Evidence in Agriculture: Information Constraints to Technology Adoption
The TOPS Program and the Agricultural Technology Adoption Initiative (ATAI) hosted Kyle Murphy, Policy Manager of MIT's Abdul Latif Jameel's Poverty Action Lab (J-PAL), to present on emerging insights from the Randomized Control Trial (RCT) literature on information constraints to agricultural technology adoption among smallholder farmers in the developing world. The session reviewed results from evaluations on agricultural extension, social networks, and improving the pedagogical method of extension services.
Event Presentation - Kyle Murphy, J-PAL
This was the second session in a series on emerging insights from randomized evaluations hosted by The TOPS Program and ATAI.
To listen to the first session, visit Randomized Evaluations in Practice: Opportunities and Challenges.
The TOPS Program and ATAI will collaborate on a series of presentations in 2017, offering both in-person and online participation options. Subsequent sessions will include such topics:
- Evidence in Agriculture: Risk and Credit - an overview of emerging insights from the RCT literature on how exposure to risk constraints influence smallholder farmers' decisions around technology adoption, and on credit constraints to agricultural technology adoption among smallholder farmers in the developing world. The session will review results from evaluations on weather-based index insurance, stress-tolerant seed varieties, microcredit, biometric credit bureaus, flexible collateral arrangements, and harvest/planting time loans. How does weather risk influence farmers' decisions? What solutions are the most promising going forward? How does lack of access to credit constrain farmers from adopting to potentially profitable technologies? Can the microcredit model be adapted to better serve rural smallholders?
- Globally Informed Locally Grounded Program Development - focuses on how lessons from smaller evaluations can generalize to inform larger program design across contexts, as well as how general lessons from the literature can be used to increase impacts. What types of data are useful to different parts of your theory of change? How is evidence from different contexts useful? How can you use very contextual and experimental evidence to inform your program design?
More about ATAI
ATAI is a joint research initiative of J-PAL and UC Berkeley's Center for Effective Global Action (CEGA). ATAI's mission is to develop and rigorously test programs that improve adoption and profitable use of agricultural technology by small-scale farmers in South Asia and Sub-Saharan Africa. ATAI also works with practitioners, funders, and developers to define the most critical issues, technologies and adoption strategies in agricultural developmnent, to ensure that the resulting research has maximum impact on the lives of the poor. USAID's Office of Food for Peace has been exploring ways in which ATAI's expertise, particularly around applying randomized control trial research methodologies to program planning and decision-making, can be harnessed to the benefit of the FSN Network.