of individuals is 2,870,315. In the case of North Macedonia, the total number of households covered was 567,753 and the number of individuals was 2,076,247. In both cases we nowcast incomes to what would be observed in 2019 levels using official GDP per capita growth and passthrough rates for each country.<sup>14</sup> For Moldova we use the 2019 Household Budget Survey (HBS), which is a nationally representative household survey conducted annually that captures information on income, expenses, and consumption in addition to demographic and social indicators. The 2019 HBS covered 4,408 households containing 11,355 individuals.

In the simulations we use an expenditure-based poverty threshold of $5.5 per day; and the same threshold is used to calculate the poverty gap squared, our main welfare indicator. The estimated poverty rates for each country before the shocks are 32%, 12% and 17% for Albania, Moldova, and North Macedonia, respectively. The poverty gap squared indexes are 6.7, 1.3 and 3.5, while the Gini indexes are 0.37, 0.32 and 0.33.

# Methodology

To determine the poverty and distributional impacts of alternative responses to shocks, we use survey-based microsimulations (Bourguignon and Spadaro 2006). We consider three types of shocks and assess the impacts of the different responses described in Table 2 for each type of shock. As a baseline shock we use the first wave of the Covid-19 Pandemic shock as simulated by World Bank (2020d, 2020b, 2020a), where individuals were exposed to the shock to varying degrees depending on the industry where they work and the type of workers they are. We then simulate two additional shocks of the same magnitude (i.e., with the same aggregate labor income losses), but with different distributional impacts. The second shock consists of a proportional loss of income that is equal for all workers who report labor income. This is an extreme case that wants to assess optimal responses under shocks that do not affect welfare rankings across the population; the third shock is the opposite – it affects fewer households, randomly, but workers affected lose all their labor incomes. Under the third shock there will therefore be households who suddenly become extreme poor and drop to the bottom of the income distribution – a case that many common targeting methodologies, such as Proxy-Means Tests, are not able to capture.<sup>15</sup> Specifically, the algorithm for the simulations loops over three layers: the type of shock, the overall budget of the social assistance response, and the different ways to identify (target) program beneficiaries. The first layer in the simulation is the shock. Once each shock is modeled and income losses have taken place, we proceed to the second layer and iterate over two social assistance package budgets. Given a shock and budget, we proceed to distribute each budget equally for each of the ten targeting cases in Table 2, one at a time. Once the budget is distributed equally among the chosen beneficiaries, we compute poverty and inequality indexes, as well as statistics regarding the average value of transfers, the

> 14 Official GDP per capita growth rates are from WDI. Albania: 3.9% (2017), 4.3% (2018); North Macedonia: 2.8% (2018). Passthrough rates are 1 and 0.87 percent for Albania and North Macedonia, respectively.

> 15 We acknowledge that the two modeled shocks (besides the “COVID-19” one) may not be very realistic, as most shocks are not random –even those considered exogenous such as natural disasters. Indeed, most shocks hit groups that share common characteristics, whether geographic, demographic or socio-economic. To some extent, the COVID-19 shock already captures these differential impacts as it varies according to the type of employment and sector of the worker. The other two shocks are extreme in nature but help to set certain bounds to the analysis of the quality of responses.

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