Exploring the nexus between climate finance, rural–urban disparities, and rural brain drain in Somalia: the mediating role of climate resilience - Nor, Mohamed Ibrahim (Frontiers in Sociology, 2025)
Rural brain drain poses a major development challenge in fragile, climate-vulnerable contexts where climate shocks, economic disparities, and weak governance converge. This study investigates how climate-sensitive development financing (CSDF), climate resilience (CR), and rural–urban disparity (RUD) interact to influence rural brain retention (RBR) in Southwest Somalia, a conflict- and drought-prone region. Using data from 118 rural households, the study applies Partial Least Squares Structural Equation Modeling (PLS-SEM) supported by confirmatory factor analysis (CFA) to test a reflective four-factor model. The model examines both direct and mediated effects between CSDF, CR, RUD, and RBR. The results reveal that climate resilience is central to rural brain retention, acting as a key pathway through which climate-sensitive financing strengthens local adaptive capacity. Conversely, rural–urban disparities undermine resilience and exacerbate skilled outmigration. The findings highlight the importance of integrated, context-sensitive strategies that enhance resilience and improve opportunities in rural areas. Integrating climate-sensitive financing into rural development agendas can enhance adaptive capacity and reduce skilled outmigration. Development partners should prioritize concessional funding, youth-led entrepreneurship, and climate-smart infrastructure while addressing service and opportunity gaps between rural and urban areas. This is among the first empirical studies to model rural brain retention—rather than migration—linking it to climate finance, resilience, and structural disparities in a fragile context. The study advances migration and adaptation theory by positioning climate resilience as a mediator between financial investments, disparities, and human capital retention. It operationalizes CSDF as a measurable construct and demonstrates the utility of advanced SEM techniques in data-scarce, high-risk environments.