MBA_LOCATION CHOICE OF FIES THE CASE OF ASIA

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Over the last 6 decades or so of globalization, foreign direct investment (FDI) has played an important role in stimulating international trade, facilitated the global integration and growth of many developing and emerging economies. FDI is considered as one type of investment funds contributing to the growth of a country in general and the development of economy in particular because of some key reasons. For instance, FDI generates a huge number of impacts on all sectors in the national economy relating to financial development of various industries, the level of economy’s openness and so on. Beginning in the mid1980s, world foreign direct investment (FDI) flows increased rapidly with a growing number of multinational enterprises (MNEs) as the engine of the increased international economic activities. Both industrialized and developing countries are becoming more receptive to FDI flows such that a majority of FDI policy changes in these countries are in the direction of more liberalization of FDI inflows (United Nations, 1992) as there are various benefits which FDI brought for economic growth. Asia, in particular has benefited enormously from FDI flows into the region. According to UNCTAD’s World Investment Report 2014, Asia was the worlds top recipient region of foreign direct investment (FDI), accounting for nearly 30 per cent of global FDI inflows. Total inflows to developing Asia (excluding West Asia) amounted to 382 billion in 2013, 4 per cent higher than in 2012. FDI flows to developing Asia in 2017 remained at the level of 2016 (476 billion), according to UNCTAD’s World Investment Report 2018. The region regained its position as the largest recipient of FDI in the world as its share in global inflows rose from 25% in 2016 to 33% in 2017. Worldwide investors have long been interested in Asia as an investment destination thanks to the growth of local and regional markets, the region’s enormous natural resources and its strategic location for exportoriented production. DAO THI NGOC LAN * LOCATION CHOICE OF FIES: THE CASE OF ASIA * 2019 MINISTRY OF EDUCATION AND TRAINING FOREIGN TRADE UNIVERSITY MASTER THESIS LOCATION CHOICE OF FIES: THE CASE OF ASIA Specialization: Master of Research in International Economics FULL NAME: DAO THI NGOC LAN Hanoi – 2019 MINISTRY OF EDUCATION AND TRAINING FOREIGN TRADE UNIVERSITY MASTER THESIS LOCATION CHOICE OF FIES: THE CASE OF ASIA Major: Master of Research in International Economics Specialization: International Economics Code: 8310106 Full name: Dao Thi Ngoc Lan Supervisor: Ass Prof, Dr Nguyen Thi Tuong Anh Hanoi – 2019 ABSTRACT Considered as one of the most important factors in one country’s economy growth, Foreign Direct Investment (FDI) is properly appreciated in both developed and developing countries Especially developing Asia, already the largest recipient region of FDI flows, registered an FDI rise of per cent to $512 billion in 2018, with positive growth occurring in all subregions, according to the World Investment Report 2019 (UNCTAD, 2019) This study, therefore, analyses the determinants of foreign-invested enterprises’ (FIEs) locational choice in Asia region over a period of 10 years from 2008 to 2017 The estimation is for a panel of 30 Asian countries and based on the random fixed effect model The results highlight that a country’s market size and regulatory quality of a government have a large positive impact on the on the Asian FDI inflow attractiveness Also, the research reaffirmed the previous literature that a country with higher corruption index is less attractive in terms of locational investment decision of FIEs Surprisingly, results indicate that working population, infrastructure, inflation, openness to trade, economic growth, natural resources and political stability are not significant determinants of FDI inflows in Asia during 2008 and 2017 STATEMENT OF ORIGINAL AUTHORSHIP I, Dao Thi Ngoc Lan, confirm that this Master's Thesis has been written solely by the undersigned and contains the work of no other person or persons except where explicitly identified to the contrary I also state that said Master's Thesis has not been submitted elsewhere for the fulfilment of any other qualification I make this statement in full knowledge of and understanding that, should it be found to be false, I will not receive a grade and may face disciplinary proceedings ACKNOWLEDGMENTS I would like to express my deep gratitude to people who supported and assisted me in completing my dissertation Firstly, I would like to thank my supervisor sincerely, Ass Prof, Dr Nguyen Thi Tuong Anh This thesis has benefited substantially from her guidelines, suggestions, comments and encouragements Without her help, it could have been impossible for me to finish my research Secondly, I would like to sincerely thank my lecturer in Econometrics, Dr Chu Thi Mai Phuong, who has made many value comments and suggestions in the formulating of the research model and estimation method Besides, I also would like to show my appreciation to the whole of staff in the Department of Graduate Studies, Foreign Trade University throughout our study process, especially Dr Cao Thi Hong Vinh for her thoughtful administrative arrangements, patience and tremendous encouragement during our thesis writing process Moreover, I desire to show my heartfelt appreciation to my parents, my brother and my family as well They always stand by me to strongly encourage in completing my whole study Finally, I feel very thankful for my friends even if they are my classmates in the Master of Research in International Economics or not, since they supported me sincerely and enthusiastically in many ways when I was embarking on this tough yet beneficial higher education path Dao Thi Ngoc Lan July, 2019 TABLE OF CONTENTS ABSTRACT i STATEMENT OF ORIGINAL AUTHORSHIP ii ACKNOWLEDGMENTS iii LIST OF ABBREVIATIONS .vi LIST OF TABLES AND FIGURES viii CHAPTER 1: INTRODUCTION 1.1 Rationale of the thesis 1.2 Objective of the thesis 1.3 Methodology 1.4 Scope of the thesis 1.5 Structure of the thesis CHAPTER 2: LITERATURE REVIEW .6 2.1 Theoretical foundation 2.1.1 What is FDI? 2.1.2 Why should countries encourage FDI inflow? .10 2.2 Background of Economy and FDI Inflows in Asia 13 2.2.1 The Asian economies 13 2.2.2 The facts of FDI flows into Asia 16 2.3 Determinants of FDI Inflows 30 2.3.1 Market size 31 2.3.2 Working population 32 2.3.3 Infrastructure .32 2.4.4 Inflation 33 2.3.5 Openness to Trade 33 2.3.6 Economic growth 34 2.3.7 Corruption index 34 2.3.8 Natural resources 35 2.3.9 Political Stability 35 2.3.10 Regulatory quality 36 CHAPTER 3: THE MODEL 38 3.1 Model research 38 3.2 Description of variables 42 3.2.1 Dependent variable 42 3.2.2 Independent variables 43 3.3 Data collection 49 3.4 Estimation method 50 CHAPTER 4: FINDINGS AND DISCUSSION 53 4.1 Results of the regression model 53 4.2 Discussions on the outcomes of model 56 4.3 Summary 59 CHAPTER 5: POLICY IMPLICATIONS 61 CONCLUSION 63 REFERENCES 66 APPENDICES .70 LIST OF ABBREVIATIONS BCC: Business Cooperation Contract BEA: Bureau of Economic Analysis BI: Brownfield Investment BLT: Build -Lease-Transfer BOO: Build-Own-Operate BOT: Build-Operate-Transfer BT: Build-Transfer BTL: Build-Transfer-Lease BTO: Build-Transfer-Operate CLMV: Cambodia, Laos, Myanmar and Viet Nam EU: European Union FDI: Foreign Direct Investment FE: Fixed effects FEM: Fixed effect model FIEs: Foreign Invested Enterprise GDP: Gross Domestic Product GI: Greenfield Investment IIP: Index of industrial production IMF: International Monetary Fund M&A: A merger and acquisition MNE: Multinational Enterprise O&M: Operate-Manage OECD: Organization for Economic Cooperation and Development OLS: Ordinary Least Square method PPP: Public and Private Partnership RE Random effects REM: Random effect model WTO: World Trade Organization LIST OF TABLES AND FIGURES Table 1: Asia: Real GDP 15 Table 2: Top 10 Global and Asian Foreign Direct Investment Recipients in recent years (Millions of dollars) .18 Table 3: Expected effects of explanatory variables on FDI inflow into Asia 48 Table 4: Descriptive statistics of variables in model 52 Table 5: Correlation matrix of the explanatory variables .54 Table 6: Results of analysis 55 Table 7: Descriptive statistics of variables in model 69 Table 8: Correlation matrix of the explanatory variables .70 Table 9: OLS model .71 Table 10: Random effect model .72 Table 11: Breusch and Pagan Lagrangian multiplier test for random effects 73 Table 12: Fixed effect model 74 Table 13: Results of Hausman Test 75 Table 14: Robust regression for random effect model 76 Table 15: Results of analysis 77 Figure 1: Top 10 recipients of FDI flows in developing Asia in 2017 (millions of US dollars) Figure 2: FDI inflows, 2012–2018 17 Figure 3: FDI trend for China during 2008 – 2018 comparing to East Asia and Asia 19 Figure 4: FDI trend for Hong Kong (China) during 2008 – 2018 comparing to East Asia and Asia 20 Figure 5: FDI trend for Republic of Korea during 2008 – 2018 comparing to East Asia and Asia 20 Figure 6: FDI trend for Singapore during 2008 – 2018 comparing to South-East Asia and Asia 21 Figure 7: FDI trend for Indonesia during 2008 – 2018 comparing to South-East Asia and Asia 22 Figure 8: FDI trend for Thailand during 2008 – 2018 comparing to South-East Asia and Asia 23 Figure 9: FDI trend for CLMV countries during 2008 – 2018 comparing to SouthEast Asia 24 Figure 10: FDI trend for India during 2008 – 2018 comparing to South Asia and Asia .25 Figure 11: FDI trend for Bangladesh during 2008 – 2018 comparing to South Asia and Asia 25 Figure 12: FDI trend for Sri Lanka during 2008 – 2018 comparing to South Asia and Asia 26 Figure 13: FDI trend for Pakistan during 2008 – 2018 comparing to South Asia and Asia 27 Figure 14: FDI trend for Turkey during 2008 – 2018 comparing to West Asia and Asia 28 Figure 15: FDI trend for Saudi Arabia during 2008 – 2018 comparing to West Asia and Asia 29 Figure 16: FDI trend for Saudi Arabia during 2008 – 2018 comparing to West Asia and Asia 29 Goel, D., 2002 Impact of Infrastructure on Productivity: Case of Indian Registered Manufacturing Centre for Development Economics, (106), pp.3– 33 Graybill, F.A., 1994 Regression analysis: concepts and applications Duxbury Resource Center Hasli, A., Ho, C.S.F and Ibrahim, N.A., 2015 Determinants of FDI inflow in Asia Journal of Emerging Economies and Islamic Research, 3(3), pp.1–9 Hill, C., 2008 International business: Competing in the global market place Strategic Direction, 24(9) International Monetary Fund, 2018 Asia Pacific Regional Economic Outlook, May 2018 Jenkins, C and Thomas, L., 2002 Foreign direct investment in Southern Africa: Determinants, characteristics and implications for economic growth and poverty alleviation CSAE, University of Oxford Jordaan, J., 2004 Foreign Direct Investment and Neighbouring Influences [online] Available at: Kakar, Z.K and Khilji, B.A., 2011 Impact of FDI and trade openness on economic growth: A comparative study of Pakistan and Malaysia Theoretical and Applied Economics, 11(11), p.53 Kasasbeh, H.A., Mdanat, M.F and Khasawneh, R., 2018 Corruption and FDI Inflows: Evidence from a Small Developing Economy Asian Economic and Financial Review, 8(8), pp.1075–1085 Kaufmann, D., Kraay, A and Mastruzzi, M., 2011 The Worldwide Governance Indicators: Methodology and Analytical Issues Hague Journal on the Rule of Law, [online] 3(Junio), pp.220–246 Available at: Kim, Soyoung, Sunghyun Kim, and Y.C., 2013 Determinants of international capital flows in Korea: Push vs pull factors Korea and the world economy, 14(3), pp.447–474 Kim, H and Haksoon, K., 2010 Political Stability and Foreign Direct Investment International Journal of Economics and Finance, 2(3), pp.59–71 Kurtishi-kastrati, S., 2013 The Effects of Foreign Direct Investments for Host Country ’ s Economy Introduction : The Benefits of FDI for Host Country ’ s Economy European Journal of Interdisciplinary Studies, 5(1), pp.26–38 Lee, C.-C and Chang, C.-P., 2009 FDI, financial development, and economic growth: international evidence Journal of applied economics, 12(2), pp.249– 271 Liargovas, P.G and Skandalis, K.S., 2012 Foreign direct investment and trade openness: The case of developing economies Social indicators research, 106(2), pp.323–331 Lucas, R.E., 1990 Why doesn’t capital flow from rich to poor countries? American Economic Review, 80(2), pp.92–96 Mah, J.S., 2010 Foreign direct investment inflows and economic growth of China Journal of Policy Modeling, 32(1), pp.155–158 Neter, J., 1983 Applied linear regression models Noorbakhsh, F., Paloni, A and Youssef, A., 2001 Human capital and FDI inflows to developing countries: New empirical evidence World Development, 29(9), pp.1593–1610 Nsouli, M.S.M., 2004 New Partnership for Africa’s Development: Macroeconomics, Institutions, and Poverty International Monetary Fund Pla Gutierrez, K., 2015 The effect of corruption on FDI in Argentina: has corruption acted as a negative determinant discouraging FDI? Rao, M.S and Pillai, K.R., 2013 Determinants of Inward FDI to India: A Factor Analysis of Panel Data The Journal Contemporary Management Research, 7(1), pp.1–16 Rodrik, D., 1999 The new global economy and developing countries: making openness work Scaperlanda, A.E and Mauer, L.J., 1969 The Determinants of U.S Direct Investment in the EEC The American Economic Review, 59(4), pp.558–568 Svyatoslavovich, Mariev Oleg, Drapkin Igor Mikhailovich, and R.H., 2016 Determinants of FDI inflows: The case of Russian regions Ekonomika regiona [Economy of Region], 12(4), pp.1244–1252 Torriti, J and Ikpe, E., 2015 Administrative costs of regulation and foreign direct investment: the Standard Cost Model in non-OECD countries Review of World Economics, 151(1), pp.127–144 UNCTAD, 2019 World Investment Report 2018 Special Economic Zones WTO., 1996 Trade and Foreign Direct Investment WTO News: 1996 Press Releases Wu, J., Li, S and Selover, D.D., 2012 Foreign direct investment vs foreign portfolio investment Management International Review, 52(5), pp.643–670 Zaman, Q., Donghui, Z., Yasin, G., Zaman, S and Imran, M., 2018 Trade Openness and FDI Inflows : A Comparative Study of Asian Countries 7(2), pp.386–396 APPENDICES Table 7: Descriptive statistics of variables in model sum FDI L.lnGDP L.Labor L.Infrastructure L.Inflation L.Open L.Growth L.CI L.NR L.PS L.RQ Variable Descriptive Source Mean SD Min Max FDI Asian country’s FDI (USD) World Bank 1.87E+10 4.46E+10 -5.03E+09 2.91E+11 GDP Asian country’s GDP (USD) World Bank 26.08186 1.40795 23.68189 30.04613 Labor Population ages 15-64/ working population (% of total) World Bank 68.27627 6.978963 53.61504 85.8724 Tele Infrastructure by fixed telephone subscriptions (per 100 World Bank and ITU 1.85E+01 1.65E+01 0.470191 62.08586 people) Inf Inflation rate (annual %) World Bank and ADB 5.46E+00 5.73E+00 -12.6 39.26602 Open Trade (% of GDP), the sum of exports and imports of goods World Bank 9.99E+01 8.54E+01 1.67E-01 4.43E+02 and services measured as share of GDP Growth GDP growth (annual %) World Bank 4.69E+00 4.21E+00 -1.67E+01 1.96E+01 CI Corruption Perceptions Index Transparency 4.03E+01 1.98E+01 1.30E+01 9.30E+01 International NR Total natural resources rents (% of GDP) World Bank 1.35E+01 1.59E+01 3.41E-04 6.08E+01 PS Political Stability and Absence of Violence/Terrorism WGI -4.81E-01 1.06E+00 -2.81E+00 1.50E+00 RQ Regulatory Quality, the ability of the government to WGI -8.77E-02 9.96E-01 -2.24E+00 2.26E+00 formulate and implement sound policies and regulations that permit and promote private sector development N 300 Source: Author’s calculations in STATA software Table 8: Correlation matrix of the explanatory variables corr FDI L.lnGDP L.Labor L.Infrastructure L.Inflation L.Open L.Growth L.CI L.NR L.PS L.RQ (obs=269) FDI FDI L L L L lnGDP Labor Infrastructure Inflation Open lnGDP L1 0.5259 L1 0.2048 0.1729 0.2636 0.4011 0.3175 Labor Infrastructur e L1 Inflation L L L Growth CI L L L NR PS RQ L1 -0.1465 -0.1973 -0.1651 -0.1573 -0.1842 Open L1 0.2721 -0.0508 0.3725 0.4602 L1 0.1123 -0.0736 0.0524 -0.2486 L1 0.2281 0.3681 0.4873 0.662 L1 -0.2176 -0.238 0.1947 -0.2256 L1 0.1723 0.1784 0.679 0.4987 -0.2954 0.5308 L1 0.2395 0.3518 0.3708 0.646 -0.4516 0.6175 -0.2505 Growth -0.016 -0.0831 CI -0.4195 0.6197 -0.1727 NR 0.129 -0.1135 0.138 -0.1904 PS 0.0371 0.7064 0.113 0.9055 -0.2737 0.5585 RQ Source: Author’s calculations in STATA software Table 9: OLS model reg FDI L.lnGDP L.Labor L.Infrastructure L.Inflation L.Open L.Growth L.CI L.NR L.PS L.RQ Source SS df MS Number of obs = 269 Model 2.5785E+23 10 2.5785E+22 F( 10, 258) = 21.83 Residual 3.0471E+23 258 1.1811E+21 Prob > F = 0.0000 Total 5.6256E+23 268 2.0991E+21 R-squared = 0.4584 Adj R-squared = 0.4374 Root MSE = 3.4E+10 FDI Coef Std Err t P>t [95% Conf Interval] lnGDP L1 2.11E+10 1.86E+09 11.32 1.74E+10 2.47E+10 L1 7.57E+08 4.23E+08 1.79 0.074 -7.52E+07 1.59E+09 L1 2.30E+07 1.88E+08 0.12 0.903 -3.47E+08 3.93E+08 L1 -4.95E+08 4.38E+08 -1.13 0.259 -1.36E+09 3.68E+08 L1 2.70E+08 3.63E+07 7.43 1.98E+08 3.41E+08 L1 1.64E+09 5.62E+08 2.92 0.004 5.37E+08 2.75E+09 L1 -8.19E+08 3.12E+08 -2.63 0.009 -1.43E+09 -2.05E+08 L1 -3.14E+08 1.52E+08 -2.07 0.039 -6.12E+08 -1.54E+07 Labor Infrastructur e Inflation Open Growth CI NR PS L1 -1.70E+09 3.65E+09 -0.47 0.642 -8.88E+09 5.48E+09 L1 -1.14E+09 5.69E+09 -0.2 0.841 -1.23E+10 1.01E+10 -5.78E+11 5.39E+10 -10.73 -6.85E+11 -4.72E+11 RQ _cons Source: Author’s calculations in STATA software Table 10: Random effect model xtreg FDI L.lnGDP L.Labor L.Infrastructure L.Inflation L.Open L.Growth L.CI L.NR L.PS L.RQ, re Random-effects GLS regression Number of obs = 269 Group variable: ID Number of groups = 30 R-sq: within = 0.0608 Obs per group: = between = 0.3818 avg = overall = 0.3543 max = Wald chi2(10) = 28.29 corr(u_i, X) = (assumed) Prob > chi2 = 0.0016 FDI Coef Std Err z P>z [95% Conf Interval] lnGDP L1 1.24E+10 3.43E+09 3.62 0.000 5.67E+09 1.91E+10 Labor L1 -4.58E+08 7.68E+08 0.60 0.551 -1.96E+09 1.05E+09 Infrastructure L1 1.74E+08 2.97E+08 0.59 0.558 -4.08E+08 7.56E+08 Inflation L1 -9.08E+07 2.29E+08 0.40 0.691 -5.39E+08 3.57E+08 Open L1 8.52E+07 5.89E+07 1.45 0.148 -3.03E+07 2.01E+08 Growth L1 2.48E+08 2.70E+08 0.92 0.358 -2.80E+08 7.76E+08 CI L1 -9.12E+08 2.77E+08 3.29 0.001 -1.46E+09 -3.68E+08 NR L1 -1.19E+08 1.84E+08 0.65 0.518 -4.79E+08 2.42E+08 PS L1 3.82E+09 3.74E+09 1.02 0.307 -3.51E+09 1.11E+10 RQ L1 1.12E+10 5.52E+09 2.02 0.043 3.31E+08 2.20E+10 _cons -2.44E+11 9.26E+10 2.64 0.008 -4.25E+11 -6.25E+10 sigma_u 3.72E+10 sigma_e 1.32E+10 rho 0.88780835 (fraction of variance due to u_i) Source: Author’s calculations in STATA software Table 11: Breusch and Pagan Lagrangian multiplier test for random effects FDI[ID,t] = Xb + u[ID] + e[ID,t] Estimated results: FDI e u Test: Var(u) = Var 2.10E+21 1.75E+20 1.39E+21 chibar2(01) = 695.63 Prob > chibar2 = 0.0000 Source: Author’s calculations in STATA software sd = sqrt(Var) 4.58E+10 1.32E+10 3.72E+10 Table 12: Fixed effect model xtreg FDI L.lnGDP L.Labor L.Infrastructure L.Inflation L.Open L.Growth L.CI L.NR L.PS L.RQ, fe Fixed-effects (within) regression Number of obs = 269 Group variable: ID Number of groups = 30 R-sq: within = 0.0709 Obs per group: = between = 0.0969 avg = 9.0 overall = 0.0951 max = F(10,229) = 1.75 corr(u_i, Xb) = -0.0330 Prob > F = 0.0715 FDI Coef Std Err t P>t [95% Conf Interval] lnGDP L1 9.18E+09 4.49E+09 2.04 0.042 3.26E+08 1.80E+10 Labor L1 -1.26E+09 1.07E+09 1.18 0.240 -3.36E+09 8.44E+08 Infrastructure L1 -2.58E+07 3.82E+08 0.07 0.946 -7.79E+08 7.27E+08 Inflation L1 -1.60E+08 2.35E+08 0.68 0.496 -6.24E+08 3.03E+08 Open L1 1.87E+07 7.09E+07 0.26 0.792 -1.21E+08 1.58E+08 Growth L1 2.02E+08 2.76E+08 0.73 0.464 -3.41E+08 7.46E+08 CI L1 -9.04E+08 3.03E+08 2.98 0.003 -1.50E+09 -3.07E+08 NR L1 -5.09E+07 2.03E+08 0.25 0.802 -4.50E+08 3.48E+08 PS L1 2.51E+09 4.05E+09 0.62 0.537 -5.47E+09 1.05E+10 RQ L1 1.13E+10 6.18E+09 1.83 0.069 -8.87E+08 2.35E+10 _cons -9.65E+10 1.17E+11 0.83 0.409 -3.26E+11 1.33E+11 sigma_u 4.24E+10 sigma_e 1.32E+10 rho 0.91127896 (fraction of variance due to u_i) F test that all u_i=0: F(29, 229) = 52.05 Prob > F = 0.0000 Source: Author’s calculations in STATA software Table 13: Results of Hausman Test hausman fem Coefficients (b) (B) (b-B) sqrt(diag(V_b-V_B)) fem Difference S.E L.lnGDP 9.18E+09 1.24E+10 -3.21E+09 2.90E+09 L.Labor -1260000000 -458000000 -8.00E+08 7.40E+08 L.Infrastructure -2.58E+07 1.74E+08 -2.00E+08 2.41E+08 L.Inflation -160000000 -90800000 -6.97E+07 5.65E+07 L.Open 1.87E+07 8.52E+07 -6.65E+07 3.94E+07 L.Growth 2.02E+08 2.48E+08 -4.54E+07 5.84E+07 L.CI -904000000 -912000000 7501460 1.22E+08 L.NR -50900000 -119000000 6.79E+07 8.50E+07 L.PS 2.51E+09 3.82E+09 -1.31E+09 1.56E+09 L.RQ 1.13E+10 1.12E+10 1.44E+08 2.78E+09 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(10) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 12.22 Prob>chi2 = 0.2708 (V_b-V_B is not positive definite) Source: Author’s calculations in STATA software Random effect model is selected against a pooled OLS model Table 14: Robust regression for random effect model xtreg FDI L.lnGDP L.Labor L.Infrastructure L.Inflation L.Open L.Growth L.CI L.NR L.PS L.RQ, re robust Random-effects GLS regression Number of obs = 269 Group variable: ID Number of groups = 30 R-sq: within = 0.0608 Obs per group: = between = 0.3818 avg = 9.0 overall = 0.3543 max = Wald chi2(10) = 14.59 corr(u_i, X) = (assumed) Prob > chi2 = 0.1477 (Std Err adjusted for 30 clusters in ID) Robust FDI Coef Std Err z P>z [95% Conf Interval] lnGDP L1 1.24E+10 5.69E+09 2.18 0.029 1.24E+09 2.35E+10 Labor L1 -4.58E+08 8.39E+08 0.55 0.585 -2.10E+09 1.19E+09 Infrastructure L1 1.74E+08 2.09E+08 0.83 0.406 -2.36E+08 5.83E+08 Inflation L1 -9.08E+07 9.67E+07 0.94 0.348 -2.80E+08 9.88E+07 Open L1 8.52E+07 9.42E+07 0.90 0.366 -9.94E+07 2.70E+08 Growth L1 2.48E+08 2.15E+08 1.15 0.249 -1.74E+08 6.70E+08 CI L1 -9.12E+08 4.56E+08 2.00 0.046 -1.81E+09 -1.74E+07 NR L1 -1.19E+08 1.10E+08 1.08 0.280 -3.34E+08 9.66E+07 PS L1 3.82E+09 3.00E+09 1.27 0.202 -2.05E+09 9.69E+09 RQ L1 1.12E+10 6.07E+09 1.84 0.066 -7.37E+08 2.30E+10 1.67E+11 1.46 0.145 _cons -2.44E+11 -5.72E+11 sigma_u 3.72E+10 sigma_e 1.32E+10 rho 0.88780835 (fraction of variance due to u_i) Source: Author’s calculations in STATA software 8.41E+10 Table 15: Results of analysis esttab ols rem fem last, r2 star(* 0.1 ** 0.05 *** 0.01) brackets nogap (1) OLS L.lnGDP 2.10773e+10*** [11.32] L.Labor 757190595.9* [1.79] L.Infrastructure 23025577 [0.12] L.Inflation -495282262.6 [-1.13] L.Open 269589764.5*** [7.43] L.Growth 1.64367e+09*** [2.92] L.CI -819434151.1*** [-2.63] L.NR -313914119.0** [-2.07] L.PS -1.70E+09 [-0.47] L.RQ -1.14E+09 [-0.20] _cons -5.78434e+11*** [-10.73] N 269 R-sq 0.458 t statistics in brackets * p
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