How to incentivize peanut producers to adopt post-harvest aflatoxin control measures: A field experiment in Haiti
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".