Competition in the banking sector The effect on GDP

Transcription

1 Competition in the banking sector The effect on GDP Bachelor Thesis Patrick Hereijgers Administration Number: Business Administration Tilburg University May 18, 2012 Supervisor: Mintra Dwarkasing

2 Abstract In this paper, two measurement methods are used to determine the level of competition in the banking sector. First the HHI s of 2006 and 2009 are compared to each other to see if the concentration on the market has changed during the financial crisis. We found mixed results, so we are not able to conclude that the HHI increased because of the decreasing numbers of banks. Then we did research on the link between HHI and the H-statistic of Panzar and Rosse (1987). This relationship was not very convincing, something that is confirmed by among others Molyneux et al. (1994) and Claessens and Laeven (2003). Then we investigated the effect of the HHI on the GDP. We used a linear regression model with in total seven variables. We found evidence that the HHI and the GDP are negatively related. A high level of HHI is not desirable for the global economy. 1

3 Chapter 1 Introduction The banking sector is a special industry. The degree of competition in the financial market does not only matter for the production of the services, or the quality of the products and the degree of innovation in the sector. This industry is different because of the link between competition and stability. Stability is very important in the financial markets. Excessive competition in the banking market has been one of the factors contributing to the financial crises (Claessens and Laeven, 2003). So, for the banking sector it is important that the competition in the market is not that fierce, that the stability is under pressure. But why is the stability of the financial system so important for policy-makers? Governments of countries all over the world supported banks which were in financial difficulties, because they wanted to protect their stability. That the stability on the market is that important becomes clear when you think about the function of banks. Banks provide financial services that are necessary in the society to undertake anything (Northcott, 2004). When a bank does this in an efficient way, this contributes to economic growth. Therefore, bank failures lead to economic costs. These costs can increase greatly, because of the fact that banks are connected with each other closely, and so a shock to one bank can lead to failures by other banks too. A result of the financial crises is that this sector is nowadays under great attention of research. Competition analyses in economics are done to look at the safety of the financial system. An adequate degree of competition is supposed to safeguard financial stability and therefore, the topic is an interest among the bank supervisors, who are in need of devices for monitoring the evolution of the banking competition (De Rozas, 2007). However, in the academic literature there is no consensus about the degree of competition. The majority of the studies done in the last decade report the existence of monopolistic competition on the banking market, but the results are still rather mixed. The degree varies between the countries, also in the European Union. The European banking markets are undergoing large changes at the moment, caused by the deregulation of financial services, the establishment of the Economic and Monetary Union and the developments in information technology (Bikker and Haaf, 2001). Another trend in European banking is the wave of mergers, which eventually leads to increasing competition (Gardener and Molyneux, 1990). 2

4 About the empirical method to determine the level of competition in the banking sector, there is no consensus. According to Northcott (2004), there is not just one measure that is the best to gauge competition in this industry. In general, the measurement methods of competition may be divided into two mainstreams: the structural and the non-structural approach (Bikker and Haaf, 2001). At this moment, the non-structural approaches like the Panzar-Rosse variable and the Bresnahan model are more popular than the structural approaches. This is because the non-structural models measure competition without using explicit information about the structure of the market. This is probably better, because of the fact that the relationship between the structure of the market and the competition on the market is doubted in some literature (De Rozas, 2007). Therefore, in chapter 3 this paper will describe the different measures of competition that are used in the literature and they will be compared with each other. First, in chapter 2 there is a literature review of some leading papers and articles in this field. In chapter 3 there is also a theoretical section about the important factors that determine the degree of competition in the banking sector. In section 4, there will be an empirical analysis with the use of a linear regression model. In this chapter, the Herfindahl-Hirschman index is going to be compared with the Panzar and Rosse method. Furthermore, also the effect of the degree of concentration on the market on the gross domestic product of the country is going to be empirically investigated. This thesis ends with the concluding remarks in chapter 5. Chapter 2 Literature Review In this section of the paper the background of competition in banking will be discussed. This chapter will give an overview of the existing literature on this topic and will look at some leading papers and articles in this field closely. The papers will be discussed on what exactly is investigated and the main results of the research. There has been a lot of research on this topic, especially because there is no consensus about the right measurement method to determine the degree of competition in the banking industry. In this review, both the measurement methods as well as the theoretic part of the paper will come forward. In the article Concurrentie op de Nederlandse bancaire markt (2007), Boot and Schinkel explain the difficulties about determining the right measurement method to measure the level of competition in the banking sector. They claim that the banking sector is not just one 3

5 market. The industry consists of different relevant markets, which should be considered when one is speaking about the competition on the banking market. They explain that the standard way to determine the relevant market, is using the SSNIP-test, the Small but Significant Nontransitory Increase in price test. In this method, you start with a small group of strong substitutes as basis. Via the model, examined is whether an increase in price by for example 5 percent, is profitable. Is that not the case, than you can add the next substitute. You repeat that action until an increased price is profitable. Then you determined a relevant market. Another item that Boot and Schinkel (2007) consider is about the uniqueness of the market. As explained in the introduction, the banking industry is a special one. It is important for society that there is a stable environment in which the banks operate. Therefore, rivalry in this sector is often considered undesirable. Supervisors, who keep an eye on the stability of the market, are therefore reluctant when it is about competition. Another thing that is special in this sector is about the accession of new companies. This is almost impossible. The banking industry is relatively concentrated, so there are a couple of big players on the market. They can hold the accession of upcoming banks to the industry. The third peculiarity Boot and Schinkel name is about the collaboration between the banks. In banking, there are large partnerships between the banks. For example, their payment options are all aligned. Also, due to the financial distress, there have been mergers on the Dutch banking market. This also leads to an even stronger concentration of the market and therefore, the barrier to enter the market becomes even bigger. These characteristics are the main reasons that the banking industry is so different from other industries. Furthermore, Boot and Schinkel (2007) debate the way competition should be measured. A measurement method often used, is the Hirschmann-Herfindahl Index (HHI). This index reflects the market shares of the company s in the industry and searches for the asymmetry in that. A high HHI number reflects a monopolistic market, with a HHI of 1 stands for a monopoly. The index is used in the United States in the enforcement process of antitrust laws in banking. When there is an application for a merger between two banks, that request will be automatically approved without further investigation if the guidelines coherent with the HHI are satisfied. One part of the guideline says that before the merger, the market HHI should not exceed Another point that should be satisfied, is that in the post-merger situation, the increase in HHI due to the merger should be less than 0.02 (Bikker & Haaf, 2000). Also in the European Union the threshold of 0.2 is used, so when there is an application for a merger in 4

6 a country with a HHI higher then 0.2, this application is extensively investigated (Schoenmaker, 2011). This is an important feature of concentration ratios like the HHI. They are able to see what the consequence of mergers are, but also for situations when there is a new entrant at the market, or when one party exit from it. Therefore, the HHI measure can be used in structural models to explain the competitive performance in the banking industry as a result of market structure. A tool that Boot and Schinkel (2007) name as specific for the banking industry is the Panzar- Rosse variable. This measurement relates the degree of competition to the change in the turnover of the product, when the marginal production costs of that product changes. The application of this measurement is though very inconvenient. To compute this variable exactly, you need information from the bookkeeping of the company s. That can be very difficult, because of the fact that the internal allocation of the costs is very difficult to understand for outsiders and due to the difference between the fiscal accounting rules and the real economic value. Still, this paper wants to give some attention to that Panzar-Rosse variable. In 1987 John Panzar and James Rosse developed a test with which they can determine whether an industry is in a monopoly. This method relates the degree of competition on a market to the reaction of the turnover of a product when there is a change in the marginal production costs of that same product. Panzar and Rosse give a method with which you can determine whether a market is in a monopoly-condition or that there is a perfect competition in the industry. Assumed is that under perfect competition, an increase in input prices raises the marginal costs and the total revenues by the same amount as the rise in the costs. On the other hand, an industry is a monopoly if the increase in the input prices will also increase the marginal costs, but reduces the equilibrium output and so, also the total revenues are reduced (Claessens and Laeven, 2003). A point of critique on the Panzar-Rosse statistic is the fact that the empirical implication of the test is that it must be undertaken on observations that are in long-run equilibrium (De Rozas, 2007). Claessens and Laeven (2003) applied the Panzar and Rosse methodology to estimate the degree of competition in the banking systems in 50 different countries. They state that the level of competition in the banking sector matters, because of the link between competition and stability in the financial sector. 5

7 Excessive competition in for example East Asia had been one of the factors contributing to the financial crises. Stability is desirable in the financial sector. Excessive competition can disturb that stability and can lead to undesirable outcomes, like bank failures. Claessens and Laeven (2003) distinguish two possible empirical tests for competition in the financial sector. First, the model of Bresnahan (1989) comes forward. The basic idea of the model is that profit-maximizing firms in equilibrium will choose prices and quantities such, that the marginal costs are equal to the marginal revenue. With this test a parameter can be estimated that gives the degree of the competition, varying from perfect competition (value of statistic is zero) to market power, with a value of one. A draw-back of the Bresnahan test is that it is suffering from a multicollinearity problem, as described by Perloff and Shen (2001). The other model is the Panzar-Rosse model, as described before. They choose for this model because of the fact that they also want to study differences among banks. The Panzar-Rosse H statistic is interpreted as follows. H<0 indicates a monopoly; H=1 indicates perfect competition; and 0<H<1 indicates monopolistic competition. Because this interpretation assumes that the market is in a long-run equilibrium, they also uses parameter E the check whether that is the case in the banking industry. They used data for the years and their sample includes all types of bank (commercial banks, saving banks, cooperative banks and bank holding companies). This resulted in observations. To reduce this sample, they applied some selection criteria. The outliers were deleted from the database, and also the countries with less than 50 bankyear observations were removed. They also deleted countries with less than 20 banks. Eventually this led to 50 countries. They estimated the H-statistic on the basis of four models. The models varied in the way of the dependent variable and the technique. As dependent variable they did use twice gross interest revenue and twice total revenues and as technique they used in model 1 and 3 the pooled OLS and in model 2 and 4 fixed effects. With these differences you can form exactly four models. What they found was an average H-statistic varying between the four models from 0.60 to This result suggests that monopolistic competition is the best description of the degree of competition. They did not find any strong patterns between the type of countries. Although, they did see that some of the largest countries have relatively low values for the H-statistics. 6

8 Model 1 applied on the United States for example resulted in a value of 0.15, so that value is much below the average and it is close to monopoly. Claessens and Laeven (2003) conclude that a lack of entry restrictions in the banking sector leads to a more competitive banking industry, just like a greater foreign bank presence. On the other hand, activity restrictions on commercial banks can reduce competition in the sector. So, it seems like being more open to new entry is a factor that is crucial for the competition in banking. They find no significant evidence that banking system concentration is negatively associated with the degree of competition. None of the four H-statistics are negatively correlated with bank concentration. In fact, they find evidence for a positive relation, so that more concentrated banking systems are more competitive. Similarly, the number of banks variable is also never significant positively related to the competition indicator and sometimes they even find a negative link, indicating that the less banks, the more competitive the system is, although they could not make this sentence statistically significant. So, they found little evidence that variables describing the banking system structure can really help to explain the measured competitiveness. Luis Guitiérrez de Rozas (2007) wants to determine the level of competition in the Spanish banking world. He uses just like Claessens and Laeven (2003) the methodology presented by Panzar and Rosse (1987). In his paper, de Rozas used an extensive twenty-year-long period, from 1986 to He used data from Banco de España, the Spanish supervisory authority of the banking industry. In the sample, de Rozas did not include cooperative banks due to the lack of relevant data. Also banks with just limited presence in Spain are excluded. De Rozas found that the level of competition is quite higher than reported in the literature. In the case of the large banks, it is really close to perfect competition, so the H-statistic is close to 1. He also concludes that the contestability theory holds for the banking industry. This theory assumes that firms can enter or leave any market rapidly without losing their capital (Molyneux et al., 1994). This is because the contestability theory believes that every potential competitor possesses the same cost function as firms that are already in the industry. So, with the contestability theory you can conclude that the negative relationship between concentration and competition is not always there (Boot and Schinkel, 2007). 7

9 Sometimes it can be that the market is under such a pressure from possible entrants that they have to keep their prices down. Excess profits are not earned in the equilibrium of such sectors. Another study that focuses on the degree of the competition in the banking sector, is the research done by Bikker and Haaf (2001). They used the Panzar-Rosse model, because they want to distinguish three groups of banks: small or local banks, medium-sized banks and large or international banks. They apply this model to banks from 23 countries in a time frame from 1988 till They distinct three groups based on the size of the banks. They found that, with increasing the size of the bank, on average also the H-statistic increased and so, it comes closer to perfect competition. The average for small banks is 0,64, for the middle-sized banks the average is 0,75 and for the largest 10% of the total sample, the H- statistic is 0,86. So, the banking market is characterized by monopolistic competition, although in some cases, perfect competition cannot be ruled out. Also interesting to see is the differences they find between countries. In the medium-sized banks category for example, Japan had a value of 0,07, while a country like the Netherlands scored 0,87 in the Panzar-Rosse model. It is not a coincidence that the Netherlands are in Europe and Japan is not. Bikker and Haaf (2001) conclude that the competition in banking seems to be weaker in non-european countries. They devote this to the structure of the European banking industry. This changed during the 1980s in a response to domestic deregulations and in anticipation of the EU-wide regulatory changed. Another thing that contributes to the higher degree of competition in European countries is the situation with the mergers in Europe (Gardener and Molyneux, 1990). Mergers contribute to the forming of large-sized banks that could compete with the large, dominant banks in the industry. The competition then becomes heavier, as showed by the three groups Bikker and Haaf (2001) investigated. Molyneux, Lloyd-Williams and Thornton (1994) explain the differences between Europe and the other parts of the world. They found that the banking sectors in Germany, the United Kingdom, France and Italy are by far the largest. From those four countries, the banking industries in Italy and France are the most concentrated. Molyneux et al. (1994) claim that in those countries also the regulations were historically been seen as the most restrictive. 8

10 In France and Italy, the government owned a considerable amount of banks: 42% of the total banking sector assets in France was in 1989 owned by the French government and in Italy the government owned 67%. (Gardener and Molyneux, 1990) So, Molyneux et al. (1994) link governmental ownership and concentration. But they do not give a relationship between the increased concentration and the effect on the level of competition. They state that it is difficult to appraise the implications for the competition when the concentration increases, especially in the light of the contestability theory, mentioned before. The traditional viewpoint would suggest that collusion and other anti-competitive practices would happen, when the concentration is increased. The contestability theory claims otherwise. There is a preference for larger size banks in Europe, but Molyneux et al. (1994) claim that this can be forced by the economies of scale theory. Another point already mentioned was the domestic deregulation in the countries. The European Community wanted to regulate more as a whole for the banking industry. In 1973, the EC Council of Ministers adopted some treaties that aimed to ensure the equal treatment of banks of member states regarding entry into domestic markets. They wanted the borders to disappear. Also in 1977, 1983 and 1986 the European Community accepted treaties that deregulate the tasks of the countries (Molyneux et al. 1994). But as Baltensperger and Dermine (1990) pointed out, in the late 1980s the European banking markets were still far from fully integrated. Banks remained subject to supervision by the host country and so they were subject to different requirements, something the European Community tried to discourage with those treaties. Chapter 3 Theoretical framework This section of the paper consists of two paragraphs. First it is determined which factors drive the competition in the banking sector. In the second paragraph, it is decided which method is used for the empirical research in chapter 4, where this paper will investigate whether competition measures influence GDP in a country. From the literature, it became clear that there are a lot of ways of how you can measure the degree of the competition on the banking market. There are structural and non-structural approaches which are both used to determine the level of competition in the market. Every method has its strong and weak points and there is not academic consensus about which measurement should always be used. 9

11 In the second paragraph, this paper gives an analysis of the measurement methods and on the basis of that inquiry it is determined which measure for competition is used in chapter Which factors drive competition in the banking industry? If one thing comes forward out of the literature, it is that the competition policy in the financial sector is complex and can be hard to analyze (Claessens, 2009). In contrast with other industries, in the banking sector policymakers are reluctant when there is a lot of competition on the market. When usually a large degree of competition is beneficial for the consumer, in the banking industry that is not the case. Excessive competition has an effect on the financial stability in the market. When the competition is that fierce, it can lead to financial distress by the banks, or even to bankruptcy. It is clear that that is not desirable in this market, because we saw in the last decade that the problems on the financial markets are one of the reasons of the financial crisis nowadays. This crisis makes clear that many policies are needed in order to ensure that the objective of stability on the financial market is maintained. But which factors drive the degree of completion in the banking industry? An important factor that determines the degree of competition in and the stability of the banking system, is the way entry barriers make it difficult for possible new competitors to enter the market (Claessens and Laeven, 2003). They found that countries with fewer entry and activity restrictions have a higher competitiveness score. A way to lower those entry restrictions is to prevent that there exists a lot of information asymmetry on the market. As Boot and Thakor (2000) showed, increased interbank competition results in more relationship loans between the banks reciprocally. When there is doubt by a possible new entrant about the lending relationships in the industry, this can become a barrier to enter into the system (Claessens and Laeven, 2003). When the entry barriers are weakened, the loan rates in the equilibrium of the market will decline. Another effect of relaxing the barriers is the fact that the interest rates on deposits will increase. So, that is advantageous for the consumers. That the interest rate on deposits does go up is because the market is more contestable when it is less difficult to enter the market. And as we saw already in chapter 2, the contestability of a market is positively linked with bank efficiency and stability. This is supported by the empirical research done by Barth, Caprio and 10

12 Levine (2002), who found that tighter entry requirements are negatively linked with bank efficiency, because the interest rate margin and the overhead expenditures increase. Gelos and Roldós (2004) named the allowance for foreign banks to enter the domestic market as one of the consequences of lowered entry barriers in the industry. They did empirical research via the Panzar-Rosse method to check whether more foreign bank participation actually lead to more competition intensity. This is the case and therefore, they support the view that lowered barriers to entry leads to a higher degree of competition on the banking sector. Another point that has an effect on the degree of competition is the way how the banking industry is regulated. Some studies showed that the competition in European banking is heavier than in non-european markets. Among other, Bikker and Haaf (2001) claim this is because of the changed industry in European since the 1980s. The European Union pleaded for domestic deregulation and more central organized EU-wide banking. The major consequence of this changing structure of European banking is increased competition. This is partly due the fact that the deregulation measures eventually leads to mergers (Gardener and Molyneux, 1990). Mergers of small-sized banks into a larger bank increases the competition in the market, because this merged bank can now compete with the large, dominant banks in the industry. Therefore, the competition becomes heavier. All these factors influence the degree of competition on the European banking market. But how do we measure that level of concurrence? That is explained in the next paragraph. 2. Which measure is the most optimal one to determine the degree of the competition in banking? There are a lot of measurement methods that are used in the academic literature to determine the degree of competition in banking. These methods can be classified in two types: the structural approaches and the non-structural. The structural approach is based on two assumptions: the Structure-Conduct-Performance (SCP) paradigm and the efficiency hypothesis. The aim of the structural indicators is to see if there is a relationship between the structural features of an industry and firm performance (Rooijakkers, 2011). Firm performance is often measured by profit indexes. The structural features of the industry are among others indicated by market shares and concentration ratios. 11

13 The SCP paradigm (Bain, 1951) links market concentration to the level of competition. The assumption is that a higher level of concentration results in behavior by larger firms that is non-competitive. So, it is hypothesized that higher concentration is positively related with higher market power. The efficiency hypothesis assumes an indirect link between competition and concentration. It states that efficient firms do well in the industry and therefore increase in size and logically then they also increase in market share and therefore generate more profit. This approach leads to problems. First about how the performance of the company is measured. The structural methods are based on the assumption that the structural features like the concentration ratios are responsible for a higher profit. With this, the SCP-approach ignores the fact that a higher profit can also have other causes then structural characteristics. The problem by determining the structural features is there is an assumption about the relationship between concentration and competition, as explained in chapter 2. With just the structural features, the competitiveness of an industry cannot be analyzed well (Claessens and Laeven, 2004). Despite of this remarks, some types of the structural approaches are still used to determine the degree of competition. In chapter 2 we saw that the HHI is still a popular measure of concentration. For example, the index is used by authorities when they get an application for a merger. An important characteristic of the HHI is that it gives a lot of weight to larger firms and that it takes into account the entire market, contrary the CRk-method. That method only uses the market shares of the k largest banks in the country. A disadvantage of the HHI is that it only takes that market shares into account and neglects other factors, like the competitive behavior of banks (Berger et al., 2004). Besides the structural approaches, we also know some non-structural methods. The main focus by these approaches is on measuring the monopoly power on the market. In contestable markets, these indicators of competition are a good manner to test for competitive behavior in contestable markets (Rooijakkers, 2011). These models emphasize more the competitive conduct of the firms, in contrast to using a lot of information about the structure of the market. In recent literature, the methodology from Panzar and Rosse is seen as the most optimal one of all non-structural competition measurement methods. With this methodology, investigated is to which extent a change in factor input prices is reflected in the revenues earned. With the H-statistic Panzar and Rosse determined, you can measure the degree of competition, as 12

14 explained in chapter 2. The main advantage of this model is that it uses bank-level data and allows for bank-specific differences in the production function. Therefore, we can study the differences of the competition between types of banks also (Claessens and Laeven, 2003). A drawback of this method is that the industry should be in a long-run equilibrium to use this method. This should be tested when the H-statistic is used. Another weak point of this method is that the H-statistic sometimes turns out to be negative (Toolsema, 2002). Claessens and Laeven (2003) assume that also in that situation the H-statistic indicates a monopoly. However, they do not give an explanation for that. The question of this paragraph was which model is the most optimal one to determine the degree of competition in the banking industry. This paper explains that every method has its advantages, but certainly also its drawbacks. So, there is not one correct method, every methodology has its pros and cons. Theoretically, the non-structural methods are seen as more trustworthy, because those models emphasize the competing behavior of the firms. In contrast, the structural models are more interested is the market structure of the industry. That relationship is often doubted in the academic literature. However, also in recent literature it is assumed in general that a highly concentrated market is less competing (Boot & Schinkel, 2007). In this paper, both a structural and a non-structural measurement are used. As structural measure, we chose the HHI above the CRk-method, because the HHI takes the market shares as weights and it includes each bank in the industry, contrary to the CRk-method. So, the HHI contains the full information of the market, because it captures features of the entire distribution of banks size (Bikker & Haaf, 2000). With the HHI method, this paper tries to say something about the competition in the industry and also wants to link this to the GDP of countries. The non-structural method used is that from Panzar and Rosse. The Panzar-Rosse model is used frequently in the recent literature, but it is hard to get the right information to compute that H-statistic. For this paper, computing the H-statistic with the use of the Panzar and Rosse method is out of reach, so we used information about this statistic from Casu and Girardone (2006). 13

15 Chapter 4 Empirical study In this chapter, this paper uses different measures of banking competition by which it will try to determine the effect of competition on the financial market on the gross domestic product (GDP). Through an empirical study the HHI is going to be determined and with the use of the non-structural Panzar-Rosse method a comparison is made between the HHI and the H- statistic for several countries. This chapter contains also a study in which the relationship between a higher concentration in the market and the GDP in a country is investigated. 1. Comparing the HHI from 2006 and 2009 for 16 EU-countries The markets shares of the different banks are calculate with the use of total assets on their balance sheets. So, the market share S of bank i in country k is determined as follows: S i = total assets bank i / total assets banking sector country k (1) With the market shares of all banks in country n, the HHI is indicated as follows: HHI = (2) To determine whether the banking market became more concentrated in the last turbulent decade, this paper focuses on the HHI of the years 2006, just before the real problems in the financial markets became clear, and 2009, when the world was in a crisis for a couple of years. The study focuses mainly on the old member states of the European Union. Only Hungary and Poland are included, because these countries also have a significant financial industry. This papers hypothesis is that the number of banks at the continent declined, because of the fact that there were a lot of mergers in that time-interval, due to the financial distress some financial institutions faced. This decline in the number of banks will lead to a higher concentration index. Besides the number of banks in the industry, the sensitivity of the HHIratio is also determined by the inequality of the market shares among the different banks. Davies (1979) hypotheses that the HHI becomes less sensitive to changes in the number of banks the larger the number of banks on the market. 14

16 Table 1 presents the HHI for some countries for the years 2006 and The numbers in the table are derived from the study from Schoenmaker (2011). Country HHI 2006 HHI 2009 Country HHI 2006 HHI 2009 Austria 0,0534 0,0414 Italy 0,0220 0,0353 Belgium 0,2041 0,1622 Luxembourg 0,0294 0,0288 Denmark 0,1071 0,1042 Netherlands 0,1822 0,2032 Finland 0,2560 0,3120 Poland 0,0599 0,0574 France 0,0726 0,0605 Portugal 0,1134 0,1150 Germany 0,0178 0,0206 Spain 0,0442 0,0507 Greece 0,1101 0,1184 Sweden 0,0856 0,0899 Hungary 0,0823 0,0861 United Kingdom 0,0394 0,0467 Table 1 HHI 2006 and HHI 2009 for 16 EU-countries From table 1, it becomes clear that there are large differences between EU-countries. For example, The Netherlands, Belgium and Finland have a relatively high HHI, while Italy, Germany and Luxembourg have a low concentration index. In general, countries with a large number of banks have a low concentration index. This is confirmed by the example of Italy and Germany. In particular Germany, where the financial market is very large with in 2009 almost 2000 banks (Schoenmaker, 2011), it is shown that there is a negative relation between the number of banks and the concentration of the banking industry. That a country like Luxembourg has such a low HHI is therefore remarkable, because there are just 150 banks in that country. A factor that is possibly responsible for this is the fact that the total assets of Luxembourg s financial market are very small. Also remarkable is that in Luxembourg domestic banks have just 6 percent of the total banking market in hands (Schoenmaker, 2011). These factors could be responsible for the exceptional position of the country. Vice versa it also works. The Netherlands, Belgium and Finland are reported to have a high concentration index. So, following the hypothesis that the amount of banks and the concentration index are negatively related, this should mean that these countries have a small number of banks. This is the case. For example, Belgium has a very small banking market with just hundred banks active in the country, and with a HHI of 0,1622 in 2009, it is one of the most concentrated markets. 15

17 Another item that triggers is the very high HHI of Finland. The country has a HHI of 0,3120 in That means that for example mergers and acquisitions are not desirable in that country, because the threshold of 0,2 is passed. A thing that is remarkable about the situation in Finland is that it has, just like Luxembourg, a very high penetration of foreign banks in their domestic market. Two-third of the total assets in that market belongs to foreign markets. 2. Comparing HHI and the H-statistic (Panzar and Rosse) In the literature review and the theoretical part of this paper the Panzar and Rosse method came extensively forward. This method is the most used method in the recent literature. The H statistic measures the sum of the elasticity s of banks total revenue with respect to input prices, as we explained in previous chapters. Also in the paper of Casu and Girardone (2006) the Panzar and Rosse method was replicated. For the period between the years 1997 en 2003 the average H-statistic was calculated. The H-statistic is calculated as: H = (3) with R i referring to bank-revenue (were * indicates an equilibrium value) and W i referring to a vector of m factor input prices of bank i. As said before, a value of 0 (or lower) for the H- statistic means that the market is in monopoly, and H equaling 1 indicates a perfect competition on the market. Any value between those values means that there is monopolistic competition. The Panzar and Rosse method is one of the non-structural methods for determining bank competition, while the HHI measure is a structural one. Expected is that a higher HHI, so a relatively high degree of concentration will lead to a low value for the H-statistic, which we get when we follow the Panzar and Rosse method. This is because a low value of the H- statistic is determined as a monopoly by Panzar and Rosse (1987) and a high value of the HHI would refer also to that situation. This paper is going to check that statement on the basis of the H-statistic s for the period , which are investigated by Casu and Girardone (2006). With the use of data gathered from Bankscope, this paper determines the HHI of

18 This paper calculated the indexes using formula (2), so using the total assets to determine the market shares. Both the HHI and the H-statistic are shown in table 2. Country HHI 2003 H-statistic Austria 0,0992 0,154 Belgium 0,3406 0,779 Denmark 0,1676 0,050 France 0,0820 0,201 Germany 0,0311 0,368 Greece 0,7745 0,000 Luxembourg 0,0530 0,656 Netherlands 0,1272 0,287 United Kingdom 0,0800 0,327 Table 2 HHI (own calculations) and H-statistic (Casu & Girardone, 2006) for 9 EU-countries A restriction of this study was that some banks did not have a known value for their total assets in Bankscope. Therefore, the total assets of the country were too low and some banks did get a huge market share. So, this drawback forced to delete the values of Finland, Italy, Portugal, Sweden and Spain. When compared to table 1, also Poland and Hungary are not in table 2. This is because Casu and Girardone (2006) only researched the old member states of the European Union, not the new entrants, and therefore the H-statistic of these countries was unknown. For the remaining countries, in some situations the negative relationship which we first expected between the HHI en the H-statistic is very clear. For example by Greece, were Casu and Girardone (2006) found an H-statistic of 0,000; we expect a very high HHI. That is the case, with a HHI of 0,7745. However, Casu and Girardone (2006) found real monopoly, but that was not the case via the HHI method. Another situation where it is just around is in the case of Luxembourg. In this country, this paper found a HHI of 0,0530, based on the data from Bankscope for the year Casu and Girardone (2006) found with the Panzar-Rosse method an H-statistic of 0,656, so that is relatively very high. However, as concluded before by studies done about the relationship between market structure and competition (Claessens and Laeven, 2003 & Molyneux et al., 1994), the relation is not very convincing. We saw the relationship by Greece and Luxembourg and also 17

19 Germany and the UK have a negative sign, but in the other cases, this paper did not find a negative correlation. In fact, in some cases it looked more like a positive relationship between structure and competition, so that more concentrated banking industries are more competitive. See for example Belgium: the HHI is far above the mean, so you would expect a very small H-statistic. In table 2 we see however that the H-statistic is also far above the average value. Therefore, also for this paper it is hard to conclude that HHI and the H-statistic are negatively related. 3. Effect of concentration on the GDP Next, we are going to investigate the effect of the degree of concentration in the banking market on the GDP in a country. This is the first time in literature that the banking competition is linked to the GDP. It might be expected that banking concentration has an effect on national GDP, because of the impact the financial market has on the domestic economy as a whole. First we have to think about the direction of the relationship. In general it is assumed that a monopoly is not desirable for the economic wealth in a country. The market is in that state not efficient. And as we saw before, a high level of HHI is determined as monopolistic competition, with a HHI of 1 stands for a monopoly. So, we could say that we expect that a relatively low HHI will lead to a relatively high GDP. For sure, there are a lot of other factors which determines the growth of the GDP. Therefore, we also have to take this into account in our formula. The other factors we take into consideration are the unemployment rate, the governmental expenditures, the HICP inflation rate, foreign direct investment and total export. The data for the first three variables are derived from Eurostat, the last two we found with the use of information of the Worldbank. The year 2009 is used because for that year, we also have the data for the HHI index. We derived them earlier from Schoenmaker (2011). We also used information by the Worldbank to find the GDP s per country for

20 To determine the relationship and the degree of influence from the level of competition on the banking sector on the GDP, we use the following formula: GDPi = (4) with i stands for the country, HHI for the concentration measure, UNE for the Unemployment rate, GOV for the Governmental Expenditures (as percentage of GDP), INF for inflation, FDI for Foreign Direct Investment and EXP for total export (as percentage of GDP). The mean, standard deviation and minimum & maximum values can be found in Appendix A. As stated above, we expect a negative relation between HHI and the GDP. With respect to the variable Unemployment rate, we also think that there is a negative relation. A high unemployment rate is considered bad for the economy, and therefore the GDP. A positive relation is likely between Governmental expenditures and GDP, because these expenditures by the authorities are often aimed to keep the economy in a country running. Inflation is considered as negative, so there should not be a positive coefficient. FDI can have a positive influence on the economy, when the human capital level in a country is sufficient (Borensztein et al., 1998). Because in this case, the countries are all developed EU-states, we expect in our regression a positive relation between GDP and FDI. Finally, a higher level of export should lead to a higher GDP, because the net export (export-import) is added in the definition of GDP. First we derive a Pearson correlation matrix to determine the direction and the strength of the bivariate relationships among the seven variables we use in this model. The matrix is in Appendix B. Out of the matrix we can conclude that the significance of the correlations is quite high, what might point out that some relationships we expect there to be, are somewhat controversial. Between our special independent variable of interest, the HHI, and the dependent variable GDP is a two-tailed significance of 0,100, with a Pearson correlation of - 0,426. This negative relation is coherent with our hypothesis. We take a closer look with respect to the effect of the independent variables on the GDP through the regression analysis from equitation 4. In table 3 the results of this linear regression are displayed, which we got with the use of STATA. 19

21 The R-square of this regression is 0,5507, meaning that about 55% of the variation in the data set is explained by this model. We use the robust model. Variable β coefficient Significance Constant ,692 HHI ,174 Unemployment Rate -24,89 0,800 Governmental Expenses 0,673 0,995 Inflation -42,79 0,805 FDI 14,35 0,072 Export -30,41 0,012 Table 3 Regression results eq. 4. All β coefficients are divided by one milliard for scaling reasons, except FDI. If we compare the coefficients with our assumptions made earlier, only the β of export is different than expected. Our special variable of interest, the HHI, is again negatively related with GDP. The significance is 0,174, so that goes in the good direction, but this could perhaps be lowered with a more extensive model. Also the significance of FDI and Export are good. To really conclude something with respect to the positive or negative sign of the β coefficient, we do another regression, but this time with the natural logarithm of GDP. With this sensitivity analysis we want to strengthen our claims. So, this linear regression is as follows: LN GDPi = (5) The coefficients of this equation are given by table 4. We see that the signs of the coefficients are still the same for this adjusted regression. Therefore the likelihood of the correctness increases. 20

22 Variable β coefficient Significance Constant 28,958 0,001 HHI -2,997 0,404 Unemployment -0,028 0,779 rate Governmental 0,002 0,983 Expenses Inflation -0,050 0,784 FDI 0,0125 0,087 Export -0,038 0,000 Table 4 Regression results eq. 5. FDI is multiplied by one milliard for scaling reasons. We have now three sources from which we can conclude that our variable of interest, the HHI, is negatively correlated with GDP. The correlation matrix, the regression analysis with GDP as dependent variable and the analysis with ln GDP gave all a negative sign for the β coefficient belonging to the independent variable HHI. Although the results are not statistically significant, this all supports our hypothesis that a state of monopoly is considered as non desirable for the economic wealth in a country, and therefore the HHI and the GDP are negatively related. Chapter 5 Conclusion In this paper, it becomes clear that the banking industry is not just an ordinary sector. It is a special one, because of the link between stability on the one side and competition on the other. Due to the financial distress in the global economy, the stability component of the banking sector got in trouble. Because of the fact that the impact of the banking industry is important for the financial stability, policy-makers tried to create a banking environment that was able to survive the financial crises, but still was competitive. Deregulations of the financial services in the European Union, removal of entry barriers and foster efficient competition became a point of attention. Due to these measures we also saw a trend of mergers and acquisitions in the industry. 21

23 This paper investigated the interaction between competition and concentration in the banking sector. To obtain a clear view, this paper used two measurement-methods: the structural Herfindahl-Hirschmann Index and the non-structural Panzar and Rosse method. First, this paper compared the HHI for the years 2006 and 2009 for sixteen European countries. To determine whether the banking market became more concentrated during the crises, we chose for those years. The research pointed out that there are large differences between the EU-countries. Countries like the Netherlands and Belgium has a relatively high HHI, while powerful countries like Germany and Italy has a relatively low concentration index. Unfortunately, our hypothesis that the HHI should have been increased in 2009 with respect to 2006 because of the decreasing number of banks, was not supported by the results. We found mixed outcomes and therefore we could not conclude our hypothesis. Another hypothesis that was formed in this paper is about the relationship between the HHI and the H-statistic. Because a value of 0 or lower for the H-statistic means that the market is in monopoly, we expect that a relatively low value for the H-statistic will mean a relatively higher HHI value. This is because a high value of HHI also refers to the monopoly situation. We did this empirical research with the use of the H-statistic for European countries that Casu and Girardone (2006) found. With own calculations, the HHI of 2003 was determined. Unfortunately, this study was restricted because we could not derive the total assets from some countries. Therefore we had only nine observations. As also concluded before by Claessens and Laeven (2003) and Molyneux et al. (1994), the supposed relation between the HHI and the H-statistic is not very convincing. However we found support for the relationship in for example Greece and Luxembourg, there also were countries were the expected negative relationship was not that clear. In some cases, for example Belgium, it was even just the opposite: their HHI is far above the mean, so we expect a small H-statistic. That was not the case, that statistic was also relatively large. Therefore, it is hard to conclude that HHI and the H-statistic are negatively related, just as previous research could not conclude that. Another point this paper looked at, is the role of HHI when it is about the GDP. In a regression model with six independent variables, our main interest is the relationship between HHI and the dependent variable GDP. Clear is that a relatively high HHI stands for 22