UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI THE MINH HA CITY GUE VIET THE NAM NETHE RLAND S VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE NEXU S BET WEE N INSTI TUTI ONS, FORE IGN AID, AND FORE IGN DIRECT INVESTME NT BY LE M TUE MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, JULY 2015 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE NEXU S BET WEE N INSTI TUTI ONS, FORE IGN AID, AND FORE IGN DIRE CT INVESTME NT A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS BY LE M TUE Academic Supervisor: Dr DINH CONG KHAI HO CHI MINH CITY, JULY 2015 CERTIFICATION I certify that the substance of this thesis has not already been submitted for any degree, and has not been currently submitted for any other degree I certify that, to the best of my knowledge, help received in preparing this thesis and all sources used have been acknowledged in this thesis July 2, 2015 Le M Tue ACKNOWLEDGMENT I am grateful to Dinh Cong Khai, my academic supervisor, and Pham Khanh Nam, a member of the VNP scientific committee, for helpful and detailed comments ABSTRACT This paper examines the mutual relationship between foreign aid and foreign direct investment (FDI), which might be ambiguous by reverse causality or simultaneity problems Using the dual-approach dynamics-balanced (DADB) model, we are able to point out that both bilateral and multilateral aid could lead to more FDI, and the impact of the latter could be even larger than that of the former The institutional effect of multilateral aid is proposed to explain this phenomenon Interestingly, the role of political stability could surpass those of democracy and control of corruption in having more aid disbursements CONTENTS INTRODUCTION 1.1 Practical Motivation and Research Problems 1.2 Research Objectives 1.3 Structure LITERATURE REVIEW MODEL AND DATA 3.1 Dual-Approach Framework 3.2 Dual-Approach Dynamics-Balanced Model RESULTS 4.1 Independent Marginal Effects between Institutions 4.2 Reliability and Robustness Checks CONCLUDING REMARKS 5.1 Empirical Findings 5.2 Policy Implication 5.3 Research Contribution, Implication, and Limitatio 5.4 Future Research REFERENCES APPENDIX A APPENDIX B LIST OF FIGURES Figure 1: The Nexus between Institutions, Foreign Aid, and FDI LIST OF TABLES Table 1: Descriptive Statistics Table 2: Independent Marginal Effects between Institutions, Foreign Aid, and FDI, 19962012 Table 3: The Impacts of Different Institutional Measures on FDI and Foreign Aid Table 4: Independent Estimation of the Dynamic FDI Equation Table 5: Independent Estimation of the Dynamic Aid Equations LIST OF APPENDICES Table A 1: Variables and Data Sources Table B 1: Regression Result of Table 2, column (1) Table B 2: Regression Result of Table 2, column (2) Table B 3: Regression Result of Table 2, column (3) Table B 4: Coefficient of INSA index in Table 3, Panel A, column AA Table B 5: Coefficient of INSA index in Table 3, Panel A, column BA Table B 6: Coefficient of INSA index in Table 3, Panel A, column MA Table B 7: Regression Result of Table 4, column (7) Table B 8: Regression Result of Table 4, column (8) Table B 9: Regression Result of Table 4, column (9) Table B 10: Regression Result of Table 5, column (7) Table B 11: Regression Result of Table 5, column (8) Table B 12: Regression Result of Table 5, column (9) Figure B 1: Scree Plot of Eigenvalues of Components for Five Variables of INSF index Figure B 2: Scree Plot of Eigenvalues of Components for Three Variables of INSA index INTRODUCTION 1.1 Practical Motivation and Research Problems From the behavior aspect, when a country receives more aid from a donor, it will acknowledge the generosity of that donor, easily cooperate with that sovereign partner, and create a concessionary legal environment for the enterprises of that donor (Kimura & Todo, 2010; Rodrik, 1995, p 25) From the effectiveness aspect, when a country receives more aid from all donors, it is able to improve the social and economic infrastructures, thus the human capital as well as the total factor productivity increase accordingly (Harms & Lutz, 2006) Hence, the recipient country would be able to attract more FDI from any other countries due to its increasing competitiveness In both of the explanations, foreign aid should be a significant factor of private capital inflows, which are generally accepted to vigorously promote growth, technology, and employment in the host country Nevertheless, research studies have not found a robust relationship between foreign aid and FDI (Alesina & Dollar, 2000; Harms & Lutz, 2006) Harms and Lutz (2006) suggest that one should consider the role of political and institutional characteristics when quantifying this relationship Indeed, institutional quality of the host country itself is an important direct magnet of private capital inflows Abundant empirical studies have pointed out the negative causality of a bad institution to the inflows of FDI Ironically, several countries which are perceived as having high corruption and low political, institutional profiles still have large inflows of FDI (Habib & Leon, 2002) In the aspect of modeling, the influence of foreign aid on FDI is difficult to estimate due to the problems of simultaneity and reverse causality By using lagged variables as instruments, 2SLS and GMM methods, to some extent, could alleviate such endogeneity However, the treatment is purely technical and does not reflect the nature of the problems Asiedu, Jin, and Nandwa (2009) propose a simultaneous equations model that could solve these problems In this approach, foreign aid and FDI are determined at the same time, and each of them is the determinant of the other While the dual approach is undoubtedly a superb idea, the applied model and the results of this research nonetheless contain some flaws and contradictions First, there is no institutional determinant in the aid equation Second, in the aid equation, the positive coefficient of FDI could be interpreted that while foreign aid reduces FDI, FDI could, however, increase foreign aid Table B 4: Coefficient of INSA index in Table 3, Panel A, column AA Three-stage least-squares regression Equation infdigdp aidgdp infdigdp aidgdp 37 Table B 5: Coefficient of INSA index in Table 3, Panel A, column BA Three-stage least-squares regression Equation infdigdp aidgdp infdigdp aidgdp 38 Table B 6: Coefficient of INSA index in Table 3, Panel A, column MA Three-stage least-squares regression Equation infdigdp aidgdp infdigdp aidgdp 39 Table B 7: Regression Result of Table 4, column (7) Dynamic panel-data estimation, one-step system GMM Group variable: imfid Time variable Number of instruments = 123 F(6, 129) Prob > F la la la lag2 Arellano- Bond Arellano-Bond Sargan test of overid restrictions: chi2(116) (Not robust, but not weakened by many instruments.) Hansen test of overid restrictions: chi2(116) (Robust, but weakened by many instruments.) Table B 8: Regression Result of Table 4, column (8) Dynamic panel-data estimation, one-step system GMM Group variable: imfid Time variable Number of instruments = 123 F(6, 129) Prob > F la la la lag2 Arellano-Bond test for AR(1) Arellano-Bond test for AR(2) Sargan test of overid restrictions: chi2(116) (Not robust, but not weakened by many instruments.) Hansen test of overid restrictions: chi2(116) (Robust, but weakened by many instruments.) 40 Table B 9: Regression Result of Table 4, column (9) Dynamic panel-data estimation, one-step system GMM Group variable: imfid Time variable : year Number of instruments = 123 F(6, 129) Prob > F la la la lag2 Table B 10: Regression Result of Table 5, column (7) Dynamic panel-data estimation, one-step system GMM Group variable: imfid Time variable : year Number of instruments = 124 F(7, 125) Prob > F c Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of overid restrictions: chi2(116) (Not robust, but not weakened by many instruments.) Hansen test of overid restrictions: chi2(116) (Robust, but weakened by many instruments.) 41 Table B 11: Regression Result of Table 5, column (8) Dynamic panel-data estimation, one-step system GMM Group variable: imfid Time variable : year Number of instruments = 124 F(7, 125) Prob > F Arellano -Bond test for AR(1) in first differences: z = Arellano- Bond test for AR(2) in first differences: z = Sargan test of overid restrictions: chi2(116) = 536.89 Prob > chi2 = 0.000 (Not robust, but not weakened by many instruments.) Hansen test of overid restrictions: chi2(116) = 122.01 Prob > chi2 = 0.333 (Robust, but weakened by many instruments.) 42 Table B 12: Regression Result of Table 5, column (9) Dynamic panel-data estimation, one-step system GMM Group variable: imfid Time variable : year Number of instruments = 124 F(7, 125) Prob > F Arellano-Bond test for AR(1) in first differences: z = Arellano- Bond test for AR(2) in first differences: z = -1.45 -0.30 Pr > z = Pr > z = 0.147 0.765 Sargan test of overid restrictions: chi2(116) = 579.65 Prob > chi2 = 0.000 (Not robust, but not weakened by many instruments.) Hansen test of overid restrictions: chi2(116) = 120.08 Prob > chi2 = 0.379 (Robust, but weakened by many instruments.) 43 Eigenvalues2 Figure B 1: Scree Plot of Eigenvalues of Components for Five Variables of INSF index .5 Eigenvalues51 52 Figure B 2: Scree Plot of Eigenvalues of Components for Three Variables of INSA index 44 ... approach, foreign aid and FDI are determined at the same time, and each of them is the determinant of the other While the dual approach is undoubtedly a superb idea, the applied model and the results... the future On one hand, the human capital and the living conditions are the attractive factors of foreign investment On the other hand, the low ability to work and pay debt of the old population... opposite linkages between foreign aid and FDI Furthermore, when data is available, we might replace the flows of foreign aid and FDI by their respective stocks to investigate whether the DADB model