Accounting for Voter Heterogeneity within and across Districts with a Factor-Analytic Voter-Choice Model

In this study, we propose a model of individual voter behavior that can be applied to aggregate data at the district (or precinct) levels while accounting for differences in political preferences across districts and across voters within each district. Our model produces a mapping of the competing candidates and electoral districts on a latent “issues” space that describes how political preferences in each district deviate from the average voter and how each candidate caters to average voter preferences within each district. We formulate our model as a random-coefficients nested logit model in which the voter first evaluates the candidates to decide whether or not to cast his or her vote, and then chooses the candidate who provides him or her with the highest value. Because we allow the random coefficient to vary not only across districts but also across unobservable voters within each district, the model avoids the Independence of Irrelevant Alternatives Assumption both across districts and within each district, thereby accounting for the cannibalization of votes among similar candidates within and across voting districts. We illustrate our proposed model by calibrating it to the actual voting data from the first stage of a two-stage state governor election in the Brazilian state of Santa Catarina, and then using the estimates to predict the final outcome of the second stage.

[1]  Kevin M. Quinn,et al.  Bayesian Factor Analysis for Mixed Ordinal and Continuous Responses , 2004, Political Analysis.

[2]  Jay K. Dow,et al.  Multinomial probit and multinomial logit: a comparison of choice models for voting research , 2004 .

[3]  James W. Endersby,et al.  Issues, the Spatial Theory of Voting, and British General Elections: A Comparison of Proximity and Directional Models , 2003 .

[4]  Kenneth E. Train,et al.  Discrete Choice Methods with Simulation , 2016 .

[5]  Application of Theil group logit methods to district-level vote shares: tests of prospective and retrospective voting in the 1991, 1993, and 1997 Polish elections , 2002 .

[6]  John E. Jackson.  A Seemingly Unrelated Regression Model for Analyzing Multiparty Elections , 2002, Political Analysis.

[7]  Jason Wittenberg,et al.  An Easy and Accurate Regression Model for Multiparty Electoral Data , 2002, Political Analysis.

[8]  Gary King,et al.  A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data , 2002, Political Analysis.

[9]  M. Wedel,et al.  Factor analysis with (mixed) observed and latent variables in the exponential family , 2001 .

[10]  Stuart Macdonald,et al.  Sophistry versus Science: On Further Efforts to Rehabilitate the Proximity Model , 2001, The Journal of Politics.

[11]  Garrett Glasgow,et al.  Mixed Logit Models for Multiparty Elections , 2001, Political Analysis.

[12]  Langche Zeng,et al.  A Heteroscedastic Generalized Extreme Value Discrete Choice Model , 2000 .

[13]  Gary King,et al.  A Statistical Model for Multiparty Electoral Data , 1999, American Political Science Review.

[14]  Christian Gourieroux,et al.  Simulation-based econometric methods , 1996 .

[15]  Allan L. McCutcheon,et al.  Cross-Level Inference , 1995 .

[16]  Peter J. Coughlin,et al.  Probabilistic Voting Theory , 1992 .

[17]  Terry Elrod,et al.  Choice Map: Inferring a Product-Market Map from Panel Data , 1988 .

[18]  Melvin J. Hinich,et al.  The Spatial Theory Of Voting , 1984 .

[19]  Shmuel Nitzan,et al.  Electoral outcomes with probabilistic voting and Nash social welfare maxima , 1981 .

[20]  Michael Boss.  Economic theory of democracy , 1974 .

[21]  D. McFadden.  Conditional logit analysis of qualitative choice behavior , 1972 .

[22]  G. Thompson,et al.  The Theory of Committees and Elections. , 1959 .