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Luận văn thạc sỹ quốc tế TECHNICAL EFFICIENCY FULL DISSERTATION

ABSTRACT
The dissertation aims to investigate the level of technical efficiency and
determinants of technical efficiency in commercial banks of Vietnam in the period
of 5 years, from 2011-2015. The sample set includes 10 commercial banks, in
which 4 state-owned banks and 6 joint-stock banks. The author applies the Data
envelopment analysis (DEA) approach to examine the level of technical efficiency
in Vietnamese commercial banks. The findings show that Vietnamese commercial
banks has improving tendency in technical efficiency in the whole period, in spite
of some fluctuations. In two bank types, state-owned banks have tendency to
obtain better technical efficiency scores than joint-stock ones. In addition, the
total productivity for all banks is calculated by Malmquist index, which indicates
that Vietnamese banks have been weak at technological advance and technical
efficiency. For the purpose of exploring factors impacting level of technical
efficiency in banks, Tobit regression model is conducted. The empirical findings
show the positive and significant correlation between technical efficiency and
some factors of total asset size and equity ratio. Meanwhile, technical efficiency
has negative and significant relationship with loan ratio, non-performing debt,
and government ownership.

ACKNOWLEGEMENTS


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I would like to express my gratitude to my supervisor who is always beside me
during the period of doing this dissertation. He shows me how to do an academic
business research with the application of theories that I learnt previously.
I would like to thank my parents, my wife and my kids who always encourage me
to complete this master course.
I would like to show appreciation to my manager and colleagues to assist me
some tasks when I had to struggle with assignments and dissertation in this
course.
Finally, I honestly recognize the enthusiastic support of all lectures and
administrators in both Leeds Beckett University and Academy of Finance during
the program.

Contents

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LISTS OF FIGURES AND TABLES

LIST OF FIGURES
Figure 4.1 Charter capitals of some commercial banks after merge and acquisition
in 2015
Figure 4.2 Average technical efficiency of commercial banks in Vietnam from
2011 to 2015
Figure 4.3 Technical efficiency of joint-stock commercial banks in Vietnam from
2011 to 2015
Figure 4.4 Average technical efficiency of commercial banks in Vietnam in the
period of 2011-2015

LIST OF TABLES
Table 4.1 Bank types and quantity in Vietnamese banking sector
Table 4.2 Description of inputs and outputs used DEA model
Table 4.3 Average technical efficiency of two groups of ownership in Vietnamese
commercial banks from 2011-2015
Table 4.5 Variations in Total Factor Productivity of All Banks and Its specific
elements per year
Table 4.5 Variations of total factor productivity and its specific elements in


Vietnamese banks in the period of 2011-2015
Table 4.6 Descriptive statistics of variables in Tobit model
Table 4.7 Result of Tobit regression of factors

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4


CHAPTER 1 INTRODUCTION

1.1

Research background

Commercial banks play a vital part in the connection of economic constituents in
the economy and contribution to the development of financial sector. The
financial system is considered as the backbone of the economy, so the banking
sector is represented for the main pillar of business. The growth of national
economy is largely reliant on the operations of banking sector.
In Vietnam, banking sector changes strongly current years. It grows rapidly in
terms of both asset and number. Together with the operations of traditional
banking, commercial banks provide a lot of new services and products to bring
the satisfaction to customers. Otherwise, these commercial banks also enhance
continuously facilities, infrastructure as well as employ advanced technologies in
their operations, which supports to enhance the technical effectiveness in each
bank as well as the commercial banking sector.
Nevertheless, Vietnamese banking sector in general and commercial banks in
particular have their ineffectiveness in operations during the time of uncertainty.
Remarkably, in the period of crisis and economic downturn since 2008,
Vietnamese banking industry had experienced numerous difficulties and fell into
the overloaded situation. During the time of 2011-2015, the increasing rate of
credit in banks was just around 20%, the historic lowest level of Vietnamese
banking system. Besides, the proportion of non-performing deb had an increasing
trend, reaching 51% in 2013. The absorption of capital has been very poor in
Vietnamese economy. So, the major features of Vietnamese banking industry
were weak growth of loan and deposit, high ratio of non-performing loan and
revenues primarily dependent on credit activities. Although the national
government and commercial banks issued several new policies to deal with these
matters, they seemed to be ineffective in encountering these challenges.
Therefore, it is important that the authorities and bank’s top executives have
long-term perspectives and propose long-run strategies to manage bank
operations.
To look for the reasons for this, it is necessary to analyze and understand the way
of

performance

of

commercial

bank,

especially

the

elements

creating

dissimilarities in performance among commercial banks such as technical
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effectiveness. It can say that technical effectiveness plays an important role in
bringing the successfulness to commercial banks in the fierce competition of
banking sector.

Consequently, if commercial banks want to improve their

performance as well as the position in the banking sector, they should focus on
technical effectiveness. There are many authors studying this issue. And this
paper is desired to provide a helpful viewpoint in relation to how commercial
banks’ technical effectiveness is and what elements influencing the banking
technical effectiveness are. Therefore, the topic “evaluation of technical
efficiency in Vietnamese commercial banks” is chosen.
Understanding the considerable influence of banking system on the development
of national economy, numerous researches have been conducted on the theme of
bank efficiency. Recently, several scholars in Vietnam have done studies related
to this topic. For instance, Phu (2002) applies the functions of production and cost
to evaluate the efficiency of commercial banks. But the drawbacks of this
research are just to recognize costs and the function of estimating cost in order
to build the model. So, the bank efficiency is not mentioned specifically. Anh
(2004) assess factors causing the inefficiency in Vietnamese rural and
development banks; however the author just establishes the framework. Thanh
(2010) uses the model of data envelopment analysis to assess the input
efficiency in Vietnamese commercial banks. The findings indicate that input
efficiency of banks is rather good; however it is possible to improve the efficiency
of inputs to higher level. Truc and Danh (2012) explore that small banks suffered
bad consequences from global economic crisis rather than large and medium
ones by using financial ratios to evaluate the technical efficiency in Vietnamese
banks. Hung (2008) applies the quantitative approach to investigate the
performance of Vietnamese banking system. Nevertheless, a drawback of this
study was the dataset before 2008 as many weaknesses of banking sectors were
shown after 2008. Additionally, the study concentrates on too much the
explanation of theories and the conclusions have not specified determinants of
bank efficiency. So, it is essential to conduct researches about the bank efficiency
in this situation.
So, the thesis will bring a deep understanding about the technical efficiency of
banks and factors impacting the efficiency of banks in the context of Vietnam.
Evaluating technical efficiency of banks and exploring its determinants is
important and useful for policy makers, banks’ managers and investors to make

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decisions. The thesis is also groundwork to fulfill the policy frames more
appropriately in the course of management of Vietnamese commercial banks.

1.2

Research aims and objectives

The purposes of this paper are to discuss the evaluation of Vietnamese
commercial banks’ technical effectiveness in the period of 2011 -2015 and to
determine components influencing technical effectiveness.
To achieve these purposes, the paper needs to solve the below objectives:


Measuring Vietnamese commercial banks’ technical effectiveness in

the period of 2011 -2015

Comparing and analyzing the technical effectiveness’s results
among banks

Exploring

components

influencing

technical

effectiveness

of

Vietnamese commercial banks

1.3

Research questions

With the purpose of achieving the research aim and objectives, the author will
find the answers for questions below:


What levels of technical efficiency are in commercial banks of

Vietnam?

What

factors

affect

the

technical

efficiency

of

Vietnamese

commercial banks?

1.4

Research boundary

The focus of the dissertation is to evaluate technical efficiency of banks and
reveal factors affecting technical efficiency in banks with technical efficiency as
the key pointer to measure bank performance. The sample set includes 10
commercial banks operating in Vietnam. Data are collected in the time between
2011-2015.

1.5

Significance of the research

This paper will enrich empirical findings about assessing technical efficiency of
Vietnamese banks and understanding the impacting factors by showing practical
proofs in sample set. Furthermore, the results of the dissertation will be valuable
source for reference in terms of officials and corporate managers who will gain
insight understandings about the technical efficiency in Vietnamese banks. From

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that, they can conduct suitable strategies and frameworks concerning to banking
controlling or evaluating benchmarks.

1.6

Dissertation structure

The thesis has five chapters
Chapter 1: Introduction
Chapter 1 presents research background by showing reasons choosing the topic
for this dissertation. The author also specifies research aims, objectives and
questions in this dissertation. The boundary and significance of this research are
indicated clearly.
Chapter 2: Literature Review
The theories relating to bank efficiency, measuring methods of efficiency and
determinants of bank efficiency are reviewed critically by underpinning diversity
of researchers’ views in previous researches. Chapter 2 is seen as the important
pillar for the dissertation.
Chapter 3: Methodology
Chapter 3 will explain the research methods applied in this paper. The procedures
of selecting methodology and gathering data are portrayed. Then, the
hypotheses will be developed to examine in next chapter.
Chapter 4: Empirical findings
The empirical findings will be shown in Chapter 4. The efficiency of banks will be
revealed by comparison, analysis and interpretation in order to identify the most
efficient banks and the most influential factors on efficiency of banks.
Chapter 5: Discussion of findings and conclusion
Chapter 5 will discuss differences or similarities between previous studies and
obtained findings in this research. Key notions and results are summarized and
synthesized. From that, the author will make a conclusion about the whole
research: research aims and objectives, literature reviews, methodology and
achieved results.

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CHAPTER 2 LITERATURE REVIEW

2.1

Bank efficiency
2.1.1 Definition of efficiency
Efficiency is defined as the evaluation of performance generated from the least
funds of time, human resources and assets (Aschenbach, 2010). It is indicated as
the percentage of total output in comparison with total input under a specific
circumstance. The assessment of firm or sectorial efficiency is important for the
government and company directors to calculate the level of output that a
company or industry can produce if improving the efficiency without exploiting
more resources (Farrell, 2953).
2.1.2 Types of efficiency
Efficiency is categorized into three main types: allocative efficiency, cost
efficiency and technical efficiency.


Allocative efficiency

Allocative efficiency is known as the allocation of economic resources to adapt
economic needs, which is the manufacturing of those commodities and services
that the society most wants (Anderton, 2006). It means that commodities and
services are offered appropriately with the demand of consumers. A company
could achieve the efficiency in production of a product, but clients do not need
this product, so it is considered as allocative inefficiency. Therefore, allocative
efficiency is to generate an output at the intersected point of marginal benefits to
consumers and the marginal cost of manufacturing.


Productive efficiency

Productive efficiency is the capability of a company to generate full output at the
lowest expenses with the optimal usage of resources (Higson, 2011). Productive
efficiency is obtained in case that a company manufactures goods and services at
the lowest costs. When a company reaches the productive efficiency, it is
impossible to manufacture more commodities without utilizing more resources or
lowering the quality. Thus, the company should find a balance in the exploitation
of resources, manufacturing capacity and commodities’ quality.


Technical efficiency

Technical efficiency is the effectiveness with a specific set of inputs exploited to
generate an output. A firm obtains the technical efficiency if it manufactures the
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maximum volume of output from the lowest number of inputs, including human
resources, capital and technology (Kumbhakar and Lovell, 2003). A firm is
regarded as technical inefficiency if it uses too many resources than required.
Technical efficiency occurs when it is impossible to increase the output without
improving more inputs (Koopmans, 1951). The level of technical efficiency is
rated from 0 to 1.
2.1.3 Bank’s technical efficiency
According to Bhattacharyya et al (1997), technical efficiency in banking sector is
understood as the capacity to transfer input resources into diverse banking
products and services. A commercial bank acquires technical efficiency when its
performance is on the frontier line. Under the assumption of constant returns-toscale (CRS), technical efficiency is evaluated to recognize the inefficiency
resulting from factors of input, output or the size of banks.
As stated by the theory of system, commercial banks are efficient when: (1) they
can change inputs into outputs or generate profits, or decrease expenditures to
improve the competitive advantage with other banks; (2) they can ensure the
prospect of safe operations.
The relationship between the commercial banks’ performance and the growth of
domestic economic is strongly constructive as commercial banks play the role as
financial intermediaries which move free cash from citizens to corporations
needing capital for business and investment. Therefore, the change on
commercial banks’ performance will largely influence on the whole economic
activities.
A lot of researchers carried out studies concerning to examination of bank
efficiency. In the research of Casu and Molyneux (2003), they investigate the
impact of the efficiency score of European banking system on banks in each
country. The authors use Tobit regression to evaluate the impact of each
country’s specific factors concerning to bank efficiency to measure determinants
of banks in Europe. The findings show that the efficiency levels of banking system
are different between European countries due to specific country-factors linked
with banking advanced technology.
Isik and Hassan (2002) assess the input and output efficiency in banks by
applying non-parametric and parametric approaches in Turkish banks in the
period of 1988-2006. The obtained results indicate that dissimilar characteristics
of banks considerably impact the efficiency and different banking characteristics
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impact the variations in bank efficiency; and the efficiencies in cost and profit has
a little decrease across periods.
In the research of Chen, Skully and Brown (2005), bank efficiency before and
after Chinese government conducted the deregulation scheme in 1993-2000 are
investigated to reveal the difference of Chinese banks. Results show banks which
are owned by the state and smaller sized are often more efficient than those at
average size. Moreover, Chinese banks often have better technical efficiency
than allocate efficiency. The banks have better cost efficiency after the scheme of
deregulation in 1994.
In Canada, technical efficiency of banks in the period of 1983-1987 is explored by
two scholars named Nathan and Neave (1992). Researchers uncover that the cost
efficiency in large-sized banks were not as good as small-sized banks.
The examination of Berger, Hanweck and Humphrey (1987) on 413 subsidiaries
of national banks and 241 commercial banks owned by the government in 1983
is carried out. The inputs of efficiency consist of capital and labor, and the
outputs of efficiency are call deposit, term deposit, mortgage credit volume,
installment credit volume. The economic efficiency of 241 commercial banks
having state ownership was averagely at 0.96, while this figure was at 0.98 in
subsidiaries of national banks. So, subsidiaries of national banks perform more
productively than commercial banks.
Fukuyama (1993) uses the DEA method to assess the efficiency of 143
commercial banks in Japan. The inputs of bank efficiency include: labor, tangible
assets and intangible assets; and the outputs of bank efficiency comprise:
incomes from lending and financial services. The study result indicates that the
efficiency of most banks enhanced with bank size, and banks having total asset
of more than 8 billion JPY got the best productivity.
In the context of Vietnam, the efficiency of banks have been paid attention and
evaluated by scholars, such as: Huong (2002) shows some suggestions to
enhance the efficiency on investment activities in commercial banks, Dan (2004)
applies the statistical method to investigate the technical efficiency in
commercial banks, Binh (2005) measures the competitiveness level of domestic
banks after joining the global and regional integration agreements.
In the measurement of technical efficiency, the functions of production and cost
are applied by Phu (2002) to evaluate the efficiency of commercial banks.
Nevertheless, the drawback of this paper is to fairly recognize costs and use cost
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function to figure out the boundary of the framework. Therefore, the inefficient
aspect at banks is not apparently revealed. On the other hand, Anh (2004)
measures unproductive factors in a state-owned commercial bank – Agribank, but
merely the function is built. Thanh (2010) judges how commercial banks in
Vietnam use efficiently input resources by applying DEA method. The finding
indicates that input resources are used efficiently by commercial banks, but at
moderate level and highly potential to improve the efficient usage of input
resources in the future. By employing traditional methods of analyzing financial
ratios, Truc and Danh (2012) evaluate technical efficiency in commercial banks
and discover that small banks are more easily vulnerable by the consequences of
global downturns than other medium banks.
In general, studies about bank efficiency are undertaken in developed nations,
rather than developing economies. Researches concerning to technical efficiency
measurement are not executed deeply in the case of Vietnam. Therefore, it is
necessary to make a specific analysis paper about bank efficiency in Vietnam.

2.2

Measurement of bank efficiency

In order to evaluate the efficiency of banks or corporations, four methods are
used popularly, namely: stochastic frontier approach (SFA), data envelopment
analysis (DEA), distribution-free approach (DFA) and thick frontier approach (TFA).
SFA and DEA are applied most by scholars to assess bank efficiency.
SFA method was developed by Aigner, Lovell and Schimidt (1977) and Meeusen
and van den Broeck (1977) with the aim of calculating the corporate efficiency in
production. The method is built on the ground of the econometric theory and
under the assumption that input and output has a stochastic relationship. SFA
method is applied to evaluate efficiency in various industries, such as: Greene
(2004) and Gerdtham et al. (1999) assess efficiency of healthcare centers, Diaz
and Sanchez (2008) examine the efficiency of manufacturing enterprises, Wang
(2003) investigate the efficiency of financial institutions.
Charnes, Cooper and Rhodes (1978) developed DEA approach deriving from the
theory of Farrell (1957) about the application of non-parametric approach and
mathematical practice to measure the level of efficiency. DEA concerns to the
usage of linear encoding method to construct the frontier of non-parametric
piecewise with the purpose of calculating efficient point on the frontline. DEA
assesses the efficiency of decision-making units (DMU) which are corporations,
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banks or institutions… The result of DEA shows whether DMUs can achieve the
efficiency or not. Researchers implement this approach to measure the efficiency
in many sectors, such as retailing sales (Thomas et al., 1998; Keh and Chu,
2003); farming sector (Mao and Koo, 1997).
Besides, there are a lot of researches using DEA and SFA approaches to evaluate
bank efficiency. Ferrier and Lovell (1990), the assessment of around 600 banks in
the United States are done in terms of cost structure in 1984. The outcomes of
efficiency generated from DEA approach are found to be higher than those from
SFA approach. Thus, DEA is more flexible to run data.
Two these approaches of DEA and SFA are also used by Sheldon (1994) to
estimate cost efficiency of Switzerland banks in the period of 1987 -1991. It is
found that the cost efficiency of banks calculated by SFA is at 56%, whereas DEA
measures the cost efficiency of banks at 3.9%. The considerable gap between
two approaches in efficiency measurement proves the doubt about the
appropriate estimate of cost function. Nevertheless, the research of Resti (1997)
which applies two similar approaches to measure efficiency of Italian banks
shows the little divergence between two outcomes.
In the United States, the performance of banks is assessed in the study of Bauer
and Berger (1984) by using SFA method. The operation of US banking system is
detected to be inefficient, but not in the aspect of scale or scope. This efficiency
is explained by the burden for banks to cut operational expenses, unite
compulsorily with other healthier banks, or exit the market. SFA approach is
implemented in the different study of Altunbas et al (1994) to analyze German
and Italian banks.
By employing DEA approach, technical efficiency of commercial banks is
indicated to be negative association with bank size in the paper of Field (1990).
The efficiency of bank subsidiaries in Canada is also explored by DEA method (Wu
et al, 2006). The efficiency of commercial banks in Greek in the period of 20002004 is evaluated by Pasiouras (2008). He points out that the provision amount
for credit loss has positive relationship with the efficiency level of banks
measured by DEA approach.

By using the same analysis method, Brazilian

banks’ efficiency is revealed in the aspect of cost, technical and allocative (Staub
et al., 2010). The scholar gave the empirical result that the cost efficiency of
Brazilian banks is less than those of EU and US banks. Meanwhile, cost efficiency
of state-ownership banks is higher than those in overseas and private banks.
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Overall, two approaches of SFA and DEA are applied singly or jointly to
investigate and evaluate efficiency of companies, banks or specific industry.
However, studies relating to efficiency are carried out mainly in advanced
economies. So, there is a large room to conduct research in developing
economies.

2.3

Model of factors affecting bank efficiency

CAMEL framework is often used to identify determinants of bank efficiency,
consisting of capital, asset, management, earning and liquidity. Besides, factors
impacting bank efficiency are selected on the ground of previous researches and
requests of directors in bank management. Upon considering prior studies about
bank efficiency, such as: Altunbas et al (1994), Das (2002), Forster and Shaffer
(2005), Staub et al. (2010), and Pasiouras (2008). Five factors are chosen to
evaluate the influence on bank efficiency, comprising: bank size, loan ratio,
capital ratio, non-performing loan, type of ownership.

2.3.1 Factors affecting bank efficiency
2.3.1.1 Bank size
Bank size is measured by the natural logarithm of total bank assets. Logically, it
is predictable that bank size and efficiency have positive relationship. The largersize commercial banks possess the abundant resources of capital, employees,
technological infrastructure, thus they have higher probability to increase the
overall efficiency. However, when problems occur due to mistakes in bank
operations and administration, these banks would suffer worse consequences
than small banks. Thus, small-size banks are evaluated to obtain better efficiency
in the difficult period.
In previous studies, researchers found that bank size have strong relationship
with the efficiency. It is assumed that large banks have competent and skillful
managers or have advantage of economic scale in cost as the directors
emphasize dominantly on profits (Evanoff and Israilevich, 1991).
The earlier researches show different results about the correlation between size
and efficiency. In the paper of Berger, Hunter and Timme (1993), the affirmative
relationship between efficiency and size may not apparent because factors
representative are not convincing. Larger companies able to manufacture at the
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full capacity will reach the efficient point. It means that greater banks may
generate higher level of profits at a specific cost as they have progressively
improved in size during a given time. Inversely, small banks are difficult to have
enough capacity to obtain the productivity in a short term. Besides, it is likelihood
that companies with higher level of efficiency have better competitive
advantage, and consequently, they are increasingly large. Reda and Isick (2006)
support that bank size have significant association with efficiency.
Findings about the linkage between size and efficiency are conflicting in different
researches. Ataullah and Le (2006) and Ajlouni, Hmedat and Hmedat (2011)
realize that larger banks tend to be better efficient when they enlarge the size of
asset. In the research of Chen, Skully and Brown (2005), banks with large and
small size have tendency to perform better than medium-size ones. However, this
finding is opposite with the US banks where the curve of average cost is flat,
signifying the efficiency of medium-sized banks.
By contrast, Almumani (2013) indicates that the performance of small-size banks
is better than medium and large-size ones in the aspect of technical efficiency.
Isik and Hassan (2002) show the findings about the inverse relationship between
size and efficiency. Small and medium-size banks tend to spend large proportion
of budgets to operate activities, but they can get better technical and scale
efficiency than larger-size ones. DeYoung and Nolle (1994) and Kaparakis, Miller
and Noulas (1994) find that smaller banks can obtain the efficiency in profit
higher than large banks and efficiency evaluation reveals no scale biases on the
bank size. Havrylchyk (2006) and Avkiran (1999) also confirm these findings by
showing the insignificant relationship between bank size and efficiency.
From the results of existing literature, the researcher assumes that:
H1: The relationship between bank size and technical efficiency is positive in
Vietnamese commercial banks.
2.3.1.2 Equity capital
Equity capital is total amount of capital contributed by investors/shareholders of
banks. The ratio of equity and total assets is the proportion of equity capital
amount of banks and its asset amount. The higher ratio of equity results in the
increase of return on equity. In other words, stakeholders can mitigate risk and
get better profit when investing in the bank. Commercial banks tend to use high
ratio of leverage which is contributed by depositors and creditors, while the ratio
of equity accounts for only 8%. However, the proportion of equity has a
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significant role in maintaining the stable activities of banks and is an important
source for banks to develop and expand the business in long term. The equity
capital is useful to minimize the potential risks arising from banking operations
and protective actions in case of insolvency. If banks have the amount of loss
much higher than the capital, they may be in threat of insolvency and default.
Therefore, the maintenance of adequate capital is important for commercial
banks.
The proportion of equity capital could have positive and negative impact on the
efficiency of commercial banks. In existing literature, the relationship between
equity ratio and bank efficiency is found to be different. Girardone et al (2004)
indicate the opposite relationship between equity and inefficiency in Italian
banks. Das (2002) also shows that the Indian banks had higher ratio of equity will
perform better in the period of 1995 – 2001. Besides, Pasiouras et al. (2007)
affirm that Greek banks’ technical efficiency is positive with the amount of equity
capital during the time interval of 2000-2004. The positive correlation between
equity and bank efficiency is supported by different scholars as Mester (1996),
Pastor et al (1997), and Carbo et al (1999).
However, the research of Casu and Molyneux (2003) reveals that the correlation
between equity ratio and efficiency was insignificant in European banks in the
time of 1993 -1997.
The majority of studies explore that bank’s efficiency and the equity ratio have
positive relationship. Therefore, it is assumed that
H2: The relationship between equity and technical efficiency is positive in
Vietnamese commercial banks.
2.3.1.3 Loan to total assets
The ratio of loan to total assets is the proportion of total lending volume on total
bank’s asset. This ratio reveals the risk level of liquidity in banks as it shows the
allocation of low liquid asset in total asset. When banks provide larger volume of
loans, the operational cost is reduced and thus increasing progressively the
amount of loans to customers (Isik and Hassan, 2003).
Sufian (2009) explores that Malaysian banks having higher proportion of loans
tend to obtain higher level of efficiency in 1997. Supporting this result, Sufian and
Habibullah (2010) and Sufian and Noor (2009) testify that banks with more
efficiency often have larger volume of loan in the cases of Thailand and Islamic.

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However, Barr et al (1999) indicate that higher proportion of loan on total assets
will result in the lower efficiency of banks because of higher potential risks.
From the findings above, it is supposed that
H3: The relationship between the ratio of loan and bank’s technical efficiency is
positive.
2.3.1.4 Non-performing loans
Non-performing loans are loans whose interest and principle has been paid
overdue for a long time. Non-performing loans arise from controllable and
uncontrollable factors (Berger and Mester, 1997). Controllable factors include
quality management, credit appraisal and policies, loan structure and credentials.
Uncontrollable factors consist of harsh business environment, unexpected
modification in regulations and laws, problems from borrower’s business and
catastrophic events. Due to the impact of external factors, loss from loans is
impossible to discard totally. The understanding of this will be helpful to accept
the risk of credit, which results in lower profit. Therefore, bank managers should
take into account the loss arising from loans because bank employees are
difficult to predict exactly if the borrower can pay interest and principle on due
date or not.
Several researches about the efficiency have examined the level of bank
efficiency in the relation with the quality of asset. In earlier papers, assessment of
non-performing loans have straightly been combined to manage asset quality in
the functions of cost and profits, thus indicators of efficiency are achieved. Other
scholars applied different methods, and include the variable of non-performing
loans as in the regression model (Reda & Isik, 2006; Mester, 1994).
In the research of Das and Ghosh (2006), regardless of selecting input and
output, the non-performing loan and inefficiency have positive relationship.
Berger and DeYong (1997) state the hypothesis of “bad management” that the
growth of non-performing loan reduces the efficiency of financial institutions
because of increasing cost in administration, supervision and sales-off.
Some academicians as Wheelock & Wilson (1995) and Barr & Siems (1994)
explore that weak banks tend to be positioned far from the optimal practice
frontier. Thus, despite of high proportion of non-performing loans, these banks
still have tendency to show the efficiency in cost. Studies of Karim, Chan &
Hassan (2010) and Kwan & Eisenbeis (1995) provide supportive evidence that
efficiency and non-performing loans have increasing correlation in banks. Fan and
17


Shaffer (2004) also affirm that the association between non-performing loans and
efficiency in US large banks was negative, but no statistically significant.
The author proposes the hypothesis 4:
H4: The relationship between non-performing loans and technical efficiency is
negative in Vietnamese commercial banks.
2.3.1.5 Ownership structure
The importance of ownership structure has been concentrated on the stage of
reforming banking activities, especially in developing nations. The big inquiry is
how to maximize the structure of ownership and management in banks to
enhance the mutual benefits of stakeholders and directors (Spong et al., 1995).
The ownership of government is supported by the belief that the actions of
government aims to ensure the best advantages for the general society; so, the
state ownership is useful to reduce common costs and improve efficiency. The
contribution of the government in the operation of banking system is hoped to
encourage more capital to develop the domestic economy and bring other social
benefits for the society instead of focusing on economic benefits (Megginson,
2005).
However, the expectations about advantages of state-owned banks are failure
and their banking activities have worse efficiency than private banks. It is
believed that the purpose of the state in banking control is primarily to increase
its advantages rather than social improvement in spite of risk aspects and
unproductive asset distribution.
The correlation between ownership structure and efficiency in banks has been
studied by many scholars, especially in economies that the state has a key part
in the operation of banking system. Several empirical results indicate that the
association between state ownership and efficiency in banks is converse. Credit
for political and social aims often fall into the inefficiency. Although the
government monitors and directs commercial banks towards some specific
benefits, the financial detriments counterweight the returns. Conversely, the
private ownership is thought to be improve the performance of banks by handling
the troubles between directors and stakeholders and encourage investors to
investigate the execution of management. Therefore, the elimination of state
ownership in the banking sector by privatizing may be most expected.
Das (2002) indicates that Indian banks can enhance their performance if the
capital is contributed more by the state. Sathye (2003) also uncovers that private
18


commercial banks tend to be weaker in efficiency than public and overseas
commercial banks in the context of India. Supporting this finding, Unal, Aktas and
Acikalin (2007) prove that state-owned banks have the same efficiency with
private banks and even perform better at several facets.
Sufian and Noor (2009) hold the opposite view that the efficiency score of private
banks is higher than those of public banks in the situation of Italian economy.
Hussein (2003) reinforces the above outcome when doing a research on 17 banks
in Sudan, Islamic in the period of 1990-2000 with the application of stochastic
cost frontier model. He finds that in three groups of state-owned, private and
overseas banks, state-owned banks are the worst at cost efficiency.
In the situation of Vietnam, commercial banks have been progressively
privatized; however the state still holds a large percentage of shares in several
key banks. The state-owned banks are defined as the holdings above 50% by the
state. Joint-stock banks have an insignificant part of state capital or none. Due to
the abundant experience in operating banking services and higher volume of
capital, state-owned banks are supposed to be more efficient that joint-stock
banks. Thus, the hypothesis 5 is
H5: The relationship between state-owned banks and technical efficiency is
positive.
2.3.2 Proposal research model

Loan to total assets
(+)
Equity capital

Non-performing loans

(+)

(-)

Bank size

Bank technical

(+)

efficiency

This dissertation will test 5 hypotheses:
19

State ownership (+)


H1: The relationship between size and technical efficiency in Vietnamese banks is
positive
H2: The relationship between equity capital and technical efficiency in
Vietnamese banks is positive
H3: The relationship between loan ratio and technical efficiency in Vietnamese
banks is positive
H4: The relationship between non-performing loans and technical efficiency in
Vietnamese banks is negative
H5: The relationship between state-owned banks and technical efficiency in
Vietnamese banks is positive

20


CHAPTER 3 METHODOLOGY
3.1 Data Envelopment Analysis (DEA)
DEA was built by Charnes, Cooper and Rhodes (1978) for the sake of measuring
corporate efficiency in the public sector, especially on non-profit corporations
where the amount of account profit is at insignificant value, different outputs are
generated with different inputs, and it is complicated to recognize efficient
correlation of input and output. The formation of this approach arises when prices
might be unavailable or unreliable and hypotheses of cost minimization and profit
maximization might be unsuitable (Bauer et al., 1998)
DEA approach is viewed as a standard for efficiency evaluation (Canhoto &
Dermine, 2003). This approach was firstly applied in banking sector by Sherman
and Gold (1985). Currently, DEA is one of the most popular methods to measure
efficiency of banks.


Definition and Assumptions

DEA frontier is built as a part of linear combination linking the group of best
observation in sample set, so producing a convex Production Possibility Set (PPS).
Therefore, scores of DEA efficiency for a particular decision-making unit (DMU)
are not expressed by an absolute figure, however relative to other DMUs in the
particular group of data (Casu and Molyneux, 2003). Cooper et al. (2006) indicate
that the method’s name is called as the technique it covers observations for the
purpose of recognizing a frontier which is employed to assess observations
signifying the performance of measured units by recognizing the sources and
volumes of inefficiency in every input and output for single DMU and DMUs are
positioned on the frontier of efficiency.
DEA model was firstly constructed on the foundation of constant return to scale
(CRS) which is often seen as CCR framework. After that, Banker, Charnes and
Cooper (1984) broadened this model to variable returns to scale (VRS). It permits
the recognition of whether a DMU is functioning at growing, constant, or declining

21


return to scale and generally acknowledged as BCC model. The model is also
benefit for identifying separately technical and scale inefficiency. Technical
efficiency is the score of efficiency in CRS framework, while scale efficiency
entails running data in both two models of CRS and VRS and equals to the ratio of
CRS and VRS scores.
The score of scale efficiency = CRS score/ VRS score
It is not required pre-designated weights for many inputs and outputs, but it
recognizes a group of most productive DMUs, where the scores of DMU’s
efficiency are measured. The score of efficiency reveals a chain of weights as
regulated from the data, and the highest efficiency point is made by one for
every output and one for every input.
A suggesting group for an inefficient DMU is set up the efficient DMUs, which an
inefficient DMU is assessed contrary. The suggesting group is the non-parametric
form of the frontier of efficiency according to the parametric methods. Besides,
this suggesting group permits management to position and comprehend the
essence of inefficiencies by assessing the inefficient DMU with its equivalent
efficient complement (Sherman and Gold, 1985).
The slack based measure (SBM) of efficient DMUs is recommend by Tone (2001)
for the sake of integrating coincident input surplus and output deficits, and
therefore explained as the outcome of input and output inefficiencies. Moreover,
the process solves with the regular zero apportioned weights in the traditional
assessments by distributing weights to entirely inputs and outputs of DMUs with
the exemption of non-positive data. Likewise, SBM tries to locate the maximum
practical returns in contrary to the CCR concentrating on recovering the
maximum proportion of real output on real input.
Andersen and Petersen (1993) enlarge the expansion of DEA to deeper examine
and rate the efficient DMUs from prior frameworks. Their perspective assesses
DMU with a linear grouping of efficient DMYs in the trial, so DMU is discarded.
This directed to Tone (2002) combining the SBM and super-efficiency model (SEM)
built initially by Andersen and Petersen (1993) in order to get the slack-based
evaluation of super-efficiency. The SEM is viewed as an evaluation of stability, in
case that the input data is exposed to inaccuracy or fluctuation across periods,
and offers ways of assessing the aspect of which these modifications could
happen without infringing that DMU’s position as being an efficient element
(Cook and Seiford, 2009).
22


Casu and Molyneux (2003) recommend that Tobit regression model is applied to
examine the impact of numerous factors relating to specific nation and
environment on the efficiency of banks and condensed data can be comprised,
despite of several questions for verifying the validity of this method (Simar and
Wilson, 2007). Besides, the bootstrapping approach is implemented to correct the
integral dependency of DEA applied in regression model. Grosskopf (1996) states
that dependency is validated as the efficiency score of DEA is relative, rather
than absolute index of efficiency; so breaking the individual assumption in the
model of regression and proposing that typical process is unenforceable
(Grosskopf, 1996). The assumptions of DEA on the ground of unit uniformity are
evaluated as followed: (1) units are (1) units are presumed to involving in the
same operations, generating equivalent output set and popular technologies are
implemented; (2) the same series of resources is accessible to units; (3) units
are functioning in the same environment (Dyson et al., 2001); (4) the sufficient
sample size ought to be bigger than the outcome of input and output numbers
(Cooper et al., 2006) or the quantity of sample needs to be triple than those of
inputs and outputs at least.
Strengths of DEA
The advantages of DEA is that it is not essential for explicit or apparently stated
description of a purposeful form and enacts less organisation on the form of
efficiency frontier and data is permitted to express themselves (Wheelock and
Wilson, 2006). It solves different inputs and outputs specified in various units of
measurement, and concentrates on most efficient frontier, instead of tendency
for centralized population (Chen et al., 2005).
Siems and Barr (1998) propose that DEA model is helpful for policy-makers
because of balancing off-site supervising instrument. Moreover, they present
features of DEA model to comprise: a firm cost-effective and mathematical
underlining; substitute practical and combined or theoretical best-practice units;
the capacity to take into consideration trade-offs and replacements between the
standard metrics, and

the suggestion for enhancement on different corporate

aspects.
In comparison of three approaches: DEA, SFA and TFA, Bauer et al. (1998)
indicate several proofs to show that the efficiency is fluctuated across period and
the efficiency is almost the same in the stability in two approaches of parametric
and non-parametric. A significant distinction between methods is that DEA overall

23


is more stable than other approaches. Likewise, Sickles (2005) examines
efficiency measurement of parametric and non-parametric, consisting of DEA,
and shows that DEA is a better measurement tool of technical efficiencies varying
across time for corporations, and also implements well in place of accurate and
predicted efficiency of companies, especially, once there is a growing trend in the
number of cross-sections and time series.
Limitations of DEA
However, the most disagreement of DEA is concerning on the lack of ability to
combine random error. For banks, their costs which are lower than the
measurement level would be categorized as most efficient, and other adverse
impact out of the management of banks would be recognized as inefficiency
(Mester, 1996). The case becomes severer when there occurs random error on
the frontier of efficiency or a leading recommending set, it impacts the deliberate
efficiency of all companies that are contrasted (Bauer et al, 1998).
Besides, DEA only employs input and output data and may not directly consist of
input prices, so it causes difficulties to approximate allocative inefficiency
(Berger, 1993). Nevertheless, this matter is against the initial opinion of Farrell
(1957) relating to the difficulties resulting from price efficiency.
Bauer et al (1998) indicate that a possible problem for DEA is the matter of selfidentifiers

and

near-self-identifiers.

Every

company

merely

could

be

in

comparison with other companies on the frontier with the similar outputs or vice
versa adding more limitations applied on the ground of comparability of quality
controls.
Therefore, no any companies in different aspects could lead to companies being
evaluated as highly efficiency or self-identified of 100% because of shortage of
equivalent companies in the limited variables. Basically, this matter usually
arises in case of small size of observations comparing with the input and output
size, and other restrictions leading to a problem of coordinating all aspects.
Brown (2006) indicates that the drawback of DEA is the deterministic and
troubled in assessing variables errors. Likewise, Fried et al (2002) recommend
that the majority of DEA frameworks and practically all operative DEA
frameworks are deterministic, therefore incapable to take the stochastic
constituent of a DMU. This encourages these researchers to build a DEA on the
ground of model consisting of a stochastic factor proposed to separate the
influence of luck from the environmental influence and managerial execution.
24


However, this view is cost-effectively and statistically illogical because the
deterministic characteristics of DEA may be strongest element.
3.1.2 Malmquist productivity index
In addition to the DEA model, several indexes are applied to measure the
efficiency, comprising: Fisher index, Tornqvist index and Malmquist index. This
research decides to use Malmquist index due to some benefits. Initially, it is not
required to assume profit maximization or minimization. Secondly, it is
unnecessary to get data relating to input and output price.
Productivity index is significant to investigate the level of efficiency and its
variation over different period. The index of Malmquist shows the variation of
total factor productivity (TFP). It is calculated by the ratio of the distance of
output and input vectors in two successive periods, proportional to a directed
technology (Coelli et al., 2005). The productivity index of Malmquist is explained
by Färe et al. (1994) as the formula below:

In which:
Mj

= Malmquist productivity index

Dj

= Distance function

x and y = inputs and outputs from t to t+1.
The components constituting Malmquist index are displayed in the formula. The

proportion of

shows the variation of technical efficiency (EFFCH) in

the period of t and t+1. The proportion of

is the

arithmetical mean of two index which signifies for the change in technology
(TECHCH) in the period of t to t+1. So, the variation of Malmquist productivity
index is imparted as the outcome of two components EFFCH and TECHCH.

25


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