Department of Political Science, Penn State University
Title: Network Event History Analysis for Modeling Public Policy Adoption with Latent Diffusion Networks
Research on the diffusion of public policies across jurisdictional units has long identified the choices made by neighboring units as a key external determinant of policy adoption. However, operationalizing the competitive and/or educational forces that drive this process with geographic contiguity is partially a choice made for empirical convenience. Recent work provides a methodology for inferring latent, dynamic networks connecting units based on repeated adoption decisions rather than shared borders. Based on the current state-of-the-art, diffusion network inference must be conducted using analytical tools that are separate from the main empirical methods for studying public policy adoption---discrete-time event history models. We offer two contributions relevant to the empirical study of public policy diffusion. First, we introduce Network Event History Analysis (NEHA)---a modeling framework that incorporates inference regarding latent diffusion pathways into the conventional model used for discrete-time event history analysis. Second, with an extensive application to the study of policy adoption in the American states, we evaluate the role of inferred networks in shaping states' decisions to adopt. Focusing on the literature on policy diffusion in the American states, we replicate six statistical models of policy adoption, updating the models to incorporate diffusion network structure. We show that the incorporation of networks improves model fit for some, but not all, of the models. We also find considerable variance in the marginal effect of network-based parameters on the probability of adoption. We conclude that the NEHA is a valuable method for incorporating diffusion networks into the study of public policy diffusion, and that network inference, in general, can be a useful addition to policy adoption studies.