On The Heterogeneity In The Judicial Efficiency Literature: A Meta-Regression Analysis

This study performs a meta-regression analysis with the aim of explaining the differences in results obtained when estimating a frontier for judicial institutions. The metadata set comprises 264 efficiency scores retrieved from 36 papers published from 1992 to 2019. The empirical setting allows for the control of publication bias as in Aiello and Bonanno (2018; 2019) and is based on a random-effects model estimated with the two-step random-effects maximum likelihood (REML) technique proposed by Gallet and Doucouliagos (2014). Results show that primary papers using parametric methods yield higher efficiency scores than non-parametric studies. Additionally, the estimated efficiency scores in primary papers decrease with the number of inputs and outputs and the sample size. Importantly, the efficiency scores from studies that analyse the first-instance courts are higher than those obtained for the appeal courts. Meta-regressions also highlight that the efficiency scores retrieved from primary papers focusing on a specific court type (only tax, civil or criminal) are higher than those obtained when the analysed courts are mixed.