Impacts on NAFTA Members of Multilateral and Regional Trading Arrangements and Initiatives and Harmonization of NAFTA's External Tariffs
Bibliographic record
Abstract
We have used the Michigan Model of World Production and Trade to simulate the economic effects on the NAFTA member countries and other major trading countries/regions of a prospective new round of WTO multilateral trade negotiations, the variety of free trade agreements (FTAs) that the NAFTA members have negotiated or are considering, and the adoption of a system of common external tariffs by the NAFTA members. We estimate that an assumed reduction of post-Uruguay Round tariffs on agricultural and industrial products and services barriers by 33 percent in a new WTO trade round would increase world welfare by $613.0 billion, with gains of $177.3 billion for the United States, $13.5 billion for Canada, $6.5 billion for Mexico, and significant gains for all other industrialized and developing countries. If there were global free trade, world welfare would increase three-fold to $1.9 trillion and the country/region gains would be similarly larger. Regional FTAs such as an expansion of NAFTA to include Chile and a Western Hemisphere FTA would increase global and member-country welfare but much less than a new WTO multilateral trade round would. Separate bilateral FTAs negotiated or being considered by Canada, Mexico, and the United States would have positive, though generally small, welfare effects on the partner countries, but potentially disruptive sectoral employment shifts in some countries. There would be trade diversion and detrimental welfare effects on some nonmember countries for both the regional and bilateral FTAs analyzed. If the NAFTA members were to adopt a system of common external tariffs to replace their existing differentiated external tariffs, a system based on trade weights would have less distortive effects on trade and welfare than a system based on simple averages or production-weighted tariffs.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".