U.S. Consumers’ Preference and Willingness to Pay for Country‐of‐Origin‐Labeled Beef Steak and Food Safety Enhancements
Bibliographic record
Abstract
While previous studies have investigated country‐of‐origin effect from various angles, the extent to which Country‐of‐Origin Labelling (COOL) affects U.S. beef imports from specific countries remains unexplored. Using data from 1,079 consumers from the United States, we examined consumers’ willingness to pay (WTP) for Canadian and Australian beefsteaks. We also estimated WTP for bovine spongiform encephalopathy (BSE)–tested traceability‐enabled, tenderness‐assured, and natural beef. The results from both a mixed logit model and a latent class model (LCM) revealed unobserved taste heterogeneity and important differences in the WTP between the imported and domestic steak. The LCM, for instance, estimated the range of discount needed for consumers to switch from U.S. to Canadian steak as $1.09 to $35.12 per pound. This strongly suggested that U.S. consumers prefer domestic‐originated beef to imported beef. In addition, consumers were found to be willing to pay significant amount for BSE‐tested, traceability‐enabled, and tenderness‐assured beef. Bien que des études antérieures aient examiné les répercussions de l’étiquetage du pays d’origine sous différents angles, les répercussions de cet étiquetage obligatoire sur les importations étatsuniennes de bœuf en provenance de pays spécifiques ne l’ont pas été. À l’aide de données tirées d’un échantillon de 1079 consommateurs étatsuniens, nous avons examiné le consentement à payer (CAP) pour du bifteck en provenance du Canada et de l’Australie. Nous avons également examiné le CAP des consommateurs pour du bœuf provenant d’un animal ayant subi un test de dépistage de l’ESB, traçable, de tendreté assurée et naturel. Les résultats obtenus à l’aide d’un modèle logit mixte et d’un modèle à classes latentes ont révélé une hétérogénéité non observée du goût et des écarts importants dans le CAP pour du bifteck provenant des États‐Unis et de l’extérieur du pays. Le modèle à classes latentes, par exemple, a révélé que les écarts de rabais nécessaires pour que les consommateurs délaissent le bifteck américain pour le bifteck canadien variaient de 1,09 $à 35,12 $ la livre. Ces résultats montrent clairement que les consommateurs étatsuniens préfèrent le bœuf des États‐Unis plutôt que le bœuf importé. Les résultats montrent également que les consommateurs sont prêts à payer plus cher pour du bœuf provenant d’un animal ayant subi un test de dépistage de l’ESB, traçable et de tendreté assurée.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".