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Record W2891194726 · doi:10.3386/w14176

School Competition and Efficiency with Publicly Funded Catholic Schools

2008· preprint· en· W2891194726 on OpenAlexafffundabout
David Card, Martin Dooley, A. Abigail Payne

Bibliographic record

VenueNational Bureau of Economic Research · 2008
Typepreprint
Languageen
FieldSocial Sciences
TopicAmerican Constitutional Law and Politics
Canadian institutionsMcMaster University
FundersMcMaster UniversityOntario Innovation Trust
KeywordsCompetition (biology)Mathematics educationPolitical sciencePsychologyBiology

Abstract

fetched live from OpenAlex

The province of Ontario has two publicly funded school systems: secular schools (known as public schools) that are open to all students, and separate schools that are open to children with Catholic backgrounds. The systems are administered independently and receive equal funding per student. In this paper we use detailed school and student-level data to assess whether competition between the systems leads to improved efficiency. Building on a simple model of school choice, we argue that incentives for effort will be greater in areas where there are more Catholic families, and where these families are less committed to a particular system. To measure the local determinants of cross-system competition we study the effects of school openings on enrollment growth at nearby elementary schools. We find significant cross-system responses to school openings, with a magnitude that is proportional to the fraction of Catholics in the area, and is higher in more rapidly growing areas. We then test whether schools that face greater cross-system competition have higher productivity, as measured by test score gains between 3rd and 6th grade. We estimate a statistically significant but modest-sized impact of potential competition on the growth rate of student achievement. The estimates suggest that extending competition to all students would raise average test scores in 6th grade by 6-8% of a standard deviation.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.235
GPT teacher head0.487
Teacher spread0.252 · 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; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations44
Published2008
Admission routes3
Has abstractyes

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