Journal of Banking & Finance 38 (2014) 64–77
Contents lists available at ScienceDirect
Journal of Banking & Finance
journal homepage: www.elsevier.com/locate/jbf
Bank competition and financial stability in Asia Pacific
Xiaoqing (Maggie) Fu a, Yongjia (Rebecca) Lin a, Philip Molyneux b,⇑,1
a
b
Faculty of Business Administration, University of Macau, Taipa, Macau
Bangor Business School, Bangor University, UK
a r t i c l e
i n f o
Article history:
Received 21 May 2013
Accepted 20 September 2013
Available online 1 October 2013
JEL classification:
G21
G28
Keywords:
Bank competition
Financial stability
Regulation
Banks in Asia Pacific
a b s t r a c t
Analysis of the tradeoff between competition and financial stability has been at the center of academic
and policy debate for over two decades and especially since the 2007–2008 global financial crises. Here
we use information on 14 Asia Pacific economies from 2003 to 2010 to investigate the influence of bank
competition, concentration, regulation and national institutions on individual bank fragility as measured
by the probability of bankruptcy and the bank’s Z-score. The results suggest that greater concentration
fosters financial fragility and that lower pricing power also induces bank risk exposure after controlling
for a variety of macroeconomic, bank-specific, regulatory and institutional factors. In terms of regulations
and institutions, the results show that tougher entry restrictions may benefit bank stability, whereas
stronger deposit insurance schemes are associated with greater bank fragility.
Ó 2013 Elsevier B.V. All rights reserved.
1. Introduction
The impact of bank competition on financial stability has been a
focus of academic and policy debate over the last two decades and
particularly since the 2007–2008 global financial crises (Beck,
2008; Carletti, 2008; Careletti, 2010; Acharya and Richardson,
2009; Beck et al., 2010; OECD, 2011). Under the traditional competition-fragility view, banks cannot earn monopoly rents in competitive markets and this results in lower profits, capital ratios and
charter values. This makes banks less able to withstand demandor supply-side shocks and encourages excessive risk-taking
(Marcus, 1984; Keeley, 1990). Alternatively, the competitionstability view suggests that competition leads to greater stability.
A less competitive banking market may lead to more risk-taking
if the big banks are deemed too important to fail and as such obtain
implicit (or explicit) subsidies via government safety nets (Mishkin, 1999). In addition, banks with more market power tend to
charge higher loan rates, which may induce borrowers to assume
greater risk leading to greater default. In competitive banking
⇑ Corresponding author. Tel.: +44 1248382170.
E-mail addresses: [email protected] (Xiaoqing (Maggie) Fu), [email protected]
(Yongjia (Rebecca) Lin), [email protected] (P. Molyneux).
1
The authors are grateful for funding from the University of Macau. We highly
appreciate the comments from the BOFIT Research Seminar, Australasian Finance &
Banking Conference, Auckland Finance Meeting, and FMA Asian Conference Doctoral
Student Consortium, especially those from Franklin Allen, Iikka Korhonen, Minghua
Liu, Ronald Masulis and Yukihiro Yasuda. All errors are our responsibility.
0378-4266/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.jbankfin.2013.09.012
markets loan rates are lower, Too-Big-To-Fail issues and safety
net subsidies are smaller, and this results in a positive link between
bank competition and stability (Boyd and De Nicoló, 2005). It could
also be the case, as noted by Martinez-Miera and Repullo (2010)
that bank competition and stability are linked in a non-linear
manner, and in a similar vein Berger et al. (2009) argue that competition and concentration may coexist and can simultaneously
induce stability or fragility.
As noted above, recent studies on the causes of the credit
crunch have highlighted deregulation and excessive competition
as factors that led to financial sector meltdowns in the US and
the UK (Llewellyn, 2007; Brunnermeier, 2009; Milne, 2009; OECD,
2011). Moreover, it is of interest to assess whether the relationship
between banking competition and financial stability has been affected after the outbreak of the recent financial crisis. While a substantial literature has emerged addressing this critical issue,2 to our
knowledge, the problem has been inadequately covered for banks
operating across the Asia Pacific region.3 Against this backdrop our
paper investigates the impact of bank competition on financial
2
Beck (2008) and Carletti (2008, 2010) provide excellent surveys of the literature.
Soedarmon et al. (2011) and Liu et al. (2012) estimate the competition-stability
nexus for banks in 12 Asian economies and four South East Asian countries,
respectively. In addition, a small number of cross-country empirical studies include
several Asia Pacific economies into their large sample sets in testing this relationship.
See, for example, Beck et al. (2006a), Boyd et al. (2006), Evrensel (2008), Berger et al.
(2009), Schaeck et al. (2009), Behr et al. (2010), Turk Ariss (2010), and Anginer et al.
(2012).
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Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77
stability for 14 Asia Pacific economies over the period from 2003 to
2010 and extends the previous empirical literature in several
respects.4
First, previous studies have focused on using Z-scores or evidence of a real bank crisis as measures of banking sector risk/stability. Here we extend the analysis by employing the probability
of bankruptcy as an indicator of individual bank fragility.5 A real
banking crisis can be an accurate indicator of banking sector stability, but its significance may be distorted for the following reasons:
(1) banking crises are defined and announced differently across
countries; (2) regulators may be less inclined to report bank insolvencies because they may imply regulatory failure; and finally (3)
regulators are reluctant to announce the failures of banks that play
a key role within the system because they wish to avoid contagion
effects (Uhde and Heimeshoff, 2009). The probability of bankruptcy,
computed using the Black and Scholes (1973) and Merton (1974)
contingent claims approaches provide a more appealing alternative.
Compared to the use of accounting-based models (e.g., Z-score), this
market-based measure of stability has the following advantages: (1)
in efficient markets, stock prices reflect all available information; (2)
market variables are unlikely to be influenced by firm’s accounting
policies; and (3) market prices reflect future expected cash flows
and thus should be more appropriate for use for prediction purposes.
Second, according to the structure-conduct-performance proposition, competition and concentration are inversely related; a more
concentrated market will feature a lower degree of competition.
However, criticisms of this view have led to a shift away from
the presumption that structure is the most important determinant
of the level of competition. Instead, proponents of what is now
known as the New Industrial Organization (NIO) literature, such
as Schmalensee (1982), argue that the strategies (conduct) of individual firms are equally, if not more, important than concentration,
in explaining competitive conditions. Also, the related emergence
of the theory of contestability (Baumol, 1982; Baumol et al.,
1982) has spawned a variety of non-structural indicators of competition aimed at identifying firm conduct.6 In our study we include
both structural and non-structural measures of competition to
examine the concentration, competition and stability nexus in Asia
Pacific banking.7
Thirdly, we incorporate both regulatory and institutional environmental factors in our models and also highlight the impact of
the global turmoil on individual risk exposure in the region. Following Berger et al. (2009), we adopt an instrumental variable
technique with a Generalized Method of Moments (GMM) estimator to address potential endogeneity problems between bank competition and risk. We also include a series of sensitivity analyses
using different model specifications.
4
See, for example, De Nicoló et al. (2003), Beck et al. (2006a), Boyd et al. (2006),
Yeyati and Micco (2007), Berger et al. (2009), Schaeck and Cihak (2008), Schaeck et al.
(2009), Uhde and Heimeshoff (2009), Behr et al. (2010), Turk Ariss (2010), Agoraki
et al. (2011), Soedarmon et al. (2011), and Liu et al. (2012).
5
The Z-score is also used in this study to determine the robustness of our results.
6
These include measures of competition between oligopolists such as Iwata (1974)
and those that test for competitive behavior in contestable markets, Bresnahan (1982,
1989), Lau (1982) and Panzar and Rosse (1987). These indicators have been developed
from (static) theory of the firm models under equilibrium conditions and mainly use
some form of price mark-up over a competitive benchmark, such as price over
marginal cost for the Lerner index and price over marginal revenue for the Bresnahan
(1982) measure. The main exception is the Panzar and Rosse (1987) indicator that
measures the relationship between changes in factor input prices and revenues
earned by firms. See also Koetter et al. (2012) for recent studies using adjusted-Lerner
indices to measure market power in banking.
7
The structural approach focuses on market structure measures such as market
shares, concentration ratios for the largest sets of firms, and a Hirschman–Herfindahl
index. Structural indicators measure actual market shares but do not allow inferences
regarding the competitive behavior of banks. Non-structural measures are used to
quantify bank pricing behavior. They include the Lerner index and the Panzar Rosse
H-statistic (Berger et al., 2004).
65
Overall our results suggest that greater concentration fosters
financial fragility, whereas lower pricing power also induces bank
risk exposure after controlling for macroeconomic, bank-specific,
regulatory and institutional factors. This finding supports the
neutral view of the competition-stability relationship. It also
implies that some banks in the region are able to attain greater
discretion in price-setting to boost profits and reduce insolvency
risk through channels other than increased concentration, such
as product differentiation. Furthermore, there is evidence that
larger banks are more likely to fail than their smaller counterparts.
In addition, our results indicate that tougher entry restrictions may
benefit bank stability, whereas stronger deposit insurance schemes
appear to create greater bank fragility.
The remainder of the paper is organized as follows. Section 2
provides a review of the literature on competition and stability
in banking. Section 3 introduces the econometric methodology.
Section 4 describes the data used in the econometric tests. Section 5 presents the empirical results and Section 6 are the
conclusions.
2. Literature review
Under the traditional competition-fragility hypothesis, competitive and/or less concentrated banking systems are more fragile. The ‘‘charter/franchise value’’ of banking, as modeled by
Marcus (1984), and Keeley (1990), suggests that competition
drives banks to undertake risk-taking strategies due to the contraction of the latter’s franchise value. These models show that
a higher charter or franchise value arising from increased market
power may deter excessive risk-taking by the bank’s management. Because higher franchise value results in greater opportunity costs during bankruptcy, bank managers and shareholders
may become more reluctant to engage in risky activities improving bank asset quality.
Diamond (1984), Ramakrishnan and Thakor (1984), Boyd and
Prescott (1986), Williamson (1986), and others show that more
concentrated banking systems are composed of larger banks
and that larger banks can capitalize on economies of scale and
scope and better diversify their portfolios. Smith (1984) argues
that banking relationships may endure for longer periods in less
competitive environments if the information on the probability
distribution of depositors’ liquidity needs is private. Hence,
greater concentration and less competition could reduce liability
risk and lead to greater stability in banking. Boot and Greenbaum (1993) and Allen and Gale (2000, 2004) suggest that in
a more competitive environment, banks earn less informational
rent from their relationships with borrowers, which reduces
their incentives to properly screen borrowers and increases the
risk of fragility.
Competition can impact stability through contagion. Using a
model of financial contagion in the interbank market Allen and
Gale (2000) suggest that under perfect competition, all banks are
price takers and none have an incentive to provide liquidity to
troubled banks. As a result, troubled banks eventually fail with
negative repercussions for the entire sector. Similarly, Saez and
Shi (2004) argue that banks can cooperate, act strategically and
help other banks to cope with temporary liquidity shortages in a
market characterized by imperfect competition. Allen and Gale
(2000) also find that a concentrated banking system with a small
number of large institutions is more stable because banks are
easier to monitor, less burdened by supervision, and therefore
more resilient to shocks. Boot and Thakor (2000) suggest that
larger banks tend to engage in ‘‘credit reputation/rating’’ because
making fewer high-quality credit investments can increase the
return of individual investments and thereby encourage financial
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Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77
soundness. Additionally, larger banks are assumed to enjoy comparative advantages related to the provision of credit monitoring
services.
Allen and Gale (2004) claim that financial crises are more likely
to occur in less concentrated banking systems due to the absence
of powerful providers of financial products that could reap benefits
from the high profits that thus serve as a buffer against asset quality deterioration. Similarly, Boyd et al. (2004) state that the presence larger (monopolistic) banks in concentrated banking
systems might enhance profits and thus reduce financial fragility
by providing higher ‘‘capital buffers’’ that protect these systems
against external macroeconomic and liquidity shocks.
A different argument among proponents of the competition-fragility hypothesis is that deposit insurance schemes can reduce fragility by preventing bank runs but also introduce moral hazard by
providing incentives to banks to engage in riskier activities. Thus,
in more competitive environments, more generous deposit insurance may undermine bank stability (Diamond and Dybvig, 1983;
Matutes and Vives, 1996). In addition, Hellmann et al. (2000) suggest that deposit interest rate ceilings are still necessary to prevent
banks from taking excessive risk in competitive markets, although
minimum capital requirements can boost the charter value.
Under the alternative competition-stability hypothesis, more
competitive and/or less concentrated banking systems are more
stable. The ‘‘too big to fail’’ doctrine (Mishkin, 1999, 2006; Barth
et al., 2012b) indicates that policymakers are more concerned
about bank failures when the number of banks in a concentrated
banking system is low. Thus, these large banks are often more
likely to receive public guarantees or subsidies, which may generate a moral hazard problem, encourage risk-taking behavior and
intensify financial fragility (Kane, 2010; Rosenblum, 2011). Moreover, contagion risk may increase in a concentrated banking system with larger banks.
Caminal and Matutes (2002) claim that lower competition can
result in reduced credit rationing and larger loans, ultimately
increasing the probability of bank failure. Boyd and De Nicoló
(2005) argue that concentrated banking systems allow banks to
charge higher loan rates, which may encourage borrowers to assume greater risk. Consequently, the volume of non-performing
loans may increase, resulting in a higher probability of bank failure.
However, Martinez-Miera and Repullo (2010) suggests that higher
loan rates also produce higher interest revenues for banks. This dynamic might generate a U-shaped relationship between bank competition and stability.
Beck et al. (2006a,b) suggest that bank size is positively correlated with organizational complexity; for example, monitoring a
large bank is more difficult than monitoring a small bank. Accordingly, as firm size increases, transparency may decrease as a result
of expansion across multiple geographic markets and business
lines and the use of sophisticated financial instruments that facilitate the establishment of complex corporate organizations. These
developments may reduce managerial efficiency and internal corporate control and may increase operational risk. Increasing organizational complexity can render both market discipline and
regulatory action less effective in preventing excessive risk exposure (Cetorelli et al., 2007).
However, as indicated in Berger et al. (2009), the two strands of
the literature do not necessarily produce opposing predictions
regarding the relationship between bank competition and financial
stability. The aforementioned authors argue that bank risks may
not increase even if market power encourages riskier asset portfolios because banks may protect their charter values by using other
methods to offset the greater risk exposure. Such methods may include increasing equity capital, reducing interest rate risk, and selling credit derivatives. As noted earlier, market structure measures
may not be good measures of competition and this (to some ex-
tent) has been confirmed by Berger et al. (2004) and Beck (2008)
who show that banking industry concentration can influence stability through channels other than competition.
A substantial empirical literature has emerged testing for concentration, competition and banking stability relationships across
countries. Yeyati and Micco (2007), for instance, use a sample of
commercial banks from eight Latin American countries over the
period 1993–2002 and find a positive link between bank risk (as
measured by the Z-score) and competition (as captured by the
Panzar and Rosse 1987, H-statistic), whereas the coefficient for
bank concentration is not significant. This result lends support to
the competition-fragility paradigm. Schaeck and Cihak (2008)
analyze the relationship between bank competition and soundness
using a sample of more than 3600 banks from ten European
countries and more than 8900 US banks for the period from 1995
to 2005. They suggest that competition as measured by the Boone
indicator increases bank soundness by increasing efficiency and
that more concentrated banking markets benefit from financial
stability. Using data from 31 systemic banking crises in 45 countries for the period from 1980 to 2005, Schaeck et al. (2009) show
that competition (as captured by the Panzar Rosse H-statistic) reduces the likelihood of a crisis and increases the time to crisis, even
after they control for banking system concentration, which is negatively related to financial fragility.
In a similar study, Berger et al. (2009) use a sample of 8235
banks from 23 industrial countries over 1999–2005 and find that
banks with market power (measured using the Lerner index) have
less overall risk exposure, as captured by their Z-scores. These findings support the traditional competition-fragility view. On the
other hand, they show that bank-level market power also results
in riskier loan portfolios, as indicated by non-performing loan ratios. Berger et al. (2009) argue that banks can protect their charter
value from higher loan risk by holding more equity capital. More
recently, Anginer et al. (2012) examine the relationship between
competition according to the Lerner index and systemic stability
as captured by default risk under Merton’s (1974) contingent claim
pricing framework. Using a sample of 1872 publicly traded banks
from 63 countries between 1997 and 2009, they find a positive
relationship between competition and systemic stability (and the
results remain the same even when they conduct a robustness
check using bank asset concentration as an alternative proxy for
bank competition).
Liu et al. (2012) introduce a variety of bank-specific risk indicators (the ratio of loan-loss provisions to total loans, loan-loss reserves to total loans, after-tax ROA volatility, and the natural
logarithm of the Z-index) to investigate similar relationships for
banks operating in South East Asia (Indonesia, Malaysia, the Philippines and Vietnam) between 1998 and 2008. They find that competition measured using the Panzar Rosse H-statistic is inversely
and significantly related to most risk indicators except the natural
logarithm of the Z-index, which suggests that competition does not
erode bank stability. The researchers also find that concentration is
negatively associated with bank risk, whereas regulatory restrictions positively influence bank fragility.
Overall, cross-country evidence yields mixed results regarding
the relationship between bank concentration, competition, and
stability. Meanwhile, the findings do confirm that concentration
and competition can coexist and may influence financial stability
through different channels.
3. Methodology
We test whether bank concentration and competition influence
bank stability employing bank-level data from 14 Asia Pacific economies. To address potential endogeneity issues associated with
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Table 1
Variable definitions and sources.
Variable
Dependent variables
Probability of bankruptcy
Z-score
Independent variables
CR3
LERNER
E-LERNER
SIZE
LLP
NIM
Entry restrictions
Definition
Data sources
The bank-level probability of bankruptcy based on method of Bharath and
Shumway (2008)
The bank-level Z-score; a larger value means less overall bank risk and higher
bank stability
Bankscope, Datastream
A country-level structural indicator of bank concentration, measured by the
concentration of assets held by the three largest banks in each country, with
higher value indicating greater market concentration
A bank-level non-structural indicator of bank competition, measured by the
Lerner index using fixed-effects method, with higher values indicating less
competition in the banking sector
A bank-level non-structural indicator of bank competition, measured by the
efficiency-adjusted Lerner index using a stochastic frontier analysis approach,
with higher values indicating less competition in the banking sector
The natural logarithm of total assets in thousands of USD
The ratio of loan loss provisions to total assets
Bank’s net interest income as a share of its interest-bearing (total earning) assets
Ratio of entry applications denied to applications received from domestic and
foreign banks
World Bank database on financial development
structure and Bankscope
Capital requirements
Minimum regulatory capital-to-assets ratio per country
Deposit insurance
A dummy variable that takes a value of one if the country has deposit insurance,
and zero otherwise
RGDP
CRISIS
Rate of real GDP growth rate
A dummy variable that takes a value of one for the years 2008–2009, and zero
otherwise
Instrumental variables
Activity restrictions
Financial freedom
Property rights
Index measure that indicates whether bank activities in the
securities, insurance and real estate markets, ownership and control of
non-financial firms are unrestricted, permitted, restricted or prohibited
The aggregate indicator ranges from 1 to 4. A higher value indicates greater
activity restrictions arising from legal requirements
The indicator of the openness of the banking system is a composite index of
whether government interference exists in the financial sector, such as regulation,
financial products, allocation of credit, whether foreign banks are free to
operate. Higher values indicate fewer restrictions on banking freedoms
The Heritage Foundation property rights protection index.
A higher value signifies weaker protection
measures of market power, we use an instrumental variable
technique with a GMM estimator.8 Our panel data model has the
following general form:
Bank Risk ¼ f ðConcentration; Competition; Bank Controls;
Regulatory and Institutional Controls; Macro ControlsÞ
ð1Þ
Notes on our dependent, explanatory and instrumental variables as
well as data sources are presented in Table 1.
3.1. Market-based risk measure
Black and Scholes’s (1973) and Merton’s (1974) Distance to Default model is used to estimate the insolvency risk of listed banks.
The model has been widely used in empirical research.9 However,
there is only one paper employing this model in comparing the performance of market-based and accounting-based bankruptcy prediction models (Agarwal and Taffler, 2008). The Distance to Default
8
GMM is more efficient than 2SLS because it accounts for heteroskedasticity (Hall,
2005).
9
For example, see Hillegeist et al. (2004), Vassalou and Xing (2004), Gropp et al.
(2004, 2006), Akhigbe et al. (2007), Chan-Lau and Sy (2007), Duffie et al. (2007),
Bharath and Shumway (2008), and Campbell et al. (2008).
Bankscope
Bankscope
Bankscope
BankScope
BankScope
BankScope
World Bank Survey of Bank Regulation and
Supervision (for details see Barth et al., 2008,
2012a,b)
World Bank Survey of Bank Regulation and
Supervision (for details see Barth et al., 2008,
2012a,b)
World Bank Survey of Bank Regulation and
Supervision (for details see Barth et al., 2008,
2012a,b)
World Economic Outlook Database, IMF
Compiled by the authors
World Bank Survey of Bank Regulation and
Supervision (for details see Barth et al., 2008,
2012a,b)
Heritage Foundation (2010)
Heritage Foundation (2010)
model views equity as a call option on the assets of a firm, with a
strike price equal to the face value of the liabilities at time T when
the liabilities mature. At time T, equity holders exercise their option
and pay off the debt holders if the value of the firm’s assets is greater
than the face value of its liabilities. Otherwise, if the value of the assets is insufficient to fully repay the firm’s debts, the call option becomes worthless, and equity holders let it expire. In this scenario, the
firm files for bankruptcy, and ownership is assumed to be transferred to the debt holders at no cost, whereas the payoff for equity
holders is zero. Estimates for the probability of bankruptcy are given
by McDonald (2002). They are modified for dividends, and they reflect the fact that the stream of dividends paid by the firm accrues
to the equity holders:
0
B ln
P ¼ N @
V A D
r2 1
þ u d 2A T C
pffiffiffi
A
rA T
ð2Þ
where P is the probability of bankruptcy, N( ) is the cumulative normal density function, VA is the value of assets, D is the face value of
debts proxied by total liabilities, u is the expected return, d is the
dividend rate estimated as total dividends/(total liabilities + market
value of equity), rA is the standard deviation of assets (asset volatility), and T is the time to expiration (taken to be 1-year).
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Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77
VA, u and rA are non-observable. This study uses the following
method outlined by Bharath and Shumway (2008):
VA ¼ VE þ D
rA ¼
VE
D
rE þ rD
VA
VA
ð3Þ
ð4Þ
rD ¼ 0:05 þ 0:25 rE :
ð5Þ
u ¼ r i;t1 :
ð6Þ
where VE is the market value of common equity, VA is the total value
of assets, D is the face value of debts proxied by total liabilities, rA is
the standard deviation of assets (asset volatility), rE is the standard
deviation of daily stock returns multiplied by the square root of the
average number of trading days in the year (set at 252 trading
days), u is the expected return, and ri,t1 is the bank’s stock returns
over the previous year.10
3.2. Accounting-based risk measure
For our accounting based risk measure we use the Z-score
which is widely used in the literature as a stability indicator (see,
for instance, Boyd and Runkle, 1993; Lepetit et al., 2008; Laeven
and Levine, 2009; Čihák and Hesse, 2010). Using accounting information on asset returns, its volatility and leverage, the Z-score is
calculated as follows:
Z it ¼
ROAit þ Eit =TAit
rROAit
competition, the Lerner index = 0; under a pure monopoly, the Lerner index = 1. A Lerner index < 0 implies pricing below the marginal
cost and could result, for example, from non-optimal bank behavior.
Algebraically, the Lerner index is calculated as follows:
Lernerit ¼ ðPTAit MCTAit Þ=PTAit
ð8Þ
where PTAit is the price of total assets proxied by the ratio of total
revenues (interest and non-interest income) to total assets for bank
i at time t, and MCTAit is the marginal cost of total assets for bank i at
time t.
Following Fernández de Guevara et al. (2005) and
Carbó-Valverde et al. (2009), we can calculate the output price
(PTAit ) as the ratio of total revenues (interest and non-interest
income) to total assets. Given the limited information on prices
for loans and deposits,13 we use a single indicator of banking
activity, namely total assets as a measure of bank output, as
suggested by Shaffer (1993) and Berg and Kim (1994). Assuming that
the heterogeneous flow of goods and services supplied by a bank is
proportional to its total assets, the output price includes both interest income and non-interest income. Following Hasan and Marton
(2003), Soedarmon et al. (2011), Sun and Chang (2011) and Jiang
et al. (2013) we use a two input cost function specification that tends
to be used in emerging market bank efficiency studies (due to data
availability issues) to estimate marginal costs. We also cross check
with a three-input cost function specification and also follow Koetter
et al. (2008, and 2012) and estimate the efficiency-adjusted Lerner
index using a stochastic frontier analysis approach for another
robustness test.14
ð7Þ
where ROA is the return on assets, E/TA is the equity to total assets
ratio, and rROA is the standard deviation of return on assets.
The Z-score is inversely related to the probability of a bank’s
insolvency. A bank becomes insolvent when its asset value drops
below its debt and the Z-score shows the number of standard deviations that a bank’s return has to fall below its expected value to
deplete equity and make the bank insolvent.
3.3. Concentration and competition measures
First, based on the structural approach, the degree of market
concentration is used. Market concentration is measured as the ratio of the assets of the three largest banks to the total assets of the
banking system in the country in question (CR3). Second, a nonstructural indicator, the Lerner index (LERNER), is used to measure
the degree of competition. This indicator has been widely used in
recent bank research.11 The Lerner index captures the capacity of
price power by calculating the difference between price and marginal cost as a percentage of price.12 The degree of competition is given by the range 0 < Lerner index < 1. In the case of perfect
10
Hillegeist et al. (2004) use this approach to assess the probability of bankruptcy
and they note that ‘‘since expected returns cannot be negative, we set the expected
growth rate equal to the risk-free rate in these cases (p. 10)’’. Bharath and Shumway
(2008) also use the risk-free rate to replace the expected return on assets as a
robustness check (p. 1348). In our sample, the risk-free rates range between 0.20 and
5, whereas the expected returns are negative during the crisis period. Therefore, we
follow these two studies and replace the expected return with the risk-free rate when
the former is negative.
11
For example, see Claessens and Laeven (2004), Maudos and Fernández de Guevara
(2004), Fernández de Guevara et al. (2005), Berger et al. (2009), and Maudos and Solís
(2009).
12
The H-statistic, developed by Panzar and Rosse (1987), is an alternative tool for
inferring the degree of competition in the banking industry. It is computed from
reduced form revenue equations, and it measures the sum of the elasticities of a
bank’s revenue with respect to the bank’s input prices (Claessens and Laeven, 2004). A
critical feature of the Panzar Rosse H-statistic is that the test must be undertaken in
long-run equilibrium.
3.4. Other control variables
Following Schaeck and Cihak (2008), Laeven and Levine (2009)
and Uhde and Heimeshoff (2009), we also include a range of bankspecific variables. A bank’s asset size (SIZE) is defined as the logarithm of its total assets. The ratio of loan-loss provisions to total assets (LLP) is used to measure output quality and the way in which
managers invest in high risk assets. The net interest margin (NIM)
is employed to track the profitability of a bank’s investing and
lending activities.
Beck et al. (2006a) argue that there are two reasons why crosscountry differences in bank regulatory policies and national institutions should be considered in assessing the relationship between
bank competition and financial stability. First, this approach provides a simple robustness test for the competition-stability relationship. Second, it presents additional information about the
links between bank regulations, national institutions, and financial
stability. Hence, following previous studies (Beck et al. 2006a; Laeven and Levine 2009; Delis et al. 2011, and Goddard et al. 2011),
we also control for bank regulations and institutional environments in investigating the effects of concentration and competition
on bank stability.
Deposit insurance is a dummy variable that takes a value of one if
a country has explicit deposit insurance and a value of zero
otherwise.15 Credible deposit insurance can enhance financial stability by decreasing the likelihood of depositor runs. Conversely, if the
capital positions and risk-taking of insured institutions are not supervised carefully, insurers tend to accrue loss exposures that undermine bank stability over the long-run. Capital requirement indicates
the minimum capital requirement (capital-to-assets ratio) per coun13
Loan revenue data do not separate earned income from fixed income investments,
and the financial costs of deposits are included with those of other liability products.
14
Appendix A presents the translog cost function used to estimate bank marginal
cost.
15
In our sample, five countries (Australia, China, Sri Lanka, Pakistan, and Thailand)
do not have deposit insurance.
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Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77
Table 2
Descriptive statistics.
Variable
Probability of bankruptcy
Z-score
CR3
Conventional Lerner index (LERNER)
Efficiency-adjusted Lerner index (E-LERNER)
Bank size (SIZE)
Loan loss provision% (LLP)
Net interest margin% (NIM)
Real GDP growth% (RGDP)
Global financial crisis (CRISIS)
Entry restrictions
Capital requirements%
Deposit insurance
Activity restrictions
Financial freedom
Property rights
Listed banks
Listed and non-listed banks
Obs.
Mean
Std. dev.
Min
Max
Obs.
Mean
Std. dev.
Min
Max
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
0.18
40.86
0.44
0.31
0.26
16.22
1.80
2.79
3.96
0.28
0.13
8.42
0.86
10.84
44.85
57.56
0.18
32.69
0.11
0.14
0.14
1.60
4.27
1.56
3.88
0.45
0.25
0.69
0.35
1.97
14.43
18.84
0.54
2.69
0.26
1.26
1.32
10.11
0
1.49
6.29
0
0
8
0
4
30
20
0
196.64
0.99
0.68
0.65
21.05
149
11.04
14.47
1
0.92
10
1
16
90
90
4069
4069
4069
4069
4069
4069
4069
4069
4069
4069
4069
4069
4069
4069
4069
39.78
0.46
0.32
0.27
15.59
1.69
2.94
5.37
0.27
0.09
8.32
0.75
11.28
44
52.45
47.14
0.13
0.18
0.19
2
3.82
2.69
4.23
0.44
0.22
0.63
0.43
2.59
16.59
22.03
40.28
0.26
2.75
2.79
1.78
0
60.57
6.29
0
0
8
0
4
30
20
681.92
0.99
0.82
0.81
21.4
149
39.36
14.47
1
0.92
10
1
16
90
90
The probability of bankruptcy is a market-based bank-level measure of financial fragility that is calculated using the method developed by Bharath and Shumway (2008). The
Z-score is an accounting-based bank-level indicator of financial stability. The conventional Lerner index (LERNER) is a bank-level indicator of bank competition that is calculated
as the difference between price and marginal cost as a percentage of price using fixed effect regression. The efficiency-adjusted Lerner index (ELERNER) is a bank-level
efficiency-adjusted indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using a stochastic frontier analysis
approach. CR3 is a country-level structural indicator of bank concentration calculated as the fraction of assets held by the three largest banks in each country. SIZE is the
natural logarithm of total assets in thousands of USD. LLP is the ratio of loan loss provisions to total assets. NIM is the ratio of net interest income to interest-bearing (total
earning) assets. RGDP is the rate of real GDP growth. Entry restrictions is the ratio of entry applications denied to applications received from domestic and foreign banks. Global
financial crisis is a dummy variable that takes a value of one for the years 2008–2009 and zero otherwise. Activity restrictions is an aggregate index measure that indicate
whether bank activities in the securities, insurance and real estate markets and the ownership and control of non-financial firms are unrestricted, permitted, restricted or
prohibited. The capital requirement is the minimum regulatory capital-to-assets ratio per country. Financial freedom is an indicator of the openness of the banking system; it
functions as a composite index of government interference in the financial sector, including regulations on financial products, allocation of credit, whether foreign banks are
free to operate and other factors. Deposit insurance is a dummy variable that takes a value of one if the country has deposit insurance and zero otherwise. Property rights are
measured using the Heritage Foundation property rights protection index.
try, which is interpreted as another entry barrier indicator. In addition, greater equity capital encourages prudent behavior. Hence,
greater capital requirements are expected to indicate a more stable
banking market. The variable entry restrictions is the ratio of the number of banking licence applications denied to the number of applications received from domestic and foreign entities. The effect of this
control variable on bank stability is expected to be ambiguous because restricted entry may reduce competitive pressure and thereby
increase domestic bank profits, but it may also induce market inefficiencies. The rate of real GDP growth (RGDP) is used as a proxy for the
fluctuations in economic activity. CRISIS is a dummy variable that
takes a value of one for the years 2008–2009 and zero otherwise.
To deal with the potential presence of endogeneity and heteroskedasticity, following Berger et al. (2009), we employ a GMM panel data estimator using activity restrictions, financial freedom, and
property rights as instruments. Activity restrictions are a key determinant of the scope of a bank’s ability to provide fee-paying services. This measure reflects the level of regulatory restrictiveness
for bank participation in securities market, insurance activities,
real estate activities, and the ownership of non-financial firms.
Financial freedom is an indicator of the openness of a financial system. This measure indicates the extent of government involvement
in the financial sector, considering regulation, financial products,
and the allocation of credit; the freedom of foreign banks to operate; and the degree of regulation of financial market activities. Finally, the protection of property rights is an important pre-requisite
for a well-functioning financial system. A higher value of the Heritage Foundation property rights protection index signifies weaker
protection of property rights.
4. Data
The sample data focus on commercial banks in 14 Asia Pacific
economies over 2003 and 2010. Financial information and stock
market information, converted to US dollars, are obtained from the
Bankscope database by Bureau van Dijk and are supplemented by
information from Datastream. Banking sector concentration ratios
are obtained from the updated version of the World Bank database
on financial development structures and supplemented by the
Bankscope database; real GDP growth data are taken from the
World Economic Outlook by the International Monetary Fund
(IMF); and information on regulations and the institutional environment come from several sources, including the World Bank database
on ‘‘Bank Regulation and Supervision’’ (developed by Barth et al.,
2001 and updated by Barth et al., 2006, 2008 and Barth et al.,
2012a) and the 2010 index of Economic Freedom, which was published by The Wall Street Journal and The Heritage Foundation.16
After excluding banks with (1) missing, negative or zero values
for the cost function needed to calculate the Lerner index, (2) missing values for loan loss provisions, and (3) missing Z-score values,
we obtain a final sample that includes unbalanced panel data for
14 Asia Pacific economies, with 4069 observations (see Appendix
B). The subsample for listed banks includes 1500 observations
(see Appendix C). All of the data are deflated by their corresponding year CPIs to the 2003 price level to control for inflation effects.
Table 2 presents the descriptive statistics for all variables used in
the study. All bank-level variables are averaged by bank for the
period from 2003 to 2010, and the country-level variables are averaged by country for the same study period. Comparing listed banks
with non-listed banks in the sample, Table 2 shows that on average
listed sample banks enjoy a higher Z-score, lower loan loss provision ratio, and larger in size, whereas non-listed banks have a higher Lerner index and rely more on interest income. Moreover,
markets with listed banks are less concentrated, subject to less
activity restrictions, have a higher capital requirement ratio, and
enjoy more financial freedom and better property rights protection. Listed banks join the deposit insurance scheme in the markets
with more entry restrictions.
16
Please refer to Table 1 for details.
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Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77
Table 3
Concentration, Competition, and Stability measures.
Listed banks
Obs.
Listed and non-listed banks
CR3
LERNER
E-LERNER
Prob. of bankruptcy
Obs.
CR3
LERNER
ELERNER
Z-score
Panel A: mean by year
2003
153
2004
167
2005
185
2006
188
2007
197
2008
203
2009
211
2010
196
0.4748
0.4305
0.4181
0.4175
0.4297
0.4472
0.4483
0.4550
0.3191
0.3351
0.3273
0.3116
0.3028
0.2585
0.3151
0.3336
0.2712
0.2885
0.2807
0.2637
0.2529
0.2056
0.2651
0.2828
0.2004
0.0940
0.0834
0.0799
0.1862
0.2561
0.3369
0.1523
423
460
519
549
565
552
534
467
0.4767
0.4596
0.4593
0.4624
0.4644
0.4661
0.4655
0.4647
0.3154
0.3363
0.3173
0.3076
0.3142
0.2826
0.3206
0.3545
0.2724
0.2943
0.2750
0.2645
0.2695
0.2351
0.2740
0.3072
39.2417
42.0883
40.778
40.2366
39.448
37.3431
39.9518
39.4407
Panel B: mean by country
Australia
48
China
35
Hong Kong
32
India
226
Indonesia
134
Japan
597
Korea
31
Malaysia
24
Pakistan
98
Philippines
78
Singapore
16
Sri Lanka
55
Taiwan
62
Thailand
64
0.6827
0.5191
0.7064
0.3409
0.4562
0.4089
0.5057
0.4563
0.4376
0.5088
0.9156
0.6165
0.2719
0.4550
0.2954
0.4343
0.4268
0.3093
0.2653
0.3091
0.3486
0.4315
0.2671
0.3175
0.4889
0.2669
0.2753
0.3622
0.2291
0.3811
0.3823
0.2598
0.2270
0.2538
0.3009
0.3842
0.2316
0.2769
0.4410
0.2365
0.2236
0.3147
0.0820
0.1295
0.0447
0.1330
0.1265
0.2616
0.1872
0.0362
0.0936
0.1145
0.0670
0.1409
0.1974
0.1167
111
700
193
447
409
988
126
191
174
184
67
81
253
145
0.6577
0.5387
0.6971
0.3389
0.4583
0.4077
0.5033
0.4571
0.4404
0.4953
0.9145
0.6171
0.2712
0.4539
0.3206
0.3914
0.3683
0.3106
0.2991
0.3074
0.3380
0.3945
0.2129
0.2448
0.3315
0.2147
0.3126
0.2520
0.2740
0.3518
0.3281
0.2663
0.2661
0.2521
0.2866
0.3547
0.1766
0.2070
0.2856
0.1857
0.2646
0.2070
44.5649
40.6025
41.2276
42.8772
47.6483
39.9749
28.4216
47.0183
17.5765
40.8976
62.3927
34.9779
28.714
34.2761
The probability of bankruptcy is a market-based bank-level measure of financial fragility that is calculated using the method developed by Bharath and Shumway (2008). The
Z-score is an accounting-based bank-level indicator of financial stability. LERNER is a bank-level indicator of bank competition calculated as the difference between price and
marginal cost as a percentage of price using fixed effect regression. ELERNER is a bank-level efficiency-adjusted indicator of bank competition calculated as the difference
between price and marginal cost as a percentage of price using a stochastic frontier analysis approach. CR3 is a country-level structural indicator of bank concentration
calculated as the fraction of assets held by the three largest banks in each country.
Table 3 presents a summary of our concentration, competition
and bank stability measures from 2003 to 2010 for 14 Asia Pacific
countries by year (panel A) and by country (panel B). The pattern
derived from the sample of listed banks is quite similar to that
from the whole sample. Thus, we focus on the sample of listed
banks. Based on the market measure of bank stability, bank risk increased overall from 2007 to 2009. The results imply that bank performance was most affected over 2009, a finding also confirmed by
the IMF (2009). Bank risk decreased dramatically in 2010, which
implies that this region was initially hit hard by the global crisis
but has rapidly rebounded. Comparing bank risk by country using
the market-based measure indicates that on average, banks operating in Malaysia, Hong Kong, and Singapore are exposed to lower
risk than those in other Asia Pacific economies. Meanwhile, Japanese, Taiwanese, and Korean banks are the most fragile.
When the findings regarding market concentration and competition are compared by year, the structural and non-structural
measures reveal different trends. The trend for the Lerner index
(non-structural measure) is descending between 2005 and 2008
suggesting a decrease in pricing power, whereas industry concentration (structural measure) increases over the same period. The
Lerner index exhibits varying degrees of market power across
countries. Singapore has the highest efficiency-adjusted Lerner index value (0.44), whereas Taiwan has the lowest value (0.22). Concentration also varies across countries. The results suggest that
concentration of assets held by the three largest banks in Singapore
is 91.6%, indicating that the system is dominated by these banks.
However, concentration in Taiwan is relatively low at 27.2%.
5. Empirical results
Table 4 presents the main results that indicate the impact of bank
concentration and competition on financial stability. Two different
risk exposure indicators are used as the dependent variables that
proxy for financial stability: the probability of bankruptcy for listed
banks (specifications 1–4) and the Z-score for both listed and non-
listed banks (specifications 5–8). We use the First Stage F-test and
the Hansen’s J test to test for the relevance and validity of the instruments of the degree of market power, respectively. The Second Stage
F-test is also used to test for goodness of fit for all regression models.
The results support the use of the GMM panel data estimator.
Based on market measures, Table 4 indicates the significantly
negative correlation for the Lerner index used in regression (1),
suggesting that increases in the degree of bank pricing power are
positively related to individual bank stability in Asia Pacific. Meanwhile, the coefficient of bank concentration is significantly positive, indicating that banks in more concentrated markets face
greater risk. The robustness of the results is verified using regression specifications (2)–(4). The findings lend support to the neutral
view of the competition-stability nexus as both the competitionstability and competition-fragility views can be simultaneously valid. In this case, excessive concentration and lower pricing power
simultaneously lead to bank fragility.
Our findings vary from those of most previous studies which focus on banks operating in a specific geographic region such as Latin
America,17 or a broader area.18 However, the results findings (we believe) are not surprising for banks operating in Asia Pacific. On the
one hand, most countries in this region (developing countries in particular) have adopted ‘‘finance for growth’’ policies for a long period.
The protected, larger banks in these concentrated banking systems
17
Yeyati and Micco (2007) use a sample of commercial banks from eight Latin
American countries over the period 1993–2002 and find a positive link between bank
risk (as measured by the Z-score) and competition (as captured by the Panzar and
Rosse, 1987, H-statistic), whereas the coefficient of bank concentration is
insignificant.
18
Using data from 31 systemic banking crises in 45 countries for the period from
1980 to 2005, Schaeck et al. (2009) show that competition reduces the likelihood of a
crisis and increases the time to crisis and concentration is positively related to
financial fragility. Anginer et al. (2012) use a sample of 1,872 publicly traded banks
from 63 countries between 1997 and 2009 and find a positive relationship between
competition and systemic stability and a negative relationship between concentration
and systemic stability.
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Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77
Table 4
Concentration, competition, and financial stability.
Dependent variable: prob. of bankruptcy
LERNER
CR3
SIZE
LLP
NIM
RGDP
CRISIS
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
1.3250**
(0.5237)
2.2529***
(0.5459)
0.0700***
(0.0271)
0.5129
(0.8826)
0.0260
(0.0255)
0.0040
(0.0031)
0.0642**
(0.0250)
1.0569**
(0.5258)
2.5413***
(0.5498)
0.0623**
(0.0269)
0.2709
(0.8516)
0.0195
(0.0239)
0.0015
(0.0031)
0.0822***
(0.0252)
0.1791***
(0.0623)
1.4570***
(0.5175)
2.2277***
(0.5612)
0.0641**
(0.0286)
0.9785
(0.9875)
0.0277
(0.0266)
0.0038
(0.0032)
0.0622**
(0.0257)
1.5704***
(0.5559)
2.1215***
(0.5727)
0.0671**
(0.0282)
0.7478
(0.9410)
0.0296
(0.0275)
0.0052
(0.0033)
0.0480*
(0.0272)
53.4755***
(18.1775)
46.3050***
(9.0731)
3.0907***
(0.9571)
23.1895
(33.3662)
0.2372
(0.1593)
0.0475
(0.0826)
1.1194
(0.6965)
50.0600***
(18.4541)
49.0417***
(9.5586)
3.1619***
(0.9242)
28.1637
(33.6761)
0.2588
(0.1590)
0.0707
(0.0813)
0.9282
(0.7283)
3.8920
(3.1427)
50.5653***
(17.0571)
46.6299***
(8.8796)
3.0507***
(0.9380)
27.5442
(34.3624)
0.2575*
(0.1539)
0.0409
(0.0807)
1.0695
(0.6760)
57.0264***
(20.4786)
45.7716***
(9.4445)
3.0621***
(0.9675)
20.3329
(33.4386)
0.2111
(0.1730)
0.0456
(0.0841)
1.2774
(0.8057)
Entry restrictions
Capital Requirement
0.0587
(0.0435)
Deposit Insurance
First Stage F-test (LERNER)
First Stage F-test (CR3)
Hansen’s J v2
(P-value)
Second Stage F-test
No. of observations
Dependent variable: Z-score
8.25***
117.24***
0.859
(0.3541)
66.85***
1320
8.01***
134.54***
0.126
(0.7226)
69.60***
1320
8.72***
120.29***
1.064
(0.3023)
54.38***
1320
1.3472
(1.0743)
0.0618**
(0.0309)
7.78***
108.27***
2.619
(0.1056)
51.07***
1320
10.36***
234.57***
0.871
(0.3508)
16.51***
3299
9.91***
226.27***
0.503
(0.4783)
15.49***
3299
11.23***
236.17***
0.849
(0.3569)
15.21***
3299
0.9649
(1.5376)
9.68***
233.67***
1.169
(0.2797)
14.14***
3299
Results from GMM panel data estimations to explain the impacts of bank concentration and competition on financial stability. The first dependent variable (specifications 1–
4) is the probability of bankruptcy, which is a market-based bank-level measure of financial fragility that is calculated using the method developed by Bharath and Shumway
(2008). The second dependent variable (specifications 4–8) is Z-score, which is an accounting-based bank-level indicator of financial soundness. LERNER is a bank-level
indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using the stochastic frontier analysis approach. CR3 is a
country-level structural indicator of bank concentration calculated as the fraction of assets held by the three largest banks in each country. SIZE is the natural logarithm of
total assets in thousands of USD. NIM is the ratio of net interest income to interest-bearing (total earning) assets. LLP is the ratio of loan loss provisions to total assets. RGDP is
the rate of real GDP growth. Crisis is a dummy variable that takes a value of one for the years 2008–2009 and zero otherwise. Deposit insurance is a dummy variable that takes
a value of one if the country has deposit insurance and zero otherwise. Capital requirement is the minimum regulatory capital-to-assets ratio for each country. Entry restrictions
is the ratio of entry applications denied to applications received from domestic and foreign banks. The instrumental variables include activity restrictions, financial freedom,
and property rights.
***
Indicate significance at the 1% levels, respectively. Robust standard errors are in parentheses.
**
Indicate significance at the 5% levels, respectively. Robust standard errors are in parentheses.
*
Indicate significance at the 10% levels, respectively. Robust standard errors are in parentheses.
channel resources to ‘‘priority sectors’’. Their borrowers become ‘‘too
large to fail’’, and hence, banks lose their incentive to develop an
appropriate credit culture and may find themselves faced with relatively high levels of non-performing loans. In addition, banks are the
most important source of public savings in the majority of Asia Pacific economies, which also makes them ‘‘too-big or too-systemicallyimportant-to-fail’’ possibly leading to moral hazard problems
(Sheng, 2009).
On the other hand, according to Elzinga and Mills (2011), the
Lerner index is a ‘‘better indicator of a firm’s price-setting discretion than its ability to sustain monopoly prices’’ (p. 1). Thus, the results may imply that banks in this region are able to obtain greater
discretion in terms of price-setting to boost their profits and reduce their insolvency risk through channels other than increased
concentration (product differentiation).19 In other words, greater
19
For example, as indicated in a survey report provided by the IDC Financial Insights
Asia/Pacific division, banks across the Asia Pacific region are considering the uniquely
Asian opportunities for sustainable growth that have been generated by governments
identifying new priority industries such as aerospace and defense in Singapore, green
technology in China, and high technology in Taiwan and China. Banks in this region
have identified two strategic technology initiatives that they can use to expand their
reach and profitability – risk management and channel efficiency. The focus on risk
management has mainly been generated by the growing availability and sophistication of analytics technologies, whereas the emphasis on channel efficiency stems
from the vast expansion of mobility across the region. As a result, there are a growing
number of innovative strategic IT projects that drive business differentiation in Asia
Pacific banks (IDC, 2012).
pricing power enhances the ability of banks to generate higher ‘‘capital buffers’’ to protect them against external macroeconomic and
liquidity shocks.
Among other control variables, the significantly positive
coefficient for bank size suggests that larger banks face greater risk.
Laeven and Levine (2009) also find the same result. The crisis
dummy is positively and significantly related to bank risk, which
implies that banks are more fragile during financial turmoil. In
considering regulatory and institutional environments, we find
that entry restrictions are significantly and negatively associated
with the probability of bankruptcy, which suggests that a lower
level of competitive pressure induces greater fragility for listed
banks. This result is consistent with the empirical findings of
Uhde and Heimeshoff (2009), who find that restricted market entry
is likely to enhance bank stability in Western European banking.
Deposit insurance is significantly associated with a higher probability of bankruptcy, supporting the moral hazard argument
regarding excessive risk-taking when a financial safety net is
available. Again, the result is similar to the findings of Laeven
and Levine (2009).
Table 4 also examines the impact of bank concentration and
competition on the soundness of both listed and non-listed banks
using the Z-score as a proxy for financial stability. The results of
regressions (5)–(8) show that the Lerner index is positively and
significantly related to the Z-score, whereas the coefficient of
concentration is significantly negative. The finding confirms that
lower pricing power and excessive concentration may simulta-
72
Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77
neously lead to bank fragility. Meanwhile, the coefficient on bank
size is significantly negative, which is also consistent with our previous finding that larger banks face greater risk.
We undertake a variety of robustness tests on our main models.
First, following Koetter et al. (2008, 2012) and Turk Ariss (2010),
we use the efficiency-adjusted Lerner index to replace the conventional Lerner index as a measure of banking market competition.
Our main results are similar (see Appendix D). Second, following
Berger et al. (2009), we also use a quadratic term for the Lerner
index (namely, LERNER2) to capture a possible non-linear relationship between competition and stability. The coefficient of the quadratic term is significantly negative for the probability of
bankruptcy model and positive for the Z-score model. Based on
the inflection points calculated, the results remain unchanged
and are reported in Appendix E. Third, we use Tobit regression
models to estimate the competition-stability nexus for listed
banks, because the probability of bankruptcy is between zero and
one. The main results are maintained (see Appendix F). Fourth,
we employ the Lerner index estimated using a three-input cost
function specification replacing the one estimated using the twoinput specification. Overall, the key findings remain unchanged
(see Appendix G).
6. Conclusions
This study investigates the competition-stability nexus using
cross-country data from 14 Asia Pacific countries for the period
from 2003 to 2010. Both market-based and accounting-based risk
measures are employed to measure individual bank fragility for
the first time. Meanwhile, both concentration and competition
indicators are included in the models to determine their impacts
on bank stability. The initial results show a substantial shift in
the average risk exposure of banks over the entire sample period,
accompanied by gradual increases in concentration and competition. The main results not only highlight the significant negative
association between the Lerner index and individual bank risk
but also illustrate the significant positive relationship between
the concentration ratio and bank fragility. In other words, the findings provide support for the neutral view of the competitionstability nexus, indicating that the competition-stability and competition-fragility theories can simultaneously apply to Asia Pacific
banking markets. The results also confirm that bank concentration
is an insufficient measure of bank competitiveness. Overall our
findings hold when we control for an array of bank-specific,
macroeconomic, regulatory and institutional factors.
In addition, our analyses indicate that smaller bank size
may improve financial soundness. In terms of regulations and
institutions, the results show that tougher entry restrictions may
enhance bank stability, whereas stronger deposit insurance
schemes negatively influence financial soundness. Unsurprisingly,
banks are found to be more fragile during the recent financial
crisis.
The findings highlight several important issues for policymakers in Asia Pacific economies. First, to prevent excessive concentration, regulators should adopt a more cautious approach to
evaluating and approving merger and acquisitions at the national
level. Policymakers should also seek to reduce policy lending by
encouraging banks to develop stronger independent credit cultures. Second, to improve the efficiency of resource allocation
within an economy, regulators should encourage financial innovation among banks based on the premise of effective risk management, which also enables banks to become more stable via
product innovation. Third, a certain level of entry restriction is
needed for both domestic and foreign entrants to maintain financial soundness. This suggests there should be greater scrutiny of
foreign banks that seek to make acquisitions in Asia Pacific countries. Finally, deposit insurance schemes appear to foster moral
hazard and risk shifting behavior so any policy moves to increase
coverage should be treated with caution as this could have the
unintended consequence of boosting risk as opposed to promoting
stability.
Appendix A. Translog cost function for estimating bank
marginal cost
To derive MCTAit , the following translog cost function is estimated while capturing bank specificities using bank fixed effects:
ln TCit ¼ a0 þ
2
X
a1 ln wjit þ
j¼1
2 X
2
1X
ajk ln wkit þ b1 ln TAit
2 j¼1 k¼1
2
X
1
1
2
j
þ b2 ðlnTAit Þ þ
b2j ln TAit lnwit þ c1t T c2t T2
2
2
j¼1
þ
2
X
c3t T ln wjit þ c4t T ln TAit þ ei
ð9Þ
j¼1
MCTAit ¼
@TCit
¼
@TAit
b1 þ b2 ln TAit þ
!
2
X
TCit
b2j ln wjit þ c4t T
TA
it
j¼1
ð10Þ
where TCi is the bank’s total costs, TAi is the total assets, wi is the
price of the factors of production, defined as follows: w1 is the price
of purchased funds: interest expenses/total deposits and short-term
funding, w2 is the price of labor and physical capital: non-interest
Table B1
Number of both listed and non-listed banks in sample. Source: BankScope (Bureau Van Dijk).
2003
2004
2005
2006
2007
2008
2009
2010
Total
Australia
China
Hong Kong
India
Indonesia
Japan
Korea
Malaysia
Pakistan
Philippines
Singapore
Sri Lanka
Taiwan
Thailand
8
46
11
58
48
130
17
22
16
14
4
9
24
16
9
54
25
57
51
129
17
23
17
22
6
10
24
16
13
71
30
56
56
126
19
25
22
28
9
10
36
18
17
98
29
58
55
124
18
24
24
27
10
11
35
19
19
117
28
58
56
127
16
24
25
23
10
9
33
20
18
110
26
56
55
127
15
24
26
24
9
9
33
20
14
107
24
54
52
122
15
25
23
24
10
11
34
19
13
97
20
50
36
103
9
24
21
22
9
12
34
17
111
700
193
447
409
988
126
191
174
184
67
81
253
145
Total
423
460
519
549
565
552
534
467
4069
73
Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77
Table C1
Number of listed banks in sample. Source: BankScope (Bureau Van Dijk).
Australia
China
Hong Kong
India
Indonesia
Japan
Korea
Malaysia
Pakistan
Philippines
Singapore
Sri Lanka
Taiwan
Thailand
Total
2003
2004
2005
2006
2007
2008
2009
2010
Total
6
2
4
14
12
74
4
3
7
8
2
5
4
8
6
2
4
22
14
74
4
3
8
10
2
6
4
8
6
3
4
26
17
75
4
3
11
10
2
7
9
8
6
3
4
27
18
75
4
3
12
10
2
7
9
8
6
4
4
31
18
76
4
3
15
10
2
7
9
8
6
5
4
35
20
75
4
3
15
10
2
7
9
8
6
9
4
36
20
77
4
3
15
10
2
8
9
8
6
7
4
35
15
71
3
3
15
10
2
8
9
8
48
35
32
226
134
597
31
24
98
78
16
55
62
64
153
167
185
188
197
203
211
196
1500
Table D1
Concentration, competition, and financial stability (using efficiency-adjusted Lerner index).
Dependent variable: prob. of bankruptcy
E-LERNER
CR3
SIZE
LLP
NIM
RGDP
CRISIS
(2)
(3)
(4)
(5)
(6)
(7)
(8)
1.2413**
(0.4886)
2.2323***
(0.5494)
0.0610**
(0.0252)
0.4634
(0.8652)
0.0246
(0.0249)
0.0041
(0.0031)
0.0638**
(0.0250)
0.9908**
(0.4899)
2.5235***
(0.5531)
0.0551**
(0.0249)
0.2298
(0.8345)
0.0184
(0.0233)
0.0016
(0.0031)
0.0820***
(0.0252)
0.1804***
(0.0618)
1.3632***
(0.4824)
2.2064***
(0.5641)
0.0543**
(0.0266)
0.9220
(0.9635)
0.0262
(0.0260)
0.0039
(0.0032)
0.0618**
(0.0257)
1.4688***
(0.5183)
2.1004***
(0.5761)
0.0564**
(0.0263)
0.6894
(0.9221)
0.0280
(0.0268)
0.0053
(0.0033)
0.0476*
(0.0272)
50.3293***
(17.0441)
45.6700***
(9.1422)
2.6723***
(0.9133)
24.1559
(33.1960)
0.2416
(0.1571)
0.0466
(0.0824)
1.1244
(0.6966)
47.1489***
(17.2802)
48.4773***
(9.6322)
2.7750***
(0.8851)
29.0915
(33.4751)
0.2626*
(0.1570)
0.0703
(0.0812)
0.9327
(0.7267)
3.9543
(3.0849)
47.6084***
(16.0116)
46.0288***
(8.9477)
2.6554***
(0.9002)
28.4276
(34.1855)
0.2615*
(0.1521)
0.0401
(0.0806)
1.0746
(0.6764)
53.4184***
(19.1364)
45.1490***
(9.5238)
2.6184***
(0.9121)
21.5510
(33.2407)
0.2178
(0.1702)
0.0448
(0.0838)
1.2706
(0.8029)
Entry restrictions
Capital requirement
0.0582
(0.0428)
1.3380
(1.0507)
**
Deposit insurance
First Stage F-test (ELERNER)
First Stage F-test (CR3)
Hansen’s J v2
(P-value)
Second Stage F-test
No. of observations
Dependent variable: Z-score
(1)
***
8.52
117.24***
0.856
(0.3547)
66.52***
1320
***
8.29
134.54***
0.120
(0.7291)
69.41***
1320
***
9.02
120.29***
1.061
(0.3029)
54.15***
1320
0.0619
(0.0308)
8.05***
108.27***
2.631
(0.1048)
50.75***
1320
***
10.78
234.57***
0.864
(0.3526)
16.59***
3299
***
10.34
226.27***
0.492
(0.4828)
15.52***
3299
***
11.66
236.17***
0.843
(0.3586)
15.26***
3299
0.8995
(1.5083)
10.15***
233.67***
1.140
(0.2857)
14.26***
3299
Results from GMM panel data estimations to explain the impacts of bank concentration and competition on financial stability. The first dependent variable (specifications 1–
4) is the probability of bankruptcy, which is a market-based bank-level measure of financial fragility that is calculated using the method developed by Bharath and Shumway
(2008). The second dependent variable (specifications 4–8) is Z-score, which is an accounting-based bank-level indicator of financial soundness. ELERNER is a bank-level
efficiency-adjusted indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using the stochastic frontier analysis
approach. CR3 is a country-level structural indicator of bank concentration calculated as the fraction of assets held by the three largest banks in each country. SIZE is the
natural logarithm of total assets in thousands of USD. NIM is the ratio of net interest income to interest-bearing (total earning) assets. LLP is the ratio of loan loss provisions to
total assets. RGDP is the rate of real GDP growth. Crisis is a dummy variable that takes a value of one for the years 2008–2009 and zero otherwise. Deposit insurance is a
dummy variable that takes a value of one if the country has deposit insurance and zero otherwise. Capital requirement is the minimum regulatory capital-to-assets ratio for
each country. Entry restrictions is the ratio of entry applications denied to applications received from domestic and foreign banks. The instrumental variables include activity
restrictions, financial freedom, and property rights.
***
Indicate significance at the 1% levels, respectively. Robust standard errors are in parentheses.
**
Indicate significance at the 5% levels, respectively. Robust standard errors are in parentheses.
*
Indicate significance at the 10% levels, respectively. Robust standard errors are in parentheses.
expenses/fixed assets,20 T is the time trend that captures the influence of technological changes that lead to shifts in the cost function
over time, and e is the error term.
Following Hasan and Marton (2003), Soedarmon et al. (2011),
Sun and Chang (2011) and Jiang et al. (2013) we use a two input
cost function specification (we also re-estimate the translog cost
function with three inputs – purchased funds, labor and physical
capital – as a further robustness test to investigate market power
and risk issues, the sample size falls but results are in-line with
the two input specification, (see Appendix G). As usual,21 symmetry
restrictions apply to this function (i.e. ajk = akj). Meanwhile, the total
cost and input price terms are normalized by w2. This imposes linear
20
Because of the lack of labor data, non-interest expenses are used as a proxy for
labor and physical capital costs.
21
See Claessens and Laeven (2004), Maudos and Fernández de Guevara (2004),
Fernández de Guevara et al. (2005), Berger et al. (2009), and Maudos and Solís (2009).
74
Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77
Table E1
Test for non-linear relationship.
Dependent variable: probability of bankruptcy
Dependent variable: Z-score
(1)
(3)
(2)
1.8018**
(0.8726)
4.3906*
(2.3836)
LERNER
LERNER2
2.2149***
(0.7124)
4.0066*
(2.2827)
0.0771*
(0.0419)
0.8852
(2.2487)
0.0236
(0.0600)
0.0031
(0.0064)
0.0145
(0.0465)
0.275
8.52***
1.70
0.151
(0.6980)
20.92***
1320
ELERNER
ELERNER2
SIZE
LLP
NIM
RGDP
CRISIS
Inflection point
First Stage F-test (LERNER/ELERNER/CR3)
First Stage F-test (LERNER2/ELERNER2/CR32)
Hansen’s J v2
(P-value)
Second Stage F-test
No. of observations
(4)
51.6104
(37.2177)
143.7211***
(46.0858)
0.1088***
(0.0402)
1.1718
(2.2720)
0.0202
(0.0600)
0.0031
(0.0061)
0.0167
(0.0449)
0.205
8.25***
2.35*
0.044
(0.8340)
22.25***
1320
62.2878*
(33.1187)
140.1059***
(47.1578)
1.3844
(1.1248)
60.9301
(55.7692)
0.0437
(0.2759)
0.1212
(0.1430)
2.4364**
(1.1467)
0.222
10.36***
9.99***
0.137
(0.7108)
6.37***
3299
2.0614
(1.2560)
60.9575
(54.9509)
0.0187
(0.2700)
0.1090
(0.1386)
2.3794**
(1.1293)
0.180
10.78***
7.53***
0.058
(0.8097)
6.76***
3299
Results from GMM panel data estimations to explain the impacts of bank concentration and competition on financial stability. The first dependent variable (specifications 1–
2) is the probability of bankruptcy, which is a market-based bank-level measure of financial fragility that is calculated using the method developed by Bharath and Shumway
(2008). The second dependent variable (specifications 3–4) is Z-score, which is an accounting-based bank-level indicator of financial soundness. LERNER is a bank-level
indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using the stochastic frontier analysis approach. ELERNER is
a bank-level efficiency-adjusted indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using the stochastic
frontier analysis approach. CR3 is a country-level structural indicator of bank concentration calculated as the fraction of assets held by the three largest banks in each country.
SIZE is the natural logarithm of total assets in thousands of USD. NIM is the ratio of net interest income to interest-bearing (total earning) assets. LLP is the ratio of loan loss
provisions to total assets. RGDP is the rate of real GDP growth. Crisis is a dummy variable that takes a value of one for the years 2008–2009 and zero otherwise.
***
Indicate significance at the 1% levels, respectively. Robust standard errors are in parentheses.
**
Indicate significance at the 5% levels, respectively. Robust standard errors are in parentheses.
*
Indicate significance at the 10% levels, respectively. Robust standard errors are in parentheses.
Table F1
Concentration, competition, and probability of bankruptcy (Tobit regression).
(1)
LERNER
ELERNER
CR3
SIZE
LLP
NIM
RGDP
CRISIS
Country effect
Wald test
No. of observations
(2)
0.1880***
(0.0331)
0.4596***
(0.0748)
0.0025
(0.0037)
0.1758*
(0.0898)
0.0090**
(0.0041)
0.0028
(0.0021)
0.1454***
(0.0122)
yes
1051.70***
1500
0.1799***
(0.0310)
0.4588***
(0.0748)
0.0015
(0.0036)
0.1773**
(0.0896)
0.0090**
(0.0041)
0.0029
(0.0021)
0.1451***
(0.0122)
yes
1055.12***
1500
This table presents the results of Tobit regressions. The dependent variable is the probability of bankruptcy, which is a market-based bank-level
measure of financial fragility calculated using the method developed by Bharath and Shumway (2008). LERNER is a bank-level indicator of bank
competition calculated as the difference between price and marginal cost as a percentage of price using fixed effect regression. ELERNER is a
bank-level efficiency-adjusted indicator of bank competition calculated as the difference between price and marginal cost as a percentage of
price using a stochastic frontier analysis approach. CR3 is a country-level structural indicator of bank concentration calculated as the fraction of
assets held by the three largest banks in each country. SIZE is the natural logarithm of total assets in thousands of USD. LLP is the ratio of loan loss
provisions to total assets. NIM is the ratio of net interest income to interest-bearing (total earning) assets. RGDP is the rate of real GDP growth.
Crisis is a dummy variable that takes a value of one for the years 2008–2009 and zero otherwise.
***
Indicate significance at the 1% levels, respectively. Robust standard errors are in parentheses.
**
Indicate significance at the 5% levels, respectively. Robust standard errors are in parentheses.
*
Indicate significance at the 10% levels, respectively. Robust standard errors are in parentheses.
75
Xiaoqing (Maggie) Fu et al. / Journal of Banking & Finance 38 (2014) 64–77
Table G1
Concentration, competition, and financial stability – LERNER estimated using a 3-input specification (robustness check).
LERNER
Dependent variable: Prob. of bankruptcy
Dependent variable: Z-score
(1)
(3)
SIZE
LLP
NIM
CRISIS
First Stage F-test
First Stage F-test (CR3)
Hansen’s J v2
Second Stage F-test
No. of observations
(4)
51.8100*
(29.8397)
0.2389
(0.9480)
0.0059
(0.0198)
1.6558
(1.5140)
0.0415
(0.0435)
0.1262***
(0.0195)
1.5496**
(0.7050)
0.2819
(0.9521)
0.0054
(0.0199)
1.6724
(1.5641)
0.0406
(0.0448)
0.1260***
(0.0198)
99.7069**
(40.3922)
3.7235*
(1.9838)
105.5617*
(62.6924)
0.0438
(0.3333)
2.0772
(1.3357)
55.6842*
(33.7837)
105.8960**
(45.1951)
3.9748*
(2.2412)
114.6456
(71.6090)
0.0029
(0.3784)
2.2418
(1.5081)
9.98***
12.68***
1.897
22.41***
786
9.48***
12.68***
2.266
22.43***
786
9.07***
40.15***
1.193
3.41***
2120
8.77***
40.15***
0.699
2.92***
2120
ELERNER
CR3
(2)
1.5303**
(0.6683)
Using the three-input specification to derive LERNER measures the number of observations reduces from 3299 to 2120 for listed and non-listed banks, and from 1320 to 786
for listed banks. We have to drop RGDP in our GMM model to avoid multicollineary problems, because the correlation coefficient between RGDP and CRISIS is 0.4548 for
listed banks and 0.4222 for listed and non-listed banks. Results from GMM panel data estimations explain the impact of bank concentration and competition on financial
stability. The first dependent variable (specifications 1–2) is the probability of bankruptcy, which is a market-based bank-level measure of financial fragility calculated using
the method developed by Bharath and Shumway (2008). The second dependent variable (specifications 3–4) is Z-score, which is an accounting-based bank-level indicator of
financial soundness. LERNER is a bank-level indicator of bank competition calculated as the difference between price and marginal cost as a percentage of price using the
stochastic frontier analysis approach. In this case the LERNER is calculated using a three input specification (purchased funds, labor and physical capital). CR3 is a countrylevel structural indicator of bank concentration calculated as the fraction of assets held by the three largest banks in each country. SIZE is the natural logarithm of total assets
in thousands of USD. NIM is the ratio of net interest income to interest-bearing (total earning) assets. LLP is the ratio of loan loss provisions to total assets. Crisis is a dummy
variable that takes a value of one for the years 2008–2009 and zero otherwise. The instrumental variables include activity restrictions, financial freedom, and property rights.
Overall, the key findings remain unchanged.
***
Indicate significance at the 1% levels, respectively. Robust standard errors are in parentheses.
**
Indicate significance at the 5% levels, respectively. Robust standard errors are in parentheses.
*
Indicate significance at the 10% levels, respectively. Robust standard errors are in parentheses.
homogeneity to ensure that the cost minimizing bundle does not
change if all of the input prices are multiplied by the same positive
scalar. Thus, only changes in the ratios of the input prices affect the
allocation of inputs. Following Lozano-Vivas and Pasiouras (2010),
we also include ln(equity) in the efficiency model to control for
the effect of risk. We then use the system GMM model to test the
link between market power and financial stability. The key results
are reported in Appendix G and remain the same.
In addition, following Koetter et al. (2008), we estimate Eq. (10)
using a stochastic cost frontier approach and calculate marginal
costs (MC SFA
TAit ).
Appendix B
See Table B1.
Appendix C
See Table C1.
Appendix D
See Table D1.
Appendix E
See Table E1.
Appendix F
See Table F1.
Appendix G
See Table G1.
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