From Efficiency to Illness
Do Highly Automatable Jobs Take a Toll on Health in Germany
Bibliographic Data
| ID | 21334710 |
|---|---|
| Authors | Mariia Vasiakina (0000-0003-4382-7676, Max Planck Institute for Demographic Research (MPIDR) Rostock Mecklenburg‐Vorpommern Germany, corresponding author), Christian Dudel (0000-0002-2985-6684, Max Planck Institute for Demographic Research (MPIDR) Rostock Mecklenburg‐Vorpommern Germany) |
| Year | 2026 |
| Volume | 57 |
| Issue | 4 |
| Pages | 297-308 |
| Publication date | 2026-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Industrial Relations Journal (JOURNAL) |
| Journal identifiers | ISSN: 0019-8692 • E-ISSN: 1468-2338 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/irj.70034 |
| OpenAlex | W7143378472 |
| Language | EN |
| References cited | 44 |
Automation transforms work at a rapid pace, with gradually increasing shares of the workforce at risk of being replaced by machines. However, little is known about how this risk is affecting workers. In this study, we examine the relationship between exposure to high automation risk at work and both subjective (self‐reported health, anxiety, and health satisfaction) and objective (healthcare use and sickness absence) health outcomes of workers in Germany. We base our analysis on survey data from the German Socio‐Economic Panel (SOEP) and administrative data from the Occupational Panel for Germany (2013–2022). Employing panel regression, we demonstrate that for workers, exposure to high automation risk at the occupational level is associated with lower self‐reported health and health satisfaction, as well as increased sickness absence. No significant effects are observed for anxiety and healthcare use. Our heterogeneity analysis reveals only minor variations in the effects across several demographic and occupational characteristics. We also conduct multiple robustness checks (i.e., alternative model specifications and risk measures with different thresholds), with the results remaining largely consistent with our main findings
German · Health care · Occupational safety and health · Panel data · Robustness (evolution) · Toll · Work (physics) · Workforce · Health, Environment, Cognitive Aging · Human-Automation Interaction and Safety · Workplace Health and Well-being
Self-Rated Health and Public Health
Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity
Explaining Job Polarization
The impacts of digital transformation on the labour market
On the Pooling of Time Series and Cross Section Data
The Skill Content of Recent Technological Change
The future of employment
The German Socio-Economic Panel (Soep)
Robot Adoption at German Plants
The Occupational Panel for Germany
Transformation to Industrial Artificial Intelligence and Workers' Mental Health
Job quality and automation
Displaced or depressed? Working in automatable jobs and mental health
Trends in gender differences in health at working ages among West and East Germans
People versus machines
Health inequalities in Germany
The rise of artificial intelligence, the fall of human wellbeing
The impact of automation and artificial intelligence on worker well-being
Death by Robots? Automation and Working-Age Mortality in the United States
How Important is Methodology for the Estimates of the Determinants of Happiness
Comparing Incomparable Survey Responses
Testing prospective effects in longitudinal research
The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market
County-level job automation risk and health
Maternal health, well-being, and employment transitions
| Citation velocity | historical |
|---|---|
| Highly cited | No |