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Published March 1999 | Published
Journal Article Open

A Statistical Model for Multiparty Electoral Data

Abstract

We propose a comprehensive statistical model for analyzing multiparty, district-level elections. This model, which provides a tool for comparative politics research analogous 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 the 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.

Additional Information

© 1999 American Political Science Association. An earlier version of this paper was presented at the 1997 meetings of the Midwest Political Science Association, Chicago, and won the Pi Sigma Alpha Award for the best paper presented there. Our thanks to Jim Alt, Larry Bartels, Neal Beck, Gary Cox, Nick Cox, Mo Fiorina, M. F. Fuller, Dave Grether, Mike Herron, James Honaker, Chuanhai Liu, Ken Scheve, Ken Shepsle, and Bob Sherman for helpful suggestions; Josh Tucker for useful suggestions and research assistance; Gary Cox for his British election data; and Selina Chen for her help and expertise in collecting additional British data. Burt Monroe saw the virtues of the compositional data analysis literature at essentially the same time as we did, and we appreciate his comments. For research support, Jonathan N. Katz thanks the Haynes Foundation, and Gary King thanks the National Science Foundation (SBR-9729884), the Centers for Disease Control and Prevention (Division of Diabetes Translation), the National Institutes on Aging, the World Health Organization, and the Global Forum for Health Research. All information, data, and software necessary to replicate the results in this article are available in a replication data set to be deposited in the ICPSR's Publication Related Archive upon publication (ICPSR PRA #1190).

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