Can results-based prizes to private sector incentivize technology adoption by farmers? Evidence from the AgResults Nigeria project that uses prizes to incentivize adoption of AflasafeTM
Bibliographic record
Abstract
Abstract Background The AgResults initiative tests the efficacy of results-based prizes to scale-up smallholder technology adoption. In Nigeria the project awarded a $18.75/ton prize for private sector actors who aggregated maize from smallholders that was treated by Aflasafe—a biocontrol that addresses liver cancer-causing aflatoxin contamination in maize. This paper examines the impact of AgResults initiative on smallholder farmers. Methods This evaluation estimates the causal effect of the AgResults program on farmer outcomes by comparing survey data from AgResults farmers to survey data from a matched comparison group of farmers. To improve balance, we use propensity score weights. In considering inestimable selection bias, we describe several key considerations, including the inclusion of comparison areas for treatment post-evaluation. Results The project increased Aflasafe adoption by 56% points, farmers earned 16% more net maize income on average. However, the majority of farmers in villages engaged by the project did not know about Aflasafe’s health benefits. This suggests that complimentary donor-directed efforts may still be needed to generate general awareness about the technologies whose benefit is not immediately visible to the smallholders. With the prize focused on aggregation, private sector actors may have reduced incentive to raise awareness about Aflasafe’s health benefits in case farmers held back Aflasafe-treated maize for consumption. Conclusions This paper highlights the potential of results-based prizes to engage the private sector in solving development problems. However, it also equally highlights the gaps that such an approach may have, arguing for the need for having complimentary efforts to address those gaps. This is particularly the case when the technology’s benefits are not perceived by the consumer (aflatoxins are not visible to the eye, and the health benefits are not immediate), or when the technology results in positive externalities (final consumers of Aflasafe-treated maize also benefit). Broader consumer awareness needs to be raised to promote continued development of the market for Aflasafe-treated maize, aflatoxin standards need to be enforced, and aflatoxin testing needs to be more easily available.
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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 | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".