Lower Tax on Innovation Output: The Uneasy Case Against an Innovation Box in Canada
Bibliographic record
Abstract
The innovation box (also known as a patent box) offers a reduced rate of tax for firms that create and commercialize certain forms of intellectual property (IP). The innovation box has been justified on the basis that it discourages firms from relocating valuable intellectual property to lesser taxed jurisdictions, and also that it encourages real activity including R&D and related commercialization. Some authors have argued that the Canadian government should implement an innovation box. In the 2022 Federal Budget, the federal government indicated that it would study whether an innovation box would be beneficial for Canada. This thesis investigates the arguments offered in support of implementing an innovation box in Canada. The thesis first explores the argument that the innovation would discourage firms from migrating IP income outside of the country using profit shifting techniques. The thesis then explores the argument that the innovation box would lead to greater R&D and related commercialization. These arguments are explored using several sources including a unique database of patent application for a sample of the top Canadian headquartered R&D performers in Canada (with custom computer code), corporate tax data obtained from the Canada Revenue Agency and an informal interview with the leader of international tax with a “big 4” accounting firm. In addition, numerous secondary sources were consulted including academic journals and government reports. The central claim of the thesis is that the innovation box would not offer any tangible benefit to Canada. There is little evidence that profit shifting using IP is a significant drain on the Canadian treasury. For various reasons, there is little reason to believe that an innovation box would encourage greater R&D or related commercialization in Canada.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".