Willingness‐to‐Pay for Safe Vegetables: A Comparative Analysis Between Wet Market and Supermarket Shoppers in Urban Cambodia
Bibliographic record
Abstract
ABSTRACT Consumers in low‐ and middle‐income countries are increasingly worried about food safety, but markets for safe produce remain underdeveloped and do not offer farmers a premium price. This study used the Becker–DeGroot–Marschak experimental auction design to identify the market for safe vegetables by assessing consumers' willingness to pay for both internationally and locally certified vegetables. The study involved 585 shoppers at wet markets and supermarkets in Phnom Penh, Cambodia. The factors influencing willingness to pay were analysed using Pooled Ordinary Least Squares. Compared to uncertified vegetables, shoppers were willing to pay 100% more for those certified as United States Department of Agriculture Organic and 55% more for those certified under Cambodian Global Agricultural Practices. Providing information about both types of certification increased shoppers' willingness to pay for certified vegetables, with a greater increase for the international label than the local one. For the local label, information only increased willingness to pay among supermarket shoppers, not wet market shoppers. Our findings highlight the importance of age and income in shaping consumers' willingness to pay for safe produce. We offer recommendations to improve food purchasing choices in low‐income countries, emphasising the need for food safety information that is tailored to different populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".