Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-10T10:40:10.989Z Has data issue: false hasContentIssue false
  • English
  • Français

Approaches to Modeling the Adoption and Diffusion of Policies with Multiple Components

Published online by Cambridge University Press:  25 January 2021

Frederick J. Boehmke
Frederick J. Boehmke*
Affiliation:
University of Iowa
Get access Rights & Permissions [Opens in a new window]

Abstract

Scholars have begun to move beyond the dichotomous dependent variable—indicating whether a state adopts a policy or not in a given year—usually employed in event history analysis. In particular, they have devoted increasing attention to the components of policies that states adopt. I discuss a variety of estimators that have been employed to analyze the adoption and modification of policies with multiple components, including various forms of event history analysis, OLS, and event count models. With various modifications, the researcher can estimate models that treat each component as distinct, pool these models to leverage commonalities across components, or treat the components as identical parts of the same process. Each of these has its strengths and may be appropriate in certain circumstances. Nonetheless, in the majority of cases, some version of event history analysis for multiple or repeat failures is likely to be preferred. The different approaches are illustrated by studying state adoption of various obesity-related policies.

Type
The Practical Researcher
Information
State Politics & Policy Quarterly , Volume 9 , Issue 2 , Summer 2009 , pp. 229 - 252
Copyright
Copyright © 2009 by the Board of Trustees of the University of Illinois

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allison, Paul D. 1984. Event History Analysis: Regression for Longitudinal Event Data. Sage University Paper Series on Quantitative Applications in the Social Sciences, series no. 07–046. Beverly Hills, CA: Sage Publications.CrossRefGoogle Scholar
Alt, James E., King, Gary, and Signorino, Curtis. 2001. “Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data.” Political Analysis 9:21–44.CrossRefGoogle Scholar
Beck, Nathaniel, Katz, Jonathan N., and Tucker, Richard. 1998. “Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable.” American Journal of Political Science 42:1260–88.CrossRefGoogle Scholar
Berry, Frances Stokes, and Berry, William D.. 1990. “State Lottery Adoptions as Policy Innovations: An Event History Analysis.” American Political Science Review 84:395–415.CrossRefGoogle Scholar
Boehmke, Frederick J. 2005. The Indirect Effect of Direct Legislation: How Institutions Shape Interest Groups Systems. Columbus, OH: Ohio State University Press.Google Scholar
Boehmke, Frederick J., and Witmer, Richard. 2004. “Disentangling Diffusion: The Effects of Social Learning and Economic Competition on State Policy Innovation and Expansion.” Political Research Quarterly 57:39–51.CrossRefGoogle Scholar
Box-Steffensmeier, Janet M., and Jones, Bradford D.. 2004. Event History Modeling: A Guide for Social Sciences. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Box-Steffensmeier, Janet M., and Zorn, Christopher. 2002. “Duration Models for Repeated Events.” The Journal of Politics 64:1069–94.CrossRefGoogle Scholar
Cameron, A. Colin, and Trivedi, Pravin K.. 1998. Regression Analysis of Count Data. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Council of State Governments. Various years. The Book of the States. Lexington, KY: Council of State Governments.Google Scholar
Erikson, Robert S., Wright, Gerald C., and McIver, John P.. 1993. Statehouse Democracy: Public Opinion and Policy in the American States. Cambridge, UK: Cambridge University Press.Google Scholar
Flegal, Katherine M., Carroll, Margaret D., Ogden, Cynthia L., and Johnson, Clifford L.. 2002. “Prevalence and Trends in Obesity Among US Adults, 1999–2000.” Journal of the American Medical Association 288:1723–27.Google Scholar
Imhof, Sara. 2006. Promoting a “Good Death”: An Investigation of the Determinants that Explain State Medical Board Adoptions of Pain Management-Enhancing Policies in the United States. Ph.D. dissertation, University of Iowa.Google Scholar
Jacoby, William G., and Schneider, Saundra K.. 2001. “Variability in State Policy Priorities: An Empirical Analysis.” The Journal of Politics 63:544–68.CrossRefGoogle Scholar
Jones, Bradford S., and Branton, Regina P.. 2005. “Beyond Logit and Probit: Cox Duration Models for State Policy Adoption.” State Politics and Policy Quarterly 5:420–43.CrossRefGoogle Scholar
Karch, Andrew. 2007. “Emerging Issues and Future Directions in State Policy Diffusion Research.” State Politics and Policy Quarterly 7:54–80.CrossRefGoogle Scholar
Kersh, Rogan, and Morone, James A.. 2005. “Obesity, Courts, and the New Politics of Public Health.” Journal of Health Politics, Policy, and Law 30:839–68.CrossRefGoogle ScholarPubMed
King, Gary. 1989a. “Statistical Models for Political Science Event Counts: Bias in Conventional Procedures and Evidence for the Exponential Poisson Regression Model.” American Journal of Political Science 32:838–63.Google Scholar
King, Gary. 1989b. “Variance Specification in Event Count Models: From Restrictive Assumptions to a Generalized Estimator.” American Journal of Political Science 33:762–84.CrossRefGoogle Scholar
King, James D. 2000. “Changes in Professionalism in U.S. State Legislatures.” Legislative Studies Quarterly 25:327–43.CrossRefGoogle Scholar
Maddala, G.G. 1983. Limited Dependent and Qualitative Variables in Econometrics. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Mokdad, Ali H., Serdula, Mary K., Dietz, William H., Bowman, Barbara A., Marks, James S., and Koplan, Jeffrey P.. 1999. “The Spread of the Obesity Epidemic in the United States, 1991–1998.” Journal of the American Medical Association 282:1519–22.Google ScholarPubMed
Prentice, R. L., Williams, B. J., and Peterson, A. V.. 1981. “On the Regression Analysis of Multivariate Failure Time Data.” Biometrika 68:373–9.CrossRefGoogle Scholar
Shipan, Charles R., and Volden, Craig. 2006. “Bottom-Up Federalism: The Diffusion of Antismoking Policies from U.S. Cities to States.” American Journal of Political Science 50:825–43.CrossRefGoogle Scholar
Shipan, Charles R., and Volden, Craig. 2007. “Local Antismoking Laws and the Mechanisms of Policy Diffusion.” Paper presented at the Annual Meeting of the Midwest Political Science Association, Chicago, IL, April 12–15.Google Scholar
U.S. Bureau of the Census. Various years. Statistical Abstract of the United States. Washington, DC: U.S. Government Printing Office.Google Scholar
Volden, Craig. 2006. “States as Policy Laboratories: Emulating Success in the Children's Health Insurance Program.” American Journal of Political Science 50:294–312.CrossRefGoogle Scholar
Wei, L. J., Lin, D. Y., and Weissfeld, L.. 1989. “Regression Analysis of Multivariate Incomplete Failure Time Data by Modeling Marginal Distributions.” Journal of the American Statistical Association 84:1065–73.CrossRefGoogle Scholar
Yackee, Susan Webb. 2005. “Coalitions in Conflict: Competing Interests in State Medical Malpractice Laws.” Paper Presented at the State Politics and Policy Conference, East Lansing, MI, May 13–14.Google Scholar
Yu, Eric C. H. 2005. “The Impact of Welfare Reforms on Welfare Caseload.” Paper presented at the State Politics and Policy Conference, East Lansing, MI, May 13–14.Google Scholar