unlicensed 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 CREDIT | Coef Std Err t P>|t| [95% Conf Interval] + + 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 CREDIT | Coef Std Err t P>|t| [95% Conf Interval] + + 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 M2 | Coef Std Err t P>|t| [95% Conf Interval] + + 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 M2 | Coef Std Err t P>|t| [95% Conf Interval] + + 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] + + - 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] + + - 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] + + - 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] + + - 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 sd = sqrt(Var) -+ - Prob > chibar2 = 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 sd = sqrt(Var) -+ - Prob > chibar2 = 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 sd = sqrt(Var) -+ - Prob > chibar2 = 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 sd = sqrt(Var) -+ - Prob > chibar2 = 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 = 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) 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 = 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 (Std Err adjusted for 36 clusters in code) - + - 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) = -0.3136 (Std Err adjusted for 36 clusters in code) - + .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 (Std Err adjusted for 35 clusters in code) - + - 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) = -0.4234 (Std Err adjusted for 35 clusters in code) - + .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 CREDIT | Coef Std Err t P>|t| [95% Conf Interval] + 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 CREDIT | Coef Std Err t P>|t| [95% Conf Interval] + 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 M2 | Coef Std Err t P>|t| [95% Conf Interval] + 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 M2 | Coef Std Err t P>|t| [95% Conf Interval] + 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