Document Type
Article
Abstract
Mental health problems are prevalent among China’s internal migrant workers. However, research on the relationship between socioeconomic status (SES) and mental health is insufficient. Therefore, this study, utilizing the China’s National Dynamic Monitoring Survey data from a sample of 15,997 migrant workers aged 15–59 years to explore differences in the relationship between migrants’ objective and subjective SES and mental health status in 2015. Both the mediating effect of perceived interpersonal discrimination and the moderating effect of age were examined through structural equation modeling. The results indicated that subjective SES has a stronger direct relationship with mental health than objective SES. Perceived interpersonal discrimination mediated the association between subjective SES and mental health. Furthermore, a much stronger relationship was found between subjective SES and perceived interpersonal discrimination among migrants older than 24 years of age than younger migrant groups. The results showed that, compared with traditional objective SES indicators, subjective SES could be a more sensitive index for identifying those migrant workers with a high risk of mental health problems. In addition, reducing interpersonal discrimination toward migrants can alleviate their mental health problems. And we should pay more attention to older migrant workers since they are more likely to prone to interpersonal discrimination and mental health issues.
Digital Object Identifier (DOI)
Publication Info
Published in PlOS One, Volume 17, Issue 9, 2022.
Rights
© 2022 Shao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
APA Citation
Shao, Y., Huang, Y., Li, X., & Tong, L. (2022). Association between socioeconomic status and mental health among China’s migrant workers: A moderated mediation model. PLOS ONE, 17(9). https://doi.org/10.1371/journal.pone.0274669