Competition and financial stability in the Norwegian banking sector

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Competition and financial stability in the Norwegian banking sector

Eirik Bille Holter

Master in Economics Department of Economics UNIVERSITY OF OSLO

May 2019

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Competition and financial stability in the Norwegian banking sector http://www.duo.uio.no

Print: Reprosentralen, University of Oslo

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Abstract

Competition and financial distress in the banking sector has been a hot topic over the last couple of decades. Banks play a crucial role in the economy and the environment they operate in are constantly changing. 1^st of January 2018, the second Payment Service Directive (PSD2) came into force for countries in the EU and EEA. One of its many purposes is to expose the sector to more competition. The Norwegian banking sector has earlier experienced increased competition from deregulation and liberalization which among other factors led to a systemic crisis, costing the nation 2% of its GDP. In this paper I investigate whether competition introduced to the Norwegian banking sector show evidence of destabilizing or stabilizing the financial system, exploring two well established hypotheses.

By employing a system GMM estimation with data covering over 176 banks during the time period 1995 – 2017, I find evidence in support of the competition-fragility hypothesis, that is, increasing instability from competition. Furthermore, the estimations show signs of a non- linear relationship where the Norwegian deposit market has a lower tolerance for competition, than the loan market. I also find that the impact of increasing activity in innovation have ambiguous effects on stability.

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Preface

This paper marks the end of my formal education, and the beginning of the unformal real-life education. Halfway into my first year as a master student, January 2018, the second Payment Service Directive for the EU and the EEA was put to force. Being close to this change as a part-time employee in a bank, sparked the interest of investigating the effects of competition within the banking industry.

I would like to thank my supervisor Jin Cao for inspiring me further with the course “The Economics of Banking”, and for giving me valuable comments and advice.

Thanks to all the users at Statalist that provided me with helpful comments and guides for the technicalities of the analysis.

A special thanks to my girlfriend, mom and dad for the support and love.

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List of figures and tables

FIGURE 1: YEARLY REAL-ESTATE TRANSACTIONS ... 23

FIGURE 2: HERFINDAHL-HIRSCHMAN INDEX FOR SELECTED EU COUNTRIES ... 25

FIGURE 3: AGGREGATED MARKET SHARES BASED ON THE HERFINDAHL-HIRCSHMAN INDEX 26 FIGURE 4: AGGREGATED MARKET SHARES FOR THE 5^TH LARGEST BANKS ... 26

FIGURE 5: MEDIAN OF THE Z-INDEX AND THE RATIO OF NON-PERFORMING LOANS ... 27

FIGURE B.1: TOTAL ASSETS FOR THE THREE BIGGEST BANKS IN NORWAY ... 51

FIGURE B.2: YEARLY ESTIMATION OF THE H-STATISTIC ... 51

FIGURE B.3: HISTOGRAM OF RESIDUALS FOR THE LOGGED Z-SCORE ... 52

FIGURE B.4: HISTOGRAM OF RESIDUALS FOR THE Z-SCORE ... 52

FIGURE B.5: HISTOGRAM OF RESIDUALS FOR THE NPL ... 53

FIGURE B.6: HISTOGRAM OF RESIDUALS FOR THE LOGGED NPL ... 53

FIGURE B.7:QUADRATIC PREDICTION PLOT OF CR5 (LOANS) ON THE Z-SCORE. ... 54

TABLE 1: Z-INDEX ESTIMATES – SYSTEM GMM ... 35

TABLE 2: NPL ESTIMATES – SYSTEM GMM ... 38

TABLE C.3: NUMBER OF BANKS BY TYPE ... 55

TABLE C.4: SUMMARY OF VARIABLES. ... 57

TABLE C.5: STATISTIC SUMMARY ... 58

TABLE C.6: H-STATISTIC ESTIMATES ... 59

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1. Introduction

In any economy, banks play an important role as a financial intermediary. Banks essentially serve as a bridge between lenders and borrowers, supplying the market with liquidity. In addition, banks are also key in the payment infrastructure.¹ For the financial system to work properly it is extremely important that these abilities operate in a safe and reliable manner. If markets under pressure struggle with liquidity, or payment structures fails, economic growth may slow down, and in a worst-case scenario send the economy into a downturn. Diamond and Dybvig, (1983) showed that an economy in absence of deposit insurance and suspension of convertibility, could in fact overturn a bank purely driven by belief. Therefore, due to its fragility, a bank is heavily regulated. The regulatory framework for a modern intermediary includes, amongst many, level of risk exposure, minimum requirements of equity, compulsory reports for internal control and risk management.² Over the last decades, the framework has been deregulated and liberalized, starting in the 1970s with relaxation of ownership- and branching restrictions on banks in the United States, followed by easing caps on deposit rates (Kroszner & Strahan, 2014). Since, there have been numerous occurrences of financial stress worldwide, the biggest of them being the financial crisis during the years 2007 to 2009. In the 1990s, the Norwegian banking sector experienced such form of financial trauma after a deregulation of the industry in 1985, a lending boom and a following rapid increase in consumption rates. Many factors played a role in what was about to come, but there is no doubt competition within the banking sector roughened. Policy makers’ and bank leaders’

lack of experience with credit markets, combined with a sole focus on stealing market shares played a huge role in leading the financial sector into a period characterized as a systemic crisis (G. Moe, A. Solheim & Vale, 2004; Kroszner & Strahan, 2014; Reinhart & Rogoff, 2009).

Development in technology, deregulation and opening of entry barriers through the new payment service directive (PSD2) has created a new form of competition for financial intermediaries. In this paper, I investigate the link between financial stability and banking competition by using a system GMM estimation and data from the Norwegian banking sector

1 In 2017 customers transferred a total of 738 million Norwegian Kroner (MNOK) through giro transfers, and made card payments for a total of 2 284 MNOK (Norges Bank, 2018b).

2 The words bank(s) and financial- intermediaries or institutions are used interchangeably thought the paper.

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over the last 22 years. All estimations and figures are attained by the use of the statistical program STATA and Microsoft Excel.

The next chapter discuss the existing literature on this topic, followed by a presentation of the most popular measurements on stability and competition. Chapter 3 discuss the existing empirical evidence, followed by an introduction of the Norwegian banking sector with its history, and modern development in chapter 4. Chapter 5 presents the model, methodical framework and data used for the empirical analysis, followed by the empirical results in chapter 6. Implications of the results I addressed in chapter 7. Lastly, in chapter 8, I conclude on the findings from the analysis, based on the explored theory. Appendices with figures and tables is listed at last.

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2. Literature review

Competition has been studied by economists for some time and one of the most famous economists, Adam Smith argue that competition is a way of allocating scarce resources to the most productive and highly value uses. Smith’s view and prevailing treatment of competition challenged the opposing practice of monopolistic markets, and was practiced for the next century (Stigler, 2008). Economic theory suggest competition increases the supply thus decrease prices, leading to an efficient market. With less competition, companies retain profits, hence increasing probability to survive during busts. This type of reasoning portrays an environment where the market is perfect, essentially with no market failures. However, increased profits may not be retained but rather shared as dividends to the owners.

Researchers and policy makers agree that competition in the banking market is different. The banking market is subject to asymmetric information, behavior of moral hazard and other externalities.³ These market frictions affect how the intermediaries operate and exert risks.

Banks has many important roles in the economy and are in many ways interlinked to each other. One of the most central tasks it has is known as “transformation of maturity”.

Essentially it means pooling money from “short-term” investors with the means of

distributing it to “long-term” borrowers. This provides markets with liquidity and is crucial for fostering economic growth. Therefore, if the supply of liquidity is disturbed, economic growth may slow down (Reinhart & Rogoff, 2009). Banks are heavily involved in payment infrastructure and interbank lending and therefore are they not only key in serving liquidity, but also important for basic functionality and robustness in the financial market. The

intermediaries are also special to other firms in the way that they meet competition in two channels; on the liability and the asset side. For a bank to lend money it needs deposits, and the higher the deposit rate, the more attractive it is for depositors, ceteris paribus. On the assets side banks compete on loan rates, as borrowers minimize their cost function, according to consumer theory. However, as banks mainly fund lending with deposits, an increasing deposit rate and decreasing loan rates, their margins decrease and thus increase their fragility.

Therefore, banks face a trade-off between higher fragility or lower returns. So, stability and fragility may occur from both parts of the balance sheet.

3 Moral hazard is a phenomenon that occurs in markets with asymmetric information. In this context the bank does not observe borrower’s behavior such that the borrower may not work for the banks best interest.

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After decades of deregulation and several financial crises, the topic of competition and financial stability is taken more interest of by researchers and policy makers. Existing literature present ambiguous results of the effects on financial stability by changes in

competition. There are mainly two hypotheses on the effects; fragility and stability. The first section of this chapter addresses the literature speaking for the competition-fragility

hypotheses arguing increased competition destabilize banks. The second part presents the arguments other hypotheses claiming the opposite, namely competition-stability.

2.1 The competition-fragility hypotheses

2.1.1 The charter value hypothesis

In the beginning of the 1990s economists started exploring the field of competition in banking and the way it affected financial stability. Keeley (1990) first introduced what is called the charter value hypothesis. It is also known as the competition-fragility hypothesis. A banks charter or franchise value can be thought of as assets that are intangible; reputation, superior knowledge in the financial markets and economic rents.⁴ It represents the future expected stream of economic rents gained through its market power, and is considered to be the difference between the market value of the banks equity and the equity invested by

shareholders (Acharya, 1996; Furlong, 2006; Keeley, 1990). Risk-shifting is a keyword for many of the theories on banking stability and competition. By assuming banks optimize the portfolio problem, that is, deciding their allocation of assets based on prices and returns, Keeley (1990) shows how deposit insurance and government intervention distort bank-

managers risk-taking behavior. He reasons that when competition is introduced to the market, a banks charter value is threatened to be reduced. To make up for this, banks reallocate their assets in more risky positions to gain a higher yield. Essentially the bank shifts the risk over to the depositors. Ultimately, greater risk-taking leads to higher probability of default, and according to Keeley, destabilize the financial system. Deposit insurance removes depositors’

incentives to monitor the banks behavior. Furthermore, the presence of limited liability ant the phenomena of “too-big-to-fail”, makes bank managers not take full responsibility, forcing the government to bail out systemic important banks in trouble.⁵ These considerations amplify the effect of the risk-taking behavior resulting in a greater probability of insolvency.

4 The words charter value and franchise value are used interchangeably throughout the paper.

5 An elaboration of this phenomena is presented in section 2.2.2.

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A rather intuitive way of dealing with this type of risk-behavior is to make the risk-takers take greater ownership. That is, to make the banks has their own “skin-in-the-game”. By

introducing capital requirements banks invest less in riskier assets. However, capital requirements as the only tool for prudency will create pareto-inefficient outcomes in the market (Keeley, 1990; Hellmann, Murdock & Stiglitz, 2000). Hellmann, Murdock and Stiglitz (2000) argue that there is a second effect of introducing capital requirements. Holding more capital in the future can be thought of as reducing future charter value. To accommodate for this, again banks reallocates to more risky assets. When analyzing the effects of hazardous behavior, one should have in mind that this only considers the asset side of the banks’ balance sheet. The loan portfolio of a bank is a big part of its assets, but its riskiness may not affect the overall riskiness of the bank. In concentrated markets banks has a relative high charter value and thus a higher opportunity cost of going bankrupt; there is no charter value if the bank is out of business. Then banks have incentives to exert less risky behavior to preserve their franchise value and thus behave more prudently. Moreover, prudent banks contribute to a more sound and stable financial system. (Keeley, 1990; Beck, 2008; Berger, Klapper, &

Turk-Ariss, 2008).

Hellmann, Murdock and Stiglitz (2000) showed that competition on the liability side may also affect stability. From a microeconomic perspective, deposits are the main input factor to produce credit, whilst in the reality of modern banking, the intermediaries increasingly finance lending through the bond market.⁶ Competition on deposit rates reduce banks margins, making their charter value fall, ceteris paribus. Furthermore, Hellmann, Murdock and Stiglitz (2000) argue that because of deposit insurance and government intervention, banks incentives to take risks is strengthened. To mitigate this market-stealing-effect, they suggest deposit-rate controls as an instrument to preserve the incentives of prudent behavior.

By introducing a ceiling on deposit rates, the belief is that capital requirements can be

relaxed, and thus restore the incentives (Hellmann, Murdock & Stiglitz, 2000). As mentioned in the introduction, the Norwegian banking sector were deregulated over many years which led to great competition both in the deposit and lending market. The first deregulation came in 1985 and lifted the cap on deposit rates. Next there was a boom in lending, and among other factors, it ultimately this all ended in a systemic crisis (G. Moe et al., 2004; Keeley, 1990).

Hellmann, Murdock and Stiglitz (2000) discusses a similar scenario in Japan commonly

6 In 2017, Norwegian banks funded around 40% of their liabilities with deposits from customers, and around 30% with bonds (Norges Bank, 2018a).

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referred to as the “lost decade”, where excessive risk-taking led the economy into a long crisis. They argue that financial liberalization and reduced entry barriers led to increased competition, resulting in risky lending by Japanese banks.⁷

2.1.2 Competition and contagion in the interbank market

The interbank market is where banks lend each other money on a day-to-day basis, handling short-term liquidity needs. Changes in the competitive environment within this market may exacerbate the effects of a distressed bank. Allen and Gale (2000) show this by using a basic microeconomic model. Banks in different regions benefit in trading liquidity through

liquidating and supplying deposits, but this channel proves to be contagious for financial distress.⁸ A bank that has trouble fulfilling short term obligations, may lend deposits from other banks through the interbank market. However, opening for trade does not mean the supply of liquidity in the market increase. If the withdrawal of deposits exceeds the overall supply, the banks in trouble must liquidate long assets or liquidate claims on other banks to meet its obligations. Liquidating long assets is costly such that banks want to liquidate their claim on other banks instead. This interlinkage and overlapping of claims can be critical in situations of distress where such claims might be denied. In a worst-case scenario bank runs or bankruptcy may be the result. Allan and Gale (2000) stress that the contagion rely on the market structure, whether its complete or incomplete. Complete markets may debilitate the impact of distress, since the initial shock is transmitted to all banks, making the shock is more dispersed. In an incomplete market, fewer banks bear the shock such that the probability of a distressed bank meeting its obligations is lower, resulting in a greater chance for contagion of distress and possibly ending in bank runs and bankruptcies.

2.1.3 Relationship banking

Financial intermediaries have incentives to screen and monitor the borrowers, since they only get their share if the loan is repaid. Thus, a good evaluation of borrowers’ creditworthiness is necessary. Theory suggest this evaluation-process develops a relationship between the two parties and gives the bank monopoly on information of its borrowers. Since this information is not publicly available, competitors stay uninformed and the better information the informed have, the lower the competition. Increased competition may reduce the informed margins, thus destroy the relation if the borrower switches bank. If either the margins of the informed

7 For further readings, see Hayashi & Prescott (2002).

8 Allen and Gale (2000) suggest regions can be interpreted as banks operating in different categories and sectors.

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falls, or the borrower switches, the probability of the loan defaulting might increase. In turn, this can lead to greater instability. However, competition may foster better relationships. If the borrower gets better terms from negotiating, its probability of success increase, thus

decreasing the probability of defaulting (Vives, 2016). There are empirical reports on this topic, and it presents ambiguous results. Entrants in a market subject to asymmetric

information will be exposed to the adverse selection problem if there are no common filters.⁹ That is, if there are no common criteria for screening or common register for denied

applicants (Vives, 2016).

Entrant banks is subject to adverse selection problems by not knowing about bad borrowers rejected by the market. Shaffer (1998) address this question quantitatively. By using data from US banks in the period 1986-95, he finds that entrant banks and experienced banks operating in less concentrated markets, is exposed to higher credit risks and loan loss rates.¹⁰ Jiménez and Saurina (2004) analyze how collateral, type of lender and bank-borrower

relationship affect the probability of default for bank loans within the Spanish banking sector.

They find that when the relationship between the borrower and the bank is close, the banks willingness to take more risk increases. Existence of informational rents and a competitive environment they pose as plausible factors affecting this relationship, in line with existing theory. They also find savings banks take more risk than commercial banks. The latter result is contrary to the theory that commercial banks managed by shareholders is riskier than savings banks as a self-owning institution (Saunders et al. as cited by Jiménez & Saurina, 2004, pp. 2210)

2.2 The competition-stability hypothesis

The many theories of competition-fragility, whether liberalization and or deregulation lead to instability, were broadly accepted among researchers and regulators for years. There are however different theories focusing the opposite effect of competition, namely fragility.

9 The problem of adverse selection occurs in markets with asymmetric information and is different from the moral hazard problem as adverse selection, in this context, occur when the borrowers have more and better information than the banks. That is, the banks do not observe the type of borrower, opposed to the moral hazard problem when the bank don not observe actions of the borrowers.

10 Also referred to as charge-off rates.

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2.2.1 Adverse selection, moral hazard and credit rationing

Stiglitz and Weiss, (1981) theoretically show that the loan market could be characterized by credit rationing in equilibrium. They argue that in less competitive sectors, high loan rates yield high loan portfolio risk due to adverse selection and moral hazard problems. To identify good borrowers, banks must screen their customers and one of the screening devices they have, according to Stiglitz and Weiss (1981), is the interest rate. High loan rates attract riskier borrowers because they perceive their ability to repay the loan as low. Concurrently,

increasing the interest rate may induce the borrower to change behavior by undertaking riskier projects because their initial payoff is lessened. Banks considering these effects would

identify a tipping point at which the optimal lending rate attracts the good borrowers, excluding the bad borrowers. Thus, we have credit rationing in the credit market. The result can be interpreted as that in less competitive markets, the volume of non-performing loans increases due to the adverse selection and moral hazard problems, adding to the banks risk exposure and further destabilize the bank (Berger, Klapper & Turk-Ariss, 2008).

Boyd and De Nicoló (2005) presented an opposing view to the charter value hypothesis, known as the moral hazard, or competition-stability hypothesis. Their argument follows from the environment banks operate in, and how it is opposed to adverse selection problems and moral hazard. Financial intermediaries cannot observe all the costs related to the borrower, and in market with low competition banks use their market power to exploit borrowers offering high loan rates. Thus, encouraging borrowers to take more risk, increasing the probability of loan loss. Boyd and De Nicoló (2005) argue that in such markets, increasing competition lowers loan rates such that the borrowers have less incentives to exert risky behavior. In turn, less loan loss stabilizes the bank and thus improve financial stability. See the Appendix A for the model that these arguments follow from.

Their model and argumentation, however, is not uncriticized. Boyd and De Nicoló (2005) assumes that defaulting loans is perfectly correlated with a banks’ probability of failure.

Martinez-Miera and Repullo (2010) prove this is not a very realistic assumption. They argue that there is imperfect correlation and considers the fact that decreasing loan rates reduce a banks’ profits, hence there is a balance of two effects working in each direction. As presented above, Boyd and De Nicoló (2005) argue for a risk-shifting effect improving stability, while Martinez-Miera and Repullo (2010) introduce what they call the margin-effect. Increasing competition reduce revenues from performing loans, which in turn lowers the banks buffer

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and increasing their fragility. In less concentrated loan markets, they argue this margin-effect always dominate, but in markets with many banks, the answer is ambiguous. The result of their analysis is what they introduce as an U-formed relation between competition and

stability, in contrast to a monotonic as in (Boyd & De Nicoló, 2005). As the number of banks increase, banks’ probability of default decrease until it shifts at a certain point. From that point the probability of insolvency increases.

2.2.2 Bail-out policies - “too-big-to-fail”

As goal of preserving financial stability, many countries have regulative policies serving as a safety net, not only for the banks, but also for all the small investors and depositors. Such policies may be bail-outs where the government take control of banks in distress, inject equity, or guarantee for deposits. Some researchers and policy makers disagree with the competition-fragility hypothesis by arguing that in concentrated markets, such policies discourage the actors to behave prudently. As banks monitor their borrowers, regulators monitor the banks and if we assume size is positively correlated with complexity, large banks are more difficult to monitor. The potential consequence of a systemic crisis rationalizes such bail-out policies. However, since the regulators have difficulties monitoring banks behavior and observing their actions, bank managers have incentives of exerting a higher risk.

Ultimately, a concentrated market is less stable than a market with more and smaller banks (Beck, Demirguc-Kunt, & Levine, 2005; Vives, 2016). Hellman, Murdock and Stiglitz (2000) claim that whether a country has any formal policy on bail-outs or not, will have no difference if a crisis were to occur as there will be bail-out anyway. They cite a commentator stating the following: “There are two kinds of countries: those that has deposit insurance, and those that don't yet know that they have it” (pp. 148).

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3. Measuring stability and competition

Empirical investigation about the relationship between stability and competition, require precise definitions and methods. Basic microeconomic theory suggests markets with perfect competition yield zero profit for suppliers such that the price level can indicate the degree of competition. Banks prices and returns are not directly observable and therefore, quantifying them is not easy. However, there are an extensive amount of empirical studies on the relation using a variety of measures serving as proxies for prices and other competition measures. The section present the most popular ones.

3.1 Stability measures

The research question for this paper is how competition within the banking sector in Norway affect the financial stability. In order to answer this, it is necessary to know what the terms signify. The central bank of Norway define what a stable financial system is in the following words; “Financial stability implies a financial system that is resilient to shocks and thus capable of channeling funds, executing payments and distributing risk efficiently.” (Norges Bank, 2018a, pp. 2). The bank also publishes a general overview of Norway’s financial system every year, and in the paper of 2018 the bank identifies three important. The first is that it provides consumers and businesses with opportunities for borrowing and investing.

Second, it fosters stable and secure payment services. Third is that it operates a proper risk management. With these arguments along with the need to ensure intermediaries fulfill these tasks the central bank legislate the need of regulation (Norges Bank, 2018a).

There are however no final or unambiguous definition of financial stability. Schinasi (2004) define financial stability in the following way;

“A financial system is in a range of stability whenever it is capable of facilitating (rather than impeding) the performance of an economy, and of dissipating financial imbalances that arise endogenously or as a result of significant adverse and unanticipated events.” (pp.8).

This can be interpreted as a system that nurture economic performance and is rigged for unexpected shocks and events that may disturb its functioning.

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A variety of reports on the relation between financial stability and banking competition, control for episodes of financial distress, or systemic crises. That is, episodes were the system is not stable or behave like described above. A widely used and accepted definition among researchers is presented by Demirguc-Kunt and Detragiache (1998). They identify several conditions that needs to hold for the episode to be categorized as a period of systemic distress.

The properties are as follows:

“

1. The NPL-ratio during the period exceeds 10 percent.

2. The cost of the rescue-operation, including bail-outs, was at least 2 percent of national GDP.

3. Banking sector problems resulted in a large-scale nationalization of banks.

4. Extensive bank runs took place or emergency measures such as deposit freezes, prolonged bank holidays, or generalized deposit guarantees were enacted by the government in response to the crisis.” (pp. 12).

In previous empirical work, these episodes are usually treated as dummy variables to control for their effects. There are however alternatives to using episodes of systemic distress as a measurement. As we established in section 2, the interbank market is contagious for financial distress and history has proven that several systemic failures started with one distressed bank (Beck, 2008). The two most common measurements on stability consider individual banking data. Non-preforming loans over total loans is a ratio that express a banks exposure to risk and indicate how fragile it is, thus serving as a proxy for loan portfolio risk (Beck, 2008).

The non-performing loan ratio (NPL) is given by the following formula:

NPL =Non-performing loans Total loans

(3.1)

Increasing non-performing loans, increases the ratio and thus lowers the loan portfolio quality.

The Z-score, or Z-index, is also a widely used measure for financial stability. It captures a banks probability of defaulting and works an inverse proxy for overall bank risk (Berger, Klapper, & Turk-Ariss, 2008). It is derived by the following formula:

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Z-score_& = ROA_& + EQ_&/TA_&

𝜎(ROA_&)

(3.2)

where 𝑖 denote the individual bank, ROA is the banks return on assets, EQ is the total amount of equity, TA is the total amount of assets and 𝜎(ROA_&) is the standard deviation of the banks return on assets. Note that both indicators do assess an individual banks credit risk, not its probability of failure. It does however indicate how healthy a bank is (Beck, 2008; Berger, Klapper, & Turk-Ariss, 2008). The Z-score represents the number of standard deviations, below the mean, by which profits must fall as to just deplete equity capital. It can also be interpreted as a measure of banks buffers and how volatile it is. So, with high volatility in the ROA, the Z-score lowers (Boyd, De Niccolo & Jalal, 2006).

3.2 Competition measures and empirical evidence

Existing literature divide the approaches on measuring competition into two categories, a structural and a non-structural approach. The structural approach is based on both the Structure-Conduct-Performance (SCP) Paradigm, merger analysis and other forms of

Industrial Organization theories. The SCP Paradigm mainly consist of two hypotheses; market power and efficiency. According to the market power hypothesis, firms in concentrated markets are protected by entry-barriers, that is, each slice of the pie is bigger. The efficiency hypothesis presents an opposing view; more efficient banks attract market shares, thus collect more profits. Supporters of the SCP Paradigm use the level of concentration to explain the degree of competition. It assumes that higher level of concentration within a market, makes collusion more attractive, thus reducing the competition. The SCP Paradigm lacks backup from microeconomic theory; nonetheless, it has been widely used by researchers evaluating competition within the banking industry, showing significant results. A non-structural approach typically address competition by assessing bank-level attributes, not including market structure relationships. In banking stability analysis, the Panzar and Rosse model, applying the reduced-form revenue approach is popular. Another measure widely used, but difficult to attain is the Lerner Index (Bikker & Haaf, 2002; Bikker & Haaf, 2000; Vives, 2016). The next section contains an elaboration of the most popular measures for the two approaches.

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3.2.1 Structural measures

Concentration measures capture the structural features of a market essentially by summing the market shares of all firms in the sector. An important feature is that it captures the effect of entries, exits and mergers, however, its estimates should be taken with reservation. A highly concentrated market does not necessarily indicate that the degree of competition is low (Bikker

& Haaf, 2000). Two of the most popular uses of concentration measures are the Herfindahl- Hirschman Index (HHI) and the n-bank concentration ratio (CRn). The CRn measure the concentration within a market by summing the n largest banks markets shares, and can be modeled as follows:

𝐶𝑅₅ = 6 𝑆_&

5

&89

(3.3)

where 𝑆_& is the market share of bank 𝑖, and n is the number of banks in the sector. The CRn

concentration measure gives equal weight to all the banks and is easy to employ in in

situations where data is limited, which is one of the reasons that it is frequently used (Bikker

& Haaf, 2000). The Herfindahl-Hirschman Index captures market concentration as well but weigh the banks individually. It is represented by the following formula:

𝐻𝐻𝐼 = 6 𝑆_&^<

=

&89

(3.4)

The differences between the two measures are the extension of “n”-banks, to all banks (“N”) in the sector, along with the squared-term. When evaluating applications of mergers, the Department of Justice in the US employ the HHI as a measure of the degree of competition.

They suggest that an HHI between 1500 - 2500 points is moderately concentrated while an HHI above 2500 is highly concentrated (Bikker & Haaf, 2000).

Some researchers also apply what is called the price-cost margin to assess the degree of competition. The price-cost margin is essentially the difference between the price and cost of lending. However, this measure has been criticized for not being able to capture possible cross-subsidization of products in an universal banking market. Bikker and Haaf (2000) argue the failure to do so can present misleading pictures of the sectors performance.

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3.2.2 Non-structural measures

As a non-structural measure of competition, a widely used measurement is the H-statistic, derived from a reduced form revenue model. It was developed by Panzar and Rosse (1987) and evaluates firms elasticities in total revenue with respect to their input prices. By

aggregating the results for an entire sector, it provides a way of determining the degree of competition. The H-statistic, which is the sum of elasticities for the reduced form revenue function, can be modeled as follows:

𝐻 = 6( 𝜕𝑅_&

𝜕𝑊_@_A

B

@89

)(𝑊_@_A

𝑅_& ) (3.5)

Where 𝑅_& an individual firms’ revenue in equilibrium, satisfied by the zero-profit constraint and 𝑊_@& is the firms input factors. The H-statistic ranges from −∞ < 𝐻 ≤ 1 and if 𝐻 < 0, the market is characterized as monopolistic. An H-statistic ranging between 0 < 𝐻 < 1 is defined as monopolistic competitive.¹¹ For values of H equal to unity, the market is identified as perfectly competitive (Bikker & Haaf, 2002).

The original model presented by Panzar and Rosse (1987) must be modified to fit the banking firm model and there are mainly three empirical versions of the Panzar-Rosse model.The most popular model is called the Panzar-Rosse price equation and can be modeled as follows:

log 𝑇𝑅_&N = 𝛼 + 𝛽₉logQ𝑊_9,&NS + 𝛽_<logQ𝑊<,&NS + 𝛽_TlogQ𝑊_T,&NS + 𝛽_UlogQ𝑌_9,&NS + 𝛽_WlogQ𝑌<,&NS + 𝛽_XlogQ𝑌<,&NS + 𝛽_Y𝑌𝐷

+ 𝜀_&N

(3.6)

Where the dependent variable 𝑇𝑅_&N reflects total revenue of bank 𝑖 at time 𝑡, 𝑊_& the respective input factors, and 𝑌_& the control variables (Bikker & Haaf, 2002). In a banking perspective, total revenue includes interest income, and income from fee-based- and off-balance sheet activities. Only until recent years, the inclusion of other income than from interests has been relevant due to their increased importance. Furthermore, banks prices and costs are hard to

11 Monopolistic competition is characterized as a market with many producers, no entry barriers, producers selling differentiated products, with a limited degree of impact on the price level.

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observe directly, such that the use of proxies is necessary in a non-structural approach. The input prices are the average funding rate, wage rates and cost of physical capital. Average funding rate (W1) is assumed to be reflected by the ratio of interest expenses and total

deposits. A common proxy for reflecting the wage rate (W2), is personnel expenses over total assets. However, a more precise measure for wage rates would be the ratio of personnel expenses and employees, but as not all banks report the number of employees, getting reliable estimates is not uncomplicated (Bikker, Shaffer & Spierdijk, 2012). The input price (W3) is the price of physical capital and is assumed to be mirrored by operating and administration costs over total assets. Banks behave differently thus there is a need to control for bank specific characteristics. Existing literature suggest including credit risk, leverage and size.

Credit risk is reflected by the ratio of total loans over total assets (Y1), leverage by equity over total assets (Y2), and size by total assets. 𝑌𝐷 is a denotation for yearly dummy variables dummy and is included for time-fixed effects. All variables should be log-transformed such that the coefficients can be interpreted as constant elasticities (Bikker & Spierdijk, 2008).

Model (3.6) however is shown to produce upward biased estimates of the H-statistic. Bikker, Shaffer and Spierdijk (2012) proved by using data from over 17 000 banks over 10 years that the scaled revenue equation produced invalid estimations of the statistic, such that it cannot be used to evaluate the degree of competition as the condition of monopoly will consistently be rejected. They do find that the unscaled version can be used, though with some caveats to consider. By not controlling for scale differences in banks revenues, the estimate of the error term will with high probability be heteroscedastic. This can however be sorted by using clustered or robust the standard errors, which will be discussed further in chapter 7.

Furthermore, by including total assets in the denominator for the explanatory variables, the H- statistic may be distorted if the correlation between the log of total assets and the other explanatory variables is high (Bikker, Shaffer & Spierdijk, 2012). The unscaled reduced revenue equation can is modeled as follows:

log 𝑇𝑅_&N = 𝛼 + 𝛽₉logQ𝑊_9,&NS + 𝛽_<logQ𝑊<,&NS + 𝛽_TlogQ𝑊_T,&NS + 𝛽_UlogQ𝑌_9,&NS + 𝛽_WlogQ𝑌<,&NS + 𝛽_X𝑌𝐷 + 𝜀_&N

(3.7)

For the model to hold, there are some assumptions. First, the banking market is in a long-run equilibrium and banks operate with a Cobb Douglas production function. Second, demand is

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assumed to have constant elasticity (Bikker, Shaffer & Spierdijk, 2012). A method to test the long-run equilibrium condition is to estimate the following model:

log 𝑅𝑂𝐴_&N = 𝛼 + 𝛽₉logQ𝑊_9,&NS + 𝛽_<logQ𝑊<,&NS + 𝛽_TlogQ𝑊_T,&NS + 𝛽_UlogQ𝑌_9,&NS + 𝛽_WlogQ𝑌<,&NS + 𝛽_X𝑌𝐷 + 𝜀_&N

(3.8)

If the market is in equilibrium the factor prices is not correlated with the returns. The reasoning is that firms in a competitive market should have equal risk-adjusted rates of returns. So, if the condition holds, the coefficients of the input prices is equal to zero, that is H^ROA = 0. If however H^ROA are significantly negative, according to Bikker, Shaffer and Spierdijk (2012), the market must not necessarily be in a structural equilibrium, given the H- statistic is negative. Their reasoning is that the market demand is to some degree elastic, such that the monopolist is not able to fully pass all costs over to the consumer.

Apart from the H-statistic, another popular non-structural measure is the Lerner Index. It measures market power expressed by the difference in output price and marginal cost, divided output price in a profit maximum. If the Lerner Index equals zero, the marginal revenue equals the marginal cost, thus we have perfect competition. Hence, an increasing Lerner Index indicate decreasing competition.

3.3 Empirical evidence

The different hypotheses whether there is a trade-off between competition and stability has been tested with many different approaches and measures. Most of the modern literature finds ambiguous results, and in this section I present some of the evidence.

The first to test the relation were Michael C. Keeley. By using data on 150 US banks in the period between 1970 and 1986, Keeley (1990) built an empirical foundation to the charter value hypothesis. To measure charter value through market power, he utilizes Tobin’s q. It is defined as the market value of assets over book value of assets. In measuring bank insolvency risk he use market value of common equity over market value of equity, plus the book value of liabilities and interest cost on checkable deposits.¹² Keeley (1990) also control for bank

12 Checkable deposits are the most liquid deposit accounts.

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characteristic and macroeconomic factors such as periods of liberalization, deposit demand to total deposits, ratio of foreign deposits to total deposits and the ratio loans to assets. To test the relation, he employs the ordinary least squares (OLS) framework and the two-stage least squares (TSLS) framework. He found that banks with more market power held more capital relative to assets and that they had a lower default risk. Keeley (1990) argue that deregulation in the US market during the period, caused franchise values to decline thus increasing banks fragility, thus providing empirical foundation to his initial theory of competition-fragility.

Boyd, De Nicoló, & Jalal, (2006) explored the connection of (in)stability and competition by empirically testing two different models, one based on the charter value hypothesis and the other based on the theory presented by Boyd and De Nicoló (2005). The model framework of Boyd and De Nicoló (2005) is presented in Appendix A. In measuring financial stability, they use the Z-score, defined in section 3.2. In measuring competition, they adopt the Herfindahl- Hirschman Index measured in three different ways. They include HHI based on total loans, total deposits and total assets. Their model also controls for country- and bank-specific characteristics such as banks size and their different production technologies. A common microeconomic assumption is that firms operate with identical production technologies. Boyd, De Nicoló and Jalal (2006) adapt their models without this assumption using the ratio of non- interest operating costs to total income, to reflect the banks technical efficiency. They also disregard the assumption that not all banks are the same size in equilibrium and control for it by using the natural logarithm of total bank assets. By using both cross-sectional and panel data in the period of 1994 to 2004 for US banks, and banks in 134 non-industrialized countries, they employ three different regression methods to test the two models. The first regression method is OLS with state-fixed effects, then adding clustered standard errors to correct for locational correlation. The last method they employ is the generalized method of moments (GMM) with HHI and bank size as instrument variables. Boyd, De Nicoló and Jalal (2006) argue that the HHI and the banks’ assets size likely are endogenous functions of regional economic conditions, such that the use of GMM is necessary. In their first model, they allow for competition only in deposits which predicts banks’ risk of failure increase in competition, in line with the charter-value hypothesis. The second model, competition is introduced both in loan- and deposit markets providing evidence with opposite effects of competition than the former. They conclude that there are no compelling evidence, nor theory in support of the charter value hypothesis. Furthermore, Boyd, De Nicoló (2006) strengthen

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their conclusion by stating that there was no previous empirical work that combined measures that fit good for both competition and stability, like their analysis.

In a policy review for the Federal Reserve Bank of New York by Cetorelli et al. (2007), the authors investigate how the financial market concentration’ in the US affect market stability through different volatility measures by the use of a two-step regression method. The

explained variables include two forms of loans; syndicated loans and investment-grade bonds underwriting. Cetorelli et al. (2007) believes the volatility measures serves as a reasonable proxy for market resiliency to a range of supply and demand shocks in the financial market.

They argue the measures capture deviations in spreads disturbing supply and demand, hence reflecting factors destabilizing the market. As a measure for competition they use

concentration as a proxy for competition, employing the HHI. Their findings are somewhat ambiguous. With syndicated loans as dependent variable they see generally a negative relationship; as market concentration increases, the volatility in the market lowers. But for investment-graded bonds they get mixed result. For low levels of HHI, the relationship is negative until a tipping point where it turns positive for higher levels of HHI. Furthermore, by excluding a possible biased part of the sample, they obtain estimates suggesting a generally negative relationship between concentration and bank stability. Cetorelli et al. (2007) do however conclude that their findings are a counter-example to literature claiming the relationship is not ambiguous, by providing evidence that the relationship is in fact ambiguous.

Egan, Hortacsu, and Matvos (2015) investigate how competition interact with financial health in a market with insured and uninsured and finds evidence using data from US commercial banks in the period between 2002 and 2013. They assume that uninsured and insured depositors have different preferences in the choice of bank, and that banks compete on deposits. Among other assumptions this they develop a structural model which they

empirically tests. Based on their sample, they find that a large amount of uninsured deposits within the sector, can lead to fragile banks, given its elasticity of deposits to financial distress.

Another interesting results from their analysis is that bank stability and welfare is not

positively linear. In the model they develop, there is a tipping point to which welfare decrease with increasing banking stability.

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In a world-wide investigation of competition in the banking industry, and its development over time, Bikker and Spierdijk (2008) finds evidence that countries in the Euro area experienced a decline in competition after a structural break. To assess competition, they employ the H-statistic derived from a model developed by Panzar and Rosse (1998). To control for effects of the different economics environment across countries, they correct for, market capitalization, real annual GDP growth, long and short rates. Bikker and Spierdijk (2008) first estimate the statistic with yearly and recursive estimation but their sample were too short in order to obtain reliable results. However, with a parametric approach using non- linear least squares they found that many western and Eastern European countries experienced a decrease in competition during the period between 1994 and 2004. They confirm this with a structural model and find also that it especially decreased after a structural break between 2001 and 2002, right after the formal establishment of the Economic and Monetary Union and the digital euro. Bikker and Spierdijk (2008) also attribute the downward trend in competition with increased consolidation and more complex banking products.

Modern banking is not characterized only by serving as a financial intermediary. In a

financial conglomerate we may also find banks providing other services as asset management, insurance and investment banking. Banks nowadays also supply more market-based and off- balance products whereas non-interest income has increased (Vives, 2016). Beck et al. (2014) is one of the first to explore how financial innovation affect real sector growth, its volatility and banking fragility. To measure bank fragility, they use the Z-score, indicating how close a bank is to insolvency. They claim that the Z-score is highly skewed, hence using the natural logarithm of the score is fitting. Also, for a higher statistical power they compute the standard deviation of the ROA over a 4-year window. Furthermore, in assessing competition they include the HHI and dummy variable for foreign owed banks. The latter inclusion controls for effects of financial innovation that spills over from one country to another. Moreover, they employ bank-specific factors to control for omitted variables bias by capturing unobservable heterogeneity. One of them is the loan to asset ratio as a proxy for credit risk. In measuring financial innovation, Beck et al. (2014) adopt mainly two variables dependent on the sectors expenditure on R&D; Financial R&D Intensity, value added, and Financial R&D Intensity, cost based, where the value-added expenditure measures the contribution to GDP by an individual intermediation institution. In testing the connection Beck et al. (2014) use OLS and finds that in countries with high levels of financial innovations, banks tend to have a lower Z- score, thus being closer to insolvency. All their measures of financial innovations affect the

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solvency-measure significantly negative which is in-line with a hypothesis called the innovation-fragility hypothesis; more innovation calls for higher probability of financial distress. They also find that banks with higher loan to asset ratios and banks with to-big-to- fail statuses are less prone to insolvency.¹³ Beck et al. (2014) also investigates how innovation within the sector affect the economic growth of the country and finds evidence that this

relationship is positive. Hence, concluding there are both dark and bright sides of financial innovation. They argue that the increased innovation encourages to greater risk-taking

behavior which in turn benefit firms and households, enhancing economic growth and capital allocation efficiency. Hence, the increased level of risk makes banks more prone to losses when profits are volatile.

3.3.1 Evidence from the Norwegian banking sector

There are few studies focusing on the relationship between competition and stability only in the Norwegian banking sector. In many cross-country studies Norway is included, however most of these use numbers from the BankScope database with an average of around 50 banks included. See e.g., Beck, Demirguc-Kunt, & Levine (2005) and Berger, Klapper and Turk- Ariss (2008).

Heimdal and Solberg (2015) assessed the relation within the sector by employing the NPL rate as explained variable and confirms the theory of Martinez-Miera and Repullo (2010), that there is a non-linear relationship between market concentration and bank stability, and that it support the competition-fragility hypothesis.

13 Beck et al. (2014) define banks with too-big-to-fail status as those that have 10% or more of the market share of the country’s total deposits.

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4. The Norwegian banking sector

In this paper, the investigation of the relationship between competition and financial stability is limited to the Norwegian banking sector. The accounting data is retrieved from Finance Norway, complemented by macro-level data from Statistics Norway (SSB) and OECD Statistics. The panel data is unbalanced and includes observations for 238 individual banks.

Following most of the empirical literature and in order to obtain more precise estimates, banks that have less than 5 years of consecutive observations is removed. This limits the sample to 182 banks with 2,274 observations. The banks financial statements contain yearly account data from the period 1995 to 2017.¹⁴ Mergers has not been controlled for, other than those that have just changed name. It is debated whether companies that merged behave differently after but for this paper the assumption that they do not, are maintained as an analysis of this assumption is beyond the scope of this paper.¹⁵ The formats for the account data have changed over the years such that all the financial statements had to be collected and sorted manually.¹⁶ All regressions is performed in STATA and a summary of the statistics is listed in table C.3 in Appendix C.

The next section presents the structural history and its development, followed by a presentation of the sector today.

4.1. History and times of distress

The first bank in Norway, Christiana Sparebank, opened in 1822 and was organized as a savings bank.¹⁷ Its initial purpose was to defeat poverty by pooling small amounts of money from investors to lend entrepreneurs capital for their start-ups. The savings bank form roots to today’s largest bank of Norway, DNB (Gram, 2014). Over 100 years later the banking sector was characterized by a locally anchored and decentralized sector. Between the 1950s and 1960s, there were around 600 savings banks and 68-89 commercial banks. In the 60's, a committee, later called the “Area-committee”, was appointed by the savings bank union to assess the structure of Norwegian savings banks due to expected changes in the housing

14 Only banks mandatory to report is included in the data set.

15 See Bikker, Shaffer, & Spierdijk, (2012), Hempell, (2002) and Kishan & Opiela, (2000).

16 This procedure was done with Microsoft Excel and customized macros.

17 A savings banks are organized as a self-owned institution, while a commercial bank is organized as a joint stock company.

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market, employment in the country, and thus the banking industry. The committee argued that the industry had to align with the development of the primary sector. At that time the

regulatory framework gave small banks limited opportunities of risk diversification, and in combination with the need for more competent human resources, the committee stressed that the number of banks needed to be reduced to keep up with the change (Gram, 2014; Meinich

& Munthe, 2015; Sparebankforeningen, 2019).

In the 70s there were broad consensus that the concentration process was too slow and in response to that a new committee, the planning committee, were set to speed up the process.

In later years, after several closures and mergers, the number of banks quickly fell and in 1987 there were 173 savings banks, 20 commercial banks and 8 foreign banks. Moreover, 132 of these had under 100 million USD in assets under management (Eitrheim, Gerdrup, &

Klovland, 2004; Eitrheim, Grytten, & Klovland, 2007; G. Moe et al., 2004).

During the time the Norwegian currency were connected to the gold standard, there were a banking crisis approximately every decade until the mid-20^th century. At the end of the 20^th century, the banking sector were subject to liberalization and deregulation. In 1984 the Norwegian government opened the borders for foreign banks and up until this time, there had also been a cap on lending rates. This was lifted over the next two years, leading to immediate responses in both private consumption and bank lending. Private consumption went up 15%

and bank lending went into a boom directly impacting the real-estate prices (G. Moe et al., 2004; Gram, 2017).

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FIGURE 1: Yearly real-estate transactions

Figure 1: Yearly real-estate transactions for four of Norway’s biggest cities; Oslo, Kristiansand, Trondheim and Bergen Source: Figure 1b in Eitrheim and Erlandsen (2004).

Eitrheim and Erlandsen (2004) reconstructed the Norwegian housing prices all the way back to 1819, and from their total sample of transactions for the four big cities, it is evident that after liberalization and deregulation, the real estate prices increased tremendously during the 80s. This was followed by a huge fall in the beginning of the 90s, just in line with the

beginning of the systemic banking crisis. Regulators and policy makers did not have

experience from competitive credit markets as the previous decades were characterized as a heavily regulated regime with a clear presence of credit rationing. Competition in the lending market challenged the banks screening and monitoring abilities. In 1985, the oil price fell, and Norway’s trade balance went from positive to negative. The Norwegian Krone was devalued in 1986 and in 1988 the Norwegian economy entered a recession. The following two years, 13 small- to medium-sized banks went bankrupt. The systemic crisis began in 1990 when the Commercial Banks’, and Savings Banks’ own Guarantee Funds, ensuring the customers deposits, were depleted. The large banks lost a lot of their equity and shareholder capital which in the end resulted in the government intervening. Total gross fiscal cost of this is estimated to be 2% of the Norwegian gross domestic product at that time and on average, a financial crisis increase unemployment rates by 7%, decrease national GDP by 9%, and increase government debt by 86% (G. Moe et al., 2004; Reinhart & Rogoff, 2009).

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In 2003 two of the largest banks DnB and Gjensidige NOR merged after negotiations with the Norwegian Competition Authority. Two months after the Department of Finance authorized the merger, the Norwegian Competition Authority made the decision to intervene as there was with great possibility that the merger would lead to a social economic loss due to market delineation. They further argued that both banks operated in regional and local markets, conditions that would worsen the effect. The counterparts however argued that since most of their services was done by telecommunication, their market was national

(Finansdepartementet, 2003). Since the merger, DNB has been the largest bank in managing assets. As Figure 3 illustrates, the concentration within the sector experienced a huge jump, indicating lower degree of competition.

4.2 The market structure today

During the period 1995 to 2017, the total number of banks within the Norwegian sector have decreased by 16. Only two of these banks discontinued operation due to big losses, whereas the rest were merged into bigger banks. Today there are 417 companies with a banking license while only 131 of them were operating within the sector in 2018 (Finanstilsynet 2019).

In 2017 three of the largest banks had a combined market share in gross lending at around 50%. Danske Bank, which is the third largest in gross lending, was at the same time half as big as Nordea. The next biggest bank at the time was Handelsbanken with 5,6% market share of gross lending. That same year, the same three banks also managed 60% of the total assets in the sector. DNB, which is the largest in both measures, were over three times bigger than the second largest bank, Nordea, in total assets. In comparison, Sweden’s five biggest banks managed the same percentage of the sectors assets as the three biggest in Norway in 2017.

Figure 2 display the changes of the HHI concentration measure for Norway compared to other north European countries. Finland had the most concentrated sector for many years with indices up to 3400 points, while Germany and the UK had the lowest with points as low as 114 and 190 respectively. (Finance Norway, 2019; ECB, 2018).

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FIGURE 2

Figure 2: Herfindahl-Hirschman Index for selected EU countries and Norway in the period 1997-2017. Calculations is based on total assets. Source: ECB (2018); Finance Norway (2019).

Concentration within the Norwegian sector has changed significantly over the last decades which is illustrated by Figure 3 and 4. Both the HHI and CR5 measures are calculated based on assets, loans and deposits to reflect their differences. The first significant change in all measures, is between the period 2003-2004. This reflects the merger between two of the biggest banks at that time, Gjensidige NOR and DnB Bank. The next big change is in 2011, this time DNB Bank and Nordlandsbanken merged. Furthermore, from the beginning of the period all measures seem pretty aligned until around 2007. After that, all measures start to deviate, especially the CR5 based on loans. A possible explanation is the transition to International Financial Reporting Standards (IFRS) (Finance Norway, 2019). This will be addressed in chapter 7. Overall, the concentration seems to have increased, especially for the asset based concentration measures.

0 500 1000 1500 2000 2500 3000 3500

Belgia

Germany

Denmark

Finland

United Kingdom

Ireland

Netherland

Sweden

Norway

Herfindahl-Hirschman Index

1997 2001 2005 2009 2013 2017

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FIGURE 3: Development of the HHI based on assets, loans and deposits

Figure 3: Aggregated market shares for all banks within the Norwegian banking sector in the period 1995-2017, based on the Herfindahl-Hirschman Index. Source: Finance Norway (2019)

FIGURE 4: Development of the CR5 based on assets, loans and deposits

Figure 4: Aggregated market shares for the 5^th largest banks (CR5) for the Norwegian banking sector in the period 1995- 2017. Source: Finance Norway (2019)

10152025HHI

1995 2000 2005 2010 2015

YEAR

HHI Assets HHI Deposits HHI Loans

55606570CR5

1995 2000 2005 2010 2015

YEAR

CR5 Assets CR5 Deposits CR5 Loans

Competition and financial stability in the Norwegian banking sector