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Record W4298331687 · doi:10.46692/9781847425256.012

Gender inequality in poverty in affluent nations: the role of single motherhood and the state

2001· other· en· W4298331687 on OpenAlexaboutno aff
Karen L. Christopher, Paula England, Sara McLanahan, Katherin Ross, Timothy M. Smeeding

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyInequalityGender inequalityState (computer science)Single mothersSociologyPolitical scienceDevelopment economicsDemographic economicsGender studiesEconomicsEconomic growthPsychologyMathematicsDevelopmental psychology

Abstract

fetched live from OpenAlex

Introduction Women have higher poverty rates than men in almost all societies (Casper et al, 1994). In this chapter, we compare modern nations on this dimension. We use the Luxembourg Income Study (LIS) to compare women’s and men’s poverty rates in eight Western industrialised countries circa the early 1990s: the United States, Australia, Canada, France, West Germany, the Netherlands, Sweden, and the United Kingdom. We define individuals to be in poverty if they live in households with incomes below half the median for their nation. We examine, for each country, the ratio of women’s to men’s poverty rate. We then use simple demographic simulation methods to estimate how this gender disparity is affected by how prevalent single motherhood is, and by state tax and transfer programmes that may particularly help households headed by women. Our guiding framework emphasises a web of interdependencies. Individuals rely on others (family members, employers, or the state) to obtain money and what it can buy. In addition, we have relationships with other people – as friends, spouses, employees, fellow citizens or neighbours – and in this we are reliant on the labour of those who reared these people. In this second emphasis, our analysis is inspired by feminist interrogation of who pays the costs of children (England and Folbre, 1999; Folbre, 1994a; 1994b). In this view, an important reason why more women than men are in poor households is because women are paying more of the costs of children than men. Folbre (1994a) argues that many members of society share in the benefits of children being brought up well. Most of us are dependent upon those who rear children for our ability to find caring friends, a spouse, trustworthy neighbours or employees. But we seldom recognise this dependency, and market mechanisms don’t get all the beneficiaries to pay the parents or others who reared children. Often when services have this ‘public good’ aspect, as for example with national defence or highways, the state steps in to socialise the costs. Many social welfare programmes, in effect, socialise some of the costs of rearing children. But states differ in how much they do this, and this may affect how much individual mothers bear the costs of children relative to individual fathers, and relative to male and female taxpayers.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.022
GPT teacher head0.275
Teacher spread0.253 · 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

Citations2
Published2001
Admission routes1
Has abstractyes

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