Bibliographic record
Abstract
Several recent proposals have advocated using the income tax system to collect user fees to help fund the health care system.While there is a considerable amount of research investigating both how individuals respond to tax incentives for employer provided health insurance and on the effects of user fees payable at the point of service on the use of health care services, there is limited evidence on how individuals respond to tax incentives when these are not realized until taxes are paid.This paper uses existing exemptions in the Canadian tax code that allow individuals to deduct the cost of health care or health insurance from their taxable income in order to identify the tax price elasticity of demand for health care when price changes are realized at the end of the tax year.Our results suggest that despite not realizing the tax benefit at the time of purchase, individuals are quite responsive to changes in the tax price of health care.Our elasticity estimates for a wide range of health care products are well within the range of traditional price elasticity estimates, including in particular our estimates for prescription drugs.We also find some evidence that suggests individuals trade off risk sharing through traditional insurance companies with risk sharing through the tax code.That is, as the tax price of health care decreases, individuals spend more on health care, but spend less on health insurance.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".