Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Bank Competition in Emerging Asian Countries: Pre-Post
the Global Financial Crisis
Hanh Thi My Phan* and Kevin Daly**
The paper investigates the competitive structure of banking industry in six
emerging Asian countries (Bangladesh, Indonesia, India, Philippines,
Thailand, and Vietnam) during the period 2005-2012. By using Conduct
Parameter approach, we found that monopolistic competition is the best
description of competitive structure in banking industry of these countries
in almost period. It is worth noting that banks in some emerging Asian
countries such as Philippines, Bangladesh, India, and Vietnam did not
behave as optimizing banks in some years over this period. By contrast,
banks operated under conditions of Perfect competition during period of
the crisis for India and in 2008 for Vietnam. The results reveal that the
trends of competition follow V-shapes and inverted V shapes.
Interestingly, the bottom and the top of these V-shapes and inverted Vshapes occurred in 2008, 2009, and 2010 when the global financial crisis
happened. This shows that the trend of competition in the banking
systems of emerging countries changed remarkably during the global
financial crisis.
JEL Codes: G21, L13
1. Introduction
Over the past decade, banks in emerging markets have undergone significant
restructuring in terms of ownership (i.e. bank privatization via mergers and
acquisitions), interest rate deregulation and increased competition from foreign
banks (Turk Ariss, 2010). Studies by Claessens and Laeven (2004), Yeyati and
Micco (2007), Yildirim and Philippatos (2007), Wu et al. (2010), Jeon et al. (2011)
have demonstrated that foreign bank entry has enhanced competition across
emerging market economies. Other studies by Jeon et al. (2011), Turk Ariss (2010)
and Williams (2012)’s have focused on the effects of changed ownership on
competition in banking in emerging markets.
Previous studies investigating the competitive structure of banking in the context of
developed countries such as the USA and Europe have been performed by Shaffer
(1982), Nathan and Neave (1989), Molyneux et al. (1994), Vesala (1995), Coccorese
(1998), (2004), Bikker and Groeneveld (2000), De Bandt and Davis (2000), Hempell
(2002), Bikker and Haaf (2002), Weill (2004), Casu and Girardone (2006) to name a
few.
Only a handful of studies have focused on banking across emerging markets in
particular since the recent global financial crisis. This paper contributes to the latter
by investigating the competitive structure of banking across six emerging Asian
countries over the period of 2005-2012.
* Hanh Thi My Phan, School of Business, University of Western Sydney, Australia
Email: phanmyhanhnt@yahoo.com.vn
** Dr. Kevin Daly, School of Business, University of Western Sydney, Australia,
Email: K.daly@uws.edu.au
1
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
This paper is structured as follows; the next section reviews the literature on
competition in banking across both developed and developing countries. In Sections
3 and 4 we specify
our empirical model and discuss our data, respectively. Section 5 provides an indepth discussion about our empirical results. Finally, Section 6 provides a summary
and conclusion.
2. Literature Review
In the literature, bank competition is investigated by structural and non-structural
approaches (Delis et al., 2008). The former approach employs market concentration,
measured by the concentration ratio (CR) or Herfindahl-Hirshman index (HHI).
However, this indicator is viewed by some researchers as a weak and poor estimate
of bank competition (Berger et al. 2004; Claessens & Laeven 2004, 2005; de
Guevara, Maudos & Pérez 2005; Maudos & de Guevara 2004, and Schaeck, Klaus
& Cihak 2010). The concern here is that market concentration tells us little about the
degree of competition in banking and is according to Schaeck et al. (2009) an
inappropriate measure of bank competition.
The alternative non-structural or New Empirical Industrial Organization (NEIO)
approaches have become popular with recent bank studies since these have several
advantages over the structural approaches. For instance, the non-structural
approaches to measuring bank competition does not assume the existence of a
negative relation between the level of concentration and competition but rather
assumes contestability in banking competition whereby the degree of potential
competition rather than the degree of concentration are investigated. In addition, it is
not necessary to identify a geographic market because market power can be
indicated by individual banks’ behaviors (Casu and Girardone, 2006). Therefore,
instead of investigating the competitive environment via a structural approach, the
NEIO approaches tries to measure or estimate the level of competition directly.
These approaches evaluate the competitive conditions of the banking industry or the
market power via the pricing conduct of banks. Here, the degree of competition
amongst banks is often measured by The Panzar-Rosse (P-R) revenue test, Lerner
index, and Conduct parameter method.
The revenue test is based on the Panzar-Rosse (1987) model which estimates a Hstatistic to reflects the degree of competition. This test aims to evaluate the effect of
changes in input prices on banks’ revenues. Several studies such as Casu and
Girardone (2006), Schaeck & Cihak (2010), Mlambo & Ncube (2011) have employed
this indicator to measure bank competition. However, a weakness of the
Panzar&Rosse H-statistic is that its usefulness only applies when estimating the
level of bank competition at national level rather than at the specific individual banklevel. Furthermore, the H-statistic is considered as a continuous and long run
measure under the assumption of long-run equilibrium which is a further limitation of
the method (Shaffer 2004, Schaeck, Klaus & Cihak 2010, Liu, Molyneux & Nguyen
2012).
To overcome H-statistic test’s shortcomings, empirical studies used another
measurement for competition, the Lerner index (Lerner, 1934), calculated as the
2
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
ratio of disparity between price and marginal cost to price. This index is considered a
bank-level and time-varying proxy for market power (Angelini and Cetorelli, 2003,
Maudos and de Guevara, 2007, Coccorese and Pellecchia, 2010). Studies
investigating the nexus between bank competition – efficiency relationships,
employed the Lerner indices as measures of market power in EU banking markets
(Maudos & Guevara 2007, Casu & Girardone 2009,Coccorese & Pellecchia
2010)and the US (Koetter, Kolari, & Spierdijk, 2008, 2012). This measure was also
used to estimate bank-level market power in a model of net interest margin for the
new EU candidate countries over 1996-2006 period (Kasman et al., 2010) as well as
in a study on competition and financial stability in the case of 23 developed countries
(Berger et al., 2009).
An alternative method to estimate the degree of competition at both the industry and
bank firm-level is Conduct parameter method. This approach reflects banks’
behavior via an estimated parameter that represents their degree of market power. It
can be expressed as a Conjectural Variation Coefficient (Iwata, 1974), or as a markup test (Bresnahan, 1989). Mark-up tests are based on the principle that profitmaximizing banks in equilibrium will select prices at which marginal costs and
perceived marginal revenue are equal. This test, therefore, estimates θ which
represents the degree of market power of a bank by calculating the deviation of a
bank’s perceived marginal revenue schedule from its demand schedule. This
technique and its variants have been applied in bank competition studies by Uchida
and Tsutsui (2005), Brissimis et al. (2008), Delis et al. (2008), Delis and Tsionas
(2009), Soedarmono, Machrouh, & Tarazi (2011, 2013).
A suggested approach in the study of Uchida and Tsutsui (2005), namely the
Cournot-type theoretical framework, provides yearly estimates of the degree of
competition (θ). By using a translog cost function, Uchida & Tsutsui (2005)
investigated the degree of short-run competition in Japanese banking. They found an
improvement in bank competition over the period from 1974 to 2000, especially
throughout 1970s and the first half of the 1980s. Following Uchida & Tsutsui (2005),
Brissimis, Delis & Papanikolaou (2008) captured the banking industry average
competition levels per year (θ) in ten newly acceded European countries during the
period of 1994–2005. They derived θ from a simultaneous estimation of three
equations (e.g. translog cost, revenue and inverse loan function). By using bank
panel data and seemingly unrelated regressions, θ is derived separately for each
country. The results were mixed with Bulgaria and Romania assessed as fairly
competitive while Lithuania and Slovenia became anticompetitive with a θ close to 1,
while the remaining countries operated in a monopolistic competitive structure.
Values for competition levels changed over time and tended to converge to the
median value of θ.
Based on Uchida & Tsutsui (2005)’s Cournot-type theoretical framework, Delis and
Tsionas (2009) suggested a method for the joint estimation of industry-level
competition and bank-level efficiency. This method combines NEIO approach with
the stochastic frontier approach, and employs the local maximum likelihood (LML)
technique. LML technique makes estimation of market power convenient and relaxes
some theoretical and empirical assumptions. With the sample of EMU commercial
banks from 1999 to 2006, their findings suggested that most banks’ performed at
fairly competitive levels.
3
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
In the context of the Latin American banking system, Williams (2012) used a
Conventional Lerner indices for both loan and deposit markets and Efficiency
adjusted Lerner indices for asset markets to estimate market power. Nevertheless,
the former were very high (around 0.8), the later were much smaller (under 0.3). The
results showed that the banking system in general was monopolistically competitive
and became fairly anticompetitive over the period 1985-1990 and the year 1995
(Lerner indices equaled to over 0.9) in both loan and deposit markets but fairly
competitive between 1986 and 1992 in asset markets. Similar, examined the
association between market power, capital cost and profit efficiency, based on
estimated values of Conventional Lerner indices from around 0.3 to 0.4, Fang et al.
(2011) concluded that banking systems of six South-Eastern European transition
nations operated under conditions of monopolistic competition in the decade 1998 2008.
Within Asian banking, Soedarmono (2010), Soedarmono, Machrouh & Tarazi (2011,
2013) applied Uchida & Tsutsui (2005)’s Cournot-type theoretical framework as a
measure of banking industries’ market power. In particular, Soedarmono examined
yearly average market power in the context of 10 Asian countries between 1999 and
2007. After the 1997 Asian crisis, the Asian banking structure changed significantly
due to the fast development of banking consolidations as well as M&As growth. The
findings also indicated an increase in market power associated with a decrease in
competition across the Asian banking sector over this period.
3. Model Specification
In our study we employ a non-structural approach based on explicit information
regards costs and demand to estimate banks’ behavior. The level of banks’ market
power is reflected via estimates of banks’ behavior (Coccorese, 2009). Following
Uchida & Tsutsui (2005), Brissimis, Delis & Papanikolaou (2008), and Soedarmono,
Machrouh & Tarazi (2011, 2013), our study employed the new industrial organization
approach to measure competition across emerging markets. By using this approach,
the degree of market power is estimated endogenously across the banking industry
in each country .Similar to Brissimis, Delis & Papanikolaou (2008), Delis and Tsionas
(2009), and Soedarmono, Machrouh & Tarazi (2011), we employed three equations
involving a translog cost function (equation 1), a revenue function (equation 2), and
an inverse loan demand function (equation 3 and 4) to estimate the degree of
competition in some emerging countries. The system of equations includes equation
(1), (2) and (3) for model 1, and equation (1), (2) and (4) for model 2. θ represents
our proxy for market power and is identified via the system of equations as follows.
(1) lnCit = b0 + b1̅̅̅̅̅̅̅ + b2(̅̅̅̅̅̅̅)2 + b3̅̅̅̅̅̅̅ + b4(̅̅̅̅̅̅̅)2 + b5̅̅̅̅̅̅̅
+ b6(̅̅̅̅̅̅̅)2+ b7(̅̅̅̅̅̅̅ )(̅̅̅̅̅̅̅) + b8(̅̅̅̅̅̅̅ )(̅̅̅̅̅̅̅) + b9(̅̅̅̅̅̅̅ )(̅̅̅̅̅̅̅)
+
(2) Rit = Rit + ritqit+ Cit(b1 + b2̅̅̅̅̅̅̅ + b7 ̅̅̅̅̅̅̅ + b8̅̅̅̅̅̅̅)
+ Cit
(3) ln Pit = g0 –
(b3 + b4 ̅̅̅̅̅̅̅ + b8̅̅̅̅̅̅̅+ b9̅̅̅̅̅̅̅) +
ln Qt + g1 lnGDPt+ g2lnOPLit+
4
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
(4) ln Pit = g3 –
Variables
C
q
Q
d
w
R
r
P
OPL
GDP
IR
TA
e
ln qit + g4 lnGDPt+ g5 lnIRt+ g6 lnTAit +
Definition
Total expenses
Total earning assets
Sum of qit
Total deposit and short term funding
Total operating expenses to total assets
Total revenue equals sum of interest revenue and non-interest
revenue
Ratio of interest expenses to total deposits
Ratio of total revenues to total earning assets
Ratio of operating expenses to total loans
Annual real gross domestic product
Interest rate (interbank rate, refinancing rate or one-day
repurchase rate)
Total assets
Error term
In the above equations ηt = - (Pt/Qt) (∂Qt/∂Pt) measures the market demand elasticity
for total earning assets. Variables with upper bars are calculated by deviations from
their means in each time period. According to this approach, θ, is defined as the
conjectural variation elasticity for total banking industry outputs with respect to ith
bank’s output, providing us with a measure of market power or bank competition. A
higher (lower) θ shows a decrease (increase) in bank competition. In theory, values
of θ are bound between 0 and 1, where θ = 0 implies perfect competition, and θ = 1
implies pure monopoly. Values of θ in between indicate monopolistic competition.
However, θ <0 implies that marginal cost is higher than price, possibly due to a nonoptimizing behavior of banks. In the special case of Cournot competition, θ i,t =
market share of bank i in time t.
Like Uchida and Tsutsui (2005), the study employs three-stage least squares (3SLS)
to estimate simultaneously the system of three equations. We use year dummy
variables to estimate θt and time dummy variables for every two years to estimate η t
in equation (2) and (3) for model 1, and in equation (2) and (4) for model 2 due to a
linear dependence of η on GDP in the equation (3) and (4). Rank variables of qi,t, di,t,
ri,t, Ci,t, Pi,t, Ri,t, and exogenous variables such as OPLi,t, Qt (in model 1), qit, IRt, TAit
(in model 2), wi,t, GDPt, and year dummies are used as instrumental variables.
4. Data and Sample
To investigate the trend of bank competition in emerging banking industries pre and
post global financial crisis of 2008-2009, the sample consists of domestic
commercial banks in six emerging Asian countries, e.g. Bangladesh (36 banks),
India (38 banks), Indonesia (36 banks), Philippines (24 banks), Thailand (19 banks),
and Vietnam (28 banks), during the period 2005-2012. Although increased foreign
bank entry in the financial system has been allowed due to liberalization, a level
playing field for both foreign banks and domestic banks have not been created
5
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
completely in some emerging countries. Hence, foreign commercial banks and
foreign bank branches were excluded from the analysis.
We employed panel data for domestic commercial banks in each country over 20052012 period and all bank-specific data was retrieved from Bankscope Fitch-IBCA.
For country-specific data, data for the real Gross domestic product (GDP) was
collected from World Economic Outlook of International Monetary Fund. IR is defined
as interbank rate (for Bangladesh, India, Indonesia, Philippines), one-day repurchase
rate (for Thailand), and refinancing rate (for Vietnam) that were collected from
www.tradingeconomics.com. After eliminating missing data, the total number of
observations is 1,447 for six countries including Bangladesh (288), India (304),
Indonesia (288), Philippines (192), Thailand (152), and Vietnam (223). Descriptive
statistics for all variables are shown in Table 1.
6
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Table 1: Descriptive statistics
Variables
Mean
Bangladesh
R
13034
P
0.156
C
7860
q
84409
d
85406
r
0.067
w
0.026
GDP
4909255
OPL
0.037
IR
9.324
TA
97020
Number of
observations
288
India
R
144028
P
0.116
C
93175
q
1270352
d
1174804
r
0.064
w
0.018
GDP
46153935
OPL
0.031
IR
6.542
TA
1404304
Number
of 304
observations
Indonesia
R
7844106
P
0.169
C
4073328
q
51048433
d
47259706
r
0.061
w
0.034
GDP
2152606271
OPL
0.064
IR
6.687
TA
58029225
Number
of 288
observations
Standard
deviation
Minimum
Maximum
11610
0.029
7020
80166
86762
0.019
0.016
676535
0.022
2.966
89909
630
0.053
288
4213
4323
0.001
0.010
3932175
0.015
4.824
5731
66224
0.297
39314
500106
594549
0.173
0.185
6005389
0.203
13.473
500106
190629
0.031
119517
1660354
1540699
0.039
0.013
7776381
0.023
1.527
1849138
3761
0.057
1908
38845
22281
0.031
0.0004
34517105
0.0004
3.608
41579
1649764
0.332
1046102
14190472
13316931
0.568
0.106
57772710
0.194
8.422
15662610
13365263
0.105
6270575
89317705
82719406
0.025
0.013
281983506
0.064
1.503
101360709
7300
0.066
5200
40500
19500
0.007
0.008
1750815200
0.010
4.037
57100
75876700
1.143
33946000
587047800
458147300
0.207
0.085
2618139200
0.881
8.963
635618700
7
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Variables
Philippines
R
P
C
q
d
r
w
GDP
OPL
IR
TA
Number
observations
Thailand
R
P
C
qit
d
r
w
GDP
OPL
IR
TA
Number
observations
Vietnam
R
P
C
q
d
r
w
GDP
OPL
IR
TA
Number
observations
Table 1: Descriptive statistics (cont.)
Mean
Standard
Minimum
deviation
Maximum
20203
0.122
10327
180675
162144
0.034
0.030
5337619
0.080
5.620
206162
192
20992
0.024
10462
198550
183243
0.017
0.008
576479
0.054
1.438
224563
1177
0.067
610
7139
6325
0.011
0.012
4481281
0.029
4.025
7912
118298
0.199
57673
1172037
974292
0.143
0.062
6314865
0.437
7.825
1244408
45692
0.083
22498
572851
509270
0.027
0.021
4361365
0.032
2.927
613334
of 152
44689
0.021
21252
561160
507339
0.016
0.012
309230
0.036
1.047
599447
64
0.014
49
647
117
0.002
0.004
3858019
0.005
1.354
771
176021
0.180
104491
2200830
2050941
0.142
0.102
4895634
0.363
4.792
2338099
11113807
0.124
7636425
82973021
79264412
0.071
0.015
503992866
0.028
8.865
93322819
of 223
18143370
0.039
12592356
120465395
113401386
0.028
0.006
71640605
0.010
2.669
130275237
2654
0.017
1608
106145
40416
0.009
0.002
393030768
0.004
5.917
144861
126514346
0.268
89434198
621530144
598289088
0.153
0.045
613396463
0.077
13.333
665039653
of
Note: R,C,q,d,TA, and GDP are measured in one million national currency of each
country in the sample; IR is in percent; P, w, r, OPL are ratios.
8
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
5. Results
The results of our estimates for θt in model 1 and 2 for six emerging countries
employing 3SLS with 95% confidence interval are presented in the table 2.
Table 2: Results of estimated θt
Country
Bangladesh
Parameter
Model 1
Model 2
Estimate
p-value
Estimate
p-value
θ 2005
0.297
[0.000]
-2.321
[0.000]
θ 2006
0.285
[0.000]
-2.221
[0.000]
θ 2007
0.333
[0.000]
-2.485
[0.000]
θ 2008
0.403
[0.000]
-3.010
[0.000]
θ 2009
0.371
[0.000]
-2.664
[0.000]
θ 2010
0.496
[0.000]
-3.555
[0.000]
θ 2011
0.324
[0.000]
-2.456
[0.000]
θ 2012
0.300
[0.000]
-2.268
[0.000]
2
R
0.873
0.873
Country
India
Parameter
Model 1
Model 2
Estimate
p-value
Estimate
p-value
θ 2005
0.333
[0.639]
-0.021
[0.639]
θ 2006
0.546
[0.345]
-0.035
[0.345]
θ 2007
-0.200
[0.640]
0.013
[0.640]
θ 2008
-0.425
[0.219]
0.028
[0.219]
θ 2009
-1.500
[0.000]
0.095
[0.000]
θ 2010
-0.428
[0.114]
0.027
[0.114]
θ 2011
0.630
[0.003]
-0.041
[0.003]
θ 2012
0.946
[0.000]
-0.062
[0.000]
2
R
0.299
0.299
Country
Indonesia
Parameter
Model 1
Model 2
Estimate
p-value
Estimate
p-value
θ 2005
0.119
[0.014]
0.112
[0.014]
θ 2006
0.083
[0.035]
0.078
[0.035]
θ 2007
0.215
[0.000]
0.202
[0.000]
θ 2008
0.232
[0.000]
0.218
[0.000]
θ 2009
0.224
[0.000]
0.211
[0.000]
θ 2010
0.283
[0.000]
0.266
[0.000]
θ 2011
0.235
[0.000]
0.220
[0.000]
θ 2012
0.208
[0.000]
0.195
[0.000]
2
R
0.889
0.889
9
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Country
Parameter
θ 2005
θ 2006
θ 2007
θ 2008
θ 2009
θ 2010
θ 2011
θ 2012
R2
Country
Parameter
θ 2005
θ 2006
θ 2007
θ 2008
θ 2009
θ 2010
θ 2011
θ 2012
R2
Country
Parameter
θ 2005
θ 2006
θ 2007
θ 2008
θ 2009
θ 2010
θ 2011
θ 2012
R2
Table 2: Results of estimated θt (cont.)
Philippines
Model 1
Model 2
Estimate
p-value
Estimate
-0.455
[0.076]
-0.356
-0.200
[0.318]
-0.156
0.113
[0.562]
0.088
0.577
[0.003]
0.451
0.876
[0.000]
0.691
0.421
[0.010]
0.332
0.329
[0.035]
0.258
0.209
[0.144]
0.164
0.228
0.228
Thailand
Model 1
Model 2
Estimate
p-value
Estimate
0.386
[0.000]
0.394
0.260
[0.000]
0.266
0.254
[0.000]
0.263
0.329
[0.000]
0.340
0.373
[0.000]
0.392
0.351
[0.000]
0.369
0.267
[0.000]
0.273
0.228
[0.000]
0.233
0.946
0.946
Vietnam
Model 1
Model 2
Estimate
p-value
Estimate
0.726
[0.309]
-0.200
1.240
[0.020]
-0.342
0.556
[0.143]
-0.149
-0.344
[0.153]
0.092
0.325
[0.219]
-0.083
0.819
[0.000]
-0.210
1.298
[0.000]
-0.336
1.396
[0.000]
-0.362
0.790
0.790
p-value
[0.076]
[0.318]
[0.562]
[0.003]
[0.000]
[0.010]
[0.035]
[0.144]
p-value
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
p-value
[0.309]
[0.020]
[0.143]
[0.153]
[0.219]
[0.000]
[0.000]
[0.000]
Competition structure
Due to the estimated values of θt between 0 and 1 in both Models 1 and 2,
monopolistic competition is the best description of competitive structure in banking
for Indonesia and Thailand during 2005-2012, and the Philippines over 2007 and
2012. Regards the Philippines, the negative values of θt in the first two years implies
that the marginal cost is higher than price, possibly due to a non-optimizing behavior
of banks while banking competitive structures in 2007 appeared to represent perfect
competition.
For the other countries in our sample, the estimated results for model 1 and 2 are
different. For instance, in Bangladesh, model 1 indicated that banks were operated
under conditions of monopolistic competition whereas model 2 concluded that
10
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
marginal cost is higher than price because θ is negative. Over 2005-2006 and 20112012, Monopolistic competition appears to represent the competitive structure of the
Indian banking industry (for model 1) but the banking systems seemed to behave at
non-optimization level (for model 2). By contrast, during 2007-2010, non-optimizing
behavior of banks for model 1 compared to perfect competition for model 2 was
description for competition of Indian banking industry. In the case of Vietnam, model
1 showed that banks operated under conditions of monopolistic competition except
for 2006, 2011 - 2012 (Monopoly), and 2008 (non - optimation levels). Nevertheless,
results from model 2 indicated that marginal cost is higher than price, possibly due to
a non-optimizing behavior of banks in Vietnam, except for the year 2008 strong
competition between banks resulted in perfect competition. The estimates for θ t in
model 1 (θ1) are higher than in model 2 (θ2) in almost every period, except for
Thailand over this period.
Figure 1: Estimates of θ1 for model 1 and θ2 for model 2 of banking in Vietnam
Vietnam
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
2005
2006
2007
2008
θ 1 (3sls)
2009
2010
2011
2012
θ 2 (3sls)
11
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Figure 2: Estimates of θ1 for model 1 and θ2 for model 2 of banking in India
India
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
2005
2006
2007
2008
2009
θ 1 (3sls)
2010
2011
2012
θ 2 (3sls)
Figure 3: Estimates of θ1 for model 1 and θ2 for model 2 of banking in Bangladesh
Bangladesh
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-2.5
-3.0
-3.5
-4.0
2005
2006
2007
2008
θ 1 (3sls)
2009
2010
2011
2012
θ 2 (3sls)
12
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Figure 4: Estimates of θ1 for model 1 and θ2 for model 2 of banking in Indonesia
Indonesia
0.30
0.25
0.20
0.15
0.10
0.05
2005
2006
2007
2008
2009
θ 1 (3sls)
2010
2011
2012
θ 2 (3sls)
Figure 5: Estimates of θ1 for model 1 and θ2 for model 2 of banking in Philippines
Philippines
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
2005
2006
2007
2008
θ 1 (3sls)
2009
2010
2011
2012
θ 2 (3sls)
13
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
Figure 6: Estimates of θ1 for model 1 and θ2 for model 2 of banking in Thailand
Thailand
0.45
0.40
0.35
0.30
0.25
0.20
2005
2006
2007
2008
θ 1 (3sls)
2009
2010
2011
2012
θ 2 (3sls)
The trends of bank competition
The trends for θt follow a V-shape and inverted V-shape. V-shapes were recorded for
Bangladesh (model 2), India and Vietnam (model 1). Inverted V-shape appears for
Bangladesh (model 1), India and Vietnam (model 2), Indonesia, Philippines, and
Thailand (both models). Interestingly, the bottom and the top of these V- and
inverted V-shape occur in 2008 (for Vietnam), 2009 (for India, Philippines, and
Thailand) and 2010 (for Bangladesh and Indonesia) when the global financial crisis
happened. This shows that the trend of competition in the banking systems of
emerging countries changed remarkably during the global financial crisis.
6. Conclusions
To understand the changes in competition across emerging countries pre and post
global financial crisis of 2008-2009, this paper investigates the competitive structure
of banking industry as well as trends in competition across six emerging Asian
countries (Bangladesh, Indonesia, India, Philippines, Thailand and Vietnam) during
the period 2005-2012 covering the global financial crisis of 2008-2009.
In terms of competition levels, by using the conduct parameter approach, the
estimated results in model 1 and 2 are fairly different. Estimates for θt in model 1 (θ1)
are higher than in model 2 (θ2) in almost every period, except for Thailand over this
period. Monopolistic competition is the best description of competitive structure in
banking industry of Indonesia and Thailand during 2005-2012, and of Philippines
over 2007 and 2012, Bangladesh (for model 1), India (for model 1 except for the
period of 2007-2010), Vietnam (in four years: 2005, 2007, 2009, and 2010 for model
1). It is worth noting that in some emerging Asian countries such as Bangladesh (for
model 2), Philippines (in 2005 and 2006 for both models), India (for model 2 except
14
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
the period of 2007-2010 and for model 1 over 2007-2010), and Vietnam (in 2008 for
model 1 but over the period of 2005-2012 except 2008 for model 2), their banks did
not behave as optimizing banks. By contrast, in some countries, banks operated
under conditions of Perfect competition during period of the crisis (for India over
2007-2010 period in model 2) or both Monopoly (in 2006, 2011, and 2012) and
perfect competition (in 2008) in model 1 and model 2, respectively in the case of
Vietnam.
Turning to the trend of bank competition, the trends for θt follow a V-shape and
inverted V-shape. V-shapes were recorded for Bangladesh (model 2), India and
Vietnam (model 1), inverted V-shape appears for Bangladesh (model 1), India and
Vietnam (model 2), Indonesia, Philippines, and Thailand (both models). Interestingly,
the bottom and the top of these V- and inverted V-shape occur in 2008 (for Vietnam),
2009 (for India, Philippines, and Thailand) and 2010 (for Bangladesh and Indonesia)
when the global financial crisis happened. This shows that the trend of competition in
the banking systems of emerging countries changed remarkably during the global
financial crisis.
Based on the results of competition in two models, the paper concluded that θt of
banking industries follows inverted V- shapes for Philippines Thailand, and
Indonesia. Therefore, bank competition in these countries declined before 2009 (for
Philippines and Thailand) and 2010 (for Indonesia) and then recovered. Besides, the
trend of bank competition in Vietnam and India had the similar pattern. In particular,
in Model 1, competition improved until 2009 for India and 2008 for Vietnam then
decreased in the later period; however, in model 2 θt follows inverted V shapes, in
the other words, trend of competition follows V shapes. By contrast, for Bangladesh,
competition declined before recovering in 2010 in model 1 and vice versa in model 2.
References
ANGELINI, P. & CETORELLI, N. 2003. The Effects of Regulatory Reform on Competition in
the Banking Industry. Journal of Money, Credit and Banking, 35, 663-684.
BERGER, A. N., KLAPPER, L. F. & TURK-ARISS, R. 2009. Bank Competition and Financial
Stability. Journal of Financial Services Research, 35, 99-118.
BIKKER, J. A. & GROENEVELD, J. M. 2000. Competition and concentration in the EU
banking industry. Kredit and Kapital, 33, 62-98.
BIKKER, J. A. & HAAF, K. 2002. Competition, concentration and their relationship: An
empirical analysis of the banking industry. Journal of Banking & Finance, 26, 21912214.
BRESNAHAN, T. F. 1989. Empirical studies of Industries with market power. In:
SCHMALENSEE, R. & WILLIG, R. D. (eds.) Handbook of Industrial Organisation.
Elsevier, Amsterdam.
BRISSIMIS, S. N., DELIS, M. D. & PAPANIKOLAOU, N. I. 2008. Exploring the nexus
between banking sector reform and performance: Evidence from newly acceded EU
countries. Journal of Banking & Finance, 32, 2674-2683.
CASU, B. & GIRARDONE, C. 2006. Bank competition, concentration and efficiency in the
single European market. The Manchester School, 74, 441-468.
CASU, B. & GIRARDONE, C. 2009. Does competition lead to efficiency? The case of EU
commercial
banks
[Online].
Available:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1200362 2012].
15
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
CLAESSENS, S. & LAEVEN, L. 2004. What Drives Bank Competition? Some International
Evidence. Journal of Money, Credit and Banking, 36, 563-583.
COCCORESE, P. 1998. Assessing the competitive conditions in the Italian Banking system:
Some empirical evidence. Banca Nazionale del Lavoro Quarterly Review, 51, 171171.
COCCORESE, P. 2004. Banking competition and macroeconomic conditions: a
disaggregate analysis. Journal of International Financial Markets, Institutions and
Money, 14, 203-219.
COCCORESE, P. 2009. Market power in local banking monopolies. Journal of Banking &
Finance, 33, 1196-1210.
COCCORESE, P. & PELLECCHIA, A. 2010. Testing the ‘Quiet Life’ Hypothesis in the Italian
Banking Industry. Economic Notes, 39, 173-202.
DE BANDT, O. & DAVIS, E. P. 2000. Competition, contestability and market structure in
European banking sectors on the eve of EMU. Journal of Banking & Finance, 24,
1045-1066.
DELIS, M. D., STAIKOURAS, K. C. & VARLAGAS, P. T. 2008. On the Measurement of
Market Power in the Banking Industry. Journal of Business Finance & Accounting,
35, 1023-1047.
DELIS, M. D. & TSIONAS, E. G. 2009. The joint estimation of bank-level market power and
efficiency. Journal of Banking & Finance, 33, 1842-1850.
FANG, Y., HASAN, I. & MARTON, K. 2011. Bank efficiency in South-Eastern Europe.
Economics of Transition, 19, 495-520.
HEMPELL, H. S. 2002. Testing for competition among German banks. Deutsche
Bundesbank, Economic Research Centre, Discussion Paper 04/02
IWATA, G. 1974. Measurement of Conjectural Variations in Oligopoly. Econometrica, 42,
947-966.
JEON, B. N., OLIVERO, M. P. & WU, J. 2011. Do foreign banks increase competition?
Evidence from emerging Asian and Latin American banking markets. Journal of
Banking & Finance, 35, 856-875.
KASMAN, A., TUNC, G., VARDAR, G. & OKAN, B. 2010. Consolidation and commercial
bank net interest margins: Evidence from the old and new European Union members
and candidate countries. Economic Modelling, 27, 648-655.
LERNER, A. P. 1934. The Concept of Monopoly and the Measurement of Monopoly Power.
The Review of Economic Studies, 1, 157-175.
LIU, H., MOLYNEUX, P. & NGUYEN, L. H. 2012. Competition and risk in South East Asian
commercial banking. Applied Economics, 44, 3627-3644.
MAUDOS, J. & DE GUEVARA, J. F. 2007. The cost of market power in banking: Social
welfare loss vs. cost inefficiency. Journal of Banking & Finance, 31, 2103-2125.
MLAMBO, K. & NCUBE, M. 2011. Competition and Efficiency in the Banking Sector in South
Africa. African Development Review, 23, 4-15.
MOLYNEUX, P., LLOYD-WILLIAMS, D. M. & THORNTON, J. 1994. Competitive conditions
in European banking. Journal of Banking & Finance, 18, 445-459.
NATHAN, A. & NEAVE, E. H. 1989. Competition and Contestability in Canada's Financial
System: Empirical Results. The Canadian Journal of Economics / Revue canadienne
d'Economique, 22, 576-594.
PANZAR, J. C. & ROSSE, J. N. 1987. Testing For "Monopoly" Equilibrium. The Journal of
Industrial Economics, 35, 443-456.
SCHAECK, K. & CIHAK, M. 2010. Competition, efficiency, and soundness in banking: an
industrial organization perspective. European banking center discussion Paper No.
2010-20S.
SCHAECK, K., CIHAK, M. & WOLFE, S. 2009. Are Competitive Banking Systems More
Stable? Journal of Money, Credit and Banking, 41, 711-734.
16
Proceedings of World Business and Social Science Research Conference
24-25 October, 2013, Novotel Bangkok on Siam Square, Bangkok, Thailand, ISBN: 978-1-922069-33-7
SHAFFER, S. A non-structural test for competition in financial markets. Proceedings of a
conference on bank structure and competition, 1982 Federal Reserve Bank of
Chicago. 225-243.
SHAFFER, S. 2004. Comment on "What Drives Bank Competition? Some International
Evidence" by Stijn Claessens and Luc Laeven. Journal of Money, Credit and
Banking, 36, 585-592.
SOEDARMONO, W. 2010. Bank competition, institution and economic development:
evidence from Asia during 1999-2007. Economics Bulletin, 30, 2119-2133.
SOEDARMONO, W., MACHROUH, F. & TARAZI, A. 2013. Bank competition, crisis and risk
taking: Evidence from emerging markets in Asia. Journal of International Financial
Markets, Institutions and Money, 23, 196-221.
TURK ARISS, R. 2010. On the implications of market power in banking: Evidence from
developing countries. Journal of Banking & Finance, 34, 765-775.
UCHIDA, H. & TSUTSUI, Y. 2005. Has competition in the Japanese banking sector
improved? Journal of Banking & Finance, 29, 419-439.
VESALA, J. 1995. Testing for competition in banking: behavioural evidence from Findland.
Bank of Finland Studies, E: 1.
WEILL, L. 2004. On the relationship between competition and efficiency in the EU banking
sectors. Kredit and Kapital, 37, 329-352.
WILLIAMS, J. 2012. Efficiency and market power in Latin American banking. Journal of
Financial Stability, 8, 263-276.
WU, J., JEON, B. N. & LUCA, A. 2010. Foreign bank penetration, resource allocation and
economic growth: evidence from emerging economies. Journal of Economic
Integration, 25, 166-192.
YEYATI, E. L. & MICCO, A. 2007. Concentration and foreign penetration in Latin American
banking sectors: Impact on competition and risk. Journal of Banking & Finance, 31,
1633-1647.
YILDIRIM, H. S. & PHILIPPATOS, G. C. 2007. Restructuring, consolidation and competition
in Latin American banking markets. Journal of Banking & Finance, 31, 629-639.
17