low-income countries, the impact of the economic shutdowns is less clear since subsistence agricultural farmers rather than low-skilled urban workers tend to be in the bottom of the income distribution there.

Thirdly, in response to the economic shutdowns, countries around the world implemented emergency transfers and other mitigating economic mechanisms at a record speed (Gentilini 2020). To the extent that these transfers were targeted towards the poorest, they are likely to have reduced, mitigated, or reversed the impact of the pandemic on inequality and poverty. Given that wealthier countries often have more resources available for such transfers, it is likely that this effect has been greater for high-income countries.

Fourthly, the connectivity of the global economy means that the pandemic will have had consequences on poverty and inequality for a country even if it was not hit by the pandemic, did not shut down parts of its economy, and did not implement any emergency transfers. Countries might have seen a lower demand for goods they normally export, seen remittances go down, and experienced a sharp drop in tourism. All of this contributes to slowing down growth and hence increasing poverty.

Finally, it is important to note that our study ends in 2020. Beyond 2020, different factors have been at play, such as access to vaccines, the elimination of emergency social support, and inflation.

# **2. Data**

Our starting point is the country-level distributions of welfare from the Poverty and Inequality Platform (PIP), which is the World Bank’s database of country-level, regional, and global estimates of poverty and inequality.<sup>3</sup> PIP contains more than 2,000 surveys from 169 countries covering 97.7% of the world’s population. The data available in PIP are standardized as far as possible but differences exist with regard to the method of data collection, and whether the welfare aggregate is based on income or consumption. PIP reports per capita household income or consumption reported in 2017 purchasing power parities. PIP directly uses survey micro data wherever available and supplements these with binned income data.<sup>4</sup> 3 In 2022, PovcalNet was replaced by PIP. The data in PIP feeds into the United Nations’ monitoring of the first target of the first Sustainable Development Goal, to end extreme poverty. Most of the data in PIP is based on the Global Monitoring Database, which is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty. We use the September 2022 version of PIP. 4 For 156 of the countries, housing 73% of the world’s population, survey micro data are available. For an additional 8 economies (Australia; Canada; Germany; Israel; Japan; the Republic of Korea; Taiwan, China; and the United States), or 8% of the world’s population, grouped income data of 400 bins are available. We treat these bins as microdata. Finally, for China, United Arab Emirates, Algeria, and Trinidad and Tobago, constituting about 17% of the world’s population, only decile or ventile shares and the overall mean income or consumption is available. For these countries, PIP fits a General Quadratic Lorenz curve or a Beta Lorenz curve, chooses the one that gives the best fit, and uses it to recover a full distribution.

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