Formulary Apportionment and Group Taxation in the European Union: Insights from the United States and Canada
Bibliographic record
Abstract
In 2001, the European Commission endorsed a future company tax strategy that would allow EU companies the option of calculating their EU profits on a common consolidated tax base and allow Member States to tax their share of that base at national rates. Implementing this strategy requires developing a formula to distribute the common tax base across the Member States. Although EU Member States currently do not use formulary methods to distribute a common consolidate tax base across national boundaries, Canada and the United States have extensive experience using formulary methods to distribute income across sub-national boundaries. Thus, the European Union can turn to North America to gain valuable insights into the design of a formulary apportionment system with common base taxation. This paper evaluates key issues that may arise when implementing common consolidated base taxation with formulary apportionment in the EU. These issues include the formula design, the definition of the company group and the definition and scope of the tax base. The paper also discusses potential economic consequences that may arise and suggests a potential apportionment system for the European Union.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".