Marginal Tax Rates of Canadian Public Firms and the Applicability of Marginal Tax Rates in Corporate Decisions*
Bibliographic record
Abstract
ABSTRACT While several US studies demonstrate the importance of using marginal tax rates (MTRs) to study corporate decisions and tax incentives, research using MTR in the Canadian setting is limited. The MTR literature has made several improvements on the estimation process using US firms, but these advances have not been made available for Canadian firms. In this study, we first incorporate the improved MTR simulation procedure based on the US literature for 19,551 firm‐years of publicly listed nonfinancial Canadian firms from 2006 to 2021. We then explicitly test the relation between our simulated MTR and two corporate decisions: debt policy and investment choice. We find that only our simulated MTR shows a positive and statistically significant relation with debt, while other tax rate proxies, including the trichotomous measures, fail to show this theorized relation. We find that both MTR and statutory tax rate show a significantly negative relation with investment, and the negative relation is mitigated by accelerated tax depreciation policies.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".