• climate change
• climate-migration nexus
• econometrics
• human mobility
• migration
• multi-level analysis
• panel data

This thesis examines the relationship between climate change and migration through the integration of two original and highly granular datasets: the global net migration estimates developed by Niva et al. (2023), and the subnational climate indicators constructed by Gortan et al. (2024). A significant contribution of this work lies in the extensive effort to harmonize these datasets across spatial and temporal dimensions, enabling a novel multi-level empirical analysis. By applying two complementary econometric models to panel data spanning national, regional, and local units over two decades (2000–2019), the thesis investigates how long-term climatic anomalies influence human mobility. The first model estimates the effects of climate variability on net migration flows through the use of socio-economic regressors, while the second directly explores the climate–migration nexus by adopting a fixed-effects framework inspired by Burke et al. (2019). The results suggest that climate change significantly affects migration patterns, with the strongest and most consistent effects emerging at the regional level. The analysis reveals spatial heterogeneity in both magnitude and direction of the impacts, pointing to the importance of localized policy responses and cautioning against one-size-fits-all solutions. This study contributes to the climate–migration literature by offering a data-rich and spatially disaggregated empirical perspective.