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.
INTRODUCTION
PROBLEM STATEMENT
1.1.1 The significance of the research’s topic
1.1.1.1 The importance of the research the role of knowledge spillovers on innovation at sector level
Innovation and knowledge spillover are crucial for the economic development of countries, particularly in developing nations Theoretical frameworks such as endogenous growth theory and new growth theory highlight the significance of innovation and technological change as key drivers of long-term economic growth.
Innovation often stems from investments in knowledge and the resulting knowledge spillovers Griliches (1992) highlighted that these investments tend to benefit third-party firms that can commercialize ideas without bearing the full costs Key scholars like Romer (1986), Lucas (1988, 1993), and Grossman and Helpman (1991) emphasized that knowledge spillovers play a crucial role in driving endogenous growth.
The success of four Asian industrial countries underscores the critical role of innovation in industrialization However, Vietnam's performance in the 2016 Global Innovation Index, with a score of 35.4 and a rank of 59 out of 128 countries, highlights its need for improvement, especially compared to ASEAN peers like Singapore (7), Malaysia (33), and Thailand (48) Consequently, enhancing innovation capacity has become a key focus for Vietnamese policymakers, who must understand the mechanisms of knowledge spillover to drive economic growth and development.
The capacity for innovation at the sector level, along with the channels for knowledge spillovers, plays a crucial role in shaping effective policies for growth driven by innovation Analyzing sectors provides valuable insights into the relationship between knowledge accumulation and diffusion Mehrizi and Ve (2008) highlighted that sector-level analysis helps connect firm-specific factors with broader macroeconomic conditions Similarly, Malerba (2002) underscored the importance of sector-level analysis in exploring innovative and production activities Furthermore, Padoan (1999) noted that adopting a sectoral perspective can enhance the understanding of knowledge accumulation and diffusion processes.
The innovation capacity of a firm or sector is influenced not only by its own knowledge and technology but also by knowledge spillovers from other firms and sectors This interdependence highlights the importance of collaboration and shared expertise in driving innovation, as noted by Aghion and Jaravel.
Innovations in one firm or sector often rely on knowledge generated by innovations in others, leading to a spillover effect that can reduce firms' incentives to invest in Research and Development (R&D) However, recent studies by economists Cohen and Levithal (1989) and Onodera (2009) propose that R&D efforts can actually improve a firm's absorptive capacity This enhancement not only benefits the firm itself but also contributes to the advancement of its sector through internal spillovers and facilitates knowledge transfer to other sectors through external spillovers.
Research on the influence of knowledge spillover channels on sector innovation capacity is limited Most existing studies have focused on how sector characteristics affect innovation (Castellacci, 2008; Yurtseven & Tandoğan, 2012; Hage et al., 2013; Piqueres et al., 2015), while only a handful of recent works have explored the role of R&D spillover (Autant-Bernard and Lesage, 2011; Ang and Madsen, 2013; Malerba et al., 2013) Additionally, there is a scarcity of studies that address both sectoral and regional factors in the context of spillovers (Buerger and Cantner, 2011; Aiello and Ricotta).
Vietnam, there are few studies on innovation and most of these studies focused on firm level (Jordan, 2015; Doan Thi Hong Van and Bui Le Nhat Uyen, 2017;
This study, building on the work of Le Thi Ngoc Bich et al (2017) and Tran Hoai Nam et al (2017), aims to explore the impact of knowledge spillover on innovation across sectors It focuses on three key channels: research and development (R&D), foreign direct investment (FDI), and trade activities, utilizing spatial regression models for analysis.
1.1.1.2 The importance of the research on heterogeneity of firms’ TFP in considering both firms’ characteristics and spillover effects from sectors and regions
Sustainable economic development cannot solely rely on capital and labor; it requires Total Factor Productivity (TFP), which represents the output not attributed to these inputs In the Solow model (1956), TFP is viewed as a key driver of technical change essential for sustainable growth Countries like Korea and Singapore have demonstrated that TFP growth is crucial for their development, with Korea experiencing a significant increase from 8.3% in 1970-1980 to 31.5% in 1980-1990, as reported by the Central Institute for Economic Management (CIEM) in 2010 Consequently, enhancing TFP has become a central focus of development policy.
To derive valuable policy implications for development, it is essential to analyze the determinants of Total Factor Productivity (TFP), with technological progress being a key factor for sustained growth (Solow, 1956) However, Acemoglu (2009) argues that TFP heterogeneity is not solely attributable to technology; even firms using identical technologies can achieve varying efficiencies Capeda and Ramos (2015) further explain that TFP can be influenced by various components, including economies of scale and resource combination methods in production Despite adopting similar technologies, firms may still exhibit TFP differences due to sector characteristics or geographical location.
The variability in firms' total factor productivity (TFP) primarily stems from differences in their characteristics, including size, production technology, and human capital Additionally, sector-specific characteristics and external economies of scale significantly influence firm performance As highlighted by Krugman and Obstfeld (2009), the concentration of industries within a region can generate positive externalities such as specialized suppliers, labor market pooling, and knowledge spillovers Furthermore, the local environment, characterized by varying quantities and qualities of resources, can differentially benefit firms Their research supports the notion that external economies encourage the localization of sectors, prompting firms to cluster in proximity to maximize these advantages, as evidenced by the success of localized industries like Silicon Valley in California and the financial sector in New York.
Examining the determinants of firms' Total Factor Productivity (TFP) through a multilevel cross-classified model is crucial, as it isolates the impacts of various factors at different levels, including firm, sectoral, and regional dimensions This multi-level analysis effectively addresses error correlation among firms operating at the same level, providing a more accurate assessment of variance Unlike single-condition models that often underestimate variance by treating the entire sample as a whole, the multilevel cross-classified model ensures that variance at each level is appropriately evaluated.
However, most of studies on firms’ TFP focused on the determinants as firms’ characteristics (Sjoholm 1999; Blalock and Veloso, 2006; Waldkirch and Ofosu, 2010; Lopez, 2008; Baptist and Teal, 2014; Fernandes, 2008; Seker and Saliola,
2018) In Vietnam, studies on TFP are still very limited (CIEM, 2010) although
Total Factor Productivity (TFP) is increasingly recognized as a crucial factor in assessing the quality of development This research introduces a novel approach to examining TFP in Vietnam through the use of a multileveled cross-classified model Furthermore, the findings could inform policy recommendations that benefit not only individual firms but also entire sectors and regions.
1.1.1.3 The importance of research on the role of knowledge spillovers on sectoral innovation and firms’ TFP in the manufacturing industries in Vietnam
The manufacturing sector has significantly contributed to Vietnam's economy, accounting for 18.35% of the GDP and emerging as the leading industry in 2016 (APO, 2016) Additionally, from 2010 to 2016, it consistently ranked second in labor participation, following only the agriculture, forestry, and fishing sectors (APO).
2016) In comparison to other countries in the region, the output growth of manufacturing sector in Vietnam was in the top five at 9.3% in the period from
2000 to 2014 Moreover, the manufacturing in Vietnam was also the leading sector with the biggest contribution share to output growth at 26,98%.
Despite its significant role in the economy, Vietnam's manufacturing sector experienced low labor productivity growth of only 2.7% from 2000 to 2014, ranking just above Brunei and Indonesia, and far below China's impressive 7.6% Additionally, the percentage of enterprises adopting modifications to existing technology was a mere 0.87% between 2010 and 2014 When it comes to innovation activities, which encompass acquiring patents and developing new technologies, only 9.02% of enterprises engaged in such efforts, highlighting the need for improvement in Vietnam's manufacturing sector.
RESEARCH OBJECTIVES AND QUESTIONS
This study aims to explore how knowledge spillover influences innovation within different sectors The innovation capabilities of a firm or sector are derived not only from their internal knowledge and technology but also from the knowledge and advancements of other firms and sectors, highlighting the significance of knowledge spillovers.
2015) 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?
This study aims to explore how firm-level, regional, and sectoral characteristics influence firms' total factor productivity (TFP), addressing key research questions related to these impacts.
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 non-exported 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?
RESEARCH METHODOLOGY and RESEARCH SCOPE
This study investigates the impact of three channels of knowledge spillovers on sector innovation capacity by employing Spatial Regression analysis Additionally, a cross-classified model is utilized to assess the heterogeneity in firm productivity across three determinant groups: sector, regional, and firm levels The research leverages data from the Vietnam Enterprises Survey (VES) and the Vietnam Technology and Competitiveness Survey (TCS), alongside Input-Output (I/O) data.
Vietnam in 2012 Besides, the study also uses the annually surveyed data on province of General Statistics Office (GSO).
The analysis focuses on the impact of R&D, FDI, and trade on innovation within specific sectors, utilizing data from Vietnamese manufacturing firms between 2010 and 2014 Sectoral relationships are established through intermediary transactions identified in Vietnam's Input-Output framework.
2012 By spatial regression model, the study finds the direct as well as indirect impact of R&D, FDI and trade on sectoral innovation.
This study analyzes the impact of firm-level, regional, and sectoral characteristics on total factor productivity (TFP) in Vietnamese manufacturing firms from 2011 to 2014 Utilizing TCS and VES data, it examines firm-specific attributes and sectoral characteristics Additionally, annual data from the Province Competitive Index (PCI) is incorporated to evaluate provincial human resources.
RESEARCH CONTRIBUTION
This study offers valuable contributions from both theoretical and policy perspectives by developing a framework and testing the hypothesis of knowledge spillover at the sector level Utilizing a Spatial Regression Model, it investigates the knowledge spillovers among sectors while exploring contextual factors influencing firms' total factor productivity (TFP) The application of a Cross-classified Model allows for an analysis of the spillover effects of innovation activities at the sector level and human resources at the provincial level on firms' TFP These insights are particularly relevant for Vietnam, as identifying core spillover factors that enhance sector innovation capacity provides crucial information for policymakers Furthermore, the Cross-classified Model highlights the significance of firm characteristics and sectoral and provincial attributes in relation to firms' TFP.
This study builds upon the frameworks of knowledge spillovers established by Cohen and Levinthal (1989) and Griliches (1992), examining both intra-sector and inter-sector dynamics Utilizing Spatial Regression Models, it investigates the direct and indirect effects of R&D, FDI, and trade on sectoral innovation This approach provides valuable insights for identifying effective channels to boost innovation activities across sectors.
This study utilized the Cross-Classified Multilevel Model to investigate the heterogeneity of firms' Total Factor Productivity (TFP) in Vietnam, assessing the influence of firm-level, sectoral, and provincial factors Additionally, it highlighted the spillover effects of sectoral innovation based on Griliches' framework and provincial human resources as per Moretti's research The findings suggest that understanding these spillover effects can inform effective sectoral and provincial policies aimed at boosting productivity.
STRUCTURE OF THIS STUDY
This study is organized into five chapters, beginning with an Introduction The second chapter provides a Literature Review, which includes both the Theoretical Framework and Empirical Studies related to two overarching objectives The subsequent chapter focuses on Methodology, detailing the Spatial Regression Model and the Cross-Classified Model.
This article outlines the structure of the research, including the Model Specification, Variable Measurement, and Data It features two main chapters: one focusing on the results and discussions regarding Sectoral Innovation and Spillover Effects, and the other addressing the heterogeneity in Total Factor Productivity (TFP) among Vietnamese manufacturing firms The final chapter presents the Conclusion and Policy Implications.
LITERATURE REVIEW
DEFINITION AND CONCEPTS
Knowledge spillovers occur when entities benefit from others' investments in knowledge without bearing the full cost, stemming from the public good characteristics of knowledge, which is non-rival and non-excludable Arrow (1962) highlighted that knowledge's non-rival nature allows it to be used without depletion, while its non-excludable nature makes it difficult to restrict others from utilizing it Kaiser (1960) noted that such spillovers often arise from inadequate protection of innovations within firms Griliches (1992) further emphasized that investments in knowledge are likely to spill over, enabling third-party firms to commercialize ideas without incurring the complete expenses associated with accessing and implementing them.
Knowledge spillovers can occur voluntarily or involuntarily among agents, as highlighted by Romer (1990) Knowledge is generally categorized into codified and tacit forms While codified knowledge, such as patents, can be partially protected as explicit information, tacit knowledge poses a greater risk for unintentional disclosure since it resides in the skills and experiences of employees Consequently, tacit knowledge is a significant contributor to knowledge spillover.
Firms can access external knowledge through various methods, including market transactions, formal collaborations, and informal interactions They may purchase knowledge assets outright or engage in partnerships with other organizations to enhance their knowledge base Additionally, firms can tap into valuable insights from customers, suppliers, and competitors, allowing them to identify market opportunities and emerging trends by understanding customer needs and expectations Knowledge can also be gleaned from the products provided by suppliers and competitors, further enriching a firm's external knowledge pool.
Most of studies on knowledge spillovers made distinguish between
Knowledge flows can be categorized as 'horizontal' or 'vertical' spillover Horizontal spillover occurs when both the knowledge sender and receiver operate within the same industry, while vertical spillover takes place when they belong to different industries For firms to effectively recognize, access, or adopt external knowledge, they must possess a certain level of absorptive capacity, which refers to their ability to recognize, absorb, and utilize new external information (Cohen and Levinthal, 1989).
Knowledge spillover is crucial in both economic literature and public policy Griliches (1992) highlighted that the "New" growth economics suggests that sustainable economic growth cannot continue at a steady pace without significant externalities, spillovers, or other factors contributing to social increasing returns.
A recent study from 2018 highlights that the interplay between spillover effects and other externalities significantly influences whether an economy underinvests or overinvests in knowledge In developed nations, strong spillover effects have led to substantial subsidies for knowledge investment For developing countries, leveraging knowledge spillovers could be a key strategy to boost economic growth.
Innovation encompasses a range of definitions based on novelty, area of change, and measurement It is characterized by five key aspects: the launch of a completely new product, the implementation of an untested production method, the exploration of a previously unentered market, the sourcing of new raw materials or semi-finished products, and the establishment of a new organizational structure within an industry According to the OECD (2005), innovation is defined as the execution of a new or significantly enhanced product (goods or services), process, marketing method, or organizational method in business practices, workplace organization, or external relations.
Innovation differs from invention primarily in its novelty degree, with invention being defined as a "global first" or "new to the world" concept that holds little value unless applied In contrast, innovation encompasses ideas that are "new to the firm" or "new to the market" (Onodera, 2009) This distinction leads to two main types of innovation: radical and incremental Radical innovations often stem from inventions or other forms of intellectual creation (Barbieri & Álvares, 2016), while incremental innovations typically emerge from specific activities implemented without formal processes Consequently, the terms improvement and incremental innovation are often used interchangeably, with continuous improvements signifying ongoing incremental innovations.
The concept of innovation, particularly in the context of change, is largely rooted in the Schumpeterian approach (1943) As noted by Martin (2016), during the 1960s, innovation was primarily understood and evaluated through the lens of technology-driven advancements in manufacturing, with a focus on research and development (R&D) and patenting, reflecting the dominance of manufacturing in the economies of developed countries Early definitions emphasized the importance of both product and process innovation (Pavitt).
The trend of economic development has led to increased focus on organizational growth and marketing strategies, particularly in relation to innovation as defined by the OECD (2005) Innovation encompasses the introduction of new or significantly improved products, processes, marketing methods, or organizational practices within a business context It can manifest in various forms, being new to the firm rather than globally, and plays a crucial role in enhancing productivity and employment.
Different measurements of innovation arise from varying research objectives, methodologies, and interpretations of novelty Martin (2016) highlights that in the era of technology-driven innovation, which often involves patenting, pioneers in innovation studies have developed measurement tools utilizing indicators such as R&D funding, the number of researchers, and patent counts Patent counts are applicable in assessing regional innovation (Ponds et al., 2010; Capello & Lenzi, 2016; Wang et al., 2016) as well as firm innovation (Guan et al., 2015; Lin, 2015; Blazsek & Escribano, 2016; Li et al., 2017; Qiu et al., 2017).
Guan and Pang, 2017) and sectoral innovation (Buerger and Cantner, 2011). However, Martin (2016) argued that such indicators may be ‘missing’ much innovative activities that is incremental or is not patented.
2.1.3 Knowledge production function and the determination of innovation in this study
This study utilizes the knowledge production function (KPF) framework, originally introduced by Pakes and Griliches in 1984, to analyze innovation and its driving factors within the model Pakes and Griliches presented a simplified diagram of the knowledge production function to illustrate these concepts.
Figure 2.1 The framework of knowledge production function
The diagram illustrates that K˙ is generated by a knowledge production function (KPF) that converts previous research expenditures (R) and a disturbance term (U) into inventions This disturbance term accounts for the impact of additional nonformal R&D inputs and the inherent unpredictability involved in the invention process.
Pakes and Griliches (1984) analyzed the transformation function from r to k˙, proposing a Cobb-Douglas form for the Knowledge Production Function (KPF) that incorporates firm-specific constants and a time trend Their model is expressed as k˙ it = ai + bt + ∑ 8 c r i,t–c + u i,t, where ui,t represents an independent and identically distributed disturbance that is uncorrelated with r, capturing the randomness in the KPF The term ai accounts for firm-specific variations in the effectiveness of research efforts, influenced by factors such as appropriability environments, opportunities, and managerial abilities.
Next, the relationship between p and k˙ was presented as follows: pi,t = dt + þ kt˙
Where þ is the elasticity of patents with respect to knowledge increments, and d is a measure of the trend in factors determining the propensity to patent.
The component v∗ represents the portion of the detrended variance in patents that remains unexplained by the detrended changes in knowledge increments; thus, the variance in v∗ is considered "noise" within the patent measurement.
Combining two above equations, they reached the preferred functional form in their analysis as follows: pi,t = α + yt + ∑ wc r i,t–c + ∑ 5 Φ c r i,– c
O wc = þ8c , y = þ b + d, Φ = þƒ, 5i = vi + þ ui, and ci,t = vi,t + þ ui,t
THEORETICAL FRAMEWORK
2.2.1 Developing model on Knowledge Spillovers at sector-level
Due to the limited existing empirical and theoretical research on the interplay between R&D, FDI, trade activities, and innovation at the sector level, we initiated the development of a functional equation to establish connections among these variables within our dataset.
Cohen and Levinthal (1989) constructed a model of firm’s stock knowledge as follow: zi = Mi + yi (8 ∑j*i
The firm's stock of technological and scientific knowledge is influenced by its investment in R&D, denoted as Mi The firm's absorptive capacity, represented by yi, reflects its ability to assimilate and exploit knowledge available in the public domain Additionally, the degree of intra-industry spillovers, represented by 8, and the level of extra-industry knowledge, T, further impact the firm's innovation potential Other firms' investments in research and development also play a crucial role in shaping the competitive landscape.
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 sector s: Zs n i=
Similarly, Ms is the total input of knowledge in the sector s: Mc =∑ N Mi. (2.21)
Zc = Mc + 8 ∑ yi ( Mc − Mi) + N T ∑N
In the industry, N represents the total number of firms, while n denotes the subset of firms engaged in innovation activities Additionally, m indicates the number of firms actively involved in research and development (R&D) efforts within the industry.
Then the average knowledge stock in the industry is Z̅ = Zc
The average R&D investment in the industry is M¯ = Mc
We assume that zi = Z̅ + ði and Mi = M¯+ &i
n Zc + ∑ n ði = Mc + Mc 8 ∑ yi - 8 ∑N
Replace (2.28) into (2.31) we have: n Z c + ∑ ði = (N N
n Zc + ∑n ði = N Mc + (N − 1) Mc 8 ∑N yi + (Mc − N Mc ) +
(2.38) Besides intra-industry spillover, Griliches (1992) suggested the more complicated spillover when a whole array of industries which “borrow” different i= 1 i=
The aggregate knowledge acquired by the ith industry from various sources is influenced by their economic and technological proximity, as articulated by Griliches (1992).
Where Kj measures the levels available sources of knowledge while wij is the
“weighting” function that can be interpreted bas the effective fraction of knowledge in sector j by industry i One of earlier suggestions was based on
The "vertical" borrowing approach, as described by Griliches (1992), utilizes input-output tables to assess the inter-industry closeness based on their purchasing relationships This study focuses on examining rent knowledge spillovers both within and beyond a sector, differentiating them from pure knowledge spillovers by emphasizing those embedded in transaction goods Consequently, employing the transaction matrix derived from the Input and Output table is deemed most suitable for this analysis.
According to Griliches (1992), the total aggregate knowledge utilized by the ith industry from various sources is represented by T in equation (2.38) This T signifies the cumulative technological and scientific knowledge derived from all other sectors (k ≠ s), highlighting the interconnectedness of industry knowledge across n sectors.
The equation (2.40) illustrates the direct and indirect impacts of knowledge spillovers resulting from R&D, FDI, and trade activities, as outlined in the methodology chapter of our model.
In the equation (2.41), the left-hand side express the percentage of firms in the industry having innovation activities This percentage may depend on the R&D
Investment in the industry is directly linked to the percentage of firms engaged in R&D activities, highlighting the significant impact of R&D on innovation within the industry, as discussed in the methodology chapter.
2.2.2 Channels of knowledge spillovers and the research hypothesis of the first objective
The percentage of firms engaged in innovation activities within an industry is influenced by the spillover of technological and scientific knowledge from other industries, as indicated in equation (2.41) This knowledge can stem from R&D investments made in both the industry itself and in other sectors Griliches (1979) highlighted that the level of knowledge in any industry is not solely based on its own R&D investments but is also shaped by knowledge acquired from other sectors Consequently, the productivity of an industry is reliant on the R&D efforts of various industries This study aims to test the hypothesis that R&D in a specific sector positively impacts its innovation, while also considering the indirect influence of R&D in other sectors on that sector's innovation.
H12: The sectoral innovation in the sector i may be positively affected by the R&D from other related sectors.
In addition to R&D efforts, the technological and scientific advancements in an industry stem from both R&D investments and the vertical linkages created through foreign direct investment (FDI) transactions, as well as the embodied knowledge gained from trade activities Previous studies on technological progress highlight the significance of these factors in driving innovation and growth within industries.
According to Engelbrecht (1997), international trade and foreign direct investment (FDI) serve as the primary means for transferring external knowledge and foreign technologies between countries Engaging in activities like importing, exporting, and FDI not only facilitates this transfer but also fosters communication channels that encourage cross-border learning in areas such as production techniques, product design, organizational strategies, consumer preferences, and market conditions.
Knowledge spillover effects from foreign direct investment (FDI) are significant, as highlighted by studies from Jaffe (1986), Jaffe et al (1993), and Keller (2002) Geographical proximity plays a crucial role in these spillovers, suggesting that FDI may generate more externalities than international trade Hofmann and Wan (2013) identified various channels for knowledge spillover from FDI, which include horizontal or intra-industry spillovers, vertical inter-industry spillovers both downstream and upstream, and those occurring through the labor market.
Hofmann and Wan (2013) identified that foreign direct investment (FDI) can impact domestic firms in the same industry through four main channels: competition, imitation and adoption, labor turnover, and second-round effects via input suppliers The presence of multinational enterprises (MNEs) intensifies competition, potentially lowering prices and market shares for local firms, while simultaneously motivating them to innovate MNEs typically possess superior advantages in technology, management, and human capital, leading to knowledge spillovers as domestic firms may imitate their products and practices, a phenomenon known as the demonstration effect or learning by watching (Wang and Blomstrom, 1992) Additionally, domestic firms can benefit from labor turnover by hiring former MNE employees, facilitating knowledge exchange through social networks Lastly, second-round effects occur as domestic firms improve their operations through enhanced input and intermediate suppliers, driven by MNE requirements.
Foreign Direct Investment (FDI) can create significant vertical externalities that impact domestic firms across various industries, categorized as backward and forward vertical FDI Backward vertical externalities arise when multinational enterprises (MNEs) act as suppliers to domestic customers, while forward vertical externalities occur when MNEs serve as customers to domestic suppliers MNEs can facilitate direct knowledge transfer and demand higher quality products from suppliers, leading to improved standards Additionally, they may offer technological support and managerial training, further enhancing supplier capabilities The increased demand for intermediate goods from MNEs can help domestic suppliers achieve economies of scale, resulting in positive externalities through knowledge transfer and improved input quality and variety.
Markusen and Venables (1997) developed an analytical framework to evaluate the impact of industrial linkages, highlighting that foreign direct investment (FDI) can alter supply and demand dynamics across interconnected industries While FDI introduces increased competition that can adversely affect local businesses, it can also create beneficial effects for firms in other sectors For example, the influx of FDI may result in price reductions, fostering a more competitive market environment.
EMPIRICAL STUDIES
2.3.1 Empirical Studies on determinants of sectoral innovation
There are three main unit levels in investigating determinants of innovation.
Research at the sectoral level is less prevalent compared to national and firm levels, as noted by Buerger & Cantner (2011), Chamberlin et al (2010), Piqueres et al (2015), and Malerba et al (2013) Various theories, including R&D mainstream theory, industry lifecycles, evolution theory, and sectoral innovation systems, often explain the factors influencing sectoral innovation Malerba et al (2013) specifically examined national and international intersectoral R&D spillovers and their impact on innovative activities across six major industrialized nations from 1980 onward.
In 2000, Piqueres et al (2015) highlighted the importance of various factors influencing sectoral innovation capacity, drawing on industry lifecycles, evolution theory, and sectoral innovation systems They developed a model outlining the determinants of sector innovation capacity, which includes prior innovation experience, innovation infrastructure, and the overall structure of the sector.
Due to the scarcity of sectoral data, most studies on innovation at the sectoral level rely on generating sectoral variables from firm-level data Sectoral innovation is often measured through metrics such as firm patent counts (Capello and Lenzi, 2016; Piqueres et al., 2015; Malerba et al., 2013; Li et al., 2017; Lin, 2015), innovation counts (Castellacci, 2008), or product sales value (Hashi & Stojcic, 2013) A straightforward approach to estimate sectoral innovation is by calculating the number of firms with at least one type of innovation (Aiello and Ricotta, 2015) or determining the proportion of firms engaged in innovative activities, such as patents or other innovation types outlined in the Oslo Manual (Piqueres et al., 2015).
Piqueres et al (2015) conducted factor analysis to develop a robust sectoral innovation variable, while other studies, such as Capello and Lenzi (2016), examined sectoral innovation by assigning weights based on the significance of different innovation levels.
Spillover variables, crucial for understanding sectoral innovation, are complex to construct and include various types such as R&D spillover, innovation spillover, and FDI spillover A straightforward method for constructing spillover variables involves summing values without weighting For example, Tian (2016) assessed intra-sector FDI spillover by evaluating foreign presence within a sector and inter-sector spillover based on foreign presence outside the sector of domestic firms Researchers often apply weights based on linkages, similarities, or geographical proximity among sectors or firms Goya et al (2016) calculated intra-industry spillover innovation using the total R&D stock within a sector at a given time, while inter-industry spillover innovation was determined by the total R&D stock from other sectors, adjusted for intermediate purchases between sectors, typically derived from Input and Output analyses.
Various estimation methods are employed to investigate the determinants of sectoral innovation, ranging from OLS regression to multi-level analysis Multiple regression techniques, particularly two or three-stage least squares (2SLS or 3SLS), are commonly utilized (Chen et al., 2015; Kováč & Žigić, 2016; Lee, Kim, & Lee, 2017; Luo, Guo, & Jia, 2017) Studies focusing on dependent variables like patent counts often adopt Tobit or negative binomial regression methods (Lọpple et al., 2016; Buerger & Cantner, 2011; Castellacci, 2008) Additionally, research that emphasizes the geographical characteristics of sectors frequently employs spatial regression techniques (Autant-Bernard & Lesage).
Several studies have examined the combined impact of sectoral and regional factors on firm innovation, often utilizing multi-level regression analysis (Guan & Pang, 2017; Aiello and Ricotta, 2015).
Recent studies on sectoral innovation highlight several key determinants, with some research indicating that not all factors are related to spillover effects (Piqueres et al., 2015; Chamberlin, Doutriaux, & Hector, 2010) Notably, R&D has been shown to positively influence sectoral innovation, with Autant-Bernard & Lesage (2011) confirming the beneficial impacts of both private and public R&D Additionally, Kaygalak & Reid (2016) found that innovation processes in Turkey are more geographically concentrated than based on organizational proximity Furthermore, Malerba et al (2013) emphasized the significant role of intersectoral R&D spillover effects, revealing that domestic R&D exerts a stronger influence on innovation activities compared to international R&D.
Most sectoral innovation research has concentrated on developed countries within the European Union (EU), with numerous studies examining specific countries such as France (Autant-Bernard & Lesage, 2011), Germany (Bade et al., 2015; Buerger and Cantner, 2011), Spain (Piqueres et al., 2015), Turkey (Yurtseven and Tandoğan, 2012), Canada (Chamberlin et al., 2010), Japan, and Korea (Jung and Lee, 2010) Additionally, some research has analyzed groups of countries, including six developed nations (Barcenilla-Visús et al., 2014) and ten Asian countries.
Research on spillover effects, particularly in R&D and knowledge transfer, is predominantly conducted in developed nations like the U.S and France Significant studies include those by Blazsek & Escribano (2016) and Capello & Lenzi (2016) in the U.S., and Autant-Bernard & Lesage (2011) in France Additionally, innovation spillovers have been examined across ten Asian countries (Chen & Yang, 2011) and in Spain (Stare & Damijan, 2015) A substantial body of research on FDI spillovers has focused specifically on China, with key contributions from Li et al (2017) and others.
Research on sectoral innovation and R&D spillover in Vietnam is scarce, primarily focusing on firm-level analysis (Jordan, 2015; Doan Thi Hong Van and Bui Le Nhat Uyen, 2017; Tran Thi Bich et al., 2017; Srholec, 2011; Nham Tuan et al., 2016; Nguyen Ngoc Anh et al., 2008) A questionnaire survey conducted in November highlights these limitations in the existing literature.
A study conducted by Tran Hoai Nam et al (2017) analyzed 500 firms across Hanoi, Danang, and Ho Chi Minh City, identifying six key determinants of innovation: awareness of innovation, innovation strategy and policy, organization for innovation, human resources for innovation, and absorptive capacity Similarly, research by Doan Thi Hong Van and Bui Le Nhat Uyen (2017) with a sample of 380 enterprises in various sectors revealed that innovation capacity is positively influenced by total quality management, internal human resources, absorptive capacity, government support, and collaboration networks Additionally, Jordan (2015) highlighted that firms in the Red River Delta Region, including Hanoi, exhibited a higher likelihood of product innovation compared to those in other regions Investigating the impact of innovation on firm performance in supporting industries in Hanoi, Nham Tuan et al (2016) found positive effects of process, marketing, and organizational innovations Furthermore, Nguyen Ngoc Anh et al (2008) explored the relationship between innovation and exports in Small Medium Enterprises (SMEs) in Vietnam, demonstrating that innovation—measured through new products and production processes—plays a crucial role in enhancing export performance.
The enhancement of current products significantly influences exports among Vietnamese SMEs Additionally, the research identified potential endogeneity in export activities, which could result in biased estimates of innovation in earlier studies.
2.3.2 Empirical Studies on channels of knowledge spillover and applications of Spatial Regression Model
Numerous studies have explored the impact of regional knowledge spillovers on innovation and growth (Huggins and Thompson, 2015; Lọpple et al., 2016; Ponds et al., 2010; Shang et al., 2012), highlighting that advancements in intellectual and communication technology have diminished the effects of spatial distance on knowledge transfer This section reviews empirical research on the channels of knowledge spillover and the applications of Spatial Regression Models A primary source of knowledge generation is research and development (R&D) activities, which have been shown to significantly influence firms' innovation (Raymond and St-Pierre, 2010) and total factor productivity (Wieser, 2005) The increasing focus on R&D spillover effects began with Griliches (1979), who examined the returns on R&D investments Since then, numerous studies have investigated R&D spillover among firms (Bloom and Reenen, 2007; Kovac and Zigic, 2016; Khazabi and Quyen, 2017; Savin and Egbetokun, 2016; Yang and Maskus, 2001), as well as at regional (Rodriguez-Pose and Villarreal, 2015) and country levels (Ang and Madsen, 2013; Tientao et al.).
In their 2013 study, Malberba et al examined the impact of R&D spillovers on innovation across various levels, including national, international, intra-sectoral, and inter-sectoral contexts, focusing on six industrialized countries during a specified period.
1980-2000 Moralles et al (2016) found the positive R&D spillover effects among industries in Brazil.