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Record W4214775385 · doi:10.1177/01640275211046322

My Wife Is My Insurance Policy: Household Bargaining and Couples’ Purchase of Long-Term Care Insurance

2022· article· en· W4214775385 on OpenAlexaff
Sharon Tennyson, Hae Kyung Yang, Frances Woolley

Bibliographic record

VenueResearch on Aging · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsSpouseLong-term care insuranceWifeBargaining powerAsset (computer security)WelfareDemographic economicsBusinessTest (biology)Term (time)EconomicsActuarial scienceHealth insuranceLabour economicsHealth careLong-term careMicroeconomicsNursingMedicineEconomic growth

Abstract

fetched live from OpenAlex

This paper examines household decisions over long-term care insurance (LTCI) purchases through a bargaining lens. Long-term care insurance purchase is a discrete decision around which spouses' interests may diverge substantially. The cost of buying LTCI is typically borne by both spouses, but the benefits of LTCI go disproportionately to women, who are more likely to need long-term care for themselves, and to benefit from the asset protection and other support LTCI offers in the event their husband needs care. Using panel data on married couples ages 50-75 from the US Health and Retirement Study (HRS), we test and find support for the hypothesis that spouses' relative bargaining power is related to LTCI purchase decisions. In particular, when husbands have final say in household decisions, LTCI coverage is less likely. The findings suggest that spouse's relative bargaining power matters for health care choices and, therefore, for the welfare of older men and women.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.097
GPT teacher head0.399
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

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