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Duration Dependence, Functional Form, and Corrected Standard Errors: Improving EHA Models of State Policy Diffusion

Published online by Cambridge University Press:  25 January 2021

Jack Buckley  and
Chad Westerland
Jack Buckley
Affiliation:
Boston College
Chad Westerland
Affiliation:
State University of New York at Stony Brook
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Abstract

Discrete event history analysis (EHA) is the analytic tool of choice for many scholars of policy diffusion across American states. Unfortunately, the policy diffusion literature largely ignores several important specification issues for EHA models: duration dependence, choice of functional form, and the computation of standard errors corrected for temporal and spatial dependence. We use data from Berry and Berry's (1990) seminal study of state lottery diffusion to demonstrate ways to deal properly with these issues.

Type
The Practical Researcher
Information
State Politics & Policy Quarterly , Volume 4 , Issue 1 , Spring 2004 , pp. 94 - 113
Copyright
Copyright © Board of Trustees of the University of Illinois

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References

Aldrich, John H., and Nelson, Forrest D.. 1984. Linear Probability, Logit, and Probit Models. Beverly Hills, CA: Sage.CrossRefGoogle Scholar
Allison, Paul D. 1984. Event History Analysis: Regression for Longitudinal Data. Newbury Park, CA: Sage.CrossRefGoogle Scholar
Anselin, Luc. 1988. Spatial Econometrics: Methods and Models. Dordrecht, The Netherlands: Kluwer.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
Beck, Nathaniel, and Jackman, Simon. 1998. “Beyond Linearity by Default: Generalized Additive Models.” American Journal of Political Science 42:596–627.CrossRefGoogle Scholar
Bennett, D. Scott. 1998. “Integrating and Testing Models of Rivalry Duration.” American Journal of Political Science 42:1200–32.CrossRefGoogle Scholar
Bennett, D. Scott. 1999. “Parametric Models, Duration Dependence, and Time-Varying Data Revisited.” American Journal of Political Science 43:256–70.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
Berry, Frances Stokes, and Berry, William D.. 1991. “Specifying a Model of State Policy Innovation (Response).” American Political Science Review 85:573–9.Google Scholar
Berry, Frances Stokes, and Berry, William D.. 1992. “Tax Innovation in the States: Capitalizing on Political Opportunity.” American Journal of Political Science 36:715–42.CrossRefGoogle Scholar
Berry, Frances Stokes, and Berry, William D.. 1999. “Innovation and Diffusion Models in Policy Research.” In Theories of the Policy Process, ed. Sabatier, Paul A.. Boulder, CO: Westview.Google Scholar
Box-Steffensmeier, Janet M., and Jones, Bradford S.. N.d. Timing and Political Change: Event History Modeling in Political Science. Ann Arbor, MI: University of Michigan Press.Google Scholar
Congdon, Peter. 2003. Applied Bayesian Modeling. West Sussex, UK: Wiley.CrossRefGoogle Scholar
Dubin, R. 1995. “Estimating Logit Models with Spatial Dependence.” In New Directions in Spatial Econometrics, eds. Anselin, Luc, and Florax, R.. Berlin: Springer-Verlag.Google Scholar
Efron, Bradley, and Tibshirani, Robert J.. 1993. An Introduction to the Bootstrap. New York: Chapman and Hall.CrossRefGoogle Scholar
Frant, Howard. 1991. “Specifying a Model of State Policy Innovation.” American Political Science Review 85:571–3.CrossRefGoogle Scholar
Gilks, W. R., Thomas, A., and Spiegelhalter, David J.. 1994. “A Language and Program for Complex Bayesian Modeling.” The Statistician 43:169–78.CrossRefGoogle Scholar
Gill, Jeff. 2001. “Whose Variance Is It Anyway? Interpreting Empirical Models with State-Level Data.” State Politics and Policy Quarterly 1:318–38.CrossRefGoogle Scholar
Gill, Jeff. 2002. Bayesian Methods: A Social and Behavioral Sciences Approach. Boca Raton, FL: Chapman and Hall/CRC.CrossRefGoogle Scholar
Greene, William H. 2000. Econometric Analysis. 4th ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome. 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer-Verlag.CrossRefGoogle Scholar
Heagerty, Patrick J., Ward, Michael D., and Gleditsch, Kristian Skrede. 2002. “Windows of Opportunity: Window Subseries Empirical Variance Estimators in International Relations.” Political Analysis 10:304–17.CrossRefGoogle Scholar
Heagerty, Patrick J., and Lumley, Thomas. 2000. “Window Subsampling of Estimating Functions with Application to Regression Models.” Journal of the American Statistical Association 95:197–211.CrossRefGoogle Scholar
Huber, P. J. 1967. “The Behavior of Maximum-Likelihood Estimates Under Non-Standard Conditions.” In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, eds. L. LeCam and J. Neyman. Berkeley, CA: University of California Press.Google Scholar
King, Gary, and Zeng, Langche. 2001. “Logistic Regression in Rare Events Data.” Political Analysis 9:137–63.CrossRefGoogle Scholar
McCullagh, P., and Nelder, J. A.. 1989. Generalized Linear Models. 2nd ed. London: Chapman and Hall.CrossRefGoogle Scholar
Mintrom, Michael. 1997. “Policy Entrepreneurs and the Diffusion of Innovation.” American Journal of Political Science 42:738–70.Google Scholar
Mintrom, Michael, and Vergari, Sandra. 1998. “Policy Networks and Innovation Diffusion: The Case of Education Reform.” Journal of Politics 60:126–48.CrossRefGoogle Scholar
Mooney, Christopher Z. 1997. “Social Learning or Political Threat: Is the Regional Effect on the Diffusion of State Policy Always Positive and Unidirectional?” Presented at the Annual Meeting of the Southern Political Science Association, Norfolk, VA.Google Scholar
Mooney, Christopher Z. 2001. “Modeling Regional Effects on State Policy Diffusion.” Political Research Quarterly 54:103–24.CrossRefGoogle Scholar
Mooney, Christopher Z., and Lee, Mei-Hsien. 1995. “Legislating Morality in the American States: The Case of Pre-Roe Abortion Regulation Reform.” American Journal of Political Science 39:599–627.CrossRefGoogle Scholar
Mooney, Christopher Z., and Duval, Robert D.. 1993. Bootstrapping: A Non-Parametric Approach to Statistical Inference. Newbury Park, CA: Sage.CrossRefGoogle Scholar
Nagler, Jonathan. 1994. “Scobit: An Alternative Estimator to Probit and Logit.” American Journal of Political Science 38:230–55.CrossRefGoogle Scholar
Raftery, A. E., and Lewis, S. M.. 1992. “How Many Iterations in the Gibbs Sampler?” In Bayesian Statistics 4, eds. Bernando, J. M., Smith, A. F. M., David, A. P., and Berger, J. O.. Oxford, UK: Oxford University Press.Google Scholar
Raftery, A.E., and Lewis, S. M.. 1996. “Implementing MCMC.” In Markov Chain Monte Carlo in Practice, eds. Gilks, W. R., Richardson, S., and Spiegelhalter, D. J.. New York: Chapman and Hall.Google Scholar
Rogers, W. H. 1993. “sg17: Regression Standard Errors in Clustered Samples.” Stata Technical Bulletin 53:32–5.Google Scholar
Spiegelhalter, David J., Thomas, Andrew, and Best, N. G.. 1999. WinBUGS Version 1.2 User Manual. Cambridge, UK: MRC Biostatistics Unit.Google Scholar
Tobler, W. 1970. “A Computer Movie Simulating Growth in the Detroit Region.” Economic Geography 46:234–40.CrossRefGoogle Scholar
Tobler, W. 1979. “Cellular Geography.” In Philosophy in Geography, eds. Gale, S. and Olsson, G.. Dordrecht, The Netherlands: Reidel.Google Scholar
Walker, Jack L. 1969. “The Diffusion of Innovations among the States.” American Political Science Review 63:880–99.CrossRefGoogle Scholar
Western, Bruce, and Jackman, Simon. 1994. “Bayesian Inference for Comparative Research.” American Political Science Review 88:412–23.CrossRefGoogle Scholar
White, H. 1980. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica 48:817–30.CrossRefGoogle Scholar
Zorn, Christopher J. 2000. “Modeling Duration Dependence.” Political Analysis 8:367–80.CrossRefGoogle Scholar
Zorn, Christopher J. 2001. “Generalized Estimating Equation Models for Correlated Data: A Review with Applications.” American Journal of Political Science 45:470–90.CrossRefGoogle Scholar