Using a Two-Step Clustering Approach to Examine Judiciary Efficiency in European Countries

Maria Stachova () and Jan Hunady ()
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Maria Stachova: Matej Bel University, Faculty of Economics, Department of Quantitative Methods and Information Systems, Tajovského 10, 975 90, Banská Bystrica, Slovakia
Jan Hunady: Matej Bel University, Faculty of Economics, Department of Quantitative Methods and Information Systems, Tajovského 10, 975 90, Banská Bystrica, Slovakia

Economic Research Guardian, 2025, vol. 15, issue 1, 142-160

Abstract: Panel data, also known as longitudinal data, is collected and analysed across various research areas. This type of data consists of statistical objects that are periodically observed over time. In comparison to cross-sectional data, there are significantly fewer clustering techniques available for panel data. Therefore, the main objective of the contribution is to present a two-step clustering approach. In the first step, the panel data are transformed into a static form using a set of proposed characteristics that capture time dynamics. In the second step, the objects are clustered using conventional spatial clustering algorithms, such as K-means clustering or hierarchical partitioning. The clustering performance of this approach is then compared to that of the well-known KML method using real panel data sets. These datasets include indicators that assess the effectiveness of courts at the first instance level. Factors like digitalisation in the public sector are affecting the judiciary's efficiency during this period. The methodology implemented allowed us to categorise European countries based on the efficiency of their courts while capturing the dynamic trends. This approach is generally helpful in assessing and comparing the efficiency of public spending and evaluating the quality of public institutions, including courts.

Keywords: Panel data; Clustering; CluMP algorithm; KML algorithm; courts’ effectiveness (search for similar items in EconPapers)
JEL-codes: C33 H59 H83 (search for similar items in EconPapers)
Date: 2025
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