Problem statement
According to Decree No 56/2009/ND-CP, small and medium-sized enterprises (SMEs) in Vietnam are defined as firms with 10 to 300 employees and total equity below a specified threshold This decree aims to support the development of SMEs, recognizing their vital role in the Vietnamese economy The regulation provides guidelines and assistance programs to foster growth, innovation, and sustainability among these businesses Proper understanding of the criteria and available support is essential for SMEs seeking to leverage government policies for their development.
As of March 2015, small and medium-sized enterprises (SMEs) in Vietnam account for over 90% of all enterprises, highlighting their vital role in the economy These SMEs generate more than half a billion jobs annually, underscoring their significance as major employment creators Additionally, they contribute approximately 40% to Vietnam's overall GDP, making them a key driver of economic growth in the country.
SMEs play an important role to the sustainable growth of the economy
Enhancing SME productivity is an urgent priority for the Vietnamese Government, as economic growth heavily relies on the efficiency of domestic firms Key drivers of increased productivity include innovation and technological advancement (Bartelsman & Doms, 2000), yet Vietnamese SMEs continue to face operational challenges that lead to inefficiencies A significant obstacle is the lack of awareness among SMEs regarding the importance of fostering innovation and adopting new technologies to boost productivity Despite the widely recognized contribution of innovation to firm performance, many SMEs in Vietnam have not yet prioritized these strategies, hindering their growth potential.
Research and Development (R&D) expenditure is a widely used metric for measuring innovation in empirical studies Numerous studies have explored the relationship between a firm's R&D spending and its overall performance, highlighting the importance of innovation investment for business success Understanding this link can help firms optimize their R&D strategies to enhance competitiveness and drive growth.
Studies have shown a strong correlation between certain variables, including findings by Siedschlag, Zhang, and Cahill (2010), Belderbos, Carree, and Lokshin (2004), and Crespi and Pianta (2009) However, in the Vietnamese context, small and medium-sized enterprises (SMEs) rarely report significant investments in research and development activities, indicating a gap in R&D expenditure transparency among these firms.
Small and medium-sized enterprises (SMEs) tend to engage in less formal innovation activities and participate in a variety of different initiatives compared to larger firms This informal approach presents significant challenges for researching the impact of innovation on SME productivity, particularly within the Vietnamese context.
The relationship between innovation and SME productivity in Vietnam requires further investigation, as the economy is predominantly driven by SMEs with relatively low technological levels Understanding this connection is crucial for policymakers to effectively promote innovative activities that enhance both firm growth and national economic development Developing deeper insights in this area will help guide strategic initiatives aimed at fostering innovation, improving competitiveness, and supporting the sustainable growth of Vietnamese SMEs.
Research objectives
This study aims to provide additional evidence on the positive relationship between innovation and productivity among Vietnamese SMEs Its primary objective is to define and quantify how innovation impacts firm-level productivity within the context of Vietnamese small and medium-sized enterprises By analyzing this relationship, the research offers valuable insights for policymakers and business owners seeking to enhance competitiveness and growth through innovation Understanding the link between innovation and productivity can help Vietnamese SMEs adopt effective strategies to boost performance and sustain long-term success.
Research questions
The study aims to provide empirical evidence for the main questions emerged:
Is there any relationship between innovation and productivity in the context of SMEs in Vietnam? If yes, then how does innovation can affect SMEs productivity?
Research motivations
This study provides a closer look at Vietnamese SMEs’ productivity using the Levinsohn and Petrin (2003) approach, examining how innovation-driven changes influence productivity levels Despite the crucial role of innovation in development, its outcomes are uncertain, making it difficult to predict whether innovation activities will succeed This research highlights the impact of innovation on SME performance and emphasizes the unpredictable nature of innovation outcomes in the Vietnamese manufacturing sector.
Research indicates that creating value added for firms is essential for boosting productivity, providing policymakers with evidence on effective resource allocation strategies In developing countries like Vietnam, this topic is particularly important for two reasons: firstly, the benefits of innovation are often underutilized, and secondly, limited national resources constrain innovation efforts despite its critical role in driving global growth.
Research scope and data
The study aims to determine the relationship between innovation and productivity in Vietnam SMEs from 2005 to 2013 in six selected industries include:
This study focuses on six key industries: foods, wood and wood-related products, rubber and plastic products, non-metallic mineral products, fabricated metal products, and furniture These industries were selected because they represent the largest segments within the dataset, accounting for nearly 70% of all SMEs in the five-round survey, making them highly representative of the overall dataset It is important to note that at the time of data collection, the 2015 survey dataset was not yet fully gathered or published, which may impact the comprehensiveness of the analysis.
As such, data used in this study only ends in 2013.
The structure of this study
This study contains five chapters which can be presented as follow:
Chapter 2 provides theoretical and empirical studies on the relationship between innovation and productivity Chapter 2 begins with Schumpeter Theory of Innovation that explains the role of innovation to economic growth Then this chapter reviews the concept of productivity and the methods of how productivity can be estimated as well as its determinants In addition, the definition of innovation and how it is measured are discussed in the chapter The relationship between these two concepts has been reviewed through literature
Chapter 3 presents the methodology which is utilised in the study An overview of Vietnam SMEs is discussed On the ground of literature review in Chapter 2, the conceptual framework is constructed The measurement of relevant variables and tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
4 regression techniques are described In addition, this section also includes the process of how to filter data
Chapter 4 presents empirical results Statistical descriptive of data is presented in this chapter Then, the findings on Vietnam SMEs’ productivity are described and discussed The results of regression in relation to the relationship between innovation and productivity are presented in this chapter
Chapter 5 provides the summary of the main results and proposes some policy implications based on the results described in Chapter 4 This Chapter also includes research limitation and suggests some further research direction in the future tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Schumpeter Theory of Innovation – How does Innovation play its role in
Schumpeter was seen as a person who built very first basic foundation to the theory of innovation and economic development In his famous book The Theory of
Joseph Schumpeter, in his 1912 work on Economic Development, emphasized that the overall economy follows its own business cycle, significantly influenced by technological innovations He explained that when a new technology is introduced at the right time, the economy adapts, leading to increased employment and an upward phase in the business cycle Conversely, if the technology is introduced during economic saturation, it may render the economy more vulnerable to negative shocks and potential depressions Schumpeter also highlighted the importance of firms willing to take risks and invest early in emerging technologies to capitalize on profits before competitors do, driving economic growth through innovation.
Joseph Schumpeter significantly contributed to economic theories through his works such as "The Theory of Economic Development" (1934), "Capitalism, Socialism and Democracy" (1942), and "Business Cycles" (1939) He emphasized the crucial role of innovation and entrepreneurship in driving economic growth and development, arguing that innovation is the primary catalyst for progress in capitalist economies.
6 as emphasized the role of entrepreneur of smoothing the mechanism in which revolutionarily technical changes occurs via innovation and push the economy out of its steady state
Schumpeter explained the development of the economy is mainly driven by innovation which he categorized into five types:
(i) launching new products, whether these are about improving a part of products or totally new to the market,
Introducing innovative production methods enables companies to enhance efficiency and stay competitive in the market Expanding into new markets that were previously untapped allows for greater growth opportunities and increased revenue streams Additionally, discovering new sources and suppliers for raw materials and other inputs in the production process helps optimize supply chains and reduce costs, driving overall business success.
(v) acquiring new market structures in any industry (i.e changing the monopoly position)
Innovation drives economic growth through a four-step process: invention, innovation, diffusion, and imitation, with diffusion and imitation having the most significant impact According to Schumpeter, while early-stage innovations yield limited immediate gains, economies experience increased sales and cost savings during diffusion and imitation, prompting greater investment in these phases However, ideas alone are insufficient; successful implementation depends on entrepreneurs who allocate resources towards replacing old technologies with new, a process Schumpeter termed "creative destruction." This continuous cycle of technological renewal underpins economic development fueled by innovation.
Productivity: concept and measurements
Productivity measures the efficiency of converting inputs into outputs within a firm, industry, or country It is typically defined as the ratio of output to input in the manufacturing process High productivity serves as a key indicator of overall economic performance, reflecting how effectively resources are utilized across different levels of the economy.
Productivity is influenced by the availability of input resources and the ability to add value during the production process When inputs are scarce or not utilized efficiently, a firm's productivity declines Conversely, creating value-added activities with available resources enhances overall productivity Optimizing input usage and focusing on value-adding steps in manufacturing are key strategies for improving efficiency and output.
Productivity can be assessed using various methods, primarily categorized into two groups: single-factor productivity measures, which evaluate output relative to a single input, and multifactor or total factor productivity measures, which analyze output in relation to multiple inputs.
Single-factor productivity measurement includes labor and capital productivity, calculated as the ratio of input quantities to gross output or value added, providing a simple but partial view of efficiency These measures focus only on individual inputs and do not account for how inputs are combined in the production process To obtain a more comprehensive measure, multifactor productivity (or total factor productivity) considers the contribution of multiple inputs simultaneously Therefore, this research utilizes total factor productivity to better assess firms’ overall productivity and efficiency.
Estimating total factor productivity (TFP) through production function estimators is a widely used approach to analyze efficiency and productivity growth in various industries This method helps identify the contribution of technological progress and input utilization to overall economic performance By applying production function models, researchers can accurately measure the impact of different factors on output, providing valuable insights for policy development and strategic decision-making TFP estimation plays a crucial role in understanding long-term economic growth and competitiveness in the global market.
The relationship between foreign direct investment (FDI) and domestic firms’ productivity has been extensively studied, with Javorcik (2004) highlighting how FDI can enhance the efficiency of local firms Additionally, research by Hall et al (2009) demonstrates that investment in R&D significantly boosts firm productivity and innovation capabilities Furthermore, Chun et al (2015) explore the impact of information technology on productivity growth, emphasizing its role in modernizing domestic firms These relationships are primarily analyzed using simple Cobb-Douglas production function regressions, providing insights into the interconnected effects of FDI, R&D, and information technology on domestic firms’ performance.
Where 𝑌 𝑗 represents firm j’s output, 𝐾 𝑗 is physical capital stock, 𝐿 𝑗 is labor input and 𝐴 𝑗 denotes for firm’s level of efficiency, 𝛽 𝑘 and 𝛽 𝑙 are output elasticities with respect to capital and labor
Based on the definition of productivity above, 𝐴 𝑗 is referred to Total Factor Productivity and could be derived by taking natural logs of (1):
Total Factor Productivity (TFP) is composed of two key components: the average productivity across all firms over time, denoted as 𝛽 0, and the deviation from this average, represented by 𝜀 𝑗𝑡 The deviation term 𝜀 𝑗𝑡 captures unobserved factors that influence a firm's output outside of input variations This deviation can be further broken down into firm-level productivity, 𝑤 𝑗𝑡, and an independent and identically distributed (i.i.d.) component, 𝑣 𝑗𝑡, reflecting random shocks affecting firm performance.
Therefore researchers can get firm’s productivity from estimating (3) and solving for 𝑤 𝑗𝑡 :
Then, the exponential of 𝑤̂ 𝑗𝑡 is the result of firm-level productivity
Research on calculating Total Factor Productivity (TFP) primarily follows two approaches: non-parametric and parametric methods The non-parametric approach, with growth accounting as its most prevalent technique, was popularized by Robert Solow in 1957 through his work on technological change and production functions Under assumptions of constant returns to scale and competitive factor markets, the growth accounting method quantifies TFP growth by analyzing input and output data to identify productivity improvements beyond input increases.
Changes in output growth are primarily explained by variations in different types of inputs and total factor productivity While growth accounting is a reliable and established method, it cannot determine causality, such as whether technological investment drives productivity growth or vice versa Econometric techniques, including production function estimators, are used to assess total factor productivity by analyzing the relationship between inputs and output These methods offer benefits like testing parameter significance and addressing endogeneity issues, leading to more accurate insights into productivity dynamics.
The non-parametric growth accounting method was developed by Robert Solow in 1957 to analyze how technological change influences economic growth through the aggregate production function Growth accounting evaluates the extent to which economic growth is driven by input contributions, such as capital and labor, versus technological progress that shifts the production function upward This approach assumes constant returns to scale, meaning the total elasticities of all inputs in the production process sum to one (i.e., 𝛽𝑘 + 𝛽𝑙 = 1), allowing for a clear separation of input-driven growth and technological advancements.
When assessing productivity, input factors are typically weighted according to their income shares for country-level analyses (Cardona et al., 2013), or by their cost shares when evaluating firm-level productivity.
Productivity is calculated by solving equation (4) without econometric sense
The Solow residual (𝑤̂ 𝑗𝑡) is a key indicator of productivity growth, indicating a positive value when the output growth rate exceeds the growth rate of input factors It captures not only advances in technological progress but also encompasses other factors influencing overall efficiency beyond input variables According to Schreyer (2001), the Solow residual reflects improvements in productivity driven by technological innovation and external efficiency factors, making it a vital measure for understanding economic growth dynamics.
Endogeneity may pose a problem due to a potential relationship between input decisions and unobserved productivity shocks Addressing this issue is crucial for ensuring accurate analysis of productivity and input choices Proper identification strategies can help mitigate endogeneity biases, leading to more reliable results in productivity research.
Firms often adjust their input levels in response to productivity shocks, increasing investment when experiencing positive shocks and reducing workforce during unfavorable ones This dynamic behavior can lead to biased and inconsistent estimates of input coefficients in OLS regressions, as highlighted by Eberhardt and Helmers Understanding these adjustments is crucial for accurate productivity analysis and modeling firm behavior under varying economic conditions.
After the problem of endogeneity arises in production function estimation, there are several solutions have been developed and applied in the literature:
Instrumental Variables (IV) regression is a key econometric technique used to address endogeneity issues in empirical research The development of dynamic panel estimators, notably by Arellano and Bond (1991) and Blundell and Bond (1998), has significantly advanced this area, introducing the Generalized Method of Moments (GMM) approach for more robust and efficient estimation These methods are widely applied in analyzing panel data where lagged variables are used as instruments Additionally, the influential work of Olley and Pakes has contributed to the estimation of production functions and firm-level productivity, enhancing the understanding of dynamic relationships in economic data.
(1996) which is categorized as ‘structural estimators’, then been further developed by Levinsohn and Petrin (2003)
Innovation: concept and measurements
The Oslo Manual (OECD 2005, p.46) defines innovation as the implementation of a new or significantly improved product, process, marketing method, or organizational practice This widely accepted definition is frequently referenced by researchers and institutions in studies and surveys exploring various aspects of innovation, highlighting its importance in driving business growth and competitiveness.
There are four type of innovations which proposed in Oslo Manual (OECD 2005):
Product innovation involves introducing new goods or services to the market that significantly improve upon current offerings in terms of appearance, technical features, new functionalities, or user-friendliness This process focuses on enhancing existing products or creating entirely new ones to meet evolving consumer needs and preferences Effective product innovation can drive competitive advantage and stimulate market growth by offering unique and improved solutions.
Process innovation involves enhancing the methods of producing and distributing goods and services, leading to reduced costs or improved product quality It primarily focuses on the techniques, supporting equipment, and software used in the production process By implementing process innovation, businesses can achieve greater efficiency and competitiveness in their operations.
Marketing innovation involves enhancing product design, pricing strategies, and promotional campaigns through new marketing methods tailored to meet customer needs These innovative approaches help businesses capture market share and boost sales effectively.
Organizational innovation involves changes in organizational structure and business environment that help reduce administrative costs and improve overall efficiency Implementing these innovations can streamline processes, enhance adaptability, and promote sustainable growth within the organization Embracing organizational innovation is essential for maintaining competitiveness and fostering long-term success in a dynamic market.
17 transaction cost, improving work efficiency In addition, organizational innovation not only involves internal activities but also external relations improvements (with suppliers, clients, state agencies, etc.)
It is essential to clearly differentiate between various types of innovation, as some innovations may possess characteristics of more than one category For example, introducing a new product often requires a new production process, classifying it as both product and process innovation The Oslo Manual (2005) offers detailed guidelines to help identify and distinguish these different types of innovation effectively.
Different types of innovation can be easily represented by dummy variables which applied by many studies such as Hall, Lotti, & Mairesse (2008), Griffith, Huergo, Harrison, & Mairesse (2006), Mairesse, Mohnen, & Kremp (2005), Mairesse
Research by Robin (2009), Polder, Van Leeuwen et al (2009) highlights that existing measurement methods may not adequately capture differences in innovation intensity among firms Mohnen and Hall (2013) argue that using simple innovation dummy variables can be misleading, especially when comparing firms of different sizes, as large firms often engage in multiple innovation activities across four different types Consequently, this approach may inaccurately suggest that large firms are more innovative than small firms, underscoring the need for more nuanced measurement methods to accurately assess innovation across varying firm sizes.
Innovation can be assessed using both input and output approaches Input approaches focus on a firm's efforts to develop new products, enhance production processes, explore new markets, and increase overall efficiency Conversely, output approaches measure innovation through tangible results such as the launch of new products, successful process improvements, cost reductions, and efficiency gains (Mohnen and Hall, 2013).
Innovation is often primarily measured by Research and Development (R&D) expenditures, which focus on developing new products and production methods However, many firms engage in a variety of non-R&D activities that also contribute to innovation According to the Oslo Manual (2005), it is important to recognize that innovation extends beyond R&D investments to include different types of activities that foster innovative outcomes.
Non-R&D innovation activities play a vital role in fostering firm innovation and efficiency These activities include purchasing patents, paying royalties, acquiring scientific information, and modifying technologies to suit specific needs Additionally, firms can enhance their labor knowledge and skills through internal training, invest in new equipment and software, and upgrade facilities to improve their innovative processes Improvements in management structures and new product introduction methods also contribute to innovation beyond traditional R&D efforts According to the Oslo Manual (2005), measuring innovation should incorporate these non-R&D activities alongside R&D expenses, as they collectively aim to enhance a firm’s innovative capacity Moreover, skilled employees are critical assets in innovation, as they facilitate technology adoption, manage manufacturing operations, and solve technological challenges, emphasizing the importance of human capital in innovation measurement.
Innovation performance is primarily measured by the quality and impact of new products, often reflected in revenue figures The percentage of revenue derived from new or enhanced products through innovative processes serves as a key indicator of a firm's overall innovation success This metric effectively demonstrates how innovation contributes to a company’s financial performance and competitive advantage, being widely adopted in academic studies like those by Miguel Benavente.
(2006), Jefferson et al (2006), Siedschlag et al (2010) Another measurement of innovation effect on firm’s performance is cost deduction due to process innovation (Peters, 2008).
How has the relationship between innovation and firms’ performance been
performance been analysed in the literature?
The relationship between innovation and a firm’s performance has been extensively analyzed in the literature, with no clear consensus Some studies indicate that innovation positively influences firm performance, which can be measured through various performance indicators Conversely, other research highlights potential negative impacts of innovation on firm outcomes, suggesting a complex and nuanced relationship.
Innovation positively impacts a firm’s performance through a process where innovation inputs lead to innovative outputs, which in turn enhance overall firm performance (Crépon, Duguet, & Mairesse, 1998) The CDM model, developed by Crépon et al (1998), estimates the relationship between innovation and performance and has been widely applied and refined by researchers like Benavente (2006), Janz et al (2003), and Mairesse et al (2005) Siedschlag, Zhang, and Cahill (2010) employed the CDM model using panel data from 723 Irish firms in the Community Innovation Survey (2004-2008), controlling for factors such as foreign ownership and international trade activities.
CDM model with three stages of estimation: (i) firm's decision to invest in innovation;
This study examines the relationship between innovation inputs, innovation output, and final production output The findings indicate that foreign-owned firms and domestic firms engaged in export activities are more likely to invest in innovation and generate innovation outputs compared to firms with only domestic activities Interestingly, innovation expenditure does not significantly influence innovation output Additionally, the results reveal a positive relationship between innovation outputs and labor productivity, underscoring the importance of innovation for enhancing firm performance.
The relationship between innovation and firm performance is widely regarded as causal, with studies by Belderbos, Carree, and Lokshin (2004), Lokshin, Belderbos, and Carree (2008), Parisi, Schiantarelli, and Sembenelli (2006), and Santos, Basso, Kimura, and Kayo (2014) supporting this view Innovation drives the creation of new products, enhances production processes, and improves business practices, leading to increased firm efficiency and better performance Conversely, higher-performing firms are more likely to invest in innovation, suggesting a bidirectional relationship To address the endogeneity concerns arising from this causality, many studies employ instrumental variable (IV) or Generalized Method of Moments (GMM) estimations.
Belderbos, Carree, and Lokshin (2004) analyzed the impact of innovation on firm performance in the Netherlands using data from the Community Innovation Survey of 1996 and 1998, covering 2,056 manufacturing firms Their study highlights that both internal innovations and external collaborations significantly influence company performance, emphasizing the importance of continuous innovation for competitive advantage The research provides valuable insights into how innovation activities contribute to growth and productivity in the manufacturing sector.
20 activities but also external innovative collaboration have taken into account in determining this relationship They measure firm’s innovation by two set of variables:
(i) internal innovation activities represented by internal innovation expenditure per sales and (ii) external innovation collaboration through R&D cooperation dummies with competitors, suppliers, customers and universities or other research institutions
The firm's performance is measured through labor productivity growth and sales of newly introduced products An IV regression model was used to establish the causal link between innovation and productivity, controlling for firm size, industry dummies, ownership status, and demand-pull and cost-push factors Past productivity levels were included to account for their influence on current productivity growth The study found that various types of R&D collaboration and innovation intensity significantly and positively impact productivity growth, although innovation intensity showed no significant effect on the growth of innovation sales.
Lokshin, Belderbos, and Carree (2008) found a significant positive relationship between both internal and external innovation activities and labor productivity Internal innovation, measured by in-firm R&D expenditure, and external innovation, represented by contracted R&D expenses with other firms, both contribute to enhancing productivity Their study, which applied GMM estimation to a dynamic panel from 304 Dutch manufacturing firms between 1996 and 2001, highlights that internal and external R&D are complementary, exhibiting decreasing returns to scale The results emphasize that internal R&D plays a crucial role in improving firm productivity, while external R&D significantly impacts only when sufficient internal R&D investment has been made.
Parisi, Schiantarelli, and Sembenelli (2006) conducted research on 941 manufacturing firms in Italy, utilizing data from surveys conducted in 1995 and 1998 to analyze the relationship between innovation and firm performance They used indicators such as product and process dummies and R&D expenditure as a percentage of output to measure innovation activities.
This study investigates the impact of innovation on firm performance using two analytical approaches First, a Cobb-Douglas production function examines the relationship between output growth and innovation variables, instrumented by lagged factors such as output per labor, materials per labor, capital per labor, R&D intensity, and firm size Second, Total Factor Productivity (TFP), calculated using Levinsohn and Petrin’s (2003) method, is regressed against the same innovation variables The findings reveal a positive effect of both process and product innovation on productivity, with process innovation having a larger impact than product innovation These results are consistent and robust across both methodologies, confirming the beneficial role of innovation in enhancing firm productivity.
Several studies have found no significant impact of innovation on productivity Santos, Basso, Kimura, and Kayo (2014) concluded that innovation efforts from investments do not significantly explain a firm's performance Similarly, Li and Atuahene-Gima (2001) highlighted that the uncertain nature of innovation often leads to insignificant effects on firm performance, as innovation activities are risky and resource-intensive with unpredictable outcomes Additionally, Branzei and Vertinsky (2006) emphasized that successful innovation requires specific organizational resources and capabilities to generate positive results for firms.
In the Vietnamese context, there is a growing interest in studying innovation, productivity, and related concepts Nguyen et al (2007) examined the relationship between innovation and export performance of Vietnamese SMEs in 2005, using variables such as whether a firm introduces new products, new processes, or improves existing products to represent innovation Vu and Doan (2015) also employed these innovation indicators along with marketing changes to analyze their impact on SME performance, measured by gross profit within a year Their study identified endogeneity issues in the innovation-performance relationship and addressed them using a 2SLS model Overall, these studies highlight the significance of innovation in enhancing the performance of Vietnamese small and medium enterprises.
22 efforts in product, production process or marketing do have positive impact on firm’s performance
Productivity for Vietnamese firms have been analysed widely in the literature
Ha and Kiyota (2014) used data from Annual Survey on Enterprises from 2000 to
In 2009, non-parametric methods were utilized to estimate a firm's total factor productivity (TFP) when analyzing the relationship between firm productivity and turnover Yang and Huang (2012) applied the Levinsohn and Petrin approach to accurately measure TFP for Vietnamese SMEs, providing valuable insights into the productivity dynamics of small and medium-sized enterprises in Vietnam.
However they focused on the effect of trade liberalization on productivity which is different with this study
This study examines the crucial role of innovation in enhancing the productivity of SMEs in Vietnam, a transitioning economy It assesses various aspects of innovation efforts, including innovative expenditure intensity, the presence of innovation activities, and the proportion of high-quality employees within the total labor force The findings highlight that increased innovation investment and a skilled workforce significantly contribute to improved firm productivity in the Vietnamese SME sector.
Productivity in the study is assessed using the Levinsohn and Petrin approach, which effectively addresses endogeneity issues caused by the potential correlation between input decisions and productivity shocks This robust method for estimating total factor productivity (TFP) is rarely utilized in the context of Vietnam, making its application notably significant for capturing accurate productivity measurements in this environment.
Appendix 2 provides the summaries on the related empirical studies on the relationship between innovation and firm’s performance. tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
An overview of Vietnamese Small and Medium-sized Enterprises
Small and Medium-sized Enterprises (SMEs) are typically classified by international organizations and countries based on key criteria such as the number of employees, total revenues, and total assets or equity These classifications help standardize definitions across different regions and facilitate effective policy implementation, in accordance with national regulations like the Decree.
According to Decree No 56/2009/ND-CP, which outlines support for the development of small and medium-sized enterprises (SMEs) in Vietnam, SMEs are defined as companies with 10 to 300 employees and total equity below a specified threshold This regulation aims to promote the growth and sustainability of SMEs by providing tailored assistance and resources, fostering a robust business environment in Vietnam.
100 billion dong The details of the classification following Decree No 56/2009/ND-
Table 3.1 presents the comprehensive overview of the CP, highlighting key data and analysis The table provides essential information relevant to the study, offering insights into its structure and findings This detailed presentation supports understanding of the reported results and their implications for future research.
Table 3.1: Classification of SMEs in Vietnam
Micro enterprises Small enterprises Medium enterprises
Industry Average no of employee
Average no of employee Total assets Average no of employee Total assets Agriculture, forestry and fishery
Industry and construction < 10 10-200 < VND 20 bil 200-300 VND 20–100 bil
Services < 10 10-50 < VND 10 bil 50-100 VND 10–50 bil
Source: Government’s decree No 56/2009/ND-CP
Since the implementation of the Enterprises Law in 2005, Vietnam has experienced a significant increase in the number of enterprises, including a substantial rise in SMEs Between 2006 and 2013, the total number of Vietnamese SMEs more than doubled, growing from 120,074 in 2006 to 367,300 in 2013, indicating a remarkable expansion of the small and medium enterprise sector in the country.
In Vietnam, micro, small, and medium-sized enterprises consistently represent the largest share of total firms, accounting for approximately 96% to 98% annually from 2006 to 2013 While the total number of enterprises has steadily increased over this period, their sizes have gradually declined Specifically, in 2006, micro firms (with 1-10 employees) made up 61% of all firms, increasing slightly to 66% in 2013, while small firms comprised 35% in 2006 and decreased to 32% in 2013 Conversely, large firms declined significantly from 4% in 2006 to just 1.6% in 2013 This shrinking trend of medium and large enterprises hampers Vietnam’s economic development and diminishes the country's capacity to compete effectively in international markets.
Figure 3.1: Number of enterprises at 31/12 (by size of employees)
Small and Medium-sized Enterprises (SMEs) have experienced significant growth, representing the largest portion of all enterprises and making substantial contributions to the economy According to the Mid-term Evaluation Report on the Implementation of the Developing Small and Medium-sized Enterprises Plan (2011-2015), SMEs play a crucial role in economic development through four main channels: boosting GDP, increasing government revenue, driving total investment, and creating employment opportunities.
Between 2009 and 2012, non-state owned enterprises, predominantly SMEs accounting for 98.6%, contributed approximately 48-49% to the total GDP, making them the largest sector in the economy Meanwhile, state-owned enterprises, with 59.3% being SMEs, saw their GDP contribution decline from 37.32%, indicating a shifting economic landscape favoring private sector growth.
Between 2009 and 2012, the share of this sector increased from 20% to 32.57% as a result of the government's privatization policies targeting these firms Foreign direct investment (FDI) plays a significant role, with 78.8% of these enterprises being SMEs, contributing steadily around 17-18% to the total GDP throughout this period.
In 2012, due to the evolving GDP structure and ongoing privatization efforts, small and medium-sized enterprises (SMEs) in the non-state owned sector are expected to play an increasingly vital role in driving future GDP growth.
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The total amount of taxes and fees that SMEs has contributed accounting for a large part in government budget since they are the key sector in the economy In
2010, SMEs contributed over VND 181,060 billion dong to the state budget, accounting for 41% total collected taxes and fees from all enterprises In 2011 and
2012, these figures are VND 181,210 billion dong (34%) and VND 205,260 billion dong (34%)
SMEs play a significant role in the national investment landscape, contributing a substantial portion to the overall economy In 2010, SMEs invested VND 236,119 billion, representing 32% of total enterprise sector investments This figure surged to VND 699,690 billion (57%) in 2011, driven mainly by micro and small-sized enterprises, before declining to VND 235,463 billion (29%) in 2012 Throughout 2010 to 2012, small-sized firms consistently accounted for the largest share of enterprise investments, ranging from 61% to 68%. -Boost your SME content with SEO-smart summaries that capture growth trends perfectly – [Learn more](https://pollinations.ai/redirect/draftalpha)
SMEs play a vital role in Vietnam’s economy by creating and sustaining a significant number of jobs In 2010, Vietnamese SMEs provided over 4.35 million jobs, representing nearly 45% of employment in the enterprise sector By 2012, this number increased by 16.83% to 5.09 million, accounting for 47% of total jobs, highlighting the expanding contribution of SMEs to employment Additionally, workers' incomes in the SME sector have improved over time, reflecting their positive impact on the livelihoods of many Vietnamese employees.
42 million dong/employee/year and this figure reaches to VND 46 million in 2011 and continuous increases to VND 61 million in 2012, approximately 90% average income of employee in the whole enterprise sector
Small and Medium Enterprises (SMEs) are vital to Vietnam’s economic growth, significantly contributing to national income, government revenue, and overall investment They play a crucial role in creating employment opportunities, stabilizing workers’ income, and enhancing living standards Despite these important contributions, Vietnamese SMEs continue to face numerous challenges that hinder their development and sustainability.
Small and medium-sized firms often face challenges due to insufficient internal capital and limited access to external funding sources Their constrained internal capital stocks hinder growth and expansion, making it difficult to secure necessary external investments This capital shortage impacts their ability to compete effectively and sustain long-term development.
Access to external capital sources such as banks and financial institutions is often limited, primarily due to the lack of collateral, untrustworthy financial operations, complex procedures, and insufficient information This shortage of funding hampers growth and development opportunities for businesses facing these financial barriers.
Therefore they usually fail to raise capital when they want to expand the market, change technology or invest in new projects
Methodology
Based on relevant theories and empirical research, this study constructs and illustrates a comprehensive conceptual framework, depicted in Figure 3.2 below, to guide the analysis and understanding of key relationships within the research topic.
The impact of innovation on productivity can be estimated through two stages:
Total factor productivity (TFP) for each firm will be estimated using the Levinsohn and Petrin (2003) approach applied to the production function The analysis considers input variables such as labor, capital, and intermediate inputs, while the output variable is measured as value added This method provides an accurate assessment of firm efficiency and productivity growth, essential for understanding technological progress and competitive advantage Using this approach ensures reliable TFP estimates that are crucial for performance evaluation and policy implications.
This study investigates the relationship between innovation and productivity by regressing the total factor productivity (TFP) of each firm, obtained from stage (1), against various innovation proxies These proxies include a dummy variable indicating investments in innovation activities, innovation expenditure intensity, and the share of high-quality employees within the total labor force Grounded in Schumpeter’s Theory of Innovation and supported by empirical research such as Siedschlag, Zhang, and Cahill (2010), Belderbos, Carree, and Lokshin (2004), Crespi and Pianta (2009), Santos et al (2014), and Lokshin, Belderbos, and Carree (2008), this analysis aims to elucidate how innovation contributes to firm productivity improvements.
In addition to innovation variables, the regression model incorporates control variables such as firm age, firm size, capital structure, and past firm productivity, as empirical studies by Cucculelli et al (2014), De Kok et al (2006), and Huergo and others suggest these factors are significantly related to a firm's productivity.
Output (Value added) Total factor productivity
- Share of high-quality labor in total labor force
- Past total factor productivity tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Jaumandreu (2004); Dhawan (2001); Margaritis and Psillaki (2010) and
The detail methodology for above two stages are presented in the next sections
3.2.2.1 First stage: Total factor productivity estimation using Levinsohn and Petrin (2003) approach
Levinsohn and Petrin (2003) developed an innovative method to calculate Total Factor Productivity (TFP) within the framework of Olley and Pakes (1996) Instead of employing investment as a proxy, they utilized intermediate inputs to better control for unobserved productivity, which is part of the error term correlated with the input Their approach demonstrated that using intermediate inputs as a proxy yields more consistent and reliable coefficient estimates, enhancing the accuracy of TFP measurement in empirical analyses.
This study utilizes the Linear Programming (LP) approach to estimate firm productivity, focusing on the value-added case The LP method begins with a Cobb-Douglas production function expressed in logs, incorporating two inputs: labor (l), which is freely variable, and capital (k), a state variable The output measure in this analysis is the firm's value-added (y) for firm i in year t, providing a comprehensive basis for productivity assessment.
Error term 𝜀 𝑗𝑡 can be divided by two components: unobservable productivity (productivity shocks that correlated to inputs) 𝑤 𝑗𝑡 and an i.i.d component which does not affect inputs choice decision 𝑣 𝑗𝑡 Rewrite (7) we have:
Under the assumption of perfect market competition, prices for inputs and outputs are identical across all firms in the market, simplifying demand analysis Intermediate input demand can be expressed as a function of capital and unobservable productivity, represented as 𝑖 𝑗𝑡 = 𝑖 𝑡 (𝑘 𝑗𝑡 , 𝑤 𝑗𝑡 ), without explicitly relying on input and output prices This indicates that demand for intermediate inputs is correlated with the firm's capital stock and unobserved productivity levels, highlighting the influence of these factors on input demand in competitive markets.
30 shocks The function is index by time t to allow for any changes in inputs and output prices across time
The monotonicity condition assumes that, conditional on capital, an increase in productivity or a positive productivity shock leads to greater use of intermediate inputs This is because higher productivity increases the marginal revenue of intermediate inputs, prompting firms to produce more and consequently use more inputs Under perfect market competition, this assumption is straightforward and clear However, in less competitive markets, such as oligopolies, firms may not increase output in response to positive productivity shocks, as doing so could reduce prices and negatively impact sales.
Under the monotonicity assumption, the intermediate inputs demand function can be inverted to express productivity shocks \(w_t\) as a function of intermediate inputs and capital, i.e., \(w_{jt} = w_t(i_{jt}, k_{jt})\) This approach allows productivity shocks to be proxied using intermediate inputs, facilitating their identification and measurement within the model.
Substituting productivity shock function into (8) get:
To estimate the coefficients of input variables in equation (7), LP suggested a process of two stages:
The first stage: Estimating coefficient of freely variable labor:
Equation (9) is a partially linear model, linear in labor variable and non-linear in intermediate inputs and capital Based on Robinson (1988), this type of equation can be estimated effectively using a semiparametric approach In this method, the predicted values of output (𝑦 𝑗𝑡) and labor (𝑙 𝑗𝑖) are derived conditionally on intermediate inputs (𝑖 𝑗𝑡) and capital (𝑘 𝑗𝑡) using weighted least squares with a second-order approximation, capturing the non-linear relationships between variables for more accurate estimation.
Equation (9) then can be rewritten as:
𝐸(𝑦 𝑗𝑡 |𝑖 𝑗𝑡 , 𝑘 𝑗𝑡 ) = 𝛽 𝑙 𝐸(𝑙 𝑗𝑖 |𝑖 𝑗𝑡 , 𝑘 𝑗𝑡 ) + 𝜑 𝑡 (𝑖 𝑗𝑡 , 𝑘 𝑗𝑡 ) (10) (it is noted that: 𝐸(𝑣 𝑗𝑡 |𝑖 𝑗𝑡 , 𝑘 𝑗𝑡 ) = 0 tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Labor coefficient 𝛽 𝑙 in equation (11) can be estimated using no-intercept Ordinary Least Squares and yield unbiased and consistent result since 𝑣 𝑗𝑡 is uncorrelated with inputs by assumption
The second stage: Estimation of capital coefficient:
State variable capital is assumed to respond gradually to productivity shocks 𝑤 When 𝑤 is decomposed into forecastable and non-forecastable components, capital tends to adjust primarily to the predictable part of the shock Additionally, LP assumes that the unobservable productivity 𝑤 follows a first-order Markov process, influencing how capital dynamics are modeled in response to different types of productivity changes.
𝑤 𝑗𝑡 = 𝐸(𝑤 𝑗𝑡 |𝑤 𝑗𝑡−1 ) + 𝜂 𝑗𝑡 (12) where: 𝜂 𝑗𝑡 is the “news” in unobservable productivity 𝑤
Two moment conditions to follows:
𝐸(𝜂 𝑗𝑡 + 𝑣 𝑗𝑡 |𝑘 𝑗𝑡 ) = 𝐸(𝜂 𝑗𝑡 |𝑘 𝑗𝑡 ) + 𝐸(𝑣 𝑗𝑡 |𝑘 𝑗𝑡 ) = 0 which means capital is mean independent of unpredictable productivity and:
𝐸(𝜂 𝑗𝑡 + 𝑣 𝑗𝑡 |𝑖 𝑗𝑡−1 ) = 𝐸(𝜂 𝑗𝑡 |𝑖 𝑗𝑡−1 ) + 𝐸(𝑣 𝑗𝑡 |𝑖 𝑗𝑡−1 ) = 0, referring that decision in intermediate input choices in previous period do not have relationship with the unpredictable productivity in in the following period
Next step would be choosing candidate value for (𝛽 𝑘 ), let denotes- (𝛽 𝑘 ∗ ), equation (8) then can be rewritten as:
𝑦 𝑗𝑡 − 𝛽 𝑙 𝑙 𝑗𝑡 − 𝛽 𝑘 ∗ 𝑘 𝑗𝑡 = 𝑤 𝑗𝑡 + 𝑣 𝑗𝑡 (13) Together with 𝛽 𝑙 estimated from first stage, computing 𝑤 𝑗𝑡 ̂+ 𝑣 𝑗𝑡 as:
The initial estimation of 𝜑 𝑡 obtained from the first stage can be applied to subsequent functions to enhance accuracy and reliability This process is essential for optimizing model performance and ensuring precise results in data analysis Implementing this methodology allows for continuous improvement and better predictive capabilities in various applications.
𝑤̂ = 𝜑 𝑗𝑡−1 ̂ − 𝛽 𝑡−1 𝑘 ∗ 𝑘 𝑗𝑡−1 (15) The result of (14) then be regressed against result got from (15) using quadratic least square to get the estimation of 𝐸(𝑤 𝑗𝑡 |𝑤 𝑗𝑡−1 )
Substituting (12) on (13) then re-arranging the equation yields:
𝑦 𝑗𝑡 − 𝛽 𝑙 𝑙 𝑗𝑡 − 𝛽 𝑘 ∗ 𝑘 𝑗𝑡 − 𝐸(𝑤 𝑗𝑡 |𝑤 𝑗𝑡−1 ) = 𝑣 𝑗𝑡 + 𝜂 𝑗𝑡 All variables in the left hand side have been estimated through these above steps, therefore 𝑣 𝑗𝑡 ̂+ 𝜂 𝑗𝑡 can be computed straightforward It turns to the algorithm of choosing (𝛽 𝑘 ∗ ) to minimize 𝑣 𝑗𝑡 ̂+ 𝜂 𝑗𝑡
Levinsohn and Petrin's two-stage estimation process effectively addresses endogeneity issues caused by the correlation between input choices and unobservable productivity This method ensures consistent estimation of input coefficients, leading to more accurate calculation of Total Factor Productivity (TFP).
Stata offers the "levpet" command, a user-friendly tool designed to simplify the calculation of Total Factor Productivity (TFP) In this study, the researchers utilized the "levpet" command to efficiently and accurately compute TFP, ensuring reliable results and streamlined analysis.
3.2.2.2 Second stage: Regression model - How does innovation affect firms’ productivity?
According to the related concepts and empirical studies about innovation- productivity relationship at firm level, the dynamic panel regression including lagged effect of dependent variables is constructed as below:
The analysis examines the impact of innovation variables—specifically, the presence of innovation (dummy variable), innovation expenditure intensity, and the share of high-quality labor in the firm's total workforce—on firm performance Control variables such as the firm's age, size, and capital structure are also included to ensure robust results This comprehensive approach enables a clear understanding of how innovation efforts influence firm outcomes while accounting for firm-specific characteristics.
Research hypotheses and concept measurements
As mentioned in the previous chapters, innovation could have positive impact on firm’s productivity (Belderbos, Carree and Lokshin (2004), Crespi and Pianta
This study explores the positive relationship between innovation and firm productivity within Vietnamese SMEs, building on previous research by Rosenbusch, Brinckmann, and Bausch (2011), as well as Lokshin, Belderbos, and Carree (2008) Innovation is analyzed through two key components: innovation expenditure intensity and the proportion of high-quality labor within the total workforce The findings highlight that increased innovation investment and a higher share of skilled labor significantly enhance the productivity of small and medium enterprises in Vietnam.
In a further details, two hypotheses about the relationship between innovation and firm’s productivity are tested in this study are illustrated as below:
Hypothesis H 1 : Innovation expenditure intensity have the relationship with firm’s productivity in positive direction
Hypothesis H 2 : High-quality labor in total firm’s labor force have positive impact on firm’s productivity
This study employs two-stage estimation to determine the innovation – firm’s total factor productivity Variables used include:
Stage 1: three input variables (labor, intermediate inputs and physical capital) and output (value added)
Stage 2: dependent variable (total factor productivity), innovation variables (innovation expenditure intensity, dummy for innovation, share of high-quality employee in labor force), control variables (firm’s age, firm’s size and firm’s capital structure) and past level of productivity
Table 3.2 summarizes the key concepts and measurement methods for the variables discussed, providing clear definitions and relevant data collection techniques This comprehensive overview ensures accurate analysis and helps in understanding the relationships between the variables Proper measurement is essential for reliable research outcomes and contributes to the overall validity of the study.
Table 3.2: Concepts and measurements of variables used in the study
L Labor used in firm's production process in survey year
Number of regular employees at the end of survey year
C Value of physical capital used in firm's production process in survey year
Average value of physical assets at two point time: at the beginning and at the end of survey year
I Value of intermediate inputs used in firm's production process in survey year
Average value of intermediate inputs used in the production process at the beginning and at the end of survey year
Intermediate inputs include raw material and indirect inputs
Y Value added generated by firm within year
Value added generated by firm within year
TFP Total Factor Productivity Total factor productivity determined by
LP approach obtained from stage 1
Inv_exp Innovation expenditure intensity
Share of innovation expenditures in total firm's investment in survey year
Innovation expenditures include: investment in R&D, investment in human upgrading, investment in buying patents and investment in equipment and machinery
D_Inv Dummy on innovation Coded 1 if firm have invested in innovation activities and 0 otherwise
Share_L Share of high-quality employee in total labor force
Share of professional employee in total labor force
Size Firm's size Ln of total assets
Age of a firm is calculated by subtracting its established year from the survey year If a firm provides varying establishment year information across different surveys, the earliest reported year (from the first survey) is used to determine its age This method ensures consistency when assessing the firm's age over multiple survey periods Properly understanding firm age is essential for analyzing business longevity and assessing developmental stages in research studies.
Cap_struc Firm's capital structure Ratio of outstanding debt over total assets
L.TFP Lag one time of firm’s total factor productivity
Lag one time of firm’s total factor productivity
Source: Author’s analysis tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Data sources
This study analyzes survey data from Vietnamese SMEs collected in 2005, 2007, 2009, 2011, and 2013 through a collaborative effort by ILSSA (under MOLISA), the Stockholm School of Economics, and the University of Copenhagen The surveys encompass ten provinces and cities—Hanoi, Ho Chi Minh City, Ha Tay, Hai Phong, Phu Tho, Khanh Hoa, Nghe An, Lam Dong, Quang Nam, and Long An—aiming to represent the Vietnamese SME population Each survey sampled 2,500 firms, with 80% of the firms being consistent across surveys and 20% replaced due to reasons like non-operation or non-response, ensuring a comprehensive and representative dataset for analyzing Vietnamese small and medium-sized enterprises.
This study focuses on analyzing the six largest industries—foods, wood and wood-related products, rubber and plastic products, non-metallic mineral products, fabricated metal products, and furniture—out of approximately fifty reported industries (2-digit VSIC 2007) These six industries collectively account for nearly 70% of all SMEs surveyed across five rounds, making them representative of the overall dataset and providing valuable insights into broader industry trends.
Table 3.3: Number of observation in selected industries in dataset
Year No of obs in selected industries Total obs in raw data %
The study applies essential filtering techniques to eliminate missing data and outliers, ensuring data quality for more accurate analysis These preprocessing steps are detailed below to maintain transparency and reliability in the research methodology.
In Stage 1, estimating Total Factor Productivity (TFP) using the linear programming (LP) approach requires comprehensive data on output (revenue) and input factors, including labor, intermediate inputs, and capital Firms lacking any of these key data points are excluded from the dataset to ensure accurate TFP measurement The descriptive statistics for the remaining firms are summarized in Table 3.3, providing an overview of the data used in the analysis.
Table 3.4: Number of observation after filtering
In Stage 2, variables such as the firm’s age and total assets (used as a proxy for firm size) exhibit a wide range of values To ensure accurate analysis, the study employs a filtering process to eliminate outliers that could bias the results due to measurement errors or recording inaccuracies Specifically, any observation exceeding the mean value plus two standard deviations is classified as an outlier and removed from the dataset This outlier removal procedure is systematically applied to both the firm’s age and total assets variables, ensuring the robustness and reliability of the estimation results.
38 selected industry The summary statistics of data after filtering are presented in Table 3.5 below:
Table 3.5: Number of observation after filtering in stage 2
Industry Total obs No of outliers
Source: Author’s calculation tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg