Measurement of Efficiency in Greek Banking Industries in The Light of the Financial Crisis

Abstract

1. Introduction The efficiency of European banks has been one of the major topics in monetary and financial environment. The financial integration, the greater deregulation and the technological change of the European banking system forced financial institutions to strive for greater operational efficiency (Liapis et al., 2013). The enhanced competition due to the globalization of the banking system, the expansion of ATM's and e-banking (Pasiouras, 2006) has encouraged banks to improve the efficiency of their operations. However, the collapse of Lehman Brothers led to a global financial crisis that affected the real economy in Europe. The global financial crisis affected adversely the European banks that forced to minimize operational inefficiencies. To the best of our knowledge, the purpose of this study is to investigate the impact of the global financial crisis on the efficiency of Greek banks during the period 2008-2010. Although there is a well established literature on measurement of European banking efficiency a few studies are focused on the impact of global financial crisis on banking efficiency. This paper contributes to previous work and applies a DEA model in order to extract technical and scale efficiency scores of 20 Greek banking institutions for three years and find answers to the following concerns. How efficient are Greek banks? How the efficiency of Greek banks changed due to the global financial crisis? How the global financial crisis affected technical and scale efficiency of Greek banks? The remainder of this study is organized below as following. Chapter 2 analyses the concepts of efficiency and Chapter 3 reviews major studies in literature about efficiency in Greek banking sector. Chapter 4 presents the existing methodology and Chapter 5 concludes the data collected for three years. Chapter 6 indicates the empirical results for three years and Chapter 7 sums up the major conclusions of the study. 2. Conceptual Framework Farrell (1957) proposed two components in order to define efficiency. The first component is technical efficiency and the second is allocative efficiency. First of all, technical efficiency reflects the ability of a DMU to minimize inputs in order to produce a given level of outputs. Allocative efficiency reflects the ability of a DMU to use inputs in optimal proportions given their respective prices and production technology. It is worth mentioning that the level of efficiency of the individual firm is the ratio of total weighted outputs to total weighted inputs. The decision making unit (DMU) is the entity (business, regional, sector, country) that transforms n inputs into m outputs based on a specific technology. Coelli et al. (1997) define total efficiency measures as the product of technical efficiency and allocative efficiency. Efficiency ratio ranges between zero and one. An efficiency score of one denotes a fully efficient DMU while any other deviation from one indicates inefficiency. For example, an efficiency score measured against a cost frontier of 80% indicates that the DMU could have reduced cost by 20% without altering its output vector. (Brack and Jimborean, 2009) Figure 1 indicates that [DMU.sub.1], [DMU.sub.3], [DMU.sub.5] are efficient as they lie on the efficient part of the production frontier. Particularly, DMU3 is efficient under CRS and [DMU.sub.1], [DMU.sub.5] are efficient under VRS. Essentially, [DMU.sub.1] operates under increasing returns to scale (IRS) and is subject to economies of scale while [DMU.sub.5] operates under decreasing returns to scale (DRS) and is subject to diseconomies of scale. On the other hand, [DMU.sub.2] and [DMU.sub.4] lie inside the production frontier and they are inefficient while [DMU.sub.6] is inefficient although it lies on the frontier as the same amounts of outputs can be clearly produced with less input. (Webb, 2003, Brack and Jimborean, 2009). [FIGURE 1 OMITTED] The non-parametric approach, specifically the DEA model, measure scale efficiency by estimating two technical efficiency scores under the assumptions of CRS (2) and VRS (3). …