Public Redistribution and Inequality in a Period of Fiscal Consolidation: A Decomposition Analysis for Canada in the 1980s and 1990s
Bibliographic record
Abstract
This article documents the evolution of income inequality during Canada's major fiscal consolidation of the mid‐1980s to the mid‐1990s, one of the most spectacular fiscal turnarounds in recent economic history. Our main objective is to understand why overall market income inequality rose between 1986 and 1996, while that of disposable income did not. To analyse this question, we use data from the Survey of Consumer Finances and two distinct decomposition methodologies. Our results show that both the automatic stabilisation effect of transfer programmes and the rise in personal income taxes explain the performance of the Canadian tax and transfer system in offsetting market income inequality growth during the fiscal consolidation decade. Canada's fiscal consolidation episode, which placed more weight on rising taxes than on cuts in transfer programmes, was thus an inequality reducing factor.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".