The Revised Case of IP Regimes Under the GloBE Rules: A Canadian Perspective
Bibliographic record
Abstract
After action 5 of the base erosion and profit shifting (BEPS) project identified intellectual property (IP) regimes as harmful, the Organisation for Economic Co-operation and Development (OECD) proceeded to recommend that favourable tax treatment be available only under a nexus approach. More than 25 countries now offer a form of IP regime. The introduction of the global minimum tax may limit the effectiveness of IP regimes. The impact of this tax will vary with the circumstances, however. A comprehensive examination of the optimal alignment of innovation tax incentives for multinational enterprises (MNEs) must be undertaken under the GloBE rules. In this paper, the authors begin by providing an overview of the global context of IP regime adoption, and they summarize some research findings with respect to the effectiveness of this type of preferential tax regime in achieving the objectives set by governments. Next, they present examples to illustrate, in simple terms, some of the interactions that must be considered when these regimes are being designed in a Canadian context. From the MNEs' perspective, the impact of the interaction between the GloBE rules and research and development (R & D) incentives will depend on, among other things, (1) the location of the IP income, tangible assets, and R & D activities; (2) the proportion of IP income; and (3) the possibility of leveraging the corporate structure. From a tax policy perspective, the introduction of the GloBE rules will in some cases not alter the suitability of an IP-preferential regime when the effective tax rate remains above 15 percent. When the GloBE rules apply, however, the characteristics of the tax regime—for example, IP regime tax rates, R & D incentive mechanisms, and (most of all) the additive effect of the measures (federal plus provincial measures, and R & D credits plus IP deductions)—will alter the results. From the perspective of tax competition, the opportunity to adopt an IP regime also depends on the perceived risks of losing or deterring R & D activities now that IP regimes are related to local R & D activities and are widespread. Finally, the federal-provincial context complicates the design and the deployment of a tax policy that is aimed at stimulating innovation and that will require some form of negotiation, if not cooperation, with the implementation of a global minimum tax.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".