luận án tiến sĩ lan tỏa tri thức, cải tiến đổi mới cấp ngành và năng suất nhân tố tổng hợp (TFP) của doanh nghiệp trường hợp nghiên cứu ở ngành công nghiệp chế tạo ở việt nam

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY ***** NGUYEN THI HOANG OANH KNOWLEDGE SPILLOVER, SECTORAL INNOVATION AND FIRM TOTAL FACTOR PRODUCTIVITY: THE CASE OF MANUFACTURING INDUSTRIES IN VIETNAM PhD THESIS HO CHI MINH CITY, 2021 MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY ***** NGUYEN THI HOANG OANH KNOWLEDGE SPILLOVER, SECTORAL INNOVATION AND FIRM TOTAL FACTOR PRODUCTIVITY: THE CASE OF MANUFACTURING INDUSTRIES IN VIETNAM Major: Development Economics Code: 9310105 PhD Thesis ACADEMIC ADVISORS Dr Pham Khanh Nam Dr Pham Hoang Van HO CHI MINH CITY, 2021 i COMMITMENTS I hereby declare that this dissertation is my own work and it has not been previously submitted for a degree elsewhere All the references from works done by other authors have been explicitly cited To the best of my knowledge, I would like to certify that the above statements are true Ho Chi Minh City, January 2021 Nguyen Thi Hoang Oanh ii ACKNOWLEDGEMENT First of all, I would like to express my deepest gratitude to my academic advisers Dr Pham Khanh Nam and Dr Pham Hoang Van for their valuable advices and continuous support during my study They have given me autonomy on doing my research while continuing to provide me valuable feedbacks and encouragement Second, I would like to thank the School of Economics and University of Economics Ho Chi Minh City for providing me the best environment for my study and doing my research I also would like to thank the board of professors and independent external reviewers on giving me a lot of useful comments on my research All their comments enable me to improve and complete this version of my thesis Last but not least, I am extremely grateful to my family for their love, support and sacrifice Without them, the completion of my dissertation would not have been possible Ho Chi Minh City, January 2021 Nguyen Thi Hoang Oanh iii Table of Contents COMMITMENTS ACKNOWLEDGEMENT ABBREVIATIONS LIST OF TABLES LIST OF FIGURES Abstract Tóm tắt CHAPTER INTRODUCTION 1.1 PROBLEM STATEMENT 1.1.1 The significance of the research’s topic 1.1.1.1 sector level 1.1.1.2 both firms’ characteristics and spillover effects from sectors and regions 1.1.1.3 innovation and firms’ TFP in the manufacturing industries in Vietnam 1.1.2 The gaps and the new aspects in this thesis 1.1.2.1 1.1.2.2 1.1.2.3 1.2 RESEARCH OBJECTIVES AND QUESTIONS 1.3 RESEARCH METHODOLOGY and RESEARCH SCOPE 1.4 RESEARCH CONTRIBUTION 1.5 STRUCTURE OF THIS STUDY CHAPTER LITERATURE REVIEW 2.1 DEFINITION AND CONCEPTS 2.1.1 Knowledge spillovers 2.1.2 Innovation 2.1.3 Knowledge production function and the determ 2.1.4 Sectoral Innovation System (SIS) and its determ 2.1.5 Total Factor Productivity (TFP) 2.1.5.1 2.1.5.2 iv 2.1.5.3 2.2 TFP measu THEORETICAL FRAMEWORK 2.2.1 Developing model o 2.2.2 Channels of knowle 2.2.3 Theoretical framewo 2.2.3.1 Debates o 2.2.3.2 Human ca 2.2.4 the second objective 2.3 Multilevel modeling EMPIRICAL STUDIES 2.3.1 Empirical Studies on 2.3.2 Regression Model Empirical Studies o 2.3.3 Empirical Studies on CHAPTER RESEARCH METHODOLOGY 3.1 THE RESEARCH MODEL ON SECTORAL INNOVATION 3.1.1 Model Specification 3.1.1.1 The spatia 3.1.1.2 Estimation 3.1.1.3 Measuring 3.1.1.4 Measurem 3.1.1.5 Hypothesi 3.1.2 Data 3.2 The research model of Cross-Classified Model 3.2.1 Measurem 3.2.2 Data for m 3.2.3 Applicatio 3.2.4 The Resea CHAPTER SECTORAL INNOVATION AND SPILLOVER EFFECTS: RESULTS FROM SPATIAL REGRESSION MODELS AND DISCUSSIONS 4.1 OVERVIEW OF RESEARCH AND DEVELOPMENT (R&D) ACTIVITIES AND PATENTS IN VIETNAM 4.1.1 R&D expenditure an 4.1.2 Funding sources and 4.1.3 The human resources in R&D activities v 4.1.4 The overview of the 4.1.5 manufacturing industries Overview of transfe 4.2 DESCRIPTIVE STATISTICS 4.3 RESULTS OF THE MODEL ESTIMATION 4.4 DISCUSSION ON THE RESULTS CHAPTER HETEROGENEITY IN TFP OF VIETNAMESE MANUFACTURING FIRMS: RESULTS FROM CROSS-CLASSIFIED MODELS AND DISCUSSIONS 5.1 THE MANUFACTURING INDUSTRIES AND FIRMS’ PRODUCTIVITY 5.1.1 The importance of m 5.1.2 economic activities in Vietnam The value added an 5.1.3 in Vietnam Overview of capital, 5.2 Summary on characteristics of Vietnamese manufacturing enterprises in 126 5.3 THE RESULTS OF MODEL ESTIMATION 5.3.1 The Cross classified 5.3.2 The Fixed effect mod 5.3.3 The multilevel mode 5.3.4 The multilevel mode 5.3.5 The multilevel mode 5.3.6 The multilevel mode 5.4 SUMMARY ON RESULTS AND DISCUSSION CHAPTER CONCLUSION AND POLICY IMPLICATIONS 6.1 CONCLUSIONS ON IMPORTANT FINDINGS 6.2 SOME POLICY IMPLICATIONS 6.3 LIMITATIONS AND FUTHER RESEARCH REFERENCES APPENDIX Table A1 Description of Sectors Table A2 The distribution of provinces by regions Table A3 Test on SDM versus SEM and SAR B Spatial Regression Model in analysis on Knowledge Spillover among Sectors B1 General idea of Spatial Regression Model vi B2 Knowledge Spillover among Sectors under Spatial Regression Model approach xxx B3 Robust Hausman Test xxxii vii ABBREVIATIONS Words APO ASEAN CCMM CIEM DERG FDI FE GDP GERD GSO GTFP IO LBE LP NIS NISTPASS OECD OP Ph.D QML R&D RE RIS S&T SAC SAR SDM SEM SIC SIS SME TCS TFP VAT VES VPC VSIC Meanings Asian Productivity Organization Associate East Asia Nation Cross-Classified Multilevel Model Central Institute for Economic Management Development Economics Research Group Foreign Direct Investment Fixed Effects Gross Domestic Product Gross expenditure on R&D General Statistics Office Green Total Factor Productivity Input Output Learning by Exporting Levinsohn and Petrin National Innovation System National Institute for S&T Policy and Strategy Studies Organization for Economic Co-operation and Development Olley-Pakes Philosophy Doctor Quasi-Maximum Likelihood Research and Development Random Effects Regional Innovation System Science and Technology Spatial Autoregressive Model with Auto Regressive Spatial Autoregressive Model Spatial Durbin Model Spatial Error Model Standard Industrial Classification Sectoral Innovation System Small and Medium Enterprise Technology and Competitiveness Survey Total Factor Productivity Value Added Tax Vietnam Enterprise Survey Variance Partitioning Coefficient Vietnam Standard Industrial Classification viii LIST OF TABLES Table 3.1 Description of variables in the models 69 Table 3.2 Description of variables in the model of the second objective 81 Table 4.1 Gross expenditure on R&D (GERD) and intensity of GERD 84 Table 4.2 Investment on Science &Technology and Research & Development in 2013 87 Table 4.3 Registration for patents by nationality of applicants 93 Table 4.4 Number of approved patents by nationality of the holders 94 Table 4.5 Descriptive Statistics of variables in the model of the first objective .100 Table 4.6 Correlation matrix of variables in the model 102 Table 4.7 Estimation results of the model by fixed and random effects 105 Table 4.8 Estimation results of the model by Spatial Regression with fixed year effects 106 Table 4.9 Estimation results of the model by Spatial Regression with random sector effects 108 Table 4.10 Estimation results of the model by Spatial Regression with fixed year effects 109 Table 4.11 Estimation results of the model by Spatial Regression with random sector effects 110 Table 5.1 The number of sectors per province 126 Table 5.2 The number of provinces that firms in a sector has located 127 Table 5.3 The descriptive statistics of variables in the model 128 Table 5.4 The correlation matrix between variables in the model 129 Table 5.5 The descriptive statistics of the logarithm of TFP in the data sample .130 Table 5.6 The result of Cross classified model with no predictors 132 Table 5.7 The result of firm fixed effect model 134 Table 5.8 The result of region Fixed effect and sector Random effect model 135 xviii Tuncay, S., 2015 Financial Openness and Total Factor Productivity in Turkey Procedia Economics and Finance 30(15), pp 848–862 doi: 10.1016/S2212-5671(15)01335-0 Tzokas, N., Kim, Y A., Akbar, H., & Al-Dajani, H., 2015 Absorptive capacity and performance: The role of customer relationship and technological capabilities in high-tech SMEs Industrial Marketing Management, 47, 134–142 doi:10.1016/j.indmarman.2015.02.033 UNESCO, 1968 Provisional guide to the collection of science statistics United Nations Educational Scientific and Cultural Organization, COM/MD/3, PARIS, 31 December 1968 Vu Hoang Duong & Le Van Hung, 2017 FDI Spill-Overs, Absorptive Capacity and Domestic Firms’ Technical Efficiency in Vietnamese Wearing Apparel Industry Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 65(3): 1075–1084 Waldkirch, A & Ofosu, A., 2010 Foreign presence, Spillovers, and Productivity: Evidence from Ghana World Development, Vol 38, No.8, pp 1114-1126 Wang, C C and Wu, A., 2016 Geographical FDI knowledge spillover and innovation of indigenous firms in China International Business Review Elsevier Ltd, 25(4), pp 895–906 doi: 10.1016/j.ibusrev.2015.12.004 Wang, C C., & Wu, A., 2016 Geographical FDI knowledge spillover and innovation of indigenous firms in China International Business Review, 25(4), 895– 906 https://doi.org/10.1016/j.ibusrev.2015.12.004 Wang, Z et al., 2016 Analyzing the Space-Time Dynamics of Innovation in China: ESDA and Spatial Panel Approaches Growth and Change, 47(1), pp 111– 129 doi: 10.1111/grow.12115 Wang, Z., Cheng, Y., Ye, X., & Wei, Y H D., 2016 Analyzing the Space-Time Dynamics of Innovation in China: ESDA and Spatial Panel Approaches Growth and Change, 47(1), 111–129 https://doi.org/10.1111/grow.12115 Wei, T., & Liu, Y., 2018 Estimation of resource-speci fic technological change Technological Forecasting & Social Change doi: 10.1016/j.techfore.2018.08.006 Westphal, L., Rhee, Y and Pursell, G., 1984 Sources of technological capability in South Korea In M Fransman and K King (eds), Technological Capability in the Third World London: Macmillan Wieser, R., 2005 Research And Development Productivity And Spillovers: Empirical Evidence At The Firm Level Journal of Economic Surveys, 19(4), 587–621 doi:10.1111/j.0950-0804.2005.00260 Wooldridge, J M., 2009 On estimating firm-level production functions using proxy variables to control for unobservable Economics Letters, 104(3), 112– 114 doi:10.1016/j.econlet.2009.04.026 xix Wooldridge, J.M., 2005 On estimating firm-level production functions using proxy variables to control for unobservable Unpublished manuscript World Bank, 1993 The East Asia miracle Economic growth and economic policy New York: Oxford University Press Yang, G and Maskus, K E., 2001 Intellectual property rights, licensing, and innovation in an endogenous product-cycle model Journal of International Economics, 53(1), pp 169–187 doi: 10.1016/S0022-1996(00)00062-3 Yu, J., de Jong, R & Lee, L.F., 2008 Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and t are large Journal of econometrics, 146:118-134 Yurtseven, A E., & Tandoğan, V S., 2012 Patterns of innovation and intra-industry heterogeneity in Turkey International Review of Applied Economics, 26(5), 657–671 https://doi.org/10.1080/02692171.2011.631900 Zhang, L., 2017 The knowledge spillover effects of FDI on the productivity and efficiency of research activities in China China Economic Review, 42, 1– 14 https://doi.org/10.1016/j.chieco.2016.11.001 xx APPENDIX Table A1 Description of Sectors Code in VSIC 2007 1010 1020 1030 1040 to 1050 1061 to 1071 1072 to 1073 1074 to 1075 1079 1080 1101 to 1102 The classification by Pavitt (1984) was based on sources of technology, requirements of users, and possibilities for appropriation in firms Supplier dominated firms (group 1) are mainly in traditional sectors of manufacturing like agriculture, housebuilding, informal household production Scale intensive producers (group 2) have principal activities in food products, metal manufacturing, shipbuilding, motor vehicles, glass and cement Science based firms (group 3) are to be found in the chemical and in the electronic/electrical sectors xxi 1103 to S_48 1104 1311 to S_51 1313 1321 S_52 1322 S_53 1420 S_54 1511 to S_55 1512 1610 to S_56 1629 1701 to S_57 1709 1811 to S_58 1820 2011 to S_62 2013 2021 to S_65 2030 2100 S_67 2211 to S_68 2212 2220 S_69 2310 S_70 2391 to S_71 2393 xxii 2394 to S_72 2399 2410 S_74 2420 to S_75 2432 2511 to S_76 2599 2610 to S_77 2680 2710 to S_81 2732 2740 to S_84 2790 2811 to S_87 2829 2910 to S_89 3099 3100 S_94 3211 to S_95 3290 3311 to 3320 S_98 xxiii Table A2 The distribution of provinces by regions Region Province Region NorthWest Lào Cai Lai Châu Sơn La Điện Biên n Bái Hịa Bình Thái Nguyên Hà Giang Cao Bằng Bắc Kạn Tuyên Quang Thái Nguyên Phú Thọ Lạng Sơn Quảng Ninh Bắc Giang North Central NorthEast Province South Central Red River Delta Vĩnh Phúc Bắc Ninh Hải Dương Hưng Yên Thái Bình Hà Nam Nam Định Hà Nội Hải Phịng Ninh Bình Highlands Region Province South East Bình Phước Tây Ninh Bình Dương Đồng Nai BRVT South West Tp.HCM Long An Tiền Giang Bến Tre Vĩnh Long Đồng Tháp An Giang Kiên Giang Cần Thơ Hậu Giang Sóc Trăng Bạc Liêu Cà Mau Notes: This list includes the provinces in the data The number of provinces were reduced from 63 provinces to 62 provinces (no presence of Tra Vinh Province by merging the data in the period xxiv Table A3 Test on SDM versus SEM and SAR Model Test S_RD_mean=[Wx]S_FDI_Supplier= [Wx]S_FDI_Customer [Wx]S_InputImport= [Wx] S_export =0 Chi2 (5) 31.63 25.42 19.76 16.31 23.68 25.42 Table A4 Test on SDM versus SAC Model Obs 190 190 190 190 190 190 Model Obs 190 190 190 190 Table A5 LR test to compare the model and the model in the Table 4.7 LR chi2(3) = 1240.89 Likelihood-ratio test Prob > chi2 = 0.0000 (Assumption: model1 nested in model3) Table A6 LR test to compare the model and the model in the Table 4.7 Likelihood-ratio test (Assumption: model2 nested in model3) xxv Table A7 Collinearity Diagnostics in the model with Innovation activities as the Dependent variable Vaiable Innovation Activities R&D activities Input suppliers Output customers Imported Input Exported Output Capital per worker Monopoly Concentration level Mean VIF Table A8 Collinearity Diagnostics in the model with Modification activities as the Dependent variable Vaiable Modification Activities R&D activities Input suppliers Output customers Imported Input Exported Output Capital per worker Monopoly Concentration level Mean VIF from to from to xxvi Table A9 Wooldridge test for autocorrelation in panel data with Innovation activities as the Dependent variable H0: no first-order autocorrelation F (1, 37) = Prob > F = Table A10 Wooldridge test for autocorrelation in panel data with Modification activities as the Dependent variable H0: no first-order autocorrelation F( 1, 37) = 2,000 1,000 Number of firms 3,000 4,000 Prob > F = Figure A1 Number of firms by sectors 10 20 30 40 10 20 30 40 xxvii 35 36 37 62 65 67 Figure A2 The correlation of Innovation activities and R&D activities in each year in the period In particular, the relationship between innovation activities and R&D activities is described in each year (Figure 3.19) There seems to be a slightly positive relationship between innovation activities and R&D activities This positive relationship may be more revealed in the year of 2010, 2011 and 2013 In comparison to the average level of innovation activities and R&D activities in the period, there may be more sectors having R&D activities in 2010, but more sector having innovation activities in 2014 xxviii 10 2030 40 35 10 20 30 40 45 02 54 10 20 30 40 67 04 75 10 20 30 40 94 2010 10 2011 2012 2013 15 2014 Figure A3 The correlation of Innovation activities and R&D activities by each sector in the period The positive correlation between innovation activities and R&D activities seems to be clearer in consideration of each sector (Figure 3.20) Some sectors such as sector number 41 and number 67 had both R&D activities and innovation activities to be higher than the average level of all sectors in all years in the period The innovation activities in 2013 seem to be below the average level in all sectors, except the sector number 41 and number 45 xxix B Spatial Regression Model in analysis on Knowledge Spillover among Sectors B1 General idea of Spatial Regression Model Spatial Regression Model has become a prominent tool for measuring spatial spillover It is widely acknowledged that what occurs in one region may be related to what happens in neighboring regions Several economic and socio-demographic variables may be referred to spatial clustering or geographic-based correlation such as unemployment, crime rates, house prices, per capital health expenditures and the alike (Solle Olle, 2003; Moscone and Knapp,2005; Reveilli,2005; Kostov,2009; Elhorst and Freret,2009; Elhorst et al.,2010) It is obvious that unemployment, crime rates or house prices in this region may have some effects on that in other regions These regions could be countries, states, census tracts or zip codes As stipulated by Tobler (1979), everything is related to everything else, but closer things more so These relations may be observed and analyzed by spatial regression models In spatial relations, the variable change for a specific unit may have effect on the change of other units that is regressed by different typical spatial models The change in dependent variable of this unit may be correlated with that variable’s change of other units This case is appropriate with the application of Spatial Autoregressive Model (SAR) In other case, the change in unobserved factors of this unit may be affected by the change in these factors of other units that is solved by Spatial Error Model (SEM) Besides, when there exists the effect of an explanatory variable’s change for a specific unit on the unit itself and, potentially, all other unit indirectly, Spatial Durbin Model (SDM) is prominent method to explore these relations In general, spatial regression models determines the relations among units basing on the correlation matrix that indicates which regions are spatially related with a given region This is usually a square symmetric RxR matrix xxx with (i,j) element equal to if regions i and j are neighbors of one another, and zero otherwise The diagonal elements of this “spatial neighbors” matrix are conventionally set to zero Depending on the border between regions, LeSage and Pace (2009) pointed out four ways to construct such a matrix including linear contiguity, rook contiguity, bishop contiguity and queen contiguity Besides, distance-based criteria is also used to determine neighboring relations among regions This approach can be expanded in a lot of ways by different distances or weights The usage of different methods in determining the matrix depends on the context In practice, the spatial neighbors’ matrices are usually slightly transformed into spatial weights matrices The most common transformation method is to make the sum of each row in the neighbor matrix to be unity In this method, called “row-standardization”, each element in a row is divided by the sum of the elements in the row Therefore, a spatial weights matrix W, with element wij is defined by !H = Depending on the method, the value of !zH is determined differently Under border approach, !zH is equal to when two regions satisfy the criteria of the same border Another typical criterion to define ! zH is distance-based contiguity with d ij to be the distance between (centroids of) regions i and j !zH is defined to be if dij