Farm Heterogeneity in Biotechnology Adoption with Risk and Learning: an Application to U.S. Corn

We investigate the role of risk and learning in biotechnology adoption, with an empirical focus on the adoption of Genetically Modified (GM) corn in the U.S. Corn Belt. Relying on the Kalman filter algorithm, a conceptual structural dynamic programming (DP) model is developed to capture the relative roles of individual and social learning with farm risk preferences. Farm heterogeneity is explored by comparing parameter estimates for the early-, the intermediate-, and the late- adopters by adoption timing. Results show relative risk aversion coefficient is higher for the late adopter and lower for the early adopter. This reflects that farmers’ adoption timing is related to their degree of risk aversion: the more risk-averse farmers are, the later they adopt GM technology. Also, the social learning parameter, representing the strength of information externalities, is higher for the late adopter and lower for the early adopter, indicating that early adopters rely less on information externalities, while late adopters rely more on information from their neighbors.