Introduction to SPPQ Special Issue on Policy Diffusion

610366SPAXXX10.1177/1532440015610366State Politics & Policy QuarterlyBoehmke and Pacheco research-article2015 Introduction Introduction to SPPQ Special Issue on Policy Diffusion State Politics & Policy Quarterly 2016, Vol. 16(1) 3 7 The Author(s) 2015 Reprints and permissions: sagepub.com/journalspermissions.nav DOI: 10.1177/1532440015610366 sppq.sagepub.com Frederick J. Boehmke 1 and Julianna Pacheco 1 Since Walker s (1969) pathbreaking article, over 200 articles have been written on the topic of policy innovation in American politics (Graham, Shipan, and Volden 2013) spanning such policy topics as welfare reform (e.g., Rom, Peterson, and Scheve 1998), school choice (Mintrom 1997), criminal justice (e.g., Makse and Volden 2011), public health (e.g., Shipan and Volden 2006), and Indian gaming (Boehmke and Witmer 2004), to name just a few. Scholars researching policy innovation and diffusion continue to ask why some states adopt certain policies or innovate and others do not? What explains the geographic and temporal patterns of policy diffusion? What is the process through which one state s policy activity influences current or future activities among other states? Why do certain policies diffuse more rapidly than others? How much of the policy process is influenced by internal state characteristics compared with external forces? The study of policy innovation and diffusion continues to expand and change rapidly, particularly in the last decade (Graham, Shipan, and Volden 2013). While much has been learned through the single policy event history analysis (EHA) approach first introduced by F. S. Berry and Berry (1990) in their classic study on lottery adoptions, scholars have begun to exhaust the great variety of questions that can be studied or answered with this single method. There have been both theoretical and methodological pushes to advance the study of policy diffusion, whether through more rigorous theorizing (Pacheco 2012; Volden, Ting, and Carpenter 2008), new measures of key explanatory concepts (W. D. Berry and Baybeck 2005; Shipan and Volden 2006), new measures of policy outcomes (Boehmke and Skinner 2012; Boushey 2010; Karch 2007), or new methods for studying innovation (Boehmke 2009; Volden 2006). Given the rapid pace of development and the multiple directions it took, we convened a small gathering of experts in the field hosted by the University of Iowa and sponsored by the Benjamin F. Shambaugh Memorial Fund and the Department of Political Science to assess the state of the field and to discuss directions for moving 1 The University of Iowa, Iowa City, IA, USA Corresponding Author: Frederick J. Boehmke, The University of Iowa, 341 Schaeffer Hall, Iowa City, IA 52242, USA. Email: frederick-boehmke@uiowa.edu

4 State Politics & Policy Quarterly 16(1) forward. We sought a broad conversation and scheduled 15 papers discussing policy diffusion between the American states as well as between countries. These presentations demonstrated that the pace of innovation in the policy diffusion literature continues to be high, with recurring conversations about what we seek to test and the best way to test it as well as continued methodological innovation through the application of spatial econometrics, text analysis, network analysis, and experiments. The conference led to an open call for submissions to put together this special issue of State Politics & Policy Quarterly (SPPQ) on policy diffusion in the American states. The issue begins with a short commentary by Gilardi (2016) titled Four Ways We Can Improve Policy Diffusion Research. Heeding the call for American politics scholars to integrate their work with other subfields (Graham, Shipan, and Volden 2013), Gilardi cites extensive literature from comparative politics and international relations and suggests the following strategies for improvement: (1) consistency in concepts and measurement, (2) clarification of the goals of the diffusion research, (3) attention to the research design, and (4) discussion of practical implications. It is our hope that scholars reflect upon these recommendations in the future. The remaining articles can be divided into providing primarily theoretical or methodological advances to the study of policy diffusion in the American states. Theoretical Advances Karch and Rosenthal (2016) highlight that policy diffusion processes need not only occur horizontally across states. Diffusion can also occur vertically (also called bottom-up diffusion) as the national government learns about policy successes or failures from the 50 states. Oftentimes, policy advocates strategically begin at the state level in hopes that policy adoptions will bubble-up to the federal level, especially in cases where Congress has failed to act such as with sales taxation associated with electronic commerce. Karch and Rosenthal (2016) finds evidence of federal policy learning with electronic commerce, yet only in the early stages of the legislative process when specific laws and rhetoric were under consideration by policy makers. There is less evidence that policy learning occurs in the later stages of policy adoption. Building off the policy learning mechanism of policy diffusion, Volden (2016) explores the extent to which states abandon policies after learning about failures in other states. Using a dyad-based EHA, Volden (2016) finds that states are more likely to abandon welfare-to-work policies during the formative years of the Temporary Assistance for Needy Families (TANF) after learning that a state s policy failed in its objectives, such as not decreasing unemployment in welfare recipients, welfare rolls, or overall poverty rates. Moreover, learning from policy failures is amplified in state pairings that are ideologically similar as well as among professional state legislatures. Nicholson-Crotty and Carley (2016) also find evidence that diffusion occurs via policy learning, but remind us that learning can occur through the implementation process. More specifically, Nicholson-Crotty and Carley (2016) argue that lawmakers will be particularly likely to adopt effective policies from states that share similar

Boehmke and Pacheco 5 implementation capacities, such as when two states have similarly regulated electricity markets. Using a dyadic approach on the probability of a state to adopt Renewable Portfolio Standards (RPS), the authors find that policy learning about the effectiveness of RPS is magnified when states have similar regulation markets. This research implies that lawmakers consider their likelihood of replicating success when emulating effective policies. Methodological Advances Building off of recent work that uses large datasets of policies (e.g., Boehmke and Skinner 2012), Mallison (2016) uses survival analysis to create a new estimate of policy adoption speed from 1902 to 2010. Armed with this continuous estimate, Mallison then shows how useful such a measure is by exploring whether diffusion speeds up over time and how policy characteristics (e.g., policy complexity and salience) and the clustering of policies influence the speed of adoption. We concur with Mallison that a continuous measure of adoption speed holds promise for our understanding of the mechanisms of policy diffusion as well as how the political or policy environment influences the spread of policies. Kreitzer and Boehmke (2016) highlight the importance of choosing the correct estimation strategy for studying the policy diffusion process across multiple issues. Using Monte Carlo simulations, the authors show that multilevel models with random coefficients produce better estimates than strategies using fixed effects or clustering to model heterogeneity across policies. Kreitzer and Boehmke (2016) go on to show the value of this estimation technique on a unique dataset of 29 anti-abortion rights policies from 1973 to 2013. Scholars are advised to incorporate random effects and random coefficients into pooled event history analyses (PEHA) when appropriate in their own data. Future Directions These articles and the other research presented at the conference demonstrate that the study of policy diffusion remains a revitalized field that has taken advantage of new methodological innovations to study its core issues and has been the impetus for developing new methods in its own right. Given the complexity of the underlying process of diffusion and the development of ever-more nuanced theories of the diffusion process, bringing new methods to bear and identifying creative ways to use existing tools remain critical for the field to move forward. At its core, policy adoption remains a binary outcome, and often a rare event. But rich concepts underlie adoption and diffusion and scholars have increasingly begun to find ways to measure these richer concepts, whether at the micro or macro levels. Combining this with further theoretical advances and, perhaps even more important, attention to theoretical clarity in empirical tests will allow the field to continue to move forward apace.

6 State Politics & Policy Quarterly 16(1) Acknowledgments The impetus of this special issue came out of a conference hosted by the Department of Political Science at the University of Iowa in March 2014 titled New Frontiers in Policy Diffusion. We thank the following conference participants for their insightful comments and lively discussions: Graeme Boushey, Fabrizio Gilardi, Andy Karch, Pam McCann, Sean Nicholson-Crotty, Craig Volden, Bruce Desmarias, Zach Elkins, Jude Hayes, Rachael Hinkle, Adam Hughes, Rebecca Kreitzer, Jonathan Ring, Emily Schilling, and Rob McGrath. We also thank the SPPQ editors, Chris Bonneau and Kris Kanthak, as well as anonymous reviewers, for their assistance in compiling the special issue. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Support for the conference was provided by the Benjamin F. Shambaugh Memorial Fund and the Department of Political Science. References Berry, Frances Stokes, and William D. Berry. 1990. State Lottery Adoptions as Policy Innovations: An Event History Analysis. American Political Science Review 84 (2): 395 415. Berry, William D., and Brady Baybeck. 2005. Using Geographic Information Systems to Study Interstate Competition. American Political Science Review 99 (4): 505 19. Boehmke, Frederick J. 2009. Approaches to Modeling the Adoption and Diffusion of Policies with Multiple Components. State Politics & Policy Quarterly 9 (2): 229 52. Boehmke, Frederick J., and Paul Skinner. 2012. State Policy Innovativeness Revisited. State Politics & Policy Quarterly 12 (3): 303 29. Boehmke, Frederick J., and Richard Witmer. 2004. Disentangling Diffusion: The Effects of Social Learning and Economic Competition on State Policy Innovation and Expansion. Political Research Quarterly 57 (1): 39 51. Boushey, Graeme. 2010. Policy Diffusion Dynamics in America. New York: Cambridge University Press. Gilardi, Fabrizio. 2016. Four Ways We Can Improve Policy Diffusion Research. State Politics & Policy Quarterly 16 (1): 8 21. Graham, Erin R., Charles R. Shipan, and Craig Volden. 2013. The Diffusion of Policy Diffusion Research in Political Science. British Journal of Political Science 43 (3): 673 701. Karch, Andrew, and Aaron Rosenthal. 2016. Vertical Diffusion and the Shifting Politics of Electronic Comerce. State Politics & Policy Quarterly 16 (1): 22 43. doi:10.1177/ 1532440015593811. Kreitzer, Rebecca J., and Frederick J. Boehmke. 2016. Modeling Heterogeneity in Pooled Event History Analysis. State Politics & Policy Quarterly 16 (1): 121 141. doi:10.1177/ 1532440015592798. Makse, Todd, and Craig Volden. 2011. The Role of Policy Attributes in the Diffusion of Innovations. The Journal of Politics 73 (1): 108 24.

Boehmke and Pacheco 7 Mallison, Daniel J. 2016. Building a Better Speed Trap: Measuring Policy Adoption Speed in the American States. State Politics & Policy Quarterly 16 (1): 98 120. doi:10.1177/1532440015596088. Mintrom, Michael. 1997. Policy Entrepreneurs and the Diffusion of Innovation. American Journal of Political Science 41 (3): 738 70. Nicholson-Crotty, Sean, and Sanya Carley. 2016. Effectiveness, Implementation, and Policy Diffusion: Or Can We Make That Work for Us? State Politics & Policy Quarterly 16 (1): 78 97. doi:10.1177/1532440015588764. Pacheco, Julianna. 2012. The Social Contagion Model: Exploring the Role of Public Opinion on the Diffusion of Antismoking Legislation across the American States. The Journal of Politics 74 (1): 187 202. Rom, Mark C., Paul Peterson, and Kenneth F. Scheve, Jr. 1998. Interstate Competition and Welfare Policy. Publius 28 (3): 17 37. Shipan, Charles R., and Craig Volden. 2006. Bottom-Up Federalism: The Diffusion of Antismoking Policies from U.S. Cities to States. American Journal of Political Science 50 (4): 825 43. Volden, Craig. 2006. States as Policy Laboratories: Emulating Successes in the Children s Health Insurance Program. American Journal of Political Science 50 (2): 294 312. Volden, Craig, Michael M. Ting, and Daniel P. Carpenter. 2008. A Formal Model of Learning and Policy Diffusion. American Political Science Review 102 (3): 319 32. Volden, Craig. 2016. Failures: Diffusion, Learning, and Policy Abandonment. State Politics & Policy Quarterly 16 (1): 44 77. doi:10.1177/1532440015588910. Walker, Jack L. 1969. The Diffusion of Innovations among the American States. American Political Science Review 63 (3): 880 99. Author Biographies Frederick J. Boehmke is professor of Political Science and Director of the Iowa Social Science Research Center. He received his PhD from California Institute of Technology in 2000 and spent two years as a Robert Wood Johnson Scholar in Health Policy Research at the University of Michigan. Julianna Pacheco is assistant professor of Political Science. She received her PhD from The Pennslyvania State University in 2010 and was a Robert Wood Johnson Health and Society Scholar at the University of Michigan from 2010-2012.