Understanding Student Loan Repayment Problems: Evidence from the Canada Student Loans Program
Bibliographic record
Abstract
Almost a third of borrowers in the Canada Student Loans Program who recently left school experienced some form of loan repayment problem. There are various reasons for these repayment difficulties: lack of income, high debt amounts, or views on the importance of repayment. Understanding the extent to which these sources contribute to loan repayment problems can help policymakers design better student loan programs and devise effective repayment assistance schemes. For example, if repayment problems occur mostly due to poor post-schooling labor market outcomes, increased repayment enforcement might prove ineffective, whereas making loan limits contingent on expected post-schooling incomes might reduce problematic loans in creditors ’ portfolios. In order to explore the causes of student loan repayment problems, we exploit data from a unique survey, which was specifically designed for this purpose, alongside matched records from the administrative data of the Canada Student Loan Program. We find that post-schooling income, outstanding debt amounts and perceptions about the importance of repayment are all significant contributors to repayment problems. For example, we find that borrowers with a yearly income less than $20000 are 42 % more likely to have a repayment problem; and that borrowers who regard student loans as the least important
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".