Lost in aggregation? On the importance of local food price data for food poverty estimates

International organizations, governments, and NGOs routinely rely on welfare effect estimates for social programming in crisis situations. Often, these estimation models incorporate national consumer price index data as an integral predictor. This paper contends that utilizing aggregate price data can be misleading due to spatial disparities in price trends. To explore this, we analyze shifts in food poverty estimates by employing local market price data instead of national consumer price index data. Utilizing a dataset from seven West African countries, we highlight significant spatial variation in cereal prices at the local level following the outbreak of COVID-19. Model estimates indicate an increase in food poverty of almost 10% during the pandemic's first wave due to food price increases. Sourcing cereal prices from local markets, instead of national CPI statistics, results in a 5% inclusion and 2% exclusion error, yet similar mean estimates. Our findings underscore the need for systematic collection of local price data for effective policymaking, such as CPI adjustments to social transfers and the allocation of relief funds.