The Insurance Implications of Government Student Loan Repayment Schemes
Bibliographic record
Abstract
We use new administrative data that links detailed information on Canadian student loan recipients with their repayment and income histories from the Canada Student Loans Program (CSLP), income tax filings, and post-secondary schooling records to measure the extent to which student borrowers adjust loan repayments to insure against income variation.Several mechanisms are available for students to adjust loan repayments in response to income fluctuations: formal, like CSLP's Repayment Assistance Plan; and informal, such as delinquency or default.Borrowers can also make larger payments than required should they experience unexpectedly high income.Indeed, loan payments are shown to increase in income, more so in early years and for individuals with higher initial debt.More formally, we estimate that on average, an unexpected $1,000 change in year-over-year income is associated with a $30 change in loan payment: from a $50 change the year after graduation, declining to a $20 change 5 years after graduation.Loan repayments are also used to absorb income variation that is more permanent in nature: for borrowers whose income is consistently below or above expected income at graduation, the magnitude of average repayment adjustment is similar to the average yearly response.
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".