Can Network Theory based Targeting Increase Technology Adoption?

Abstract

In order to induce farmers to adopt agricultural technologies in Malawi, we apply diffusion models of simple and complex contagion on rich social network data from 200 villages in Malawi to identify optimal seed farmers to target and train on the new technologies. 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 proxy for the social network (geographic distance between households) to identify seed farmers. Adoption rates over three years are greater in villages that received the theory-based data intensive targeting treatments. The data, interpreted through the lens of the theory, yield insights on the nature of diffusion, and are most consistent with a learning environment where farmers need to know more than one person with knowledge of the technology before they adopt themselves.