It’s all relative: consistent marginal effects with willingness to pay and willingness to accept framing in experimental auctions
Bibliographic record
Abstract
When eliciting consumer preferences for controversial products – an increasing number of which exist due to increasing demographic diversity and political polarization – conventional assumptions that all individuals derive positive marginal utility from consumption are challenged. It is relatively easy to adjust hypothetical stated preference questionnaires to include negative willingness to pay (WTP), but few studies on controversial products investigate how individuals behave using incentive-compatible revealed preference techniques. Using a framed field experiment with 292 adult subjects, we fill this gap by comparing the differences and similarities between a set of results that arise from the Becker-DeGroot-Marschak (BDM) mechanism between WTP versus willingness to accept (WTA) elicitation methods. This study has two main findings. First, in economic experiments eliciting preferences for controversial products, neither the WTP nor the WTA method fully discovers the true valuation range across all participants. Second, despite framing effects that give rise to different bid distributions, relative revealed preferences for the examined products are consistent under various interventions, indicating that WTP and WTA estimates have consistent policy implications.
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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 | 0.051 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".