Policy Forum: The Taxation of Capital Gains—Principles, Practice, and Directions for Reform
Bibliographic record
Abstract
The taxation of capital gains is particularly complex given their relatively infrequent receipt, the different ways in which they are generated, and worries about harming productivity. There are theoretical arguments in support of policies ranging from zero rates to high rates of tax on capital. In this article, the author first discusses the impact of capital gains on inequality, which often motivates discussions about how gains should be taxed. He then sets out the principles that determine how gains should be taxed—in particular, how the tax rate should relate to tax rates on labour income. The author proposes that capital gains tax rates should be equalized with income tax rates, subject to provisions to allow gains to be "smoothed" over time and to remove inflation from the tax base. He highlights key transitional issues in moving to such a tax structure. Finally, he discusses specific lessons for Canada.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.020 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".