The role of learning in technology adoption: Evidence on hybrid rice adoption in Bihar, India

Much empirical research has shown that individuals’ decisions to adopt a new technology are the result of learning–both through personal experimentation through observing the experimentation of others. Yet even casual observation would suggest significant heterogeneity of learning processes, manifesting itself in widely varying patterns of adoption over space and time. This paper explores this heterogeneity in the context of early adoption of hybrid rice in rural India. Using specially designed experiments conducted as part of a primary survey in the field, we identify which of four broad learning heuristics most accurately reflects individuals’ information processing strategies. Linking these learning heuristics with observed use of rice hybrids, we demonstrate that pure Bayesian learning is well suited for the tinkering and marginal adjustments that are required to learn about a technology like hybrid rice, but it is also more cognitively taxing than other learning styles requiring a longer memory and more complex updating processes. Consequently, only about 25 percent of the farmers in our sample can be characterized as pure Bayesian learners. Present-biased learning and relying on first impressions will likely hinder adoption of a technology like hybrid rice, even after controlling for access to credit and a rudimentary proxy for intelligence.