Abstract:

Firms often decide whether to adopt an innovation of uncertain value in markets where the outcomes of earlier adopters are observed. This paper introduces a flexible Bayesian model suitable for the analysis of social learning, competition, and diffusion in such environments. Agents in the model have (potentially misspecified) theories of how others’ profits relate to their own, and use these to make their adoption decisions. When adopting, agents steal business from and inform others. I estimate the model exploiting a unique reform in Illinois that legalized slot machines, and empirically study how information and adoption diffuse through a network. This setting is well-suited for such analysis, since gambling data are publicly available, adoption is a discrete action, and the set of potential adopters (liquor license holders) is defined by law. I find that establishments that observe more adoption or higher neighbors’ profits are more likely to adopt themselves, yet learning could improve since they do not use all the relevant information. Establishments have diffuse priors and they learn from more neighbors than they compete with. The direction and extent to which learning affects adoption are ex-ante ambiguous. In two counterfactual exercises I show that increasing information availability or learning substantially increases both adoption and total profits in the market.