Hypothetical Bias for Private Goods: Does Cheap Talk Make a Difference?
Classification
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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Economists and market researchers often need to accurately gauge consumers’ willingness-to-pay for private goods. The experimental literature has identified a problem of hypothetical bias when using stated preferences techniques, such as open-ended questions. It has been suggested that using a cheap talk script has the potential to resolve this bias. Yet, few empirical studies on the efficiency of cheap talk for private goods exist. This study uses a between-subjects experimental design to compare consumers’ willingness-to-pay for DHA-enriched milk using three elicitation methods: 1) Hypothetical open-ended stated preference question, without monetary consequence for the respondent; 2) Idem to the first with the addition of a cheap talk script; and 3) A Vickrey auction with real monetary consequences. In this experiment subjects have the choice to participate, or not, at each period. Our results indicate a significant hypothetical bias. While the use of cheap talk has no impact on this bias, it does however increase the level of participation to the market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it