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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
of the specifications.
Second, we apply a macro-micro simulation methodology in five countries that combines pre-
COVID-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
changes in the shares of the vulnerable population and middle class.
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
employment projections based on the calculation of employment elasticities.
Finally, we validate our estimations for Brazil using 2019 and 2020 microdata from the PNAD-
Contínua. 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
channels.
Our results can be summarized as follows:
(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.
(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.
(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
