Credit access reduces food insecurity in Horn of Africa by two percent
Credit Access is Associated with Improved Food Security in the Horn of Africa
Machine Learning
Summary
Food security is threatened by climate change, especially in vulnerable areas like the Horn of Africa. This study finds that having better access to credit is linked to a small but meaningful decrease in the number of people facing severe food shortages. The authors use machine learning on diverse data from 2015 to 2022 to carefully estimate this effect. Their work helps understand how financial tools can support food security in crisis regions where data is often limited.
What this means in practice
- •For development finance teams: Design credit programs informed by data linking financial access and food security improvements in crisis regions.
- •For humanitarian policy planners: Use causal estimates to prioritize financial interventions in food insecurity hotspots to reduce crisis impact.
Authors
Jordi Cerdà-Bautista, Vasileios Sitokonstantinou, José Manuel Veiga López-Peña, Duccio Piovani, José María Tárraga, Gustau Camps-Valls
Abstract
The intensification of climate change poses a growing threat to food security, especially in vulnerable communities. This study employs an observational machine-learning framework to estimate the causal association between access to credit and acute food insecurity in Somalia and across the Horn of Africa, drawing on a harmonized dataset spanning key environmental, socioeconomic, and conflict-related factors from 2015 to 2022. Results indicate that greater credit access is associated with a 2% reduction in acute food insecurity at the population level over the study period. Given that, on average, 16% of the population is in crisis, this effect represents a meaningful shift within the at-risk group. We interpret these estimates under explicit identification assumptions and complement them with robustness and refutation tests. The results provide context-specific evidence on how financial access correlates with food security outcomes in data-scarce, crisis-affected settings, and offer a transparent framework for integrating heterogeneous data sources when randomized evaluations are infeasible.