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Can Network Theory-based Targeting Increase Technology Adoption?

Lori Beaman, Ariel BenYishay, Jeremy Magruder () and Ahmed Mobarak ()

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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. Keywords: Social Learning, Agricultural Technology Adoption, Complex Contagion, Malawi JEL Classification Codes: O16, O13

New Economics Papers: this item is included in nep-dev
Date: 2018-08
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http://arxiv.org/pdf/1808.01205 Latest version (application/pdf)

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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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