Machine Learning Guided Outlook of Global Food Insecurity Consistent with Macroeconomic Forecasts

Motivated by the deterioration in global food security conditions, this paper develops aparsimonious machine learning model to derive a multi-year outlook of global severe food insecurity from macro-economicprojections. The objective is to provide forecasts that are internally consistent with wider economic assessments,allowing both food security policies and economic development policies to be informed by a cohesive set ofexpectations. The model is validated on holdout data that explicitly test the ability to forecast new data fromhistory and extrapolate beyond observed intervals. It is then applied to the World Economic Outlook database of April2022 to project the severely food insecure population across all 144 World Bank lending countries. The analysis estimatesthat the global severely food insecure population may remain above 1 billion through 2027 unless large-scaleinterventions are made. The paper also explores counterfactual scenarios, first to investigate additionalrisks in a downside economic scenario, and second, to investigate whether restoring macroeconomic targets issufficient to revert food insecurity back to pre-pandemic levels. The paper concludes that the proposed model providesa robust and low-cost approach to maintain reliable long-term projections and produce scenario analyses that canbe revised systematically and interpreted within the context of available economic outlooks.