Bank competition and stability: Reconciling con‡icting empirical evidence Thorsten Becky

Olivier De Jonghez

Glenn Schepensx

Abstract Theoretical models and empirical results o¤er con‡icting evidence on the relationship between bank competition and bank stability. This paper aims to reconcile the seemingly contrasting evidence on the bank competition-bank soundness relationship. We develop a uni…ed framework to assess how regulation, supervision and other institutional factors may make it more likely that the data favor one theory over the other (charter value paradigm versus risk-shifting paradigm). Cross-country heterogeneity in these factors allows us to test the assumptions and predictions of various theoretical models. We show that an increase in competition will have a larger impact on banks’ risk taking incentives in countries with stricter activity restrictions, more herding in revenue structure and unconcentrated banking markets. The authors would like to thank seminar participants at HEC Paris, Ghent University and Tilburg University for interesting discussions and helpful comments. y CentER,

European Banking Center, Tilburg University and CEPR.

z CentER,

European Banking Center, Tilburg University.

x Corresponding

author: [email protected]. Department of Financial Economics, Ghent University.

1

1

Introduction

The impact of bank competition on …nancial stability remains a widely debated and controversial issue, both among policymakers and academics. The belief that …ercer competition among banks would lead to a more e¤ective banking system initiated a deregulating spiral in the late 70s and early 80s. While the deregulation of branching and activity restrictions may have resulted in more intense competition among banks, with positive repercussions, it may as well have had the unintended consequence of increasing banking sector instability.1 Similarly, the international process of banking liberalization seemingly has gone hand in hand with an increased occurrence of systemic banking crises in the last two decades of the twentieth century, culminating in the global …nancial crisis of 2007-2009. However, there is no academic consensus on whether bank competition leads to more or less stability in the banking system. On the one hand, an increase in loan market competition leads to lower lending rates and hence lower interest margins. As banks’ franchise values erode, this may create incentives to gamble and may lead to a shift towards riskier activities, because of the limited liability by bank shareholders that e¤ectively turns bank equity into a put option on banks ’pro…ts. On the other hand, a similar argument but in the opposite direction can be made for the bank’s borrowers. If entrepreneurs are confronted with lower loan rates, they will choose safer projects and have fewer incentives for aggressive risk taking, i.e. the adverse selection and moral hazard problems will be mitigated. These two opposite e¤ects may help in explaining why empirical studies across di¤erent samples and time periods fail to …nd a consensus on which e¤ect dominates. Moreover, comparing the results of di¤erent studies is complicated by the use of di¤erent competition and risk measures. A similar inconclusive debate as on the relationship between competition and stability has been led on the e¤ect of the regulatory framework on banks ’risk-taking incentives and ultimately bank stability. On the one hand, capital regulation and interest rate and activity restrictions 1 See

among other Keeley (1990) and Jayaratne and Strahan (1998)

2

are seen as fostering stability (Hellmann, Murdock, and Stiglitz (2000)); on the other hand, they might lead to rent-seeking and might prevent banks from reaping necessary diversi…cation and scale bene…ts. The role of deposit insurance schemes has been especially controversial. While often introduced to protect small depositors’ lifetime savings and to prevent bank runs, they also provide perverse incentives to banks to take aggressive and excessive risks. These perverse incentives are held less in check in weak supervisory frameworks (Demirguc-Kunt and Detragiache (2002)). This paper combines the two literatures and assesses whether the relationship between competition and stability varies across markets with di¤erent regulatory frameworks, market structures and levels of institutional development. Speci…cally, while holding the measure of bank competition and stability constant across samples, we document that support for either the competitionstability or competition-fragility view varies across countries and over time. Next, we identify and test the possible channels that may create cross-country variation in the competition-stability relationship. While we identify several country characteristics that explain the cross-country variation in the competition-stability relationship, a large amount is still unexplained. Finally, based on our results, we try to reconcile the seemingly con‡icting existing empirical results on the competition-stability relationship. As way of motivation, consider the cross-country variation in the relationship between competition and stability. In our sample of banks in 62 countries, the pairwise correlation2 between the Lerner index and the Z-score, widely used proxies for market power and bank soundness, respectively, is 0:258.3 Figure 1, however, reveals that this full sample correlation masks a substantial 2 We

refer to simple pairwise correlation coe¢ cients in the introduction. A similar story can be made with

regression based conditional relationships. However, for ease of exposition, we postpone this to later sections. 3 The

Lerner index is the ratio of the di¤erene between price and marginal cost and the price, with higher

values indicating higher market power. The Z-score is an accounting measure of bank distress. It is measured

3

degree of country-level heterogeneity. Each bar in Figure 1 corresponds to the country-speci…c pairwise correlation between market power and bank soundness. The average pairwise correlation over the 62 countries resembles the full sample correlation. However, there is a large amount of heterogeneity in the competition-stability relationship, with correlations ranging from below -0.2 to above 0.5. In some countries, the correlation is negative and signi…cant. In many others it is not statistically di¤erent from zero. In most countries, the number is positive and signi…cant. Rather than being interested in the sign of the relationship, we are interested in the cross-sectional dispersion. Speci…cally, we are interested in which country-speci…c features make it more likely that competition is less harmful or more bene…cial for bank soundness. Exploring the variation in the competition-stability relationship is important for academics and policy makers alike.

The academic debate on the e¤ect of competition on bank stability

has been inconclusive and by exploring factors that can explain variation in the relationship, this paper contributes to the resolution of the puzzle. Policy makers have been concerned about the e¤ect of deregulation and the consequent competition on bank stability but have also discussed di¤erent elements of the regulatory framework that have both an impact on competition and directly on stability, including deposit insurance capital regulation and activity restrictions. This paper shows a critical role for the regulatory framework in explaining the variation across countries and over time in the relationship between competition and stability and has therefore important policy repercussions. Our results are not only of interest for the ongoing competition-stability debate, but have broader implications for the development of stress tests and macroeconomic models. The 200709 …nancial crisis acted as a catalyst for economic models that embed a banking sector into a full-‡edged macroeconomic model. For instance, Gerali, Neri, Sessa, and Signoretti (2010) conas the sum of accounting pro…ts and the capital to asset ratio, divided by the volatility of pro…ts. As such, it indicates with how many standard deviations pro…ts can fall before capital is depleted.

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tribute to the literature by adding a banking industry characterized by imperfect competition to an otherwise standard DSGE model. These extended macro-…nance models should help academics and policymakers in their understanding of the interaction between the …nancial sector and the real economy. Such models are then used to conduct stress tests which give an indication of the dynamics and the magnitude of the shock transmission process and the resilience of the banking sector to shocks (see e.g., Hirtle, Schuermann, and Stiroh (2009)). Our paper will help in understanding and explaining why and how the results of these stress tests may di¤er across countries. Moreover, our results may help in pinning down the magnitude of the parameters needed to calibrate such models as we show why and to what extent the impact of bank competition on banks’ Z-score, one measure of bank …nancial strength, di¤ers considerably across countries. Our paper builds on a rich theoretical and empirical literature exploring the relationship between competition and stability in the banking system. On the one hand, the competitionfragility view posits that more competition among banks leads to more fragility. This “charter value” view of banking, as theoretically modeled by Marcus (1984) and Keeley (1990), sees banks as choosing the risk of their asset portfolio. Bank owners, however, have incentives to shift risks to depositors, as in a world of limited liability they only participate in the up-side part of this risk taking. In a more competitive environment with more pressures on pro…ts, banks have higher incentives to take more excessive risks, resulting in higher fragility. In systems with restricted entry and therefore limited competition, on the other hand, banks have better pro…t opportunities, capital cushions and therefore fewer incentives to take aggressive risks, with positive repercussions for …nancial stability. In addition, in more competitive environment, banks earn fewer informational rents from their relationship with borrowers, reducing their incentives to properly screen borrowers, again increasing the risk of fragility (Boot and Thakor (1993), Allen and Gale (2000), Allen and Gale (2004)). The competition-stability hypothesis, on the other

5

hand, argues that more competitive banking systems result in more rather than less stability. Speci…cally, Boyd and De Nicolo (2005) show that lower lending rates reduce the entrepreneurs cost of borrowing and increase the success rate of entrepreneurs’investments. In addition, these …rms will refrain from excessive risk-taking to protect their increased franchise value. As a consequence, banks will face lower credit risk on their loan portfolio in more competitive markets, which should lead to increased banking sector stability. However, more recent extensions of the Boyd and De Nicolo (2005) model that allow for imperfect correlation in loan defaults (MartinezMiera and Repullo (2010); Hakenes and Schnabel (2007)) show that the relationship between competition and risk is U-shaped. Hence, the impact of an increase in competition can go either way, depending on other factors. Wagner (2010) extends the Boyd and De Nicolo (2005) model and allows for risk choices made by borrowers as well as banks. If lending rates decline due to more competition, banks have less to lose in case a borrower defaults. Hence, a bank may …nd it optimal to switch to …nancing riskier projects4 , which overturns the Boyd and De Nicolo (2005) results. The standard response to con‡icting theoretical predictions is to let the data speak. Numerous authors have used di¤erent samples, risk measures and competition proxies to discriminate between the competition-fragility and competition-stability view.5 Empirical studies for speci…c countries – many if not most for the U.S. – have not come to conclusive evidence for an either stability-enhancing or stability-undermining role of competition. The cross-country literature has found that more concentrated banking systems are less likely to su¤er a systemic banking crisis as are more competitive banking systems (Beck, Demirguc-Kunt, and Levine (2006); Schaeck, Cihak, and Wolfe (2009)). There seems also evidence that banks in more competitive banking 4 Other

authors have also shown that more intense competition may induce banks to (i) switch to more risky,

opaque borrowers (Dell’Ariccia and Marquez (2004)), and (ii) acquire less information on borrowers (Hauswald and Marquez (2006)). 5 For

an overview, see ?.

6

systems hold more capital, thus compensating for potentially higher risk they are taking (Schaeck and Martin (Forthcoming), Berger, Klapper, and Turk Ariss (2009)). A consequence of the recent theoretical extensions is that the predicted impact of competition on bank stability moved from a bipolar setting (good or bad per se) to a continuous approach (settings that are better or worse in relative terms). These models lead to new testable implications that exceed a mere assessment of the sign of the coe¢ cient of bank market power. For example, by allowing loan defaults to be imperfectly correlated, the Martinez-Miera and Repullo (2010) model and the Hakenes and Schnabel (2007) model imply that the impact of competition on risk is a¤ected by regulatory constraints on asset diversi…cation, since the latter will a¤ect the correlation structure of loan defaults. Our results suggest that an increase in competition will have a larger impact on banks’risk taking incentives in countries with stricter activity restrictions, more herding in revenue structure and unconcentrated banking markets. These …ndings are con…rmed both in cross-sectional regressions as well as when we allow for additional time-series variation in the competitionstability relationship. However, we also …nd that a large part of the cross-country variation in the competition-stability relationship cannot be explained, which constitutes a challenge for further research. The remainder of the paper is structured as follows. Section 2 discusses di¤erent factors that might explain the variation in the competition-stability relationship documented in Figure 1. Section 3 introduces data and methodology, while section 4 presents the main results. Section 5 presents robustness, while section 6 concludes with policy implications.

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2

Competition-stability relationship - theoretical considerations

We argue that country-speci…c features may a¤ect the existing empirical evidence on the relationship between competition and stability via three possible channels. First, a certain type of regulation may limit the extent to which banks can or will engage in riskier activities if their franchise values are eroded. For example, regulatory capital requirements should limit the extent to which banks can follow risk-taking incentives if banks’charter value is eroded. Second, country-speci…c characteristics may also a¤ect the adverse selection problem that banks face if they charge higher loan rates. For example, lending relationships or credit registries may reduce the likelihood that entrepreneurs will chose riskier project in response to higher loan rates. Third, institutional characteristics may a¤ect the proportion of systematic and idiosyncratic risk in loan defaults and may make it hence more likely that the empirical data favor one theory over the other. For example, regulatory constraints on asset, revenue or geographical diversi…cation may make it more likely that loan defaults are highly correlated and hence lead to the empirical outcome that competition is good for …nancial stability. More speci…cally, let

de-

note the estimated e¤ect of bank market power on stability. This point estimate is in‡uenced by three factors:

CF

> 0;

CS

< 0; p(CF ) 2 [0; 1] where

CF

denotes the stability welfare

gains of a unit increase in market power (competition-fragility hypothesis),

CS

denotes the

stability loss as a result of a unit increase in market power (competition-stability hypothesis) and p(CF ) indicates how likely it is that one theory dominates over the other. We conjecture that

= p(CF )

CF

+ (1

p(CF ))

CS .

Our conjecture is that the regulatory environment,

strength of supervision and the institutional framework of a country a¤ect

CF ;

CS

and p(CF ).

More speci…cally, let x denote the speci…c feature under investigation. A change in x (or two samples with di¤erent x) can lead to a di¤erent estimated impact of market power on stability

8

via three di¤erent channels: @ @p(CF ) = @x @x

CF

@p(CF ) @x

CS

+ p(CF )

@

CF

@x

+ (1

p(CF ))

@

CS

@x

The relative strength of each of these three channels may explain why di¤erent studies obtain di¤erent results in terms of sign and magnitude. That is, certain country-speci…c features may make the assumption and prediction of a given theoretical model more realistic. In the remainder of this section, we describe which country-speci…c features may play a role and why. We also introduce speci…c measures that capture these di¤erent market-speci…c characteristics.

2.1

Herding

A …rst important market characteristic that can in‡uence the relationship between competition and stability is covariation of banks’behavior, also known as herding. Acharya and Yorulmazer (2007) and Brown and Dinc (forthcoming) show that the supervisory decision to intervene a failing bank is subject to an implicit too-many-to-fail problem: when the number of bank failures is large, the regulator …nds it ex-post optimal to bail out some or all failed banks. This gives banks incentives to herd and increases the risk that many banks may fail together. Hence, herding behavior may also a¤ect banks’ incentive to increase risk-taking in response to an increase in competition. Bank activity herding is measured by a heterogeneous banking system indicator that measures whether there are substantial di¤erences among di¤erent …nancial institutions within a country. It is calculated as the within country standard deviation of the non-interest income share. If all banks in a country have a similar business model (either voluntarily or forced by regulation), the indicator will be low. When bank activities are highly correlated across banks, a rise in competition will do more damage to a banks franchise value since they do not have any other activities to fall back on. Thus, we can hypothesize that competition will have a CF > 0. It is stronger impact on bank risk behavior in more homogeneous banking systems, i.e. @ @x

important to note in this context that we do not relate the actual activity structure of banks to 9

the relationship between competition and stability, but rather the variation in activity structure within a market.

We also look at herding in terms of risk taking behavior (systemic risk).

If a majority of banks has a high risk appetite, it is very well possible that other banks feel the pressure to take on more risk due to the herding incentives described above. Therefore, we hypothesize that banks operating in an environment with a high risk taking standard, will have a stronger incentive to increase risk taking when competition changes.

2.2

Market structure

A second important market characteristics that might in‡uence the relationship between competition and stability is the structure of the market. Martinez-Miera and Repullo (2010) and Hakenes and Schnabel (2007) show that a lower correlation of loan defaults makes it more likely that …ercer competition harms stability. A bank’s potential to reduce the correlation of its loan portfolio and other revenues is clearly a¤ected by restrictions on functional or geographical diversi…cation. If x is a proxy for diversi…cation, we conjecture that

@p(CF ) @x

> 0.

Market concentration, measured by the Hirschmann-Her…ndahl index, may also play a role in assessing the strength of the competition-stability relationship. While these measures are not good proxies for competition6 , market concentration may play an important role in determining the relationship between competition and stability. Fewer banks in the economy (more concentrated banking markets) make supervision more e¤ective and accurate. If bank supervisors have to monitor fewer banks, they may observe malpractices (risk-shifting, loan portfolio concentration) in a more timely fashion. According to Allen and Gale (2000), countries with a larger number of banks (such as the US) are, ceteris paribus, more likely to support the competition-fragility view compared to banking sectors dominated by fewer larger banks (such as Canada), i.e. 6 See

@

CF

@x

< 0 where x stands for the degree of concentration. Moreover, with

Claessens and Laeven (2004).

10

fewer banks in the system, entrepreneurs may behave more prudently as they have fewer outside options when they default on their loans, which raises the franchise value of the bank. On the other hand, the lower the number of banks in a country, the more they will be interconnected, which may again encourage risk-taking behavior if banks perceive themselves as too-important-to fail, i.e.

@

CF

@x

> 0. The e¤ect of market structure on the competition-stability relationship is

therefore a priori ambiguous.

2.3

Regulatory and supervisory framework

A third group of country traits that in‡uence the relationship between competition and stability consists of regulations designed to protect bank charter values and to prevent risk-seeking behavior if charters are eroded. High capital levels reduce the moral hazard incentives to take aggressive risks. More stringent (risk-based) capital regulation may therefore limit the CF negative in‡uence that competition may have on stability ( @ @x < 0). Hellmann, Murdock,

and Stiglitz (2000), however, show that even with capital requirements, deposit interest rate ceilings are still necessary to prevent banks from excessive risk-taking in competitive markets. Furthermore, Allen, Carletti, and Marquez (2010) show that borrowers prefer well capitalized banks, since these banks have a relatively higher incentive to monitor, which improves …rm performance. They …nd that franchise value and capital are substitute ways of providing banks with monitoring incentives. Also, a recent study by Mehran and Thakor (2010) shows that there is a positive relationship between bank capital holdings and total bank value. This rise in bank value and the borrower preferences should induce a rise in bank charter value, thus lowering the banks’ risk appetite. These e¤ects allow us to hypothesize that higher capital regulation limits the negative e¤ect of competition on bank stability. In other words, the average impact of an increase in competition on bank fragility is larger in countries with weak capital regulation vis-à-vis strict capital requirement regimes.

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Another popular regulatory measure to increase the stability of banking systems is deposit insurance, as it reduces the risk of bank runs. On the other hand, generous deposit insurance schemes might increase moral hazard and thus increase risk-taking incentives in more competitive CF > 0 (see, e.g., Demirguc-Kunt and Kane (2002)). environments, i.e. @ @x

E¤ective banking supervision can be important for several reasons. First, monitoring banks is both costly and di¢ cult for both depositors and shareholders, which can lead to suboptimal bank risk behavior. Secondly, bank failures may be very costly, due to the crucial role banks play within the economic system. Taking these points into account, more e¤ective supervision should provide incentives to limit bank risk taking and thus could soften the e¤ect of competition on risk taking. On the other hand, Boot and Thakor (1993) show that supervisors may pursue self interest, which may lead to suboptimal actions. We integrate two supervisory variables into our analysis, a dummy that indicates whether there is more than one supervisor and an external governance indicator. The e¤ect of having multiple supervisors is not unambiguous. Kahn and Santos (2005) argue that if a single institution is responsible for di¤erent regulatory functions, it may not be able to su¢ ciently monitor all banks. Also, having multiple supervisors may lead to di¤erent supervisory approaches, which can generate useful information which would otherwise be neglected (Llewellyn (1999)). From this point of view, having multiple supervisors should reduce banks risk taking incentives. On the other hand, a single supervisory institution may be preferred because it reduces the chance of taking con‡icting policy measures. Furthermore, Llewellyn (1999) argues that a single authority could prevent gaps in the regulation that could arise when there are multiple supervisors and that having multiple supervisors could lead to supervisory arbitrage, thus relaxing the overall supervision. The empirical evidence on this topic is rather scarce. The only study that extensively focuses on the number of supervisors is Barth, Dopico, Nolle, and Wilcox (2002) who …nd evidence for the supervisory arbitrage theory when there are multiple supervisors. The external governance indicator measures the strength of

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external auditors, …nancial statement transparency and accounting practices and the existence of external ratings, thus the degree of potential market discipline. Having a wide range of private control mechanisms such as external audit and external ratings should dampen the risk incentives of a bank.

2.4

Institutional and …nancial development

A fourth set of country traits that can in‡uence the competition-stability relationship is the institutional framework and …nancial system structure in which banks operate. First, we consider the contractual framework. Loan defaults can arise if a borrower is unable or unwilling to repay her loan. In the latter case, contract enforcement possibilities will be of great importance for banks. If a borrower knows that a bank will have to go through numerous procedures, wait for several weeks/months or simply has to pay large fees to enforce a contract, she will have a greater incentive to evade the loan repayment. A part of the Boyd and De Nicolo (2005) explanation of the competition-stability view relies on the fact that lower loan rates will reduce the entrepreneurs’borrowing cost and thus will increase the success rate of his project, which lowers the loan default probability. However, when operating in countries with protracted contract enforcement procedures, the entrepreneur has a counteracting incentive to repay his loan, independent of his success rate. Thus, we expect that a change in competition will be more harmful to stability when operating in a country with low credit enforcement standards. In other words, a rise in credit enforcement reduces the risk-shifting incentives of entrepreneurs, CS > 0. i.e. @ @x

Take next the credit information sharing framework. Credit registry institutions are public or private entities which collect information on the creditworthiness of borrowers. The existence of these institutions facilitates the exchange of credit information among banks and among investors. The existence of credit registers is expected to reduce both adverse selection

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and moral hazard problems that are inherent on being in the lending business. Pagano and Jappelli (1993) show that information sharing lowers adverse selection problems by lowering the selection cost for lenders. Kallberg and Udell (2003) con…rm these …ndings when studying private information exchanges in the U.S.. They …nd that private credit registry information is valuable in assessing borrower quality, after controlling for information that would be available to a single institution. Information sharing also tends to reduce moral hazard incentives through reputation e¤ects (see, e.g. Diamond (1989)). As borrowers realize that it will be hard to get a loan at another institution when they default on their current loan, they will have a stronger incentive to repay and they will choose safer project (Padilla and Pagano (2000), Vercammen (1995)). Furthermore, Houston, Lin, Lin, and Ma (2010) show for a sample of nearly 2400 banks in 69 countries that greater information sharing leads to higher bank pro…ts and lowers bank risk. This leads us to hypothesize that countries with better information sharing systems will encounter smaller e¤ects on stability when competition changes, since better information systems increases a banks’franchise value and will lower the entrepreneurs’incentive to take more risk. Finally, we consider …nancial structure and, more speci…cally, competition for banks coming from …nancial markets. More developed stock markets make it easier for …rms to switch between bank-based and market-based funding. This could lead to an additional e¤ect of a change in competition on bank risk behavior. As mentioned above, Boyd and De Nicolo (2005) show that a higher loan interest rate (due to lower competition) leads to a higher loan default probability. Martinez-Miera and Repullo (2010) add that, when loans are not perfectly correlated, higher interest rates also raise pro…ts on non-defaulting loans. In countries with strong developed capital markets, however, …rms will have the possibility to substitute loans with market-based funding, thus lowering the total amount of loans and bank pro…t. This leads us to hypothesize that, ceteris paribus, it is more likely to …nd positive e¤ects of competition on bank stability in countries with well developed …nancial markets.

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3

Data and methodology

This section consists of two parts. First, we describe the sample composition and data sources. Next, we explain how we allow for a country-level variation in the estimated impact of competition on stability. In this section, we also describe how we compute the bank-speci…c measures of soundness and market power.

3.1

Data sources

We combine several data sources. We obtain information on banks’ balance sheet and income statement from Bankscope. Bankscope is a database compiled by Fitch/Bureau Van Dijck that contains information on banks around the globe, based on publicly available data-sources. Moreover, the information in the database is harmonized and provided in a global format7 that facilitates the international comparison of banks’…nancial statements. Admittedly, in general this comes at the cost of losing detailed information. However, this is not an issue for the information we need in our analysis. The period of analysis is 1994-2006, and hence is not contaminated by the exceptional events of the 2007-09 global …nancial crisis. If banks report information at the consolidated level, we delete the unconsolidated entries of the group from the sample to avoid double counting. We apply a number of selection criteria to arrive at our sample. First, we exclude countries for which we have information on less than 50 bank-year observations. Second, we delete banks that report information for less than three consecutive years, as our risk measure is computed over rolling windows of three years. Third, we drop bank-year observations that do not have data available on basic variables drop. Subsequently, we winsorize all variables at the 1 percent level to mitigate the impact of outliers and to enhance robustness of the standard errors. While most of the bank-speci…c variables are ratios, variables in levels (such as size) are expressed in 2007 US dollars using a GDP de‡ator. 7 As

of April 2009, the global format is replaced by the ’Fitch Universal Format on Bankscope’.

15

The bank-speci…c data are linked to various country-level datasets that contain information on the regulatory framework, strength of supervision and other institutional features. More speci…cally, we employ data from the 2007 issue of the Bank regulation and supervision dataset compiled by the World Bank (Barth, Caprio, and Levine (2008)). Additional information is obtained from the Heritage Foundation and the World Development Indicators. Filtering the bank-speci…c database and matching it with the country-level datasets yields a sample of banks from 62 countries. The sample consists of a mix of developed and developing countries.

3.2

Empirical framework

In the literature, there are two main approaches to assessing the relationship between competition and stability: a single country or multiple country setup. In a cross-country setup, proxies of market power at the bank- or country-level are related to bank soundness (in a linear or quadratic speci…cation). The sign of the coe¢ cient(s) then indicate whether competition helps or harms stability (or whether there is a turning point at which there is a sign reversal). These studies provide insight into the average relationship between competition and stability for the set of countries under investigation (e.g.: developing countries as in Turk Ariss (2010), developed countries as in Berger, Klapper, and Turk Ariss (2009), the European Union as in Schaeck and Cihak (2010)), while controlling for other country-speci…c factors such as macro-economic conditions, regulation and supervision. However, single country studies (such as Keeley (1990), Salas and Saurina (2003), Jimenez, Lopez, and Saurina Salas (2010), Boyd, De Nicolo, and Jalal (2006)) document a large degree of variation in the competition-stability relationship. This indicates that these other country-speci…c factors may not only have a level e¤ect but also a slope e¤ect. Hence, it is not only important to control for the impact of these factors on risk but also on how they shape the competition-stability relationship. For example, activity restrictions (i.e. allowing banks to enter real estate, insurance or underwriting) may not only a¤ect the aggregate

16

level of risk, but may also in‡uence the extent to which loan market power a¤ect bank risk-taking incentives. Put di¤erently, these variables may also determine whether it is more likely to …nd support for the franchise value paradigm compared to the risk-shifting hypothesis or vice versa. This results in the following setup:

Riski;j;t = c +

j Competitioni;j;t 1

+

j Xi;j;t 1

+ Zj;t + "i;j;t

(1)

In this setup, the indices i; j ; t stand respectively for bank, country and time. The impact of competition (as well as any other bank-speci…c variable, Xi;j;t ) on risk is allowed to vary at the country level. This is denoted by giving the corresponding (vector of) coe¢ cient(s) a j subscript. The vector of bank-speci…c variables, Xi;t

1,

characterizes a bank’s business model. In particular,

we include proxies for the funding structure (share of wholesale funding in total funding), asset (loans to assets ratio) and revenue mix (share of non-interest income in total income) as well bank size (natural logarithm of total assets), credit risk (loan loss provisions to interest income) and asset growth. In addition, we include specialization dummies to allow for di¤erent intercepts for commercial banks, bank holding companies, saving banks and cooperatives. Summary statistics on the control variables that determine bank soundness are in the upper part of Table 1. In addition, time-varying country-speci…c characteristics may also a¤ect bank soundness are included in the vector Zj;t . We hypothesize that

j

can be modelled as a function of (a subset

of) these country-speci…c factors. To gain insight in the potential drivers of heterogeneity in , we take a two-step approach. In a …rst step, we relate bank market power to a measure of bank soundness. This relationship is assessed at the country level. More speci…cally, for each country in our sample, we estimate the following equation:

Riski;t = c +

Competitioni;t

17

1

+ Xi;t

1

+ vt + "i;t

(2)

Including time …xed e¤ects and estimating this equation country by country creates many advantages. First, we allow for the maximum extent of heterogeneity in the competition-stability trade-o¤ across countries. Second, the time dummies di¤er in each country regression and hence indirectly capture the level e¤ect of country-speci…c regulation or the business cycle on bank risk. In the second step, we will explore which country-speci…c variables explain the heterogeneity in the estimated

3.3

j s.

Indicators of market power and bank soundness

In this subsection, we describe how we measure competition, discuss the correlation with other measures of competition and introduce our indicator of bank soundness.

3.3.1

The Lerner index: measure of pricing power

For our analysis, we need a measure of market power that varies at the bank level rather than a competition or concentration proxy at the country level. The Lerner index is an obvious candidate as it captures the extent to which banks can increase the marginal price beyond the marginal cost. Conditional on having an estimate of the marginal price and cost, we can construct the Lerner index for each bank and each year, as follows:

Lerneri;t =

Pi;t

M Ci;t Pi;t

(3)

where Pi;t is proxied by the ratio of total operating income to total assets. As banks have the

opportunity to expand their activities into non-interest generating activities, we include both interest and non-interest revenue. The marginal cost, M Ci;t , is derived from a translog cost function. As Berger, Klapper, and Turk Ariss (2009), we model the total operating cost of running the bank as a function of a single, aggregate output proxy, Qi;t , and three input prices, j wi;t , with j 2 f1; 2; 3g. More speci…cally, we estimate:

18

ln Ci;t =

0+ 1

ln Qi;t +

3 X

2 2 (ln Qi;t ) +

j=1

j

3 X 3 X j ln wi;t +

j=1 k=1

j;k

3 X j k ln wi;t ln wi;t +

j

j ln wi;t ln Qi;t +vt +"i;t

j=1

(4)

We also include time dummies to capture technological progress as well as varying business cycle conditions, as well as a bank specialization dummy. Homogeneity of degree one in input 3 3 X X prices is obtained by imposing the restrictions: j = 1; j = 0 and 8 k 2 f1; 2; 3g : j=1

3 X

j;k

j=1

= 0. Marginal cost is then obtained as follows:

j=1

M Ci;t =

Ci;t @Ci;t = @Qi;t Qi;t

1 j wi;t @ b 1 + 2b 2 ln Qi;t + bj ln 3 A wi;t j=1 0

2 X

(5)

in which Ci;t measures total operating costs (interest expenses, personnel and other administrative or operating costs), Qi;t represents a proxy for bank output or total assets for bank i at time t. The three input prices capture the price of …xed assets, the price of labor and the price of borrowed funds. They are constructed as respectively 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 and money market funding. Following Berger, Klapper, and Turk Ariss (2009), Equation (4) is estimated separately for each country in the sample to re‡ect potentially di¤erent technologies. Table 1 presents summary statistics on the variables needed to construct the Lerner index as well as the estimated Lerner index. The average Lerner index at the country level is 10%, but varies across countries, from

8:6% in Serbia to 21:3% in Denmark.

19

3.3.2

The Z-Score: measure of bank soundness

In our analysis, bank risk is measured using the Z-score. The Z-score measures the distance from insolvency (Roy (1952)) and is calculated as

Zi;t =

ROAi;t + (E=A)i;t (ROA)i;t

where ROA is return on assets, E=A denotes the equity to asset ratio and

(6) (ROA) is the

standard deviation of return on assets. While in large parts of the literature the volatility of pro…ts is computed over the full sample period, we use a three-year rolling time window for the standard deviation of ROA to allow for variation in the denominator of the Z-score. This approach avoids that the Z-scores are exclusively driven by variation in the levels of capital and pro…tability (Schaeck and Cihak (2010)). Moreover, given the unbalanced nature of our panel dataset, it avoids that the denominator is computed over di¤erent window lengths for di¤erent banks. The Z-score can be interpreted as the number of standard deviations by which returns would have to fall from the mean to wipe out all equity in the bank (Boyd and Runkle (1993)). A higher Z-score implies a lower probability of insolvency, providing a more direct measure of soundness than, for example, simple leverage measures. Because the Z-score is highly skewed, we use the natural logarithm of Z-score to smooth out higher values8 . Table 1 shows that the average value of ln(Z-score) slightly exceeds four with a standard deviation of 1:14. Our indicator of market power is signi…cantly correlated with the Z-score and other indicators of competition, but also with indicators of market concentration. Table 2 presents correlations on the country-year level between the Z-score, the Lerner index if market power as well as bank and country-level indicators of competition and market structure. The Lerner index is positively and 8 Others

have used the transformation ln(1+Z-score) to avoid truncating the dependent variable at zero. We

take the natural logarithm after winsorizing the data at the 1% level. As none of the Z-scores is lower than zero after winsorizing, this approach is similar, save for a rescaling, to the former approach and winsorizing after the transformation.

20

signi…cantly correlated with the Z-score, consistent with Figure 1. The Lerner index is negatively and signi…cantly correlated with the average market share of banks in a country and year, while there is no signi…cant correlation with the number of banks. The Lerner index is higher for more concentrated banking markets, as measured by the Her…ndahl index. Interestingly, there is no signi…cant correlation between the Lerner index of market power and two other industry-level behavioral indicators of bank competition, the H-Statistics and the Boone index.

4

The competition-stability relationship: explaining crosscountry variation

4.1

The competition-stability relationship: Cross-country heterogeneity

In the Introduction, we mentioned that the full sample unconditional correlation between the Lerner index and the Z-score is positive (0:258), but that this number hides a substantial amount of cross-country variation (see Figure 1). Using a regression framework, we show that this relationship also holds when conditioning on other variables . Following common practice in the literature, we regress the bank soundness measure on the Lerner index and a wide range of control variables using our total sample. The results are presented in table 3. The …rst column shows OLS estimates, whereas the second column are IV (2SLS) regression results. In the second column, we take into account that market power may be endogenous. The instruments are loan growth and lagged values of the Lerner index. We employ the panel structure of the database and control for …xed heterogeneity at the country and time level by including country and time …xed e¤ects. The standard errors are robust and clustered at the

21

bank level. Moreover, to avoid that our results are driven by countries that are overrepresented in our sample, we weigh each variable with the inverse of the number of banks in the country. Doing so, we give equal weight to each country. We again …nd a positive and signi…cant e¤ect of a change in market power on bank stability. This result is in line with existing literature that also uses the Lerner index as a market power proxy (see, e.g. Berger, Klapper, and Turk Ariss (2009)). However, as already mentioned in the introduction, our interest is less on arguing that competition is good or bad, but rather on uncovering which country characteristics make the impact better of worse. Therefore, it is crucial to show that the regression-based methods also indicate that there is a substantial degree of heterogeneity. Figure 2 is very similar to Figure 1 of the introduction. The correlation between the conditional and unconditional correlation is 0:69 and highly signi…cant. Both bar charts show that the unconditional and conditional correlations are positive in most countries. In Figure 2, the height of the bars shows the magnitude of the coe¢ cient of the Lerner index when estimating Equation (2) for each country separately. The bars are sorted from low to high and the country labels are mentioned on the X-axis. The coe¢ cients that are signi…cantly di¤erent from zero have a lighter shade. The average of the 62 estimated coe¢ cients equals 1.044, which resembles the full sample coe¢ cient. Hence, on average, it seems that the franchise value paradigm dominates the risk-shifting hypothesis. In response to an increase in market power, banks will behave more prudently to protect their monopoly rents that create a larger franchise value. Or vice versa, an increase in competition increases banks’ appetite for risk-taking. However, there is a large amount of heterogeneity in the competition-stability relationship. The standard deviation of the coe¢ cient across the 62 countries is 1.444. A quick look at the country labels on the X-axis also reveals that it is not just a developed versus developing countries story or that regions exhibit similar behavior. In the remainder of this section, we will empirically explore what drives this

22

high cross-country variation in the competition-stability relationship.

4.2

A binary classi…cation approach

Our goal is to shed light on the underlying factors that drive this heterogeneous impact. Uncovering which institutional features drive the cross-sectional variation in b j will allow us to identify

which banking sectors will not be harmed (or harmed less) by more intense banking competi-

tion. To assess the impact of regulatory, supervision and institutional features on the estimated competition-stability trade-o¤, we …rst classify the sample countries into two distinct groups according to the median value of each such speci…c feature. We perform two sorts of tests9 , which di¤er in their treatment of potential cross-country di¤erences in the impact of bank-speci…c characteristics on bank risk. More speci…cally, we employ a SEPARATE and POOLED approach. Both approaches di¤er in the amount of heterogeneity they allow in the relationship between control variables and bank risk. In the SEPARATE test methodology, we employ t-tests to compare the di¤erence in magnitude of the competition-stability relationship across the two groups. More speci…cally, we perform t-tests on two measures. On the one hand, we look at variation in the (unconditional) correlation between the Z-score and the lagged Lerner index. On the other hand, we also estimate Equation (2) separately for each country, and then average the estimated values within each group formed based on a particular institutional characteristic. The POOLED methodology estimates Equation (2) across all banks from countries with a speci…c institutional feature. We thereby impose common slopes (both on the Lerner index and the control variables) within each group. For example, we estimate equation (2) across all countries with weak activity restrictions, and then across all banks in the strong activity restriction countries. We 9 Oztekin

and Flannery (2008) perform a similar kind of analysis to examine how country-speci…c features a¤ect

the adjustment speed at which …rms converge to their target leverage.

23

thus estimate a single coe¢ cient for each group of countries10 , and test whether the coe¢ cients di¤er between the two groups of countries. To compare the impact of the Lerner index on bank stability across the two groups, we employ Chow tests. The POOLED approach is implemented using OLS as well as IV(2SLS). According to Oztekin and Flannery (2008), the separate and pooled methodologies each have its own merits. Averaging individual country regressions in the SEPARATE method allows for full heterogeneity in parameter estimates and error variances. However, estimating Equation (2) for countries with few banks might also yield noisy coe¢ cient estimates. The POOLED method assumes slope and error variance homogeneity across countries, raising the possibility that the gains from pooling would outweigh any costs imposed by ignoring the inherent heterogeneity in the slope estimates. Since we believe that neither method is superior, we document our results using both approaches. Table 5 consists of two panels. The …rst panel corresponds to the separate approach, whereas the second panel contains the results of the pooled approach. Summary statistics on the countryspeci…c variables are reported in Table 4. Variable de…nitions and sources are reported where they occur for the …rst time in the text. The country-speci…c variables are obtained from various sources and may vary at a di¤erent level. Some variables are (almost) constant over time, other vary by country and over time. Moreover, not all variables are available for all countries or all time periods. In Table 4, the summary statistics of the country-speci…c variables are categorized in four 1 0 Larger

countries with more banks may be overrepresented in a particular group. For example, the majority

of banks within the sample operate in the US. On the one hand, could this lead to US-biased results, on the other hand does it make a fair comparison with the separate approach invalid. Therefore, we give equal weight to each country in the pooled approach. The weight that each individual bank observation gets is proportional to 1=ni , where ni equals the number of observations for country i. Moreover, we include time and country …xed e¤ects and cluster the standard errors at the bank level.

24

groups. The results on the SEPARATE and POOLED tests will be described in a similar order.

4.2.1

Herding

As discussed above, we use two proxies of herding. The heterogeneous banking system indicator measures whether there are substantial revenue di¤erences among di¤erent …nancial institutions within a country. It is calculated as the within country standard deviation of the non-interest income share. If all banks in a country have a similar business model (either voluntarily or forced by regulation), the indicator will be low. A higher value indicates that the banking system is more diverse. In heterogeneous banking systems, an increase in competition is less detrimental for bank risk compared to homogeneous banking systems. A second indicator of the too-manyto-fail problem is the aggregate Z-score. This variable is a proxy of systemic risk. Lower values of the aggregate Z-score points to an overall reduction in banking sector soundness and larger scope for herding as the likelihood of joint failures is larger in unstable banking sectors. The results in Table 5 indicate that competition will do more harm when banks’activities are highly correlated as they mimic their rivals’business model11 . We also …nd signi…cant and robust evidence that banks operating in less stable banking systems will gamble more in response to an increase in competition compared to stable banking sectors, in which it is more likely that default will be an idiosyncratic event. Herding thus exacerbates the negative impact of competition on stability.

4.2.2

Market structure

Market structure consists of several di¤erent dimensions. First, we look at whether overall activity restrictions limit the types of banks in the country. Therefore we include an activity restriction index, taken from the World Bank’s “Bank regulation and supervision”database (Barth, Caprio, 1 1 We

also tried a similar measure of herding on the funding side, which, however, does not seem to matter for

the competition-stability relationship.

25

and Levine (2008)), which measures the degree to which banks are permitted to engage in feebased activities related to securities, insurance and real estate rather than more traditional interest spread-based activities. Lower values of the index indicate that no restrictions are placed on this type of diversi…cation by banks and higher values indicate that such diversi…cation is prohibited. Next, we look at the Hirschmann-Her…ndahl index (based on total assets) as a proxy of bank market concentration12 . Larger values denote a more disperse distribution of market shares and hence higher concentration. Finally, we also check whether the existence of entry barriers, as a proxy of the contestability of the market, a¤ect the strength of the competitionstability relationship. Entry barriers is an index measuring the degree to which applications to receive a banking licence have been denied over the past …ve years. Higher values of the index indicate greater stringency, hence less competition. The results in Table 5 indicate that the impact of competition on bank risk is larger in banking sectors that face higher restrictions. The di¤erences are signi…cant in all test set-ups (unconditional versus conditional, separate versus pooled). An increase in competition (lower Lerner index) will lead, ceteris paribus, to a much larger reduction in Z-scores for banks operating in countries with stricter restrictions on di¤erent …nancial activities. If we classify the countries in our sample in a low and high concentrated group, we …nd that the positive impact of market power on stability is larger in concentrated banking systems. Put di¤erently, from a …nancial stability perspective, an increase in loan market competition is more detrimental in countries with more disperse bank market structures. This is in line with Allen and Gale (2000). In the separate and pooled approaches, the mean of the high group is lower than the mean of the low group. The means are signi…cantly di¤erent in the two groups for the classi…cation based on the Her…ndahl index. Finally, using the fraction of entry applications denied to classify countries in 1 2 We

also looked at a Hirschmann-Her…ndahl index based on total loans and a CR3 ratio as concentration

proxies. Both indicators lead to similar results as for the HHI based on total assets.

26

a low and high entry barriers group yields insigni…cant and unstable results. The pooled and separate approach yield opposite conclusions. This perceived inconsistency with the previous …ndings is probably due to the much smaller set of countries for which we observe this variable (42 instead of 62).

4.2.3

Regulatory and supervisory structure

To test the hypothesis that capital regulation reduces the negative impact of more competition on stability, we divide the countries in the sample in two groups based on a capital stringency index. The capital stringency index checks whether there are explicit requirements regarding the amount of capital that a bank should have. A higher index indicates greater stringency. In addition to capital regulation, deposit insurance schemes have been designed to protect depositors from excessive risk-taking by banks. Deposit insurance coverage is proxied by deposit insurance coverage relative to GDP per capita. This variable taken from the Deposit Insurance Around the World database of the World Bank (Demirguc-Kunt, Karacaovali, and Laeven (2005)). Banking supervision is captured by an external governance index and a dummy that equals one if there are multiple supervisors in a country. The external governance index allows us to check the in‡uence of private monitoring mechanism, while the supervisory dummy gives more info on the structure of public supervision. The results in Table 5 show that the average unconditional correlation between the Lerner index and the Z-score is larger in the set of countries with a capital stringency index below the mean. Hence, an increase in competition is more harmful for bank stability in countries with weak capital regulation. However, the averages are not signi…cantly di¤erent across both groups. Hence, we …nd no support for the theories that predict that stricter capital requirements are a substitute for franchise values and will prevent to take on excessive risks when they are faced with more intense competition. Looking at deposit insurance coverage relative to GDP per capita, it

27

is clear that countries with more generous deposit insurance systems face a worse competitionstability trade-o¤. As a more generous deposit insurance system reduces the e¤ectiveness of outside monitors, banks have a larger incentive to gamble in response to a shrinking Lerner index. When we divide the countries in our sample in a high and low group based on the external governance index, we see that countries with weak external monitoring mechanisms react stronger to changes in competition. Thus, an increase in loan market competition will be more harmful for bank stability in countries with a low focus on private monitoring. On the other hand, having multiple bank supervisors leads to a stronger impact of loan market competition on bank risk taking behavior, while the impact of competition is far milder when there is only one supervisor. Multiple supervisors may lead to coordination problems if they have supervisory responsibilities over di¤erent activities. For both measures, the four approaches yield di¤erences of consistent magnitude, though the statistical signi…cance at the conventional levels is not achieved across all methods.

4.2.4

Institutional and …nancial development

We hypothesized that variables capturing the extent to which information on borrowers (and defaults) are shared and the cost of enforcing contracts may a¤ect the risk-shifting incentives of entrepreneurs. We use a dummy to capture whether there is a public or a private registry present in a country, as well as an indicator of credit information depth. This variable captures the di¤erence in information content between the registers in di¤erent countries, since some of them only collect limited information on large borrowers, while others have extensive information on a whole range of borrowers and their characteristics (Miller (2003)). The index ranges between 0 and 6, with a higher value indicating that there is more information available.. Both variables are based on the Doing Business database from the World Bank. From the same database we also use the a proxy measuring the contract enforcement cost in a country, thus a negative indicator

28

of the e¢ ciency of the contractual framework. Furthermore, we use stock market turnover , i.e. the ratio of stocks traded to stocks listed, as indicator of …nancial market development. Finally, we consider a sample split according to GDP per capita, the most general indicator of economic and institutional development. Table 5 shows that the split up based on the credit registry dummy does not lead to signi…cant di¤erences in the competition-stability trade-o¤. Also, the depth of credit information and the cost of contract enforcement do not play a decisive role in the competition-stability relationship. The stock market turnover variable, on the other hand, leads to signi…cant and consistent …ndings over the four methods. Our results show that banks in countries with a high stock market turnover tend to have higher risk taking incentives when there is a rise in competition. Finally, we …nd no signi…cant di¤erence in the competition-stability relationship across countries with di¤erent levels of economic development, as measured by GDP per capita.

4.3

Competition-stability relationship: a continuous approach

The binary classi…cation approach provides evidence that many country-speci…c characteristics may have an e¤ect on the competition-stability trade-o¤. One could still wonder (i) to what extent these variables capture di¤erent information and do they still have an impact when they are simultaneously controlled for, and (ii) whether the relationship is con…rmed using all the variability in the proxy (rather than creating high and low groups) . In this section, we provide further insight into these issues. Based on the results reported in Table 5, we select a number of variables to include in a continuous rather than binary analysis. Country-speci…c variables are only included if they satisfy the following criteria: (i) they have to be signi…cant in the IV(2SLS) case, (ii) they have to be signi…cant in more than half of the test setups in Table 5. This leaves us with Herding - Revenues, Systemic risk, Activity Restrictions, HHI , Multiple Supervisors, and Stock Market Turnover ratio. Although the variable GDP per capita only leads to signi…cant

29

di¤erences in the pooled IV approach, we also include it to exclude that our results are driven by the degree of economic development. Table 6 provides information on the correlation matrix of these six variables as well as the (un)conditional correlation coe¢ cients at the country level. The binary approach showed that these characteristics potentially play an important role in the competition-stability relationship. The correlation table con…rms these …ndings and also provides information on how these countryspeci…c variables are correlated with each other. Table 7 provides information on estimation results of regressions of the following form:

Riski;j;t = c + (

0

+

j Zj;t )

Competitioni;j;t

1

+

j Xi;j;t 1

+ vt + vj + "i;j;t

(7)

where Zj;t is either just one of the above-mentioned country-speci…c characteristics or a vector containing all of them. The …rst column shows the outcome for the baseline regression at the bank level, i.e. when regressing our stability measure (Z-score) on the Lerner index, a group of bank-speci…c control variables and GDP per capita. In each subsequent column, we add an interaction term of the Lerner index with a country-speci…c characteristic. In the last column, we show the result when we add all interaction terms simultaneously. For ease of comparability of the economic signi…cance, all country-speci…c variables have been normalized to have zero mean and unit variance. In the characteristic-by-characteristic regressions, each of the interaction terms is signi…cant (except the systemic risk proxy) with the expected sign. As we selected the variables based on their signi…cance in the binary approach, this is not surprising. When exploiting all of the heterogeneity rather than only classifying them into a high or low group, we reinforce the previous …ndings. The last column shows that almost all results hold when they are simultaneously controlled for. We …nd strong and convincing multivariate evidence that competition is more harmful for stability in countries where (i) banks herd more in terms of revenue structure, (ii) there are more restrictions on the permissible range of activities, (iii)

30

banks have multiple supervisors, (iv) they operate in less concentrated markets and (v) GDP per capita is lower. Stock market turnover is no longer signi…cant because of its high correlation with GDP per capita. Similarly, the high correlation of systemic risk with Activity Restrictions and HHI also in‡ates its standard errors. However, in the case of systemic risk, the variable does not show up signi…cantly even in the individual characteristic regression (column (3)). Interestingly, the absolute value of the coe¢ cients of the signi…cant variables are almost equal in magnitude (between 0:16 and 0:21). As we normalized the variables, they all seem to have an equally important e¤ect in economic terms. The coe¢ cient on the Lerner index without interaction is 1:04. A one standard deviation increase in one of these variables hence leads to a 15% to 20% change in the impact of competition on stability.

5 5.1

Additional results Time variation in the competition-stability relationship

So far, our analysis has focused on the cross-country di¤erences in the relationship between competition and bank stability. In this part, we allow for additional variation over time in these country-speci…c relationships. More speci…cally, for each country we regress our bank stability measure (Z-score) on the Lerner index, a group of bank speci…c control variables and GDP per capita, while using …ve year rolling windows. In this way, we retrieve time-varying (conditional) correlations between the Lerner index and bank stability. Since our sample period is 1994-2006, and taking into account that we lag our independent variables with one period, we get a maximum of eight country-speci…c conditional correlations. The retrieved correlations are subsequently regressed on country-speci…c variables measured at the …rst year of the …ve year windows. Table 8 displays the results for three di¤erent regression speci…cations using this setup. In the …rst column, we show the results coming from a regression of the conditional,

31

time-varying correlations on country-speci…c characteristics, while clustering the standard errors over time. In the second column, we add a lagged correlation term to the explanatory variables. In this way, we control for the persistence in the time varying correlation, caused by our rolling window estimations. Standard errors are again clustered over time. The third regression also includes a lagged term, but the error terms are now clustered over the countries instead of over time. The results in column 1 of Table 8 show that country characteristics explain only 18 percent of the variation in the conditional correlation coe¢ cients. Hence, after taking into account countryspeci…c characteristics (and having considered a wide range of potential drivers), there is still a lot of unexplained variation in the competition-stability relationship. If this heterogeneity is all random, this is worrying as it implies that it is di¢ cult to design an optimal regulatory setting to minimize the negative e¤ect of competition on stability. The results of this time-varying analysis largely con…rm our previous …ndings. First of all, the impact of revenue herding on stability is negative and signi…cant for all three speci…cations. Thus, banks in countries where the majority of the banks collect their revenues from the same type of business will respond stronger – in terms of risk behavior - to a change in competition than banks in dispersed markets. In other words, heterogeneous banking markets are more likely to support the competition-stability theorem. Furthermore, the results show that more activity restrictions have a positive and signi…cant impact on the correlation coe¢ cient. Financial institutions that are allowed to become universal banks will face imperfectly correlated revenue streams, which in‡uences their reaction to a change in competition. This result is consistent over the three regression speci…cations and in line with our previous …ndings It again con…rms that the impact of competition on bank risk behavior will be larger in markets with a more concentrated bank focus. The fact that more diversi…ed banks tend to have higher charter values seems to dominate the higher risk taking incentive for diversi…ed banks due to the lower correlation

32

in loan defaults. The third country characteristic that has a consistent signi…cant impact on bank stability is market concentration. Although not signi…cant in the …rst speci…cation, the Her…ndahl concentration index has a signi…cant and negative impact on the conditional correlation coe¢ cient when adding the lagged dependent variable to the regression. Banks operating in less concentrated markets react stronger to a change in competition, which is similar to our previous …ndings. A small concentrated market is easier to monitor by the bank supervisor and banks operating in concentrated markets may also have higher franchise values, which reduces their risk appetite. Besides these three characteristics, there are two other variables (Multiple Supervisors and GDP per capita) that showed up signi…cantly in Table 7. Their signi…cance is not con…rmed in this setup.

5.2

Alternative risk measures

Until now, we have used the Z-score as our preferred bank risk measure. The Z-score combines bank equity over total assets, return on assets, and the volatility of the returns to come up with a measure of bank stability. It thus combines three di¤erent risk aspects. In this part, we will look at the reaction of these three subcomponents when the level of competition changes. We are particularly interested whether the subcomponents all move in the same direction or whether there is a component overruling the other two subcomponents. Furthermore, we also look at the amount of non-performing loans as a potential alternative bank risk measure. The Table 9 con…rms our …ndings while using the alternative risk measures. The …rst column shows the standard results when using the Z-score as stability measure and regressing it on the Lerner index and a group of bank-speci…c control variables. For regression (2) to (5) we use four alternative stability measures, being non-performing loans and the three subcomponents of the Zscore. For each stability measure we perform the basic regression with country-year …xed e¤ects. When using non-performing loans as stability measure, we leave out the loan loss provisions

33

over interest income ratio as a control variable, since both variables are heavily related to each other. The results show that all subcomponents of the Z-score react in a similar way to a change in competition. A rise in market power leads to a higher equity ratio, more return on assets and lower return volatility. Furthermore, a rise in competition also leads to less non-performing loans. The results for the alternative risk measures are thus in line with the Z-score results; we again …nd consistent and signi…cant evidence in favour of the competition-fragility theorem. A rise in market power (measured by the Lerner index) leads to more stability, independent of the stability proxy we use.

5.3

Bank-variation in the competition-stability relationship

So far, we have exploited cross-country and time-series variation in the competition-stability relationship. However, banks’risk-taking incentives might be also in‡uenced by their own relative position in the market. Speci…cally, we posit that failing banks have a greater incentive to exploit competition towards more aggressive risk-taking. Further, banks with a larger market-share and that therefore consider themselves too-big-to-fail might also exploit increasing competition to take more aggressive risks. This subsection assesses whether such bank-level variation exists. Table 10 shows the impact of competition on bank stability while controlling for the potential impact of failing banks. The …rst column shows our baseline competition-stability regression. In the second and the third column, we interact the Lerner index with an exit dummy. In the second regression, the exit dummy equals one in the two years before the bank leaves the sample. These banks seem to react less intense when competition changes. However, notice that this dummy does not discriminate between defaults and distressed mergers at the one hand and ’normal’ mergers or acquisitions at the other hand. Therefore, in the third regression, the exit dummy only equals one in the two years before a bank leaves the sample when the bank had a negative return on assets in that period. In this way, we only capture the banks that actually where

34

in distress before they leave the sample. The signi…cant and positive interaction term between competition and the exit dummy indicates that these banks that are in trouble before leaving the sample react more strongly to a change in competition. Thus, banks that are in distress gamble even more than others when competition rises, probably because there is not much left to loose for them. In the fourth regression, we only look at banks that did not exit the sample (Distressed Exit Dummy =0), while adding interaction terms between the Lerner index and country-speci…c characteristics that potentially in‡uence the competition-stability relationship. The results show that market power still has a positive impact on bank stability for these banks. Furthermore, as shown in our previous analysis, banks operating in a country with high activity restrictions or with a highly liquid stock market tend to react stronger to a change in competition. Table 11 shows the results for the baseline competition-stability regression while controlling for the impact of bank market share. The …rst column retakes our baseline results, while we interact the Lerner index with a bank’s market share (measured in terms of total assets) in the second regression. This allows us to check whether banks with a higher market share have an incentive to take more risk, because they can potentially see themselves as too-big-to-fail. The results indicate that there is no direct too-big-to-fail e¤ect in‡uencing the competition-stability relationship. In the third column, we do a similar exercise, but now using a market share dummy that equals one for bank with a market share that is larger than 10 percent. Again, we do not …nd a signi…cant direct e¤ect of a banks’market share on the competition-stability relationship.

6

Conclusion

This paper aims to reconcile the seemingly contrasting evidence on the bank competition-bank soundness relationship. Theoretical models and empirical results o¤er con‡icting evidence. A …rst look at a worldwide sample of banks tells us that the relationship between market power and bank soundness is positive. Hence, on average, it seems that the franchise value paradigm 35

dominates the risk-shifting hypothesis. In response to an increase in market power, banks will behave more prudently to protect their monopoly rents that create a larger franchise value. Or vice versa, an increase in competition increases banks’ appetite for risk-taking. However, this full sample relationship hides a substantial amount of cross-sectional heterogeneity, with estimates ranging from negative to positive, with many countries showing insigni…cant relationships between competition and stability. We develop a framework to assess how regulation, supervision and other institutional factors may make it more likely that the data favor one theory over the other, i.e. the charter value paradigm over the risk-shifting paradigm. We show that an increase in competition will have a larger impact on banks’risk taking incentives in countries with stricter activity restrictions, more herding in revenue structure and unconcentrated banking markets. Our …ndings help in understanding the seemingly con‡icting empirical evidence. Most studies tend to …nd results in favour of the competition-fragility view. However, if one would sample banks from countries/regions with concentrated banking markets and homogeneous operations (either homogeneous because of regulatory restrictions or due to herding), obtaining the opposite …nding need not be inconsistent. Our …ndings have important policy repercussions. They suggest that activity restrictions and herding trends can exacerbate the negative impact of competition on bank stability so that regulatory reforms have to take this into account. We show that the too-many-to-fail phenomenon is worse in more competitive environments. On the other hand, capital regulations seem to have less of an in‡uence on the relationship between competition and stability, which puts the current debate on capital bu¤ers somewhat in perspective.

References Acharya, Viral V., and T. Yorulmazer, 2007, Too many to fail–an analysis of time-inconsistency in bank closure policies, Journal of Financial Intermediation 16, 1–31. 36

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Table 1: Summary Statistics This table shows the total sample summary statistics for the bank speci…c variables used throughout the paper. Bank speci…c data is retrieved from the Bureau Van Dijck Bankscope database. The full sample contains 112074 observations. The table consists of three parts. Panel A contains information on the mean and standard deviation of the variables that are used as control variables in the competition - stability regressions. The impact of banks’ business model on bank soundness is proxied via its funding structure (share of wholesale funding equals the share of money market funding in money market funding and total deposits), asset mix (loans to total assets) and revenue composition (non-interest income in total income). We also control for bank size, credit risk (loan loss provisions to total interest income) and bank strategy (annual growth in total assets). We have four types of banks in our sample: Commercial Banks, Cooperative Banks, Savings Banks and Bank Holding Companies. Panel B summarizes the variables that are needed to construct the Lerner index. The Lerner index is the relative markup of price over marginal cost. The average price of bank activities equals the ratio of total revenues over total assets. Marginal costs are obtained after estimating a translog cost function. Using a translog speci…cation, we relate banks’total operating cost to three input prices. They are constructed as respectively 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 and money market funding. Panel C contains info on the main variables of interest: market power and bank riskiness. Market power is measured through the Lerner index, whereas our bank stability indicator is the natural logarithm of the Z-score. The Z-score is calculated as the sum of equity over total assets and return on assets divided by the three year rolling standard deviation of return on assets.

Variable Mean Standard Deviation Determinants of Bank Soundness Share of Wholesale Funding 0.0439 0.0943 Loans to Total Assets 59.9764 17.6376 Non-Interest Revenue Share 0.228 0.1625 ln(Total Assets) 5.9873 1.822 Loan Loss Provisions to Interest Income 0.1094 0.1663 Annual Growth in Total Assets 6.7748 17.507 Commercial Bank dummy 0.6052 0.4888 Cooperative Bank dummy 0.1485 0.3556 Savings Bank dummy 0.1382 0.3451 Bank Holding Company dummy 0.1081 0.3105 Translog Cost Function Total Operating Cost 208.6049 815.1252 Price of Fixed Assets 1.4845 2.7043 Price of Labor 0.016 0.0091 Price of Funding 0.0359 0.0322 Average Price of bank activities 0.0751 0.0424 Marginal Cost 0.0634 0.0379 Market Power and Bank Soundness ln(Z-score) 4.1106 1.1417 Lerner 0.1548 0.1366

42

43 Boone

-H-statistic

HHI(TA)

nbanks

Market Share

Variables Lerner

Bank Soundness 0.420 (0.000) -0.372 (0.000) -0.187 (0.000) 0.020 (0.625) 0.047 (0.244) 0.072 (0.074)

Lerner -0.087 (0.018) -0.021 (0.613) 0.104 (0.005) 0.047 (0.199) 0.031 (0.395)

1.000

Market Share 0.444 (0.000) 0.477 (0.000) 0.038 (0.297) -0.004 (0.909)

1.000

nbanks 0.280 (0.000) -0.026 (0.520) -0.099 (0.014)

1.000

0.112 (0.002) 0.011 (0.762)

1.000

HHI(TA)

This table provides information on the correlation between bank soundness and various proxies of bank market power, market structure and competition. Correlation measures are obtained at the time-varying country level. If a variable varies at a higher level (like bank soundness) it is …rst averaged at the time-country level. Bank soundness is the natural logarithm of the Z-score. The Lerner index is a bank-speci…c, time–varying measure of market power. Market Share is the average market share of a bank in a country in a given year. nbanks is the inverse of the number of banks in a country. HHI(TA) is the Hirschmann-Her…ndahl index of concentration of total assets. The more disperse the market structure, the lower this value will be. The last two measures are estimated structural competition measures. The estimations are done at the country level over …ve year rolling windows. We take the opposite of the Panzar-Rosse H-statistics, such that a higher value also indicates an increase in competition. Finally, the Boone indicator is a new measure of competition following Boone (2008).

Table 2: Bank Soundness and Competition measures: correlation table

0.153 (0.000)

1.000

-H-statistic

Table 3: Full sample regressions This table contains information on the relationship between competition and stability in the total sample. The total sample consists of 62 countries and spans the time period 1994-2006. Bank soundness (ln Z-score) is the dependent variable and is regressed on a competition proxy (Lerner index) , a group of bank speci…c control variables (including specialization dummies) and GDP per capita. We employ the panel structure of the database and control for …xed heterogeneity at the country and time level by including country and time …xed e¤ects. The standard errors are robust and clustered at the bank level. To mitigate the impact of reverse causality, we use one period lagged values of the independent variables. The …rst two columns show OLS estimates, whereas the third column are IV (2SLS) regression results. In the …rst column, we include year and country …xed e¤ects. In the second column, we interact them to account for time-varying country speci…c heterogeneity. In the third column, we take into account that market power may be endogenous. The instruments are loan growth and lagged values of the Lerner index. The Stock-Yogo weak ID test critical values at the 15 per cent level is 11.59 and at the 10 per cent level is 19.93. To avoid that our results are driven by countries that are overrepresented in our sample, we weigh each variable with the inverse of the number of banks in the country. Doing so, we give equal weight to each country.

VARIABLES Lerner index Share of Wholesale Funding Loans to Total Assets Non-Interest Revenue Share ln(Total Assets) Loan Loss Provisions to Interest Income Annual Growth in Total Assets GDP per Capita Constant

Observations R-squared Type dummies Year dummies Country dummies Year x Country dummies Instruments

(OLS) ln(Z-score)

(OLS) ln(Z-score)

(IV) ln(Z-score)

1.001*** (0.0919) 0.00899 (0.104) 0.00284*** (0.000802) -0.627*** (0.0812) 0.0256*** (0.00838) -0.601*** (0.0601) -0.00287*** (0.000496) -2.86e-05** (1.21e-05) 3.237*** (0.153)

0.971*** (0.0957) -0.0128 (0.109) 0.00196** (0.000810) -0.624*** (0.0845) 0.0238*** (0.00863) -0.523*** (0.0618) -0.00369*** (0.000557)

2.129*** (0.169) 0.0966 (0.113) 0.00231*** (0.000837) -0.690*** (0.0872) 0.00550 (0.00912) -0.295*** (0.0714) -0.00303*** (0.000519) -3.61e-05*** (1.25e-05) 3.435*** (0.180)

112,074 0.284 YES YES YES NO

112,074 0.349 YES

2.841*** (0.211)

YES

F-stat(…rst step IV) Jstat p-value

111,179 0.266 YES YES YES NO lagged Lerner and Loan Growth 741.3 0.839 0.360

Robust standard errors in parentheses *** p