Who Benefits from Inconsistent Multinational Tax Transfer‐Pricing Rules?*
Bibliographic record
Abstract
Abstract This paper uses a strategic tax compliance model to examine taxpayer reporting and tax authority audit strategies in an international setting with two tax authorities. The setting features both information asymmetry between the taxpayer and the tax authorities and inconsistent tax transfer‐pricing rules. The latter creates the possibility of each country trying to tax the same income. We study the effect of the probability of transfer‐price rule inconsistency on the strategies and payoffs of the taxpayer and the tax authorities. We find that an increase in the probability of transfer‐price rule inconsistency induces more aggressive auditing by governments. It therefore deters taxpayers from shifting income to the country with the lower tax rate in situations in which the transfer‐pricing rules are consistent, and can either increase or decrease the income reported to the low‐tax‐rate country in cases in which the transfer‐pricing rules are inconsistent. We find that an increase in transfer‐price rule inconsistency could either increase or decrease the taxpayer's expected tax liability and could either increase or decrease the deadweight loss from auditing. Our results call into question the conventional wisdom that the prospect of double taxation due to transfer‐price rule inconsistency increases a firm's expected tax liability and governments' expected audit costs.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".