Learning from Self and Learning from Others : Experimental Evidence from Bangladesh

Can decentralizing demonstration accelerate learning about new technologies This paper randomizes access to a fixed demonstration kit for new flood-saline-resilient seeds across villages in Bangladesh, with demonstration either by a single farmer or spread across many farmers. In the short run, higher learning from self and others under decentralization increases technology adoption. In the long run, the impacts of any demonstration persist, but the additional impacts of decentralization vanish. A Bayesian model of learning the returns to a new technology suggests belief dispersion caused noisy adoption along the learning path, and farmers’ expected gains from demonstration are four times higher under decentralization.