US International Corporate Taxation after the Tax Cuts and Jobs Act
Bibliographic record
Abstract
The root dilemma that informs the past, present, and future of US international taxation is the tension between two desiderata: protecting the corporate tax base from erosion and ensuring the competitiveness of US multinational firms in the world economy. This article begins by exploring that tension, discussing the evidence behind these competing policy goals. It then considers the international tax provisions of the Tax Cuts and Jobs Act of 2017. TCJA enacted transformative changes in US corporate tax policy, but it did not resolve long held policy concerns. While research on TCJA is in early stages, evidence indicates that TCJA substantially reduced corporate tax revenues, that TCJA’s international provisions (as a whole) raised less revenue than expected, that offshoring and profit shifting remain large policy concerns, that changes in US multinational company competitiveness were mixed, and that underlying trends in wages and investment did not change due to TCJA. While TCJA was unable to resolve the tension between competitiveness and tax base protection, the Pillar 2 international tax agreement shows more promise in that regard. As countries throughout the world implement a “country-by-country” minimum tax on multinational income of 15 percent, this has the potential to disrupt long-standing arguments about international corporate taxation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".