Year: 2026
Type: article
Study design: Observational Study
Abstract: The relationship between labor market status and individual health has been a central topic in academic research for decades. The findings remain inconclusive despite extensive studies, largely due to variations in national contexts, study periods, and health indicators used. This study focuses on Russia and examines the direct causal effect of employment status on self-reported health outcomes. We employ a quasi-experimental Difference-in-Differences (DID) approach to address potential biases arising from self-selection and indirect selection (confounding). Our analysis is based on data from waves 24–31 (2015–2022) of the Russian Longitudinal Monitoring Survey – Higher School of Economics (RLMS-HSE), a nationally representative panel dataset. The results confirm a significant causal relationship between employment status and self-reported health. However, the analysis does not conclusively confirm or reject the hypothesized negative impact of unemployment on health outcomes in the Russian population, highlighting the need for further investigation. Moreover, the extended analysis reveals a significant positive association of higher education levels with self-reported health, while being divorced is associated with a negative impact on health outcomes. Additionally, we run synthetic DID models to demonstrate that our results are robust to changes in specifications. Our findings have important policy implications, providing a strong foundation for developing initiatives focused on expanding employment opportunities, ultimately contributing to improved population health. In Russia, where healthcare costs are state-funded, these measures can enhance population health and reduce long-term government healthcare expenditures.
Source: Population and Economics
Institution: Siberian Branch of the Russian Academy of Sciences
Language: English
Subfield: General Health Professions
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
Funder: Russian Science Foundation
OpenAlex ID: W7154280289