Bibliographic record
Abstract
BACKGROUND: Like most of other developed countries, Canada experienced baby boom in the 20 years after World War II. With the eldest baby turned 65 in 2011, it is expected that a considerable number of people will retire in coming years and consequently, retired people will soon constitute a significant part of Canadian population. In this context, an interesting question would be how retirement impacts mental health. This question is related to the well-being of the retired population as well as to over all health care expenditures. AIMS: The major objective of this study is to examine the impact of retirement on mental health as measured by the Short Form Depression Scale. This study further conducts separate analyses to examine whether the impact of retirement on mental health differs between males and females, and among different education and income groups. METHODS: This study uses large scale Canadian National Population Heath Survey (Longitudinal Component) data and adopts fixed effect method and fixed effect instrumental variable method to deal with possible endogeneity problem. RESULTS: After controlling for unobserved individual specific heterogeneity, the study found that retirement has an insignificant impact on depression. As a robustness check, the study utilizes logit, conditional fixed effect logit, and fixed effect instrumental variable regression on a dichotomous variable representing depression and found that retirement has an insignificant impact on depression. The study further examined this issue using different subgroups based on gender, education and marital status, and again found that impacts of retirement on depression are not statistically significant. IMPLICATIONS FOR POLICY: Though the coefficients are statistically insignificant, however, most of the results are economically meaningful since the magnitudes are relatively large, implying very large effects. The effects of retirement on mental health appear to be complex and multidimensional; however, based on the FE-IV models, most of the effects seem to suggest that there may be some increase in depression symptoms. The findings of this study will have important policy implications. If retirement worsens mental health, then policy encouraging retirement may actually increase health care expenditures. On the other hand, if retirement improves mental health, then such policy will likely to decrease health care expenditure. Studies based on data from Canada and other OECD countries suggest that the provisions of social security programs themselves often provide strong incentive to leave the labor force early. The finding of this study that retirement has negative impact on mental health in Canada will imply that current Canadian policy of encouraging early retirement is likely to increase mental health care expenditure. IMPLICATIONS FOR FURTHER RESEARCH: There are a number of ways to extend this study. Depending on the availability of data, future studies can focus on sub populations: voluntary retiree/ involuntary retirement, early retiree/ late retiree and complete retiree/ partial retiree. Future study can also conduct more detailed analysis by including variables such as previous job characteristics, voluntary activity during retirement and family characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".