How to incentivize peanut producers to adopt post-harvest aflatoxin control measures: A field experiment in Haiti
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Bibliographic record
Abstract
• Haitian supermarket consumers declare a 21.1 % premium for aflatoxin-safe peanut butter. • This premium is enough to compensate peanut farmers. • Scenarios for potential hypothetical bias and intermediary margin are considered. • Market mechanisms can help control aflatoxins in Haitian peanut butter. • Various factors influence farmer compensation, aflatoxin levels, and consumer premium. Aflatoxin contamination in peanuts represents a significant public health concern in many developing countries, including Haiti, a low-income country. Although simple post-harvest mitigation measures exist, their adoption by Haitian farmers remains limited. This study assesses the willingness to accept (WTA) of peanut producers in Haiti to implement post-harvest measures, using a reversed Becker-DeGroot-Marschak (BDM) auction. It also tests the effect on WTA of a conditional market access. For one group the research project commits to purchase peanuts at a predetermined price if aflatoxin levels meet a maximum of 10 parts per billion, while another group receives unconditional market access at the same predetermined price. Moreover, Haitian supermarket consumers’ willingness to pay (WTP) a premium for a local peanut butter (mamba) certified to meet aflatoxin standards is explored. Results indicate that conditional market access generates higher WTA. Haitian supermarket consumers show strong interest in certified peanut butter, with a declared premium of 21.1 % over the market price of a 16-ounce jar of non-certified peanut butter. If we consider intermediary margins as high as 86 % of the final consumer price—well above the current 68 % reported in previous studies—as well as for a potential hypothetical bias as high as two thirds of the stated WTP, this premium is sufficient to incentivize Haitian peanut producers, measured by their observed WTA. Thus, a market solution to the aflatoxin problem in Haiti seems plausible for supermarket consumers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it