Public Finance in the Real World: Through the Lens (Down the Rabbit Hole?) of Transfer Pricing
Bibliographic record
Abstract
The current lack of confidence in the international rules for taxing the global profits of multinational enterprises (MNEs) has three underlying causes: (1) tax rules are not universal or natural; (2) taxes must be practical, administrable, and collectible; and (3) tax policy is a domain where national sovereignty and multilateralism are both important and conflictual. As a result, in the real world of public finance, the principles and norms of international tax must be tempered with the need for practicality and respect for national sovereignty. Transfer pricing, which affects how an MNE's global profits are allocated among countries, provides a good illustration of the difficult problem of implementing public finance principles and norms in the real world. Criticisms of the arm's-length principle have led the Organisation for Economic Co-operation and Development to recommend formulary approaches to transfer pricing in the pillar 1 and 2 proposals of its base erosion and profit shifting project. Instead, we propose a solution that draws its inspiration from the distinction made by the International Centre for Settlement of Investment Disputes between "investment" and "trade" that underlies the four-factor Salini test: contribution, assets, risk, and duration. We argue that the Salini test provides useful insights into the conundrum of "source" and a way out of the current lack of confidence in the international tax system. Our work builds on, and pays homage to, Richard Bird's lifelong contributions to public finance.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.025 | 0.038 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".