The effect of financial development on economic growth evidence from asian countries

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The effect of financial development on economic growth evidence from asian countries

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UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE EFFECT OF FINANCIAL DEVELOPMENT ON ECONOMIC GROWTH: EVIDENCE FROM ASIAN COUNTRIES BY TRẦN THANH GIANG MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, JULY 2014 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE EFFECT OF FINANCIAL DEVELOPMENT ON ECONOMIC GROWTH: EVIDENCE FROM ASIAN COUNTRIES A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By TRẦN THANH GIANG Academic Supervisor: ASSOC PROF DR NGUYỄN VĂN NGÃI HO CHI MINH CITY, JULY 2014 ii DECLARATION This is to certify that this thesis entitled “The effect of financial development on economic growth: evidence from Asian countries”, which is submitted by me in fulfillment of the requirements for the degree of Master of Art in Development Economic to the Vietnam – The Netherlands Programme The thesis constitutes only my original work and due supervision and acknowledgement have been made in the text to all materials used Trần Thanh Giang iii ACKNOWLEGEMENT I would not be able to write and finish my dissertation without the help and support of people surrounding me Above all, I would like to express my greatest appreciation to my supervisor, Assoc Prof Nguyễn Văn Ngãi, for his invaluable comments and advices, patient guidance, encouragement in during the time of doing this thesis I have been strikingly lucky to have supervisor who cared so much my thesis and answered to all my questions Without his guidance, my thesis would not have been possible I would also like to offer my special thanks to Dr Trương Đăng Thụy and Dr Phạm Khánh Nam for the econometric guidance and valuable suggestions that help to develop this thesis Besides my mentors, special thanks also to all the lecturers at the Vietnam – Netherlands Program for their knowledge of all the course, during the time I studied at the program In addition, I would like to thank my friends and people who are always beside me and support for my thesis but are not above mentioned Last, but not least, I am very deeply grateful to my family Without their warm encouragement and attention, I would not be possible to complete this dissertation iv ABBREVIATIONS WB World Bank OECD Organization for Economic Cooperation and Development MENA Countries in the Middle East and North Africa GLS Generalized Least Squares OLS Ordinary Least Squares FEM Fix Effects Model REM Random Effects Model GMM The Generalized Method of Moments Estimation v ABSTRACT This study estimates the effect of financial development on economic growth in Asian countries in the period from 2000 to 2011 Based on unbalanced panel data, this effect is examined by Fixed effects model (FEM) and the first difference Generalizes Methods of Moments approach (GMM) The findings indicate that financial development has significant impacts on economic growth on both estimation techniques However, these impacts depend significantly on estimation methods and proxies for financial development The results of FEM and first difference GMM imply that financial depth and domestic credit to private sector have negative impact on growth, but there is no relationship between stock market development and economic growth On the other hand, while a positive relationship between the ratio of commercial – central bank assets and growth rate of real GDP per capita is shown by FEM, this indicator is not related to growth rates in GMM results Key words: Financial development, Economic growth, relationship, effect, endogeneity, fixed effects, random effects, Asian countries vi TABLE OF CONTENTS LIST OF TABLES ix LIST OF FIGURES x CHAPTER 1: INTRODUCTION 1.1 Problem statements 1.2 Research objectives 1.3 Research scope and data 1.4 Research structure CHAPTER 2: LTERATURE REVIEW 2.1 Theoretical literature 2.1.1 Endogenous growth theory 2.1.2 Theories of financial development 2.2 Empirical studies 16 CHAPTER 3: RESEARCH METHODOLOGY 29 3.1 Model Specification 29 3.2 Measurements of Variables 31 3.2.1 Measurements of financial development 31 3.2.2 The determinants of economic growth 34 3.3 Data collection 39 3.4 Research methodology 39 3.4.1The common constant method (Pooled OLS) 40 3.4.2 The random effects method (REM) 41 3.4.3 The Fixed effects method (FEM) 41 3.4.4 Choice of panel regression model 42 3.4.5 The generalized method of moments estimation (GMM) 45 vii CHAPTER 4: RESEARCH RESULTS 48 4.1 Overview the economic growth and the financial development in the regions of Asia 48 4.1.1 Overview the economic growth in the regions of Asia in the period 2000 - 2011: 48 4.1.2 Overview the financial development in the regions of Asia in 2000 - 2011 50 4.2 The descriptive statistic of the sample 57 4.3 Empirical results 62 4.3.1 Results of tests for panel regression model 62 4.3.2 Discussions on the research results 65 4.3.3 Discussions on the results of first difference GMM 69 CHAPTER 5: CONCLUSION AND POLICY IMPLICATION 75 5.1 Conclusions 75 5.2 Policy implications 77 5.3 Research limitations 78 5.4 Suggestions for further research 79 REFERENCES 81 APPENDIX A 85 APPENDIX B 91 APPENDIX C: DESCRIPTIVE STATISTIC OF VARIABLE 94 APPENDIX D: PANEL REGRESSION MODEL 96 APPENDIX E: RESULTS OF BREUSCH – PAGAN LM TEST 102 APPENDIX F: RESULTS OF HAUSMAN TEST 103 APPENDIX G: THE REGRESSION MODEL RESULTS OF FIRST – DIFFERENCE GMM 105 viii LIST OF TABLES Table 3.1: The expected sign of variables in model 38 Table 3.2: Tests for choosing a panel regression model 44 Table 4.1 Descriptive statistics of the sample observation 58 Table 4.2: The corrrelation on the sample observations 61 Table 4.3: The results of F test and Breusch – Pagan test 63 Table 4.4: The results of Hausman test 63 Table 4.5: The results of FEM regression model 64 Table 4.6: The results of first difference GMM 70 ix LIST OF FIGURES Figure 2.1: The role of financial development in economic growth 27 Figure 3.1: Analytical framework 37 Figure 4.1: The average growth rate of real GDP per capita in Asia regions in 2000 – 2011 50 Figure 4.2: Financial development in Central Asia 51 Figure 4.3: Financial development in South - East Asia 51 Figure 4.4: Financial development in South Asia 52 Figure 4.5: Financial development in Eastern Asia 53 Figure 4.6: Financial development in Western Asia 54 Figure 4.7: The ratio of liquid liabilities to GDP across Asia regions 55 Figure 4.8: The ratio of domestic credit to private sector to GDP across Asia regions 55 Figure 4.9: The ratio of commercial – central bank assets across Asia regions 56 Figure 4.10: The ratio of stock market capitalization to GDP across Asia regions 56 Figure 4.11: The scatter diagram among dependent variable and financial development variables 59 Figure 4.12: The scatter diagram among dependent variable and control variables 60 x Figure B.5: Growth rate of real GDP per capita in Central Asia (%) Growth rate of real GDP per capita in Central Asia (%) 40 30 20 10 -10 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 -20 Armenia Azerbaijan Georgia Kazakhstan Kyrgyz Republic Tajikistan Turkey Figure B.6: Financial development in Asian developing countries Financial development in developing countries 100 90 80 Ratio of Liquid liabilities to GDP 70 60 Ratio of Commercial central bank assets 50 40 Ratio of domestic credit to private sector to GDP 30 20 Ratio of stock market capatilization to GDP 10 93 Figure B.7: Financial development in Asian developed countries Financial development in developed countries 160 140 Ratio of Liquid liabilities to GDP 120 100 Ratio of Commercial central bank assets 80 60 Ratio of domestic credit to private sector to GDP 40 Ratio of stock market capatilization to GDP 20 APPENDIX C: DESCRIPTIVE STATISTIC OF VARIABLE 0 005 05 Density Density 01 015 Figure C.1: Data distribution figures of Financial development indicators 100 200 300 20 40 60 BANK 80 100 Density 005 01 0 005 Density 01 015 02 015 DEPTH 50 100 CREDIT 150 200 200 400 STOCK 94 600 Density 0 05 02 Density 04 15 06 Figure C.2: Data distribution figures of control variables -10 GROWTH_1 10 -20 20 20 inf 40 60 Density 0 02 005 04 Density 06 01 08 015 -20 10 15 GOV 20 25 30 02 01 Density 015 005 20 40 60 EDU 100 200 300 TO 025 80 100 95 400 APPENDIX D: PANEL REGRESSION MODEL Estimation results of model Figure D.1 -1: The pooled OLS model Source SS df MS Model Residual 575.892541 5041.8858 328 115.178508 15.3716031 Total 5617.77834 333 16.8702052 GROWTH Coef DEPTH INF GOV TO EDU _cons -.0096109 0314105 -.1962185 0057002 0095793 5.998403 Std Err .0047422 02727 0483809 0040521 0122061 8767924 t -2.03 1.15 -4.06 1.41 0.78 6.84 Number of obs F( 5, 328) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.044 0.250 0.000 0.160 0.433 0.000 = = = = = = 334 7.49 0.0000 0.1025 0.0888 3.9207 [95% Conf Interval] -.0189399 -.0222357 -.2913945 -.0022713 -.0144329 4.273557 -.0002819 0850568 -.1010425 0136716 0335914 7.723249 Figure D.1-2: The FEM model Fixed-effects (within) regression Group variable: id Number of obs Number of groups = = 334 32 R-sq: Obs per group: = avg = max = 10.4 12 within = 0.1261 between = 0.0828 overall = 0.0559 corr(u_i, Xb) F(5,297) Prob > F = -0.8124 GROWTH Coef DEPTH INF GOV TO EDU _cons -.0459941 0181456 -.2912321 0668945 -.0011982 4.796717 0190295 0282139 1067523 0136771 0377244 3.269838 sigma_u sigma_e rho 4.1115071 3.3522938 60067769 (fraction of variance due to u_i) F test that all u_i=0: Std Err t P>|t| = = -2.42 0.64 -2.73 4.89 -0.03 1.47 F(31, 297) = 4.89 96 0.016 0.521 0.007 0.000 0.975 0.143 8.57 0.0000 [95% Conf Interval] -.0834439 -.0373788 -.5013189 0399782 -.0754391 -1.638271 -.0085444 0736701 -.0811452 0938109 0730428 11.23171 Prob > F = 0.0000 Figure D.1-3: The REM MODEL Random-effects GLS regression Group variable: id Number of obs Number of groups = = 334 32 R-sq: Obs per group: = avg = max = 10.4 12 within = 0.0978 between = 0.1588 overall = 0.0913 Random effects u_i ~ Gaussian corr(u_i, X) = (assumed) Wald chi2(5) Prob > chi2 GROWTH Coef DEPTH INF GOV TO EDU _cons -.0174571 0393201 -.2218614 0184767 0139279 5.25352 0082673 0269725 0712008 0071266 019114 1.503053 sigma_u sigma_e rho 2.053621 3.3522938 2728758 (fraction of variance due to u_i) Std Err z -2.11 1.46 -3.12 2.59 0.73 3.50 P>|z| 0.035 0.145 0.002 0.010 0.466 0.000 = = 29.55 0.0000 [95% Conf Interval] -.0336606 -.0135451 -.3614125 0045087 -.0235349 2.307591 -.0012535 0921853 -.0823104 0324446 0513906 8.199448 Estimation results of model Figure D.2-1: The pooled OLS model Source SS df MS Model Residual 522.042893 4453.52723 295 104.408579 15.0967025 Total 4975.57012 300 16.5852337 GROWTH Coef BANK INF GOV TO EDU _cons 0107968 0285552 -.2480927 0080281 0155202 4.60912 Std Err .0177247 0274753 0506151 0056802 0125624 1.706022 t 0.61 1.04 -4.90 1.41 1.24 2.70 97 Number of obs F( 5, 295) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.543 0.300 0.000 0.159 0.218 0.007 = = = = = = 301 6.92 0.0000 0.1049 0.0898 3.8854 [95% Conf Interval] -.0240862 -.0255171 -.347705 -.0031507 -.0092032 1.251604 0456798 0826276 -.1484803 0192069 0402436 7.966637 Figure D.2-2: The FEM MODEL Fixed-effects (within) regression Group variable: id Number of obs Number of groups = = 301 30 R-sq: Obs per group: = avg = max = 10.0 12 within = 0.1656 between = 0.0842 overall = 0.0689 corr(u_i, Xb) F(5,266) Prob > F = -0.8498 GROWTH Coef BANK INF GOV TO EDU _cons 0712947 -.022958 -.6250647 0760589 -.0589488 4.207273 0301948 0295659 1197558 0163898 0377705 3.383438 sigma_u sigma_e rho 4.7597486 3.2010708 68856496 (fraction of variance due to u_i) F test that all u_i=0: Std Err t P>|t| = = 2.36 -0.78 -5.22 4.64 -1.56 1.24 F(29, 266) = 0.019 0.438 0.000 0.000 0.120 0.215 10.56 0.0000 [95% Conf Interval] 0118435 -.0811709 -.8608545 0437886 -.1333159 -2.454454 5.81 1307459 0352549 -.3892749 1083291 0154184 10.869 Prob > F = 0.0000 Figure D.2-3: The REM MODEL Random-effects GLS regression Group variable: id Number of obs Number of groups = = 301 30 R-sq: Obs per group: = avg = max = 10.0 12 within = 0.1347 between = 0.1411 overall = 0.0915 Random effects u_i ~ Gaussian corr(u_i, X) = (assumed) Std Err Wald chi2(5) Prob > chi2 GROWTH Coef z BANK INF GOV TO EDU _cons 0284564 0137372 -.3425755 0273702 0169124 2.700434 0231654 0281362 0765049 0098496 0197052 2.312929 sigma_u sigma_e rho 2.0920598 3.2010708 29929179 (fraction of variance due to u_i) 1.23 0.49 -4.48 2.78 0.86 1.17 98 P>|z| 0.219 0.625 0.000 0.005 0.391 0.243 = = 32.54 0.0000 [95% Conf Interval] -.0169469 -.0414087 -.4925223 0080652 -.0217091 -1.832824 0738597 0688832 -.1926287 0466751 055534 7.233692 Estimation results of model Figure D.3-1: The pooled OLS model Source SS df MS Model Residual 628.040221 4968.65208 321 125.608044 15.4786669 Total 5596.6923 326 17.1677678 GROWTH Coef CREDIT INF GOV TO EDU _cons -.0133036 028056 -.2233368 0039021 0207128 5.753535 Std Err .0059683 0277754 0506562 0037007 0132289 8851951 t -2.23 1.01 -4.41 1.05 1.57 6.50 Number of obs F( 5, 321) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.027 0.313 0.000 0.292 0.118 0.000 = = = = = = 327 8.11 0.0000 0.1122 0.0984 3.9343 [95% Conf Interval] -.0250456 -.0265888 -.3229969 -.0033786 -.0053135 4.012018 -.0015617 0827008 -.1236768 0111828 0467391 7.495052 Figure D.3-2: The FEM MODEL Fixed-effects (within) regression Group variable: id Number of obs Number of groups = = 327 32 R-sq: Obs per group: = avg = max = 10.2 12 within = 0.1419 between = 0.0880 overall = 0.0572 corr(u_i, Xb) F(5,290) Prob > F = -0.8400 GROWTH Coef CREDIT INF GOV TO EDU _cons -.0773631 0142092 -.2424867 0649857 0159684 3.97082 0212231 028273 1078179 013314 0389221 3.339713 sigma_u sigma_e rho 4.5106778 3.3426731 64550832 (fraction of variance due to u_i) F test that all u_i=0: Std Err t P>|t| = = -3.65 0.50 -2.25 4.88 0.41 1.19 F(31, 290) = 4.99 99 0.000 0.616 0.025 0.000 0.682 0.235 9.59 0.0000 [95% Conf Interval] -.1191339 -.041437 -.4546915 0387815 -.0606373 -2.602329 -.0355922 0698555 -.0302818 0911899 092574 10.54397 Prob > F = 0.0000 Figure D.3-3: The REM MODEL Random-effects GLS regression Group variable: id Number of obs Number of groups = = 327 32 R-sq: Obs per group: = avg = max = 10.2 12 within = 0.1073 between = 0.1683 overall = 0.0974 Random effects u_i ~ Gaussian corr(u_i, X) = (assumed) Wald chi2(5) Prob > chi2 GROWTH Coef CREDIT INF GOV TO EDU _cons -.0269785 0335913 -.2231705 0164404 0244525 4.945361 0102772 0272808 0730493 0066426 0203186 1.544365 sigma_u sigma_e rho 2.0892602 3.3426731 28091619 (fraction of variance due to u_i) Std Err z P>|z| -2.63 1.23 -3.06 2.48 1.20 3.20 0.009 0.218 0.002 0.013 0.229 0.001 = = 31.28 0.0000 [95% Conf Interval] -.0471214 -.019878 -.3663445 0034212 -.0153713 1.918462 -.0068355 0870606 -.0799966 0294597 0642762 7.972261 Estimation results of model Figure D.4-1: The pooled OLS model Source SS df MS Model Residual 402.749508 3950.00013 255 80.5499015 15.4901966 Total 4352.74964 260 16.7413448 GROWTH Coef STOCK INF GOV TO EDU _cons -.0068085 0477213 -.2260206 0059663 0271913 4.545108 Std Err .0049619 0306011 0621749 0059755 0168323 1.111186 t -1.37 1.56 -3.64 1.00 1.62 4.09 100 Number of obs F( 5, 255) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.171 0.120 0.000 0.319 0.107 0.000 = = = = = = 261 5.20 0.0001 0.0925 0.0747 3.9358 [95% Conf Interval] -.0165799 -.0125418 -.3484623 -.0058014 -.0059567 2.356837 002963 1079845 -.1035789 017734 0603393 6.733378 Figure D.4-2: The FEM MODEL Fixed-effects (within) regression Group variable: id Number of obs Number of groups = = 261 25 R-sq: Obs per group: = avg = max = 10.4 12 within = 0.1168 between = 0.0499 overall = 0.0354 corr(u_i, Xb) F(5,231) Prob > F = -0.8270 = = GROWTH Coef STOCK INF GOV TO EDU _cons -.0097339 0336772 -.4759169 0522821 0019463 5.808138 009788 0338324 1243896 016863 0462949 4.201582 sigma_u sigma_e rho 4.2388812 3.4500397 60152594 (fraction of variance due to u_i) F test that all u_i=0: Std Err t P>|t| -0.99 1.00 -3.83 3.10 0.04 1.38 F(24, 231) = 0.321 0.321 0.000 0.002 0.967 0.168 4.20 6.11 0.0000 [95% Conf Interval] -.029019 -.0329823 -.7210002 0190573 -.0892679 -2.470184 0095512 1003367 -.2308336 085507 0931605 14.08646 Prob > F = 0.0000 Figure D.4-3: The REM MODEL Random-effects GLS regression Group variable: id Number of obs Number of groups = = 261 25 R-sq: Obs per group: = avg = max = 10.4 12 within = 0.0873 between = 0.1530 overall = 0.0869 Random effects u_i ~ Gaussian corr(u_i, X) = (assumed) Wald chi2(5) Prob > chi2 GROWTH Coef Std Err z STOCK INF GOV TO EDU _cons -.0069336 0538534 -.3011605 0109858 0367918 4.297176 0068862 0315876 0847686 008949 0249 1.858546 sigma_u sigma_e rho 1.9587931 3.4500397 24377109 (fraction of variance due to u_i) -1.01 1.70 -3.55 1.23 1.48 2.31 101 P>|z| 0.314 0.088 0.000 0.220 0.140 0.021 = = 22.13 0.0005 [95% Conf Interval] -.0204302 -.0080571 -.467304 -.006554 -.0120112 6544919 006563 115764 -.135017 0285256 0855948 7.93986 APPENDIX E: RESULTS OF BREUSCH – PAGAN LM TEST Figure E.1: Breusch – Pagan LM Test for Pooled OLS and REM of model 1DEPTH Breusch and Pagan Lagrangian multiplier test for random effects GROWTH[id,t] = Xb + u[id] + e[id,t] Estimated results: Var GROWTH e u Test: sd = sqrt(Var) 16.87021 11.23787 4.217359 4.107336 3.352294 2.053621 Var(u) = chi2(1) = Prob > chi2 = 70.73 0.0000 Figure E.2: Breusch – Pagan LM Test for Pooled OLS and REM of model BANK Breusch and Pagan Lagrangian multiplier test for random effects GROWTH[id,t] = Xb + u[id] + e[id,t] Estimated results: Var GROWTH e u Test: sd = sqrt(Var) 16.58523 10.24685 4.376714 4.072497 3.201071 2.09206 Var(u) = chi2(1) = Prob > chi2 = 86.68 0.0000 Figure E.3: Breusch – Pagan LM Test for Pooled OLS and REM of model CREDIT Breusch and Pagan Lagrangian multiplier test for random effects GROWTH[id,t] = Xb + u[id] + e[id,t] Estimated results: Var GROWTH e u Test: sd = sqrt(Var) 17.16777 11.17346 4.365008 4.143401 3.342673 2.08926 Var(u) = chi2(1) = Prob > chi2 = 65.40 0.0000 102 Figure E.4: Breusch – Pagan LM Test for Pooled OLS and REM of model – STOCK Breusch and Pagan Lagrangian multiplier test for random effects GROWTH[id,t] = Xb + u[id] + e[id,t] Estimated results: Var GROWTH e u Test: sd = sqrt(Var) 16.74134 11.90277 3.83687 4.091619 3.45004 1.958793 Var(u) = chi2(1) = Prob > chi2 = 45.33 0.0000 APPENDIX F: RESULTS OF HAUSMAN TEST Figure F.1: Hausman test for FEM and REM of model – DEPTH Coefficients (b) (B) fixed random DEPTH INF GOV TO EDU -.0459941 0181456 -.2912321 0668945 -.0011982 (b-B) Difference -.0174571 0393201 -.2218614 0184767 0139279 -.0285371 -.0211745 -.0693706 0484179 -.015126 sqrt(diag(V_b-V_B)) S.E .0171398 0082768 0795393 0116737 0325236 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(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 20.16 Prob>chi2 = 0.0012 103 Figure F.2: Hausman test for FEM and REM of model – BANK Coefficients (b) (B) fixed random BANK INF GOV TO EDU 0712947 -.022958 -.6250647 0760589 -.0589488 (b-B) Difference 0284564 0137372 -.3425755 0273702 0169124 sqrt(diag(V_b-V_B)) S.E .0428382 -.0366952 -.2824892 0486887 -.0758612 0193673 0090826 0921328 0131 0322229 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(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 24.82 Prob>chi2 = 0.0002 Figure F.3: Hausman test for FEM and REM of model –CREDIT Coefficients (b) (B) fixed random CREDIT INF GOV TO EDU -.0773631 0142092 -.2424867 0649857 0159684 -.0269785 0335913 -.2231705 0164404 0244525 (b-B) Difference -.0503846 -.0193821 -.0193161 0485453 -.0084841 sqrt(diag(V_b-V_B)) S.E .0185688 0074244 0793001 0115385 0331976 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(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 23.55 Prob>chi2 = 0.0003 Figure F.4: Hausman test for FEM and REM of model – STOCK Coefficients (b) (B) fixed random STOCK INF GOV TO EDU -.0097339 0336772 -.4759169 0522821 0019463 -.0069336 0538534 -.3011605 0109858 0367918 (b-B) Difference -.0028003 -.0201762 -.1747564 0412963 -.0348455 sqrt(diag(V_b-V_B)) S.E .0069559 0121183 0910333 0142925 0390283 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(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 12.40 Prob>chi2 = 0.0297 104 APPENDIX G: THE REGRESSION MODEL RESULTS OF FIRST – DIFFERENCE GMM Figure G.1: First difference GMM of model –DEPTH Instrumental variable (GMM) regression Number of obs = 214 Wald chi2(6) = 57.36 Prob > chi = 0.0000 R – squared = 0.1932 Root MSE = 3.8832 GMM weight matrix: Robust GROWTHit Coef GROWTHi, t-1 0503047 Robust Std Err 4350783 -.1382538 z P> |z| [ 95% Conf Interval] 0.12 0.908 -.8024331 9030425 561286 -2.46 0.014 -.2482639 -.282437 0133705 0382841 0.35 0.727 -.0616649 0884059 -.5657275 268372 -2.11 0.035 -1.091727 -.039728 TOit 1024867 0363412 2.82 0.005 0312592 1737142 EDUit -.233941 2124433 -1.10 0.271 -.6503223 1824403 _cons 0492278 322095 0.15 0.879 -.5820668 6805224 DEPTHit INFit GOVit Instrumented: GROWTHi, t-1 Instruments: DEPTHit INFitGOVitTOitEDUit GROWTH_n4 Figure G.2: First difference GMM of model –BANK Instrumental variable (GMM) regression Number of obs Wald chi2(6) Prob > chi R – squared Root MSE GMM weight matrix: Robust GROWTHi, t-1 2422392 Robust Std Err 4683615 BANKit 0583932 INFit GOVit GROWTHit Coef z P> |z| = 190 = 32.56 = 0.0000 = 0.0156 = 4.3118 [ 95% Conf Interval] 0.52 0.605 -.6757325 1.160211 049697 1.17 0.240 -.0390112 1557976 0148773 0489025 0.30 0.761 -.0809698 1107243 -.7909463 2816325 -2.81 0.005 -1.342936 -.2389569 847065 0504605 1.68 0.093 -.0141942 1836072 EDUit -.2400032 2484416 -0.97 0.334 -.7269398 2469335 _cons -.0194185 343102 -0.06 0.955 -.6918861 6530491 TOit Instrumented: GROWTHi, t-1 Instruments: BANKit INFitGOVitTOitEDUit GROWTH_n4 105 Figure G.3: First difference GMM of model –CREDIT Instrumental variable (GMM) regression Number of obs = 210 Wald chi2(6) = 64.35 Prob > chi = 0.0000 R – squared = 0.2383 Root MSE = 3.7978 GMM weight matrix: Robust z P> |z| [95% Conf Interval] -.023368 Robust Std Err 3624047 -0.06 0.949 -.7336683 -.1732655 0584235 -2.97 0.003 -.2877734 -.0587575 INFit 0016365 0319282 0.05 0.959 -.0609417 0642146 GOVit -.5458838 2519901 -2.17 0.030 -1.039417 -.0519922 1171291 032153 3.64 0.000 0541104 1801478 -.1983322 1994108 -0.99 0.320 -.5891703 1925058 1109649 334401 0.33 0.740 -.5444491 7663788 GROWTHit Coef GROWTHi, t-1 CREDITit TOit EDUit _cons 6869322 Instrumented: GROWTHi, t-1 Instruments: CREDITit INFit GOVit TOit EDUit GROWTH_n4 Figure G.4: First difference GMM of model –STOCK Instrumental variable (GMM) regression Number of obs Wald chi2(6) Prob > chi R – squared Root MSE GMM weight matrix: Robust = 173 = 75.75 = 0.0000 = 0.3277 = 3.5458 Robust Std Err 6331143 -0.61 0.544 -1.624663 8570996 012684 0138801 0.91 0.361 -.0145205 0398886 0446467 0761401 0.59 0.558 -.1045851 1938785 -.8256605 3323747 1284711 0516158 2.49 0.013 0273061 2296361 EDUit -.1385895 2070761 -0.67 0.503 -.5444512 2672721 _cons 34006616 2675744 -1.27 0.204 -.8644978 1843746 GROWTHit GROWTHi, t-1 STOCKit INFit GOVit TOit Coef -.3837816 z -2.48 P> |z| 0.013 Instrumented: GROWTHi, t-1 Instruments: STOCKit INFit GOVit TOit EDUit GROWTH_n4 106 [ 95% Conf Interval] -1.477102 17442189 Figure G.5: Test the relevance of instrument variable for model –DEPTH Test of endogeneity (orthogonality conditions) Ho: variables are exogenous GMM C statistic chi2(1) = 672249 (p = 0.4123) Figure G.6: Test the relevance of instrument variable for model –BANK Test of endogeneity (orthogonality conditions) Ho: variables are exogenous GMM C statistic chi2(1) = 1.77781 (p = 0.1824) Figure G.7: Test the relevance of instrument variable for model –CREDIT Test of endogeneity (orthogonality conditions) Ho: variables are exogenous GMM C statistic chi2(1) = 46497 (p = 0.4953) Figure G.8: Test the relevance of instrument variable for model -STOCK Test of endogeneity (orthogonality conditions) Ho: variables are exogenous GMM C statistic chi2(1) = 026778 (p = 0.8700) 107 ... between the liquidity level of stock market and the development of banking sector with economic growth Secondly, the contribution of financial development to the economy is mainly through the growth. .. investigate the impact of financial development on economic growth in Asian countries 1.2 Research objectives This study aims to: Analyze the situation of financial development and economic growth. .. function to growth It proves that the development of financial system is only necessary condition, but it is not sufficient condition for economic growth From the results of vector error correction

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