As COVID-19 threatens the food security of vulnerable populations across the globe, there is an increasingneed to identify places that are affected most in order to target aid. We propose a two-step approach topredict changes in food insecurity risk caused by income shocks at a granular level using existinghousehold-level data and external information on aggregate income shocks. We apply this approach toassess changes in food insecurity risk during the pandemic in Vietnam. Using national household surveydata between 2010 and 2018, we first estimate that a 10% decrease in income leads to a 3.5% increase infood insecurity. We then use the 2019 national Labor Force Survey to predict changes in the share of foodinsecurehouseholds caused by the income shocks during the pandemic for 702 districts. We find that thesmall, predicted change in food insecurity risk at the national level masks substantial variation at the districtlevel, 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.
Feb 22, 2022