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

UNIVERSITY OF ECONOMICS ERASMUS UNVERSITY ROTTERDAM HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS 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 i ABSTRACT This study measures economic efficiency of commercial banks in Viet Nam and its determinants The Stochastic Frontier Model is applied for a profit function We include internal factors (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 other macroeconomic environment variables) in the profit function The empirical results show that the efficiency of Vietnamese commercial banks seems to be fluctuated steeply over the period 20082013 In terms of the efficiency of different groups of commercial banks, the profit efficiency of the State-owned commercial banks and Private commercial banks follow different trend Particularly, the state owned commercial bank’s group seems to have extremely higher profit efficiency score then private commercial bank ii LIST OF ABBREVIATION LSDV Least squares with dummy variables GLS Generalized least squares SFA Stochastic Frontier Analysis SFM Stochastic Frontier Model DEA Development Envelopment Analysis PE Profit efficiency CRER Credit risk LIQD Liquidity CON Concentration DEV Banking development INF Inflation rate GDP GDP SIZE SIZ PROFIT Total profit PBT Profit before tax X1 Labor X2 Capital X3 Fund W1 Price of labor W2 Price of capital W3 Price of fund Y1 Loan Y2 Other earning asset P1 Price of loan P2 Price of other earning asset TE Technical efficiency EE Economic efficiency AE Allocative efficiency Obs Number of observations SOBs State-owned commercial banks PCBs Private commercial banks iii LIST OF TABLES Table 3.1: Variables of potential correlates of profit efficiency function 32 Table 3.2: Data sample of observations over period 2008-2013 43 Table 4.1: Overview of variable in profit function 35 Table 4.2: A summary statistics of profit before tax, inputs, outputs and prices used in the profit efficiency estimation for the whole period 2008-2013 38 Table 4.3: A summary statistics of inputs, outputs and prices used in the profit efficiency estimations: (million VND units for X1, X2, X3, Y1, Y2) 39 Table 4.4: A summary statistics of potential correlated of profit efficiency .40 Table 4.5: Potential correlates of profit before tax (PBT) with time-invariant fixed-effects model 41 Table 4.6: Banking profit efficiency scores in the period 2008-2013 with time-invariant fixedeffects model .42 Table 4.7: Potential correlates profit efficiency through time-invariant fixed-effect model .43 Table 4.8: Potential correlates of profit before tax (PBT) with Cornwell et al (1990) time varying fixed-effects model 44 Table 4.9: Banking profit efficiency scores in the period 2008-2013 with time-varying Battese and Coelli (1995) model 45 Table 4.10: Potential correlates profit efficiency with Kumbhakar (1990) time-varying parametric model (half-normal) 46 Table 4.11: Average banking profit efficiency score of 27 commercial bank in Vietnam and its ranking from 2008 to 2013 48 LIST OF FIGURES Figure 1.1: Input-oriented efficiency 13 Figure 1.2: Output-oriented efficiency .14 Figure 4.1: Graph of scatter between Profit before tax (Pbt) and total number of staff (X1) from 2008 to 2013 36 Figure 4.2: Graph of scatter between Profit before tax (Pbt) and total fixed - asset (X2) from 2008 to 2013 36 Figure 4.3: Graph of scatter between Profit before tax (Pbt) and total deposit from customer (X3) from 2008 to 2013 .36 Figure 4.4: Graph of scatter between Profit before tax (Pbt) and total loan from customers (Y1) from 2008 to 2013 36 Figure 4.5: Graph of scatter between Profit before tax (Pbt) and total asset (Y2) from 2008 to 2013 36 Figure 4.6: Graph of scatter between Profit before tax (Pbt) and Price of labor (W1) from 2008 to 2013 .36 Figure 4.7: Graph of scatter between Profit before tax (Pbt) and Price of capital (W2) from 2008 to 2013 .37 Figure 4.8: Graph of scatter between Profit before tax (Pbt) and Price of fund (W3) from 2008 to 2013 .37 Figure 4.9: Graph of scatter between Profit before tax (Pbt) and Price of loan (P1) from 2008 to 2013 37 Figure 4.10: Graph of scatter between Profit before tax (Pbt) and Price of other earning asset (P2) from 2008 to 2013 37 Figure 4.11: Profit efficiency score from 2009 to 2013 with time-invariant fixed-effects model 43 Figure 4.12: Profit efficiency score of SOBs, PCBs and all banks from 2009 to 2013 with timevarying Battese and Coelli (1995) model 46 LIST OF APPENDIXES: Appendix 1: List of Vietnamese commercial banks in Vietnam Data was collected from www.taichinhvietnam.com and sorted based on their charter capital 57 Appendix 2: The result of estimation about the determinants of banking profit before tax and its potential correlate through different type of model 59 Appendix 3: Descriptive statistics of profit efficiency estimation through different type of model 67 TABLE OF CONTENTS ABSTRACT ii LIST OF ABBREVIATION iii LIST OF TABLES iv LIST OF FIGURES v LIST OF APPENDIXES vi CHAPTER I: INTRODUCTION Introduction: Research objective CHAPTER II: LITERATURE REVIEW .2 Theory of the efficiency 2 Reviews of empirical study on bank efficiency 2.1 Overview on bank efficiency 2.2 Determinants of banking efficiency 2.3 Bank efficiency measurement .9 CHAPTER III: DATA AND METHODOLOGY 21 Overview of the banking industry in Vietnam 21 Analytical framework 23 Research method .23 Theoretical Model .23 Estimation Methodology 24 Model specification 25 Functional form 26 Profit Efficiency 26 Variables description 28 10 Data sources: .33 CHAPTER IV: RESULTS AND DISCUSSION 35 Descriptive statistics 35 Regression results 38 2.1 Time-invariant fixed-effects model 38 vii 2.2 Time-varying fixed-effects model 42 CHAPTER V: CONCLUSION 47 REFERENCES 49 APPENDIX 54 viii Vietnam – Netherlands Programme for M.A in Development Economics CHAPTER I: INTRODUCTION Introduction: In most countries, the banking sector is considered as a key indicator to ensure the national economy operates smoothly Banking industry is widely considered as the backbone of an economy and highly regulated in many countries Banks usually has been considered as key players in providing capital and stimulating economic development In the study of Demirguc & Huizinga (2000), it showed that when an economy is developed at a certain level, it requires an intensive support from the stable and sustainable national financial system Prior studies in the body of literature indicate that there are a wide range of factors affecting the bank efficiency which can be classified into two main groups namely external factors (e.g macroeconomic factors and industrial factors) and internal factors of commercial banks (e.g size, losses, liquidity, and other factors) Particularly, Hasan and Marton (2003) examines the relationship between the development and efficiency of banking sector while Hou, Wang, and Zhang (2014) measure the linkages among market structure, risk taking and efficiency of commercial banks Due to their importance on the financial sector and their influence 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 to support this sector to stabilize and develop Like other countries, the banking system in Vietnam provides many products from retail banking such as saving accounts, deposits, credit/debit card, mortgage, loans to commercial banking such as business loan, capital equity, risk management, and credit services As commercial banks have evolved in Vietnam before the investment banks, this service is one of the most sensitive business which suffers direct and indirect impacts on several intrinsic obstacles of the economy as well as the external effects, thus stabilizing the currency and the banking system play an important factor, primarily in the financial system stabilization Therefore, understanding the efficiency of the banking system as well those factors affecting the banking operations attracts a lot of attention from many economists and scholars all over the world In measuring efficiency, people usually choose between technical efficiency and economic efficiency We choose economic efficiency as this is more comprehensive In this thesis, the profit function is used to measure economic efficiency Research objective While the capital funds raised for the national economy from those commercial banks are enormous, limited research was carried out about how these banks operate and whether they operate efficiently With a proper measurement about banking efficiency together with a wealth of predictive determinants will help the public to indicate how well a certain commercial bank operate in the competitive market like Vietnam Secondly, by improving the performance of individual banks, the whole national banking system will function much more efficiently and effectively However, at the moment, most of studies focused on qualifying the efficiency of the commercial banks (Nguyen, Roca, & Sharma, 2004) or determining 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 the current financial growth at the national scale and the efficiency performance of commercial banks in Vietnam In details, this thesis is to understand the connection between the economic efficiency and the economic growth of the banking system based on two following smaller objectives:  How efficient are commercial banks in Vietnam?  Which factors affect the economic efficiency of commercial banks? This is the research of Vietnamese banks efficiency that will convey the managers to identify 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 resource inputted In addition, this will be a useful research to analyze inefficiencies and minimize it in order to improve the performance of Vietnamese banking industry Moreover, this research also demonstrates the reality of Vietnamese banking industry, which will be a useful guide for foreign investors to start financing their equity in this dynamic industry Chortareas, G E., Garza-García, J G., and Girardone, C (2012) Competition, efficiency and interest rate margins in latin american banking International Review of Financial Analysis, 24(0), 93-103 Coelli, T J., Rao, D S P., O’Donnell, C J., and Battese, G E (2005) Data envelopment analysis An introduction to efficiency and productivity analysis (pp 161-181): Springer US Delis, M D (2012) Bank competition, financial reform, and institutions: The importance of being developed Journal of Development Economics, 97(2), 450-465 Economist Intelligence Unit (2005) Vietnam regulations: Banking sector is liberalising (pp n/a-n/a) New York, United States Färe, R., Grosskopf, S., and Lovell, C A K (1988) Scale elasticity and scale efficiency Journal of Institutional and Theoretical Economics (JITE) / Zeitschrift für die gesamte Staatswissenschaft, 144(4), 721-729 Farrell, M J (1957) The measurement of productive efficiency Journal of the Royal Statistical Society, 120(3), 253 - 290 Federal Reserve Bank of San Francisco (2011) Banking reform in vietnam Retrieved from http://www.frbsf.org/publications/banking/asiafocus/2011/june-banking- reform-in-vietnam.pdf Førsund, F R., and Hjalmarsson, L (1979) Generalised farrell measures of efficiency: An application to milk processing in swedish dairy plants The Economic Journal, 89(354), 294-315 Fu, X., Lin, Y., and Molyneux, P (2014) Bank efficiency and shareholder value in asia pacific Journal of International Financial Markets, Institutions and Money, 33(0), 200-222 Goddard, J., Molyneux, P., and Williams, J (2014) Dealing with cross-firm heterogeneity in bank efficiency estimates: Some evidence from latin america Journal of Banking and Finance, 40(0), 130-142 50 Hasan, I., and Marton, K (2003) Development and efficiency of the banking sector in a transitional economy: Hungarian experience Journal of Banking and Finance, 27(12), 2249-2271 Hou, X., Wang, Q., and Zhang, Q (2014) Market structure, risk taking, and the efficiency of chinese commercial banks Emerging Markets Review, 20(0), 75-88 Jiang, C., Yao, S., and Zhang, Z (2009) The effects of governance changes on bank efficiency in china: A stochastic distance function approach China Economic Review, 20(4), 717-731 Kohers, T., Huang, M.-h., and Kohers, N (2000) Market perception of efficiency in bank holding company mergers: The roles of the dea and sfa models in capturing merger potential Review of Financial Economics, 9(2), 101-120 Kwan, S H (2006) The x-efficiency of commercial banks in hong kong Journal of Banking and Finance, 30(4), 1127-1147 Lensink, R., Meesters, A., and Naaborg, I (2008) Bank efficiency and foreign ownership: Do good institutions matter Journal of Banking and Finance, 32, 834 - 844 Liadaki, A., and Gaganis, C (2010) Efficiency and stock performance of eu banks: Is there a relationship? 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of china's insurance industry after wto accession China Economic Review, 18(1), 66-86 Yin, H., Yang, J., and Mehran, J (2013) An empirical study of bank efficiency in china after wto accession Global Finance Journal, 24(2), 153-170 Pavlos Almanidis, Giannis Karagiannis, Robin C Sickles Semi-nonparametric spline modifications to the Cornwell–Schmidt–Sickles estimator: an analysis of US banking productivity Empirical Economic, 2015 Sangho Kim et al (2002) Public Sector Capital and the Production Efficiency of U.S Regional Manufacturing Industries Japanese economic review, 12/2002 Peter Dawson et al (2000) Estimating Coaching Efficiency in Professional Team Sports: Evidence from English Association Football Scottish journal of political Economy, 9/2000 Young Hoon Lee (2006) A stochastic production frontier model with group-specific temporal variation in technical efficiency European Journal of Operational research, volume 174 issue 3, November 2006, Pages 1616–1630 K.Kim et al Analysis on the Technical Efficiency and Productivity Growth of the Korean Cable SOs: A Stochastic Frontier Approach Contributions to economic (2009) Zhu, W and Zhao, D (2007) Effect of Subsidy Policy on Technical Efficiency: An Empirical Study of China's Urban Public Transportation International Conference on Transportation Engineering 2007 Kumbhakar S.C Temporal patterns of technical efficiency: Results from competing models International Journal of Industrial Organization (1997) Claudia Ginrardone et al (2004) Analysing determinants of banking efficiency: the case of Italian banks Applied economic, 2004, 36, 215-227 APPENDIX Appendix 1: List of Vietnamese commercial banks in Vietnam Data was collected from www.taichinhvietnam.com and sorted based on their charter capital (Billion VND dong) No Name of bank Charter Year of capital opening Type of banks Agribank 20.708 1988 State-owned commercial bank Vietinbank 20.230 1988 State-owned commercial bank Vietcombank 19.698 1963 State-owned commercial bank BIDV 14.600 1957 Joint-stock commercial bank Eximbank 12.355 1989 Joint-stock commercial bank Maritime Bank 11.750 1991 Joint-stock commercial bank Asia Commercial Bank 11.252 1993 Joint-stock commercial bank Sacombank 10.739 1989 Joint-stock commercial bank Saigon commercial bank 10.583 2011 Joint-stock commercial bank 10 PV Bank 9.000 2013 Joint-stock commercial bank 11 Techcombank 8.788 1993 Joint-stock commercial bank 12 Military Bank 7.300 1990 Joint-stock commercial bank 13 LienVietPostBank 6.460 2008 Joint-stock commercial bank 14 VPBank 6.347 1993 Joint-stock commercial bank 15 HDBank 5.450 1993 Joint-stock commercial bank 16 SeABank 5.335 1993 Joint-stock commercial bank 17 Saigon-Hanoi commercial bank 5.000 1993 Joint-stock commercial bank 18 Construction Bank 5.000 2015 Joint-stock commercial bank 19 DongA Bank 4.500 1992 Joint-stock commercial bank No Name of bank Charter Year of capital opening Type of banks 20 Vietnaminternational commercial bank 4.250 1996 Joint-stock commercial bank 21 Oceanbank 4.000 1993 Joint-stock commercial bank 22 Southern Bank 4.000 1993 Joint-stock commercial bank 23 National citizen commercial bank 3.500 1995 Joint-stock commercial bank 24 Oriented commercial bank 3.400 1996 Joint-stock commercial bank 25 Dai A Bank 3.100 1996 Joint-stock commercial bank 26 GPBank 3.018 2005 Joint-stock commercial bank 27 NAS bank 3.000 1994 Joint-stock commercial bank 28 Viet Capital Bank 3.000 1992 Joint-stock commercial bank 29 KienLong Bank 3.000 1995 Joint-stock commercial bank 30 Nam A Bank 3.000 1992 Joint-stock commercial bank 31 Saigonbank 3.000 1987 Joint-stock commercial bank 32 VietABank 3.000 2003 Joint-stock commercial bank 33 VietBank 3.000 2007 Joint-stock commercial bank 34 Petrolimex Bank 3.000 1993 Joint-stock commercial bank 35 TienPhongBank 3.000 2008 Joint-stock commercial bank 36 An Binh Bank 2.700 1993 Joint-stock commercial bank 37 BaoVietBank 1.500 2008 Joint-stock commercial bank Appendix 2: The result of estimation about the determinants of banking profit before tax and its potential correlate through different type of model 2.1 Schmidt and Sickles, 1984 with fixed effect model sfpanel lnpbt lnp1 lnp2 prlabor prcapital prfund,model(fe) Time-invariant fixed-effects model (LSDV) Group variable: bank Time variable: year lnpbt Coef lnp1 lnp2 prlabor prcapital prfund _cons 4.198393 3397084 -.039442 -.2517396 -3.735177 18.60677 sigma_u sigma_v 1.2368158 55908501 Std Err 1.37591 0978474 3482624 2052482 1.506359 2.109864 Number of obs of Number groups Obs per group: avg = = = = max = z 3.05 3.47 -0.11 -1.23 -2.48 8.82 P>|z| 0.002 0.001 0.910 0.220 0.013 0.000 120 27 4.4 [95% Conf Interval] 1.501659 1479309 -.7220238 -.6540186 -6.687587 14.47152 6.895126 5314859 6431397 1505394 -.7827683 22.74203 2.2 (Schmidt and Sickles, 1984) with random effect model sfpanel lnpbt lnp1 lnp2 prlabor prcapital prfund,model(regls) Time-invariant Random-effects model (FGLS) Group variable: bank Time variable: year lnpbt Coef lnp1 lnp2 prlabor prcapital prfund _cons 3.178243 3427337 3996413 0428911 -3.022183 11.64578 sigma_u sigma_v 60162966 55908501 Std Err 1.351252 1090995 3946904 2286064 1.48116 2.164916 Number of obs Number of groups Obs per group: avg max z 2.35 3.14 1.01 0.19 -2.04 5.38 P>|z| 0.019 0.002 0.311 0.851 0.041 0.000 = = = = = 120 27 4.4 [95% Conf Interval] 529838 1289026 -.3739377 -.4051693 -5.925203 7.402626 5.826647 5565648 1.17322 4909514 -.1191634 15.88894 2.3 Battese and Coelli (1988) with u truncated normal sfpanel lnpbt lnp1 lnp2 prlabor prcapital prfund,model(bc88) Time-invariant model (truncated-normal) Group variable: bank Time variable: year Log likelihood = lnpbt Number of obs Number of groups Obs per group: avg max Prob > chi2 Wald chi2(5) -136.8706 Coef Std Err z P>|z| = = = = = 120 27 4.4 = = 0.0004 22.90 [95% Conf Interval] Frontier lnp1 lnp2 prlabor prcapital prfund _cons 3.694703 3457527 1665881 -.1094272 -3.366826 17.62234 1.279968 0950796 3458668 2035166 1.396376 2.452743 2.89 3.64 0.48 -0.54 -2.41 7.18 0.004 0.000 0.630 0.591 0.016 0.000 1.186012 1594 -.5112983 -.5083124 -6.103673 12.81505 6.203395 5321053 8444745 289458 -.629979 22.42963 /lnsigma2 /ilgtgamma /mu 4090443 1.36415 2.272285 3091456 4394098 6328651 1.32 3.10 3.59 0.186 0.002 0.000 -.1968699 5029228 1.031892 1.014959 2.225377 3.512678 sigma2 gamma sigma_u2 sigma_v2 1.505378 7964334 1.198934 3064448 4653811 0712403 4694979 0467788 8212975 623146 2787346 2147601 2.759249 9025054 2.119133 3981296 2.4 Cornwell et al (1990) time varying effects which follows a function of time sfpanel lnpbt lnp1 lnp2 prlabor prcapital prfund,model(fecss) Time-varying fixed-effects model (CSS Modified-LSDV) Number of obs = Group variable: bank Number of groups = Time variable: year Obs per group: = avg = max = lnpbt Coef lnp1 lnp2 prlabor prcapital prfund 2.472617 4491118 1307675 -.2320278 -2.534667 sigma_u sigma_v 1.2025942 33526097 Std Err 1.708415 1018857 5388136 2207682 1.862956 z 1.45 4.41 0.24 -1.05 -1.36 P>|z| 0.148 0.000 0.808 0.293 0.174 120 27 4.4 [95% Conf Interval] -.8758157 2494195 -.9252878 -.6647255 -6.185994 5.821049 6488041 1.186823 2006699 1.11666 2.5 Kumbhakar (1990) with the time varying sfpanel lnpbt lnp1 lnp2 prlabor prcapital prfund,model(kumb90) Time-varying parametric model (half-normal) Group variable: bank Time variable: year Log likelihood = lnpbt Prob > chi2 Wald chi2(5) -114.0995 Coef Number of obs Number of groups Obs per group: avg max Std Err z P>|z| = = = = = 120 27 4.4 = = 0.0000 64.27 [95% Conf Interval] Frontier lnp1 lnp2 prlabor prcapital prfund _cons 4.163957 3315176 1.013599 074006 -4.42011 7.890089 9977763 0822256 2979419 1806788 1.089532 2.206345 4.17 4.03 3.40 0.41 -4.06 3.58 0.000 0.000 0.001 0.682 0.000 0.000 2.208352 1703584 4296433 -.2801179 -6.555553 3.565733 6.119563 4926768 1.597554 4281299 -2.284666 12.21445 b c 9882546 -.2068163 172745 0328004 5.72 -6.31 0.000 0.000 6496806 -.271104 1.326829 -.1425287 /sigmau_2 /sigmav_2 15.93036 2199874 7.255593 0332395 2.20 6.62 0.028 0.000 1.709659 1548391 30.15106 2851357 sigma_u sigma_v lambda 3.991285 4690281 8.509693 9089293 0354345 9054828 4.39 13.24 9.40 0.000 0.000 0.000 1.307539 393496 6.73498 5.490998 533981 10.28441 Bt 60 2.6 Battese and Coelli (1992) with a technical efficiency and truncated normal distribution at zero sfpanel lnpbt lnp1 lnp2 prlabor prcapital prfund,model(bc92) Time-varying decay model (truncated-normal) Group variable: bank Time variable: year Log likelihood = lnpbt Number of obs Number of groups Obs per group: avg max Prob > chi2 Wald chi2(5) -125.4620 Coef Std Err z P>|z| = = = = = 120 27 4.4 = = 0.0000 66.11 [95% Conf Interval] Frontier lnp1 lnp2 prlabor prcapital prfund _cons 4.70956 2310566 1.083232 2178284 -4.566797 5.704297 1.123927 0913601 3431991 2079262 1.229133 2.791917 4.19 2.53 3.16 1.05 -3.72 2.04 0.000 0.011 0.002 0.295 0.000 0.041 2.506703 0519941 4105743 -.1896995 -6.975854 2322407 6.912417 410119 1.75589 6253563 -2.15774 11.17635 /lnsigma2 /ilgtgamma /mu /eta 7532815 1.875455 9752062 -.2272321 5956937 6989891 9503405 0749525 1.26 2.68 1.03 -3.03 0.206 0.007 0.305 0.002 -.4142568 5054614 -.8874269 -.3741363 1.92082 3.245449 2.837839 -.0803279 sigma2 gamma sigma_u2 sigma_v2 2.123958 8670882 1.841659 2822991 1.265229 0805559 1.263531 0424967 6608313 6237419 -.6348154 1990071 6.826553 9625092 4.318134 3655912 61 2.7 Battese and Coelli (1995) with a technical efficiency and truncated normal distribution at zero sfpanel lnpbt lnp1 lnp2 prlabor prcapital prfund,model(bc95) Inefficiency effects model (truncated-normal) Group variable: bank Time variable: year Log likelihood = lnpbt Number of obs Number of groups Obs per group: avg max Prob > chi2 Wald chi2(5) -151.3222 Coef Std Err z P>|z| = = = = = 120 27 4.4 = = 0.0000 78764.35 [95% Conf Interval] Frontier lnp1 lnp2 prlabor prcapital prfund _cons 3.293586 2042989 1.049241 2874152 -3.850424 3.883808 9211122 0777567 6974293 371301 1.337038 2.182924 3.58 2.63 1.50 0.77 -2.88 1.78 0.000 0.009 0.132 0.439 0.004 0.075 1.488239 0518986 -.3176957 -.4403213 -6.470971 -.3946456 5.098932 3566992 2.416177 1.015152 -1.229878 8.162261 _cons 9885463 2770013 3.57 0.000 4456337 1.531459 _cons 4658394 2655103 1.75 0.079 -.0545512 9862299 _cons -19.17327 26.28532 -0.73 0.466 -70.69155 32.34501 sigma_u sigma_v lambda 1.26228 0000686 18389.84 1675742 0009021 1675951 7.53 0.08 1.1e+05 0.000 0.939 0.000 973093 4.46e-16 18389.52 1.637409 1.06e+07 18390.17 Mu Usigma Vsigma 2.8 Lee and Schmidt (1993) with a function with the time function is replaced by a set of dummy variables sfpanel lnpbt lnp1 lnp2 prlabor prcapital prfund,model(fels) Time-varying fixed-effects model (Iterative LS) Number of obs = Group variable: bank Number of groups = Time variable: year Obs per group: = avg = max = lnpbt Coef Std Err Frontier lnp1 lnp2 prlabor prcapital prfund 3.226146 1385396 4782888 7531739 -3.130212 1.286743 0948973 4403415 1760138 1.411741 sigma_u sigma_v 82028172 57796677 z 2.51 1.46 1.09 4.28 -2.22 P>|z| 0.012 0.144 0.277 0.000 0.027 120 27 4.4 [95% Conf Interval] 7041766 -.0474558 -.3847647 4081931 -5.897173 5.748115 324535 1.341342 1.098155 -.3632501 Appendix 3: Descriptive statistics of profit efficiency estimation through different type of model sum eff1 eff2 eff3 eff4 eff5 eff7 eff8 eff10 Variable Obs Mean Std Dev Min Max eff1 eff2 eff3 eff4 eff5 120 120 162 120 120 1897591 3040625 2414794 4292832 331005 2471796 246255 1820754 2636527 2549261 0170183 0618459 0417799 0276929 0079115 1 7498389 9224332 9998103 eff7 eff8 eff10 162 120 118 5435435 5957178 2356303 2574238 3225025 2609426 0175849 028772 0060706 9540524 9999998 ... considers about the inefficiency of the banking system based on the results of banking production, it means that if the bank operates with the lower profit and higher cost than another bank in the system... to find out the relationship between banking profit efficiency and what are determinants of banking efficiency using the panel data of the period 2008-2013 of twenty-seven banks in Viet Nam including... banking system play an important factor, primarily in the financial system stabilization Therefore, understanding the efficiency of the banking system as well those factors affecting the banking