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Record W2140250405

Post-Secondary Attendance by Parental Income: Comparing the U.S. and Canada

2010· preprint· en· W2140250405 on OpenAlexaboutno aff
Philippe Belley, Marc Frenette, Lance Lochner

Bibliographic record

VenueEconstor (Econstor) · 2010
Typepreprint
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAttendanceEarningsDemographic economicsStudent loanEducational attainmentFamily incomeNational Longitudinal SurveysEconomicsLoanConsumption (sociology)Government (linguistics)Survey data collectionCohortEconomic growthFinanceSociologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

This paper makes three contributions to the literature on educational attainment gaps by family income.First, we conduct a parallel empirical analysis of the effects of parental income on postsecondary (PS) attendance for recent high school cohorts in both the U.S. and Canada using data from the 1997 Cohort of the National Longitudinal Survey of Youth and Youth in Transition Survey.We estimate substantially smaller PS attendance gaps by parental income in Canada relative to the U.S., even after controlling for family background and adolescent cognitive achievement.Second, we develop an intergenerational schooling choice model that sheds light on the role of four potentially important determinants of the family income -PS attendance gap: (i) borrowing constraints, (ii) a 'consumption value' of attending PS school, (iii) the earnings structure, and (iv) tuition policies and the structure of financial aid.Third, we document Canada -U.S. differences in financial returns to PS schooling, tuition policy, and financial aid, discussing the extent to which these differences contribute to the stronger family income -attendance relationship in the U.S. Most notably, we document the dependence of both non-repayable financial aid and government student loan access on parental income in both countries.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, 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 score0.211
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.282
Teacher spread0.261 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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