IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v8y2025i2p38-d1655133.html
   My bibliography  Save this article

Determinants of Blank and Null Votes in the Brazilian Presidential Elections

Author

Listed:
  • Renata Rojas Guerra

    (Department of Statistics, Universidade Federal de Santa Maria, Av. Roraima 1000, Santa Maria 97105-340, Brazil)

  • Kerolene De Souza Moraes

    (Department of Statistics, Universidade Federal de Santa Maria, Av. Roraima 1000, Santa Maria 97105-340, Brazil)

  • Fernando De Jesus Moreira Junior

    (Department of Statistics, Universidade Federal de Santa Maria, Av. Roraima 1000, Santa Maria 97105-340, Brazil)

  • Fernando A. Peña-Ramírez

    (Department of Statistics, Universidade Federal de Santa Maria, Av. Roraima 1000, Santa Maria 97105-340, Brazil)

  • Ryan Novaes Pereira

    (Department of Statistics, Faculty of Science and Technology, São Paulo State University (UNESP), Rua Sen. Roberto Simonsen 305, Presidente Prudente 19060-080, Brazil)

Abstract

This study analyzes the factors influencing the proportions of blank and null votes in Brazilian municipalities during the 2018 presidential elections. The behavior of the variable of interest is examined using unit regression models within the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) framework. Specifically, five different unit regression models are explored, beta, simplex, Kumaraswamy, unit Weibull, and reflected unit Burr XII regressions, each incorporating submodels for both indexed distribution parameters. The beta regression model emerges as the best fit through rigorous model selection and diagnostic procedures. The findings reveal that the disaggregated municipal human development index (MHDI), particularly its income, longevity, and education dimensions, along with the municipality’s geographic region, significantly affect voting behavior. Notably, higher income and longevity values are linked to greater proportions of blank and null votes, whereas the educational level exhibits a negative relationship with the variable of interest. Additionally, municipalities in the Southeast region tend to have higher average proportions of blank and null votes. In terms of variability, the ability of a municipality’s population to acquire goods and services is shown to negatively influence the dispersion of vote proportions, while municipalities in the Northeast, North, and Southeast regions exhibit distinct patterns of variation compared to other regions. These results provide valuable insights into electoral participation’s socioeconomic and regional determinants, contributing to broader discussions on political engagement and democratic representation in Brazil.

Suggested Citation

  • Renata Rojas Guerra & Kerolene De Souza Moraes & Fernando De Jesus Moreira Junior & Fernando A. Peña-Ramírez & Ryan Novaes Pereira, 2025. "Determinants of Blank and Null Votes in the Brazilian Presidential Elections," Stats, MDPI, vol. 8(2), pages 1-20, May.
    • Handle: RePEc:gam:jstats:v:8:y:2025:i:2:p:38-:d:1655133
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/8/2/38/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/8/2/38/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pablo Mitnik & Sunyoung Baek, 2013. "The Kumaraswamy distribution: median-dispersion re-parameterizations for regression modeling and simulation-based estimation," Statistical Papers, Springer, vol. 54(1), pages 177-192, February.
    2. Cristine Rauber & Francisco Cribari-Neto & Fábio M. Bayer, 2020. "Improved testing inferences for beta regressions with parametric mean link function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 687-717, December.
    3. Renata Rojas Guerra & Fernando A. Peña-Ramírez & Marcelo Bourguignon, 2021. "The unit extended Weibull families of distributions and its applications," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(16), pages 3174-3192, December.
    4. Barndorff-Nielsen, O. E. & Jørgensen, B., 1991. "Some parametric models on the simplex," Journal of Multivariate Analysis, Elsevier, vol. 39(1), pages 106-116, October.
    5. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    6. Guilherme Pumi & Cristine Rauber & Fábio M. Bayer, 2020. "Kumaraswamy regression model with Aranda-Ordaz link function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 1051-1071, December.
    7. Diego Ramos Canterle & Fábio Mariano Bayer, 2019. "Variable dispersion beta regressions with parametric link functions," Statistical Papers, Springer, vol. 60(5), pages 1541-1567, October.
    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. Guilherme Pumi & Taiane Schaedler Prass & Cleiton Guollo Taufemback, 2024. "Unit-Weibull autoregressive moving average models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(1), pages 204-229, March.
    2. Josmar Mazucheli & Bruna Alves & Mustafa Ç. Korkmaz & Víctor Leiva, 2022. "Vasicek Quantile and Mean Regression Models for Bounded Data: New Formulation, Mathematical Derivations, and Numerical Applications," Mathematics, MDPI, vol. 10(9), pages 1-23, April.
    3. Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos, 2021. "Autoregressive conditional proportion: A multiplicative-error model for (0,1)-valued time series," MPRA Paper 110954, University Library of Munich, Germany, revised 06 Dec 2021.
    4. Idika E. Okorie & Emmanuel Afuecheta, 2024. "An Alternative to the Beta Regression Model with Applications to OECD Employment and Cancer Data," Annals of Data Science, Springer, vol. 11(3), pages 887-908, June.
    5. Alisson de Oliveira Silva & Jonas Weverson de Ararújo Silva & Patrícia L Espinheira, 2022. "Bootstrap-based inferential improvements to the simplex nonlinear regression model," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-27, August.
    6. Ricardo Rasmussen Petterle & Wagner Hugo Bonat & Cassius Tadeu Scarpin, 2019. "Quasi-beta Longitudinal Regression Model Applied to Water Quality Index Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 346-368, June.
    7. Fernando A Peña-Ramírez & Renata R Guerra & Charles Peixoto Mafalda, 2023. "The unit ratio-extended Weibull family and the dropout rate in Brazilian undergraduate courses," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-22, November.
    8. Cristine Rauber & Francisco Cribari-Neto & Fábio M. Bayer, 2020. "Improved testing inferences for beta regressions with parametric mean link function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 687-717, December.
    9. Tatiane Fontana Ribeiro & Fernando A Peña-Ramírez & Renata Rojas Guerra & Gauss M Cordeiro, 2022. "Another unit Burr XII quantile regression model based on the different reparameterization applied to dropout in Brazilian undergraduate courses," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-25, November.
    10. Rashad A. R. Bantan & Christophe Chesneau & Farrukh Jamal & Mohammed Elgarhy & Muhammad H. Tahir & Aqib Ali & Muhammad Zubair & Sania Anam, 2020. "Some New Facts about the Unit-Rayleigh Distribution with Applications," Mathematics, MDPI, vol. 8(11), pages 1-23, November.
    11. Abdul Ghaniyyu Abubakari & Suleman Nasiru & Christophe Chesneau, 2024. "A unit Weibull loss distribution with quantile regression and practical applications to actuarial science," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(4), pages 1-29.
    12. Patrícia L. Espinheira & Alisson Oliveira Silva, 2020. "Residual and influence analysis to a general class of simplex regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 523-552, June.
    13. Fábio M. Bayer & Francisco Cribari‐Neto & Jéssica Santos, 2021. "Inflated Kumaraswamy regressions with application to water supply and sanitation in Brazil," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(4), pages 453-481, November.
    14. Luiz M A Lima-Filho & Tarciana Liberal Pereira & Tatiene C Souza & Fábio M Bayer, 2020. "Process monitoring using inflated beta regression control chart," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-20, July.
    15. Guilherme Pumi & Cristine Rauber & Fábio M. Bayer, 2020. "Kumaraswamy regression model with Aranda-Ordaz link function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 1051-1071, December.
    16. Mustafa Ç. Korkmaz & Emrah Altun & Morad Alizadeh & M. El-Morshedy, 2021. "The Log Exponential-Power Distribution: Properties, Estimations and Quantile Regression Model," Mathematics, MDPI, vol. 9(21), pages 1-19, October.
    17. Fahimeh Tourani-Farani & Zeynab Aghabazaz & Iraj Kazemi, 2025. "A class of transformed joint quantile time series models with applications to health studies," Computational Statistics, Springer, vol. 40(3), pages 1147-1170, March.
    18. Lucio Masserini & Matilde Bini & Monica Pratesi, 2017. "Effectiveness of non-selective evaluation test scores for predicting first-year performance in university career: a zero-inflated beta regression approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 693-708, March.
    19. Jay Verkuilen & Michael Smithson, 2012. "Mixed and Mixture Regression Models for Continuous Bounded Responses Using the Beta Distribution," Journal of Educational and Behavioral Statistics, , vol. 37(1), pages 82-113, February.
    20. Antonio Calcagnì & Luigi Lombardi, 2022. "Modeling random and non-random decision uncertainty in ratings data: a fuzzy beta model," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(1), pages 145-173, March.

    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:gam:jstats:v:8:y:2025:i:2:p:38-:d:1655133. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.