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Record W2568924698

Long Term Educational Attainment of Private High School Students in Québec: Estimates of Treatment Effects from Longitudinal Data

2016· preprint· en· W2568924698 on OpenAlexaboutno aff
David Lapierre, Pierre Lefèbvre, Philip Merrigan

Bibliographic record

VenueEconstor (Econstor) · 2016
Typepreprint
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsGraduation (instrument)Educational attainmentDemographic economicsPanel dataSubsidyFixed effects modelAverage treatment effectLongitudinal studyAttendanceMatching (statistics)Selection biasOmitted-variable biasGovernment (linguistics)StatisticEconomicsEconometricsDemographyEstimatorPsychologyStatisticsEconomic growthSociologyMathematics
DOInot available

Abstract

fetched live from OpenAlex

Very few studies analyze the long-term educational effects of private secondary school attendance while controlling for socioeconomic status. In Québec, the second most populous Canadian province, twenty percent of students at this level are enrolled in private schools subsidized by the government that however sets a relatively low ceiling for the fees in exchange for subsidies. Selection bias arising from a host of factors, preclude simplistic comparisons of their educational results with those of their public sector peers. This study uses the first four longitudinal waves of the two cohorts from Statistics Canada?s Youth in Transition Survey (YITS) to estimate the average treatment on the treated effect of private school on the high school graduation rate within the expected number of years after starting high school (5), enrolment in postsecondary institutions at age 19, university enrolment at age 21 or more, university graduation at age 24 or more, and enrolment in a professional degree program. The econometric estimation of treatment effects is based on a particular entropy balancing algorithm with a large set of key balancing covariates. Results are validated by a simulation-based sensitivity analysis for matching estimators. We find large, positive, robust, and statistically significant effects of private schooling on almost all outcomes analyzed. Most results are not sensitive to simulations of omitted variable bias.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.343
Teacher spread0.311 · 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 designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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