Eliciting Willingness‐to‐Pay through Multiple Experimental Procedures: Evidence from Lab‐in‐the‐Field in Rural Ghana
Bibliographic record
Abstract
Abstract This paper has the objectives of (a) comparing estimated willingness‐to‐pay (WTP) across three elicitation mechanisms (a Becker‐DeGroot‐Marschak [BDM] auction, a kth price auction, and a choice experiment [CE]) and (b) examining how these vary by participation fee. The product under consideration is kenkey made with nutritious maize, biofortified with vitamin A, which gives it a distinct orange color, in contrast to the white and yellow varieties that are traditionally consumed. We use an experiment consisting of 14 treatment arms, conducted in rural Ghana. Our estimation strategy explicitly accounts for the censored (typically at the market price) nature of the bids in the auctions, and the apparently lexicographic choices of several individuals in the CE. We find no evidence of economically meaningful (defined by the minimum currency unit of five pesewas) differences in WTP (although they may be statistically significant) across elicitation mechanisms, or by participation fee, a result that is in contrast to that found in much of the literature. A secondary finding is that the provision of nutrition information positively and significantly affects the marginal WTP for the new maize.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".