Do Marketing Margins Change with Food Scares? Examining the Effects of Food Recalls and Disease Outbreaks in the U.S. Red Meat Industry
Bibliographic record
Abstract
ABSTRACT This study examines the impact of different food scare events on marketing margins in the U.S. beef and pork industries. The authors analyze how market stresses induced by the Food Safety Inspection Service (FSIS) recalls and bovine spongiform encephalopathy (BSE) outbreaks affect price spreads and the extent of price transmission at the slaughter‐to‐wholesale and wholesale‐to‐retail levels. They use monthly national data for the period 1986–2008, which includes records of FSIS recalls of varying severity and BSE events in the United States and Canada. The authors account for immediate and delayed effects of food scares and for potential cross effects across industries and countries. The results indicate that beef and food recalls do not affect their corresponding price margins and overall food safety incidents have minor cross‐industry and cross‐country effects. However, BSE discoveries in the United States considerably affect marketing margins in the beef industry, particularly at the wholesale‐to‐retail level. Interestingly, subsequent discoveries had smaller impacts on price margins. Bovine spongiform encephalopathy outbreaks also appear to affect the extent of price transmission between wholesalers and retailers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".