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A cluster analysis of vote transitions

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  • Puig, Xavier
  • Ginebra, Josep

Abstract

To help settle the debate triggered the day after any election around the origin and destination of the vote of winners and losers, a Bayesian analysis of the results in a pair of consecutive elections is proposed. It is based on a model that simultaneously carries out a cluster analysis of the areas in which the results are broken into and links the results in the two elections of areas in a given cluster through a vote switch matrix. The number of clusters is chosen both through predictive checks as well as by testing whether the residuals are spatially correlated or not. The analysis is tried on the results in Barcelona of a pair of consecutive elections held just four months apart, in 2003 for the Catalan parliament and in 2004 for the Spanish parliament. The proposed approach, which reconstructs individual behavior from aggregated data, can be exported to be a solution for any ecological inference problem where one cannot assume that all the areas are exchangeable the way typically assumed by other ecological inference methods.

Suggested Citation

  • Puig, Xavier & Ginebra, Josep, 2014. "A cluster analysis of vote transitions," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 328-344.
    • Handle: RePEc:eee:csdana:v:70:y:2014:i:c:p:328-344
    DOI: 10.1016/j.csda.2013.10.006
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    References listed on IDEAS

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    1. Jon Wakefield, 2004. "Ecological inference for 2 × 2 tables (with discussion)," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 385-445, July.
    2. Andrew Gelman & David K. Park & Stephen Ansolabehere & Phillip N. Price & Lorraine C. Minnite, 2001. "Models, assumptions and model checking in ecological regressions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 101-118.
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    4. Ori Rosen & Wenxin Jiang & Gary King & Martin A. Tanner, 2001. "Bayesian and Frequentist Inference for Ecological Inference: The R×C Case," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(2), pages 134-156, July.
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    1. Roberto Colombi & Antonio Forcina, 2016. "Latent class models for ecological inference on voters transitions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(4), pages 501-517, November.
    2. Eric J. Beh & Duy Tran & Irene L. Hudson, 2024. "A generalisation of the aggregate association index (AAI): incorporating a linear transformation of the cells of a 2 × 2 table," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 87(5), pages 1-33, July.

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