The tale of the tails: Canadian income inequality in the 1980s and 1990s
Bibliographic record
Abstract
Abstract. We present new evidence on levels and trends in after‐tax income inequality in Canada between 1980 and 2000. We argue that existing data sources may miss changes in the tails of the income distribution, and that many of the changes in the income distribution have been in the tails. For this reason, we turn to an alternative source. In particular, we construct data on after‐tax and transfer income using Census files augmented with predicted taxes based on information available from administrative tax data. Using these data, we find that Canadian after‐tax inequality levels are substantially higher than has been previously recognized, primarily because income levels are lower at the bottom of the distribution than in commonly used survey data. We also find larger long‐term increases in after‐tax income inequality and far more variability over the economic cycle. This raises interesting questions about the role of the tax and transfer system in mitigating both trends and fluctuations in market income inequality.
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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 | 0.005 | 0.011 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".