Three Essays on the Demand of Imported and Domestic Meat and Livestock in the United States
Bibliographic record
Abstract
This dissertation studies the demand for imported and domestic demand models for meat and livestock. The first essay focuses on the separability between import and domestic meat demand and the performance of static versus dynamic models of consumer behavior. A new dynamic system of demand functions is developed and used to test the separability restrictions on U.S. meat consumption data. Our results indicate that imported meat consumption is non-separable from the U.S. consumption and a dynamic specification of the AIDS model is superior to the static AIDS model. The second essay analyzes the demand for domestic and imported livestock by the US meat processing industry and explores the existence of long-run relationships in the derived demand models which are required for the specification of dynamic demand models. The results indicate that the static inverse input demand model performed better than the dynamic models for both the beef and pork processing industries. The results of this study indicate that there is not a long run relationship in the variables of the inverse demand models for livestock. The third essay investigates the impact of the discovery BSE in Canadian cattle on the imported and domestic demand for livestock and meat in the United States. The analysis is based on the results of the first and second essays of the dissertation. A multi-market partial equilibrium model is utilized to simulate the effects of policy-induced shifts in quantities of imports supplied from Canada on the meat and livestock industries. Our simulation results predict small effects on cattle and the results are similar to prediction from Armington type models even though separability strongly rejected.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".