Can Network Theory-Based Targeting Increase Technology Adoption? (WP-18-26)


WP-18-26

Lori Beaman, Ariel Ben Yishay, Jeremy Magruder, Ahmed Mushfiq Mobarak

In order to induce farmers to adopt a productive new agricultural technology, the researchers 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. The 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 the researchers identify methods to realize these gains at low cost to policymakers.

Lori Beaman, Associate Professor of Economics and IPR Fellow, Northwestern University 

Ariel Ben Yishay, Associate Professor of Economics, College of William and Mary

Jeremy Magruder, Associate Professor of Agricultural and Resource Economics, University of California, Berkeley

Ahmed Mushfiq Mobarak, Professor of Economics, Yale University

PDF icon Download working paper PDF