Examining Government Consolidation With Panel Data Methods


Panel data methods help understand relationships between variables (e.g., features of individuals, firms, states, taxpayers) located in specific environments (e.g., within jurisdictions, established groups, cohorts, geographic locations), over time. Because these environments have certain unique characteristics, the differences between the variables occurring because of the environments they are located in may lead to observable or unobservable heterogeneity in the data. To account for this variation, to correct for heterogeneity in statistical estimates, and to increase the efficiency of estimators, researchers use panel data methods. Because panel data models capture more variance, less collinearity among the variables, more degrees of freedom, and higher efficiency of the estimators, their estimates are more informative compared with the estimates calculated by time-series or cross-sectional ordinary least squares regression methods. This case introduces panel data regressions and reviews the two approaches to their estimation: fixed-effects regression methods and random-effects regression methods. Next, it explains the conditions appropriate for using each method and shows how to test for assumptions the random-effects regression method needs. This case is based on a peer-reviewed paper examining assessor consolidation reform in the state of Indiana in 2008 and its effects on cost savings. In this study, all observations were located within the counties and the data spanned between 2005 and 2014. While the original publication demonstrated the cost savings that resulted from the consolidation reform, the purpose of this case is to show how to use panel data to estimate economies of scale in the public sector.

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