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Estimating the Impact of State Policies and Institutions with Mixed-Level Data

Researchers often seek to understand the effects of state policies or institutions on individual behavior or other outcomes in sub-state-level observational units (e.g., election results in state legislative districts). However, standard estimation methods applied to such models do not properly account for the clustering of observations within states and may lead researchers to overstate the statistical significance of state-level factors. We discuss the theory behind two approaches to dealing with clusteringclustered standard errors and multilevel modeling. We then demonstrate the relevance of this topic by replicating a recent study of the effects of state post-registration laws on voter turnout (Wolfinger, Highton, and Mullin 2005). While we view clustered standard errors as a more straightforward, feasible approach, especially when working with large datasets or many cross-level interactions, our purpose in this Practical Researcher piece is to draw attention to the issue of clustering in state and local politics research.

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File URL: http://economics.missouri.edu/working-papers/2006/wp0603_milyo.pdf
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Paper provided by Department of Economics, University of Missouri in its series Working Papers with number 0603.

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Length: 18 pgs.
Date of creation: 22 Feb 2006
Date of revision:
Handle: RePEc:umc:wpaper:0603
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Web page: http://economics.missouri.edu/

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  1. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
  2. Moulton, Brent R, 1990. "An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Unit," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 334-38, May.
  3. Gabor Kezdi, 2005. "Robus Standard Error Estimation in Fixed-Effects Panel Models," Econometrics 0508018, EconWPA.
  4. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
  5. Froot, Kenneth A., 1989. "Consistent Covariance Matrix Estimation with Cross-Sectional Dependence and Heteroskedasticity in Financial Data," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 24(03), pages 333-355, September.
  6. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2002. "How Much Should We Trust Differences-in-Differences Estimates?," NBER Working Papers 8841, National Bureau of Economic Research, Inc.
  7. Rick L. Williams, 2000. "A Note on Robust Variance Estimation for Cluster-Correlated Data," Biometrics, The International Biometric Society, vol. 56(2), pages 645-646, 06.
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