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Analyzing the impacts of socio-economic factors on French departmental elections with CoDa methods

Author

Listed:
  • Thi-Huong-An Nguyen

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Thibault Laurent

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Christine Thomas-Agnan

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Anne Ruiz-Gazen

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

The vote shares by party on a given subdivision of a territory form a vector called composition (mathematically, a vector belonging to a simplex). It is interesting to model these shares and study the impact of the characteristics of the territorial units on the outcome of the elections. In the political economy literature, few regression models are adapted to the case of more than two political parties. In the statistical literature, there are regression models adapted to share vectors including Compositional Data (CoDa) models, but also Dirichlet models, and others. Our goal is to discuss and illustrate the use CoDa regression models for political economy models for more than two parties. The models are fitted on French electoral data of the 2015 departmental elections.

Suggested Citation

  • Thi-Huong-An Nguyen & Thibault Laurent & Christine Thomas-Agnan & Anne Ruiz-Gazen, 2022. "Analyzing the impacts of socio-economic factors on French departmental elections with CoDa methods," Post-Print hal-03721994, HAL.
  • Handle: RePEc:hal:journl:hal-03721994
    DOI: 10.1080/02664763.2020.1858274
    Note: View the original document on HAL open archive server: https://hal.science/hal-03721994
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    References listed on IDEAS

    as
    1. Honaker, James & Katz, Jonathan N. & King, Gary, 2002. "A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data," Political Analysis, Cambridge University Press, vol. 10(1), pages 84-100, January.
    2. Lewis, Jeffrey B. & Linzer, Drew A., 2005. "Estimating Regression Models in Which the Dependent Variable Is Based on Estimates," Political Analysis, Cambridge University Press, vol. 13(4), pages 345-364.
    3. Joanna Morais & Christine Thomas-Agnan & Michel Simioni, 2018. "Using compositional and Dirichlet models for market share regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(9), pages 1670-1689, July.
    4. Katz, Jonathan N. & King, Gary, 1999. "A Statistical Model for Multiparty Electoral Data," American Political Science Review, Cambridge University Press, vol. 93(1), pages 15-32, March.
    5. Jiajia Chen & Xiaoqin Zhang & Shengjia Li, 2017. "Multiple linear regression with compositional response and covariates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2270-2285, September.
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    Cited by:

    1. Jacob Fiksel & Scott Zeger & Abhirup Datta, 2022. "A transformation‐free linear regression for compositional outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(3), pages 974-987, September.
    2. Thi Huong An Nguyen & Anne Ruiz-Gazen & Christine Thomas-Agnan & Thibault Laurent, 2019. "Multivariate Student versus Multivariate Gaussian Regression Models with Application to Finance," JRFM, MDPI, vol. 12(1), pages 1-21, February.
    3. Dargel, Lukas & Thomas-Agnan, Christine, 2024. "Pairwise share ratio interpretations of compositional regression models," Computational Statistics & Data Analysis, Elsevier, vol. 195(C).
    4. Thomas-Agnan, Christine & Morais, Joanna, 2019. "Covariates impacts in compositional models and simplicial derivatives," TSE Working Papers 19-1057, Toulouse School of Economics (TSE).
    5. Joanna Morais & Christine Thomas-Agnan, 2021. "Impact of covariates in compositional models and simplicial derivatives," Post-Print hal-03180682, HAL.
    6. Bačo, Tomáš & Baumöhl, Eduard, 2021. "Socioeconomic factors and shifts in ideological orientation among political parties: Parliamentary elections in Slovakia from 1998 to 2020," EconStor Preprints 246584, ZBW - Leibniz Information Centre for Economics.

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    More about this item

    Keywords

    Political economy; Compositional regression models; Multiparty; Vote shares; French departmental election; Gaussian distribution;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • P16 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Capitalist Institutions; Welfare State

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