MétaCan
Menu
Back to cohort
Record W4410561023 · doi:10.1215/00703370-11958785

The Effects of Extended Parental Benefits on Parents’ Employment and Earnings in Canada

2025· article· en· W4410561023 on OpenAlexaffabout
Youjin Choi, Rachel Margolis, Anders Holm

Bibliographic record

VenueDemography · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsWestern UniversityStatistics Canada
Fundersnot available
KeywordsEarningsParental leaveDemographic economicsSurvey of Income and Program ParticipationEconomicsLabour economics

Abstract

fetched live from OpenAlex

Paid parental benefits, with individually earmarked time for mothers and fathers, aim to promote gender equality in labor force participation, wages, and childcare. The Canadian province of Québec expanded parental benefits over and above the federal policy in 2006 with the Québec Parental Insurance Plan (QPIP), which introduced paid paternity leave and lower eligibility criteria as its key features. This policy aimed to increase gender equality by encouraging fathers to use parental benefits and expanding coverage to low-income parents. Using Canadian administrative data and exploiting the policy changes in 2006 as a natural experiment, we examine the effects of Québec's extended parental benefits policy on parents' employment and earnings over 10 years after the transition to parenthood. First, we find that fathers' use of parental benefits had positive long-run effects on mothers' and fathers' earnings 8-10 years after a first birth. Second, we find that among women with low earnings before the transition to parenthood, QPIP increased the likelihood of employment 1-7 years after a first birth. This article provides the first evidence that a policy dramatically expanding parental benefits and encouraging use among both parents can have long-term positive effects on parents' labor market outcomes.

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)0.000
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 score0.297
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.006
GPT teacher head0.233
Teacher spread0.227 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueDemographySame topicGender, Labor, and Family DynamicsFrench-language works237,207