Occupational status and health disparities among workers—An empirical study based on China health and nutrition survey data

This study uses data from the China Health and Nutrition Survey (CHNS) in 2004, 2006, and 2015 and employs a fixed-effects model based on Driscoll-Kraay standard errors to investigate the impact of occupation type, employment type, and work unit type on workers' health and the underlying mechanisms. The main findings suggest that higher levels of occupation type and employment type are associated with better self-rated health among workers, but simultaneously increase the probability of chronic diseases, demonstrating a "dual effect" of occupational characteristics on health. Additionally, workers in the public sector have a higher probability of chronic diseases and lower self-rated health compared to those in the non-public sector. Furthermore, the impact of occupation type, employment type, and work unit type on health is greater for male workers than for female workers. The negative impact of an increase in occupation type on chronic diseases is significantly higher in the absence of overtime work and for workers engaged in moderate and heavy physical labor. The mechanism analysis reveals that work intensity, labor income, and work hours play a crucial role in explaining the impact of occupation on health, jointly accounting for a significant portion of the impact of employment type on chronic diseases, occupation type on self-rated health, and work unit type on self-rated health.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2025-05-23
DOI
https://doi.org/10.1371/journal.pone.0324144
Primary Topic
Workplace Health and Well-being
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Occupational status and health disparities among workers—An empirical study based on China health and nutrition survey data

Yingji Li, Qing Li
PLoS ONE
Workplace Health and Well-being
article

Occupational status and health disparities among workers—An empirical study based on China health and nutrition survey data

Yingji Li, Qing Li
article en

Abstract

This study uses data from the China Health and Nutrition Survey (CHNS) in 2004, 2006, and 2015 and employs a fixed-effects model based on Driscoll-Kraay standard errors to investigate the impact of occupation type, employment type, and work unit type on workers' health and the underlying mechanisms. The main findings suggest that higher levels of occupation type and employment type are associated with better self-rated health among workers, but simultaneously increase the probability of chronic diseases, demonstrating a "dual effect" of occupational characteristics on health. Additionally, workers in the public sector have a higher probability of chronic diseases and lower self-rated health compared to those in the non-public sector. Furthermore, the impact of occupation type, employment type, and work unit type on health is greater for male workers than for female workers. The negative impact of an increase in occupation type on chronic diseases is significantly higher in the absence of overtime work and for workers engaged in moderate and heavy physical labor. The mechanism analysis reveals that work intensity, labor income, and work hours play a crucial role in explaining the impact of occupation on health, jointly accounting for a significant portion of the impact of employment type on chronic diseases, occupation type on self-rated health, and work unit type on self-rated health.

PLoS ONEVol. 20(5)
Yunnan University (CN), Henan University of Economic and Law (CN)
Decent work and economic growth
Openalex Percentile: Top 81%
Workplace Health and Well-being
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.