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Analysis of Judiciary Expenditure and Productivity Using Machine Learning Techniques

Author

Listed:
  • Fernando Freire Vasconcelos

    (School of Economics, Administration, Accounting and Actuarial Science, São Paulo University, São Paulo 05508-101, Brazil)

  • Renato Máximo Sátiro

    (“Luiz de Queiroz” School of Agriculture, São Paulo University, São Paulo 05508-101, Brazil)

  • Luiz Paulo Lopes Fávero

    (School of Economics, Administration, Accounting and Actuarial Science, São Paulo University, São Paulo 05508-101, Brazil)

  • Gabriela Troyano Bortoloto

    (School of Economics, Administration, Accounting and Actuarial Science, São Paulo University, São Paulo 05508-101, Brazil)

  • Hamilton Luiz Corrêa

    (School of Economics, Administration, Accounting and Actuarial Science, São Paulo University, São Paulo 05508-101, Brazil)

Abstract

Maintaining the judiciary requires a high level of budgetary expenditure, but the specifics of this relationship have not yet been fully explored. While several studies have examined the impact of spending on the judiciary through measures related to productivity and performance, none have used machine learning techniques. This study examines the productivity of the court system based on expenditures and other variables using machine learning techniques. In the clustering process Brazilian courts are ranked according to their productivity, while in the neural network step it is verified which characteristics are most relevant at the budgetary level related to judicial productivity for each cluster formed in the first step. The final neural network model supports the results of Pearson’s parametric correlation test, which found no significant correlation between expenditure and productivity. The findings from this study demonstrate the importance of understanding that increasing public budget expenditures alone is not sufficient to improve the efficiency of the judicial system. Instead, other administrative measures are necessary to meet the demands of the Brazilian judiciary and improve service delivery rates. These results offer important theoretical and managerial contributions to the field.

Suggested Citation

  • Fernando Freire Vasconcelos & Renato Máximo Sátiro & Luiz Paulo Lopes Fávero & Gabriela Troyano Bortoloto & Hamilton Luiz Corrêa, 2023. "Analysis of Judiciary Expenditure and Productivity Using Machine Learning Techniques," Mathematics, MDPI, vol. 11(14), pages 1-19, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3195-:d:1198907
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    References listed on IDEAS

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