> 17We have computed the _R_ 0 for all the countries in the sample using our calibrated model. The postlockdown _R_ 0 is similar across countries and is close to those reported for the European countries in Flaxman et al. (2020). The average _R_ 0 among the 85 countries one week after the reference lockdown is 0.57. In comparison, the same statistic among the 11 European countries in Flaxman et al. (2020) is 0.66. Given that the _R_ 0 estimates in Flaxman et al. (2020) is our calibration target, the consistency among 85 countries provides some reassurance, but not external validation. Our estimates are also broadly consistent with the _R_ 0’s reported in the economics literature, such as Atkeson et al. (2020) and Fern ́andez-Villaverde and Jones (2020). While our estimated _R_ 0 is not directly comparable to the _R_ 0 reported in the papers above - the main difference is that the _R_ 0’s in our paper are computed based on simulated data assuming a 7-week reference lockdown while the _R_ 0’s reported in these two papers are based on the actual data where the strength and length of the lockdown policies vary - the general consistency is reassuring. For example, the average _R_ 0 reported in Fern ́andez-Villaverde and Jones (2020) in May 2020, a period immediately after the first wave of lockdown policies in the world, is 0.86. Atkeson et al. (2020) shows that after the initial outbreak, the estimated _R_ 0 usually hovers around 1.0. Figure 1 in Atkeson et al. (2020) shows that _R_ 0 usually first drops below 1.0 at around 50 days after the initial outbreak, before increasing again to hover around 1.0. The dip in _R_ 0 is probably due to the lockdown policies implemented immediately after the outbreak.

> 18A corner solution in this step occurs for the four countries that we have dropped (Denmark, Sweden Switzerland, and Norway). If the country imposes a mild lockdown, the implied _μ_  ̄ for this country will be low, which makes it impossible to reach the target _R_ 0 even with _ξ_ = _∞_ .

> 19Infection fatality rates calculated with data from China might not be generalizable to other countries because of factors such as prevalence of comorbidities and quality of health services (Ghisolfi et al., 2020; Demombynes, 2020). More recent meta-analyses found mortality rates in line with those reported by Verity et al. (2020) (Levin et al., 2020; Meyerowitz-Katz and Merone, 2020). It is important to note, however, that these analyses overwhelmingly rely on studies from high-income countries.

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