Willingness-to-Pay For Information: Experimental Evidence on Product Traceability From the U.S.A., Canada, the U.K., and Japan
Bibliographic record
Abstract
Traceable product systems provide a tool to track the inputs of a final good throughout the entire production chain. This tool can provide valuable information to consumers on verifiable characteristics of the product, can improve the speed of product recall, and can help identify areas of inefficiency in the product chain. Recent examples of traceable systems include those used in the diamond, lumber, and food industries. This article reports results from a case study on traceability using Vickrey auctions to generate willingness-to-pay (WTP) data for traceability and related product characteristics. Specifically, we examine WTP for traceable meat, which is a timely topic given that major customers and competitors in the multi-billion dollar red-meat market are all implementing traceable meat systems. However, the largest player in world red-meat markets, the U.S., is lagging in the development of these systems. We conduct comparable auctions in the U.S., Canada, the U.K., and Japan and find that subjects are willing to pay a nontrivial premium for traceability, but the same subjects show even higher WTP for traceability-provided characteristics like additional meat safety and humane animal treatment guarantees. The implication is that producers can likely implement such a traceable meat system profitably by tailoring the verifiable characteristics of the product to consumer preferences. For other types of traceable products, these results highlight the importance of full exploitation of traceable systems by providing consumers with the additional product information that only a traceable system can verify.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".