The Transfer-Pricing Profit-Split Method After BEPS: Back to the Future
Bibliographic record
Abstract
In 2018, the Organisation for Economic Co-operation and Development/Group of Twenty (OECD/G20) Inclusive Framework on base erosion and profit shifting (BEPS): action 10 issued revised guidance on the transactional profit-split method. Regrettably, the revised guidance failed to provide the opportunity for the profit-split method to be more often the most appropriate transfer-pricing method. The revised guidance expressly states that the lack of comparable uncontrolled transactions, by itself, is not a basis for the use of the profit-split method. Under the former guidance, the profit-split method was used infrequently. In the revised guidance, the threshold requirements for the use of the profit-split method are still restrictive. Consequently, it is likely that the profit-split method will rarely be the most appropriate transfer-pricing method. Nevertheless, the residual profit-split method is being considered for BEPS action 1, on the taxation of the digital economy. Two of the proposals under pillar 1 of the Inclusive Framework's 2019 short policy note involve the use of the residual profit-split method to allocate profits. These proposals involve new profit allocation rules that go beyond the arm's-length principle.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".