How Does Transfer Pricing Risk Affect Premiums in Cross‐Border Mergers and Acquisitions?
Bibliographic record
Abstract
ABSTRACT This study investigates how transfer pricing risk affects the premiums in cross‐border mergers and acquisitions (M&A). Differences in the rigor of transfer pricing enforcement and the severity and clarity of rules across countries create differential risk of material costs for multinationals as they expand globally. We use 448 country‐level transfer pricing risk assessments by global transfer pricing partners and managers from two firms in 33 countries to develop a metric of country‐year transfer pricing risks. The resulting measure of transfer pricing risk is used to analyze the premiums of 3,103 cross‐border M&A from 2000 to 2012. We find that lower bid premiums are associated with higher transfer pricing risk in the target's country. We find the relation is stronger when expected future transfer pricing benefits are larger. Our results, consistent with the views of experts in the field, provide the first archival evidence that acquirers consider synergies created by future tax planning when estimating the value of a target.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".