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RUHR ECONOMIC PAPERS Ansgar Belke Ulrich Haskamp Ralph Setzer Regional Bank Efficiency and its Effect on Regional Growth in Normal and Bad Times #586 Imprint Ruhr Economic Papers Published by Ruhr-Universität

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RUHR ECONOMIC PAPERS Ansgar Belke Ulrich Haskamp Ralph Setzer Regional Bank Efficiency and its Effect on Regional Growth in Normal and Bad Times #586 Imprint Ruhr Economic Papers Published by Ruhr-Universität Bochum (RUB), Department of Economics Universitätsstr. 150, Bochum, Germany Technische Universität Dortmund, Department of Economic and Social Sciences Vogelpothsweg 87, Dortmund, Germany Universität Duisburg-Essen, Department of Economics Universitätsstr. 12, Essen, Germany Rheinisch-Westfälisches Institut für Wirtschaftsforschung (RWI) Hohenzollernstr. 1-3, Essen, Germany Editors Prof. Dr. Thomas K. Bauer RUB, Department of Economics, Empirical Economics Phone: +49 (0) 234/ , Prof. Dr. Wolfgang Leininger Technische Universität Dortmund, Department of Economic and Social Sciences Economics Microeconomics Phone: +49 (0) 231/ , Prof. Dr. Volker Clausen University of Duisburg-Essen, Department of Economics International Economics Phone: +49 (0) 201/ , Prof. Dr. Roland Döhrn, Prof. Dr. Manuel Frondel, Prof. Dr. Jochen Kluve RWI, Phone: +49 (0) 201/ , Editorial Office Sabine Weiler RWI, Phone: +49 (0) 201/ , Ruhr Economic Papers #586 Responsible Editor: Volker Clausen All rights reserved. Bochum, Dortmund, Duisburg, Essen, Germany, 2015 ISSN (online) ISBN The working papers published in the Series constitute work in progress circulated to stimulate discussion and critical comments. Views expressed represent exclusively the authors own opinions and do not necessarily reflect those of the editors. Ruhr Economic Papers #586 Ansgar Belke, Ulrich Haskamp, and Ralph Setzer Regional Bank Efficiency and its Effect on Regional Growth in Normal and Bad Times Bibliografische Informationen der Deutschen Nationalbibliothek Die Deutsche Bibliothek verzeichnet diese Publikation in der deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über: abrufbar. ISSN (online) ISBN Ansgar Belke, Ulrich Haskamp, and Ralph Setzer 1 Regional Bank Efficiency and its Effect on Regional Growth in Normal and Bad Times Abstract The financial crisis affected regions in Europe in a different magnitude. This is why we examine whether regions which incorporate banks with a higher intermediation quality grow faster in normal times and are more resilient in bad ones. For this purpose, we measure the intermediation quality of a bank by estimating its profit and cost efficiency while taking the changing banking environment after the nancial crisis into account. Next, we aggregate the efficiencies of all banks within a NUTS 2 region to obtain a regional proxy for financial quality in twelve European countries. Our results show that relatively more profit efficient banks foster growth in their region. The link between financial quality and growth is valid in normal and in bad times. These results provide evidence to the importance of swiftly restoring bank protability in euro area crisis countries through addressing high non-performing loans ratios and decisive actions on bank recapitalization. JEL Classification: G21, O16, O47, O52 Keywords: Bank efficiency; financial development; regional growth; europe October Ansgar Belke and Ulrich Haskamp, UDE; Ralph Setzer, European Central Bank. The views expressed are those of the authors and do not necessarily reflect those of their institutions. All correspondence to: Ulrich Haskamp, University of Duisburg-Essen, Department of Economics, Chair for Macroeconomics, Universitätsstr. 12, Essen, Germany, 1. Introduction Growth divergences across European regions have been large and persistent. Some European regions have been experiencing steady growth, while in others growth has remained anaemic (Quah, 1996; Cuaresma et al., 2014). One of the reasons for this finding could be that banks perform differently in their financial intermediation function across regions. For example, easier access to credit increases resources that could be channeled into investment. There are many studies which analyzed the relationship between financial volume and growth in cross-country studies (Levine, 2005). However, Hasan et al. (2009) criticize, firstly, that cross-country studies suffer from sample heterogeneity as they cover very different economies. Therefore, a solution is to concentrate on regions to use also within-country variation (Higgins et al., 2006). 2 Secondly, Hasan et al. (2009) argue that financial development cannot only be measured by the credit to GDP ratio - a financial volume measure. In fact, Rousseau & Wachtel (2011) show there has been only a weak link between financial volume and growth in developed countries over recent times. Therefore, Hasan et al. (2009) provide another channel of the influence of banks on regional productivity growth. Specifically, they showed for eleven European countries over the period that the intermediation ability of a bank should not be assessed alone by the volume of funds which are shifted from savers to borrowers, but also by its quality, i.e. by its ability to channel funds to its most productive uses at a reasonable interest rate. A bank s intermediation quality can be measured by its efficiency in converting inputs into outputs while either minimizing costs or maximizing profits. A more efficient bank is assumed to foster growth as it is able to select the optimal projects to fund while calculating the optimal cost of lending given the projects risks. These considerations are supported by the recent financial crisis in the euro area which was driven by financial intermediaries inefficient allocation of resources to sectors where the marginal product of capital was low. This implied that capital accumulation was not associated with technological change and hence higher potential growth. Indeed, in a number of euro area economies capital flowed disproportionately into the non-tradable sector (construction, financial services, public sector) that pushed up wages without adequately raising productivity, and which gave rise to large intra-euro area current account imbalances, high indebtedness and major economic disruptions (Praet, 2014). Furthermore, the sluggish recovery in euro area crisis countries suggests that during the bad time of the crisis, there has been too little good deleveraging and too much ever-greening and forbearance, undermining the ability of banks to support the upswing and the reallocation of labor and capital towards more productive uses. Firstly, we test whether Hasan et al. (2009) s finding of a positive link between the efficiency of 2 Further examples of studies examining the effect of financial development on regional growth are Guiso et al. (2004) and Moretti (2014) using Italian firm data, Pascali (2014) for long-term effects in Italy, Koetter & Wedow (2010) taking Bundesbank data about German banks, and Kendall (2012) examining Indian district data. 4 banks in a region and productivity growth holds for an updated and extended data set for twelve European countries. As our sample includes the financial crisis and its aftermath, we thereby contribute to the literature by examining whether the results of Hasan et al. (2009) are valid in normal as well as in bad times. In addition we also address the differing banking environment in European countries. After 2007 the regulation for banks was tightened and the non-performing loans ratio increased. Estimating a bank s efficiency would be biased without accounting for these changes. For example, if a government introduces a stricter banking regulation which reduces its banking sector s profits, the efficiency estimation would wrongfully account the reduced profits to inefficiency if one neglects these changes. As a further innovation to the literature, we demonstrate that the strength of the relationship between financial quality and productivity growth is dependent on the level of development of a region. Firms in less developed regions have more problems in obtaining funding and investments have a relatively higher marginal productivity (Guiso et al., 2004; Hakenes et al., 2015). If such a region includes more efficient banks, which are able to identify the right firms to finance, it, firstly, gains credits for its firms and, secondly, catches up faster in productivity. This result bears interesting policy implications on how to increase the speed of convergence of European regions. Potential instruments to foster bank efficiency are by adjusting regulations for savings and cooperative banks, fostering investment in commercial banks IT or, in light of the financial crisis, swiftly addressing the high non-performing loans ratios in many countries (Barth et al., 2013; Beccalli, 2007; Koetter & Poghosyan, 2009). Our results are corroborated by several robustness tests. Firstly, we confirm our findings for an estimation which excluded financial centers to account for the fact that we assigned a bank to a region by its headquarters - a procedure which could be considered heuristic for large commercial banks which usually are operating nationwide and are based in financial centers. Additionally, we exclude either large banks or all banks but savings banks from the sample as the latter is forced by law to operate only regionally. Another approach to deal with across region spillovers is to specifically model them. We use a spatial-lag model to account for spillovers of financial development from neighboring regions. All estimations confirm our findings. As policy makers are not only interested in economic growth, but also in reducing unemployment, we also test whether fostering bank efficiency is a potential tool to do so. While we find evidence for this hypothesis using our complete sample, it cannot be said that a region with more efficient banks is more resilient against rising unemployment during a turmoil period as the recent financial crisis. The outline of this work is as follows: Firstly, the methodology of the analysis will be presented. After an exposition and explanation of the regional growth equation and of the estimation of banks efficiency, a description of the data follows. Secondly, empirical evidence will be presented with an additional chapter on robustness. Lastly, we conclude our results. 5 2. Methodology 2.1. Regional Growth Equation The regions in Europe display different growth patterns. Furthermore, although financial regulation in the euro area is being harmonized, banks are different in their efficiency of channeling funds across regions (Bos & Kool, 2006). An example is Italy in which the amount of credit, the interest charged for loans and bank efficiency varies strongly between the northern and southern regions (Pascali, 2014; Montagnoli et al., 2015; Giordano et al., 2013). To test whether these differences in financial volume and quality of European regions affect growth, we follow Levine et al. (2000) and estimate a dynamic panel growth model of the following form: ΔY r,t = α + β 1 ΔY r,t 1 + β 2 lnfq r,t + β 3 lnfv r,t + β 4 lnx r,t + μ r + ɛ r,t (1) where ΔY is the growth rate of GDP per worker. We take GDP per worker as the variable of interest as this measure is determining the productivity of an economy. Still, we also present results for GDP per capita and unemployment as dependent variables. FQ and FV denote financial quality and financial volume, respectively. Financial quality is represented by the weighted average estimated bank efficiency of a region. 3. The weighting was done according to a bank s market share of a region s loans. Financial volume is measured by the regionally aggregated value of loans relative to GDP. Details on the calculation of the financial development variables follow later in Section The additional variables contained in X control for further regional and country-specific variables 4 and μ r represents an unobserved region-specific effect. The subscript r indicates the European NUTS 2 region and t the year. Equation (1) cannot be estimated with basic panel techniques as the lagged GDP variable is correlated with the unobserved region-specific effect μ r. However, μ r can be eliminated by taking first differences. The result is that the differenced lagged dependent variable and the differenced error term are correlated. Arellano & Bond (1991) therefore suggest to use lagged levels as instruments for the differenced lagged dependent variable, the difference GMM estimator, if the error term is not autocorrelated. For further precision, we use the system-gmm estimator of Arellano & Bond (1995), which includes additionally lagged differences of the dependent variable as instruments, as Blundell & Bond (1998) showed that this approach is more efficient than the difference GMM 3 As we use an estimated variable in our regression, we are confronted with a generated regressors problem if the error term of the first stage is not normally distributed. In our case, however, we can be sure that the latter is the case as every deviation of the error term from the normal distribution is regarded as inefficiency by the stochastic frontier estimation. For details on the latter see Section As regional controls we employ the growth rate of the working population and education. The latter is measured as the share of persons between 25 and 64 that obtained tertiary education on the first or second stage. These two variables and regional GDP were obtained from Eurostat for NUTS 2 regions. The country-specific variables are capturing the differences between countries in terms of the banking sector or economic freedom. 6 estimator. Our explanatory variables financial quality and, especially, financial volume may suffer from endogeneity as a growing economy can result in an increasing demand for credits and a growing financial industry. To deal with this potential reverse causality, we follow Levine et al. (2000) and specify both variables as endogenous and, thus, include their lagged levels and differences as instruments. Details follow in Chapter Estimation of Banks Efficiency Bank efficiency is measured by a banks relative ability to convert its inputs into output while maximizing profits or minimizing costs. A bank is inefficient if it uses too many inputs or allocates them in wrong proportions. This relative measurement of efficiency is less affected by endogeneity criticism than financial volume measures because a bank s relative ability to convert its inputs should influence growth independently of whether the economy is growing fast or slowly. An efficient bank should support growth of an economy through its good intermediary function, i.e. by selecting the optimal projects for funding while assigning the optimal costs given risks at the same time (Hakenes et al., 2015). In the following, we assume that banks demand as inputs fixed assets, borrowed funds and labor which can be used at given factor prices, W, to produce outputs, Y, as loans to customers and other banks, and earning assets. We also include equity, z, as a net output, and a time trend. Furthermore, we either assume that banks minimize total costs, TOC, or maximize pre-tax profits, PBT. We estimate these two concepts of efficiency making use of stochastic frontier analysis. Taking this as a starting point, we gain the following translog stochastic frontier: lnlhs i,t = α i + J β j lnx i,j,t K J β j,k lnx i,j,t lnx i,k,t +lnz i,t + t + t 2 + ɛ i,t (2) j k j where LHS represents either total costs or pre-tax profits of a bank i, and X includes the previously mentioned inputs at given factor prices W, outputs Y and equity z. The error terms structure is assumed to be ɛ i,t = v i,t ± u i,t. Therefore, profits or costs differ from the optimal point either because of random noise, v i,t,orinefficiency,u i,t. Random noise is assumed to be i.i.d. and v i N(0,σv). 2 Furthermore, we impose the usual linear homogeneity restriction for the cost function by normalizing total costs, pre-tax profits and input prices by one of the input prices. Following Restrepo-Toban & Kumbhakar (2014), we do not impose such a restriction for the profit function. The translog specification is unproblematic for analyzing cost efficiency. For profit efficiency, however, we encounter the problem that we cannot take the log of negative profits. Hence, we follow Bos & Koetter (2011) and use a negative profit indicator approach. Thus, we do not delete bank observations with negative profits, but we specify (before taking logs) its profits to be 1 and add 7 an indicator variable that takes the absolute value of the losses. For banks with positive profits the indicator variable is zero in logs. Thereby, we keep the complete sample and include the important information about losses. To improve the estimation, we follow Lozano-Vivas et al. (2002) and include country-specific differences of financial systems in the stochastic frontier. They found that neglecting such differences, which still exist between European countries, can bias the inefficiency estimates. In line with the literature, we include net output z and country-specific variables as determinants of the inefficiency distribution to capture the regulatory demand for equity as well as macroeconomic and banking-market differences between countries. 5 This is of considerable importance as the financial crisis is included in the estimation sample period which led to stricter regulation and a worse macroeconomic environment for banks. 6 Inefficiency is therefore assumed to be of the following structure: u i N(μ + dz, σu). 2 μ is the estimated mean of the inefficiency distribution and d is a vector including the estimated coefficients of Z, the country-specific variables and the bank s equity, z. We also follow the standard frontier assumption of a half normal distribution for the inefficiency term. Lastly, a bank-specific fixed effect, α i, is introduced to capture the remaining heterogeneity. So, if a bank deviates from optimal profits or costs given its use of inputs, this is accounted to be due to inefficiency or random noise. With this specification we estimate Equation (2) using a maximum likelihood estimation of a fixed-effects panel frontier with time-variant efficiency (Greene, 2005). Thus, we do not impose a monotonous trend for banks efficiency, but allow for variation over time. The efficiency of the stochastic frontier can then be obtained by exp( u i,t ). A cost efficiency value of 80% represents, for example, that a bank could have produced the same amount of outputs with the usage of only 80% of its inputs. A profit efficiency value of 60% implies that the bank could have gained 40% more profits if it had used its inputs efficiently Data Financial Development We have obtained unconsolidated financial data for 3,878 banks from twelve European countries between the years 2000 and 2013 from the Bankscope database. We dropped banks as central banks, securities firms and bank holdings from the sample is the end of the sample as regional GDP data is not available for more recent years. The financial data include loans, y 1, earning assets, y 2, bank loans, y 3, other operating expenses over fixed assets, w1, expenses for personnel over fixed assets 7, w2, total interest expenditure over funding, w3, equity, z, total costs, TOC, and pre-tax 5 Thereby, z is included in the kernel and as a determinant of the inefficiency distribution of Equation (2). 6 Further details follow in Section Usually, it is suggested to divide personnel expenses by the number of employees. However, if the latter is not available, it is common to divide it by fixed assets. An example for this is Lozano-Vivas & Pasiouras (2010). They 8 profits, PBT, allowing us to estimate cost and profit efficiency. The country-specific variables which are used, following Lozano-Vivas et al. (2002), as determinants for the inefficiency distribution of the stochastic frontier estimation are taken from various sources. These variables contro
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