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

General Summary

State governments are commonly referred to as 'laboratories of democracy?'because for many

decades 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 policies

that can be emulated by other governments across the country; a process referred to as policy

diffusion. 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 and

data to facilitate the large-scale computational and data-intensive study of policy diffusion. They

seek to develop and utilize the largest database on states' decisions to adopt policies to better

understand which states are policy leaders, which are policy followers, and why. This will

provide a novel, expansive, and systematic view of the policy diffusion process that covers the

American states. As a result, policymakers and other interested stakeholders will have access to a

comprehensive look at the spread of innovation among state governments regarding financial,

environmental, health, security, and other social problem domains.

Technical Aspects

This 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, at

least tripling the size of the largest database currently available and yielding a sample of policies

that is more representative of the universe of policies for which states make laws. It will also

leverage these new data to map and analyze the network pathways according to which policies

diffuse (i.e., establish states that are leaders and followers in the diffusion of policies). As

recently published research by the principal investigators shows, cutting-edge computational

methods can be applied to large databases containing information on when policies were adopted

by which governments in order to identify underlying networks along which policies persistently

diffuse. Another key contribution from this research is to improve upon the existing method for

inferring network ties in ways that are most suitable for policy diffusion research (as well as

social science more generally) and implement it in a user-friendly package in the R statistical

environment. A final contribution, which is intended to maximize the potential user-base of these

data and methods, will be to build an interactive online portal to the data, complete with

visualizations and automated analytics.