Child care policy and child care burden: Policy feedback effects and distributive implications of regulatory decisions
Bibliographic record
Abstract
The policy feedback literature highlights that the design of public policies can affect recipients’ experience of those policies and programs. In this paper, we examine the largely unexplored distributional implications of market-based early childhood education and care (ECEC) services. We present the results of a quasi-behavioral conjoint survey of 606 parents in the City of Toronto. Grouping parent respondents by income and access to public subsidies, we find evidence that access to public subsidies influences the ECEC preferences of lower income parents. We explore these findings with respect to how non-subsidized lower income parents experience the market for ECEC. We find evidence that non-subsidized lower income parents are more cost-conscious; this is likely to result in their using less well-regulated ECEC that is more variable in quality. In turning to less well-regulated care, the burden of performing oversight and quality assessments falls on these parents. However, our study finds that lower income non-subsidized parents report the least engagement with learning about ECEC, suggesting that they are likely to be the least able to effectively monitor their children’s care arrangements. We explore the implications of these findings regarding the effects of policy on vulnerable children’s access to high quality ECEC services.
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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 | 0.026 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".