The economic crisis triggered by COVID-19 has caused a world-wide economic downturn, and the deepest GDP contraction in Latin America since the beginning of the XX
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century. One of the most dramatic outcomes of the crisis is the increase in poverty, but its extent will remain unknown until household income data is collected and analyzed. We propose a simple approach to provide early estimates, micro-simulating the short-run effect of the crisis on the poverty rate. It combines household level micro-data, estimates on the feasibility of working from home, information on key public policies (e.g., cash-transfers, unemployment insurance), and forecasts of GDP contraction. This approach, which can be easily adapted and applied to different countries, allows to
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the current poverty level and the poverty-reducing effect of public policies, while providing full micro-macro consistency between heterogeneous impacts on households and the shock to aggregate GDP. Moreover, it enables to estimate the effect on informal and self-employed workers, of utmost importance in developing countries. We illustrate the methodology with an application for Uruguay, finding that during the first full trimester of the crisis, the poverty rate grew by more than 38%, reaching 11.8% up from 8.5%. Moreover, cash transfers implemented by the government in the period had a positive but very limited effect in mitigating this poverty spike, which could be neutralized with additional transfers worth under 0.5% of Uruguay’s annual GDP.
There is a large gap between income estimates used in inequality studies and macroeconomic statistics. This makes it hard to assess how economic growth is distributed across the population, and to what extent mainstream distributional statistics are an accurate representation of income flows. We take stock of these discrepancies by confronting estimates of the income distribution from surveys, administrative records and aggregates from the system of national accounts, thoroughly documenting them over the past two decades for ten Latin American countries. We find that surveys only account for around half of the macroeconomic income in the region. Measurement gaps account for just over half of the overall gap on average, while the rest is due to conceptual differences across data sets. Measurement gaps have been growing fast for many countries, the bulk being due to non-covered capital income. We also compare the top tails in administrative data and surveys, finding diverging averages—especially for non-wage incomes—and different shapes. We discuss the degree to which inequality levels and trends could be affected. (Stone Center on Socio-Economic Inequality Working Paper)
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