Foreign Holding Companies and the <scp>US</scp> Taxation of Foreign Earnings: Evidence from the Tax Increase Prevention and Reconciliation Act of 2005*
Bibliographic record
Abstract
ABSTRACT I analyze US multinationals' (MNCs) use of foreign holding companies in their organizational structures and the impact of holding companies on internal capital markets. The look‐thru rule in the Tax Increase Prevention and Reconciliation Act of 2005 (TIPRA) reduces the after‐tax cost of foreign intercompany financing transactions. I use TIPRA as a natural experimental setting to test whether a shift in US tax policy that reduces the cost of moving foreign capital increased firms' reliance on foreign holding company subsidiaries. I find that MNCs responded to TIPRA by creating more foreign holding companies. Furthermore, consistent with the policy objectives of TIPRA, I document that MNCs that rely on holding companies gained tax efficiencies in their post‐TIPRA foreign internal capital markets, reducing domestic taxation on foreign earnings and easing financial constraints. Overall, my results expand our understanding of foreign organizational structure decisions and their internal financing benefits. I contribute to the tax literature by documenting a response to TIPRA that sheds light on the growing complexity of foreign subsidiary ownership structures.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".