Bibliographic record
Abstract
This study develops a quantitative analysis of the impact of the Canada-United States-Mexico Agreement (USMCA), as signed on 30 November 2018. The USMCA provides a major overhaul of the NAFTA legal text based largely on the Trans-Pacific Partnership, but only minor changes to market access. The modelling approach consists of developing impacts of the USMCA on the member countries as estimated using a dynamic global computable general equilibrium (CGE) model. The main modelling challenge is to quantify the policy shock, which is unusual in that it has no traditional tariff liberalization and has many features that promise to be restrictive of trade. The impact of the USMCA is assessed against a baseline that reflects an in-force NAFTA. These results can, however, be compared to the impacts of NAFTA lapsing to infer the difference between the USMCA and the hard NAFTA exit scenario. We evaluate non-tariff measures based on the extent to which the USMCA reduces/increases the parties’ scores on indexes measuring restrictiveness of their regimes for goods, services, and investment. For goods, we examine possible improvements upon the WTO Trade Facilitation Agreement (TFA) commitments for the North American economies as measured by the OECD’s Trade Facilitation Indicators (TFI). For services, we consider the liberalization implied by the services commitments evaluated on the basis of changes to the parties’ scores under the OECD’s Services Trade Restrictiveness Index (STRI). For investment, we consider the changes implied against the parties’ scores on the OECD’s Foreign Direct Investment Restrictiveness (FDIR) index. For services and investment, we consider the value of binding market access commitments.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".