A Diffusion Network Event History Estimator

Abstract

Research on the diffusion of political decisions across jurisdictions typically accounts for units’ influence over each other with (1) observable measures or (2) by inferring latent network ties from past decisions. The former approach assumes that interdependence is static and perfectly captured by the data. The latter mitigates these issues but requires analytical tools that are separate from the main empirical methods for studying diffusion. As a solution, we introduce network event history analysis (NEHA), which incorporates latent network inference into conventional discrete-time event history models. We demonstrate NEHA’s unique methodological and substantive benefits in applications to policy adoption in the American states. Researchers can analyze the ties and structure of inferred networks to refine model specifications, evaluate diffusion mechanisms, or test new or existing hypotheses. By capturing targeted relationships unexplained by standard covariates, NEHA can improve models, facilitate richer theoretical development, and permit novel analyses of the diffusion process.