welfare by the household income or expenditure recorded in surveys (similar to Milanovic’s earlier work) and do not distribute total income from national accounts like others have done (for a discussion of one such method, see Piketty et al 2018). An advantage of our approach is that our inequality measures are consistent with the global poverty measures produced by the World Bank.

Due to the paucity of household survey data for 2020, despite our best efforts of using alternative data sources, the estimate of inequality and poverty in 2020 must be viewed with a higher level of uncertainty than usual. Even among the countries that conducted traditional household surveys, many were forced to modify the survey design, change the mode of the survey – from face-to-face interviews to phone interviews – or capture one-time transfer payments traditionally not accounted for in household surveys. Neither China nor India, which always play a large role in driving global results, have household survey data for 2020 available yet, further exacerbating uncertainty. The increases in both global inequality and extreme poverty are driven by India and the choice of methodology for data in India plays a large role in the variance of the estimate. Yet, it is important to underscore that the qualitative results are robust to all available data options for India.

The rest of the paper is organized as follows. Section 1 outlines some of the possible mechanisms that could influence the impact of COVID-19 on poverty and inequality. Section 2 explains the data sources we use. Section 3 details our methodology to recover distributions for 2020. Section 4 covers our main results. Section 5 contains some robustness checks. Section 6 concludes.

# **1. Potential mechanisms**

The pandemic, its economic consequences, and the policy responses to these consequences have impacted inequality and poverty across and within countries in a variety of ways. Several channels work in opposite directions, and hence ex-ante the impact of the pandemic on countrylevel or global inequality and poverty is not clear.

Firstly, the virus itself may have impacted poverty. When household members got ill and in worst cases died because of contracting the virus, they were unable to generate an income, which may have pushed their household into poverty. Other family members may have stopped working to care for the ill, further exacerbating this effect. Across countries, this suggests that the greater the severity of the pandemic, all else equal, the greater the implications for poverty.

Secondly, most countries responded to the pandemic by shutting down parts of the economy. During these shutdowns, individuals of certain occupations were unable to work and earn an income. Some individuals’ jobs may not have been directly affected, but with schools and daycares closed, they had to take care of children rather than working. All else equal, the longer and more severe these shutdowns were, the greater the impacts on poverty. Given that the occupations most affected by these shutdowns tend to be low-skilled urban households (Bundervoet et al. 2022), it is likely that this channel will have increased inequality in middle- and high-income countries where low-skilled urban households are among the lowest incomes. In 4