The Impact of Center-Based Childcare Attendance on Early Child Development: Evidence From the French Elfe Cohort
Bibliographic record
Abstract
Proponents of early childhood education and care programs cite evidence that high-quality center-based childcare has positive impacts on child development, particularly for disadvantaged children. However, much of this evidence stems from randomized evaluations of small-scale intensive programs based in the United States and other Anglo/English-speaking countries. Evidence is more mixed with respect to widespread or universal center-based childcare provision. In addition, most evidence is based on childcare experiences of 3- to 5-year-old children; less is known about the impact of center-based care in earlier childhood. The French context is particularly suited to such interrogation because the majority of French children who attend center-based care do so in high-quality, state-funded, state-regulated centers, known as crèches, and before age 3. We use data from a large, nationally representative French birth cohort, the Étude Longitudinale Français depuis l'Enfance (Elfe), and an instrumental variables strategy that leverages exogenous variation in both birth quarter and local crèche supply to estimate whether crèche attendance at age 1 has an impact on language, motor skills, and child behavior at age 2. Results indicate that crèche attendance has a positive impact on language skills, no impact on motor skills, and a negative impact on behavior. Moreover, the positive impact on language skills is particularly concentrated among disadvantaged children. This implies that facilitating increased crèche access among disadvantaged families may hold potential for decreasing early socioeconomic disparities in language development and, given the importance of early development for later-life outcomes, thereby have an impact on long-term population inequalities.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".