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 individualbehavior or other outcomes in sub-state-level observational units (e.g., election results in statelegislative districts). However, standard estimation methods applied to such models do notproperly account for the clustering of observations within states and may lead researchers tooverstate the statistical significance of state-level factors. We discuss the theory behind twoapproaches to dealing with clustering clustered standard errors and multilevel modeling. Wethen demonstrate the relevance of this topic by replicating a recent study of the effects of statepost-registration laws on voter turnout (Wolfinger, Highton, and Mullin 2005). While we viewclustered standard errors as a more straightforward, feasible approach, especially when workingwith large datasets or many cross-level interactions, our purpose in this Practical Researcherpiece is to draw attention to the issue of clustering in state and local politics research.
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- 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.
- Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, Oxford University Press, vol. 119(1), pages 249-275.
- White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
- 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.
- Whitney K. Newey & Kenneth D. West, 1986.
"A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix,"
NBER Technical Working Papers
0055, National Bureau of Economic Research, Inc.
- Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
- 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 703-708, May.
- Gabor Kezdi, 2005. "Robus Standard Error Estimation in Fixed-Effects Panel Models," Econometrics 0508018, EconWPA.
- 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 334-338, May.
- Bénédicte Vidaillet & V. D'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
- 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.
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