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Record W7016050758

A welfare analysis of universal childcare : lessons from a Canadian reform

2024· other· en· W7016050758 on OpenAlexfundaboutno aff

Bibliographic record

VenueWarwick Research Archive Portal (University of Warwick) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchToulouse School of Economics
KeywordsCounterfactual thinkingWelfareLiberian dollarGovernment (linguistics)Marginal utilityYield (engineering)Marginal valueWelfare reformValue (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

Leveraging the introduction of universal low-fee daycare in Québec in 1997, we assess the welfare effect of universal childcare provision. First, using novel data on local daycare coverage and a difference-in-differences design, we show that positive impacts on maternal labor supply and childcare use are greater in areas with larger daycare expansion, suggesting that childcare availability, not just affordability, drives these responses. We then estimate the policy's Marginal Value of Public Funds (MVPF), defined as the ratio of beneficiaries' utility gains to net governmental costs. Unlike the standard sufficient-statistics metric, which assumes a marginal change in fiscal policy, we quantify the beneficiaries' utility gains through a model of maternal labor supply and childcare choices. This allows us to relax the common marginal-policy assumption and to incorporate non-pecuniary benefits for parents. Our results indicate substantial welfare gains from universal policies, with approximately $3.5 of benefits per dollar of net government spending - over twice the amount captured by the sufficient-statistics metric. Counterfactual simulations suggest that allocating more resources to increasing availability, rather than improving affordability, could yield even larger social returns.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.540
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0190.008
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0370.003

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 head0.029
GPT teacher head0.292
Teacher spread0.263 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes2
Has abstractyes

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