Short‐term effect of retirement on health: Evidence from nonparametric fuzzy regression discontinuity design
Bibliographic record
Abstract
We estimate the short-term effect of retirement on health in the US using the Health and Retirement Study survey. We use the nonparametric fuzzy regression discontinuity design to avoid assuming any functional form on the age-health profile and minimize potential bias in identifying the causal effect of retirement on health status in the short term. Estimates indicate an 8% decline in the cognitive functioning score of retirees and a 28% increase in the CESD depression scale. The likelihood of being in good health status declined by 16%. The transition from working to retirement has more significant negative impacts on males than females. In addition, retirement has more considerable adverse effects on less-educated individuals compared to high-educated individuals. The short-term effects of retirement on health are consistent and robust across different bandwidths, weighting kernel functions, and age-profile specifications. Moreover, the Treatment Effect Derivative test results highly support the external validity of the nonparametric estimates of the retirement effect on health.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".