Is Debt Relief as Good as Liquidity? The Impact of Prospective Student Debt on Post-Secondary Attendance among Low-Income Youth
Bibliographic record
Abstract
In this study, I estimate the impact of offering two large non-refundable grants to low-income Canadian youth on postsecondary attendance. The grants had two interesting features. First, they were clawed back from loans, thus reducing costs but providing no additional liquidity. Second, the grants were only available to students if parental income was below a fixed threshold. This sharp discontinuity in the offer of the grants provides for near ideal conditions to study their causal impact, closely mimicking random assignment. Despite the large size of the grants (up to $6, 000 or $7, 000), the fact that students were automatically assessed for the grants with their regular student loans application, and evidence that most Canadian youth are at least aware of non-refundable study grant opportunities, I find that the grants had no impact on postsecondary or university attendance. Some policy implications are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".