发表机构
Universitat de València; Wageningen University & Research; Joint Research Centre (JRC), European Commission; World Food Programme (WFP), United Nations; Internal Displacement Monitoring Centre (IDMC)（瓦伦西亚大学; 瓦赫宁根大学及研究中心; 欧盟委员会联合研究中心; 联合国世界粮食计划署; 境内流离失所监测中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用观测性机器学习框架，基于2015-2022年多源数据，发现信贷获取增加与非洲之角急性粮食不安全率降低2%相关，为数据稀缺危机地区提供了金融与粮食安全关联的证据。
AI 中文摘要
气候变化的加剧对粮食安全构成日益严重的威胁，尤其是在脆弱社区。本研究采用观测性机器学习框架，基于2015年至2022年间涵盖关键环境、社会经济和冲突相关因素的统一数据集，估计信贷获取与索马里及整个非洲之角地区急性粮食不安全之间的因果关联。结果表明，在研究期间，信贷获取的增加与人口层面急性粮食不安全率降低2%相关。鉴于平均有16%的人口处于危机状态，这一效应代表了高风险群体内的显著转变。我们在明确的识别假设下解释这些估计，并通过稳健性和反证检验加以补充。研究结果提供了关于在数据稀缺、受危机影响的背景下，金融获取如何与粮食安全结果相关的具体情境证据，并为在随机评估不可行时整合异构数据源提供了透明框架。
英文摘要
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.
