A Model of Multinational Income Shifting and an Application to Tax Planning with E-Commerce
Bibliographic record
Abstract
ABSTRACT: This manuscript develops an investment model that incorporates the joint consideration of income shifting by multinational parents to or from a foreign subsidiary and the decision to repatriate or reinvest foreign earnings. The model demonstrates that, while there is always an incentive to shift income into the U.S. from high-foreign-tax-rate subsidiaries, income shifting out of the U.S. to low-tax-rate countries occurs only under certain conditions. The model explicitly shows how the firms' required rate of return for foreign investments affects both repatriation and income shifting decisions. We show how the model can be used to refine extant research. We then apply it to a novel setting—using e-commerce for tax planning. We find firms in manufacturing industries with high levels of e-commerce have economically significant lower cash effective tax rates. This effect is magnified for firms that are less likely to have taxable repatriations. JEL Classifications: G38, H25, H32, M41.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".