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Record W2988713146 · doi:10.1177/0260107919874249

The Gender Gap in Market Work Hours Among Canadians: Examining Essential(ist) Linkages to Parenting Time and Household Labour Hours

2019· article· en· W2988713146 on OpenAlexaffabout
Tom Buchanan, Adian McFarlane, Anupam Das

Bibliographic record

VenueJournal of Interdisciplinary Economics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsThe King's UniversityWestern UniversityMount Royal University
Fundersnot available
KeywordsGender gapGender inequalityInequalityWork (physics)EconomicsDemographic economicsGender pay gapDomestic workLabour economicsMarriage marketPaid workWork hoursTime-use surveyChild labourWorking hoursWage

Abstract

fetched live from OpenAlex

Using 2015 Canadian time diary data, we analyse how the gender gap in market work hours is linked to gender inequality in parenting time and household labour hours (N = 2,296). Among Canadians who are 15–34 years of age, we examine three family groupings, single without children, married without children and married with children. For the married with children group, we focus on respondents with at least one child aged 0–4 years. We find that the gender gap in market work is not significant for those single and married without children. For the married without children group, a gender gap exists for household labour. This suggests that a gender gap in household labour exists prior to the onset of children. As expected, a large gender gap in market work presents itself for married/common law respondents with young children. Half of the gender gap in market work is explained by household labour hours and parenting time. Our study demonstrates that time allocations contribute substantively to gender inequality in market work. Yet, the large unexplained part of the gap suggests that this issue is larger and more complex than mere bargaining decisions about domestic and market time. JEL: I24, J13, J16, C10

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.045
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.271
Teacher spread0.247 · 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 teacher head, 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

Citations4
Published2019
Admission routes2
Has abstractyes

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