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Record W2109101118 · doi:10.3386/w17218

Post-Secondary Attendance by Parental Income in the U.S. and Canada: What Role for Financial Aid Policy?

2011· preprint· en· W2109101118 on OpenAlexafffundabout
Philippe Belley, Marc Frenette, Lance Lochner

Bibliographic record

VenueNational Bureau of Economic Research · 2011
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsMcMaster UniversityWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAttendanceEconomicsFinanceBusinessDemographic economicsEconomic growth

Abstract

fetched live from OpenAlex

This paper examines the implications of tuition and need-based financial aid policies for family income -post-secondary (PS) attendance relationships. We first conduct a parallel empirical analysis of the effects of parental income on 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, adolescent cognitive achievement, and local residence fixed effects. We next document that U.S. public tuition and financial aid policies are actually more generous to low-income youth than are Canadian policies. By contrast, Canada offers more generous aid to middle-class youth than does the U.S. These findings suggest that the much stronger family income -PS attendance relationship in the U.S. is not driven by differences in the need-based nature of financial aid policies. Based on previous estimates of the effects of tuition and aid on PS attendance, we consider how much stronger income -attendance relationships would be in the absence of need-based aid and how much additional aid would need to be offered to lower income families to eliminate existing income -attendance gaps entirely.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.076
GPT teacher head0.367
Teacher spread0.291 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations18
Published2011
Admission routes3
Has abstractyes

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