For jobs or housing in the Metropolis: A Life Course Analysis of Small-town Migrants’ Settlement Intentions Through Machine Learning

Migration from small towns to metropolises is a global trend, but migrants often face a dilemma between employment opportunities and prohibitive living costs. In China, this is exacerbated by institutional barriers like the hukou system, trapping many in a state of temporary residence without full welfare benefits. Settlement intention is thus a complex nonlinear process, yet understanding of these dynamics remains limited. Employing the XGBoost-SHAP methodology on China Migrants Dynamic Survey data, this study analyzes the non-linear effects of employment and housing on the settlement intentions of small-town migrants. The findings reveal significant threshold effects, where factors of urban attraction transform into repulsion. Counterintuitively, highly educated migrants exhibit a lower tolerance for metropolitan pressures, as their versatile human capital allows them to find a better work-life balance in medium-sized cities. In contrast, less-educated migrants show higher tolerance, compelled by constrained employment alternatives to accept precarious incomplete settlement. This educational divide is sharpened by life course events: for the highly educated, having children decreases their tolerance for megacities, while a structural dilemma forces the less-educated to remain even after having children.