United States Manufacturing Direct Investment and Trade: The Case of Canada and Mexico under NAFTA and Earlier Trade Liberalization Measures
Bibliographic record
Abstract
This paper examines the apparent impact of the formation of the North American Free Trade Agreement (NAFTA) and earlier relevant trade liberalization measures on U.S. manufacturing direct investment and trade vis-¨¤-vis Canada and Mexico. Employing a maximum likelihood regression approach that focuses on the relationship between U.S. manufacturing direct investment in Canada and Mexico and its manufacturing trade with each of these countries, as well as the real GDP of each country, serving as a ¡°gravity model¡± proxy variable, empirical results are presented for the period 1989-2013. The results suggest that the process of regional economic integration in North America, with its concomitant relaxation of trade barriers, has served to modify what one would otherwise expect on the basis of the conceptual frameworks and scenarios that have thus far been developed in the literature with regard to the relationship of foreign direct investment to international trade. With regard to the expected relevance of gravity-type influences on U.S. direct manufacturing investment in its two NAFTA partners, for Canada these influences appear to be confirmed, but not so for Mexico, owing to the close interconnection between U.S. manufacturing direct investment in Mexico and its Mexican manufacturing trade.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".