The Impact of Retirement on Well-Being in Canada
Bibliographic record
Abstract
This dissertation investigates the effects of retirement on various dimensions of well-being, including psychological, general, physical, and mental health, among Canada's aging population. The primary objective of the study is to explore how retirement influences life satisfaction (psychological well-being), general health, physical health, and mental well-being. The dissertation is divided into three main parts: the first examines life satisfaction using the 2008–2009 Canadian Community Health Survey – Healthy Aging (CCHS), a cross-sectional dataset; the second explores life satisfaction using the longitudinal Canadian Longitudinal Study on Aging (CLSA); and the third focuses on the effects of retirement on general, physical, and mental health using CLSA data. To address self-selection, reverse causality, and unobserved heterogeneity, the research employs econometric techniques, including instrumental variables, fixed effects models, difference-in-differences analysis, and fuzzy regression discontinuity design (FRDD). These methods provide reliable causal estimates while controlling for factors such as age, gender, marital status, education, income, and health status. The findings show that retirement has a positive and significant effect on life satisfaction, after controlling for a wide set of socioeconomic and demographic factors, with this effect remaining robust across both cross-sectional and longitudinal data. Retirement also improves general and mental health, likely due to reduced work-related stress and increased opportunities for social engagement. However, physical activity levels decline after retirement, reflecting reduced occupational movement. These health effects vary by retirement type, voluntariness, and sociodemographic factors, with complete and voluntary retirement offering the most substantial benefits. General health improves in the short term, while mental health gains become more pronounced over time. Additional analysis highlights that men experience greater improvements in mental health, while women report better general health. Higher education levels amplify the positive effects of retirement across all health outcomes, including physical activity. Social support and engagement consistently enhance well-being, while household and regional factors play a minimal role. These findings carry important policy implications, highlighting the need for flexible retirement options and programs that promote physical activity and social engagement. As Canada’s population ages, these insights can inform policies that foster healthy aging and improve retirees’ overall quality of life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".