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Record W3124522969 · doi:10.1111/caje.12250

Male wage inequality and marital dissolution: Is there a link?

2017· article· en· W3124522969 on OpenAlexaffvenue
Andriana Bellou

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRemarriageEconomicsInequalityWageWage dispersionDemographic economicsWage inequalityLabour economicsDispersion (optics)Economic inequalityEfficiency wageSociologyMathematics

Abstract

fetched live from OpenAlex

Abstract After almost a century‐long pattern of rising marital instability, divorce rates levelled off in 1980 and have been declining ever since. The timing of deceleration and decline in the rates of marital dissolution interestingly coincides with a period of substantial growth in wage inequality. This paper establishes a novel connection between the two phenomena and discusses potential explanations for the underlying link. Using female marital histories in a duration analysis framework combined with regional and temporal variation in the pattern of male wage dispersion, I show that inequality has a significant stabilizing effect on the marriage. Quantitatively, increases in male wage dispersion can roughly explain up to 30% of the fall in the mean separation probability between 1979 and 1990. Several plausible explanations are discussed: changes in spousal labour supplies, female wage inequality, income uncertainty, social capital as well as a hypothesis where inequality renders the option to divorce less attractive by making remarriage more difficult.

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)0.005
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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.130
GPT teacher head0.231
Teacher spread0.101 · 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

Citations11
Published2017
Admission routes2
Has abstractyes

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