Collaborative Research: An Expanded Framework for Inferring Public Policy Diffusion Networks

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General SummaryState governments are commonly referred to as "laboratories of democracy?"because for manydecades they have experimented with new policy solutions to society's most important problems.Public policy scholars and practitioners of politics often look to the states for innovative policiesthat can be emulated by other governments across the country; a process referred to as policydiffusion. However, the complex relationships between the states (that is, the patterns,pathways, and mechanisms underlying how policies actually diffuse from one state to another)are not well understood. In this research, the PIs develop innovative tools anddata to facilitate the large-scale computational and data-intensive study of policy diffusion. Theyseek to develop and utilize the largest database on states' decisions to adopt policies to betterunderstand which states are policy leaders, which are policy followers, and why. This willprovide a novel, expansive, and systematic view of the policy diffusion process that covers theAmerican states. As a result, policymakers and other interested stakeholders will have access to acomprehensive look at the spread of innovation among state governments regarding financial,environmental, health, security, and other social problem domains.Technical AspectsThis research seeks to make three central contributions to the study of public policy diffusion.First, it will produce a database on the adoption of hundreds of policies in the American states, atleast tripling the size of the largest database currently available and yielding a sample of policiesthat is more representative of the universe of policies for which states make laws. It will alsoleverage these new data to map and analyze the network pathways according to which policiesdiffuse (i.e., establish states that are leaders and followers in the diffusion of policies). Asrecently published research by the principal investigators shows, cutting-edge computationalmethods can be applied to large databases containing information on when policies were adoptedby which governments in order to identify underlying networks along which policies persistentlydiffuse. Another key contribution from this research is to improve upon the existing method forinferring network ties in ways that are most suitable for policy diffusion research (as well associal science more generally) and implement it in a user-friendly package in the R statisticalenvironment. A final contribution, which is intended to maximize the potential user-base of thesedata and methods, will be to build an interactive online portal to the data, complete withvisualizations and automated analytics.