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Comparing Dynamic Pies: A Strategy for Modeling Compositional Variables in Time and Space*

Abstract

Across a broad range of fields in political science, there are many theoretically interesting dependent variables that can be characterized as compositions. We build on recent work that has developed strategies for modeling variation in such variables over time by extending them to models of time series cross-sectional data. We discuss how researchers can incorporate the influence of contextual variables and spatial relationships into such models. To demonstrate the utility of our proposed strategies, we present a methodological illustration using an analysis of budgetary expenditures in the US states.

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Christine S. Lipsmeyer is an Associate Professor in the Department of Political Science, Texas A&M University, 2010 Allen Building, 4348 TAMU, College Station, TX 77843-4348 (lipsmeyer@pols.tamu.edu). Andrew Q. Philips is an Assistant Professor in the Department of Political Science, University of Colorado Boulder, UCB 333, Boulder, CO 80309-0333 (andrew.philips@colorado.edu). Amanda Rutherford is an Assistant Professor in the School of Public and Environmental Affairs, Indiana University, 1315 E 10th Street, Bloomington, IN 47405-1106 (aruther@indiana.edu). Guy D. Whitten is a Professor in the Department of Political Science, Texas A&M University, 2010 Allen Building, 4348 TAMU, College Station, TX 77843-4348 (g-whitten@pols.tamu.edu). Earlier versions of this paper were presented at the 2015 conference on “Innovations in Comparative Political Methodology” at Texas A&M University, the 2015 MPSA meetings, and at the University of São Paulo. The authors thank these audiences for their helpful comments. To view supplementary material for this article, please visit https://doi.org/10.1017/psrm.2017.39

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Political Science Research and Methods
  • ISSN: 2049-8470
  • EISSN: 2049-8489
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