Are U.S. Multinational Corporations Becoming More Aggressive Income Shifters?
Bibliographic record
Abstract
ABSTRACT This paper examines income shifting of U.S. multinational companies over the past two decades. Domestic and foreign policy makers are increasingly concerned with the effect of income shifting on dwindling tax revenues, however, extant research on income shifting by U.S. multinational enterprises is mixed. We address the disconnect between the academic literature and the policy maker's perceptions by examining the extent of multijurisdictional income shifting by U.S. multinational companies. We directly address conflicting results in extant literature and show that using either multiperiod proxies or instrumental variables overcomes weaknesses of annual proxies in this setting. Our tests show that U.S. companies have become more active at shifting income out of the United States as the regulatory costs of shifting have changed. Holding tax rate differences between U.S. and foreign jurisdictions constant, our empirical estimates suggest that our sample of 380 corporations with low average foreign tax rates collectively shifts approximately $10 billion of additional income out of the United States annually during 2005–2009 relative to 1998–2002 due to varying regulatory costs of shifting.
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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 | 0.001 | 0.003 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".