Researchers conducted a randomized evaluation in Kenya to test various methods to target subsidies to farmers most able to experiment on behalf of their community.

The adoption of modern agricultural technologies can lead to increased productivity and profitability, particularly for small farmers in low- and middle-income countries such as Kenya. However, farmers need credible information about the technology to decide whether it suits their investment needs; this can be a challenge, as many farmers face information constraints to technologies, even when they are available on the market.¹ 

Research suggests that experimentation by select, appropriate farmers in the community, whose demonstrations serve as a guide for other farmers in similar conditions, can help inform adoption decisions.² However, farmers vary in suitability to experiment for the rest of the community, including their level of social skills, willingness to invest in a new technology, and willingness to share information. This makes identifying the appropriate ones critical for spreading valuable information about the technology. What are the incentive-compatible ways to identify a suitable experimenting farmer and maximize social learning?

Researchers conducted a randomized evaluation to measure the impact of different selection mechanisms on the level of experimentation and information diffusion. A total of 390 villages in Bungama, Busia and Siaya counties in western Kenya participated in the study. All farmers received a 15 percent discount for a manually operated irrigation pump, attended practical information sessions on using the pump and the importance of cooperation during experimentation to increase social learning, and received a lottery ticket to win a pump. 

In each village, one to two farmers were selected as experimenters to receive a free pump by either a standard lottery with equal odds; their willingness to pay for a higher chance of winning the pump; their willingness to work for a higher chance of winning the pump; or based on neighbors' votes for the best experimenter in the village. These groups were measured against a comparison group, villages where no farmer was selected as an experimenter. Researchers conducted follow-up surveys to measure the experimentation rate of adopters, information passing to other farmers in the community, and pump usage knowledge and beliefs about its returns among all farmers.

Results will be available later in 2024.

Sources

1. Omotilewa, Oluwatoba J., Jacob Ricker‐Gilbert, and John Herbert Ainembabazi. "Subsidies for agricultural technology adoption: Evidence from a randomized experiment with improved grain storage bags in Uganda." American Journal of Agricultural Economics 101, no. 3 (2019): 753-772.

Shiferaw, Bekele, Tewodros Kebede, Menale Kassie, and Monica Fisher. "Market imperfections, access to information and technology adoption in Uganda: Challenges of overcoming multiple constraints." Agricultural economics 46, no. 4 (2015): 475-488.

2. Beaman, Lori, Ariel BenYishay, Jeremy Magruder, and Ahmed Mushfiq Mobarak. "Can network theory-based targeting increase technology adoption?." American Economic Review 111, no. 6 (2021): 1918-1943.

Conley, Timothy G., and Christopher R. Udry. "Learning about a new technology: Pineapple in Ghana." American Economic Review 100, no. 1 (2010): 35-69.

Chassang, Sylvain, Gerard Padró i Miquel, and Erik Snowberg. "Selective trials: A principal-agent approach to randomized controlled experiments." American Economic Review 102, no. 4 (2012): 1279-1309.