The causal effect of trade unions on workers' health: A parametric g-formula approach using longitudinal data from the Panel Study of Income Dynamics (PSID)
Gonzalez-Hijon, J.; Hamarat, N.; Kromydas, T.; Wels, J.
Show abstract
BackgroundTrade unions are increasingly recognized as public health actors. They both protect workers health by ensuring workplace health and safety and, indirectly, by providing economic advantages to their members. However, previous research has not addressed the direct and indirect causal pathways between trade unions and health and only focused on membership, omitting the important role that the presence of trade union has within the workplace. MethodsWe used two decades of nationally representative longitudinal data from the United States (PSID 2001-2021; {approx}26,000 individuals; {approx}117,000 person-years), used Exploratory Causal Discovery to detect causal pathways and applied parametric g-formula modelling to account for time-varying confounding. FindingsSustained union membership was associated with lower psychological distress (Mean Ratio - MR 0.944; 95% CI 0.916-0.974), with a persistent direct effect after adjusting for income, working hours, and housing (MR 0.937; 95% CI 0.907-0.968). Always workplace union presence showed an association (MR 0.972; 95% CI 0.945-0.999) that attenuated after adjusting for mediators. Improvements in self-reported health were primarily mediated by income and working-hours pathways (always union membership total adjust MR 0.991; 95% CI 0.983-1.000; always union presence total adjust: MR 0.990; 95% CI 0.982- 0.998). InterpretationBy examining both union membership and workplace union presence, this study captures the individual and institutional dimensions of unionization that have not been simultaneously addressed in prior research. It highlights the significant role trade unions play in improving workers health and the threat the erosion of collective bargaining in the US might pose for population health.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Impacts of COVID-19 on sick leave 92%
- Genetic evidence for causal relationships between age at natural menopause and the risk of aging-associated adverse health outcomes 90%
- The Causal Effects of Health Conditions and Risk Factors on Social and Socioeconomic Outcomes: Mendelian Randomization in UK Biobank 90%
Similar papers in this journal
- Racial Differences in Associations Between Adverse Childhood Experiences and Physical, Mental, and Behavioral Health 92%
- Excess death among Latino people in California during the COVID-19 pandemic 91%
- What Explains the Socioeconomic Status-Health Gradient? Evidence from Workplace COVID-19 Infections 90%
Similar papers in this journal
- Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases 91%
- Genetic predictors of participation in optional components of UK Biobank 90%
- Post-acute symptoms, new onset diagnoses and health problems 6 to 12 months after SARS-CoV-2 infection: a nationwide questionnaire study in the adult Danish population 90%
Similar papers in this journal
- Parental Income Gradients in Adult Health: A National Cohort Study 93%
- Exploring health in the UK Biobank: associations with sociodemographic characteristics, psychosocial factors, lifestyle and environmental exposures 92%
- The UK Coronavirus Job Retention Scheme and changes in diet, physical activity and sleep during the COVID-19 pandemic: Evidence from eight longitudinal studies 92%
Similar papers in this journal
- The Impact of Late-Career Job Loss and Genotype on Body Mass Index 94%
- A longitudinal causal graph analysis investigating modifiable risk factors and obesity in a European cohort of children and adolescents 92%
- Cross-classification between self-rated health and health status: longitudinal analyses of all-cause mortality and leading causes of death in the UK 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.