State Learning and Emulation: Exploring Ambiguities in Policy Diffusion Mechanisms

What is the difference between policy learning and emulation, and how does the salience of an issue impact each process? Understanding how and why policy diffuses has been the focus of the field over the previous two decades. This has culminated in a paradigm where policy can diffuse through one of four key mechanisms. Only two of these mechanisms, coercion and competition, have fully developed explanations for when they happen. Learning and emulation can have ambiguous definitions in the literature and a limited understanding of when each might occur. I explore the relationship between the two to bring our understanding of these mechanisms to the level that exists for competition and coercion. My research design exploits the nature of US election policy to do so. Using election policy from the National Conference of State Legislatures and election turnout data from the State Legislative Election Returns database, I identify that learning and emulation are distinct concepts, each with different influences on the patterns of policy diffusion. Policy salience and vertical learning processes can also impact the diffusion mechanism choice, with salient policies being more likely to diffuse via policy learning. I provide a more detailed understanding of emulation and learning, as well as how they are measured. I also provide policy-specific results to US election policy diffusion, specifically the effects of the Shelby v. Holder voting rights decision on how states adopt policy.