Top income shares in Canada: recent trends and policy implications
Bibliographic record
Abstract
Abstract. According to Canadian taxfiler data, over the last thirty years there has been a surge in the income shares of the top 1%, top 0.1 % and top 0.01 % of income recipients, even with longitudinal smoothing by individual using three- or five-year moving averages. Top shares fell in 2008 and 2009, but only by a fraction of the overall surge. Alberta, British Columbia, and Ontario have much more pronounced surges than other provinces. Part of the Canadian surge is likely attributable toU.S. factors, but a comprehensive expla-nation remains elusive. Even so, I draw implications for policies that might achieve some support from across the political spectrum, including the elimination of tax preferences that favour those with high incomes, the promotion of shareholder democracy and, to maintain Canada’s relatively high intergenerational mobility, continued wide accessibility to healthcare and education. Portion des plus hauts revenus au Canada: tendances récentes et implications pour les politiques. Selon les dossiers des contribuables canadiens, il y a eu une brusque montée dans la part des revenus de ceux qui sont dans le premier percentile, et dans les segments 0,01 % et 0,001 % au sommet de la distribution des récipiendaires de revenus au cours des
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".