What You Don't Know Can't Help You: Lessons of Behavioural Economics for Tax-Based Student Aid
Bibliographic record
Abstract
Canada’s federal and provincial governments spend a lot of money subsidizing postsecondary students. Tuition and education/textbook tax credits, in particular, cost the federal government around $1.6 billion in 2012 – a sum much greater than the net cost of the Canada Student Loan Program. These credits lower dramatically the cost of attending postsecondary education. Unlike other programs that support postsecondary education, there has not been a formal evaluation of the effectiveness of these tax measures, but there is good reason to conclude that they are poor policy. The immediate benefits of the credits go disproportionately to students from relatively well-off families, who are not relatively sensitive to the costs of postsecondary education, with students from lower-income families benefiting from them only after they have finished their education and have enough taxable income to claim the credit. Lessons from economics and from more recent innovations in behavioural economics emphasize that flaws in the design of postsecondary tax credits mean that they are unlikely to have any effect on youths’ decisions to undertake or cope with the costs of postsecondary education. A simple change to the tax credits – making them refundable instead of non-refundable – would go a long way to making them more efficient and equitable. Whereas a non-refundable tax credit can’t reduce the amount of tax owed to less than zero, a refundable tax credit can reduce your tax below zero and provide a refund. This change would provide a more immediate benefit to students from low-income families who need it most.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".