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Estimation of voter transitions and the ecological fallacy

Author

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  • Antonio Forcina

    (Dipartimento di Economia)

  • Davide Pellegrino

    (Università degli Studi di Torino: Politecnico di Torino)

Abstract

This paper attempts an investigation into the features of ecological fallacy in the context of estimation of voter transitions between two elections. After reviewing some theoretical findings from a statistical point of view, we discuss two tools for checking whether bias is present: (1) fitting models with covariates; (2) comparing the standard errors of transition probabilities computed under ideal conditions against those based on bootstrap methods. Concerning the effect of covariates, we describe two different data generating mechanisms, depending on whether voting decisions are affected by variables measured at the (1) aggregate or (2) the individual level. By theoretical arguments and empirical evidence, we show that, while modelling the effect of covariates removes bias in the first case, it may fail in the second because only aggregate level covariates are usually available. Our investigation relies on the analysis of real and artificial data sets: the latter are obtained by a computer software which mimics voting behaviour and is such that, artificial electoral data with designed size and direction of ecological bias can be produced. An application to a recent election in the city of Turin is also used to illustrate our methodology and findings.

Suggested Citation

  • Antonio Forcina & Davide Pellegrino, 2019. "Estimation of voter transitions and the ecological fallacy," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 1859-1874, July.
    • Handle: RePEc:spr:qualqt:v:53:y:2019:i:4:d:10.1007_s11135-019-00845-1
    DOI: 10.1007/s11135-019-00845-1
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    References listed on IDEAS

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    1. Carolina Plescia & Lorenzo De Sio, 2018. "An evaluation of the performance and suitability of R × C methods for ecological inference with known true values," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 669-683, March.
    2. Michela Gnaldi & Venera Tomaselli & Antonio Forcina, 2018. "Ecological Fallacy and Covariates: New Insights based on Multilevel Modelling of Individual Data," International Statistical Review, International Statistical Institute, vol. 86(1), pages 119-135, April.
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    6. D. James Greiner & Kevin M. Quinn, 2009. "R×C ecological inference: bounds, correlations, flexibility and transparency of assumptions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 67-81, January.
    7. 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.
    8. A. Forcina & M. Gnaldi & B. Bracalente, 2012. "A revised Brown and Payne model of voting behaviour applied to the 2009 elections in Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 109-119, March.
    9. Jon Wakefield, 2004. "Ecological inference for 2 × 2 tables," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 385-425, July.
    10. 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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      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
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