An evaluation framework and empirical evidence on the effect of pay-for-results programs on the development of markets for welfare-enhancing agricultural technologies
Bibliographic record
Abstract
Abstract Background Donors and international development organizations increasingly recognize that private sector investment and creativity are needed to enhance global food security. Pay-for-results schemes are receiving greater attention as a means to catalyze private sector investment in sustainable, inclusive markets for goods and technologies that achieve food security and agriculture development goals. In pay-for-results schemes, the development organization promises prizes to private sector actors for achieving pre-specified goals. Method We describe an evaluation framework to help development organizations learn from both successful and failed pay-for-results projects to achieve agriculture and food security outcomes. Applying the evaluation framework, we describe the findings from four pay-for-results projects sponsored by AgResults, a multilateral initiative funded by development organizations from four countries (Australia, Canada, the UK, and the US) and the Bill & Melinda Gates Foundation. Results The lessons highlighted from these examples illustrate the importance of structuring the prize to encourage the creation of competitive agricultural markets; aligning the prize structure with the development goal of improving smallholder farmers’ food security; and constructing a theory of change that reflects a thorough understanding of the baseline market, enabling environment, and underlying assumptions about competitors’ response to the prize. Conclusions Our work has several policy implications: Under certain conditions, pay-for-results mechanisms can help develop competitive, smallholder-inclusive agricultural markets and reduce food insecurity. Prize competitions offering multiyear, proportional prizes are more conducive than grand prizes to fostering the development of competitive agricultural markets. The enabling environment plays a significant role in pay-for-results mechanisms’ success or failure. Private sector-led actions alone may not be sufficient to adequately address the targeted development challenge.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".