Understanding Strategic Behavior and Its Contribution to Hypothetical Bias When Eliciting Values for a Private Good
Bibliographic record
Abstract
Understanding market demand and premiums paid for food products with social and environmental attributes is increasingly important to ensure adequate information for the agri‐food system. Stated preference (SP) surveys are a flexible and affordable approach to elicit values, however the presence of hypothetical bias compromises their reliability. In this study, we seek to identify strategic behaviors and how they relate to hypothetical bias in SP survey for private goods. An online survey was conducted to measure willingness‐to‐pay (WTP) for regular and free‐run eggs using two treatments, a nonhypothetical experimental auction and an open‐ended elicitation question. We find that the bias associated with the presence of strategic behavior in the open‐ended elicitation survey can be isolated by calculating premiums, which are defined as the difference between declared values for free‐run eggs and regular eggs. The insight gained from this study can be used to improve experimental design of hypothetical SPs surveys and significantly reduce hypothetical bias. Comprendre la demande et la volonté de payer pour des biens alimentaires avec des caractéristiques sociales et environnementales est important afin d'assurer une information adéquate pour le secteur agroalimentaire. Les sondages déclaratifs sont une approche flexible et peu coûteuse pour capturer les valeurs, par contre la présence d'un biais hypothétique réduit la fiabilité des valeurs ainsi estimées. Dans cette étude nous tentons d'identifier le comportement stratégique en lien avec le biais hypothétique intrinsèque aux sondages déclaratifs. À cet effet, nous réalisons un sondage en ligne qui mesure le consentement à payer pour des œufs réguliers et de poules en liberté en utilisant deux traitements, une enchère non hypothétique et une question ouverte. Nous trouvons que le comportement stratégique présent en situation déclarative peut être isoler en calculant la prime, laquelle est définie comme étant la différence entre la valeur déclarée pour les œufs de poules en liberté et des œufs réguliers. Les résultats de cette étude peuvent être utilisés pour améliorer le design expérimental des enquêtes de préférences révélées et limiter le biais hypothétique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".