Leveraging Social Networks to Enhance Agricultural Extension Collaboration between Lori Beaman, Ariel BenYishay, Paul Fatch, Jeremy Magruder and Mushfiq Mobarak
Leveraging Social Networks to Enhance Agricultural Extension: Lessons from an RCT study by Paul Fatch
Jun 19, 2015
New technologies diffuse through inter-personal ties, as social network members are often the most credible source of information. We apply models of simple and complex contagion on rich social network data from 200 villages in Malawi to identify seed farmers who would maximize technology adoption in theory, assuming that a specific contagion model correctly predicts diffusion patterns. A randomized controlled trial compares these theory-driven network targeting approaches to simpler, scalable strategies that either rely on a government extension worker or an easily measurable trait (geographic centrality) to identify seed farmers. Adoption rates over three years are greater in villages that received the theory based data intensive treatments. The data, interpreted through contagion theory, yield insights on the nature of diffusion, and are most consistent with a complex learning environment.
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Leveraging Social Networks to Enhance
Agricultural Extension Collaboration between Lori Beaman,
Ariel BenYishay, Paul Fatch, Jeremy
Magruder and Mushfiq Mobarak
MOTIVATION
Improving food security, raising farm incomes, and reducing environmental damage depend on smallholder adoption of new
technologies
Technologies that would minimize adverse environmental effects and increase long-term yields exist, but have yet to be adopted on a wide
scale
Low productivity in agriculture and environmentally unsustainable farming challenges are pressing development challenges for Malawi
2
Impact evaluation: Central Questions
What are the most effective ways to convey
information about new technologies to farmers?
What can MoAIWD do to increase rates of adoption
of technologies that will increase long-run
productivity and ensure sustainable use of natural
resources?
3
Motivation for peer farmers
When making decisions, people may be influenced by friends and neighbors
Will allow AEDOs to take advantage of existing channels of social networks, which may increase their ability to convey information
Finding the right partner farmers could be a low-cost way for the Ministry to boost adoption rates
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Selected Districts
Conservation Agriculture
Districts
Mwanza
Machinga
Nkhotakota
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Technologies
Conservation Agriculture
• Pit Planting • Use basins
instead of ridges • Very low
adoption at start of project
Crop Residue Management
• Composting
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Role of the seed farmer
Implement the new technology
on their own farm
Talk to their friends and neighbors about what they are doing
Try to convince people in their social groups to
adopt the new technology
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Ridges vs Pit Planting
Ridges Pit Planting
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Evaluation Strategy
• Selection of partners to maximize adoption using theoretical diffusion model and detailed social network data
• 100 villages
Network Partners
• Use geography as a proxy to full social network mapping along with diffusion model
• Policy relevant alternative to Network Partners treatment, since low cost and scalable
• 50 villages
Geo Partners
• Business as usual: extension agent chooses partners
• 50 villages Benchmark
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An Example Network 10
Data and Timeline 11
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Figure 1: Training Partner Farmers on Pit planting Increases Adoption
Trained Not trained
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Adoption of PP Increases over Benchmark
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Figure 2: Adoption Rates across Network, Geo and Benchmark partner villages
Network partners Geo partners Benchmark partners
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Expect Strongest Effects in Places that didn’t know about technology before the project
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Figure 3: Adoption Rates in Villages with low pit planting use at Baseline
Network Partners Geo Partners Benchmark Partners
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Does the choice of partner farmers matter? Yes
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Year 1 Year 2 Year 3
Figure 3: Any adoption in the village (excluding trained partners)
Network partners Geo partners Benchmark partners
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Who we train mattered
Gradual adoption over time in all villages (from 0 to 8% over 3 years), but
Social network treatments increased adoption over benchmark
Remember, this is above extension workers choosing carefully – not obvious that network data would beat this
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Conclusions
• First: There are important and valuable technologies for which information is the only constraint to adoption
• Farmers, like the rest of us, are not perfectly informed
• Sometimes, even with these technologies, adoption is slow and difficult (still fairly low and increasing in year 3)
• Previous studies: social learning is important for tech adoption
17
Conclusions
• This study: We can identify partners that increase the speed of diffusion through social networks
• Looks like best is to treat the densest part of the network intensively rather than going for broad-based exposure.
• These partners are, though, hard to identify
• Avenue for future research (& collaboration to bring this to scale!): how to make networks work for policy in a more cost effective way
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