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Record W2092494375 · doi:10.1080/00036840600949330

Evidence on the dynamics of unemployment by gender

2008· article· en· W2092494375 on OpenAlexaboutno aff
Hervé Queneau, Amit Sen

Bibliographic record

VenueApplied Economics · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentUnit rootEconomicsPersistence (discontinuity)HysteresisNatural rate of unemploymentEmpirical evidenceDemographic economicsUnit (ring theory)Dynamics (music)Labour economicsUnemployment rateEconometricsMacroeconomicsPsychology

Abstract

fetched live from OpenAlex

We present empirical evidence regarding unemployment dynamics for women and men in eight OECD countries. Unit-root tests are used to examine the unemployment dynamics of women and men. Failure to reject the unit-root hypothesis is consistent with unemployment hysteresis. Rejection of the unit-root hypothesis indicates that unemployment dynamics are best explained by the natural rate of unemployment or the structuralist view. We find evidence of gender differences in unemployment dynamics in Canada, Germany and the US, but not in other countries. While there are some differences in the extent of persistence across gender and across countries, the degree of persistence for both female and male unemployment rates is fairly low in all countries. Our results, therefore, contrast with substantial empirical evidence of high levels of unemployment persistence in European countries.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.077
GPT teacher head0.222
Teacher spread0.145 · 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

Citations29
Published2008
Admission routes1
Has abstractyes

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