Bank concentration and efficiency of commercial banks in Vietnam

666 | ICUEH2017



Bank concentration and efficiency of
commercial banks in Vietnam
LE NGUYEN QUYNH HUONG
University of Economics HCMC –

NGUYEN HUU BINH
University of Economics HCMC –

Abstract
The relationship between bank concentration and bank efficiency remains a controversial
topic. This paper investigates to what degree bank concentration dampens or enhances the
response of bank efficiency in Vietnam and vice versa. This study applies Concentration Ratio
(CR) and Herfindahl - Hirschman Index (HHI) as proxies of bank concentration, while efficiency
scores are calculated by stochastic frontier approach (SFA) and data envelopment analysis (DEA).
To test the Structure Conduct Performance (SCP) and Efficient Structure (ES) paradigm, the
authors use Granger causality approach. However, regarding the causality running from bank
efficiency and bank concentration, the results are complex: we find the causality running from
concentration to efficiency is weak, whereas efficiency Granger-caused negatively competition.
Over a relatively long time period, from 2007 to 2014, the more efficient commercial banks
operated in the less concentrated market.
Keywords: Vietnam; bank concentration; efficiency; structure conduct performance.

1. Introduction
In the process of integration into the world economy, Vietnam's financial market is
under great pressure. Strong competition among commercial banks would be a great
opportunity for the banking sector if Vietnam domestic banks are more adaptable and
operate more efficiently, especially under the Restructuring Plan. Thus, operational

efficiency becomes a vital part for the survival of a bank in the increasingly competitive
environment. The relationship between bank concentration and bank efficiency,
especially in Vietnam, is open to doubt and highly ambiguous. There are numerous
studies testing for this relationship. Some concentrate on the Structure Conduct


Le Nguyen Quynh Huong & Nguyen Huu Binh| 667



Performance (SCP) paradigm (Bikker & Haaf, 2002a; Deltuvaitė, Vaškelaitis, &
Pranckevičiūtė, 2015; T. P. T. Nguyen & Nghiem, 2016), while others support the reverse
relationship namely efficient structure hypothesis (ES), which considers that bank
efficiency positively influence on market concentration (Punt & Van Rooij, 2003; Weill,
2004). Recently, this topic has received tremendous attention in Vietnam, and only three
studies found hitherto (Chinh & Tiến, 2016; Huyền, 2016; Thơm & Thủy, 2016).
Unfortunately, no study analyses simultaneously the relationship between bank
concentration and efficiency by using Granger causality. Thus, this is a noticeable
research gap needed further investigation.
The purpose of this paper is to examine the relationship between bank concentration
and efficiency by using the application of Granger causality method. It also tests Structure
Conduct Performance and Efficient Structure hypothesis. The rest of the paper is
structured as follow. Section 2 presents a brief overview of Vietnamese banking system.
Section 3 contains the previous related literature. Section 4 describes the methodology
and the data. Section 5 contains the empirical results while section 6 gives conclusions
and policy recommendations.
2. Overview of Vietnamese banking system
According to the State Bank of Vietnam (SBV), the history of banking activities is
divided into four stages, including 2 critical periods: 1986 - 2001 (reforming from the
mono-banking system into the two-tier banking system) and after 2011 (restructuring the

Vietnamese banking system). The process of restructuring the banking system and cleanup bad debts has implemented drastically under Vietnam’s banking restructuring
Scheme in 2011-2015 (Decision 254, 1/3/2012) and Non-performing debt settlement
Scheme of credit institutions (Decision 843, 31/5/2013). These Schemes focus on some
central goals, including controlling the weak credit institutions, bad debts, development
of the banking system and to contribute significantly to macroeconomic stability,
removing difficulties for production and business, promoting economic growth. To sum
up, the process of restructuring of Vietnam's banking system consists:
•

The privatisation of state-owned commercial banks.

•

Increasing the financial scale and capacity: raising capital, acquisitions and
mergers, expanding mobilisation.


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•

Improving asset quality, credit quality and reduce bad debt.

Vietnamese commercial banking system can be classified into 4 main groups: (1) Stateowned commercial bank, (2) Joint stock commercial bank, (3) Foreign commercial bank,
and (4) Joint venture commercial bank. Figure 1 shows the number of commercial banks
as well as Non-performing loans (NPLs) over the period of 8 years. It is noticed that Stateowned banks and foreign banks still remained in number, while Joint stock commercial
banks decreased their number from 40 in 2008 to 30 in 2014. According to Vietnam’s
banking restructuring Scheme mentioned above, some weak banks (Joint-stock

commercial banks) took actively and hospitably M&A with other leading banks resulted
in the drop in the number of commercial banks from 52 in 2007 to 44 in 2014. For
example, Vietnam Tin Nghia Bank together with SCB and First Bank of VN merged into
SCB, Western Bank and PVFC consolidated in PVcombank, Habubank is acquired by SHB,
etc. Because of high NPLs in weak banks, merging with leading banks could be an efficient
solution encouraged by SBV in order to strengthen and improve the competition of
Vietnamese domestic banks. NPLs figures shown in Figure 1 followed an upward trend,
from 2% (2007) to 4.55% (2013). After reaching a peak at 4.55% in 2013, NPLs decreased
significantly to 3.25%. It is doubtful that some banks could “cook the book”, deliberately
failed to comply with regulations on debt classification and recorded bad debts in financial
statements lower than actual. However, some argue that 2014 is the first year Vietnam
Asset Management Company (VAMC) bought bad debt from troubled banks and moved a
considerable amount of NPLs out of banks’ financial statements (approximately 123
thousand billion VND, according to SBV – 23/12/2014).


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45
40
35
30
25
20
15
10
5
0

2007 2008 2009 2010 2011 2012 2013 2014

Stateownedcommercial
bank

5

5

5

5

5

5

5

5

Jointstockcommercialbank

37

40

40

37

37

34

34

30

Jointventurecommercial
bank

5

5

5

5

4

4

4

4

Foreigncommercialbank

5

5

5

5

5

5

5

5

Non-performingloans

5%
5%
4%
4%
3%
3%
2%
2%
1%
1%
0%

Non-performingloans

Numberofbanks



2% 3.50% 2.20% 2.60% 3.40% 4.08% 4.55% 3.25%

Figure 1. Number of Vietnamese banks and NPLs from 2007 to 2014
Source: Annual Statements of State Bank of Vietnam (SBV)

3. Literature review
This section reviews the theoretical and empirical results between bank concentration
and efficiency.
There have been long theoretical debates about the relationship between market
concentration and efficiency. These debates dated back to three distinct hypotheses that
reflect the opinions on this relationship.
Two hypothesis in the structural approach including the traditional StructureConduct-Performance (SCP) hypothesis, which is originated from the traditional
industrial organisation literature, and the Efficient Structure (ES) hypothesis. In which,
SCP hypothesis argues the direct positive link between market concentration and
profitability based on the presumption that banks in a high concentrated market can
collude to earn higher profits resulting in efficiency (Bain, 1951, 1956). ES hypothesis,
meanwhile, assumes a reverse causality that efficient banks are more profitable and gain
market shares, resulting in a concentrated market. In other words, the higher efficiency
of market leads to the higher market concentration (Demsetz, 1973). The “quiet life” (QL)


670 | ICUEH2017

hypothesis developed by Hicks (1935), by contrast, supports a negative relationship
between market concentration and performance. Following this, firms with market
concentration tend to make few efforts to maximise efficiency. Because managers in these
firms may have no motivation and enjoy the monopoly profit of a “quiet life”, and this
may result in inefficient operation.
Based on these hypotheses, there were a numerous number of studies performed in
the banking sector in many parts of the world. Some of the studies are summarised in
Table 1.
Table 1
Authors
Homma, Tsutsui, and Uchida
(2014)
Fu and Heffernan (2009)

Years/
Period
1974-2005

1985–2002

Nation/Region

Japan

China

Hypothesis
tested

Result

QL

Supported

ES

Supported

SCP

Supported

QL

Rejected

ES

Rejected

SCP

Supported

ES

Rejected

Lloyd-Williams, Molyneux, and

Thornton (1994)

1986-1988

Spanish

Molyneux and Forbes (1995)

1986-1989

European banking
industry

SCP

Supported

ES

Rejected

Goldberg and Rai (1996)

1988-1991

11 European
countries

SCP

Rejected

ES

Supported

Coccorese and Pellecchia (2010)

1992–2007

Italy

QL

Supported

Al-Muharrami and Matthews
(2009)

1993-2002

Arab GCC banking

SCP

Supported

QL

Rejected

Koetter and Vins (2008)

1996-2006

Germany

QL

Rejected

Fang, Hasan, and Marton (2011)

1998–2008

South-Eastern
Europe

SCP

Supported

Berger and Hannan (1998)

1998

U.S

ES

Supported

Casu and Girardone (2009)

2000-2005

5 EU countries

QL

Rejected

ES

Rejected

Ferreira (2013)

1996-2008

27 EU countries

SCP

Supported

ES

Rejected

Nguyen, Stewart (2013)

1999-2009

Vietnam

SCP

Rejected

ES

Rejected


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Authors

Years/

Nation/Region

Period
Zhang, Jiang, Qu, and Wang
(2013)

2003-2010

Brazil, Russia,
India, China

Celik and Kaplan (2016)

2008-2013

Turkey

Hypothesis
tested

Result

QL

Supported

SCP

Rejected

ES

Supported

As can be seen from the Table 1, there are differences in the results of empirical studies
concerning the relationship between bank concentration and efficiency proposed by three
hypotheses mentioned above. This shows that the relationship between bank
concentration and efficiency depends on the characteristics of each country and region.

This paper uses Granger causality to test simultaneously both SCP and ES in the case of
Vietnam.
4. Methodology
To test the Granger causality relationship between bank concentration and bank
efficiency, this section explains the methodological framework and the data: how to
measure bank concentration and bank efficiency, how to choose inputs and outputs from
financial statements of commercial banks, and the Granger causality procedure.
4.1.

Bank concentration

The market concentration is scaled from low to high, and in this regard, the market
is catalogued into four cases: (1) perfect competition, (2) monopolistic competition,
(3) oligopoly and (4) monopoly. The market which is considered as perfect
competition is addressed as low concentrated, and on the opposite side of the scale the concentration of market which tends to monopoly is evaluated as high (Boďa,
2014).


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Market
structures

Perfect
competition

Monopolistic
competition

Oligopoly

Low concentration

Monopoly

High concentration

There are a number of market concentration indicators based on the calculation of
market shares. Among other things, two standard and popular ways to measure
concentration level are Concentration Ratio (CR) and Helfindhal-Hirschman Index (HHI).
The other well-known indicators of concentration ratio are the Coefficient of variation,
the Hall-Tideman Index (HTI), and the Comprehensive industrial concentration index.
Table 2 gives a brief overview of these concentration measures except for CR and HHI.
However, because of general consensus, data validation and straightforwardness, this
paper use CRk and HHI to measure the concentration in Vietnamese banking market.
Technically, both CRk and HHI do not require to rank and sort in descending order all
banks based on their market shares.
The k bank concentration ratio
The k Bank Concentration ratio is the simplest and required limited data measure of
concentration. Nevertheless, this measure only emphasises on kth leading banks while
neglecting the small banks. Moreover, there is no rule for determination of the value of k,
so k can be chosen on an ad hoc basis (often, k = 3, 4, 5, 8).
The Concentration ratio of k banks is calculated as:
#

CR # =

S&
&'(

th

where: S& is the market share of i bank.


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k represents the number of banks on the market.
The value of this indicator varies from 0 (perfect competition) to 1. The market is
considered as oligopoly, if k > 1 or monopoly, if k = 1.
This study adopts the Concentration Ratio - CR4, which means the market share of
the four largest firms. In the case of Vietnam, we conventionally define four largest banks
or “big-four” Vietnamese banks as BIDV, Vietcombank, Vietinbank, and Agribank. Here,
we use the percentage share of the total assets held by the four largest banks for CR4.
Helfindhal-Hirschman Index (HHI)
HHI is calculated by the sum of the squares of market shares of all banks on the
market. This index is defined as:
-

S&,

HHI =
&'(

where: S&, is the square of market share of i bank.
th

n represents the number of banks on the market.
HHI spreads widely as U.S. Department of Justice has used it since the 1980s to
measure potential mergers issues or antitrust concerns. However, there is no convention
to classify a market into high, moderate and low concentrated catalogue. This problem
can be addressed by using the consensus from U.S. Department of Justice (DOJ) & Federal
Commission Trade (FCT) and The European Commission.
According to U.S. Department of Justice (DOJ) & Federal Commission Trade (FCT),
Horizontal Merger Guidelines § 5.2 (2010), and The European Commission, the
interpretation of HHI is as follows:
Concentration degree
High
Moderate
Low
Source: European Commission and DOJ + FTC

Value of HHI
The European Commission

DOJ & FCT

> 2000

> 2500

1000 – 2000

1500 – 2500

< 1000

< 1500


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HHI sometimes is called full-information index as it captures features of the whole
banking system. For this reason, this paper chooses HHI to measure the concentration
ratio of Vietnamese banking market.
Table 2 summarises the key features of other concentration measures which are
mentioned at the beginning of this section (Bikker & Haaf, 2002b; Boďa, 2014):
Table 2
A brief overview of HTI, CIC, CV
Concentration
measure

Definition

Range

HTI
Hall-Tideman index

Comprehensive
industrial
concentration index

Coefficient of
variation

4.2.

1
=
2 &'( is& − 1
CIC
= s(
+

&',

s&, (1

(0,1]

Emphasis on the absolute number of banks.
Enriching HHI by the number of banks
which cause entry and exit barriers.

(0,1]

Suitable for cartel markets (monopoly). It
combines both relative dispersion and
absolute magnitude. Stressing on the
dominance of the largest bank.

[0,∞)

Not including the number of banks.

Simple to understand (this is a standard
relative measure of variation of nominal
variables). No consensus at which value
may be considered as high or low.

+ 1 − s& )
CV
1
=
n
− 1),

&'(

(ns&

Typical features

Bank efficiency

Defining output, input variables in banking sector
The determination of the input - output variables in banking field is a controversy
issue. Berger and Humphrey (1992) determined inputs and outputs in many different
perspectives (National Bureau of Economic Research - NBER study "Output Measurement
in the Service Sectors”, Chapter 7 - Measurement and efficiency issues in commercial
banking). Briefly, these viewpoints include three main approaches:
Intermediation Approach: banks are financial institutions, intermediation between
borrowers and lenders. Therefore, outputs are probably defined as loans and other assets,
while inputs will be deposits and other liabilities. This method was developed by Sealey
and Lindley (1977).

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User cost Approach: This method determines the inputs or outputs based on the ability
to contribute to revenue for the bank. If the financial returns on an asset exceed the
opportunity cost of funds or if the financial costs of liability are less than the opportunity
cost, then the instrument is considered to be a financial output (Berger & Humphrey,
1992).
Value-added Approach: This approach considers all asset and liability categories to
have output characteristic rather than distinguish inputs from outputs in a mutually
exclusive way. The categories having substantial value added, as judged using an external
source of operating cost allocations, are employed as the important outputs. Others are
treated as representing mainly either unimportant outputs, intermediate products, or
inputs, depending on the specifics of the category (Berger & Humphrey, 1992).
Measuring bank efficiency
Charnes, Cooper, and Rhodes (1978) is the first team using Data Envelopment Analysis
model (DEA) to measure the efficiency of decision-making units (DMUs). DEA model is a
non-parametric estimation which is widely used in myriad fields since 1957. The global
private banking sector, particularly, has been applied DEA model in research (Nathan &
Neave, 1992) (Miller & Noulas, 1996), (Iršová & Havránek, 2010), (Luo, Yao, Chen, &
Wang, 2011).
Data envelopment analysis (DEA) is a linear programming formulation for measuring
the relative performance of organisational units where the presence of multiple inputs
and outputs makes comparisons difficult. Efficiency scores are then calculated from the
frontiers generated by a sequence of linear programs (convex combinations of DMUs).
Assuming there are n banks, each bank can create s output by using m different inputs.
The relative efficiency score of a DMU p could be assessed by solving a fractional program,

which is defined by extremal optimization (maximization) of the ratio of weighted sum
of outputs to weighted multiple inputs (aka virtual output to virtual input ratio), then
subject to the constraints of non-decreasing weights and efficiency measure (the earlier
mentioned ratio) less than or equal to one. To sum up, this involves finding the optimal
weights so that efficiency measure is maximised (banks choose their input and output
weights that maximise their efficiency scores).


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?
#'( v# y#>
B
A'( uA xA>

max
s. t.

?
#'( v# y#&
B
A'( uA xA&

≤ 1∀i, v# , uA ≥ 0∀k, j

where: k = 1, …, s; j = 1, …, m; i = 1, …, n
yki: output k produced by bank i,
xji: input j used by bank i,
vk and uj are weights given to output k and input j.

However, this research will not go too deep into the complex theoretical part of the
DEA estimations but focus primarily on the empirical side of the methods that concern
measuring efficiency.
Another common method of measuring efficiency, developed by Aigner, Lovell, and
Schmidt (1977) and Meeusen and van Den Broeck (1977), is the Stochastic Frontier
Approach (SFA). SFA method divides residuals into 2 groups: inefficiencies and noise, and
using some assumptions about the inefficiencies’ distribution. One part of residuals is
called normal statistical noise (Vit) and the rest is noise inefficiency (Uit). Vit is assumed to
be independent of the explanatory variables and have the same distribution iid ~ N (0,
s,L ) and represents the statistical noise, measurement error, and other random events
(e.g., economic conditions, earthquakes, weather, strikes, luck) beyond the company's
control. Inefficiency Uit (aka inefficiency error term - non-negative) represents
inefficiency factors and assumptions is truncated at 0 and idd ~N (µ, s,L ). At the same
time, Uit is assumed to be independent of Vit. The canonical formulation that serves as the
foundation for other variations is the model:
Y = b’X + v – u,
where Y is the observed outcome, b’X + v is the optimal, or frontier goal (i.e. maximal
production output or minimum cost) pursued by the individual. The amount by which
the observed individual fails to reach the optimum (the frontier) is u. Alternatively, there
is a commonly used – the Translog function:
Yit = exp [Xit b + (Vit - Uit)]

i = 1, …, K, t = 1, …, T


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where: Yit: output, the output of the ith enterprise, at time t

Xit: Vector KX1 input of ith now, at time t
b: Vector Kx1 of unknown factors
Vit: “noise” error term - symmetric (i.e. normal distribution)
Uit: “inefficiency error term” - non-negative (i.e. half-normal distribution)
SFA has become the method commonly used because of many prominent advantages
(Coelli & Perelman, 2000; Cuesta & Orea, 2002; Färe, Grosskopf, Lovell, & Yaisawarng,
1993; Grosskopf, Margaritis, & Valdmanis, 1995). Whereas SFA is more appropriate for
emerging markets where measurement errors and uncertainties of the economic
environment are more likely to prevail (Zhang et al., 2013), we use both DEA and SFA for
Vietnam case.

Figure 2. DEA and SFA Frontier


Here, we adopt DEA input-oriented and follow the intermediation approach. The
intermediation approach, originally proposed by Sealey and Lindley (1977), is appropriate
when banks operate as independent entities (Bos & Kool, 2006) and take into account
interest expenses. It seems appropriate to evaluate commercial banks in Vietnam because
interest expenses present at least more than half of total costs in general (Berger &
Humphrey, 1997). In particular, this study uses interest expenses and other operating
expenses presenting for the banks’ inputs, and net interest revenue, other operating
income for the banks’ outputs.


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To control multiple inputs and to allow a nonlinear relationship between the bank's
total income and inputs, this paper uses Fiorentino's proposed translog function

(Fiorentino, Karmann, & Koetter, 2006; Fontani & Vitali, 2014). Sharing the DEA data
set, the translog function has two inputs, namely interest expense and other interest
expense, as follows:
ln(Yit) = b0 + b1 ln(Xit1) + b2 ln(Xit2) + b3 ln(Xit1) ln(Xit2) + b4 ln(Xit1)2 + b5 ln(Xit2)2 +
(Vit - Uit)
Where:

Yit: outputs (total revenue)

Xit1, Xit2: inputs (interest expense and other interest expense)
b: Vector Kx1 of unknown factors
Vit and Uit are assumed to have standard and semi-standard distributions,
respectively.
4.3.

Granger causality model

Granger causality is a statistical concept of causality that is based on the prediction.
Granger causality (or "G-causality") was developed in 1969 by Professor Clive Granger
and has been widely used in economics since the 1960s. Following Casu and Girardone
(2009), we use autoregressive-distributed linear specification to disentangle the
relationship between concentration and efficiency. The lags (K, J) are determined by
Augmented Dickey-Fuller. Its mathematical formulation takes the following form:
S

Q

yM = ∂O +

yMP# α# +

#'(

xMPA βA + ϑM
A'(

where yM and xM are represented alternatively by mentioned above measures of
concentration and efficiency, and ϑ&M is disturbance term. We first run OLS and then
employ endogeneity test. Next, we test ES and SCP, and null hypothesis is β( =… = βA =
0. If ES is hold, the coefficients for efficiency is positive and significant. If SCP is hold,
there are positive and significant coefficients of concentration.
Data
Our data are collected from financial statements of 21 commercial banks in Vietnam
from 2007 to 2014. We cannot cover financial data from the whole Vietnamese banking


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system due to the limit in collecting data. Nineteen of 21 banks are joint stock commercial
banks, one is foreign bank and the remaining is State-owned bank.
To compute concentration ratio in the first stage, we use the percentage share of total
assets of four largest banks. In the second stage, we measure the efficiency scores by
adopting DEA and SFA method with inputs as interest expenses and other interest
expenses. In the third stage, we test the Granger causality between concentration ratio
(measured on the first stage) and efficiency scores (measured on the second stage, then
multiply each bank score by their market shares). Appendix 1 presents description and
statistics of variables used in measuring efficiency scores in the second stage. It can be
seen that “big-4” always are State-owned commercial banks and dominate the whole
banking system between 2007 and 2014.

5. Empirical results
5.1.

Concentration index of Vietnamese commercial banks

Appendix 2 reports HHI and CR4 of Vietnamese banking system between 2007 and
2014. In 2008, both concentration ratios reached their peaks (1440 for HHI, 0.77 for CR4),
suggesting Vietnamese banks faced challenge of strong competition. In 2008, two new
banks (Tiên Phong Bank and Liên Việt Bank) were granted the license of establishment
by SBV after a decade no new bank set up. Moreover, SBV officially issued the first 100%
foreign subsidiary bank licenses to HSBC, ANZ and Standard Chartered, opening a new
period for the operation of foreign banks in Vietnam. Therefore, there was a potential
threat which was posed by not only local competitors but also foreign banks, leading to
high concentration in 2008.
Over the following four years, both concentration ratios fell gradually and reached
their lowest points in 2012. Thereafter, they increased steadily during 2013-2014 due to
the booming M&A activities (for example, Western Bank and Petro Vietnam Financial
Company, Construction Bank and Vietinbank, Mekong Housing Bank and BIDV). This is
the effect of the M&A process that has formed a number of large-scale banks in terms of
total assets. However, the concentration ratio of Vietnamese banking system is considered
relatively low (HHI < 1500), suggesting that high competition in the banking market.
High completion, in turn, could enhance the performance and efficiency of banking
system (Bính, 2015).


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5.2.

Efficiency scores of Vietnamese commercial banks

In measuring efficiency, we adopt both SFA and DEA approach. Taking the available
data, the SFA specifies two empirical models – the SFA True random effects and fixed
effects. Next, Hausman-test allows us to confirm whether to use Random or Fixed effects.
Hausman-test result is shown in Table 3 (Prob>chi2 = 0.0000), suggesting that using
SFA True random effects are more robust and consistent.
Table 3
Hausman test for SFA True random effects and fixed effects
Coefficients
(b)

(B)

(b-B)

Sqrt (diag(V_b -V_B))

Tfe

tre

Difference

S.E.

Ln Interest Expense

0.24832

0.039392

0.208928

0.352019

Ln Other Interest Expense

0.185185

0.602509

-0.41732

0.30878

Ln (Interest Expense.Other IE)

0.00269

-0.02035

0.023045

0.053423

0.012671

0.031644

-0.01897

0.031928

0.007267

-0.01014

0.017403

0.021178

Ln (Interest Expense)

2

Ln (Other Interest Expense)

2

b = consistent under Ho and Ha; obtained from sfpanel
B = inconsistent under Ha, efficient under Ho; obtained from sfpanel
Test: Ho: difference in coefficients not systematic
chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=

170.15

Prob>chi2 = 0.0000

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Table 4
Efficiency scores estimated by SFA and DEA
Bank name
(dmu)

SFA Random
effects - jlms

DEA SCALE

VCB

0.853794

0.846826

ACB

0.866551

0.843874

SHB

0.908021

0.839725

VID

0.942865

0.839618

ABB

0.89097

0.833539

EXM

0.90221

0.83213

AGR

0.81185

0.825763

NCB

0.909856

0.821062

PGB

0.9342

0.819591

SAC

0.872095

0.81909

SHI

0.897082

0.818368

BIDV

0.812259

0.815861

VIB

0.892994

0.808407

MB

0.863921

0.803189

CTG

0.843139

0.793373

OCE

0.871425

0.783218

OCB

0.908507

0.757279

VPB

0.879565

0.750967

MHB

0.894391

0.749243

MEK

0.919637

0.739595

SEA

0.902765

0.724531

Efficiencyscores
jlms(SFAapproach)

VID
PGB
MEK

1

AGR

SCALE(DEAapproach)

BIDV
CTG

0.8

VCB

0.6
0.4

NCB

MB

0.2

OCB

ACB

0

SHB

OCE

SEA

SAC

EXM

VPB
MHB

VIB

ABB

Table 4 shows the average efficiency scores of commercial banks in Vietnam in the
period of 2007-2014 by DEA VRS input-oriented and SFA True random effects. It is
obvious that there are differences between SFA and DEA results. Reported figures in Table
4 imply that according to SFA approach banks scored low efficiency are State-owned
commercial banks (Vietcombank, Vietinbank, BIDV and Agribank are ranked low). Noted
that jlms is named for SFA Scores and DEA Scale is chosen to represent for DEA Scores.
5.3.

Granger causality

Firstly, we test the stationarity of the series, using augmented Dickey-Fuller test.
Lags are included and the null hypothesis is non-stationary existing. The decision of

682 | ICUEH2017



choosing whether random walk with drift or without drift is based on the shapes of
the trend line graph in Figure 3. Both variables Scale and jlms of each banks are
adjusted by multiplying by their market shares in percentage, then name them as
Scale-adjusted and jlms-adjusted.

.86
.76

.84

.78

.845

jlmsa
.8 .82

scalea
.85 .855

.84

.86

Figure 3. Trend line of Scale-adjusted, jlms-adjusted, HHI, CR4

2008

2010
year

2012

2014

2006

2008

2010
year

2012

2014

2006

2008

2010
year

2012

2014

2006

2008

2010
year

2012

2014

.65

.7

cr4

.75

hhi
1000 1100 1200 1300 1400

.8

2006



Table 5

ADF test
Scale-adjusted

Jlms-adjusted

CR4

HHI

MacKinnon approximate p=value for Z(t)
lag (0)

0.5828

0.0585

0.1209

0.1202

lag (1)

0.0000

0.5997

0.0077

0.0021

Table 5 illustrates that only jlms-adjusted (jlms-a) is station while Scale-adjusted
(Scale-a), CR4 and HHI are station with 1 time lag. Thus, we decide on lags for scale-a,
jlms-a, cr4, hhi (1,0,1,1, respectively).


Le Nguyen Quynh Huong & Nguyen Huu Binh| 683



Then we test endogeneity of all models to whether or not to apply GMM to robust the
results. We discover that all explanatory variables are exogenous variables, means Cov
(Xjt, ϑjt ) = 0, with j is j-th model. Whenever OLS estimators are as well as GMM
estimators, no need to use GMM.
The results obtained from testing the hypotheses put forward to explain the SCP and
ES relationship are presented in Table 6, ES hypothesis test, it is also clear that the bank
efficiency of the previous year (first lags) has a negative and statistically significant
influence on bank concentration, while the influence of the same year is not statistically
significant. Increasing in bank efficiency Granger-causes a fall in both HHI and CR4 index,
meaning scale efficiency positively Granger-causes competition. This results are
consistent with findings of Ferreira (2013); T. N. Nguyen and Stewart (2013); Casu and
Girardone (2009) and reject the ES hypothesis in Vietnam. Based on the signs of
regression coefficients, noticeably, this study makes an unambiguous conclusion that ES
Hypothesis should be rejected in transition economy like Vietnam. One possible
explanation is that Vietnamese banking system is considered highly regulated and “overprotected”. In a highly regulated and “over-protected” market, efficient banks compared
to State owned banks (inefficient banks) hardly continue high profits because efficient
banks cannot have advantages and create barriers to market entry. The policy makers
should notice that each policy intervention or interventionism could adversely affect the
development of the banking system and distort the structure of the system. Another
explanation could be that the business strategies of large Vietnamese banks during this
period were focused on raising capital, loans, assets, deposits, branch networks and

reducing NPLs. Thus, revenue, interest income and profit before tax were not the most
propriety missions of banks (T. N. Nguyen & Stewart, 2013). Panels (b) and (d) in Table
6 show that the first lags of competition are significantly (different from zero), indicating
that competition at time t is influenced by previous year's competition.
With regard to the causality running from bank concentration (measured by CR4 and
HHI) to DEA scale efficiency and SFA jlms, the results presented in the later Table 6 are
inconsistent and contradictory. DEA-efficiency is affected positively by concentration and
previous year’s efficiency, while there is a negative influence from concentration to SFA
jlms. However, this result is not significant, implying that concentration does not Granger
cause to the efficiency of Vietnam’s banks. Overall, the evidence for Vietnam commercial
banks does not support either the ES or SCP hypothesis.


684 | ICUEH2017



Table 6
Granger test
ES test
dependent variable:

HHI

CR4

Explanatory variables: a) Lag HHI, jlms-a b) Lag HHI, Lag scale-a
coef

P > |t|

c) Lag CR4, jlms-a

d) Lag CR4, Lag scale-a

coef

P > |t|

coef

P > |t|

coef

P > |t|

Explanatory 1

0.590599 0.159

0.5278723

0.037

0.6089294

0.142

0.5698613

0.031

Explanatory 2

-707.3363 0.743

Cons
Granger test
(Prob > F)
Ho: no granger cause

-11378.62

0.046

-0.2040081

0.744

-2.990502

0.068

1037.119

0.615

10180.09

0.041

0.431276

0.54

2.827062

0.056

-

0.7435

-

0.0461

-

0.7438

-

0.0679

SCP test
dependent variable:
Explanatory variables:

jlms-a
a) Lag HHI
coef

Explanatory 1
Explanatory 2
cons
Granger test
(Prob > F)
Ho: no granger cause

scale-a
b) Lag CR4

P > |t|

-0.0000941 0.195

c) Lag scale-a, Lag CR4 d) Lag scale-a, Lag HHI

coef

P > |t|

coef

P > |t|

coef

P > |t|

-0.03451581

0.152

0.2104672

0.61

0.2132968

0.611

-

-

-

-

0.0763758

0.24

0.0000219

0.258

0.9289959

0

1.058251

0.001

0.6163318

0.139

0.6411903

0.128

-

0.1946

-

0.1519

-

0.2397

-

0.2578

6. Conclusions and policy implications
This paper employs Granger causality to examine the relationship between bank
concentration and efficiency. The data is collected from the consolidated accounting
statements of 21 commercial banks in Vietnam from 2007 to 2014.
To measure bank concentration, we opt to use two common approaches:
Concentration Ratio (CR4) and Helfindhal-Hirschman Index (HHI). The results reveal
that there is a decline of concentration ratio from 2008 to 2012, and then it rose slightly
over the following two years. It is apparent that the booming M&A in 2012-2014 results
in the increase of bank concentration in these years. In general, Vietnam's banking


Le Nguyen Quynh Huong & Nguyen Huu Binh| 685



concentration is still ranked at a low level with high competition, however, the situation
is expected to be reversed after Restructuring Plan of SBV ends in 2020. One must
concede that the findings of low concentration of Vietnamese banking sector are “good
message” for bank customers, but do not offer any guidance in which directions the
Vietnamese banking sector should be regulated in the future. High competition could be
considered as a threat for domestic banks, especially after the ASEAN Economic
Community in 2015 (aggressive competition from ASEAN and Japanese banks into the
domestic market). SBV can act only within the scope of its competence and try to maintain
changes in the current level of concentration and endeavour to prevent unnecessary M&A
that would contribute to lowering competition and increasing bank concentration.
For bank efficiency, we applied DEA and SFA estimation and found that most
inefficient commercial banks are State-owned banks. We believe that the SBV needs to
strengthen the whole banking system by restructuring the State-owned banks into

privatisation. There is also a need to well-prepare for merger and acquisition procedure
of some small and inefficient domestic banks when the bail-out sources are not only
funded by SBV but also from foreign organizations. The SBV should prepare for specific
scenarios and management policies when Asian Development Bank (ADB) has announced
a plan to cooperate with a Vietnamese company to buy one of the “VND 0 banks” in 2017.
Our empirical results do not, in general, support either SCP and ES. The regression
models did not yield the reliable results due to the statistically insignificant regression
coefficients and the reversed sign of them. We also test endogeneity of all models to
whether or not to apply GMM to robust the results. Due to the fact that all explanatory
variables are exogenous, there no need to apply GMM estimator. We found that bank
concentration was related negatively to bank efficiency and positively to previous year
concentration. Although according to ES, the banking industry will become more
concentrated under competition conditions if some banks are more efficient, the results
of this study are reversed. Then, efficient banks cannot maintain competitive advantage
and create barriers to entry partly because of the intervention of SBV through preferential
monetary policies for the State-owned banks. Therefore, we come up with a suggestion
that the governmental regulation and intervention are inappropriate policies since they
might impose penalties on efficient banks and discourage the proper functioning of the
banking market mechanism. Regarding SCP-test, however, control variables (market
concentration, lag 1-year efficiency) are positive and insignificant. This outcome is likely


686 | ICUEH2017



due to rigid regulatory rules governing banking activities and strict control over interest
rates, which also prevented State-owned banks (“big 4”/large bank) from enjoying
monopoly profits, thereby ruling out any opportunity to opt for a market power.
Taken together, the findings of this study indicate that the Vietnamese banking sector

data do not provide a support either SCP or ES hypothesis, but consistent with those for
China, 5 European countries, Arab, Vietnam, etc. found in previous work. Hence our
results suggest that the model might content a Restructuring Banking system-dependingvariable. During this phase, the “big 4” banks were State-owned banks with the special
power, subsidised by the government to make loans to designated sectors and firms.
Moreover, SBV nominated State-owned banks to buy weak banks. Thus, neither
concentration nor efficiency significantly affected the profitability and competition
advantage.



Le Nguyen Quynh Huong & Nguyen Huu Binh| 687



Appendix
Appendix 1

a



dmu

Other Operating
a
Income

Net Interest
a
Revenue

Interest Expense

ABB

106845.4

1348978

1923224

550750

ACB

1355550

4325250

9206750

1120000

AGR

2700930

1.86E+07

3.26E+07

8833979

BIDV

3641813

1.05E+07

1.70E+07

5499425

CTG

946337.5

2.71E+07

1.68E+07

3443013

EXM

497012.5

2837025

5178838

980300

MB

884403.9

4050737

4382324

991124.1

MEK

694.875

423972.7

221367.6

53927.29

MHB

67051.5

981724

2996416

2057219

NCB

59138.88

477154.3

1197087

256031.9

OCB

29062.5

776400

1179213

237687.5

OCE

24442.86

934514.3

2622529

171357.1

PGB

101617.1

538995.3

818956.2

57044.19

SAC

987487.5

4321375

6447475

1263025

SEA

8570

825526.4

3006232

823692

SHB

212978.9

1346778

3802997

543880.1

SHI

254216.7

786950

209001

209283.3

VCB

3453025

8996875

1.30E+07

1533625

VIB

478190.1

2299211

3286245

747503

VID

76216.38

231006.8

249598

52483.89

VPB

436616.6

2185179

3186149

1388455

million VND

a

Other Interest
a
Expense


688 | ICUEH2017



Appendix 2

ESTIMATEDCONCENTRATION RATIOS
1600

0.78

1400

0.76
0.74

1200

HHI

0.7

800

0.68

600

0.66

400

0.64

200
0

CR4

0.72

1000

0.62
2007

2008

2009

2010

2011

2012

2013

2014

0.6

HHI 1380.39 1440.07 1271.08 1080.18 1028.39 1093.8 1114.64 1157.86
CR4

0.76

0.77

0.73

0.67

0.66

0.66

0.67

0.69

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