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July 13, 2026Acta PsychologicaOpen Access

What drives generational differences in subjective well-being? A machine learning study of Chinese migrant workers in the Yangtze River Delta

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Authors

WXWei XuZXZhihui XianYNYingxin Niu

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Overview

Machine learning reveals generational disparities in subjective well-being of Chinese migrant workers, suggesting targeted policy implications.

Key Points

  • This study aims to identify factors influencing subjective well-being differences between generations of migrant workers in China.
  • Analyzed survey data from migrant workers in the Yangtze River Delta using machine learning techniques.
  • Employed Shapley additive explanations (SHAP) to assess the contributions of various factors to subjective well-being.
  • Applied Social Quality Theory's four-dimensional framework to interpret generational differences.
  • New-generation migrant workers report lower subjective well-being levels than first-generation workers.
  • First-generation well-being is mainly linked to social security factors like work stability, while new-generation well-being also relates to family support and urban integration.
  • Linear and nonlinear relationships are found, including inverted U-shaped patterns for urban integration and socio-economic status, with no positive welfare system association for either generation.

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a54807d475c38bf615a5628https://doi.org/10.1016/j.actpsy.2026.107425
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