The Economic Effect of COOL on the Mexican and United States Cattle Price Relationship
Bibliographic record
Abstract
Country of Origin Labeling (COOL) was introduced in 2002 but not implemented until September, 2008. COOL required covered commodities to indicate their country of origin. Among other commodities, COOL applied to muscle cuts and ground beef. Canada and Mexico won a WTO complaint against the U.S. forcing USDA to rewrite the COOL regulations. The WTO finding on the re-written COOL regulation is due to become public any day. This paper analyzes the impact of COOL on stocker and feeder cattle price differences between the U.S. and Mexico. Cattle trade with Mexico is a longstanding market relationship. The U.S. imports stocker and feeder cattle from Mexico. Weekly AMS reported prices of Mexican cattle and Texas feeder cattle prices are used to construct a price spread. An econometric model is developed to analyze factors that affect the Mexican-U.S. feeder cattle price spread. A dummy variable is included for COOL implementation. COOL was found to have a statistically significant positive affect on the price spread for 300-400 and 500-600 pound feeder cattle. The results indicate that the Mexican cattle have received a significant discount following COOL implementation.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".