Modeling interprovincial migration in China using dual mechanism ERGM: toward resilient and sustainable mobility

Understanding the dynamics of internal migration is essential for promoting regional resilience and sustainable development. This study applies a weighted Exponential Random Graph Model (ERGM) to investigate the structural and functional evolution of China’s interprovincial migration networks in 2000, 2010, and 2020. By integrating embeddedness, information cascades, resource endowments, proximity, and emergence theory, the analysis reveals the dual influence of endogenous and exogenous mechanisms. Results highlight that information cascades are a significant endogenous mechanism influencing migration outcomes. Specifically, mutual and popularity cascades are positively correlate with migration network formation, while transitive cascades exhibit an inhibitory effect. Exogenous drivers, including favorable economic, educational, healthcare, ecological attributes, cultural similarity, and geographic proximity, consistently facilitate migration flows. The interprovincial migration networks have shifted from a “core-periphery clusters” to a pattern of “key agglomeration and regional balance.” The dominance of endogenous mechanisms underscores the self-reinforcing nature of migration systems. These findings provide critical insights for designing evidence-based policies that address regional inequality, enhance human mobility infrastructure, and foster sustainable and inclusive internal migration systems.