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As COVID-19 threatens the food security of vulnerable populations across the globe, there is an increasing need to identify places that are affected most in order to target aid. We propose a two-step approach to predict changes in food insecurity risk caused by income shocks at a granular level using existing household-level data and external information on aggregate income shocks. We apply this approach to assess changes in food insecurity risk during the pandemic in Vietnam. Using national household survey data between 2010 and 2018, we first estimate that a 10% decrease in income leads to a 3.5% increase in food insecurity. We then use the 2019 national Labor Force Survey to predict changes in the share of food-insecure households caused by the income shocks during the pandemic for 702 districts. We find that the small, predicted change in food insecurity risk at the national level masks substantial variation at the district level, and changes in food insecurity risk are larger among young children. Food relief policies, therefore, should prioritize a small number of districts predicted to be severely affected. |
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