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Record W2104034039 · doi:10.1080/13545700701716649

Working for less? Women's part-time wage penalties across countries

2007· article· en· W2104034039 on OpenAlexaboutno aff
Elena Bardasi, Janet C. Gornick

Bibliographic record

VenueFeminist Economics · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
FundersEuropean CommissionUniversity of Pennsylvania
KeywordsWageEconomicsDemographic economicsDistribution (mathematics)Full-timeLabour economicsPosition (finance)Differential (mechanical device)Economic growth

Abstract

fetched live from OpenAlex

This paper investigates wage gaps between part- and full-time women workers in six OECD countries in the mid-1990s. Using comparable micro-data from the Luxembourg Income Study (LIS), for Canada, Germany, Italy, Sweden, the UK, and the US, the paper first assesses cross-national variation in the direction, magnitude, and composition of the part-time/full-time wage differential. Then it analyzes variations across these countries in occupational segregation between part- and full-time workers. The paper finds a part-time wage penalty among women workers in all countries, except Sweden. Other than in Sweden, occupational differences between part- and full-time workers dominate the portion of the wage gap that is explained by observed differences between the two groups of workers. Across countries, the degree of occupational segregation between female part- and full-time workers is negatively correlated with the position of part-time workers' wages in the full-time wage distribution.

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)0.004
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.035
GPT teacher head0.250
Teacher spread0.215 · 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

Citations260
Published2007
Admission routes1
Has abstractyes

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