Efficiency of the CEE Countries Banking System: a DEA Model Evaluation

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1 Vision 2020: Innovation, Development Sustainability, and Economic Growth 1009 Efficiency of the CEE Countries Banking System: a DEA Model Evaluation Jana Erina, Riga Technical University, Riga, Latvia, Jana.Erina@rtu.lv Ingars Erins, Riga Technical University, Riga, Latvia, Ingars.Erins@rtu.lv Abstract Purpose The aim of this article is to empirically evaluate cost-benefit efficiency of the banks of seven CEE states (the Czech Republic, Hungary, Latvia, Lithuania, Poland, Slovakia, Slovenia) in the periods before and during the crisis, and in the period of economic recovery using Stochastic Frontier Analysis. Design/methodology/approach In order to achieve the results of the present research, the qualitative overview of the scientific literature on Data Envelopment Analysis (DEA) has been performed. To gain the required data, the authors of the present paper have analyzed the work of the CEE countries banks in the reporting period of The financial indicators applied for the needs of the present research have been obtained from the data available on the web page of the Association of Latvian Commercial Banks, Eurostat, European Central Bank, as well as from the annual reports of the banks. Findings Based on the two DEA models, the findings showed that banking systems most effectively operate in Lithuania and Slovenia, while the Latvian banking system is most inefficient. Research limitations/implications The research performed has provided information on the efficiency rate of the banks operating in CEE countries, which allows making decisions considering future perspectives of each bank not only from the standpoint of a shareholder, but also from the standpoint of a client and bank personnel. Originality/value Taking into consideration how important the efficiently developed banking system is, especially in the states with a high competitive dynamics (including Latvia), the banks should change the present strategy to constantly improve their efficiency rate. Keywords: CEE countries, Banks, DEA, Efficiency Introduction Banking system plays an important role in the development of the national economy of any state, both for investors and borrowers. Economic growth of any country can be ensured only if savings are effectively directed for investment. Thus, the absence of involvement of the banking system is one of the hurdles in the centrally planned economy. Strategically investors in the transition economy also pay more and more attention to bank efficiency and the means to ensure it (Berger et al, 2010). In this regards, the issue of bank efficiency and its analysis is important both in microeconomic and macroeconomic perspective. At the microeconomic level efficiency is of great significance in the transition economy in particular, taking into consideration the fact that the competition is growing and subsidiaries of the foreign banks enter financial sector markets of each country (Caviglia et al, 2002). Lensink and Hermes (2004) in their research discovered that entering of the foreign banks into the local market to a great extent depends on the degree of development of the state economy and the banking sector. This fact is attested also by rapid development of the banking sectors of CEE states, which started when these states joined the EU.

2 1010 Vision 2020: Innovation, Development Sustainability, and Economic Growth Bank efficiency analysis methods can be conditionally divided into two categories: parametric and non-parametric, which are based on Stochastic Frontier Approach (SFA) and Distribution Free Approach (DFA). SFA method in some sources is also referred to as econometric frontier approach (EFA), developed by Aigner, Lovell and Schmidt in The essence of this method is based on the concepts of cost-benefit efficiency. In turn, DFA does not reflect definite division into inefficient and accidental errors. Date Envelopment Analysis (DEA) is a deterministic methodology, which allows determining relative efficiency that is based on input and output data. It is also called Decision Making Unit (DMU). The research conducted and the literature available on bank efficiency assessment mainly address the banks of the USA and Asian countries, comparatively less sources discuss the banks of European countries. Using DEA method in the analysis of different countries it has been discovered that foreign banks work with lower efficiency than local banks (Grigorian and Manole, 2002; Hasan and Marton, 2003; San et al, 2011). In turn, the studies of other scientists demonstrate the opposite: foreign banks are more efficient than the local (Berger et al, 2010; Uiboupin, 2004). The goal of this article is to evaluate cost-benefit efficiency of the banks of CEE states in the reporting period of To achieve the goal the following research methods were used: qualitative overview of the scientific literature on Data Envelopment Analysis (DEA), including the monographic and descriptive method, as well as deterministic, non-parametric approach. Literature Review The issue of bank efficiency and its assessment becomes topical not only in the period of economic transition or in the time of the financial crisis; it is also an indispensable aspect of the everyday work of financial institutions. In the available literature bank efficiency assessment is mainly based on the premise that all banks work according to the same model, excluding such influencing factors as economic situation in the country and its marker system, within which the banks operate. For example, Dietsch and Lozano-Vivas (2000) consider that not including specific indicators of each country into the assessment of efficiency may give erroneous results. Grigorian and Manole (2002) expressed similar views, stressing that when comparing the efficiency of the banks across different states specific environment indicators of each country should be taken into account. Before joining the EU the banks of CEE states were subjected to deregulation and liberalisation, which left an impact on bank performance indexes and their efficiency (Berger et al, 2000). Performing the research many authors have discovered that the increase of proportion of the foreign banks can also increase the overall bank efficiency (Levine, 2001). In turn, other authors stress that the better developed the economy of the state is, the more developed is its financial sector (Levine, 1997; Khan and Semlali, 2000; Guiso et al, 2009). Rossi et al (2005) analysed cost-benefit efficiency of the banks of nine CEE states in the period from 1995 to 2002 using the stochastic frontier analysis. The obtained data demonstrated that banks with low cost efficiency show higher profitability. In contrast, Semih et al (2007), Rossi et al (2005) discovered that there is no strong correlation between efficiency and loan quality, however, cost efficiency is indeed influenced by external factors. Weill (2007) performing research from 1996 to 2000 on the banks of CEE and Western European countries using stochastic frontier approach has come to the conclusion that there are differences in cost efficiency of Western and CEE countries banks. For example, the banks of the Czech Republic and Hungary are as efficient as the banks of Western European states, except the banks in Greece and Portugal. A more significant increase in efficiency has been observed in the banks of CEE states

3 Vision 2020: Innovation, Development Sustainability, and Economic Growth 1011 rather than in the Western European banks. Differences in efficiency of the analysed countries are explained by the fact that CEE banks have been influenced by the transition economy as well as government performance. Semih et al (2007) having analysed cost-benefit efficiency of the banks of 12 CEE states in the period from 1993 to 2000 have concluded that the banks of Poland and Slovenia operate most efficiently, whereas the banks of Russia and the Baltic States (Latvia, Lithuania and Estonia) are least efficient. The authors have also discovered that higher efficiency is characteristic of larger banks and the banks that mainly concentrate on deposits; large proportion of bad loans and high concentration of banks decease efficiency; higher efficiency is demonstrated by foreign banks rather than local. Fang et al (2011) have come to a similar conclusion that foreign banks work by far more efficiently that the local banks. In the later studies on CEE state banks it has been discovered that the banking sectors of the Czech Republic and Romania work most efficiently, and that efficiency has grown since these countries joined the EU (Andries, 2011) DEA analysis principles DEA is an alternative analytical tool for regression analysis. The main difference between DEA and regression analysis is that the latter is based on comparison of mean DMU indicators, but DEA is a point method, which compares each DMU with the best DMU indicator. One of the first studies on non-parametric efficiency assessment method was performed in In this study technical production efficiency was determined using one input and output variable (Farrell, 1957). Slightly later this method was elaborated by Banker et al. (1984) and by Fare et al. (1985). Only in 1978 Charnes et al. named the previously used non-parametric method DEA CCR model. The model of Banker et al (1984) is called BCC in the scientific literature. The principle of the CCR model is based on the performance efficiency assessment concept elaborated by Farrell (1957), which uses either many input variables and just one output variable, or many input and output variables. Charnes et al (1978) have applied linear combinations in this model, assuming that yield value is fixed. BCC model, in its turn, is based on variable output indicators on the scale. The relative DMU efficiency mathematically is expressed as the ratio of the sum of the output variables and the sum of the input variables. Methodology The authors of the present paper have chosen the following countries as representative countries of Central and Eastern Europe (CEE) in order to perform the analysis: the Czech Republic, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia. All countries mentioned above joined the European Union in The number of the banks in the CEE countries as on 1 December 2011 is provided in Table 1. Table 1: The Number of banks in the CEE countries Country Number of banks (local and subsidiaries of the foreign banks) Number of the branches The Czech Republic Hungary Latvia Lithuania Poland Slovakia Slovenia

4 1012 Vision 2020: Innovation, Development Sustainability, and Economic Growth In order to achieve the results of the present research, the qualitative overview of the scientific literature on Data Envelopment Analysis (DEA) has been performed. To gain the required data, the authors of the present paper have analysed the work of the CEE countries in the reporting period of The financial indicators applied for the needs of the present research have been obtained from the data available on the web page of the Association of Latvian Commercial Banks and European Central Bank. In order to assess the efficiency of the banking system of the CEE countries, the authors have applied the DEA methodology, which is based on the deterministic, nonparametric approach applying various input and output indicators. The input and output indicators have been identified taking into consideration the studies conducted by other scientists. Having analysed 48 scientific papers on the research on bank efficiency assessment applying the DEA methodology within the period of , which was conducted in the USA and Asian countries, the authors of the paper have learned that the employees, capital, deposits, and return on assets are considered as input variables, while loans, profitability, and related revenues as output variables. Having summarised 40 studies on the European states banks within the period of , the authors of the paper have noticed that labour, physical capital, and funds are most frequently used as the input variables, while deposits, net loans and other earning assets as the output variables. Taking into consideration the results of the scientific paper analysis and the data available, in order to assess the efficiency of the CEE states banks the authors have used two input (X 1, X 2 ) and output (Y 1, Y 2 ) variables: X 1 labor X 2 capital Y 1 loans Y 2 - deposits The descriptive statistics data for the input and output variables of the CEE states banks used for the needs of the present research are provided in Table 2 Table 2: Descriptive Statistics for the Input and Output Variables of the CEE States Banks within the Period of Variables Mean St. Dev. Minimum Maximum X 1 (number of bank employees) 45, , , , X 2 (EUR 99, , , , millions) Y 1 (EUR 65, , , , millions) Y 2 (EUR 61, , , , millions) To assess the efficiency of the banks, a variety of DEA models exists, however, for the needs of the present research, the authors have employed the most frequently used models CCR DEA and BCC DEA input and output oriented. The authors of the paper have applied the following formula to calculate the overall bank efficiency rate: (1)

5 Vision 2020: Innovation, Development Sustainability, and Economic Growth 1013 Research Results In order to assess the cost-benefit efficiency of the CEE states banks, the authors have initially analysed the input and output variables applying the CCR model. The obtained data on the CEE states banks economic efficiency indicators for the period of are provide in Table 3. Table 3: CEE Countries Banks Economic Efficiency Based on CCR Model Efficiency Country Mean Evolution The Czech Republic Hungary Lithuania Latvia Poland Slovenia Slovakia Having studied the available scientific literature on the bank efficiency it can be stated that banks are considered efficiently operating if the efficiency index exceeds or is equal to 1, and vice versa if the efficiency index is below 1. Taking into consideration the obtained data it can be concluded that the most efficiently operating banks are in Lithuania and Slovenia, while other CEE states banks are considered to be inefficient. The data on the CEE countries banks economic efficiency assessment based on BCC model are provided in Table 4. Table 4: CEE Countries Banks Economic Efficiency Based on BCC Model Efficiency Country Mean Evolution The Czech Republic Hungary Lithuania Latvia Poland Slovenia Slovakia Having studied the data provided in Table 4, the authors of the research have come to the conclusion that when assessing the economic efficiency based on BCC model, the most efficiently operating banks are in the Czech Republic, Lithuania, Poland and Slovenia, while the lowest efficiency rate is calculated for the Latvian commercial banks. The economic efficiency assessment performed has shown that the efficiency indicators are lower for the CCR model than for the BCC model. This assumption is also substantiated by the results of the research performed by other scientists (Wang and Cullinane, 2006; Park and De, 2004). The authors of the paper have calculated the overall economic efficiency rate as the ratio of the sum of the mean values of each state obtained using the CCR model and the sum of the mean values of each state obtained using the BCC model. The authors have summarized the calculated economic efficiency rate and the results provided by other scientists in Table 5.

6 1014 Vision 2020: Innovation, Development Sustainability, and Economic Growth Country Table 5: CEE countries banks efficiency by DEA Grigorian Weill (2007), Delis and and Manole research Papanikolaou (2002), period (2011), research 2000 research period period The Czech Republic Hungary Lithuania N/a Latvia Poland Slovenia Slovakia Authors, research period Having analysed the data provided in Table 5, the authors of the paper have come to the conclusion that the efficiency rate of the CEE states banks have increased after their accession to the EU, which is also substantiated by the results of the research performed by Andries (2011). While Grigorian and Manole (2002), Delis and Papnikolaou (2011) have assessed Slovenian banks as having the highest efficiency rate. Conclusion Using the non-parametric DEA methodology approach it is possible to evaluate the bank's economic and technical efficiency. The results of research showed that the CEE countries bank efficiency has decreased when the financial crisis started, with some exceptions. The authors have also studied the works on the application of DEA model written by the foreign scientists. Both the performed analysis and the research on bank efficiency rate conducted by other scientists have indicated that the efficiency rate of the CEE states banks have increased after the accession of the corresponding CEE states to the EU. Therefore, the changes in the economic situation influence the fluctuations of the bank efficiency rate. The data obtained have also indicated that the increase of the ratio of the bad loans in credit portfolio can influence the banks efficiency rate, which is also proved by Berger and Deyong (1997). The increase of the ratio of bad loans in credit portfolio has especially influenced the work of the Latvian commercial banking system. Taking into consideration how important the efficiently developed banking system is, especially in the states with a high competitive dynamics (including Latvia), the banks should change the present strategy to constantly improve their efficiency rate. In the future the authors see a need to perform additional researches on the other influencing factors of the DEA models. References Aigner, D., Lovell, C. A. L. and Schmidt, P. (1977) Formulation and estimation of stochastic frontier production function models, Journal of econometrics, 6(1), Andries, A. M. (2011) The determinants of bank efficiency and productivity growth in the Central and Eastern European banking systems, Eastern European Economics, 49(6), Association of Latvian Commercial Banks, (2013). Indices of bank activities in Q4 of [Online], [Retrieved March 1, 2013],

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