Development and Effectiveness of Controlled-Foreign-Company Rules : Empirical evidence from European multinational companies
Bibliographic record
Abstract
This thesis studies the development of CFC rules and assesses the effect that \nCFC rules have on capital structure decisions of MNCs. CFC rules are an anti-taxavoidance \nmeasure that aims to prevent profit shifting. If CFC rules are applied, income \nof a foreign affiliate is added to the tax base of the parent and, therefore, taxed at the \ntax rate of the parent’s country of residence. \nFirst, we review the development of CFC regimes in Europe, the US, and \nCanada (2000 - 2015). Second, we create a panel data set of European companies with \nparents headquartered in Europe, the US, or Canada (2004 - 2015). This data set, which \ncontains financial and historical ownership data that is obtained from Amadeus and \nOrbis databases, respectively, is further used in econometric analysis. \nOur empirical analysis suggests that a parent country’s CFC rules have a \nnegative effect on an affiliate’s total debt-to-asset ratio and an increase in the strictness \nof CFC rules is associated with a further decrease in leverage. These findings also hold \nwhen we control for thin-capitalization rules and transfer pricing rules. Therefore, it \ncan be argued that CFC rules make internal lending as a profit shifting channel less \nattractive for MNCs. Furthermore, the results suggest that also thin-capitalization rules \nand transfer pricing rules are effective in limiting profit shifting activities by European \nMNCs. \nWe find that since 2006, when the European Court of Justice issued a landmark \ndecision in the Cadbury-Schweppes (C-196/04) case, the negative effect of CFC rules \non an affiliate’s leverage has weakened. Nevertheless, we argue that the role of CFC \nrules in corporate decision making should not be disregarded.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".