Efficiency in banking: a meta-regression analysis

Francesco Aiello () and Graziella Bonanno

International Review of Applied Economics, 2016, vol. 30, issue 1, 112-149

Abstract: One learns two main lessons from studying the great quantity of banking efficiency literature. These lessons regard the heterogeneity in results and the absence of a comprehensive review aimed at understanding the reasons for this variability. Surprisingly, although this issue is well-known, it has not been systematically analyzed before. In order to fill this gap, we perform a Meta-Regression-Analysis (MRA) by examining 1661 efficiency scores retrieved from 120 papers published over the period 2000--2014. The meta-regression is estimated by using the Random Effects Multilevel Model (REML) because it controls for within- and between-study heterogeneity. The analysis yields four main results. First, parametric methods yield lower levels of banking efficiency than nonparametric studies. This holds true even after controlling for the approach used in selecting the inputs and outputs of the frontier. Secondly, we show that banking efficiency is highest when using the value-added approach, followed by estimates from studies based on the intermediation method, whereas those based on the hybrid approach are the lowest. Thirdly, efficiency scores are also determined by the quality of studies and the number of observations and variables used in the primary papers. As far as the effects of sample size, dimension and quality of papers are concerned, there are significant differences in sign and magnitude between parametric and nonparametric studies. Finally, cost efficiency is found to be higher than profit efficiency. Interestingly, MRA results are robust to the potential outliers in efficiency and sample size distributions.

Date: 2016
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Citations: View citations in EconPapers (23)

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DOI: 10.1080/02692171.2015.1070131

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