www.umich.edu/~rmstern / An Econometric Analysis of Trade Diversion under NAFTA *
Bibliographic record
Abstract
We provide an econometric analysis of whether or not the tariff preferences extended to Canada and Mexico under NAFTA may have resulted in trade diversion. A review of previous studies, both descriptive and econometric, suggests that trade diversion has occurred especially as evidenced by Mexico’s increased shares of U.S. imports apparently at the expense of several Asian countries. We use a conceptual framework based on a partial-equilibrium model of differentiated product industries under monopolistic competition for many countries. The model is implemented empirically using a fixed-effect panel analysis of U.S. imports at the Harmonized System (HS) 2-digit level for the period, 1992-98. Of the 70 sets of regressions that were run, the coefficients of the tariff rates were statistically significant in 15 cases. The strongest evidence of trade diversion was found mainly for U.S. imports of textile and apparel products. We also estimated regressions for selected commodities at the HS 4-digit level. The results suggest trade diversion for textiles, apparel, and some footwear products but not for trade in motor cars and vehicles and television receivers, which may have been more influenced by changes in foreign direct investment and outsourcing rather than tariff preferences.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.104 | 0.029 |
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".