The nexus between Institutions, foreign Aid and foreign direct investment

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 NEXUS BETWEEN INSTITUTIONS, FOREIGN AID, AND FOREIGN DIRECT INVESTMENT 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 NEXUS BETWEEN INSTITUTIONS, FOREIGN AID, AND FOREIGN DIRECT INVESTMENT 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 2 LITERATURE REVIEW 3 MODEL AND DATA 3.1 Dual-Approach Framework 3.2 Dual-Approach Dynamics-Balanced Model RESULTS 12 4.1 Independent Marginal Effects between Institutions, Foreign Aid, and FDI 12 4.2 Reliability and Robustness Checks 14 CONCLUDING REMARKS 29 5.1 Empirical Findings 29 5.2 Policy Implication 29 5.3 Research Contribution, Implication, and Limitations 30 5.4 Future Research 30 REFERENCES 31 APPENDIX A 33 APPENDIX B 34 LIST OF FIGURES Figure 1: The Nexus between Institutions, Foreign Aid, and FDI LIST OF TABLES Table 1: Descriptive Statistics 10 Table 2: Independent Marginal Effects between Institutions, Foreign Aid, and FDI, 19962012 18 Table 3: The Impacts of Different Institutional Measures on FDI and Foreign Aid 22 Table 4: Independent Estimation of the Dynamic FDI Equation 25 Table 5: Independent Estimation of the Dynamic Aid Equations 27 LIST OF APPENDICES Table A 1: Variables and Data Sources 33 Table B 1: Regression Result of Table 2, column (1) 34 Table B 2: Regression Result of Table 2, column (2) 35 Table B 3: Regression Result of Table 2, column (3) 36 Table B 4: Coefficient of INSA index in Table 3, Panel A, column AA 37 Table B 5: Coefficient of INSA index in Table 3, Panel A, column BA 38 Table B 6: Coefficient of INSA index in Table 3, Panel A, column MA 39 Table B 7: Regression Result of Table 4, column (7) 40 Table B 8: Regression Result of Table 4, column (8) 40 Table B 9: Regression Result of Table 4, column (9) 41 Table B 10: Regression Result of Table 5, column (7) 41 Table B 11: Regression Result of Table 5, column (8) 42 Table B 12: Regression Result of Table 5, column (9) 43 Figure B 1: Scree Plot of Eigenvalues of Components for Five Variables of INSF index 44 Figure B 2: Scree Plot of Eigenvalues of Components for Three Variables of INSA index 44 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 1.2 Research Objectives This paper aims to modify the simultaneous equations model of Asiedu et al (2009) and visualize the intricate relationship between foreign aid and FDI, which might comprise simultaneity and reverse causality With regard to purposes, while Asiedu et al (2009) focus on the alleviating role of foreign aid on the adverse effect of expropriation risk on FDI, we concentrate on the effect of foreign aid on final FDI With regard to samples, we use both low-income and middle-income subsamples to support our analysis, whereas it is only the low-income countries in Asiedu et al (2009) In comparison with Harms and Lutz (2006), we apply a different model with different proxies of variables and a more recent period to assess the effect of foreign aid on FDI After setting up the framework and specifying the according model, we use the data to illustrate the mutual relationship between foreign aid and FDI Using this result, we expect to figure out whether multilateral aid could actually lead to more FDI As for researchers concerned with the determinants of FDI and foreign aid, this paper provides more empirical evidence on the role of institutions In particular, we reexamine whether better institutions could attract more FDI as postulated in theory and found in many research studies By the way, we also appraise the importance of different institutional measures on FDI In the other side, democracy (freedom), control of corruption, and political stability help a country receive more foreign aid? 1.3 Structure In Chapter 2, this paper briefly reviews a trade theory which is widely used to explain the investment decision of foreign investors and some empirical results based on this theory We mainly concentrate on the papers that have institutions and foreign aid as the determinants of private capital inflows In Chapter 3, we explain the dual-approach framework and the regression model The variables and data sources are also described in this chapter The empirical findings and associated explanations are located in Chapter Chapter recapitulates the results for making policy and research 2 LITERATURE REVIEW We first review the OLI theory on the investment decision of foreign investors; then we come into the papers mentioning institutions and foreign aid as separate explanatory variables of FDI; next, we have a look on the research which embeds the political and institutional factors into the influence of foreign aid on FDI Lastly, we summarize the institutional measures which might affect foreign aid Dunning (1988, 1998, 2001) built up the OLI paradigm as a general framework to explain the activities of foreign investors The ownership advantages are classified as the O component and emphasize the comparative advantages of firms which can expand their business abroad Analyzing the O component provides us with information about the nature of products and the ability of firms The location advantages define the L component and are related to the human and natural resources, the favorable conditions for production, business, research activities, and the market size in the host country The internalization advantages belong to the I component and focus on the aspect of how to lower transaction costs, as firms decide whether importing intermediate products from markets or internalizing foreign suppliers into their production chain The I component could be taken into analysis by firms at the time of choosing the destination Political and institutional factors of the host country are considered as the location advantages in the OLI framework The influence mechanism of these factors on foreign investors are mentioned in the papers such as Habib and Leon (2002) and Dunning and Lundan (2008) On the empirical side, Habib and Leon (2002) find out a negative relationship between corruption levels in the host countries and their inflows of FDI According to Habib and Leon, foreign investors might see corruption as violating social and professional ethics and increasing unnecessary costs Moreover, paying bribes is strictly prohibited in the home countries of some foreign investors such as the United States (Hines, 1995) Busse and Hefeker (2007) examine the impacts of government stability, law and order, absence of internal and external conflicts, lack of ethnic tensions, control of corruption, democracy, and bureaucratic performance on FDI inflows to developing countries in the period 1984-2003 The paper does show positive relationships between such measures and private capital inflows With the same period of research, Bénassy-Quéré, Coupet, and Mayer Wei (2000) views corruption as a kind of tax on foreign investors interest, long maturity date, and no commercial constraints of multilateral aid also help to ease the debt burden, and thus it should be contemplated as the first priority when financing development projects In addition, regulatory quality, control of corruption, rule of law, and political stability in the host country are other major concerns of foreign investors Such institutional measures can absolutely be changed if the government has the appetite to so Another implication is that those countries which are perceived as having high corruption could improve their institutional quality in order to have more FDI, even though they already have a large amount of FDI inflows As regards foreign aid, enhancing political rights, civil liberties, control of corruption, and political stability are feasible policies for any government to have more bilateral and multilateral aid Finally, foreign investors, to some extent, can use multilateral aid as a safety signal for their investment, especially south-south investment 5.3 Research Contribution, Implication, and Limitations To my knowledge, this is the first paper that finds out the positive impact of multilateral aid on FDI and introduces the DADB model The positive causality of aggregate aid on FDI is not the result which could be found by many studies so far In relation to theory, the results of this paper suggest that the role of foreign aid in making the infrastructures of destination countries better could be considered in prospective OLI-based empirical research, especially for LDCs While the institutional effect of multilateral aid is purely an advantage, the (extended) vanguard effect of bilateral aid is both an advantage and a disadvantage of a location under the scope of OLI theory One limitation of the DADB model is that it still does not take into account unobserved individual-level effects Yet, as a trade-off, it has achieved the purpose of the research: to visualize the mutual relationship between foreign aid and FDI 5.4 Future Research Future research will explore how institutions affect the two 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 is applicable anymore and whether a country with larger accumulated foreign aid also has larger accumulated FDI Le M Tue The Vietnam-Netherlands Programme 30 REFERENCES Acemoglu, D., Naidu, S., Restrepo, P., & Robinson, J A (2014) Democracy Does Cause Growth National Bureau of Economic Research Working Paper Series, No 20004 Alesina, A., & Dollar, D (2000) Who Gives Foreign Aid to Whom and Why? 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The Review of Economics and Statistics, 82(1), 1-11 doi: 10.2307/2646667 World Bank (2012) World Governance Indicators Washington, D.C.: The World Bank World Bank (2014) World Development Indicators Washington, D.C.: The World Bank 32 APPENDIX A Table A 1: Variables and Data Sources Variable Description Source infdigdp agaidgdp biaidgdp muaidgdp regulate Net FDI inflows (% GDP) Aggregate aid (% GDP) Bilateral aid (% GDP) Multilateral aid (% GDP) Regulatory quality, range: -2.5 (weak) to 2.5 (strong), index Control of corruption, range: -2.5 (weak) to 2.5 (strong), index Government efficiency, range: -2.5 (weak) to 2.5 (strong), index Rule of law, range: -2.5 (weak) to 2.5 (strong), index Political stability and absence of violence/terrorism, range: -2.5 (weak) to 2.5 (strong), index Composite institutional index of (1) regulatory quality, (2) control of corruption, (3) government efficiency, (4) rule of law, and (5) political stability Composite institutional index of (1) freedom score, (2) control of corruption, and (3) political stability Freedom in the world score, range (1-7), lower score higher freedom and democracy GDP per capita in constant 2005 US$ GDP growth (annual, %) Population General government gross debt (% GDP) Total density of communication utilities (per 100 people): telephone lines, mobile cellular and internet subscribers (Exports + Imports)/GDP (%) Financial development: money and quasi money (M2) as % of GDP Inflation, consumer prices (annual %) Young-age dependency ratio (% of working-age population) Old-age dependency ratio (% of working-age population) WDI OECD OECD OECD WGI ctrlcorr goveff rule polstbl govindex insaindex fiwscore gdppc gdpgrow pop debtgdp utilcom tradeopen m2gdp infc youngdep olddep WGI WGI WGI WGI Author’s calculation Author’s calculation Freedom House WDI WDI WDI WEO WDI WDI WDI WDI WDI WDI Note: WEO (IMF, 2014), WDI (World Bank, 2014), WGI (World Bank, 2012) 33 APPENDIX B Table B 1: Regression Result of Table 2, column (1) Three-stage least-squares regression Equation Obs Parms RMSE "R-sq" F-Stat P infdigdp aidgdp 1312 1312 5.262244 4.378219 0.4247 0.7239 161.41 493.04 0.0000 0.0000 Coef infdigdp govindex aidgdp laginfdigdp lag2gdpgrow lag2utilcom lag2tradeopen _cons Std Err t P>|t| [95% Conf Interval] 192941 1446996 5578062 0558889 0088495 0112835 -.1635071 087729 0233651 0230554 0288825 0033153 0037306 4171654 2.20 6.19 24.19 1.94 2.67 3.02 -0.39 0.028 0.000 0.000 0.053 0.008 0.003 0.695 0209154 0988835 5125975 -.0007459 0023486 0039682 -.9815157 3649665 1905156 6030149 1125237 0153504 0185988 6545014 -.0383036 -.2573959 564128 -8.725887 0339251 07829 0182054 1.423342 -1.13 -3.29 30.99 -6.13 0.259 0.001 0.000 0.000 -.1048265 -.4109128 5284295 -11.51688 0282193 -.1038791 5998264 -5.934893 4582088 0955077 4.80 0.000 2709302 6454874 0086541 -.577695 50.90207 00258 0748521 5.547387 3.35 -7.72 9.18 0.001 0.000 0.000 0035951 -.7244705 40.02434 0137131 -.4309194 61.77979 aidgdp infdigdp fiwscore lagaidgdp lag2lngdppc c.lag2lngdppc# c.lag2lngdppc lag2debtgdp lnpop _cons 34 Table B 2: Regression Result of Table 2, column (2) Three-stage least-squares regression Equation Obs Parms RMSE "R-sq" F-Stat P infdigdp aidgdp 1312 1312 5.274181 3.279438 0.4221 0.6679 158.69 378.65 0.0000 0.0000 Coef infdigdp govindex aidgdp laginfdigdp lag2gdpgrow lag2utilcom lag2tradeopen _cons Std Err t P>|t| [95% Conf Interval] 1856059 1822603 564014 0565513 0076033 0102835 1366255 0879598 0355605 0231153 0289558 0033213 0037271 4098271 2.11 5.13 24.40 1.95 2.29 2.76 0.33 0.035 0.000 0.000 0.051 0.022 0.006 0.739 0131278 1125307 5186877 -.0002274 0010906 0029752 -.6669936 3580839 2519899 6093402 11333 014116 0175919 9402447 -.0284821 -.1312825 597438 -3.433308 0251168 0584082 0178812 1.04244 -1.13 -2.25 33.41 -3.29 0.257 0.025 0.000 0.001 -.0777329 -.2458136 5623753 -5.477401 0207687 -.0167515 6325008 -1.389215 1576006 0705704 2.23 0.026 019221 2959801 0041746 -.3702638 24.08141 0019338 0559904 4.002227 2.16 -6.61 6.02 0.031 0.000 0.000 0003827 -.4800538 16.23355 0079666 -.2604737 31.92927 aidgdp infdigdp fiwscore lagaidgdp lag2lngdppc c.lag2lngdppc# c.lag2lngdppc lag2debtgdp lnpop _cons 35 Table B 3: Regression Result of Table 2, column (3) Three-stage least-squares regression Equation Obs Parms RMSE "R-sq" F-Stat P infdigdp aidgdp 1312 1312 5.288637 1.85442 0.4189 0.6962 160.98 430.57 0.0000 0.0000 Coef infdigdp govindex aidgdp laginfdigdp lag2gdpgrow lag2utilcom lag2tradeopen _cons Std Err t P>|t| [95% Conf Interval] 1919366 3868792 5598472 0568883 0084672 0123398 -.2640524 0877701 0574587 022878 0288922 0032545 0037505 4148135 2.19 6.73 24.47 1.97 2.60 3.29 -0.64 0.029 0.000 0.000 0.049 0.009 0.001 0.524 0198306 27421 5149864 0002343 0020855 0049855 -1.077449 3640427 4995484 604708 1135423 0148489 0196941 5493444 -.0121789 -.1290024 503375 -5.711948 0144338 0331938 0209994 6356694 -0.84 -3.89 23.97 -8.99 0.399 0.000 0.000 0.000 -.0404817 -.1940912 4621979 -6.958416 0161238 -.0639135 5445521 -4.465481 3278152 0421457 7.78 0.000 2451728 4104576 0046348 -.2117308 28.49451 0010816 0313564 2.502276 4.29 -6.75 11.39 0.000 0.000 0.000 002514 -.2732168 23.58787 0067556 -.1502449 33.40116 aidgdp infdigdp fiwscore lagaidgdp lag2lngdppc c.lag2lngdppc# c.lag2lngdppc lag2debtgdp lnpop _cons 36 Table B 4: Coefficient of INSA index in Table 3, Panel A, column AA Three-stage least-squares regression Equation Obs Parms RMSE "R-sq" F-Stat P infdigdp aidgdp 1312 1312 5.26182 4.370231 0.4248 0.7249 161.32 495.72 0.0000 0.0000 Coef infdigdp govindex aidgdp laginfdigdp lag2gdpgrow lag2utilcom lag2tradeopen _cons Std Err t P>|t| [95% Conf Interval] 1909537 1429957 5582536 0558106 0087875 0112729 -.1510584 0877265 0233071 02305 0288826 0033143 0037305 4169026 2.18 6.14 24.22 1.93 2.65 3.02 -0.36 0.030 0.000 0.000 0.053 0.008 0.003 0.717 0189332 0972934 5130556 -.0008245 0022887 0039579 -.9685518 3629742 1886981 6034517 1124457 0152864 0185879 6664349 -.0428392 4714157 5605182 -8.174653 0338275 1136152 0182508 1.409686 -1.27 4.15 30.71 -5.80 0.205 0.000 0.000 0.000 -.1091706 2486307 5247307 -10.93887 0234921 6942007 5963056 -5.410438 4100916 0949624 4.32 0.000 2238823 5963008 0097597 -.5128405 47.4224 0025979 0783872 5.479014 3.76 -6.54 8.66 0.000 0.000 0.000 0046656 -.6665479 36.67874 0148537 -.3591331 58.16605 aidgdp infdigdp insaindex lagaidgdp lag2lngdppc c.lag2lngdppc# c.lag2lngdppc lag2debtgdp lnpop _cons 37 Table B 5: Coefficient of INSA index in Table 3, Panel A, column BA Three-stage least-squares regression Equation Obs Parms RMSE "R-sq" F-Stat P infdigdp aidgdp 1312 1312 5.273933 3.275613 0.4222 0.6687 158.61 380.16 0.0000 0.0000 Coef infdigdp govindex aidgdp laginfdigdp lag2gdpgrow lag2utilcom lag2tradeopen _cons Std Err t P>|t| [95% Conf Interval] 1867868 179979 5642398 0567235 007517 0102767 1476292 0879528 0354532 0231087 0289517 0033196 0037265 4095203 2.12 5.08 24.42 1.96 2.26 2.76 0.36 0.034 0.000 0.000 0.050 0.024 0.006 0.719 0143224 1104596 5189266 -.0000471 0010076 0029694 -.6553883 3592511 2494983 6095531 1134942 0140263 017584 9506467 -.0313949 2637822 5956063 -3.116749 0250728 0845952 0178904 1.03535 -1.25 3.12 33.29 -3.01 0.211 0.002 0.000 0.003 -.0805595 0979017 5605256 -5.14694 0177698 4296627 6306871 -1.086559 1301594 0703864 1.85 0.065 -.0078594 2681782 004784 -.3300683 22.08801 0019467 0587774 3.972806 2.46 -5.62 5.56 0.014 0.000 0.000 0009667 -.4453234 14.29784 0086012 -.2148131 29.87818 aidgdp infdigdp insaindex lagaidgdp lag2lngdppc c.lag2lngdppc# c.lag2lngdppc lag2debtgdp lnpop _cons 38 Table B 6: Coefficient of INSA index in Table 3, Panel A, column MA Three-stage least-squares regression Equation Obs Parms RMSE "R-sq" F-Stat P infdigdp aidgdp 1312 1312 5.287994 1.851179 0.4191 0.6973 160.91 432.76 0.0000 0.0000 Coef infdigdp govindex aidgdp laginfdigdp lag2gdpgrow lag2utilcom lag2tradeopen _cons Std Err t P>|t| [95% Conf Interval] 1786798 3841179 5605925 056085 0086062 0123192 -.2607986 0879243 0574295 0228781 0289038 0032561 0037515 414856 2.03 6.69 24.50 1.94 2.64 3.28 -0.63 0.042 0.000 0.000 0.052 0.008 0.001 0.530 0062712 2715059 5157314 -.0005917 0022214 0049629 -1.074279 3510883 4967299 6054535 1127617 0149909 0196754 5526815 -.0132057 2152985 4974703 -5.480575 0143916 048452 0211434 6280901 -0.92 4.44 23.53 -8.73 0.359 0.000 0.000 0.000 -.0414259 1202902 4560109 -6.71218 0150145 3103068 5389298 -4.24897 3072468 0417408 7.36 0.000 2253984 3890952 0051244 -.1845736 26.97108 0010894 032871 2.460435 4.70 -5.62 10.96 0.000 0.000 0.000 0029882 -.2490295 22.14647 0072606 -.1201176 31.79568 aidgdp infdigdp insaindex lagaidgdp lag2lngdppc c.lag2lngdppc# c.lag2lngdppc lag2debtgdp lnpop _cons 39 Table B 7: Regression Result of Table 4, column (7) Dynamic panel-data estimation, one-step system GMM Number of obs Number of groups Obs per group: avg max Group variable: imfid Time variable : year Number of instruments = 123 93.71 = F(6, 129) 0.000 Prob > F = infdigdp Coef govindex aidgdp laginfdigdp lag2gdpgrow lag2utilcom lag2tradeopen _cons 2380248 2070995 489228 0593284 0139958 0132821 -.7269243 Robust Std Err t 1029741 0842136 1186851 0451713 0063307 0062149 8148364 2.31 2.46 4.12 1.31 2.21 2.14 -0.89 P>|t| 0.022 0.015 0.000 0.191 0.029 0.034 0.374 Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(116) = 618.96 but not weakened by many instruments.) overid restrictions: chi2(116) = 120.01 weakened by many instruments.) = = = = = 1537 130 11.82 14 [95% Conf Interval] 0342881 0404809 2544066 -.0300442 0014703 0009858 -2.339098 -2.56 0.18 4417615 3737182 7240493 148701 0265214 0255784 8852496 Pr > z = Pr > z = 0.010 0.856 Prob > chi2 = 0.000 Prob > chi2 = 0.381 Table B 8: Regression Result of Table 4, column (8) Dynamic panel-data estimation, one-step system GMM Number of obs Number of groups Obs per group: avg max Group variable: imfid Time variable : year Number of instruments = 123 72.04 = F(6, 129) Prob > F = 0.000 infdigdp Coef govindex aidgdp laginfdigdp lag2gdpgrow lag2utilcom lag2tradeopen _cons 2282307 256309 4937751 0637124 0123576 012077 -.3096705 Robust Std Err t 2.25 1.99 4.18 1.32 1.94 2.00 -0.39 1014349 1285109 1181094 0481679 0063675 0060324 7891544 P>|t| 0.026 0.048 0.000 0.188 0.054 0.047 0.695 Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(116) = 603.07 but not weakened by many instruments.) overid restrictions: chi2(116) = 123.11 weakened by many instruments.) 40 = = = = = 1537 130 11.82 14 [95% Conf Interval] 0275394 002047 2600929 -.031589 -.0002406 0001417 -1.871032 -2.61 -0.02 4289221 510571 7274574 1590139 0249557 0240123 1.251691 Pr > z = Pr > z = 0.009 0.988 Prob > chi2 = 0.000 Prob > chi2 = 0.308 Table B 9: Regression Result of Table 4, column (9) Dynamic panel-data estimation, one-step system GMM Number of obs Number of groups Obs per group: avg max Group variable: imfid Time variable : year Number of instruments = 123 = 113.49 F(6, 129) Prob > F = 0.000 infdigdp Coef govindex aidgdp laginfdigdp lag2gdpgrow lag2utilcom lag2tradeopen _cons 2007069 4905417 5117775 0428812 0115317 0141414 -.5506081 Robust Std Err t 0931901 1542035 1054431 0425304 0047162 0061888 5862545 P>|t| 2.15 3.18 4.85 1.01 2.45 2.29 -0.94 0.033 0.002 0.000 0.315 0.016 0.024 0.349 = = = = = 1537 130 11.82 14 [95% Conf Interval] 0163279 1854464 3031558 -.0412664 0022005 0018968 -1.710527 3850858 7956371 7203992 1270287 0208628 0263861 6093107 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) = 139.89 Prob > F = 0.000 Number of obs Number of groups Obs per group: avg max Robust Std Err = = = = = 1437 126 11.40 14 aidgdp Coef infdigdp insaindex lagaidgdp lag2lngdppc -.000011 7525089 4831472 -6.5823 0512869 2544338 0709565 3.934489 -0.00 2.96 6.81 -1.67 1.000 0.004 0.000 0.097 -.1015142 2489528 3427154 -14.36914 1014923 1.256065 623579 1.204543 2642384 2642451 1.00 0.319 -.2587355 7872123 0241604 -.8128585 48.13831 0108354 2472221 14.65431 2.23 -3.29 3.28 0.028 0.001 0.001 0027158 -1.302142 19.13562 0456049 -.3235753 77.141 c.lag2lngdppc# c.lag2lngdppc lag2debtgdp lnpop _cons t P>|t| Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(116) = 607.87 but not weakened by many instruments.) overid restrictions: chi2(116) = 121.99 weakened by many instruments.) 41 [95% Conf Interval] -1.76 1.47 Pr > z = Pr > z = 0.078 0.142 Prob > chi2 = 0.000 Prob > chi2 = 0.333 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) = 143.57 Prob > F = 0.000 Number of obs Number of groups Obs per group: avg max Robust Std Err Coef infdigdp insaindex lagaidgdp lag2lngdppc -.0070849 3885303 5936378 -.5954528 0366113 1540346 1215039 2.30223 -0.19 2.52 4.89 -0.26 0.847 0.013 0.000 0.796 -.0795433 0836767 3531664 -5.151852 0653735 6933839 8341092 3.960946 -.0482114 1577698 -0.31 0.760 -.3604574 2640346 0124058 -.5351091 16.27724 0076282 1549214 8.414567 1.63 -3.45 1.93 0.106 0.001 0.055 -.0026913 -.8417178 -.3762308 0275028 -.2285005 32.93071 lag2debtgdp lnpop _cons P>|t| 1437 126 11.40 14 aidgdp c.lag2lngdppc# c.lag2lngdppc t = = = = = Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(116) = 536.89 but not weakened by many instruments.) overid restrictions: chi2(116) = 122.01 weakened by many instruments.) 42 [95% Conf Interval] -1.99 1.30 Pr > z = Pr > z = 0.047 0.195 Prob > chi2 = 0.000 Prob > chi2 = 0.333 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) = 160.63 Prob > F = 0.000 Number of obs Number of groups Obs per group: avg max Robust Std Err Coef infdigdp insaindex lagaidgdp lag2lngdppc -.0146111 3236754 37808 -6.965905 0085929 1098354 0422015 1.695313 -1.70 2.95 8.96 -4.11 0.092 0.004 0.000 0.000 -.0316175 1062975 2945579 -10.32114 0023953 5410532 461602 -3.610671 3900247 110271 3.54 0.001 1717848 6082647 0124225 -.1848659 33.15753 0038698 0542548 6.570723 3.21 -3.41 5.05 0.002 0.001 0.000 0047636 -.2922428 20.15326 0200814 -.077489 46.16181 lag2debtgdp lnpop _cons P>|t| 1437 126 11.40 14 aidgdp c.lag2lngdppc# c.lag2lngdppc t = = = = = Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(116) = 579.65 but not weakened by many instruments.) overid restrictions: chi2(116) = 120.08 weakened by many instruments.) 43 [95% Conf Interval] -1.45 -0.30 Pr > z = Pr > z = 0.147 0.765 Prob > chi2 = 0.000 Prob > chi2 = 0.379 Eigenvalues Figure B 1: Scree Plot of Eigenvalues of Components for Five Variables of INSF index 95% CI Eigenvalues Mean Eigenvalues 1.5 2.5 Figure B 2: Scree Plot of Eigenvalues of Components for Three Variables of INSA index 1.5 95% CI Eigenvalues 44 2.5 Mean