The impact of remittances on financial development in selected asian countries

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE IMPACT OF REMITTANCES ON FINANCIAL DEVELOPMENT IN SELECTED ASIAN COUNTRIES BY HUYNH THI MY CHI MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, DECEMBER 2016 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 IMPACT OF REMITTANCES ON FINANCIAL DEVELOPMENT IN SELECTED ASIAN COUNTRIES A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By HUYNH THI MY CHI Academic Supervisor: DR NGUYEN VAN NGAI HO CHI MINH CITY, DECEMBER 2016 DECLARATION “This declaration is to certify that this thesis entitled “The Impact of Remittances on Financial Development in Selected Asian Countries” which is conducted and submitted by me in partial fulfilment of the requirements for the degree of the Vietnam – The Netherlands Programme The thesis constitutes only my original works and due supervision and acknowledgement have been made in the text to all materials used.” Huynh Thi My Chi ACKNOWLEDGEMENTS I would like to express my greatest appreciation to persons who greatly supported and contributed to this thesis by supervision and encouragement First of all, I am deeply grateful to my supervisor, Assoc., Prof Nguyen Van Ngai, for his guidance, enthusiasm, support, dedication and invaluable comments and advices It was my privilege to have his supervision Without his encouragement and support, I would not have been able to complete this thesis I am also very obliged to Prof Nguyen Trong Hoai, Dr Pham Khanh Nam for their valuable comments and suggestions for my Concept Note and Thesis Research Design My special thanks to Dr Truong Dang Thuy for his encouragements, advices and enthusiasm to help me finish the thesis I would also like to thank all VNP staff for their diligent assistance I am thankful to my friends from VNP who supported, encouraged and shared experiences for my thesis completion Besides, my sincere thankfulness also goes to my company’s managers and colleagues who kindly and understandingly facilitated my master studying Finally, I am most grateful to my family for their endless support and encouragements to me all the way through my journeys ABBREVIATIONS FDI : Foreign Direct Investment FEM : Fixed Effect Model GDP : Gross Domestic Product GDPPC : Gross Domestic Product Per Capita GMM : Generalized Method of Moments ODA : Official Development Assistant REM : Random Effect Model ABSTRACT Although the impact of remittances on economic growth and poverty has always been a controversial problem for researchers and policy makers as remittance inflow have been becoming one of the largest external capital sources for many countries, its direct effect on financial development merely attract more attention after the financial crisis of 2007-2008 In an effort to contribute to empirical studies on this issue, this study utilizes fixed effect, random effect and system Generalized Method of Moments (GMM) to investigate the direct impact of remittances on two dimensions of financial development comprising the percentage of domestic credit to private sector by banks and broad money to GDP in thirty-seven Asian countries during the period 1990-2014 Furthermore, this study also examines whether there are different effects of remittance inflows in high, middle and low-income countries in this area The results show that an increase in remittances seems to have no impact on financial development in general while there are mix results regarding the different income groups of countries in Asia In particular, while no evidence on the impact of remittances on both measurements of financial development as the whole region are obtained in middle and low-income countries, there is a significant and positive effect of these flows on the ratio of domestic credit to private sector by banks to GDP despite an insignificant influence on broad money to GDP in high income countries Keywords: Remittances, Financial development, Asia, Income Group TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES CHAPTER 1: INTRODUCTION 1.1 Problem statement 1.2 Research objectives 1.3 Scope and data of the study 1.4 Structure of the study CHAPTER 2: LITERATURE REVIEWS 2.1 Theory of remittances and financial development 2.1.1 The concepts and channels of remittances 2.1.2 Definitions of financial development 2.1.3 The role of remittances in financial development 2.2 Empirical studies 10 2.3 Other determinants of financial development 15 CHAPTER 3: MODEL SPECIFICATION AND DATA .18 3.1 Model specification 18 3.2 Data sources 21 3.3 Estimation methods 23 3.3.1 Pooled OLS model 24 3.3.2 Fixed effect model 25 3.3.3 Random effect model 26 3.3.4 Tests for choosing sufficient model 26 3.3.5 The system generalized method of moment estimation 28 CHAPTER 4: THE IMPACT OF REMITTANCES ON FINANCIAL DEVELOPMENT IN ASIA 31 4.1 Overview of remittance inflows and financial development in Asia 31 4.1.1 Overview of remittance inflows to Asia from 1990 to 2014 31 4.1.2 Overview of financial development in Asia from 1990 to 2014 36 4.2 Empirical results 40 4.2.1 Descriptive statistic 40 4.2.2 Empirical results 45 CHAPTER 5: CONCLUSIONS AND POLICY IMLICATIONS .54 5.1 Conclusions 54 5.2 Policy implications 55 5.3 Limitations and further researches 56 REFERENCES APPENDIX I APPENDIX II: THE REGRESSION RESULTS LIST OF TABLES Table 3.1: The definition and expected sign of variables .22 Table 4.1: The summary statistics of variables 41 Table 4.2: The correlation between variables 44 Table 4.3: The results of tests for choosing models .45 Table 4.4: The results of FEM with robust 47 Table 4.5: The results of system GMM 49 Table 4.6: Summary of the impact of remittances on financial development in Asia and different income groups by FEM and system GMM .50 LIST OF FIGURES Figure 4.1: Remittances received by areas in the world from 1990 to 2014 (US$ billion) 31 Figure 4.2: Top 10 remittance recipient countries in 2014 (US$ billion) .32 Figure 4.3: Top 10 remittance recipient countries in 2014 (% GDP) 33 Figure 4.4: Remittances to areas in Asia from 1990 to 2014 (US$ billion) 34 Figure 4.5: Remittances received by income groups in the world from 1990 to 2014 (US$ billion) .35 Figure 4.6: Remittances received by income groups in Asia from 1990 to 2014 ($US billion) 36 Figure 4.7: Domestic credit to private sector by banks (% of GDP) in Asia from 1990 to 2014 37 Figure 4.8: Broad money as % of GDP in Asia from 1990 to 2014 .38 Figure 4.9: The average ratio of domestic credit to private sector by banks to GDP across income groups in Asia (%) .39 Figure 4.10: The average ratio of broad money as % of GDP across income groups in Asia 39 Figure 4.11: Correlation between domestic credit to private sector by banks (%GDP) and remittance inflows (%GDP) and other controlling variables 42 Figure 4.12: Correlation between broad money (%GDP) and remittance inflows (%GDP) and other controlling variables 43 Result of equation (1) by FEM xtreg CREDIT REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, fe Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 699 36 R-sq: Obs per group: = avg = max = 19.4 25 within = 0.3200 between = 0.1791 overall = 0.3097 corr(u_i, Xb) = -0.2915 F(6,657) Prob > F = = 51.54 0.0000 CREDIT | Coef Std Err t P>|t| [95% Conf Interval] + -REMIT | 3734473 1739304 2.15 0.032 0319208 7149738 GDPPC | 0016842 0002496 6.75 0.000 001194 0021743 LNGDP | 11.88314 2.453112 4.84 0.000 7.066252 16.70002 INF | -.0237079 0594917 -0.40 0.690 -.1405246 0931089 FINANCIALOPENNESS | 22.27592 4.106088 5.43 0.000 14.21329 30.33856 TRADEOPENNESS | 0941891 0442767 2.13 0.034 0072482 1811301 _cons | -277.6653 59.53601 -4.66 0.000 -394.569 -160.7615 + -sigma_u | 45.438543 sigma_e | 16.871812 rho | 87883382 (fraction of variance due to u_i) F test that all u_i=0: F(35, 657) = 64.61 Prob > F = 0.0000 Result of equation (2) by FEM xtreg CREDIT REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, fe Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 699 36 R-sq: Obs per group: = avg = max = 19.4 25 within = 0.3375 between = 0.1909 overall = 0.3423 corr(u_i, Xb) = -0.3136 F(8,655) Prob > F = = 41.70 0.0000 CREDIT | Coef Std Err t P>|t| [95% Conf Interval] + -REMIT | 3249448 1866753 1.74 0.082 -.0416094 6914991 REMIT_HIGH | 18.68919 4.528686 4.13 0.000 9.796702 27.58169 REMIT_MIDDLE | 1648921 2689052 0.61 0.540 -.363128 6929122 GDPPC | 0018311 0002497 7.33 0.000 0013408 0023215 LNGDP | 11.14733 2.480295 4.49 0.000 6.277039 16.01762 INF | -.024802 0588158 -0.42 0.673 -.1402923 0906884 FINANCIALOPENNESS | 24.58052 4.100262 5.99 0.000 16.52928 32.63176 TRADEOPENNESS | 0871429 043806 1.99 0.047 0011258 17316 _cons | -262.2778 60.14002 -4.36 0.000 -380.3683 -144.1873 + -sigma_u | 46.797333 sigma_e | 16.679626 rho | 88728227 (fraction of variance due to u_i) F test that all u_i=0: F(35, 655) = 55.82 Prob > F = 0.0000 Result of equation (3) by FEM Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 674 35 R-sq: Obs per group: = avg = max = 19.3 25 within = 0.6074 between = 0.1902 overall = 0.3693 corr(u_i, Xb) = -0.3808 F(6,633) Prob > F = = 163.22 0.0000 M2 | Coef Std Err t P>|t| [95% Conf Interval] + -REMIT | 2938719 1113628 2.64 0.009 0751866 5125572 GDPPC | 001841 0001759 10.47 0.000 0014956 0021863 LNGDP | 19.3266 1.594238 12.12 0.000 16.19597 22.45724 INF | -.031237 0381282 -0.82 0.413 -.1061101 0436361 FINANCIALOPENNESS | 6.38807 2.728308 2.34 0.020 1.030441 11.7457 TRADEOPENNESS | 2420957 028831 8.40 0.000 1854798 2987116 _cons | -455.3315 38.75055 -11.75 0.000 -531.4267 -379.2363 + -sigma_u | 57.841233 sigma_e | 10.776757 rho | 9664509 (fraction of variance due to u_i) F test that all u_i=0: F(34, 633) = 230.23 Prob > F = 0.0000 Result of equation (4) by FEM xtreg M2 REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, fe Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 674 35 R-sq: Obs per group: = avg = max = 19.3 25 within = 0.6130 between = 0.1788 overall = 0.3497 corr(u_i, Xb) = -0.4234 F(8,631) Prob > F = = 124.95 0.0000 M2 | Coef Std Err t P>|t| [95% Conf Interval] + -REMIT | 4304403 1200561 3.59 0.000 1946824 6661981 REMIT_HIGH | 1.975491 3.243214 0.61 0.543 -4.393308 8.344291 REMIT_MIDDLE | -.5078442 1729705 -2.94 0.003 -.8475118 -.1681767 GDPPC | 0018115 000177 10.24 0.000 001464 0021591 LNGDP | 20.27213 1.623084 12.49 0.000 17.08483 23.45943 INF | -.0308403 0379141 -0.81 0.416 -.1052934 0436129 FINANCIALOPENNESS | 6.176824 2.760999 2.24 0.026 754966 11.59868 TRADEOPENNESS | 2407214 0287956 8.36 0.000 1841746 2972682 _cons | -478.1534 39.41685 -12.13 0.000 -555.5575 -400.7493 + -sigma_u | 59.368218 sigma_e | 10.71616 rho | 96844661 (fraction of variance due to u_i) F test that all u_i=0: F(34, 631) = 215.35 Prob > F = 0.0000 Result of equation (1) by REM xtreg CREDIT REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS Random-effects GLS regression Group variable: code Number of obs Number of groups = = 699 36 R-sq: Obs per group: = avg = max = 19.4 25 within = 0.3147 between = 0.2064 overall = 0.3284 corr(u_i, X) = (assumed) Wald chi2(6) Prob > chi2 = = 290.51 0.0000 CREDIT | Coef Std Err z P>|z| [95% Conf Interval] + -REMIT | 4092651 1671524 2.45 0.014 0816525 7368777 GDPPC | 0011848 0001987 5.96 0.000 0007952 0015743 LNGDP | 11.18949 1.740983 6.43 0.000 7.777224 14.60175 INF | -.0665004 0599486 -1.11 0.267 -.1839974 0509967 FINANCIALOPENNESS | 24.23398 4.070236 5.95 0.000 16.25646 32.21149 TRADEOPENNESS | 1349976 0418367 3.23 0.001 0529992 2169959 _cons | -265.6446 42.57769 -6.24 0.000 -349.0954 -182.1939 + -sigma_u | 30.172241 sigma_e | 16.871812 rho | 76179686 (fraction of variance due to u_i) - 10 Result of equation (2) by REM xtreg CREDIT REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS Random-effects GLS regression Group variable: code Number of obs Number of groups = = 699 36 R-sq: Obs per group: = avg = max = 19.4 25 within = 0.3279 between = 0.2363 overall = 0.3704 corr(u_i, X) = (assumed) Wald chi2(8) Prob > chi2 = = 304.82 0.0000 CREDIT | Coef Std Err z P>|z| [95% Conf Interval] + -REMIT | 339069 1843703 1.84 0.066 -.0222902 7004281 REMIT_HIGH | 17.11416 4.567107 3.75 0.000 8.162797 26.06553 REMIT_MIDDLE | 23341 2676162 0.87 0.383 -.2911081 7579281 GDPPC | 001137 0001899 5.99 0.000 0007649 0015091 LNGDP | 10.79716 1.644715 6.56 0.000 7.573582 14.02075 INF | -.0770848 0599546 -1.29 0.199 -.1945937 0404241 FINANCIALOPENNESS | 26.79683 4.097709 6.54 0.000 18.76546 34.82819 TRADEOPENNESS | 1349993 0414463 3.26 0.001 053766 2162325 _cons | -257.8749 40.33023 -6.39 0.000 -336.9207 -178.8291 + -sigma_u | 26.074446 sigma_e | 16.679626 rho | 70961924 (fraction of variance due to u_i) - 11 Result of equation (3) by REM xtreg M2 REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS Random-effects GLS regression Group variable: code Number of obs Number of groups = = 674 35 R-sq: Obs per group: = avg = max = 19.3 25 within = 0.6062 between = 0.1992 overall = 0.3797 corr(u_i, X) = (assumed) Wald chi2(6) Prob > chi2 = = 928.76 0.0000 M2 | Coef Std Err z P>|z| [95% Conf Interval] + -REMIT | 3531678 1115207 3.17 0.002 1345913 5717443 GDPPC | 0016509 0001649 10.01 0.000 0013277 001974 LNGDP | 18.50758 1.454525 12.72 0.000 15.65676 21.35839 INF | -.0490542 03878 -1.26 0.206 -.1250615 0269532 FINANCIALOPENNESS | 7.396905 2.768831 2.67 0.008 1.970097 12.82371 TRADEOPENNESS | 2566667 0287755 8.92 0.000 2002678 3130657 _cons | -438.4478 35.6064 -12.31 0.000 -508.2351 -368.6606 + -sigma_u | 41.150693 sigma_e | 10.776757 rho | 935818 (fraction of variance due to u_i) - 12 Result of equation (4) by REM xtreg M2 REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS Random-effects GLS regression Group variable: code Number of obs Number of groups = = 674 35 R-sq: Obs per group: = avg = max = 19.3 25 within = 0.6113 between = 0.1895 overall = 0.3643 corr(u_i, X) = (assumed) Wald chi2(8) Prob > chi2 = = 931.02 0.0000 M2 | Coef Std Err z P>|z| [95% Conf Interval] + -REMIT | 4741564 1219161 3.89 0.000 2352053 7131076 REMIT_HIGH | 851582 3.317811 0.26 0.797 -5.651209 7.354373 REMIT_MIDDLE | -.4131369 176143 -2.35 0.019 -.7583708 -.0679031 GDPPC | 001613 0001652 9.76 0.000 0012892 0019367 LNGDP | 19.06374 1.469447 12.97 0.000 16.18368 21.9438 INF | -.051037 0388239 -1.31 0.189 -.1271304 0250564 FINANCIALOPENNESS | 7.261745 2.81942 2.58 0.010 1.735782 12.78771 TRADEOPENNESS | 2587039 0288998 8.95 0.000 2020613 3153465 _cons | -451.6724 35.88742 -12.59 0.000 -522.0105 -381.3344 + -sigma_u | 39.026425 sigma_e | 10.71616 rho | 92988813 (fraction of variance due to u_i) - 13 Breuschs – Pagan LM test of equation (1) xttest0 Breusch and Pagan Lagrangian multiplier test for random effects CREDIT[code,t] = Xb + u[code] + e[code,t] Estimated results: | Var -+ CREDIT | 2171.51 e | 284.658 u | 910.3641 Test: sd = sqrt(Var) 46.59946 16.87181 30.17224 Var(u) = chibar2(01) = Prob > chibar2 = 2976.63 0.0000 14 Breuschs – Pagan LM test of equation (2) xttest0 Breusch and Pagan Lagrangian multiplier test for random effects CREDIT[code,t] = Xb + u[code] + e[code,t] Estimated results: | Var -+ CREDIT | 2171.51 e | 278.2099 u | 679.8767 Test: sd = sqrt(Var) 46.59946 16.67963 26.07445 Var(u) = chibar2(01) = Prob > chibar2 = 2476.28 0.0000 15 Breuschs – Pagan LM test of equation (3) xttest0 Breusch and Pagan Lagrangian multiplier test for random effects M2[code,t] = Xb + u[code] + e[code,t] Estimated results: | Var -+ M2 | 2861.967 e | 116.1385 u | 1693.38 Test: sd = sqrt(Var) 53.49735 10.77676 41.15069 Var(u) = chibar2(01) = Prob > chibar2 = 3114.32 0.0000 16 Breuschs – Pagan LM test of equation (4) xttest0 Breusch and Pagan Lagrangian multiplier test for random effects M2[code,t] = Xb + u[code] + e[code,t] Estimated results: | Var -+ M2 | 2861.967 e | 114.8361 u | 1523.062 Test: sd = sqrt(Var) 53.49735 10.71616 39.02642 Var(u) = chibar2(01) = Prob > chibar2 = 3128.74 0.0000 17 Hausman test of equation (1) hausman fixed random,sigmamore Note: the rank of the differenced variance matrix (5) does not equal the number of coefficients being tested (6); be sure this is what you expect, or there may be problems computing the test Examine the output of your estimators for anything unexpected and possibly consider scaling your variables so that the coefficients are on a similar scale Coefficients -| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fixed random Difference S.E -+ -REMIT | 3734473 4092651 -.0358178 0604033 GDPPC | 0016842 0011848 0004994 0001599 LNGDP | 11.88314 11.18949 6936487 1.803506 INF | -.0237079 -.0665004 0427925 0100902 FINANCIALO~S | 22.27592 24.23398 -1.958051 1.018868 TRADEOPENN~S | 0941891 1349976 -.0408084 0172263 -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) = 31.69 Prob>chi2 = 0.0000 18 Hausman test of equation (2) hausman fixed random,sigmamore Note: the rank of the differenced variance matrix (7) does not equal the number of coefficients being tested (8); be sure this is what you expect, or there may be problems computing the test Examine the output of your estimators for anything unexpected and possibly consider scaling your variables so that the coefficients are on a similar scale Coefficients -| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fixed random Difference S.E -+ -REMIT | 3249448 339069 -.0141242 0585273 REMIT_HIGH | 18.68919 17.11416 1.575033 1.0785 REMIT_MIDDLE | 1648921 23341 -.0685179 0776192 GDPPC | 0018311 001137 0006941 0001758 LNGDP | 11.14733 10.79716 3501632 1.974969 INF | -.024802 -.0770848 0522828 0109492 FINANCIALO~S | 24.58052 26.79683 -2.216309 1.122895 TRADEOPENN~S | 0871429 1349993 -.0478564 0185122 -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) = 41.94 Prob>chi2 = 0.0000 19 Hausman test of equation (3) hausman fixed random,sigmamore Note: the rank of the differenced variance matrix (5) does not equal the number of coefficients being tested (6); be sure this is what you expect, or there may be problems computing the test Examine the output of your estimators for anything unexpected and possibly consider scaling your variables so that the coefficients are on a similar scale Coefficients -| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fixed random Difference S.E -+ -REMIT | 2938719 3531678 -.0592959 0224873 GDPPC | 001841 0016509 0001901 0000714 LNGDP | 19.3266 18.50758 8190285 7326629 INF | -.031237 -.0490542 0178172 0036435 FINANCIALO~S | 6.38807 7.396905 -1.008836 319168 TRADEOPENN~S | 2420957 2566667 -.014571 0062806 -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) = 33.60 Prob>chi2 = 0.0000 20 Hausman test of equation (4) hausman fixed random,sigmamore Note: the rank of the differenced variance matrix (7) does not equal the number of coefficients being tested (8); be sure this is what you expect, or there may be problems computing the test Examine the output of your estimators for anything unexpected and possibly consider scaling your variables so that the coefficients are on a similar scale Coefficients -| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fixed random Difference S.E -+ -REMIT | 4304403 4741564 -.0437162 0198629 REMIT_HIGH | 1.975491 851582 1.123909 356266 REMIT_MIDDLE | -.5078442 -.4131369 -.0947073 0254096 GDPPC | 0018115 001613 0001986 0000766 LNGDP | 20.27213 19.06374 1.208388 7934091 INF | -.0308403 -.051037 0201967 0037979 FINANCIALO~S | 6.176824 7.261745 -1.084921 3473939 TRADEOPENN~S | 2407214 2587039 -.0179825 0065251 -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) = 44.01 Prob>chi2 = 0.0000 21 Result of equation (1) by FEM with robust xtreg CREDIT REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, fe r Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 699 36 R-sq: Obs per group: = avg = max = 19.4 25 within = 0.3200 between = 0.1791 overall = 0.3097 corr(u_i, Xb) = -0.2915 F(6,35) Prob > F = = 12.20 0.0000 (Std Err adjusted for 36 clusters in code) | Robust CREDIT | Coef Std Err t P>|t| [95% Conf Interval] + -REMIT | 3734473 2300381 1.62 0.113 -.0935549 8404496 GDPPC | 0016842 0011945 1.41 0.167 -.0007409 0041092 LNGDP | 11.88314 4.985376 2.38 0.023 1.762286 22.00399 INF | -.0237079 0383598 -0.62 0.541 -.1015823 0541666 FINANCIALOPENNESS | 22.27592 8.334838 2.67 0.011 5.355303 39.19655 TRADEOPENNESS | 0941891 1094212 0.86 0.395 -.1279477 316326 _cons | -277.6653 116.5702 -2.38 0.023 -514.3154 -41.01508 + -sigma_u | 45.438543 sigma_e | 16.871812 rho | 87883382 (fraction of variance due to u_i) - 22 Result of equation (2) by FEM with robust xtreg CREDIT REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, fe r Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 699 36 R-sq: Obs per group: = avg = max = 19.4 25 within = 0.3375 between = 0.1909 overall = 0.3423 corr(u_i, Xb) F(8,35) Prob > F = -0.3136 = = 11.20 0.0000 (Std Err adjusted for 36 clusters in code) | Robust CREDIT | Coef Std Err t P>|t| [95% Conf Interval] + -REMIT | 3249448 2649823 1.23 0.228 -.2129978 8628874 REMIT_HIGH | 18.68919 7.097914 2.63 0.013 4.279664 33.09873 REMIT_MIDDLE | 1648921 2774354 0.59 0.556 -.3983318 728116 GDPPC | 0018311 0011578 1.58 0.123 -.0005193 0041816 LNGDP | 11.14733 5.130455 2.17 0.037 7319498 21.56271 INF | -.024802 0394337 -0.63 0.533 -.1048567 0552528 FINANCIALOPENNESS | 24.58052 7.429819 3.31 0.002 9.497182 39.66385 TRADEOPENNESS | 0871429 1031763 0.84 0.404 -.1223161 2966019 _cons | -262.2778 120.0222 -2.19 0.036 -505.9358 -18.61975 + -sigma_u | 46.797333 sigma_e | 16.679626 rho | 88728227 (fraction of variance due to u_i) .test REMIT REMIT_HIGH REMIT_MIDDLE ( 1) ( 2) ( 3) REMIT = REMIT_HIGH = REMIT_MIDDLE = F( 3, 35) = Prob > F = 4.51 0.0089 23 Result of equation (3) by FEM with robust xtreg M2 REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, fe r Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 674 35 R-sq: Obs per group: = avg = max = 19.3 25 within = 0.6074 between = 0.1902 overall = 0.3693 corr(u_i, Xb) = -0.3808 F(6,34) Prob > F = = 12.36 0.0000 (Std Err adjusted for 35 clusters in code) | Robust M2 | Coef Std Err t P>|t| [95% Conf Interval] + -REMIT | 2938719 2568168 1.14 0.260 -.2280427 8157865 GDPPC | 001841 0008173 2.25 0.031 0001801 0035018 LNGDP | 19.3266 5.833566 3.31 0.002 7.471372 31.18184 INF | -.031237 0492367 -0.63 0.530 -.131298 0688241 FINANCIALOPENNESS | 6.38807 6.509756 0.98 0.333 -6.841347 19.61749 TRADEOPENNESS | 2420957 0708248 3.42 0.002 0981623 386029 _cons | -455.3315 140.5875 -3.24 0.003 -741.0396 -169.6234 + -sigma_u | 57.841233 sigma_e | 10.776757 rho | 9664509 (fraction of variance due to u_i) - 24 Result of equation (4) by FEM with robust xtreg M2 REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, fe r Fixed-effects (within) regression Group variable: code Number of obs Number of groups = = 674 35 R-sq: Obs per group: = avg = max = 19.3 25 within = 0.6130 between = 0.1788 overall = 0.3497 corr(u_i, Xb) F(8,34) Prob > F = -0.4234 = = 12.91 0.0000 (Std Err adjusted for 35 clusters in code) | Robust M2 | Coef Std Err t P>|t| [95% Conf Interval] + -REMIT | 4304403 2722296 1.58 0.123 -.1227968 9836773 REMIT_HIGH | 1.975491 13.51297 0.15 0.885 -25.48617 29.43715 REMIT_MIDDLE | -.5078442 2845456 -1.78 0.083 -1.08611 0704219 GDPPC | 0018115 0008505 2.13 0.040 0000831 00354 LNGDP | 20.27213 5.857076 3.46 0.001 8.369117 32.17514 INF | -.0308403 0522309 -0.59 0.559 -.1369862 0753057 FINANCIALOPENNESS | 6.176824 6.748893 0.92 0.367 -7.538576 19.89222 TRADEOPENNESS | 2407214 0660723 3.64 0.001 1064464 3749965 _cons | -478.1534 140.9402 -3.39 0.002 -764.5783 -191.7285 + -sigma_u | 59.368218 sigma_e | 10.71616 rho | 96844661 (fraction of variance due to u_i) .test REMIT REMIT_HIGH REMIT_MIDDLE ( 1) ( 2) ( 3) REMIT = REMIT_HIGH = REMIT_MIDDLE = F( 3, 34) = Prob > F = 1.16 0.3390 25 Result of equation (5) by system GMM Dynamic panel-data estimation, one-step system GMM -Group variable: code Number of obs = 625 Time variable : year Number of groups = 36 Number of instruments = Obs per group: = F(7, 617) = 6151.74 avg = 17.36 Prob > F = 0.000 max = 23 CREDIT | Coef Std Err t P>|t| [95% Conf Interval] + -CREDIT | L1 | 9838308 0066582 147.76 0.000 9707553 9969064 | REMIT | -.0277577 0323711 -0.86 0.392 -.0913285 0358131 GDPPC | 000049 0000277 1.77 0.077 -5.39e-06 0001034 LNGDP | -.1095696 1520272 -0.72 0.471 -.4081231 1889839 INF | -.077817 0169666 -4.59 0.000 -.1111363 -.0444976 FINANCIALOPENNESS | -1.193222 8314943 -1.44 0.152 -2.826124 4396799 TRADEOPENNESS | 0122349 0071549 1.71 0.088 -.0018159 0262858 _cons | 5.373752 4.096574 1.31 0.190 -2.671167 13.41867 Instruments for levels equation Standard GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS L.REMIT L2.CREDIT _cons 26 Result of equation (6) by system GMM xtabond2 CREDIT l1.CREDIT REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, iv( GDPPC LNGDP INF FINANCIALO > PENNESS TRADEOPENNESS l1.REMIT l1.REMIT_HIGH l1.REMIT_MIDDLE l2.CREDIT,eq(level)) small arlevels Favoring speed over space To switch, type or click on mata: mata set matafavor space, perm Dynamic panel-data estimation, one-step system GMM -Group variable: code Number of obs = 625 Time variable : year Number of groups = 36 Number of instruments = 10 Obs per group: = F(9, 615) = 4851.96 avg = 17.36 Prob > F = 0.000 max = 23 CREDIT | Coef Std Err t P>|t| [95% Conf Interval] + -CREDIT | L1 | 9771188 0072232 135.28 0.000 9629336 9913039 | REMIT | -.0077992 0392346 -0.20 0.842 -.0848491 0692508 REMIT_HIGH | 3.30484 1.307392 2.53 0.012 7373462 5.872334 REMIT_MIDDLE | -.0238959 0550876 -0.43 0.665 -.1320785 0842868 GDPPC | 0000238 0000293 0.81 0.416 -.0000337 0000813 LNGDP | 0192888 1597831 0.12 0.904 -.2944977 3330754 INF | -.0800792 0169113 -4.74 0.000 -.1132902 -.0468682 FINANCIALOPENNESS | -.7029093 8702677 -0.81 0.420 -2.411966 1.006148 TRADEOPENNESS | 014597 0071926 2.03 0.043 0004719 028722 _cons | 1.976765 4.299557 0.46 0.646 -6.466829 10.42036 Instruments for levels equation Standard GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS L.REMIT L.REMIT_HIGH L.REMIT_MIDDLE L2.CREDIT _cons -.test REMIT REMIT_HIGH REMIT_MIDDLE ( 1) ( 2) ( 3) REMIT = REMIT_HIGH = REMIT_MIDDLE = F( 3, 615) = Prob > F = 2.49 0.0593 27 Result of equation (7) by system GMM xtabond2 M2 l1.M2 REMIT GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, iv( GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS l1.REMIT l > 2.M2,eq(level)) small arlevels Favoring speed over space To switch, type or click on mata: mata set matafavor space, perm Dynamic panel-data estimation, one-step system GMM -Group variable: code Number of obs = 602 Time variable : year Number of groups = 35 Number of instruments = Obs per group: = F(7, 594) = 7831.74 avg = 17.20 Prob > F = 0.000 max = 23 M2 | Coef Std Err t P>|t| [95% Conf Interval] + -M2 | L1 | 9936289 0061673 161.11 0.000 9815165 1.005741 | REMIT | -.0113328 0350881 -0.32 0.747 -.0802447 0575791 GDPPC | 0000247 0000288 0.86 0.391 -.0000318 0000813 LNGDP | 2303206 1745863 1.32 0.188 -.112561 5732022 INF | -.0896665 0172437 -5.20 0.000 -.1235325 -.0558005 FINANCIALOPENNESS | -.0810879 8715184 -0.09 0.926 -1.79272 1.630544 TRADEOPENNESS | 0019923 0069409 0.29 0.774 -.0116394 0156241 _cons | -2.862192 4.613469 -0.62 0.535 -11.92289 6.198504 Instruments for levels equation Standard GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS L.REMIT L2.M2 _cons 28 Result of equation (8) by system GMM xtabond2 M2 l1.M2 REMIT REMIT_HIGH REMIT_MIDDLE GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS, iv( GDPPC LNGDP INF FINANCIALOPENNESS > TRADEOPENNESS l1.REMIT l1.REMIT_HIGH l1.REMIT_MIDDLE l2.M2,eq(level)) small arlevels Favoring speed over space To switch, type or click on mata: mata set matafavor space, perm Dynamic panel-data estimation, one-step system GMM -Group variable: code Number of obs = 602 Time variable : year Number of groups = 35 Number of instruments = 10 Obs per group: = F(9, 592) = 6058.00 avg = 17.20 Prob > F = 0.000 max = 23 M2 | Coef Std Err t P>|t| [95% Conf Interval] + -M2 | L1 | 9953435 0064532 154.24 0.000 9826696 1.008017 | REMIT | 0092411 0405543 0.23 0.820 -.0704067 0888888 REMIT_HIGH | -.1006358 1.456401 -0.07 0.945 -2.960978 2.759706 REMIT_MIDDLE | -.057186 0585484 -0.98 0.329 -.1721739 0578019 GDPPC | 0000181 0000312 0.58 0.562 -.0000432 0000794 LNGDP | 2265281 1752476 1.29 0.197 -.1176545 5707107 INF | -.0900247 0173014 -5.20 0.000 -.1240042 -.0560452 FINANCIALOPENNESS | 1374827 9077767 0.15 0.880 -1.645372 1.920337 TRADEOPENNESS | 0021695 0069678 0.31 0.756 -.0115151 0158541 _cons | -2.896876 4.626626 -0.63 0.531 -11.98347 6.189722 Instruments for levels equation Standard GDPPC LNGDP INF FINANCIALOPENNESS TRADEOPENNESS L.REMIT L.REMIT_HIGH L.REMIT_MIDDLE L2.M2 _cons -.test REMIT REMIT_HIGH REMIT_MIDDLE ( 1) ( 2) ( 3) REMIT = REMIT_HIGH = REMIT_MIDDLE = F( 3, 592) = Prob > F = 0.35 0.7906 ... negative impact of inflations on financial development in most of former studies, the influences of these determinants on financial development in Asia and different income groups of countries in this... of the most concerns in examining the impact of remittances on financial development since higher level of financial development might result in higher recorded remittances either since financial. .. (Nyamongo, 2012) 2.1.2 Definitions of financial development According to the definitions of World Bank, financial sector is the combination of institutions, instruments, markets, and the legal and regulatory