Post-Secondary Attendance by Parental Income in the U.S. and Canada: What Role for Financial Aid Policy? forthcoming in Canadian Journal of Economics
Bibliographic record
Abstract
We examine the extent to which tuition and need-based aid policies explain important differences in family income – post-secondary attendance relationships between Canada and the U.S. Using data from recent cohorts, we estimate substantially smaller attendance gaps by parental income in Canada relative to the U.S., even after controlling for family background, 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. Equalizing these policies across Canada and the U.S. would likely lead to a greater difference in income attendance gradients. Résumé Nous étudions les frais de scolarite ́ et l’aide financière afin d’expliquer d’importantes différences entre le Canada et les États-Unis quant a ̀ la relation entre le revenu parental et la fréquentation des études postsecondaires. Nous trouvons que les écarts entre les taux de fréquentation des jeunes adultes de différents niveaux de revenu familial sont considérablement plus faibles au Canada qu’aux États-Unis, et ce, même en tenant compte des acquis cognitifs, des effets fixes résidentiels, et d’autres caractéristiques familiales. Nous documentons aussi le fait que l’aide financière aux étudiants de famille a ̀ bas revenus est beaucoup plus généreuse aux États-Unis qu’au Canada. S’ils avaient les mêmes politiques d’aide financière, les différences entre les États-Unis et le Canada quant a ̀ la relation entre le revenu parental et les études postsecondaires seraient plus prononcées. 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".