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Can Network Theory-based Targeting Increase Technology Adoption?

Lori Beaman, Ariel BenYishay, Jeremy Magruder () and Ahmed Mobarak ()
Additional contact information
Lori Beaman: Northwestern University
Ariel BenYishay: College of William and Mary

No 1062, Working Papers from Economic Growth Center, Yale University

Abstract: In order to induce farmers to adopt a productive new agricultural technology, we apply simple and complex contagion diffusion models on rich social network data from 200 villages in Malawi to identify seed farmers to target and train on the new technology. A randomized controlled trial compares these theory-driven network targeting approaches to simpler strategies that either rely on a government extension worker or an easily measurable proxy for the social network (geographic distance between households) to identify seed farmers. Our results indicate that technology diffusion is characterized by a complex contagion learning environment in which most farmers need to learn from multiple people before they adopt themselves. Network theory based targeting can out-perform traditional approaches to extension, and we identify methods to realize these gains at low cost to policymakers.

Pages: 61 pages
Date: 2018-08
New Economics Papers: this item is included in nep-dev
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Working Paper: Can Network Theory-based Targeting Increase Technology Adoption? (2018) Downloads
Working Paper: Can Network Theory-based Targeting Increase Technology Adoption? (2018) Downloads
Working Paper: Can Network Theory-based Targeting Increase Technology Adoption? (2018) Downloads
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