Designer's Corner - Willingness-To-Pay (WTP): The New-Old Kid on the Economic Evaluation Block
Bibliographic record
Abstract
Cost-benefit analysis (CBA) is defined in the methodology literature as a form of economic evaluation whereby both costs and consequences are measured in monetary terms (Drummond, O'Brien, Stoddart, & Torrance, 1997). In recent years we have witnessed renewed enthusiasm for CBA and contingent valuation (CV) methodology, in particular the willingness-to-pay (WTP) approach to measuring the consequences of health-care programs (Diener, O'Brien, & Gafni, 1998; Klose, 1999). This renewed enthusiasm stems partly from the congruence between the empiric method used to measure the outcome (i.e., WTP) and the theoretical foundation of CBA in welfare theory (Mishan, 1971). This type of analysis also enables direct comparison of benefits and costs, as the two are measured in the same units. An added attraction of CBA is that the same principle of net benefit (i.e., benefit minus cost) can be applied to other sectors such as transport or environment, permitting intersectoral comparisons of resource use. The maximum amount that an individual is willing to pay for goods or services is a common economics measure of the value of those goods or services to the individual. Yet only in recent years have we witnessed renewed enthusiasm for the use of WTP survey techniques in estimating monetary values for improved morbidity and mortality risks. As was observed as far back as a decade ago (Johannesson & Jonsson, 1991), environmental economics and health economics developed differently with respect to evaluation methods. While CBA (and WTP) has evolved into the most common method for valuing environmental
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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 | 0.066 | 0.132 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".