Whence the Beef: The Effect of Repealing Mandatory Country of Origin Labeling (COOL) Using a Vertically Integrated Armington Model with Monte Carlo Simulation
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Bibliographic record
Abstract
Increasingly, international trade policy analysis explores the economic effects of changes in ad‐valorem tariffs or equivalent nontariff measures on vertically integrated markets for which high quality data are unavailable. Standard Constant Elasticity of Substitution (CES) Armington models fail to account for either vertical linkages or parameter uncertainty. Here, we introduce a vertically integrated, nested two‐sector Armington model that incorporates uncertainty in the estimates of Armington elasticities through Monte Carlo simulation. As an illustrative case, we model the effects of changes in country of origin labeling (COOL) rules on the market shares of cattle in the U.S. beef market. By accounting for parameter uncertainty in this way, we are able to estimate the distribution of potential effects of repealing mandatory COOL. Ultimately, we predict that, in all but the most extreme cases, Mexico and Canada would not gain as much market share from the repeal of mandatory COOL as they claim in their World Trade Organization (WTO) filings against the regulation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it