Working Paper: NBER ID: w24912
Authors: Lori Beaman; Ariel Benyishay; Jeremy Magruder; Ahmed Mushfiq Mobarak
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: Technology Adoption; Network Theory; Agricultural Extension; Randomized Controlled Trial; Malawi
JEL Codes: O13; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| network theory-based targeting (D85) | technology adoption (O33) |
| complex contagion targeting (E44) | technology adoption (O33) |
| complex contagion targeting (E44) | higher likelihood of adoption by others (C92) |
| theory-driven targeting of optimal seed farmers (Q16) | greater technology diffusion (O33) |
| favorable conditions (P17) | larger and sustained gains in adoption (O36) |
| lack of information (D89) | failure to adopt (J12) |
| traditional extension strategies (O36) | lower technology adoption (O33) |