Assessing banking sectors’ efficiency of financially troubled Eurozone countries

Journal Pre-proof Assessing Banking Sectors’ Efficiency of Financially Troubled Eurozone Countries Apostolos G. Christopoulos, Ioannis G. Dokas, Sofia Katsimardou, Eleftherios Spyromitros

PII:

S0275-5319(19)30498-2

DOI:

https://doi.org/10.1016/j.ribaf.2019.101121

Reference:

RIBAF 101121

To appear in:

Research in International Business and Finance

Received Date:

2 May 2019

Revised Date:

26 September 2019

Accepted Date:

18 October 2019

Please cite this article as: Christopoulos AG, Dokas IG, Katsimardou S, Spyromitros E, Assessing Banking Sectors’ Efficiency of Financially Troubled Eurozone Countries, Research in International Business and Finance (2019), doi: https://doi.org/10.1016/j.ribaf.2019.101121

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Assessing Banking Sectors’ Efficiency of Financially Troubled Eurozone Countries

Apostolos G. Christopoulos National and Kapodistrian University of Athens, Department of Economics, 1 Sofokleous street, 10559 Athens, Greece, e-mail: [email protected]

Ioannis G. Dokas Democritus University of Thrace, Department of Economics, University Campus, 69100 Komotini, Greece, e-mail: [email protected]

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Sofia Katsimardou

Democritus University of Thrace, Department of Economics, University Campus, 69100 Komotini, Greece, e-mail: [email protected]

Eleftherios Spyromitros

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Democritus University of Thrace, Department of Economics, University Campus, 69100 Komotini, Greece, e-mail: [email protected] Graphical abstract

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Abstract

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The aim of this paper is to assess the relative banking efficiency of the Eurozone’s soft underbelly (i.e., the so called PIIGS countries: Portugal, Ireland, Italy, Greece and Spain) in the period after the outburst of the financial crisis. It is one of the few that attempts to

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measure banking efficiency in the periphery of Eurozone in the outburst of the financial crisis using a battery of efficiency evaluation techniques for robustness reasons. This study relies on

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a modeling framework consisting of data envelopment analysis (DEA), bootstrapping, Malmquist Productivity Index and truncated regression which is applied on accounting and macroeconomic data spanning from 2009 to 2015. Findings show statistical evidence of a high degree of inefficiency in most of the examined banks. The application of the truncated regression indicate several financial variables as driving forces, offering thus important warning signs for banking performance. Indicators such as Tier 1 Capital to Risk Weighted Assets, Tangible Common Equity to Risk–Weighted Assets, Risk-Weighted Assets to Total Assets, Non-Performing Assets to Total Assets and Net Income Margin contribute in banks' 2

efficiency score. The above financial variables need to be closely monitored by the bank’s decision makers. Moreover, inflation and high levels of General Government Debt to GDP ratio also affect the efficiency of banks.

JEL Classification: C44, G21.

Key Words: Bank Efficiency; PIIGS; DEA; Bootstrap; Malmquist Productivity Index; Truncated Regression. . 1.

INTRODUCTION

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According to previous studies (Overbeek, 2012; Baldwin and Giavazzi, 2015; Beker, 2016; Papadamou and Spyromitros, 2016; Filoso et al., 2017), the causes of the European sovereign debt crisis started to occur right after the introduction of the Euro currency and the establishment of the Euro area. The institutional design of the European Monetary Union

created competitiveness issues for the periphery countries of the euro area by eliminating the

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traditional monetary instruments that allowed these countries to maintain their

competitiveness, i.e., a currency devaluation. During this period, most of the Southern

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Eurozone governments suffered from increased wages in the presence of high levels of inflation, affecting domestic export companies which had to deal with a competitive disadvantage and lost trade shares. At the same time, by borrowing money at very low rates,

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most of the Southern Eurozone governments increased their budget deficits. In other words, the financial confidence placed on the United Europe project along with the common currency effect on export trade, facilitated access to funding for smaller and

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less competitive Eurozone economies, creating thus intra-Eurozone lending/borrowing imbalances. A part of this was to private borrowers (mostly in Ireland and Spain) and another part to public borrowers (mostly Greece and Portugal). Consequently, the latter countries

crisis.

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increased their sovereign debt portfolios and thus were exposed to a possible sovereign debt

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In the Euro area, it was after the unfolding of the global financial crisis that the

dynamic linkages between sovereign debt portfolios of the weaker economies and their banking sectors became evident, with banks struggling to maintain financial confidence and minimize possible capital outflows. European governments in order to maintain liquidity in their financial institutions justified the acquisition of their debt obligations, which in turn led to questioning the reliability and efficiency of those countries’ banking sector. A popular way to test the efficiency of banks is the use of models with financial ratios as explanatory variables (see, among others, Molyneux and Thornton, 1992; Berger, 1995; 3

Spathis and Kosmidou, 1999; Athanasoglou et al., 2008; Kosmidou and Zopounidis, 2008; Georgantopoulos and Tsamis, 2013). Nevertheless, the use of financial ratios has been characterized by several drawbacks. For example, financial ratios do not take into account differences in the business undertaken by different banks, which in turn reflects different combinations of inputs and outputs (Tripe, 2004). According to Bauer et al. (1998), frontier efficiency analysis is superior to the financial ratio analysis since it is based on the recognition than some banks will not be as successful as others in achieving their objectives. In this research the methodological approach includes three stages analysis. In the first stage the use of DEA establishes a classification ranking of the banks of PIIGS. This ranking is based on accounting figures, emphasizing on revenues. The CCR (output-oriented)

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version of DEA is adopted. In order to take into account, the sensitivity of efficiency measures to sampling variations of the estimated frontier (Simar and Wilson, 1998), bootstrap methodology has been used allowing us to estimate bias-corrected DEA efficiency scores. In the second stage, Malmquist Productivity Index (MPI) is introduced in order to estimate the

total factor productivity change (TFPC) in the revenues efficiency framework. Finally, a

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truncated regression is implemented, where the bootstrap results are used as the dependent

variable, in order to examine the effect of financial and macroeconomic factors in the banking

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efficiency level.

Moreover this paper extends the recent financial literature and contributes significantly in two ways: Firstly, to the best of our knowledge this study is the one of the few

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that examines the relative efficiency of the banking sector of the low performing economies in the Eurozone i.e. the PIIGS countries during the sovereign debt crisis period using several components of DEA, such as MPI and Bootstrap. Secondly, a truncated regression model is

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applied to concentrate the possible variables which affect bank performance. In our analysis, based on Fernandes et al. (2018) approach on banking sector performance of the PIIGS countries, alternative inputs/outputs are used in the various stages of the proposed

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methodology and additional measures are included to incorporate the regulatory framework. All the above provide a suitable quantitative basis to draw good quality estimation

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results. This study is important for policy makers in Europe, since the understanding and the monitoring of the soft underbelly of the European economies may help the integrated Europe to overpass similar financial events in the future. According to the results of DEA, the reaction of the banking sector in the aftermath of the global financial crisis differs between the PIIGS members. In effect, Portuguese and Italian banks seem to perform better on average in the period under investigation. Moreover, it appears that the change of the efficiency score between 2009 and 2015 is negative for the majority of the sample of the banks, underlining the fact that the banking sector of PIIGS has not fully recovered from the crisis. Additionally, 4

MPI results show a progress in average Total Factor Productivity Change (TFPC) for the majority of the banks which compose our sample. Finally, the truncated regression analysis highlights the role of regulatory, profitability and macroeconomic variables as determinants of banks’ efficiency. The rest of the paper is organized as follows: Section 2 briefly describes the literature related to the problem investigated. Section 3 presents the methodology and data. Section 4 presents and discusses the results, while section 5 concludes.

2. LITERATURE REVIEW

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Over the last decades, bank efficiency has been examined using either parametric or non-parametric efficient frontier techniques. Among the parametric techniques, we have the

Stochastic Frontier Approach (SFA), the Distribution Free Approach (DFA) and the Thick

frontier approach (TFA), while non-parametric techniques incorporate Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH). The main difference of the two approaches is

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that non parametric techniques do not require a specific functional form in order to estimate the frontier, in comparison to parametric techniques (Bhatia et. al., 2018). SFA is the most

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popular parametric technique but requires very strict assumptions regarding the form of the efficient frontier (Biener et al., 2016). Bonin et al. (2005), Beccalli et al. (2006) and Belke et al. (2016) and Sarmiento and Galan (2017) use SFA for bank efficiency evaluation. On the

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other hand, DEA is the most frequently used non-parametric technique for investigating bank efficiency in almost all countries (see among others, Lozano-Vivas et al., 2002; Casu and Girardone, 2006; Brissimis et al., 2008; Olgu and Weyman-Jones, 2008; Akther et al., 2013;

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Tan and Floros, 2013; Tan and Floros, 2018). Moreover, Pasiouras (2008) applied DEA in order to examine an international sample consisting of 715 banks from 95 countries1. There are also a few studies such as Casu et al. (2004), Weill (2004) and Delis et al.

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(2008) that provide comparison among various techniques. The majority of the existing literature using European evidence focuses on individual countries (Halkos and Salamouris,

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2004; Pasiouras et al., 2008; Tortosa-Ausina et al., 2008; Zen and Baldan, 2008). But over the last few years, there is a growing number of studies that examine cross-country evidence. In their analysis, most of them use banks from leading European countries such as Spain, Italy, France and Germany (Pastor, 2002; Casu and Molyneux, 2003; Beccalli et al., 2006; Bolt and Humphrey, 2010), and Bergendahl (1998) on Scandinavian countries. Moreover, Kevork et al. (2017) conduct their analysis on the global financial crisis and sovereign debt 1

Konara et al. (2019) use a DEA technique to assess efficiency on a sample of emerging economies.

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crisis in Europe using data from 11 Eastern European Countries. Finally, Degl’Innocenti et al. (2017) investigate bank efficiency in transition economies using countries from Central and Eastern Europe. According to the previous references, DEA is a suitable method to estimate the relative efficiency status of European banks. However, numerous studies have introduced bootstrap technique in order to enhance the reliability of DEA results (Casu and Molyneux, 2003; Brissimis et al., 2008). In addition, the introduction of Malmquist Productivity Index enhanced the power of the proposed methodology in the investigation process of bank efficiency. As a significant component of DEA framework, MPI offers an insight of whether there were productivity improvements amongst DMUs over time (Brissimis et al., 2008; Olgu

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and Weyman-Jones, 2008; Tortosa-Ausina, et al. 2008). The inputs-outputs which are used in the various versions of DEA, describe the individual characteristics of banks according to the main assumptions of the proposed DEA

model. The combination of inputs –outputs could give emphasis to the figure of net revenues

after provisions. (Output oriented). In this case the issue of the maximization of net revenues

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after provisions has been illustrated from specific inputs according to a primary theoretical framework. However an indirect effect on the efficiency score from other factors is possible.

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Several studies investigate the impact of various factors on the performance of DMUs’ with econometric techniques such as Tobit (Maudos et al., 2002; Pastor, 2002; Casu and Molyneux, 2003; Rezitis, 2006; Pasiouras, 2008) and Truncated regression (Chortareas et

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al., 2012; Chortareas et al., 2013; Goulati and Kumar, 2017; Tan and Anchor, 20172). In this study, the second tool is adopted as a more advanced and reliable approach according to the findings of the relative literature. Fernandes et al. (2018) focus on PIIGS banking sector

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providing significant findings. More analytically, a negative impact on banks’ productivity from liquidity and credit risk was observed, while capital and profit risk have a positive effect on the banks’ performance. Also, during the crisis period the financial development of the

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banking sector in the area of PIIGS was limited.

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3. DATA & RESEARCH METHODOLOGY The data set consists of accounting figures from financial statements of commercial

banks from Ireland (2), Portugal (3), Spain (5), Italy (6), Greece (4), as well as macroeconomic variables and covers the period 2009-2015. Annual financial data were taken by Bloomberg and annual data on GDP growth, inflation and General Government Debt to 2

It is to notice than Tan and Anchor (2017) also employ another econometric technique, namely the logit fractional regression model of Papke and Wooldridge’s (1996) to assess the impact of risk and competition on bank efficiency.

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GDP ratio come from the IMF’s World Economic Outlook Database. The structure of the methodology includes three stages of analysis. In the first stage, an output oriented DEA model (CCR) is implemented in order to establish a classification of the banks according to the relative efficiency score. DEA is a technique for measuring the relative efficiency of DMUs, using multiple inputs to produce multiple outputs measured in different units. The proposed DEA version introduced in Charnes et. al. (1978), under the assumption of constant returns to scale has been formulated as:

 m  max zk     si  sr   i 1  n

 x j 1

j ij

n

 y j 1

j

rj

 si  xik

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subject to

i  1,..., m; (k  1, 2,...n)

 sr  zk yk

r  1,..., s;

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 j , si , sr  0 i, j, r

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0    1

where zk is the proportional increase in output for the kth firm,  j is the intensity factor

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showing the contribution of firm j in the derivation of the efficiency of firm k, si , sr are, respectively, the input and output slack variables and ε is a very small positive number used as a lower bound to input/output weights and it is also used to scale the input/output slacks in The original model (CCR) uses constant returns to scale and

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the envelopment model.

provides reliable estimations. The choice of CCR model is justified from the specific characteristic of the firms’ sample like the common currency exchange risk, the common

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regulatory framework, and the same auditing authority (ECB), as a member of the euro

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monetary zone. Table 1 provides the inputs and the one output of the proposed DEA model.

Table 1 “Variables of DEA model” Model 1 Inputs - Outputs Variables Inputs Operating Cost (V1) Total Assets (V2)

Output Net Revenues after provisions (V4)

Number of Employees (V3) 7

The selection process of these variables is based on the literature findings. Specifically, as far as the efficiency evaluation process of European banks is concerned, two approaches can be seen in the relative literature. The first one is the production approach which considers banks as production units and the second one is the intermediation approach under which banks are considered as the intermediary between savers and investors (Sealey and Lindley, 1977). In this research, intermediation approach has been selected since according to Berger and Humphrey (1997), is more suitable when evaluating entire financial institutions and their profitability. Based on similar studies on banking efficiency evaluation, total assets (Chiu et al., 2014; Markovic et al., 2015; Kevork et al., 2017), the number of

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employees (Mukherjee et al., 2002; Markovic et al., 2015; Kevork et al., 2017) and operating costs (Kuosmanen and Post, 2001; Mukherjee et al., 2002; Avkiran, 2015) have been selected

as inputs whereas net revenues after provisions as output (Mukherjee et al., 2002; Markovic et al., 2015)3.

In order to overcome structural deficiencies that are differently biased when standard

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DEA model is used (Staat, 2002), bootstrap methodology, a statistical simulation process, has

been selected. In obtaining the empirical distribution of the explanatory variables,

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(inputs/outputs) it relies on a procedure of random sampling with replacement. The bootstrapping of the sample, along with Simar and Wilson (1998), is carried out by generating



an appropriately large number (B) of pseudo-samples of y j  y ij ,..., y nm *

*

 where n is the

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*

number of inputs and m is the number of units. The sample of the bootstrap is represented by B

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 *   ib  . This is in turn used to draw an estimate of the bias for each of the estimators as  b 1 *

*

1 B * . The estimation of the confidence intervals for  i can be  B b1 ib

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 i   i , where  i 

B

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 *  estimated using the empirical distribution   ib  as follows:  b 1



i lower bound





, i upper bound  i

*( a )

, i

*(1 a )

3



An interesting study by Floros et al. (2019) highlights the importance of choosing deposits as an input or output respectively, when investigating the efficiency of PIGS banks over the period 2008-2014.

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*

where i  2i  i stands for the bias corrected estimator of  i .

In the second stage, Malmquist Productivity Index is introduced, in order to estimate the productivity changes of DMUs, as a result of the calculation of the relative performance of a DMU at different periods of time using the technology of a base period. In the case of the banking sector the relative literature is very rich. Casu et al. (2004) compared parametric and non-parametric estimates of productivity change in European banking between 1994 and 2000, while Grifell-Tatjé and Lovell (1996) studied productive efficiency and total factor productivity change in Spanish savings banks over the 1986-1991 post-deregulation period. Kevork et al. (2017), using a probabilisting version in the cases of technical, efficiency, pure

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efficiency, scale efficiency, scale change factor and scale bias of technical change investigated the status of EU banks productivity level in the US prime crisis time and during

the EU sovereign debt crisis. Fare et al. (1994) defined the output-oriented MPI between the year t and t+1 as the ratio of the distance function for each year relevant to a common techonology as follows:

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d0t  yt 1 , xt 1  MPI  t , d0  yt , xt  t 0

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where the subscript 0 indicates the firm under estimation  yt 1 , xt 1  , while d0t  yt 1 , xt 1  and

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d0t  yt , xt  are the input distance functions for unit 0 at time t+1 and t, respectively and measured against the technology at time t. If the base year is t+1, then the MPI for the t+1 period is:

d0t 1  yt 1 , xt 1  . d0t 1  yt , xt 

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MPI 0t 1 

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Moreover MPI0 could be expressed as a geometric mean of the two indices, evaluated with respect to period t and period t+1 technologies as follows: 1

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 d0t  yt 1 , xt 1  d0t 1  yt 1 , xt 1   2 MPI 0   t  t 1  . d 0  yt , xt    d0  yt , xt 

A more analitically presentation of this index is given by: 1

d  y , xt 1   d 0t  yt 1 , xt 1  d 0t 1  yt 1 , xt 1   2 MPI 0   t 1   . d  y , xt   d0t  yt , xt  d 0  yt , xt   t 1 0 t 1 t 1 0 t

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Efficiency Change (EC)

Technological Change (TC)

where technical efficiency change indicates improvements in efficiency relative to the frontier and technological change indicates shifts in the frontier of the different units. As far as the interpretation of Malmquist Productivity Index is concerned, a value more than one indicates progress of the unit in the TFPC from the period t to the period t + 1, a value less than one indicates decline and finally a value of one indicates no change in TFPC. In the third stage, a truncated regression is applied. This approach was proposed by

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Simar and Wilson (2007), in order to draw insights about the relationship between the relative efficiency scores of banks and other features such as the regulatory framework. Additionally,

this approach offers the ability to examine the impact of various exogenous factors on banks’ efficiency. The mathematic formula of the proposed regression is presented as:

where

j  1,..., n

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bc

T E j  Z j   j ,

j  1,..., n are the banks under investigation and  j

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 j  1  Z j .

N  0,  2  , such that

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This is defined as: bc

The dependent variable T E j is the bias-corrected efficiency scores produced by bootstrap

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methodology instead of the original CCR DEA efficiency scores. Z j , the independent variables, is a row vector of specific financial ratios which in conjunction with the vector δ, that is needed to be estimated, will give as an insight of the driving forces of banks efficiency.

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 j is the statistical noise term of each bank and its’ truncated normal distribution

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N  0,  2  is limited by the condition  j  1  Z j .

The above truncated regression model can be estimated by maximizing the

corresponding likelihood function. Moreover, bootstrap confidence intervals for the estimated parameters can be obtained. More details regarding the bootstrap procedure can be found in the relative work of Simar and Wilson, 2007. Table 2 presents the variables which are used in the regression model.

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Table 2 “Explanatory variables in the truncated regression ” Field Regulation Regulation Size Regulation Profitability Regulation Leverage Profitability Macro Macro Macro

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Variables Symbol Tier1 Capital to Risk-Weighted Assets V1 Tangible Common Equity to Risk – Weighted Assets V2 LOG(Total Assets) V3 Risk -Weighted Assets to Total Assets V4 Non Performing Assets to Total Assets V5 Total Risk Based Capital V6 Total debt/Total Assets V7 Net Income Margin V8 Inflation V9 GDP (% change) V 10 General Government Debt to GDP (dummy) V 11

Our motivation for this approach is that financial ratios are good indicators of the financial position of financial entities, and more importantly they provide a good picture of

the efficiency level of a bank in using its current and non-current assets. Moreover, the role of

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regulatory capital following Basel Accords is taken into account by considering the Tier1 Capital to Total Risk-Weighted Assets (V1), the Tangible Common Equity Capital to Total

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Risk-Weighted Assets (V2) and Total Risk Based Capital Ratio (V6). The choice of these variables is justified on the ground that strict capital rules may increase lending costs and reduce the available liquidity, which may lead economies to lower lending and investment

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levels, affecting thus the efficiency of banks (Goddard et al., 2010). However, there is a strand of the literature suggesting that banks that are highly capitalized are less likely to engage in excessive risk-taking activities (Lee and Hsieh, 2013; Admati et al., 2014). As a

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result, banks tend to perform better due to less distortions in lending decisions and lower moral hazard. It is to mention at according to Tran et al. (2016), regulatory capital is negatively related to bank profitability for higher capitalized banks but positively related to

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profitability for lower capitalized banks. Other bank control variables such as Risk–Weighted Asset to Total Assets (V4)

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which measures bank risk taking as the bank's risk-weighted assets divided by total assets and Size (V3), which is the logarithm of total assets, are also considered. One of the most important figures during the crisis concerns the Non Performing

Assets (NPAs) which were rapidly increased in the banks of all PIIGS countries. This is measured by the Non Performing Assets to Total Assets (V5) where NPAs are loans with payments in delay at least for 90 days. When NPAs increase fast, the provisions are used to cover loses and thus reduce equally the available capital to generate new loans. In the worst case scenario, provisions may not be adequate to cover the loss and it may be needed to ask 11

for recapitalization. In such case, the bank needs a bail-out or a bail-in solution. Another interesting ratio is Total debt/Total Assets (V7) where one can see in which way each bank chose to finance its assets. In the case of high debt financing, the bank will face more difficulties in a crisis period. The Net Income Margin is a ratio that is always interesting for evaluating the performance of banks operating in a competitive environment. Therefore, income margin is very useful to judge for the competitiveness of each bank. A high value of this ratio also indicates that banks succeed economies of scale and/or economies of scope. Furthermore, despite the financial ratios, we also include in our regression the role of several macroeconomic variables. The variables selected are GDP growth (V9), inflation (V10) and General Government Debt to GDP ratio (GovDebt) (V11). In periods of economic

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growth, it is expected that the performance of banks will be increased. Changes in the inflation are expected to negatively affect the efficiency of banks, especially if their inflation

expectations are not fulfilled, leading thus to potential losses due to interest rate adjustments. GovDebt is an important macroeconomic variable characterizing the financial health of a government. High levels of GovDebt positively affect their yields and therefore the

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performance of banks investing on such debt securities4.

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In the sense of Reinhart et al. (2012), we consider a threshold over 100% for the variable GovDebt to reflect high values of government debt to GDP ratio.

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4. DISCUSSION OF THE RESULTS 4.1 DEA and BOOTSTRAP Tables 3 and 4 provide the results of the first stage analysis and allow us to draw crucial conclusions. According to the findings of Table 3, examining the average efficiency of banks per country, the highest efficiency scores have been received by the banking sectors of Portugal and Italy, while Spain succeeded the third place in the ranking among the PIIGS members. The banking sector of Greece was classified in the fourth place and the banking

Table 3 Results of DEA model

Y

2009

2010

2011

2013

2014

2015

AVE

1

Banko –BPI

Portugal

0.59

1.00

1.00

1.00

0.86

0.88

1.00

0.90

2

Banko –Comercial

Portugal

0.55

0.62

0.52

0.52

0.52

0.61

0.67

0.57

3

Caixa General

Portugal

1.00

0.73

0.52

0.61

0.38

0.44

0.49

0.60

4

Allied Irish Banks

Ireland

0.12

0.00

0.00

0.00

0.04

0.79

0.89

0.26

5

Bank of Ireland (BKIR)

Ireland

0.46

0.48

0.42

0.23

0.38

0.67

0.77

0.49

6

Banca Popalre Dell Emilia Romagna

Italy

0.89

1.00

0.95

0.91

0.97

0.86

0.86

0.92

7

Banca Popolare Di Milano

Italy

1.00

0.98

1.00

1.00

1.00

1.00

0.97

0.99

8

Banco Popolare SC

Italy

0.86

0.50

0.48

0.60

0.64

0.40

0.63

0.59

9

Unione di Banche Italiane SpA

Italy

0.86

0.56

0.51

0.64

0.86

0.65

0.62

0.67

10

Unicredit SpA

Italy

0.67

0.28

0.20

0.37

0.50

0.49

0.39

0.41

11

Intesa Sanpaolo SpA

Italy

0.90

0.32

0.25

0.54

0.72

0.53

0.43

0.53

12

Alpha Bank

Greece

0.67

0.75

0.81

0.44

0.33

0.46

0.22

0.53

13

Eurobank

Greece

0.62

0.59

0.13

0.32

0.00

0.23

0.20

0.30

14

National Bank of Greece

Greece

0.90

0.58

0.45

0.42

0.83

0.25

0.00

0.49

15

Piraeus Bank

Greece

0.60

0.82

0.50

0.40

0.03

0.00

0.12

0.35

16

Banco Bilbao Vizcaya Argentaria SA

Spain

0.97

0.40

0.26

0.46

0.98

0.60

0.44

0.59

17

Banco de Sabadell SA

Spain

1.00

0.67

0.63

0.48

0.71

0.61

0.53

0.66

18

Banco Popular Espanol SA

Spain

0.81

0.55

0.52

0.20

0.73

0.55

0.50

0.55

19

Banco Santander

Spain

0.86

0.38

0.25

0.42

1.00

0.62

0.49

0.57

20

Bankinter SA

Spain

0.96

1.00

1.00

1.00

1.00

1.00

1.00

0.99

13

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BANKS

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2012

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COUNTR

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sector of Ireland in the last position.

0.69

0.38

0.69

0.42

0.67

This finding can be interpreted as the result of an effective adjustment of the two economies (Portugal and Italy) in the aftermath of the global economic crisis of 2008. Especially for Portugal, it seems that the memorandum policy had a positive effect. The 67% of Portuguese banks provides a positive change in their efficiency score at the end of the period. In contrast, this change is negative for the Italian banks. However, during our sample period the change of the efficiency score in the case of the Italian banks presented a smaller variation as compared to the banks of Spain, Ireland and Greece, whereas the banks of Ireland appear to have a huge improvement during the last years (2014 and 2015). Generally, the reaction of national banking sectors in the financial crisis differs between the PIIGS members, and this behavior is justified obviously by two factors: the

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changes in the business cycle and the individual structure of each banking sector. Focusing on the case of the Irish banking sector for the first five years of the examined period, it seems

that the global financial crisis had a strong negative effect. The latter could be explained from the collapse of the real estate market in the US. The majority of the securities which composed the investment portfolio of Irish banks were connected with the performance of this

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market. However, the credit expansion of Irish banks was negatively affected, establishing a failure status for these institutions.

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In the case of Greece, debt crisis and the implemented economic policy during the period 2010-2015, had a significant effect on the viability of the national banking system. Deposits were reduced dramatically as a result of the fear for a possible GRexit and as a result

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of the huge decrease in the disposable income, which could be justified on the ground of economic recession and the austerity measures. The NPAs of Greek banks had a significant rise, while the “PSI haircut” had a negative effect on the performance of these institutions.

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However, some of the memorandum policy reforms contributed positively, especially in the fields of the reorganization of the public sector. However, the political change in Greece in 2015 resulted in a new crisis which harmed the reliability of the Greek banking sector and led

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to capital controls which remained in effect up to 2018. The results of DEA analysis in Table 3 confirm the impact of this political change on the performance of the four systemic banks.

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Table 4 suggests some interesting findings regarding the behavior of efficiency score

for the banks of our sample over time. Considering 2009 as the basis year, it seems that the change of the efficiency score at the end of the period is negative for the majority (75%) of the sample of the banks.

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Table 4 The Change of the relative efficiency score (basis year 2009) 2012 1.00 0.52 0.61 0.00 0.23 0.91 1.00 0.60 0.64 0.37 0.54 0.44 0.32 0.42 0.40 0.46 0.48 0.20 0.42 1.00

2013 0.86 0.52 0.38 0.04 0.38 0.97 1.00 0.64 0.86 0.50 0.72 0.33 0.00 0.83 0.03 0.98 0.71 0.73 1.00 1.00

2014 0.88 0.61 0.44 0.79 0.67 0.86 1.00 0.40 0.65 0.49 0.53 0.46 0.23 0.25 0.00 0.60 0.61 0.55 0.62 1.00

2015 1.00 0.67 0.49 0.89 0.77 0.86 0.97 0.63 0.62 0.39 0.43 0.22 0.20 0.00 0.12 0.44 0.53 0.50 0.49 1.00

Change + + + + +

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2011 1.00 0.52 0.52 0.00 0.42 0.95 1.00 0.48 0.51 0.20 0.25 0.81 0.13 0.45 0.50 0.26 0.63 0.52 0.25 1.00

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2010 1.00 0.62 0.73 0.00 0.48 1.00 0.98 0.50 0.56 0.28 0.32 0.75 0.59 0.58 0.82 0.40 0.67 0.55 0.38 1.00

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2009 0.59 0.55 1.00 0.12 0.46 0.89 1.00 0.86 0.86 0.67 0.90 0.67 0.62 0.90 0.60 0.97 1.00 0.81 0.86 0.96

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

BANKS Banko -BPI Banko -Comercial Caixa General Allied Irish Banks Bank of Ireland (BKIR) Banca Popalre Dell Emilia Romagna Banca Popolare Di Milano Banco Popolare SC Unione di Banche Italiane SpA Unicredit SpA Intesa Sanpaolo SpA Alpha Bank Eurobank National Bank of Greece Piraeus Bank Banco Bilbao Vizcaya Argentaria SA Banco de Sabadell SA Banco Popular Espanol SA Banco Santander Bankinter SA

The latter fact could be explained from the consequences of the recession on the economy and the level of compliance of the banks’ management to the regulations. Table 5

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presents bootstrap results. In order to overcome structural deficiencies that are differently biased when standard DEA model is used (Staat, 2002), bootstrap statistical procedure has been implemented. We measure the efficiency in terms of Shephard’s output distance

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functions estimates. An efficient bank will receive the value of one in the efficiency score. Since in our analysis an output orientation approach has been selected, a value less than one

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shows how much the output (Revenues (Net after provisions)) should be increased while keeping all the inputs (Operating Cost, Total Assets and Number of Employees) at their current levels, in order each bank to be considered as efficient. In the following table the average results of DEA and bootstrap models are presented for all banks of PIIGS and for each country’s banks separately.

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Table 5 Bootstrap Results Samples # Period

20 3 2 6 4 5

Bootstrap bias

Variance estimates

Lower 95% C.I.

Upper 95% C.I.

0,11 0,13 0,12 0,10 0,05 0,17

0,01 0,01 0,01 0,004 0,002 0,02

0,47 0,54 0,26 0,55 0,34 0,50

0,59 0,68 0,37 0,67 0,41 0,66

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PIIGS Portugal Ireland Italy Greece Spain

DEA distance function estimates 0,60 0,69 0,38 0,69 0,42 0,67

(2009 - 2015) Biascorrected distance function estimates 0,49 0,57 0,26 0,59 0,37 0,50

For the total sample of banks (PIIGS), DEA model gives an average uncorrected

efficiency score of 0.6 while bootstrap gives an average bias-corrected distance function

estimate of 0.49 (average bootstrap bias of 0.11). This bias-corrected distance function estimate suggests that keeping all the inputs constant, revenues (Net after provisions) could

could have been increased between 41% and 53%.

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have been expanded by 51%. Moreover, the 95% confidence interval shows that revenues

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As far as per country analysis is concerned, Portugal and Spain obtained the highest DEA model average efficiency estimate of 0.69, while bootstrap gives an average biascorrected distance function estimate of 0.57 for Portugal and 0.59 for Italy (average bootstrap

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bias for DEA scores of 0.13 and 0.10, respectively). These bias-corrected distance function estimate suggest that keeping operating cost, total assets and number of employees constant, Revenues (Net after provisions) could have been expanded by 43% for Portugal and 41% for

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Italy. In addition, the average 95% confidence intervals show that revenues could have been increased between 32% and 46% (Portugal) and between 33% and 45% (Italy). Spanish banks received a relative high DEA model average efficiency estimate of 0.67, whereas the

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average bias-corrected distance function estimate is 0.5 (average bootstrap bias for DEA scores of 0.17). This latter indicates that by retaining all the inputs at their current levels,

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output could have been expanded by 50% (average 95% confidence intervals between 34% and 50%).

In the worst position, according to DEA average efficiency estimates are Greece

(0.42) and Ireland (0.38), with bootstrap bias of 0.05 and 0.12, respectively. Revenues could have been increased by 63% for Greece and 74% for Ireland (average 95% confidence intervals between 59% and 66% for Greece and between 63% and 74% for Ireland).

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4.2 MALMQUIST (Total Factor Productivity Analysis)

In this stage, in order to determine whether there were productivity improvements among banks over the period of interest, Malmquist Total Factor Productivity Index has been used. In our analysis, the same variables, as in the case of the DEA model, have been used. In the following Table 6, the results of the conducted analysis for each bank are presented. An Irish bank (bank 4) was ranked first in terms of productivity growth. There was an average positive increase in Total Factor Productivity Change of 59.9%. This can be decomposed into 39.2% improvement in efficiency and 14.9% improvement in technology. On the other hand, a Greek bank (bank 14) was ranked as the worst, as far as

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productivity change is concerned. There was an average decrease in Total Productivity Change during the period of interest of -71.9%. This negative change was due to a reduction of -75.5% in efficiency although there was a technological improvement of 15.1%. All of this negative change in efficiency is attributed to a decline in pure technical efficiency since there

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was no change in terms of scale efficiency.

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Table 6 Malmquist index summary of bank means BANKS EC TC PEC SC TFPC 1,091 1,127 1,089 1,002 1,23 1 1,035 1,131 1,059 0,978 1,171 2 0,889 1,123 0,924 0,962 0,998 3 1,392 1,149 1,402 0,993 1,599 4 1,088 1,131 1,105 0,985 1,231 5 0,993 1,159 1,011 0,983 1,151 6 0,994 1,138 0,998 0,996 1,131 7 0,949 1,141 0,982 0,967 1,083 8 0,946 1,153 0,981 0,964 1,091 9 0,913 1,150 1,013 0,902 1,05 10 0,885 1,175 1,011 0,875 1,04 11 0,832 1,119 0,852 0,977 0,931 12 0,829 1,140 0,849 0,977 0,946 13 0,245 1,151 0,245 1,000 0,281 14 0,768 1,148 0,794 0,968 0,882 15 0,878 1,140 1,000 0,878 1,001 16 0,899 1,186 0,971 0,926 1,066 17 0,921 1,198 0,957 0,962 1,103 18 0,910 1,124 1,000 0,910 1,023 19 1,007 1,220 1,000 1,007 1,229 20

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RANKING 3 5 16 1 2 6 7 10 9 12 13 18 17 20 19 15 11 8 14 4

In Table 7, a summary of Malmquist results for PIIGS and for each country separately is presented. According to the results, 75% of banks showed a progress in average Total Factor Productivity Change (TFPC). Among them, the Irish banks showed the best performance (59.9% and 23.1% respectively). On the other hand, all Greek banks and one from Portugal count for the 25% of banks that performed TFPC<1, ranging between -71.9% and -0.2%. As far as EC is concerned, 75% of banks showed a decline in EC ranging between -75.5% and -0.6% and only 25% showed a progress ranging between 0.7% and 39.2%. On the other hand, banks from all countries had TC>1. If we combine this fact with the very low standard deviation of TC (0.026), we conclude that all banks performed under a similar

SC 0,961 0,040 0,875 1,007 85% 5% 10%

TFPC 1,062 0,239 0,281 1,599 25% 0% 75%

0,981 0,989 0,948 0,981 0,937

1,133 1,415 1,091 0,760 1,084

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PEC 0,962 0,208 0,245 1,402 50% 15% 35% 1,024 1,254 0,999 0,685 0,986

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Table 7 Malmquist results for PIIGS EC TC 1,150 MEAN (PIIGS) 0,923 0,207 0,026 SD(PIIGS) 0,245 1,119 MIN(PIIGS) 1,392 1,220 MAX(PIIGS) 75% 0% PER CENT<1 0% 0% PER CENT =0 25% 100% PER CENT>1 MEAN 1,005 1,127 PORTUGAL 1,240 1,140 IRELAND 0,947 1,153 ITALY 0,669 1,140 GREECE 0,923 1,174 SPAIN

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technological level in the period of interest.

The leading country that performed an increase in TFPC was Ireland (followed by

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Portugal, Italy and Spain, respectively). Irish banks performed an average positive increase in TFPC of 41.5% that can be analyzed to 24% improvement in efficiency and 14%

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improvement in technology. The only country that showed a decrease in TFPC of -24% was Greece. This finding could be explained by the specific conditions in the Greek economy as a result of the debt crisis since 2009. Especially, the reduction in national savings, the drop in investments, the high level of unemployment, etc. This negative change was due to -33.1% reduction in efficiency although there was a technological improvement of 14%. In Portugal, the average positive increase in TFPC of 13.3% is mainly due to the 12.7% improvement in technology since the increase in EC is only 0.5%. In contrast, Italy and Spain showed a decline in EC of -5.3% and -7.7% respectively despite the technological improvement of 18

15.3% for Italy and 17.4% for Spain. These findings for each country could be illustrated by the specific economic conditions during the investigated period. 4.3 TRUNCATED REGRESSION In this section, two alternative models are established in order to estimate the effect of other factors in the banks’ efficiency score. In the first model only financial variables are introduced. 4.3.1 First Model –Financial Variables According to the results of Table 8, it seems that four variables plays significant role

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in the final configuration of the banks’ performance. More analytically, it is shown that

regulatory capital (V1) negatively affects Net Revenues after provisions controlling for bank characteristics. This result is in line with the argument that higher capital requirements may

lower bank efficiency through higher opportunity costs. However, this negative relationship is

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not robust to different capital measures (i.e., tangible common equity capital ratio and total

risk based capital ratio). Risk Weighted Assets Ratio (V4) is positively associated with bank efficiency through its positive effect on bank’s (net) interest income and therefore on bank’s

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(net) revenues. An alternative interpretation of this result is the effective adjustment of the banks in their operating environment risks. In this case an effective management of total asset

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is succeeded, with a positive impact on the bank’s performance. Concerning the effect of bank size on bank efficiency, there is no significant relationship which is in line with the results obtained by Athanasoglou et al. (2008) and

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Trujillo-Ponce (2013) for Greek and Spanish banks, respectively. As expected, the Non Performing Assets ratio (V5) is negatively related to our measure of efficiency since nonperforming assets reduce the performance of total assets. In our approach as measure of

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efficiency is defined the net revenues after provisions. However, the accounting provisions during the recent crisis are configured in lower levels in relation to the real amount of the NPA. The inability to accurately predict the appropriate level of accounting provisions could

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be attributed to the decrease in disposable income, but also to the loss of a significant part of the value of collaterals covering mainly a substantial part of housing and investment loans.

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Table 8 “Truncated Regression Results” Model 1 Percentile Observed Variables Coef. constant 0.9637986 V1 -0.0226297** V2 0.0075777 V3 -0.031155 V4 0.0042996*** V5 -0.0138757*** V6 -0.0078529 V7 -0.0038408 V8 0.0016866***

Bootstrap Std. Err. z 0.4027319 2.39 0.011159 -2.03 0.007065 1.07 0.0606781 -0.51 0.001613 2.67 0.003689 -3.76 0,0131658 -0.60 0.0024273 -1.58 0.0003634 4.64

p z 0.017 0.043 0.283 0.608 0.008 0.000 0.551 0.114 0.000

95% conf. Interval 0.1575808 1.714313 -0.0447355 -0.0016363 -0.0052396 0.0221562 -0.1531175 0.0806809 0.0011474 0.007395 -0.0212847 -0.006425 -0.0330354 0.0184529 -0.0087943 0.0010091 0.0009593 0.0023419

Notes: The dependent variable is the efficiency scores derived from the bootstrap method. ** Denotes significant

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at 5% and *** denotes significant at 1%.

Net Income Margin (V8) is positively linked to bank efficiency. The positive sign of the coefficient is consistent with our expectation since this index is an indicator of how profitable a company is relative to its total assets. The higher the ratio, the more efficient is the bank at

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generating earnings by using its assets and thus net bank revenues are also increased. Finally,

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total debt to total assets (V7) is not significant in relation to the efficiency score.

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4.3.2 Second Model - “Combination of Financial and Macroeconomic Variables”

In this model, a combination of financial and macroeconomic variables are considered. According to the results of Table 9, both financial and macroeconomic variables proved to be

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significant as determinants for the efficiency score of banks.

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Table 9 “Truncated Regression Results” Model 2

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Variables Observed Coef. constant 0.7573676 V1 -0.0163438 V2 0.0140461** V3 -0.0142513 V4 0.0044653*** V5 -0.0197446*** V6 -0.0161062 V7 0.0015758 V8 0.0017164*** V9 -0.0318976** V10 -0.007316

Bootstrap Std. Err. 0.3688279 0.0109532 0.0071748 0.0541422 0.0013928 0.0040739 0.0128249 0.002497 0.0003354 0.0147296 0.0057483

Percentile z 2.05 -1.49 1.96 -0.26 3.21 -4.85 -1.26 -0.63 5.12 -2.17 -1.27 20

p z 0.040 0.136 0.050 0.792 0.001 0.000 0.209 0.528 0.000 0.030 0.203

95% conf. Interval 0.0050314 1.442009 -0.0368666 0.0044204 0.0011006 0.0284875 -0.1214683 0.0884881 0.0016624 0.0072425 -0.0278241 -0.0121111 -0.0403079 0.0117941 -0.0065213 0.0035908 0.0009303 0.0021841 -0.600767 -0.0004681 -0.193365 0.0029417

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0.1456134***

0.0546417

2.66

0.008

0.0390923

0.2492041

Notes: The dependent variable is the efficiency scores derived from the bootstrap method. ** Denotes significant at 5% and *** denotes significant at 1%.

Comparing the results of the two models, four financial variables play a significant role in the final configuration of the banks’ performance. Notable is the fact that the effect of regulation on the banks’ efficiency status remain strong. Moreover, it appears that the macroeconomic environment contributes to banks’ performance. The point estimates on inflation suggest that banks were not able to target inflation correctly, leading thus to imperfect interest rate adjustments and potential losses (Pasiouras and Kosmidou, 2007). Moreover, we cannot confirm the argument that economic growth

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enhances the performance of the financial sector. According to the results, GDP growth do not have an impact on the efficiency score of PIIGS banks in the investigated period. Finally,

it is shown that high levels of General Government Debt to GDP ratio as a percentage of GDP (GovDebt) may exert a positive effect on the efficiency of banks. In other words, the financial position of a government affects the interest rates on its debt securities, and thus the

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performance of banks holding such securities on its portfolio. In our case, by considering a

dummy taking the value 1 if GovDebt exceeds 100%, we verify the above relationship. In

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other words, the financial position of a government affects the interest rates on its debt securities, and thus the performance of banks holding such securities in their portfolio. The above findings highlight the significant role of monetary policy in the Eurozone area. While

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the economies of the PIIGS operates under the common monetary framework, the benefits from their participation of the Eurozone are not allocated equally in the member countries. This fact is absolutely justified by the individual characteristics of the economies. Moreover,

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the risk associated with an increase in public debt may imply a negative effect on the

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performance of banks if financial markets stop the financing of a country, leading to default.

5. CONCLUSIONS

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The global economic crisis had also a strong effect on the operation of the European banking system, through its effect on the assets and liabilities of a bank’s balance sheet. Also the differences in the structure of these economies such as the size and the productive orientation create a specific framework relevant to the banking sector strategy in the national level. According to the results of our research, the reaction of national banking sectors in the recession, differs between the PIIGS members and this behavior, is theoretically justified by several economic and financial factors. In this research our interesting is specifically focused on the financial and regulatory features of the banks, while the effect of the macroeconomic 21

environment is also investigated. Methodologically, the proposed approach used a combination of the DEA components such as the fundamental DEA output oriented, Bootstrap technique, the Malmquist Productivity Index and finally the version of truncated regression based on Simar and Wilson’s approach. Evaluating the results of DEA in the first stage of our analysis it seems that sovereign debt crisis indirectly affects the performance of the banking sector of PIIGS, especially in the case of Greece. According to the DEA results, the majority of the banks present a negative change in the efficiency score in the end of the investigated period. This finding could be illustrated as a result of the economic policy which is followed by all members of the Eurozone and the weakness of this policy to be compatible with the specific macroeconomic characteristics of

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the PIIGS countries. These findings are supported by the results of the proposed empirical analysis using financial and regulatory variables. DEA results are enhanced by the bootstrap model and the primary results are confirmed. According to the Malmquist Productivity Index results, 75% of banks have shown a progress in average Total Factor Productivity Change

(TFPC). As far as EC is concerned, 75% of banks showed a decline in EC ranging between -

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75.5% and -0.6% and only 25% showed a progress ranging between 0.7% and 39.2%. On the

other hand, banks from all countries had TC>1. If we combine this latter result with the very

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low standard deviation of TC (0.026), we conclude that all banks performed under a similar technological level in the period of interest. In the last stage of the proposed analysis, the truncated regression results of the first model, show a strong effect on the efficiency score

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generated by four financial ratios: Tier 1 Capital to Risk Weighted Assets, Risk -Weighted Assets to Total Assets, Non Performing Assets to Total Assets and Net Income Margin. These results show that the regulatory framework and profitability strongly affect the efficiency

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score of banks. An increase in Tier 1 Capital to Risk Weighted Assets results in a reduction in the banks’ efficiency score. This effect is explained by the fact that an increase in the total capitals for regulatory purposes will reduce banking activities with an expected return on

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capital. The same result on bank’s efficiency is also observed for the Non Performing Assets to Total Assets, which is a quality indicator relevant to the bank’s assets. On the other hand,

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there is a positive effect related to the rise of the Risk -Weighted Asset to Total assets and Net Income Margin. In the case of the first ratio it seems a better adjustment in the risks creating a safety environment for the banks operation. The second ratio offers estimations relevant to a more effective management of administrative and operating costs. In the case of the second model, the effect of regulation and the profitability in the banks’ efficiency status remain significant and additionally it is shown that macroeconomic factors have an impact on the performance of banks.

22

Evaluating the findings of this research, especially in the stages of DEA and truncated regression, it is clear that this study could offer significant implications in the field of banks’ performance evaluation in the aftermath of the global financial crisis. It seems that the regulatory framework and the macroeconomic environment play a crucial role in the banks’ efficiency configuration. In the light of this evidence, the policy design in micro and macro level could be more compatible and flexible in relation with the issues raised. Moreover, MPI analysis offers significant findings in the case of economies with strong macroeconomic imbalances. The banking sector of these countries is difficult to obtain a high performance as a result of technological change, during the business cycle. All these findings could be evaluated by both bank managers and policy makers. The analysis performed on the results of

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the MPI could be enhanced by the quantification of information on specific restructuring strategies for each bank of the sample used, emphasizing potential mergers or acquisitions.

Finally, the present study raises questions about the continuation of research in new avenues

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such as the comparison of PIIGS banks with those of other euro area economies.

Acknowledgements

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We would like to thank the editor and two anonymous reviewers for their helpful comments

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in an earlier version of this paper. All possible remaining errors are our own.

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