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Record W7010516495

Impacts of universal early education on children's outcomes

2023· other· en· W7010516495 on OpenAlexaboutno aff

Bibliographic record

VenueAaltodoc (Aalto University) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsDisadvantagedCounterfactual thinkingSocioeconomic statusSubsidyAffect (linguistics)Early childhoodEarly childhood educationCognitive developmentChild care
DOInot available

Abstract

fetched live from OpenAlex

Early childhood education is high on the policy agenda in many countries, with growing interest in making subsidized child care universally accessible to children from all backgrounds. Proponents see universal early education as an equitable means to promote child development and support parental employment, but the costs are high and may exceed the benefits for advantaged children. This thesis reviews empirical economics literature aiming to estimate causal impacts of universal child care programs on children’s outcomes. Findings from programs in the United States, Canada, Norway, Denmark, and Germany indicate that universal child care can substantially affect children’s development and long-term socioeconomic success, either in the positive or negative direction. Significant results are found for behavioral and cognitive development, health, educational attainment, earnings, and crime. Variation in the effects across programs could be explained by differences in children’s background characteristics, in program type and quality, and in the counterfactual modes of care. Overall, universal early education appears to benefit disadvantaged children the most, pointing to its potentially equalizing role. Moreover, high-quality center-based care appears to show greater promise in improving children’s long-run prospects than family day care, especially when it displaces informal non-parental care as opposed to home care.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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: Other
Teacher disagreement score0.151
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.008

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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
Published2023
Admission routes1
Has abstractyes

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