Publication Type
Book Chapter
Version
acceptedVersion
Publication Date
1-2004
Abstract
The estimation of vote splitting in mixed-member electoral systems is a common problem in electoral studies, where the goal of researchers is to estimate individual voter transitions between parties on two different ballots cast simultaneously. Because the ballots are cast separately and secretly, however, voter choice on the two ballots must be recreated from separately tabulated aggregate data. The problem is therefore of one of making ecological inferences. Because of the multiparty contexts normally found where mixed-member electoral rules are used, furthermore, the problem involves large-table (R × C) ecological inference. In this chapter we show how vote-splitting problems in multiparty systems can be formulated as ecological inference problems and adapted for use with King's (1997) ecological inference procedure. We demonstrate this process by estimating vote splitting in the 1996 Italian legislative elections between voters casting party-based list ballots in proportional representation districts and candidate-based plurality ballots in single-member districts. Our example illustrates the pitfalls and payoffs of estimating vote splitting in multiparty contexts, and points to directions for future research in multiparty voting contexts using R × C ecological inference.
Discipline
Models and Methods | Political Science
Research Areas
Political Science
Publication
Ecological inference: New methodological strategies
Editor
Gary King, Ori Rosen, & Martin A. Tanner
First Page
333
Last Page
350
ISBN
9780521835138
Identifier
10.1017/CBO9780511510595.016
Publisher
Cambridge University Press
City or Country
Cambridge
Citation
BENOIT, Kenneth, LAVER, Michael, & GIANNETTI, Daniela. (2004). Multiparty split-ticket voting estimation as an ecological inference problem. In Ecological inference: New methodological strategies (pp. 333-350). Cambridge: Cambridge University Press.
Available at: https://ink.library.smu.edu.sg/soss_research/4007
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1017/CBO9780511510595.016