preprints.org > social sciences > government > doi: 10.20944/preprints202306.0105.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 1 June 2023 / Approved: 1 June 2023 / Online: 1 June 2023 (15:31:49 CEST)

Maintaining the judiciary's power requires a high level of budgetary expenditure, but the specifics of this relationship have yet to be fully explored. Although several studies have analyzed the impact of spending in the judiciary through productivity and performance-related measures, none have employed machine learning techniques. This study examines the productivity of the judiciary based on spending and other variables using machine learning techniques, including clustering and neural networks. The final neural network model supports the results of Pearson's parametric correlation test, which found no significant correlation between expenditure and productivity. This study's findings demonstrate the importance of understanding that increased public budgetary expenditure alone is insufficient for improving the judiciary's efficiency. Instead, other administrative and technical measures are necessary to meet the demands of the Brazilian judiciary and improve service delivery rates. These findings offer important theoretical and managerial contributions to the field.

Productivity; Judiciary; Machine Learning

Social Sciences, Government

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