Running Title: The Relationship between Tax and Incentive Transfer Prices
Bibliographic record
Abstract
* We wish to thank Deloitte transfer pricing specialists in Toronto and Sydney and seminar participants at the Australian Graduate School of Management for their valuable comments. We are also grateful for many detailed and constructive comments from the coeditor and two referees. The usual disclaimer applies. Keeping Two Sets of Books: The Relationship between Tax & Incentive Transfer Prices This paper studies two distinct roles that transfer prices play within multinational enterprises operating in two tax jurisdictions. Assuming that the multinational enterprise chooses one transfer price for tax purpose and another for providing incentives to its subsidiary’s manager, we analyze the relationship between these two transfer prices. The two transfer prices are independent if taxable income is assessed based on the formula apportionment approach in both jurisdictions. Under the separate entity approach, however, they are interdependent: they both decrease as the expected penalty for noncompliance with the arm’s length principle increases; the tax transfer price decreases and the incentive transfer price increases as the marginal cost of production increases. We also study how the relationship changes depending on whether the incentive transfer price is negotiated or dictated by the parent company. Our results are shown to be robust to different market and tax environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.087 | 0.004 |
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".