The Effects of Health and Wealth Shocks on Retirement Decisions
Bibliographic record
Abstract
etirement decisions both affect and are affected by health status.Health status, in turn, has been linked to net worth.And according to the life cycle model of savings, retirement has an important effect on net worth because retirees begin to expend their assets to maintain consumption once they leave the labor force.Given the multidirectionality of all these influences at this three-factor nexus, it has been difficult to separate the direct impacts of health and wealth on retirement from the reciprocal effect of retirement on health and wealth.This is our goal in the present article.Many studies have found that health shocks predict retirement decisions (see, e.g., Hagan, Jones, and Rice, 2009).For example, using fixed effects estimators and instrumenting subjective health by "health stock, " Disney, Emmerson, and Wakefield (2006) find that ill health strongly predicts early retirement among respondents older than 50 years of age in the British Household Panel Survey.Health limitations have also been shown to have a similar impact on early retirement decisions.However, the evidence does not completely support the claim that health shocks lead to exit from the labor force.For example, French (2005) finds that health is not among the more important determinants of job exit at older ages.Both health status and net worth can affect retirement decisions.In some cases, early retirement may be precipitated by a shock to an individual's health and/or economic status.The authors examine how health and wealth shocks affect retirement decisions.They use data from the Panel Study of Income Dynamics to estimate a first-differences model of health and wealth shocks on retirement over the course of the 2000s in the United States.Their results suggest that acute health shocks are associated with labor market exits for older American men but not women.These results appear particularly strong for blacks, whose labor force participation seems particularly sensitive to health status, which may be due to different occupations for blacks and whites.(JEL J26, I12, D91)
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".