Subject variation more than values clarification explains the reliability of willingness to pay estimates
Bibliographic record
Abstract
In a recent article in this journal, Smith offers additional evidence to support his claim that the test-retest reliability of willingness to pay measures increases along with willingness to pay because people take more time to consider their answers for the more highly valued (and therefore more 'expensive') goods. Unfortunately, by repeating a common misconception about what reliability actually measures, he overlooks an alternative explanation for the relationship he observed; namely, that subject variation increases with willingness to pay and that it is this, rather than any reduction in measurement error, that explains his findings. We show that 75% of the increase in reliability comes from increases in subject variation (that is different views about the value of good health), and that the relationship between measurement error and willingness to pay is not as simple as Smith suggests. However, our critique of Smith's paper should not be construed as criticism of the ideas being explored. We need to better understand the responses people give to contingent valuation exercises. Such understanding has to be based on a better appreciation of what reliability is and on more robust testing of alternative hypotheses.
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 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 | 0.036 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.028 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".