Innovation and productivity of SMEs in vietnam firm level panel data evidence

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 INNOVATION AND PRODUCTIVITY OF VIETNAMESE SMALL AND MEDIUM ENTERPRISES FIRM LEVEL PANEL DATA EVIDENCE BY HO THI MAI ANH MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, December 2013 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 INNOVATION AND PRODUCTIVITY OF VIETNAMESE SMALL AND MEDIUM ENTERPRISES FIRM LEVEL PANEL DATA EVIDENCE A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By HO THI MAI ANH Academic Supervisor: Dr PHAM DINH LONG HO CHI MINH CITY, December 2013 DECLARATION This is to certify that this thesis entitled “Innovation and Productivity of Vietnamese Small and Medium Enterprises: firm level panel data evidence” which is submitted in fulfillment of the requirements for the degree of Master of Art in Development Economic to the Vietnam – The Netherlands Programme The thesis constitutes only my original work and due supervision and acknowledgement have been made in the text to all material used CERTIFICATION ii | P a g e ACKNOWLEDGEMENT Above all, I would like to thank and gratefully express the special appreciation to my supervisor - Dr Pham Dinh Long for all of his guidance, useful recommendations and valuable comments I could not be able to complete this thesis without his help and support I would like to acknowledge the great works from the Vietnam – The Netherlands Programme team, especially all of lecturers who have put their enthusiastic into the lectures for students Personally, I would like to thank Dr Truong Dang Thuy and Dr Pham Khanh Nam, who have greatly supported me in the courses and in thesis writing process Special thanks go to my family, friends and colleagues for their motivation and encouragement during my study in this program 5|Pa ge ABBREVIATIONS SME Small and Medium Enterprise CDM Crepon Duguet Model CIEM Central Institute of Economic Management FE Fixed effect model GDP Gross Domestic Products GSO General Statistics Office of Vietnam HCMC Ho Chi Minh City ISIC International Standard Industrial Classification ILSSA Institute of Labor Science and Social Affairs MFP Multi Factor Productivity OECD Organization for Economic Co-operation and Development R&D Research & Development RE Random effect model SFA Stochastic frontier analysis TFP Total Factor Productivity WTO World Trade Organization ABSTRACT Innovation is considered an essential factor for motivating the productivity of nations and firms Innovation and productivity are connected by multidimensional relationships and investigated in many countries However, there is very limited research in this field for Viet Nam This paper examines the relationship between innovation and productivity of Small and Medium Enterprises (SMEs) by using Viet Nam SMEs survey balanced panel data in 2007 and 2009 Cobb-Douglas production function and the fixed effect model are employed throughout the thesis The author has found that the presence of innovation has positive effects on a manufacturing firm’s productivity In addition, this study also looks at the impact of firm size, firm location and manufacturing sector on the relationship between innovation and SMEs’ productivity Key words: innovation, productivity, SMEs Viet Nam TABLE OF CONTENTS LIST OF TABLES VIII LIST OF FIGURES IX CHAPTER 1.1 1.2 1.3 1.4 1.5 INTRODUCTION PROBLEM STATEMENT RESEARCH OBJECTIVES RESEARCH QUESTIONS SCOPE OF THE STUDY STRUCTURE OF THE STUDY CHAPTER LITERATURE REVIEW 2.1 PRODUCTIVITY: CONCEPTS AND MEASUREMENTS 2.2 INNOVATION: CONCEPTS AND MEASUREMENTS 2.3 RELATIONSHIP OF INNOVATION AND PRODUCTIVITY 2.3.3 EMPIRICAL REVIEW OF INNOVATION AND PRODUCTIVITY RELATIONSHIP 10 2.3.4 DETERMINANTS OF THE INNOVATION IMPACT 13 2.3.5 INNOVATION AND FIRM PRODUCTIVITY IN VIET NAM 16 2.3.6 CHAPTER REMARK 16 CHAPTER RESEARCH METHODOLOGY AND DATA 17 3.1 AN OVERVIEW OF SMES IN VIET NAM 17 3.2 CONCEPTUAL FRAMEWORK AND MODEL SPECIFICATION 23 3.2.1 CONCEPTUAL FRAMEWORK 23 3.1.2 MODEL SPECIFICATION 23 3.3 RESEARCH HYPOTHESES 26 3.4 DEFINITIONS OF VARIABLES AND CONCEPTS 26 3.5 DATA COLLECTION 29 3.6 METHODOLOGY 29 3.6.1 RANDOM EFFECT REGRESSION MODEL (RE) 30 3.6.2 FIXED EFFECT REGRESSION MODEL 30 3.6.3 SELECTION BETWEEN RE AND FE MODEL BY HAUSMAN TEST 30 3.7 MEASUREMENTS OF VARIABLES 31 CHAPTER 4.1 DATA ANALYSIS 34 EMPIRICAL RESULTS 34 CHAPTER REMARK 41 CHAPTER 5.1 5.2 CONCLUSIONS 42 CONCLUSION AND POLICY IMPLICATION 42 RESEARCH LIMITATION AND FUTURE STUDY 44 REFERENCES 46 APPENDIX A:DESCRIPTION OF DATASET 49 APPENDIX B: REGRESSION RESULTS 50 APPENDIX C: HAUSMAN TEST RESULTS 54 APPENDIX D: INDUSTRY CLASSIFICATION 57 APPENDIX E - EMPIRICAL STUDIES ON RELATIONSHIP BETWEEN INNOVATION AND PRODUCTIVITY 58 LIST OF TABLES Table 1: Summary of main definitions of SME in selected economies .17 Table 2: Number of Enterprises by Sector 2006 – 2011 19 Table 3: Labor Productivity by Firm size and Location 20 Table 4: Labor Productivity by Sector .20 Table 5: Innovation Rates in Manufacturing SMEs 21 Table 6: Diversification and Innovation Rates (%) 22 Table 7: Diversification and Innovation Rates, by Sector (%) 22 Table 8: Definitions & Measurement of Variables and Expected Sign of Coefficients .27 Table 9: Descriptive Statistic of Variables 34 Table 10: Regression results 36 Table 11: Hausman Test Results 37 Table 12: Regression results with regards to employee size 37 Table 13: Regression results with regards to employee size - groups 38 Table 14: Regression results with regards to firm location 38 Table 15: Regression results with regards to firm location - groups 39 Table 16: Regression results with regards to manufacturing sector 40 Table 17: Regression results with regards to manufacturing sector- groups 40 viii | P a g e Table B-3: Regression result Model (2) - RE xtreg lnOAperL lnKAperL lnMAperL lnL innovation InLsize Random-effects GLS regression Group variable: firmid Number of obs Number of groups R-sq: within = 0.8128 between = 0.8832 overall = 0.8700 Obs per group: = avg = max = Random effects u_i ~ Gaussian corr(u_i, X) = (assumed) Wald chi2(5) Prob > chi2 lnOAperL Coef Std Err lnKAperL lnMAperL lnL innovation InLsize _cons 075251 6411797 0754226 0025202 022743 3.453783 004719 0046407 0064351 0172401 019028 0598597 sigma_u sigma_e rho 20876594 26716442 37911621 (fraction z P>|z| 15.95 138.16 11.72 0.15 1.20 57.70 0.000 0.000 0.000 0.884 0.232 0.000 of variance due = = 4186 2110 2.0 = 24936.81 = 0.0000 [95% Conf Interval] 066002 6320841 06281 -.0312698 -.0145511 3.33646 3.57110 to u_i) Source: Author’s calculation Table B-4: Regression result Model (2) - FE xtreg lnOAperL lnKAperL lnMAperL lnL innovation InLsize, fe Fixed-effects (within) regression Group variable: firmid Number of obs Number of groups R-sq: within = 0.8235 between = 0.8167 overall = 0.8194 Obs per group: = avg = max = corr(u_i, Xb) = 0.2050 F(5,2071) Prob > F lnOAperL Coef Std Err lnKAperL lnMAperL lnL innovation InLsize _cons 0402848 6120385 -.0973427 0474205 -.0268129 4.486592 007456 006756 0152904 0217827 0247135 114408 sigma_u sigma_e rho 36428572 26716442 65025231 (fraction F test that all u_i=0: t 5.40 90.59 -6.37 2.18 -1.08 39.22 P>|t| 0.000 0.000 0.000 0.030 0.278 0.000 of variance due F(2109, 2071) = 2.22 = = = = 4186 2110 2.0 1933.06 0.0000 [95% Conf Interval] 0256628 5987893 -.1273287 0047023 -.0752788 4.262225 -.067356 4.71095 to u_i) Prob > F = 0.0000 Source: Author’s calculation 66 | P a g e Table B-5: Regression result Model (3) - RE xtreg lnOAperL lnKAperL lnMAperL lnL innovation InLocation Random-effects GLS regression Group variable: firmid Number of obs Number of groups R-sq: within = 0.8123 between = 0.8841 overall = 0.8706 Obs per group: = avg = max = Random effects u_i ~ Gaussian corr(u_i, X) = (assumed) Wald chi2(5) Prob > chi2 lnOAperL Coef Std Err lnKAperL lnMAperL lnL innovation InLocation _cons 0737981 6406835 0688563 -.0016768 058822 3.486165 0047343 0046376 0055204 0123062 0172885 0602105 sigma_u sigma_e rho 20797586 26682879 37792371 (fraction z 15.59 138.15 12.47 -0.14 3.40 57.90 P>|z| 4186 2110 2.0 = 25017.87 = 0.0000 [95% Conf Interval] 0.000 0.000 0.000 0.892 0.001 0.000 of variance due = = 064519 631594 0580364 -.0257965 0249372 3.368154 3.60417 to u_i) Source: Author’s calculation Table B-6: Regression result Model (3) - FE xtreg lnOAperL lnKAperL lnMAperL lnL innovation InLocation, fe Fixed-effects (within) regression Number of obs = 4186 Group variable: firmid Number of groups = 2110 R-sq: within = 0.8240 between = 0.8100 overall = 0.8143 Obs per group: = avg = max = corr(u_i, Xb) = 0.1993 F(5,2071) Prob > F lnOAperL Coef Std Err lnKAperL lnMAperL lnL innovation InLocation _cons 0400784 612038 -.0942923 0556297 -.0706182 4.484393 0074471 0067473 0148668 0172175 0279287 1141296 sigma_u sigma_e rho 37039794 26682879 65834811 (fraction F test that all u_i=0: Source: Author’s calculation t 5.38 90.71 -6.34 3.23 -2.53 39.29 P>|t| 0.000 0.000 0.000 0.001 0.012 0.000 of variance due F(2109, 2071) = 2.22 = = 2.0 1938.96 0.0000 [95% Conf Interval] 0254739 5988058 -.1234477 0218644 -.1253894 4.260573 -.065136 -.01584 4.70821 to u_i) Prob > F = 0.0000 Table B-7: Regression result Model (4) - RE xtreg lnOAperL lnKAperL lnMAperL lnL innovation In_High Random-effects GLS regression Group variable: firmid Number of obs Number of groups R-sq: within = 0.8132 between = 0.8829 overall = 0.8699 Obs per group: = avg = max = Random effects u_i ~ Gaussian corr(u_i, X) = (assumed) Wald chi2(5) Prob > chi2 lnOAperL Coef Std Err lnKAperL lnMAperL lnL innovation In_High _cons 0753286 6411869 0712497 0268699 -.0100902 3.45932 0047202 0046422 0054841 0193808 0197762 0597695 sigma_u sigma_e rho 21008212 26718228 38204784 (fraction z 15.96 138.12 12.99 1.39 -0.51 57.88 P>|z| 0.000 0.000 0.000 0.166 0.610 0.000 of variance due = = 4186 2110 2.0 = 24903.14 = 0.0000 [95% Conf Interval] 0660772 6320884 060501 -.0111158 -.0488508 3.342174 3.57646 to u_i) Source: Author’s calculation Table B-8: Regression result Model (4) - FE xtreg lnOAperL lnKAperL lnMAperL lnL innovation In_High, fe Fixed-effects (within) regression Group variable: firmid Number of obs Number of groups R-sq: within = 0.8235 between = 0.8161 overall = 0.8190 Obs per group: = avg = max = corr(u_i, Xb) = 0.2048 F(5,2071) Prob > F lnOAperL Coef Std Err lnKAperL lnMAperL lnL innovation In_High _cons 0400982 6118667 -.0939256 0507251 -.0261341 4.485119 0074618 0067571 0148891 0266599 0275453 1143814 sigma_u sigma_e rho 36486488 26718228 65094416 (fraction F test that all u_i=0: Source: Author’s calculation t 5.37 90.55 -6.31 1.90 -0.95 39.21 P>|t| 0.000 0.000 0.000 0.057 0.343 0.000 of variance due F(2109, 2071) = 2.23 = = = = 4186 2110 2.0 1932.74 0.0000 [95% Conf Interval] 0254648 5986153 -.1231249 -.001558 -.0801534 4.260804 -.064726 4.70943 to u_i) Prob > F = 0.0000 APPENDIX C: HAUSMAN TEST RESULTS Table C-1: Hausman Test result Model (1) hausman fixed random lnKAperL lnMAperL lnL innovation Coefficients (B) (b) rando fixed m 0403773 0753398 6119739 6411617 -.0935365 071272 0289898 0186494 (b-B) Difference -.0349625 -.0291878 -.1648085 0103404 sqrt(diag(V_b-V_B)) S.E .0057718 0049093 0138362 0083497 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(4) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 154.75 Prob>chi2 = 0.0000 Source: Author’s calculation Table C-2: Hausman Test result Model (2) hausman fixed random lnKAperL lnMAperL lnL innovation InLsize Coefficients (B) (b) rando fixed m 0402848 075251 6120385 6411797 -.0973427 0754226 0474205 0025202 -.0268129 022743 (b-B) Difference -.0349663 -.0291412 -.1727652 0449004 -.0495559 sqrt(diag(V_b-V_B)) S.E .0057726 0049099 0138703 013314 01577 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(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 170.40 Prob>chi2 = 0.0000 Source: Author’s calculation Table C-3: Hausman Test result Model (3) hausman fixed random lnKAperL lnMAperL lnL innovation InLocation Coefficients (B) (b) rando fixed m 0400784 0737981 612038 6406835 -.0942923 0688563 0556297 -.0016768 -.0706182 058822 (b-B) Difference -.0337197 -.0286455 -.1631486 0573065 -.1294402 sqrt(diag(V_b-V_B)) S.E .0057485 0049009 0138038 0120415 0219344 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(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 175.83 Prob>chi2 = 0.0000 Source: Author’s calculation Table C-4: Hausman Test result Model (4) hausman fixed random lnKAperL lnMAperL lnL innovation In_High Coefficients (B) (b) rando fixed m 0400982 0753286 6118667 6411869 -.0939256 0712497 0507251 0268699 -.0261341 -.0100902 (b-B) Difference -.0352303 -.0293202 -.1651754 0238552 -.0160439 sqrt(diag(V_b-V_B)) S.E .0057791 0049101 0138423 0183067 0191741 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(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 155.48 Prob>chi2 = 0.0000 Source: Author’s calculation Table C-5: Het Test result regress lnOAperL lnKAperL lnMAperL lnL innovation Source SS Model Residual Total lnOAperL lnKAperL lnMAperL lnL innovation _cons df MS Number of obs = 4186 F( 4, 4181) = 6993.25 Prob > F = 0.0000 R-squared = 0.8700 Adj R-squared = 0.8698 Root MSE = 33999 3233.44605 808.361512 483.288806 4181 115591678 3716.73486 4185 888108687 Coef .0764296 6475528 0815 012375 3.435645 Std Err .0045721 0045694 0049266 011145 0579639 t 16.72 141.71 16.54 1.11 59.27 P>|t| [95% Conf Interval] 0.000 0.000 0.000 0.267 0.000 0674659 6385943 0718411 -.0094752 3.322005 3.54928 Source: Author’s calculation hettest lnKAperL lnMAperL lnL innovation Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: lnKAperL lnMAperL lnL innovation chi2(4) = 5955.95 Prob > chi2 = 0.0000 Source: Author’s calculation Table C-5: Correlation test result cor lnOAperL lnKAperL lnMAperL lnL innovation InLsize InLocation In_High (obs=4186) lnOAperL lnKAperL lnMAperL lnOAperL lnKAperL lnMAperL lnL innovation InLsize InLocation In_High 1.0000 0.4505 0.9216 0.2724 0.1584 0.0246 0.2111 0.1418 1.0000 0.3839 0.1661 0.1173 0.0426 0.1981 0.1046 1.0000 0.1791 0.1261 0.0321 0.1557 0.1174 lnL innova~n InLsize InLoca~n In_High 1.0000 0.3140 -0.2122 0.3021 0.2611 1.0000 0.6597 0.4933 0.8317 1.0000 0.1896 0.5433 1.0000 0.4768 1.0000 APPENDIX D: INDUSTRY CLASSIFICATION OECD Classification - Manufacturing industries classified according to their global technological intensity - ISIC Revision (Note: in the study, there are only two subgroups: low-tech and high-tech sector The medium high-tech and medium low-tech are combined to form an aggregated high-tech sector) OECD classification High-technology Aerospace Computers, office machinery Electronics-communications Pharmaceuticals Scientific instruments Medium-high-technology Motor vehicles Electrical machinery Chemicals Other transport equipment 10.Non-electrical machinery Medium-low-technology 11 Rubber and plastic products 12 Shipbuilding 13 Other manufacturing 14 Non-ferrous metals 15 Non-metallic mineral products 16 Fabricated metal products 17 Petroleum refining 18 Ferrous metals Low-technology 19 Paper printing 20 Textile and clothing 21 Food, beverages, and tobacco 22 Wood and furniture Source: ISIC Revision 3845 3825 3832 3522 385 3843 3832 3522 3842+3844+3849 3825 355+356 3841 39 372 36 381 351+354 371 http://epp.eurostat.ec.europa.eu/cache/ITY_SDDS/Annexes/hrst_st_esms_an9.pdf 34 32 31 33 APPENDIX E - EMPIRICAL STUDIES ON RELATIONSHIP BETWEEN INNOVATION AND PRODUCTIVITY # Authors & Title Data Variables & Concepts Model & Methodology Results Macro level Latin America, Asia "New Technology, Human Capital, Total Factor Productivity and Growth Process for Developing Countries!" Developing countries and developed countries in the 20th century Färe, Grosskopf et al (1994) 17 OECD countries Technical change, Malmquist index of total over the period 1979- productivity growth productivity growth 1988 “Productivity growth, technical progress, and efficiency change in industrialized countries." TFP, growth, technology change, human capital Solow, Ramsey Model Convex-Concave Production Function Human Capital Growth Model Cobb-Douglas production function OLS regression Le Van (2008) Pooled time-series data of 71 non-oil exporting countries (Barro & Lee) Growth in developing countries based mainly on physical capital accumulation Growth in developed countries motivated essentially by human capital and technology change factor U.S productivity growth is slightly higher than average, all of which is due to technical change Japan's productivity growth is the highest in the sample, with almost half due to efficiency change CDM Model – developed by Crepon (1998) This model explained the productivity of firms in correlation with innovation output, knowledge and investment in research development Larger firms engage in innovative activities than small firms Micro level Masso and Vahter (2008) Estonia "Technological innovation and productivity in latetransition Estonia: econometric evidence from innovation surveys." Community Innovation Survey, CIS (19982000) with 3161 firms & CIS (2002-2004) with 1747 firms in Technological innovation, productivity (Figure 1) Firm size has an insignificant impact on product and a positive impact on the process innovation only process innovation has a positive significant effect on TFP 73 | P a g e effect of innovation on productivity – not only on the productivity in the last year of the innovation survey, but also one and two years after the survey Tobit Model F ri ts c h a n d M e s c h e d e ( 0 ) "Pr od uct inn ova tio n, pro ces s i n n o v a t i o n , a n d s i z e " German 1800 enterprises firm size Cohen and Kleppe r (1996a, b) approa ch R&D exp asizeb ln (R&D = ln a + standard l regression R&D expenditure rises less than proportional Chudnovsk y, López et al (2006) "Innovation and productivity in developing countries: A study of Argentine manufacturi ng firms’ behavior (1992– 2001)." with size ses that perform R&D tend to be more innovative than large enterprises small enterpri small enterprises spend a much higher proportion of their R&D budget on new Argentin ean manufact uring firms Innova Innovation surveys tive Panel data for 19922001 s Miguel Benavente (2006) Chile – less developed country "The role of research and innovation in promoting productivit y in Chile." 1995-1998 tive inputs Innova output Firm’s productivity (sales per employee) Fixed effect estimators on innovation expenditure intensity and productivity CDM Model – investmen ALS t methodology Innovatio Tobit & Probit intensity Market share Productivi ty on Labor productivity is, on average, 14.1% higher in innovators than in non-innovators Multinomial logit on innovation outcomes Large firms have a higher probability Four groups of of engaging in innovation outcomes: innovation non-innovators, activities and product innovators, becoming process innovators innovators and combined innovators R&D n products than new processes Labor skills have positive and significant effect on the estimation of productivity Neither research expenditure nor innovation has a significant impact on innovation sales and productivity in the short-run Productivity is measured as value added per worker Roper and Love (2002) innovation and export performance: evidence from uk and german manufacturing plants UK, German Innovation Plan-level data Export performance Product Survey Development RD Spill-over effects 1700 UK manufacturing plants and 1300 German plants Probit Model Innovation is positively related to export probability in both countries UK: the scale of plants’ innovation is related positively to export propensity Be effective in their ability to exploit spill-overs from the innovation Germany: negative relationship between the scale of innovation activity and export performance non-innovators are to absorb regional and supply-chain spill-over effects Innovative firms are generally found to discourage exporting Huergo and Jaumandreu (2004) Productivity growth Firms’ age, process innovation and Firm age productivity growth Process innovation firms enter the market experiencing high productivity growth and that above-average growth rates tend to last for many years, but also that productivity growth of surviving firms converges Process innovations at some point then lead to extra productivity growth, which also tends to persist somewhat attenuated for a number of years 60 | P a g e Dhawan (2001) Using a large panel of Firm size publicly traded US Productivity firms, parameters of the production technology for large and small firms are estimated for the 1970–1989 period Cobb Douglas production function Ghosal and Nair-Reichert (2009) North America and Europe Cobb Douglas production function Investments in modernization, innovation and gains in productivity: Evidence from firms in the global paper industry 19 firms in pulp and paper industry Firm size and productivity differential: theory and evidence from a panel of US firms Time series data: 19952004 Sources: Compustat, Thompson Financial Cassiman, Golovko et al (2010) Innovation, exports and productivity Firm performance: profitability and productivity Control variables: mergers and acquisitions, R&D expenditure, capital intensity panel of Spanish Innovation manufacturing firms Export Spanish Productivity ESEE survey Fixed and Random effect model Performance = f (modernization investment, innovation activity, control variables) Sweden 1996-1998 Productivity are different across the firms R&D expenditures don’t significant contribution have OLS regression Kolmogorov–Smirnov equality-ofdistributions test, We compare the productivity distributions of different samples of firms product innovation –not process innovation – affects productivity and induces small non-exporting firms to enter the export market Innovating firms show higher productivity levels and grow faster than non-innovators SME firms running from 1990 until 1998 Lööf and Heshmati (2006) smaller firms exhibit a higher profit rate, lower survival probability and difficulty in accessing the capital market small firms are significantly more productive but also more risky than their large counterparts Small firms facing market uncertainties, capital constraints and other challenges undertake actions that make them more efficient than large firms but is achieved at the cost of increasing their riskiness Dependent Variables: Generalized 77 | P a g e On the Relationship 3000 service and between Innovation and manufacturing firms Performance: A Sensitive 1300 in innovation Analysis sample R&D intensity Innovation sales worker Labor productivity Tobit per Independent Variables: Size, human capital, innovation sales per worker Semi-parametric Least Squares (SLS) Mohne n, Mairess e et al (2006) Innovat ion: A Compar ison Across Seven Europe an Countri es Seven European Countries CIS1 microaggregate d data in 1992 8000 firm and 5700 innovatio n sample Innovat G ive e intensit n y R&D e intensit r y a Size l i z e d T o b i t Viet Nam Lang, Lin et al (2012) M e d i a te effe ct of tech nolo gy innovat ion capabil ities investm ent capability and firm p er fo r m a n ce in V ie t N a m V rm i s e t N a m m a n u f a c t u r i n g f i I n n o v a ti o n T e c h n o l o g i c a l i n n o v a ti o n c a p a b il iti su es rv C ey o qu m est p io et nn iti air v e e wa p s er de fo sig r ne m d a co n lle c cte e d da ta fro m en ter pri se s in th e hi gh tec h m a n u f a c t u r i n g i n d u s t r y & S M E s i n V i e t N a m T he sa m pl es w er e nd o ml y se le ct ed fo r m “T he R ol e of Te ch no lo gy , Inv est me nt and Ow ner shi p Str uct ure in Pro duc tivi ty Per for ma nce of the Ma nuf act uri ng Sec tor in Vie t Na m – BS PS CIE M proj ect” t h e s t u d y i d e n t i f i e d t e r p r i s e a r e r e q u i r e d t h a t t o V i e t i n v e s t N a m t o e n e n h a n c e s e v e n d i m e n s i o n s o f T I C s l e a r n i n g , R & D , r e s o u r c e a l l o c a t i o n , m a n u f a c t u ri n g , m a r k e t, o r g a n i z a ti o n a l, a n d s tr a t e g i c p l a nning capabilitie s Investmen t capa bility can lead direct ly competiti ve performan ce through TICs Investment capability had positive effects on their TICs were positively correlated to their firm competitive performance Logit / Probit model Three measures of innovation are sig Export, Innovation Innovation is measured by Export = β0 + β1X + θ2 Innovation + ε and instrumental variable (Z) new products,new Innovation = γZ + ε production improvementprocess products or of existing al (2008) SMEs Viet Nam Survey “Innovation and exports in Viet Nam's SME– sector." 2005 ) SMEs Viet Nam SurveyTechnical Vietnamese performanceefficiency non-state manufacturing Stochastic SMEs frontier operated analysis at high (SFA)level – of technical efficiency Technical efficiency performance of Vietnamese manufacturing small and medium enterprises Influencing factors of technical efficiency are: firm age, size, location, subcontracting, long-te using function function production production Cobb-Douglas Cross-sectional data 2002, 2005, in of 2007 New product innovation has positive impact technical efficiency in 2002, negative impa and onTranslog manufacturing SMEs ... measuring the product and process innovation, innovation activity and expenditure, impacts of innovation, other sources and findings of innovation of European enterprises Most of the firms in Europe... technology innovation and productivity of a firm? (ii) What are the roles of firm size, firm location, and manufacturing sector on the impact of innovation and firm productivity? 1.4 SCOPE OF THE... RELATIONSHIP OF INNOVATION AND PRODUCTIVITY 2.3.3 EMPIRICAL REVIEW OF INNOVATION AND PRODUCTIVITY RELATIONSHIP 10 2.3.4 DETERMINANTS OF THE INNOVATION IMPACT 13 2.3.5 INNOVATION AND