An analysis of the economic efficiency of the banking sector in vietnam

Table 3.1: Variables of potential correlates of profit efficiency function...32Table 3.2: Data sample of observations over period 2008-2013...43Table 4.1: Overview of variable in profit

HO CHI MINH CITY

AN ANALYSIS OF THE ECONOMIC EFFICIENCY OF

THE BANKING SECTOR IN VIETNAM

BY

NGUYEN THI PHUONG THAO

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, NOVEMBER 2015

This study measures economic efficiency of commercial banks in Viet Nam and itsdeterminants The Stochastic Frontier Model is applied for a profit function We include internalfactors (including price of fund, price of physical capital, price of labor, price of loan and price

of other earning assets) and macroeconomic factors (economic growth, inflation rate, and othermacroeconomic environment variables) in the profit function The empirical results show that theefficiency of Vietnamese commercial banks seems to be fluctuated steeply over the period 2008-

2013 In terms of the efficiency of different groups of commercial banks, the profit efficiency ofthe State-owned commercial banks and Private commercial banks follow different trend.Particularly, the state owned commercial bank’s group seems to have extremely higher profitefficiency score then private commercial bank

Generalized least squares

Stochastic Frontier Analysis

Stochastic Frontier Model

Development Envelopment AnalysisProfit efficiency

State-owned commercial banks

Private commercial banks

Table 3.1: Variables of potential correlates of profit efficiency function 32Table 3.2: Data sample of observations over period 2008-2013 43Table 4.1: Overview of variable in profit function 35Table 4.2: A summary statistics of profit before tax, inputs, outputs and prices used in theprofit efficiency estimation for the whole period 2008-2013 38Table 4.3: A summary statistics of inputs, outputs and prices used in the profit efficiencyestimations: (million VND units for X1, X2, X3, Y1, Y2) 39Table 4.4: A summary statistics of potential correlated of profit efficiency 40Table 4.5: Potential correlates of profit before tax (PBT) with time-invariant fixed-effectsmodel 41Table 4.6: Banking profit efficiency scores in the period 2008-2013 with time-invariant fixed-effects model 42Table 4.7: Potential correlates profit efficiency through time-invariant fixed-effect model 43Table 4.8: Potential correlates of profit before tax (PBT) with Cornwell et al (1990) timevarying fixed-effects model 44Table 4.9: Banking profit efficiency scores in the period 2008-2013 with time-varying Batteseand Coelli (1995) model 45Table 4.10: Potential correlates profit efficiency with Kumbhakar (1990) time-varying

parametric model (half-normal) 46Table 4.11: Average banking profit efficiency score of 27 commercial bank in Vietnam and itsranking from 2008 to 2013 48

Figure 1.1: Input-oriented efficiency 13Figure 1.2: Output-oriented efficiency 14Figure 4.1: Graph of scatter between Profit before tax (Pbt) and total number of staff (X1) from

2008 to 2013 36Figure 4.2: Graph of scatter between Profit before tax (Pbt) and total fixed - asset (X2) from

2008 to 2013 36Figure 4.3: Graph of scatter between Profit before tax (Pbt) and total deposit from customer(X3) from 2008 to 2013 36Figure 4.4: Graph of scatter between Profit before tax (Pbt) and total loan from customers (Y1)from 2008 to 2013 36Figure 4.5: Graph of scatter between Profit before tax (Pbt) and total asset (Y2) from 2008 to2013 36Figure 4.6: Graph of scatter between Profit before tax (Pbt) and Price of labor (W1) from 2008 to

Appendix 1: List of Vietnamese commercial banks in Vietnam Data was collected from

www.taichinhvietnam.com and sorted based on their charter capital 57Appendix 2: The result of estimation about the determinants of banking profit before tax and itspotential correlate through different type of model 59Appendix 3: Descriptive statistics of profit efficiency estimation through different type of model67

ABSTRACT ii

LIST OF ABBREVIATION iii

LIST OF TABLES iv

LIST OF FIGURES v

LIST OF APPENDIXES: vi

CHAPTER I: INTRODUCTION 0

1 Introduction: 0

2 Research objective 1

CHAPTER II: LITERATURE REVIEW 2

1 Theory of the efficiency 2

2 Reviews of empirical study on bank efficiency 5

2.1 Overview on bank efficiency 5

2.2 Determinants of banking efficiency 7

2.3 Bank efficiency measurement 9

CHAPTER III: DATA AND METHODOLOGY 21

1 Overview of the banking industry in Vietnam 21

2 Analytical framework 23

3 Research method 23

4 Theoretical Model 23

5 Estimation Methodology 24

6 Model specification 25

7 Functional form 26

8 Profit Efficiency 26

9 Variables description 28

10 Data sources: 33

CHAPTER IV: RESULTS AND DISCUSSION 35

1 Descriptive statistics 35

2 Regression results 38

2.1 Time-invariant fixed-effects model 38

CHAPTER V: CONCLUSION 47 REFERENCES 49 APPENDIX 54

Prior studies in the body of literature indicate that there are a wide range of factorsaffecting the bank efficiency which can be classified into two main groups namely externalfactors (e.g macroeconomic factors and industrial factors) and internal factors of commercialbanks (e.g size, losses, liquidity, and other factors) Particularly, Hasan and Marton (2003)examines the relationship between the development and efficiency of banking sector whileHou, Wang, and Zhang (2014) measure the linkages among market structure, risk taking andefficiency of commercial banks Due to their importance on the financial sector and theirinfluence on the national economy, this sector requires a high regulation by the government.However, in most developing countries like Vietnam, there is lacks of perfect regulation tosupport this sector to stabilize and develop Like other countries, the banking system inVietnam provides many products from retail banking such as saving accounts, deposits,credit/debit card, mortgage, loans to commercial banking such as business loan, capitalequity, risk management, and credit services.

As commercial banks have evolved in Vietnam before the investment banks, thisservice is one of the most sensitive business which suffers direct and indirect impacts onseveral intrinsic obstacles of the economy as well as the external effects, thus stabilizing thecurrency and the banking system play an important factor, primarily in the financial systemstabilization Therefore, understanding the efficiency of the banking system as well thosefactors affecting the banking operations attracts a lot of attention from many economists andscholars all over the world In measuring efficiency, people usually choose between technicalefficiency and economic efficiency We choose economic efficiency as this is morecomprehensive In this thesis, the profit function is used to measure economic efficiency

2 Research objective

While the capital funds raised for the national economy from those commercial banksare enormous, limited research was carried out about how these banks operate and whetherthey operate efficiently With a proper measurement about banking efficiency together with awealth of predictive determinants will help the public to indicate how well a certaincommercial bank operate in the competitive market like Vietnam Secondly, by improvingthe performance of individual banks, the whole national banking system will function muchmore efficiently and effectively However, at the moment, most of studies focused onqualifying the efficiency of the commercial banks (Nguyen, Roca, & Sharma, 2004) ordetermining certain large-scale economic factors affecting commercial banks (Athanasoglou,Brissimis, & Delis, 2008) without quantifying these factors to rank these commercial banks

in the global scale Hence, the aim of this thesis is to verify the connection between thecurrent financial growth at the national scale and the efficiency performance of commercialbanks in Vietnam

In details, this thesis is to understand the connection between the economic efficiencyand the economic growth of the banking system based on two following smaller objectives:

 Which factors affect the economic efficiency of commercial banks?

This is the research of Vietnamese banks efficiency that will convey the managers toidentify the banks’ weakness as well as the causes of those weaknesses so that they can come

up with the right strategies, which will bring the best result given the same level of resourceinputted In addition, this will be a useful research to analyze inefficiencies and minimize it inorder to improve the performance of Vietnamese banking industry Moreover, this researchalso demonstrates the reality of Vietnamese banking industry, which will be a useful guidefor foreign investors to start financing their equity in this dynamic industry

CHAPTER II: LITERATURE REVIEW

The concept “efficiency” in general and “bank efficiency” in particular are commonterms in the body of literature of economic discipline These could be used to refer to thecompetitive capability of entities in the economy The chapter covers a broad review onfoundational theories on efficiency, bank efficiency and characteristics of the terms Themeasurement approaches as well as factors affecting bank efficiency

1 Theory of the efficiency

In the production economics, the definitions of efficiency and productivity are twoconcepts proxy for two different things Firstly, the definition of “productivity” and

“efficiency” in terms of firm production has to be differentiated Clearly speaking,

“productivity” considers the entire elements that decide the level of output achieved with theamount of input given Efficiency, however, have a different meaning compared toproductivity

Efficiency includes three types (technical efficiency, allocative efficiency andeconomic efficiency) The function of economic efficiency consists of profit efficiency andcost efficiency In this study, profit efficiency function is used to measure economicefficiency

It relates to the production frontier This frontier presents the level of output that can

be reached a peak with the same level of input amount The firm, which produces on thisfrontier, will be considered efficiency Also, going beyond this frontier is unrealistic becausethere is a limitation of its performing ability Similarly, it is inefficient producing below thisfrontier The further distance, the more inefficiency the firm is

Even though productivity and efficiency are two separate concepts, they are closelyrelated Therefore, if the firm expects to improve its productivities, they will have to producemore efficiently Other elements that makes the level of productivity are changed in thequantities and proportion of inputs (changing its scale efficiency), an innovation oftechnology (change in technology level), or according (Coelli et al., 2005) we makecombinations between all above factors

The definition of efficiency is about the transforming performance between thenumbers of inputs into outputs (Forsund & Hjalmarrson, 1979) Hence, the efficiency is

usually considered as an essential competitive force of entities in an economy such as abranch, industry, or an entire system However, as the society gets more advanced, thedefinition of efficiency changes to be more specific For example, Saha & Ravisankar (2000)focused on the basic stage to measure the value of output at a given amount of input from theengineering perspective In order to attain an efficient point, Koopmans (1951) proposed thenecessary of balancing the equivalent unit of different outputs In other word, the mostefficiency is the point at which one output is used to maximize a given amount of the input.Given that, Debreu (1951) and Sephard (1953) developed a quantitative measurement ofefficiency for the outputs and inputs respectively Debreu (1951) measured the distancebetween the produced output and the predicted output that could have been produced from agiven amount of inputs while Sephard (1953) measured the difference the actual input and theminimum possible amount of inputs.

Later, Farrel (1957) brought the measurement of efficiency into the next level byestablishing the distance functions between efficient point and practical producing point – thetheory of Production Possibility Frontier (PPF) Based on the PPF theory, Kablan (2010)measured the efficiency by measuring the optimal set of points of inputs in order to produceone unit of output which is defined as the Production Possibility Frontier Line (PPF 8 Line)and compared with the actual production unit The firm is called to be efficient only when itsoutcome is on the PPF line and vice versus Moreover, the theory of PPF can also beapproached from two sides: Input-Oriented approach (IO) and Output-Oriented approach(OO) (Farrell, 1957) While the firm uses IO to measure the minimum amount of inputs toproduce a given set of outputs, OO is used to predict the maximum level of outputs from agiven level of inputs

Although there is no consensus on the proper results of IO and OO in bankingefficiency measurement, the IO approach is mostly preferred than the OO one because banksare able to focus on controlling the inputs (cost) rather than relying on the outputs (outcome)(Dipasha et al., 2012) although there are some exceptions by employing both approaches(Beccalli, Casu, & Girardone, 2006)

Among the above studies, Farrell (1957) was one of the first pioneer who proposedthe efficiency as a technical term that can be measured by two main elements Figure 1.1demonstrates a firm with two inputs X1 and X2, YY’ is an isoquant which shows everyminimum set of inputs that could be used to produce a given output If a firm operates on this

isoquant (the frontier), it will be technically efficient in an input-oriented way for the reasonthat the inputs amount of this firm is minimized The iso-cost line CC’ (which can beconstructed when the input-price ratio is known) determines the optimal proportion of inputs

in order to archive lowest cost

Figure 1.1: Input-oriented efficiency

The first one is technical efficiency (TE) which focus on the ability of bank to achievethe maximal output with a given fixed set of inputs, Technical efficiency (TE) can becalculated by the percentage rate of OR/OP, the second one is allocative efficiency (AE)equals the percentage rate of OS/OR The multiplication of AE and TE expresses the overallefficiency of the firm, called economic efficiency (EE) (i.e.EE = AE × TE) Figure 1.2illustrates the case where the bank uses one input and produces one output The f(X) curvedetermines the maximum output can be obtained by using each level of input X (the frontier).The firm will be technical efficient operating on this frontier In this situation, TE equals BD/BE

Figure 1.2: Output-oriented efficiency

Regarding to the concept of efficiency measurement in banking sector, a bank can besaid to be efficient only if it has an ability to produce an expected result with a minimumeffort or resources.

In the body of literature, bank efficiency has been discussed in a variety of studiesfrom Berger & DeYoung (1997), Berger and Humphrey (1997), Timothy J Coelli (1998),and Bonin, Hasan, & Wachte (2005) In order to evaluate the efficiency of commercial banks,several measurement criteria need to be taken into account such as scale efficiency, allocativeefficiency, productive efficiency, technical efficiency However, as knowledge of bankefficiency has evolved, many factors have to be considered as suggested by Fu et al (2014),Berger, Hasan, and Zhou (2009), and Tecles and Tabak (2010) These studies suggested thebank efficiency measurement can be determined by both internal and external factors

In summary, the theoretical foundation on efficiency and bank efficiency have beenwell-established and attracted a lot of academic interests who not only studied about theefficiency measurement but also evaluate different determinant of bank efficiency in theaspect of economic world

Measurements and analyses of TE were conducted by a huge number of studies withtwo main approaches – Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis(SFA) The next section briefly discusses these two methods

2 Reviews of empirical study on bank efficiency

2.1 Overview on bank efficiency

Bank efficiency has been applied since 19th century when Sherman and Gold (1985)applied specific DEA (CCR Model) to analyze the efficiencies of 14 branches of a USsavings bank Their study was able to indicate six out of 14 branches were operatinginefficiently at that time Milin Sathye (2002) also applied a similar methodology to measurethe efficiency of 94 India banks during the period of 1997-1998 He developed twoindependent models to rank the efficiency based on the variation of inputs and outputs In thefirst model, inputs are expenses, both interest and non-interest while outputs are nominated asnet income accordingly Hence, compared to the private sector and foreign commercialbanks, the public sector banks have showed a higher score in the average efficiency in India

In the second model, he designated inputs as deposits and staff numbers, while outputs as net

loans and non-interest income As a result, the second model showed the opposite result,meaning the private sector commercial banks scored a higher mean efficiency than the publicsector ones.

Among the prior studies, the banking efficiency received an greatest academic interestamong many industries (Liadaki & Gaganis, 2010; Saha & Ravisankar, 2000; Yin, Yang, &Mehran, 2013) As given by Saha and Ravisankar (2000), the bank efficiency is of the mostreliable indicator to drive the competitveness in the banking industry The authors argued thatthe commercial banks require an efficient operation system to gain more chances to sustaintheir business Tecles and Tabak (2010) indicated that the efficiency of the banking sectorplays a vital role in both the finance development sector as well as the economic growth Acomprehensive review on the efficiency of financial institutions can be seen in the study byBerger and Humphrey (1997)

Taking into account the banking industry of a specific country, a study by Tecles andTabak (2010) measured the level of efficiency of156 commercial banks in Brazil from 2007

to 2010 and determine different bank’s characteristics that are associated with, e.g size,ownership, market share, non – performing loans and equity value As a result, it showed thatthe larger size the banks were, the more efficient the banks got Bank efficiency measurementhas also been applied for the Vietnamese banking system as Vietnam is in the transitioneconomy with the high economic growth in recent years Concise evidences can be foundfrom reports by Nguyen (2011) and Lieu and Vo (2012) Chao and Nguyen (2006), forexample, researched the methodology to evaluate the efficiency of commercial banks inVietnam Labor and various kinds of expense were used to measure the total loans as outputsand total deposits as inputs for efficiency measurement similarly to model by Milin Sathye(2002) The results were consistent to other research in the region, meaning, the larger assetsize of banks have exhibited 11 times higher efficiency than the small ones Ngo (2010) alsoconducted a research to estimate the performance of 22 Vietnamese commercial banks in

2008 The study also used labor, capital and funds as well as income for testing variables Theconclusion is that these banks achieved the average efficiency scores close to the optimalscore, as an evidence for the bank improvement in Vietnam

Another conduct by Nguyen (2011) focused on measuring the efficiency 20commercial banks in Vietnam in the 3-year period (2007-2010) with more efficiencydeterminants to be evaluated In details, labor, fixed assets and deposits as inputs, and interest

and non-interest income as outputs are all taken into account The finding indicated that thestate-owned banks had lower efficiency scores as compared to their join-stock commercialbanks competitors Later, a study of Lieu and Vo (2012) showed that since 2006, thecompetition between banks was extremely high due to the financial crisis, especially with thewide fluctuation in in the input value of deposits and changes in the operating efficiencies ofjoint stock banks However, the research scope of the study was still limited as a traditionalmethodology was applied in evaluating key financial ratios of commercial banks.

In the recent years, the stock market of Vietnam has been considered as one of thefastest growing markets among emerging countries, hence more commercial banks has listedtheir stocks on on the stock exchange However, very few studies have focused on theefficiency of the banking sector in Vietnam due to data limitation Hence, it is important toset a standard measurement of banking efficiency of Vietnamese commercial banks whichwill provides valuable information for managers, customers and investors

2.2 Determinants of banking efficiency

2.2.1 Macroeconomic factors

There have been many researches regarding the relationship between macroeconomicindicators and banking efficiency As a result, several economic factors are proven to havethe impacts on bank efficiency such as economic growth and stock performance

For example, Avadi and Arbak (2013) researched the relationship between bank’sefficiency and economic growth especially in the southern Mediterranean region Afteranalyzing insight information and data, they found out that the profits from banking industryhad a huge impact on the economic growth Also, Ferreira (2012) also conduct a study of thesame topic but in the European The outcome of this study was the evidence of how thebank’s cost efficiency affect the economic growth, especially GDP

Regarding the stock market performance, Beccalli et al., (2006) in his researchsuggested that bank’s efficiency also has impacts on the stock market performance The studywas focused on measuring various European bank’s efficiency and used those results to provethat changes in cost efficiencies of target banks may cause changes in the prices of bankshares, that means bank’s efficiency and the stock market are having a relationship, throughwhich it could create changes to the economy Similar results could be found in the study by

Liadaki and Gaganis (2010) who indicate the changes in both cost and profit efficiency haveaffected stock prices of commercial banks operating in EU markets over the period from

2002 to 2006

2.2.2 Banking industry factors

It could be seen that there are a wide range of banking industry factors taken intoconsideration with regard to the bank performance in the body of literature Some commonfactors can be indicated such as industry development, industry competition, industryconcentration or industry growth, and other factors These factors are discussed in severalstudies by Athanasoglou, Brissimis, and Delis (2008), Owoputi (2013), and Pulungan andYustika ((2014)

A study by Athanasoglou, Brissimis, and Delis (2008) examines the impacts ofindustry specific factors and bank profitability of Greek commercial banks over the period

1985 – 2001 Particularly, the authors use two determinants including ownership andconcentration to measure the industry specific indicators; however, they find industrystructure does not significantly affect bank profitability

Naceur and Omran (2011) investigate the effect of two indicators of the bankingsystem, namely regulation and competition, on bank margin and cost efficiency across 11countries over the 1988 – 2005 period Additionally, taken into account the industrydevelopment indicator, the authors use the size of the credit to private sector as a percentage

of the GDP to measure the impacts of bank financing in the entire economy The proxies ofthe banking sector development seem to have no impacts on the bank market but havenegative signs with the cost efficiency They conclude that the well – developed bankingsector may lower its operating costs

2.2.3 Bank Specified factors

There are several factors that can be utilized to measure specific characteristics of thebank, such as financial leverage, and the bank’s size, equity and risk, etc However, anevidence mentioned in an observational studies shows the keen interest between the bankspecific indicators and the performance of the bank (how liquid and profitable it is)

A study conducted by Fu et al., investigates the relationships between the return onequity and both cost and profit efficiency Several bank specific factors mentioned above

were used in the study The findings indicate that there is a significant influence of the banksize, market risk and credit risk (bank specific factors) Other bank specific factors, equity tototal assets and return on assets ratios, were also used by Lensink, Meesters, and Naaborg in astudy to examine the impact of both factors on cost efficiency The researchers examined theefficiency of the bank and the foreign ownership of a large commercial bank in more than

100 countries (from 1998 to 2003)

In conclusion, many prior studies were taken into consideration when determining therelationship between macroeconomic factors, and bank specific factors and performance(ratios) However, it is worth to mention that the researchers have also taken intoconsideration that bank efficiency has emerged recently, and that the findings in theobservational studies on bank efficiency and factors are not consistently supported This leads

to the need of further investigating the relationships between the bank efficiency andindicators of commercial banks including industry specific and bank specific factors

2.3 Bank efficiency measurement

In this literature, different methods will be discussed that can be used to measure bankefficiency These methods are based on different aspects of bank efficiency As given by Yin,Yang, and Mehran (2013), two specific aspects are taken into account for bank efficiencymeasurement in terms of achieving the maximization of output or requiring the minimalinputs As such two approaches namely the production approach and the intermediationapproach can be applied to determine the bank inputs and outputs (Sufian, 2009; Yin et al.,2013) Particularly, the production approach considers loan, deposit and other financialservices as inputs and the intermediation approach considers deposits, labors, and capitals asits input In the production approach, output variables are bank deposit and loan transactionswhile in the intermediation approach, bank loans and investments are known as outputs However, as indicated by Yin, Yang, and Mehran (2013) and Thoraneenitiyan and Avkiran(2009), commercial banks focus on intermediation services rather than physical goods, hencethe intermediation approach will be more appropriately used to measure their bank efficiency

Regarding to the methodology to measure bank efficiency, many studies haveproposed several methods such as Berger and Humphrey (1997) and Beccalli et al (2006).The most traditional and simple method is to use key financial ratios to analyze bank’sperformance However, due to the increasing input/output determinants in bank efficiency

measurement, a large number of authors support two more advanced group classes, namelyparametric and nonparametric approaches Particularly, the nonparametric methods cover twomain approaches which are Data Envelopment Analysis (DEA) and Free Disposal Hull(FDH) The methods draw a linear connection to rank the best practicing DMUs bycomparing the operation of two different banks in one sample set These methods do notrequire any specific production function and are suitable for a small size of data set On theother hand, the parametric method includes Stochastic Frontier Analysis (SFA) or moreadvanced techniques such as Thick Frontier Approach (TFA) and Distribution Free Approach(DFA) The later method requires more variable and specifies a functional form for efficiencyfunctions such as production function, profit function or cost function For specificcommercial bank industry in particular to determine its profit efficiency, as commercial banks

do not produce physical goods but provide intermediation services, both the inputs andoutputs of banks must be determined by different groups of inputs and outputs Among theseabove methods, the DEA and SFA are commonly used by many researchers in recent studies(Kohers, Huang, & Kohers, 2000; Thoraneenitiyan & Avkiran, 2009), hence, the details ofthese two methods are given as follow

2.3.1 Data Envelopment Analysis model and Stochastic frontier model

a Data Envelopment Analysis model:

The Data Envelopment Analysis (DEA), categorized as a non–parametric method, is alinear programming model As given by Farrel (1957), the model was developed from thepiecewise -linear convex hull approach and further developed by Charnes, Cooper, andRhodes (1978) who employed a mathematical planning model with “constant returns toscale” (CCR model) to measure the technical efficiency frontier Then, Banker et al (1984)extended the model by providing variable returns to scale and output orientation In the DEAmethod, no functional form for the data is required It allows a production frontier reflected inthe weights for the inputs and outputs can be calculated

In the DEA method, how a particular decision making unit (DMU or bank in thisstudy) operated is examined relative to the other units in the sample Based on the DMUs orbanks, the production frontier set, is then set to rank from the most efficient banks to the mostinefficient ones using efficiency scores

Profit efficiency model

While cost efficiency focuses only on cost, profit efficiency considers both cost and

revenue aspects Accordingly, the profit efficiency model developed by Färe, Grosskopf, and

Lovell (1988) is employed to measure profit efficiency for bank j as follows

The assumptions to this model is a combination of the optimal output supply vector y∗j = (y∗j1 …y∗js) and the optimal input demand

vector x ∗ j = (x ∗ j1 …x ∗ jm) in a possible production curve set that the same condition of given input prices c and output prices r, the profit will be

maximized, and the bank j will be the best one when it uses least level of input to produce optimal output with a bank’s linear combination is

obtained, it leads this

hypothetica l bank to have an optimal profit P∗ j = ∑s rjsy∗js which, by

as follows

∑ y − ∑

b Stochastic frontier model

The first SFA model was developed by Aigner, Lovell and Schmidt (1977)and Broek, Førsund, Hjalmarsson, and Meeusen (1980) based on the production function

estimation namely the Cobb-Douglas production function Later, several other functions are

listed in the SFA model including Quadratic, Translog, Generalized Leontief, Normalized

Quadratic, and Constant Elasticity of Substitution (CES) forms (Griffin, Montgomery, and

Rister, 1987) Among those function, Cobb-Douglas and Translog function are most

commonly used (Jiang et al., 2009; Liadaki & Gaganis, 2010; Yin et al., 2013) Frequently,

the Cobb-Douglas model is used for its parsimony while the Translog model is used for its

flexibility More particularly, the Cobb-Douglas functional form assumes there is no change

in the value of production elasticity and other factor substitution; while the Translogfunctional form does not As such, The production function can be tested in the Translogform to provide more realistic and less restrictive results

However the Translog model poses some weakness when the number of parametersincreases, it appears cross and squared terms On the other hand, if the number ofobservations is not limited, this increase in parameters leads to the decrease in freedom.Given that, it is necessary to combine both models provide consistent results of bankefficiency Examples can be seen in the study by Kohers et al.(2000), and Thoraneenitiyanand Avkiran (2009)

Profit efficiency model:

On the other hand, using the SFA methodology, according to Berger and Mester(1997), the measurement of the profit efficiency model can be quantified similarly to the costefficiency model with the exception that the inefficiency values are with a negative sign inthe regression as follow

Ln

c Trade-offs between DEA and SFA

Based on many difference from approaching method, there are many advantages anddisadvantages when we make a choice using DEA or SFA in our research It means thatresearchers have to face the trade-offs when researchers use DEA or SFA to estimate thestudy According to Ray & Mukherjee, 1995 DEA is a non-parametric method, adeterministic approach, it does not mention about the specification of the production functionmeanwhile SFA is a parametric method, a stochastic technique using econometric tools andmust have a model specification of production function From that key difference, DEA isconsidered to be non-statistical, which assumes that the data have no noise Data noise cancome from measurement errors or random factors which can be controlled by banks This

restrict seems to be stubborn in realistic situation SFA is statistical, so it allows and takesinto account statistical noise In other words, it is more flexible with real world data, in whichrandom factors and error in collecting is unavoidable.

Wagstaff, 1989 found that using SFA must satisfy assumptions about the functionalforms, specifications of the model, distribution of parameters and error terms In DEAapproach, every bank counts as inefficiency operating when every factor that leads the bankaway from its frontier While in SFA, the residual will be decomposed into two components.One part, which is not under the control of the firm itself, is accounted to be the noise and hasthe zero mean The other part, which is known as the inefficiency, is the weakness of the bankwhich makes it produce below the frontier So, generally, the efficiency measured from SFAwill be higher relatively (Ferrier & Lovell, 1990) DEA has the advantages with the ability to

be applied in various complicated condition of production Without the requirement of adefinite production function, it helps simplifying the linkage from inputs to outputs of aproduction process Without statistical properties, no test can be used to test DEA’s goodness

of fit or specification In spite of having troubles with model specification, SFA still haseconometric tools to test whether the model is suitable or not The most beneficial advantage

of SFA is the capability of dealing with statistical noise

Generally, for industries that the production processes are controlled strictly, DEAseems to be the better choice of measuring efficiency This is because the random fluctuation

in these industries is minimized and the production process is very stable (from a givenamount of inputs, the number and quality of outputs is likely to be determined precisely) Forthose natures of the industries the thesis analyzed, SFA is the better model to be applied Thenext part discusses in detail SFA method with panel data models

Overall, both the DEA and SFA approach have both advantages as well asdisadvantanges While the DEA methodology is sufficient to estimate the efficiency of agiven banking system by avoiding misspecification issue Yin et al (2013) illustrated that theDEA approach might lead to some random errors in the ill-defined economic environments

In fact, Jiang et al (2009) had suggested that SFA method is reliable in a context of transitioneconomies However, given that the SFA approach requires an intensive parametric factors,profit efficiency calculation might become exhaustive especially with the multi-hierarchybanking system in developing countries like Vietnam

2.3.2 Stochastic frontier model with panel data

In this section, an overview of stochastic frontier model (SFM) with panel data(repeated observations on each firm) will be present to explain the motivation for the models

in banking efficiency estimation By using panel data rather than cross-sectional data setting,estimation techniques can be carried out without strong distribution assumptions on eachvariable and repeated observations with panel data can be considered as a substitute ofindependence between technical efficiency error and other parameters This allows theefficiency measurement to be more robust and consistent (Kumbhabar & Lovell, 2000)

A typical stochastic frontier model firstly developed by Aigner, Lovell and Schmidt (1977) and Meeusen and van den Broeck (1977) can be written as below:

(1) = + x + − , i = 1, …, N

Where i is a production unit (firm), is the log of output, x is the log of input, v is normal noise, and u is a measure of technical inefficiency Three variables x, v, and u are assumed to be independent, and v and u are assumed in different types of distributions The

formula (1) can be computed as follows

(2) y i = f(x i ;β) exp(vβ) exp(v) exp(v i ) exp(−u i ), i = 1, 2, , N.

Later, several authors have generalized this model for the panel data format setting (N firms

in period time T) by applying time parameters in the assumptions on y, x, , and u Here two

main SFA models are considered in which technical efficiency is constant through time(time-invariant model) or allowed to vary for each firm (time-varying model) Especially,

technical inefficiency error (u) explains differences across different firm performances as

well as the same firm performance in different time periods While technical efficiencymeasurement allows to determine which firms operate efficiency, technical inefficiency errorcomponent permits firms to be able to identify causes of inefficiency and learn how tobecome efficient in practice As a result, several models have been developed in order tomake the most appropriate estimation for the technical inefficiency component

a Time-invariant Technical Efficiency

First, it begins by assuming technical inefficiency u i to be constant over time and

restrictedly non-negative (u i ≥ 0) Based on model (1), the time-invariant SFM model is as

i Fixed-effect model by Schmidt and Sickles (1984)

The fixed-effected model is the simplest one which has been developed by Schmidt

and Sickles (1984) In the FE model, the technical inefficiency u i is constant over time butvarying among different firms, hence there is no distribution assumption about technical

inefficiency error u i and all the variables are uncorrelated to each other Since there is no

distribution assumptions on u i , technical efficiency (TE i) of individual firm can be estimated

by introducing least squares with dummy variables (LSDV) followed by equation (4):

0 is estimated from 0 = maxi{ 0i }

u i is estimated from u i = 0 - 0i

TE i is estimated from TE i = exp {-u i }

In the FE model, at least one firm is assumed to be 100% technically efficient andother firms’ efficiencies are relatively compared to the top firm The LSDV estimationmethod of technical inefficiency component can be consistent as both the number of firmsand time period assessment are large enough (e.g T  ∞ and N  ∞).

ii Random-effect model by Schmidt and Sickles (1984)

While in the FE model, the u i is fixed, in the RE model, the u i value is randomlydistributed but with constant mean and variance This allows the technical inefficiency ui tovary across different firms but still constant for each firm Again, all the variables (e.g

between inefficiency error u i and the input x it , or between inefficiency u i and noise errors v it)are uncorrelated to each other Now the model (4) can be written as follows

iii Maximum likelihood model by Pitt and Lee (1981)

For time-invariant technical efficiency, a conventional maximum likelihood method

can be adapted to estimate the effect β) exp(v and the technical inefficiency error u i by makingindependent distribution assumptions for botherror components, the idiosyncratic noise vit

and technical inefficiency term ui There are several distributional assumption models that can

be used in the maximum likelihood method including half normal, exponential, normal-gamma, and normal-truncated normal models Pitt and Lee (1981) wasthe first one to formulate and estimate time-invariant panel data model using the normal-halfnormal distributional assumption

normal-b Time-varying technical efficiency

In a competitive operating environment like financial services, the assumption of invariant efficiency sometimes is unrealistic, e.g it is unlikely that technical inefficiency termremains unchanged especially through a long time panel data setting (Cornwell et al

time-1990) Hence with a long panel, it is more desirable to relax this assumption, leading to the development of time-varying technical efficiency.

Similar to time-invariant technical efficiency, we can use a maximum likelihood or fixed effects or random effects approaches to estimate this model

We can illust the stochastic frontier model using panel data with time-varying

technical efficiency as follows

(6) ln = + ln + (based on model 3)

With is time varying firm effects which follows a function of time

The model 6 is also written as a Cobb-Douglas function based on model 4

(7) ln t = 0t + ∑ n n ln x n t + t − t = it + ∑ n n ln x n t + t where it = 0t - u it

i Fixed effects and random-effects model by Cornwell et al (1990)

In term of the fixed-effect model, the technical efficiency of each bank in the givenperiod t is estimated as:

TE it = exp{-u it } wheret=β) exp(v ^ 0 − β) exp(v ^

Hence, in each period t, at least one firm’ technical efficiency is estimated to be100%, however, the most efficient bank level can change over the time (Zhu, W and Zhao,D., 2007)

As the same reason to the random-effect model for time-invariant technicalefficiency, uit can vary over the time but also can be random distributed across differentfirms Hence, Cornwell et al (1990) also use GLS random-effect estimator like Schmidt andSickles (1984) to evaluate the technical efficiency in condition of time-varying The estimatormodel can be written as:

TE it = exp{-u it } where = it − ^

Where it =( ^ i ) by comparing the specific effect of each firm to the most efficient firm in that year.

However, since the degree of freedom of these parameters is heavily affected in small

N (firms) and T (time), GLS estimation remains inconsistent Hence, an alternativeformulation is proposed by Lee and Schmidt (1993) as follows

=B( ) , where B(t) is set as time dummy variables B(t), is fixed through time butvaried across firms and follows a half normal distribution

Similarly, if temporal error components vit and uit are independent and available fordistributional assumption, we can use the likelihood function to evaluate time-varyingtechnical efficiency (Sangho Kim and al., 2002) Three estimators have been developed byKumbhakar (1990), Lee and Schmidt (1993), and Battese and Coelli (1992)

Kumbhakar (1990) estimated the temporal technical inefficiency error as follows

^ = ^( )× ^ where u i is followed half-normal distribution

The suggested time function is:

Hence, Green (2005) suggested that uit should be uncorrelated to the rest of modelparameters, that means, the most efficient firm identity should be consistent over time whilethe technical inefficiency vary freely through time (Green, 2008) This kind of so-calledheterogeneity has been discussed by Farsi, Filippini, and Kuenzle (2003) as factors beyondthe control of firms but belongs to business environment As a result, Greene (2005) suggests

“true” fixed and random effects model as follows

“True” fixed model is described as:

With is a random constant term that varies across firms

*Note: Green (2005) used the Cobb-Douglas form

In conclusion, with the power of better analysis using panel data, stochastic frontiermodels with panel data have been used widely to estimate the firm efficiency in both time-invariant and time-varying manners Within these formulations, both fixed-effect, random-effect, and maximum likelihood models can be adapted to estimate the efficiencyperformance in both measure and empirical works Firstly, with the fixed effect estimator, anintroduction of dummy variables as LSDV allows us to estimate the models unbiasedly.However, by using dummy variables, all explanatory variables might be lost throughcalculations, leaving the produced technical efficiency performance inconsistently Secondly,with the random effect estimator, the technical inefficiency error can be randomly distributedunder a GLS model, however, leading to the fact that the explanatory variables can bebiasedly correlated with the technical inefficiency error Hence, whenever the explanatoryvariables are tested to unbiased with the technical inefficiency suggested by Hausman andTaylor (1981), random-effect models can be demonstrated to be powerful and consistentestimation methodology Lastly, Green (2005) has also challenged our assumption abouttime-varying function with the heterogeneity of firms by relaxing the technical inefficiencyerrors across time and firms

Lastly, xt-frontier calculation from the program STATA is shown to be powerful inestimating the parameters of stochastic cost or production frontier models The programallows to calculate both time-invariant and time-varying technical efficiency based onmaximum likelihood estimator by Battese and Coelli (1992) with truncated-normal randomvariables.

CHAPTER III: DATA AND METHODOLOGY

This chapter gives descriptions about current status of banking industry of Vietnam aswell as the research model, variables and data used in the research Particularly, details onspecific models and measurement of variables are given based on reviews of prior studies.The data collection is introduced and discussed

1 Overview of the banking industry in Vietnam

The first independent bank in Vietnam was opened after the August Revolution in

1945, namely Vietnam National Bank (later named the State Bank of Vietnam) and served asthe first commercial bank in Ho Chi Minh City which handles personal savings and businessloans Since 1992, the Vietnamese banking system pioneers in attracting foreign investmentand integrating into the global economy From the beginning, the banking sector was based

on the single/double-level banking system with four govern bent-controlled commercialbanks with a limited scale in management or services Nowadays, Vietnam banking system isimproving rapidly in different aspects such as credit unions, credit cards, insurance, stockbrokerage, or investment funds Current Vietnam banking system consists of a variety ofbanking ownerships such as national or state-owned, joint-stock, joint-venture, co-operative,private-limited, or foreign banks These banks serves different economic functions such aslocal banking for local financial business, foreigner banking with 100% foreign investment,retail banking for individual and small businesses, business banking for mid-level financialservices, cooperate banking for large business entities, private banking for fund equity, andinvestment banking for those large-scale financial markets

With effective and customer-oriented reorganization and development strategies, theVietnamese banking system especially in the joint-stock commercial banking has improvedsignificantly in both quality and scale diversity as compared to those from the 80s At first,the commercial bank licenses were divided into two categories based on their minimalcapital The urban joint-stock commercial banks require at a minimum of 50billion Vietnamdong while the suburban ones require 2billion Vietnam dong The momentary policy haschanged three times with the highlights: (i) to increase the suburban commercial banks’minimal capital from 2 to 5 billion Vietnam dong, (ii) to set a threshold of 100 billionVietnam dong for all urban banks, (iii) to set a new threshold of all joint-stock commercialbanks of 3000 billion Vietnam dong until the end of 2010

As a result, until 31/12/2009, there are in total 37 joint-stock commercial banks whichcontribute 42% of the whole Vietnam banking system The total capital raised approximately100.000 billion dong, which is twice as much as all the state-owned commercial banks andconstituted 60% of total national capital The whole commercial bank network has developeddramatically all over the country – from Lang Son to South East provinces, even the mostremote areas The whole collected capital from these banks contributes approximately 42% ofthe banking capital system.

In summary, over the last two decades following the restructuring plan, the Vietnam’sbanking sector has experienced significant developments On the one hand, the supervisionfunction of the State Bank of Vietnam is clearly separating into the commercial function ofcommercial banks The structure of commercial banks, on the other hand, is graduallystrengthening with a participation of new strong financial institutions in accordance with theliberalization and international globalization with the global economy

The current Vietnam commercial banking is still considered to be immature ascompared with other countries in SEA and internationally, which lead to the instability inboth economic and financial industry of the country The banking sector has undergone re-organization since 1990 in order to limit the government dominancy and stimulated thebanking development permanently in terms of ownerships and increase in the number of banknames In details, the number of for-profit commercial joint stock banks has increaseddramatically However due to low public confidence, regulatory and managerial limitations,the commercial banking system still struggles to find their positions in the Asian financialmarkets as well as the global sector From 1991 to 1993, the number of commercial banksjumped from 4 to 41 and reached 51 on 1997 After the global financial crisis in 1997, severalcommercial banks filed bankruptcy or license withdrawal due to inefficient operation, as aresult a decrease in the number of operating banks In the period of 2002-2007, most ofcommercial banks speed up the reorganization process to stabilize and developed themanagement structure in terms of constructive financial management, mergers andacquisitions, take-over of those small inefficient commercial banks in the end of 1990s.Moreover, the number of branches and representative offices of international banks werelikely increased in this period according to the US-Vietnam Bilaterial Trade Agreement andASEAN Framework Agreement on Services As a result, the proportion of local commercialbanks decreased visibly from the top of 73% in 1993 down to 40% in 2007

Currently, there are about 40 commercial banks operating in Vietnam which are fromstate-owned and urban joint-stock commercial (it is described more details in Table 1)excluding government banks, equity funds, wholly foreign-owned banks, and foreign banksrepresentatives.

2 Analytical framework

Furthermore, in order to have a deeper analysis, one of the most important aspect ofVietnamese banking industry, which is profitability, was estimated by the stochastic frontieranalysis (SFA), first proposed by Aigner, Lovell, and Schmidt (1977) and Meeusen and vander Broeck (1977) We particularly apply the SFA model developed by Green (2005) toexamine potential correlates of bank efficiency over the period 2008-2013 to investigatingthe determinants of bank efficiency

3 Research method

There are different indicators implied on the purpose of researching banking industrydevelopment However, since the “banking industry development” and “bank concentrationindex” have the most recent data and it is always available, it is the most important among allthe indicators Regarding the research methods, the Stochastic Frontier Approach is used tomeasure profit efficiency This method is also applied to examine the relationship betweenthe bank efficiency and banking industry development in Vietnam

For reasons discussed earlier, we employ the stochastic frontier analysis (SFA), firstproposed by Aigner, Lovell, and Schmidt (1977) and Meeusen and van der Broeck (1977) toestimate the most important aspects of bank efficiency in Vietnam: profitability Weparticularly apply the SFA model developed by Green (2005) to examine potential correlates

of bank efficiency over the period 2008-2013

In particular, a profit function is determined as the given input prices (W) and outputprices (P), the profit is got the maximization:

π= π (W, P) (1)

This function must have characteristics as follow: convex in the output prices (P) andconcave in the input prices (W) Moreover, the function of profit must be in conditions ofnon-increasing in the prices of input (W) and non-decreasing in the prices of output (P)

5 Estimation Methodology

In this study, the Stochastic Frontier Approach (SFA) is used to analyze the bankingefficiency, and then it provides the efficiency scores of each bank and ranks them Thisapproach considers about the inefficiency of the banking system based on the results ofbanking production, it means that if the bank operates with the lower profit and higher costthan another bank in the system with the same environment and conditions, so we canconclude this bank is inefficient In particular, because of the existence of possible firminefficiency and the random noise, the observations of total banking profit will be deviatedfrom maximum profit efficiency frontier

As a result, when we use the Stochastic Frontier Approach (SFA) to estimatemethodology, we must require a specific functional form which is constructed by suitablevariables of input and output under random noises in data exists over time Moreover,Stochastic Frontier Approach (SFA) also includes many differences between each bank andfor this reason, it is clear to allow for both random errors and environmental factors

For Stochastic Frontier Approach (SFA) with panel data, accordingly to Berger andMester (1997) we can write the profit function in logs as follow

ln(πit) = π ( ) + - (2)

Where the profit function is defined as negative sign with inefficiency term ( , ) in the regression, is proxy of total profit of each bank with ith bank (i= 1, 2, 3, N) at the tth year (t= 1, 2,…) , P it is the vector of the prices of output prices, W it is the vector of the prices of input in the banking industry.

6 Model specification

Regarding the inefficiency terms we must mention about distributional assumption There

are three popular distributions for u consist of truncated normal, half normal and exponential

normal Exponential normal distribution and half normal distribution have the advantages anddisadvantages when we apply for measuring the efficiency of bank The truncated normaldistribution +( , 2) is assumed distribution of inefficiency term in this study, this is the mostflexible one

There are some determinants of the parameter for inefficiency can be modeled asfollow

Where is the vector of determinants of and is the vector of corresponding parameters.This is description of the inefficiency model and this model will be simultaneously evaluatedwith the Stochastic Frontier Approach (SFA) In this study, determinants of inefficiencyterms which are proxy by Z is described in Table 3.1 Therefore, this study assumed that thefunction of Z must be followed a linear function form

There are many various models develop to indicate that how the inefficiency termchange overtime when we apply SFA with panel data As discussion in chapter II, we candivide those models into two types: time varying models and time invariant model Based onthe estimation techniques, SFA with panel data models can also be classified into two groups:random effects and fixed effects Greene (2005), Cornwell et al (1990), Schmidt and Sickles(1984),and Lee and Schmidt (1993) mentioned about fixed effects models which do notrequire the assumption that inefficiency term is not correlated with the rest of the model.Similarly, Kumbhakar (1990), Pitt and Lee (1981), Schmidt and Sickles (1984), Battese andCoelli (1988, 1992, 1995), and “true” random effects model in Greene (2005) indicated thatrandom effects models which require the assumption of uncorrelated between the inefficiencyterm and other variables in the model In this study, we chose the model of Cornwell et al.(1990) time varying effects which follows a function of time, with αit is time varying firmeffects which follows a function of time The author mentions a quadratic function of timewith parameters vary across firms described as:

7 Functional form

In this study, based on the characteristics of Translog functional form is popular forits flexibility with the drawbacks on tractability and parsimony, it is used to descript theprofit function as follow

The level of banking inefficiency can be calculated as the ratio of observed cost/profit

to the optimal cost/profit (i.e the output when the bank is fully efficient or the value of iszero)

In the progress of finding banking efficiency result, this research found that there areendogenous phenomena between profit efficiency and input variables such as price of labor(W1), price of capital (W2) and price of fund (W3) We can define endogenous phenomena

as an appearance of correlate between one of variable in the model with error terms Theendogeneity can raise ability error in the result of measurement, auto-regression with auto-correlated errors, omitted variables and simultaneity Basing on these endogenousphenomena, this study generated the lag-variable to treat the endogeneity like lagW1, lagW2,lagW3 for endogenous variables consist of price of labor, price of capital and price of fund,respectively

After that, this research run regression for endogenous variables and then make apredict functions to generate a new variable such as Prlabor, Prcapital, Prfund and three newvariables will replace to W1, W2, W3 in the regression

Furthermore, we run Stochastic frontier model with panel data regression to evaluatethe effects of inputs and outputs on profit before tax basing on fixed effect model (Schmidt

( + )

and Sickles, 1984) and then calculating the efficiency of every bank with time-invariant technical efficiency and with time varying technical efficiency:

 Cornwell et al (1990) time varying effects which follows a function of time, with αit

is time varying firm effects which follows a function of time The authors mentions aquadratic function of time with parameters vary across firms described as:

 Battese and Coelli (1992 and 1995) with a technical efficiency and truncated normal distribution at zero:

= ( ) (Battese and Coelli 1992)

 Lee and Schmidt (1993) with a function with the time function is replaced by a set of dummy variables:

27

 True” fixed effects model and “true” random effects model (Greene, 2005): The

“True” fixed model is described as:

The “true” random effects model can be written as:

=( + )+ ′ + − With is a random constant term that varies across firms That is the way to have a result of

banking efficiency.

Furthermore, after all progress to find out the results of banking profit efficiency, thisstudy will continue to estimate potential correlated variables on profit efficiency throughrunning a regression between profit efficiency and its explanatory variables like as bankingconcentration (CON), banking development (DEV), credit risk (CRER), liquidity (LIQD),bank size (SIZ), economic growth (GDP) and inflation rate (INF)

There is another way to find out the result of profit efficiency simultaneously estimatethe effect of determinants on profit efficiency by combining the Sfpanel and Emean methodfor many different models mentioned above

After making all regressions with different types of mentioned model, Stata systemcannot give results for these models because the conditional mean model is not allowed forall above model For this reason, this study will apply the first way by dividing regressioninto two steep such as the first step is running regression to recognize the determinants ofbanking profit and profit efficiency, the second steep is running regression to find out whatare relationships between profit efficiency and its determinants

9 Variables description

In many years, appropriate definitions of bank’s input and output are one of theambiguous problems for many researchers to investigate the banking profit efficiency in theworld (Claudia Girardone et al., 2004) There are many obvious concepts of bankingproducts, meanwhile, the concepts of input and output variables of banks are not clearlyrecognized In general, in particular context of measurement banking indicators, depending

on each different purpose of the research, we will have a special set of relevant input andoutput variables which are influent with these goals

The approach we used in this study to define input and output items is theintermediary approach of Sealey and Lindley (1977) which is considered as the mostpreferred approach in measuring bank efficiency (Maudos et al, 2002; Koetter, 2006).Accordingly, this approach posits that banks as financial intermediaries primarily channelfinancial funds from savers to investors More specifically, banks use labor, capital, depositsand other borrowed funds to produce loans and other earning assets To provide output Yi atoutput prices Pi, banks demand input quantities Xi at given prices Wi that maximize totalprofit (Profit).

In the line with the literature, we define three input and two output categories Inputsare concluded: number of employees (X1), fixed assets (X2) and total funds (X3) The threeinput prices include wage rate (W1) measured as ratio of salaries and related expenses foremployees to labor (X1); price of physical capital (W2) defined as the ratio of rents, taxes,duties, fees, insurances and other administrative cost to fixed asset (X2); and price of fund(W3) measured as the ratio of total interest expense to deposit from customer (X3) Similarly,outputs consist of the two following variables: net loans (Y1) and other earning assets (Y2)with corresponding prices as per unit interest income (P1) defined as the ratio of total interestincome from loan to total loan for customer (Y1) and noninterest operating income (P2)measured as the ratio of other non-interest income to total asset (Y2)

The table 3.1 below will summarize the detail of all variables will mention to thedetailed inputs, outputs and profit in SFA efficiency model are specified as follow

Table 3.1: Overview of variable in profit function

measurement

Dependent variable

Profit Total Profit Total bank profit: It means net profit before tax, million VND

it equals total operating incomes (interest incomeand other interest income) minus total operatingexpenses minus provisions and allowances forcredit losses

Independent variables

Input

Input prices

W1 Price of labor Ratio of salaries and related expenses for

employees to labor (X1)

W2 Price of capital Ratio of rents, taxes, duties, fees, insurances and

other administrative cost to fixed asset (X2)

W3 Price of fund Ratio of total interest expense to deposit from

customer (X3)Output

Output prices

P1 Price of loan Ratio of total interest income from loan to total

loan for customer (Y1)

P2 Price of other Ratio of other non-interest income to total asset

earning asset (Y2)Moreover, we will show the graph of scatter between dependent variables (Profitbefore tax Pbt) and its independent variables (such as Total number of staff X1, Fixed-assets

X2, Total funding X3, Net loans Y1, and other earning assets Y2), prices of input (consist of

W1 equals Total labor cost/X1, W2 equals other operating expenses/X2, W3 equals total

interest expenses/X3) and prices of output (including P1 equals total interest income/Y1, P2

equals noninterest operating income/Y2) as follows

Ln(Total number of staff X1)

Figure 3.1: Graph of scatter between Profit before tax (Pbt) and total

number of staff (X1) from 2008 to 2013

Ln(Total deposit from customer X3)

Figure 3.3: Graph of scatter between Profit before tax (Pbt) and total deposit

from customer (X3) from 2008 to 2013

21 20

Ln(Total loan to customers Y1)

Figure 3.4: Graph of scatter between Profit before tax (Pbt) and total loan

from customers (Y1) from 2008 to 2013

Ln(Price of labor W1)