Innovation and productivity in small and medium enterprises a case study of vietnam

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS INNOVATION AND PRODUCTIVITY IN SMALL AND MEDIUM ENTERPRISES: A CASE STUDY OF VIETNAM By PHAM DO TUONG VY MASTER OF ART IN DEVELOPMENT ECONOMICS HCMC, NOVEMBER 2016 University of Economics International Institute of Social Study Ho Chi Minh City The Hague Vietnam The Netherlands VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS INNOVATION AND PRODUCTIVITY IN SMALL AND MEDIUM ENTERPRISES: A CASE STUDY OF VIETNAM by Pham Do Tuong Vy A Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Art in Development Economics Academic Supervisor: Dr Vo Hong Duc DECLARATION I hereby declare that this thesis entitled “Innovation and Productivity in Small and Medium sized Enterprises: A case study of Vietnam”, which is written and submitted by me in accordance with the requirement for the degree of Master of Art in Development Economics to the Vietnam – The Netherlands Programme This is my original work and all sources of knowledge carried in this thesis have been duly acknowledged HCMC, November 2016 PHẠM ĐỖ TƯỜNG VY ACKNOWLEDGEMENT I would like to take this opportunity to express my deepest gratitude for the help, support and encouragement of the following people, who have contributed to the completion of this thesis in their very own ways Above all, I would like to express my immeasurable appreciation to my supervisor – Dr Võ Hồng Đức for his precious time, support and advices to make this thesis completed Furthermore, I would like to send my great thanks to all the lecturers and staffs at the Vietnam – The Netherlands Programme for their knowledge and supports during my time joining in the program In specific, I am extremely grateful to Dr Phạm Khánh Nam and Dr Trương Đăng Thụy for their valuable guidance and support in the courses and thesis writing process To all of my friends in Class 21 and my colleagues at TPF, I could never thankful enough for your encouragement and support until the very end of this thesis Last but not least, my deepest thanks and love to my parents, who have always been beside me Without their unconditional love, none of this would have been possible ABBREVIATION 2SLS Two Stage Least Squares CDM Crépon, Duguet and Mairesse FE Fixed Effect GMM Generalized Method of Moments GSO General Statistic Office IV Instrument Variables LP Levinsohn and Petrin OLS Ordinary Least Squares OP Olley and Parker R&D Research and Development SMEs Small and Medium-sized Enterprises TFP Total Factor Productivity ABSTRACT The majority of enterprises in Vietnam is categorized as small and medium sized (SMEs) firms which play an important role to the sustainable growth of the Vietnamese economy As such, improving the productivity of the SMEs is essentially needed and this request becomes a crucial mission for the governments It is generally accepted that innovation and technology improvement are key drivers of productivity (Bartelsman & Doms, 2000) However, they have not been well-acknowledged by the SMEs in Vietnam even though their huge contribution to firm’s productivity is unarguable This study aims to examine the relationship between innovation and productivity in the Small and Medium-sized Enterprises (SMEs) in Vietnam To establish and quantify this relationship, this study employs the two-stage process: (i) the estimation of total factor productivity for each firm; and (ii) a determination of an innovation – productivity relationship In the first stage, total factor productivity is estimated based on production function using the input and output approach However, firms might adjust their input level according to expected productivity shock As such, a potential endogeneity caused by possible relationship between input decision and productivity shocks (unobserved productivity shock) might exist To deal with this problem of endogeneity, an approach developed by Levinsohn and Petrin is applied to estimate firm’s total productivity In the second stage, the system- GMM approach is adopted to examine the relationship between innovation and productivity An unbalanced panel dataset from five Small and Medium-sized Enterprises surveys from 2005 to 2013 is used in this study Findings from this study indicate that, in the context of Vietnam, when innovation is measured as innovation expenditure intensity and high-quality labor share in total firm’s labor force, innovation activities provide positive and significant impact on firm’s productivity In addition, past value of firm’s productivity also has significant relationship with its current level This finding implies that higher (lower) level of current productivity could lead to higher (lower) level of productivity in the future The study also provides empirical evidence to confirm that larger firms might perform better than the relatively smaller firms In contrast, capital structure provides negative impact on firm’s productivity However, this study fails to provide any evidence to support the view that longevity of firm does provide significant impact on productivity of firms Key words: Vietnam SMEs; Total factor productivity; Productivity Shock; Innovation, GMM TABLE OF CONTENTS CHAPTER INTRODUCTION 1.1 Problem statement 1.2 Research objectives 1.3 Research questions 1.4 Research motivations 1.5 Research scope and data 1.6 The structure of this study CHAPTER LITERATURE REVIEW 2.1 Schumpeter Theory of Innovation – How does Innovation play its role in economic development? 2.2 Productivity: concept and measurements 2.3 2.4 2.1.1 Concept 2.1.2 Measurements 2.1.3 General productivity determinants 12 Innovation: concept and measurements 16 2.1.4 Concept 16 2.1.5 Measurements 17 How has the relationship between innovation and firms’ performance been analysed in the literature? 18 CHAPTER 23 RESEARCH METHODOLOGY 23 3.1 An overview of Vietnamese Small and Medium-sized Enterprises 23 3.1.1 Statistic overview 23 3.1.2 Difficulties 26 3.2 Methodology 27 3.1.3 Conceptual framework 27 3.1.4 Model identification 29 3.3 Research hypotheses and concept measurements 34 3.1.5 Research hypotheses 34 3.1.6 Concept and variable measurements 34 3.4 Data sources 36 CHAPTER 39 EMPIRICAL RESULTS 39 4.1 Total Factor Productivity of Vietnamese SMEs 39 4.1.1 Data descriptions 39 4.1.2 Total factor productivity from production function estimation of Vietnamese SMEs 42 4.2 Innovation – Firm’s productivity relationship 45 4.1.3 Data descriptions 45 4.1.4 The relationship between innovation expenditure intensity and firm’s productivity 46 4.1.5 The relationship between high-quality labor share in total firm’s labor force and their productivity 49 CHAPTER 52 CONCLUSION AND POLICY IMPLICATION 52 5.1 Conclusion remarks 52 5.2 Policy implications 54 5.3 Limitation and potential future research 54 REFERENCES 56 APPENDIX 1: Empirical studies on general productivity determinants 62 APPENDIX 2: Empirical studies on relationship between innovation and firm’s performance 65 APPENDIX 3: Durbin – Wu Hausman test for endogeneity 69 APPENDIX 4: Durbin – Wu Hausman test for endogeneity 71 LIST OF TABLES AND FIGURES Table 3.1: Classification of SMEs in Vietnam Table 3.2: Concepts and measurements of variables used in the study Table 3.3: Number of observation in selected industries in dataset Table 3.4: Number of observation after filtering Table 3.5: Number of observation after filtering in stage Table 4.1: Descriptive statistics of production function variables Table 4.2: Comparison of OLS, Fixed Effect and LP estimators in Foods, Woods and Rubber and Plastics Table 4.3: Comparison of OLS, Fixed Effect and LP estimators in Non-metallic mineral, Fabricated metal and Furniture Table 4.4: Descriptive statistics of TFP and its determinants Table 4.5: Regression results of innovation expenditure intensity and firm’s productivity Table 4.6: Regression results of high-quality labor share in total labor force and firm’s productivity Figure 3.1: Number of enterprises at 31/12 (by size of employees) Figure 3.2: Conceptual framework Jensen, M C (1986) Agency cost of free cash flow, corporate finance, and takeovers Corporate Finance, and Takeovers American Economic Review, 76(2) Jensen, M C., & Meckling, W H (1976) Theory of the firm: Managerial behavior, agency costs and ownership structure Journal of financial economics, 3(4), 305-360 Ha, D T T., & Kiyota, K (2014) Firm‐Level Evidence on Productivity Differentials and Turnover in Vietnamese Manufacturing Japanese Economic Review, 65(2), 193-217 Hall, B H., Lotti, F., & Mairesse, J (2008) Employment, innovation, and productivity: evidence from Italian microdata Industrial and Corporate Change, 17(4), 813-839 Hansen, L P (1982) Large sample properties of generalized method of moments estimators Econometrica: Journal of the Econometric Society, 1029-1054 Holtz-Eakin, D., Newey, W., & Rosen, H S (1988) Estimating vector autoregressions with panel data Econometrica: Journal of the Econometric Society, 1371-1395 Huergo, E., & Jaumandreu, J (2004) Firms' age, process innovation and productivity growth International Journal of Industrial Organization, 22(4), 541-559 Keele, L., & Kelly, N J (2006) Dynamic models for dynamic theories: The ins and outs of lagged dependent variables Political analysis, 14(2), 186-205 Keller, W., & Yeaple, S R (2009) Multinational enterprises, international trade, and productivity growth: firm-level evidence from the United States The Review of Economics and Statistics, 91(4), 821-831 Kim, E (2006) The impact of family ownership and capital structures on productivity performance of Korean manufacturing firms: Corporate governance and the “chaebol problem” Journal of the Japanese and International Economies, 20(2), 209-233 Li, H., & Atuahene-Gima, K (2001) Product innovation strategy and the performance of new technology ventures in China Academy of Management Journal, 44(6), 1123-1134 Lokshin, B., Belderbos, R., & Carree, M (2008) The productivity effects of internal and external R&D: Evidence from a dynamic panel data model Oxford bulletin of Economics and Statistics, 70(3), 399-413 Mairesse, J., Mohnen, P., Kremp, E., & KREMP, E (2005) The importance of R&D and innovation for productivity: A reexamination in light of the French innovation survey Annales d'Economie et de Statistique, 487-527 Mairesse, J., & Robin, S (2009) Innovation and productivity: a firm-level analysis for French Manufacturing and Services using CIS3 and CIS4 data (19982000 and 2002-2004) Paris: CREST-ENSAE Marschak, J., & Andrews, W H (1944) Random simultaneous equations and the theory of production Econometrica, Journal of the Econometric Society, 143- 205 Margaritis, D., & Psillaki, M (2010) Capital structure, equity ownership and firm performance.Journal of Banking & Finance, 34(3), 621-632 Miguel Benavente, J (2006) The role of research and innovation in promoting productivity in Chile.Economics of Innovation and New Technology, 15(4-5), 301-315 Mohnen, P., & Hall, B H (2013) Innovation and productivity: An update Eurasian Business Review, 3(1), 47-65 Mortensen, P S., & Bloch, C W (2005) Oslo Manual-Guidelines for collecting and interpreting innovation data Organisation for Economic Cooporation and Development, OECD Myers, S C (1977) Determinants of corporate borrowing Journal of financial economics, 5(2), 147-175 Nelson, R R (1973) Recent exercises in growth accounting: new understanding or dead end? The American Economic Review, 63(3), 462-468 Nguyen, N A., Pham, Q E., Nguyen, D C., & Nguyen, D N (2007) Innovation and Export of Vietnam’s SME Sector’, MPRA Paper No 3256 Development and Policies Research Center Nickell, S (1981) Biases in dynamic models with fixed effects Econometrica: Journal of the Econometric Society, 1417-1426 Olley, G S., & Pakes, A (1992) The dynamics of productivity in the telecommunications equipment industry (No w3977) National Bureau of Economic Research Parisi, M L., Schiantarelli, F., & Sembenelli, A (2006) Productivity, innovation and R&D: Micro evidence for Italy European Economic Review,50(8), 2037- 2061 Petrin, A., Poi, B P., & Levinsohn, J (2004) Production function estimation in Stata using inputs to control for unobservables Stata journal, 4, 113-123 Polder, M., Leeuwen, G V., Mohnen, P., & Raymond, W (2009) Productivity effects of innovation modes Santos, D F L., Basso, L F C., Kimura, H., & Kayo, E K (2014) Innovation efforts and performances of Brazilian firms Journal of Business Research, 67(4), 527-535 Roodman, D (2006) How to xtabond2: An introduction to difference and system GMM in Stata Center for Global Development working paper, (103) Rosenbusch, N., Brinckmann, J., & Bausch, A (2011) Is innovation always beneficial? A meta-analysis of the relationship between innovation and performance in SMEs Journal of business Venturing, 26(4), 441-457 Schreyer, P., & Pilat, D (2001) Measuring productivity OECD Economic studies, 33(2), 127-170 Schumpeter, J A (1912) 1934, The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest and the Business Cycle.Trans Redvers Opie Cambridge, MA: Harvard University Press Schumpeter, J A (1942) Socialism, capitalism and democracy Harper and Brothers Schumpeter, J A (1939) Business cycles: a theoretical, historical, and statistical analysis of the capitalist process McGraw-Hill Siedschlag, I., Zhang, X., & Cahill, B (2010) The effects of the internationalisation of firms on innovation and productivity Economic and Social Research Institute Working Paper, (363) Siegel, D., & Griliches, Z (1992) Purchased services, outsourcing, computers, and productivity in manufacturing In Output measurement in the service sectors (pp 429-460) University of Chicago Press Solow, R M (1957) Technical change and the aggregate production function The review of Economics and Statistics, 312-320 Tovar, B., Ramos-Real, F J., & De Almeida, E F (2011) Firm size and productivity Evidence from the electricity distribution industry in Brazil Energy Policy, 39(2), 826-833 Van Beveren, I (2012) Total factor productivity estimation: A practical review Journal of economic surveys, 26(1), 98-128 Vu, H N., & Doan, Q H (2015) Innovation and Performance of Enterprises: The Case of SMEs in Vietnam White, H (1982) Regularity conditions for Cox's test of hypotheses Journal of Econometrics, 19(2), 301-318 non-nested APPENDIX 1: Empirical studies on general productivity determinants References Objectives Data Methodology Key conclusions Cucculelli et - Pioneer on analyzing al (2014) the relationship between family ownership status and TFP in Italia context - Determine the impact of firm age on TFP in family owned firm in Italia De Kok et Estimate the al (2006) relationship between firm age and productivity growth Data from Xth Capitalia - UniCredit survey conducted in Italia from 2004 to 2006 Two-stage approach: - Stage 1: TPF is determined using Levinsohn and Petrin (2003) approach - Stage 2: OLS estimation: regressing TPF obtained from stage with firm age, family-managed status and vector of other control variables (firm size, human capital, firms listed in stock market, capital intensity, and ownership concentration.) Data from Production survey for Dutch manufacturing industry firms from 1994-1999 Huergo and Examine the Jaumandreu relationship between (2004) innovation introduction and productivity growth If this relationship exist, whether firm life span matter? Dhawan Examine the effect of firm size on the firm (2001) productivity in US Unbalanced panel data set of over 2300 Spanish manufacturing firms from 1990 to 1998 - Productivity is calculated using growth accounting - At the first few years of operation, firm method productivity is lower than average - OLS estimation with dependent variable is - After ten years of operation, firm age have productivity growth and independent variables are no impact on productivity firm age and control for size, organizational structure and sectors in manufacturing industry - Semiparametric regression for assumed nonlinear- Firms at early stage enjoy high relationship between age and productivity growth productivity growth (at around (5%) and which calculated from Solow residual this rate decreases continuously for years - Adding innovation variable to the regression to until equal the average productivity (about estimate the relationship between innovation and 2%) productivity growth - Additionally control for innovation results in higher productivity growth but then decreases after years - Defined productivity as median value of capital - Small firms pay higher interest rate than and labor ratio large firms Data from COMPUSTAT database from 1970-1989, US 92 - Productivity of non-family firms is higher than family-managed firms, but the different is small (3.5%-5%) - Firm age has positive impact on TFP of family-managed firms - OLS estimation of Cobb-Douglas production function is regressed separately for large firms and small firms to examine whether small firms and large firms are different in cost of capital - Construct equation on productivity differential between small and large firms Examine the effect of Panel data from 17 - Productivity is estimated using Stochastic Frontier firm size on the Brazilian electricity Analysis TFP is decomposed into three performance of distribution firms from components: (i) pure technical efficiency change, Brazilian Electricity 1998 to 2005 (ii) scale efficiency change, (iii) technical change Industry - Through Scale efficiency change and Technical change, different in firm size can explain productivity differential Tovar et al (2011) Margaritis Examine the and Psillaki relationship between (2010) firm leverage and their performance Test whether firm efficiency has any effect on their choice of capital structure Determine the effect of ownership on firm capital structure and efficiency Kim (2006) Assess the relationship between family ownership Firm level panel data of France from 2002 to 2005 in three industries: Chemicals, Computers and Textiles - Using Data Envelopment Analysis and distance function approach to estimate firm efficiency Firm leverage is measured as debt to total asset ratio Dynamic OLS estimation to identify the relationship among firm efficiency, leverage and ownership status, other control variables: profitability (profit/total assets), asset structure (fixed tangible assets/total assets), growth opportunities (intangible assets/total assets), size Panel data of Korean - Define Chaebols firms as family-managed, debtmanufacturing firms dependent, diverse business activities from 1991 to 1998 - Small firms are more productive than large firms In the period of 1998-2005, TPF growth is at 0.9%/year, Technical change growth rate at 4.9%/year, Pure technical efficiency change growth rate at -3.7%/year, unchanged at Scale efficiency change Firm size is positively correlated with TFP growth Therefore mergers of small electricity distribution firms could lead to gain in productivity At low level of leverage, firm's level of efficiency has positive affect on firm leverage Firm efficiency also has relation to assets structure, growth opportunities, size, profitability, ownership status - Family ownership concentration has significant positive impact on firm productivity, but at decreasing rate statuses, capital structure on firm productivity Aw, Roberts Analyze firm behavior and Winston in entering export (2007) market, investing in R&D and investing in improving labor force quality Assess the impact of these above activities on firm productivity Keller and Yeaple (2009) Examine the effect of FDI and international trade on productivity growth in US - Productivity is measured as TFP using multilateral index approach - Pooled OLS estimation between TFP and family ownership concentration, capital structure and other control variables - Significant negative effect of debt ratio on productivity - Family ownership concentration affect productivity in Chaebols much stronger than non-Chaebols firms - Debt ratio has positive impact on Chaebols firms’ productivity Panel data of Taiwanese - Applied standard bivariate probit model to analyze - Firms with higher productivity are more firms in electronic firm decision on participate export market and likely to involve in export However this industry in 1986, 1991 invest in R&D/worker training The explanatory effect is not significant with choice to and 1996 variables: firm age, capital stock, wages, invest in R&D/worker training productivity (calculated using OP approach), - Key determinants of firm's survival are interaction terms, etc entrant status and capital stock - Using maximum likelihood estimate to identify - Current productivity level does affect the determinants of firm decision to survive future productivity Investment in - Using maximum likelihood estimate to determine R&D/worker training and export the marginal contribution of investment in experience have significant positive R&D/worker training on future productivity relationship with firm productivity, and the relationship between these two factors are complement COMPUSTAT panel - Firm productivity is determined using Olley and - FDI activities have significant positive data of 1,277 US firms Pakes (1996) method impact on TPF growth (account for 11% from 1987 to 1996 - FDI activities is represented by the share of MNE TFP growth) The relationship is then affiliate industry employment robust - OLS estimation for the relationship between - Import affect TFP growth with the productivity growth and FDI and international trade same direction, but weaker than FDI In addition, there is no evidence on robust result on import and TFP growth APPENDIX 2: Empirical studies on relationship between innovation and firm’s performance Reference Data Siedschlag, Zhang and Cahill (2010) Panel data of Labor 723 firms from productivity Community Innovation Survey of Ireland in period of 2004-2008 -CDM model contains three (1) stages: (1) firm's decision to invest in innovation; (2) determine innovation output using innovation inputs and (3) (2) innovation output and other production inputs in the relationship with final output production Belderbos, Carree and Lokshin (2004) IV regression between output variable and innovation variables, control for firm size, 2-digit industry dummies, ownership status, demand-pull and cost-push Productivity in the previous period also is included Crespi Pianta (2009) Panel data from Community Innovation Survey in Netherlands in 1996 and 1998 for 2056 manufacturing firms 32 industries (including manufacturing Innovation input: innovation expenditure (rather than R&D expenditure) per employee Innovation output: Three types of innovation dummies: product, process and organizational innovation; interactions; innovation sales share Labor -Innovation expenditure per productivity sales growth/Innovati -Dummy variables for four of R&D ve sales types (products that cooperation: competitors, customers, are new to the suppliers, universities or other market) research institutions productivity growth Labor productivity growth Diversify the impact of innovation Product and process innovations; effort to on productivity growth through build technological competitive advantages, three separated models: general and Output measurement Innovation measurement - Innovation expenditure per employee Methodology Conclusion Foreign owned firms and domestic firms involved in export activities are more likely to invest in innovation than firms with domestic activities only Foreign owned firms and domestic firms involved in export activities are more likely to have innovation output Innovation expenditure have no significant effect on innovation output (3) Innovation outputs have positive relationship with labor productivity Different types of R&D collaboration and innovation intensity significantly and positively affect productivity growth (but innovation intensity on innovative sales share) and service sectors) in Europe countries from 1996 to 2001 Parisi, Schiantarelli and Sembenelli (2006) 941 manufacturing firms in Italy from two surveys in 1995 and 1998 Santos, Basso, Kimura and Kayo (2014) Data of Brazilian firms in 2000, 2003, 2005 in which: Technological competitiveness: percentage of firms have patents; percentage of firms targetingon improving product quality Cost competitiveness: spending on acquisition of new machinery per employee; percentage of firms targeting on flexible the production process - Gross output -Process and product growth innovation dummies - TFP growth - R&D expenditure as (TFP is percentage of output determined using Levinsohn and Petrin (2003) approach) ROA ROS ROE - Human capital - Ratio of training expenditure to sales - Ratio of internal R&D expenditure to sales model (reflect product and process cost advantages significantly contribute to innovation); technological growth in productivity competitiveness model and cost competitiveness model Cobb-Douglas production function to estimate the impact of innovation activities on output growth, instrumented by lag of ln(output/labor), ln(material/labor), ln(capital/labor), R&D intensity, size) - Tornquist index of TFP is regressed against innovation variables, instrumented by the same set of variables above - Descriptive and quantitative approach: factor analysis - Structural equation modelling - Positive impact of process and product innovation on productivity In details, the impact of process innovation is bigger than product innovation These results are robust in both approaches: TFP growth and CobbDouglas production function estimation - R&D intensity does not have significant impact on productivity Innovation efforts from innovative investment not significant explain firm's performance Causality relationship between innovation and performance Innovation information from PINTEC of Brazilian Institute of Geography and Statistics Financial information from Serasa and Gazeta Mercantil Rosenbusch, 42 empirical Categorized into studies on three types: Brinckmann and Bausch 21,270 SMEs accounting returns (2011) growth stock market performance Lokshin, Belderbos and Carree (2008) 304 Netherlands manufacturing firms from 1996 to 2001 - Ratio of acquisition of machinery expenditure to sales - Ratio of external R&D expenditure to sales - Ration of introduction of technological innovation expenditure to sales - Innovation orientation Meta-analyses on 42 empirical - Positive effect of innovation on firm's - Innovation input studies of 21,270 SMEs performance when innovation is measured in measurements: internal three types, but the effect of innovation (R&D expenditure) and orientation is larger than innovation outcomes external (R&D - Effect of innovation on firm's performance in cooperation) young firms is larger than in more established - Innovation output firms measurements - Internal innovation activities have significant (innovation sales, impact on performance while innovative patents) collaboration with external involves have no significant impact on performance - Cultural environment also have impact on the innovation-performance relationship Labor Internal R&D GMM estimation for the dynamic - Internal and external R&D are complement productivity: net expenditure panel equation from augmented in the relationship with productivity with value added per External R&D Cobb-Douglas production decreasing returns to scales effect employee expenditure (contracted function Innovation variables - Internal R&D have positive impact on firm's R&D) included in the model are: internal productivity, this relationship is robust with dynamic R&D expenditure, external R&D different expenditure, quadratic forms and techniques interaction form panel econometric APPENDIX 3: Durbin – Wu Hausman test for endogeneity IV (2SLS) estimation Estimates efficient for homoskedasticity only Statistics consistent for homoskedasticity only Number of obs = F( 6, 1942) = 1949 50.27 Prob > F = 0.0000 Total (centered) SS = 386439909.4 Centered R2 = -0.9527 Total (uncentered) SS = 655444519.7 Uncentered R2 = -0.1513 Residual SS = 754587147.2 Root MSE = 622.2 -TFP | Coef Std Err z P>|z| [95% Conf Interval] -+ -invexpintensity | -1657.575 803.1596 -2.06 0.039 -3231.739 -83.4112 dummy | 968.7459 471.593 2.05 0.040 44.44065 1893.051 706479 0460183 15.35 0.000 6162849 7966732 lntotalassets | -21.87988 28.06836 -0.78 0.436 -76.89286 33.13309 capitalstructure | -372.9817 203.9315 -1.83 0.067 -772.6802 26.71677 firmage | -3.555249 2.472536 -1.44 0.150 -8.40133 1.290831 _cons | 598.6118 444.9574 1.35 0.179 -273.4886 1470.712 | TFP | L1 | | -Underidentification test (Anderson canon corr LM statistic): Chi-sq(1) P-val = 7.005 0.0081 -Weak identification test (Cragg-Donald Wald F statistic): 7.005 Stock-Yogo weak ID test critical values: 10% maximal IV size 16.38 15% maximal IV size 8.96 20% maximal IV size 6.66 25% maximal IV size 5.53 Source: Stock-Yogo (2005) Reproduced by permission 99 -Sargan statistic (overidentification test of all instruments): 0.000 (equation exactly identified) -endog- option: Endogeneity test of endogenous regressors: 12.577 Chi-sq(1) P-val = Regressors tested: 0.0004 invexpintensity -Instrumented: invexpintensity Included instruments: dummy L.TFP lntotalassets capitalstructure firmage Excluded instruments: L.invexpintensity APPENDIX 4: Durbin – Wu Hausman test for endogeneity IV (2SLS) estimation Estimates efficient for homoskedasticity only Statistics consistent for homoskedasticity only Number of obs = F( 7, 2899) = 2907 155.87 Prob > F = 0.0000 Total (centered) SS = 467122455.9 Centered R2 = 0.2650 Total (uncentered) SS = 799333085.9 Uncentered R2 = 0.5705 Residual SS = 343351960.1 Root MSE = 343.7 -TFP | Coef Std Err z P>|z| [95% Conf Interval] -+ -highqualitylabor | -395.2885 285.819 -1.38 0.167 -955.4834 164.9064 dummy | -17.08177 18.7009 -0.91 0.361 -53.73487 19.57132 6299597 0219493 28.70 0.000 5869398 6729796 invexpintensity | 27.09339 22.10942 1.23 0.220 -16.24029 70.42706 lntotalassets | 42.31543 6.82258 6.20 0.000 28.94342 55.68744 capitalstructure | -35.67232 26.2863 -1.36 0.175 -87.19252 15.84788 firmage | -1.741898 9786339 -1.78 0.075 -3.659985 176189 _cons | -356.4369 84.88587 -4.20 0.000 -522.8101 -190.0636 | TFP | L1 | | -Underidentification test (Anderson canon corr LM statistic): Chi-sq(1) P-val = 313.770 0.0000 -Weak identification test (Cragg-Donald Wald F statistic): 350.767 Stock-Yogo weak ID test critical values: 10% maximal IV size 16.38 15% maximal IV size 8.96 20% maximal IV size 6.66 25% maximal IV size 5.53 Source: Stock-Yogo (2005) Reproduced by permission -Sargan statistic (overidentification test of all instruments): 0.000 (equation exactly identified) -endog- option: Endogeneity test of endogenous regressors: 6.684 Chi-sq(1) P-val = Regressors tested: 0.0097 highqualitylabor -Instrumented: highqualitylabor Included instruments: dummy L.TFP invexpintensity lntotalassets capitalstructure firmage Excluded instruments: L.highqualitylabor ... 1996 and 1998 for 2056 manufacturing firms to analyse the impact of innovation and firm performance in Netherlands Not only internal innovation activities but also external innovative collaboration... firm’s productivity is unarguable This study aims to examine the relationship between innovation and productivity in the Small and Medium- sized Enterprises (SMEs) in Vietnam To establish and quantify... context of Vietnam, when innovation is measured as innovation expenditure intensity and high-quality labor share in total firm’s labor force, innovation activities provide positive and significant