<mark>five developing countries during 2020 –Brazil, Sri Lanka, the Philippines, South Africa, and</mark> Türkiye.

<mark>The objectives are threefold. The first is to understand the extent to which employment projections based on GDP elasticities may be biased during a crisis. Changes in employment levels are a key input into these macro-micro models. When employment data is not available, projections of job gains or losses are needed, and these are often based on employment-to-GDP elasticities. The second objective is to better understand the projected distributional impacts of the economic shock during the first year of the pandemic in five countries from different regions of the world. Finally, the third objective is to better understand in Brazil, where post-crisis data on shock magnitudes exist, the extent to which the proposed methodology generates accurate estimates of the</mark> distributional impacts of the shock.

<mark>The combination of pre-COVID-19 microdata with National Accounts data from 2020 to estimate 2020 poverty rates is similar to other studies (ECLAC, 2020; Brum and De Rosa, 2021; World Bank, 2021). The methodology used here differs from these studies in two respects. First, we use a different procedure than in ECLAC (2020) and Brum and De Rosa (2021) to simulate changes in individual and household labor incomes. In particular, similar to World Bank (2021), we predict an individual probability of becoming unemployed, as a function of both sector of employment and formality status.</mark><sup>3</sup> <mark>Second, we differ in the consideration of government transfers and work from home possibilities. Unlike several other studies, our study does not include changes in government transfers in the simulation of household income changes, due to lack of information across all the countries under study here and thus to maintain comparability.</mark><sup>4</sup> <mark>As such, it predicts total income changes, poverty rates, and shares of vulnerable and middle-class populations that</mark> can be attributed to job losses, labor income changes, and changes in remittances.

<mark>We proceed by, first, presenting an in-depth analysis of employment elasticity projections for 15 developing countries selected based on the availability of post-crisis employment data from Labor Force Surveys or other official sources. The sample includes 5 countries from the Europe and Central Asia region, 6 from Latin America and the Caribbean, 3 from East Asia and Pacific, and one from Sub-Saharan Africa. When no employment data is available, employment elasticities are a crucial input to project employment changes and simulate the distributional impact of a shock. However, these estimates may provide biased inputs during a crisis period and, especially, during the recent pandemic when large drops in GDP were observed. Employment projections based on elasticities may overestimate the employment loss, for instance, because most countries have implemented crisis-relief measures such as cash transfers and wage subsidies that may have protected employment but not been reflected in GDP growth. We therefore use pre-COVID-19 data on GDP and employment levels to perform a sensitivity analysis using different model specifications. The projected employment levels are then compared with observed 2020 values. The differences or biases between actual and projected employment levels are correlated with the</mark>

> 3 For example, some studies do not distinguish the job loss channel by sector of activity (Brum and De Rosa, 2021) or assign the probability of losing a job exogenously (ECLAC, 2020) . The former, however, consider amenability of working from home when predicting job loss.

> 4 Brum and Da Rosa (2021), World Bank (2021), and ECLAC (2020) include government transfers in the simulation of household income changes.

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