Sector Innovation Capacity in Vietnamese Enterprises: Spillover Effects from Research and Development

Sector Innovation Capacity in Vietnamese Enterprises: Spillover Effects from Research and Development Nguyen Thi Hoang Oanh University of Economics Ho Chi Minh City, Vietnam Abstract This study investigates the spillover effects of Research Development (R&D), Foreign Direct Investment (FDI) and Trade on sector innovation capacity of Vietnamese Enterprises in manufacturing industries The spillover effects are estimated under the idea of Spatial Regression; however, the weight matrix herein is Transaction weight matrix rather than Spatial weight matrix This matrix is constructed from Input Output Table to reflect the relations between two sectors By aggregating the data of firms of Vietnam Enterprises Survey (VES) and Vietnam Technology and Competitiveness Survey (TCS) from 2010 to 2013, this study explore some interesting findings Firstly, R&D of a sector not only has directly positive impact on innovation capacity of that sector but also has indirectly positive impact on innovation capacity of other related sectors Secondly, transaction to FDI customers seems to have positive effect on sector innovation capacity Thirdly, export has only directly positive impact on sector innovation capacity In contrast, the higher imported input of a sector, the lower innovation capacity of that sector and other related sectors Finally, the scale-intensive producers sector and the specialized equipment supplier sector may have stronger spillover effect than supplier-dominated sectors Keywords: Sector Innovation capacity, R&D Spillovers, FDI Spillovers, Trade and Innovation, Knowledge Spillover Problem Statement Looking back into the history of Vietnam, innovation continues playing a key role for the development of the country Vietnam had considerably positive transformation due to the economic reform referred to as doi moi that involved a move from central planning to a greater role for marketization and internationalization in Vietnamese economic and business activity Nowadays, in the context of the industry 4.0, everything changes rapidly overtime; especially the impressively advanced technology in the world put a pressure on developing countries to enhance its innovation capacity Under theoretical perspective, the endogenous growth and new growth theories all emphasize the importance of innovation or technology change on long run growth (Romer, 1986) The role of innovation was also confirmed by the success of four industrial countries (NICs) in Asia in their industrialization Meanwhile, according to the survey of World Intellectual Property Organization (WIPO), the Global Innovation Index (GII) 2016 (INSEAD, 2016) of Vietnam was scored only at 35,4 over 100, ranked at 59 over 128 surveyed countries Among ASEAN countries, Vietnam lagged behind by Singapore (7), Malaysia (33) and Thailand (48) Therefore, how to enhance innovation capacity for economic growth is primarily question for policymakers in Vietnam The innovation capacity at sector level and channels of knowledge spillovers on innovation are necessarily conducted to give valuable implications for innovated-enhanced growth policy The analysis on sector raises the opportunity in investigation the relationship between knowledge accumulation and diffusion Mehrizi 265 and Ve (2008) argued that the main reason for sector-level analysis is the “lost” ring of chain which links firm level factors to macro-economic conditions Malerba(2002) also stated that sectors provide a key level of analysis for economists, business scholars, technologists and economic historian in the examination of innovative and production activities Meanwhile, innovation capacity of a firm or a sector stems from not only their own knowledge and technology but also from those of other firms or other sectors, understood as knowledge spillovers 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 This spillover have long argued to diminish firms ‘incentive to conduct investment, particularly Research and Development (R&D) for innovation However, economists (Cohen and Levithal,1989; Onodera,2009) recently suggested that firms’ R&D could enhance their absorptive capacity as well as has contribution on upgrading their belonging sector through internal spillovers in the industry and the other sectors through external spillovers out the industry Therefore, the research on the role of R&D spillover among sectors on innovation may be an evidence to test theories Besides, knowledge spillovers from Foreign Direct Investment (FDI) companies and trade is also considered in innovationenhanced policy The investigation on these channels of knowledge spillovers in Vietnam may provide policymakers valuable information in determining the appropriate channel to enhance sector innovation capacity for growth In our knowledge, there are few studies on the roles of channels of knowledge spillover on sector innovation capacity Most studies investigated the impact of sector characteristics on innovation (Castellacci, 2008) (Chamberlin, Doutriaux, & Hector, 2010)(Yurtseven & Tandoğan, 2012)(Hage, Mote, & Jordan, 2013)(Piqueres Garcia, Serrano Bedia, & Lopez Fernandez, 2015) and few recent studies examined the role of R&D spillover (Autant-Bernard & Lesage, 2011; Ang & Madsen, 2013; Malerba, Mancusi, & Montobbio, 2013) In Vietnam, there are few studies on innovation and most of these studies focused on firm level (Jordan, 2015; Van and Uyen, 2017; Bich et al.,2017 ) This study shall investigate the impact of three channels of knowledge spillover respectively from R&D, FDI and trade on sector innovation capacity In particular, the study shall focus on three following objective questions:  How is sector innovation capacity affected by directly R&D of this sector and by indirectly R&D from other sectors in manufacturing industries in Vietnam?  How does transaction with FDI firms impact directly and indirectly on sector innovation capacity in manufacturing industries in Vietnam?  How is sector innovation capacity affected directly and indirectly by the imported input and export in manufacturing industries in Vietnam? Literature Review 2.1 Theoretical framework 2.1.1 Innovation Several studies pay more attention development of organization and marketing terms and base on innovation definition in Oslo Manual of OECD(2005) This manual defined innovation as the introduction of a new or significantly improved product (goods or services); a new or significantly improved process, a new marketing method, or a new organization method in terms of business practice, organization of workplace The novelty degree of innovation make it distinguish from invention Meanwhile invention is restricted to a “global first” or “new to the world” and of little value in of it is not put to use; innovation includes “new to the firm” or “new to the market” (Onodera, 2009) This generates the classification of two opposite types of 266 innovation including radical and incremental innovation According to Barbieri & Álvares (2016), ideas for radical innovations are inventions, models, proposals, plans and other ways of explaining an intellectual creation; meanwhile, ideas for incremental innovations arise from the achievement of specific activities and are often implemented without a formal process Therefore, the terms improvement and incremental innovation are often used interchangeably, so that continuous improvements would mean continuous incremental innovations In respect of the area of change in innovation, albeit this invariably relates to something new, most of definitions stem from Schumpeterian approach According to Martin (2016), during the 1960s, ‘innovation’ is commonly conceptualized, defined and measured in terms of technology-based innovation for manufacturing and generally involving R&D and patenting due to the predomination of manufacturing in the economics of developed nations Initially, definitions have strong emphasis on product and process innovation (Pavitt, 1984) In sum, innovation can take several forms and it could simply be new to the firm rather than to be new to the world as whole and has impact on productivity and employment The different measurement on innovation result from different research objectives, the choice of approach and the interpretation of the concept novelty According to Martin (2016), in the era of technology based innovation which frequently involved patenting, innovation studies pioneers develop tools for measuring innovation by indicators such as R&D funding, number of researchers, and patents Patent count may be used in regional innovation (Ponds, van Oort, & Frenken, 2010; Capello & Lenzi, 2016; (Wang, Cheng, Ye, & Wei, 2016); in firm innovation (Guan, Zhang, & Yan, 2015; Lin, 2015; Blazsek & Escribano, 2016; Li, Sutherland, & Ning, 2017; Qiu, Liu, & Gao, 2017) and sectoral innovation (Buerger & Cantner, 2011) However, Martin (2016) argued that such indicators may be ‘missing’ much innovative activities that is incremental or is nor patented 2.1.2 Sectoral Innovation System (SIS) It is necessary to investigate innovation under system approach Porto Gómez, Ortegi, & ZabalaIturriagagoitiab (2016) argued that firms rarely innovate in isolation In fact, they need to interact and cooperate with other economic actors, not only to explore new sources of knowledge but also to exploit already existing ones Accordingly, the unit of analysis should be the system rather than the individual agent; that leads to the concept of innovation systems There are several innovation systems including National Innovation System (NIS); Regional Innovation System (RIS); Sectoral Innovation System (SIS) (Malerba, 2002) and recent developed system Regional Open Sectoral Innovation System (ROSIS) ((Porto Gómez, Ortegi, & Zabala-Iturriagagoitiab, 2016) In research on innovation, sectoral system has been recently developed but plays a very important role According to Malerba (2002), the founder of sectoral innovation system, sectors provide a key level of analysis for economists, nosiness scholars, technologists and economic historians in the examination of innovative and production activities He proposed that a sectoral system is a set of products and the set of agents carrying out market and non-market interactions for the creation, production and sale of those products A sectoral system has a specific knowledge base, technologies, inputs and demand Agents are individuals and organizations at various levels of aggregation They interact through process of communication, exchange, cooperation, competition and command, and these interactions are shaped by institutions Therefore, he suggested that the sectoral innovation system could be used to explain the creation, absorption, sharing and utilization of knowledge and innovation in a sector Basing on the sectoral innovation system framework of Malerba (2002), MEHRIZI & Ve (2008) suggested several components of a sectoral model These components could be abstract variables such as export, FDI, Herfin-dahl index and agglomeration of industry in a specific region as well as actual actors such as research organizations, government agencies and firms; and activities such as R&D, production and innovation There are several important elements to be considered in most analyses of sectoral systems The principal element are firms which include also user and suppliers who have different types of relationships with 267 innovating, producing or selling firms According to Malerba (2002), firms are the key factors in a sectoral system They are involved in the innovation, production and sale of sectoral products, and in the generation, adoption and use of new technologies He also proposed the role of suppliers of components and subsystems in affecting innovation, productivity increases and competitiveness of downstream sectors Suppliers are characterized by specific attributes, knowledge and competencies, with more or less close relationships with producers Besides, he appreciated the role of geographical boundaries in analysis of sectoral systems He suggested that often a sectoral system is highly localized and frequently defines the specialization of the whole local area as in the case of machinery, some traditional industries and even information of the whole local area 2.1.3 Three channels of knowledge spillovers: R&D, FDI and imported input on sectoral innovation Economists conventionally appreciate the role of R&D in generating new knowledge or innovation Besides, Cohen and Levinthal (1989) as attached in Aghion & Jaravel (2015) proposed that R&D also enhances the firm’s ability to assimilate and exploit existing information which is called absorptive capacity R&D spillover is originated from the spillover of knowledge created by R&D Cohen and Levinthal (1989) described knowledge spillover like a radio signal or smoke pollution, its effects are thought to be costless realized by all firm located within the neighborhood of the emission Therefore, R&D spillover is defined as the indirect contribution that R&D expenditures in a sector have on the growth of productivity in other industries Basing on R&D definition, R&D spillover is estimated by weighting the stock of R&D of other sectors and to use it as a measure of inter-sectoral R&D spillovers This could be done by two strategies that stem from two types of knowledge spillover including rent spillover and pure knowledge spillover According to Castellacci (2008), the first is to use the transaction-based weights, such as inter-industry sales or investment flows which corresponds to the concept of rent spillover (a pecuniary exchange between provider and the recipient of technology), while the second is to construct measures of technological distance between industries which implicitly focuses on pure knowledge spillovers (the public nature of knowledge) Besides inter-sectoral R&D spillover, there are two other strands of studies including international knowledge spillover and the geographical scope of R&D spillovers The former is argued by Romer (1986) that R&D sector affect the growth of foreign countries through cross-border trade and knowledge flows Meanwhile, the former focus on the extent to which spillovers are local, rather than national or international and is recently called localized knowledge spillovers (LKS) In respect of empirical strategy, Castellacci (2008) concluded that the common strategy is to use a knowledge production function to estimates the relationships between R&D and innovative output, measured in terms of patents or innovation counts Other studies are not explicitly based on production function approach, and make use of different methodologies based on the analysis of patents citations or new approach announcement The role of R&D spillover was initially stipulated in Romer (1986) which is widely regarded as the origins of the new growth theory The principal element of Romer’s model is the demonstration of how new knowledge created by individuals companies can produce positive externalities in terms of production capacity of other companies because knowledge is not entirely patentable As the result, investment undertaken across sectors generate new knowledge which is subsequently disseminated which raise the general level of knowledge throughout the economy In order to understand the dual roles of R&D which are both the generation of new knowledge as well as the enhancement of absorptive capacity, Cohen and Levinthal (1989) construct a model of firm’s stock knowledge as follow: 𝑧𝑖 = 𝑀𝑖 + 𝛾𝑖 (𝜃 ∑𝑗≠𝑖 𝑀𝑗 + T) Where 𝑧𝑖 is stock of technological and scientific knowledge of the firm i; 𝑀𝑖 is a firm’s investment in R&D; 𝛾𝑖 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; 𝜃 is the degree of intra-industry spillovers and T is the level of extraindustry knowledge Other firm’s investment in research and development is 𝑀𝑗 for j≠i also contribute to 𝑧𝑖 268 Basing on this framework, Malerba et al (2013) constructed a model to investigate the relative effects of national and international, intrasectoral and intersectoral R&D spillovers on innovative activity He argued that technology is typically considered as non-rival and R&D investments have both private and public returns R&D expenditures therefore create new knowledge and technology can be used – locally or internationally – within the same industry and locally and internationally – in different industries New technologies can be transmitted across countries through different activities,16 for example, through trade in capital goods and intermediate goods and services, both inward and outward foreign direct investment, movement of natural persons, contact with suppliers and customers, licensing agreements, and learning by doing (Onodera, 2009) Multiple channels are often used and it is extremely difficult to isolate the effects of each channel FDI is firstly considered as not only capital widening factor but also a capital deepening through technology spillovers Brems (1970) considered FDI simply as a second capital input factor in production function However, Findlay(1980) suggested that FDI could be an important channel for knowledge spillovers from the developed to less developed countries A widely recognized model of the spillover effect of FDI has been recently developed by Borensztein, De Gregorio and Lee(BDL) (1998) to confirm the role of capital deepening of FDI They argued that the existence of foreign firms in the domestic market makes it easier for domestic firms to access new technology and invent new types of capital goods themselves In particular, domestic firms may be able to produce sophisticated capital goods themselves through imitation or adaptation by knowledge spillover from FDI In addition, FDI and movement of natural persons may be more important in the transmission of disembodied or tacit technologies Recently, Onodera (2009) argued on the role of multinationals on an economy First, innovation and average productivity will increase as firms become multinationals or their affiliates Secondly, if the share of multinationals and affiliates increase in the economy, this will also increase innovation and average productivity in the general economy Thirdly, there can also be an indirect effect, when there are spillovers to other purely domestic companies through enhanced competition, imitation and demonstration, worker mobility and spin-offs, and backward and forward linkages Besides, the innovation capacity could be also enhanced by knowledge spillovers from trade According to (Onodera, 2009), trade is an important conduit for the international transfer of technology and diffusion of innovation Firstly, innovation could be upgraded through Learning by Exporting Alvarez, R and López, R (2005) found a substantial amount of anecdotal evidence which point to the existence of “Learning-byExporting” whereby foreign customers provided information about product designs, materials, labelling, packaging and shipping, assistance to reduce costs and control quality, help in the factory layout In the other aspect, Imports of capital goods or inputs are recognized as an important conduit for technology diffusion as foreign machinery can embody more technology than domestic machinery, especially in the case of developing countries (Onodera, 2009) In particular, trade in intermediates is growing and domestic production increasingly relies on technology gained from foreign inputs as a source of innovation Technology gained through intermediate products may especially be important for exporters who are subject to greater international competition Damijan and Kostevc (2015) explained that adding and dropping of imported input varieties seem to be important for firms aiming to optimize their input mix and, in addition, to innovate products and introduce new varieties into firms’ export product scope In addition, international business literature suggested that firms engaging in either import or export activities are likely to benefit from the contacts with their suppliers and customers as well as from the increased competition faced in larger foreign markets Therefore, both importing as well as exporting activities may help firms become more innovative in terms of their production processes or products, which could, in turn, impact productivity growth and/or firm survival in the long run However, in some aspects, trade may have negative effects on innovation through negative effect of imports on scale economies, decreased rent available for innovation Increased competition through imports has a disciplining effect on domestic industries Recently, Navas (2015) clarified that differences in the degree of competition generate substantial differences in firms' innovative responses to 269 trade liberalisation A movement from autarky to free trade promotes innovation and productivity growth in those sectors which are initially less competitive 2.2 Empirical Studies Most studies on innovation at sectoral level have to generate sectoral variables from firm level data due to the lack of sectoral data Sectoral innovation is usually constructed from firm’s patent count (Capello & Lenzi, 2016; Piqueres Garcia, Serrano Bedia, & Lopez Fernandez,2015; Malerba et al., 2013; Li, Sutherland, & Ning, 2017; Lin, 2015) or innovation count (Castellacci, 2008) or value of sale of product (Hashi & Stojcic, 2013) The most simplest way to proxy sectoral innovation from firm level data is calculate the number of firms have at least one type of innovation (Aiello, Pupo, & Ricotta, 2015) or the proportion of firm having innovation activity (patents or some of innovation types stipulated by Oslo Manual) in the sector (Piqueres Garcia et al., 2015) In order to test the robustness of sectoral innovation proxies, Piqueres Garcia et al (2015) applied factor analysis to construct sectoral innovation variable Some other studies scrutinized the sectoral innovation by using the weight determined by the importance level of innovation (Capello & Lenzi, 2016) Besides sectoral innovation, spillovers variable is also a complex one to construct There are several types of spillover variables such as R&D spillover (Autant-bernard & Lesage, 2011; Moralles & Nascimento Rebelatto, 2016; Malerba et al., 2013), innovation spillover (Chen & Guan, 2016 ; Y.-H Chen, Nie, & Wang, 2015; Kováč & Žigić, 2016) or FDI spillover (Tian, 2016; C C Wang & Wu, 2016) The simplest way to construct spillover variable is to make sum without weighted For instance, Tian (2016) calculated inside FDI spillover of a sector by the foreign presence in that sector and the outside FDI spillover if a sector is the foreign presence outside the sector where a domestic firm is located The authors usually weight the spillover by the linkage between sectors or the similarity or geographical proximity among firms or sectors Goya, Vayá, & Suriñach (2016) calculated intra-industry spillover innovation by the total R&D stock carried in the sector at specific time and inter-industry spillover innovation by the total of the R&D stock from other sector weighted by the intermediate purchase between two sectors which usually stems from Input and Output There are various estimation method in investigating determinants of sectoral innovation from OLS regression to multi-level analysis Multiple regression is usually used with two or three stages least square (2 or SLS) ( Kováč & Žigić, 2016; Lee, Kim, & Lee, 2017; Luo, Guo, & Jia, 2017) Some studies with dependent variable such as patent count usually utilized Tobit or negative binominal regression (Läpple, Renwick, Cullinan, & Thorne, 2016; Buerger & Cantner, 2011; Castellacci, 2008) Some other studies which paid attention on geographical characteristic of the sectors applied spatial regression ((Autant-bernard & Lesage, 2011) There also existed some studies that jointly analyzed the effect of sectoral level and regional level on firm innovation Those studies usually applied multi-level regression (JianCheng Guan & Pang, 2017; Aiello, Pupo, & Ricotta, 2015) In regarding to the findings of sectoral innovation, some studies confirmed the determinants not related to spillover (Piqueres Garcia, Serrano Bedia, & Lopez Fernandez, 2015; Chamberlin, Doutriaux, & Hector, 2010) Several studies found the positive effect of R&D on sectoral innovation (Autant-bernard & Lesage, 2011; Kaygalak & Reid, 2016; Malerba et al., 2013) Autant-bernard & Lesage (2011) confirmed that both private R&D and public R&D have positive effect on sectoral innovation Kaygalak & Reid(2016) explored that innovation process in Turkey are highly concentrated geographically than organizational proximity Malerba et al (2013) showed that intersectoral effect of R&D spillover have key impact on innovation activities and that domestic R&D has a stronger effect than international R&D Research Methodology 3.1 Research Model 270 Theoretical framework has implied on the spillover effects of knowledge to innovation It means that capacity of innovation in this sector does not only depend on R&D, FDI and Trade of this sector but also those of the other sector Moreover, capacity of innovation in this sector may have effect on this capacity of the other sector through transactions This study shall investigate this spillover basing on the interesting ideas of spatial regression models Spatial regression models exploit the complicated dependence structure between units, the effect of an explanatory variable’s change for a specific unit will affect the unit itself and, potentially, all other units indirectly (Belotti, Hughes and Mortari, 2016).While locations are attributed to the dependence structure between units in spatial models, this study suggest that the transaction among sectors shall determine the dependence structure among sectors to build up the following model: 𝑦𝑖𝑡 = δ ∑𝑛𝑗=1 ᵂ𝑖𝑗 𝑦𝑗𝑡 + ∑𝑛𝑗=1 ᵂ𝑖𝑗 𝑋𝑘𝑗𝑡 θk + Zkt βk + 𝜇𝑖 +εit (*) (t=1……T, i=1…n) In this model, yit is the innovation capacity of sector i in the period t W ij yjt is the interaction weighted dependent variable, ᵂ𝑖𝑗 𝑋𝑘𝑗𝑡 is the interaction weighted regressors and Zk are control variables This model shall be implemented on the basis of spatial econometric However, wij herein is the transaction weights matrix rather than spatial weights matrix This matrix reveals the interdependence between sectors basing the transaction of input between sectors It means that the dependence of the sector i on the sector j is determined by the input value is supplied from the sector j to the sector i This study constructs the transaction weighted matrix basing on the data of Input/Output table in the year of 2012 To row-standardize the matrix, the study divide each element in a row by sum of elements in the row Thus a transaction weights matrix W, with element wij is defined by Wij = 𝑤 ̃𝑖𝑗 / ∑𝑗 𝑤 ̃𝑖𝑗 3.2 Model Estimation Strategy The dependent variable 𝑦𝑖𝑡 in (1) is respectively measured by S_modified and S_Innovation Each this variable shall be regressed by the following strategy Firstly, the model only consists of the interaction weighted regressors , ᵂ𝑖𝑗 𝑋𝑘𝑗𝑡 , namely S_RD_meanit, S_FDI_Supplierit, S_FDI_Customerit, S_exportit and S_InputImportit Then each model shall be added by control variables, Zkt, including S_hMono, S_Competitors and S_CtoS Finally, the dummy variables which divided the sectors according to the classification of Pavitt (1984) are included in the model to investigate whether the spillover has different among groups of sectors Additionally, the study excludes the interaction weighted dependent variable as on the basis of Moran test, there does not exist the interaction weighted dependent variable In particular, the following models are respectively regressed: S_modifiedit = β1 S_RD_meanit + β2 S_FDI_Supplierit + β3 S_FDI_Customerit + β4 S_InputImportit + β5 S_exportit + δ1 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_RD_meanit + δ2 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_FDI_Supplierit + δ3 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_FDI_Customerit + δ4 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_InputImportit + δ5 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_exportit + 𝜇𝑖 +εit (1) S_modifiedit = β1 S_RD_meanit + β2 S_FDI_Supplierit + β3 S_FDI_Customerit + β4 S_InputImportit + β5 S_exportit + δ1 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_RD_meanit + δ2 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_FDI_Supplierit + δ3 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_FDI_Customerit + δ4 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_InputImportit + δ5 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_exportit + β5 S_Competitors + β6 S_CtoS + β7 S_hMono + 𝜇𝑖 +εit (2) S_Innovationit = β1 S_RD_meanit + β2 S_FDI_Supplierit + β3 S_FDI_Customerit + β4 S_InputImportit + β5 S_exportit + δ1 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_RD_meanit + δ2 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_FDI_Supplierit + δ3 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_FDI_Customerit + δ4 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_InputImportit + δ5 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_exportit + 𝜇𝑖 +εit (3) S_Innovationit = β1 S_RD_meanit + β2 S_FDI_Supplierit + β3 S_FDI_Customerit + β4 S_InputImportit + β5 S_exportit + δ1 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_RD_meanit + δ2 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_FDI_Supplierit + δ3 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_FDI_Customerit + δ4 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_InputImportit + δ5 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_exportit + β5 S_Competitors + β6 S_CtoS + β7 S_hMono +εit (4) 271 + 𝜇𝑖 S_Innovationit = β1 S_RD_meanit + β2 S_FDI_Supplierit + β3 S_FDI_Customerit + β4 S_InputImportit + β5 S_exportit + δ1 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_RD_meanit + δ2 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_FDI_Supplierit + δ3 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_FDI_Customerit + δ4 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_InputImportit + δ5 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_exportit β6 PSector2 + β7 PSector3 + β8 PSector4 + 𝜇𝑖 +εit (5) S_Innovationit = β1 S_RD_meanit + β2 S_FDI_Supplierit + β3 S_FDI_Customerit + β4 S_InputImportit + β5 S_exportit + δ1 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_RD_meanit + δ2 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_FDI_Supplierit + δ3 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_FDI_Customerit + δ4 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_InputImportit + δ5 ∑𝑛𝑗=1 ᵂ𝑖𝑗 S_exportit + β5 S_Competitors + β6 S_CtoS + β7 S_hMono + β8 PSector2 + β9 PSector3 + β10 PSector4 + 𝜇𝑖 +εit (6) These model are implemented to test the following hypothesis: H1a: The research development in the sector i may have positive impact on the innovation capacity of this sector H1b: The innovation capacity in the sector i may be positively affected by R&D from other related sectors H2a: The transaction with FDI enterprises in the sector i may enhance the innovation capacity of this sector H2b: The innovation capacity in the sector i may be affected by the transaction with FDI enterprises in other related sectors H3a: The export of the sector i may upgrade the innovation capacity of this sector H3b: The innovation capacity of the sector i may be affected by export of other related sectors H4a: The input import of the sector i may have positive impact on the innovation capacity of this sector H4b: The innovation capacity of the sector i may be affected by the input import of other related sectors Table Description of variables in the models Variables Description Variable name Measurement yit The innovation capacity S_modifiedit The percentage of firms having modification on existing technology in the sector i The percentage of firms having at least one following activities: obtaining patents, having modification of the existing technology, having developed any technology which is interesting for other firm or used outside the enterprise in the sector i The percentage of firms doing R&D activities The average percentage of input in the sector i is supplier by FDI firms The average percentage of output in the sector i is sold to FDI firms The average percentage of input in the sector i is imported The average percentage of output in the sector i is exported S_Innovationit Xit Zit The R&D activities in the sector The suppliers in the sector i are FDI firms S_RD_meanit The customers in the sector i are FDI firms S_FDI_Customerit Input Import of the sector i Export of the sector i S_InputImportit Control variables Competition in the sector i S_FDI_Supplierit S_Exportit S_Competitors 272 The average number competitors in the sector i Expected signs of + + + +/+ + Profitability in the sector i S_CtoS Monopoly in the sector i S_hMono Dummy variables PSector1 PSector2 PSector3 PSector4 The average cost is divided by the average sales in the sector I in the previous period t-1 The average number of firms in the sector i perceived themselves as significant autonomy in setting prices Supplier dominated sectors Scale-intensive producer sectors Science-based sectors Specialized equipment supplier sectors - ? 3.3 Data This study aggregates the sectoral data basing on the firms ‘information from Vietnam Enterprises Survey (VES) and Vietnam Technology and Competitiveness Survey (TCS) from the year of 2010 to 2013 These survey have been done in the collaboration of the Centre Institute for Economic Management (CIEM), the General Statistics Office (GSO) and the Development Economics Research Group (DERG) of the Department of Economics (DoE), University of Copenhagen The TCS is a part of the GSO VES that focuses on innovation and technology of firms which are the subset of firms surveyed in VES and conducted annually from 2010 to 2014 According to CIEM et al (2015), this is a rare and valuable data due to its longitudinal nature of the dataset and the detail information on innovation and technology which is unique not only for Vietnam but also for emerging countries Moreover, the panel data allows to look at the country’s performance of manufacturing sector overtime through a series of carefully chosen indicators While the VES collects firm-level data provides general information about firm characteristics or production and output, the TCS focuses on firm-level measures of investment in technology innovation In particular, TCS enables researchers to have in-depth examination of channels through which firms improve methods, process and physical equipment used in production Additionally, the survey examines the transfer of technologies and spillovers in the productive economy (CIEM et al., 2015) Besides VES, this study adopts the Input/Output Table in the year of 2012 to construct the weigh matrix of interdependence between sectors In particular, only manufacturing sectors are collected and analyzed 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 Basing on the division of sector in Input/Output Table, the study make group of manufacturing sectors in VSIC to 38 manufacturing sectors in correspondence to Input/Output table (as detail in The Appendix 1) This generates a panel data of 152 observations from the year of 2010 to 2013 Result Analysis and Policy Implication 4.1 Some information on transfer channel of technology and innovation in Vietnam Figure Main Supplier of Technology 273 Source: CIEM et al (2015) The Figure illustrates the trend of technology transfer from the Vietnamese and foreign firms in the same and other sector It can be seen from the figure that technology transfer from Vietnamese firms outnumbered the one from foreign firms ate the beginning of the period However, the trend of technology transfer from foreign firm remained steady whereas the transfer from Vietnamese firms fluctuated sharply in the period from 2009 to 2010 In particular, the technology transfer from Vietnamese firms in the same sector considerably decreased from the peak at approximately 100 percentages in the year of 2009 to under 20 percentages in the following years In contrast, the technology transfer from Vietnamese firms in the other sector dramatically increased from around 0% in the year of 2009 to the peak at 70 percentages in the year of 2010 It can be said that the Vietnamese firms in the other sector had more and more important role than the ones in the same sector from the year of 2010 and this trends remained stable in the following years Figure The technology transfer from Customers Source: CIEM et al (2015) The Figure demonstrates the technology transfer from Domestic and International customers from the year of 2009 to 2013 There was few technology transfers from Domestic and International customers in the period Though the technology transfer from the customers remained steady, this transfer was at very low rate In most of the years, the technology transfer from Vietnamese customers was under 10 percentages while the one from foreign customers remained under percentages In general, technology transfer from the customers have not made considerable contribution in this period Figure The main export markets of Vietnamese firms 274 Source: CIEM et al (2015) The Figure shows the top ten export markets of Vietnamese firm from the year of 2009 to 2013 It is apparent from the figure that most of top ten export markets are Asia countries, except the United States and two Europe countries (Germany and France) In addition, the rank of top ten export markets had a slight change through the years If in the year of 2009, the top three export markets are respectively Japan, Taiwan and China, in the following years, the United States reached to be the first export market Besides, the share of export sales was less concentrated on the top three markets at the end of the period In the year of 2009, the top three markets accounted for more than 60 percentages of exported sales However, the exported sales have steeply increased in the other top ten markets in the following years It could be said that this was a good signal due to the diversified trend and less dependence on only few export markets of Vietnamese firms Figure Sources of Imported Inputs of Vietnamese firms Source: CIEM et al (2015) The Figure presents the main sources of imported inputs of Vietnamese firms The top seven countries which supplied input to Vietnamese firms were easily recognized from the figure and the rank of these countries remained stable from the year of 2009 to 2013 China, Taiwan and Japan were the top four suppliers of inputs in this period with more than 50 percentages of imported input amount In particular, the inputs imported from China and Taiwan had a slight increasing trend while the ones from other top seven suppliers remained steady Figure Originality of Research Output 275 Source: CIEM et al (2015) The Figure depicts the Research and Development activities and the Research Output of Vietnamese firm from the year of 2009 to 2013 Looking first at share of firm performing R&D activities There were 38,731 firms having R&D investment, accounted for around 9% of the surveyed sample since 2009 As can be seen from the figure, the number of firms performing R&D activities had a significant decline from 769 firms in 2009 to 477 firms in 2013 This decrease could be also seen though the difference between the number of all firms and balanced firms In the period from 2009 to 2013, the total number of firms investing in R&D are 3,470; however, only 2,392 firms invested in R&D through the years in the period In terms of the research output of this R&D investment, most of the output are new to enterprises or new to market It is represented that only 3% of research expenditure is spent for frontier research which is dedicated to generate a product new to the world The majority of research expenditure (53%) is dedicated to develop technology that is new to the market Moreover, it can be seen that the research investment was more and more spent for the output new to the market that the one new to only enterprises It could be said that that signal is good to enhancing innovative capacities of Vietnamese firms Figure Share of firms doing research and adaptation Source: CIEM et al (2015) The Figure indicates the share of firm doing research and adaption which is to purchase already existing technology and adapt it to the firm’s needs The 2009-2013 TCS panel data contains repeated information about research and adaptation for around seven thousand firms (CIEM, 2015) As can be seen from the figure, 276 majority of surveyed enterprises did not engage in any technology adaptation or R&D activities Only 7% of firms pursue either R&D or adaptation, while 3% of firms pursue both R&D and adaptation in the balanced panel It is noticed that there was a declining trend in any form of adaptation and R&D activities, with adaptation declining sharply from 16% to 3% in 2013 Meanwhile, R&D activities have returned to 5% after peaking at 8% in 2010 It can be concluded that both R&D and adaption activities have been not enhanced by Vietnamese firms during the period 4.2 Descriptive Statistics on variables in the model Table Descriptive Statistics Variable Observation Mean Standard Deviation Min Max S_Patent 152 2.11 4.70 38.00 S_T_modified 152 0.72 1.22 9.19 S_Innovation 152 45.53 40.15 100 S_RD_mean 152 1.76 2.49 18.37 S_FDI_Supplier 152 3.33 4.47 20.40 S_FDI_Customer 152 20.61 15.27 61.50 S_InputImport 152 32.33 19.29 76.48 S_export 152 31.35 22.41 0.23 84.55 S_Competitor 152 28.00 13.75 7.46 87.36 S_CtoS 152 0.94 0.64 0.64 8.44 S_hMono 152 20.04 6.38 50 The Table presents descriptive statistics of variables in the model in the period from the year of 2010 to 2013 There are 38 analyzed sectors through consecutively years that generates a panel data of 152 observations at sector level As stated in the previous section, innovation capacity of the sectors is measured by S_Patent, S_T_modified, S_Innovation Looking first at S_Patent which is the total number of international or national Patent obtained by firms in each sector As shown in the Table 2, the average number of these Patents is 38 analyzed sectors is 2.11 The majority of sectors had no Patents during the period but there still exist the sector which obtained the peaked average number of 38 Patents The next variables revealing innovation capacity are S_T_modified which shows the percentage of firms in the sector which have modification of already existing technologies that are new to the enterprise or to the country It can be seen in the Table 2, the average percentage of firms which have modified the existing technologies is only 0.72% across sectors The number of firms having these activities are still at low level when the sector has highest level of modifying the existing technology at only 9.19% of total number of firms The final variable of innovation capacity is S_Innovation constructed by a complex proxy This variable considers the share of firms which have at least one of the following activities including: obtaining patents, having modification of the existing technology, having developed any technology which is interesting for other firm or used outside the enterprise According to the Table 2, the average percentage of firms which have at least one of the above innovation activities is 45.53 % across sectors The deviation of this variable is considerably high as a number of sectors have no firm performing these activities whereas there exists some sectors have all surveyed firms doing at least one of these activities In terms of R&D activities, the average percentage of firms performing these activities in sectors was significantly small at only 1.76% The sector having highest percentage of firms doing these activities peaked at 18.37% In regarding to the commercial transaction with FDI firms, the percentage of output sold to FDI firms sharply outnumbered the percentage of input supplied by FDI firms As can be seen in the table, the 277 average percentage of sales stem from FDI customers is 20.6% whereas the average percentage of input from FDI suppliers is only 3.33% There existed the sectors which have up to averaged 61.5% of its output sold to FDI customers It could be confirmed that the FDI firms are more likely to be customer than to be supplier of Vietnamese firm In respect of trade, the average percentage of exported sales is equivalent with the average percentage of imported input across sectors at slightly higher than 30% There existed the sector which has no export or no imported input Meanwhile, there are the sector which have the highest average percentage of sales to be exported at 84.55% and the sector which have the peaked average percentage of imported input at 76.48% Finally, regarding to controlled variables, there are variables constructed to measure the competition level, the market structure and the profitability in each sector According to the Table 2, the average number of competitors perceived by firms across sectors is 13.75 There is the sector perceived at strongly competitive level with more than 87 competitors; whereas, there is the sector with only approximately 14 competitors In respect of market structure, this characteristic is measured by perceived as significant autonomy in setting prices The average percentages of firms perceived them to be significant autonomy in setting prices across sectors is 20.04% There are the sector having only 5% of firms perceived them to be significant autonomy in setting prices but also the sector with up to 50% of firms having that perception In regarding of profitability, the study calculates the average cost over sales in the previous period by the variable S_CtoS According to the Table 2, the sector has highest profitability at only 64.35 percentages of cost over sales However, there still exists the sector which have costs many times higher than sales 4.3 Analysis on Model Results Table The estimation results of the regression model and model S_T_modified S_T_modified Direct Indirect Total Direct Indirect Total S_RD_mean 0.268*** 0.170*** 0.438*** 0.252*** 0.147*** 0.399*** S_FDI_Supplier -0.055*** -0.100** -0.155*** -0.042** -0.126*** -0.168*** S_FDI_Customer 0.011 0.036*** 0.047*** 0.012 0.046*** 0.058*** S_InputImport -0.014** -0.037*** -0.051*** -0.015*** -0.038*** -0.053*** S_export 0.008* 0.025*** 0.033*** 0.006 0.024*** 0.030*** -0.016*** -0.001 -0.017*** S_Competitors S_CtoS -0.102 -0.004 -0.105 S_hMono -0.020 -0.0008 -0.021 152 152 152 n 152 152 152 Adj R2 within 0.24 0.3 between 0.75 0.75 overall 0.53 0.56 Hausman test: chi2(11) = 7.59 Hausman test: chi2(14) = 11.18 Prob>=chi2 = 0.749 Prob>=chi2 = 0.6722 ***: 1% significant level; **: 5% significant level; *: 10% significant level As shown in the Table 3, the Hausman test approved the random effect model Besides the study also the test of Spatial Autoregressive Model (SAM) over Spatial Durbin Model (SDM) and the test results 278 approved the random effect SDM model In general, the sign and significant level of both models have considerably consistent The probability of changing existing technology in the sector i may be positively affected not only by directly its level of R&D activity but also by indirectly others’ level of R&D activity, the transaction with FDI customer and export Among three channels of knowledge spillover, R&D activity has strongest level of spillover at significant level 1% In particular, if the average percentage of number of firms which engaged in R&D activities increase 1% in the sector i, the average number of firms in this sector which may have change in existing technology may be increased by 0.268% as in the model or by 0.252% as in the model In addition, if the average percentage of number of firms which engaged in R&D activities increase 1% in the other sectors, the average number of firms in the sector i which may have change in existing technology may be increased by 0.17% as in the model or by 0.147% as in the model This result confirmed both the internal sector and external sector of R&D spillovers This exploration is consistent with the findings of Moralles et al.(2016), Piqueres Garcia et al.(2015) and Cohen and Levinthal (1989) Moreover, this finding is an evidence of some theory arguments that firms invest in own R&D to be able to utilize information which is available externally and R&D obviously generates innovations, it also develops the firm’s ability to identify assimilate, and exploit knowledge from the environment that is called a firm’s learning or “absorptive” capacity (Cohen and Levinthal ,1989; Aghion and Jaravel, 2015) In respect of policy, this evidence encourages the government to develop R&D policy to enhance innovation capacity for sustainable growth In contrast, FDI supplier and imported input may have both directly and indirectly negative effect on the probability of changing existing technology in a sector Most of these effects have significant level at 1% These negative effects implies that the dependence on input from FDI or foreigner’s firm may constrain the domestic firms in adaption or changes in existing technology Table The estimation results of the regression model and model S_Innovation S_Innovation Direct Indirect Total Direct Indirect Total S_RD_mean 3.303*** 6.820*** 10.122*** 3.208*** 6.742*** 9.950*** S_FDI_Supplier 1.730*** 3.336*** 5.067*** 1.740*** 3.018** 4.758*** S_FDI_Customer 0.979*** 0.880** 1.859*** 0.995*** 0.953** 1.947*** S_InputImport -0.220*** -0.639** -0.860** -0.246*** -0.705** -0.951** S_export 0.232*** -0.272 -0.040 0.241*** -0.210 0.030 S_Competitors -0.044 -0.051 -0.095 S_CtoS -0.955 -1.136 -2.091 S_hMono 0.076 0.099 0.174 152 152 152 n 152 152 152 Adj R2 within 0.94 0.94 between 0.15 0.16 overall 0.9 0.9 ***: 1% significant level; **: 5% significant level; *: 10% significant level In comparison with S_modified, S_Innovation considers innovation activities not only changing the existing technology but also the number of patents or technology which is interested or used by other firms However, the findings as in the Table are gradually consistent with the ones in the Table The innovation capacity of the sector i is also significantly positively affected not only by R&D activities and FDI customers 279 in this sector but also by these determinants of the other sectors Additionally, imported input has also negative direct and indirect effects on sector innovation capacity at high significant level However, these marginal effects of all these determinants on S_Innovation (as in the Table 4) are all considerably higher than the ones on S_Modified (as in the Table 3) In contrast, customers which are FDI firms herein may have positive effects on innovation capacity that is consistent with Kováč and Žigić (2016) Export activity is found to have positive direct effect on innovation capacity at 1% significant level but no significant indirect effect on the other sectors Table The estimation results of the regression model and model S_Innovation S_Innovation Direct Indirect Total Direct Indirect Total S_RD_mean 3.156*** 5.304*** 8.459*** 3.159*** 5.301*** 8.460*** S_FDI_Supplier 1.876*** 3.046*** 4.922*** 1.860*** 3.053*** 4.913*** S_FDI_Customer 0.911*** 0.792** 1.703*** 0.921*** 0.761** 1.682*** S_InputImport -0.178** -0.483* -0.661** -0.182* -0.457 -0.639** S_export 0.216*** -0.139 0.077 0.219** -0.130 0.089 S_Competitors 0.013 0.014 0.026 S_CtoS -0.625 -0.649 -1.274 S_hMono 0.041 0.046 0.087 PSector2 6.023*** 6.226* 12.248** 6.060* 6.467* 12.528** PSector3 1.509 1.598 3.107 1.484 1.776 3.260 PSector4 12.741** 13.383** 26.124** 12.828** 13.776** 26.604* 152 152 152 152 152 152 n Adj R2 within 0.93 0.94 between 0.21 0.2 overall 0.91 0.91 ***: 1% significant level; **: 5% significant level; *: 10% significant level The Table shows the determinants on sector innovation capacity in consideration of different groups of sector In comparison with the Table 4, all these findings are consistent with the ones in the Table Additionally, the Table presents that in comparison with the supplier dominated sector (PSector1), the scaleintensive producers sector (PSector2) and the specialized equipment supplier sector (PSector 4) may have stronger positive effects on innovation capacity It means that firms in these sectors may have internal spillovers among firms in the sector Also these sectors may also have higher absorptive capacity of external knowledge from the other sectors This evidence is available for policymakers in choosing the core sectors to enhance innovation capacity of the economy 4.4 Conclusion This study have interesting findings which are not only considered as an evidence of theory on knowledge spillover but also valuable for policymakers in enhancing innovation capacity The first finding is to confirm the role of R&D as well as the existence of R&D spillover on sector innovation capacity This is obvious evidence on the theory suggested by Cohen and Levinthal (1989) that R&D does not only generate invention but also enhance firms’ absorptive capacity to imitate or adapt the existing technology Moreover, the R&D spillover effect also brings policymakers to a valuable evidence to enhance subsidy on R&D for innovation 280 capacity The next findings bring an evidence on the role of transaction with FDI firms on innovation capacity The more output is supplied to FDI firms, the more knowledge domestic firms may gain to enhance innovation capacity However, the dependence of imported input may distort innovation capacity both directly and indirectly This implies that there should have policy to develop the sectors which supply inputs 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Literature... innovation including radical and incremental innovation According to Barbieri & Álvares (2016), ideas for radical innovations are inventions, models, proposals, plans and other ways of explaining