We put forward a new approach to studying issue definition within the context of policy diffusion. Most studies of policy diffusion—which is the process by which policy making in one government affects policymaking in other governments—have focused on policy adoptions. We shift the focus to an important but neglected aspect of this process: the issue definition stage. We use topic models to estimate how policies are framed during this stage and how these frames are predicted by prior policy adoptions. Focusing on smoking restriction in U.S. states, our analysis draws upon an original dataset of over 52,000 paragraphs from newspapers covering 49 states between 1996 and 2013. We find that frames regarding the policy’s concrete implications are predicted by prior adoptions within a state’s diffusion network, while frames regarding its normative justifications are not. Our approach and findings open the way for a new perspective to studying policy diffusion in many different areas.

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Fabrizio Gilardi is a professor of policy analysis in the Department of Political Science of the University of Zurich and an editor of the Journal of Public Policy and DeFacto. He studies policy diffusion, gender & politics, and digital democracy. He is the co-founder of the Digital Democracy Lab.