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Persistent Policy Pathways: Inferring Diffusion Networks in the American States

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  • DESMARAIS, BRUCE A.
  • HARDEN, JEFFREY J.
  • BOEHMKE, FREDERICK J.

Abstract

The transmission of ideas, information, and resources forms the core of many issues studied in political science, including collective action, cooperation, and development. While these processes imply dynamic connections among political actors, researchers often cannot observe such interdependence. One example is public policy diffusion, which has long been a focus of multiple subfields. In the American state politics context, diffusion is commonly conceptualized as a dyadic process whereby states adopt policies (in part) because other states have adopted them. This implies a policy diffusion network connecting the states. Using a dataset of 187 policies, we introduce and apply an algorithm that infers this network from persistent diffusion patterns. The results contribute to knowledge on state policy diffusion in several respects. Additionally, in introducing network inference to political science, we provide scholars across the discipline with a general framework for empirically recovering the latent and dynamic interdependence among political actors.

Suggested Citation

  • Desmarais, Bruce A. & Harden, Jeffrey J. & Boehmke, Frederick J., 2015. "Persistent Policy Pathways: Inferring Diffusion Networks in the American States," American Political Science Review, Cambridge University Press, vol. 109(2), pages 392-406, May.
  • Handle: RePEc:cup:apsrev:v:109:y:2015:i:02:p:392-406_00
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    Citations

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    Cited by:

    1. Michael J. Motta, 2021. "Diffusion and Typology: The Invention and Early Adoption of Medicinal Marijuana and Offshore Wind Policies," Social Science Quarterly, Southwestern Social Science Association, vol. 102(1), pages 567-584, January.
    2. Steven J. Balla & Zhoudan Xie, 2023. "The durability of governance reform: A two‐wave audit of notice and comment policymaking in China," Regulation & Governance, John Wiley & Sons, vol. 17(2), pages 549-569, April.
    3. Brian Y. An & Adam Butz & Min-Kyeong Cha & Joshua L. Mitchell, 2023. "Following neighbors or regional leaders? Unpacking the effect of geographic proximity in local climate policy diffusion," Policy Sciences, Springer;Society of Policy Sciences, vol. 56(4), pages 825-868, December.
    4. Prysmakova, Palina, 2020. "Generation learning framework: Applying Margaret Mead's typology to agenda-setting stage of policy diffusion," Studia z Polityki Publicznej / Public Policy Studies, Warsaw School of Economics, vol. 7(2), pages 1-25, July.
    5. Côme Billard & Anna Creti & Antoine Mandel, 2020. "How Environmental Policies Spread? A Network Approach to Diffusion in the U.S," Working Papers 2020.12, FAERE - French Association of Environmental and Resource Economists.
    6. Yunxiang Zhang & Shichen Wang, 2021. "How does policy innovation diffuse among Chinese local governments? A qualitative comparative analysis of River Chief Innovation," Public Administration & Development, Blackwell Publishing, vol. 41(1), pages 34-47, February.
    7. Daniel M. Butler & Jeffrey J. Harden, 2023. "Can Institutional Reform Protect Election Certification?," The ANNALS of the American Academy of Political and Social Science, , vol. 708(1), pages 257-270, July.
    8. Kim Yeaji & Antenangeli Leonardo & Kirkland Justin, 2016. "Measurement Error and Attenuation Bias in Exponential Random Graph Models," Statistics, Politics and Policy, De Gruyter, vol. 7(1-2), pages 29-54, December.
    9. Fabrizio Gilardi & Charles R. Shipan & Bruno Wüest, 2021. "Policy Diffusion: The Issue‐Definition Stage," American Journal of Political Science, John Wiley & Sons, vol. 65(1), pages 21-35, January.
    10. Thomas Malang & Philip Leifeld, 2021. "The Latent Diffusion Network among National Parliaments in the Early Warning System of the European Union," Journal of Common Market Studies, Wiley Blackwell, vol. 59(4), pages 873-890, July.
    11. Clark, Duncan A. & Macinko, James & Porfiri, Maurizio, 2022. "What factors drive state firearm law adoption? An application of exponential-family random graph models," Social Science & Medicine, Elsevier, vol. 305(C).

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