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Statistical analysis of Brazilian electoral campaigns via Benford’s law

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  • Gamermann, Daniel
  • Antunes, Felipe Leite

Abstract

The principle of democracy is that the people govern through elected representatives. Therefore, a democracy is healthy as long as the elected politicians do represent the people. We have analyzed data from the Brazilian electoral court (Tribunal Superior Eleitoral, TSE) concerning money donations for the electoral campaigns and the election results. Our work points to two conclusions that combined may be in conflict with the democratic principle: money is the determining factor on whether a candidate is elected or not (opposed to representativeness); secondly, the use of Benford’s Law to analyze the declared donations received by the parties and electoral campaigns shows either possible manipulations in the declarations or a significant number of donations that might not have been spontaneous from the donors. The better term that describes Brazil’s government system is plutocracy (govern by the wealthy).

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  • Gamermann, Daniel & Antunes, Felipe Leite, 2018. "Statistical analysis of Brazilian electoral campaigns via Benford’s law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 171-188.
    • Handle: RePEc:eee:phsmap:v:496:y:2018:i:c:p:171-188
    DOI: 10.1016/j.physa.2017.12.120
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    as
    1. Shao, Lijing & Ma, Bo-Qiang, 2010. "The significant digit law in statistical physics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3109-3116.
    2. González, M.C & Herrmann, H.J, 2004. "Scaling of the propagation of epidemics in a system of mobile agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 340(4), pages 741-748.
    3. Tam Cho, Wendy K. & Gaines, Brian J., 2007. "Breaking the (Benford) Law: Statistical Fraud Detection in Campaign Finance," The American Statistician, American Statistical Association, vol. 61, pages 218-223, August.
    4. Cantú, Francisco & Saiegh, Sebastián M., 2011. "Fraudulent Democracy? An Analysis of Argentina's Infamous Decade Using Supervised Machine Learning," Political Analysis, Cambridge University Press, vol. 19(4), pages 409-433.
    5. Vivien Marx, 2013. "The big challenges of big data," Nature, Nature, vol. 498(7453), pages 255-260, June.
    6. Stanley, Michael H. R. & Buldyrev, Sergey V. & Havlin, Shlomo & Mantegna, Rosario N. & Salinger, Michael A. & Eugene Stanley, H., 1995. "Zipf plots and the size distribution of firms," Economics Letters, Elsevier, vol. 49(4), pages 453-457, October.
    7. Boudewijn F. Roukema, 2014. "A first-digit anomaly in the 2009 Iranian presidential election," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 164-199, January.
    8. Boris Podobnik & Vuk Vukovic & H Eugene Stanley, 2015. "Does the Wage Gap between Private and Public Sectors Encourage Political Corruption?," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-15, October.
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