thus holding within-country inequality fixed.<sup>9</sup> Seventh, for the 3% of the global population with no micro data in PIP, in line with what the World Bank does for global poverty calculation, we assign the average regional distributions.<sup>10</sup> Given that one of our main purposes is to study how inequality within countries changed in 2020, we prefer methods that can speak to this over projections using per capita GDP growth rates for all households. For the purpose of studying how poverty changed in 2020, we also prefer methods that model distributional changes. Though projections using per capita growth rates may work well to predict poverty in normal times, there are strong reasons to believe that it is less appropriate in 2020. In many countries, the pandemic impacted households differentially based on their occupation, location, and age, and resulted in a large government response to the crisis (e.g. Bundervoet et al., 2022; Kugler et al., 2021). These events cast doubt on whether a GDPbased projection works well in 2020.

Table 1 reports the share of the population (panel a) and the number of countries (panel b) covered by each data source used in 2020. The data sources are ordered from the most preferred on the left column (survey data) to the least preferred on the right (regional average). We have either survey data or tabulated data for 28 countries (covering 42% of the world’s population). Our preferred methods include the first four columns – household surveys, tabulations, phone surveys, and estimates from the literature. This includes 101 countries that cover 82% of the global population.

In addition to the observed 2020 distribution, we also construct a counterfactual 2020 distribution. The income and consumption distributions would have certainly changed in 2020 even in the absence of the COVID-19 pandemic. Hence, the change from 2019 to 2020 captures both the changes due to the pandemic and those changes that would have happened even without the pandemic. For instance, a number of countries in Sub-Saharan Africa were expected to have negative per capita GDP growth before the pandemic. Hence, not all negative income growth in 2020 can be attributed to the pandemic. To estimate the net impact of the pandemic, we construct a counterfactual distribution for 2020 using growth forecasts from before the pandemic.<sup>11</sup> This counterfactual distribution potentially captures all the anticipated changes before the pandemic but not including the pandemic. The difference between the observed 2020 distribution and the counterfactual distribution provides an estimate of the net impact of the pandemic. The counterfactual distribution is further discussed in Section 3.2.

> 9 We use per capita GDP growth rates from the World Development Indicators (WDI).

> 10 We construct our global data set with grouped data of 1,000 bins of national welfare for each country in 2019 derived using the microdata in PIP. This is then projected to 2020 using the growth rates available from various data sources described in this section.

> 11 We use the forecasts that were published in January 2020 in the World Bank’s Global Economic Prospects

> (GEP). It is important to note that the counterfactual projection is distribution neutral.

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