(Luận văn) determinants of bank profitability in vietnam, does foreign presence matter

INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS t to UNIVERSITY OF ECONOMICS ng hi ep w VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN n lo DEVELOPMENT ECONOMICS ad ju y th yi pl DETERMINANTS OF BANK PROFITABILITY IN VIETNAM: DOES FOREIGN PRESENCE MATTER? n ua al n va ll fu oi m nh at MASTER OF ARTS IN DEVELOPMENT ECONOMICS z z om l.c gm an Lu Dr DUONG NHU HUNG k Academic Supervisor jm TRUONG UY PHAP ht vb By n va ey t re HCM city, December 2014 CERTIFICATION “I hereby claim that the substance of this thesis has not ever been submitted for any t to degree and has not been currently submitted for any other degree ng hi I certify that to the best attempt of my knowledge and support received during the ep preparation phrase of this thesis and all resources have been acknowledged in this w thesis” n lo ad TRUONG UY PHAP y th ju Date: … December 2014 yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm an Lu n va ey t re i ACKNOWLEDGEMENT I would not be able to write and finish my thesis without the help and support t to of people surrounding me ng hi Above all, I would like to express my greatest appreciation to my supervisor, ep Dr Duong Nhu Hung, for his precious comments, advices and guidance, w encouragement during the writing period Without his guidance, my thesis would n lo not be completed ad I would also like to offer my special thanks to Dr Truong Dang Thuy for the y th econometric guidance and valuable suggestions that help to develop this thesis ju yi Besides my mentors, special thanks also to all the lecturers at the Vietnam – pl phrase n ua al Netherlands Program for their expert knowledge and sharing during the coursework n va In addition, I really appreciate my friends and people who are always beside ll fu me and provide supports for my thesis oi m Last, but not least, I am very deeply grateful to my family Without their at thesis nh warm encouragement and patience, I would not be possible to complete well this z z k jm ht vb gm TRUONG UY PHAP om l.c an Lu n va ey t re ii ABSTRACT t to The present paper seeks to examine the determinants of bank profitability in ng hi Vietnam during the period 2004–2013 The empirical findings suggest that credit ep risk, non-interest income, operating expense and liquidity variables have a w statistically significantly impact on bank profitability The empirical findings n lo suggest that credit risk and expense preference behaviour are negatively related to ad banks' profitability, while non-interest income and liquidity have a positive impact y th During the period under study, the results suggest that inflation has a negative ju yi impact on bank profitability, while the impacts of economic growth and money pl ua al supply have not significantly explained the variations in the profitability of the Vietnamese banks Besides, foreign presence from the macroeconomic view and n n va enterprise view not show the significant relationship with bank profitability In ll fu other words, the opening up policy to allow more foreign entry entering into oi m domestic banking market and relaxing of foreign ownership restriction in local banks are considered unclear for policy implications at nh z z Key words: bank profitability, foreign presence, ownership, Vietnam, determinants k jm ht vb om l.c gm an Lu n va ey t re iii ABBREVIATION t to ng hi ep SBV State Bank of Vietnam SOBs State-Owned Banks JSBs Joint Stock commercial banks Joint Venture banks w JVBs n 100% foreign-owned banks lo FOBs ad GMM yi Random Effect Model pl Ordinary Least Square ua al OLS Fixed Effect Model ju REM y th FEM Generalized method of moments Least Square Dummy Variable GDP Gross Domestic Product WTO World Trade Organization FS Foreign Shareholding FSP Foreign Strategic partner FP Foreign presence ROA Return on Assets NIM Net interest margin n LSDV n va ll fu oi m at nh z z k jm ht vb om l.c gm an Lu n va ey t re iv TABLE OF CONTENTS t to CHAPTER 1: INTRODUCTION ng hi ep Problem statement 1.2 Research objective 1.3 Research questions w 1.1 n lo 1.4 The scope of the study ad 1.5 Structure of the study y th CHAPTER 2: LITERATURE REVIEWS ju Theory of Bank profitability’s determinant 2.2 Foreign presence/ ownership and its impact on bank profitability 10 yi 2.1 pl ua al CHAPTER 3: RESEARCH METHODOLOGY AND DATA 16 n Research methodology 16 n va 3.1 ll fu 3.1.1 Estimation panel regression model: 16 oi m 3.1.2 Test for an appropriate panel data regression choice 18 Model specification 20 3.3 Variable measurement and test hypothesis 25 at nh 3.2 z z 3.3.1 Bank profitability definition 25 vb 3.3.2 Internal determinants 26 ht jm 3.3.3 External determinants 29 k 3.3.4 Foreign presence and foreign shareholding 30 gm Estimation strategy & Data collection 32 l.c 3.4 om CHAPTER 4: RESEARCH RESULTS & DISCUSSION 35 Vietnam banking background 35 4.2 Descriptive statistic of the sample 39 4.3 Empirical results & findings discussion 45 an Lu 4.1 Discussion of findings 48 v ey 4.4 t re 4.3.2 Empirical results 46 n va 4.3.1 Result of Test for panel regression model 45 4.4.1 Overall model 48 4.4.2 Internal determinants 49 t to 4.4.3 External determinants 52 ng hi 4.4.4 Foreign presence 53 ep CHAPTER 5: CONCLUSION 55 5.1 Conclusion 55 w n Recommendations 57 5.3 Limitations & suggestion for future studies 58 lo 5.2 ad y th APPENDIX A: INFORMATION OF BANK OBSERVATION SAMPLE 62 ju yi APPENDIX B: SUMMARY OF SOME MAIN EMPIRICAL LITERATURE pl REVIEWS 71 al n ua APPENDIX C: DESCRIPTIVE SUMMARY ANALYSIS 77 n va APPENDIX D: PANEL REGRESSION MODEL 79 ll fu APPENDIX E: RESULTS OF BREUSCH – PAGAN LM TEST 87 oi m APPENDIX F: RESULTS OF HAUSMAN TEST 89 nh APPENDIX G: RESULT OF ROBUST STANDARD ERROR (FIX at HETEROSKEDASTICITY) 91 z z k jm ht vb om l.c gm an Lu n va ey t re vi LIST OF FIGURES t to ng Figure 2.1: Analytical framework of determinants of bank profitability 15 hi Figure 4.1: Trend of foreign presence in Vietnam 38 ep Figure 4.2: Average bank’ equity by different levels of foreign presence .42 w Figure 4.3: Average bank’ total assets by different levels of foreign presence 42 n lo Figure 4.4: Average bank’ return on asset by different levels of foreign presence 43 ad y th Figure 4.5: Average bank’ net interest margin by different levels of foreign presence ju .43 yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm an Lu n va ey t re vii LIST OF TABLES t to ng hi Table 3.1: Tests for choosing the appropriate panel regression model .20 ep Table 3.2: Definition, notation and expected signs of relevant variables 22 w n Table 3.3: Data collection sources 34 lo ad Table 4.1: Numbers of banking institutions in Vietnam (2004-2013) 37 y th Table 4.2: Performance of banking industry and its contribution to Vietnam ju economy 39 yi pl Table 4.3: Summary statistics for all variables in this research 40 al ua Table 4.4: Correlation analysis among independent variables 44 n Table 4.5: Results of F test and Breusch – Pagan Test .45 va n Table 4.6: Results of Hausman Test 45 fu ll Table 4.7: Result of Test for heteroskedasticity .46 m oi Table 4.8: Results of the final models .47 at nh z z k jm ht vb om l.c gm an Lu n va ey t re viii CHAPTER 1: INTRODUCTION 1.1 Problem statement t to In the world of financial liberalization during the last few decades, in terms of ng facilitating the critical roles of international trade, the rise of international banks has hi ep become important significantly Banks have expanded multinationally by not only establishing foreign susidiaries and branches as local incoporation formation but also by w n taking over estabished foreign banks As other corporations, banks have always operated lo ad with the aims of profit maximization The foreign banks operates in developing y th countries will be considered as the same strategy afterward However, to what extent ju will the foreign bank operating in particular country yield a better return than those yi pl domestic competitors, or vise-versa? This is the question leading to many reseaches ua al conducted recently n There have been some researches regarding the impact of the internal factors such va n as bank specific characteristics (size, risk level, liquidity status and so on) and external fu ll factors such as inflation, GDP from the country-specific factors on banking profitability m oi (Pasiouras and Kosmidou, 2007) These determinants have been anlayzed not only in a at nh series of pooled countries but also in a single country However, to integrate the effect of z foreign ownership structure into this link is critically necessary especially for developing z countries where they are concentrating on the progress of financial liberalization and vb jm ht seeking for foreign entry to enhance the domestic market As part of the privatization and financial liberalization strategy, more and more developing countries allow foreign- k l.c gm owed banks or greenfield bank (Bonin et al, 2005) to operate in their domestic market As stated in their study, privatization of banking sector, particularly in developing om countries is usally accompany with complex procedures and phrases These may an Lu dramatically change the whole banking sector operation in which they would affect to the value of banking, growth rate, risk exposures and defintely performance of banks ownership type besides domestic state-owned or private-owned banks, will foreign ownership, either at greenfield bank or at domestic bank, with the role of strategic ey partner program (Shen et al., 2009) Since foreign banks represent one of bank t re certain limit of foreign investor in their domestic commercial bank so-called strategic n va Some markets such as China followed a more prudent approach where they open a APPENDIX D: PANEL REGRESSION MODEL Estimation result of Model 1: t to ng Figure D.1-1: Pooled OLS of Model hi reg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg ep Source | SS df MS Number of obs = F( w -+ 9, 315 305) = 29.98 n 015570019 001730002 Prob > F = 0.0000 Residual | 017602004 305 000057711 R-squared = 0.4694 Adj R-squared = 0.4537 lo Model | ad y th -+ ju Total | 033172022 314 000105643 Root MSE = 0076 yi pl al roa | Coef Std Err t P>|t| [95% Conf Interval] ua -+ -10.34 0.000 4981522 7323342 0580929 -5.21 0.000 -.4168732 -.1882459 0005981 0004362 1.37 0.171 -.0002603 0014565 llp_ta | -.3160525 123447 -2.56 0.011 -.5589682 -.0731368 eqass | 0432139 0059742 7.23 0.000 031458 0549698 tl_dstf | 0108173 0019028 0.000 0070731 0145616 gdp_g | 099305 0679406 1.46 0.145 -.0343866 2329966 inf | 0246593 0084328 2.92 0080655 0412532 msg | 0029653 0044211 0.67 0.503 -.0057345 011665 _cons | -.0121083 0078666 -1.54 0.125 oi m lnta | ll -.3025595 fu nie_ta | n 6152432 va 0595043 nh n nii_ta | 5.68 at z z 0.004 jm ht vb -.027588 0033714 k gm Figure D.1-2: Figure : REM – Model l.c xtreg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg,re Number of obs = Group variable: bank Number of groups = R-sq: = 0.4222 Obs per group: = between = 0.4905 avg = 6.4 overall = 0.4504 max = 10 Wald chi2(9) = 231.20 Prob > chi2 = 0.0000 Coef Std Err z P>|z| [95% Conf Interval] -+ -nii_ta | 5485638 0582358 9.42 79 0.000 4344237 6627038 ey roa | t re n = (assumed) va corr(u_i, X) 49 an Lu within 315 om Random-effects GLS regression t to -.3634942 0586701 -6.20 0.000 -.4784855 -.2485029 lnta | 1.32e-06 0005246 0.00 0.998 -.0010268 0010295 llp_ta | -.3553174 1194248 -2.98 0.003 -.5893857 -.1212491 eqass | 0312726 0057563 5.43 0.000 0199905 0425547 tl_dstf | 0098466 0019749 4.99 0.000 0059758 0137174 gdp_g | 0513383 0605799 0.85 0.397 -.0673961 1700726 inf | 0272292 0069391 3.92 0.000 0136289 0408296 msg | 0047159 0036571 1.29 0.197 -.0024519 0118837 _cons | 0002185 0087667 0.02 0.980 -.0169639 0174008 ng nie_ta | hi ep w n -+ lo 00475025 sigma_e | 00595359 rho | 38898234 ad sigma_u | ju y th (fraction of variance due to u_i) yi pl Figure D.1-2a: FEM using ‘xtreg - fe’ – ‘within’ estimator for Model al ua xtreg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg,fe Number of obs = 315 Group variable: bank Number of groups = 49 Obs per group: = avg = 6.4 max = 10 = 21.83 = 0.0000 n Fixed-effects (within) regression = 0.4332 ll fu between = 0.3675 n within va R-sq: oi m overall = 0.4037 nh = 0.0398 Prob > F at corr(u_i, Xb) F(9,257) z -Coef Std Err t P>|t| z roa | [95% Conf Interval] vb -+ -.4031921 6446108 nie_ta | -.4165573 0635934 -6.55 0.000 -.5417878 -.2913267 lnta | -.0005972 0006406 -0.93 0.352 -.0018587 0006642 llp_ta | -.3851267 1233687 -3.12 0.002 -.6280689 -.1421845 eqass | 024052 0061201 3.93 0.000 012 tl_dstf | 0075553 0021903 3.45 0.001 0032421 0118685 gdp_g | 0037714 0639493 0.06 0.953 -.1221599 1297027 inf | 0272624 0068077 4.00 0.000 0138564 0406684 msg | 0048617 0036018 1.35 0.178 -.0022311 0119546 _cons | 0128473 0105029 1.22 0.222 -.0078354 0335301 l.c 0.000 gm 8.55 k 0612975 jm 5239015 ht nii_ta | 0361039 om an Lu 00595359 rho | 58132363 ey sigma_e | t re 00701533 n sigma_u | va -+ (fraction of variance due to u_i) 80 F test that all u_i=0: F(48, 257) = 4.99 Prob > F = 0.0000 Figure D.1-2b: FEM using LSDV for Model t to reg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg i.bank ng Source | SS df MS Number of obs = hi -+ F( 57, 315 257) = 11.91 ep 024062609 57 000422151 Prob > F = 0.0000 Residual | 009109413 257 000035445 R-squared = 0.7254 Adj R-squared = 0.6645 Root MSE 00595 w Model | n -+ lo Total | 033172022 314 000105643 = ad -Coef y th roa | Std Err t P>|t| [95% Conf Interval] 5239015 0612975 8.55 0.000 4031921 6446108 pl 0635934 -6.55 0.000 -.5417878 -.2913267 yi nii_ta | ju -+ -.4165573 lnta | -.0005972 0006406 -0.93 0.352 -.0018587 0006642 llp_ta | -.3851267 1233687 -3.12 0.002 -.6280689 -.1421845 eqass | 024052 0061201 va 3.93 0.000 012 0361039 tl_dstf | 0075553 0021903 n 3.45 0.001 0032421 0118685 gdp_g | 0037714 0639493 0.06 0.953 -.1221599 1297027 inf | 0272624 0068077 4.00 0.000 0138564 0406684 msg | 0048617 0036018 oi 0.178 -.0022311 0119546 n ua al nie_ta | ll fu m 1.35 at nh z *48 dummy banks will not be displayed here, please prefer to Stata file for detail z vb jm ht Estimation result for Model Figure D.2-1: Pooled OLS – Model k SS df MS Number of obs = -+ F( 10, 00155857 Prob > F Residual | 01758632 304 00005785 R-squared 033172022 314 000105643 = 0.0000 = 0.4698 Adj R-squared = 0.4524 Root MSE 00761 = Coef Std Err t P>|t| [95% Conf Interval] nii_ta | 6133539 059686 10.28 0.000 4959039 7308038 nie_ta | -.2991703 0585255 -5.11 0.000 -.4143367 -.1840038 lnta | 0007135 0004898 1.46 0.146 -.0002503 0016773 81 ey -+ t re roa | n va Total | 26.94 an Lu 10 315 om 015585703 304) = Model | -+ l.c Source | gm reg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg fp -.3277836 1256315 -2.61 0.010 -.5750009 -.0805662 eqass | 0441923 0062696 7.05 0.000 0318551 0565296 tl_dstf | 010665 0019274 5.53 0.000 0068723 0144578 gdp_g | 0704193 0877754 0.80 0.423 -.1023049 2431436 inf | 0261857 0089373 2.93 0.004 0085988 0437726 msg | 003041 0044288 0.69 0.493 -.005674 0117559 fp | -.0044186 0084859 -0.52 0.603 -.0211172 0122801 _cons | -.0101737 0087084 -1.17 0.244 -.02731 0069627 t to llp_ta | ng hi ep w - n lo Figure D.2-2: REM – Model ad xtreg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg fp,re Number of obs = 315 Group variable: bank Number of groups = 49 R-sq: Obs per group: = avg = 6.4 max = 10 Wald chi2(10) = 230.72 Prob > chi2 = 0.0000 = 0.4216 yi within ju y th Random-effects GLS regression pl between = 0.4957 n ua al overall = 0.4517 = (assumed) n va corr(u_i, X) -Std Err z ll Coef fu roa | P>|z| [95% Conf Interval] oi m -+ -.058469 9.38 nie_ta | -.3617069 058846 -6.15 lnta | 0001078 0006791 0.16 llp_ta | -.3588123 1211502 eqass | 0318764 tl_dstf | 0.000 4338181 6630122 0.000 -.477043 -.2463709 0.874 -.0012231 0014387 -2.96 0.003 -.5962624 -.1213621 0061232 5.21 0.000 0098815 0019771 5.00 0.000 gdp_g | 0434366 0724945 0.60 inf | 0279313 0076744 msg | 0047965 fp | _cons | at z z vb 019875 0438777 ht 5484151 nh nii_ta | 0137567 0.549 -.0986499 1855232 3.64 0.000 0128898 0429729 0036928 1.30 0.194 -.0024413 0120343 -.0018105 0081766 -0.22 0.825 -.0178364 0000455 0087691 0.01 0.996 -.0171417 k jm 0060064 l.c gm 0142155 om 0172326 sigma_e | 00596136 rho | 38040082 (fraction of variance due to u_i) n 00467101 va sigma_u | an Lu -+ ey t re Figure D.2-3a – FEM using ‘xtreg - fe’ – ‘within’ estimator for Model xtreg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg fp,fe Fixed-effects (within) regression Number of obs 82 = 315 Group variable: bank Number of groups = 49 R-sq: = 0.4340 Obs per group: = between = 0.3464 avg = 6.4 overall = 0.3930 max = 10 F(10,256) = 19.63 Prob > F = 0.0000 within t to ng corr(u_i, Xb) = -0.0141 hi ep roa | Coef Std Err t P>|t| [95% Conf Interval] w -+ n nii_ta | 0614368 8.55 0.000 4044662 6464379 nie_ta | -.4168279 0636782 -6.55 0.000 -.5422276 -.2914281 lnta | -.0011218 0011156 -1.01 0.316 -.0033187 0010751 llp_ta | -.3816055 1236815 -3.09 0.002 -.6251683 -.1380427 0227501 0065333 3.48 0.001 0098843 0356159 tl_dstf | 0072405 0022605 3.20 0.002 0027889 0116921 gdp_g | 0215208 0710902 0.30 0.762 -.1184752 1615168 inf | 0245306 2.95 0.003 0081661 0408952 msg | 0043357 0037209 1.17 0.245 -.0029917 0116631 fp | 0061937 0107758 0.57 0.566 -.0150269 0274142 _cons | 0157523 0116681 1.35 0.178 -.0072253 03873 lo 5254521 ad ju y th yi eqass | pl ua al 0083099 n n va ll fu 00596136 rho | 58695302 at sigma_e | nh 00710636 oi sigma_u | m -+ (fraction of variance due to u_i) z z -F(48, 256) = 4.98 Prob > F = 0.0000 jm ht vb F test that all u_i=0: Figure D.2-3b – FEM using LSDV for Model k SS df MS Number of obs = -+ F( 58, l.c Source | gm reg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg fp i.bank 315 256) = 11.68 om 024074349 58 000415075 Prob > F = 0.0000 Residual | 009097673 256 000035538 R-squared = 0.7257 Adj R-squared = 0.6636 Root MSE 00596 -+ -.033172022 314 000105643 = Coef Std Err t P>|t| [95% Conf Interval] nii_ta | 5254521 0614368 8.55 0.000 4044662 6464379 nie_ta | -.4168279 0636782 -6.55 0.000 -.5422276 -.2914281 lnta | -.0011218 0011156 -1.01 0.316 -.0033187 0010751 83 ey -+ t re roa | n va Total | an Lu Model | -.3816055 1236815 -3.09 0.002 -.6251683 -.1380427 eqass | 0227501 0065333 3.48 0.001 0098843 0356159 tl_dstf | 0072405 0022605 3.20 0.002 0027889 0116921 gdp_g | 0215208 0710902 0.30 0.762 -.1184752 1615168 inf | 0245306 0083099 2.95 0.003 0081661 0408952 msg | 0043357 0037209 1.17 0.245 -.0029917 0116631 fp | 0061937 0107758 0.57 0.566 -.0150269 0274142 _cons | 0177537 0117375 1.51 0.132 -.0053606 040868 t to llp_ta | ng hi ep w n *48 dummy banks will not be displayed here, please prefer to Stata file for detail lo ad y th Estimation result for model ju Figure D.3-1: Pooled OLS – Model yi SS df al Source | pl reg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg fs MS Number of obs = F( 10, Residual | 01737715 10 001579487 304 000057162 n 015794873 va Model | n ua -+ Total | 033172022 314 ll fu -+ -.000105643 315 304) = 27.63 Prob > F = 0.0000 R-squared = 0.4762 Adj R-squared = 0.4589 Root MSE 00756 = m oi -Coef Std Err t P>|t| [95% Conf Interval] nh roa | at -+ -.5865314 0609639 9.62 0.000 nie_ta | -.3218198 0586254 -5.49 0.000 lnta | 0006751 0004359 1.55 0.122 llp_ta | -.3046423 1229922 -2.48 0.014 -.5466661 -.0626184 eqass | 041428 0060135 6.89 0.000 0295947 0532614 tl_dstf | 011333 0019115 5.93 0.000 0075716 gdp_g | 1026484 0676372 1.52 0.130 -.0304479 inf | 0251673 0083965 3.00 0.003 0086448 msg | 0033397 0044041 0.76 0.449 -.0053266 012006 fs | 0033815 001705 1.98 0.048 0000265 0067365 _cons | -.0134442 007858 -1.71 0.088 -.0289071 0020187 z nii_ta | z 7064962 -.4371827 -.2064568 -.0001826 0015329 k jm ht vb 4665667 l.c gm 0150944 2357447 om 0416899 an Lu n t re Figure D.3-2: REM – Model ey xtreg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg fs, re Random-effects GLS regression Number of obs = 315 Group variable: bank Number of groups = 49 R-sq: Obs per group: = within va = 0.4214 84 between = 0.4935 avg = 6.4 overall = 0.4583 max = 10 Wald chi2(10) = 231.95 Prob > chi2 = 0.0000 t to corr(u_i, X) = (assumed) ng roa | Coef Std Err z P>|z| [95% Conf Interval] hi ep -+ -.5395554 0588473 9.17 0.000 4242168 654894 nie_ta | -.3715765 0591259 -6.28 0.000 -.4874611 -.2556919 1.61e-06 0005259 0.00 0.998 -.0010291 0010323 llp_ta | -.350307 1194639 -2.93 0.003 -.5844519 -.1161621 eqass | 03047 0057941 5.26 0.000 0191137 0418262 tl_dstf | 0100632 0019925 5.05 0.000 006158 0139684 05481 0606898 0.90 0.366 -.0641399 1737599 inf | 0272502 006925 3.94 0.000 0136774 040823 msg | 0048241 0036514 1.32 0.186 -.0023325 0119806 fs | 0025125 0.97 0.330 -.002544 007569 _cons | -.0003379 -0.04 0.969 -.0176204 0169446 nii_ta | w n lnta | lo ad ju y th yi gdp_g | pl ua al 0025799 n va 0088178 n -+ rho | 39717136 (fraction of variance due to u_i) oi 00595184 m sigma_e | ll 00483107 fu sigma_u | at nh Figure D.3-3a : FEM Using ‘xtreg - fe’ – ‘within’ estimator – Model z z xtreg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg fs, fe Number of obs vb = 315 Group variable: bank Number of groups ht = 49 R-sq: = 0.4358 Obs per group: = between = 0.2051 avg = 6.4 overall = 0.3000 max = 10 F(10,256) = 19.77 Prob > F = 0.0000 om = -0.0922 l.c gm corr(u_i, Xb) k within jm Fixed-effects (within) regression an Lu -Coef Std Err t P>|t| [95% Conf Interval] 061288 8.57 0.000 4043043 6456902 nie_ta | -.4210397 063712 -6.61 0.000 -.546506 -.2955733 lnta | -.0004073 0006644 -0.61 0.540 -.0017157 0009011 llp_ta | -.3798348 1234312 -3.08 0.002 -.6229046 -.1367651 eqass | 0249491 0061752 4.04 0.000 0127884 0371097 85 ey 5249972 t re nii_ta | n -+ va roa | .0074394 0021923 3.39 0.001 0031221 0117567 gdp_g | -.0026103 0642068 -0.04 0.968 -.129051 1238304 inf | 0280298 0068432 4.10 0.000 0145537 041506 msg | 0048757 0036008 1.35 0.177 -.0022153 0119666 fs | -.0083261 0077621 -1.07 0.284 -.0236117 0069595 _cons | 0126487 0105015 1.20 0.230 -.0080316 033329 t to tl_dstf | ng hi ep -+ w sigma_u | 00788826 sigma_e | 00595184 n rho | 63722739 (fraction of variance due to u_i) lo ad -F(48, 256) = 4.89 Prob > F = 0.0000 ju y th F test that all u_i=0: yi Figure D.3-3b : FEM Using LSDV – Model pl reg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg fs i.bank SS ua al Source | df MS Number of obs = F( 58, n -+ Residual | 009068653 58 000415575 256 n 024103369 va Model | 000035424 fu ll -+ -.033172022 314 m Total | 000105643 315 256) = 11.73 Prob > F = 0.0000 R-squared = 0.7266 Adj R-squared = 0.6647 Root MSE 00595 = oi -Std Err t P>|t| [95% Conf Interval] at Coef nh roa | z -+ -.061288 8.57 0.000 nie_ta | -.4210397 063712 -6.61 0.000 lnta | -.0004073 0006644 -0.61 0.540 llp_ta | -.3798348 1234312 -3.08 eqass | 0249491 0061752 tl_dstf | 0074394 gdp_g | 4043043 6456902 vb -.546506 -.2955733 ht 5249972 z nii_ta | 0009011 0.002 -.6229046 -.1367651 4.04 0.000 0127884 0371097 0021923 3.39 0.001 0031221 0117567 -.0026103 0642068 -0.04 0.968 -.129051 inf | 0280298 0068432 4.10 0.000 0145537 041506 msg | 0048757 0036008 1.35 0.177 -.0022153 0119666 fs | -.0083261 0077621 -1.07 0.284 -.0236117 0069595 _cons | 0153593 0107046 1.43 0.153 -.0057209 0364395 k jm -.0017157 l.c gm 1238304 om an Lu n ey t re *48 dummy banks will not be displayed here, please prefer to Stata file for detail va 86 APPENDIX E: RESULTS OF BREUSCH – PAGAN LM TEST Figure E.1: Breusch – Pagan LM Test for Pooled OLS and REM of model t to xttest0 ng Breusch and Pagan Lagrangian multiplier test for random effects hi ep roa[bank,t] = Xb + u[bank] + e[bank,t] Estimated results: w | Var sd = sqrt(Var) n -+ - lo ad 0001056 0102783 e | 0000354 0059536 u | 0000226 0047503 Var(u) = yi Test: ju y th roa | 59.37 Prob > chibar2 = 0.0000 pl chibar2(01) = n ua al n va Figure E.2: Breusch – Pagan LM Test for Pooled OLS and REM of model fu xttest0 ll Breusch and Pagan Lagrangian multiplier test for random effects m oi roa[bank,t] = Xb + u[bank] + e[bank,t] Var sd = sqrt(Var) at | nh Estimated results: z -+ - z e | 0000355 0059614 u | 0000218 004671 k Var(u) = jm 0102783 ht 0001056 vb Test: roa | Prob > chibar2 = 0.0000 om l.c 59.33 gm chibar2(01) = xttest0 Var sd = sqrt(Var) -+ roa | 0001056 0102783 e | 0000354 0059518 87 ey | t re Estimated results: n roa[bank,t] = Xb + u[bank] + e[bank,t] va Breusch and Pagan Lagrangian multiplier test for random effects an Lu Figure E.3: Breusch – Pagan LM Test for Pooled OLS and REM of model u | Test: 0000233 0048311 Var(u) = t to chibar2(01) = 57.85 Prob > chibar2 = 0.0000 ng hi ep w n lo ad ju y th yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm an Lu n va ey t re 88 APPENDIX F: RESULTS OF HAUSMAN TEST Figure F.1: Hausman test for FEM and REM of model hausman fe1 re1 t to Coefficients ng hi ep | (b) (B) | fe1 re1 (b-B) sqrt(diag(V_b-V_B)) Difference S.E -+ w 5239015 5485638 -.0246623 0191305 nie_ta | -.4165573 -.3634942 -.0530631 0245345 lnta | -.0005972 1.32e-06 -.0005985 0003676 llp_ta | -.3851267 -.3553174 -.0298093 0309443 024052 0312726 -.0072207 0020787 0098466 -.0022913 0009471 0037714 0513383 -.0475669 0204839 inf | 0272624 0272292 0000332 msg | 0048617 0047159 0001458 n nii_ta | lo ad y th eqass | ju tl_dstf | 0075553 yi pl gdp_g | n ua al va b = consistent under Ho and Ha; obtained from xtreg n Test: Ho: ll fu B = inconsistent under Ha, efficient under Ho; obtained from xtreg difference in coefficients not systematic m oi chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B) Prob>chi2 = 0.0002 at 31.74 nh = z (V_b-V_B is not positive definite) z jm ht vb Figure F.2: Hausman test for FEM and REM of model hausman fe2 re2 k (b) (B) | fe2 re2 (b-B) Difference sqrt(diag(V_b-V_B)) l.c | gm Coefficients S.E om -+ nie_ta | -.4168279 -.3617069 -.0551209 0243322 lnta | -.0011218 0001078 -.0012296 0008851 llp_ta | -.3816055 -.3588123 -.0227932 0248945 eqass | 0227501 0318764 -.0091263 0022781 tl_dstf | 0072405 0098815 -.002641 0010958 gdp_g | 0215208 0434366 -.0219159 inf | 0245306 0279313 -.0034007 0031872 msg | 0043357 0047965 -.0004608 0004558 89 ey 0188642 t re -.0229631 n 5484151 va 5254521 an Lu nii_ta | fp | 0061937 -.0018105 0080041 0070186 -b = consistent under Ho and Ha; obtained from xtreg t to B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic ng chi2(10) = (b-B)'[(V_b-V_B)^(-1)](b-B) hi ep = 33.99 Prob>chi2 = 0.0002 w (V_b-V_B is not positive definite) n lo ad Figure F.3: Hausman test for FEM and REM of model (B) pl | (b) re3 yi | Coefficients ju y th hausman fe3 re3 fe3 (b-B) sqrt(diag(V_b-V_B)) Difference S.E al -.0145582 0171236 -.3715765 -.0494632 0237349 1.61e-06 -.0004089 0004061 fu nii_ta | 5249972 nie_ta | -.4210397 lnta | -.0004073 llp_ta | -.3798348 -.350307 -.0295278 0310424 eqass | 0249491 03047 -.0055209 0021358 tl_dstf | 0074394 0100632 -.0026238 0009145 gdp_g | -.0026103 05481 inf | 0280298 0272502 msg | 0048757 fs | -.0083261 n 5395554 n ua -+ va ll oi m nh 0209583 0007796 0048241 0000516 0025125 -.0108386 at -.0574203 z z vb 0073208 ht jm k b = consistent under Ho and Ha; obtained from xtreg Ho: difference in coefficients not systematic chi2(10) = (b-B)'[(V_b-V_B)^(-1)](b-B) Prob>chi2 = 0.0006 (V_b-V_B is not positive definite) an Lu 30.91 om = l.c Test: gm B = inconsistent under Ha, efficient under Ho; obtained from xtreg n va ey t re 90 APPENDIX G: RESULT OF ROBUST STANDARD ERROR (FIX HETEROSKEDASTICITY) t to Test heteroskedasticity for fixed effect model ng hi Figure G.1-1: test heteroskedasticity for Model ep xttest3 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model w n H0: sigma(i)^2 = sigma^2 for all i = 8.2e+36 ad 0.0000 lo chi2 (49) Prob>chi2 = ju y th yi Figure G.1-2: test heteroskedasticity for Model pl xttest3 al ua Modified Wald test for groupwise heteroskedasticity in fixed effect regression model n H0: sigma(i)^2 = sigma^2 for all i = 0.0000 n Prob>chi2 = 7.2e+34 va chi2 (49) ll fu m oi Figure G.1-3: test heteroskedasticity for Model nh xttest3 at Modified Wald test for groupwise heteroskedasticity in fixed effect regression model z = 5.6e+34 ht Prob>chi2 = vb chi2 (49) z H0: sigma(i)^2 = sigma^2 for all i 0.0000 k jm Figure G.2-1: result of FEM with robust standard error for Model l.c gm Result of regression model FEM with robust standard error om xtreg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg, fe robust Number of obs = Group variable: bank Number of groups = 49 R-sq: = 0.4332 Obs per group: = between = 0.3675 avg = 6.4 overall = 0.4037 max = 10 F(9,48) = 16.80 Prob > F = 0.0000 within 91 ey t re (Std Err adjusted for 49 clusters in bank) n = 0.0398 va corr(u_i, Xb) 315 an Lu Fixed-effects (within) regression | Robust roa | Coef Std Err t P>|t| [95% Conf Interval] -+ -.5239015 0983832 5.33 0.000 3260889 7217141 nie_ta | -.4165573 1665908 -2.50 0.016 -.7515104 -.0816041 lnta | -.0005972 0009464 -0.63 0.531 -.0025001 0013057 llp_ta | -.3851267 1154085 -3.34 0.002 -.6171709 -.1530824 eqass | 024052 014759 1.63 0.110 -.005623 053727 tl_dstf | 0075553 0020561 3.67 0.001 0034213 0116893 0037714 0915054 0.04 0.967 -.1802125 1877553 inf | 0272624 0071561 3.81 0.000 0128741 0416507 msg | 0048617 0029936 1.62 0.111 -.0011573 0108807 _cons | 0128473 016189 0.79 0.431 -.0197027 0453974 t to nii_ta | ng hi ep w n gdp_g | lo ad ju y th yi -+ -.00701533 sigma_e | 00595359 rho | 58132363 pl sigma_u | ua al (fraction of variance due to u_i) n n va fu ll Figure G.2-2: result of FEM with robust standard error for Model m oi xtreg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg fp, fe robust 315 Number of groups = 49 = 0.4340 Obs per group: = z within = at Group variable: bank R-sq: Number of obs nh Fixed-effects (within) regression z vb between = 0.3464 F(10,48) = -0.0141 Prob > F max = 10 k corr(u_i, Xb) 6.4 jm ht overall = 0.3930 avg = = 15.54 = 0.0000 gm (Std Err adjusted for 49 clusters in bank) l.c roa | Robust Coef Std Err t P>|t| om | [95% Conf Interval] an Lu -+ -0.000 3266819 7242222 nie_ta | -.4168279 1672953 -2.49 0.016 -.7531976 -.0804581 lnta | -.0011218 0015862 -0.71 0.483 -.004311 0020673 llp_ta | -.3816055 1149539 -3.32 0.002 -.6127358 -.1504753 eqass | 0227501 0151873 1.50 0.141 -.0077861 0532863 tl_dstf | 0072405 0020984 3.45 0.001 0030213 0114597 gdp_g | 0215208 0802662 0.27 0.790 -.1398652 1829067 inf | 0245306 0085788 2.86 0.006 0072817 0417796 92 ey 5.32 t re 0988594 n 5254521 va nii_ta | msg | 0043357 0031409 1.38 0.174 -.0019794 0106508 fp | 0061937 0110244 0.56 0.577 -.0159724 0283597 _cons | 0157523 0191765 0.82 0.415 -.0228045 0543092 t to -+ ng 00710636 sigma_e | 00596136 rho | 58695302 hi sigma_u | ep (fraction of variance due to u_i) w n lo Figure G.2-3: result of FEM with robust standard error for Model ad xtreg roa nii_ta nie_ta lnta llp_ta eqass tl_dstf gdp_g inf msg fs, fe robust ju y th Fixed-effects (within) regression = 315 Group variable: bank Number of groups = 49 R-sq: = 0.4358 Obs per group: = between = 0.2051 avg = 6.4 max = 10 F(10,48) = 15.19 Prob > F = 0.0000 yi Number of obs pl overall = 0.3000 n ua al within ll fu = -0.0922 n va corr(u_i, Xb) (Std Err adjusted for 49 clusters in bank) m oi Coef Std Err t P>|t| [95% Conf Interval] at roa | Robust nh | z -+ z 5249972 0977026 5.37 0.000 328553 7214415 nie_ta | -.4210397 1662923 -2.53 0.015 -.7553928 -.0866866 lnta | -.0004073 0009685 -0.42 0.676 -.0023547 00154 llp_ta | -.3798348 1162425 -3.27 0.002 -.613556 -.1461137 eqass | 0249491 0152715 1.63 0.109 -.0057562 tl_dstf | 0074394 0020598 3.61 0.001 003298 gdp_g | -.0026103 0902344 -0.03 0.977 -.1840387 inf | 0280298 0068209 4.11 0.000 0143155 0417442 msg | 0048757 0029896 1.63 0.109 -.0011354 0108867 fs | -.0083261 0086144 -0.97 0.339 -.0256466 0089943 _cons | 0126487 0162789 0.78 0.441 -.0200822 0453797 k jm ht vb nii_ta | l.c gm 0556544 0115808 om 1788182 an Lu 00595184 rho | 63722739 ey sigma_e | t re 00788826 n sigma_u | va -+ (fraction of variance due to u_i) 93