Machine learning (ML) holds potential to predict hunger crises before they occur. Yet, ML models embed crucial choices that affect their utility. We develop a prototype model to predict food insecurity across three countries in sub-Saharan Africa. Readily available data on prices, assets and weather all influence our model predictions. Our model obtains 55-84% accuracy, substantially outperforming a logit and ML models using only time and location. We highlight key principles for transparency and demonstrate how modeling choices between recall and accuracy can be tailored to policy-maker needs. Our work provides a path for future modeling efforts in this area.

Globally, over 800 million people are food insecure. Current methods for identifying food insecurity crises are not based on statistical models and fail to systematically incorporate readily available data on prices, weather, and demographics. As a result, policymakers cannot rapidly identify food insecure populations, hampering responses to mitigate hunger. We develop a replicable, near real-time model incorporating spatially and temporally granular market data, remotely-sensed rainfall and geographic data, and demographic characteristics. We train the model on 2010-2011 data from Malawi and forecast 2013 food security. Our model correctly identifies the food security status of 77% of the most food insecure village clusters in 2013 while the prevailing approach fails to correctly classify any of these village clusters. Our results show the power of modeling food insecurity to provide early warning and suggest model-driven approaches could dramatically improve food insecurity responses.

Many countries, particularly in the developing world, use public stockholding programs to stabilize price for both farmers and consumers. Governments directly purchase and store staple grains, and then sell them to processors or consumers often at a substantial subsidy. Despite the substantial costs of these stockholding programs, little is known about their effectiveness in mitigating the retail price swings. This paper estimates the effects of the purchase and sales activities of the Zambian Food Reserve Agency (FRA) on maize market prices across more than thirty markets in Zambia using monthly price data from 2003 to 2008. To deal with the endogeneity in purchases and sales, we use predicted FRA purchase and sales targets as instrumental variables. Controlling for other policies in place, we find evidence that FRA activities stabilize retail prices in the major district markets within the cropping year. Results show that FRA purchases raise local prices for surplus maize producers for about 5% on average during the time of harvest and FRA sales help to lower the price during the lean season up to 7%. On the other hand, we are only able to find evidence of the FRA reducing price volatility between years in a few district markets.