Who uses early childhood education and care services? Comparing socioeconomic selection across five western policy contexts
Bibliographic record
Abstract
Abstract Growing evidence suggests that children’s participation in early childhood education and care (ECEC), especially center-based services, is associated with positive outcomes, particularly for children over one year of age and children of low socioeconomic backgrounds. This signals an important opportunity for reducing socioeconomic disparities in young children’s development. Many western countries have adopted policies to encourage maternal employment, facilitate ECEC service use, or both, often focusing on disadvantaged families. Yet few studies to date have tested the impact of these policies for reducing socioeconomic selection into ECEC. This study integrates data from five cohorts of children living in different western, high-income countries (UK, USA, Netherlands, Canada, and Norway; total N = 21,437). We compare participation rates and socioeconomic selection into ECEC across the different policy contexts in infancy (5–9 months) and early childhood (36–41 months). Policy environments where parents had access to at least 6 months of paid maternity/parental leave had lower ECEC participation in infancy but higher participation in early childhood. Higher participation rates were also associated with universal ECEC subsidies (i.e., not targeted to low-income families). In general, low income, low maternal education and having more than one child were associated with reduced use of ECEC. Selection effects related to low income and number of children were reduced in countries with universal ECEC subsidies when out-of-pocket fees were income-adjusted or reduced for subsequent children, respectively. Most socioeconomic selection effects were reduced in Norway, the only country to invest more than 1% of its GDP into early childhood. Nevertheless, low maternal education was consistently associated with reduced use of ECEC services across all countries. Among families using services however, there were few selection effects for the type of ECEC setting (center-based vs. non-center-based), particularly in early childhood. In sum, this comparative study suggests wide variations in ECEC participation that can be linked to the policy context, and highlights key policy elements which may reduce socioeconomic disparities in ECEC use.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".