A Statistical Model for Multiparty Electoral Data

American Political Science Review, Vol. 93, No. 1, pp. 15-32, March 1999

18 Pages Posted: 16 Jan 2008

See all articles by Jonathan N. Katz

Jonathan N. Katz

California Institute of Technology - Division of the Humanities and Social Sciences

Gary King

Harvard University

Abstract

We propose a comprehensive statistical model for analyzing multiparty, district-level elections. This model, which provides a tool for comparative politics research analagous to that which regression analysis provides in the American two-party context, can be used to explain or predict how geographic distributions of electoral results depend upon economic conditions, neighborhood ethnic compositions, campaign spending, and other features of the election campaign or aggregate areas. We also provide new graphical representations for data exploration, model evaluation, and substantive interpretation. We illustrate the use of this model by attempting to resolve a controversy over the size of and trend in electoral advantage of incumbency in Britain. Contrary to previous analyses, all based on measures now known to be biased, we demonstrate that the advantage is small but meaningful, varies substantially across the parties, and is not growing. Finally, we show how to estimate the party from which each party's advantage is predominantly drawn.

Suggested Citation

Katz, Jonathan N. and King, Gary, A Statistical Model for Multiparty Electoral Data. American Political Science Review, Vol. 93, No. 1, pp. 15-32, March 1999 , Available at SSRN: https://ssrn.com/abstract=1083762

Jonathan N. Katz (Contact Author)

California Institute of Technology - Division of the Humanities and Social Sciences ( email )

1200 East California Blvd.
Pasadena, CA 91125
United States
626-395-4191 (Phone)

HOME PAGE: http://jkatz.caltech.edu

Gary King

Harvard University ( email )

1737 Cambridge St.
Institute for Quantitative Social Science
Cambridge, MA 02138
United States
617-500-7570 (Phone)

HOME PAGE: http://gking.harvard.edu

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