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Record W2478613176 · doi:10.1016/s0194-3960(00)13004-5

The changing economic status of disabled women, 1982–1991 Trends and their determinants

2004· book-chapter· en· W2478613176 on OpenAlexaboutno aff
Robert Haveman, Karen Holden, Barbara Wolfe, Paul A. Smith, Kathryn Wilson

Bibliographic record

VenueResearch in human capital and development · 2004
Typebook-chapter
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyEarningsSocial securityDemographic economicsDisability insuranceSample (material)Economic securityEconomicsQuarter (Canadian coin)Poverty thresholdSocioeconomic statusSocioeconomicsDemographyEconomic growthGeographySociologyPopulation

Abstract

fetched live from OpenAlex

In this paper, we provide an assessment of the intertemporal economic well-being of a representative sample of females who became new Social Security Disability Insurance (SSDI) beneficiaries in 1982. We compare their economic circumstances over the 1982 to 1991 period with those of both disabled men who became new SSDI beneficiaries in 1982, and a matched sample of nondisabled females who had sufficient work experience for benefit eligibility should they have become disabled. In 1982, the new SSDI women beneficiaries were a relatively poor segment of U.S. society. One quarter of them lived in poverty, and 48 percent had incomes below 150 percent of the poverty line. Over the subsequent decade, some of those married in 1982 lost husbands and the income contributed by their husbands. Yet, as of 1991, over one half of these disabled women lived in families with income below 150 percent of the poverty line. Social Security benefits to disabled women have played an important, and growing, role in sustaining economic status. Nevertheless, the level of well-being of these women lies substantially below that of the comparison groups, and for some groups of the women, well-being trends were negative both absolutely and relative to the comparison groups. We statistically relate the poverty status of these new female recipients to sociodemographic factors that would be expected to contribute to low well-being, and simulate the effect of Social Security benefits in reducing poverty and replacing earnings. We suggest a number of SSDI-related policy changes that could, at low cost, reduce poverty among those women with the highest incidence 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)0.003
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 score0.345
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.235
GPT teacher head0.429
Teacher spread0.194 · 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

Citations19
Published2004
Admission routes1
Has abstractyes

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