Social Integration Among Rural-to-Urban Migrants: A Dynamic Analysis Using a Hybrid Approach Integrating Structural Equation Modeling and Machine Learning.

Journal: Evaluation review
Published Date: Nov 25, 2025

Abstract

Against the backdrop of China's new urbanization and regional coordination strategies, the social integration of rural-to-urban migrants has become an increasingly critical indicator of state governance modernization and institutional performance. Considering rural migrants in Jilin Province as the research subject, particularly in the context of its agricultural revitalization and urbanization policies, this study constructs an "embedding-activation" theoretical framework grounded in institutional theory and social capital theory. It proposes a mechanism in which formal institutions are embedded within informal institutions and generate effective governance outcomes through cognitive transformation facilitated by social capital. Empirically, the study applies structural equation modeling to identify the direct and indirect pathways through which policy cognition, social trust, identity, and economic integration influence social integration. Additionally, machine learning methods are employed to reveal the marginal contributions and nonlinear transition characteristics of individual variables, bridging the gap between causal explanation and predictive accuracy. The results demonstrate that social capital functions as a cognitive trigger for activating institutional efficacy and plays a key mediating role in promoting migrants' social integration. The success of institutional incentives depends on the accumulation of individual cognition and the threshold effects of trust and identity mechanisms, resulting in a behavioral shift from passive acceptance to active engagement. This dynamic promotes the expansion of scope and deepening of structure in social integration. By adopting an institution-cognition-behavior analytical lens, this study enriches the micro-foundations of institutional theory and provides support for enhancing the evaluation, responsiveness, and effectiveness of migration.

Authors

  • Xintao Li
    Department of Urology, Air Force Medical Center, Beijing, China.
  • Qi Qin
    Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Yantai Information Technology Research Institute of SJTU, Shanghai Engineering Research Center for Intelligent diagnosis and treatment instrument, Shanghai 200240, China. Electronic address: [email protected].
  • Tianxu Zheng
    School of International Relations and Public Affairs, Fudan University, Shanghai, China.
  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.

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