Bank concentration and efficiency of commercial banks in vietnam

666 | ICUEH2017Bank concentration and efficiency of commercial banks in Vietnam LE NGUYEN QUYNH HUONGUniversity of Economics HCMC – quynh_huong@ueh.edu.vn NGUYEN HUU BINHUniversity of Ec

666 | ICUEH2017

Bank concentration and efficiency of

commercial banks in Vietnam

LE NGUYEN QUYNH HUONGUniversity of Economics HCMC – quynh_huong@ueh.edu.vn

NGUYEN HUU BINHUniversity of Economics HCMC – huubinh_ais@ueh.edu.vn

Abstract

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

2007 to 2014, the more efficient commercial banks operated in the less concentrated market.

Keywords: Vietnam; bank concentration; efficiency; structure

conduct performance.

1 Introduction

In the process of integration into the world economy, Vietnam'sfinancial market is under great pressure Strong competitionamong commercial banks would be a great opportunity for thebanking sector if Vietnam domestic banks are more adaptable andoperate more efficiently, especially under the Restructuring Plan.Thus, operational efficiency becomes a vital part for the survival

of a bank in the increasingly competitive environment Therelationship between bank concentration and bank efficiency,especially in Vietnam, is open to doubt and highly ambiguous.There are numerous studies testing for this relationship Someconcentrate on the Structure Conduct

Le Nguyen Quynh Huong & Nguyen Huu Binh| 667

Performance (SCP) paradigm (Bikker & Haaf, 2002a; Deltuvaitė,Vaškelaitis, & Pranckevičiūtė, 2015; T P T Nguyen & Nghiem,2016), while others support the reverse relationship namelyefficient structure hypothesis (ES), which considers that bankefficiency positively influence on market concentration (Punt &Van Rooij, 2003; Weill, 2004) Recently, this topic has receivedtremendous attention in Vietnam, and only three studies foundhitherto (Chinh & Tiến, 2016; Huyền, 2016; Thơm & Thủy, 2016).Unfortunately, no study analyses simultaneously the relationshipbetween bank concentration and efficiency by using Grangercausality Thus, this is a noticeable research gap needed furtherinvestigation

The purpose of this paper is to examine the relationshipbetween bank concentration and efficiency by using theapplication of Granger causality method It also tests StructureConduct Performance and Efficient Structure hypothesis The rest

of the paper is structured as follow Section 2 presents a briefoverview of Vietnamese banking system Section 3 contains theprevious related literature Section 4 describes the methodologyand the data Section 5 contains the empirical results whilesection 6 gives conclusions and policy recommendations

2 Overview of Vietnamese banking system

According to the State Bank of Vietnam (SBV), the history ofbanking activities is divided into four stages, including 2 criticalperiods: 1986 - 2001 (reforming from the mono-banking system intothe two-tier banking system) and after 2011 (restructuring theVietnamese banking system) The process of restructuring thebanking system and clean-up bad debts has implemented drastically

under Vietnam’s banking restructuring Scheme in 2011-2015 (Decision 254, 1/3/2012) and Non-performing debt settlement

Scheme of credit institutions (Decision 843, 31/5/2013) These

Schemes focus on some central goals, including controlling the weak

credit institutions, bad debts, development of the banking systemand to contribute significantly to macroeconomic stability, removingdifficulties for production and business, promoting economic growth

To sum up, the process of restructuring of Vietnam's banking systemconsists:

• The privatisation of state-owned commercial banks

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

in 2014 According to Vietnam’s banking restructuring Schemementioned above, some weak banks (Joint-stock commercialbanks) took actively and hospitably M&A with other leading banksresulted in the drop in the number of commercial banks from 52 in

2007 to 44 in 2014 For example, Vietnam Tin Nghia Banktogether with SCB and First Bank of VN merged into SCB, WesternBank and PVFC consolidated in PVcombank, Habubank is acquired

by SHB, etc Because of high NPLs in weak banks, merging withleading banks could be an efficient solution encouraged by SBV inorder to strengthen and improve the competition of Vietnamesedomestic banks NPLs figures shown in Figure 1 followed anupward trend, from 2% (2007) to 4.55% (2013) After reaching apeak at 4.55% in 2013, NPLs decreased significantly to 3.25% It

is doubtful that some banks could “cook the book”, deliberatelyfailed to comply with regulations on debt classification andrecorded bad debts in financial statements lower than actual

However, some argue that 2014 is the first year Vietnam Asset Management Company (VAMC) bought bad debt from troubled banks and moved a considerable amount of NPLs out of banks’

financial statements (approximately 123 thousand billion VND,according to SBV – 23/12/2014)

Le Nguyen Quynh Huong & Nguyen Huu Binh| 669

Joint venture commercial

bank Foreign commercial bank

Non-performing loans

Figure 1 Number of Vietnamese banks and NPLs from 2007 to 2014

Source: Annual Statements of State Bank of Vietnam (SBV)

Two hypothesis in the structural approach including the traditionalStructure-Conduct-Performance (SCP) hypothesis, which is originatedfrom the traditional industrial organisation literature, and theEfficient Structure (ES) hypothesis In which, SCP hypothesis arguesthe direct positive link between market concentration and

profitability based on the presumption that banks in a highconcentrated market can collude to earn higher profits resulting inefficiency (Bain, 1951, 1956) ES hypothesis, meanwhile, assumes areverse causality that efficient banks are more profitable and gainmarket shares, resulting in a concentrated market In other words,the higher efficiency of market leads to the higher marketconcentration (Demsetz, 1973) The “quiet life” (QL)

670 | ICUEH2017

hypothesis developed by Hicks (1935), by contrast, supports anegative relationship between market concentration andperformance Following this, firms with market concentration tend

to make few efforts to maximise efficiency Because managers inthese firms may have no motivation and enjoy the monopolyprofit of a “quiet life”, and this may result in inefficient operation.Based on these hypotheses, there were a numerous number ofstudies performed in the banking sector in many parts of theworld Some of the studies are summarised in Table 1

Molyneux and Forbes (1995)

Goldberg and Rai (1996)

Coccorese and Pellecchia (2010)

Al-Muharrami and Matthews

(2009)

Koetter and Vins (2008)

Fang, Hasan, and Marton (2011)

Berger and Hannan (1998)

Casu and Girardone (2009)

Ferreira (2013)

Nguyen, Stewart (2013)

Hypothesis tested

Zhang, Jiang, Qu, and Wang

(2013)

Celik and Kaplan (2016)

As can be seen from the Table 1, there are differences in theresults of empirical studies concerning the relationship betweenbank concentration and efficiency proposed by three hypothesesmentioned above This shows that the relationship between bankconcentration and efficiency depends on the characteristics ofeach country and region This paper uses Granger causality totest simultaneously both SCP and ES in the case of Vietnam

4 Methodology

To test the Granger causality relationship between bankconcentration and bank efficiency, this section explains themethodological framework and the data: how to measure bankconcentration and bank efficiency, how to choose inputs andoutputs from financial statements of commercial banks, and theGranger causality procedure

4.1 Bank concentration

The market concentration is scaled from low to high, and in this regard, the market

is catalogued into four cases: (1) perfect competition, (2) monopolistic competition,

(3) oligopoly and (4) monopoly The market which is considered as perfectcompetition is addressed as low concentrated, and on the opposite side of the scale

- the concentration of market which tends to monopoly is evaluated as high (Boďa,2014)

672 | ICUEH2017

Market structures

Perfect competition

concentration

There are a number of market concentration indicators based onthe calculation of market shares Among other things, two standardand popular ways to measure concentration level are ConcentrationRatio (CR) and Helfindhal-Hirschman Index (HHI) The other well-known indicators of concentration ratio are the Coefficient ofvariation, the Hall-Tideman Index (HTI), and the Comprehensiveindustrial concentration index Table 2 gives a brief overview of theseconcentration measures except for CR and HHI

However, because of general consensus, data validation andstraightforwardness, this paper use CRk and HHI to measure theconcentration in Vietnamese banking market Technically, bothCRk and HHI do not require to rank and sort in descending orderall banks based on their market shares

The k bank concentration ratio

The k Bank Concentration ratio is the simplest and requiredlimited data measure of concentration Nevertheless, thismeasure only emphasises on kth leading banks while neglectingthe small banks Moreover, there is no rule for determination ofthe value of k, so k can be chosen on an ad hoc basis (often, k =

Le Nguyen Quynh Huong & Nguyen Huu Binh| 673

k represents the number of banks on the market

The value of this indicator varies from 0 (perfect competition) to

1 The market is considered as oligopoly, if k > 1 or monopoly, if k

= 1

This study adopts the Concentration Ratio - CR4, which means themarket share of the four largest firms In the case of Vietnam, weconventionally define four largest banks or “big-four” Vietnamesebanks as BIDV, Vietcombank, Vietinbank, and Agribank Here, we usethe percentage share of the total assets held by the four largestbanks for CR4

Helfindhal-Hirschman Index (HHI)

HHI is calculated by the sum of the squares of market shares ofall banks on the market This index is defined as:

HHI = S &,

-&'(

n represents the number of banks on the market.

HHI spreads widely as U.S Department of Justice has used itsince the 1980s to measure potential mergers issues or antitrustconcerns However, there is no convention to classify a marketinto high, moderate and low concentrated catalogue Thisproblem can be addressed by using the consensus from U.S.Department of Justice (DOJ) & Federal Commission Trade (FCT)and The European Commission

According to U.S Department of Justice (DOJ) & FederalCommission Trade (FCT), Horizontal Merger Guidelines § 5.2(2010), and The European Commission, the interpretation of HHI

674 | ICUEH2017

HHI sometimes is called full-information index as it capturesfeatures of the whole banking system For this reason, this paperchooses HHI to measure the concentration ratio of Vietnamesebanking market

Table 2 summarises the key features of other concentrationmeasures which are mentioned at the beginning of this section(Bikker & Haaf, 2002b; Boďa, 2014):

= n

− 1) ,

Range Typical features

Emphasis on the absolute number

of banks.

(0,1] Enriching HHI by the number

of banks which cause entry and exit barriers.

Suitable for cartel markets (monopoly) It

combines both relative dispersion and

(0,1]

absolute magnitude

Stressing on the dominance

of the largest bank.

Not including the number of banks Simple to understand (this is a standard

[0,∞) relative measure of variation of

nominal variables) No consensus at which value may

be considered as high or low.

4.2 Bank efficiency

Defining output, input variables in banking sector

The determination of the input - output variables in bankingfield is a controversy issue Berger and Humphrey (1992)determined inputs and outputs in many different perspectives(National Bureau of Economic Research - NBER study "OutputMeasurement in the Service Sectors”, Chapter 7 - Measurementand efficiency issues in commercial banking) Briefly, theseviewpoints include three main approaches:

Intermediation Approach: banks are financial institutions,intermediation between borrowers and lenders Therefore, outputsare probably defined as loans and other assets, while inputs will

be deposits and other liabilities This method was developed bySealey and Lindley (1977).

Le Nguyen Quynh Huong & Nguyen Huu Binh| 675

User cost Approach: This method determines the inputs oroutputs based on the ability to contribute to revenue for the bank

If the financial returns on an asset exceed the opportunity cost offunds or if the financial costs of liability are less than theopportunity cost, then the instrument is considered to be afinancial output (Berger & Humphrey, 1992)

Value-added Approach: This approach considers all asset andliability categories to have output characteristic rather thandistinguish inputs from outputs in a mutually exclusive way Thecategories having substantial value added, as judged using anexternal source of operating cost allocations, are employed as theimportant outputs Others are treated as representing mainly eitherunimportant outputs, intermediate products, or inputs, depending onthe specifics of the category (Berger & Humphrey, 1992)

Measuring bank efficiency

Charnes, Cooper, and Rhodes (1978) is the first team usingData Envelopment Analysis model (DEA) to measure the efficiency

of decision-making units (DMUs) DEA model is a non-parametricestimation which is widely used in myriad fields since 1957 Theglobal private banking sector, particularly, has been applied DEAmodel in research (Nathan & Neave, 1992) (Miller & Noulas,1996), (Iršová & Havránek, 2010), (Luo, Yao, Chen, & Wang,2011)

Data envelopment analysis (DEA) is a linear programmingformulation for measuring the relative performance of organisationalunits where the presence of multiple inputs and outputs makescomparisons difficult Efficiency scores are then calculated from thefrontiers generated by a sequence of linear programs (convexcombinations of DMUs)

Assuming there are n banks, each bank can create s output byusing m different inputs The relative efficiency score of a DMU pcould be assessed by solving a fractional program, which isdefined by extremal optimization (maximization) of the ratio ofweighted sum of outputs to weighted multiple inputs (aka virtualoutput to virtual input ratio), then subject to the constraints ofnon-decreasing weights and efficiency measure (the earliermentioned ratio) less than or equal to one To sum up, thisinvolves finding the optimal weights so that efficiency measure is

maximised (banks choose their input and output weights thatmaximise their efficiency scores).

yki: output k produced by bank i,

xji: input j used by bank i,

vk and uj are weights given to output k and input j

However, this research will not go too deep into the complextheoretical part of the DEA estimations but focus primarily on theempirical side of the methods that concern measuring efficiency.Another common method of measuring efficiency, developed by Aigner, Lovell, and Schmidt (1977) and Meeusen and van Den Broeck (1977), is the Stochastic Frontier Approach (SFA) SFA method divides residuals into 2 groups: inefficiencies and noise, and using some assumptions about the inefficiencies’ distribution One part of residuals is called normal statistical noise (V it ) and the rest is noise inefficiency (U it ) V it is assumed to be independent of the explanatory variables and have the same distribution iid ~ N (0, s,L ) and represents the statistical noise, measurement error, and other random events (e.g., economic conditions, earthquakes, weather, strikes, luck) beyond the company's control Inefficiency U it

(aka inefficiency error term - non-negative) represents inefficiency factors and assumptions is truncated at 0 and idd ~N (µ, s,L ) At the same time, U it is assumed

to be independent of V it The canonical formulation that serves as the foundation for other variations is the model:

Y = b’X + v – u,

where Y is the observed outcome, b’X + v is the optimal, orfrontier goal (i.e maximal production output or minimum cost)pursued by the individual The amount by which the observedindividual fails to reach the optimum (the frontier) is u.Alternatively, there is a commonly used – the Translog function:

Yit = exp [Xit b + (Vit - Uit)] i = 1, …, K, t = 1, …, T

Le Nguyen Quynh Huong & Nguyen Huu Binh| 677

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

Xit: Vector KX1 input of ith now, at time t

b: Vector Kx1 of unknown factors

Vit: “noise” error term - symmetric (i.e normal distribution)

Uit: “inefficiency error term” - non-negative (i.e half-normal distribution)

SFA has become the method commonly used because of manyprominent advantages (Coelli & Perelman, 2000; Cuesta & Orea,2002; Färe, Grosskopf, Lovell, & Yaisawarng, 1993; Grosskopf,Margaritis, & Valdmanis, 1995) Whereas SFA is more appropriatefor emerging markets where measurement errors anduncertainties of the economic environment are more likely toprevail (Zhang et al., 2013), we use both DEA and SFA for Vietnamcase

Figure 2 DEA and SFA Frontier

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

678 | ICUEH2017

To control multiple inputs and to allow a nonlinear relationshipbetween the bank's total income and inputs, this paper usesFiorentino's proposed translog function (Fiorentino, Karmann, &Koetter, 2006; Fontani & Vitali, 2014) Sharing the DEA data set,the translog function has two inputs, namely interest expense andother interest expense, as follows:

ln(Y it ) = b 0 + b 1 ln(X it1 ) + b 2 ln(X it2 ) + b 3 ln(X it1 ) ln(X it2 ) + b 4

ln(X it1 ) 2 + b 5 ln(X it2 ) 2 + (V it - U it )

Where: Yit: outputs (total revenue)

Xit1, Xit2: inputs (interest expense and other interest

expense)

b: Vector Kx1 of unknown factors

Vit and Uit are assumed to have standard and

semi-standard distributions,

respectively

4.3 Granger causality model

Granger causality is a statistical concept of causality that isbased on the prediction Granger causality (or "G-causality") wasdeveloped in 1969 by Professor Clive Granger and has beenwidely used in economics since the 1960s Following Casu andGirardone (2009), we use autoregressive-distributed linearspecification to disentangle the relationship betweenconcentration and efficiency The lags (K, J) are determined byAugmented Dickey-Fuller Its mathematical formulation takes thefollowing form:

Data

Our data are collected from financial statements of 21commercial banks in Vietnam from 2007 to 2014 We cannotcover financial data from the whole Vietnamese banking