IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-03721994.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://hal.science/hal-03721994/document
    Download Restriction: no

    File URL: https://libkey.io/10.1080/02664763.2020.1858274?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Thomas-Agnan, Christine & Morais, Joanna, 2019. "Covariates impacts in compositional models and simplicial derivatives," TSE Working Papers 19-1057, Toulouse School of Economics (TSE).
    3. 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.
    4. Joanna Morais & Christine Thomas-Agnan, 2021. "Impact of covariates in compositional models and simplicial derivatives," Post-Print hal-03180682, HAL.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Julia Cage & Yasmine Bekkouche, 2018. "The Price of a Vote: Evidence from France, 1993-2014," Sciences Po publications 12614, Sciences Po.
    2. Bekkouche, Yasmine & Cagé, Julia & Dewitte, Edgard, 2022. "The heterogeneous price of a vote: Evidence from multiparty systems, 1993–2017," Journal of Public Economics, Elsevier, vol. 206(C).
    3. Smeets, Valerie & Warzynski, Frederic, 2006. "Job creation, job destruction and voting behavior in Poland," European Journal of Political Economy, Elsevier, vol. 22(2), pages 503-519, June.
    4. Joanna Morais & Christine Thomas-Agnan & Michel Simioni, 2017. "Interpretation of explanatory variables impacts in compositional regression models," Working Papers hal-01563362, HAL.
    5. Bekkouche, Yasmine & Cagé, Julia & Dewitte, Edgard, 2022. "The heterogeneous price of a vote: Evidence from multiparty systems, 1993–2017," Journal of Public Economics, Elsevier, vol. 206(C).
    6. Yasmine Bekkouche & Julia Cage, 2019. "The Heterogeneous Price of a Vote: Evidence from France, 1993-2014," SciencePo Working papers Main hal-03393084, HAL.
    7. Cagé, Julia & Bekkouche, Yasmine, 2018. "The Heterogeneous Price of a Vote: Evidence from France, 1993-2014," CEPR Discussion Papers 12614, C.E.P.R. Discussion Papers.
    8. Julia Cage & Yasmine Bekkouche, 2018. "The Price of a Vote: Evidence from France, 1993-2014," SciencePo Working papers Main hal-03393149, HAL.
    9. Jens Hainmueller & Holger Lutz Kern, 2005. "Incumbency Effects in German and British Elections: A Quasi- Experimental Approach," Public Economics 0505009, University Library of Munich, Germany.
    10. Yasmine Bekkouche & Julia Cage & Edgard Dewitte, 2022. "The Heterogeneous Price of a Vote: Evidence from Multiparty Systems, 1993-2017," SciencePo Working papers Main hal-03389172, HAL.
    11. repec:hal:spmain:info:hdl:2441/7rcgbs4v788terphdvb6a5e8t8 is not listed on IDEAS
    12. repec:hal:spmain:info:hdl:2441/10lirmbd5p8h4ae52oi51b4cka is not listed on IDEAS
    13. repec:hal:spmain:info:hdl:2441/2ahul47tb09rvqfl9eelv7o5ca is not listed on IDEAS
    14. Juan José Egozcue & Vera Pawlowsky-Glahn, 2019. "Compositional data: the sample space and its structure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 599-638, September.
    15. repec:hal:wpspec:info:hdl:2441/2ahul47tb09rvqfl9eelv7o5ca is not listed on IDEAS
    16. Scott Basinger & Damon Cann & Michael Ensley, 2012. "Voter response to congressional campaigns: new techniques for analyzing aggregate electoral behavior," Public Choice, Springer, vol. 150(3), pages 771-792, March.
    17. repec:hal:wpspec:info:hdl:2441/10lirmbd5p8h4ae52oi51b4cka is not listed on IDEAS
    18. Yasmine Bekkouche & Julia Cage, 2018. "The Price of a Vote: Evidence from France, 1993-2014," Working Papers Series 68, Institute for New Economic Thinking.
    19. Dostie, Benoit & Dupré, Ruth, 2012. "“The people's will”: Canadians and the 1898 referendum on alcohol prohibition," Explorations in Economic History, Elsevier, vol. 49(4), pages 498-515.
    20. Julia Cage & Edgard Dewitte, 2021. "It Takes Money to Make MPs: Evidence from 150 Years of British Campaign Spending," SciencePo Working papers Main hal-03384143, HAL.
    21. Arzheimer, Kai & Evans, Jocelyn, 2010. "Bread and butter à la française: Multiparty forecasts of the French legislative vote (1981-2007)," International Journal of Forecasting, Elsevier, vol. 26(1), pages 19-31, January.
    22. Georgiadis, Georgios & Schumann, Ben, 2021. "Dominant-currency pricing and the global output spillovers from US dollar appreciation," Journal of International Economics, Elsevier, vol. 133(C).
    23. Alessandro Gavazza & Mattia Nardotto & Tommaso Valletti, 2019. "Internet and Politics: Evidence from U.K. Local Elections and Local Government Policies," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(5), pages 2092-2135.
    24. Julia Cage & Edgard Dewitte, 2021. "It Takes Money to Make MPs: Evidence from 150 Years of British Campaign Spending," Sciences Po publications 2021-08, Sciences Po.
    25. Gerdes, Christer & Wadensjö, Eskil, 2008. "The Impact of Immigration on Election Outcomes in Danish Municipalities," IZA Discussion Papers 3586, Institute of Labor Economics (IZA).

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-03721994. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.