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Record W7113520488

Child Penalties in Canada

2023· article· en· W7113520488 on OpenAlexfundaboutno aff

Bibliographic record

VenueEconstor (Econstor) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
FundersFonds de Recherche du Québec - SantéSocial Sciences and Humanities Research Council of CanadaUniversity of WaterlooCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsEarningsSubsidyExploitLongitudinal dataDifferential (mechanical device)Longitudinal studyChild careMaternity leaveParental leaveDeveloped country
DOInot available

Abstract

fetched live from OpenAlex

Having children has a sizeable impact on women's labour outcomes, but not on men's. The differential effects of children by gender are referred to as child penalties, and are now documented in many countries. In this paper, we exploit the Longitudinal and International Study of Adults to estimate Canadian child penalties in both earnings and employment for a period going from five years before the birth of the first child to 10 years after. Using an event study methodology (Kleven et al., 2019a), we find large and persistent negative effects of parenthood for mothers, but not fathers. Mothers' earnings decrease by 49% the year of birth, with a penalty still at 34.3% 10 years after; the corresponding penalty in employment down 14.2%. We also document larger negative impacts of parenthood for women who had multiple children or those with a lower education level. We finally provide suggestive evidence that family policies such as parental leave and subsidized childcare may help reduce child penalties.

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 score0.039
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.252
Teacher spread0.234 · 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
Published2023
Admission routes2
Has abstractyes

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