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Record W2131753306 · doi:10.1093/geronb/gbn045

The Income and Wealth Packages of Older Women in Cross-National Perspective

2009· article· en· W2131753306 on OpenAlexfundno aff
Janet C. Gornick, Eva Sierminska, Timothy M. Smeeding

Bibliographic record

VenueThe Journals of Gerontology Series B · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsnot available
FundersMemorial University of Newfoundland
KeywordsPerspective (graphical)Demographic economicsEconomicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

OBJECTIVES: We assess the income and wealth packages of older women's (age 65+ years) households and the extent to which low income is paired with low wealth, across a group of six high-income countries. METHODS: We use data on income and net worth from the Luxembourg Wealth Study, a new cross-national microdatabase. We define income poverty as having household income less than 50% of the national median and asset poverty as holding financial assets equivalent to less than 6 months of income at the poverty threshold. RESULTS: Older women typically have less income than do members of younger households at the national median, but their wealth holdings are generally much higher than their country's median wealth holdings. Older women's households in the United States report the highest net worth across these countries, in part because older American women have comparatively high rates of homeownership. However, American older women are also substantially more likely to be income poor. They also report high levels of asset poverty, as do women across all our comparison countries, with Sweden as a partial exception. DISCUSSION: Further research is needed to identify the most vulnerable subgroups, to integrate analyses of necessary expenditures, and to assess policy implications.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.365
Teacher spread0.344 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations57
Published2009
Admission routes1
Has abstractyes

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