Revisiting U.S. country of origin labeling trade damage estimates how does an equilibrium displacement model perform under different scenarios
Bibliographic record
Abstract
Abstract Mexico and Canada successfully challenged the U.S. mandatory country of origin labeling (COOL) requirements for beef and pork as inconsistent with World Trade Organization (WTO) rules, which ultimately led to arbitration over the level of trade lost due to the COOL measure. During this phase of the dispute, Mexico, Canada, and the United States provided the Arbitration Panel with estimates of the trade losses caused by COOL that were produced using different quantitative methods. The U.S. estimates were based on an equilibrium displacement model (EDM). This article presents a version of the EDM used by the U.S. Government to calculate trade losses due to COOL. The Panel developed its own analysis combining econometric analysis and an EDM that used only supply‐side information to calculate changes in Canadian and Mexican livestock trade. The U.S. EDM includes both the supply and demand sides of the market. We use the U.S. EDM and the Panel's assumptions to re‐estimate the value of lost trade due to COOL. The inclusion of demand‐side effects and domestic COOL costs produces lower estimated trade damages than those produced using the Panel's analysis, validating the EDM as a useful quantitative tool for this type of trade policy analysis.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".