Top-End Progressivity and Federal Tax Preferences in Canada: Estimates from Personal Income Tax Data
Bibliographic record
Abstract
This article presents a first step toward consideration of how tax preferences may affect the progressivity of the Canadian federal personal income tax system at the top end. The authors consider 60 tax expenditures listed by the Department of Finance and use taxfiler data to attribute the shares to the top 1 percent, top 0.1 percent, and top 0.01 percent of income recipients. The Department of Finance estimates are made under the assumption of no behavioural change. The authors relax this slightly by assuming that behavioural change does not vary by income group. They define a tax preference as top-end progressive if the share of the preference's benefits received by top income recipients is less than their income share. Most tax expenditures are estimated to be top-end progressive except, as expected, those involving capital income and stock options. Similar findings hold for an alternative definition of top-end progressive based on tax payments. The results are consistent with those for the United States by Nguyen, Nunns, Toder, and Williams (2012) and Brown, Gale, and Looney (2012).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".