IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v57y2023i1d10.1007_s11135-022-01367-z.html
   My bibliography  Save this article

Estimation of electoral volatility parameters employing ecological inference methods

Author

Listed:
  • Pablo Sandoval

    (Universidad Santo Tomás)

  • Silvia Ojeda

    (Universidad Nacional de Córdoba)

Abstract

The general purpose of this work consists in to relate the statistical methods for the estimation of voter transitions rates based on aggregate data, with the problem of inferring the composition of the electorate in a democratic system in seven categories of voters once the second of two consecutive voting processes has been carried out. To know the electorate composition between stable and unstable voters is a matter of relevance to sociology and political science regarding comparative research. Available options to infer these values—electoral polls and panel surveys—present reliability issues arising from lack of recall or concealing on the voting behavior. In view of this situation, we propose an original estimation strategy consisting in to locate the unknown quantities within of a matrix whose sums of entries by rows and columns are known; based on this, such magnitudes can be estimated resorting to Ecological inference methods. The proposal was applied to the case of competition between political conglomerates in Chile for the period 1993–2009, using two types of estimation methods with aggregate data available in the free software R. One of those methods rendered results consistent with previous evidence proceeding from polls. We conclude that the proposed strategy can be replicable on a larger-scale application, even though these methods must, in parallel, remain subject to evaluation and improvement.

Suggested Citation

  • Pablo Sandoval & Silvia Ojeda, 2023. "Estimation of electoral volatility parameters employing ecological inference methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 405-426, February.
    • Handle: RePEc:spr:qualqt:v:57:y:2023:i:1:d:10.1007_s11135-022-01367-z
    DOI: 10.1007/s11135-022-01367-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-022-01367-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11135-022-01367-z?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Luana Russo, 2014. "Estimating floating voters: a comparison between the ecological inference and the survey methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(3), pages 1667-1683, May.
    3. André Klima & Thomas Schlesinger & Paul W. Thurner & Helmut Küchenhoff, 2019. "Combining Aggregate Data and Exit Polls for the Estimation of Voter Transitions," Sociological Methods & Research, , vol. 48(2), pages 296-325, May.
    4. Luana Russo & Laurent Beauguitte, 2014. "Aggregation level matters: evidence from french electoral data," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(2), pages 923-938, March.
    5. 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.
    6. 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.
    7. Gary King & Ori Rosen & Martin A. Tanner, 1999. "Binomial-Beta Hierarchical Models for Ecological Inference," Sociological Methods & Research, , vol. 28(1), pages 61-90, August.
    Full references (including those not matched with items on IDEAS)

    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. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • 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.
    2. 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.
    3. Irene L. Hudson & Linda Moore & Eric J. Beh & David G. Steel, 2010. "Ecological inference techniques: an empirical evaluation using data describing gender and voter turnout at New Zealand elections, 1893–1919," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 185-213, January.
    4. Rob Eisinga, 2009. "The beta‐binomial convolution model for 2×2 tables with missing cell counts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(1), pages 24-42, February.
    5. 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.
    6. Sarah Moon, 2024. "Partial Identification of Individual-Level Parameters Using Aggregate Data in a Nonparametric Binary Outcome Model," Papers 2403.07236, arXiv.org, revised Apr 2024.
    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. William Reed, 2003. "Information and Economic Interdependence," Journal of Conflict Resolution, Peace Science Society (International), vol. 47(1), pages 54-71, February.
    9. Pelzer, B. & Eisinga, R. & Franses, Ph.H.B.F., 2002. "Ecological panel inference in repeated cross sections," Econometric Institute Research Papers EI 2002-22, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. de Bromhead, Alan & Fernihough, Alan & Hargaden, Enda, 2020. "Representation of the People: Franchise Extension and the “Sinn Féin Election” in Ireland, 1918," The Journal of Economic History, Cambridge University Press, vol. 80(3), pages 886-925, September.
    11. Andrew D. Martin, 2003. "Bayesian Inference for Heterogeneous Event Counts," Sociological Methods & Research, , vol. 32(1), pages 30-63, August.
    12. Puig, Xavier & Ginebra, Josep, 2014. "A cluster analysis of vote transitions," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 328-344.
    13. Joan G. Staniswalis, 2008. "Incorporating Marginal Covariate Information in a Nonparametric Regression Model for a Sample of R×C Tables," Biometrics, The International Biometric Society, vol. 64(4), pages 1054-1061, December.
    14. 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.
    15. Ludo Peeters & Coro Chasco-Yrigoyen, 2005. "Ecological Inference And Spatial Heterogeneity - A New Approach Based On Entropy Econometrics," ERSA conference papers ersa05p705, European Regional Science Association.
    16. Lehmann, Sibylle H., 2010. "The German Elections in the 1870s: Why Germany Turned from Liberalism to Protectionism," The Journal of Economic History, Cambridge University Press, vol. 70(1), pages 146-178, March.
    17. Álvaro J. Corral & David L. Leal, 2020. "Latinos por Trump? Latinos and the 2016 Presidential Election," Social Science Quarterly, Southwestern Social Science Association, vol. 101(3), pages 1115-1131, May.
    18. 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.
    19. Zax Jeffrey S., 2012. "Single Regression Estimates of Voting Choices When Turnout is Unknown," Statistics, Politics and Policy, De Gruyter, vol. 4(1), pages 1-22, October.
    20. 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.

    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:spr:qualqt:v:57:y:2023:i:1:d:10.1007_s11135-022-01367-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.