Has the Single Supervisory Mechanism Lived up to its Promise? A Euro Area Perspective on the Competition-Stability Nexus

Master Thesis Economics & Finance

Has the Single Supervisory Mechanism Lived up to

its Promise? A Euro Area Perspective on the

Competition-Stability Nexus

Martijn Vrugt

S2345668

Supervisor:

MSc. D. Vullings

Abstract:

This paper studies the relationship between competition and stability for the banking sector in the Euro countries for the period 2012-2016. It is valuable to analyze this relationship because policy makers often have to make decisions which affect both competition and stability. In this panel data study, the Lerner index is used as the proxy for competition and the ln(Z-score) as the proxy for stability. This paper finds evidence which supports the competition-fragility hypothesis. In addition, this paper analyzes the impact of the supervisory change in the banking sector, from the national supervisory authorities to the Single Supervisory mechanism, which became operative as of November 2014. This paper finds that the implementation of the SSM has intensified the relationship between competition and stability. Moreover, this paper finds evidence that there exists an inverted U-shaped relationship between competition and stability.

Keywords: Bank competition; Bank stability; Single Supervisory Mechanism; Lerner Index; ln(Z-score); charter-value hypothesis.

1. Introduction

Many elements were responsible for the creation of the financial crisis of 2007-2009. One of these elements which contributed in the run-up to the financial crisis was the competitive pressure in the banking industry. In the 1970’s and 80’s there was a transformation of the perspective from governments around the globe that a competitive banking environment contributes to a stable economy. The transformation of the perspective from the government started a spiral of deregulation in the banking market. The financial liberalization decreased the regulatory costs for the bank and therefore should, according to the structure-conduct-performance hypothesis have a positive effect on the efficiency and productivity (Lee & Hsieh, 2013). However, the increase in competition of the banking industry diminished lending margins. It was the competitive pressure that provoked incentives to record sector beating profits and returns on equity (Bell & Hindmoor, 2018). Because of the competitive pressure and decreased lending margins, executives had to find other sources of income through financial innovation, securitization and excessive risk-taking. This established the foundation for the crisis which ultimately led to government bail outs with billions of taxpayers’ money.

Along with the financial crisis of 2007-2009, the Euro area faced a different crisis. This crisis started at the end of 2009 and lasted until 2013, the European debt crisis. This crisis demonstrated the vulnerability of the relationship that banks have with their home country i.e. the bank-sovereign nexus. Because of the lack of supranational supervision, the ECB had to take extraordinary measures throughout the crisis to save the Euro. The measures that the ECB enacted to save the euro include longer term refinancing operations, reducing the policy rate, allowing banks to get easier access to refinancing due to fixed rate full allotment and temporary extension of the collateral list. Furthermore, to overcome the distortions in the government bond market throughout the Euro area, the ECB initiated the securities market program and outright monetary transactions to provide liquidity to the euro system.

Crises cause a significant drop in stability, welfare and place a high burden on taxpayers (Allen and Gale, 2004). Therefore, it is important that the banks are properly supervised and regulated. The global financial crisis of 2007-2009 and the European debt crisis showed how fragile the financial system is when it is not correctly supervised and regulated. Besides the lack of adequate supervision and regulation, this episode of financial hardship emphasized how globally connected the financial system has become. Supervision and competition play a large role in establishing a stable financial system. Therefore, I will analyze the relationship between competition and stability in the Euro area over the period 2012-2016. Moreover, whether this relationship has changed due to the implementation of the supervisory unification in the Eurozone.

In reaction to the European debt crisis the European union founded the Banking union. The Banking union was created to solve the bank-sovereign nexus. The Banking union is based on two pillars, the single supervisory mechanism (SSM) and the single resolution mechanism (SRM). The SSM supervises and the SRM establishes a resolution procedure for banks. The European Parliament voted in favor of the Single Supervisory Mechanism regulations on 12 September 2013. On October the 13th _{the Council of the European Union gave their consent. One year later, on the}

4th _{of November 2014, the SSM became operative in the Euro area. In addition to the SSM, the}

prudential supervisor of banks in the Euro area and those in the non-euro member states that choose to join the Banking Union (De Haan et al, 2015). Moreover, banks are supervised along the same guidelines which are stated in the single rule book. This single rule book reduces the problem of comparing apples and oranges in terms of stability across nations. The supervisory task is executed by a joint supervisory team. A joint supervisory team is led by a coordinator who has a different nationality than the country in which the headquarter of a specific bank is located. The ECB directly supervises systemically important banks which make up over 80 percent of total assets in the European banking sector. The banks that do not fall under the direct supervision of the ECB are supervised by the national supervisory authorities. The ECB has the power to take over the supervision of a bank if the ECB believes a bank has become systemically important. The SSM has three objectives. First, ensure the safety and soundness of the European banking system. Second, increase the financial integration and stability. Last, ensure consistent supervision (ECB, 2014). Identifying the effect of the supervisory change on the competition stability relationship might be of prodigious influence since financial instability comes at high cost. For example, Allen and Gale (2004) report that for developed and developing countries the banking resolution cost for a crisis is estimated at 12% and 17.5% of GDP respectively. These results can be separated between a banking and twin crisis. A twin crisis is represented by the co-occurrence of a banking and currency crisis. In the case of a banking crisis the fiscal costs are estimated to be 4.5% of GDP and in the case of a twin crisis this number increases to 23% of GDP. Moreover, they report the average cumulative output losses as percentage of GDP. The output loss is 23.8% for developed countries and 13.9% for emerging countries. These numbers indicate that there are real costs during times of a crisis. In addition to the significant costs that a financial crisis causes, it is also detrimental to financial integration. Globan and Soric (2017) investigate financial integration for the European Union over the years 1995-2014. They show that financial integration has decreased since the financial crisis of 2007-2009. Furthermore, they suggest that policy efforts could be made at the national and supranational level to enhance financial integration. The implementation of the Single Supervisory Mechanism in the European Union could increase financial stability and cross-border integration. Financial and cross-border integration could derive benefits from the cost reduction caused by the increase in competition (Fernandez et al, 2007). The benefits of the cross-border and financial integration originate from competitive forces that affect the availability of financing from one country to the next. If two borrowers in two different countries present two projects which are of equal credit risk, they should face similar probability of success. However, if the banking market is fragmentated along national borders this is not possible - if a bank is able to lend money more cheaply to domestic businesses due to barriers (Hakkarainen, 2018). As the market becomes more integrated the probability of success of the two borrowers from the different countries will converge. Hence, create a more equal level playing field.

reducing the home bias and the ability to protect the national champions even further. Because of the previous mentioned arguments, I believe that competition will increase in the Euro area due to the supervisory change. The SSM tries to reduce these barriers, increase the cross-border and financial integration and establish healthy competition in the European banking market. The SSM attempts to decrease these barriers because frictions distort the business climate and decrease the stability of the banking system.

The implied reduction of the entry barriers caused by the implementation of the SSM gives banks the possibility to open new subsidiaries within the Euro area in which they were unable to do business before. This will likely increase competition in the Euro area as consumers have the opportunity to choose from more banks to conduct their business. As competition will likely increase, the bank has two options, increase their risk or decrease the lending margins. As the banks are less able to increase their risk-taking behavior it will decrease its interest margin and attract more creditworthy borrowers. This implied reduction of the entry barriers could affect the competition stability relationship. Previous to the SSM, the barriers to enter the banking market were regulated at the national supervisory authorities. A change in their policy to reduce these barriers will have only a small effect on stability at the level of the Euro area. Since the implementation of the SSM, the banks are subjected to the same regulatory framework in which the ECB is in charge of regulating entry barriers. When there is a change in this framework the whole banking market has to comply with these rules. For that reason, a change in the regulatory policy to lower these barriers will have a larger effect on stability than before. When the barriers to enter are reduced, the participating countries of the SSM will become more aligned and integrated. Lower entry barriers will result in a larger effect of competition on stability as all banks under the supervision of the SSM have to adhere to this reform.

of this reduction, the participating countries of the SSM will become more aligned and integrated which will result in a larger effect of competition on stability as all banks under the supervision of the SSM have to adhere to this reform. The ECB is since the SSM the regulator of entry and exit rules in the banking market. Therefore, the national governments are no longer able to protect their national champions. The banks are more tightly monitored than before, therefore are less able to increase their risk-taking behavior in reaction to the increased competitive pressure caused by the reduced entry barriers. The banks have to decrease their interest rate margins until this no longer has a stabilizing effect (Martinez-Miera & Rapullo, 2010). This reduces the adverse selection problem and increases the effect of competition on stability.

The identification of the effect of the SSM on the competition stability relationship might give rise to an endogeneity problem, because the SSM both affects stability and competition. This possible endogeneity problem complicates the identification of the effect of the SSM. The competition parameter will incorporate the effect of the SSM from both stability and competition. To be able to identify this relationship I have to make a number of assumptions. First, that the implementation of the SSM will have a positive one-time shock to stability. This because banks have had a period of time to adapt to the new rules. In the time between the vote of the European Parliament and the implementation of the SSM banks had the opportunity to accommodate to the new rules. Therefore, banks will likely be better managed. Second, I believe that the SSM increases competition through the financial integration process due to reduced entry barriers. This adaptation in competition will only change gradually as it takes time and capital for a bank to set up new subsidiaries and establish new relations.

This paper builds on the existing literature and provides new insight whether the supervisory unification in the Euro area affects the competition stability relationship. I will use the SSM as a natural experiment and compare the before and after implementation date samples to see whether there exists an effect of the SSM. The structure of this paper is as follows, Section 2 presents the relevant literature. In Section 3 and 4 the methodology and data are highlighted. In Section 5 the empirical results are discussed. Section 6 concludes.

2. Literature Review

The academic literature is dominated by two views on the relationship between competition and stability. These views are the competition fragility and competition stability hypothesis. I will highlight the findings of the literature and present how these papers are related to the supervisory change in the Euro area.

to an increase in the interest rate margin. The increase in the loan rate induces borrowers to engage in moral hazard, this will increase their probability of default. Because of this risk-shifting effect the borrowers will take on more risky projects. This will affect the volatility of the bank’s earnings. In addition, Boyd and De Nicoló (2005) argue that the loan defaults at a bank are perfectly correlated. In their model, banks do not have access to equity markets and therefore the balance sheet identity requires total loans equals total deposits. This implies that the bank and loan probability of failure coincide. They justify this claim by stating that banks lend to entrepreneurs whose returns are perfectly correlated, this corresponds with the assumption that the risk of each loan can be divided into a systemic and idiosyncratic part. When the pool of entrepreneurs is large, the idiosyncratic part can be completely diversified away. This assumption leads them to the conclusion that competition between banks reduces risk. This study fits and builds on the work of Keeley (1990) and Boyd & de Nicoló (2005) as it studies how the SSM affect the competition-stability nexus in the Euro banking market. Furthermore, the SSM could alter the competition in the market, hence, the charter value and risk-taking behavior of the banks. This could be the consequence of two mechanism that are distorted through the implementation. The charter value hypothesis (Keeley, 1990) is no longer an option for banks. The ability to take excessive risk that is caused by an increase in the level of competition has complicated, because they are now more tightly monitored. Moreover, the argument of Boyd and de Nicolò (2005) has lost some credibility. Their mechanism states that banks will charge high loan rates if the banking market is uncompetitive. This will cause increased default rates and as a result decrease stability. Because the SSM has tightened supervision on banks, they are less able to charge excessive rates. Therefore, their suggested theory might not be applicable.

for banks since they are now more tightly monitored due to the SSM. I believe that the margin effect will dominate due to the new regulation in the Euro area. Banks have to lower their interest margin to stay competitive. However, as the margin diminishes, banks will eventually fail. Therefore, I suggest that competition will enhance stability until competition no longer has a positive effect on stability. This implies that too much competition deteriorates stability. In this study I will analyze whether there exists a U-shaped relation between competition and stability. Moreover, if the risk-shifting or the margin effect dominates within the Euro area.

There are several studies that shed light on the relationship between competition and the probability of the emergence of a crisis. For instance, Beck et al (2006) study the effect between competition and the likelihood of a crisis. They found that crises are less likely in economies with more concentrated banking systems. In contrast to Beck et al (2006), Schaeck et al (2009) find that banking systems with a high level of competition are less likely to experience a systemic banking crisis. They use the H-statistic as measure for competition. The H-statistic measures the ability of a bank to pass on increases in factor input prices to customers i.e. how does a change in input prices change the revenues. In their model they analyze the effect of competition and concentration and show that they have independent effects on the timing and probability of observing a banking crisis. They suggest that competition and concentration describe different components of the banking system. Moreover, they do not find evidence that competition increases the risk of a crisis. In addition to the previous findings from Beck et al (2006), they found that regulating policies and institutions that distort competition are associated with greater banking system fragility. Furthermore, Barth et al. (2003) state that higher entry barriers result in a less stable banking system. Hence, high levels of contestability in the banking market contributes to stability. As frictions distort the business climate and decrease the stability of the banking system, the SSM tries to reduce these barriers and increase the cross-border and financial integration within the European Union. They lower these barriers because they want to create a healthy level playing field and enhance competition. I will complement these studies by analyzing if competition has changed since the implementation of the SSM, this through the mechanism of cross-border and financial integration, and whether this affected the competition stability relationship.

In summary, the competition-stability nexus has not reached a conclusion. As Allen and Gale (2004) formulated it: the relationship between competition and financial stability is more complex than a simple trade-off. Nevertheless, the literature presents a variety of new insights between the market structure of banks and financial stability over the past years.

3. Methodology

3.1 Econometric model

There exists an abundance of measures for competition in the literature. Examples of these competition measures are: the Lerner index (Beck et al, 2013; Soendarmono et al, 2013), Tobin’s Q (Keeley, 1990), the number of banks, market share, concentration ratio’s (Akins et al, 2016), the Herfindahl-index (Akins et al, 2016), bank mergers and the Panzar-Rosse H-statistic (1987). Likewise, there exist numerous proxies for stability. Examples of stability measures are: nonperforming loans as a share of total loans, the Z-score (Beck et al, 2013; Soendarmono et al, 2013), equity-total assets ratio, profit volatility in terms of ROA (Beck et al, 2013) and ROE, profitability in terms of ROA (Akins et al, 2016) and ROE.

In light of all these measures and the main purpose of this study I will follow the current literature in their choices of applying one proxy for stability and competition and perform robustness analysis with different proxies for competition and stability. I will use the following standardized equation (1) estimating the relationship between competition and stability.

𝑆𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦_{𝑖,𝑗,𝑡} = 𝐶 + 𝛽_𝑗,𝑡∗ 𝐶𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑖𝑜𝑛_{𝑖,𝑗,𝑡−1} + 𝛾 ∗ 𝑋_{𝑖,𝑗,𝑡−1}+ 𝑣_𝑗,𝑡 + 𝜀_{𝑖,𝑗,𝑡} (1). Where i is a bank index, j is the country index and t a time index and X is a set of control variables. Moreover, v are country ×year indicator variables to control for differences over time and across countries. Including these indicators control for macroeconomic shocks which could influence the competition stability relationship. Last, 𝜀 represents the error term. As control variables the share of wholesale funding, the ratio of loans to total assets, non-interest revenue, the natural logarithm of total assets, loan loss provision to interest income and annual growth of total assets are included. These control variables are often used in the literature (Beck et al, 2013; Berger et al, 2008; Demirguc-Kunt and Huizinga, 2010). The summary statistics of the bank variables are given in table 1.

In the primary analyses the natural logarithm of the Z-score will be used for the stability measure. The Z-score is a measure that has become standard practice in the literature for measuring stability at the bank level (Beck et al, 2013; Berger et al, 2008; Demirguc-Kunt and Huizinga, 2010). The

Z-score is given by equation (2):

𝑍_𝑖,𝑡 =(𝑅𝑂𝐴𝑖,𝑡+ 𝐸 𝑇𝐴⁄ 𝑖,𝑡)

In the main analyses the Lerner-Index is applied as the measure for competition. This measure gives the mark-up of the bank’s average price over the marginal costs. The Lerner-Index is a proxy for current and future profits which originate from the pricing power. This aspect is associated and fits in well with the theory of the franchise value. The franchise value theory formulates that the conceptual place the bank has in the customers mind is so distinct that the bank is able to charge above marginal costs. The Lerner index is a measure which has in almost all observations a value between 0 and 1. Occasionally, the Lerner index turns negative, because a bank operates below marginal cost. This implies that the bank is not financially stable and goes through financial hardship. The higher the value of the Lerner index the more pricing power the bank has, i.e. the less competition exists in the market. The main advantage of the Lerner index is that it is a bank specific measure which varies over time. This aspect of the Lerner index allows for comparing market power between banks over the periods (Leon, 2015). Other proxies that are available for the competition measure are represented at the country level and bounds the competition to national borders. Examples of these measures are the HHI, concentration ratio’s and market shares. In contrast, The Lerner-Index is a measure which is calculated at the bank level. The Lerner-Index is presented in equation (3)

𝐿𝐼_𝑖,𝑡 =(𝑃𝑖,𝑡− 𝑀𝐶𝑖,𝑡)

𝑃_𝑖,𝑡 (3). The Lerner-Index is calculated as the ratio of total operating income to total assets, which is represented with the Pi,t , minus the marginal costs, that is displayed as MCi,t, divided by Pi,t ..The marginal costs are derived from a trans-log cost function. This cost function is displayed in equation (4)

ln 𝐶𝑜𝑠𝑡𝑖,𝑡 = 𝛽0+ 𝛽1ln 𝑄𝑖,𝑡+ 𝛽2ln 𝑄𝑖,𝑡2 + ∑𝑘=13 𝛾𝑘,𝑡ln 𝑊𝑘,𝑖,𝑡 + ∑3𝑘=1𝜙𝑘ln 𝑄𝑖,𝑡ln 𝑊𝑘,𝑖,𝑡+ ∑3𝑘=1∑3𝑗=1ln 𝑊𝑘,𝑖,𝑡ln 𝑊𝑗,𝑖,𝑡+ 𝜀𝑖,𝑡(4).

Where Qi,t is total assets for bank i at time t. Wk,i,t represents the three input prices W1,i,t, W2,i,t and

W3,i,t. They are respectively the price of fixed assets, price of labor and price of funding. These

input prices are calculated as the ratio of operating and administrative expenses to total assets, staff expenses over total assets and interest expenses to total deposits and short-term funding. Year and country indicator variables are included to reflect potential different technologies across countries and time. The marginal costs are than calculated as presented in equation (5).

𝑀𝐶_𝑖,𝑡 = 𝐶𝑜𝑠𝑡𝑖,𝑡

𝑄_𝑖,𝑡 [𝛽1+ 2 ∗ 𝛽2ln 𝑄𝑖,𝑡 + ∑ 𝜙𝑘

3

𝑘=1

ln 𝑊_{𝑘,𝑖,𝑡}] (5).

Table 1: Summary Statistics Bank Specific variables

Table 1 presents the bank specific variables that are used throughout this study. It represents the proxies of bank soundness that are used as control variables in the regression analyses. I control for the funding structure, the asset mix, the revenue-structure, bank size, credit risk and business strategy. The funding structure is given as the share of wholesale funding (this is the ratio of wholesale funding to total funding excluding derivatives). The asset mix is represented by the ratio of loans to total assets. For the revenue-structure the non-interest revenue share is used (which is presented with the ratio of non-interest income to operating revenues). Bank size is measured by taking the log of total assets. Credit risk is controlled for through the share of loan loss provision to interest income and business strategy with the annual asset growth. In addition to the control variables the inputs for the trans-log cost function are presented by the total operating cost, price of fixed assets, labor, funding, average bank activities and marginal cost. In the last two rows the proxies which are used for competition and stability are presented, the Lerner index and Z-score respectively.

3.2 Heteroskedasticity and endogeneity concerns

To test for the presence of heteroskedasticity I perform a Breusch-Pagan test. This test indicates that there is heteroskedasticity in the data. To mitigate for the problem of heteroskedasticity, White robust standard errors clustered at the country-year level are employed. I use clustered standard errors at the country-year level, because the unobservables at the bank level ln(Z-score) belonging to the same country- year will be correlated. While the bank level ln(Z-score) will be less correlated with the bank level ln(Z-score) that are located in a different country and takes place at a different time.

To overcome the endogeneity problems regarding omitted variables and reverse causality I apply country-year fixed effects. These country-year fixed effects control for unobservable variables which impact the relationship between competition and stability within each country-year

Variable Mean Standard deviation

Determinants of bank Soundness:

Share of wholesale funding 0.2012 0.1674

Loans to total assets 0.5850 0.1513

Non-interest revenue share 0.3578 0.1278

Ln (total assets) 13.5021 1.6908

Loan loss provisions to interest income 0.0931 0.1712

Annual growth in total assets -0.020 0.1013

Trans-log cost function:

Total operating cost 142.938 593.392

Price of fixed assets (W1) 0.0129 0.0086

Price of Labor (W2) 0.0122 0.0056

Price of Funding (W3) 0.0112 0.0080

Average price of bank activities 0.0944 0.0276

Marginal cost 0.0430 0.0133

Market Power & Bank Stability:

Lerner Index 0.5438 0.1591

observation. I use lagged values, t -1, of the control variables in the regression analysis to deal with the reverse causality problem. Since it is unlikely that the ln(Z-score) at time t explains the lagged values of the control variables. However, the lagged control variables have explanatory power regarding the ln(Z-score).

As the banking union is established on two pillars, the SSM and SRM, a change in the competition stability relationship might be the consequence of both these pillars. However, the SRM is a resolution mechanism which is set up to save or restructure a bank in case it has financial difficulties. Therefore, the SRM will not have a large impact on this relationship beforehand. The SSM on the other hand is a proactive mechanism which tries to minimize the chance on a bank failure by ensuring consistent supervision. It impacts the relationship between competition and stability in advance. Moreover, the SSM follows the Basel Accords and implements and supervises whether these are carried out correctly. Because of these aspects I consider the SSM to be exogenous and the reason for a change in the competition stability relationship.

4. Data

To analyze the relationship between competition and stability I use panel-data obtained from the database Orbis bank focus. The bank specific variables are obtained for 19 Euro countries which are under the supervision of the SSM as of November 2014. The sample under study is limited to commercial, savings and cooperative banks. I have removed the unconsolidated data if the same bank had observations available at the consolidated level. These observations are removed to avoid the possibility to count the same bank twice. The total sample consists of 14.871 bank-year observations over the years 2012 to 2016.

To acquire the total dataset for the analyses the bank-year observations that do not have values for the basic bank-specific variable total assets are removed. Moreover, the dataset is winsorized at the 1 and 99 percent level. This process removes the top and bottom 1 percent from the dataset to remove possible outliers.

have a loan loss provision of 9.3 percent, this is in line with the literature. Last, I control for the banks business strategy by incorporating the annual asset growth. The average asset growth over the period 2012-2016 is minus 2.0 percent. This indicates that the average bank has become smaller in terms of assets. The reason could be that a lot of banks had to undergo financial restructuring as a consequence of the financial crisis.

In the second part, the inputs for trans-log cost function and determinants of the Lerner index are given. They are total operating costs, price of fixed assets, price of labor, price of funding, the average price of banking activities and the marginal costs. The values for the variables are 142.938, 0.0129, 0.0122, 0.0112, 0,0944 and 0.0430 respectively. The price of fixed assets, labor and funding are calculated as the share of other operating and administrative expenses to total assets, the ratio of personnel expenses to total assets and the ratio of interest expenses to total deposits. These values comply with the literature, except the price of funding and marginal costs has significantly decreased. The reason for this decrease is that the ECB had to decrease the interest rate substantially in the years after the crisis to provide liquidity to the economy. Moreover, the decrease in the funding costs explains the drop in the marginal costs.

In the last section the proxies that are used for competition and stability are presented, the Lerner index and Z-score respectively. The Lerner index in the Eurozone is on average 0.5438. This value is higher than presented in the literature. An explanation could be that the funding cost have decreased in the period of 2012-2016. Decreased funding cost lower the marginal cost. A decrease in the marginal cost increases the mark-up, hence an increase in the Lerner index. Moreover, the ln(Z-score) is 4.2079, which complies with the literature.

5. Empirical Results

5.1 The homogeneous competition stability relationship within the Euro area

The pooled regression results for the homogeneous relationship are presented in table 2. The focus for the homogeneous relationship between stability and competition in this study is given by the ln(Z-score) and the Lerner index, respectively. In the regression analysis country-year fixed effects are incorporated to mitigate for unobservable heterogeneity at the country-year observations. Therefore, the within country-year variation for the stability-competition linkage is exploited. The literature is indecisive about the competition stability relationship. In this study I find a positive and significant relationship between the Lerner index and the ln(Z-score). This suggests that competition decreases stability. Moreover, the results comply with the literature that finds evidence in favor of the competition-fragility hypothesis (Beck et al, 2013; Berger et al, 2008). Moreover, these results are in line with the charter-value hypothesis which is presented by Keeley (1990). This indicates that for the period of 2012-2016 in the Euro countries more competition would decrease stability.

dependent variable. The relationship between competition and stability is significant and positive. Moreover, it is economically meaningful. A one standard deviation change in the Lerner index, which equals 0.1591, results on average in a change of 38.8% in the ln(Z-score). A different way of interpreting this result, the number of standard deviations profit has to fall before the banks’ capital is exhausted, is reduced by 38.8% if the market power is decreased by one standard deviation. The results for the homogeneous relationship between competition and stability are presented in table 2. The control variables that are shown in table 1 are incorporated in the regression results of table 2.

The first column of table 2 presents the stability competition relationship. In the second and third column I replace the stability measure with the different components of the Z-score. These are the negative of the standard deviation of the return on assets and equity over total assets. By conducting these regressions, I rule out the possibility of a spurious relationship, because both the Lerner index and Z-score are calculated by using bank profits (Beck et al, 2013). In both regressions the Lerner index has a positive impact on the components of the Z-score. An increase in the market power decreases earnings volatility. Volatile earnings have a negative effect on bank stability (Kasman & Kirbas-Kasman, 2013). This indicates that a lower level of competition increases earnings stability and supports that stability is enhanced when competition decreases. Moreover, if the earnings of a bank are less volatile it will be less likely that the bank will be hit by financial hardship because volatile earnings might lead to more uncertainty about the level of equity capital (Albertazzi & Gamabacorta, 2009). An increase in market power leads to a decrease of the leverage ratio. The leverage ratio is a proxy for a bank managers risk aversion. Risk averse bank managers tend to hold more equity capital than risk loving managers (Busch & Kick, 2015). As banks decrease their leverage ratio the probability of financial distress decreases because a higher level of equity gives the bank more protection in case of a negative shock. The effect of both the components of the Z-score are grounded in the literature as having a stabilizing effect on bank stability. Therefore, the relationship between the stability and competition measure is unlikely to be spurious.

2012-2016. However, I do not have data to investigate whether a monopolistic market enhances or deteriorates stability, because in the period under study there is to my knowledge no monopolistic market within the Euro area. Therefore, this is a hypothesis which could be studied in the future.

5.2 The impact of the supervisory transition on the competition-stability nexus in the Euro area

The results of the impact of the supervisory transition are presented in table 3. In this section I analyze whether the change of supervision from the national to the supranational level, from the national supervisory authorities to the SSM, has had an impact on the competition stability relationship. The dataset is divided into a before and after SSM sample. The before SSM sample covers the period 2012-2014 and the after sample covers 2015-2016. The first three columns of table 3 present the before SSM sample. The last three columns display the results of the after SSM sample.

First, I will study whether the supervisory transition has resulted in more or less competition in the banking sector of the Euro countries. When analyzing the before and after sample, it can be observed that on average the Lerner index in the Euro area has decreased from 0.5923 to 0.5192, a decrease of 12.3%. This means that the pricing power of the banks has diminished. Hence, competition has increased. This is an indication that the banking market in the Euro market has become more integrated and in turn more competitive since the SSM.

Next, I will turn to the impact of the increased effect of competition on stability. These results are presented in table 3. To give the results of this regression validity I have to make the assumption that the SSM will cause a one-time shock to stability. Figure 1 depicts the mean development of the ln(Z-score) (right y-axis) and the Lerner index (left y-axis). Between 2013 and 2014 the stability jumps up, the one-time positive shock to stability. The reason for this jump could be the proposed assumption that banks have had time to adjust to the implementation of the SSM between 12 September 2013 and 4 November 2014. However, this should be taken with caution, as during these years the ECB implemented unconventional monetary actions which could have influenced the stability in the Eurozone. This jump in the data should be taken with caution, but it is an indication that the supervisory unification in the Euro area has had an impact. After the jump the stability decreases slightly over the years 2015 and 2016. The effect of the SSM on competition only changes gradually as banks need time to move to new markets. The Lerner index decreases gradual over the period 2012-2016. If the effect of the SSM on stability is not a one-time positive shock, the parameter of the competition variable could be underestimated, as it captures both the effect of the increase in stability and competition.

stability. Prior to the implementation of the SSM the competition stability relationship was on the more horizontal part of the relationship. The point of the competition stability relationship could have moved back to the steeper area because competition increased after the implementation of the SSM. Therefore, increasing the marginal effect of competition on stability. Therefore, these results indicate that there is an inverted U-shaped relationship.

Figure 1: The mean value development of the stability and competition measure in the Euro area for the

period 2012-2016

In this figure the development of the mean value of the stability measure, ln(Z-score) (left y-axis), and the competition measure, Lerner index (right y-axis) for the 19 countries within the Eurozone are presented for the period 2012-2016. The time variable given in years is depicted on the x-axis.

Theoretical work has demonstrated that there might exist an inverted U-shaped relationship between competition and stability (Martinez-Mierra and Rapullo, 2010). The squared Lerner index is incorporated in columns 2 and 6 to see whether the inverted U-shape holds for the different time periods. In the 2012-2014 sample the results are not significant and therefore an indication that there does not exists an inverted U-shaped relationship. Nonetheless in the 2015-2016 sample I find significant results that there is such a relationship. However, when the competition measure is 0.8460 the relationship changes of sign. This includes only 0.06% of the observations in the after SSM sample. In columns 3 and 7 I incorporate the Lerner index to the third power. The third power of the Lerner index is applicable in this range because it resembles an inverted U-shape. This regression excludes the squared term as it turns insignificant when including the third power of the Lerner index. In the 2012-2014 sample there are no significant results for an inverted U-shape relationship. In the period of the sample 2015-2016 the results turn significant. However, the sign only changes when the competition index has reached 0.7342. This vertex point is substantially different in comparison with the regression including the squared Lerner index, but still includes only 2.92% of the observations in the sample after the SSM.

columns 1 and 5 are used to observe the effect of the SSM on the competition stability relationship. The Chow-test gives a statistic of 3.93, which is significant at the 5% level. This indicates that there is a significant change in the effect of competition on stability after the implementation of the SSM. This result is not only significant but also economically meaningful. Before the SSM was implemented a one standard deviation change in the Lerner index, which equals 0.1475, results on average in a change of 23.5% in the ln(Z-score). After the implementation of the SSM a one standard deviation change in the Lerner index, which equals 0.1599, results on average in a change of 42.9% in the ln(Z-score). This is an increase in the effect of the Lerner index on stability of 80.9%.

As I hypothesized, competition has increased in the period after the implementation of the SSM. Moreover, the magnitude of the coefficient of the Lerner index on stability has become larger. This indicates that a decrease in competition further improves the stability of a bank after the implementation of the SSM. Furthermore, I hypothesized that the effect of competition on stability had increased in response to the implementation of the SSM. More competition would lead to more stability, the Lerner index would have a negative parameter implying a positive effect of competition on stability. The reason for this is that banks are unable to take on more risk in response to the increase in competition because they are more tightly monitored. Therefore, banks have to decrease their interest rate margins to stay competitive, as Boyd & de Nicolo (2005) suggested. This reduces the adverse selection problem and increases the effect of competition on stability. However, contradictory with my hypothesis the results indicate that less competition contributes to stability. The findings in this study can be the consequence of two mechanism that are distorted through the implementation. Since banks are now more tightly monitored the charter value hypothesis (Keeley, 1990) is less of an option. Moreover, the argument of Boyd and de Nicolò (2005) which states that banks will charge higher loan rates resulting in increased default rates and as a result decrease stability, has lost some credibility. Banks are less able to charge excessive rates and therefore their suggested theory might not be applicable because the SSM has tightened supervision.

Table 2: Results for the homogenous relationship between competition and stability

This table present the regression results of the homogeneous relationship between competition and stability. For the regression analysis the ln(Z-score) is used as a proxy for stability and simultaneously acts as the dependent variable. I regress the ln(Z-score) on the proxy for competition, the Lerner index and the lagged control variables. For each regression I control for unobserved country-year variation by including country × year dummy variables. To control for the presence of heteroskedasticity I include White robust standard errors clustered at the country-year level. To alleviate for reverse causality, I use lagged variables of the control variables. The first column presents the main regression results. In the second and third column I replace the stability measure with the different components of the Z-score. These are the negative of the standard deviation of the return on assets and equity over total assets. By conducting these regressions, I rule out the possibility of a spurious relationship because both the Lerner index and Z-score are calculated by using bank profits. In the fourth column I replace the Lerner index by its subcomponents, these are the average price of bank activities and marginal costs. In the fifth column the Lerner index is replaced by the market share depending on total assets. The sixth column changes the stability proxy from the Lerner index to the Herfindahl index. The seventh column looks at the effect of a squared term of the competition proxy. The last column presents the base regression excluding the asset growth control variable. The number of observations vary due to data availability. The robust standard errors are given in parentheses.

* p < 0.10 ** p < 0.05 *** p < 0.01

Variables Ln(Z-score) -Ln(sd(ROA)) E/TA Ln(Z-score) Ln(Z-score) Ln(Z-score) Ln(Z-score) Ln(Z-score)

Lerner Index 2.4400*** (0.4014) 1.9378*** (0.3297) 0.026** (0.0042) 5.2044*** (0.8680) 2.2442*** (0.3882)

Lerner Index squared -3.1047***

(0.7079)

Herfindahl index (total assets) 0.0009***

(0.001)

Market share (total assets) 2.1175***

(0.6674)

Share of wholesale funding -0.0067***

(0.0015) -0.0059*** (0.0019) -0.0001 (0.0001) -0.0076*** (0.0014) -0.0035** (0.0013) -0.0036*** (0.0013) -0.0053*** (0.0018) -0.0062*** (0.0015)

Loans to total assets 0.0022

(0.0021) 0.0021 (0.0016) 0.0001 (0.0001) 0.0035 (0.0026) 0.0016 (0.0018) 0.0013 (0.0019) 0.0015 (0.0020) 0.0027 (0.0018)

Non-interest revenue share -0.0034 **

(0.0014) -0.0048*** (0.0012) 0.0001 (0.001) 0.0019 (0.0017) -0.0068*** (0.0011) -0.0071*** (0.0011) -0.0045*** (0.0015) -0.0032* (0.0017) Ln (total assets) 0.0532** (0.0211) 0.1186*** (0.0191) -0.0062*** (0.0009) 0.0202 (0.0156) -0.0051 (0.0210) 0.0100 (0.0183) 0.0466** (0.0221) 0.0489** (0.0194)

Loan loss provisions to interest income -1.1543***

(0.1636) -0.8528*** (0.1532) -0.0315*** (0.0077) -0.7762** (0.3160) -0.6445* (0.3656) -0.6464* (0.3702) -1.2274*** (0.1551) -1.1456*** (0.1522)

Annual growth in total assets -0.0009

(0.0020) -0.0005 (0.0022) -0.0002 (0.0001) -0.0071*** (0.0018) -0.0058** (0.0022) -0.0062*** (0.0022) -0.0003 (0.0023) Constant 0.3940 (0.6769) 1.1625** (0.5140) 0.1979*** (0.0284) 2.7491*** (0.6400) -0.3252* (0.5942) 3.7695*** (0.4203) 2.1402*** (0.6512) 0.2958 (0.6356)

Average price of bank activities 4.6840**

(2.0622)

Marginal costs -27.0846***

(4.9144)

Observations 6893 6900 6902 6963 7345 7305 6843 7619

R-squared 0.1158 0.0980 0.3432 0.1496 0.0859 0.0831 0.1146 0.1199

Time×country dummies yes yes yes yes yes yes yes yes

Moreover, when financial integration and competition increases due to the reduced entry barriers in the Euro area, lenders have the ability to switch banks. Therefore, decrease the advantage they can exert, hence lower the moral hazard problem. Prior to the SSM, national supervisory authorities regulated the entry and exit of banks within their national borders. A change in one of these authorities’ policy only affected their nation. With the unification, the ECB regulates the entry and exit of the banks for the entire Euro area. When there is a change in this regulatory framework, the whole banking market has to comply with these rules. For that reason, a change in the regulatory policy to lower these barriers will have a larger effect on stability than before. Financial and border integration are expected benefits of this unification. The increase in financial and cross-border integration derives its benefits from increased competition. This mechanism leads to an increase of competition and enlarges the impact on stability through better managed banks. This suggested theory supports my empirical findings, competition has risen after the implementation of the SSM and the effect of less competition on stability has increased as only the well managed banks will stay in the financial system.

5.3 Robustness analysis, limitations and future research 5.3.1 Robustness analysis

In the fourth column of table 2 the Lerner index is replaced by the components of the Lerner index, these are the average price of banking activities and the marginal costs. I perform this robustness analysis because it presents some insights whether the pricing power of the bank originates from the loan market or the funding costs. The average price of banking activities is a strong indicator for the pricing power in the loan market and the marginal cost is a powerful proxy of funding (Beck et al, 2013). Both results of these variables are in line with the competition-fragility hypothesis. The average price of banking activities is positive and significantly associated with the ln(Z-score), which indicates that pricing power in the loan market is associated with bank stability. Furthermore, marginal costs are negative and significantly related to bank stability. This illustrates that higher funding costs are associated with bank instability. Therefore, an increase in the Lerner index that arises from prices, costs or both is related to more stability.

In the fifth and sixth column of table 2 the proxy for competition, the Lerner index, is replaced by the market-share and Herfindahl index, respectively. Both of these measures are calculated based on total assets. The market-share is calculated for each bank within the same country and year. The Herfindahl index is a country-wide measure which is calculated for each country-year observation. I test for the relationship between the Herfindahl index and market share with stability because it could be that the banking market within the Eurozone is still fragmented by their national borders. The Lerner index measures the pricing power of the bank for each year and does not demarcate the banking market by national border. In addition to the national border argument, I perform these robustness analyses to test whether different measures for competition result in the same effect on stability.

Table 3: Results of the supervisory transformation in the Euro area on the competition stability relationship

This table presents the results for the supervisory transformation. For the regression analysis I use the ln(Z-score) as a proxy for stability and it simultaneously acts as the dependent variable. I regress the ln(Z-score) on the proxy for competition, the Lerner index and the lagged control variables. For each regression I control for unobserved country-year variation by including country × year dummy variables. To control for the presence of heteroskedasticity I include White robust standard errors clustered at the country year level. To alleviate for reverse causality, I use lagged variables of the control variables. The sample is split up in a before and after supervisory change. The first three columns display the regression results before the supervisory change (2012-2014). The last three columns show the regression results after the supervisory change (2015-2016). The number of countries has changed from 18 to 19 between the two samples because Lithuania entered the Eurozone in 2015. The robust standard errors are given in parentheses.

* p < 0.10 ** p < 0.05 *** p < 0.01 Pre-SSM, 2012-2014 Post-SSM, 2015-2016

Variables Ln(Z- score) Ln(Z-score) Ln(Z-score) Ln(Z-score) Ln(Z-score) Ln(Z-score)

Lerner Index 1.5946*** (0.3750) 3.0889 (1.8638) 2.8287*** (0.8831) 2.6884*** (0.4159) 5.6732*** (0.7949) 4.5611*** (0.7049) LernerIndex^2 -1.8097 (1.6518) -3.3492*** (0.6779) LernerIndex^3 -1.6875 (1.0041) -2.8198*** (0.7389)

Share of wholesale funding -0.0020

(0.0031) -0.0008 (0.0036) -0.0011 (0.0036) -0.0082*** (0.0018) -0.0067*** (0.0020) -0.0064*** (0.0021)

Loans to total assets 0.0008

(0.0015) 0.0002 (0.0014) 0.0002 (0.0016) 0.0028 (0.0026) 0.0022 (0.0026) 0.0018 (0.0026)

Non-interest revenue share -0.0017

(0.0026) -0.0023 (0.0027) -0.0024 (0.0028) -0.0039** (0.0016) -0.0052*** (0.0017) -0.0054*** (0.0016) Ln (total assets) 0.04149** (0.1664) 0.0296* (0.1487) 0.0339** (0.0159) 0.0583** (0.0271) 0.0541* (0.0284) 0.0525* (0.0284)

Loan loss provisions to interest income -1.7270***

(0.2893) -1.7946*** (0.2570) -1.7371*** (0.2757) -0.9767*** (0.1413) -1.0534*** (0.1435) -1.0089*** (0.1418)

Annual growth in total assets -0.0008

(0.0039) -0.0020 (0.0042) -0.0003 (0.0039) -0.0009 (0.0023) 0.0005 (0.0025) 0.0009 (0.0027) Constant 1.1247** (0.5367) 3.0864*** (0.6573) 0.9850 (0.5061) 2.3636*** (0.7464) 1.9551*** (0.7215) 2.0997*** (0.7488) Chow-test (F-statistic) 3.93** 3.28* 1.27 3.93** 3.28* 1.27 Observations 1875 1858 1875 5018 4985 5018 R-squared 0.1461 0.1447 0.1495 0.1070 0.1058 0.1153

Time×country dummies yes yes yes yes yes yes

change of 8.5% in the ln(Z-score). The sign for these two measures supports that competition decreases stability. This shows that the sign of the competition stability relationship in the Euro area is robust to the measurement for competition. This increases the confidence of the results in the main analyses that competition causes instability in the Euro area.

In the eighth column of table 2 the control variable asset growth is taken out. This variable is removed because it affects the amount of data on which the regression is run significantly. The coefficient of the Lerner index stays positive and does not change significantly. The movement of a one standard deviation in the Lerner index between the base scenario and without the asset growth results in a decrease of 2.9% on the effect of competition on stability.

When I control for the macroeconomic variables inflation rate and the GDP real growth rate in the linear relationship in column 1 of table 2, the effect of the Lerner index stays positive and significant on stability. This effect increases by 3%. Moreover, if I incorporate the macroeconomic variables in column 1 and 5 of table 3, this increases the effect of the Lerner index for both time periods with 3%. The Chow-test statistic is still significant at the 10 percent level indicating that there is a change in the competition stability relationship.

5.3.2 Limitations

It is difficult to address the change in the competition stability relationship only to the SSM, since there could be other variables affecting this relationship. A possible way of solving this issue is by performing a diff-in-diff analysis. In this analysis the only difference between the control and treatment group should be that one group is under the supervision of the SSM and the other not. However, as economic experiments are not performed in laboratories it is difficult to be able to execute such an analysis. Unfortunately, the data used in this study varies due to the data availability. This might complicate the interpretation and comparability of the results as they are not completely based on the same sample.

The SSM increases both stability and competition. To be able to identify the parameter for competition and come up with interpretation for this coefficient I have to make an assumption that the impact of the SSM on stability is a positive one-time shock. This assumption might be strong and therefore underestimate the parameter for the competition variable. This coefficient will include the effect that the SSM has on stability as well as competition. This affects the interpretability of the results since this could entail an endogeneity problem.

5.3.3 Future research

be achieved. Creating a capital union could make companies less dependent on bank funding. Because of this funding diversification, companies become less dependent on one source of funding and could better handle shocks. Future studies could investigate whether the capital union will change the competition stability nexus in the Eurozone.

6. Conclusion

The literature on the competition stability nexus is divided into two views, the competition fragility and competition stability view. The competition fragility view states that competition decreases stability. The competition stability view argues that competition enhances stability. In this study the competition stability relationship for the Euro countries during the period 2012-2016 is analyzed. The results of this paper support the competition-fragility hypothesis. The theoretical explanation for this result can be found in Keeley (1990). Keeley (1990) argues that if competition increases banks will take excessive risk to keep their charter value. For this reason, competition deteriorates stability. Moreover, this study shows that the results are economically relevant, a one standard deviation change in the Lerner index results on average in a change of 38.8% in the ln(Z-score).

In addition, this paper studies the effect of the supervisory change in the Eurozone. I have hypothesized in this study that the effect of competition on stability will increase. The effect of an increase in competition becomes more prominent on stability due to the implementation of the SSM. The findings in this study contradict this hypothesis. The results show that since the implementation of the SSM the competition has increased by 12.3% and that the effect, the coefficient, of the Lerner index on stability has increased by 68.6%. This indicates that the effect of a decrease in competition has become more prominent. This finding could be rationalized through two channels. First, the SSM increases financial integration and in turn increases competition through the reduced entry barriers within the Euro area. Second, the SSM stimulates banks to become better managed, resulting in a stronger effect of less competition on stability as better managed banks will stay in the financial system. This result is not only significant but also economically meaningful. A one standard deviation change in the Lerner index before the SSM is implemented results on average in a change of 23.5% in the ln(Z-score). In addition, a one standard deviation change in the Lerner index after the implementation of the SSM results on average in a change of 42.9% in the ln(Z-score). Due to the implementation of the SSM this is an increase in the effect of the Lerner index on stability of 80.9%.

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