Income Inequality in Canada at the National and Subnational Levels 1982-2021
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it