Download The impact of bank competition and concentration on industrial growth

Economics Letters 124 (2014) 60–63
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Economics Letters
journal homepage: www.elsevier.com/locate/ecolet
The impact of bank competition and concentration on
industrial growth
Guy Liu a,b , Ali Mirzaei c , Sotiris Vandoros d,e,∗
a
Brunel University London, United Kingdom
b
Fudan University, China
c
American University of Afghanistan, Afghanistan
d
King’s College London, United Kingdom
e
London School of Economics and Political Science, United Kingdom
highlights
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We study the role of bank competition on growth of other industries.
We use a sample of about 6000 banks and 23 industries across 48 economies.
Non-cooperative bank competition and bank stability promote growth robustly.
Bank concentration may also have a positive effect on industrial growth.
The effect of concentration increases in the presence of higher levels of competition.
article
info
Article history:
Received 4 September 2013
Received in revised form
11 April 2014
Accepted 20 April 2014
Available online 24 April 2014
abstract
This paper studies whether bank competition affects growth of non-banking industries. We find that noncooperative bank competition and stability promote industrial growth robustly. Bank concentration may
also affect growth positively; the latter effect increases for higher levels of competition.
© 2014 Elsevier B.V. All rights reserved.
JEL classification:
G2
G3
L1
O4
Keywords:
Bank competition
Market concentration
Financial stability
Industrial growth
1. Background
The banking system is regarded as a mechanism that can convert the impact of the financial market development into growth1
∗ Correspondence to: King’s College London, Franklin–Wilkins Building, 150
Stamford Street, London SE1 9NH, United Kingdom. Tel.: +44 207 848 3879.
E-mail address: [email protected] (S. Vandoros).
1 For example, see Rajan and Zingales (1998); Vives (2001); Claessens and Laeven
(2005); Cetorelli and Strahan (2006); Maudos and Fernandez de Guevara (2006) and
Bertrand et al. (2007).
http://dx.doi.org/10.1016/j.econlet.2014.04.016
0165-1765/© 2014 Elsevier B.V. All rights reserved.
and it has been shown that competition can drive banks to reduce their lending costs, which can lead to an increase in demand
for bank funds in order to support business and growth (Berlin
and Mester, 1999; Beck et al., 2004). Previous research has suggested that competition promotes growth (Cetorelli, 2004; Cetorelli and Strahan, 2006), but it has been argued that increased
market power in combination with less competition can help relax
external financing constraints on non-financial firms (Mayer, 1988,
1990; Petersen and Rajan, 1995). It has also been observed that
external-finance-depending industries experience a slowdown in
growth when bank competition is high, as it makes it less attractive for banks to invest in the lending relationship Rajan, 1992;
G. Liu et al. / Economics Letters 124 (2014) 60–63
Petersen and Rajan, 1995; and Chen, 2007). Claessens and Laeven
(2005) found that sectors heavily dependent on bank financing
grow faster in countries where there is fierce bank competition,
while Maudos and Fernandez de Guevara (2006) suggest that the
exercise of market power enhances economic growth, supporting the lending relationship argument, with the implication that
bank competition may have a negative impact on the availability
of funds for industries.
In light of the existing literature, if we consider competition to
be a rival or non-cooperative process, then large banks can be developed through the process of a competitively rival selection (a
firm grows at the expense of the growth of its rivals). With banks
competing non-cooperatively, we would expect high concentration to be inevitable in an efficient market with a very selective
process of rival competition. In order to verify the argument proposed by this paper, which is contrary to the prevalent view on
concentrated market structure reducing competition, a key challenge would be to find out whether banks compete as rivals. In the
absence of rivalry, concentration can imply a market environment
in favour of business collusion and may weaken competition.
This study contributes to the literature by (a) identifying rival
competition in the context of banking; (b) employing a large
sample of over 6000 banks from 48 countries to examine whether
rival competition exists in the banking business across countries;
and (c) jointly studying rival competition and concentration for
their respective impact on the growth of 23 financially-dependent
industries.
2. Data and methods
We follow the approach introduced by Rajan and Zingales
(1998), who focus on analysing the effect of financial development
on growth, and test whether sectors which rely more on external
funds yield higher growth in economies with a higher level of financial development. In order to avoid the drawback of identification that arises in the cross-country regressions that are observed
in the literature on economic growth, Rajan and Zingales introduced an interaction between an industry characteristic (external
financial dependence) and a country characteristic (financial development).
In order to ensure distinct effects between bank competition,
the constraint of bank stability and financial depth, we include a
proxy of financial depth (i.e. domestic credit to private sector) in
the estimation (as in Cetorelli and Gambera, 2001; Claessens and
Laeven, 2005 and Maudos and Fernandez de Guevara, 2006). We
make a further distinction between financial depth, bank market
structure, bank stability constraint, and bank competition. When
banks are involved in rival competition, efficient banks can grow
by acquiring higher market share from inefficient banks, which inevitably leads to a more concentrated market structure in the long
run. To see if this holds, we include market structure and rival competition effects on growth in the estimation. This specification is
distinctive from existing studies that consider market structure as
a key determinant of competition (Rajan and Zingales, 1998; King
and Levine, 1993; Levine and Zervos, 1998; Cetorelli and Gambera, 2001; Cetorelli, 2004). We use country dummies to capture
any characteristic time-invariant effects of an economy on growth,
including information quality, which, according to Claessens and
Laeven (2005), can affect growth, and a variable for institutional
quality (property rights protection). Regarding financial dependence in relation to growth, raised by Rajan and Zingales (1998),
there are usually two empirical strategies used to estimate this.
One is to directly assess it using the dependence variable, and another is to integrate the dependence with other explanatory variables. The latter approach has been applied by King and Levine
(1993), Levine and Zervos (1998), Cetorelli and Gambera (2001),
and Cetorelli (2004). When an opportunity for growth arises, an industry that demonstrates high reliance on internal funds will find
61
it easier to grow, regardless of the situation in the financial sector.
However, for an industry that relies on external sources of funding, the success of the effort to secure funding will very much rely
on the circumstances in the banking sector. Therefore, the interaction term of external dependence should apply to any variable
that may affect (positively or negatively) the circumstances in the
banking sector, while they may be irrelevant to industries with
high reliance on internal funds. Therefore, apart from financial
depth, an interaction with external dependence should also apply
for concentration (Cetorelli and Gambera, 2001), competition and
stability.
We collected data for 23 industries over the period 1993–2007
for 48 emerging and mature markets2 and used OLS to estimate the
following empirical model:
Growthi,c = Const + β1 SectorDummiesi + β2 CountryDummiesc
+ β3 Share_in_value_addedi,c
+ β4 External_Dependencei × Financial_Depthc
+ β5 External_Dependencei × Bank_Competitionc
+ β6 External_Dependencei × Control_Variablesc
+ εi,c .
(1)
The dependent variable Growth is the average compounded annual
growth rate of value added in a particular sector in each country
over the period 1993–2007, based on our own calculations from
the UNIDO database. Variable share in value added represents the
value added of each sector as a percentage of the total value added
of an economy in the first year of the study period (1993), which
is also based on our own calculations from the UNIDO database.
External Dependence captures the external financial dependence of
US firms by ISIC sector over period 1980–1989, based on Rajan and
Zingales (1998). Financial Depth represents domestic credit provided to the private sector, as a proportion of GDP (data obtained
from IMF-IFC). Bank Competition is a degree of bank sector competition measured as the responsiveness of growth of bank market share to change of bank cost efficiency (source: BankScope and
own estimations based on Hay and Liu, 1997). In particular, for this
variable, we employ a simplified version of Hay and Liu’s model to
estimate efficiency competition within the context of the banking
business, which is as follows:
MS it = α + β
cit
ct
+ γ Pit + εit .
(2)
MS it is the market share of a bank i in year t; cit is the unit overhead cost (total non-interest expenses) of total assets of a bank in
year t; ct is the average overhead costs per unit of the total assets
of the bank sector in year t. Pit is the interest rate spread, implying
a price of bank assets employed for banking business. In a competitive market, we expect a negative coefficient (β ) because in any
non-cooperative competition, firms with higher costs relative to
the market average costs will grow slowly and then lose their market share. We employ a dynamic GMM panel method to estimate
β for each economy, which is then used in the empirical model.
As this variable enters the main model of the paper as a generated regressor, it can lead to a bias in the estimated coefficients
and the confidence intervals may be underestimated. For this reason, we checked the initial regressions that we performed in order to estimate β for each economy. As the coefficients are highly
2 The sample includes 25 mature markets (Australia, Austria, Belgium, Canada,
Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan,
Korea, Luxembourg, The Netherlands, New Zealand, Norway, Portugal, Spain,
Sweden, Switzerland, the United Kingdom and the United States) and 23 emerging
markets (Argentina, Brazil, Chile, China, Colombia, Czech Republic, Egypt, Estonia,
Hungary, India, Indonesia, Malaysia, Mexico, Morocco, Peru, Philippines, Poland,
Russia, Slovak Republic, Slovenia, South Africa, Thailand and Turkey).
62
G. Liu et al. / Economics Letters 124 (2014) 60–63
Table 1
Summary statistics.
Source: BankScope (Bureau Van Dijk), UNIDO Database, World Bank Database, Barth
et al. (2001), Demirguc-Kunt et al. (2004), Heritage Foundation, and estimation by
this study.
Mean
St. dev
0.028
0.045
0.358
0.060
0.047
0.414
Domestic credit to private sector/GDP
89.359
52.347
Alternative variables:
Bank credit/GDP
Stock market capitalisation/GDP
Stock market turnover ratio
106.220
75.007
73.346
58.169
49.162
53.602
Concentration (5-firm) ratio (%)
Efficiency competition
66.526
4.033
18.246
4.273
Alternative variables:
Concentration (HH index)
Competition (H-statistics)
Competition (Lerner index)(%)
2387
0.610
26.948
1633
0.175
7.548
6.461
6.708
2.598
2.106
Industry variables
Industry growth (average compounded)
Industry’s share in total industry value added
External finance dependence (all firms)
Financial depth variables
Banking variables
Control variables
Z -index
Property rights
Note: This table reports the summary statistics of the main regression variables.
Alternative variables used for robustness tests.
statistically significant in the vast majority of cases, the uncertainty
arising from the generated regressor is minimised.
Furthermore, we include a conventional alternative indicator
of bank competition: Bank concentration is a country-level indicator of bank concentration, measured as the ratio of the total
share of assets of the five largest banks over the total assets of
all banks in an economy (5-firm ratio; based on own calculations
from BankScope). Finally, we include two control variables: a proxy
for bank stability and a proxy of institutional quality. Bank stability is a measure of bank soundness, calculated as the return on assets plus capital asset ratio divided by volatility of return on assets
(source: own calculations from BankScope). This is known as the Z score, which was developed by Roy (1952) and revised by De Nicolo
(2000). For institutional quality we use Property rights, in a range
from 2 to 9, where a higher score indicates greater property protection (data obtained from the Heritage Foundation). Claessens and
Laeven (2005) state that, by using the Rajan and Zingales methodology, there should be no endogeneity of banking competition or
omitted variables problems, and so an instrumental variable approach is not required. Summary statistics and a correlation matrix
are presented in Tables 1 and 2, respectively.
3. Results
Results are presented in Table 3. The coefficient of the variable
capturing financial depth interacting with financial dependence
is positive and statistically significant in three out of six regressions (columns 1, 3 and 4), providing some evidence that industrial sectors relying on more external finance may tend to develop
faster in countries where there is more financial depth. For a given
level of external dependence, an increase in financial depth by one
percentage point (as a proportion of GDP) leads to an increase in
the real growth rate of value added by between 0.046 and 0.058%
points. The coefficient of efficiency competition is positive and
strongly statistically significant in all specifications (columns 2, 4,
and 6), providing strong evidence that competition in the banking
sector promotes growth in other sectors. Bank stability appears to
have a strongly or weakly significant positive effect on growth, depending on the specification of the model (columns 3, 4 and 6).
The effect of market concentration on growth is statistically
significant in the specification that does not include efficiency
competition or stability (column 1). For a given level of financial
dependence, an increase in the 5-firm ratio by one point leads to
an increase in the real growth rate by 0.133% points. However, the
coefficient becomes insignificant when the efficiency competition
variable is included in the model (column 4). This calls for the inclusion of an interaction term: in order to see whether concentration has a positive effect when competition is high, we include an
interaction of these two variables. According to the results, which
are presented in columns 7 and 8 of Table 3, the coefficient of the
interaction term has a positive but weakly significant effect, providing limited evidence that the effect of concentration may become higher for higher levels of competition.
Finally, we performed a sensitivity analysis for different sample
periods, different measures of financial sector development,
alternative measures of competition and stability (as presented
in Table 1), and different country sub-samples (emerging versus
advanced economies). Findings are very similar to the baseline
model and hold the same interpretation. Results are not reported,
but are available upon request.
4. Conclusions
We measured rival competition by using the efficiency competition model as a new and direct measure of bank competition. The key information embedded in this measure is the state
of competition—the non-cooperative process of banks competing in terms of size. This information enables this new measure
of competition to capture the spillover effects of bank competition on the growth of non-financial industries. Using data from 48
markets, the empirical analysis has shown that higher levels of
banking competition and banking stability lead to an increase in
industrial growth. In addition, we found some empirical evidence
that growth of industrial output is higher when bank markets are
Table 2
Correlation matrix.
Industry growth
i. Share in value added
ii. External financial dependence
iii. Domestic credit to private sector
iv. 5-firm concentration ratio
v. Efficiency competition
vi. Z -score
vii. Property rights
i
ii
iii
iv
v
vi
0.012
0.013
−0.006
−0.007
0.042
0.153***
0.267***
0.194***
0.657***
0.242***
0.108***
0.049
−0.035
−0.009
0.082**
−0.172***
0.060*
0.065**
0.001
0.160***
Notes: Definitions and data sources of the variables are in Table 1.
*
Significant at 10%.
**
Significant at 5%.
***
Significant at 1%.
−0.159***
0.017
0.031
0.034
0.035
−0.006
0.277***
0.126***
G. Liu et al. / Economics Letters 124 (2014) 60–63
63
Table 3
Regression results.
Share in value added
Financial depth
Credit to private sector ∗ FD
Bank concentration
5-firm ratio ∗ FD
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
−0.031***
−0.033***
−0.034***
−0.031***
−0.037**
−0.035***
−0.031***
−0.033**
[3.03]
[3.27]
[3.36]
[3.01]
[2.03]
[3.43]
[3.06]
[3.23]
0.046*
[1.80]
0.032
[1.22]
0.058**
[2.15]
0.050*
[1.83]
−0.010
[0.34]
0.008
[0.23]
0.037
[1.42]
0.012
[0.32]
0.091
[1.36]
0.082
[1.23]
0.098**
[2.23]
0.093**
[2.11]
0.009*
[1.77]
0.011*
[1.70]
0.133**
[2.07]
Bank competition
Efficiency competition ∗ FD
0.085
[1.28]
0.060***
[2.97]
0.048**
[2.23]
0.048**
[2.29]
Interaction term
5-firm conc ratio ∗ Efficiency competition ∗ FD
Bank stability
Z -score ∗ FD
0.453**
[2.14]
0.358*
[1.67]
Institution
Property rights ∗ FD
Industry dummies (23 sectors)
Country dummies (48 countries)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.164*
[1.94]
Yes
Yes
Number of countries
Observations
R-squared
S.E of regression
F -statistic
48
928
0.45
0.21
9.71
48
928
0.45
0.21
9.82
48
928
0.45
0.21
9.72
48
928
0.46
0.21
9.65
48
928
0.45
0.21
9.73
0.353*
[1.65]
0.093*
[1.71]
0.121
[1.63]
Yes
Yes
Yes
Yes
0.125*
[1.69]
Yes
Yes
48
928
0.46
0.21
9.67
48
928
0.45
0.21
9.61
48
928
0.46
0.21
9.48
Notes: the dependent variable is the average (compounded) real growth of value added over the period 1993–2007. Share in value added is the fraction of value added of
each sector in each country in the year 1993. FD is the external financial dependence of each sector taken from Rajan and Zingales. Robust t-values are in parentheses.
*
Significant at 10%.
**
Significant at 5%.
***
Significant at 1%.
more (competitively) concentrated. Results also show that the effect of concentration on growth may become higher for higher
levels of competition. In conclusion, we show that stronger noncooperative competition and what may be a competitively driven
concentrated banking sector promote industries to grow faster.
The policy implication of our findings is that competitively-driven
large banks promote economic growth.
Acknowledgments
We are grateful to the Editor of the Journal and an anonymous
referee for their constructive comments. Thanks are also due to
Chris Muris for suggestions on earlier versions of the paper. All
outstanding errors are our own.
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