Credit Access is Associated with Improved Food Security in the Horn of Africa

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.

Machine Learning (cs.LG)

arXiv:2609.24382 [cs.LG]

(or arXiv:2609.24382v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.24382 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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