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Record W3122326121

Increased paid maternitiy leave and children's development measured at age four to five. An empirical analysis

2011· preprint· en· W3122326121 on OpenAlexaboutno aff
Catherine Haeck

Bibliographic record

VenueLirias (KU Leuven) · 2011
Typepreprint
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsObservational studyPsychologyMatching (statistics)Developmental psychologySelection biasDemographyMedicine
DOInot available

Abstract

fetched live from OpenAlex

Parental leave policies are often enacted based on the premise that children will bene.t from an extended period of time spent with their parent. A number of research studies have looked at the effect of maternal time investments on the early development of skills, behavioral well-being and health, but the results thus far are mixed and mainly based on multivariate analysis. This approach can often not eliminate selection bias and can rarely predict the sign and magnitude of the bias. In this paper, I evaluate the effect of extended maternal care on children’s development at age 4 to 5 using observational data prior to and after the Canadian parental leave reform, which extended total paid leave from 25 to 50 weeks on December 31st, 2000. Previous research exploiting this labor supply shock found that mothers significantly increased their time at home in the first year, but generally found no significant effects on parent-reported measures of development between age 7 and 24 months. For the first time in this literature, children of mothers receiving maternity leave benefits are identified and compared with all other children. Using matching difference-in-differences, I find that the policy change had positive effects on cognitive development, measured using different standardized tests for children aged 4 and 5. Behavioral development effects are mixed and mainly not significant. Effects on the family environment and parent-reported health measures are positive and significant.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.069
GPT teacher head0.306
Teacher spread0.236 · 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.

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
Published2011
Admission routes1
Has abstractyes

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