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Record W3124094117

Market work, housework and childcare: A time use approach

2018· preprint· en· W3124094117 on OpenAlexaff
Emanuela Cardia, Paul Gomme

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2018
Typepreprint
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsConcordia UniversityUniversité de Montréal
Fundersnot available
KeywordsCounterfactual thinkingFertilityEconomicsBaby boomWork (physics)Matching (statistics)Labour economicsTime allocationProduction (economics)Demographic economicsWagePopulationPsychologyMedicineMicroeconomicsDemographySociology
DOInot available

Abstract

fetched live from OpenAlex

Raising children takes considerable time, particularly for women. Yet, the role of childcare time has received scant attention in the macroeconomics literature. We develop a life-cycle model in which the time dimension of childcare plays a central role. An important contribution of the paper is estimation of the parameters of a childcare production function using data on primary and secondary childcare time as reported in the American Time Use Survey (2003--2015). The model does a better job matching the observed life-cycle patterns of womens' time use than a model without childcare. Our counterfactual experiments show that the increase in the relative wage of women since the 1960s is an important factor in the increase in womens' work time; changes in fertility associated with the baby boom play a smaller role, and changes in the price of durables are found to have a negligible effect. We consider the effects of cheaper daycare. Not surprisingly, this experiment leads to greater use of daycare and more time allocated to market work. A knock-on effect of cheaper daycare is a substantial decline in primary childcare time.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.038
GPT teacher head0.271
Teacher spread0.233 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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