Tái cấu trúc ngân hàng và hiệu quả hoạt động ngân hàng trường hợp việt nam nơi nghiên cứu đại học kinh tế TPHCM

TABLE OF CONTENT LIST OF TABLES LIST OF FIGURES Figure 3.1 Result after the merger of the authorized capital………………………55 Figure 3.2 Result after banking consolidation and merger in 2015……………….57 ABBREVIATION 2SLS: Two-stage least squares CBs: Commercial banks DEA: Data Envelopment Analysis DMU: Decision unit ESS: Scale-efficiency hypothesis ESX: X-efficiency hypothesis GLS: Generalized least squares IMF: International monetary fund NPL: Non performing loan OLS: Ordinary Least Square QLH: Quiet-life hypothesis RMP: Relative market power ROA: Return on asset ROE: Return on Equity RQ1: Research question RQ2: Research question SBV: State Bank of Vietnam SCP: Structure, Conduct and Performance paradigm SEFF: Scale-efficient hypothesis SFA: Stochastic Frontier Analysis VAMC: Vietnam Asset Management Company XEFF: X-Efficiency ABSTRACT The link between bank restructuring and bank efficiency is an appealing area that draws strong attention from both academic and industry practitioners Vietnam offers an interesting case to analyze this link however this issue remains unexplored This thesis investigates the association between bank restructuring and bank efficiency in Vietnamese banking system The thesis employ the Data Envelopment Approach and Stochastic Frontier approach Our data sample includes 26 commercial banks covering the period 1999-2015 The finding indicates that the government’s restructuring policies in the first stage did not benefit the banks implementing the restructuring methods Regarding to the effect of different restructuring methods, the privatization of state-owned commercial banks, state intervention and mergers and acquisitions (M&As) not substantially improve efficiency Besides, it is found that the bank efficiency declines during the restructuring period not only because of the transition cost but also due to the changes of other environment variables, such as financial crisis or domestic economy slowdown The thesis contributes to the banking literature by examining the allocative and technical efficiency of Vietnamese banking system in the recent period (20012015) It shows that decreasing the government's control of the banking system makes the system more competitive On the other hand, it makes domestic banks more vulnerable to competitive factors and effects of the world crisis Through the thesis, the indicated advantage results are keys to find better solutions for restructuring the banking system Furthermore, the rate of privatization also needs to be considered to create the restructuring theory from the privatization of stateowned commercial banks The thesis has relevant policy implications Bank restructuring is an important program for emerging countries to build a prudential financial system Evaluating the association between restructuring program and bank efficiency is important to achieve a successful implementation of government policy Key words: Restructuring, Bank efficiency, Integration, Financial crisis, Economy TÓM TẮT LUẬN ÁN Mối quan hệ tái cấu trúc ngân hàng hiệu hoạt động ngân hàng mối quan tâm lớn nhà khoa học lẫn doanh nghiệp nhà làm sách Chính thế, việc đánh giá mối quan hệ đo lường tính hiệu trình tái cấu trúc hệ thống Ngân hàng thương mại Việt Nam thời gian qua yê cầu thiết Luận án nghiên cứu mối quan hệ biện pháp tái cấu trúc hệ thống NHTM trình tái cấu trúc ngân hàng thương mại tác động đến hiệu hoạt động ngân hàng toàn hệ thống NHTM Việt Nam, kinh tế mở nhỏ trình chuyển đổi để hội nhập Luận án sử dụng phương pháp DEA, SFA, Random effect, Fixed effect, 2SLS để nghiên cứu 26 NHTM Việt Nam khaong3 thời gian từ năm 1999 đến năm 2015 Kết nghiên cứu cho thấy, biện pháp trình tái cấu trúc NHTM Việt Nam chưa thực mang lại hiệu quả, cịn mang tính hình thức biện pháp chưa liệt Luận án bổ sung thêm vào lý thuyết tái cấu trúc NHTM quốc gia có kinh tế mở nhỏ, quốc gia có kinh tế mở, nhỏ dễ bị tổn thương trình tái cấu trúc, đồng thời việc lựa chọn thời điểm tái cấu trúc để hội nhập vấn đề cần phải xem xét thận trọng, có tác động lớn đến hiệu việc tái cấu trúc Tái cấu trúc ngân hàng chương trình quan trọng để nước xây dựng hệ thống tài ổn định bền vững Do việc đánh giá liên kết chương trình tái cấu trúc hiệu ngân hàng quan trọng để đạt việc thực thành cơng sách phủ Từ khóa: Tái cấu trúc, Hiệu hoạt động ngân hàng, Hội nhập, Khủng hoảng tài chính, Nền kinh tế CHAPTER INTRODUCTION 1.1 OVERVIEW Banking system is a crucial factor that controls capital flows in the economy and contributes to promote economic and social development of the nation In more than sixty years of construction and development of four state-commercial banks, thirty joint-stock commercial banks, joint-venture banks and banks with 100% foreign capital, the banking system in Vietnam is gradually developing a perfect structure In 2006, Vietnam joined WTO and started to open the market economy Restructuring the banking system is a necessity in this time, stemming from the economic situation and the demands of the people However, under the impact of the global financial crisis, Vietnam banking system soon faced with a number of risks and challenges to the daily evaluation of bank performance, which has deteriorated over the years as a consequence of profit slump, low credit growth, and rising bad debts Therefore, together with restructuring banking system to build a healthy economy, the central bank and commercial banks have been implementing measures to restructure the banking system to overcome these difficulties and improve the operational efficiency of the entire system and individual banks because of the impacts of 2008 global financial crisis With the aim of assessing the situation to restructure the banking system in Vietnam in recent years, the subject is based on previous studies on the restructuring the banking system of other countries The content of the specific research topic includes references of the previous studies on restructuring to build, to choose the model and the proposed research methodology; analyse the situation of Vietnam’s restructuring banking system through quantitative research models from which to propose recommendations and solutions to improve efficiency in the process of restructuring the banking system on a sustainable and effective basis There is a close relationship between bank restructuring and efficiency It is proved through a number of researches in many economies in the world With different restructuring measures, the results of the linkage between bank restructuring and efficiency may be positive or negative The result depends on which kind of economy that country has, the status of that economy and what problems that economy suffered A number of researches have proved that the association between bank restructuring and efficiency is positive; For example, after the banking restructuring, the bank efficiency in Turkey was significant improved (Zaim, 1995) Accordingly, privatization is a good measure to increase banking efficiency; mergers and acquisitions also has positive effect on cost and profit efficiency However, some other researchers said that the banking restructuring did not have any impact on the operating efficiency, it was suggested to even contribute to its fall; For example, Elyasiani and Mehdian (1995) indicated that the performance efficiency of the United States’ banks was unaffected after the banking restructuring Therefore, this thesis needs to research the linkage between bank restructuring and efficiency to investigate whether this relationship in an emerging economy like Vietnam’s is positive or negative This thesis focuses on studying the restructuring process of the Vietnamese banking system from 2007 to 2015, and assessing different financial structure of the system before and during the implementation stage of the restructuring process More specifically, it examines how the restructuring measures affect the performance of the commercial banks and whether the process of restructuring in the sample period increases the operational efficiency of the banks or not It also explores the effect of reform on Vietnam’s bank structure and performance Besides, different structures of the banking system are investigated between pre (2001-2006) and during restructuring period (2007-2015), the quiet-life, efficient structure, market structure conduct and performance hypothesis are tested to find the consequence 1.2 MOTIVATIONS AND RESEARCH QUESTIONS This section highlights the current literature to identify potential gaps from which the objectives and the research questions are raised Accordingly, it is structured into three sections: the method of bank restructuring (Section 1.2.1) and the structure and performance relationship in the banking system (Section 1.2.2) 1.2.1 The method of bank restructuring Restructuring program is established to solve problems of distressed banks (McComb, Gruben, & Welch, 2002) There are four main restructuring methods including merging domestic banks, giving permission to foreign bank entries, state intervention and the privatization of state-owned commercial banks Hawkin and Turner (1999) showed that domestic merger was often the least costly way of restructuring the banking system Moreover, Berger et al (1999) indicated that mergers helped to increase efficiency if greater diversification improved the risk – return tradeoffs Krishnasamy et al (2004) studied production efficiency of Malaysia’s postmerger banks in 2000–2001 and discovered that technology was a crucialfactor for increasing the productivity Unite and Sullivan (2003) also said that in Asia, it was easy for foreign banks to raise their presence and operate Daniel (1997) showed thatgovernments can improve banks’ ability effectively by several methods For instance, in Indonesia, the government created a recapitalization program where the owners provided 20% of the capital shortfall (Fane and McLeod, 2002) In Korea, the government purchased non-performing loans, subordinated debt, or subscribed to new capital to assist private banks’ recapitalization efforts Baer (1994) and Baer and Nazmi (2000) pointed out that the inefficient operation of state-commercial banks accelerated the process of recession in Brazil Landier, A., & Ueda, K (2009) The economics of bank restructuring: Understanding the options (No 2009-2012) International Monetary Fund Lee, C C., & Hsieh, M F (2013).The impact of bank capital on profitability and risk in Asian banking Journal of international money and finance, 32, 251-281 Leibenstein, H (1966) Allocative efficiency vs." X-efficiency" The American Economic Review, 392-415 Lovell, C K., & Schmidt, P (1988).A comparison of alternative approaches to the measurement of productive efficiency In Applications of modern production theory: Efficiency and productivity (pp 3-32) Springer, Dordrecht Maseno, O P (2014) The effect of Financial restructuring on the financial performance of commercial banks in Kenya Unpublished Master of Science in Finance Thesis: University of Nairobi Maudos, J., Pastor, J M., Perez, F., & Quesada, J (2002).Cost and profit efficiency in European banks Journal of international financial markets, institutions and money, 12(1), 33-58 McComb, R P., Gruben, W C., & Welch, J H (1994) Privatization and performance in the Mexican financial services industry The Quarterly Review of Economics and Finance, 34, 217-235 Meeusen, W., & van Den Broeck, J (1977) Efficiency estimation from Cobb-Douglas production functions with composed error International economic review, 435-444 Modigliani, F., & Miller, M H (1958).The cost of capital, corporation finance and the theory of investment The American economic review, 261-297 Molyneux, P., & Forbes, W (1995).Market structure and performance in European banking Applied Economics, 27(2), 155-159 Musara, M., & Fatoki, O (2010) Has technological innovations resulted in increased efficiency and cost savings for banks' customers? African Journal of Business Management, 4(9), 1813 Nakane, M I., & Weintraub, D B (2005) Bank privatization and productivity: Evidence for Brazil Journal of Banking & Finance, 29(8), 22592289 Oleka, D C., & Mgbodile, C C (2014) Recapitalization Reform and Banks’ Performance–Empirical Evidence from Nigeria Research journal of Finance and Accounting, 5(6), 96-101 Paradi, J C., & Zhu, H (2013).A survey on bank branch efficiency and performance research with data envelopment analysis Omega, 41(1), 61-79 Park, K H., & Weber, W L (2006).A note on efficiency and productivity growth in the Korean banking industry, 1992–2002 Journal of Banking & Finance, 30(8), 2371-2386 Pazarbasioglu, C., & Dziobek, M C H (1997) Lessons from systemic bank restructuring: a survey of 24 countries (No 97-161) International Monetary Fund Peltzman, S (1977).The gains and losses from industrial concentration The Journal of Law and Economics, 20(2), 229-263 Philippon, T., & Schnabl, P (2013).Efficient recapitalization The Journal of Finance, 68(1), 1-42 Pinprayong, B., &Siengtai, S (2012) Restructuring for organizational efficiency in the banking sector in Thailand: a case study of Siam Commercial Bank Far East Journal of Psychology and Business, 8(2), 29-42 Schulz, H (2006) Foreign Banks in Mexico: New Conquistadors or Agents of Change? Wharton Financial Institutions Center Working Paper, 06-11 Silberston, A (1972) Economies of scale in theory and practice The Economic Journal, 82(325), 369-391 Staub, Souza & Tabak (2010) Evolution of bank efficiency in Brazil: A DEA approach European journal of operational research, 202(1), 204-213 Tan, Y (2013) Essays on the analysis of performance and competitive condition in the Chinese banking industry (Doctoral dissertation, University of Portsmouth) Thoraneenitiyan, N., & Avkiran, N K (2009).Measuring the impact of restructuring and country-specific factors on the efficiency of post-crisis East Asian banking systems: Integrating DEA with SFA Socio-Economic Planning Sciences, 43(4), 240-252 Tofallis, C (2001) Combining two approaches to efficiency assessment Journal of the Operational Research Society, 52(11), 1225-1231 Unite, A A., & Sullivan, M J (2003) The effect of foreign entry and ownership structure on the Philippine domestic banking market Journal of Banking & Finance, 27(12), 2323-2345 Vander Vennet, R., & Gropp, R (2003) Cross-border mergers in European banking and bank efficiency In Foreign direct investment in the real and financial sector of industrial countries(pp 295-321) Springer, Berlin, Heidelberg Walker, M M (1998) Leveraged recapitalizations, operating efficiency, and stockholder wealth Financial Review, 33(3), 99-114 Waxman, M (1998) A legal restructuring Available at SSRN 172984 framework for systemic bank Weill, L (2003) Banking efficiency in transition economies Economics of transition, 11(3), 569-592 Wheelock, D C., & Wilson, P W (2000) Why banks disappear? The determinants of US bank failures and acquisitions Review of Economics and Statistics, 82(1), 127-138 Williams & Nguyen (2005) Financial liberalisation, crisis, and restructuring: A comparative study of bank performance and bank governance in South East Asia Journal of Banking & Finance, 29(8), 2119-2154 Wruck, K H (1990) Financial distress, reorganization, and organizational efficiency Journal of financial economics, 27(2), 419-444 Xiaogang, C., Skully, M., & Brown, K (2005) Banking efficiency in China: Application of DEA to pre-and post-deregulation eras: 1993–2000 China Economic Review, 16(3), 229-245 Ye, Q., Xu, Z., & Fang, D (2012).Market structure, performance, and efficiency of the Chinese banking sector Economic Change and Restructuring, 45(4), 337-358 Yudistira (2004) Efficiency in Islamic banking: An empirical analysis of 18 banks Islamic Economic Studies, 12(1), 2004 Zaim, O (1995) The effect of financial liberalization on the efficiency of Turkish commercial banks Applied Financial Economics, 5(4), 257-264 APPENDIX APENDIX A - List of banks in the study 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 An Binh Commercial Joint Stock Bank (ABBANK) Asia Commercial Bank (ACB) Eastern Asia Commercial Joint Stock Bank (DongABank) Viet nam Export – Import Commercial Joint Stock Bank (EIB) HD Bank HSCB Bank LienViet Post Commercial Joint Stock Bank Maritime Commercial Joint Stock Bank (Maritimebank) OCEAN Commercial Joint Stock Bank (Oceanbank) Military Commercial Joint Stock Bank (MBB) Vietnam Development Bank (VDB) Vietnam International Commercial Bank (VIB Bank) Vietnam Prosperity commercial joint-stock bank (VPBank) Vietnam Technological and Commercial Bank (Techcombank) Saigon-Hanoi Commercial Joint Stock Bank (SHB) SaiGonThuong Tin Commercial Joint-stock Bank (Sacombank) Southern Commercial Joint Stock Bank (Southernbank) outheast Asia Commercial Joint Stock Bank (SeAbank) Saigon Commercial Bank (SCB) Saigon Bank For Industry And Trade (Saigonbank) Industrial and Commercial Bank of Vietnam (Vietinbank) Bank For Investment And Development Of Vietnam (BIDV) Bank for Foreign Trade of Vietnam (Vietcombank) Mekong Delta Housing Development Bank (MHB) Vietnam Bank for Agriculture and Rural Development (Agribank) Hanoi Building Joint-stock Commercial Bank (Habubank) APPENDIX B - Model for testing the relationship of bank restructuring and efficiency Log likelihood = Prob > chi2 Wald chi2(7) -509.5468 Std Err z = = P>|z| 0.0000 2.36e+08 totalloan Coef [95% Conf Interval] ma si cop growth realinterestrate fiscalsurplusgdp changeintermsoftrade npl realdomesticcreditgrowth 1.723525 3.009368 1.254408 69.15366 2134821 -38.77375 -11.18154 2231217 0483719 4161118 2732049 3350119 0455895 1.516109 0194998 0058619 4.14 11.02 3.74 4.68 -7.38 11.44 8.25 0.000 0.000 0.000 0.000 0.000 0.000 0.000 9079606 2.473896 5977968 1241283 -14.15306 1849028 0368827 2.539089 3.54484 1.911019 3028358 -8.210019 2613406 059861 _cons -8.254775 2.454585 -3.36 0.001 -13.06567 -3.443877 _cons -1.47448 1786367 -8.25 0.000 -1.824602 -1.124359 Frontier Usigma Vsigma sigma_u sigma_v lambda Log likelihood = 016125 4784325 0337037 01979 0427328 0445473 0.81 11.20 0.76 0.415 0.000 0.449 Prob > chi2 Wald chi2(4) -53.3228 noninterestrevenue 0014549 4015991 -.0536074 Coef Std Err z P>|z| 1787194 5699655 1210148 = = 0.0000 1.68e+10 [95% Conf Interval] Frontier ma si cop growth realinterestrate fiscalsurplusgdp changeintermsoftrade npl realdomesticcreditgrowth -.5843642 5949205 367844 59.47478 -.1144767 -35.05525 7952452 -.0094052 -.0286525 0001595 3730.32 0000246 -4658.17 0000261 -360.44 4.43e-06 -6466.13 0.000 0.000 0.000 0.000 5946079 -.1145248 -.0094564 -.0286612 5952331 -.1144285 -.0093541 -.0286438 _cons -.6149914 2279215 -2.70 0.007 -1.061709 -.1682734 _cons -29.67793 158.0092 -0.19 0.851 -339.3703 280.0145 Usigma Vsigma sigma_u sigma_v lambda 735286 3.59e-07 2046146 0837938 0000284 0837937 8.77 0.01 2.4e+07 0.000 0.990 0.000 5881021 2.03e-74 2046146 9193056 6.37e+60 2046146 Log likelihood = -72.4772 Wald chi2(8) Std Err z = P>|z| 205.80 interestrevenue Coef [95% Conf Interval] ma si cop growth realinterestrate fiscalsurplusgdp changeintermsoftrade npl realdomesticcreditgrowth 1.567048 1.112031 -.0364539 58.75496 -.1118735 -38.26057 -4.01676 0230249 -.0290335 502845 5519707 3549319 0333228 5.900356 1.809068 0289081 0084242 3.12 2.01 -0.10 -3.36 -6.48 -2.22 0.80 -3.45 0.002 0.044 0.918 0.001 0.000 0.026 0.426 0.001 5814895 0301879 -.7321077 -.1771851 -49.82505 -7.562467 -.033634 -.0455447 2.552606 2.193873 6591998 -.046562 -26.69609 -.4710531 0796839 -.0125224 _cons -8.259371 6.14147 -1.34 0.179 -20.29643 3.777689 _cons -1.114005 2336662 -4.77 0.000 -1.571982 -.6560275 Frontier Usigma Vsigma sigma_u sigma_v lambda Log likelihood = 0160879 5729239 0280804 0494018 0669365 0839903 0.33 8.56 0.33 0.745 0.000 0.738 0000391 4556679 -.1365376 Prob > chi2 Wald chi2(7) -156.0940 Std Err z P>|z| 6.611725 7203531 1926984 = = 0.0000 8.25e+06 totaldeposit Coef [95% Conf Interval] ma si cop growth realinterestrate fiscalsurplusgdp changeintermsoftrade npl realdomesticcreditgrowth 1.848791 1.999474 1.126487 54.59113 0088172 -52.83027 -9.533401 2398265 0651225 992321 6644424 7416781 0770705 4.16181 0756418 0292208 1.86 3.01 1.52 0.11 -2.29 3.17 2.23 0.062 0.003 0.129 0.909 0.022 0.002 0.026 -.0961222 6971909 -.3271752 -.1422382 -17.6904 0915712 0078508 3.793705 3.301757 2.580149 1598725 -1.376404 3880818 1223943 _cons -6.858058 2.827802 -2.43 0.015 -12.40045 -1.315667 _cons 2517573 5120679 0.49 0.623 -.7518773 1.255392 Frontier Usigma Vsigma sigma_u sigma_v lambda 0324184 1.134145 028584 0458364 2903795 2867232 0.71 3.91 0.10 0.479 0.000 0.921 002029 6866444 -.5333831 5179724 1.873289 5905511 Log likelihood = Prob > chi2 Wald chi2(5) -14.8508 noninterestexpense Coef Std Err z = = P>|z| 0.0000 7.44e+11 [95% Conf Interval] Frontier ma si cop growth realinterestrate fiscalsurplusgdp changeintermsoftrade npl realdomesticcreditgrowth 6413692 -.0936247 2266742 1028433 -.0886737 -21.81918 1.089017 -.0219695 -.0271108 0000341 0000719 8.74e-06 7.50e-06 1.38e-06 -2745.51 3153.46 -1.0e+04 -2927.53 -2.0e+04 0.000 0.000 0.000 0.000 0.000 -.0936915 2265333 -.0886908 -.0219842 -.0271135 -.0935579 2268151 -.0886566 -.0219548 -.0271081 _cons -1.65057 2169305 -7.61 0.000 -2.075746 -1.225394 _cons -31.82879 132.1172 -0.24 0.810 -290.7738 227.1162 Usigma Vsigma sigma_u sigma_v lambda Log likelihood = 4381101 1.23e-07 3573690 0475197 8.10e-06 0475197 9.22 0.02 7.5e+07 0.000 0.988 0.000 3542073 7.23e-64 3573690 Prob > chi2 Wald chi2(9) -103.6718 Std Err z P>|z| 5418874 2.08e+49 3573690 = = 0.0000 565.03 interestexpense Coef [95% Conf Interval] ma si cop growth realinterestrate fiscalsurplusgdp changeintermsoftrade npl realdomesticcreditgrowth 1.776933 2.133488 2624511 64.42692 -.1098786 -37.49864 -7.094377 0986949 -.0022067 7466254 5917036 5303812 9.910549 0485585 9.377595 2.606738 0354996 0150924 2.38 3.61 0.49 6.50 -2.26 -4.00 -2.72 2.78 -0.15 0.017 0.000 0.621 0.000 0.024 0.000 0.006 0.005 0.884 3135739 9737703 -.777077 45.0026 -.2050515 -55.87839 -12.20349 029117 -.0317872 3.240292 3.293206 1.301979 83.85124 -.0147057 -19.1189 -1.985265 1682727 0273738 _cons -7.467075 4.013247 -1.86 0.063 -15.33289 3987455 _cons -.3202056 2374034 -1.35 0.177 -.7855077 1450964 Frontier Usigma Vsigma sigma_u sigma_v lambda 0239081 8520562 0280593 0479746 1011405 1100208 0.50 8.42 0.26 0.618 0.000 0.799 0004683 6751949 -.1875775 1.220637 1.075245 2436961 APPENDIX C - Model for testing on structure and performance Hausman Test Execution Dependen Stage Stage t Variable Model 1: Market structure and performance hypothesis and relative market power hypotheses testing Coefficients Coefficients ROA (b) (B) (b-B) sqrt(diag(V_b-V_B)) (b) (B) (b-B) sqrt(diag(V_b-V_B)) herf ms sineff xineff la ka year fe re Difference S.E -.0355345 0216262 -.0150314 -.0093205 0024168 0673252 000615 -.0176601 0015352 -.0354458 -.0042595 0032737 056183 0007105 -.0178743 0200911 0204144 -.0050611 -.0008569 0111422 -.0000955 0091966 0096053 0072389 0024245 0041206 herf ms sineff xineff la ka year b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic Coefficients (b) (B) fe1 re1 herf ms sineff xineff la ka year -10.03555 -1.739477 2.437319 -1.022181 -.6472157 7053506 -.1672568 -6.68437 -2.685584 -.414674 -.5276609 -.9285473 2137022 -.1335933 S.E -.0166797 0100557 -.0426981 0255272 0085752 0568706 -.0009097 -.012229 0072394 -.0518549 0310498 0048988 0730965 -.0007803 -.0044507 0028163 0091567 -.0055226 0036764 -.0162259 -.0001294 0176109 0530377 0179009 0166388 0029331 0056144 000106 chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 14.92 Prob>chi2 = 0.0371 (V_b-V_B is not positive definite) sqrt(diag(V_b-V_B)) S.E -3.351177 9461062 2.851993 -.4945196 2813316 4916484 -.0336635 2.530796 2.958796 3.820092 2.917422 1.034664 1.399954 0412352 herf ms sineff xineff la ka year b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 47.97 Prob>chi2 = 0.0000 Difference Test: Ho: difference in coefficients not systematic (b-B) Difference Test: Ho: difference in coefficients not systematic re b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 46.05 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) ROE fe Coefficients (b) (B) fe1 re1 -.4357955 3192645 -.7267823 4848769 0347888 -.1102292 -.01444 -.3991579 3781378 -.7243753 4206718 0161601 -.070256 -.0133313 (b-B) Difference sqrt(diag(V_b-V_B)) S.E -.0366377 -.0588732 -.002407 0642051 0186287 -.0399732 -.0011087 2364667 5533194 1881299 1710679 0300179 0607301 0013986 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 2.63 Prob>chi2 = 0.9170 HERF Coefficients (b) (B) fe2 re2 sineff xineff year la ka 0532849 0020883 -.0194881 0062571 0299525 0040353 -.0035416 -.0195577 -.0009269 0098569 (b-B) Difference 0492496 0056298 0000696 0071839 0200956 Coefficients (b) (B) fe2 re2 sqrt(diag(V_b-V_B)) S.E .0478852 0366878 0003958 0139638 0185797 sineff xineff year la ka -.3680696 3296949 -.0063514 0409194 -.0761577 -.3518779 286849 -.0067778 056582 -.0982309 (b-B) sqrt(diag(V_b-V_B)) Difference S.E -.0161918 0428459 0004264 -.0156626 0220731 0468638 0343075 0003087 013656 0236871 sineff xineff year la ka b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic -.1925137 1246002 -.0007924 029326 -.0217912 herf ms year la ka 108219 1813088 -.0008095 -.0733179 -.2987633 1503845 0969298 0001146 -.0662357 -.2612067 017534 0135342 0001658 0038528 0104009 chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 10.98 Prob>chi2 = 0.0517 Coefficients (b) (B) fe4 re4 (b-B) sqrt(diag(V_b-V_B)) Difference S.E -.0421655 084379 -.0009241 -.0070822 -.0375566 0336025 -.0248793 -.0000249 0075047 -.0194851 Test: Ho: difference in coefficients not systematic Model 2: Quiet-life Hypothesis Testing Coefficients XINEFF (B) re4 -.2261162 1494795 -.0007675 0218213 -.0023062 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 0.68 Prob>chi2 = 0.9839 (V_b-V_B is not positive definite) (b) fe4 017407 0168368 0000911 0040591 0100004 chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 22.59 Prob>chi2 = 0.0004 Coefficients (b) (B) (b-B) sqrt(diag(V_b-V_B)) re3 fe3 Difference S.E chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 1.87 Prob>chi2 = 0.8662 sineff xineff year la ka 033573 -.0150565 -.0001286 0161694 0039203 Test: Ho: difference in coefficients not systematic Test: Ho: difference in coefficients not systematic Coefficients (b) (B) fe3 re3 0073805 0026064 -.0053177 0081251 0197612 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg MS 0409535 -.0124501 -.0054464 0242945 0236815 (b-B) sqrt(diag(V_b-V_B)) Difference S.E .0581104 0159645 0106108 herf ms year la ka -.0087884 4765336 0050685 0093939 -.153569 0787496 1815321 0058003 -.0016987 -.1536272 (b-B) sqrt(diag(V_b-V_B)) Difference S.E -.0875379 2950015 -.0007318 0110926 0000581 1258883 0012996 b = consistent under Ho and Ha; obtained from xtreg b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic Test: Ho: difference in coefficients not systematic chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 15.18 Prob>chi2 = 0.0096 (V_b-V_B is not positive definite) chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 63.69 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) SINEFF Coefficients (b) (B) re5 fe5 herf ms year la ka 3358774 -.0944605 0068037 0357204 -.2147845 (b-B) Difference sqrt(diag(V_b-V_B)) S.E -.0508 0234539 -.0001703 020777 0651484 1059251 0021908 0085926 3866774 -.1179144 0069741 0149434 -.2799329 herf ms year la ka Coefficients (b) (B) fe5 re5 4788155 -.7630294 0032427 0263284 0611579 (b-B) sqrt(diag(V_b-V_B)) Difference S.E .3710548 -.0952652 0026667 0268652 0960439 1077607 -.6677642 000576 -.0005369 -.034886 1154691 b = consistent under Ho and Ha; obtained from xtreg b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic Test: Ho: difference in coefficients not systematic chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 29.07 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 43.28 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) Random Effects And Fixed Effects Execution Stage Stage Fixed-effects (within) regression Group variable: stt Number of obs Number of groups R-sq: Obs per group: within = 0.6983 between = 0.0687 overall = 0.3853 corr(u_i, Xb) = = 110 22 Random-effects GLS regression Group variable: stt Number of obs Number of groups = = 218 26 = avg = max = 5.0 R-sq: within = 0.1625 between = 0.4111 overall = 0.2405 Obs per group: = avg = max = 8.4 = = 26.78 0.0000 corr(u_i, X) Wald chi2(7) Prob > chi2 F(7,81) Prob > F = -0.3288 roa Coef herf ms sineff xineff la ka year _cons -.0355345 0216262 -.0150314 -.0093205 0024168 0673252 000615 -1.224516 Std Err .0378237 0133863 0191366 014762 0052491 0072516 0007471 1.504093 t sigma_u sigma_e rho 00735025 00384871 78482236 (fraction of variance due to u_i) -0.94 1.62 -0.79 -0.63 0.46 9.28 0.82 -0.81 P>|t| 0.350 0.110 0.434 0.530 0.646 0.000 0.413 0.418 [95% Conf Interval] -.1107918 -.0050084 -.0531071 -.0386922 -.0080273 0528968 -.0008715 -4.217189 0397229 0482609 0230444 0200512 0128609 0817536 0021014 1.768157 F test that all u_i=0: F(21, 81) = 5.23 Fixed-effects (within) regression Group variable: stt Prob > F = 0.0000 Number of obs = 110 Number of groups = 22 R-sq: within = 0.0484 between = 0.2360 overall = 0.1065 Obs per group: corr(u_i, Xb) = avg = max = 5.0 = = 0.59 0.7637 F(7,81) Prob > F = -0.0077 roe Coef herf ms sineff xineff la ka year _cons -10.03555 -1.739477 2.437319 -1.022181 -.6472157 7053506 -.1672568 337.5712 Std Err 8.793337 3.112086 4.448907 3.431896 1.220331 1.685866 1736831 349.6748 sigma_u sigma_e rho 43231827 89475587 18926736 (fraction of variance due to u_i) F test that all u_i=0: F(21, 81) = 1.11 t -1.14 -0.56 0.55 -0.30 -0.53 0.42 -0.96 0.97 P>|t| 0.257 0.578 0.585 0.767 0.597 0.677 0.338 0.337 [95% Conf Interval] -27.53153 -7.931553 -6.41461 -7.850578 -3.075291 -2.648993 -.5128317 -358.1719 7.460436 4.452598 11.28925 5.806217 1.78086 4.059694 1783181 1033.314 Prob > F = 0.3581 = (assumed) Std Err z roa Coef herf ms sineff xineff la ka year _cons -.012229 0072394 -.0518549 0310498 0048988 0730965 -.0007803 1.568912 0750633 0192255 0212077 014427 0050094 0134136 0004595 92984 sigma_u sigma_e rho 0037178 00666696 23720536 (fraction of variance due to u_i) -0.16 0.38 -2.45 2.15 0.98 5.45 -1.70 1.69 P>|z| = = 0.871 0.707 0.014 0.031 0.328 0.000 0.089 0.092 50.24 0.0000 [95% Conf Interval] -.1593504 -.030442 -.0934211 0027733 -.0049195 0468064 -.0016809 -.2535408 1348924 0449207 -.0102886 0593263 014717 0993866 0001203 3.391365 Fixed-effects (within) regression Group variable: stt Number of obs Number of groups = = 218 26 R-sq: within = 0.1730 between = 0.0185 overall = 0.1139 Obs per group: = avg = max = 8.4 corr(u_i, Xb) F(7,185) Prob > F = -0.1069 Std Err roe Coef herf ms sineff xineff la ka year _cons -.4357955 3192645 -.7267823 4848769 0347888 -.1102292 -.01444 29.1386 81159 5938354 292131 2318145 0611042 1530638 0049637 10.03883 sigma_u sigma_e rho 0497435 07017813 33440827 (fraction of variance due to u_i) F test that all u_i=0: F(25, 185) = 4.05 t -0.54 0.54 -2.49 2.09 0.57 -0.72 -2.91 2.90 P>|t| = = 0.592 0.591 0.014 0.038 0.570 0.472 0.004 0.004 5.53 0.0000 [95% Conf Interval] -2.036957 -.8522954 -1.303119 0275371 -.0857618 -.4122042 -.0242328 9.333287 1.165366 1.490825 -.1504458 9422167 1553394 1917458 -.0046472 48.94392 Prob > F = 0.0000 Fixed-effects (within) regression Group variable: stt Number of obs Number of groups = = 110 22 Random-effects GLS regression Group variable: stt Number of obs Number of groups = = 218 26 R-sq: within = 0.9405 between = 0.9718 overall = 0.9407 Obs per group: = avg = max = 5.0 R-sq: within = 0.8212 between = 0.7652 overall = 0.8156 Obs per group: = avg = max = 8.4 corr(u_i, Xb) F(5,83) Prob > F corr(u_i, X) Wald chi2(5) Prob > chi2 = -0.0870 Std Err t herf Coef sineff xineff year la ka _cons 0532849 0020883 -.0194881 0062571 0299525 39.24072 0547609 0423044 0005976 0156739 0211564 1.200129 sigma_u sigma_e rho 00486935 01155314 1508443 (fraction of variance due to u_i) 0.97 0.05 -32.61 0.40 1.42 32.70 P>|t| = = 0.333 0.961 0.000 0.691 0.161 0.000 262.34 0.0000 herf Coef .0073805 0026064 -.0053177 0081251 0197612 10.79384 015346 0096972 0001929 0032423 0095533 3872726 sigma_u sigma_e rho 00639293 F test that all u_i=0: F(21, 83) = 0.18 (fraction of variance due to u_i) 162202 08623 -.0182995 0374319 0720317 41.62773 Std Err z 0.48 0.27 -27.56 2.51 2.07 27.87 P>|z| = = sineff xineff year la ka _cons [95% Conf Interval] -.0556323 -.0820535 -.0206766 -.0249177 -.0121266 36.85372 = (assumed) 937.95 0.0000 [95% Conf Interval] 0.631 0.788 0.000 0.012 0.039 0.000 -.0226971 -.0163999 -.0056958 0017704 001037 10.0348 0374581 0216126 -.0049396 0144799 0384854 11.55288 Prob > F = 1.0000 Fixed-effects (within) regression Group variable: stt Number of obs Number of groups = = 110 22 R-sq: within = 0.2944 between = 0.1043 overall = 0.1018 Obs per group: = avg = max = 5.0 corr(u_i, Xb) F(5,83) Prob > F = 0.0867 Std Err t ms Coef sineff xineff year la ka _cons -.3680696 3296949 -.0063514 0409194 -.0761577 12.75725 1547293 119533 0016884 0442874 0597783 3.391017 sigma_u sigma_e rho 08600226 03264392 8740695 (fraction of variance due to u_i) -2.38 2.76 -3.76 0.92 -1.27 3.76 P>|t| = = 0.020 0.007 0.000 0.358 0.206 0.000 6.93 0.0000 [95% Conf Interval] -.67582 0919486 -.0097096 -.0471663 -.1950543 6.012655 F test that all u_i=0: F(21, 83) = 35.54 -.0603193 5674412 -.0029932 1290052 0427388 19.50185 Prob > F = 0.0000 Fixed-effects (within) regression Group variable: stt Number of obs Number of groups R-sq: Obs per group: within = 0.2930 between = 0.1346 overall = 0.0466 corr(u_i, Xb) = = 218 26 = avg = max = 8.4 = = 15.50 0.0000 F(5,187) Prob > F = -0.3483 ms Coef sineff xineff year la ka _cons -.2261162 1494795 -.0007675 0218213 -.0023062 1.563113 Std Err .031715 0265545 0002916 0071 0189017 584294 t sigma_u sigma_e rho 05352302 00873717 97404394 (fraction of variance due to u_i) -7.13 5.63 -2.63 3.07 -0.12 2.68 P>|t| [95% Conf Interval] 0.000 0.000 0.009 0.002 0.903 0.008 -.2886814 0970946 -.0013428 0078148 -.039594 4104584 F test that all u_i=0: F(25, 187) = 142.96 -.1635511 2018644 -.0001923 0358278 0349817 2.715768 Prob > F = 0.0000 Random-effects GLS regression Group variable: stt Number of obs Number of groups = = 110 22 Fixed-effects (within) regression Group variable: stt Number of obs Number of groups = = 218 26 R-sq: within = 0.3408 between = 0.0439 overall = 0.0000 Obs per group: = avg = max = 5.0 R-sq: within = 0.3103 between = 0.0057 overall = 0.0027 Obs per group: = avg = max = 8.4 corr(u_i, X) Wald chi2(5) Prob > chi2 corr(u_i, Xb) F(5,187) Prob > F = (assumed) xineff Coef herf ms year la ka _cons 1503845 0969298 0001146 -.0662357 -.2612067 -.1098049 Std Err .3436103 0996703 0068044 0436361 0503499 13.69999 z sigma_u sigma_e rho 06775411 03511076 78830775 (fraction of variance due to u_i) 0.44 0.97 0.02 -1.52 -5.19 -0.01 P>|z| 0.662 0.331 0.987 0.129 0.000 0.994 = = 33.25 0.0000 [95% Conf Interval] -.5230794 -.0984203 -.0132219 -.1517609 -.3598907 -26.9613 8238483 29228 0134511 0192895 -.1625226 26.74169 = -0.4711 Std Err t = = xineff Coef herf ms year la ka _cons -.0087884 4765336 0050685 0093939 -.153569 -10.08815 299389 1927319 0018201 022793 0542807 3.68309 sigma_u sigma_e rho 06456282 02632508 85744547 (fraction of variance due to u_i) -0.03 2.47 2.78 0.41 -2.83 -2.74 P>|t| 0.977 0.014 0.006 0.681 0.005 0.007 [95% Conf Interval] -.5994023 0963254 001478 -.0355706 -.2606502 -17.35389 F test that all u_i=0: F(25, 187) = 32.18 Number of obs Number of groups = = 110 22 Fixed-effects (within) regression Group variable: stt Number of obs Number of groups R-sq: within = 0.3911 between = 0.1618 overall = 0.0126 Obs per group: = avg = max = 5.0 R-sq: within = 0.1798 between = 0.1861 overall = 0.0889 Obs per group: corr(u_i, X) Wald chi2(5) Prob > chi2 sineff Coef herf ms year la ka _cons 3358774 -.0944605 0068037 0357204 -.2147845 -13.66064 Std Err .2826845 0755141 0056089 0344124 0406126 11.29293 z sigma_u sigma_e rho 0396218 02708452 6815349 (fraction of variance due to u_i) 1.19 -1.25 1.21 1.04 -5.29 -1.21 P>|z| 0.235 0.211 0.225 0.299 0.000 0.226 = = 33.08 0.0000 [95% Conf Interval] -.2181741 -.2424654 -.0041895 -.0317267 -.2943837 -35.79437 8899289 0535444 017797 1031675 -.1351853 8.473094 corr(u_i, Xb) F(5,189) Prob > F = -0.8615 Std Err sineff Coef herf ms year la ka _cons 4788155 -.7630294 0032427 0263284 0611579 -6.528026 2356289 1522052 0014319 0178633 0418474 2.897542 t sigma_u sigma_e rho 05811802 02083797 88608878 (fraction of variance due to u_i) 2.03 -5.01 2.26 1.47 1.46 -2.25 F test that all u_i=0: F(25, 189) = 15.06 5818256 8567419 008659 0543584 -.0464879 -2.822402 Prob > F = 0.0000 Random-effects GLS regression Group variable: stt = (assumed) 16.82 0.0000 P>|t| 0.044 0.000 0.025 0.142 0.146 0.025 = = 220 26 = avg = max = 8.5 = = 8.28 0.0000 [95% Conf Interval] 0140151 -1.063269 000418 -.0089086 -.0213901 -12.2437 9436159 -.4627901 0060673 0615653 1437058 -.8123481 Prob > F = 0.0000 VIF Execution Dependent Variable ROA ROE HERF MS XINEFF SINEFF Stage Stage Variable VIF 1/VIF Variable VIF 1/VIF herf year sineff xineff ms la ka 19.95 19.84 2.35 2.17 1.38 1.36 1.25 0.050134 0.050412 0.424641 0.460818 0.723367 0.734213 0.798853 year herf ms sineff xineff la ka 5.69 5.43 2.08 2.04 1.93 1.73 1.41 0.175607 0.184105 0.480379 0.490137 0.517091 0.578814 0.708057 Mean VIF 6.90 Mean VIF 2.90 Variable VIF 1/VIF Variable VIF 1/VIF herf year sineff xineff ms la ka 19.95 19.84 2.35 2.17 1.38 1.36 1.25 0.050134 0.050412 0.424641 0.460818 0.723367 0.734213 0.798853 year herf ms sineff xineff la ka 5.69 5.43 2.08 2.04 1.93 1.73 1.41 0.175607 0.184105 0.480379 0.490137 0.517091 0.578814 0.708057 Mean VIF 6.90 Mean VIF 2.90 Variable VIF 1/VIF xineff sineff la year ka 1.86 1.72 1.35 1.24 1.08 0.538957 0.580131 0.740180 0.809223 0.926481 Mean VIF 1.45 Variable VIF 1/VIF sineff xineff la ka year 2.35 2.16 1.21 1.07 1.02 0.426331 0.462354 0.826495 0.938072 0.975942 Mean VIF Variable 1.56 VIF 1/VIF Variable VIF 1/VIF sineff xineff la ka year 2.35 2.16 1.21 1.07 1.02 0.426331 0.462354 0.826495 0.938072 0.975942 xineff sineff la year ka 1.86 1.72 1.35 1.24 1.08 0.538957 0.580131 0.740180 0.809223 0.926481 Mean VIF 1.56 Mean VIF 1.45 Variable VIF 1/VIF Variable VIF 1/VIF herf year ms ka la 19.94 19.79 1.38 1.20 1.13 0.050156 0.050523 0.726256 0.832231 0.882075 year herf ms la ka 5.45 5.41 1.75 1.59 1.26 0.183323 0.184936 0.570295 0.629968 0.791246 Mean VIF Variable 8.69 VIF Mean VIF 3.09 1/VIF Variable VIF 1/VIF herf year ms ka la 19.94 19.79 1.38 1.20 1.13 0.050156 0.050523 0.726256 0.832231 0.882075 year herf ms la ka 5.47 5.44 1.73 1.58 1.27 0.182717 0.183905 0.576566 0.631229 0.787582 Mean VIF 8.69 Mean VIF 3.10 A 2SLS Execution Stage ivregress 2sls sineff (ms=L.ms) herf year la ka, small Instrumental variables (2SLS) regression Source SS df MS Model Residual 05187851 280650685 86 010375702 00326338 Total 332529195 91 003654167 sineff Coef ms herf year la ka _cons 0302671 0256058 -.0002923 0816033 2683197 5414847 Instrumented: Instruments: Std Err .0748334 5581079 0112162 0414903 09798 22.58186 Number of obs F( 5, 86) Prob > F R-squared Adj R-squared Root MSE t P>|t| 0.40 0.05 -0.03 1.97 2.74 0.02 0.687 0.964 0.979 0.052 0.008 0.981 = = = = = = 92 3.16 0.0115 0.1560 0.1069 05713 [95% Conf Interval] -.1184967 -1.083876 -.0225894 -.0008766 073542 -44.34976 179031 1.135088 0220048 1640833 4630975 45.43273 ms herf year la ka L.ms ivregress 2sls xineff (ms=L.ms) herf year la ka, small Instrumental variables (2SLS) regression Source SS df MS Model Residual 022472399 41061785 86 00449448 004774626 Total 433090249 91 004759234 xineff Coef ms herf year la ka _cons 0264193 -.2747091 -.0079467 -.0507959 2035502 16.07092 Instrumented: Instruments: Std Err .0905172 6750781 013567 050186 118515 27.31464 ms herf year la ka L.ms t 0.29 -0.41 -0.59 -1.01 1.72 0.59 Number of obs F( 5, 86) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.771 0.685 0.560 0.314 0.089 0.558 = = = = = = 92 0.93 0.4664 0.0519 -0.0032 0691 [95% Conf Interval] -.153523 -1.61672 -.0349169 -.1505624 -.0320498 -38.22879 2063616 1.067302 0190235 0489705 4391501 70.37062 Stage ivregress 2sls sineff (ms=L.ms) herf year la ka, small Instrumental variables (2SLS) regression Source SS df MS Model Residual 074120024 194595607 188 014824005 001035083 Total 26871563 193 001392309 sineff Coef ms herf year la ka _cons 4537173 2724843 0035294 -.0235908 3290458 -7.129513 Instrumented: Instruments: Std Err t 0624751 3672989 0020425 0194028 0640629 4.138207 7.26 0.74 1.73 -1.22 5.14 -1.72 Number of obs F( 5, 188) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.000 0.459 0.086 0.226 0.000 0.087 = = = = = = 194 15.06 0.0000 0.2758 0.2566 03217 [95% Conf Interval] 3304751 -.4520726 -.0004999 -.0618661 2026713 -15.2928 5769596 9970412 0075586 0146844 4554204 1.033774 ms herf year la ka L.ms Instrumental variables (2SLS) regression Source SS df MS Model Residual 107915816 576435514 186 021583163 003099116 Total 68435133 191 003582991 xineff Coef ms herf year la ka _cons 18762 6634921 0122365 -.1409412 2165374 -24.51434 Instrumented: Instruments: Std Err .1094473 6410707 0035644 033906 1166372 7.221897 ms herf year la ka L.ms t 1.71 1.03 3.43 -4.16 1.86 -3.39 Number of obs F( 5, 186) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.088 0.302 0.001 0.000 0.065 0.001 = = = = = = 192 7.12 0.0000 0.1577 0.1350 05567 [95% Conf Interval] -.0282977 -.6012122 0052047 -.207831 -.0135644 -38.7617 4035377 1.928196 0192683 -.0740514 4466392 -10.26698 ... thống Ngân hàng thương mại Việt Nam thời gian qua yê cầu thiết Luận án nghiên cứu mối quan hệ biện pháp tái cấu trúc hệ thống NHTM trình tái cấu trúc ngân hàng thương mại tác động đến hiệu hoạt động. .. quan hệ tái cấu trúc ngân hàng hiệu hoạt động ngân hàng mối quan tâm lớn nhà khoa học lẫn doanh nghiệp nhà làm sách Chính thế, việc đánh giá mối quan hệ đo lường tính hiệu q trình tái cấu trúc hệ... lớn đến hiệu việc tái cấu trúc Tái cấu trúc ngân hàng chương trình quan trọng để nước xây dựng hệ thống tài ổn định bền vững Do việc đánh giá liên kết chương trình tái cấu trúc hiệu ngân hàng quan