02 Nov 2017
To the best of my ability and belief this thesis contains no materials previously published or written by another person, except where due reference is made in the next. I am completely responsible for all the contents and statements.
First of all, I owe indebtedness to my teacher, Prof. Nguyen Khac Minh, who has inspired me into the economic development field. His lectures with deep knowledge and inspiration have developed a strong passion in me. Without his help, my thesis cannot be completed.
Secondly, I would like to express my special thanks to my family my panrents, my older uncle and Do Phuong Thao for their strong belief in me, even when I was lost.
Thirdly, I feel greatly indebted to my teacher Nguyen Thi Minh, the teaching staffs, particularly teacher Giang Thanh Long and the office staffs of the Vietnam-Netherlands Master’s program in Development Economics for all their lecture and valuable assistance in my course.
This paper utilizes two-stage DEA to estimate efficiency of 33 Vietnamese domestic banks from 2006 to 2010. In the first stage, a chance-constrained DEA in both inputs and outputs is employed; we also employ chance-constrained DEA in outputs and deterministic DEA to compare between three models’ efficiency results through Banker’s asymptotic test. In the second stage, technical efficiency scores from three models are used as dependent variable in Tobit regression to determine which factors affect Vietnamese banks’ efficiency. Overall, we found that during the studied period, Vietnamese banks wasted 33% on average of input resources to produce a same amount of outputs. The key cause is technological regression. Besides, banks experienced a productivity degradation of about 0.5% from 2006 to 2010. The Banker’s asymptotic test shows that there is no difference between the efficiency scores between three models. Through Tobit regression, factors like profitability, capital adequacy, market-share, loan intensity, diversification of services significantly affect Vietnamese banks’ efficiency.
Key words: Efficiency, Bank, Vietnamese commercial bank, Malmquist index, Total factor productivity, Tobit regression, Data envelopment analysis, Chance-constrained.
TABLE OF CONTENTS
BBC
Banker, Charnes and Cooper Model
CCDEA
Chance-constrained DEA
CCR
Charnes, Cooper and Rhodes Model
DDEA
Deterministic DEA
DEA
Data envelopment analysis
DRS
Decreasing return to scale
IRS
Increasing return to scale
JSB
Joint Stock Commercial Bank
JVB
Joint Venture Bank
NIRS
Non-increasing return to scale
PE
Pure efficiency
ROA
Return on asset
ROE
Return on equity
SBV
State Bank of Vietnam
SE
Scale efficiency
SFA
Stochastic frontier analysis
SOCB
State owned commercial Banks
TE
Technical efficiency
TFP
Total factor productivity
ABB
An Binh Joint Stock Commercial Bank
ACB
Asia Commercial Bank
BANVIETB
Viet Capital Joint Stock Commercial Bank
BIDV
Bank for Investment and Development of Vietnam
CTG
Vietnam Joint Stock Commercial Bank for Industry and Trade
DAB
Dai A Joint Stock Commercial Bank
DAIAB
Dai A Joint Stock Commercial Bank
EXIMB
Vietnam Export Import Joint Stock Commercial Bank
HBB
Hanoi Building Joint Stock Commercial Bank
HDBANK
Ho Chi Minh City Housing Development Bank
KIENLONGB
Kien Long Joint Stock Commercial Bank
MB
Military Joint Stock Commercial Bank
MDB
Mekong Development Joint Stock Commercial Bank
MHB
Housing Bank of Mekong Delta
MSB
Vietnam Maritime Joint Stock Commercial Bank
NAMAB
Nam A Joint Stock Commercial Bank
NVB
Nam Viet Joint Stock Commercial Bank
OCB
Orient Joint Stock Commercial Bank
OCEANB
Ocean Joint Stock Commercial Bank
PGB
Petrolimex Joint Stock Commercial Bank
SCB
Saigon Joint Stock Commercial Bank
SEAB
south east commercial joint stock bank
SGB
Saigon Joint Stock Commercial Bank for Industry and Trade
SHB
Saigon Hanoi Joint Stock Commercial Bank
SOUTHERNB
Southern Joint Stock Commercial Bank
TCB
Vietnam Technological and Joint Stock Commercial Bank
TRUSTB
Trust Joint Stock Commercial Bank
VBARD
Bank for Agricultural and Rural Development of Vietnam
VCB
Bank for Foreign Trade of Vietnam
VIB
Vietnam International Joint Stock Commercial Bank
VIETAB
Viet A Joint Stock Commercial Bank
VPB
Vietnam Prosperity Joint Stock Commercial Bank
The measurement of productivity and efficiency as well as the decomposition of the sources of change in productivity always play an indispensable part in managerial issues of a banking system. This is because being able to comprehend those important information will certainly assist people in making better decisions like how to improve output targets such as profit while consuming the same old amount of inputs like total asset, labor and interest expense especially under the fiercely competitive markets these days. As the development of international economic integration and the liberalization of trade, the increasing pressure of competition has been put on the financial market, particularly commercial banks. Commercial banks which mainly function as an intermediary between saving sectors and investment ones are currently faced with intensive competition of other financial intermediaries from abroad. Banks without competitiveness will be replaced by other successful banks. The increasing pressure of globalization, therefore, forces commercial banks to improve its productivity.
In 1990, Vietnam carried out a rehabilitation of bank structure, which divided the system into two-tier one. From then on, the banking industry has experienced a rapid increase in the quantity of commercial banks as well as the quality of management skills (see section 2 below). Besides, it is undoubted that the banking system in terms of a financial intermediary has greatly contributed to the economic development of a country, and Vietnam is not an exception. Consequently, understanding the production efficiency of banks can help to encourage more contribution of banks to the development of the economy. Additionally, when detecting an inefficiency source, a change in technology and technical efficiency, suitable policies can be formulated to improve the performance of the system.
For evaluating productivity between organizational units, two main different approaches: non-parametric and parametric ones can be employed. Both of them have to construct a "best-practice" frontier from observed data set in order to assess relative efficiency of each unit. This is because actual frontier is unknown in reality (J.C. Paradi et al, 2004). To estimate the "best-practice" frontier, the parametric has to assume restrictions on the frontier production function and the error term. Although organizational unit can be efficient without being in the frontier due to randomness, strict restrictions or assumptions on distributional form of the error term is the major drawback of this method. On the other hand, non-parametric approach or Data Envelopment Analysis (DEA) places fewer assumptions on the frontier. It, however, is deterministic and does not allow efficient unit to be out of the frontier (Kasman and Turgutlu, 2007). To address this disadvantage of deterministic DEA, in 1993, K. C. Land et al. developed a new model for DEA – chance-constrain DEA (CCDEA). The new model included uncertainty into its inputs as well as outputs. Therefore it overcomes the disadvantage of deterministic or non-stochastic DEA model. Besides, the banking industry is the complicated service-provided one which has a large number of inputs and outputs, while the parametric approach requires a specific production function. Consequently, when examining the simultaneous relation between multiple inputs and multiple outputs, parametric approach could lead to erroneous conclusions if the function is miss-specified.
For those reasons mentioned above, this paper is denominated "A chance-constrained data envelopment analysis approach to the measurement of Vietnam’s bank efficiency and productivity from 2006 to 2010". It will utilize both deterministic DEA and chance-constrained DEA to measure efficiency of Vietnamese banking system. After that by evaluating which factors affect productivity of the system, this paper will develop some deliberate policies to improve the system’s performance.
Objective: The objective of this paper is to employ both deterministic and chance-constrained DEA to measure the performance of Vietnamese banking system and to make a comprehensive comparison between two approaches. After that, by utilizing the Malmquist total factor productivity indices (TFP) integrated with the results of those models, the paper will make a decomposition of the TFP in banking industry in Vietnam into separated sources of change: technological change and technical efficiency change including pure efficiency change and scale one. Consequently, some periods where technological change and efficiency change happened will be made clear. Additionally, this paper also uses Tobit regression to detect which factors affect Vietnamese banking system’s productivity.
Scope of study: This paper employs the dataset from annual reports of 33 domestic banks in Vietnam from 2006 to 2010. This study only focuses on domestic banks in Vietnam. There are only two types of banks in this study, including 4 state-owned commercial banks and 29 joint stock commercial banks.
The scope of study employed in this paper is due to three main reasons. Firstly, this period marked the event that Vietnam became member of WTO, which officially indicated the international integration of Vietnamese economy. Secondly, this period also witnessed the occurrence of the global financial crisis during 2007 – 2008. Those reasons implied that Vietnamese banking system had to continue its improvement – productivity, management and competitiveness to prepare for the financial liberalization and the financial crisis. Thirdly, the data source in this period is verified to be sufficient, accurate and synchronized since it is taken from official annual reports during the period.
Under the condition that only a probability of or less, observed banks will do better than best-practice banks, how to compare relative efficiency between banks in Vietnam from 2006 to 2010?
How do different kinds of efficiency contribute to total factor productivity change of Vietnamese banks from 2006 to 2010?
What factors determine the productivity of Vietnamese banking system from 2006 to 2010?
What are inputs and outputs appropriate for evaluating efficiency of a bank?
The measurement of relative efficiency between banks under deterministic conditions?
The measurement of relative efficiency between banks under stochastic conditions?
The decomposition of total factor productivity change into technical change and efficiency change?
The difference between deterministic model and chance-constrained one?
The paper is organized as follows. Section 2 reviews related literatures on DEA for evaluating the performance of banking industry. Overview of Vietnam banking system is also presented in this section. Section 3 introduces three models: deterministic DEA model, outputs chance-constrained DEA – Chen (2005) model, chance-constrained DEA in both inputs and outputs – the expansion of Chen (2005) model. Besides, in this section, Tobit model and variable selection will be introduced to determine which environment factors affect efficiency. Section 4 then provides descriptive statistics of data, the results of 3 models. Section 5 summarizes the findings of this paper and gives policy implications.
The study is constructed as follows:
Chapter 1: Introduction
Chapter 2: Literature review and introduction to Vietnamese banking system
Chapter 3: Methodology and Data
Chapter 4: Empirical results
Chapter 5: Conclusion
The necessity for measuring efficiency of Decision Making Units (DMUs – organizational units which consume similar variety of inputs to produce similar variety of outputs (Igor Jemrić and Boris VujÄić, 2002)) is essential for management. It, however, remained controversial until the development of DEA. Before DEA emerged, how to rank a DMU’s performance based on outputs and inputs encountered difficulty in selecting the average weight for each input and each output. The main reason for this was that, in the early studies on this field, researchers were unsuccessful in establishing a synthetic quantification from inputs and outputs to assess the performance of a DMU. To address this problem, Charnes, Cooper, and Rhodes (1978) suggested a series of linear program known as DEA that calculate efficiency without the need of choosing average weights in advance.
DEA which is a non-parametric approach is a series of linear programming utilized to measure relative efficiency of Decision Making Units (DMU’s). This approach evaluates relative efficiency based on estimating a "best-practice" frontier. DMUs lie on the frontier will be considered an efficient one relative to the other inefficient DMUs which is off the frontier. Fundamentally DEA is different from regression analysis approach. In regression analysis, the central tendency or the average of observations’ deportment is reflected whereas DEA approach establishes a frontier from best-performed observations and measures the divergence of the others from the frontier. Igor Jemrić and Boris VujÄić (2002) highlighted that DEA has advantage over the parametric approach in terms of less requirements imposed on the frontier production functional form. In addition, when detecting a "best-practice" frontier or a set of benchmarks of DMUs, managers can make a comparison between their DMUs and the benchmarks. Consequently, they can decide which factors (inputs) impact negatively or positively on outputs. After that, suitable adjustment to inputs such as restricting inefficient inputs and strengthening efficient ones will be took place.
After innovation of DEA, a number of studies have paid attention to this field. This paper considers the model originally developed by Charnes, Cooper, and Rhodes (1978) as Deterministic Data Envelopment Analysis model (DDEA). DDEA is the DEA model with no stochastic constrains; while Chance-constrained Data Envelopment Analysis (CCDEA) is the DEA model which allows stochastic variation in both inputs and outputs.
Significant empirical evidence for the practicality of DEA in evaluating bank efficiency was first introduced by Sherman and Gold (1985). Their study examined the performance of 14 bank branches in US. By using CCR model which employed Labor, expenses, and space as inputs and transactions as output, they highlighted that for a bank to make advancement in branches' productivity other approaches should take DEA as a advantageous accompaniment in making decision. Sigbjorn Atle Berg et al (1993) employed DEA which integrated with Malmquist index to distinguish the difference in productivity of banks between three Nordic countries. The dataset they used contained 503, 105 and 126 banks in 1990 from Finland, Norway, and Sweden respectively. When comparing productivity between those countries he found that Swedish banks accounted for highest number of banks lying on "best-practice" frontier. This indicated that banks in Sweden were much productive than the other two countries. Camanho et al (1999) suggested an application which implemented DEA to evaluate the productivity of Portuguese bank branches. The application, which emphasized the association between branch capacity and performance, acted as complementary tool for other techniques employed at the bank at that time. He found that most of the Portuguese bank branches were faced with scale inefficiency, particularly increasing return to scale. Therefore, he suggested that resources should not be realized to target higher profitability. Besides, he found that bank branch efficiency indicated higher profitability; however, the reversed is not necessary to occur. David A. Grigorian et al (2002) used DEA to examine the performance score of banks from a number of transition economies. In the paper, they determined the main targets of banks were profit maximization and the supply of transaction services. Consequence of their finding highlighted the practical usage of DEA in transition-related application. Igor Jemrić and Boris VujÄić (2002) studied performance of Croatian banks in the period of 1995-2000. Both BBC and CCR model with intermediation and operating approach are utilized. However, they didn’t employ any methods to measure the enhancement of banks’ efficiency or productivity change from year to year. This is because they only calculated efficiency for separated years. The study of Milind Sathye (2003) researched into developing country (India) banks’ productivity from 1997 to 1998. He conducted two DEA models, model A and model B, which differed in inputs and outputs chosen and he classified Indian banks into 3 groups, publicly owned, privately owned and foreign owned banks. His results showed that public banks’ efficiency score and foreign owned banks’ one are higher than that of private banks. Additionally, he also stated that banks’ strategy to rationalize staff and branches and to decrease non-performing assets could help the system to achieve better performance. This would lead to the fact that Indian banks could continued to develop even under the pressure of international competition.
Regarding weights for inputs and outputs some studies has suggested methods which integrated variable deletion and weight constraints into DEA. C. A. Knox Lovell et al (1997) assessed the performance of 545 branches of a large bank in Spain according to a number of pre-determined goals arranged by bank as well as the goals themselves. They found that, when utilizing variable deletion method in DEA suggested by Pastor et al. (1995), some goals or targets could be removed from the model with only a little loss of instruction to bank's managerial issues about the productivity of bank branches. In the paper of Athanassopoulos (1998), he asserted that it is better to take normal DEA model in combination with weight constraints. This is because lack of weight restrictions and absolute weight bound could lead to an underestimate of the relative efficiency of DMU.
In recent studies when applying DEA to measure efficiency, Tobit regression is included as a second stage to determine which factors affect efficiency. In other words, this approach is named two-stage Data Envelopment Analysis. Fethi et al (2000), examining two stage DEA to asset performance of 48 Turkish banks in 1998. Their study applies value added approach to select inputs and outputs. Inputs include the number of employees and non-labor expense, while outputs include loans, demand deposits and time deposits. After conducting DEA to determine efficiency scores of 48 banks, they consider these scores as dependent variable which is explained by number of branches, total asset, profitability, ownership and capital adequacy. They found that banks with higher profitability as well as higher capital gain more efficiency than other banks. Besides, they found that, banks which have high capital adequacy tend to be less efficient than thinly capitalized banks. This is due to the trade-off between risk and return – banks with low capital will have moral hazard incentives to invest on more high-risk portfolios. Ji-li Hu, Chiang-Ping Chen and Yi-Yuan Su (2006) utilized non-parametric approach – DEA to investigate productivity and factors which influenced productivity of 12 Chinese banks during 1996 – 2003. The author used three inputs – savings, number of workers and net fixed assets with two outputs – investment and lending. After that, based on efficiency scores estimated from DEA model, he used Tobit regression to examine which variables affected productivity of 12 Chinese banks. In the Tobit regression model, the author also used dummy variables which implied joining WTO and the Asian financial crisis. From the results estimated, the author stated that on average, there was a notable increase in technical and scale efficiency at 12 Chinese banks during the studied period. Gwahula Raphael (2013) studied the effects of bank, industry, and macroeconomic variables on banks’ efficiency in Tanzania. In the first stage, he employed DEA to estimate the technical, pure and scale efficiency scores through intermediation approach which takes into deposit, interest expense and non-interest expense as inputs and loan, investments, interest income and non-interest income as outputs. He found that, the efficiency level of banks in Tanzania declines noticeably in 2008. After that, through Tobit regression he highlighted that banks’ efficiency is significantly affected by bank specific: bank size, profitability (NIM), liquidity and capital adequacy; by industry specific: market share and concentration; and by macroeconomic specific: GDP – the only factor affect Tanzanian Banks. Besides, he also found that, non-performing loan, ownership and CPI are insignificantly affect Tanzanian banks during the studied period.
As regards panel data and productivity growth, Mette Asmild et al (2004) when investigating Canadian banking industry’s efficiency from 1981 to 2000, encountered the problem of having tow few DMUs but a large number of inputs and outputs. Due to the fact that merely five banks in Canada largely dominated at about 90% of the whole market. To deal with this problem, Asmild utilized window analysis technique in DEA which analyzes time series data. The study, moreover, employed Malmquist total factor productivity index in conjunction with the results of the series of linear program to evaluate the productivity change from one period to another period. Anthony N. Rezitis (2004) examined productivity and productivity growth of Greece banking system from 1982 to 1997 by applying DEA method and Malmquist productivity indices. Furthermore, his second objective was to make a comparison between two period 1982-1992 and 1993-1997 because there was a significant change in Greece banks’ regulations in 1992. After that he used Tobit regression of efficiency scores on banks’ factors to determine which factor affected productivity. His results exhibited that there was an increase in productivity after 1992. Besides, before 1993, efficiency enhancement was the main contributor to productivity growth, while after 1993 the cause of productivity growth was technical advancement. He also found that both pure and scale efficiency enjoyed the advantage of size and specialization of Greece banking system. However, he also mentioned that non-stochastic variation of outputs from "best-practice" frontier could cause the result of DEA model to be different from reality. J. Rebelo and V. Mendes (2000), employed DEA and Malmquist TFP to assess productivity growth of Portuguese banking system during the time of financial liberalization. The period of study was from 1990 to 1997 and the intermediation approach was utilized. They took three outputs – loans, financial applications and other banking services along with three inputs – deposits, labor and capital. They found that, there was an increase in productivity and technology in Portuguese banking system during this period. Moreover, they also highlighted that, when examining productivity, bank managers could use the ratio of asset per employee instead. Ashish Kumar and Vikas Batra (2012) applied non-parametric approach integrated with Malmquist Productivity Index to estimate productivity of 74 Indian banks. Their method decomposed productivity change into different source of changes like technical change, scale efficiency and technological change. After that, they used group wise analysis to find the different between banks. The study showed that the studied period was the one of stagnation for banking industry in India. Ashish Kumar and Vikas Batra explained that the main reason for this was scale inefficiency of the system.
Chance-constrained Data Envelopment Analysis (CCDEA) was developed to solve the disadvantage of non-stochastic variation of inputs and outputs in the deterministic DEA model. As mentioned in the Introduction section, the major drawback of deterministic DEA is that it does not allow efficient units to be out of the frontier. In reality, the process of production usually contains unsystematic factors (K. C. Land et al (1993)). For example, in agriculture, stochastic variation of outputs can be caused by unpredictable weather. In education, it is reasonable to find out that even two schools with similar student requirement entrance and teachers’ education level have different number of student who passes university. In banking system, loan and deposit can be affected by customers’ behavior like crowd psychology which is not fully captured by data. K. C. Land et al (1993) made an introduction to CCDEA model with addictive formulation (addictive DEA models take in inputs and outputs under the addictive forms). They formed the model under the condition that only a (normally ) or less of observed DMUs will do better than best-practice DMUs. Furthermore, they assumed that inputs were non-stochastic and the unit variance of outputs. After that they converted the problem into deterministic equivalent one, which can be solved by computer program. Although, K. C. Land et al (1993) introduced a method to convert a stochastic programming problem into a deterministic one, their model’s disadvantage is the assumption of unit variance as well as the assumption of non-stochastic inputs. N. K. Minh et al (2011) expanded K. C. Land et al (1993) study by removing the constraint of unit variance. They utilized expanded CCDEA model to measure efficiency of Vietnamese agricultural sector from 1995 to 2007. In their study, two models, model A and model B, are employed. The difference between the two models lied in their covariance matrix, with model A assuming unit covariance matrix of outputs while model B only assuming equal variance between outputs over time. They found that there was little deviation from the Malmquist indices of model A and model B.
Since the development of CCDEA, some researches tried to make a comparison between stochastic non-parametric method and stochastic parametric method. Adnan Kasman and Evrim Turgutlu (2007) employed DDEA, CCDEA and SFA to evaluate efficiency of Turkish Life Insurance Industry during the period of 1995-2005. The study found that firms were classified almost identical by those models; however the average efficiency scores from those models are considerably different. They stated that DEA and SFA were intrinsically different even though stochastic constrains were included into DEA (see another study which used both DEA and SFA of Chen in 2002 below).
In 1990, the necessity for incorporating stochastic constraints into DEA application which assessed performance of banks was suggested by G. D Ferrier et al (1990). In their study, they employed both econometrics and DEA, to evaluate the efficiency of 575 institutions which became members of Federal Reserve System's Functional Cost Analysis in 1984. They suggested that the lack of misspecification and measurement error in the deterministic series of linear programs was a major drawback. This is due to the fact that stochastic variation and inefficiency were considered to be the same in non-stochastic DEA. In some studies, researchers have taken into consideration both the need for the separation of stochastic variation and inefficiency (stochastic constraints) as well as the need for time series analysis in DEA. When computing performance score of 39 banks in Taiwan, Chen (2002) utilized both chance-constrained DEA approach and stochastic frontier analysis (SFA) one to find any differentiations between two of them. The comparison result exhibited a noticeable distinction between the two methods. Chen (2005) used deterministic DEA and chance-constrained DEA to make calculations of efficiency scores and productivity change for Taiwan banking system during Asian financial crisis. The study examined 46 different banks in Taiwan from two periods, 1994-1996 and 1998-2000. It showed that pure technological change mainly contributed to the improvement of productivity during the Asian financial crisis. Moreover, efficiency score from chance-constrained DEA was only a little above the score in deterministic DEA.
In earlier studies, when evaluating productivity of Vietnamese banking system, some researchers focused on traditional method namely ratio analysis. According to Joseph C. Paradi et al (2003) ratio analysis has its own reputation for being a historic standard method to measure bank productivity. By using ratio analysis, the relation between two variables is computed (such as ROA, ROE and NIM) to provide better understanding of banks’ multifaceted activities like investment, risk management, asset quality and profitability (Joseph C. Paradi et al (2003)). Le Thi Huong (2002) – improvement to efficiency of investment activity in Vietnamese commercial banks and Le Duan (2004) – applying statistic method to analyze the Vietnamese commercial banks efficiency were two of some Vietnamese studies employing ratio analysis to assess banks’ performance. However, Joseph C. Paradi et al (2003) highlighted that this method cannot establish a synthetic indicator to represent for multiple inputs and outputs in banking system; consequently, it is not sufficient to evaluate efficiency.
In Vietnam, research into DEA, especially chance-constrained DEA, to measure efficiency of banking system has been a scarcity up to now. N. V. Hung (2007) studied Vietnamese commercial banks’ efficiency by using DEA during the period of 2001-2003. He found that there was total factor productivity (TFP) improvement in studied period. This TFP improvement was contributed mainly by technical efficiency and somewhat technological enhancement. In addition, in his study in 2008, both DEA and SFA are utilizing to investigate productivity of 32 commercial banks in Vietnam from 2000 to 2005. He found that the technical efficiency score results from SFA approach (0.74 under constant return to scale and 0.729 under variant return to scale respectively) are less than that in DEA model (0.791). After that, he used Tobit model to evaluate factors that affected efficiency of those commercial banks. Consequently, he made suggestion for further efficiency improvement in Vietnamese commercial banks like the need of reduction in liquidity risk, improvement of managerial skill and encouragement of high quality of existed services as well as new services (for more detail see N. V. Hung, 2008). D. T. Ngo (2010) when applying DEA into measuring efficiency of top 22 Vietnamese commercial banks in 2008, stated that observed banks enjoyed their relatively high productivity in this year. He, nonetheless, also stated that those banks can still yield improvement in efficiency. Another study of Ngo (2012) utilized DEA window analysis to examine the productivity of 21 commercial banks in Vietnam from 1990 to 2010. He emphasized that the banking system has little to contribute to the economy, accounting for only about two-third of its capability on average. N. K. Minh et al (2012), employed DEA method to rank the performance of 145 branches of Vietnamese Agricultural bank during the period of 1997-2010. They innovated a new method which originated from slacks-based measure of efficiency model in order to rank all inefficient DMUs. In addition to this, the new method can deal with the drawback of infeasibility. Although those studied greatly contributed to recent research on Vietnamese banks efficiency, there has been no comprehensive research into non-parametric approach in terms of both deterministic and stochastic DEA. In the studies of N. V. Hung (2007), Ngo (2010) and Ngo (2012), they only utilized deterministic DEA, which has the disadvantage of not allowing efficient DMUs to be off the frontier line. While Ngo (2010) only took 2008 banks’ data into consideration, N. V. Hung (2007) compared just three-year period from 2001 to 2003 due to lack of data. N. K. Minh et al (2012) targeted evaluating only the performance of Vietnam bank for agriculture and rural development’s branches. In addition, despite that N. V. Hung (2008) made a detailed comparison between DEA and SFA, his non-parametric approach only focused on deterministic DEA.
In general, based on the practical usage of DEA application as above and the current situation of research into this field in Vietnamese banking sector, this paper will use both deterministic DEA and chance-constrain DEA to examine the relative efficiency of domestic banks in Vietnam from 2006 to 2010. The study takes account of the fact that inputs and outputs are both stochastic. This is because customers’ behavior has the reputation for intrinsically unpredictable characteristics. For example, when evaluating performance, contemplating demand for deposit, and transaction to be deterministic variables can be misleading. Besides; the delivery of bank services is directly affected by customers’ demand, which is a production relationship. It also causes the production results to be stochastic. Consequently; in the first stage, the paper will extends the model of Chen (2005) in imposing both chance-constrained conditions on inputs and outputs to measure the productivity and efficiency of Vietnamese banks, constructing a decomposition of total factor productivity change into two separated sources of change: technological change and technical efficiency change. Besides; the paper will apply 3 methods: deterministic DEA – Model A, Chance-constrained DEA (stochastic conditions on outputs) – Model B, and chance-constrained DEA (stochastic conditions on both inputs and outputs) – Model C, to make a clearer comparison between those methods. After that, in the second stage, Tobit regression will be used to analyze the factors that determine Vietnamese banking system’s efficiency. Economic insights for bank managers, therefore, will be suggested.
Before 1990, Vietnamese banking system was operated under a mono-banking mechanism in which the State Bank of Vietnam (SBV) acted as the central bank as well as a commercial bank. There was no separation between management functions and business ones and SBV was the sole bank of Vietnam at that time. Nevertheless, the need of changing from centrally planned economy into market oriented economy resulted in a reform of the banking system. In 23rd May 1990, Vietnamese government issued Decree on the State Bank of Vietnam and Decree on banking, credit and finance companies (SBV, 2012). The new mechanism consisted of the State Bank of Vietnam acting as the central bank and four State-owned commercial banks (SOCBs). Four State-owned commercial banks included Industrial Commercial Bank of Vietnam (ICB), Foreign Trade Bank of Vietnam (VCB), Bank for Agriculture and Rural Development of Vietnam (VBARD) and Bank for Investment and Development of Vietnam (BIDV). This movement broke up the State’s monopoly of the banking system. It separated the management function from commercial credit and monetary function and stimulated competition among commercial banks in the market economy.
Vietnamese banking system structure
The 1990‘s banking sector reform liberalized the banking industry as the government relaxed regulations on the sectoral specification of four State-owned commercial banks. This promoted the diversification of banking system regarding ownership, type and size (Siregar, 1999).
In addition to four State-owned commercial banks, a lot of commercial banks with different ownership forms such as: joint-stock banks (JSBs), joint-venture banks (JVBs), representative offices or branches of foreign banks (FBs), credit co-operatives, people’s credit funds and finance companies emerged. Besides, due to the liberalization of entry into the banking system, a number of banks were established. During 1990-1993 the number of Joint Stock Commercial Banks (JSCBs or JSBs) went up considerably from 4 to 41 and reached a peak at 51 in 1997 (see Chart 1). After the Asian financial crisis, this figure declined due to inefficiency in operation, bankruptcy or license withdrawal. Furthermore, in the period of 2000-2007, Vietnam carried out the financial and organizational restructure of the state-owned commercial banks as well as joint-stock commercial banks (SBV, 2012). Consequently, the number of domestic credit institutions continued to decrease slightly.
Figure .
Number of Banks in Vietnam
Source: State Bank of Vietnam (SBV), several years.
As regards foreign banks and foreign bank branches (FBs & FBBs), two majors events greatly contributed to their accession into Vietnamese banking industry were in 1998 and 2007. In 1998, the amended law on credit institutions of Vietnam which acted in accordance with the terms of the U.S.-Vietnam BTA, stipulated that 100% U.S.-owned subsidiary banks would be allowed by 2010. This revision prepare for the foundation of 100% foreign-owned banks in this country. In 2007, Vietnam became the member of the World Trade Organization (WTO), continuing its removal of legal requirement on foreign investment (Anne Ho & R. Ashle Baxter, 2011). Accordingly, the number of branches of foreign banks rose continuously to 53 in 2010, which was nearly double the figure in 1997.
In late 2010, domestic banks, foreign banks and foreign bank branches accounted for 101 banks which were comprised of 5 SOCBs, 38 JSBs, 53 FBs & FBBs and 5 JVBs (Chart 1). Despite of the fact that State-owned commercial banks have still remained their domination in the banking industry, penetration of JSBs and FBs & FBBs into this field has become deeper (VCB, 2011).
SOCBs which are the largest banks in this country originally founded to target State-owned enterprises. They possessed a huge amount of capital volume, with the four largest SOCBs having VND 64 trillion of chartered capital in 2010 (VCB, 2011).Those banks were 100% owned by the government in the time of 1990’s banking reform; however, Vietnam considered that stronger capital played an indispensable role in promoting its competitiveness ability with foreign penetration. This could be done through privatizing its commercial banks, with government still being the largest shareholder. For that reason, in April 2011, Vietnam was successful at partially privatizing two State-owned banks, which were Foreign Trade Bank of Vietnam (VCB), Industrial Commercial Bank of Vietnam (ICB) (Anne Ho & R. Ashle Baxter, 2011). Foreign Trade Bank was renamed Joint stock commercial Bank for Foreign Trade of Vietnam (VCB) and Industrial Commercial Bank of Vietnam is now known as Vietnam Joint Stock Commercial Bank for Industry and Trade (Vietinbank).
In 2005, SOCBs explained for 74.2% (see Figure 2 and Figure 3) of total deposit, which is 4 times than that of JSB and over 9 times that of JVBs, FBs & FBBs. Besides, SOCBs dominated credit market share in 2005, with the percentage of credit being 74.2%, whereas, that of other credit institutions was quite small.
JSBs which had mixture of shareholder, concentrated on retail banking, making loan to small and medium enterprises. Shareholder of JSBs comes from different kinds including public and private ones (Anne Ho & R. Ashle Baxter, 2011). Although JSBs are progressively able to obtaining market share from SOCBs, their capital volume remain small in comparison with that of SOCBs. Eximbank (EIB), ACB and STB are banks whose largest chartered capital lies between VND 9,000 billion and 11,000 billion. There are 4 banks, Military Bank (MB), Techcombank (TCB), Maritime Bank (MSB) and South East Asia Bank (SEAB), own an amount of chartered capital which is no less than VND 5 trillion. Notwithstanding, except for 7 banks mentioned above, the other JSBs’ chartered capital falls around from VND 2,000 to 3,000 billion. For that reason, the lack of liquidity at a number of banks caused them to push up interest rate over the rate cap (an interest rate race) in 2011 (VCB, 2011).
The proportion of deposit and credit belonged to JSBs continued to grow dramatically. The percentage of JSBs’ deposit increased sharply from 17.8% in 2005 to 43.4% in 2010 (see Figure 2 and Figure 3), which was slightly smaller than that of SOCBs. Additionally, the ratio of JSBs’ credit over total credit also just over doubled, rising from 16.4% in 2005 to 37.1% in 2010. Together with JVBs and FBs & FBBs, JSBs contributed to just over 50% of the credit market share. This is mainly because of the fact that JSBs was able to obtain market share from SOCBs (Anne Ho & R. Ashle Baxter, 2011).
JVBs, FBs & FBBs keep their considerable expansion into Vietnamese banking system (VCB, 2011). JVBs are established on the basis of joint venture agreement between domestic bank and one or more foreign banks. JVBs which have legal status, are independent of involving banks. Besides, FBs & FBBs at the beginning aimed at foreign investor. However; currently those banks have also diversified their customer including Vietnamese middle income class. Although their market share is still small, their superiority in retail banking, incomparable services and advanced products to other domestic banks is the reason for their succession in Vietnam (Anne Ho & R. Ashle Baxter, 2011).
The percentage of credit and deposit market share owned by these banks are 13.6% and 8.9% respectively in 2010 (see Figure 2 and Figure 3). Furthermore, their operating network is also limited in some big cities like Hanoi and Ho Chi Minh City.
Deposits market share
Credit market share
Source: VCB (2011).
From 2001 to 2010, Vietnamese banking system witnessed an average growth of credit and deposit at about 31% and 28% respectively; while the that of deposit was above 20% most of the years except for 2002 (19%).
The credit growth was above 20% in each year during this period. It reached highest value of around 54% in 2007. Besides, credit growth was much higher than GDP growth. On average, credit growth was 22% higher than that of GPD. This is due to the considerable increase of investment into Vietnam from 2005 to 2007 and government’s flexible monetary policies.
Figure .
Credit growth, Credit, % of GDP, GDP, % change,
Source: SBV – Domestic credit, %change, Domestic credit, % of GDP,
GSO – GDP, % change
Additionally, Figure 5 exhibits that compared with China, Malaysia, Indonesia and Thailand, credit growth in Vietnam was much higher than that of those countries. Domestic credit over GDP in Vietnam was 71% in 2005. This was only above that of Indonesia (46%), whereas the other three countries’ domestic credit over GDP was around 120%. However, Vietnam gradually shortened this gap, since its domestic credit over GDP indicator increased rapidly to approximately 136% in 2010. Therefore, the ratio of credit over GDP exhibited that the country was becoming a financially deepened country, with the ratio rising continuously from under 40% in 2001 to 136% in 2010 (see Figure 4).
Figure .
Domestic credit provided by bank sector, % of GDP,
Source: World Bank, 2012
The similar trend happened with deposit growth, as the growth rate of deposit was over 20% except for 2002, with the rate reached a peak of 46% in 2007. The average of deposit growth was about 29% during the period from 2001 to 2010.
The hot credit growth was the main contributor to asset bubble such as real estate. While that deposit growth kept significantly lower than credit growth over the period showed that there might be liquidity risk to the banking system (VCB, 2011).
Figure .
Deposit growth, Deposit, % of GDP, GDP, % change
Source: SBV – Deposit, % change,
GSO – GDP, % change
From 2006 to 2010, Vietnamese banking system experienced a twofold growth in total asset, with total asset raising sharply from VND 1.097 trillion in 2006 to VND 2.69 trillion in 2010 (VCB, 2011). Besides, total asset of four state-owned banks was far above that of the other commercial banks since largest the four banks accounted for about 60% of total asset of the system in 2009.
Figure .
Total asset of some largest banks in Vietnam.
Source: Banks’ annual reports, several years.
Anne Ho & R. Ashle Baxter (2011) highlighted that demand for bank’s retail services was on the rise during this time period. The reason for this was the increase in household income which was induced by the economy’s fast growth. Besides, people had tended to employ much more credit and debit cards than they did in the past. Therefore, the number of ATM per 100,000 adults and that of ATM per 1,000 km rose dramatically from 1.44 to 20.03 and from 2.72 to 42.89 respectively, with the number of ATM in 2010 being about seven times than that in 2004.
Figure .
Total liquidity composition
Source: World Bank, 2012.
Furthermore, when looking at Figure 5, it can be seen that the primary means of transaction was VND deposit, with the number raised from around 52% in 2003 to just under 70% in 2010. This exhibited that people non-cash payment activities like ATM/POS, credit card, debit card and online payment were becoming prevalent. This is also due to the fact that provision of banking services was developed notably (Ngo, 2010).
Figure .
Total liquidity composition
Source: State Bank of Vietnam (SBV), several years.
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