Retirement Reconsidered: Labor Force Participation of Older Men in the United States
Bibliographic record
Abstract
The objectives of this article are two fold. Changes in older men’s labor force participation in the United States are first described focusing on human capital and demographic variables. A model of the labor/leisure choice and the retirement decision of older menare then estimated employing Maximum Likelihood Probit. While changes in Social Security Benefit rules are a significant factor in explaining the trend of rising retirement age among older men, the focus here is on additional factors that contribute to older men’s decision to forestall retirement. Probit coefficient estimates for three distinct age cohorts verify the effects of hypothesized determinants of the decision to retire. Specifically, the coefficient on estimated earnings is negative and hasthe largest marginal effect on the decision to retire followed by years of education and retired wife. The effects of wives’ retirement decision will likely influence and forestall the retirement decision of married men as more working women reach retirement age. The rise in labor force participation rates of older men may offset rather than reverse the decline in men’s labor force participation rates that began more than a half century ago.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".