Trade in Technology Within the Free Trade Zone: The Impact of the WTO Agreement, NAFTA, and Tax Treaties on the NAFTA Signatories
Bibliographic record
Abstract
Trade in technology and related services has assumed a major role both on the world trade agenda and within the NAFTA block. The success of such cross-border trade is dependent upon three factors: protection of intellectual property,' access to foreign markets by service providers, and a minimized risk of double taxation. Each of these factors is impacted by national laws, multinational conventions, and bilateral tax treaties. The last decade has witnessed an explosion of such legislation and agreements. This article focuses on Canada, Mexico and the United States, and explores the World Trade Organization Agreement ("WTO Agreement"), the North American Free Trade Agreement ("NAFTA"), and the bilateral tax treaties entered into by Canada, Mexico, and the United States, the three NAFTA signatories, and analyzes both their impact and interaction on cross-border trade in technology and related services. The purpose of this article is to provide a framework for understanding the international environment in which trade in technology occurs within the NAFTA block. Its goal is to provide sufficient information to allow advisors to effectively plan for and structure such cross-border arrangements. Part II begins with a discussion of the multinational agreements entered into by Canada, Mexico, and the United States that effect cross-border trade in intellectual property and related services, and then focuses on the WTO Agreement and NAFTA. Part I examines the manner in which the WTO Agreement and NAFTA interact with the bilateral tax treaties entered into by the three NAFTA signatories. Part IV analyzes the effect of tax treaties on income generated in the cross-border trade in technology and related services, and highlights the significant differences in treatment among the NAFTA partners. Finally, Part V offers some recommendations and conclusions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".