Developing an International Tax Policy Strategy for NAFTA Countries
Bibliographic record
Abstract
On January 1, 1994, Canada, the United States and Mexico formed the North American Free Trade Agreement (NAFTA) to promote their economic interests by lowering barriers to international trade and investment. A concern exists that national tax differences harm or inhibit cross-border investment flows. Yet NAFTA is almost silent with respect to taxation measures. For the most part, the tax treatment of cross-border flows remains governed by bilateral tax treaties negotiated between the NAFTA states. Why does NAFTA permit national tax differences to remain a barrier to cross-border trade investment flows? The answer lies in the unique place that tax policy plays in a nation's fortunes: taxation is intensely political. Governments are forced to balance the political costs of ceding control over national tax policymaking with their desire to secure heightened economic efficiency. This Article explores how the tensions inherent in globalization play out with respect to potential tax reform efforts under NAFTA. Part I discusses problems created by the different North American tax regimes vis a vis cross-border transactions. Part II turns to Europe to see whether North Americans can draw useful lessons from the European experience with cross-border tax reform efforts. Part III asserts that, given the current political/institutional/economic environment within North America, the appropriate international policy strategy is one of heightened multilateral coordination among the NAFTA countries.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".