Is Mandatory Country of Origin Labeling a Proxy for Import Quota: A Partial Equilibrium Analysis of U.S Beef Imports?
Bibliographic record
Abstract
Over the years, there have been a number of issues pertaining to the import of beef into U.S economy. The price of beef has recorded steady increase in recent years. Internal factors such as input for raising cattle and external factors like import quota, tariffs and prices of related animal products may have accounted for the high prices of beef. Country of Origin Labeling (COOL) is a labeling U.S. Farm Bill law passed in 2008 by the United States Congress demanding meat, fruits, vegetables and peanuts to be labeled as to their country of origin. The implementation of country of origin labeling has become one of the controversial issues in the US beef industry and even Canada. The main objective of the study is to determine whether the impact of COOL is serving as an import quota on U.S. beef imports. This is achieved by employing a partial equilibrium analysis to model U.S. import demand for U.S. beef imports. This paper develops an Import Demand model for the U.S. Beef Sector to evaluate if COOL serves as an import quota for U.S. beef imports. The randomized effect model and the OLS regression were estimated. Unrestricted model to test the homogeneity and symmetry conditions: b) Restricted model with homogeneity and symmetry imposed along with a dummy variable COOL taking the value as “1” for pre-COOL, otherwise, “0.The analysis employed a partial equilibrium model to model the import demand function of beef imports. Controlling for other variables that affect the volume of imports, the estimated coefficients of MCOOL was consistently negative in all the Models. This negative effect implied that following the execution of mandatory labeling, there has been a significant increase in the imports of beef into the U.S. from the exporting countries under both the restricted models.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".