Abstract
BACKGROUND: With the rapid development of China's social economy, the state has introduced a number of social security and employment policies, which are centered on the principles of stabilizing employment, ensuring people's livelihood, and supporting key groups. Empirical research often employs cross-sectional data to examine the impact of low-quality employment on the mental health of workers, but the evidence regarding the correlation between employment quality and mental health remains insufficient. METHODS: This study aims to analyze the potential long-term impact of low-quality employment on mental health. By applying the employment arrangement typology method, we utilized the survey data from the China Family Longitudinal Survey (CFPS) from 2016 to 2018 to explore the association between employment quality and mental health, and further investigated whether the strength of work-family values (i.e., the emphasis on career success and child-rearing) and gender differences led to differences in this association pattern. CONCLUSION: Through potential category clustering analysis of representative Chinese panel data, we identified six types of employment quality: standard employment, unstable and unsustainable employment, full-time but unstable employment, mild standard employment, mixed employment, and protected parttime employment. After controlling for sociodemographic characteristics, the research results show that compared with the standard employment pattern, men engaged in unstable and unsustainable jobs, as well as women in full-time unstable jobs, have significantly lower mental health levels two years later. Although protected part-time jobs have a more negative impact on the mental health of people with middle and high working family values, the interaction analysis shows that the moderating role of values in the relationship between employment quality and mental health does not present a clear pattern among different gender groups. DISCUSSION: It is suggested that future research should replicate these findings in different countries to verify the correlation.