Migration Patterns: The Complex Roles of Poverty and Weather Extremes
Migration Patterns: The Complex Roles of Poverty and Weather Extremes
Wednesday, 9 July 2025
Location: FSE035 (Faculty of Education Sciences (FSE))
Distributed Paper
Migration, driven by global forces such as poverty, population growth, conflict, and climate change, is transforming societies worldwide. This study investigates the socio-economic and environmental determinants of migration, with Kyrgyzstan serving as an illustrative case due to its heavy reliance on labor migration. Between 1990 and 2020, the number of Kyrgyz citizens living abroad increased by 48%, reflecting migration's critical role in sustaining livelihoods. Remittances, which constitute nearly a third of the nation's GDP, place Kyrgyzstan among the top five countries globally in remittance dependence, highlighting migration's importance for economic stability.
Building on established theoretical frameworks, we adapt Stark and Bloom's (1985) labor allocation model and Guriev and Vakulenko's (2015) exploration of non-monotonic income-migration relationships. Our analysis examines both socio-economic factors, such as multidimensional poverty (MPI), and environmental stressors, including hot summers, cold winters, excessive rainfall, and dry spells. We calculate MPI and weather variables at the district level using data from the Kyrgyz Integrated Household Survey (2013–2022) and climate indices from the ERA5-Land reanalysis.
We apply a Spatial Multinomial Logit model to explore different migration outcomes—domestic, international, combined, and no migration—addressing spatial endogeneity to capture the intricate dynamics at play. The results demonstrate that a complex, non-linear interaction between poverty, weather extremes, and socio-economic conditions shapes migration decisions. Significant variations are observed across different poverty levels, reflecting the nuanced ways in which these drivers influence migration behavior.
This research contributes to the broader understanding of global migration by offering a long-term, quantitative analysis of how socio-economic and environmental factors jointly shape migration flows. It provides valuable insights into the multifaceted nature of migration, enhancing our understanding of the forces driving people to move both within and across borders.
Building on established theoretical frameworks, we adapt Stark and Bloom's (1985) labor allocation model and Guriev and Vakulenko's (2015) exploration of non-monotonic income-migration relationships. Our analysis examines both socio-economic factors, such as multidimensional poverty (MPI), and environmental stressors, including hot summers, cold winters, excessive rainfall, and dry spells. We calculate MPI and weather variables at the district level using data from the Kyrgyz Integrated Household Survey (2013–2022) and climate indices from the ERA5-Land reanalysis.
We apply a Spatial Multinomial Logit model to explore different migration outcomes—domestic, international, combined, and no migration—addressing spatial endogeneity to capture the intricate dynamics at play. The results demonstrate that a complex, non-linear interaction between poverty, weather extremes, and socio-economic conditions shapes migration decisions. Significant variations are observed across different poverty levels, reflecting the nuanced ways in which these drivers influence migration behavior.
This research contributes to the broader understanding of global migration by offering a long-term, quantitative analysis of how socio-economic and environmental factors jointly shape migration flows. It provides valuable insights into the multifaceted nature of migration, enhancing our understanding of the forces driving people to move both within and across borders.