The Limits of the International Tax Regime as a Commitment Projector
Bibliographic record
Abstract
As explained by Ronald Coase, transaction costs are the costs associated with discerning a price on a given exchange. This article conceptualizes the international tax regime as a political and legal system striving to address transaction cost challenges, and claims it has an uneven record. On the one hand, the international tax regime lowers transaction costs and hence promotes global economic growth. It does this by facilitating credible government commitments to ensure that the same cross-border profits are not taxed twice by two countries. Multinational firms are thus protected against the risk that their cross-border activities will be unduly deterred by taxation, which encourages more global economic activities.On the other hand, governments are unable to offer credible commitments that they can effectively address other important international tax policy concerns. First, despite ongoing reform efforts governments are not able to offer reasonably reliable promises that they will inhibit aggressive international tax planning that dilutes revenues in countries like the United States. Second, the international tax regime affords governments opportunities to develop their own policy solutions (such as the 2010 U.S. anti-tax evasion initiative to create a global tax information reporting system through the Foreign Account Tax Compliance Act) and thus governments can renege on earlier promises to abide by traditional international tax norms.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".