Technology Diffusion, Outcome Variability, and Social Learning: Evidence from a Field Experiment in Kenya

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Abstract

This article explores the mechanisms through which social learning mediates technology diffusion. We exploit an experiment on the dissemination of biochar, a soil amendment that can improve fertility on weathered and/or degraded soils. We find that social networks transmit information about the average benefits of adoption, but also its risk, and that observed variability inhibits uptake to a greater degree than positive average results engender it. Paradoxically, this relationship is stronger among networks that do not discuss farming, but disappears among farmer networks that do. This is resolved with a simple model of social learning about conditional, rather than unconditional benefit distributions. As farmers observe factors associated with outcomes in their networks, they constrain the distribution of their own potential outcomes. This conditional distribution diverges from the unconditional distribution that the econometrician observes. We conclude that social learning is characterized by implicit model-building by sophisticated decision makers, rather than simple herding towards observed good results.

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