Policy Forum: The Real Concentration of Capital Gains in Canada—A Longitudinal Analysis
Bibliographic record
Abstract
It is generally believed that the preferential treatment of capital gains for personal income tax purposes benefits primarily high-income taxpayers. However, since traditional tax statistics include capital gains in the total income of individuals, some individuals see their annual income artificially inflated by the one-time realization of a capital gain. The authors use Statistics Canada's Longitudinal Administrative Databank to better identify who really benefits from the preferential treatment of capital gains in Canada and to determine their frequency of realization. Specifically, the authors measure the shift of taxpayers between five income groups depending on whether or not their capital gains are considered in their total income. While wealthier taxpayers do benefit disproportionately from the preferential treatment of capital gains, the exclusion of capital gains from total income significantly reduces this disproportion. Taxpayers with more modest incomes benefit more than one would expect from the preferential treatment of capital gains, especially when they are over 55 years old. Taxpayers with higher incomes also realize capital gains more frequently than those with lower incomes. That said, among taxable capital gain filers only, this gap is much smaller, indicating that once taxpayers report capital gains, they tend to do so frequently. Taxpayers who report capital gains with regularity remain a minority, although they realize a large share of the total value of capital gains.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".