How does cost matter in health-care discrete-choice experiments?
Bibliographic record
Abstract
Willingness-to-pay (WTP) estimates derived from discrete-choice experiments (DCEs) generally assume that the marginal utility of income is constant. This assumption is consistent with theoretical expectations when costs are a small fraction of total income. We analyze the results of five DCEs that allow direct tests of this assumption. Tests indicate that marginal utility often violates theoretical expectations. We suggest that this result is an artifact of a cognitive heuristic that recodes cost levels from a numerical scale to qualitative categories. Instead of evaluating nominal costs in the context of a budget constraint, subjects may recode costs into categories such as 'low', 'medium', and 'high' and choose as if the differences between categories were equal. This simplifies the choice task, but undermines the validity of WTP estimates as welfare measures. Recoding may be a common heuristic in health-care applications when insurance coverage distorts subjects' perception of the nominal costs presented in the DCE instrument. Recoding may also distort estimates of marginal rates of substitution for other attributes with numeric levels. Incorporating 'cheap talk' or graphic representation of attribute levels may encourage subjects to be more attentive to absolute attribute levels.
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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 | 0.118 | 0.293 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".