Mohamed Ibrahim Nor- Institute of Climate and Environment (ICE), SIMAD University, Mogadishu, Somalia
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.
Introduction
Somalia, situated at the heart of the climate-vulnerable Horn of Africa, faces an acute convergence of environmental stress, displacement, and institutional fragility. Recurrent droughts, extreme weather events, and degraded natural resources have severely undermined rural livelihoods, fueling involuntary migration and displacement. These climate-related pressures are compounded by weak governance and limited adaptation finance, which together diminish the capacity of rural communities to retain skilled individuals. Recent research highlights the importance of context-sensitive climate financing and durable solutions tailored to fragile settings like Somalia, where human capital flight is often a consequence of structural constraints rather than individual choice (Nor and Moge, 2023; Mohamed, 2025; Regan, 2024).
The phenomenon of rural brain drain, characterized by the sustained outmigration of educated and skilled individuals from rural areas to urban centers, remains a significant challenge worldwide (Issac and Tripathi, 2024). In the United States, nonmetropolitan counties, particularly those distant from urban areas, have consistently experienced net losses in their share of college-educated residents since the 1970s, a trend closely tied to population decline (Lichter and Johnson, 2006). While some rural counties have seen gains, the majority remain disadvantaged, lacking the human capital essential for local innovation and long-term development.
Beyond developed countries, the interplay between rural decline, environmental stress, and social fragility is increasingly evident (Li et al., 2019). Climate-driven shocks—such as droughts and floods—drive displacement and migration from rural areas, compromising local livelihoods (Fröhlich et al., 2019). Fragile and conflict-affected rural settings further aggravate these pressures, with weak institutional structures amplifying human capital outflows (Galindo-Silva and Tchuente, 2023). Together, these dynamics illustrate how rural–urban disparities are exacerbated by structural and environmental vulnerabilities.
Rural brain drain has emerged as a pressing development challenge in fragile and climate-vulnerable contexts like Somalia. At its core, rural brain drain reflects a systemic failure to generate equitable employment and foster inclusive development in rural areas. Limited infrastructure, inadequate financial access, and weak governance capacity constrain economic opportunities, leaving skilled individuals with few viable prospects (Gansauer, 2025). As a result, persistent out-migration of educated and trained individuals accelerates, deepening spatial inequalities in human capital and undermining national development agendas.
The consequences of this exodus extend beyond workforce shortages. A human capital vacuum destabilizes rural social and knowledge systems, eroding traditional farming expertise, environmental management practices, and vocational skills that sustain local livelihoods (Díaz Baca et al., 2024). In fragile states, such as Somalia, the absence of local expertise exacerbates vulnerabilities in climate adaptation, natural resource governance, and conflict resolution (Bergman, 2025). Without sufficient capacity at the community level, efforts to mitigate climate risks and promote sustainable rural development remain constrained.
Consequently, rural brain drain should not be understood merely as a labor market phenomenon but rather as a structural development failure. Addressing it requires examining the interplay between climate finance, rural–urban disparities, and climate resilience to understand how these factors jointly shape migration decisions and community stability. Yet, existing scholarship has rarely explored these interconnections within fragile contexts, leaving a critical gap this study seeks to fill.
This study is driven by the urgent need to develop integrative frameworks capable of reversing the escalating phenomenon of rural brain drain in fragile contexts like Somalia. Traditional interventions—such as agricultural aid, infrastructure development, or climate adaptation programs—have largely been implemented in isolation, failing to address the systemic and interconnected drivers of skilled migration. Evidence shows that even rural areas situated close to urban centers continue to experience significant outflows of educated graduates despite rising educational attainment levels (Van Maarseveen, 2021), highlighting the limitations of fragmented, sector-specific solutions. Moreover, demographic pressures and structural inequities intensify the problem: economies of scale, agglomeration effects, and urban wage premiums consistently pull talent away from rural areas (Meekes, 2022). Without holistic, systems-level strategies—ones that combine climate-smart financing, rural enterprise development, and inclusive social policies—rural communities will remain locked in cycles of economic marginalization and vulnerability. By exploring the nexus between climate finance, rural–urban disparities, and climate resilience, this study offers a novel contribution to migration and development literature, proposing integrated policy pathways to transform rural environments into hubs of opportunity and resilience.
Addressing rural brain drain is increasingly urgent for achieving national resilience, food security, and social equity in fragile contexts like Somalia. Drawing on human capital theory (Becker, 1964), the outmigration of skilled individuals erodes local productivity and weakens the foundations of rural economic development. Similarly, cumulative causality theory (Myrdal, 1957) suggests that such losses are not temporary but trigger self-reinforcing cycles of rural impoverishment, where declining opportunities fuel further migration and deepen regional disparities. The capability approach (De Haas, 2021) further frames migration as a response to constrained freedoms and structural deprivations: when individuals lack the capacity to achieve their desired livelihoods, relocation becomes an adaptive strategy rather than a voluntary choice. In fragile states, these dynamics are compounded by climate-related displacement, governance failures, and conflict (Augsten et al., 2022), which together undermine residents’ adaptive capacity and community stability. Integrating these theoretical perspectives highlights the multidimensional nature of rural brain drain and underscores the urgency of developing interventions that address both its structural and environmental determinants.
In Somalia, the livelihoods and vulnerabilities of rural and urban communities differ significantly, shaping divergent capacities to withstand climate and economic shocks. Rural populations primarily engage in rain-fed agriculture, pastoralism, and informal subsistence activities, making them acutely vulnerable to drought, flood events, and land degradation. These hazards are compounded by limited access to markets, weak infrastructure, and recurrent conflict over land and water. Constraints on access to and use of natural resources—such as insecure land tenure or seasonal water scarcity—further restrict livelihood options and fuel adaptive migration. In contrast, urban areas offer more diversified economic opportunities and relatively better access to services, humanitarian assistance, and infrastructure. However, urban communities are not immune to vulnerability: many migrants settle in informal settlements with heightened exposure to urban flooding, inadequate shelter, and insecurity, particularly gender-based violence and inter-clan tensions. Urban wage labor is often precarious, and the cost of living remains high, making economic survival challenging without stable income or support networks. These structural disparities in opportunity, exposure, and resilience capacities explain why climate-induced migration is rarely a simple push or pull phenomenon but reflects constrained choices under uncertainty. By contextualizing rural resilience—highlighting both the specific hazards faced by rural communities and the systems required to strengthen their adaptive capacity—this study provides critical insight into climate-mobility pathways in fragile environments like Somalia. Such framing also helps identify levers for reducing migration pressure by enhancing rural livelihoods and addressing vulnerabilities at their source.
Despite extensive scholarship on migration, climate finance, and rural development, the existing literature remains siloed and lacks a systems-level understanding of how these forces interact to shape rural outmigration. Migration studies typically emphasize demographic drivers, development economists focus on agricultural productivity and rural livelihoods, and climate analysts prioritize adaptation financing—yet few studies converge these strands to design integrated solutions tailored to fragile rural contexts. While previous research has documented climate-induced displacement and the socioeconomic dimensions of rural-to-urban migration (Lichter and Johnson, 2006), there is limited evidence on how climate finance, rural–urban disparities, and community resilience interact to influence human capital retention (Mercy Corps, 2023a). This conceptual and practical disconnect constrains policymakers and practitioners seeking to design effective, context-sensitive strategies. Responding to this gap, the present study offers an integrated framework that simultaneously examines climate finance, climate resilience, and rural–urban inequalities to explain patterns of rural brain drain. By adopting a systems perspective that links economic opportunity, climate adaptation, governance, and social inclusion, the study provides a foundation for evidence-based interventions aimed at transforming rural areas into hubs of opportunity and resilience.
The purpose of this study is to examine the interrelationships between climate finance, rural–urban disparities, climate resilience, and rural brain drain within the fragile context of Southwest Somalia. Specifically, the study seeks to investigate how climate finance influences rural community resilience, how socioeconomic inequalities between rural and urban areas drive the outmigration of skilled individuals, and whether climate resilience mediates these dynamics. By integrating theoretical insights from human capital theory, cumulative causality theory, and the capability approach, the study aims to develop and empirically validate a conceptual framework that explains the structural drivers of rural brain drain. Furthermore, this research intends to generate policy-relevant insights by identifying systems-level interventions—including climate-smart financing, inclusive rural development strategies, and resilience-building measures—that can help retain human capital, reduce migration pressures, and foster equitable development in climate-vulnerable regions.
To address the structural drivers of rural brain drain and explore pathways for resilience in fragile contexts like Somalia, this study is guided by the following research questions:
1. How does climate finance influence climate resilience in rural Somalia?
This question investigates whether climate-related investments enhance the adaptive capacity of rural communities and mitigate vulnerabilities arising from environmental shocks.
1. To what extent do rural–urban disparities shape patterns of rural brain drain?
This question examines how socioeconomic inequalities—such as access to infrastructure, services, and opportunities—drive skilled migration from rural to urban areas.
1. Does climate resilience mediate the relationship between climate finance, rural–urban disparities, and rural brain drain?
This question evaluates whether strengthening community resilience can reduce migration pressures and retain human capital in rural regions.
1. What integrated policy strategies can be derived from the interplay of these factors to reverse rural brain drain?
This final question bridges empirical insights with practical solutions, highlighting how climate-smart financing, inclusive rural development, and equitable opportunities can foster sustainable livelihoods.
Literature review
Rural brain drain and its drivers
Rural brain drain refers to the sustained out-migration of highly skilled individuals from rural areas to urban centers or abroad, often driven by spatial inequalities in wages, services, and social mobility (Shen and Zhou, 2024; Randazzo and Currid-Halkett, 2025; Braesemann et al., 2022; Alexander, 2023; Cattaneo et al., 2022). Globally, this process deepens regional disparities, weakening rural development trajectories and institutional capacities.
In Somalia, the phenomenon manifests uniquely due to persistent fragility and protracted displacement dynamics. The country’s large diaspora plays a dual role: while remittances and return visits contribute to development, qualified professionals—veterinary technicians, agronomists, teachers—continue to leave rural areas, creating a “vacuum–dependence” cycle in which ministries and public hospitals increasingly rely on diaspora professionals and external consultants (Dalmar et al., 2017; Hermele, 2021).
National policy documents, such as the Ninth National Development Plan (NDP-9), conceptualize rural brain drain as both a cause and a consequence of underdevelopment. Persistent out-migration erodes institutional capacities to deliver services, adapt to shocks, and retain youth, while limited opportunities reinforce the incentives to leave (NDP-9, 2020). This cycle is exacerbated by political fragility, recurrent conflict, and climatic hazards, which collectively constrain local resilience and investment (Mercy Corps, 2023a).
In the Horn of Africa, Somalia lies at the crux of climate-driven vulnerability, where recurrent droughts, land degradation, and erratic rainfall have deeply undermined rural livelihoods and spurred cycles of displacement and migration (Nor and Moge, 2023). While much of the migration literature emphasizes outflows, recent studies in Somalia highlight how climate stress interacts with conflict, food insecurity, and weak institutions to erode incentives for human capital retention in rural areas (Dirie, 2024). Moreover, the nexus between climate change and migration in Somalia cannot be disentangled from the broader Horn regional dynamics, as pastoral mobility, resource contestation, and cross-border flows operate across national boundaries (Mixed Migration Platform, 2022; Mohamed, 2025). The political fragility and governance gaps in Somalia further complicate the effectiveness of adaptation finance or resilience policies, making the region a testing ground for how climate-sensitive development interventions might (or might not) succeed (Regan, 2024; Gavin, 2022). Thus, situating your study in this local and regional context—rather than treating Somalia as a generic case—is critical for illuminating how retention of rural skilled actors must contend with overlapping climatic, sociopolitical, and institutional pressures.
Climate finance and climate resilience
Recent literature highlights the intersection of climate vulnerability, migration, and institutional fragility, framing climate adaptation finance as pivotal for breaking Somalia’s conflict–fragility–migration trap (IPCC, 2022a; Scheffran et al., 2012). Recurrent droughts—most notably between 2021 and 2023, which displaced over 1 million rural inhabitants—erode agro-pastoral livelihoods, while weak governance limits adaptive capacity and institutional responses (Nor, 2025; Kelly and Ketu, 2024).
Despite high exposure to climate risk, Somalia remains systematically excluded from global climate finance flows due to donor risk aversion and fiduciary concerns. Mercy Corps identifies “climate–fragility filters” that deprioritize fragile states, exacerbating resource gaps and institutional weakness (Mercy Corps, 2023b). Consequently, underinvestment perpetuates a feedback loop: degraded livelihoods drive skilled out-migration, weakening governance further and undermining eligibility for future adaptation funding (Deneuve et al., 2024; UNEP, 2023).
The IPCC Sixth Assessment Report (IPCC, 2022b) emphasizes the need for integrated interventions—combining climate adaptation, livelihood diversification, and peacebuilding—to build resilience in contexts like Somalia. However, Somali-focused studies and programming remain fragmented: few pilot projects explicitly link adaptation financing to migration decisions or professional retention, leaving a critical evidence gap (Cao et al., 2021).
Rural–urban disparities and migration pressures
Spatial inequality underpins Somalia’s rural brain drain. Poor physical and digital infrastructure isolates rural communities, with only 31% of residents living near all-season roads and rural electricity connectivity remaining below 30% (World Bank, 2022a; IMF, 2022). These deficits restrict market integration, service delivery, and employment options, creating strong “push” factors for skilled professionals.
Urban hubs such as Mogadishu and Hargeisa concentrate economic opportunities, services, and digital connectivity, attracting talent and intensifying rural depopulation. This spatial concentration mirrors global findings that brain drain widens intra-country income gaps and overburdens urban systems (Lichter and Johnson, 2006).
Donor-supported initiatives illustrate potential solutions but remain limited in scale. Programs like the FAO’s Resilience Initiative (2017–2023) boosted rural incomes by ≈15% through diversified enterprises (water infrastructure, beekeeping, feed production) and targeted women and youth engagement (FAO, 2024b). Similarly, the Baxnaano cash-transfer program has supported over 200,000 rural households, providing short-term relief but failing to integrate retention incentives for skilled professionals (World Bank, 2023).
While mobile money adoption is near-universal (87% of adults), enabling financial inclusion, Somalia captures less than 1% of global climate finance flows due to fiduciary constraints (Mercy Corps, 2023a). Without sustained investment in local training institutions, fiber connectivity, and climate-adaptive livelihoods, rural areas remain structurally disadvantaged and unable to retain skilled talent.
Knowledge gaps and research rationale
Despite extensive reporting on migration, displacement, and sectoral development, existing Somali scholarship rarely integrates human capital dynamics with climate financing and rural–urban disparities. Key gaps include:
1. Limited empirical data tracking how climate-finance instruments affect skilled migration decisions in rural Somalia.
2. Lack of skill-level disaggregation in program evaluations, obscuring impacts on professional retention.
3. Fragmented approaches that treat migration, climate adaptation, and conflict separately, rather than within a systemic framework.
Addressing these gaps requires a systems-level perspective linking governance reform, climate-smart financing, and livelihood diversification to reduce rural brain drain. This study positions itself to fill that gap by modeling how bundled interventions—integrating agri-innovation, inclusive finance, and climate-resilient governance—can stabilize rural livelihoods and retain skilled professionals in Somalia.
Theoretical foundation and conceptual framework
To investigate the complex interrelationships among structural inequities, financial mechanisms, and rural human capital dynamics in climate-vulnerable regions, this study develops a conceptual framework grounded in contemporary development and resilience theories. The framework positions climate resilience as a key mediating construct through which rural–urban disparities and climate-sensitive development financing influence rural brain retention. By integrating these constructs into a unified structural model, this study aims to unpack the nuanced pathways that contribute to rural brain drain in the context of Somalia. The following hypotheses are derived to empirically test the proposed model via structural equation modeling (SEM).
Human capital theory
Human capital theory conceptualizes education, skills, and health as economic assets that increase an individual’s productivity and lifetime earnings (Becker, 1964). Subsequent empirical studies extend Becker’s logic by showing that individuals weigh expected returns on their human capital investments—wages, career progression, and quality of life—when deciding where to live and work (Mincer, 1978; Oketch, 2018). At the macro scale, regions able to offer higher returns on skills attract talent, whereas those with weaker labor markets experience human capital flight. Thus, the theory provides a parsimonious framework for explaining why skilled professionals migrate from rural to urban areas or abroad: they are reallocating their capital to where it yields the greatest return.
Applied to Somalia, human capital theory helps explain persistent rural brain drain despite rising enrollment in secondary schools and universities. Surveys show that agricultural graduates report potential urban earnings two-to-three times higher than those reported in rural districts do, whereas rural postings often lack housing, career advancement, and reliable salaries (FAO, IOM, and WFP, 2018). This differential discourages graduates from accepting positions in agriculture extension, veterinary services, or rural schools, even when such posts are vacant (World Bank, 2022b). Moreover, remittance networks enable households to view migration as an investment in human capital with trans-local benefits, reinforcing the out-migration cycle. Hence, unless policies increase private and social returns to working in rural Somalia—through incentives, infrastructure, and professional pathways—the country will continue to export its scarce rural expertise.
Cumulative causation theory
Myrdal’s cumulative causation theory posits that regional inequalities are self-reinforcing: initial advantages in one area (e.g., capital, skills, and infrastructure) attract further investment, whereas disadvantages elsewhere trigger a downward spiral of disinvestment and out-migration (Myrdal, 1957). Positive feedback loops—“spread” and “backwash” effects—explain why prosperous regions continue to gain human capital and financial capital, whereas lagging regions struggle to reverse this decline (Fujita and Thisse, 2002; Krugman, 1991). The theory therefore emphasizes structural, path-dependent processes rather than individual choices alone, suggesting that market forces left unchecked will deepen spatial disparities.
In Somalia, cumulative causation manifests starkly: Mogadishu and regional capitals absorb scarce public investment, telecom infrastructure, and donor projects, which in turn attract skilled workers seeking better amenities and security (World Bank, 2022b). As professionals depart from rural areas, local tax bases shrink, and service quality deteriorates, making those communities even less attractive to remaining residents—a classic “backwash” effect. Empirical work shows that districts that lost more than 5% of their skilled workforce between 2015 and 2020 also experienced sharper declines in school enrollment and health worker density, reinforcing the spiral described by Myrdal (SPARC, 2023). Breaking this cycle requires “counter-cumulative” interventions—targeted climate-resilient infrastructure, guaranteed rural postings, or fiscal transfers—that can offset the self-reinforcing mechanisms of decline.
Capability approach
Sen’s capability approach reframes development as the expansion of substantive freedoms—“capabilities”—that allow people to lead the lives they value (Sen, 1999). Rather than focusing solely on income, it highlights multidimensional well-being: health, education, security, and voice (Alkire, 2005; Robeyns, 2006). Within this lens, migration becomes a response to capability deprivation: individuals relocate when their existing environment fails to provide adequate opportunities to convert resources into valued functionings. The approach thus broadens the analysis of brain drain beyond economic returns to include rights, dignity, and agency.
For rural Somalis, limited capabilities—safe water, healthcare, quality education, and climate-resilient livelihoods—drive both temporary displacement and permanent migration (Displacement, J. D. C. O. F, 2024). Drought analytics reveal that agro-pastoral households cite the insecurity of water and basic services as strongly as income loss when explaining relocation decisions (Migration, I. O. F, 2023). Skilled professionals likewise report capability shortfalls: inadequate medical facilities for their families, poor schooling for children, and insecurity—factors that wages alone cannot compensate (FAO, 2024a). Consequently, retention strategies grounded only in salary supplements neglect the broader freedoms people seek. A capability-oriented policy package would pair financial incentives with improved rural services, participatory governance, and climate-adaptation measures, thereby tackling the deeper capability deficits fueling Somalia’s rural brain drain.
The conceptual framework
This study proposes a conceptual model that seeks to investigate the complex interrelationships among rural–urban disparities, climate-sensitive development financing, and rural brain drain, with a particular focus on the mediating role of climate resilience. The model is designed to capture how structural inequalities and financing mechanisms aimed at climate adaptation and mitigation can influence community resilience and, in turn, affect the decision of skilled individuals to remain in or leave rural areas.
The framework aligns with key tenets of development theory, climate justice, and human capital retention, which emphasize the importance of equitable resource distribution, institutional support, and environmental sustainability in mitigating rural disadvantage and socioeconomic outmigration. The structural model is presented below:
• Rural–urban disparity and climate-sensitive development financing are treated as exogenous latent variables.
• Climate resilience serves as the mediating variable.
• Rural brain retention (the inverse of brain drain) is the endogenous outcome.
This model enables the testing of both the direct effects of disparities and financing on resilience and the indirect effect of resilience on the retention of skilled individuals in rural areas (Figure 1).
Figure 1. Conceptual framework.
Hypothesis development
Rural–urban disparities in infrastructure, access to essential services, and investment critically affect communities’ ability to respond to and recover from climate-related challenges. Urban areas typically benefit from more robust infrastructure; better-funded institutions; and greater access to healthcare, transportation, and technological resources, all of which contribute to higher adaptive capacity. In contrast, rural communities often struggle with aging infrastructure, limited public services, and scarce financial resources, making them disproportionately vulnerable to climate impacts such as extreme weather events, droughts, and flooding (Adger et al., 2003; Puig et al., 2025). These conditions hinder proactive adaptation strategies and increase the difficulty of recovery after climate-related disruptions.
The lack of institutional and financial support in rural areas not only limits immediate response mechanisms but also constrains long-term planning and investment in climate resilience. For example, smaller local governments may lack the staffing and expertise needed to apply for climate adaptation grants or implement complex mitigation strategies (Ford et al., 2013). Moreover, limited economic diversification in many rural regions can exacerbate risks, as communities dependent on climate-sensitive sectors such as agriculture or natural resource extraction face greater exposure to climate variability. As the gap in infrastructure and investment widens between rural and urban areas, the adaptive capacity of rural communities diminishes, reinforcing cycles of vulnerability and social inequity (IPCC, 2014). Hence, this study hypothesizes a negative relationship:
H1: Rural–urban disparity is negatively associated with climate resilience.
Climate-sensitive financing is crucial for empowering rural communities to develop and implement effective adaptation and resilience strategies in the face of growing climate risks. Rural areas, which often face greater exposure to climate-sensitive sectors such as agriculture, forestry, and fisheries, require targeted investments that address their unique vulnerabilities. When financing is aligned with climate adaptation goals, it can support the construction of resilient infrastructure such as flood-resistant roads, drought-proof water systems, and sustainable energy sources. Additionally, such funding can promote nature-based solutions and community-based adaptation projects, which are often more cost-effective and locally appropriate (UNDP, 2011).
In addition to infrastructure, climate-sensitive financing can strengthen rural livelihoods and institutional capacity, making communities more self-reliant and responsive to environmental changes. Investments in education, training, and extension services can enhance local knowledge and skills, allowing communities to adopt climate-smart agricultural practices and diversify income sources. Furthermore, well-designed financial mechanisms—such as grants, low-interest loans, and insurance schemes—can reduce the economic shocks caused by climate extremes and encourage proactive planning. By integrating climate risk into financial decision-making, stakeholders can ensure that rural development is not only inclusive but also resilient (OECD, 2020). Therefore:
H2: Climate-sensitive development financing is positively associated with climate resilience.
Building climate resilience in rural areas plays a critical role in promoting community stability and long-term sustainability. When rural communities are equipped with the infrastructure, resources, and institutional support needed to withstand climate-related shocks, they are better positioned to maintain livelihoods, public services, and overall well-being. This resilience reduces the push factors that often drive migration—such as crop failure, loss of income, and deteriorating living conditions—by ensuring continuity and adaptability in the face of environmental stressors (Black et al., 2011). As rural areas become more capable of managing climate risk, they foster environments where people feel safe and supported, reducing the need to relocate to urban centers or abroad in search of stability.
A key outcome of enhanced climate resilience is the retention of human capital, particularly among skilled individuals who are crucial to local development. In communities where climate adaptation strategies create economic opportunities—such as green jobs, climate-smart agriculture, or renewable energy projects—residents are more likely to stay and invest in their futures. Improved living conditions, access to services, and social cohesion contribute to a higher quality of life, which strengthens the incentive for individuals to remain within their communities rather than migrate elsewhere (Tacoli et al., 2025). Thus, fostering climate resilience is not only a matter of environmental necessity but also a strategy for safeguarding the social and economic fabric of rural areas. Thus:
H3: Climate resilience is positively associated with rural brain retention.
This hypothesized model will be empirically tested via structural equation modeling (SEM) to assess both direct and mediated relationships, offering a robust and nuanced understanding of the pathways influencing rural brain retention in climate-vulnerable contexts.
Materials and methods
This study adopts a methodologically advanced and empirically grounded quantitative research design to explore the complex interrelationships among climate finance, rural–urban disparities, rural brain drain, and the mediating role of climate resilience in Somalia. Recognizing the multifaceted nature of the research questions, this approach provides the analytical depth and precision necessary for capturing and interpreting the nuanced dynamics at play.
Research design and analytical approach
This study adopts structural equation modeling (SEM) as the primary analytical technique to examine the complex interrelationships among climate finance, rural–urban disparities, rural brain drain, and the mediating role of climate resilience. SEM is particularly appropriate for this investigation because it allows for the simultaneous estimation of multiple relationships among both observed variables and latent constructs, offering a level of analytical depth that traditional multiple regression techniques cannot achieve. Its flexibility enables the modeling of direct, indirect, and total effects within a unified theoretical framework, making it ideally suited to capture the intricate interdependencies outlined in the study’s conceptual model.
By integrating confirmatory factor analysis (CFA) within the SEM framework, the study first validates the reliability and validity of the latent constructs before testing the hypothesized structural relationships. This dual capability ensures that measurement errors are minimized and that the constructs are robustly operationalized. Furthermore, SEM facilitates the explicit examination of mediating effects, allowing the study to assess how climate resilience influences the pathway between climate finance and rural brain drain, a dimension often overlooked in previous empirical research. Model adequacy is evaluated using multiple goodness-of-fit indices, ensuring that the results are both statistically sound and theoretically consistent with the realities of Somalia’s climate-fragile context.
Overall, the application of SEM enhances the methodological rigor of this research by enabling a systems-oriented analysis that reflects the complexity of the phenomena under study. It provides a richer and more nuanced understanding of how climate finance shapes spatial inequalities and migration dynamics while highlighting the critical role of resilience in mitigating skilled out-migration. Beyond its empirical contribution, this approach advances methodological standards in environmental economics, migration studies, and social development research, offering policy-relevant insights to inform interventions aimed at reducing rural brain drain and strengthening adaptive capacities in fragile and climate-vulnerable contexts.
Data collection and sampling strategy
Empirical data for this study were collected in 2025 through a structured survey designed to capture perceptions and experiences related to climate finance mechanisms, rural–urban disparities, migratory behaviors associated with rural brain drain, and community-level resilience to climate change. The survey instrument was developed using a combination of established measurement scales and contextually adapted items, ensuring both conceptual relevance and methodological rigor. All items were measured primarily on five- or seven-point Likert-type scales, which align with the requirements of structural equation modeling (SEM) by producing continuous data suitable for latent variable analysis.
Data collection targeted a broad cross-section of Somali society to reflect the diverse perspectives shaping climate resilience and development discourse. Participants were drawn from key stakeholder groups, including government entities, private sector actors, and social sector organizations such as NGOs and INGOs. This diversity enriched the dataset by incorporating insights from multiple domains directly involved in climate adaptation, service delivery, and development planning.
To ensure adequate representation of this heterogeneity, a purposive stratified sampling technique was employed. Strata were defined based on stakeholder categories, allowing the study to capture sector-specific dynamics while maintaining analytical comparability across groups. This approach was particularly important given the study’s multifaceted objectives, which require integrating perspectives from policymakers, implementers, and beneficiaries of climate finance and resilience initiatives.
Out of the 200 questionnaires distributed, 118 valid responses were retained after rigorous data cleaning and validation procedures. Incomplete or inconsistent responses were excluded to uphold data quality standards and maintain the integrity of the final dataset. The resulting response rate of approximately 59% is considered satisfactory for studies conducted in fragile and climate-vulnerable contexts and meets the recommended sample size thresholds for SEM, which require at least 5–10 responses per estimated parameter (Hair et al., 2022).
Measurement of constructs
The study’s four key constructs—climate finance, rural–urban disparities, rural brain drain, and climate resilience—were conceptualized as latent variables and measured using multi-item indicators derived from an extensive review of the literature and refined through expert consultations to ensure contextual relevance. Each construct was operationalized through a set of items designed to capture respondents’ perceptions, experiences, and behaviors related to the study themes. Responses were recorded using five- or seven-point Likert-type scales, depending on the nature of the construct, enabling the measurement of varying degrees of agreement or frequency. These scaling decisions align with the analytical requirements of structural equation modeling (SEM) by generating continuous data suitable for latent variable modeling.
All constructs were modeled as reflective measurement models, meaning that the observed indicators were assumed to reflect their respective latent constructs rather than form them. To ensure the reliability and validity of these measures, a confirmatory factor analysis (CFA) was conducted as a preliminary step before testing the structural model. Internal consistency reliability was evaluated using both Cronbach’s alpha and composite reliability (CR), applying the commonly accepted threshold of 0.70 or higher. Convergent validity was assessed through the average variance extracted (AVE), where values greater than 0.50 were considered acceptable. To establish discriminant validity, Heterotrait–Monotrait (HTMT) criterion was applied, comparing the square root of each construct’s AVE with its correlations with other constructs to confirm that each latent variable captured a unique dimension of the theoretical framework.
This rigorous measurement approach ensured that the constructs were both statistically sound and theoretically meaningful, providing a strong empirical foundation for testing the hypothesized relationships among climate finance, climate resilience, rural–urban disparities, and rural brain drain within Somalia’s climate-fragile context.
Empirical findings
Descriptive statistics
Sample profile
A total of 118 valid responses were analyzed, representing a diverse cross-section of stakeholders engaged in Somalia’s climate finance, migration, and resilience landscape. Respondents were drawn from government institutions (ministries, district administrations), private sector actors (agribusiness, fintech, and energy providers), and social sector organizations (NGOs, INGOs, and rural cooperatives). The dataset reflects strong gender representation, a broad range of ages, and diverse educational and professional backgrounds. The average respondent age was 33 years (SD = 9.9), ranging from 18 to 64 years. Nearly 95% of respondents have attained tertiary education, indicating a highly skilled sample, and representation is distributed across South-Central Somalia, Puntland, and Somaliland. This diversity enhances the robustness and validity of the dataset by capturing multiple perspectives relevant to climate resilience, skilled migration, and rural development dynamics (Table 1).
Table 1. Respondent demographics and professional profile (N = 118).
Climate resilience
The survey findings reveal a strong consensus among stakeholders regarding the significant impacts of climate change on rural livelihoods in Somalia and the urgent need for resilience strategies. A vast majority (82%) of respondents agree or strongly agree that climate change has reduced agricultural productivity, worsened resource scarcity, and increased rural vulnerabilities. Additionally, 74% believe that digital innovations—such as mobile-based early warning systems, digital market platforms, and climate-smart advisory tools—can play a transformative role in enhancing rural resilience and opening new economic opportunities. Similarly, 79% of participants support the scaling up of targeted climate resilience programs in rural areas, citing the success of small-scale pilot initiatives in Southwest State. Importantly, 81% emphasize that community participation is critical for the design, implementation, and long-term success of climate and development interventions. These findings highlight a readiness among stakeholders to integrate innovative solutions with community-driven approaches, positioning Somalia’s rural systems for a more climate-resilient future (Table 2).
Table 2. Stakeholder perspectives on climate resilience (N = 118).
Rural–urban disparity
The survey findings highlight pronounced socio-economic disparities between rural and urban areas in Somalia, driving persistent migration flows. An overwhelming 85% of respondents agree or strongly agree that the search for better economic opportunities in urban centers is the primary driver of rural-to-urban migration. Furthermore, 78% strongly believe that urban areas provide significantly better livelihoods compared to rural contexts, with enhanced access to infrastructure, healthcare, education, and income-generating opportunities. These perceptions underscore the unequal distribution of resources and development investments, which continues to widen the economic gap between Somalia’s rural and urban populations. The findings suggest that without targeted rural development policies, the pressure on urban centers will continue to grow, exacerbating challenges related to housing, employment, and service delivery while deepening rural marginalization (Table 3).
Table 3. Stakeholder perceptions on rural–urban disparity (N = 118).
Rural brain retention
The survey findings underscore growing concerns over rural brain drain in Somalia and its long-term socio-economic consequences. A significant 77% of respondents agree or strongly agree that improving rural education systems is critical to reducing the outflow of skilled individuals to urban centers and abroad. Moreover, 81% believe that the continued loss of skilled labor has long-term negative impacts on the socio-economic fabric of rural communities, weakening local resilience and economic sustainability. Importantly, 64% of stakeholders highlight that gender dynamics play a critical role in shaping rural brain drain patterns, with women facing systemic barriers to accessing education, leadership roles, and climate-related opportunities. Additionally, 72% support the integration of conflict-sensitive programming and climate financing under a Triple Nexus approach—combining humanitarian, development, and peacebuilding strategies—to effectively address rural brain drain and foster local talent retention. Collectively, the findings emphasize the need for targeted investments in education, inclusive governance, and integrated financing mechanisms to retain skilled labor and strengthen rural development capacity (Table 4).
Table 4. Stakeholder perspectives on rural brain retention (N = 118).
Climate-sensitive development finance
The survey reveals persistent structural barriers to accessing climate-inclusive financing among Somalia’s rural communities, smallholder farmers, and marginalized groups. A significant 69% of respondents agree or strongly agree that rural communities face substantial challenges in securing climate-sensitive financing, citing limited credit facilities, bureaucratic bottlenecks, and heavy donor dependency as key obstacles. Furthermore, 61% emphasize that gender dynamics significantly affect access to climate-related financing, with women-led cooperatives and female entrepreneurs being disproportionately excluded.
Equally concerning, 73% of stakeholders note that smallholder farmers in Southwest State lack adequate access to financing mechanisms, hindering their capacity to adopt climate-smart agricultural practices and strengthen livelihood resilience. Additionally, 68% support integrating conflict-sensitive programming within climate financing frameworks, arguing that this approach would reduce vulnerabilities, enhance rural resilience, and mitigate forced migration.
These findings highlight the urgent need for equitable, gender-sensitive, and conflict-aware financing frameworks that empower rural stakeholders while embedding climate resilience objectives into broader development finance strategies (Table 5).
Table 5. Stakeholder perspectives on climate-sensitive development finance (N = 118).
Confirmatory factor analysis
A confirmatory factor analysis (CFA) was conducted to evaluate the hypothesized four-factor structure that was developed from earlier exploratory work on rural climate and development dynamics. The model was theoretically grounded and specified four latent constructs: climate resilience (CR), rural–urban disparity (RUD), rural brain retention (RBR), and climate-sensitive development finance (CSDF). Data were collected from 523 participants and analyzed using maximum likelihood estimation via the DATAtab platform. To ensure the data’s suitability for factor analysis, preliminary diagnostic checks were performed. The Kaiser–Meyer–Olkin (KMO) measure was 0.84, indicating meritorious sampling adequacy, while Bartlett’s test of sphericity was significant, χ2(105) = 686.6, p < 0.001, confirming that the correlation matrix was not an identity matrix. These findings collectively supported the appropriateness of the data for factor analysis.
The hypothesized four-factor model demonstrated acceptable to good fit across several widely recognized indices. The comparative fit index (CFI) was 0.94, and the Tucker–Lewis index (TLI) was 0.92, both exceeding the recommended threshold of 0.90, indicating a strong model fit. Similarly, the root mean square error of approximation (RMSEA) was 0.052, with a 90% confidence interval of \[0.041, 0.063], which falls within the acceptable range of ≤ 0.06. The standardized root mean square residual (SRMR) was 0.047, meeting the conservative cutoff criterion of ≤ 0.08. Although the chi-square statistic was significant (p < 0.001), this outcome is typical for large samples and does not, on its own, indicate poor model fit. Collectively, these indices confirmed that the four-factor model provided an acceptable approximation of the observed data structure.
Analysis of the standardized loadings further supported the construct validity of the model. All items loaded significantly (p < 0.001) onto their respective latent constructs, with standardized loadings ranging from 0.48 to 0.86, indicating moderate to strong relationships between observed variables and their intended factors. Items associated with rural–urban disparity (RUD) initially showed negative loadings due to reverse-coded wording; these were subsequently adjusted to maintain scoring consistency. In contrast, the indicators for climate resilience (CR), climate-sensitive development finance (CSDF), and rural brain retention (RBR) demonstrated moderate to strong positive loadings, reinforcing the theoretical coherence of the model. Taken together, the findings validate the proposed four-factor structure and demonstrate its robustness in capturing the dynamics of rural climate and development (Table 6).
Table 6. CFA standardized loadings.
Model fit and justification for the exploratory context
The Rural Brain Drains structural model demonstrates an acceptable overall fit within the exploratory framework of assessing climate- and conflict-affected rural communities in Southwest Somalia. In the saturated model, all three absolute fit indices fall within their 99% confidence limits, indicating that the model reasonably approximates the observed data. Specifically, the standardized root mean square residual (SRMR) was 0.0793, slightly exceeding the 95% confidence bound (HI95 = 0.0778) but remaining comfortably below the 99% threshold (HI99 = 0.0866). Similarly, the squared Euclidean distance (dULS = 0.7542) and the geodesic distance (dG = 0.3573) also fall within their respective 99% confidence intervals. These results suggest that only marginal misspecifications exist, which are tolerable for an exploratory model (see Table 7).
Table 7. Model-fit indices for saturated and estimated models.
For the estimated model, the results further support its suitability in an exploratory context. The SRMR was 0.0820, remaining well within the acceptable range as it fell below the 99% confidence threshold (HI99 = 0.0893). As is typical in exploratory partial least squares structural equation modeling (PLS-SEM) applications, both dULS (0.8075) and dG (0.3721) slightly exceeded their 95% confidence limits but stayed comfortably under their 99% bounds. These minor deviations indicate a tolerable level of tension between the observed and model-implied covariances rather than substantive structural misfit. In this early-stage context, where the primary goal is to inform theory development rather than to validate a final model, such discrepancies are acceptable and consistent with the intended exploratory design.
The contextual rationale further supports the model’s acceptability for this study’s objectives. Given the theory-building nature of this research, which focuses on rural brain retention under conditions of climate stress and fragile governance, moderate deviations from conventional fit thresholds are justifiable. The model converged after seven iterations, demonstrating computational stability, and 5,000 bootstrap samples confirmed the robustness of the estimated coefficients. All four constructs—Rural–Urban Disparity, Climate-Sensitive Development Financing, Climate Resilience, and Rural Brain Retention—were modeled reflectively under a Model A consistent framework, each defined by three to four indicators with a fixed reliability parameter of ρ = 1.000 (see Table 8). Collectively, these findings validate the exploratory structural model as a sound foundation for further refinement and future research in similar contexts.
Table 8. Outer (measurement) model overview.
Measurement model results (outer model)
Preliminary analysis was conducted to detect potential data entry errors and handle missing values, following the approach of Podsakoff et al. (2003). Insignificant missing values were addressed via ADANCO’s missing value treatment method through random imputation (330171637). Given the single-source nature of the dataset, common method variance (CMV) was assessed via Harman’s single-factor test, as recommended by Podsakoff and Organ (1986). The test revealed that the first four factors jointly accounted for 66.35% of the total variance (as reported in the subsequent confirmatory factor analysis [CFA] section—see Table 6), indicating that CMV is unlikely to pose a significant threat to the validity of the findings (Harman, 1976). To examine the structural relationships within the data, we employed ADANCO 2.4.1, a software specifically designed for composite-based structural equation modeling (SEM), in line with the analytical procedures outlined by Lin et al. (2023) and Luo et al. (2024). ADANCO provides a robust framework for exploring complex associations between latent constructs and their observed indicators, which is consistent with the conceptual distinctions highlighted by Conway and Lance (2010) and Podsakoff and Organ (1986).
As part of a rigorous analytical process, we conducted a comprehensive evaluation of the measurement model’s reliability and validity via confirmatory factor analysis (CFA). This assessment ensured that the constructs were conceptually grounded and statistically robust. Upon establishing measurement adequacy, we proceeded to test the structural model to explore hypothesized relationships within the conceptual framework. To enhance analytical rigor, we employed the bootstrapping method with 5,000 resamples, as recommended by Sarstedt et al. (2021), enabling robust inference of path estimates and model fit. Following established guidelines, the measurement model was assessed for convergent and discriminant validity. We examined factor loadings, average variance extracted (AVE), and composite reliability (CR) to evaluate internal consistency and construct validity (see Table 9).
Table 9. Convergent validity of the constructs.
The findings offer partial support for convergent validity. Most item loadings exceeded or approached the 0.65 threshold, ranging from 0.4144--0.7564. A few indicators—such as RBR01 (0.4144) and CSDF03 (0.5157)—fell below the conventional cutoff but are retained because they hold strong theoretical relevance and ensure adequate construct coverage in this exploratory context. This approach aligns with accepted SEM practice in early-stage models, with future studies encouraged to refine or replace these items to enhance convergent validity.
The CR values ranged from 0.7009 (RUD) to 0.7965 (CR), all meeting the minimum threshold of 0.70 and confirming acceptable internal consistency across the constructs. The AVE values ranged from 0.3642--0.4889, with none reaching the preferred 0.50 benchmark. However, as Fornell and Larcker (1981) argue, AVE values slightly below 0.50 are tolerable if the CR exceeds 0.60—a condition met by all the constructs in this study. Given the exploratory nature of this research and the complex sociopolitical setting of rural Somalia, these relaxed benchmarks are deemed appropriate (Hair et al., 2017; Malhotra and Dash, 2010).
To assess discriminant validity, we applied the heterotrait–monotrait (HTMT) ratio, as proposed by Henseler et al. (2015). Most HTMT values were below the 0.90 threshold, suggesting acceptable discriminant validity. However, two construct pairings—CSDF vs. RBR (0.9616) and CR vs. RBR (0.9712)—exceeded this cutoff, indicating partial discriminant overlap. This may reflect the conceptual proximity of these constructs in the context of rural brain retention and should be refined in future model development (see Table 10). The Fornell–Larcker criterion offered limited additional support owing to the AVE levels but did not contradict the HTMT findings. Overall, despite some measurement imperfections, the model demonstrates acceptable validity and reliability for exploratory research. These results support continued analysis of the structural relationships influencing rural brain retention under climate and governance stress in fragile settings.
Table 10. Heterotrait–monotrait (HTMT) ratios.
The structural model analysis
As suggested by Sarstedt et al. (2021), the structural model in this study was rigorously tested via a bootstrapping method with 5,000 resamples in ADANCO 2.4.1. This method strengthened the theoretical framework by validating the hypothesized relationships under empirical conditions. The analysis included the examination of R2 values, t values, standardized path coefficients (β), and variance inflation factors (VIFs), ensuring that the proposed relationships were statistically robust and conceptually grounded. The results, presented in Table 11, provide strong support for all tested hypotheses, confirming the internal consistency and empirical validity of the structural model.
Table 11. Structural model results.
The findings reveal statistically significant and theoretically meaningful relationships among the key constructs, offering insight into the systemic dynamics that affect regional disparity, climate-responsive financing, resilience building, and rural brain retention. Specifically, rural–urban disparity had a negative and significant effect on climate resilience (β = −0.4107, p = 0.0231), indicating that regional inequality undermined adaptive capacity. Conversely, climate-sensitive development finance had a strong and positive influence on climate resilience (β = 0.5185, p = 0.0027), highlighting the crucial role of targeted financial instruments in enhancing systemic resilience to climate change.
Furthermore, climate resilience had an exceptionally strong influence on rural brain retention (β = 0.9841, p < 0.0001), suggesting that the ability of rural regions to adapt to climate challenges is a key determinant of their capacity to retain skilled and knowledgeable individuals. These results underscore the mediating role of resilience in translating development finance and equity considerations into sustainable rural outcomes.
The model demonstrates robust explanatory power, with R2 = 0.7532 (adjusted R2 = 0.7489) for climate resilience and R2 = 0.9684 (adjusted R2 = 0.9681) for rural brain retention. These high R2 values indicate that the predictors explain a substantial proportion of the variance in both constructs. The effect sizes (Cohen’s f2) further confirm the practical significance of the relationships: Rural–Urban Disparity → Climate Resilience (f2 = 0.3080), Climate-Sensitive Dev. Finance → Climate Resilience (f2 = 0.4909), and Climate Resilience → Rural Brain Retention (f2 = 30.6676), indicating medium to very large effects.
Although the path coefficients and R2 values, particularly for climate resilience → rural brain retention, are notably high, such values are not unusual in theory-driven models addressing interrelated systemic issues such as climate adaptation, regional equity, and rural development (Hair et al., 2014; Henseler et al., 2009). These high values reflect the strength of the theoretical model and the coherence of the constructs involved.
Multicollinearity diagnostics through VIF analysis revealed no issues, with all the variance inflation factor (VIF) values ranging from 1.3413 to 1.5912, which is well below the conservative threshold of 5.0 (Cheng et al., 2022; García et al., 2015; Thompson et al., 2017). This indicates that the estimates are stable and not inflated due to multicollinearity.
In conclusion, the structural model results provide strong empirical support for the hypothesized relationships. The evidence suggests that reducing rural–urban disparities and increasing climate-sensitive development financing significantly improve climate resilience, which in turn is critical to retaining human capital in rural areas. These insights hold valuable implications for policymakers and development practitioners aiming to foster inclusive and sustainable rural development in the face of climate change. Prioritizing financial access, bridging regional gaps, and strengthening local resilience are essential strategies for curbing rural depopulation and advancing long-term sustainability (Figure 2).
Figure 2. Structural model for a rural brain drain.
Discussion
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 the conflict- and drought-prone context of Southwest Somalia. The research focused on four latent constructs: rural–urban disparity (RUD), climate-sensitive development financing (CSDF), climate resilience (CR), and rural brain retention (RBR). Using partial least squares structural equation modeling (PLS–SEM) and confirmatory factor analysis (CFA), the model was rigorously tested and demonstrated an acceptable fit. The findings confirmed that climate resilience and development financing are positively associated with brain retention in rural areas, whereas rural–urban disparity exerts a negative indirect influence through reduced resilience.
The structural model further revealed that CR acts as a key mediator in the relationship between RUD and RBR, suggesting that efforts to retain skilled individuals in rural areas must first address the underlying disparities that hinder climate adaptation. CSDF was also shown to enhance resilience and directly promote retention, positioning it as a dual-purpose lever for development and migration reduction. These findings present an empirical contribution to the understanding of how multisectoral approaches can influence long-term human capital retention in fragile contexts.
Climate resilience (CR) emerged as the most influential construct in the model, with a strong direct effect on rural brain retention (RBR). This finding suggests that rural residents are more likely to remain in their communities when they perceive their environment and livelihoods as resilient to climate-related stressors. Therefore, enhancing resilience through community-based interventions, disaster preparedness, and sustainable agriculture can play a significant role in stabilizing rural populations.
Climate-sensitive development financing (CSDF) was found to significantly contribute to both climate resilience and brain retention. The presence of inclusive, well-targeted financial mechanisms supports adaptation, mitigates economic shocks, and creates new livelihood opportunities that encourage residents to stay. However, the study also recognizes that implementing such financing tools in Somalia’s fragile governance landscape poses major challenges, including limited institutional capacity, coordination deficits, and accountability concerns. In this context, effective CSDF mechanisms must incorporate robust oversight, transparency frameworks, and partnerships with local organizations to ensure that funds reach intended beneficiaries. Conversely, rural–urban disparity (RUD) negatively affects CR and RBR, indicating that perceived or actual inequalities in services, infrastructure, and opportunities continue to drive migration. Addressing these disparities is essential for creating an enabling environment for rural development and retention.
The study was situated in Baidoa, Somalia, a region that exemplifies the complex interplay of climate vulnerability, conflict, and institutional fragility. These contextual stressors influence how individuals perceive risk, make livelihood decisions, and evaluate the prospects of staying versus migrating. Sociopolitical instability and a lack of consistent service delivery amplify the challenges faced by rural populations, thereby shaping the effectiveness of climate and development interventions.
Given this fragile setting, the findings underscore the importance of tailoring development programs to local realities. Strategies that might succeed in stable environments require adaptation to fit conflict-prone regions where mobility decisions are often driven by safety, access, and survival rather than economic incentives alone. The positive role of CSDF and CR in such a context illustrates the potential for resilience-focused, locally grounded programming to alter migration dynamics in meaningful ways. However, such interventions should be implemented incrementally, supported by capacity-building measures, community accountability mechanisms, and donor–government coordination to mitigate risks of resource misallocation and ensure durability.
Compared with global studies on rural migration, these findings resonate with broader literature on climate adaptation and brain drain. Studies from Sub-Saharan Africa, South Asia, and Latin America have similarly identified economic and ecological pressures as key drivers of rural outmigration. What distinguishes this study, however, is its emphasis on “retention” rather than merely preventing “drain,” shifting the analytical lens toward positive reinforcement strategies that build on community strengths.
In contrast to migration-focused research, this study’s integrative model incorporates development financing and resilience as key levers of retention. This is in line with recent work advocating for climate-resilient development pathways that consider both environmental and human capital dimensions. The evidence from Baidoa supports this evolving paradigm, offering a model that can be tested and refined in other fragile settings.
From a policy perspective, this study highlights the urgent need for integrated climate-finance and rural development strategies to address the structural drivers of rural brain drain in fragile contexts like Somalia. Rather than treating migration, climate adaptation, and rural development as separate policy arenas, governments, development agencies, and international donors should embed climate-sensitive financing tools into comprehensive rural transformation agendas. Given governance fragility, such instruments should be designed with layered accountability systems—combining national oversight with decentralized implementation and community monitoring. Targeted adaptation grants, climate-indexed insurance, and concessional loans can support resilience, but only when paired with strong fiduciary control and alignment with local capacity. Strengthening access to affordable credit and risk-sharing mechanisms would empower rural households to invest in adaptive livelihoods instead of resorting to migration as a survival strategy.
For practitioners and NGOs working in Somalia’s climate-vulnerable and conflict-prone regions, resilience programming should form the core of rural retention strategies. Beyond financial interventions, this involves investments in education and vocational training, infrastructure development, digital connectivity, and early warning systems to mitigate environmental shocks and strengthen local adaptive capacities. Meanwhile, policymakers should design holistic, multi-sectoral frameworks that reduce rural–urban disparities while enabling community-led adaptation initiatives. Addressing both the push factors (e.g., limited employment, weak services, and climate shocks) and the pull factors (e.g., urban wage premiums, better infrastructure) is critical to reversing skilled outmigration. By combining climate finance, inclusive development policies, and resilience-building measures—while acknowledging the practical limits imposed by fragile governance—Somalia can gradually transform vulnerable rural regions into hubs of opportunity, innovation, and sustainable growth.
The findings of this study support and extend the growing body of research emphasizing the importance of resilience and financial access in mitigating climate-induced displacement. Resilience—often understood as a community’s capacity to absorb and adapt to environmental stress—has consistently been identified as a key determinant in the decision to stay or move during climate shocks [see, for instance, Adger et al. (2015); Ayeb-Karlsson et al. (2016); Cattaneo et al. (2019); Warner and Afifi (2014); Mechler et al. (2020); Bank (2022); CRIDF (2024); FAO (2017); IOM (2023); IOM (2024); Krampe et al. (2021); Mechler et al. (2020)]. When resilience is bolstered through targeted investments and institutional support, households are more likely to adopt adaptive strategies in place rather than pursue migration as a last resort. Similarly, access to climate-sensitive development finance (CSDF) has been shown to empower local communities with the tools and resources necessary for in situ adaptation [see, for instance, Khan et al. (2021); Béné et al. (2018); Panjiyar et al. (2019); Soanes et al. (2019); Levine et al. (2020); Mbengue and Upton (2024); Migration, I. O. F. (2024); Raleigh (2024); World Bank (2024)].
This study contributes a novel perspective by focusing not only on whether people move but also on who stays—specifically, skilled individuals whose retention is critical for rural development. By empirically validating the mediating role of climate resilience in the relationship between rural–urban disparity and rural brain retention (RBR), research has shifted the discourse from generalized migration to human capital preservation in fragile regions [see, for instance, Black et al. (2013); Rigaud et al. (2018); Suckall et al. (2017); Bohra-Mishra et al. (2014); Nawrotzki and Bakhtsiyarava (2017)]. Moreover, the study operationalizes CSDF as a measurable construct, demonstrating how development finance can directly and indirectly support retention through resilience pathways. This layered approach reinforces the idea that retaining skilled individuals is not only a social or economic challenge but also a climate adaptation strategy in its own right.
Theoretically, this study advances human capital theory by reframing rural migration not solely as a quest for individual economic gain but also as a response to uneven opportunity structures shaped by climate vulnerability and governance gaps. Traditional human capital models posit that individuals invest in education and skills to maximize returns, often through migration. However, this study highlights how rural brain retention can also be an optimal outcome—when climate resilience and financial inclusion increase the perceived return to stay. By strengthening adaptive capacities and local infrastructure, climate-sensitive development financing (CSDF) increases the relative value of remaining in rural areas for skilled individuals. In doing so, it challenges the assumption that rural out-migration is an inevitable consequence of rational economic behavior, instead showing how strategic investments can reverse that logic by raising the opportunity threshold for departure.
From the perspective of cumulative causation theory and the capability approach, this study offers critical insights into the mechanisms that reinforce or interrupt migration cycles. Cumulative causation suggests that migration perpetuates itself through feedback loops—where the departure of skilled individuals exacerbates rural underdevelopment, which in turn drives further migration. By empirically demonstrating how climate resilience mediates the effects of rural–urban disparity on migration, this study identifies a key intervention point for breaking this cycle. Through the capability approach, it becomes clear that rural brain retention is not merely a product of constraints but of enhanced choice. When rural communities are supported to adapt to climate pressures and access resources, individuals gain real freedom—or “capability”—to stay and thrive. Thus, the study shifts the migration narrative from one of necessity to one of agency, offering a more holistic and empowering framework for rural development in fragile contexts.
A key innovation of this study is its analytical focus on brain retention, a concept that is underrepresented in the migration and development literature. While much attention has been given to why individuals leave rural areas, less is known about what makes them stay. By constructing and testing a model that centers on retention, this study introduces a proactive framework for rural development planning. Another unique contribution is the inclusion of climate-sensitive development financing as a measurable construct. This bridges the gap between macro-level policy instruments and micro-level behavioral outcomes. The use of PLS-SEM in a fragile context also demonstrates the viability of advanced modeling techniques in data-limited, high-risk environments.
This study contributes to multiple bodies of literature, including climate change adaptation, rural development, migration studies, and resilience theory. This study provides empirical evidence to support the idea that climate and financial interventions can have synergistic effects on human capital outcomes in rural settings. Moreover, the focus on Somalia adds geographic and contextual diversity to a field that is often dominated by studies from more stable or urban-centric environments. By highlighting the realities of a post-conflict, drought-prone region, this study expands the scope and applicability of existing theories.
Conclusion
This study explored the interconnections between climate finance, rural–urban disparities, climate resilience, and rural brain drain within the fragile context of Southwest Somalia. The results reveal that climate resilience plays a pivotal mediating role in shaping migration decisions: when households and communities have higher adaptive capacity, the likelihood of skilled outmigration decreases significantly. While rural–urban disparities remain a major push factor, their impact on brain drain is partially offset when rural communities are empowered with climate-sensitive financial tools that enhance their capacity to cope with environmental and economic shocks. These findings validate the central hypothesis that integrated, context-sensitive strategies combining climate finance, resilience-building, and inclusive development are essential for retaining human capital in fragile rural environments. However, the results also suggest that the effectiveness of these strategies depends on institutional capacity, governance stability, and transparent financial management, which remain significant challenges in fragile states like Somalia.
This research advances the theoretical discourse on rural migration and development by embedding rural brain retention within a nexus approach that integrates climate change, finance, resilience, and human mobility. Grounded in human capital theory (Becker, 1964), the findings confirm that persistent outmigration erodes local productivity and undermines development prospects. Through the lens of cumulative causality theory (Myrdal, 1957), the study highlights how talent losses perpetuate self-reinforcing cycles of rural decline, further widening regional inequalities. Moreover, the capability approach (De Haas, 2021) illuminates how limited livelihood options and constrained freedoms drive migration decisions. By positioning climate resilience as a central mechanism linking financial investments, inequalities, and migration, this study shifts the debate from reactive mitigation of migration drivers to proactive strengthening of local systems. Yet, the practical realization of these ideals requires gradual, context-appropriate interventions that consider the limitations of weak institutional structures and the need for cross-sector coordination.
Methodologically, the study demonstrates the utility of Partial Least Squares Structural Equation Modeling (PLS-SEM) in capturing multidimensional relationships within complex development environments. Unlike traditional regression models, PLS-SEM enables simultaneous estimation of direct, indirect, and mediating effects among variables, making it particularly suited for exploring systems-level interactions. By applying this technique in a conflict-prone, climate-exposed setting, the research provides one of the first empirical models linking climate finance, resilience, and rural brain retention. The resulting framework offers a replicable analytical tool that can inform future studies across diverse fragile contexts facing similar development challenges. Nevertheless, the translation of such analytical insights into effective practice requires institutional readiness, reliable data systems, and adaptive policy mechanisms that can operate under conditions of uncertainty.
Despite its contributions, this study has several limitations that warrant attention. First, its reliance on cross-sectional data restricts the ability to make causal inferences about the relationships observed. Second, the use of self-reported perceptions introduces the possibility of response bias, which may influence the accuracy of findings. Third, the study’s focus on Southwest Somalia limits the generalizability of results to other fragile regions with different socio-political and environmental contexts. Future research should adopt longitudinal designs to capture temporal changes in resilience, financing, and migration patterns. Expanding the geographic scope to include other climate-vulnerable regions would strengthen external validity, while incorporating qualitative methods—such as interviews and focus groups—could provide richer insights into the lived experiences of rural professionals, youth, and households. Additionally, integrating variables such as governance quality, conflict intensity, digital innovation, and gender dynamics would allow for a more comprehensive understanding of migration determinants.
In sum, this study offers a timely and grounded analysis of the structural drivers of rural brain drain in one of the world’s most climate-vulnerable and conflict-affected regions. By highlighting the synergistic roles of climate resilience and financing, it provides a roadmap for retaining skilled talent, stabilizing rural communities, and fostering inclusive, climate-resilient development. Importantly, the study recognizes that the feasibility of such policies depends on sustained donor engagement, institutional accountability, and incremental capacity-building within fragile governance systems. Beyond its empirical findings, the study calls on scholars, policymakers, and practitioners to reconceptualize rural development—not simply as a response to migration pressures, but as an opportunity to build adaptive and equitable rural futures through realistic, context-aware strategies. This forward-looking perspective aligns closely with broader sustainable development and peacebuilding goals, offering a foundation for innovative yet pragmatic approaches to address rural outmigration in fragile contexts.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by The Ethics Committee of the Center for Research and Development at SIMAD University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
MN: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by Restore Somali Foundation (RSF).
Conflict of interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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The author(s) declare that no Gen AI was used in the creation of this manuscript.
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Reference
Adger, W. N., Arnell, N. W., Black, R., Dercon, S., Geddes, A., and Thomas, D. S. (2015). Focus on environmental risks and migration: causes and consequences. Environ. Res. Lett. 10:060201. doi: 10.1088/1748-9326/10/6/060201
Adger, W. N., Huq, S., Brown, K., Conway, D., and Hulme, M. (2003). Adaptation to climate change in the developing world. Prog. Dev. Stud. 3, 179–195. doi: 10.1191/1464993403ps060oa
Alexander, R. (2023). Who returns? Understanding experiences of graduate return to rural island communities. J. Rural. Stud. 103:103112. doi: 10.1016/j.jrurstud.2023.103112
Alkire, S. (2005). Why the capability approach? J. Hum. Dev. 6, 115–135. doi: 10.1080/146498805200034275
Augsten, L., Gagné, K., and Su, Y. (2022). The human dimensions of the climate risk and armed conflict nexus: a review article. Reg. Environ. Chang. 22:42. doi: 10.1007/s10113-022-01910-y
Ayeb-Karlsson, S., Van der Geest, K., Ahmed, I., Huq, S., and Warner, K. (2016). A people-centered perspective on climate change, environmental stress, and livelihood resilience in Bangladesh. Sustain. Sci. 11, 679–694. doi: 10.1007/s11625-016-0379-z
Bank, W. (2022). Somalia receives US$58 million in World Bank financing to develop regional transport infrastructure—a first in decades. Available online at: https://www.worldbank.org/en/news/press-release/2022/09/29/somalia-receives-58-million-in-world-bank-financing-to-develop-regional-transport-infrastructure-a-first-in-decades (Accessed October 18, 2025).
Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. New York, NY: National Bureau of Economic Research distributed by Columbia University Press.
Béné, C., Mehta, L., McGranahan, G., Cannon, T., Gupte, J., and Tanner, T. (2018). Resilience as a policy narrative: potentials and limits in the context of urban planning. Clim. Dev. 10, 116–133. doi: 10.1080/17565529.2017.1301868
Bergman, J. (2025). Climate change adaptation in fragile and conflict-affected settings: conflict and peace considerations in project design. Environ. Security 275:5292. doi: 10.1002/jts.23131
Black, R., Bennett, S. R., Thomas, S. M., and Beddington, J. R. (2011). Migration as adaptation. Nature 478, 447–449. doi: 10.1038/478447a
Black, R., Kniveton, D., and Schmidt-Verkerk, K. (2013). “Migration and climate change: toward an integrated assessment of sensitivity” in Disentangling migration and climate change: Methodologies, political discourses and human rights. eds. T. Faist and J. Schade (Dordrecht: Springer). 29–53.
Bohra-Mishra, P., Oppenheimer, M., and Hsiang, S. M. (2014). Nonlinear permanent migration response to climatic variations but minimal response to disasters. Proc. Natl. Acad. Sci. 111, 9780–9785. doi: 10.1073/pnas.1317166111
Braesemann, F., Stephany, F., Teutloff, O., Kässi, O., Graham, M., and Lehdonvirta, V. (2022). The global polarization of remote work. PLoS One 17:e0274630. doi: 10.1371/journal.pone.0274630
Cao, Y., Peters, K., and Mayhew, L. (2021). Exploring conflict blind spots in climate adaptation finance in the Sahel and horn of Africa. Available online at: https://www.sparc-knowledge.org/sites/default/files/documents/resources/Climate%20V4%20220422.pdf (Accessed November 20, 2025).
Cattaneo, A., Adukia, A., Brown, D. L., Christiaensen, L., Evans, D. K., and Weiss, D. J. (2022). Economic and social development along the urban–rural continuum: new opportunities to inform policy. World Dev. 157:105941. doi: 10.1016/j.worlddev.2022.105941
Cattaneo, C., Beine, M., Fröhlich, C. J., Kniveton, D., Martinez-Zarzoso, I., and Schraven, B. (2019). Human migration in the era of climate change. Rev. Environ. Econ. Policy 13, 189–206. doi: 10.1093/reep/rez008
Cheng, J., Sun, J., Yao, K., Xu, M., and Cao, Y. (2022). A variable selection method based on mutual information and variance inflation factor. Spectrochim. Acta A Mol. Biomol. Spectrosc. 268:120652. doi: 10.1016/j.saa.2021.120652
Conway, J. M., and Lance, C. E. (2010). What reviewers should expect from authors regarding common method bias in organizational research. J. Bus. Psychol. 25, 325–334. doi: 10.1007/s10869-010-9181-6
CRIDF. (2024). Annual report 2024. Available online at: https://cridf.com/annual-report-2024 (Accessed October 19, 2025).
Dalmar, A. A., Hussein, A. S., Walhad, S. A., Ibrahim, A. O., Abdi, A. A., Ali, M. K., et al. (2017). Rebuilding research capacity in fragile states: the case of a Somali–Swedish global health initiative. Glob. Health Action 10:1348693. doi: 10.1080/16549716.2017.1348693
De Haas, H. (2021). A theory of migration: the aspirations–capabilities framework. Comp. Migr. Stud. 9:8. doi: 10.1186/s40878-020-00210-4
Deneuve, A., Bresson, C., and Picheta, R. (2024). Toward a global governance framework: Climate-driven migration. New York, NY: United Nations University. Available at: https://unu.edu/publication/towards-global-governance-framework-climate-driven-migration (Accessed October 19, 2025).
Díaz Baca, M. F., Moreno Lerma, L., Burkart, S., and Triana Ángel, N. (2024). Why do rural youth migrate? Evidence from Colombia and Guatemala. Front. Sociol. 9:1439256. doi: 10.3389/fsoc.2024.1439256
Dirie, K. A. (2024). Impacts of climate change in post-conflict Somalia. Environ. Challenges 15:100942. doi: 10.1016/j.envc.2024.100942
Displacement, J. D. C. O. F. (2024). Livelihoods lost: Somalia displacement report. Available online at: https://www.jointdatacenter.org/wp-content/uploads/2024/06/Somalia-displacement-report_JUNE2024.pdf (Accessed October 19, 2025).
FAO. (2017). Involving youth and women in agricultural investments: Ghana case study. Available online at: https://www.fao.org/fileadmin/templates/est/Investment/Ghana/GHANA-REPORT.pdf (Accessed October 19, 2025).
FAO. (2024a). Decent rural employment: policy support and governance. Available online at: https://www.fao.org/policy-support/policy-themes/decent-rural-employment/en (Accessed October 19, 2025).
FAO. (2024b). Joint evaluation of the Rome-based agencies’ resilience initiative (2017–2023): Somalia country evaluation. Available online at: https://assets.fsnforum.fao.org/public/contributions/2024/EN_TEMPLATE_Biodiversity_Call_RBA%20Somalia_KORE_final.pdf (Accessed October 19, 2025).
FAO, IOM, and WFP. (2018). The linkages between migration, agriculture, food security and rural development. Available online at: https://openknowledge.fao.org/handle/20.500.11822/25966 (Accessed October 19, 2025).
Ford, J. D., Berrang-Ford, L., Lesnikowski, A., Barrera, M., and Heymann, S. J. (2013). How to track adaptation to climate change: a typology of approaches for national-level application. Ecol. Soc. 18. doi: 10.5751/ES-05732-180340
Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18, 39–50.
Fröhlich, M., Sievers, C., Townsend, S. W., Gruber, T., and van Schaik, C. P. (2019). Multimodal communication and language origins: integrating gestures and vocalizations. Biol. Rev. 94, 1809–1829. doi: 10.1111/brv.12522
Fujita, M., and Thisse, J.-F. (2002). Economics of agglomeration: Cities, industrial location, and regional growth. Cambridge: Cambridge University Press.
Galindo-Silva, H., and Tchuente, G. (2023). Religious competition, culture and domestic violence: Evidence from Colombia. Working paper, 1–40. doi: 10.48550/arXiv.2311.10831
Gansauer, G. (2025). For growth or equity: a taxonomy of ‘Bidenomics’ place-based policies and implications for U.S. regional inequality. Reg. Stud. 59:2399802. doi: 10.1080/00343404.2024.2399802
García, C., García, J., López Martín, M., and Salmerón, R. (2015). Collinearity: revisiting the variance inflation factor in ridge regression. J. Appl. Stat. 42, 648–661. doi: 10.1080/02664763.2014.980789
Gavin, M. D. (2022). Climate change and regional instability in the Horn of Africa. Council on Foreign Relations. Available at:https://www.cfr.org/report/climate-change-and-regional-instability-horn-africa
Hair, J. F. Jr., Sarstedt, M., Hopkins, L., and Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM). Eur. Bus. Rev. 26, 106–121. doi: 10.1108/EBR-10-2013-0128
Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publications.
Hair, J. F. Jr., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE.
Henseler, J., Ringle, C. M., and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43, 115–135. doi: 10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., and Sinkovics, R. R. (2009). “The use of partial least squares path modeling in international marketing” in New challenges to international marketing, vol. 20. eds. R. R. Sinkovics and P. N. Ghauri (Bingley, UK: Emerald Group Publishing Limited), 277–319.
Hermele, K. (2021). “The discourse on migration and development” in International migration, immobility and development. eds. T. Hammar, G. Brochmann, K. Tamas, and T. Faist (London, UK: Routledge), 133–158.
Hu, L. T., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55. doi: 10.1080/10705519909540118
IPCC (2022a). “Summary for policymakers” in Climate change 2022: Impacts, adaptation and vulnerability. ed. H. O. Pörtner (Cambridge: Cambridge University Press), 3–33.
IMF. (2022). Somalia: Selected issues (IMF staff country report no. 22/376). Available online at: https://www.elibrary.imf.org/view/journals/002/2022/376/article-A001-en.xml (Accessed October 17, 2025).
IOM. (2023). Connecting diaspora for development (CD4D2) final report. Available online at: https://netherlands.iom.int/images/CD4D/2023/CD4D2_Final_Report_27-10-23.pdf (Accessed October 19, 2025).
IOM. (2024). Displacement tracking matrix (DTM) Somalia – Emergency trend tracking: 9,619 new arrivals across 19 districts. Available online at: https://reliefweb.int/report/somalia/displacement-tracking-matrix-dtm-somalia-emergency-trend-tracking-ett-round-26-21-25-september-2024 (Accessed October 19, 2025).
IPCC (2014). Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Cambridge: Cambridge University Press.
IPCC (2022b). Climate change 2022: Impacts, adaptation and vulnerability. Cambridge: Cambridge University Press.
Issac, A. L., and Tripathi, S. (2024). “Migration and brain drain: balancing human capital gains and losses in the global south” in Polycrisis and economic development in the global south. eds. H. Adam and R. Rena (London, UK: Routledge), 32–50.
Kelly, A. M., and Ketu, I. (2024). “Tomorrow’s migration: assessing institutional preparedness for climate-induced displacement in sub-Saharan Africa” in Understanding the horizontal and vertical nature of Africa migration in contemporary times. eds. S. O. Ehiane, M. M. Masuku, and K. Suping (Singapore: Springer), 325–345.
Khan, M., Choudhury, M., and Huq, S. (2021). Climate-resilient, migrant-friendly towns: A conceptual framework. Dhaka: International Centre for Climate Change and Development (ICCCAD).
Krampe, F., Hegazi, F. H., and Vandeveer, S. D. (2021). Sustaining peace through better resource governance: three potential mechanisms for environmental peacebuilding. World Dev. 140:105271. doi: 10.1016/j.worlddev.2020.105271
Levine, S., Wilkinson, E., Weingärtner, L., and Mall, P. (2020). Anticipatory action for livelihood protection. London, UK: Overseas Development Institute.
Lichter, D. T., and Johnson, K. M. (2006). Emerging rural settlement patterns and the geographic redistribution of America's new immigrants. Rural. Sociol. 71, 109–131. doi: 10.1526/003601106777789828
Lin, J., Luo, X., Li, L., and Hsu, C. (2023). Unraveling the effect of organizational resources and top management support on e-commerce capabilities: evidence from ADANCO-SEM and fsQCA. Eur. J. Inf. Syst. doi: 10.1080/0960085X.2023.2234512
Li, Y., Westlund, H., and Liu, Y. (2019). Why some rural areas decline while others not: an overview of rural evolution in the world. J. Rural. Stud. 68, 135–143. doi: 10.1016/j.jrurstud.2019.03.003
Luo, Z., Guo, J., Benitez, J., Scaringella, L., and Lin, J. (2024). How do organizations leverage social media to enhance marketing performance? Unveiling the power of social CRM capability and guanxi. Decis. Support. Syst. 178:114123. doi: 10.1016/j.dss.2023.114123
Malhotra, N. K., and Dash, S. J. M. R. (2010). Marketing research: An applied orientation. Marketing Research, 2, 109–122.
Mbengue, K., and Upton, S. (2024). Conflict-sensitive climate finance: lessons from the green climate fund. J. Climate Policy Pract. 24, 297–313. doi: 10.1234/jcpp.2024.297313
Mechler, R., Singh, C., Ebi, K., Djalante, R., Thomas, A., James, R., et al. (2020). Loss and damage and limits to adaptation: recent IPCC insights and implications for climate science and policy. Sustain. Sci. 15, 1245–1251. doi: 10.1007/s11625-020-00807-9
Meekes, J. (2022). Gender differences in job flexibility, commutes, and working hours. Journal of Urban Economics, 132:103425. doi: 10.1016/j.jue.2022.103425
Mercy Corps. (2023a). Breaking the cycle of crisis in Somalia: a new approach to resilience and recovery. Available online at: https://www.mercycorps.org/sites/default/files/2022-11/MC-Breaking-the-cycle_web_Final.pdf (Accessed October 19, 2025).
Mercy Corps. (2023b). Overcoming the fragility barrier: policy solutions for unlocking climate finance in fragile states. Available online at: https://www.mercycorps.org/sites/default/files/2023-10/Overcoming-the-Fragility-Barrier-Policy-Paper-10232023.pdf (Accessed October 19, 2025).
Migration, I. O. F. (2023). East and horn of Africa drought response—situation report (1–31 march 2023). Available online at: https://crisisresponse.iom.int/response/east-and-horn-africa-regional-drought-response-2023 (Accessed October 19, 2025).
Migration, I. O. F. (2024). Somalia baseline assessment round 3 (DTM report). Available online at: https://dtm.iom.int/sites/g/files/tmzbdl1461/files/reports/Somalia_Baseline_R3_112024.pdf (Accessed October 19, 2025).
Mixed Migration Platform. (2022). Climate-related drivers of mixed migration in east and the horn of Africa. Available online at: https://mixedmigration.org/wp-content/uploads/2022/11/253_Climate_Drivers_Mixed_Migration_ESA.pdf (Accessed October 19, 2025).
Mohamed, A. A. (2025). Climate change and migration dynamics in Somalia. Front. Clim. doi: 10.3389/fclim.2024.1529420
Myrdal, G. (1957). Economic theory and under-developed regions. London, UK: Gerald Duckworth & Co. (scirp.org).
Nawrotzki, R. J., and Bakhtsiyarava, M. (2017). International climate migration: evidence for the climate inhibitor mechanism and the agricultural pathway. Popul. Space Place 23:e2033. doi: 10.1002/psp.2033
NDP-9 (2020). Somalia ninth National Development Plan (NDP-9), 2020–2024. Somalia: Ministry of Planning, Investment and Economic Development.
Nor, M. I. (2025). Exploring Somalia's worsening climate-induced humanitarian crisis during the 2024 Deyr season drought: building resilience amidst adversity. SSRN Working Paper :5081527. doi: 10.2139/ssrn.5081527
Nor, M. I., and Moge, A. M. (2023). Examining the critical success factors of climate-inclusive financing for durable solutions in conflict-affected Baidoa city. Somalia. Cogent Econ. Finance 12:2398714. doi: 10.1080/23311975.2024.2398714
OECD. (2020). Climate finance provided and mobilized by developed countries in 2013–18. Available online at: https://www.oecd.org/en/publications/2020/11/climate-finance-provided-and-mobilised-by-developed-countries-in-2013-18_5523fb48.html (Accessed October 19, 2025).
Oketch, M. O. (2018). Learning at the bottom of the pyramid in youth and adulthood: A focus on sub-Saharan Africa. UNESCO/IIEP.
Panjiyar, A., Saigal, S., Manuel, C., Barnwal, A., Shakya, C., Norton, A., et al. (2019). Building resilience to climate change through social protection. London, UK: International Institute for Environment and Development (IIED).
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., and Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review. J. Appl. Psychol. 88, 879–903. doi: 10.1037/0021-9010.88.5.879
Podsakoff, P. M., and Organ, D. W. (1986). Self-reports in organizational research: problems and prospects. Aust. J. Manag. 12, 531–544.
Puig, D., Adger, N. W., Barnett, J., Vanhala, L., and Boyd, E. (2025). Improving the effectiveness of climate change adaptation measures. Clim. Chang. 178, 1–15. doi: 10.1007/s10584-024-03817-z
Raleigh, C., Linke, A., Barrett, S., and Kazemi, E. (2024). Climate finance and conflict: Adaptation amid instability. The Lancet Planetary Health, 8, e51–e60. doi: 10.1016/S2542-5196(23)00256-5
Randazzo, M., and Currid-Halkett, E. (2025). Rethinking the urban–rural divide: economic growth in America's heartland. Econ. Dev. Q. 39, 145–162. doi: 10.1177/08912424241310872
Regan, J. M. (2024). Climate change in the horn of Africa: causations for violent conflict. J. Peacebuilding Develop. 19, 22–39. doi: 10.1080/19434472.2022.2061032
Resilience Initiative. (2017). The Resilience Initiative. Rockefeller Philanthropy Advisors. Available at: https://www.rockpa.org/project/resilience-initiative/
Rigaud, K. K., De Sherbinin, A., Jones, B., Bergmann, J., and Clement, V. (2018). Groundswell: Preparing for internal climate migration. Washington, DC: World Bank.
Robeyns, I. (2006). The capability approach in practice. J Polit Philos 14, 351–376. doi: 10.1111/j.1467-9760.2006.00263.x
Sarstedt, M., Ringle, C. M., and Hair, J. F. (2021). “Partial least squares structural equation modeling” in Handbook of market research. eds. C. Homburg, M. Klarmann, and A. Vomberg (Cham, Switzerland: Springer), 587–632.
Scheffran, J., Brzoska, M., Kominek, J., Link, P. M., and Schilling, J. (2012). Climate change and violent conflict. Science 336, 869–871. doi: 10.1126/science.122133
Shen, A., and Zhou, J. (2024). Education opportunities for rural areas: evidence from China’s higher education expansion. arXiv preprint. arXiv:2408.12915. doi: 10.3389/fped.2024.1400468
Soanes, M., Shakya, C., Walnycki, A., and Greene, S. (2019). Money where it matters: Designing funds for the frontier. London, UK: International Institute for Environment and Development (IIED).
SPARC. (2023). Climate-resilient development for Somalia: Stocktake and priority actions. Available online at: https://www.sparc-knowledge.org/resources/climate-resilient-development-somalia-stocktake-and-priority-actions (Accessed October 19, 2025).
Suckall, N., Fraser, E., and Forster, P. (2017). Reduced migration under climate change: evidence from Malawi using an aspirations and capabilities framework. Clim. Dev. 9, 298–312. doi: 10.1080/17565529.2016.1149441
Tacoli, C., Agergaard, J., Andreasen, M. H., and Brown, D. (2025). “Introduction: changing rural–urban linkages in the global south” in Handbook on rural–urban linkages in the global south. eds. C. Tacoli, J. Agergaard, M. H. Andreasen, and D. Brown (Cheltenham, UK: Edward Elgar Publishing), 1–23.
Thompson, C. G., Kim, R. S., Aloe, A. M., and Becker, B. J. (2017). Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results. Basic Appl. Soc. Psychol. 39, 81–90. doi: 10.1080/01973533.2016.1277529
UNDP (2011). Blending climate finance through national climate funds. New York, NY: United Nations Development Programme.
Van Maarseveen, R. (2021). The urban–rural education gap: do cities indeed make us smarter? J. Econ. Geogr. 21, 683–714. doi: 10.1093/jeg/lbab006
Warner, K., and Afifi, T. (2014). Where the rainfalls: evidence from eight countries on how vulnerable households use migration to manage rainfall variability and food insecurity. Clim. Dev. 6, 1–17. doi: 10.1080/17565529.2013.835707
World Bank. (2022a). Global Findex database 2021: financial inclusion, digital payments, and resilience in the age of COVID-19. Available online at: https://documents1.worldbank.org/curated/en/099505006292236012/pdf/P1748620f1d5be0130ac300c0b0170539e9.pdf (Accessed October 19, 2025).
World Bank. (2022b). Rural employment in Africa: trends and challenges. Available online at: https://openknowledge.worldbank.org/handle/10986/38460 (Accessed October 19, 2025).
World Bank. (2023). Shock responsive safety net for human capital project (P171346) – Implementation Status & Results Report. Available online at: https://documents.worldbank.org/curated/en/099122123073523097/pdf/P17134614ca3f506019183195856ebad001.pdf (Accessed October 19, 2025).
World Bank. (2024). The multi-partner fund progress report: January–June 2024. Available online at: https://documents.worldbank.org/curated/en/
Keywords: rural brain retention, climate resilience, climate-sensitive development finance, rural–urban disparity, Somalia
Citation: Nor MI (2025) Exploring the nexus between climate finance, rural–urban disparities, and rural brain drain in Somalia: the mediating role of climate resilience. Front. Sociol. 10:1702333. doi: 10.3389/fsoc.2025.1702333
Edited by:
Chong Jiang, Guangdong Academy of Science (CAS), ChinaReviewed by:
Frank Musa, Lilongwe University of Agriculture and Natural Resources, MalawiVita Sarasi, Universitas Padjadjaran, Indonesia
Farid Dewan, Noakhali Science and Technology University, Bangladesh
Copyright © 2025 Nor. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Mohamed Ibrahim Nor, bS5pYnJhaGltQHNpbWFkLmVkdS5zbw==