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Job mobility in 1990s Britain: Does gender matter?

2004· book-chapter· en· W152286995 on OpenAlexaboutno aff
Alison L. Booth, Marco Francesconi

Bibliographic record

VenueResearch in labor economics · 2004
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
Fundersnot available
KeywordsLayoffDemographic economicsQuarter (Canadian coin)Promotion (chess)Labour economicsOccupational mobilityPsychologyNeglectSocial psychologyEconomicsPolitical scienceUnemploymentGeographyEconomic growth

Abstract

fetched live from OpenAlex

This chapter examines gender differences in intra-firm and inter-firm job changes, including worker-initiated and firm-initiated separations, for white full-time British workers over the period 1991-96. We document four main findings. First, job mobility is high for both men and women, with more than one quarter of the sample changing job each year. Second, the distinction between promotions, quits and layoffs is important, suggesting that studies that either aggregate worker-initiated and firm-initiated separations or neglect within-firm mobility may provide an inappropriate picture of career mobility. Third, we find that the average male and female quit and promotion probabilities are remarkably similar, but there are significant gender differences in layoff probabilities. Fourth, we find significant gender differences in the impact of variables such as union coverage, occupation and presence of young children.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.089
GPT teacher head0.305
Teacher spread0.216 · 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

Citations56
Published2004
Admission routes1
Has abstractyes

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