The North American Free Trade Agreement: Impact on U.S. Local Economies and Employment Opportunities
Bibliographic record
Abstract
The North American Free Trade Agreement (NAFTA) was signed by President William Jefferson Clinton in 1992. Although NAFTA is a trade and tariff initiative it has profound consequences for labor and the U.S. economy. Since its implementation NAFTA has resulted in the permanent elimination of more than 766,000 employment opportunities for non-degree U.S. workers, compelled various manufacturing companies to relocate to Mexico, increased the trade deficit between the U.S. and Mexico on several occasions since 1994 and led to a decline in real wages among both U.S. and Mexican workers. While the goal of NAFTA is to reduce trade barriers between three nations, the United States, Canada and Mexico, the goal of this research endeavor is to determine the impact of NAFTA on local economies, employment opportunities in specific industries and on wages in the U.S. The premise behind this study is that reducing trade barriers increases competition among manufacturers in the three nations involved and forces U.S. manufacturers to reduce production costs in various ways. The reduction in trade barriers, particularly between the U.S. and Mexico, forces U.S. manufacturers to compete in the market place against a nation with poor labor standards and lower overhead. This clearly has profound consequences for U.S. workers, local economies and employment opportunities. This paper is an attempt to better understand and objectively assess the impact of NAFTA on U.S. workers. This paper relies on multiple statistical procedures including regression analysis to determine the strength of the relationship between the NAFTA legislation and key indicators specific to American workers and local economies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".