_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _2_ Zero Hunger is the second of the Sustainable Development Goals (SDGs) adopted by all United Nations (UN) Member States in 2015. Despite progress in reducing poverty in recent decades (World Bank Group, 2018), FAO et al. (2022) conclude that there is no longer any doubt that the world is moving backwards in its efforts to end hunger, food insecurity and malnutrition in all its forms. Close to 195 million people were estimated to be in food crisis in 2021 (Food Security Information Network, 2022), while FAO et al. (2022) estimate that the number of severely food insecure people reached 924 million in 2021.

Against this backdrop, the effects of the war in Ukraine are rippling through the global commodities markets (World Bank Group, 2022), fueling the multiple already existing humanitarian crises. The anticipated economic damages from the conflict have resulted in downward revisions of growth expectations, and upward revisions in inflation expectations (World Bank, 2022b; International Monetary Fund, 2022). Food prices have increased sharply globally, hitting vulnerable populations in low-income countries hardest and adding directly humanitarian aid costs. This occurs at a time in which low-income countries have not yet recovered from the economic blow dealt by the pandemic (World Bank, 2022a).

The wide deterioration in food security conditions calls for global long-term food insecurity outlook, along with a mechanism to update and revise projections systematically. This paper proposes a parsimonious machine learning model to derive a multi-year projection of global severe food insecurity from available macroeconomic outlooks. The model-based approach allows producing counterfactual scenarios, which can help investigate whether reaching certain macro-economic targets can be expected to sufficiently address food insecurity risks or whether more targeted interventions are needed.

The application builds the model using 2015-2019 country-level data on food insecurity conditions available for around 80 of 144 countries classified in the World Bank’s IDA and IBRD cohorts (59 IDA, 15 Blend, 70 IBRD), together with annual covariates that capture economic and structural drivers of food insecurity that can be obtained from official public sources. The model is a local-linear regression implemented using a Cubist model that utilizes decision trees to partition the data and ensemble multiple robust linear models that can extrapolate local elasticities. The algorithm provides a novel approach to combine the flexibility of decision trees with the stable extrapolation properties of linear regression techniques. The predictions are validated against different sets of holdout data that test the model’s ability to forecast large temporal changes and fill regional gaps. Since simplicity carries a premium, the accuracy of the proposed approach is also compared to that of a linear regression. The results indicate that the linear regression is not able to cope with the heterogeneity and nonlinearity in the data. The final Cubist model, however, uses 11 easy-to-interpret indicators, to explain more than 95% of the variation in out-of-sample data.

The key predictive features are easily projected forward using available economic outlooks. The model is applied to historical economic data and the WEO