Virtual Room 1: Spatial Analysis

Date: 

Tuesday, July 14, 2020, 12:00pm to 1:30pm

Causal Inference for Policy Diffusion

Naoki Egami

Network Event History Analysis for Modeling Public Policy Adoption with Latent Diffusion Networks

Bruce Desmarais, Jeffrey J. Harden, Mark Brockway, Frederick J. Boehmke, Scott LaCombe and Hanna Wallach

 

Chair: Neal Beck (New York University)

 

Co-Host: Sophie Borwein (University of Toronto)

Causal Inference for Policy Diffusion

Author(s): Naoki Egami

Discussant: Yiqing Xu (Stanford University)

 

Understanding why governments adopt policies and how policy innovations diffuse from one government to others is a central goal in all subfields of political science. Despite numerous methodological developments in the policy diffusion literature, unfortunately, fundamental issues of causal inference have been left unaddressed for decades. As a result, little is known about which substantive findings in the literature have causal interpretations. To improve causal inferences in policy diffusion studies, we make three contributions. First, we define a variety of causal effects relevant to policy diffusion questions and clarify assumptions required for causal identification. Second, we provide a general estimation method by extending the standard event history analysis commonly used in practice. Finally, we propose a sensitivity analysis method that can assess the potential influence of unmeasured confounding on causal conclusions. We illustrate the general applicability of the proposed approach using a diffusion study of abortion policies. Open-source software will be made available for implementing our methods.

Network Event History Analysis for Modeling Public Policy Adoption with Latent Diffusion Networks

Author(s): Bruce Desmarais, Jeffrey J. Harden, Mark Brockway, Frederick J. Boehmke, Scott LaCombe and Hanna Wallach

Discussant: Shahryar Minhas (Michigan State University)

 

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. Diffusion network inference is a recently-developed methodology that identifies latent, dynamic networks connecting units based on repeated adoption decisions, rather than shared borders or other similarities. 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 that address the disconnect between models for network inference and models for policy adoption. 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 a published model of policy adoption, updating it to incorporate diffusion network structure. We evaluate differences in covariate effects, and consider whether the incorporation of networks improves model. We conclude that NEHA is a valuable method for incporporating diffusion networks into the study of public policy diffusion.

 
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