Save, Spend, or Give? A Model of Housing, Family Insurance, and Savings in Old Age
Bibliographic record
Abstract
Abstract How do housing and family shape the savings, spending, and inter-generational transfer behaviour of the elderly? Using the Health and Retirement Study, we document that inter-generational transfers to children are substantially backloaded, that homeowners dis-save much more slowly than renters but often sell their houses when entering a nursing home, and that care by children slows down nursing home entry and is linked to larger bequests, particularly of housing. To rationalise these facts, we develop a dynamic, non-cooperative model of the family with an indivisible housing asset and joint bargaining between elderly parents and their children over the housing and care arrangements of the parents. The model generates realistic savings and care choices and matches the timing of transfers and home liquidations. A key novelty is the housing-as-commitment channel: In the absence of long-run family contracts, housing provides a commitment device for more efficient savings. We find that this channel increases homeownership in old age by one-third and families’ willingness to pay for houses by 5–10%. This mechanism also facilitates informal care, slows down spending, and leads to larger bequests, implications that we support empirically.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".