poverty. The underestimation is even larger when using employment projections based on elasticities instead of 2020 employment levels as inputs in the microsimulation.

<mark>Overall, the results are mixed. Estimates based on employment elasticities were reasonably accurate in most countries, although generally in the direction of overestimating employment, but they tended to be less accurate in countries with higher disruptions in the labor market. Furthermore, in the five countries considered, the macro-micro simulation methodologies capture differences in the structure of the labor market and the macroeconomic impact of the shock that led to clear differences in the projected distributional impacts. The results overall show that the middle class declined, and poverty increased for all five countries in 2020, under a scenario that did not incorporate new emergency public transfers as mitigation measures. In Brazil, however, the simulation methodology considerably underestimated the extent of (pre-transfer) income losses, suggesting that these macro-micro simulations require additional fine-tuning of the assumptions so as to: (a) allow for a reduction in hours and a corresponding reduction in earnings that occurred as part of countries’ programs protecting employment stability; and (b) take into account that the exclusion of the emergency public transfers from the model would imply that a</mark> larger private consumption shock needs to be applied in the model.

<mark>The rest of the paper is organized as follows. Section 2 presents the projections of 2020 employment levels using different employment elasticity models. Section 3 presents the macromicro simulation methodology and main results, while Section 4 discusses robustness checks and</mark> a validation exercise. Section 5 concludes.

# **2. The importance of employment elasticities and their estimation**

<mark>The elasticity of employment to GDP seeks to capture the responsiveness of labor markets to changes in macroeconomic conditions. Elasticity estimates may provide biased projections of employment levels during a crisis period when large drops in GDP are observed. For instance, in countries without labor protection mechanisms such as unemployment insurance, workers may reallocate to self-employment or to the informal sector to avoid being unemployed.</mark> We hypothesize that elasticity-based estimates would overestimate employment loss during the pandemic crisis for three reasons. First, GDP growth fell dramatically. Product losses have been unusually large for countries that depend on tourism and commodity exports and for those with limited policy space to respond (IMF, 2021). GDP losses during 2020 were as high as 11% in Spain (European region), 9.5% in the Philippines (Asian region), 8.2% in Mexico (North America), 30% in the República Boliveriana de Venezuela (South America), 25% in Lebanon (Middle East and Central Asia), and 7% in South Africa (Sub-Saharan Africa) (IMF, 2021). If the employment elasticity itself depends on the extent of the GDP loss, then an elasticity measure derived during normal years may not be accurate during shock periods. Second, most countries have implemented crisis-relief measures including cash transfers, wage subsidies and labor regulation adjustments (Gentilini et al., 2021; World Bank, 2021; ILO, 2020). Some of these measures, such as wage subsidies, may have protected employment but not been reflected in GDP growth. Third, the crisis may have reduced productivity more than employment through the 5