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Record W2159949990 · doi:10.1017/s0144686x99007904

Poor health and retirement income: the Canadian case

2000· article· en· W2159949990 on OpenAlexaffabout
Lynn McDonald, Peter Donahue

Bibliographic record

VenueAgeing and Society · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPensionDemographic economicsDisadvantagedHealth and Retirement StudySocial securityDividendEconomicsHuman capitalGerontologyEconomic growthMedicine

Abstract

fetched live from OpenAlex

Using the 1994 Canadian General Social Survey, this study examines the economic effects of retiring because of poor health. When men and women who have retired for reasons of poor health are compared to those who have retired for other reasons, the health retirees are disadvantaged on measures of their health, on human capital variables, in terms of their work history, and ultimately, in their retirement income whether personal or household. The men who retired because of ill health were less likely to receive income from a private pension or from interest and dividends. Almost half of the men reported that their financial situation was worse since their retirement. The women retirees suffered from the same disadvantages as the men although their incomes in retirement were much lower. In the multivariate analyses, health had a significant and negative effect on men's household and personal incomes but there was no effect on the incomes of women. For them, any effect that poor health might have had on household income was offset by factors associated with marriage, and the women's own socio-demographic characteristics. The findings suggest reason for policy-makers to be cautious when contemplating blanket reductions in disability/invalidity and pension rates.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.116
GPT teacher head0.390
Teacher spread0.273 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations15
Published2000
Admission routes2
Has abstractyes

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