Willingness to Pay for What? A Note on Alternative Definitions of Health Care Program Benefits for Contingent Valuation Studies
Bibliographic record
Abstract
The authors examine a number of ways in which willingness to pay (WTP) can be defined for measurement and use in a cost-benefit analysis (CBA) of a collectively funded health care program. They show how ambiguous specification of the program consequences that respondents should consider in their WTP responses can lead to problems of double counting or zero countingin a subsequent CBA. An example is whether the value of lost time from work because of poor health should be included by a CBA analyst (e.g., valued at the wage rate) as a separate cost item or whether this has already been monetized and included in respondents' WTP data. The authors highlight how differences in assumed or actual institutional structures are often ignored in measures of WTP and the consequences of this for the interpretation of WTP data.
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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 | 0.073 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".