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Knowledge spillover, sectoral innovation and firm total factor productivity The case of manufacturing industries in Vietnam.Knowledge spillover, sectoral innovation and firm total factor productivity The case of manufacturing industries in Vietnam.Knowledge spillover, sectoral innovation and firm total factor productivity The case of manufacturing industries in Vietnam.Knowledge spillover, sectoral innovation and firm total factor productivity The case of manufacturing industries in Vietnam.Knowledge spillover, sectoral innovation and firm total factor productivity The case of manufacturing industries in Vietnam.
1 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 SUMMARY OF PHD THESIS ACADEMIC ADVISORS Dr Pham Khanh Nam Dr Pham Hoang Van HO CHI MINH CITY, 2021 Dissertation is completed at: Người hướng dẫn khoa học: Academic supervisor 1: Dr Pham Khanh Nam Academic supervisor 2: Dr Pham Hoang Van Reviewer 1: ……………………………………………………… …………………………………………………………… ……… Reviewer 2:……………………………………………………… …………………………………………………………… ……… Reviewer 3:……………………………………………………… …………………………………………………………… ……… Dissertation shall be defended before the economic committee at the University level, held at: ………………………………………………………… ………………………………………………………… Time: Date This dissertation could be accessed at the following library: PUBLICATIONS OF RESULTS Nguyen Thi Hoang Oanh, 2019 Determinants of Firms’ Total Factor Productivity in Manufacturing Industry in Vietnam: An Approach of a Cross-Classified Model Journal of Asian Business and Economic Studies (JABES), Volumn 26, Special Issue 01 Available from: http://jabes.ueh.edu.vn/Home/SearchArticle?volume_id=c84365093f9c-ded2-9119-c243428cc183 Nguyen Thi Hoang Oanh, 2018 Sector Innovation Capacity in Vietnamese Enterprises: Spillover effects from Research and Development (R&D), Foreign Direct Investment (FDI) and Trade Asian Conference on Business and Economic Studies (ACBES), University of Economics Ho Chi Minh City, Ho Chi Minh City Publishing House of Economics (ISBN: 978-604-922-660-1), pp 265- 284 Abstract This study developed the framework of knowledge spillovers at sector level and investigated these spillover effects of research and development (R&D), foreign direct investment (FDI) and trade activities on sectoral innovation by Spatial Regression Models Besides, the study examined the spillover effects of sectoral innovation and provincial human resources on firms’ TFP with 7,236 enterprises in 38 sectors of Vietnamese manufacturing industries, located in 62 provinces by CrossClassified Models By Spatial Regression Models with to 38 manufacturing sectors in correspondence to Input/Output table from 2010 to 2014, the intra-industry rather than inter-industry spillover effects were found to be significant; that approved the hypothesis of MAR rather than Jacobs externalities In particular, only R&D and export activities were found to have significantly positive effects on innovation activities at sector level In contrast, FDI and import activities seem to have negative impact on innovation activities In cross-classified models, firms’ characteristics in comparison with characteristics of sectors and provinces may have the highest explanation on the heterogeneity in firms’ TFP The firm size, capital intensive and export orientation were found to have stably significantly positive impacts on firms’ TFP The sectoral innovation might turn to have positive impacts on the productivity of firms in that sector after one year Besides, the externalities of human resources in provinces on firms’ productivity were found to be positive Keywords: Knowledge Spillovers, Sectoral Innovation, TFP, Spatial Regression Model, Cross-Classified Model INTRODUCTION 1.1 Problem Statement 1.1.1 The importance of the topics in this thesis It is important to investigate the role of knowledge spillovers on innovation at sector level As stated by Aghion and Jaravel (2015), “innovations in one firm or one sector often build on knowledge that was created by innovations in another firm or sector” Mehrizi and Ve (2008) argued that sector-level analysis enables the study to link firm level determinants to macroeconomic conditions Malerba (2002) also emphasized the role of sector-level analysis in investigating innovative and production activities According to Padoan (1999), adopting a sectoral perspective may investigate the knowledge accumulation and diffusion In our knowledge, there are few studies on the roles of channels of knowledge spillover on sector innovation capacity In Vietnam, there are few studies on innovation and most of these studies focused on firm level Therefore, the first main objective in this study is to investigate the role of knowledge spillover on sectoral innovation through three channels including R&D, FDI and trade activities by spatial regression models It is also important to examine on heterogeneity of firms’ TFP in considering both firms’ characteristics and spillover effects from sectors and regions TFP is understood as the residual of output that is not contributed by the amount of capital and labor In Solow model (1956), the residual is a black box representing technical change that leads to a sustainable development Obviously, the heterogeneity in firms’ TFP is mainly originated from the differences in firms’ characteristics Acemoglu (2009) stated that “the heterogeneity in TFP are not necessarily due to technology in the narrow sense For instance, two firms have adopted the same technology but make use of these techniques in different ways with different degrees of efficiency” However, even if these firms adopted similar technology, they still have differences in TFP These differences may be originated from the characteristics of their sectors or their location It is important to examine to the determinants of firms' TFP by multileveled factors in a multilevel cross-classified model This model could isolate the impacts of elements at multilevel including firm, sectoral, regional or provincial dimensions However, most of studies on firms’ TFP focused on the determinants as firms’ characteristics In Vietnam, studies on TFP are still very limited (CIEM, 2010) although TFP is recently perceived as a key role of development quality This study could make a contribution as a new approach in investigating TFP in Vietnam by applying the multileveled cross-classified model in the second objective In addition, the study may imply policies not only for firms but also for sectors and regions 1.1.2 The gaps and the new aspects in this thesis There are three new aspects respectively on theoretical frame work, methodology and context in this study At theoretical framework, the knowledge spillover at sector level was developed by aggregating the stock of knowledge at firm level as in Cohen and Levinthal (1989) Our model is new when it indicated not only the intra-industry spillover but also the inter-industry spillover at sector level and investigated the channel of knowledge spillover from R&D, FDI and trade In addition, this study revealed the spillover effects of sectoral innovation and provincial human capital on firms’ productivity basing on the ideas of intra-industry economies of localization (Marshall, 1920), intra-sectoral spillovers (Griliches, 1992) and the role of human capital spillover on productivity (Moretti, 2004) In regarding to the methodology, the study has two new approaches Innitially, the study adopted spatial regression model in investigating sources of knowledge spillovers on sectoral innovation Then, a Cross-classified model was applied to make an efficient estimate of the effects on firms’ productivity from firm level, sectoral level and provincial level Besides, knowledge spillover, innovation and productivity, integrated in this study, is a necessary topic in the context of manufacturing sector in Vietnam In the context of Vietnam, no study investigated the determinants of firms’ TFP at firm, sector and province level by Cross-classified model Some studies have considered such as FDI transaction (Ni et al., 2015; Vu Hoang Duong and Le Van Hung, 2017; Khanh Le Phi Ho et al., 2018; Nguyen, 2017) or agglomeration economies in manufacturing industries (Francois and Nguyen, 2017; Toshitaka et al.; 2017) or import competition in the sector (Doan et al., 2016) However, there has been no study applying Cross- classified model Adopting this model in the case of 63 provinces and 38 sectors in manufacturing industry makes this study more valuable in the context of Vietnam 1.2 RESEARCH OBJECTIVES The first general objective is to investigate channels of knowledge spillovers on sectoral innovation in manufacturing industries in Vietnam, the study focuses on the following research questions: 1.1 Is sectoral innovation directly affected by R&D activities of that sector in manufacturing industries in Vietnam? 1.2 Is sectoral innovation indirectly affected by R&D activities of other sectors in manufacturing industries in Vietnam? 1.3 Is sectoral innovation directly affected by transactions with FDI enterprises in that sector in manufacturing industries in Vietnam? 1.4 Is sectoral innovation indirectly affected by transactions with FDI enterprises in other sectors in manufacturing industries in Vietnam? 1.5 Is sectoral innovation directly affected by trade activities in that sector in manufacturing industries in Vietnam? 1.6 Is sectoral innovation indirectly affected by trade activities in other sectors in manufacturing industries in Vietnam? The second objective of this study is to investigate the impacts of characteristics at firm- level, regional and sectoral level on firms’ total factor productivity (TFP) with the following research questions: 2.1 How much heterogeneity in firms’ total factor productivity is explained by firm-level, sector-level and province- level determinants? 2.2 Does firms’ size have impact on firms’ TFP in manufacturing industries in Vietnam? 2.3 Does the capital intensity in firms have impact on their TFP? 2.4 Is there difference in TFP of exported firms and nonexported firms? 2.5 Is firms’ TFP affected by their sectoral innovation in manufacturing industries in Vietnam? 2.6 Does the human resource in a province have impact on the TFP of firms in that province? 1.3 RESEARCH METHODOLOGY and RESEARCH SCOPE In order to investigate three channels of knowledge spillovers on sector innovation capacity, this study applied the Spatial Regression Then the study applied the cross classified model to examine the heterogeneity in firm productivity from three groups of determinants including sector, regional and firm level This study made use of the data of Vietnam Enterprises Survey (VES) and Vietnam Technology and Competitiveness Survey (TCS) in addition to the use of Input Output (I/O) of Vietnam in 2012 Besides, the study also used the annually surveyed data on province of General Statistics Office (GSO) The analysis unit in investigating the effect of R&D, FDI and trade on sectoral innovation is sector The sector unit is aggregated from data on Vietnamese firms in manufacturing from the year of 2010 to 2014 The relations among sectors are determined basing the intermediary transaction in the Input Output of Vietnam in 2012 By spatial regression model, the study finds the direct as well as indirect impact of R&D, FDI and trade on sectoral innovation Meanwhile, firm is the analysis unit in investigating the impacts of characteristics at firm- level, regional and sectoral level on firms’ total factor productivity (TFP) Firms are also in manufacturing industries in Vietnam with research period from the year of 2011 to 2014 Using TCS and VES data, the study accesses the characteristics at the firm level The sectoral characteristics in the model is also measured from these data In addition, the annual province data on Province Competitive Index (PCI) is also used to determine the human resources at the province 1.4 RESEARCH CONTRIBUTION This study could have contributions on theoretical perspective as well as policy implication On theoretical perspective, this study developed the framework and tested the hypothesis of knowledge spillover at sector level The study applied a new approach, Spatial Regression Model, to investigate the knowledge spillovers among sectors Besides, the study tried to explore the black box of contextual factors on firms’ TFP In particular, the study applied the Cross-classified Model to investigate the spillover effects of innovation activities at sector level and human resources at province level on firms’ TFP Determining the core spillover factors on sector innovation capacity is key information for policymakers to enhance this sector We began our construction of functional form of the equation that may connect these variables in our data at sector level from the equation at firm level of Cohen and Levinthal (1989) Cohen and Levinthal (1989) constructed a model of firm’s stock knowledge as follow: zi = Mi + yi (8 ∑j*i Mj + T) (2.19) Where zithe firm’s i stock of technological and scientific knowledge; Mi is a firm’s investment in R&D; yi is the fraction of knowledge in the public domain that the firm is able to assimilate and exploit and represents the firm’s absorptive capacity; is the degree of intra-industry spillovers and T is the level of extra-industry knowledge Other firm’s investment in research and development is Mj for j≠i also contribute to zi This model implies the intra as well as inter sectoral knowledge spillover among sectors We defined Zs to be the total output of knowledge in the n sector s: Zs =∑i= zi (2.20) Similarly, Ms is the total input of knowledge in the sector s: Mc =∑Mi (2.21) i= After the transformation, we have: n.N ∑ N i=1 + ∑n ði = M + (N − c Mc 1) ∑yi + Zc N i=1 yj &i (j ≠ i) + N T ∑ (2.38) i=N N yi In order to express the inter-industry knowledge spillover, N i= the study made a basis on the ideas of Griliches (1992) The amount of aggregate knowledge borrowed by the ith industry from all available sources was expressed by Griliches (1992) as follows: Ki = ∑wijKj Finally, we have: n Zc = N N ∑ N i=1yi ∑n i=1ði (2.39) Mc + (N − 1) Mc ∑N yi + N ∑ wkc Zk (k ≠ s) + ∑i=1 i= yj &i (j ≠ i) (2.41) Following the knowledge production function of Griliches and Pakes (1984), the study constructed the model of the sources of knowledge spillover on sectoral innovation 2.2.2 Channels of knowledge spillovers and the research hypothesis of the first objective Griliches (1979) argued that the level of knowledge in any sector or industry not only is derived from "own" research and development investments but also is affected by the knowledge borrowed or stolen from other sectors or industries Thus, the productivity of industry i will depend also on the research and development investments of industries j and h, among others Basing on this proposition, the study tests the hypothesis on the direct impact of R&D on innovation within a sector and the indirect impact of R&D in other sectors on innovation of a sector as follows: H11: The research and development (R&D) in the sector i may have positive impact on its sectoral innovation H12: The sectoral innovation in the sector i may be positively affected by the R&D from other related sectors Hofmann and Wan (2013) suggested that the horizontal externalities from FDI may have direct or indirect on domestic firms in its same industry by four channels including competition, imitation and adoption, labor turnover and second round effects through input suppliers Markusen and Venables (1997) provided an analytical framework which can assess the effects of the industrial linkages They proposed that at the industry level, the presence of FDI may change supplies and demands in a number of related industries On the basis of the potential backward and forward externalities from FDI suggested by Markusen and Venables (1997), Hofmann and Wan (2013), the study had the following hypothesis: H13: The transaction with FDI enterprises in the sector i may enhance its sectoral innovation H14: The sectoral innovation in the sector i may be affected by the transaction with FDI enterprises in other related sectors Grossman and Helpman (1991), henceforth GH who formulate a theoretical model where the foreign contribution to the local knowledge capital stock increases with the number of commercial interactions between domestic and foreign agents Basing on the assumption of Grossman and Helpman (1991) that the number of commercial interactions between domestic and foreign agents may increase the local knowledge capital stock, the study had the hypothesis on both the direct and indirect impacts of exports and imports as follows: H15a: The export of the sector i may upgrade the innovation capacity of its sectoral innovation H15b: The input import of the sector i may upgrade the innovation capacity of its sectoral innovation H16a: The sectoral innovation of the sector i may be affected by export of other related sectors H16b: The sectoral innovation of the sector i may be affected by input import of other related sectors 2.2.3 Theoretical framework of knowledge spillovers to firms 2.2.3.1 Debates on knowledge spillover of intra- sector to firms Griliches (1991) verified the intra-industry spillover effects by measuring total factor productivity as follows: +ỵ1 A = Y/X = D XN K eßt+u (2.48) y As presented, the TFP depends not only on the conventional inputs or research capital but also the contribution of the trend t in the other unmeasured factors The unmeasured factors were not specified in the framework of Griliches (1991) We argue that these unmeasured factors may include context determinants relating to sector-level and province-level The context determinants were suggested in this study to be innovation activities in the sector and level of human resources in the province 2.2.3.2 Human capital externalities from the province to firms Moretti (2004) is one of first studies that built the framework and directly estimated the human capital externalities on the productivity of manufacturing plants In order to illustrate the nature of a spatial equilibrium in the presence of human capital spillover, Moretti (2004) built a general equilibrium framework Considering two cities and two types of labor, educated and uneducated workers, he assumed that there are two types of goods, a composite good y- national traded and land h-locally traded Using a Cobb-Douglas function, each city is a competitive economy with the production of firms as following: Y = A HaHLaL KQ (2.49) where H and L are the hours worked by skilled and unskilled workers, respectively, and K is capital In order to find the possibility of human capital externalities, he allowed the productivity of plants in a city to depend on the aggregate level of human capital in the city: A = f(S)̅ in which S̅ is the fraction of college-educated workers in the city, outside the firm 2.2.4 Multilevel modeling on firms’ total factor productivity and the research hypothesis of the second objective Basing on theory on internal economies of scale (Silberston A., 1972), we test the following hypothesis: H21: The firm size may have positive effect on the firm’s productivity In decomposing the components of firms’ TFP in the United States, Solow (1962) found that the firms’ TFP is most affected by firms’ technology in comparison with capital and labor Basing on this, the study test the following hypothesis: H22: Firms with have higher capital per worker may have higher productivity Basing on the idea of learning by exporting (LBE) began to be discussed and studied (empirically and theoretically) in the mid-80s with Rhee et al (1984), Westphal et al (1984) and in the 90s with Grossman and Helpman (1991) and the World Bank (1993), the study test the following hypothesis: H23: Firms with exports activities may have higher productivity than firms without exports Basing on Griliches (1992) about the intra-industry spillover effects to firms, this study tests the hypothesis that firms located in the high localized sector may have higher TFP than the others as follows: H24: The sectoral innovation may have positive spillover effect on firms’ productivity in that sector in the same year H25: The sectoral innovation in the previous year may have positive spillover effect on firms’ productivity in that sector in the current year Basing on the framework of Moretti (2004), this study tried to explore the human capital externalities of the province to firms’ productivity in that province by the following hypothesis: H26: The vocational human resource in a province may have positive effects on firms’ productivity in that province 2.3 EMPERICAL STUDIES In this section, the study reviewed the empirical studies relating to three following topics The first one is the empirical studies on determinants of sectoral innovation The next is the empirical studies on channels of knowledge spillover and applications of Spatial Regression Model And the final one is the empirical studies on TFP Figure Theoretical framework of the study Source: By author’s own RESEARCH METHODOLOGY 3.1 The research model on Sectoral Innovation This study follows Spatial Durbin Model (SDM) which is an appropriate approach to investigate the externalities (Beer and Riedl, 2010) as follows: n yit = δ ∑ᵂij yjtj= + ∑ j= ᵂij Xkjt k + Xkit ỵk + Zkit k +it (*) (t=1……T, i=1…n) (3.1) In which the dependent variable yit in (1) is respectively measured by S_modified and S_Innovation The interaction weighted repressors,ᵂij Xkjt , namely S_RD_meanit, S_FDI_Supplierit, S_FDI_Customerit, S_exportit and S_InputImportit In this model, yit is the innovation activity of sector i in the period t Wij yjt is the interaction weighted dependent variable, ᵂij Xkjt is the interaction weighted regressors and Zkit are control variables The description of variables in the models are as in the Table 3.1 The sector of the firm is determined basing on its principal 4- digit VSIC sector The principal sector herein is meant to be the sector in that firms have highest value of production or sales or use highest number of employees In order to make it correspondent with the division of sector in Input/Output Table, the study makes group of manufacturing sectors in VSIC to 38 manufacturing sectors in correspondence to Input/Output table (as detail in The Appendix 1, page xx) This generates a panel data of 190 observations from the year of 2010 to 2014 3.2 The research on firms’ TFP heterogeneity by CrossClassified Model In order to investigate the separate effect of firm characteristics, sector and region, the simple model without any independent variables is firstly estimated as follow: yi(cj) = y000 + uc + uj + ucj + ei(cj) (3.29) Where yi(cj) is TFP of the firm in the sector s and located in the region j, y000 is the mean TFP across all sectors and all regions uc is the effect of firm i’s sector uj is the effect of firm i’s region ei(cj) is the firm level residual error term Besides, this model includes a random interaction effects between sector and region, ucj The estimation result of the equation (3.29) provides how much firm characteristics, sector and region are attributed to the heterogeneity firms ’TFP Basing on this result, the study considers to add variables on firm characteristics, sector and region specific into the following model: yi(cj) = y000 + ƒ= yƒ Xƒi(cj) + N n h= ỵh Zhij + kp= hp Spc + uc + uj + ei(cj) (3.30) Where y is the TFP of the firm i (in logs) belonging to sector s and established in region j, X is a vector of m firm-level variables which may be important determinants of TFP, Z presents the variables at the regional level, S is the variables at the sectoral level This study considered the result estimation of the equation (3.30) to include the number of variables in each investigated level This study controlled the issue of omitted variable bias and endogeneity by make comparison the results of the Crossclassified Model with the ones of the Fixed Effects model In addition, the study also applied the Hausman – Taylor method to obtain a more efficient estimator and perform valid statistical inference using that estimator The description of variables in the models are as in the Table 3.2 The VES data is used to measure the firm’s TFP from the year 2011 to 2015 In addition to the VES data, this study also makes use of the data on province of General Statistics Office (GSO) All enterprises exists during the period from 2011 to 2014 are combined into a balance panel data with the number of observations to be 7,236 enterprises per year and the total number of observations to be 28,944 This data covers 38 sectors of firms located in 62 provinces SECTORAL INNOVATION AND SPILLOVER EFFECTS: RESULTS FROM SPATIAL REGRESSION MODELS AND DISCUSSIONS The results of the models in the section 4.3 have been mostly consistent The positive direct impacts of R&D activities on both innovation activities and modification activities in a sector approved the hypothesis of Cohen and Levinthal (1989) and Griliches (1992) However, there was no evidence of indirect effects of R&D activities on the other sectors’ innovation activities or modification activities On regarding to the impact of FDI, the study has not found any positive impact as stated in the hypothesis of Hofmann and Wan (2013) and Markusen and Venables (1997) In contrast, this study found the negative impact of the number of FDI suppliers in a sector on its innovation or modification activities In respect of exports, this study found the positive impacts of exports on innovation activities or modification activities in a sector This result approved the assumption of Grossman and Helpman (1991) In contrast, the effects of imported inputs on was negative in innovation activities or modification activities in this study HETEROGENEITY IN TFP OF VIETNAMESE MANUFACTURING FIRMS: RESULTS FROM CROSSCLASSIFIED MODELS AND DISCUSSIONS The estimation results on the determinants of firms’ TFP has been consistent across the models Basing on the results of LR tests in the Table A5 and Table A6 (Appendix, page xxiv), the cross-classified model was confirmed to be better fit to the data This study carefully made the comparison between the model with random effects in both sector level and province level and the other model with fixed effects The consistence in results of random effects and fixed effects model may be a good sign to apply random effects model (Bell and Jones, 2015) This study provided evidences on determinants of firms’ TFP The positive effect of firm size on the firm’s TFP approved the hypothesis of economies of scale (Silberston A., 1972) Additionally, the study also approved the positive contribution of capital to productivity as in Solow (1962) The positive impacts of export orientation on TFP found in this study also approved for the hypothesis of learning by exporting Grossman and Helpman (1991) In regarding to the sector-level effect, the finding in this study is consistent with the hypothesis of Griliches (1992) on the spillover effects of intra-industry on firms The sectoral innovation may have positive impact on TFP of firms in that sector with some lags of time since having innovation Meanwhile, in respect of province-level effect, the finding in this study is also consistent with the theory and evidence in the research of Moretti (2004) The human capital in a province were found to be significantly positive impact on TFP of firms in that province CONCLUSION AND POLICY IMPLICATIONS The positive impact of R&D activities on innovation as well as modification activities at sector level may imply on enhancing sectoral innovation The more firms in a sector having R&D activities may enhance the innovation activities in that sector It seems that R&D activities implemented by firms are very important to innovation activities in the sector of that firm As having been described in the chapter 3, R&D activities in Vietnam was considerably implemented by State area There was also lack of linkage among R&D implementing parties such as firms, universities, government It implies that R&D activities should be encouraged more at firms These activities may have directly positive spillover impact on innovation at sector level The export activities at sector level may also have positive impacts on innovation as well as modification activities Policies enhancing export activities in the sector may generate positive spillover effects on its sectoral innovation Exporting activities should be enforced at sector level in the manufacturing sectors to obtain benefits of learning by exporting It was unexpected that FDI spillovers was found to be negative rather than positive impact on innovation or modification activities at sector level As having been described in the chapter three, Vietnamese firms did not have many transactions with foreign firms It also seems that Vietnamese firms in manufacturing was still lack of necessary capacities to take advantage of potential benefits from FDI Meanwhile, there had been a lot of preferences to attract foreign direct investment Policies should focus on enhancing absorptive capacity of domestic firms Besides, policies should have more regulations on FDI activities to take advantage of knowledge spillover from FDI Besides FDI, the input import seemed to have unexpectedly directly negative impacts on innovation as well as modification activities Importing inputs in manufacturing in Vietnam seemed to make the sector more dependence on the foreign markets rather than learn any knowledge from this imported input However, the input import may have indirectly positive impact on innovation activities It seems that policies may take advantage of imported inputs in intermediaries producing sectors rather than in final goods producing sectors Policies should also consider carefully what inputs should be imported rather than self-produced On regarding to enhance total factor productivity in Vietnamese manufacturing firms, it is important to focus policies at firm-level besides policies at sector-level or province level At firm-level, large firms in manufacturing sector may have economies of scale to obtain higher TFP Policies oriented to export or capital intensity may push the TFP of firms Foreign firms seemed to have higher TFP than domestically private firms did This implies policies which focus on learning, absorbing knowledge, technology or production method of FDI firms In respect of sector level, the spillover effects of sectoral innovation on firm productivity needed a period of time to have positive impacts on firms’ productivity This study found that these positive effects may occur after the lag of one year The sectoral innovation seemed to have negative impact on firms’ TFP due to the high cost of innovation activities This finding implies the policies to subsidy the innovation activities by sector This support may generate positive spillover effects on the TFP of firms At province level, human resource was found to have importance role to the TFP of firms in the province This implies on policies on enhancing the quality of human resources Besides the education at the higher level, vocational program may still have good contribution on the labor force Policies should also focus on developing the vocational programs ... upgrade the innovation capacity of its sectoral innovation H15b: The input import of the sector i may upgrade the innovation capacity of its sectoral innovation H16a: The sectoral innovation of the. .. nonformal R&D inputs and the inherent randomness in the production of inventions 2.1.4 Sectoral Innovation System (SIS) and its determinants According to Malerba (2002), the founder of sectoral innovation. .. activities of that sector in manufacturing industries in Vietnam? 1.2 Is sectoral innovation indirectly affected by R&D activities of other sectors in manufacturing industries in Vietnam? 1.3 Is sectoral