An empirical analysis of intergovernmental tax interaction: the case of business income taxes in Canada
Bibliographic record
Abstract
Both federal and provincial governments in Canada levy corporate taxes on businesses in their jurisdictions, which potentially gives rise to horizontal and vertical tax externalities within the federation. Using a simple model of interdependent tax choices, we estimate tax‐setting functions for the federal government, Ontario, Quebec, and an aggregate of the remaining eight provinces. We find evidence of significant vertical and horizontal tax interactions. Provincial tax rates respond negatively to the federal tax rate, while at least some provinces increase their tax rates in response to increases in the tax rates of other provinces. JEL Classification: H25, H7 Une analyse empirique de l'interaction fiscale entre gouvernements: le cas des impôts sur les revenus d'affaires au Canada. Au Canada, les gouvernements fédéral et provinciaux collectent des impôts sur le revenu de sociétés sous leur jurisdiction. Voilà qui peut donner lieu à des effets externes horizontaux et verticaux à l'intérieur de la fédération. A l'aide d'un modèle de choix fiscaux interdépendants, les auteurs calibrent les fonctions d'imposition du gouvernement fédéral, des gouvernements du Québec et de l'Ontario, et d'une entité composée des huit autres provinces. On montre que les effets verticaux et horizontaux d'interaction fiscale sont significatifs. Les taux d'imposition des provinces réagissent négativement au taux d'imposition fédéral, mais certaines provinces accroissent leur taux d'imposition en réponse à des accroissements dans les taux d'imposition d'autres provinces.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".