<mark>stringency of the social distancing measure, a mobility measure, and a measure of scale-up of cash transfer programs at the country level to understand what is behind the lack of precision in some</mark> of the specifications.

<mark>Second, we apply a macro-micro simulation methodology in five countries that combines preCOVID-19 household surveys with national accounts data for 2020. The methodology follows the household income generation model proposed by Bourguignon and Ferreira (2005), and instead of using a CGE model or macro projections to go from macro changes to micro impacts, we use actual 2020 data on employment level, sectoral GDP, private consumption, and remittances. The results of the simulation are used to project changes in household per capita income or consumption by quintiles between 2019 and 2020, changes in poverty rates at different lines, and</mark> changes in the shares of the vulnerable population and middle class.

<mark>Third, we compare the changes in poverty, vulnerable population, and middle class obtained using actual 2020 employment data in the macro-micro simulation methodology versus using</mark> employment projections based on the calculation of employment elasticities.

<mark>Finally, we validate our estimations for Brazil using 2019 and 2020 microdata from the PNADContínua. Using this survey, we calculate actual poverty rates and poverty rates excluding incomes from emergency assistance programs implemented since the pandemic started. This allows us to isolate the poverty changes that are explained by the job loss, labor income, and remittance</mark> channels.

<mark>Our results can be summarized as follows:</mark>

- <mark>(i) In most countries, employment estimates for 2020 based on elasticities were reasonably accurate. The projected levels of employment are within 5 percent of the actual levels in 11 of the 15 countries. However, in four countries the projections significantly overestimated employment levels and therefore underestimated job loss due to the crisis. This bias tends to be larger (i) for labor markets that were more disrupted by the pandemic as measured by the stringency of social distancing measures, changes in workplace mobility, and a measure of scale-up of cash transfer programs, and (ii) in the agriculture sector in comparison to the industry and service sectors.</mark>

- <mark>(ii) The simulations show declines in the per capita household income or consumption across the entire distribution. However, the impacts across the distribution varied across countries, with no clear pattern. In Sri Lanka and Brazil, for example, the simulations project larger shocks for poorer households. In Türkiye, the changes are similar across income groups. In the Philippines, the simulated reductions were largest for households in the middle three quintiles, while in South Africa, the simulated reductions were largest for the top three quintiles. When using employment projections based on elasticities instead of actual employment data, the projected income declines were smaller in all countries and quintiles.</mark>

- (iii) Actual 2020 micro data for Brazil shows that the simulations underestimated the magnitude of the shock throughout the distribution, especially for the wealthy, because they underestimated the earnings declines, and thus underestimated the increase in

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