Consolidation in the banking industry: the relation between concentration and performance : a case study in the European Union.

Consolidation in the banking

industry: the relation between

concentration and performance

A case study in the European Union

Author: Sebastiaan Klein

Student number: 5876796

Supervisor: Dr. R.E. Vlahu

Programme: MSc Business Economics

Track: Finance

Table of content

1. Introduction ... 3

2. The banking industry in Europe ... 6

2.1 Deregulation 2.2 Consolidation

3. Literature review ... 11

3.4 Concentration and competition

4. Empirical analysis ... 16

5. Discussion ... 30

6. Conclusion ... 33

7. References ... 34

1.

Introduction

One of the main goals of economic integration in Europe was the creation of a single financial market. The rationale behind this is that a single market leads to improved and more efficient financial intermediation which increases the economic growth. Over the years the European Union (EU) has deregulated the banking markets. This deregulation eased the merger and acquisition (M&A) process and resulted in a rapid consolidation within the banking industry, leading to a more concentrated market with fewer but larger banks. The main motives for a bank to engage in consolidation activity are to achieve economies of scale and scope to become more efficient, and to earn higher profits due to increased market power.

Some economists argue that this consolidation creates a moral hazard: larger banks take excessive risks because they know they are protected by the government in the case of financial distress. This phenomenon is called “too-big-to-fail” (Mishkin, 1999), and examples of banks with this status became visible during the worldwide financial crisis that emerged after the collapse of Lehman Brothers in 2008 (see for

example Fortis1_{, UBS}2 _{and Commerzbank}3_{). While policymakers aimed at improving the}

efficiency with deregulating the financial markets, it is interesting to investigate the effect of consolidation on the performance of a bank.

The implications of this consolidation on the performance of banks can be investigated by estimating the relation between the degree of concentration in the banking industry and performance of these banks. Unfortunately, the theory produces conflicting predictions about this relationship. Advocates of the concentration-stability view argue that more competition erodes the market power, leading to lower profit margins and hence reduced franchise value: the on-going concern or market value of a bank beyond its book value (Hellmann et al., 2000). With this reduced franchise value, the incentives for banks to take excessive risks increase since this could boost their returns (Keeley, 1990). Further, it is argued that a market with a few large banks is easy to monitor for supervisors, decreasing the system-wide contagion risk (Allen and Gale, 2000). Another point that is made by the proponents of the concentration-stability view,

1 _{After acquiring ABN AMRO with Banco Santander and Royal Bank of Scotland, Fortis faced financial}

distress and was bailed-out by the governments of Belgium, Luxembourg and the Netherlands in 2008 (see Steen, 2008)

2 _{UBS faced the biggest loss of any European lender and was bailed-out in 2008 (see Giles, 2008)}

3 _{The German government bailed out Commerzbank after liquidity problems due to the extra burdens of}

is that larger banks tend to engage in credit rationing, aiming to make fewer but larger investments with a higher quality. This in turn will increase the return of these investments, improving the financial soundness of a bank (Boot and Thakor, 2000). Finally, advocates of this view argue that larger banks are better able to diversify the risk of their loan-portfolio (Boyd and Prescott, 1986; Méon and Weill, 2005).

In contrast to this view, proponents of the concentration-fragility view argue that high market power increases the risk exposure of a bank. The moral hazard caused by the fail status of a bank is one cause: managers of banks that are too-big-to-fail may take excessive risks under the safety net of a government. Besides this, when larger (monopolistic) banks charge higher interest rates, borrowers will shift towards riskier projects (Boyd and De Nicoló, 2005). This will increase the number of non-performing loans, increasing the default risk of a bank. In reaction to the expected diversification benefits of large banks, advocates of the concentration-fragility argue that it is diversification that leads to reduced managerial efficiency (Cetorelli et al., 2007). Finally, Beck et al. (2006) argue that larger banks become more complex and therefore are harder to monitor, making the supervisor’s task more difficult, increasing the probability of default. It is important to note that these different theories do not automatically lead to conflicting predictions about the relationship between concentration and bank performance. The reason for this is that it is possible for a bank with high market power to have riskier loan-portfolios without increasing the overall risk of the bank.

The empirical research in this paper investigates the relation between concentration and performance in the European banking industry. The approach is similar to that of Uhde and Heimeshoff (2009), who investigate the impact of national banking market concentration on financial stability for the member states of the EU, using data ranging from 1997-2005. This paper complements the existing research, using recent data (2006-2012) for the current 28 members of the EU. Prior to the study of Uhde and Heimeshoff, empirical research did not focus on the EU exclusively (De Nicoló et al., 2004; Beck et al., 2006). By focusing exclusively on the EU, it is possible to examine the specific effects of deregulation and promoting bank consolidation in the EU. Previous papers focused on real episodes of banking crises (Beck et al., 2006) or the bank’s capital ratio as a proxy for financial soundness (Cihak et al., 2009), while in this paper

the Z-score is used as a proxy for financial soundness. The research question in this paper is: To what extent does concentration in the European Union banking market

influences the performance of banks?

The results in this paper provide evidence for a positive relationship between concentration in the banking market and the distance-to-default of a bank. Therefore, this study provides evidence for the concentration-stability theory. The results are robust when using both the market share of the five largest banks and the Herfindahl-Hirschman Index as a proxy for concentration. The model is estimated while controlling for bank-specific, macro-economic and institutional factors.

The results are relevant for policymakers deciding on the structure of the banking market and the M&A policy within this market. It is recommended that policymakers design banking market regulations in such a way that M&A activity and concentration are promoted.

2.

The banking industry in Europe

Since it is important to understand the market conditions faced by European banks, developments in the banking industry in the EU are now discussed. First the integration and deregulation of the financial markets in Europe is reviewed. After this, the motives and forces behind consolidation activity in the banking industry are discussed, followed by a discussion of the distinction between domestic and cross-border consolidation. 2.1 Integration and deregulation

The starting point for European integration was the Treaty of Rome in 1957: the goal was to transform national fragmented markets into one single European market (Dermine, 2002). The rationale behind this was that a single financial market improves financial intermediation, benefiting the economic growth. In 1992 the Treaty of Maastricht led to the completion of the internal market which holds free movement of goods, services, labor and capital within the EU. The creation of the European Monetary Union (EMU) in 1999 led to the introduction of one single currency: the Euro. To finalize the integration of the financial markets, the Financial Services Action Plan was released in 1999. With this action plan four objectives were set: a single wholesale market in the EU; open and secure retail banking and insurance markets; state-of-the-art prudential rules and supervision; and finally optimal fiscal rules for a single financial market (Dermine, 2002).

The national treatment principle was adopted with the Treaty of Rome resulting in equal regulation and supervisions for all banks operating within a country. However, barriers still existed for the financial integration in the EU, such as restrictions on capital flows and a lack of coordination between supervisors. This made banks operating in more than one country subject to different rules (Dermine, 2002). The first steps to common rules were made with the First Banking Directive in 1977 (Buch and Heinrich, 2003). Regulation on banks operating abroad was shifted from host country towards the home country of these banks. More deregulation was adopted with the Second Banking Directive in 1989, leading to a single banking license, harmonized capital requirements and limits on restrictions from a bank’s home country (Dermine, 2002).

With all these measures, the banking industry experienced a high level of consolidation activity in the past decades (see section 2.2). It is important to note that

the number of cross-border bank mergers showed a decrease since the introduction of the Euro in 1999, possibly caused by a time-consuming and inefficient merger review process (Uhde and Heimeshoff, 2009). In order to counter this trend, a revision of the regulation was made by the Second Banking Directive in 2006 to improve this merger review process.

2.2 Consolidation

The deregulation has made it easier for banks to operate abroad and it was an important force driving the rapid increase of bank consolidation in the past decades. Berger et al. (2000) show that the M&A activity in the EU grew from about $1 billion in 1985 to over $25 billion in 1999. Figure 2.1 presents the bank M&A activity in the EU from 1990 to 2002.

Figure 2.1: Mergers and acquisitions in the EU banking sector

EUR billions, six months moving averages

50 50

40 40

30 30

20 20

10 10

Jan.90 Jan.92 Jan.94 Jan.96 Jan.98 Jan.00 Jan.02

Source: ECB - Working paper series 398 (2004)

Besides the deregulation of the financial markets, several forces that drive the consolidation in the banking industry can be identified: technological progress; globalization of both the real and financial economy; and of course maximization of shareholder value through improved financial performance (De Nicoló, 2004). The latter force is one of the main motives for bank managers to engage in M&A activity (Berger et al., 1999). Maximizing shareholder value is possible through both cost reductions and revenue enhancements. Cost reductions can be attained through for example economies of scale, economies of scope, reduction of risk due to geographic or product diversification or reduction of tax obligations (Group of Ten, 2001). Increasing

revenues can be realized through for example expanding the product line, geographic expansion and of course using the increased market power to set higher prices. DeYoung et al. (2009) add to this that gaining the too-big-to-fail status is another form of maximizing shareholder value, and can be seen as a motive for consolidation. However, not only shareholders have motives for consolidation: also managers themselves are affecting these decisions. An example of a manager’s motive for consolidation can be empire building with the goal of increasing his financial compensation (De Nicoló et al., 2004). As mentioned, maximizing shareholder value is probably the main motive for banks to engage in M&A activity, but there are other forces driving the consolidation. Technological developments increased the distributional possibilities of financial services. Such developments bring scale advantages: investments in the technology are relatively large and with a large customer base, the unit costs of these investments will decrease. The technological developments and deregulation of the financial industry have promoted globalization of both the real and financial economy. Improvements in technology make it more feasible for companies to connect with other companies and customers around the world. With this globalization, the demand for global financial services also increases, encouraging (cross-border) consolidation (Group of Ten, 2001). Figure 2.2 shows that there has been an increase in market concentration between 2005 en 2012.

Figure 2.2: Banking market concentration in the EU (as measured by the market share of the five largest banks and the Herfindahl-Hirschman Index)

Although all these forces have been driving consolidation in the past decades, cross-border consolidation in the EU has been relatively limited compared to domestic consolidation (DeYoung et al., 2009). Several forces distinguish between domestic and cross-border consolidation. According to DeYoung et al. (2009) barriers which have been affecting the cross-border consolidation process are caused by several problems: managing from a distance; cultural differences; different currencies (for example between the United Kingdom and the Eurozone); different supervisory structures; and rules against foreign acquisitions. Despite the effort of the EU through deregulation as described in section 2.1, still some barriers exist for banks to engage in cross-border consolidation. These barriers are mostly caused by problems due to managerial distance, cultural differences and the persistence of implicit rules against foreign acquisitions.

Figure 2.3: Number of M&A transactions (EU) Figure 2.4: M&A transaction value (EU)

Source: ECB - Banking structures (2013) Source: ECB - Banking structures (2013)

Figures 2.3 and 2.4 present the number of M&A transactions and the value of these transactions for the period 2000-2012, and shows that the number of M&A transactions in the EU dropped by a quarter in 2008. As can be seen in figure 2.4, the

total value of transactions shows a significant decline (excluding the acquisition of ABN AMRO by the consortium of Banco Santander, Fortis and Royal Bank of Scotland in 2008). The EU cross-border and outward transactions are most affected. This decline is partly caused by supervisory issues: banks complained about a lack of transparency in the merger review process and ambiguous supervision over cross-border activities (Uhde and Heimeshoff, 2009).

With the Second Banking Directive in 2006, the EU aimed to counter the negative trend in M&A activity. This directive resulted in a more transparent merger review process and more consistent supervision across countries within the EU. However, from figures 2.3 and 2.4 it can be seen that the negative trend consolidation has not been countered until 2012. Although this is not yet the case, concentration has increased in this period as can be seen in figure 2.2. Research on the relationship between concentration and performance is necessary to examine the practical implications of the current consolidation policy. Based on the results of this research policymakers can decide on how to design the framework for the consolidation process.

3.

Literature review

In order to understand the relationship between market concentration and the performance of a bank, a review of the existing theory on this topic is necessary. The main predictions of the economic theory are presented, as well as some empirical evidence. The predictions of economic theory on the relation between concentration in the banking industry and the performance of the banks do not lead to a conclusive finding. There is a on-going debate among academia between two main theories: the concentration-stability theory versus the concentration-fragility theory.

One of the main points of the concentration-stability theory is that less concentration leads to higher profits, resulting in less excessive risk-taking behaviour by banks and higher buffers against shocks. Banks are also thought to be better in credit monitoring, better diversified and easier to monitor. In contrast to this theory, proponents of the concentration-fragility theory argue that high concentration indeed leads to higher interest rates, but this would make borrowers invest in riskier projects. Another point they make is that high concentration leads to too-big-to-fail institutions, which take excessive risks since they rely on the knowledge that they are bailed out in case of default. Finally, advocates of this view argue that larger banks are more complex and therefore harder to monitor. Based on this, these theories seem to produce conflicting predictions about the effect of setting high interest rates and the ability of regulators to monitor large institutions.

3.1 Concentration-stability hypothesis

The concentration-stabilityview argues that a high degree of concentration and thus a

high degree of market power makes it possible for banks to set prices and hence to increase their profits. This creates a higher buffer against macro-economic shocks (Keeley, 1990). Besides creating this buffer, increasing profits can lead to a higher franchise value: the market value of a bank beyond its book value. A higher franchise value increases the opportunity costs of going bankrupt, deterring bank managers from taking excessive risks (Hellmann et al., 2000). Reasoning the other way around, less concentration and thus less market power leads to a lower franchise value. This incentivizes banks to take on excessive risks to increase their returns (Berger et al., 2009). Allen and Gale (2003) illustrate this franchise or charter value hypothesis with

an agency problem between bank owners and the deposit insurance fund that arises here: the reduction in charter value increases the incentives for bank managers to take on risk, since they are backed with deposit insurance in case of default. In case the increased risk-taking leads to higher returns, the bank owners bear these profits. According to Allen and Gale, this agency problem caused a dramatic increase in bank failures during the Savings and Loan Crisis in the United States in the 1980s. The main point here is that in a concentrated market, high profits increase the charter value and therefore the opportunity costs of a failure are relatively high so bank owners do not incentivize excessive risk-taking behaviour.

Besides these implications of increased market power, advocates of the concentration-stability theory argue that banks in a concentrated market are better in credit monitoring (Berger et al., 2004). When market power is low banks earn fewer informational rents from their relationship with borrowers, reducing the screening of these borrowers which in turn decreases the performance of a bank. Boot and Thakor (2000) argue that larger banks are engaging more in credit rationing, since fewer loans of higher quality lead to improved financial soundness.

Two further points made by proponents of the concentration-stability hypothesis are that banks in a concentrated market are better diversified and easier to monitor, which makes them less fragile to economic shocks (Berger et al., 2004). According to Beck (2006), several models predict that larger banks can achieve economies of scale, thereby increasing the diversification possibilities. This is illustrated by Boyd and Prescott (1986), who found that larger banks are better in diversifying loan portfolios due to economies of scale and scope. Meon and Weill (2005) show that large banks further improve diversification by improved economies of scale and scope through geographical expansion. The final point is that high concentration, thus a relatively low number of banks in the market, makes it easier for regulators to supervise these banks which leads to improved stability (Allen and Gale, 2000).

3.2 Concentration-fragility hypothesis

The opposing concentration-fragility view argues that a high level of concentration and thus high market power, leads to a decline in performance for several reasons. First, more market power leads to higher interest rates charged to borrowers. These higher interest rates in turn attract borrowers that invest in riskier projects, to compensate for

the higher interest payments (Boyd and De Nicoló, 2005). At the same time, borrowers investing in safe projects may not take any loans due to a too high interest rate. This leads to a higher percentage of non-performing loans. Proponents of the concentration-fragility hypothesis also reject the charter value hypothesis. While this hypothesis argues that the banks are the ones choosing the riskiness of their assets, proponents of the concentration-fragility hypothesis hold that it are actually the borrowers who set the riskiness of the investments they undertake (Boyd and De Nicoló, 2005).

Another argument of the concentration-fragility theory is that banks in a more concentrated market lead to institutions that become too-big-to-fail. Mishkin (1999) argues that relative to diffuse banking systems, concentrated banking systems generally have fewer banks and this makes a possible failure of one of these banks a large concern for regulators. Banks in concentrated systems are more likely to achieve a too-big-to-fail status, leading to a moral hazard for bank managers: risk-taking is encouraged since the benefits flow to the bank owners, but in case of default, a bail-out by the government is expected. Besides this, Beck et al. (2006) argues that concentrated banking markets with few banks increase the risk of contagion of one bank to another.

Proponents of the concentration-stability hypothesis also argue that high concentration and thus few but large institutions make it harder for regulators to monitor them. Since larger banks have better opportunities to expand their business both geographically and along business lines, banks are getting more complex, making them less transparent for the regulators. This lack of transparency is harmful for the financial stability (Beck et al., 2006). The decreased transparency caused by banks getting more and more complex also harms the monitoring power of the management itself. Finally, Cetorelli et al. (2007) stress that a higher degree of risk diversification by larger banks leads to reduced managerial efficiency and less effective internal control, which is also harmful to the performance of the banks.

3.3 Empirical evidence

The empirical research produces interesting results. Boyd and Runkle (1993) investigate the relation between the size of a bank and its risk. Using data on US banking firms ranging from 1971-1990, they find that although larger size leads to less volatile stock returns, this does not mean that these banks have a lower probability of bankruptcy, as measured by the Z-score. As a possible explanation they argue that this

is caused by the capital structure: larger banks tend to hold more leverage, which obviously leads to increased risk. Beck et al. (2006) find that, using data on 69 countries from 1980-1997, crises are less likely in systems where concentration is higher. The results of Beck et al. (2006) provide evidence for the concentration-stability theory.

De Nicoló et al. (2004) find that, using data on more than 100 countries over the period 1993-2000, the five largest conglomerates in the financial industry have increased risk profiles. They also find a higher level of systemic-risk potential for banking systems where concentration is relatively high. De Nicoló et al. (2004) and Boyd and De Nicoló (2005) show that there exists a positive relationship between the concentration in the banking market and bank fragility. Their research provides evidence for the concentration-fragility view, arguing that in case of high market power banks charge higher interest rates, resulting in riskier loans and therefore increased fragility of the financial system. Also in line with this view are the results of Caminal and Matutes (2002), who show that less market power possibly leads to less credit rationing, larger loans and a higher probability of failure. Uhde and Heimeshoff (2009) find that, using data on European banks from 1997-2005, concentration in the national banking market has a negative impact on the financial soundness of banks as measured by the Z-score. Uhde and Heimeshoff control for macro-economic, bank-specific, regulatory and institutional factors.

From the review of the existing theory and empirical research it is concluded that the results are mixed: both evidence for the competition-fragility as for the competition-stability view exist. In the following sections, the relation between concentration, competition and stability in the European banking market is investigated. 3.4 Concentration and competition

Before continuing with the empirical evidence, it is important to discuss the relationship between concentration and competition. Although we focus on the relationship between concentration and performance in this paper, this relationship is important since a large amount of the theory discussed in the previous sections is built upon the implications of consolidation and concentration on market power. As mentioned, the reason for explicitly focusing on concentration in this paper is that in this way it is possible to make recommendations to policymakers about the effect of promoting consolidation on bank performance.

In the early stage of research on the relation between concentration, competition and performance the literature regarded concentration as a proxy for competition (Schaeck et al., 2006). Research by Bikker and Haaf (2004) on the effect of concentration on competition in 23 industrialized countries shows that concentration has a negative effect on competition. They argue that concentration affects competition and that increasing size of financial firms has implications for the financial stability. However, the use of concentration as a proxy for competition can seriously be disputed (Claessens and Laeven, 2004). It is argued that competition is a multifaceted issue, and that regulatory restrictions and foreign and governmental ownership also effect the competition within the market (Group of Ten, 2001). A growing body of empirical evidence shows that concentration is a poor proxy for competition (Demirgüc-Kunt et al. 2004; Claessens and Laeven, 2004). Claessens and Laeven find no empirical evidence for the negative impact of concentration on competition; they even find some evidence that more concentrated banking systems are more competitive. They conclude that competition and concentration describe different characteristics of banking systems.

If it is the case that concentration does not negatively affect competition, this could undermine the reasoning behind the theory as described in sections 3.1 and 3.2. This in turn has important implications for policymakers. For example, a banking market characterized by high entry barriers and a high concentration, may still experience strong competition (Schaeck et al., 2006).

4.

Empirical analysis

This chapter presents the empirical analysis. The question is whether banks in a concentrated environment are riskier than banks in a less concentrated environment. As explained in the previous chapter, the concentration-stability hypothesis expects banks in more concentrated markets to be less risky, while the concentration-fragility hypothesis argues that banks in a market with low market power are less risky. To test the hypotheses that these theories produce it is necessary to model the relation between performance of a bank and the concentration in the banking market. This chapter first presents the dataset and some descriptive statistics, followed by the methodology used for the empirical research. After this the regression results are presented. A discussion of these results follows in the next chapter.

4.1 Data

The empirical research in this paper is based on consolidated annual data on all commercial banks, savings banks and credit cooperatives within the 28 member countries of the EU. The period of investigation ranges from 2005 to 2012. Data on bank’s performance is annual and on bank-level basis. Data on concentration is annual and on country-level basis. Macro-economic control variables which are used are on annual country-level basis. This also applies for the institutional control variables.

Bank-specific data is retrieved from the Bankscope database from Bureau van Dijk, which contains comprehensive data on banks across the globe. Data on concentration is retrieved from the European Central Bank (ECB) statistics. Macro-economic data is retrieved from the World Development Indicator (WDI) Database of the Worldbank. To control for institutional factors such as creditor protection, there is controlled through data retrieved from the research on law and finance by La Porta et al. (1998).

Table 4.1

Variables and description

Variable Description Source

ROAA Return on average assets before taxes (ROAA) Bankscope

SD ROAA Standard deviation of ROAA Bankscope

Capital ratio Ratio of equity capital to total assets Bankscope Z-score Ratio of the sum of equity capital to total assets and

ROAA to the standard deviation of ROAA Bankscope (own calculations) Concentration (5) EU-28 concentration: fraction of assets of a

country’s total banking system’s assets held by the largest 5 domestic banks

ECB statistics

Concentration (HHI) Herfindahl-Hirschman Index computed as the sum

of the squared market shares of a country’s banks ECB statistics Net interest margin Log of accounting value of bank’s net interest

revenue as a share of its interest-bearing assets Bankscope Loan loss provisions Loan loss provisions in thousand USD Bankscope Cost-income ratio Ratio of overhead costs to total revenue Bankscope Equity ratio Ratio of equity to total assets Bankscope GDP per capita Ratio of GDP to population WDI Database GDP growth Rate of real GDP growth (annual change in %) WDI Database Inflation Log of annual change in inflation rate WDI Database Real interest rate Real short-term interest rate WDI Database Government-owned Dummy variable that takes on a value of 1 if the

bank is government-owned Bankscope British legal system Dummy variable that takes on a value of 1 if the

country's legal system is of British origin La Porta et al. (1998) French legal system Dummy variable that takes on a value of 1 if the

country's legal system is of French origin La Porta et al. (1998) German legal system Dummy variable that takes on a value of 1 if the

country's legal system is of German origin La Porta et al. (1998) Scandinavian legal system Dummy variable that takes on a value of 1 if the

country's legal system is of Scandinavian origin La Porta et al. (1998) Soviet legal system Dummy variable that takes on a value of 1 if the

country's legal system is of Soviet origin La Porta et al. (1998)

In order to control for bank-specific, macro-economic and institutional factors, several variables are added to prevent omitted variable bias. Bank-specific factors that might affect the quality of its assets are profitability, credit-risk, efficiency and leverage:

 The net interest margin functions as a proxy for profitability. Higher profitability

leads to a lower distance-to-default; thus the coefficient of the net interest margin is expected to be positive.

 The amount of loan loss provisions functions as a proxy for the quality of a

bank’s assets and its credit-risk. Intuitively, it is expected that higher credit-risk leads to a lower distance-to-default and therefore a negative coefficient for loan loss provisions is expected.

 The cost-to-income ratio functions as a proxy for the efficiency of a bank. A negative coefficient is expected: less efficiency (illustrated by a higher cost-to-income ratio) is expected to decrease the distance-to-default.

 The equity ratio functions as a proxy for leverage. Intuitively, a higher equity

ratio and thus lower leverage should decrease the distance-to-default and therefore the coefficient on the equity ratio is expected to be positive.

 The logarithm of a bank’s total assets functions as a control for the effect of the

size of a bank on the quality of its assets.

 A dummy variable measuring whether the bank is government-owned is used to

measure the effect of whether a bank is government-owned. It is assumed that government-owned banks are restrained in their risk-taking behavior so a positive sign for this dummy variable is expected.

Besides these bank-specific factors, there are several macro-economic factors that might influence the quality the assets of a bank: Gross domestic product (GDP) per capita and growth in GDP, inflation and the real interest rate:

 The GDP per capita and GDP growth are expected to have a positive coefficient:

these factors positively affect the investment opportunities, improving the quality of a bank’s assets and thus increasing its distance-to-default.

 The inflation rate in contrast is expected to have an opposite effect: a higher

inflation rate is expected to have a negative impact on the investment opportunities, worsening the quality of a bank’s assets and decreasing its distance-to-default.

 The real interest rate functions as a control for the effect of this rate on the

quality of a bank’s assets.

Finally, there is controlled for the quality of the institutional environment and the corresponding level of creditor protection following from the literature on law and finance. La Porta et al. (1998) argue that the degree of creditor protection following from a country’s legal system is positively related to the development of its economy and more specific its financial markets. Country’s with a relatively low creditor protection (e.g. Belgium) have smaller and less developed debt and equity markets. To

incorporate this effect it is necessary to control for a country’s legal system and its corresponding degree of creditor protection. This approach is widely used in similar research, see for example Beck et al. (2006) and Uhde and Heimeshoff (2009). Five dummy variables are included, incorporating the legal systems of British, French, German, Scandinavian and Soviet origin. A value of one is used if a country’s particular law system originates from one of the ‘law families’ as mentioned above, a value of zero if this is not the case.

Table 4.2 summarizes descriptive statistics of the entire dataset. For all observations from 2005 to 2012, the mean concentration ratio as measured by the market share of the five largest banks equals 51.13%. The mean Z-score for the period 2005-2012 equals 50.99, while the mean Return on average assets (ROAA) equals 0.49% for the same period. Of all the banks in the dataset, 5.87% are government-owned in 2012. For descriptive statistics on all variables used in this paper, see table 4.2 below. Tables 4.3 and 4.4 provide correlation matrices on both bank-specific variables and country-specific variables.

Table 4.2 Descriptive statistics

Variable Observations Mean Standard deviation Minimum Maximum

Concentration (5) 4768 51.12702 16.25965 21.6302 98.0959 Concentration (HHI) 4768 0.080878 0.0580492 0.0174 0.4039

Capital ratio 1998 14.62078 14.55118 -5 416.3

Net interest margin 3235 2.260776 1.981712 -3.21 32.94 Loan loss provisions 553 1654835 3127962 -981.5084 29500000 Cost-to-income ratio 3217 67.09676 35.07529 2.2 733.33 Equity ratio 3256 8.51683 6.120863 -30.52 100 Total assets 2869 122000000 349000000 117.9704 3160000000 Government-owned 4768 0.0587248 0.2351336 0 1 GDP per capita 4768 35682.09 16103.59 3733.263 114210.8 GDP growth 4768 1.220521 3.443026 -17.95499 12.23323 Inflation 4768 2.316074 2.338282 -4.639313 20.29545

Real interest rate 1938 2.741014 3.356306 -7.80269 17.99654

ROAA 3243 0.4983996 1.650351 -22.43 20.25

Z-score 1967 50.99165 70.62116 -3.65 614.22

British legal system 4768 0.1342282 0.3409331 0 1

French legal system 4768 0.4681208 0.499035 0 1

German legal system 4768 0.1208054 0.3259351 0 1

Scandinavian legal system 4768 0.0872483 0.2822282 0 1

Soviet legal system 4768 0.1895973 0.3920235 0 1

Table 4.3

Correlation matrix for bank-specific variables

Net interest margin Loan loss provisions Cost-to-income ratio Equity ratio Log (Assets) Government-owned Net interest margin 1.00

Loan loss provisions -0.0665 1.00

Cost-to-income ratio -0.2242 -0.0573 1.00 Equity ratio 0.3111 -0.2349 -0.1801 1.00 Log (Assets) -0.1766 0.5727 0.0259 -0.4992 1.00 Government-owned -0.1196 0.2237 -0.0470 -0.0855 0.1446 1.00 Source: Bankscope Table 4.4

Correlation matrix for country-specific variables

Concentration (5) Concentration (HHI) GDP per capita growth Inflation GDP Real interest rate British origin French origin German origin Scandinavian origin Soviet origin Concentration (5) 1.00 Concentration (HHI) 0.9498 1.00 GDP per capita -0.1866 -0.1253 1.00 GDP growth 0.1936 0.1619 -0.2082 1.00 Inflation 0.1749 0.1496 -0.4613 0.4470 1.00

Real interest rate 0.0436 0.0169 -0.2371 -0.4378 -0.4529 1.00

British origin -0.3270 -0.3461 0.4085 -0.0259 -0.1279 -0.3869 1.00 French origin -0.0750 0.0085 0.5145 -0.1993 -0.3173 0.1200 -0.4567 1.00 German origin -0.4340 -0.3074 0.1447 0.0317 -0.1485 0.0000 -0.1460 -0.3478 1.00 Scandinavian origin 0.0150 -0.0084 0.0707 0.0320 -0.0533 -0.0079 -0.0484 -0.0588 -0.1146 1.00 Soviet origin 0.3753 0.3136 -0.9023 0.2171 0.4431 0.2403 -0.4639 -0.5629 -0.1793 -0.0597 1.00

From these descriptive statistics on all variables in the dataset, it is interesting to look at the data on concentration in detail. Table 4.5 provides a country-level breakdown of Concentration (5) ratios for the entire sample period. The ratio’s differ significantly from country to country. Germany for example has a relatively low ratio of 33.00% in 2012, while this ratio for the Netherlands is relatively high at 82.08%.

Table 4.5 Concentration (5) ratio's4 Country 2005 2006 2007 2008 2009 2010 2011 2012 Austria 45.04 43.78 42.83 39.01 37.24 35.87 38.38 36.49 Belgium 85.29 84.39 83.42 80.84 77.12 74.86 70.77 66.34 Bulgaria 50.85 50.34 56.69 57.31 58.29 55.17 52.58 50.38 Cyprus 59.79 63.93 64.90 63.81 64.88 64.16 60.76 62.50 Czech Republic 65.49 64.06 65.71 62.05 62.40 62.50 61.78 61.48 Denmark 66.27 64.69 64.23 65.96 63.99 64.42 66.30 65.61 Estonia 98.10 97.11 95.75 94.75 93.43 92.26 90.64 89.60 Finland 82.91 82.29 81.17 82.82 82.58 83.83 80.92 79.01 France 51.87 52.33 51.84 51.16 47.21 47.40 48.27 44.62 Germany 51.63 21.99 22.00 22.74 25.01 32.60 33.55 33.00 Greece 65.60 66.30 67.70 69.50 69.19 70.64 71.99 79.47 Hungary 53.23 53.53 54.08 54.45 55.18 54.64 54.63 54.00 Ireland 47.78 48.96 50.39 55.34 58.76 56.84 53.21 56.88 Italy 26.83 26.24 33.05 31.18 30.96 39.84 39.46 39.68 Latvia 67.33 69.17 67.24 70.24 69.35 60.43 59.57 64.05 Lithuania 80.63 82.49 80.91 81.25 80.48 78.83 84.75 83.63 Luxembourg 34.54 31.54 30.58 29.71 29.31 31.11 31.21 33.08 Malta 75.28 70.93 70.22 72.82 72.79 71.28 71.96 74.46 Netherlands 84.46 85.07 86.33 86.73 85.08 84.20 83.55 82.08 Poland 48.51 46.11 46.60 44.22 43.93 43.37 43.69 44.40 Portugal 68.77 67.93 67.81 69.10 70.15 70.88 70.76 69.95 Romania 59.40 60.10 56.30 54.00 52.40 52.70 54.60 54.70 Slovakia 67.73 66.85 68.15 71.55 72.09 72.03 72.23 70.72 Slovenia 63.00 61.99 59.48 59.14 59.65 59.27 59.33 58.35 Spain 42.00 40.40 41.00 42.40 43.30 44.30 48.10 51.40 Sweden 57.26 57.79 61.01 61.86 60.66 57.78 57.81 57.42 United Kingdom 43.07 43.76 43.51 35.34 34.07 39.80 44.13 40.59

Source: World Development Indicator Database

4 _{In this table concentration refers to the Concentration (5) ratio: the sum of the market share (%) of the}

4.2 Methodology

In the empirical literature on bank stability, the Z-score is a widely used measure of the financial soundness of a bank (Boyd and Runkle, 1993; Berger et al., 2009; Uhde and Heimeshoff, 2009) and it is used as a proxy for performance in this paper as well. The Z-score is an inverse proxy for the firm’s probability to failure: it combines profitability, leverage and return volatility in one measure (Berger et al., 2009). Mathematically, the Z-score is expressed as:

Z

=

(I)

Where ROAi is the period-average return on assets for bank i as a proxy for profitability,

E/TA is the ratio of equity to total assets ratio for bank i and functions as a proxy for

leverage and σ(ROAi) is the standard deviation of return on average assets as a proxy for

return volatility. A larger value of the Z-score can be interpreted as a firm with lower risk. The inputs for calculating this Z-score are all retrieved from Bankscope.

To measure concentration, the approach of Uhde and Heimeshoff (2009) is followed. Concentration (5) ratios are computed as:

Concentration (5)c,t = (II)

where ‘Assets five largest banks’ are the assets held by the five largest domestic and foreign banks in country c, and ‘Total assets’ are the total assets of the banking system in country c. So Concentration (5) in country c is estimated by the fraction of the assets of the five largest banks of the total assets. As a robustness check also the Herfindahl-Hirschman Index (HHI) is retrieved from the ECB statistics as a proxy for concentration. The Z-score of bank i at time t is estimated using the Ordinary Least Squared (OLS) method by the equation:

Z-score (i,t) = α + 1 * Concentrationc,t + 2 * Net interest margini,t + 3 * Loan loss provisionsi,t + 4 * Cost-to-income ratioi,t + 5 * Equity ratioi,t + 6 * Log(Assets)i,t + 7 * Government-ownedi,t + 8 * GDP per capitat,c

+ 9 * GDP growtht,c + 10 * Inflationt,c + 11 * Real interest ratet,c + 12 * British origint,c + 13 * French origint,c + 14 * German origint,c + 15 * Scandinavian

origint,c + 16 * Soviet origint,c + i,t (III)

4.3 Results

This section presents the empirical results. Before presenting the estimation results from the model presented in the previous section, a country-level breakdown of the data is presented and discussed. The section concludes with the results of some robustness tests.

4.3.1 Country-level breakdown of data

In the period 2005-2012 some countries performed relatively well within the EU. Germany for example had a concentration ratio (as measured by the Concentration (5) ratio) of 51.63% in 2005 with a corresponding mean Z-score of 27.76, while in 2012 the concentration ratio decreased to a value of 33.00%, and a mean Z-score of 78.32. This could imply that concentration is negatively related to performance as measured by the Z-score. However, caution with interpreting these numbers is very important: it is possible that this increase in performance is caused by bank-specific factors such as improved profitability or efficiency, or by macro-economic factors such as growth in GDP. When looking at the Netherlands, we see a concentration ratio of 84.46% with an average Z-score of 89.64 in 2005; while the concentration ratio was 82.08 in 2012 with a corresponding average Z-score of 93.18. Another well-performing country, Finland, also saw its average Z-score increase from 26.11 to 34.05.

Interesting is also the case of the PIIGS-countries (Portugal, Italy, Ireland, Greece and Spain). In 2005 the mean distance-to-default as measured by the Z-score in Greece was already very low at 3.26, while in 2012 it was even lower at 0.03. Mean concentration ratio’s for Greece were 65.60% in 2005 and 79.47% in 2012. Besides the striking case of Greece also the other PIIGS-countries are interesting to review. While Spain shows decreasing average Z-scores (from 156.35 in 2005 to 120.13 in 2012), countries like Italy (from 18.58 to 47.25) and Portugal (from 32.07 to 40.70) saw its average Z-score increase. The decline in Spain might be caused by the restructuring in the Spanish banking market during the recent years: the government forced Spanish banks to write-off non-performing loans in order to strengthen their position in the long

run (Johnson, 2011). However, as can be seen in table 4.6, these measures worsen the Z-score of a bank in the short run.

Table 4.6

Country-level breakdown: mean values concentration5_{, assets and Z-score}

2005 2012

Country Banks (2012) Concentration Assets Z-score Concentration Assets Z-score

Austria 30 45.04 26300000 72.54 36.49 37500000 54.16 Belgium 15 85.29 - - 66.34 105000000 81.57 Bulgaria 9 50.85 - - 50.38 - - Cyprus 8 59.79 - - 62.50 - - Czech Republic 9 65.49 19200000 68.43 61.48 19600000 51.06 Denmark 31 66.27 72900000 23.07 65.61 53100000 57.14 Estonia 7 98.10 7143538 10.65 89.60 6237558 9.94 Finland 9 82.91 17400000 26.11 79.01 101000000 34.05 France 113 51.87 152000000 51.67 44.62 157000000 69.99 Germany 42 51.63 97900000 27.76 33.00 307000000 78.32 Greece 8 65.60 28300000 3.26 79.47 50000000 0.03 Hungary 14 53.23 8446547 15.35 54.00 11500000 15.67 Ireland 10 47.78 94700000 10.31 56.88 67500000 10.83 Italy 55 26.83 30200000 18.58 39.68 71600000 47.25 Latvia 17 67.33 - - 64.05 - - Lithuania 7 80.63 - - 83.63 - - Luxembourg 14 34.54 12200000 - 33.08 23800000 131.40 Malta 4 75.28 - - 74.46 - - Netherlands 22 84.46 101000000 89.64 82.08 171000000 93.18 Poland 20 48.51 12300000 43.79 44.40 14200000 30.89 Portugal 13 68.77 33200000 32.07 69.95 42700000 40.70 Romania 11 59.40 - - 54.70 - - Slovakia 8 67.73 7398796 74.33 70.72 9081749 46.60 Slovenia 11 63.00 4456499 25.04 58.35 4623555 24.49 Spain 40 42.00 164000000 156.35 51.40 147000000 120.13 Sweden 12 57.26 4548581 24.90 57.42 103000000 133.65 United Kingdom 58 43.07 222000000 58.12 40.59 231000000 48.20

Sources: Bankscope, ECB Statistics, World Development Indicator (WDI) Database and LaPorta et al. (1998)

5

In this table concentration refers to the Concentration (5) ratio: the sum of the market share (%) of the five largest banks of a country.

4.3.2 Regression results

Table 4.7 presents the main estimation results of regressing the Z-score on concentration as measured by the Concentration (5) ratio and as a robustness test also through the Herfindahl-Hirschman Index. Regression (1) is a simple regression of the Z-score on concentration without control variables for bank-specific, macro-economic or institutional factors. The second regression incorporates several bank-specific controls such as profitability (as measured by the net interest margin) and efficiency (as measured by the cost-to-income ratio). The third regression is an extension of the model with macro-economic control variables such as GDP per capita, GDP growth and inflation.

Incorporating dummy variables on the legal origin to control for the degree of creditor protection is important to capture the effect on the financial sector development of a country. However, incorporating the institutional control variables do not lead to significant results. Therefore results of the model that contains all three categories of control variables (bank-specific, macro-economic and institutional factors) are not presented in table 4.7.

Regressions (2) and (3) show a positive coefficient for concentration as measured by the Concentration (5) variable. After controlling for macro-economic factors, the results are statistically significant at the 1% level. The model of regression (3) controls for bank-specific and macro-economic factors but not for institutional factors as measured by the dummy variables on the origin of the legal system. The coefficient on concentration in this regression is 1.18: an 1% increase of the concentration ratio leads to an increase of 1.18 in the Z-score of a bank. This implies that the distance-to-default for a specific bank increases with the concentration in the banking market.

Bank-specific control variables in regression (3) almost all have expected signs on their coefficients. Profitability, controlled for through the Net interest margin, has a positive sign (although it is not statistically significant): higher profitability leads to a higher Z-score. Credit-risk is measured through Loan loss provisions and has a negative sign (significant at the 5%-level): the Z-score and thus the distance-to-default decreases with an increase in credit risk. The coefficient on efficiency (controlled for by the Cost-to-income ratio) is positive, but not significant (at α=10%).

*** = 1% confidence interval ** = 5% confidence interval * = 10% confidence interval

Table 4.7 Regression output

Z-score (1) Z-score (2) Z-score (3) Z-score (4) Z-score (5) Z-score (6)

Concentration (5) -0.14 0.13 1.18

(0.09) (0.30) (0.32)***

Concentration (HHI) -49.29 21.29 273.00

(23.23)** (82.76) (81.19)***

Net interest margin -15.40 0.86 -15.26 2.97

(4.85)*** (7.24) (4.83)*** (7.27)

Loan loss provisions -0.00000179 -0.00000331 -0.0000018 -0.00000318

(0.00000164) (0.0000016)** (0.00000164) (0.00000162)* Cost-to-income ratio -0.35 0.05 -0.35 0.08 (0.21)* (0.30) (0.21)* (0.31) Equity-ratio -2.56 4.12 -2.58 3.94 (1.48)* (3.09) (1.49)* (3.12) Log (Assets) -2.89 7.46 -2.97 7.30 (2.78) (3.04)** (2.79) (3.08)** Government -32.92 -46.63 -32.77 -45.75 (18.27)* (19.52)** (18.27)* (19.75)** GDP per capita 0.002 0.003 (0.0009)*** (0.0009)*** GDP growth 1.87 2.75 (2.90) (2.88) Inflation -12.55 -14.79 (5.42)** (5.55)***

Real interest rate -9.29 -9.14

(2.66)*** (2.69)*** _cons 58.08 180.98 -208.95 55.33 186.99 -186.08 (5.04)* (64.42)*** (86.32)** (2.59)*** (61.92)*** (87.38)** N 1967 308 125 1967 308 125 F-statistic 2.20 3.08 5.90 4.50 3.02 5.54 P-value F-statistic 0.14 0.0042 0.0000 0.0340 0.0044 0.0000 R-squared 0.0011 0.0662 0.3648 0.0023 0.0659 0.3504 Adjusted R-squared 0.0006 0.0445 0.3030 0.0018 0.0441 0.2872

The Equity ratio functions as a control for leverage, and has an expected sign: an increase in the Equity ratio (and thus lower leverage) increases the distance-to-default. The result on this coefficient is not significant at the 10%-level. To control for the size of a bank, the log of a bank’s assets is incorporated in the regression, and this control variable shows a positive sign. This implicates that larger banks tend to be less risky. One possible explanation for this finding is that larger banks experience higher opportunity costs of going bankrupt and hence act more risk-averse, as predicted by the franchise value hypothesis.

Controls for macro-economic factors also produce expected results. The coefficients of GDP per capita and GDP growth are both positive (GDP per capita produces a significant result at a 1%-level, but GDP growth does not). The coefficients on Inflation and the Real interest rate are both negative: increases in values of these variables are expected to decrease a bank’s profitability and the quality of its assets, lowering the distance-to-default. The coefficient on Inflation is significant at the 5%-level while the coefficient on the Real interest rate is significant at the 1%-5%-level.

4.3.3 Robustness tests

As a robustness test the concentration ratio as measured by the HHI is used in regressions (4)-(6). These regressions produce similar outcomes: in regressions (5) and (6) the coefficient on concentration is positive. Also here this result turns significant (α=1%) after not only controlling for bank-specific but also for macro-economic factors. The coefficient on concentration in this regression is 273.00: an increase of 1 of HHI leads to an increase in the Z-score of a bank equal to 273.00. This implies that the distance-to-default for a specific bank increases with the concentration ratio. Signs on the bank-specific and macro-economic control variables are similar to the results of regression (3), which are described in the previous section. Also when using the HHI as a proxy for concentration incorporating the institutional control variables on the origin of a country’s law system do not produce significant results. Since the results of using either the Concentration (5) ratio or the HHI as an indicator of concentration describe the same patterns, the results are not sensitive to the definition of concentration.

As another robustness check, in regressions (7) and (8) the Z-score is regressed on concentration and only macro-economic control variables. These results can be found in table 4.8. Regression (7) uses the Concentration (5) ratio as a proxy for

concentration, while regression (10) is based on the HHI. The reason for using this specification is that the bank-specific control variables could cause endogeneity problems through the components of the Z-score. The logarithm of the total assets for example, as used in regressions (1)-(3) and (4)-(6), could not only explain the Z-score of a bank, but it is also possible that this amount depends on the return on average assets, which is one of the Z-score components. Therefore, the regressions presented in table 4.8 estimate the Z-score on concentration and macro-economic control variables only. The coefficients on concentration in regression (7) and (8) are both positive and significant at the 1%-level. In regression (7), this coefficient is 0.37; while in regression (8) the coefficient is equal to 75.97. These results confirm the estimates of table 4.7.

Table 4.8

Regression output using only country-specific control variables

Z-score (7) Z-score (8) Concentration (5) 0.37 (0.08)*** Concentration (HHI) 75.97 (21.23)*** GDP per capita 0.001 0.001 (0.0002)*** (0.0002)*** GDP growth 0.88 1.12 (0.49)* (0.48)** Inflation -5.98 -6.38 (1.32)*** (1.35)***

Real interest rate -1.01 -0.89

(0.81) (0.82) _cons 9.08 21.33 (9.45) (8.93)** N 843 843 F-statistic 22.41 20.52 P-value F-statistic 0.0000 0.0000 R-squared 0.1180 0.1092 Adjusted R-squared 0.1128 0.1039 *** = 1% confidence interval ** = 5% confidence interval * = 10% confidence interval

To further test the robustness of the results in this paper, the regressions are estimated using country and time fixed-effects. In this way it is possible to control for country or time-specific effects which could cause omitted variable bias. Using these fixed effects confirms the main regression results and thereby the positive relation between concentration and performance. As a final robustness check lagged values of data on concentration measures and bank-specific factors are also incorporated to see whether these results confirm the main regression estimates. However, these results do not produce conclusive results to function as evidence for the main results in this paper.

5.

Discussion

The empirical research in this paper provides results on the relation between banking market concentration and bank performance in the EU for the period 2005-2012. In this chapter the results will be compared to the economic theory on this relationship and past results from the literature. The chapter concludes with some policy recommendations and possible directions for future research.

Empirical results in this paper provide evidence for a positive relationship between banking market concentration and the distance-to-default of a bank. These results are proven to be robust using different measures of concentration (both the Concentration (5) ratio and the Herfindahl-Hirschman Index) and several other robustness tests as described in the previous chapter. The results present evidence for the predictions of the concentration-stability theory. Several arguments of this theory could explain the positive relation between concentration in the banking market and the performance of a bank.

Allen and Gale (2003) argue that banks in more concentrated markets have better possibilities to create buffers against macro-economic shocks, improving their performance. In line with this, the franchise value hypothesis suggests that buffers leads to better performance since these buffers increase the opportunity costs of going bankrupt, preventing banks of taking excessive risks (Keeley, 1990). The main regression results provide evidence for a positive relationship between the size of a bank (as measured by the logarithm of its assets) and the distance-to-default. This is underwriting the franchise value hypothesis and is also providing evidence for the concentration-stability theory (Allen and Gale, 2003).

Other arguments from the concentration-stability theory could also explain the results of this paper. These include that banks in a concentrated market are better to perform credit monitoring (Berger et al., 2004 and Boot and Thakor, 2000); that these banks are easier to monitor and therefore more stable (Allen and Gale, 2000); and finally that these larger banks are better diversified (Berger et al., 2004 and Meon and Weill, 2005). Further research on these specific relations is necessary to provide evidence for these specific relations.

Besides comparing the results to the existing theory, it is also important to see how the results relate to empirical evidence from the literature. The literature review in chapter 3 presents mixed findings. The results in this paper are in line with the empirical results of Beck et al. (2006) who find that concentration is negatively related to the fragility of a banking system. These results are in line with the results in this paper arguing that banks in a more concentrated market will have a larger distance-to-default.

The method in this paper follows the approach of Uhde and Heimeshoff (2009) who in contrast find that concentration in the banking market decreases the distance-to-default of a bank and thereby present evidence for the concentration-fragility theory. Their results are based on 1998-2005 data on all monetary and financial institutions within the EU. Cihak et al. (2009) also find evidence for the concentration-fragility view stating that when market power is relatively low, banks tend to hold higher capital buffers which increases the distance-to-default of an institution.

The results in this paper are relevant for policymakers who are concerned with the design of the banking system and the regulation of this system. When deciding on the policy regarding consolidation in the (European) banking industry attention should be given to the implications on market concentration and stability. While the empirical research so far produces mixed results, this paper presents evidence for the concentration-stability view. These results suggest that concentration in a banking market is beneficial for the banks active in this market. Therefore it is recommended that policymakers design banking market regulations in such a way that M&A activity is promoted. However, caution when interpreting these results is important since the empirical research on the relation between concentration and performance provides mixed results.

Suggestions for further research relate to research directions which are beyond the scope of this paper. The empirical results in this paper provide evidence for the concentration-stability theory. However, in-depth research on several topics is necessary to make inferences about the arguments and underlying relationships of the concentration-stability theory. For example, the results in this paper provide evidence for a positive relationship between credit-monitoring and the stability of a bank. To see how this relation works in practice, more detailed data on credit monitoring should be

combined with data on market concentration, bank size and performance. The same applies for the relationship between concentration, size, supervision and performance of a bank, and also for the degree of diversification. Combining data on all these variables makes it possible to extend and improve the policy recommendations on the consolidation policy in the banking market.

6.

Conclusion

This paper addresses the relation between banking market concentration and performance in the EU, using data on consolidated commercial banks, savings banks and credit cooperatives within the EU ranging from 2005-2012. The theory and empirical results on this relation produces conflicting predictions on the relationship between concentration in the banking market and the performance of a bank.

The results in this paper provide evidence for a positive relationship between concentration in the banking market and the distance-to-default of a bank. In other words, a bank operating in a highly concentrated market is expected to be less risky than a bank with similar characteristics in a market where the concentration is lower. With these results the paper provides evidence for the concentration-stability theory. The results are robust when using both the market share of the five largest banks and the Herfindahl-Hirschman Index as a proxy for concentration. The model is estimated with control variables for bank-specific, macro-economic and institutional factors. Using country and time fixed effects confirm the positive relationship between concentration and performance.

The findings in this paper are relevant for policymakers deciding on the structure of the banking market and the M&A policy within this market. It is recommended that policymakers design banking market regulations in such a way that M&A activity and thereby concentration are promoted. However, caution when interpreting these results is important since the literature on this relation provides mixed empirical results.

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