A welfare analysis of universal childcare : lessons from a Canadian reform
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".