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

2026-09-21

AI Summary

研究使用机器学习方法分析了2015-2022年间信贷获取与非洲之角地区粮食安全之间的关系，发现增加信贷可降低约2%的粮食不安全状况。

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.

Problem

• credit access
• food security
• Horn of Africa
• observational machine-learning
• climate change

Innovation

• observational machine-learning framework
• credit access
• acute food insecurity
• harmonized dataset
• robustness and refutation tests