Machine Learning Guided Outlook of Global Food Insecurity Consistent with Macroeconomic Forecasts
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World Bank, Washington, DC
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Motivated by the deterioration in
global food security conditions, this paper develops a
parsimonious machine learning model to derive a multi-year
outlook of global severe food insecurity from macro-economic
projections. 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 of
expectations. The model is validated on holdout data that
explicitly test the ability to forecast new data from
history and extrapolate beyond observed intervals. It is
then applied to the World Economic Outlook database of April
2022 to project the severely food insecure population across
all 144 World Bank lending countries. The analysis estimates
that the global severely food insecure population may remain
above 1 billion through 2027 unless large-scale
interventions are made. The paper also explores
counterfactual scenarios, first to investigate additional
risks in a downside economic scenario, and second, to
investigate whether restoring macroeconomic targets is
sufficient to revert food insecurity back to pre-pandemic
levels. The paper concludes that the proposed model provides
a robust and low-cost approach to maintain reliable
long-term projections and produce scenario analyses that can
be revised systematically and interpreted within the context
of available economic outlooks.
Palabras clave
MACHINE LEARNING, MACRO-ECONOMIC PROJECTION, PRE-PANDEMIC FOOD SECURITY, FOOD INSECURITY, FOOD CRISES, HUMANITARIAN NEEDS, FOOD SECURITY POLICY, ECONOMIC SHOCKS, VULNERABILITY