Policy Forum: The Case Against Boutique Tax Credits and Similar Tax Expenditures
Bibliographic record
Abstract
One of the distinguishing features of the Harper Conservatives' tax policy was the enactment of a series of so-called boutique tax credits. Although the Canadian income tax was riddled with tax expenditures before the election of the Conservatives in 2006, a number of reasons can be identified that explain the Harper government's particular fondness for boutique tax credits. In addition to reviewing these explanations, this article defends three claims. First, boutique tax credits, along with almost all other tax expenditures, impair the legitimacy of the tax system and hobble it in the pursuit of its primary functions -- raising revenue and redistributing income. Second, analyzed as spending programs, which they are, tax expenditures, particularly boutique tax credits, violate almost every criterion of a well-designed spending program. Third, the enactment of tax expenditures circumvents almost all of the accountability and control mechanisms of sensible budgetary policy making and thus imperils democratic governance for the common good. The article suggests that all tax expenditures should be subjected to analysis by a commission of public policy experts, to rationalize the spending done through the tax system. This process would increase the integrity of the Canadian tax system, the efficiency and equity of government spending programs, and the transparency and vibrancy of Canadian democracy, and would spell the end of the unprincipled use of boutique tax credits.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.019 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.016 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".