Income Inequality in Canada at the National and Subnational Levels 1982-2021
Bibliographic record
Abstract
In this paper we estimate the distribution of all national income in Canada, and five sub-regions, from 1982 to 2021. We apply distributional national accounts (DINA) methodology to tax tabulations, combined with national accounts data and survey data. Pre-tax and post-tax income data are analysed. We find that top income shares published by Statistics Canada tend to underestimate income inequality relative to top income shares calculated using DINA, as DINA account for people who do not file taxes and for undistributed capital income that is retained in corporations. In line with previous research, income inequality in Canada increased significantly from 1982 until the mid-2000s. From 1982 until 2000, the real income of the bottom 50% of Canadians stagnated while that of the top 0.01% quadrupled. Since the mid-2000s, income inequality has decreased slightly although it remains far above the levels observed in the early 1980s. Across Canadian provinces, Ontario has consistently had higher inequality than Quebec although the gap has closed in recent years. Quebec has the most progressive tax and transfer system of the six sub-regions. In Alberta, record levels of inequality were reached in the mid-2000s and these appear to have been a significant driver of the national peak in inequality during this period. Post-tax income inequality initially fell during the pandemic because large temporary transfer programs were introduced. However, pre-tax income inequality increased, especially in 2021 when record levels of corporate profits were reached.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".