Péréquation et comportement stratégique des provinces bénéficiaires : un contre-exemple intrigant
Bibliographic record
Abstract
The work of Boadway and Hayashi (2001) and Smart (2007) tends to confirm the hypothesis that provinces who are the beneficiaries of equalization payments adopt strategic behaviours that reduce their tax capacity and thus increase these payments. In this study, we analyze the impact that a new hydroelectricity royalty paid by Hydro-Quebec to the Quebec Treasury has on total equalization payments received by the province; to do this, we consider the equalization formulas used before 2004 and after 2007. This royalty, which generates approximately $600 million each year, reduces Quebec's equalization payments by just over $100 million, using either formula. Under the terms of the current equalization formula, Quebec loses 38 cents in equalization rights for each additional dollar of income received from natural resources. The new hydroelectricity royalty and the increase in the dividend rates applied to this government-owned corporation have made it possible for the Quebec government to benefit from a transfer of $1.15 billion; on the other hand it loses $437 million in equalization payments. In this study, we provide and describe an important counter example to the hypothesis concerning the strategic behaviour of provinces that benefit from an equalization scheme.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".