INSTITUTE OF SOCIAL STUDIES UNIVERSITY OF ECONOMICS HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS ====oOo==== VIETNAM-NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS HORIZONTAL TECHNOLOGY SPILLOVER EFFECTS FROM FOREIGN DIRECT INVESMENT ON LABOUR PRODUCTIVTY IN MANUFACTURING INDUSTRY IN VIETNAM IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ART IN DEVELOPMENT ECONOMICS BY THAN HOANG DUNG ACADEMIC SUPERVISOR: DR LE THI THANH LOAN I' Ho Chi Minh, November 2011 CERTIFICATION , I hereby certify that the substance of this thesis has not been submitted for any degrees and is not being currently submitted for any other degrees I also certify that, to the best of my knowledge, and any help received in preparing the thesis and all sources used have been acknowledged in the thesis Signature Than Hoang Dung , , • ACKNOWLEDGEMENT • I have been receiving a great support from many people to complete this research I am greatly grateful to the valuable guidance, encouragement and advice from numerous individuals including Vietnam-Netherlands program lecturers, friends and my family members I would like to express my sincere appreciation to my supervisor, Dr Le Thi Thanh Loan, for her continuous support, for her valuable instructions, advices, comments and immense knowledge during the completion of my thesis I am truly grateful to Professor Peter Calkins for his precious advices and comments for improving the quality of the thesis, for his lectures in research and methodology My sincere thanks also go to Associate Professor Nguyen Trong Hoai for his lectures in econometrics, Mr Phung Thanh Binh and Mr Le Van Chon, the lectures of Vietnam- Netherlands project, for his kind help and instructions in data analysis by Eviews and Stata software Finally, I would like to send my special thanks to my friends in MDE class 14, 15 and the rest of my thesis committee for their supportive friendship, encouragement and insightful comments My special gratefulness is to my family for their love, affection and for supporting me spiritually in my learning and my career ' TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1 Problem Statement 1.2 Research Objectives 1.3 Research Questions 10 1.4 Research scope 10 1.5 Organization of the Research 11 CHAPTER 2: LITERATURE REVIEW 12 2.1 2.1.1 Foreign Direct Investment (FDI) 12 2.1.2 Productivity and Labour Productivity 13 2.1.3 Spillovers Effects 13 2.2 j Literature Review- The Concepts 12 The Economics Theory 15 2.2.1 Neoclassical Theory 15 2.2.2 Endogenous Theory 17 2.3 The Empirical Study 18 2.4 Analysis Framework 23 CHAPTER 3: RESEARCH METHODOLOGY 25 3.1 Data Collection 25 3.2 Description ofVariables 26 3.2.1 Dependent variable: Labour productivity (LABPRO) 26 3.2.2 Explanatory variables 26 3.3 Model Specification 28 3.4 Estimation Strategy 29 3.5 Hypothesis statements 30 CHAPTER 4: MODEL RESULTS AND DISCUSSION 32 4.1 Descriptive statistics 32 4.2 Estimation results 34 4.2.1 Pool regression model results 34 4.2.2 Random effects model results 34 4.2.3 Fixed effects model results 35 4.3 Results discussion 39 4.3.1 The significant effect ofFDI spillovers in Food processing industry 39 4.3.2 The influence ofFDI spillovers in Electronics and Mechanics industry 40 4.3.3 The effect of controllable variables on labour productivity 40 CHAPTER 5: CONCLUSION AND IMPLICATIONS 42 5.1 Conclusion 42 5.2 Implication 42 5.3 Research limitation 44 REFERENCES 45 APPENDICES 51 7.1 APPENDIX A MODEL RESULTS IN FOOD PROCESSING 51 7.2 APPENDIX B MODEL RESULTS IN ELECTRONICS AND MECHANICS 56 7.3 APPENDIX C OVERVIEW OF FDI IN VIETNAM 60 LIST OF TABLES Table 1: The expected signs ofindependent variables 31 Table The descriptive statistics of data sample in Food Processing industry 32 Table The correlation coefficients of the variables in Food Processing industry 33 Table Pool regression model result in Food Processing industry 34 Table Random effects model result in Food Processing industry 34 Table Breusch-Pagan Lagrange multiplier test 35 Table Fixed effects model result in Food Processing industry 36 Table Hausman test 36 Table Fixed effects model result with time fixed effects in Food Processing industry 37 Table 10 Time-fixed effects test 37 Table 11 Modified Wald test 37 Table 12 The result of the most appropriate model in Food Processing industry 38 Table 13 The summary ofresu1ts in the most appropriate model in Food Processing and Electronics and Mechanics 39 • LIST OF FIGURES Figure Foreign direct investment inflows in period 1995 - 2010 60 Figure FDI projects licensed in period 1988 - 2009 by kinds of economic activity 61 Figure Structure of investment at current prices by Foreign investment sector 62 Figure Structure ofGDP at current prices by Foreign investment sector 62 Figure Structure ofGDP at current prices by Manufacturing industry 63 ABBREVIATIONS BPM6 Balance ofPayments and International Investment Position Manual h edition FEM Fixed Effects Regression Model GSO General Statistics Office IMF International Monetary Fund MNCs Multinational Corporations OECD Organization for Economic Co-Operation and Development REM Random Effects Regression Model VSIC Vietnam Standard Industrial Classification CHAPTER 1: INTRODUCTION 1.1 Problem Statement It has been observed that within decades recently, there are some emerging countries gaining benefits from foreign direct investment and getting significant economic growth Direct effects can be seen from capital inflow, growth of local employment, application of advanced equipment, practical usage of technology Indirect effect can be seen from technology spillover from subsidiaries of multinational corporations (MNCs) to local firms in host countries Within the period of 1960s- 1970s, the inflow ofFDI has been a significant factor contributed to the rapid growth of Hong Kong, Singapore The pattern also applies to China and Vietnam in recent years as well In particular, Vietnam is considered as one of the fastest growing economies in the world with the average growth rate over percent per annum due to the successfulness in attracting a large inflow ofFDI The debate of positive effect of FDI inflows on the economic growth of host countries does not gain much attention; however, there is much discussion on whether and how FDI inflows produce technology spillovers to the labour productivity of domestic firms Some studies find that FDI brings positive technology spillovers to the productivity of domestic firms by competition, demonstration and training of employees such as Caves (1974), Blomstrom and Persson (1983), Kokko (1994), and Liu (2002) Other studies, Aitken and Harrison (1999), Kathuria (2000), believe that FDI generates negative effects to the labour productivity of domestic firms by market stealing and skill stealing These studies with mixed evidences examine FDI technology spillovers within industries, which are called intra-industry spillovers or horizontal spillovers - - In Vietnam, the number of studies on horizontal spillover effects from MNCs to Vietnamese firms is still limited, but the mixed evidences are also found in the studies Le Thanh Thuy (2007), Nguyen Thi Tue Anh et al (2006), Quoc Hoi Le and Richard Pomfret (2008), Anh Ngoc Nguyen et al (2008) and Chuc D Nguyen et al (2008) Therefore, this research investigates horizontal technology spillover effects from foreign direct investment on labour productivity of domestic firms in manufacturing industry However, due to limitation of the available data, this study only focuses on two industries representative for low technology industry (Food processing in particular) and high technology industry (Electronics and Mechanics in particular), not in the whole manufacturing industry as other studies The research applies many panel regression models including pool regression, fixed effects regression model (FEM) and random regression model (REM) The interesting findings are expected to come and contribute to policymakers in the government and firms With the findings, the government as well as firms could make a set of recommendations to take the best of advantage of FDI to improve labour productivity of domestic firms in low technology industry and high technology industry 1.2 Research Objectives The overall goal of this research is to analyze horizontal technology spillover effects from FDI on labour productivity of domestic firms in low technology industry (Food processing in particular) and high technology industry (Electronics and Mechanics industry in particular) in Vietnam Specifically, the research will focus on three objectives as follows: (i) Whether horizontal technology spillovers through FDI affect the labour productivity of domestic firms in low technology industry as Food processing Kathuria, V (2000), "Do domestic firms benefit from direct foreign investment? Evidence from Venezuela", Journal of International Development, vol 12, no 2, pp 343-369 Kokko, Ari (1994), "Technology, market characteristics, and spillovers",Journal of Development Economics, vol 11, no 2, pp 279-293 Konings, Jozef (1999), "The Effects of Direct Foreign Investment on Domestic Firms: Evidence from Firm Level Panel Data in Emerging Economies", CEPR discussion paper, No 2586 Klaus E Meyer (2004), "Perspectives on Multinational Enterprises in Emerging Economies", Journal ofInternational Business Studies, vol 35, pp 25976 Le Thanh Thuy (2007), "Does Foreign Direct Investment Have an Impact on the Growth in Labor Productivity of Vietnamese Domestic Firms?", Discussion papers ofResearch Institute of Economy, Trade and Industry, No 07021 Liu, Z (2002), "FDI and technology spillovers: Evidence from China", Journal of Comparative Economics, vol 30, no 3, pp 579-602 OECD (200 1), "Measuring Productivity- Measurement of aggregate and industry-level productivity growth", OECD Manual OECD (2008), "OECD Benchmark Definition of Foreign Direct Investment", OECD Manual, fourth edition OECD (2008), "System ofNational Accounts", Organisation for Economic Cooperation and Development 48 Nguyen Thi Tue Anh, Vu Xuan Nguyet Hong, Tran Toan Thang and Nguyen Manh Hai (2006), "The Impacts of Foreign Direct Investment on the Economic Growth in Vietnam", Research Report of Central Institute for Economic Management N Gregory Mankiw, David Romer and David N Weil (1992), "A contribution to the empirics of economic growth", The Quarterly Journal of Economics, vol 107, no 2, pp 407-43 N Gregory Mankiw, and (1995), "The Growth of Nations", Harvard Institute of Economic Research Working Papers, No 1732 Padma Mallampally (1999), "Foreign Direct Investment in Developing Countries", A quarterly magazine of the IMF- Finance and Development, vol 36, no Quoc Hoi Le and Richard Pomfret (2008), "Technology Spillovers from Foreign Direct Investment In Vietnam: Horizontal or Vertical Spillovers", Working Paper Series of Vietnam Development Forum, No 085 Romer, Paul M (1990), "Endogenous Technological Change", Journal of Political Economy., vol 98, no 5, pp 71-102 Robert M Solow (1956), "A Contribution to the Theory of Economic Growth", The Quarterly Journal of Economics, vol 70, no 1, pp 65-94 Rossitza B Wooster and DavidS Diebel (2010), "Productive Spillovers from Foreign Direct Investment in Developing Countries: A Meta-Regression Analysis", Review of Development Economics, vol 14, no 3, pp 640-655 49 Sinani, Evis and Klaus Meyer (2004), "Spillovers of Technology Transfer from FDI: The Case of Estonia", Journal of Comparative Economics, vol 32, pp 445-66 Smarzynska, Beata K (2004), "Does foreign direct investment increase the productivity of domestic firms : In search of spillovers through backward linkages", American Economic Review, vol 94, no 3, pp 605-626 Tamotsu Nakamura (2002), "Foreign Investment, Technology Transfer, and the Technology Gap: A Note", Review of Development Economics, vol 6, pp 39-47 Xiaming Liu, David Parker, Kirit Vaidya and Yingqi Wei (2001), "The Impact of Foreign Direct Investment on Labor Productivity in the Chinese Electronics Industry", International Business Review, vol 10, pp 421-39 Wang, J and Blomstrom, M (1992), "Foreign investment and technology transfer: A simple model", European Economic Review, vol 36, pp 137155 50 APPENDICES 7.1 APPENDIX A MODEL RESULTS IN FOOD PROCESSING Descriptive statistics The descriptive statistics of data sample in Food Processing industry st.ats lnLABPRO lncAPIN SCALE SKILL FDISPILL mean 3.455038 3.4566 8.327026 -1.311665 0808308 2.729331 529729 514603 9.45437 • 3442524 0571172 3.157787 7233596 1068213 11.78112 0001228 3.687105 19.33325 1916634 1305842 1.898273 6.681423 156656 0299946 • 9972355 327857 350855 p50 max skewness kurtosis The correlation coefficients of the variables in Food Processing industry LAB PRO CAP IN SCALE SKILL FDISPILL LAB PRO CAP IN SCALE 1.0000 0.5458 0.4816 0.0795 -o.0206 1.0000 0.2294 0.2079 0496 1.0000 0.0069 -o.0699 SKILL FDISPILL 1.0000 0.3153 1.0000 51 Estimation results 2.1 Pool regression model result Pool regression model result in Food Processing industry reg lrf-ABPAO lrcAPIN SCALE SKILL FDISPILL source df 55 Model Residual 2820.70312 2510.43718 2368 705.175781 1.06015083 TOLal 5331.1403 2372 2.24752964 lnLABPRO coef lncAPIN SCALE: SKILL FDISPILL _cons • 7122745 3224544 -.565974 • 5364627 01.98137 Number of obs F( 4, 2368) Prob > F R-squared Adj R-sqLlared Root MSE: MS SLd Err .0207361 015154 1146379 0985264 0869001 t 34.35 21.28 -4.94 5.44 0.23 P>!t! 0.000 0.000 0.000 0.000 0.820 2373 665.17 0.0000 0.5291 0.5283 1.0296 [95% conf Interval] 6716117 2927379 -.7907751 3432558 -.1505945 • 7529372 3521708 -.3411728 7296696 1902218 2.2 Random effects model result Random effects model result in Food Processing industry lttreg 1~ lrcAPIN SCALE SKILL FDISPILL, Random-effeCLS GLS regression Group variable: i R-sq: Number of obs Number of groups within = 0.2650 between= 0.5565 overall 0.5270 coef 2373 791 obs per group: avg max Random effects u_i - Gaussian corr(u_i, X) = (assumed) lnLABPRO re Std Err wald chi2(4) Prob > chi2 z P>lzl 6451168 2992742 -.1838828 3780614 2923689 021772 0177837 1359229 1492781 0987309 sigma_u sigma._e rho 90557632 48763046 • 77522046 (fraction of variance due to u_ i) 0.000 0.000 0.176 0.011 0.003 1529.02 0.0000 [95% conf Interval] lncAPIN SCALE SKILL FDISPILL _cons 29.63 16.83 -1.35 53 2.96 3.0 6024445 2644188 -.4502868 0854818 0988599 6877891 • 3341296 0825212 670641 485878 52 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 2.3 Pool or Random: Breusch-Pagan Lagrange multiplier test Breusch-Pagan Lagrange multiplier test Breusch and Pagan Lagrangian multiplier test for random effects lnLABPRO[i,t] = Xb + u[i] + e[i,t] Estimated results: sd = sqrt(var) var Test: lnLABPRO e 2.24753 2377835 u 8200685 var(u) 1.499176 4876305 9055763 chi (1) = Prob > chi2 = 1398 41 0.0000 As the result of LM test in table above, the p-value is smaller than 0.05 and this rejects the null hypothesis Therefore, random effects regression is more appropriate than pool OLS regression 2.4 Fixed effects model result Fixed effects model result in Food Processing industry Jttreg lrLABPRO lrx:APIN SCALE SKILL FDISPILL, fe Fixed-effects (within) regression Group variable: i Number of obs Number of groups R-sq: obs per group: avg max within = 0.2729 between= 0.3965 overa1l 0.3845 corr(u_i, xb) lnLABPRO F(4,1578) Prob > F 0.0099 coef std Err P>lt! [95% conf • 5655246 249944 232773 -1.816832 952569 0279264 0256637 1918425 • 5925712 1609525 sigma_u sigma_e rho 1.1074042 48763046 83759375 (fraction of variance due to u_i) F test that all u_i=O: F(790, 1578) = 0.000 0.000 0.225 0.002 0.000 11.37 3.0 148.05 0.0000 lncAPIN SCALE SKILL FDISPILL _cons 20.25 9.74 1.21 -3.07 5.92 2373 791 • 5107479 1996056 -.14352 -2.979142 6368658 Prob > F Interval] 6203013 • 3002825 609066 -.654522 268272 0.0000 53 2.5 Fixed or Random: Hausman test Hausman test hausman fixed random - - coefficients (b) (B) fixed random • 5655246 249944 232773 -1.816832 lncAPIN SCALE SKILL FDISPILL B Test: = Ho: (b-B) Difference sqrt(diag(v_b-V_B)) S.E .0174889 0185031 1353828 5734603 -.0795922 -.0493302 4166558 -2.194893 6451168 2992742 -.1838828 3780614 b = consistent under Ho and Ha; obtained from xtreg inconsistent under Ha, efficient under Ho; obtained from xtreg difference in coefficients not systematic chi2(4) Prob>chi2 (b-B)'[(V_b-V_B)A(-l)](b-B) 39.57 0.0000 As the result of Hausman test in the table above, the p-value is smaller than 0.05; that means fixed effects model is more appropriate than random effects model 2.6 Including time effects in a fixed effects model Fixed effects model result with time fixed effects in Food Processing industry • xi: xt:reg lrL.ABPRO lrcAPIN SCALE SKILL FOISPILL i t: • fe _It_l-3 (naturally coded; _It_l omitted) i t: Fixed-effects (within) regression Group variable: i Number of obs Number of groups R-sq: 0.2814 0.4456 0.4294 obs per group: avg max 0.0874 F(6,1576) Prob > F within between overall corr(u_i, xb) lnLABPRO = = = coef Std Err P>lt! t: 4951376 2030678 -.0337178 -2.567272 0596003 0290849 0255956 199913 6119525 0252364 0273909 1615501 sigma u sigma e rho 1.0656822 48506139 82838009 (fraction of variance due to u_i) 11.49 = [95% conf • 5521868 2532728 3584056 -1.366945 1091008 0584056 • 8601855 F(790, 1576) = 0.000 0.000 0.073 0.026 0.000 0.033 0.000 791 3.0 102.87 0.0000 lncAPIN SCALE SKILL FDISPILL _It_2 _It_3 _cons F test t.hat: all u_i=O: 18.99 9.90 1.79 -2.23 32 2.13 5.32 2373 0046791 5433097 Interval] 6092361 3034778 750529 -.1666183 1586012 1121321 1.177061 Prob > F = 0.0000 54 ( 1) ( 2) _:U_2 JL3 F( = = 0 2, 1576) Prob > F 9.38 0.0001 As the result above, p-value is smaller than 0.05 and this reject the null hypothesis; that means time fixed effects must be included a fixed effects regression 2.5 Testing for heteroskedasticity Modified Wald test • xt:'test:3 Modified wald "tes1: for groupwise he1:eroskedas"tici1:y in fixed effect regression model HO: signa(i)A2 = si!JIIchi2 0.0000 As the result of Hausman test in the table above, the p-value is smaller than 0.05; that means fixed effects model is more appropriate than random effects model 2.4 Including time effects in a fixed effects model • Fixed effects model result with time fixed effects in Electronics and Mechanics xi: xt:reg lrLABPRO lrcAPIN i.t _It._1-3 ~E SKILL FDISPILL i t fe (naturally coded; _It_1 omiHed) Fixed-effects (within) regression Group variable: i Number of obs Number of groups R-sq: obs per group: avg max within = 0.2115 between= 0.1503 overall 0.1255 corr(u_i, xb) lnLABPRO lr1CAPIN SCALE SKILL FDISPILL _It_2 Std Err t 5.78 4.33 2.95 -o.68 12.32 1.41 -1.53 P>it! [95% conf Interval] • 37472 2431662 1.275261 -.6756955 8327368 _cons sigma_u sigma_e rho 1.4322435 1.2426766 57051411 (fraction of variance due to u_i) F(537, 1070) = 1.26 47.83 0982122 1025805 1.290252 2558451 0804114 0883652 6419347 F test that all u_i=O: 3.0 0.0000 5674303 444448 3.8069.72 -.1736805 9905187 124551 -.9835032 _!1:_3 0.000 0.000 0.003 0.497 0.000 0.159 0.126 = = F(6, 1070) Prob > F -o 7308 coef 1614 538 -.0488377 -2.243097 Prob > F 7601406 • 6457298 6.338683 3283345 1.148301 2979397 2760904 0.0008 58 ~es~parm • ( 1) ( 2) Jt:* JL-2 = _:rt:_3 = F( 2, 1070) Prob > F 91.32 0.0000 As the result above, p-value is smaller than 0.05 and this reject the null hypothesis; that means time fixed effects must be included a fixed effects regression 2.5 Testing for heteroskedasticity Modified Wald test • xtt~3 Modified wald test for groupwise heteroskedasticity in fixed effect regression model HO: si~(i)A2 = si~ chi2 (538) Prob>chi2 = ' • for all i 7.5ef.OS 0.0000 The result from Modified Wald test shows that there is the presence of heteroskedasticity that need to be eliminated out of the model 2.6 The most appropriate results in Electronics and Mechanics industry The result of the most appropriate model • Xi: areg lnLABPRD lr£APIN SCALE SKILL FDISPILL i t: • absorb(i) robu~ i.t (naturally coded; _It_1 omitted) _It~1-3 Number of obs = Linear regression, absorbing indicators F( 6, 1070) Prob > F R-squared Adj R-squared Root MSE lnLABPRO t1CAPIN SCALE SKILL FDISPILL _It_2 _It_3 _cons coef • 5674303 444448 3.806972 -.1736805 9905187 124551 -.9835032 absorbed Robusl: Std Err .0926429 10601.72 1.693777 • 3418952 089476 0899383 7581131 t 6.12 4.19 2.25 -0.51 11.07 1.38 -1.30 P>!tj 0.000 0.000 0.025 0.612 0.000 0.166 0.195 1614 40.09 0.0000 0.5474 0.3177 1.2427 [95% conf Interval] • 385648 2364228 4834702 -.8445417 8149503 -.0519245 -2.47106 7492126 6524732 7.130475 4971807 1.166087 3010265 • 5040537 (538 categories) • 59 • 7.3 APPENDIX C OVERVIEW OF FDI IN VIETNAM Vietnam has successfully attracted a large inflow ofFDI in recent years, and the detail ofFDI inflows in period 1995-2010 is shown in Figure In which, the projects licensed and registered capital by kinds of economic activity are displayed in Figure Moreover, the structures of investment and GDP at current prices by foreign investment sector are respectively expressed in Figure and Figure Besides, the structure of GDP at current prices by manufacturing industry (mentioned in this research) is shown in Figure Foreign direct investment inflows in period 1995- 2010 Ill :;:) 80 70 iii 60 ' • Registered capital • Implementation capital so 40 I 30 20 10 Source: General Statistics Office of Vietnam Figure Foreign direct investment inflows in period 199 - 201 60 • Foreign direct investment projects licensed in period 1988- 2009 by kinds of economic activity Number of Total registered capital (*) Kind of economic activity No (Mill USD) projects 3,837.7 575 Argiculture and forestry 10 11 12 13 14 • Fishing Mining and quarrying Manufacturing Electricity, gas and water supply Construction Wholesale and retail trade; Repair of motor vehicles, motor cycles and personal and household goods Hotels and restaurants Transport; storage and communications Financial intermediation Real estate, renting business activities Education and training Health and social work Recreational, cultural and sporting activities Community, social and personal service 15 activities Total Source: Statistical Yearbook of Vietnam, 2009 163 130 7475 541.4 10,980.4 72 88,579.5 2,231.4 521 7,964.4 322 1,041.6 379 554 59 1867 128 73 19,402.8 8,435.3 1,103.7 45,505.7 275.8 1,033.3 129 2,838.0 llS 658.3 12,575 194,429.3 Figure FDI projects licensed in period 1988 - 2009 by kinds of economic activity j 61 I Structure of gross domestic product at current prices by Manufacturing industry 25.0% 1,800,000 20.5% 1,600,000 20.6% 21.3% 20.0% 1,400,000 1,200,000 15.0% 1,000,000 800,000 •: