Policy Forum: Is Accelerated Depreciation Good or Misguided Tax Policy?
Bibliographic record
Abstract
The authors examine the implications of Canada's response to the 2017 US tax reform. Canada's focus on accelerated tax depreciation will achieve lower marginal effective tax rates on capital for taxpaying companies, well below the US levels achieved with the Tax Cuts and Jobs Act that came into effect on January 1, 2018. By ignoring neutrality, the government offsets some of the potential gains by reducing the tax burden on capital, thereby failing to maximize efficiency gains from a better corporate tax system. Further, Canada's approach fails to respond to competitiveness effects of US reforms on corporate tax base erosion in Canada as companies shift profits to the United States. The low US tax rate on intangible income will draw certain functions to the United States. A more comprehensive approach to corporate tax reform, including some reduction in corporate income tax rates, would have been a preferable 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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.025 | 0.015 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".