Asking Willingness-to-Accept Questions in Stated Preference Surveys: A Review and Research Agenda
Bibliographic record
Abstract
Stated preference (SP) researchers have encountered an increasing number of policy problems for which a willingness-to-accept (WTA) compensation question would seem to be the most reasonable approach to structure the respondent's valuation choice task. However, most SP researchers are still reluctant to pose WTA questions to respondents due to concerns about reliability of responses and confusion about what contexts warrant a WTA question compared to a willingness-to-pay question. This review synthesizes the current literature, provides guidance on when and how to use WTA elicitation formats, and identifies research needs. We present a typology of valuation tasks that illustrates the situations in which WTA questions are appropriate and should be used to estimate welfare-theoretic measures of economic benefits—and when they should be avoided. We also discuss three different design issues that SP researchers need to consider when they use WTA questions: (a) elicitation of reference and status quo conditions, (b) incentive compatibility and private versus public goods, and (c) nonconforming responses. We conclude that good survey design makes it possible to ask respondents sensible WTA questions in many cases, yet several key research issues require attention.
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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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".