# **1. Introduction**

<mark>The COVID-19 pandemic and the associated containment measures led to dramatic declines in economic activity levels and disrupted labor markets worldwide, with potentially large distributional impacts. Empirical evidence from developed and developing countries is showing that traditionally disadvantaged workers --women, young, and low-educated workers—were hit hardest by the pandemic and, as a consequence, the patterns of existing inequalities are being</mark> exacerbated (Adams-Prassl et al., 2020; Fairlie et al., 2020; Lee et al., 2021; Kugler et al., 2021).

<mark>Workers in developing countries are often self-employed or working in informal arrangements with a relatively low earnings level. The containment measures particularly affect these groups of workers, leading to decreasing incomes and higher chances of falling into poverty. To design effective policies to protect the most vulnerable segments of the population, the distributional impacts of the pandemic need to be known. In the longer term, the calculations of prompt estimates on how individuals and households are affected during any crisis are crucial inputs for timely</mark> policy responses.

<mark>During the COVID-19 pandemic, different sources of data and methodological approaches have been used to estimate the distributional impacts of the crisis. In particular, phone surveys gained momentum during the pandemic as an instrument to gather information on the ground while respecting social distancing and lockdown policies. Phone surveys successfully collected information on respondents’ employment status and their perception of whether their household income declined, remained the same, or increased (Khamis et al., 2021; Kugler et al., 2021). A quite different approach to estimate the distributional impacts is through Computable General Equilibrium models using different scenarios of GDP reduction and transmission mechanisms, such as in the ILO (2020) and Vos et al. (2020) studies. In addition, some studies adjust prepandemic poverty lines using different scenarios of per capita income or consumption contraction or, alternatively, actual information on per capita GDP reduction (Sumner et al., 2020; Diop and Asongu, 2020). But these methodologies make strong distributional or structural assumptions,</mark> which lead the results to be sensitive to the choice of methodology.<sup>2</sup> <mark>In this study we follow a different approach, which applies a macro-micro simulation that combines pre-COVID-19 household surveys microdata with 2020 data on sectoral employment levels, sectoral GDP, private consumption, and remittances. With this information, we simulate the transmission of the COVID-19 shock via three channels – job loss, labor income declines, and changes in remittances— on household per capita income (or consumption) and the shares of poor, vulnerable, and middle-class populations in each country during 2020. We present projections for</mark>

> 2 For example, ILO (2020) projects that, using the 3.2 USD-a-day poverty line, the number of poor workers in 2020 will increase between 8.8 million and 35 million persons in 138 low- and middle-income countries compared to preCOVID-19 estimates. Sumner et al. (2020) project short-term increases in poverty of between 85 million and 580 million persons with respect to 2018 values for more than 150 countries using different poverty lines. Vos et al. (2020) predict an increase of between 12 million and 22 million in the number of extreme poor people (1.9 USD-a-day poverty line) in 30 countries of mainly Sub-Saharan Africa and South Asia in 2020. For 50 African countries, Diop and Asongu (2020) show increases of between 19 million and 26 million additional poor people in 2020 using different poverty lines.

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