Taxing data when the United States disagrees
Bibliographic record
Abstract
Abstract What is the best way to tax data-driven business models without contravening the existing global quasi-constitutionalist order on tax, trade, and investment law? A number of countries in Europe and around the world have begun imposing standalone digital services taxes pending multilateral agreement on a coordinated reform of bilateral income tax treaties (aka the OECD-G20/Inclusive Framework’s ‘Pillar 1’). But if Pillar 1 fails to materialise and countries go forward with unilateral digital services taxes, the United States and U.S. firms will likely seek redress using domestic measures as well as trade and investment treaties where applicable. This Article argues that in the face of such U.S. resistance, EU member states and countries elsewhere ought to reconsider using the income tax system to achieve their goals instead. We first review the events that led countries to avoid the income tax in favour of standalone taxes only to become embroiled in domestic U.S. trade policies. We then explain how European and other source jurisdictions for business services related to data collection, mining, and commercialisation could revisit the income tax to get to the same tax base, namely by taking an ambulatory interpretation approach to provisions in existing tax treaties in a way that renders possible to accommodate withholding taxes on those services. We show that an ambulatory interpretation approach could achieve the underlying goals of taxing data-driven businesses, in some cases even without any domestic law or treaty reform, with treaty-based rules for dispute resolution a ready tool to draw upon if and when the United States disagrees.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".