Bibliographic record
Abstract
This paper demonstrates that spousal earnings affect an individual’s decision to retire. I find that husbands with higher-earning spouses are more likely to retire following an involuntary job loss. Earlier studies show that job reduces subsequent employment, earnings, and wealth, but they do not explain why some workers return to work and others do not. I add an important dimension to these studies by considering how spousal earnings and household assets affect a worker’s post-displacement labor supply. To explore the household's problem, I develop a stylized two-period model to illustrate how labor supply responds to spousal earnings and household assets in an uncertain environment. Using data from the Health and Retirement Study, I test my theoretical model's predictions using a reduced-form empirical specification. Relative to displaced men with low-earning spouses, husbands with higher-earning wives are more likely to exit the labor force following displacement. The same effect is not detectable in the population of older women. In both populations, a displaced worker with higher household assets is less likely to return to the labor force. At the household level, job loss as a profound impact on retirement well-being. At a broader level, a reduction in the labor supply of older workers has negative fiscal consequences.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".