Direct, indirect and total effect in spatial analysis of provincial FDI in vietnam

UNIVERSITY OF ECONOMICS ERAMUS UNIVERSITY ROTTEDAM HO CHI MINH CITY INSTITUTE OF SCOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DIRECT, INDIRECT AND TOTAL EFFECT IN SPATIAL ANALYSIS OF PROVINCIAL FDI IN VIETNAM BY LE VAN THANG HO CHI MINH CITY, November 2016 UNIVERSITY OF ECONOMICS ERAMUS UNIVERSITY ROTTEDAM HO CHI MINH CITY INSTITUTE OF SCOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DIRECT, INDIRECT AND TOTAL EFFECT IN SPATIAL ANALYSIS OF PROVINCIAL FDI IN VIETNAM BY LE VAN THANG ACADEMIC SUPERVISOR DR NGUYEN LUU BAO DOAN HO CHI MINH CITY, November 2016 DECLARATION “This is to certify that this thesis entitled “Direct, Indirect and Total effect In Spatial Analysis of Provincial FDI in Vietnam”, which is submitted by me in fulfillment of the requirements for the degree of Master of Art in Development Economics to the Vietnam – The Netherlands Programme (VNP) The thesis constitutes only my original work and due supervision and acknowledgement have been made in the text to all materials used.” Le Van Thang i ACKNOWLEDGEMENT This thesis could not be accomplished without the supporting and the motivation that I have received from many people It is a pleasure to convey my gratitude to them all in my humble acknowledgment Foremost, I would like to express my sincere appreciation to Dr Nguyen Luu Bao Doan, my supervisor He gave me the greatest supporting, energetic assistance and valuable guidance as well as an infinite patient to encourage me to complete my very first research Without Dr Nguyen Luu Bao Doan, this study would never finish Besides, I also would like to give my gratitude to the Vietnam- Netherland Programme, especially to all lecturers who provided me valuable knowledge, VNP staffs for their restless assistant for the time I have been studying in VNP as well as School of Economics I would love to express my gratefulness to Prof Nguyen Trong Hoai and Dr Pham Khanh Nam for the first suggestion to encourage me to deal with a novelty field of my knowledge - spatial analysis Moreover, I would like to give my sincere thankfullness to Dr Pham Khanh Nam who has provided a valuable data source for me to complete this thesis Besides that, I would like to thank all my friends, my fellows at the University of Economics, Ho Chi Minh City, my groups and all the classmates in K20-VNP All of them are always be my side encourage and support me to complete the thesis Finally, I would like to send my gratefulness to my father, my mother, my two little brother, sister- Van Nam and Thuy Linh for their love, sacrifice, tremendous support for me not only to complete this thesis but also for my whole life ii ABBREVIATION AIC: Akaike Information Criteria ESDA: Exploratory Spatial Data Analysis EU: European Union FDI: Foreign Direct Investment GDP: Gross Domestic Product GSO: General Statistical Official GRP: Gross Regional Product PCI: Provincial Competitiveness Index LM test: Lagrange Multiplier test MPI: Minister of Planning and Investment MNE: Multinational Enterprises SAR: Spatial Autoregressive Model SDM: Spatial Durbin Model SEM: Spatial Error Model USAID: The United States Agency for International Development VCCI: Chamber of commerce and industry VIF: Variance Inflation Factor ABSTRACT This paper investigates the spatial pattern of Foreign Direct Investment (FDI) for all 63 provinces in Vietnam from 2011 to 2014 Empirical studies on locational determinants of FDI typically neglected the spatial interaction among observations which lead to inefficient and biased estimations Indeed, Moran’s I suggested by Moran, which is used to detect the spatial autocorrelation in data pattern of both dependent and independent variables, give hints of the necessity of spatial econometrics in analyzing the FDI determinants Through General To Specific approach, the Spatial Durbin Model (SDM) has been chosen as the most appropriate model, compared with other models like Non-spatial model, SpatialAutoregressive Model (SAR) and Spatial Error Model (SEM) This study finds that the FDI flow into one province negatively spatially affects FDI inflow in remaining provinces Moreover, by applying SDM, this paper econometrically estimates the impact of host province’s determinants and its neighbor determinants on its FDI inflow Keywords: Foreign Direct Investment, Moran’s I, Spatial analysis CONTENTS DECLARATION .i ACKNOWLEDGEMENT ii ABBREVIATION iii ABSTRACT iv LIST OF FIGURE vii LIST OF TALBE viii CHAPTER 1: INTRODUCTION 1.1 Problem statement 1.2 Research objective .3 1.3 Research questions 1.4 Scope of the study 1.5 Thesis structure CHAPTER 2: OVERVIEW OF FDI IN VIETNAM 2.1 Stages of foreign direct investment in Vietnam 2.2 Distribution of foreign direct investment among provinces 2.3 Country of origin 10 2.4 Sectors of foreign direct investment 11 CHAPTER 3: LITERATURE REVIEW 14 3.1 Theories about location choices of foreign direct investment 14 3.1.1 The eclectic paradigm OLI 14 3.1.2 Agglomeration and foreign direct investment 16 3.2 The inter-dependence of FDI between locations 17 3.2.1 MNE choice theory 17 3.2.2 Agglomeration effect 20 3.3 Empirical studies 21 3.3.1 Empirical studies of FDI determinants in spatial analysis 21 3.3.2 Empirical studies of FDI determinants in Vietnam 23 3.3.3 Fundamental FDI determinants 26 CHAPTER 4: DATA AND METHODOLOGY 31 4.1 Data sources 32 4.1.1 Dependent variable 32 4.1.2 Explanatory variables 33 4.1.3 Descriptive statistics 36 4.2 Spatial econometric model 37 4.2.1 Spatial Autoregressive Model 38 4.2.2 Spatial Error Model .38 4.2.3 Spatial Durbin Model 38 4.2.4 Marginal effect in Spatial Durbin Model .39 4.2.5 Model selection 40 4.3 Pre-test for spatial existent with Moran’s I 43 4.4 Spatial weight matrix 44 4.5 Comparisons of models 46 CHAPTER 5: EMPIRICAL RESULT 47 5.1 Direct effect 51 5.2 Indirect effect 54 5.3 Total effect 55 CHAPTER 6: CONCLUSION .56 6.1 Main finding .56 6.2 Policy implication 57 6.3 Limitation and future research 57 REFERENCES 59 APPENDICES 64 LIST OF FIGURE Figure 2.1: Registered, implement FDI (million USD) and Number of FDI projects Figure 2.2: The distribution of FDI in Vietnam from 1988 to 2014 Figure 2.3: The sector distribution of FDI 12 Figure 3.1: Analytical framework of FDI and determinants 31 Figure 4.1: General to Specific strategy .42 Figure 5.1: The Local Moran’s I of FDI inflow Vietnam in 2011-2012-2014 .49 vii LIST OF TALBE Table 2.1: Sharing of FDI in Vietnam from 1988 to 2014 .7 Table 2.2: Top ten countries of origin of FDI in Vietnam 11 Table 3.1: Multinational Enterprise Motivation 18 Table 4.1: The variable descriptive 35 Table 4.2: The summary statistics of variables 36 Table 5.1: The Moran’s I coefficient of FDI 47 Table 5.2: The Moran’s I coefficient of explanatory variables 48 Table 5.3: The AIC value .50 Table 5.4: The Marginal effect of Spatial Durbin Model .52 viii Pham, M H (2002) Regional Economic Development and Foreign Direct Investment Flows in Vietnam 1988-1998 Journal of the Asia Pacific economy,7(2), 12-202 Pisati, M (2001) sg162: tools for spatial data analysis Stata Technical Bulletin, 60, 21-37 Sharma, S., Wang, M., & Wong, M C (2014) FDI location and the relevance of spatial linkages: evidence from provincial and industry FDI in China Review of International Economics, 22(1), 86-104 Sun, Q., Tong, W., & Yu, Q (2002) Determinants of foreign direct investment across China Journal of international money and finance, 21(1), 79-113 Vernon, R (1966) International investment and international trade in the product cycle The quarterly journal of economics, 190-207 General Statistical Office, Statistical Yearbook 2010-2014 APPENDICES  Sectors distribution of FDI in Vietnam Source: GSO Kinds of economic activity Percentage Agriculture, forestry and fishing 1.47 Mining and quarrying 1.34 Manufacturing 55.96 Electricity, gas, stream and air conditioning supply 3.87 Water supply, sewerage, waste management and remediation activities 0.53 Construction 4.51 Wholesale and retail trade; Repair of motor vehicles and motorcycles 1.59 Transportation and storage 1.49 Accommodation and food service activities 4.43 Information and communication 1.63 Financial, banking and insurance activities 0.53 Real estate activities 19.11 Professional, scientific and technical activities 0.71 Administrative and support service activities 0.08 Education and training 0.32 Human health and social work activities 0.69 Arts, entertainment and recreation 1.44 Other service activities 0.30  Appendix 2: List of provinces recorded the zero of FDI Bac Can (2010, 2011); Dien Bien (2013, 2014); Gia Lai ( 2011, 2013, 2014) ; Lai Chau (2010); Quang Binh (2012)  Appendix 3: correlation matrix of variables FDI GDP  LAB LABCO FDI GDP 0.53 LAB 0.30 0.30 LABCO -.005 0.16 0.64 OP PORT PCI OP 0.54 0.58 0.25 0.09 PORT 0.29 0.46 0.38 0.23 0.21 PCI 0.29 0.33 0.03 -0.14 0.34 0.23 FAGG 0.60 0.46 0.22 -0.10 0.59 0.16 0.25 FAGG VIF 1/VIF 1.97 0.50 2.13 0.47 2.01 0.49 2.02 0.49 1.44 0.69 1.25 0.79 1.81 0.55 Global Moran’s I of Dependent Variable Measures of global spatial autocorrelation Weights matrix -Name: SCW Type: Imported (binary) Rowstandardized: Yes -Moran's I -Variables | I E(I) sd(I) z p-value* + dfdi0 | 0.250 -0.016 0.075 3.535 0.000 dfdi1 | 0.202 -0.016 0.080 2.725 0.006 dfdi2 | 0.035 -0.016 0.083 0.622 0.534 dfdi3 | 0.242 -0.016 0.080 3.214 0.001 -*2-tail test  Global Moran’s I of Independent Variable Measures of global spatial autocorrelation Weights matrix Name: SCW Type: Imported (binary) Rowstandardized: Yes -Moran's I -Variables | I E(I) sd(I) z p-value* + dgdp0 | 0.060 -0.016 0.067 1.134 0.257 dgdp1 | 0.063 -0.016 0.070 1.126 0.260 dgdp2 | 0.063 -0.016 0.070 1.124 0.261 dgdp3 | 0.073 -0.016 0.069 1.294 0.196 lab0 | 0.242 -0.016 0.085 3.043 0.002 lab1 | 0.247 -0.016 0.085 3.110 0.002 lab2 | 0.230 -0.016 0.085 2.888 0.004 lab3 | 0.203 -0.016 0.085 2.572 0.010 dlabco0 | 0.367 -0.016 0.087 4.399 0.000 dlabco1 | 0.373 -0.016 0.087 4.474 0.000 dlabco2 | 0.272 -0.016 0.087 3.321 0.001 dlabco3 | 0.400 -0.016 0.087 4.779 0.000 op0 | 0.230 -0.016 0.077 3.170 0.002 op1 | 0.254 -0.016 0.078 3.470 0.001 op2 | 0.336 -0.016 0.079 4.457 0.000 op3 | 0.339 -0.016 0.080 4.440 0.000 port | 0.173 -0.016 0.087 2.169 0.030 pci0 | 0.243 -0.016 0.087 2.962 0.003 pci1 | 0.239 -0.016 0.087 2.932 0.003 pci2 | 0.192 -0.016 0.087 2.386 0.017 pci3 | 0.250 -0.016 0.087 3.059 0.002 fagg0 | 0.388 -0.016 0.086 4.691 0.000 fagg1 | 0.433 -0.016 0.087 5.184 0.000 fagg2 | 0.442 -0.016 0.087 5.274 0.000 fagg3 | 0.449 -0.016 0.087 5.353 0.000 *2-tail test  Pool OLS regression Source | SS df MS Number of obs = -+ F( 7, 252 244) = 34.06 Model | 1935.75986 276.537123 Prob > F = 0.0000 Residual | 1980.9074 244 8.11847294 R-squared = 0.4942 Adj R-squared = 0.4797 Root MSE 2.8493 -+ -Total | 3916.66726 251 15.604252 = -lnfdi | Coef Std Err t P>|t| [95% Conf Interval] -+ -lngdp | 9105929 2799255 3.25 0.001 3592141 1.461972 lnlab | 2.199758 6889306 3.19 0.002 8427476 3.556768 lnlabco | -3.562939 1.453778 -2.45 0.015 -6.426495 -.6993819 lnop | 4886178 2128093 2.30 0.023 0694401 9077956 port | 4870601 5188352 0.94 0.349 -.5349072 1.509027 lnpci | 2.693149 2.66357 1.01 0.313 -2.553375 7.939674 lnfagg | 667088 1205136 5.54 0.000 4297083 9044677 _cons | 11.75943 16.47314 0.71 0.476 -20.68827 44.20714 VIF test  Variable | VIF 1/VIF -+ -lnlab | 2.13 0.470500 lnop | 2.02 0.495024 lnlabco | 2.01 0.496573 lngdp | 1.97 0.507717 lnfagg | 1.81 0.550992 port | 1.44 0.692424 lnpci | 1.25 0.798479 -+ -Mean VIF |  1.81 Summary of binary contiguity weight matrix Summary of spatial-weighting object CW Matrix | Description -+ Dimensions | 63 x 63 Stored as | 63 x 63 Links | total | 267 | mean | 4.238095 max | -  Spatial Durbin Model with binary contiguity weight matrix SDM with time fixed-effects Group variable: state Number of obs = 252 Number of groups = 63 Panel length = Time variable: year R-sq: within = 0.0192 between = 0.6817 overall = 0.5267 Mean of fixed-effects = 21.5131 Log-likelihood = -602.2214 -lnfdi | Coef Std Err z P>|z| [95% Conf Interval] -+ -Main | lngdp | 8815287 3054545 2.89 0.004 2828488 1.480209 lnlab | 1.789633 778285 2.30 0.021 2642223 3.315043 lnlabco | -3.507123 1.70015 -2.06 0.039 -6.839356 -.1748901 lnop | 2744097 2100509 1.31 0.191 -.1372825 6861018 port | 8029607 5248534 1.53 0.126 -.225733 1.831654 lnpci | 4.523734 2.684622 1.69 0.092 -.7380277 9.785496 lnfagg | 4892326 1290539 3.79 0.000 2362916 7421736 -+ -Wx | lngdp | 1.716628 6491345 2.64 0.008 4443482 2.988909 lnlab | 2.235996 1.299254 1.72 0.085 -.3104956 4.782487 lnlabco | -1.633737 2.80622 -0.58 0.560 -7.133826 3.866353 lnop | -.1163949 4258112 -0.27 0.785 -.9509695 7181797 port | -.9760618 9470394 -1.03 0.303 -2.832225 8801013 lnpci | -8.203974 4.666532 -1.76 0.079 -17.35021 9422608 lnfagg | 4832184 2515114 1.92 0.055 -.0097349 9761718 -+ 70 Spatial | rho | -.3392972 0910939 -3.72 0.000 -.517838 -.1607564 -+ -Variance | sigma2_e | 6.785361 6123693 11.08 0.000 5.585139 7.985583 -+ -LR_Direct | lngdp | 7708593 2676589 2.88 0.004 2462576 1.295461 lnlab | 1.729582 9317913 1.86 0.063 -.0966954 3.555859 lnlabco | -3.458161 1.942284 -1.78 0.075 -7.264968 348646 lnop | 2872919 211444 1.36 0.174 -.1271308 7017146 port | 9783785 5420211 1.81 0.071 -.0839633 2.04072 lnpci | 5.631912 2.857913 1.97 0.049 0305049 11.23332 lnfagg | 4625129 1345597 3.44 0.001 1987808 7262451 -+ -LR_Indirect | lngdp | 1.215549 5771316 2.11 0.035 0843914 2.346706 lnlab | 1.325236 1.167129 1.14 0.256 -.9622938 3.612766 lnlabco | -.4283758 2.586075 -0.17 0.868 -5.496989 4.640237 lnop | -.1563021 3826805 -0.41 0.683 -.9063421 5937379 port | -1.146881 8216224 -1.40 0.163 -2.757232 4634688 lnpci | -8.338653 4.311009 -1.93 0.053 -16.78808 1107689 lnfagg | 2326217 2099909 1.11 0.268 -.1789529 6441963 -+ -LR_Total | lngdp | 1.986408 5376085 3.69 0.000 9327146 3.040101 lnlab | 3.054818 9028004 3.38 0.001 1.285362 4.824274 lnlabco | -3.886537 1.754475 -2.22 0.027 -7.325244 -.4478291 lnop | 1309898 3693831 0.35 0.723 -.5929877 8549674 port | -.168503 7693632 -0.22 0.827 -1.676427 1.339421 71 lnpci | -2.706741 4.193585 -0.65 0.519 -10.92602 5.512534 lnfagg | 6951347 2024326 3.43 0.001 298374 1.091895 -Ho: difference in coeffs not systematic chi2(15) = 31.62 Prob>=chi2 = 0.0073  Test SDM over SAR ( 1) [Wx]lngdp - [Wx]lnlab = ( 2) [Wx]lngdp - [Wx]lnlabco = ( 3) [Wx]lngdp - [Wx]lnop = ( 4) [Wx]lngdp - [Wx]port = ( 5) [Wx]lngdp - [Wx]lnpci = ( 6) [Wx]lngdp - [Wx]lnfagg = ( 7) [Wx]lngdp = chi2( 7) = Prob > chi2 =  27.55 0.0003 Test SDM over SEM (1) [Wx]lngdp = -[Spatial]rho*[Main]lngdp (2) [Wx]lnlab = -[Spatial]rho*[Main]lnlab (3) [Wx]lnlabco = -[Spatial]rho*[Main]lnlabco (4) [Wx]lnop = -[Spatial]rho*[Main]lnop (5) [Wx]port = -[Spatial]rho*[Main]port (6) [Wx]lnpci = -[Spatial]rho*[Main]lnpci (7) [Wx]lnfagg = -[Spatial]rho*[Main]lnfagg chi2(7) = Prob > chi2 = 14.93 0.0369 Summary of binary contiguity weight matrix with cut-off 180km  Summary of spatial-weighting object CW180 -Matrix | Description -+ -Dimensions | 63 x 63 Stored as | 63 x 63 Links | total | 774 | mean | 12.28571 max | 21  Spatial Durbin Model with binary contiguity weight matrix with cut-off 180km SDM with time fixed-effects Number of obs = Group variable: state Number of groups = 63 Panel length = Time variable: year R-sq: within 252 = 0.0043 between = 0.7114 overall = 0.5377 Mean of fixed-effects = 113.0125 Log-likelihood = -593.1504 -lnfdi | Coef Std Err z P>|z| [95% Conf Interval] -+ -Main | lngdp | 7129332 2862624 2.49 0.013 1518692 1.273997 lnlab | 1.417406 7272654 1.95 0.051 -.0080083 2.84282 lnlabco | -2.939684 1.505191 -1.95 0.051 -5.889803 0104364 lnop | 3899052 1925438 2.03 0.043 0125262 7672841 port | 9259775 4869079 1.90 0.057 -.0283444 1.880299 lnpci | 4.502072 2.525089 1.78 0.075 -.4470125 9.451156 lnfagg | 6147891 1150063 5.35 0.000 3893808 8401975 -+ -Wx | lngdp | 9633507 1.171694 0.82 0.411 -1.333128 3.259829 lnlab | 4.319499 1.650651 2.62 0.009 1.084282 7.554717 lnlabco | -10.647 3.57467 -2.98 0.003 -17.65322 -3.640774 lnop | -1.314606 7344331 -1.79 0.073 -2.754069 124856 port | -.888663 1.253521 -0.71 0.478 -3.34552 1.568194 lnpci | -10.83901 5.842103 -1.86 0.064 -22.28932 6113043 lnfagg | 1.835162 5045094 3.64 0.000 8463413 2.823982 -+ -Spatial | rho | -.7450123 1439063 -5.18 0.000 -1.027063 -.462961 -+ -Variance | sigma2_e | 6.146415 5564041 11.05 0.000 5.055883 7.236947 -+ -LR_Direct | lngdp | 6777085 2503523 2.71 0.007 1870271 1.16839 lnlab | 1.276107 8554937 1.49 0.136 -.4006301 2.952844 lnlabco | -2.403286 1.716156 -1.40 0.161 -5.76689 9603194 lnop | 4839234 2036798 2.38 0.018 0847183 8831285 port | 1.100621 4992436 2.20 0.027 1221217 2.079121 lnpci | 5.77014 2.765344 2.09 0.037 3501656 11.19012 lnfagg | 526361 1204154 4.37 0.000 290351 7623709 -+ -LR_Indirect | lngdp | 3250731 808102 0.40 0.687 -1.258778 1.908924 lnlab | 2.072869 1.198978 1.73 0.084 -.2770839 4.422823 lnlabco | -5.504548 2.611338 -2.11 0.035 -10.62268 -.3864199 lnop | -1.029691 502238 -2.05 0.040 -2.014059 -.0453223 port | -1.09397 8750069 -1.25 0.211 -2.808952 621012 lnpci | -9.463925 4.454513 -2.12 0.034 -18.19461 -.7332404 lnfagg | 86811 3479965 2.49 0.013 1860494 1.550171 -+ -LR_Total | lngdp | 1.002782 7846046 1.28 0.201 -.535015 2.540578 lnlab | 3.348976 1.073519 3.12 0.002 1.244918 5.453034 lnlabco | -7.907834 1.874577 -4.22 0.000 -11.58194 -4.23373 lnop | -.5457673 4604498 -1.19 0.236 -1.448232 3566977 port | 0066512 8848408 0.01 0.994 -1.727605 1.740907 lnpci | -3.693784 3.997021 -0.92 0.355 -11.5278 4.140234 lnfagg | 1.394471 3261801 4.28 0.000 7551697 2.033772 -Ho: difference in coeffs not systematic chi2(15) = 41.49 Prob>=chi2 = 0.0003  Test SDM over SAR ( 1) [Wx]lngdp - [Wx]lnlab = ( 2) [Wx]lngdp - [Wx]lnlabco = ( 3) [Wx]lngdp - [Wx]lnop = ( 4) [Wx]lngdp - [Wx]port = ( 5) [Wx]lngdp - [Wx]lnpci = ( 6) [Wx]lngdp - [Wx]lnfagg = ( 7) [Wx]lngdp = chi2( 7) = Prob > chi2 = 52.10 0.0000  Test SDM over SEM (1) [Wx]lngdp = -[Spatial]rho*[Main]lngdp (2) [Wx]lnlab = -[Spatial]rho*[Main]lnlab (3) [Wx]lnlabco = -[Spatial]rho*[Main]lnlabco (4) [Wx]lnop = -[Spatial]rho*[Main]lnop (5) [Wx]port = -[Spatial]rho*[Main]port (6) [Wx]lnpci = -[Spatial]rho*[Main]lnpci (7) [Wx]lnfagg = -[Spatial]rho*[Main]lnfagg chi2(7) = Prob > chi2 = 28.39 0.000 i ... entitled ? ?Direct, Indirect and Total effect In Spatial Analysis of Provincial FDI in Vietnam? ??, which is submitted by me in fulfillment of the requirements for the degree of Master of Art in Development... of poverty reduction and improve the standard of living The following part will introduce details of FDI in Vietnam in term of stages of FDI process, provincial distribution of FDI, country of. .. al (2014) used the SAC in examining the spatial effect of FDI in China They found a complex spatial interaction of FDI between provinces in China, the positive sign of spatial lag coefficient,