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Distributional Impacts of COVID-19 in MENA
BOX 1.1
Tunisia: Using Phone Surveys and Microsimulations to Paint a COVID-19 Picture Phone surveys present real-time evidence from the ground (such as income and living standards) while microsimulations try to quantify the overall expected effects for the economy (such as poverty and welfare). How the two approaches corroborate each other can be illustrated with the case of Tunisia. As the pandemic unfolded, five waves of phone surveys were conducted. The selfreported results indicate that about half of the households saw living standards deteriorate compared with the pre-COVID-19 period, particularly among the poor and the bottom 40 percent. Those hardest hit include informal workers—especially in the private sector or self-employment—in construction, manufacturing, accommodation and food services activities, and transport. The surveys also show that the deterioration in welfare was caused by job and income loss along with higher food prices. These findings are corroborated by the microsimulations. Using pre-COVID-19 administrative data, the first exercise simulates the impact on consumption, poverty, and inequality using labor income and consumption. The second exercise simulates price effects to determine the change in disposable income. The third
exercise identifies high-risk sectors (tourism, textiles, mechanical and electrical industry, transport, commerce, and construction), which are also the industries where a large number of poor and vulnerable are likely to be employed. The microsimulations project an increase in poverty ranging from 7.3 percentage points (a more than 50 percent rise) in the optimistic scenario to 11.9 percentage points (an almost doubling) in the pessimistic scenario. They also add value by estimating the degree to which government compensatory measures can mitigate some of the losses: poverty would increase an estimated 6.5 percentage points in the optimistic scenario with mitigation measures as opposed to 7.3 percentage points without it. Put together, the two methodological approaches not only support each other’s findings but also indicate trends or furnish estimates such that they build on each other to provide a more robust picture. In Tunisia’s case, these combined results would give policy makers a better idea of which segments of the population need to be targeted (and in which sectors), along with the potential effects from mitigation measures and policies.
Lessons from this exercise also highlight the crucial role that administrative data can play for such analysis and estimates. Administrative records are less likely to be susceptible to biases relative to specially administered surveys. Moreover, the former may be collected as part of an actual state support program or exercise and be more accurate,
