NAFTA at 20: Misleading Charges and Positive Achievements
Bibliographic record
Abstract
The North American Free Trade Agreement (NAFTA) between the United States, Mexico, and Canada, which took effect 20 years ago, continues to face divided public opinion. Opponents of free trade agreements (FTAs) cite NAFTA as a job-killing precedent, while proponents argue that the economic gains from NAFTA have been considerable and unappreciated. This Policy Brief analyzes the record of NAFTA in order to clear the air so that the benefits and challenges of trade can be examined objectively. In the last 20 years, trade, investment, and economic interdependence among the three countries have grown dramatically. Nearly 2 million US jobs now depend on trade with Mexico. Closer integration with the United States and Canada has transformed Mexico's auto industry from a minor backwater into a major automotive powerhouse. The analysis presented here argues that increased trade with Mexico led to some US job losses during adjustments but that these were very small compared to the usual churn and to job losses due to other factors over the same period. The pact contributed some to wage losses in manufacturing but not to any lasting and significant increase in US unemployment. Also contrary to what opponents predicted, NAFTA did not encourage more illegal immigration to the United States. Above all, NAFTA created a new foundation for US-Mexican relations by facilitating Mexico's transition to a multiparty political state with a market-oriented system.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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; a candidate call from one teacher head, 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".