Implementing Pillar Two: Potential Conflicts with Investment Treaties
Bibliographic record
Abstract
A key objective of pillar two is to coordinate a minimum 15 percent tax on the GloBE income of certain in-scope multinational enterprises. This objective has been driven by the Organisation for Economic Co-operation and Development and focuses on global cooperation and model rules to calculate and collect the proposed tax. Tax is also a key driver in investment decisions. By design or default, the pillar two rules will clash with the typical tax incentives offered by countries to attract foreign direct investment, including tax holidays, lower tax rates, exemptions, and accelerated depreciation regimes. Often these tax incentives are offered in investment treaties. These agreements offer a win-win solution in that they set out the minimum protections that investors may rely on when making an investment in the host state, backed up by the direct remedy of binding international arbitration if those protections are not provided. For the host state, the protections provided by an investment treaty encourage inbound cash flows, and for the investor's home state, they offer the hope of repatriated profits. Although no precise numbers can be offered, clearly the tax benefits provided by investment treaties will be affected by the pillar two rules. This paper outlines some of the potential conflicts between pillar two requirements and the protections provided in investment treaties. It also offers some preliminary solutions.
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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 | 0.046 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".