Dataset to accompany publication "A meta-regression analysis on judicial efficiency literature: the role of methodological and courts diversity"

This Dataset accompanies the publication Aiello, F., Bonanno, G., & Foglia, F. (2024). A meta-regression analysis on judicial efficiency literature: the role of methodological and courts diversity. Journal of Applied Economics, 27(1), 2284010 [free available at the following link: https://www.tandfonline.com/doi/full/10.1080/15140326.2023.2284010]. Data included can be used to run other Meta-Regression Analyses on the literature of judicial efficiency. Proper citation of the original papers is mandatory when using the dataset.

The file is in an Excel spreadsheet (.csv) and easily readable.

Here, the abstract for the related paper:

This study reviews the empirical literature on judicial efficiency by conducting a meta-regression analysis. The metadata set comprises 264 efficiency scores retrieved from 36 papers published from 1992 to 2019. While the role of study design and method-specific characteristics of primary studies is evaluated, a focus also concerns courts specialisation. The results are fourfold. First, it is proven that primary papers using parametric methods yield higher efficiency scores than non-parametric studies. This is a key result, as the massive use of Data Envelopment Analysis (DEA) leads courts to be perceived as less efficient than they are. Second, the efficiency score of primary papers decreases, on average, when the sample size and the sum of inputs and outputs increase. Third, the efficiency scores obtained in studies that analyse first-instance courts are significantly higher than those obtained in studies that analyse appeal courts. Finally, primary papers focusing on a specific court type (only tax, civil or criminal) yield efficiency scores that are, on average, higher than those obtained in studies where the analysed courts are mixed. Some checks are performed to assure the robustness of the results.