Emotional Well‐Being Trajectories Before and After Statutory Retirement—Contributions of Social and Health‐Related Factors

Aging population in OECD countries and the rising mental disorder burden highlight the need to understand statutory retirement's contribution to emotional well-being. However, the relationship between statutory retirement and emotional well-being remains underexplored. Clarifying this relationship can help policymakers enhance pension systems to better support statutory retirees' well-being. This study examined emotional well-being trajectories 15 years before and after statutory retirement among 5076 City of Helsinki employees in Finland (81% women; age range 40-60 at Phase 1), and the social and health-related factors associated with these trajectories. We used prospective cohort data from the Helsinki Health Study (2000-2022) across five phases. Growth Mixture Modeling identified the trajectories, measured by the RAND-36 emotional well-being dimension. Multinomial logistic regression with average marginal effects (AMEs) and 95% confidence intervals assessed the associations between social and health-related factors and these trajectories. A three-trajectory solution was selected: 'Stable high' (85%), 'Slowly increasing' (12%), and 'Fast increasing, then fast decreasing' (3%) emotional well-being. Mentally very strenuous work, binge drinking, smoking, frequent sleep problems, and mental disorder diagnoses before retirement were associated with lower predicted probabilities of the 'Stable high' trajectory and higher predicted probabilities of the 'Slowly increasing' trajectory. Mentally very strenuous work and mental disorder diagnoses were linked to a higher predicted probability of the 'Fast increasing, then fast decreasing' trajectory. Most participants maintained high emotional well-being throughout the statutory retirement transition. A smaller group of individuals experienced lower emotional well-being before statutory retirement and a gradual improvement after, or saw an increase until retirement, followed by a rapid decline. Mentally very strenuous work, binge drinking, smoking, frequent sleep problems, and mental disorder diagnoses before retirement were associated with poorer emotional well-being trajectories. With targeted interventions we could explore whether a change in these factors could enhance emotional well-being across retirement.

Authors

Institutions

Publication Details

Journal
Scandinavian Journal of Psychology
Published
2026-01-14
DOI
https://doi.org/10.1111/sjop.70071
Primary Topic
Retirement, Disability, and Employment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Emotional Well‐Being Trajectories Before and After Statutory Retirement—Contributions of Social and Health‐Related Factors

T Lallukka, Jatta Salmela, Aapo Hiilamo, Emmi‐Susanna Katajapuu et al.
Scandinavian Journal of Psychology
Retirement, Disability, and Employment
article

Emotional Well‐Being Trajectories Before and After Statutory Retirement—Contributions of Social and Health‐Related Factors

T Lallukka, Jatta Salmela, Aapo Hiilamo, Emmi‐Susanna Katajapuu, Pauliina Saha
article en

Abstract

Aging population in OECD countries and the rising mental disorder burden highlight the need to understand statutory retirement's contribution to emotional well-being. However, the relationship between statutory retirement and emotional well-being remains underexplored. Clarifying this relationship can help policymakers enhance pension systems to better support statutory retirees' well-being. This study examined emotional well-being trajectories 15 years before and after statutory retirement among 5076 City of Helsinki employees in Finland (81% women; age range 40-60 at Phase 1), and the social and health-related factors associated with these trajectories. We used prospective cohort data from the Helsinki Health Study (2000-2022) across five phases. Growth Mixture Modeling identified the trajectories, measured by the RAND-36 emotional well-being dimension. Multinomial logistic regression with average marginal effects (AMEs) and 95% confidence intervals assessed the associations between social and health-related factors and these trajectories. A three-trajectory solution was selected: 'Stable high' (85%), 'Slowly increasing' (12%), and 'Fast increasing, then fast decreasing' (3%) emotional well-being. Mentally very strenuous work, binge drinking, smoking, frequent sleep problems, and mental disorder diagnoses before retirement were associated with lower predicted probabilities of the 'Stable high' trajectory and higher predicted probabilities of the 'Slowly increasing' trajectory. Mentally very strenuous work and mental disorder diagnoses were linked to a higher predicted probability of the 'Fast increasing, then fast decreasing' trajectory. Most participants maintained high emotional well-being throughout the statutory retirement transition. A smaller group of individuals experienced lower emotional well-being before statutory retirement and a gradual improvement after, or saw an increase until retirement, followed by a rapid decline. Mentally very strenuous work, binge drinking, smoking, frequent sleep problems, and mental disorder diagnoses before retirement were associated with poorer emotional well-being trajectories. With targeted interventions we could explore whether a change in these factors could enhance emotional well-being across retirement.

Scandinavian Journal of Psychology
University of Helsinki (FI), Max Planck Institute for Demographic Research (DE)
No poverty
Openalex Percentile: Top 91%
Retirement, Disability, and Employment
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.