household income (Ravallion 2003; Deaton 2005; Prydz et al., 2021, Lakner et al., 2022). Nevertheless, growth rates in national accounts do a fairly good job of identifying the trends in changes in poverty (Mahler et al., 2021a).<sup>3</sup> Macro growth rates are even more important at times when micro data are not widely available. Collection of micro data in the last two years has been especially challenging due to the COVID19 pandemic. Only a few countries in Africa were able to collect non-traditional sources of survey data, which we discuss and use in the next section.

The procedures to estimate the impact of the pandemic on poverty in Africa are the following (see also, Mahler et al., 2022): first, to project household surveys to 2020, we need distribution of welfare (income or consumption) in 2019 for all countries. However, household surveys are not conducted annually in most low and middle-income countries. In most cases, they are conducted in 3, 5, or even 10-year intervals. To derive a 2019 distribution of welfare for a country without a household survey in 2019, we extrapolate the welfare aggregate available in the household survey from the last available year, say 2018, using per capita GDP growth rates from the World Development Indicators. We use per capita GDP growth rates to grow household income of all households within a country. Since the applied growth rate is the same across all households, the inequality observed in that country is held fixed.<sup>4</sup> This allows us to have a baseline distribution for 2019 for all countries.<sup>5</sup> Then, using the same extrapolation method, we extrapolate the 2019 country distributions forward to 2020 and 2021. Following Mahler et al. (2022), we report two different scenarios projecting poverty for the COVID-19 affected years: (i) a series incorporating the effect of the pandemic (“with COVID-19”), and (ii) one without the effect of the pandemic (“without COVID19”). The former uses the per capita GDP growth rates from the World Bank’s June-2021 Global Economic Prospects (GEP) report, while the latter uses January-2020 GEP forecasts – that is growth forecasted before the pandemic and thus not incorporating the effects of the pandemic. Whereas (i) is the “actual” poverty series, (ii) acts as a counterfactual for the COVID-19 series – a series reflecting what could have materialized if the pandemic had not occurred. One reason for reporting the latter series is to net-out the effect of the pandemic from those changes that would have otherwise occurred without the pandemic.

Figure 3 reports the trends in the extreme poverty rate and the number of poor (in millions) in Africa from 2015 to 2021. In 2020, the number of poor in Africa was expected to rise to 449 million from 444 million in 2019 even without the pandemic. Due to the pandemic, the number of poor in 2020 is estimated to have increased to 472 million instead. Thus, we infer that COVID-19 pushed 23 million people

> 3 Short-term growth forecasts fairly predict actual growth rates. Celasun et al. (2021), who compared IMF growth forecasts and realized growth rates over the 2004-2017 period, find little differences between the two. 4 Yonzan et al. (2020) outline how poverty estimates could vary due to changes in within-country inequality. 5 The extrapolation methodology is similar to the one used by the World Bank to calculate global poverty (for instance, see Ferreira et al. 2016). For those countries without an available household survey, in line with the World Bank, we use regional averages.

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