sources – the latest household survey for each country, the World Bank’s HFPS, and national accounts data – to get around the lack of income or consumption data from surveys conducted during the pandemic. The exercise as of now covers 34 countries, which are a mix of all developing regions of the World Bank other than South Asia, and countries at different levels of development.<sup>18</sup> Briefly, the simulation methodology is as follows (see Annex B for more details): (a) using the last household survey available for each country (mostly pre-2019) to estimate the income distribution for 2019, assuming all households’ welfare have grown in accordance with growth in national accounts till 2019; (b) using a variable in HFPS indicating whether a household lost income in 2020 to predict the probability of households experiencing an income loss based on their characteristics (education, demographic, urban/rural); (c) merging these probabilities with the household survey data for 2019 to predict for each household a probability of experiencing an income loss in 2020, based on which an income loss is stochastically assigned; and (d) using sectoral growth figures from national accounts and assumptions mapping sectoral income growth to rural/urban to determine the size of income loss for each household. The exercise produces a distribution of household income in 2019 (pre-COVID-19) and 2020 (with COVID-19) for all 34 countries in the sample, using which poverty can be projected for these years.

To assess the impact of the pandemic on inequality and poverty, comparisons are made with a counterfactual 2020 income distribution, which is produced by using the last pre-pandemic sectoral GDP forecast for 2020. To isolate the impact of changes in inequality on poverty projections, another counterfactual scenario is created assuming that COVID-19 hit all households equally, by adjusting all households’ income in 2019 with the growth rate of 2020.

The estimated probability of changes in income for rural and urban households by country are shown in Annex A (Figure A-5). On average, 62 percent of rural households and 61 percent of urban households had a decline in income due to the pandemic; 31 percent and 32 percent respectively had no change in their income; and 7 percent had an increase in income in both areas. In this sample of 34 countries, the countries in SSA fared the worst where 76 percent of rural and 71 percent of urban households had an income decline. Countries in EAP and ECA fared the best with around 40 percent of households with income declines.

According to the simulations, income losses in 2020 were likely regressive in most countries – more likely for bottom 40 percent (or b40) of the distribution than for top 60 percent (or t60) in 26 out of 33 countries (Figure 12, right panel).<sup>19</sup> The gap was also larger for urban areas than for rural areas in most countries (Figure 12, left and center panels). On average, the b40 in rural areas had a 1.7 percent higher probability of experiencing a decrease in their income compared to the t60, compared to a 4.5 percent difference for urban areas. A comparison between the top and bottom income quintiles shows similar patterns. The likelihood of income loss was higher for the bottom quintile than the top quintile for 27 countries, and the average gap was 2.5 percent for rural areas versus 6.9 percent for urban areas. Thus, on the average, inequality may have increased more in urban areas than in rural areas.

The simulations project an increase in income (or consumption) inequality for most countries, albeit by a small amount. The Gini index for income in 2020 with COVID-19 is higher than what it would have been without COVID-19 for 29 out of 34 economies (Figure 13). The average expected increase in the Gini due

> 18 The 34 countries are from EAP (3), ECA (6), LAC (13), MNA (2) and SSA (10). The simulations are based on country-specific sectoral growth estimates from MPOs (April 2021) and harmonized HFPS data (as of July 2021). Results may change as growth estimates updated with the next round of MPOs (October 2021) and HFPS data are updated with surveys for more countries.

> 19 Argentina is not included for rural-urban breakdowns, since only urban data is available in the official household survey.

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