Bank concentration and efficiency of commercial banks in Vietnam

666 | ICUEH2017 Bank concentration and efficiency of commercial banks in Vietnam LE NGUYEN QUYNH HUONG University of Economics HCMC – quynh_huong@ueh.edu.vn NGUYEN HUU BINH University 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 Introduction In the process of integration into the world economy, Vietnam's financial market is under great pressure Strong competition among commercial banks would be a great opportunity for the banking sector if Vietnam domestic banks are more adaptable and operate more efficiently, especially under the Restructuring Plan Thus, operational efficiency becomes a vital part for the survival of a bank in the increasingly competitive environment The relationship between bank concentration and bank efficiency, especially in Vietnam, is open to doubt and highly ambiguous There are numerous studies testing for this relationship Some concentrate on the Structure Conduct Le Nguyen Quynh Huong & Nguyen Huu Binh| 667 Performance (SCP) paradigm (Bikker & Haaf, 2002a; Deltuvaitė, Vaškelaitis, & Pranckevičiūtė, 2015; T P T Nguyen & Nghiem, 2016), while others support the reverse relationship namely efficient structure hypothesis (ES), which considers that bank efficiency positively influence on market concentration (Punt & Van Rooij, 2003; Weill, 2004) Recently, this topic has received tremendous attention in Vietnam, and only three studies found hitherto (Chinh & Tiến, 2016; Huyền, 2016; Thơm & Thủy, 2016) Unfortunately, no study analyses simultaneously the relationship between bank concentration and efficiency by using Granger causality Thus, this is a noticeable research gap needed further investigation The purpose of this paper is to examine the relationship between bank concentration and efficiency by using the application of Granger causality method It also tests Structure Conduct Performance and Efficient Structure hypothesis The rest of the paper is structured as follow Section presents a brief overview of Vietnamese banking system Section contains the previous related literature Section describes the methodology and the data Section contains the empirical results while section gives conclusions and policy recommendations Overview of Vietnamese banking system According to the State Bank of Vietnam (SBV), the history of banking activities is divided into four stages, including critical periods: 1986 - 2001 (reforming from the mono-banking system into the two-tier banking system) and after 2011 (restructuring the Vietnamese banking system) The process of restructuring the banking system and cleanup bad debts has implemented drastically under Vietnam’s banking restructuring Scheme in 2011-2015 (Decision 254, 1/3/2012) and Non-performing debt settlement Scheme of credit institutions (Decision 843, 31/5/2013) These Schemes focus on some central goals, including controlling the weak credit institutions, bad debts, development of the banking system and to contribute significantly to macroeconomic stability, removing difficulties for production and business, promoting economic growth To sum up, the process of restructuring of Vietnam's banking system consists: • The privatisation of state-owned commercial banks • Increasing the financial scale and capacity: raising capital, acquisitions and mergers, expanding mobilisation 668 | ICUEH2017 • Improving asset quality, credit quality and reduce bad debt Vietnamese commercial banking system can be classified into main groups: (1) Stateowned commercial bank, (2) Joint stock commercial bank, (3) Foreign commercial bank, and (4) Joint venture commercial bank Figure shows the number of commercial banks as well as Non-performing loans (NPLs) over the period of years It is noticed that Stateowned banks and foreign banks still remained in number, while Joint stock commercial banks decreased their number from 40 in 2008 to 30 in 2014 According to Vietnam’s banking restructuring Scheme mentioned above, some weak banks (Joint-stock commercial banks) took actively and hospitably M&A with other leading banks resulted in the drop in the number of commercial banks from 52 in 2007 to 44 in 2014 For example, Vietnam Tin Nghia Bank together with SCB and First Bank of VN merged into SCB, Western Bank and PVFC consolidated in PVcombank, Habubank is acquired by SHB, etc Because of high NPLs in weak banks, merging with leading banks could be an efficient solution encouraged by SBV in order to strengthen and improve the competition of Vietnamese domestic banks NPLs figures shown in Figure followed an upward trend, from 2% (2007) to 4.55% (2013) After reaching a peak at 4.55% in 2013, NPLs decreased significantly to 3.25% It is doubtful that some banks could “cook the book”, deliberately failed to comply with regulations on debt classification and recorded bad debts in financial statements lower than actual However, some argue that 2014 is the first year Vietnam Asset Management Company (VAMC) bought bad debt from troubled banks and moved a considerable amount of NPLs out of banks’ financial statements (approximately 123 thousand billion VND, according to SBV – 23/12/2014) Le Nguyen Quynh Huong & Nguyen Huu Binh| 669 45 40 35 30 25 20 15 10 2007 2008 2009 2010 2011 2012 2013 2014 State owned commercial bank 5 5 5 5 Joint stock commercial bank 37 40 40 37 37 34 34 30 Joint venture commercial bank 5 5 4 4 Foreign commercial bank 5 5 5 5 Non-performing loans 5% 5% 4% 4% 3% 3% 2% 2% 1% 1% 0% Non-performing loans Number of banks 2% 3.50% 2.20% 2.60% 3.40% 4.08% 4.55% 3.25% Figure Number of Vietnamese banks and NPLs from 2007 to 2014 Source: Annual Statements of State Bank of Vietnam (SBV) Literature review This section reviews the theoretical and empirical results between bank concentration and efficiency There have been long theoretical debates about the relationship between market concentration and efficiency These debates dated back to three distinct hypotheses that reflect the opinions on this relationship Two hypothesis in the structural approach including the traditional StructureConduct-Performance (SCP) hypothesis, which is originated from the traditional industrial organisation literature, and the Efficient Structure (ES) hypothesis In which, SCP hypothesis argues the direct positive link between market concentration and profitability based on the presumption that banks in a high concentrated market can collude to earn higher profits resulting in efficiency (Bain, 1951, 1956) ES hypothesis, meanwhile, assumes a reverse causality that efficient banks are more profitable and gain market shares, resulting in a concentrated market In other words, the higher efficiency of market leads to the higher market concentration (Demsetz, 1973) The “quiet life” (QL) 670 | ICUEH2017 hypothesis developed by Hicks (1935), by contrast, supports a negative relationship between market concentration and performance Following this, firms with market concentration tend to make few efforts to maximise efficiency Because managers in these firms may have no motivation and enjoy the monopoly profit of a “quiet life”, and this may result in inefficient operation Based on these hypotheses, there were a numerous number of studies performed in the banking sector in many parts of the world Some of the studies are summarised in Table Table Authors Homma, Tsutsui, and Uchida (2014) Fu and Heffernan (2009) Years/ Period 1974-2005 1985–2002 Nation/Region Japan China Hypothesis tested Result QL Supported ES Supported SCP Supported QL Rejected ES Rejected SCP Supported ES Rejected Lloyd-Williams, Molyneux, and Thornton (1994) 1986-1988 Spanish Molyneux and Forbes (1995) 1986-1989 European banking industry SCP Supported ES Rejected Goldberg and Rai (1996) 1988-1991 11 European countries SCP Rejected ES Supported Coccorese and Pellecchia (2010) 1992–2007 Italy QL Supported Al-Muharrami and Matthews (2009) 1993-2002 Arab GCC banking SCP Supported QL Rejected Koetter and Vins (2008) 1996-2006 Germany QL Rejected Fang, Hasan, and Marton (2011) 1998–2008 South-Eastern Europe SCP Supported Berger and Hannan (1998) 1998 U.S ES Supported Casu and Girardone (2009) 2000-2005 EU countries QL Rejected ES Rejected Ferreira (2013) 1996-2008 27 EU countries SCP Supported ES Rejected Nguyen, Stewart (2013) 1999-2009 Vietnam SCP Rejected ES Rejected Le Nguyen Quynh Huong & Nguyen Huu Binh| 671 Authors Years/ Nation/Region Period Zhang, Jiang, Qu, and Wang (2013) 2003-2010 Brazil, Russia, India, China Celik and Kaplan (2016) 2008-2013 Turkey Hypothesis tested Result QL Supported SCP Rejected ES Supported As can be seen from the Table 1, there are differences in the results of empirical studies concerning the relationship between bank concentration and efficiency proposed by three hypotheses mentioned above This shows that the relationship between bank concentration and efficiency depends on the characteristics of each country and region This paper uses Granger causality to test simultaneously both SCP and ES in the case of Vietnam Methodology To test the Granger causality relationship between bank concentration and bank efficiency, this section explains the methodological framework and the data: how to measure bank concentration and bank efficiency, how to choose inputs and outputs from financial statements of commercial banks, and the Granger causality procedure 4.1 Bank concentration The market concentration is scaled from low to high, and in this regard, the market is catalogued into four cases: (1) perfect competition, (2) monopolistic competition, (3) oligopoly and (4) monopoly The market which is considered as perfect competition is addressed as low concentrated, and on the opposite side of the scale the concentration of market which tends to monopoly is evaluated as high (Boďa, 2014) 672 | ICUEH2017 Market structures Perfect competition Monopolistic competition Oligopoly Low concentration Monopoly High concentration There are a number of market concentration indicators based on the calculation of market shares Among other things, two standard and popular ways to measure concentration level are Concentration Ratio (CR) and Helfindhal-Hirschman Index (HHI) The other well-known indicators of concentration ratio are the Coefficient of variation, the Hall-Tideman Index (HTI), and the Comprehensive industrial concentration index Table gives a brief overview of these concentration measures except for CR and HHI However, because of general consensus, data validation and straightforwardness, this paper use CRk and HHI to measure the concentration in Vietnamese banking market Technically, both CRk and HHI not require to rank and sort in descending order all banks based on their market shares The k bank concentration ratio The k Bank Concentration ratio is the simplest and required limited data measure of concentration Nevertheless, this measure only emphasises on kth leading banks while neglecting the small banks Moreover, there is no rule for determination of the value of k, so k can be chosen on an ad hoc basis (often, k = 3, 4, 5, 8) The Concentration ratio of k banks is calculated as: # CR # = S& &'( th where: S& is the market share of i bank Le Nguyen Quynh Huong & Nguyen Huu Binh| 673 k represents the number of banks on the market The value of this indicator varies from (perfect competition) to The market is considered as oligopoly, if k > or monopoly, if k = This study adopts the Concentration Ratio - CR4, which means the market share of the four largest firms In the case of Vietnam, we conventionally define four largest banks or “big-four” Vietnamese banks as BIDV, Vietcombank, Vietinbank, and Agribank Here, we use the percentage share of the total assets held by the four largest banks for CR4 Helfindhal-Hirschman Index (HHI) HHI is calculated by the sum of the squares of market shares of all banks on the market This index is defined as: - S&, HHI = &'( where: S&, is the square of market share of i bank th n represents the number of banks on the market HHI spreads widely as U.S Department of Justice has used it since the 1980s to measure potential mergers issues or antitrust concerns However, there is no convention to classify a market into high, moderate and low concentrated catalogue This problem can be addressed by using the consensus from U.S Department of Justice (DOJ) & Federal Commission Trade (FCT) and The European Commission According to U.S Department of Justice (DOJ) & Federal Commission Trade (FCT), Horizontal Merger Guidelines § 5.2 (2010), and The European Commission, the interpretation of HHI is as follows: Concentration degree High Moderate Low Source: European Commission and DOJ + FTC Value of HHI The European Commission DOJ & FCT > 2000 > 2500 1000 – 2000 1500 – 2500 < 1000 < 1500 674 | ICUEH2017 HHI sometimes is called full-information index as it captures features of the whole banking system For this reason, this paper chooses HHI to measure the concentration ratio of Vietnamese banking market Table summarises the key features of other concentration measures which are mentioned at the beginning of this section (Bikker & Haaf, 2002b; Boďa, 2014): Table A brief overview of HTI, CIC, CV Concentration measure Definition Range HTI Hall-Tideman index Comprehensive industrial concentration index Coefficient of variation 4.2 = &'( i s& − CIC = s( + &', s&, (1 (0,1] Emphasis on the absolute number of banks Enriching HHI by the number of banks which cause entry and exit barriers (0,1] Suitable for cartel markets (monopoly) It combines both relative dispersion and absolute magnitude Stressing on the dominance of the largest bank [0,∞) Not including the number of banks Simple to understand (this is a standard relative measure of variation of nominal variables) No consensus at which value may be considered as high or low + − s& ) CV = n − 1), &'( (ns& Typical features Bank efficiency Defining output, input variables in banking sector The determination of the input - output variables in banking field is a controversy issue Berger and Humphrey (1992) determined inputs and outputs in many different perspectives (National Bureau of Economic Research - NBER study "Output Measurement in the Service Sectors”, Chapter - Measurement and efficiency issues in commercial banking) Briefly, these viewpoints include three main approaches: Intermediation Approach: banks are financial institutions, intermediation between borrowers and lenders Therefore, outputs are probably defined as loans and other assets, while inputs will be deposits and other liabilities This method was developed by Sealey and Lindley (1977) Le Nguyen Quynh Huong & Nguyen Huu Binh| 675 User cost Approach: This method determines the inputs or outputs based on the ability to contribute to revenue for the bank If the financial returns on an asset exceed the opportunity cost of funds or if the financial costs of liability are less than the opportunity cost, then the instrument is considered to be a financial output (Berger & Humphrey, 1992) Value-added Approach: This approach considers all asset and liability categories to have output characteristic rather than distinguish inputs from outputs in a mutually exclusive way The categories having substantial value added, as judged using an external source of operating cost allocations, are employed as the important outputs Others are treated as representing mainly either unimportant outputs, intermediate products, or inputs, depending on the specifics of the category (Berger & Humphrey, 1992) Measuring bank efficiency Charnes, Cooper, and Rhodes (1978) is the first team using Data Envelopment Analysis model (DEA) to measure the efficiency of decision-making units (DMUs) DEA model is a non-parametric estimation which is widely used in myriad fields since 1957 The global private banking sector, particularly, has been applied DEA model in research (Nathan & Neave, 1992) (Miller & Noulas, 1996), (Iršová & Havránek, 2010), (Luo, Yao, Chen, & Wang, 2011) Data envelopment analysis (DEA) is a linear programming formulation for measuring the relative performance of organisational units where the presence of multiple inputs and outputs makes comparisons difficult Efficiency scores are then calculated from the frontiers generated by a sequence of linear programs (convex combinations of DMUs) Assuming there are n banks, each bank can create s output by using m different inputs The relative efficiency score of a DMU p could be assessed by solving a fractional program, which is defined by extremal optimization (maximization) of the ratio of weighted sum of outputs to weighted multiple inputs (aka virtual output to virtual input ratio), then subject to the constraints of non-decreasing weights and efficiency measure (the earlier mentioned ratio) less than or equal to one To sum up, this involves finding the optimal weights so that efficiency measure is maximised (banks choose their input and output weights that maximise their efficiency scores) 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 many prominent advantages (Coelli & Perelman, 2000; Cuesta & Orea, 2002; Färe, Grosskopf, Lovell, & Yaisawarng, 1993; Grosskopf, Margaritis, & Valdmanis, 1995) Whereas SFA is more appropriate for emerging markets where measurement errors and uncertainties of the economic environment are more likely to prevail (Zhang et al., 2013), we use both DEA and SFA for Vietnam case Figure DEA and SFA Frontier Here, we adopt DEA input-oriented and follow the intermediation approach The intermediation approach, originally proposed by Sealey and Lindley (1977), is appropriate when banks operate as independent entities (Bos & Kool, 2006) and take into account interest expenses It seems appropriate to evaluate commercial banks in Vietnam because interest expenses present at least more than half of total costs in general (Berger & Humphrey, 1997) In particular, this study uses interest expenses and other operating expenses presenting for the banks’ inputs, and net interest revenue, other operating income for the banks’ outputs 678 | ICUEH2017 To control multiple inputs and to allow a nonlinear relationship between the bank's total income and inputs, this paper uses Fiorentino's proposed translog function (Fiorentino, Karmann, & Koetter, 2006; Fontani & Vitali, 2014) Sharing the DEA data set, the translog function has two inputs, namely interest expense and other interest expense, as follows: ln(Yit) = b0 + b1 ln(Xit1) + b2 ln(Xit2) + b3 ln(Xit1) ln(Xit2) + b4 ln(Xit1)2 + b5 ln(Xit2)2 + (Vit - Uit) Where: Yit: outputs (total revenue) Xit1, Xit2: inputs (interest expense and other interest expense) b: Vector Kx1 of unknown factors Vit and Uit are assumed to have standard and semi-standard distributions, respectively 4.3 Granger causality model Granger causality is a statistical concept of causality that is based on the prediction Granger causality (or "G-causality") was developed in 1969 by Professor Clive Granger and has been widely used in economics since the 1960s Following Casu and Girardone (2009), we use autoregressive-distributed linear specification to disentangle the relationship between concentration and efficiency The lags (K, J) are determined by Augmented Dickey-Fuller Its mathematical formulation takes the following form: S Q yM = ∂O + yMP# α# + #'( xMPA βA + ϑM A'( where yM and xM are represented alternatively by mentioned above measures of concentration and efficiency, and ϑ&M is disturbance term We first run OLS and then employ endogeneity test Next, we test ES and SCP, and null hypothesis is β( = … = βA = If ES is hold, the coefficients for efficiency is positive and significant If SCP is hold, there are positive and significant coefficients of concentration Data Our data are collected from financial statements of 21 commercial banks in Vietnam from 2007 to 2014 We cannot cover financial data from the whole Vietnamese banking Le Nguyen Quynh Huong & Nguyen Huu Binh| 679 system due to the limit in collecting data Nineteen of 21 banks are joint stock commercial banks, one is foreign bank and the remaining is State-owned bank To compute concentration ratio in the first stage, we use the percentage share of total assets of four largest banks In the second stage, we measure the efficiency scores by adopting DEA and SFA method with inputs as interest expenses and other interest expenses In the third stage, we test the Granger causality between concentration ratio (measured on the first stage) and efficiency scores (measured on the second stage, then multiply each bank score by their market shares) Appendix presents description and statistics of variables used in measuring efficiency scores in the second stage It can be seen that “big-4” always are State-owned commercial banks and dominate the whole banking system between 2007 and 2014 Empirical results 5.1 Concentration index of Vietnamese commercial banks Appendix reports HHI and CR4 of Vietnamese banking system between 2007 and 2014 In 2008, both concentration ratios reached their peaks (1440 for HHI, 0.77 for CR4), suggesting Vietnamese banks faced challenge of strong competition In 2008, two new banks (Tiên Phong Bank and Liên Việt Bank) were granted the license of establishment by SBV after a decade no new bank set up Moreover, SBV officially issued the first 100% foreign subsidiary bank licenses to HSBC, ANZ and Standard Chartered, opening a new period for the operation of foreign banks in Vietnam Therefore, there was a potential threat which was posed by not only local competitors but also foreign banks, leading to high concentration in 2008 Over the following four years, both concentration ratios fell gradually and reached their lowest points in 2012 Thereafter, they increased steadily during 2013-2014 due to the booming M&A activities (for example, Western Bank and Petro Vietnam Financial Company, Construction Bank and Vietinbank, Mekong Housing Bank and BIDV) This is the effect of the M&A process that has formed a number of large-scale banks in terms of total assets However, the concentration ratio of Vietnamese banking system is considered relatively low (HHI < 1500), suggesting that high competition in the banking market High completion, in turn, could enhance the performance and efficiency of banking system (Bính, 2015) 680 | ICUEH2017 5.2 Efficiency scores of Vietnamese commercial banks In measuring efficiency, we adopt both SFA and DEA approach Taking the available data, the SFA specifies two empirical models – the SFA True random effects and fixed effects Next, Hausman-test allows us to confirm whether to use Random or Fixed effects Hausman-test result is shown in Table (Prob>chi2 = 0.0000), suggesting that using SFA True random effects are more robust and consistent Table Hausman test for SFA True random effects and fixed effects Coefficients (b) (B) (b-B) Sqrt (diag(V_b -V_B)) Tfe tre Difference S.E Ln Interest Expense 0.24832 0.039392 0.208928 0.352019 Ln Other Interest Expense 0.185185 0.602509 -0.41732 0.30878 Ln (Interest Expense.Other IE) 0.00269 -0.02035 0.023045 0.053423 0.012671 0.031644 -0.01897 0.031928 0.007267 -0.01014 0.017403 0.021178 Ln (Interest Expense) Ln (Other Interest Expense) b = consistent under Ho and Ha; obtained from sfpanel B = inconsistent under Ha, efficient under Ho; obtained from sfpanel Test: Ho: difference in coefficients not systematic chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 170.15 Prob>chi2 = 0.0000 Le Nguyen Quynh Huong & Nguyen Huu Binh| 681 Table Efficiency scores estimated by SFA and DEA Bank name (dmu) SFA Random effects - jlms DEA SCALE VCB 0.853794 0.846826 ACB 0.866551 0.843874 SHB 0.908021 0.839725 VID 0.942865 0.839618 ABB 0.89097 0.833539 EXM 0.90221 0.83213 AGR 0.81185 0.825763 NCB 0.909856 0.821062 PGB 0.9342 0.819591 SAC 0.872095 0.81909 SHI 0.897082 0.818368 BIDV 0.812259 0.815861 VIB 0.892994 0.808407 MB 0.863921 0.803189 CTG 0.843139 0.793373 OCE 0.871425 0.783218 OCB 0.908507 0.757279 VPB 0.879565 0.750967 MHB 0.894391 0.749243 MEK 0.919637 0.739595 SEA 0.902765 0.724531 Efficiency scores jlms (SFA approach) VID PGB MEK AGR SCALE (DEA approach) BIDV CTG 0.8 VCB 0.6 0.4 NCB MB 0.2 OCB ACB SHB OCE SEA SAC EXM VPB MHB VIB ABB Table shows the average efficiency scores of commercial banks in Vietnam in the period of 2007-2014 by DEA VRS input-oriented and SFA True random effects It is obvious that there are differences between SFA and DEA results Reported figures in Table imply that according to SFA approach banks scored low efficiency are State-owned commercial banks (Vietcombank, Vietinbank, BIDV and Agribank are ranked low) Noted that jlms is named for SFA Scores and DEA Scale is chosen to represent for DEA Scores 5.3 Granger causality Firstly, we test the stationarity of the series, using augmented Dickey-Fuller test Lags are included and the null hypothesis is non-stationary existing The decision of 682 | ICUEH2017 choosing whether random walk with drift or without drift is based on the shapes of the trend line graph in Figure Both variables Scale and jlms of each banks are adjusted by multiplying by their market shares in percentage, then name them as Scale-adjusted and jlms-adjusted .86 76 84 78 845 jlmsa 82 scalea 85 855 84 86 Figure Trend line of Scale-adjusted, jlms-adjusted, HHI, CR4 2008 2010 year 2012 2014 2006 2008 2010 year 2012 2014 2006 2008 2010 year 2012 2014 2006 2008 2010 year 2012 2014 65 cr4 75 hhi 1000 1100 1200 1300 1400 2006 Table ADF test Scale-adjusted Jlms-adjusted CR4 HHI MacKinnon approximate p=value for Z(t) lag (0) 0.5828 0.0585 0.1209 0.1202 lag (1) 0.0000 0.5997 0.0077 0.0021 Table illustrates that only jlms-adjusted (jlms-a) is station while Scale-adjusted (Scale-a), CR4 and HHI are station with time lag Thus, we decide on lags for scale-a, jlms-a, cr4, hhi (1,0,1,1, respectively) Le Nguyen Quynh Huong & Nguyen Huu Binh| 683 Then we test endogeneity of all models to whether or not to apply GMM to robust the results We discover that all explanatory variables are exogenous variables, means Cov (Xjt, ϑjt ) = 0, with j is j-th model Whenever OLS estimators are as well as GMM estimators, no need to use GMM The results obtained from testing the hypotheses put forward to explain the SCP and ES relationship are presented in Table 6, ES hypothesis test, it is also clear that the bank efficiency of the previous year (first lags) has a negative and statistically significant influence on bank concentration, while the influence of the same year is not statistically significant Increasing in bank efficiency Granger-causes a fall in both HHI and CR4 index, meaning scale efficiency positively Granger-causes competition This results are consistent with findings of Ferreira (2013); T N Nguyen and Stewart (2013); Casu and Girardone (2009) and reject the ES hypothesis in Vietnam Based on the signs of regression coefficients, noticeably, this study makes an unambiguous conclusion that ES Hypothesis should be rejected in transition economy like Vietnam One possible explanation is that Vietnamese banking system is considered highly regulated and “overprotected” In a highly regulated and “over-protected” market, efficient banks compared to State owned banks (inefficient banks) hardly continue high profits because efficient banks cannot have advantages and create barriers to market entry The policy makers should notice that each policy intervention or interventionism could adversely affect the development of the banking system and distort the structure of the system Another explanation could be that the business strategies of large Vietnamese banks during this period were focused on raising capital, loans, assets, deposits, branch networks and reducing NPLs Thus, revenue, interest income and profit before tax were not the most propriety missions of banks (T N Nguyen & Stewart, 2013) Panels (b) and (d) in Table show that the first lags of competition are significantly (different from zero), indicating that competition at time t is influenced by previous year's competition With regard to the causality running from bank concentration (measured by CR4 and HHI) to DEA scale efficiency and SFA jlms, the results presented in the later Table are inconsistent and contradictory DEA-efficiency is affected positively by concentration and previous year’s efficiency, while there is a negative influence from concentration to SFA jlms However, this result is not significant, implying that concentration does not Granger cause to the efficiency of Vietnam’s banks Overall, the evidence for Vietnam commercial banks does not support either the ES or SCP hypothesis 684 | ICUEH2017 Table Granger test ES test dependent variable: HHI CR4 Explanatory variables: a) Lag HHI, jlms-a b) Lag HHI, Lag scale-a coef P > |t| c) Lag CR4, jlms-a d) Lag CR4, Lag scale-a coef P > |t| coef P > |t| coef P > |t| Explanatory 0.590599 0.159 0.5278723 0.037 0.6089294 0.142 0.5698613 0.031 Explanatory -707.3363 0.743 Cons Granger test (Prob > F) Ho: no granger cause -11378.62 0.046 -0.2040081 0.744 -2.990502 0.068 1037.119 0.615 10180.09 0.041 0.431276 0.54 2.827062 0.056 - 0.7435 - 0.0461 - 0.7438 - 0.0679 SCP test dependent variable: Explanatory variables: jlms-a a) Lag HHI coef Explanatory Explanatory cons Granger test (Prob > F) Ho: no granger cause scale-a b) Lag CR4 P > |t| -0.0000941 0.195 c) Lag scale-a, Lag CR4 d) Lag scale-a, Lag HHI coef P > |t| coef P > |t| coef P > |t| -0.03451581 0.152 0.2104672 0.61 0.2132968 0.611 - - - - 0.0763758 0.24 0.0000219 0.258 0.9289959 1.058251 0.001 0.6163318 0.139 0.6411903 0.128 - 0.1946 - 0.1519 - 0.2397 - 0.2578 Conclusions and policy implications This paper employs Granger causality to examine the relationship between bank concentration and efficiency The data is collected from the consolidated accounting statements of 21 commercial banks in Vietnam from 2007 to 2014 To measure bank concentration, we opt to use two common approaches: Concentration Ratio (CR4) and Helfindhal-Hirschman Index (HHI) The results reveal that there is a decline of concentration ratio from 2008 to 2012, and then it rose slightly over the following two years It is apparent that the booming M&A in 2012-2014 results in the increase of bank concentration in these years In general, Vietnam's banking Le Nguyen Quynh Huong & Nguyen Huu Binh| 685 concentration is still ranked at a low level with high competition, however, the situation is expected to be reversed after Restructuring Plan of SBV ends in 2020 One must concede that the findings of low concentration of Vietnamese banking sector are “good message” for bank customers, but not offer any guidance in which directions the Vietnamese banking sector should be regulated in the future High competition could be considered as a threat for domestic banks, especially after the ASEAN Economic Community in 2015 (aggressive competition from ASEAN and Japanese banks into the domestic market) SBV can act only within the scope of its competence and try to maintain changes in the current level of concentration and endeavour to prevent unnecessary M&A that would contribute to lowering competition and increasing bank concentration For bank efficiency, we applied DEA and SFA estimation and found that most inefficient commercial banks are State-owned banks We believe that the SBV needs to strengthen the whole banking system by restructuring the State-owned banks into privatisation There is also a need to well-prepare for merger and acquisition procedure of some small and inefficient domestic banks when the bail-out sources are not only funded by SBV but also from foreign organizations The SBV should prepare for specific scenarios and management policies when Asian Development Bank (ADB) has announced a plan to cooperate with a Vietnamese company to buy one of the “VND banks” in 2017 Our empirical results not, in general, support either SCP and ES The regression models did not yield the reliable results due to the statistically insignificant regression coefficients and the reversed sign of them We also test endogeneity of all models to whether or not to apply GMM to robust the results Due to the fact that all explanatory variables are exogenous, there no need to apply GMM estimator We found that bank concentration was related negatively to bank efficiency and positively to previous year concentration Although according to ES, the banking industry will become more concentrated under competition conditions if some banks are more efficient, the results of this study are reversed Then, efficient banks cannot maintain competitive advantage and create barriers to entry partly because of the intervention of SBV through preferential monetary policies for the State-owned banks Therefore, we come up with a suggestion that the governmental regulation and intervention are inappropriate policies since they might impose penalties on efficient banks and discourage the proper functioning of the banking market mechanism Regarding SCP-test, however, control variables (market concentration, lag 1-year efficiency) are positive and insignificant This outcome is likely 686 | ICUEH2017 due to rigid regulatory rules governing banking activities and strict control over interest rates, which also prevented State-owned banks (“big 4”/large bank) from enjoying monopoly profits, thereby ruling out any opportunity to opt for a market power Taken together, the findings of this study indicate that the Vietnamese banking sector data not provide a support either SCP or ES hypothesis, but consistent with those for China, European countries, Arab, Vietnam, etc found in previous work Hence our results suggest that the model might content a Restructuring Banking system-dependingvariable During this phase, the “big 4” banks were State-owned banks with the special power, subsidised by the government to make loans to designated sectors and firms Moreover, SBV nominated State-owned banks to buy weak banks Thus, neither concentration nor efficiency significantly affected the profitability and competition advantage Le Nguyen Quynh Huong & Nguyen Huu Binh| 687 Appendix Appendix a dmu Other Operating a Income Net Interest a Revenue Interest Expense ABB 106845.4 1348978 1923224 550750 ACB 1355550 4325250 9206750 1120000 AGR 2700930 1.86E+07 3.26E+07 8833979 BIDV 3641813 1.05E+07 1.70E+07 5499425 CTG 946337.5 2.71E+07 1.68E+07 3443013 EXM 497012.5 2837025 5178838 980300 MB 884403.9 4050737 4382324 991124.1 MEK 694.875 423972.7 221367.6 53927.29 MHB 67051.5 981724 2996416 2057219 NCB 59138.88 477154.3 1197087 256031.9 OCB 29062.5 776400 1179213 237687.5 OCE 24442.86 934514.3 2622529 171357.1 PGB 101617.1 538995.3 818956.2 57044.19 SAC 987487.5 4321375 6447475 1263025 SEA 8570 825526.4 3006232 823692 SHB 212978.9 1346778 3802997 543880.1 SHI 254216.7 786950 209001 209283.3 VCB 3453025 8996875 1.30E+07 1533625 VIB 478190.1 2299211 3286245 747503 VID 76216.38 231006.8 249598 52483.89 VPB 436616.6 2185179 3186149 1388455 million VND a Other Interest a Expense 688 | ICUEH2017 Appendix ESTIMATED CONCENTRATION RATIOS 1600 0.78 1400 0.76 0.74 1200 HHI 0.7 800 0.68 600 0.66 400 0.64 200 CR4 0.72 1000 0.62 2007 2008 2009 2010 2011 2012 2013 2014 0.6 HHI 1380.39 1440.07 1271.08 1080.18 1028.39 1093.8 1114.64 1157.86 CR4 0.76 0.77 0.73 0.67 0.66 0.66 0.67 0.69 References Aigner, D., Lovell, C A K., & Schmidt, P (1977) Formulation and estimation of stochastic frontier production function models Journal of Econometrics, 6(1), 21-37 Al-Muharrami, S., & Matthews, K (2009) Market power versus 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