Understanding Joint Retirement
Bibliographic record
Abstract
Evidence from different sources shows that spouses' retirement decisions are correlated. Retirement policies affecting individuals in couples are therefore also likely to affect behavior of their spouses. It is therefore important to account for joint features in modeling retirement. This paper studies a structural collective model of labor supply and retirement of both partners in a couple with interdependent preferences, imperfect knowledge of preferences of the spouse, and subjective expectations about the future. We propose a novel method to estimate preferences and the intra-household bargaining process, which relies on stated preferences data collected in the Health and Retirement Study. Respondents were asked to choose between hypothetical retirement trajectories describing the retirement ages and replacement rates of both spouses from three perspectives: considering their own preferences only, the preferences of their spouse only, or the most likely decision for the household. With these data, all model parameters are identified and potential sources of joint retirement can be disentangled. We find that males misperceive their wives' preferences, overestimating their disutility of work. Our estimates correct for this bias. They suggest that correlation in unobserved heterogeneity components of the partners' marginal utility of leisure explains a large share of joint retirement decisions. We also find significant positive complementarities in leisure, but this explains a much smaller part of joint retirement.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".