Evolution of Learning in Technology Adoption: The case of the U.S. Soybean Seed Industry.

This paper examines how the evolution of learning affects technology adoption. We use a sequential adoption model that accounts for differences between forward-looking adopters, who consider future impacts of their learning, and myopic adopters, who only consider past learning. We apply the analysis to three panels of U.S. soybean farmers representing different stages of the genetically modified (GM) seed technology diffusion path. We show that uncertainty is considerably reduced over time due to increased learning efficiency. Our results indicate that a forward-looking model fits the early adopters and early majority stages better, while both models perform equally well in the laggard stage. JEL classificationD83, Q31, Q33, Q16