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Record W2095905677 · doi:10.3138/jcfs.46.2.181

The Economic Gap Among Women in Time Spent on Housework in Former West Germany and Sweden

2015· article· en· W2095905677 on OpenAlexvenueno aff
Sanjiv Gupta, Marie Evertsson, Daniela Grunow, Magnus Nermo, Liana S Sayer

Bibliographic record

VenueJournal of Comparative Family Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsDecileEarningsDemographic economicsInequalityGender gapEconomicsGender inequalitySocioeconomic statusGender pay gapPopulationLabour economicsDemographySociology

Abstract

fetched live from OpenAlex

The quantitative scholarship on domestic labor has documented the existence of a gender gap in its performance in all countries for which data are available. Only recently have researchers begun to analyze economic disparities among women in their time spent doing housework, and their studies have been largely limited to the U.S. We extend this line of inquiry using data from two European countries, the former West Germany and Sweden. We estimate the “economic gap” in women’s housework time, which we define as the difference between the time spent by women at the lowest and highest deciles of their own earnings. We expect this gap to be smaller in Sweden given its celebrated success at reducing both gender and income inequality. Though Swedish women do spend less time on domestic labor, however, and though there is indeed less earnings inequality among them, the economic gap in their housework is only a little smaller than among women in the former West Germany. In both places, a significant negative association between women’s individual earnings and their housework time translates into economic gaps of more than 2.5 hours per week. Moreover, in both countries, women at the highest earnings decile experience a gender gap in housework that is smaller by about 4 hours per week compared to their counterparts at the lowest decile.

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 score0.112
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.122
GPT teacher head0.375
Teacher spread0.253 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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