Income Inequality and Income Taxation in Canada: Trends in the Census 1980-2005
Bibliographic record
Abstract
Faced with rising fiscal pressures and discontent over income inequality, many countries, Canada among them, are searching for remedies. Income tax systems offer an effective way of changing economic destinies, so it’s only natural for governments to regard tax policy as a panacea. The first step to a solution is to understand how income tax influences existing inequality. This paper provides an overview of trends in pre- and post-tax income distribution in Canada from 1980-2005, by drawing on a more comprehensive data source than those found in many existing studies — Canadian census data. The results are in broad agreement: money has been steadily accumulating in the top half of the income distribution since 1980, with the trend quickening after 1995. This is just as true for family after-tax incomes as it is for individual market incomes even after the impact of the income tax system is taken into account. Over the 25-year period studied, the Gini coefficient rose from 0.352 to 0.404 for pre-tax income, and from 0.312 to 0.349 for after-tax income, while the proportion of the increase undone by taxation fell to a low of 2 per cent after 1995, as the Canadian tax system became less redistributive. However, some progressive aspects remain. Improvements to refundable tax credits in the late 1990s led to a 20 per cent decline in the number of families falling under the Low-Income Cut-Off. Canada’s income tax system hasn’t kept pace with climbing pre-tax inequality, but it continues to be a useful aftertax equalizer for low-income families.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.029 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".