Key predictors of subjective well-being among Chinese people: a multi-wave national cross-sectional analysis using machine learning, 2015–2023

Subjective well-being (SWB) refers to individuals’ subjective appraisal of their own well-being and provides invaluable insights for public policy. However, the relative importance of its numerous predictors remains poorly understood. This study employed a machine learning approach with multi-wave national data to identify the key predictors of SWB among Chinese people.

Data were drawn from the 2015 (N = 10,968), 2021 (N = 5458), and 2023 (N = 11,326) three-wave repeated cross-sectional surveys of the Chinese General Social Survey. Using the XGBoost algorithm, we modeled SWB scores based on 31 predictors. Analysis of feature importance consistently identified six factors as the most robust predictors across all three waves: perception of social equity, family economic status, depression, current social class, age, and self-reported physical health.

The six-feature model achieved R2 = 0.21–0.26 in development and 0.23 in external validation, recovering 84.0–86.7% of the variance explained by the full 31-feature model, demonstrating stable predictive performance across independent cohorts. These findings identify a parsimonious set of core predictors for the Chinese population and offer an evidence-based foundation for targeted policymaking. The simplified model also holds potential for practical application in policy simulation and decision support.

This research was funded by the Phased Achievements of Guizhou Province Philosophy and Social Science Planning Project (25GZZB02). School of Psychology, Guizhou Normal University, Guiyang, China Key Laboratory of Brain Function and Brain Disease Prevention and Treatment of Guizhou Province, Guiyang, China The authors declare no competing interests. This article does not involve any studies with human participants conducted by the authors.

It is based on secondary data analysis. The empirical analysis used data from the Chinese General Social Survey (http://cgss.ruc.edu.cn/), originally collected by the National Survey Research Center at Renmin University of China. The data are publicly available for research purposes, and the data used were de-identified, ensuring no possibility of re-identification of the original subjects.

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