The impacts of export and foreign direct investment on total factor productivity evidence from cross country analysis

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 IMPACTS OF EXPORT AND FOREIGN DIRECT INVESTMENT ON TOTAL FACTOR PRODUCTIVITY: EVIDENCE FROM CROSS COUNTRY ANALYSIS BY QUAN MINH QUOC BINH MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, MARCH 2012 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 IMPACTS OF EXPORT AND FOREIGN DIRECT INVESTMENT ON TOTAL FACTOR PRODUCTIVITY: EVIDENCE FROM CROSS COUNTRY ANALYSIS A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By QUAN MINH QUOC BINH Academic Supervisors: Assc Prof Dr PHAM HOANG VAN Assc Prof Dr NGUYEN TRONG HOAI HO CHI MINH CITY, MARCH 2012 CERTIFICATION “I certify that the substance of this thesis has not already been submitted for any degree and have not been currently submitted for any other degree I certify that to the best of my knowledge and help received in preparing this thesis and all sources used have been acknowledged in this thesis.” QUAN MINH QUOC BINH ACKNOWLEDGMENTS The process of writing a thesis is a collaborative experience involving the support and helps from many people I want to express my gratitude to those who give me the tremendous support to complete this thesis I am deeply indebted to my parents for their invaluable support and constant encouragement From my early childhood, my parents always teach me valuable lessons on the importance of learning The boundless love of my parents have accompanied with me as I continue my long journey on the pathway of intellectual acquisition Associate Professor Doctor Pham Hoang Van, Lecturer at Department of Economics, Baylor University (USA), Fulbright Scholar, is a superb supervisor He has encouraged me to pursue this topic from the initial ideas to the final completion His wholehearted guidance, incredible patience, and useful discussions have enabled me to develop a deeply understanding on my thesis Equally, I wish to express my heartfelt gratitude to Associate Professor Doctor Nguyen Trong Hoai, Lecturer at Department of Economic Development, University of Economics (HCMC), my second supervisor, for his valuable suggestions during the time I write this thesis His wide knowledge, excellent advice and logical way of thinking have provided me a good basis in present this thesis I also take this chance to convey my warm and sincere thanks to Professor Arjun Singh Bedi who have a remarkable influence on my love of academic research His constructive comments have great value in my study During my time at VNP, I receive a great encouragement and help from many friends, and I am grateful to Ms Ngo Hoang Thao Trang, Ms Chau Ngoc Thao Nguyen, my close friends, for motivating me to overcome all difficulties in my life In addition, my special thanks should send to Mr Nguyen Dinh Quy (Programme Librarian) and Ms Tang Thi Xuan Hong (Programme Secretary) for their help and sharing of their resources to complete this thesis successfully and in time Finally, I take a pride in myself for working very hard to finish this thesis I realize that after each success stories, there is a process of lots of hard works, difficulties, obstacles and overcoming Even in the hardest time when I write this thesis, I always believe that great efforts will eventually to be paid off Thank for the great time to learn and to grow up Quan Minh Quoc Binh March 2012 ABSTRACT We study the role of export and foreign direct investment on total factor productivity growth in a sample of both developed and developing countries from 1996 to 2009 We start by employing growth accounting exercise to estimate TFP growth of 103 countries and find out it determinants We also provide a global picture about TFPG’s performance of countries in the world By pointing out the limitations of previous researches that fail to take into account the potential of endogeneity between FDI and TFPG as well as export and TFPG, this thesis contributes to the current literature by developing two new instrumental variables for export and FDI to overcome endogeneity problem Two new instrumental variable have proved their highly effectiveness in removing endogeneity bias We carefully check the robustness of our findings by employing panel data techniques to reestimate the model that used in cross section exercise We also employ one lagged value of FDI and export to control for problem of reversed causality running from TFPG to export and FDI In empirical analysis, my research finds a robust and positive statistically significant association between export and TFP growth The finding emphasizes the indispensable role of export in enhancing TFP growth Interestingly, my thesis asserts the existence of negative linkage between FDI and TFPG This finding is sharply contrasts with conventional wisdom of many people who think that FDI inflows will benefit for TFP growth From policy perspective, we recommend that a sound macroeconomic stability, moderate government expenditure together with policies to foster export-oriented industries are always needed for a better performance in TFP growth We also suggest that government should invest more in education to increase in quality and skill of human capital Government also needs to spend more money in training programs for the workforce to learn how to apply advanced technology into production ABBREVIATIONS FDI Foreign Direct Investment FEM Fixed Effect Model OLS Ordinary Least Square R&D Research and Development TFP Total Factor Productivity TFPG Total Factor Productivity Growth 2SLS Two Stage Least Square TABLE OF CONTENTS CHAPTER I: INTRODUCTION 1.1 Problem Statement 1.2 Research Objectives 1.3 Research Questions 1.4 Research Methodology 1.5 Organization of The Study CHAPTER II: LITERATURE REVIEW 2.1 Total Factor Productivity 2.2 Theoretical Background 2.2.1 Exogenous Growth Theory 2.2.2 Endogenous Growth Theory 2.2.2.1 Learning by Doing Model .6 2.2.2.2 R&D Model .7 2.3 Determinants of TFP & TFPG 2.4 Empirical Studies 17 2.5 Conceptual Framework 24 CHAPTER III: RESEARCH METHODOLOGY 26 3.1 Methods in TFP Level and TFP Growth Measure 26 3.1.1 The Regression Method .26 3.1.2 The Growth Accounting Method 27 3.2 Data Source 28 3.2.1 Data for Dependent Variable TFPG 29 3.2.2 Data for Independent Variables 30 3.3 Model Specification 31 3.3.1 Model for Cross Section Data 32 3.3.2 Definition of Variables used in Regression 35 3.3.3 Model for Panel Data 40 CHAPTER IV: EMPIRICAL ANALYSIS 42 4.1 Overview of TFPG Performance in The World 42 4.2 Empirical Result 47 4.2.1 Empirical Result from Cross Section Data 47 4.2.2 Empirical Result from Panel Data 51 CHAPTER V: CONCLUSION AND RECOMMENDATIONS 55 5.1 Conclusion 55 5.2 Policy Recommendation .56 5.3 Limitations and further research 57 REFERENCES 59 APPENDIX 65 LIST OF TABLES Table 2.1: Summary of empirical studies relating to the determinants of TFPG 18 Table 3.1: The definition of variables in the model 39 Table 4.1: Cross Country Regression About The Determinants of TFPG .48 Table 4.2: Panel Data Regression For The Determinants of TFPG 53 Table A1: List of 103 countries in my sample 65 Table A2: TFP growth (%) of 103 countries by regions from 1996 to 2009 67 Table A3: Ranking of average TFPG rates (%) of 103 countries 2005-2009 71 Table A4: Descriptive Statistics of Variables 73 LIST OF FIGURES Figure 2.1: Conceptual framework of determinants of TFPG 25 Figure 4.1: Trend of Total factor productivity growth 42 LIST OF APPENDICES Appendix 1: List of 103 countries in my sample 65 Appendix 2: TFP growth (%) of 103 countries by regions .67 Appendix 3: Ranking of average TFP growth rates 71 Appendix 4: Descriptive Statistics 73 Appendix 5: Regression Result From Cross Section Data 74 Appendix 6: Regression Result From Panel Data 79 Appendix 7: Statistical Tests 83 Chapter I INTRODUCTION 1.1 Problem Statement It is widely believed that productivity or efficiency of an economy is the most important determinant of income in the long run Solow (1956) explained that economic growth without technological progress (one source of productivity gains) can not be sustained and would be stopped in the long run Parente and Prescott (2004) and Hall and Jones (1996) show that productivity differences explain the large part of income differences across countries Because of the importance of productivity to income, many scholars and researchers have studied possible factors that can affect productivity In recent years, cross border investment and trade activities have increased remarkably despite the severe impacts of global financial crisis In 2010, global foreign direct investment (FDI) inflows increase $1.24 trillion, and it is expected to rise further towards $1.6 - $2 trillions in 2012 (UNCTAD, 2011) More importantly, FDI and trade are considered as important sources of economic growth, especially for developing countries However, the empirical question whether FDI and trade benefit for productivity growth in different countries at different stage of development is still a question of debated In this research, I study differences in productivity growth across countries in the world from 1996-2009 and possible factors that affect productivity growth In particular, I look at export and foreign direct investment (FDI) as two channels through which efficiency and productivity are affected If we think of productivity and efficiency as reflecting technology and production knowledge, then exports and FDI are ways that best practices from the outside can be transmitted to the domestic economy While the impacts of export and FDI on economic growth have got plenty attention by many scholars, the research on the impact of these factor on TFP still lags behind Until now, there are few studies of the impact of export and foreign APPENDIX Table A2 TFP growth (%) of 103 countries by regions from 1996 to 2009 (Continued) Annual average growth Regions 1996-1999 2000-2004 2005-2009 Algeria -2.12 -0.12 -1.61 Botswana 3.30 2.00 -1.49 Cape Verde 2.96 -0.12 3.25 Egypt 0.13 -0.02 2.17 Ethiopia -5.37 -0.18 4.67 Gabon -4.36 -2.07 -1.15 Guinea 2.89 2.43 1.14 Kenya -2.71 -1.57 -0.13 Lesotho 2.60 3.80 3.94 Madagascar -2.77 -3.34 -3.22 Mauritius 1.29 2.21 1.19 Morocco -3.43 0.95 0.85 Mozambique 3.06 1.07 2.81 Namibia -1.24 1.12 -1.03 Senegal -0.15 -0.74 -1.87 South Africa -3.01 -0.07 -0.58 Sudan -2.77 -0.96 1.43 Swaziland 1.51 3.57 1.30 Tanzania -1.53 1.33 0.92 Tunisia 1.69 1.44 1.98 Uganda 1.48 1.81 2.75 Lebanon -1.51 1.99 2.74 Jordan 1.10 3.32 4.07 Syria -1.87 0.12 0.73 Africa Middle East Source: Author’s calculation Table A2 TFP growth (%) of 103 countries by regions from 1996 to 2009 (Continued) Annual average growth 1996-1999 2000-2004 2005-2009 Regions East Asia and Pacific Australia Cambodia China Hong Kong Indonesia Japan Macao Malaysia New Zealand Philippines Singapore South Korea Thailand Vietnam Latin America Argentina Bolivia Brazil Chile Colombia Costa Rica Cuba Dominican Republic Ecuador El Salvador Guatemala Honduras Nicaragua Panama Paraguay Peru Uruguay Venezuela 1.10 -0.88 3.05 -0.58 -4.85 0.40 -5.86 0.39 1.12 -0.003 1.34 2.09 -1.88 0.74 -0.02 0.74 4.43 3.47 3.12 2.43 8.43 3.94 1.09 2.34 3.31 4.15 5.30 2.06 -0.34 1.27 6.55 3.08 3.32 0.57 4.05 2.60 -0.91 2.40 1.29 2.35 2.87 2.10 -1.27 -1.32 -1.82 -0.15 -0.20 0.86 1.24 2.21 -3.44 1.80 0.33 -2.25 0.24 0.55 -0.93 -1.79 1.55 -10.88 -1.76 0.06 0.94 1.50 -2.74 -0.83 2.69 -0.77 1.24 0.84 -0.36 2.41 -0.15 0.22 -0.13 0.93 -1.76 -3.19 3.30 1.70 1.22 -0.38 -1.12 0.33 5.27 4.19 0.67 0.39 -0.33 0.99 -0.22 3.71 1.09 3.22 4.54 0.43 Source: Author’s calculation APPENDIX Table A3 Ranking of average TFP growth rates (%) of 103 countries in 2005-2009 period Country Azerbaijan China Slovak Republic Cuba Ethiopia Uruguay Dominican Republic Jordan Macao SAR, China Tajikistan Lesotho Panama Indonesia Argentina India Cape Verde Peru Hong Kong Thailand Mozambique Uganda Lebanon Malaysia Czech Republic Belarus Poland Philippines Korea Rank 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 TFP 14.04 6.55 6.23 5.27 4.67 4.54 4.19 4.07 4.05 4.00 3.94 3.71 3.32 3.30 3.30 3.25 3.22 3.08 2.87 2.81 2.75 2.74 2.60 2.51 2.48 2.42 2.40 2.35 Country Slovenia Egypt Viet Nam Tunisia Romania Bangladesh Bolivia Malta Sudan Macedonia Swaziland Singapore Cambodia Brazil Pakistan Mauritius Guinea Paraguay Honduras Moldova Tanzania Morocco Russian Federation Syrian Arab Republic Turkey Cyprus Ecuador Japan Rank 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 TFP 2.28 2.17 2.10 1.98 1.94 1.89 1.70 1.51 1.43 1.33 1.30 1.29 1.27 1.22 1.22 1.19 1.14 1.09 0.99 0.97 0.92 0.85 0.73 0.73 0.69 0.68 0.67 0.57 Table A3 Ranking of average TFP growth rates (%) of 103 countries in 20052009 period (Continued) Country Rank TFP Country Rank TFP Switzerland 57 0.54 France 81 -0.84 Venezuela 58 0.43 New Zealand 82 -0.91 El Salvador 59 0.39 Iceland 83 -0.96 Costa Rica 60 0.33 Namibia 84 -1.03 Kyrgyz Republic 61 0.33 Denmark 85 -1.06 Greece 62 0.32 Colombia 86 -1.12 Austria 63 0.11 Gabon 87 -1.15 Netherlands 64 0.03 Sweden 88 -1.32 Lithuania 65 -0.06 United Kingdom 89 -1.32 Kenya 66 -0.13 Norway 90 -1.33 Germany 67 -0.20 Italy 91 -1.35 Nicaragua 68 -0.22 Bulgaria 92 -1.48 Armenia 69 -0.22 Botswana 93 -1.49 Finland 70 -0.33 Algeria 94 -1.61 Guatemala 71 -0.33 Spain 95 -1.67 Australia 72 -0.34 Canada 96 -1.69 Portugal 73 -0.36 Senegal 97 -1.87 Chile 74 -0.38 Mexico 98 -1.93 Luxembourg 75 -0.41 Ukraine 99 -1.96 United States 76 -0.51 Estonia 100 -2.35 Hungary 77 -0.56 Ireland 101 -2.41 South Africa 78 -0.58 Latvia 102 -2.95 Croatia 79 -0.59 Madagascar 103 -3.22 Belgium 80 -0.80 Source: Author’s calculation by employing growth accounting exercise APPENDIX Table A.4 Descriptive Statistics of Variables In Cross Section Study Variable Mean Std Dev Min Max TFPG 0.9509833 1.401627 -3.572351 4.924771 Export 43.39165 29.94215 10.83215 200.9338 FDI 7.416332 30.17139 0.0189169 308.2631 Inflation 8.309228 12.03221 -0.0238542 90.64444 HumanCapital 38.78639 18.07491 2.2 78.56667 GovExpend 15.73353 5.619339 5.013478 38.92921 LogPop 16.32853 1.643873 12.58184 20.9707 LandLocked 0.8252427 0.3816164 LogLandArea 12.04413 2.181989 3.332205 16.61138 Latitude 22.86622 28.20335 -44.28333 64.15 Source: Author’s Calculation APPENDIX Regression Result From Cross Section Data Equation 1A Linear regression Number of obs = F( 4, 70) = Prob > F = R-squared = Root MSE = TFPG Coef GovExpend Inflation LogPop HumanCapital _cons -.098094 -.0089167 -.1677642 0281788 4.132506 Robust Std Err .0257601 0088622 0767211 0078656 1.476567 t -3.81 -1.01 -2.19 3.58 2.80 P>|t| 0.000 0.318 0.032 0.001 0.007 75 6.93 0.0001 0.2572 1.0677 [95% Conf Interval] -.1494709 -.0265918 -.3207796 0124914 1.187586 -.046717 0087585 -.0147487 0438663 7.077426 Equation 2A Linear regression Number of obs = F( 5, 69) = Prob > F = R-squared = Root MSE = TFPG Coef GovExpend Inflation LogPop HumanCapital LandLocked _cons -.0933383 -.0083469 -.1542577 0261166 -.3036629 4.16235 Robust Std Err .0259526 0087235 0788376 0073026 3180835 1.529671 t -3.60 -0.96 -1.96 3.58 -0.95 2.72 P>|t| 0.001 0.342 0.054 0.001 0.343 0.008 75 5.84 0.0001 0.2652 1.0696 [95% Conf Interval] -.1451122 -.0257498 -.3115345 0115484 -.9382222 1.110741 -.0415644 009056 003019 0406849 3308963 7.21396 Equation 3A Linear regression Number of obs = 75 F( 6, 68) = 7.70 Prob > F = 0.0000 R-squared = 0.3093 Root MSE = 1.0446 TFPG Coef GovExpend Inflation LogPop HumanCapital LandLocked Export _cons -.0781032 -.0051947 -.0579818 0222952 -.3370154 0092271 2.072841 Robust Std Err .0272727 0102129 0809147 0073971 3197913 0034311 1.669096 t P>|t| -2.86 -0.51 -0.72 3.01 -1.05 2.69 1.24 0.006 0.613 0.476 0.004 0.296 0.009 0.219 [95% Conf Interval] -.132525 -.0255742 -.2194446 0075345 -.975149 0023804 -1.257788 -.0236814 0151848 103481 0370559 3011182 0160737 5.40347 Equation 4A Linear regression Number of obs = 75 F( 6, 68) = 5.06 Prob > F = 0.0002 R-squared = 0.2713 Root MSE = 1.0729 TFPG Coef GovExpend Inflation LogPop HumanCapital LandLocked FDI _cons -.0936719 -.0085395 -.1705628 0258473 -.3653131 -.002878 4.520504 Robust Std Err .0260522 0086608 0824344 0073025 3314462 0014131 1.625704 t -3.60 -0.99 -2.07 3.54 -1.10 -2.04 2.78 P>|t| 0.001 0.328 0.042 0.001 0.274 0.046 0.007 [95% Conf Interval] -.1456582 -.0258219 -.3350582 0112753 -1.026704 -.0056977 1.276462 -.0416856 0087429 -.0060675 0404193 2960775 -.0000582 7.764545 Equation 5A Linear regression Number of obs = 75 F( 7, 67) = 6.19 Prob > F = 0.0000 R-squared = 0.3374 Root MSE = 1.0308 TFPG Coef GovExpend Inflation LogPop HumanCapital LandLocked Export FDI _cons -.0740652 -.0046426 -.0650338 0204724 -.4889647 0121362 -.0066024 2.235708 Robust Std Err .0268422 0103308 0812469 0072442 3160777 0039355 0017476 1.688612 t -2.76 -0.45 -0.80 2.83 -1.55 3.08 -3.78 1.32 P>|t| 0.007 0.655 0.426 0.006 0.127 0.003 0.000 0.190 [95% Conf Interval] -.1276424 -.025263 -.2272033 0060131 -1.119858 004281 -.0100907 -1.134775 -.020488 0159777 0971357 0349318 141929 0199914 -.0031141 5.606192 Equation 6A Instrumental variable For Export We employ “Land Area” variable as an instrument for export The result in first stage regression with F>10 indicates that our instrument is a very strong instrument First-stage regressions Number of obs F( 6, 68) Prob > F R-squared Adj R-squared Root MSE Export Coef Std Err Inflation LogPop GovExpend LandLocked HumanCapital logLandArea _cons -.1212708 4729957 -.3208983 -4.915254 2248182 -9.590816 155.288 2463544 2.886406 6979441 8.196952 1796349 1.914178 36.45432 t -0.49 0.16 -0.46 -0.60 1.25 -5.01 4.26 P>|t| 0.624 0.870 0.647 0.551 0.215 0.000 0.000 Instrumental variables (2SLS) regression TFPG Export Inflation LogPop GovExpend LandLocked HumanCapital _cons Instrumented: Instruments: Coef Std Err .0239491 -.0001652 0956294 -.053795 -.3902302 0161979 -1.261036 0087494 0110386 1234433 0317708 352923 0085088 2.468175 = = = = = = [95% Conf Interval] -.6128633 -5.286737 -1.713624 -21.27202 -.1336377 -13.4105 82.54451 Number of obs Wald chi2(6) Prob > chi2 R-squared Root MSE z 2.74 -0.01 0.77 -1.69 -1.11 1.90 -0.51 P>|z| 0.006 0.988 0.439 0.090 0.269 0.057 0.609 Export Inflation LogPop GovExpend LandLocked HumanCapital logLandArea [95% Conf Interval] 0068006 -.0218005 -.1463149 -.1160647 -1.081947 -.000479 -6.098569 75 10.90 0.0000 0.4901 0.4452 24.4666 3703217 6.232729 1.071827 11.44151 5832741 -5.771134 228.0314 = = = = = 75 32.26 0.0000 0.1969 1.0726 0410976 02147 3375738 0084747 3014861 0328749 3.576498 Equation 7A Instrumental variable For FDI We employ “One year lag FDI”, “Distance to latitude”, and ““Land Area” variables as instruments for FDI The result in first stage regression with F>12 indicates that our instrument is a very strong instrument First-stage regressions Number of obs F( 8, 66) Prob > F R-squared Adj R-squared Root MSE FDI Coef Inflation LogPop GovExpend LandLocked HumanCapital lagfdi2008 logLandArea latitude _cons -.1851853 5846317 299993 -1.85342 -.0472139 1.658668 2993283 0163104 -17.91428 Robust Std Err .0502178 9738258 4236559 3.536723 0797272 1773153 8148086 0396857 12.45973 t -3.69 0.60 0.71 -0.52 -0.59 9.35 0.37 0.41 -1.44 P>|t| 0.000 0.550 0.481 0.602 0.556 0.000 0.715 0.682 0.155 Instrumental variables (2SLS) regression TFPG Coef FDI Inflation LogPop GovExpend LandLocked HumanCapital _cons -.0044317 -.0086434 -.1793655 -.093852 -.3985964 0257019 4.713862 Instrumented: Instruments: Robust Std Err .0020504 0081801 0777999 0247198 3183503 0069138 1.52116 z -2.16 -1.06 -2.31 -3.80 -1.25 3.72 3.10 P>|z| 0.031 0.291 0.021 0.000 0.211 0.000 0.002 75 13.97 0.0000 0.9186 0.9087 10.6664 [95% Conf Interval] -.2854483 -1.359674 -.5458633 -8.914716 -.2063945 1.304646 -1.32749 -.0629247 -42.79092 Number of obs Wald chi2(6) Prob > chi2 R-squared Root MSE -.0849222 2.528938 1.145849 5.207876 1119666 2.012689 1.926146 0955454 6.962367 = = = = = 75 32.79 0.0000 0.2695 1.0229 [95% Conf Interval] -.0084505 -.0246762 -.3318505 -.1423019 -1.022552 012151 1.732442 -.0004129 0073894 -.0268806 -.0454022 2253588 0392527 7.695281 FDI Inflation LogPop GovExpend LandLocked HumanCapital lagfdi2008 logLandArea latitude APPENDIX Equation 1B = = = = = = Regression Result From Panel Data Fixed-effects (within) regression Group variable: Country R-sq: within between overall Number of obs Number of groups = 0.1667 = 0.0027 = 0.0003 269 91 Obs per group: = avg = max = 3.0 F(6,90) Prob > F corr(u_i, Xb) = -0.9976 (Std Err adjusted for 91 clusters in Country) Robust Std Err = = t = = TFPG Coef P>|t| GovExpend Inflation LogPop HumanCapital Dummy96 Dummy05 _cons -.1351098 -.02254 12.86292 0480625 -.4795552 -1.476104 -208.4367 103455 0139982 3.553765 0428709 3854434 3253219 58.06484 sigma_u sigma_e rho 21.83495 2.0580919 9911939 (fraction of variance due to u_i) -1.31 -1.61 3.62 1.12 -1.24 -4.54 -3.59 0.195 0.111 0.000 0.265 0.217 0.000 0.001 5.43 0.0001 [95% Conf Interval] -.3406411 -.0503498 5.802744 -.0371081 -1.245306 -2.122412 -323.7926 0704216 0052698 19.92309 1332331 2861954 -.829795 -93.08078 Equation 2B Fixed-effects (within) regression Group variable: Country Number Number = = 269 91 R-sq: = 0.1923 = 0.0012 = 0.0000 Obs per group: = avg = max = 3.0 = F(7,90) Prob > F within between overall corr(u_i, Xb) -0.9972 (Std Err Robust Std Err TFPG Coef GovExpend Inflation LogPop HumanCapital Dummy96 Dummy05 Export _cons -.1041263 -.0246756 12.63956 0299038 -.2351499 -1.550109 0502086 -207.0949 1019919 0141335 3.582854 0412982 3980838 3232661 0231446 58.67085 sigma_u sigma_e rho 20.753428 2.0321056 99050339 (fraction adjusted for t -1.02 -1.75 3.53 0.72 -0.59 -4.80 2.17 -3.53 P>|t| 0.310 0.084 0.001 0.471 0.556 0.000 0.033 0.001 of variance due of obs of groups = = 5.02 0.0001 91 clusters in Country) [95% Conf -.3067509 -.0527542 5.521592 -.0521422 -1.026013 -2.192333 0042277 -323.6548 to u_i) Interval] 0984983 0034031 19.75752 1119498 555713 -.9078842 0961894 -90.53506 Equation 3B Fixed-effects (within) regression Group variable: Country Number of obs Number of groups = = 269 91 R-sq: Obs per group: = avg = max = 3.0 within = 0.1769 between = 0.0028 overall = 0.0003 corr(u_i, Xb) F(7,90) Prob > F = -0.9976 = = 6.58 0.0000 (Std Err adjusted for 91 clusters in Country) Robust Std Err TFPG Coef t GovExpend Inflation LogPop HumanCapital Dummy96 Dummy05 FDI _cons -.1423278 -.0229118 12.88488 049734 -.5227807 -1.468951 -.021849 -208.5413 1045857 0138299 3.59279 04256 3866764 3281176 0051755 58.70343 sigma_u sigma_e rho 22.064825 2.0514335 99143009 (fraction of variance due to u_i) -1.36 -1.66 3.59 1.17 -1.35 -4.48 -4.22 -3.55 P>|t| 0.177 0.101 0.001 0.246 0.180 0.000 0.000 0.001 [95% Conf Interval] -.3501055 -.0503873 5.747172 -.0348188 -1.290981 -2.120814 -.032131 -325.1659 0654499 0045637 20.02258 1342868 2454195 -.817088 -.011567 -91.91672 Equation 4B Fixed-effects (within) regression Group variable: Country Number of obs Number of groups = = 269 91 R-sq: Obs per group: = avg = max = 3.0 within = 0.1515 between = 0.0845 overall = 0.0776 corr(u_i, Xb) F(7,90) Prob > F = -0.6625 = = 6.40 0.0000 (Std Err adjusted for 91 clusters in Country) Robust Std Err TFPG Coef t GovExpend Inflation HumanCapital Dummy96 Dummy05 Export FDI _cons -.0500515 -.0178846 0110733 -.9426332 -.8416557 0570213 -.0266197 -.2751148 1053482 013002 0472558 3537257 2939279 0197792 0056341 2.237147 sigma_u sigma_e rho 1.8256926 2.0828819 43448238 (fraction of variance due to u_i) -0.48 -1.38 0.23 -2.66 -2.86 2.88 -4.72 -0.12 P>|t| 0.636 0.172 0.815 0.009 0.005 0.005 0.000 0.902 [95% Conf Interval] -.2593442 -.0437154 -.0828085 -1.645371 -1.425595 0177265 -.0378129 -4.719598 1592411 0079461 1049551 -.2398955 -.2577167 0963161 -.0154266 4.169369 Equation 5B Fixed-effects (within) regression Group variable: Country Number of obs Number of groups = = 269 91 R-sq: Obs per group: = avg = max = 3.0 within = 0.2073 between = 0.0012 overall = 0.0000 corr(u_i, Xb) F(8,90) Prob > F = -0.9972 = = 6.72 0.0000 (Std Err adjusted for 91 clusters in Country) Robust Std Err TFPG Coef GovExpend Inflation LogPop HumanCapital Dummy96 Dummy05 Export FDI _cons -.1099164 -.0253372 12.6446 030175 -.2640923 -1.548587 0550978 -.0266594 -207.0919 1042035 013941 3.638048 041282 404472 3253902 0212814 0053326 59.57567 sigma_u sigma_e rho 20.916924 2.0191387 99076773 (fraction of variance due to u_i) t -1.05 -1.82 3.48 0.73 -0.65 -4.76 2.59 -5.00 -3.48 P>|t| 0.294 0.072 0.001 0.467 0.515 0.000 0.011 0.000 0.001 [95% Conf Interval] -.3169349 -.0530334 5.41698 -.0518388 -1.067646 -2.195032 0128186 -.0372536 -325.4494 0971021 0023591 19.87221 1121889 5394618 -.9021428 097377 -.0160653 -88.73446 Equation 6B Fixed-effects (within) regression Group variable: Country Number of obs Number of groups = = 182 91 R-sq: Obs per group: = avg = max = 2.0 within = 0.2492 between = 0.0034 overall = 0.0026 corr(u_i, Xb) F(6,90) Prob > F = -0.9991 = = 4.82 0.0003 (Std Err adjusted for 91 clusters in Country) Robust Std Err TFPG Coef t GovExpend Inflation LogPop HumanCapital Dummy05 LaggedFDI _cons -.0807891 -.025792 23.76997 0348923 -2.031672 -.0072597 -387.646 1127587 050521 6.317485 0484228 5187206 0035842 103.9213 sigma_u sigma_e rho 40.030698 1.6000863 99840483 (fraction of variance due to u_i) -0.72 -0.51 3.76 0.72 -3.92 -2.03 -3.73 P>|t| 0.476 0.611 0.000 0.473 0.000 0.046 0.000 [95% Conf Interval] -.3048041 -.1261608 11.21918 -.061308 -3.062201 -.0143804 -594.1038 1432258 0745767 36.32075 1310925 -1.001143 -.000139 -181.1882 Equation 7B Fixed-effects (within) regression Group variable: Country Number of obs Number of groups = = 182 91 R-sq: Obs per group: = avg = max = 2.0 within = 0.2493 between = 0.0029 overall = 0.0022 corr(u_i, Xb) F(6,90) Prob > F = -0.9991 = = 3.80 0.0020 (Std Err adjusted for 91 clusters in Country) Robust Std Err TFPG Coef t GovExpend Inflation LogPop HumanCapital Dummy05 LaggedExport _cons -.0686765 -.0223716 23.50784 0366285 -1.942929 -.0203465 -382.822 1142934 0497643 6.265344 0483858 5316858 0304824 103.1485 sigma_u sigma_e rho 39.818543 1.5999844 99838802 (fraction of variance due to u_i) -0.60 -0.45 3.75 0.76 -3.65 -0.67 -3.71 P>|t| 0.549 0.654 0.000 0.451 0.000 0.506 0.000 [95% Conf Interval] -.2957403 -.121237 11.06064 -.0594984 -2.999216 -.0809051 -587.7445 1583874 0764939 35.95504 1327553 -.8866424 0402121 -177.8995 APPENDIX Statistical Tests Test for multicollinearity in Cross Section Study In order to test for multicollinearity, we estimate VIF (variance inflation factor) from Stata As a rule of thumb, if value of VIF of variable is greater than 10, there may exist multicollinearity problem in the model Let’s have a look at VIF value that we draw from our regression VIF values suggest that there is no evidence of multicollinearity in our model Variable VIF 1/VIF Export LogPop HumanCapital GovExpend FDI LandLocked Inflation 1.66 1.43 1.37 1.34 1.33 1.24 1.07 0.603690 0.700406 0.729434 0.744940 0.750166 0.807115 0.936478 Mean VIF 1.35 Hausman test in Panel Data Hausman test is a generally accepted method to help us decide whether fixed effect or random effect is more suitable regression method Hausman test checks null hypothesis whether the estimated coefficients by fixed effect method are similar to random effect method (Green, 2008, chapter 9) Because Chi-Saquare and P-value in the table below are highly statistical significant (Prob>chi2=0.022