Measuring the willingness to pay to avoid guilt: estimation using equilibrium and stated belief models
Bibliographic record
Abstract
Abstract We estimate structural models of guilt aversion to measure the population level of willingness to pay (WTP) to avoid feeling guilt by letting down another player. We compare estimates of WTP under the assumption that higher‐order beliefs are in equilibrium (i.e., consistent with the choice distribution) with models estimated using stated beliefs which relax the equilibrium requirement. We estimate WTP in the latter case by allowing stated beliefs to be correlated with guilt aversion, thus controlling for a possible source of a consensus effect. All models are estimated using data from an experiment of proposal and response conducted with a large and representative sample of the Dutch population. Our range of estimates suggests that responders are willing to pay between €0.40 and €0.80 to avoid letting down proposers by €1. Furthermore, we find that WTP estimated using stated beliefs is substantially overestimated (by a factor of two) when correlation between preferences and beliefs is not controlled for. Finally, we find no evidence that WTP is significantly related to the observable socio‐economic characteristics of players. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.019 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".