Problem statement
According to Decree No 56/2009/ND-CP, small and medium-sized enterprises (SMEs) in Vietnam are classified as businesses that employ between 10 and 300 individuals and possess total equity of less than a specified amount This decree aims to provide support for the growth and development of SMEs in the country.
As of March 2015, small and medium-sized enterprises (SMEs) in Vietnam, defined as those with a capital of up to 100 billion dong, represent over 90% of all businesses in the country These SMEs are vital to the economy, generating more than half a billion jobs annually and contributing approximately 40% to the overall GDP.
Small and medium-sized enterprises (SMEs) are vital for the sustainable growth of Vietnam's economy, making their productivity improvement a crucial mission for the government The economy's overall growth heavily relies on the productivity of these firms, which is driven by innovation and technological advancements However, many Vietnamese SMEs continue to face operational challenges, leading to inefficiencies A significant barrier is their lack of recognition regarding the importance of innovation and the adoption of new technologies in enhancing productivity, despite the well-established benefits these factors can bring to their operations.
The common measurement for innovation in empirical studies is R&D expenditure of a particular firm Various empirical studies have been conducted to quantify the relationship between R&D expenditure and firm’s performance
Research indicates a strong correlation between various factors in studies conducted by Siedschlag, Zhang, and Cahill (2010), Belderbos, Carree, and Lokshin (2004), and Crespi and Pianta (2009) However, in Vietnam, small and medium-sized enterprises have not significantly reported their investments in research and development activities.
Innovation activities in SMEs tend to be less formal and encompass a wider range of practices compared to larger companies Consequently, studying the effects of innovation on the productivity of SMEs presents several challenges, particularly within the context of Vietnam.
The relationship between innovation and productivity in small and medium-sized enterprises (SMEs) in Vietnam has been underexplored, highlighting the need for deeper investigation This is particularly crucial in an economy where SMEs play a dominant role and technology levels remain low Enhancing understanding in this area is vital for policymakers to effectively guide the development and implementation of innovative activities, ultimately fostering growth for both businesses and the country.
Research objectives
This study aims to explore the connection between innovation and productivity among Vietnamese SMEs The primary objective is to define and quantify how innovation impacts firm-level productivity within the context of these small and medium-sized enterprises in Vietnam.
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 examines the productivity of Vietnamese SMEs through the lens of Levinsohn and Petrin (2003), focusing on how shifts in innovation levels can impact productivity While innovation is vital for development, its outcomes remain uncertain, making it difficult to predict whether these activities will generate added value for firms The findings aim to provide policymakers with evidence on how to effectively allocate resources to enhance productivity This topic is particularly relevant for developing countries like Vietnam, as Indjikian and Siegel (2005) highlight that the benefits of innovation are often underutilized, and national resources for fostering new innovations are limited, despite innovation's significant role in global economic 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 due to their significant representation, accounting for nearly 70% of total small and medium enterprises (SMEs) in a comprehensive five-round survey The data utilized in this research was collected prior to the complete publication of the 2015 survey dataset, ensuring that the findings are both relevant and indicative of broader industry trends.
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 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.
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
In his seminal work "Economic Development" (1912), Schumpeter emphasized the significance of technological innovations in driving the business cycle He posited that when new technologies are introduced during a phase of economic readiness, they can lead to an upward trend as the economy adapts and fully utilizes these advancements However, if such innovations emerge during a saturated economic phase, the economy may become vulnerable to negative shocks, potentially resulting in depression Schumpeter also highlighted the necessity for firms to embrace risk and invest in new technologies early on to capitalize on profit opportunities before competitors adopt them.
Joseph Schumpeter, in his seminal works such as "The Theory of Economic Development" (1934), "Capitalism, Socialism and Democracy" (1942), and "Business Cycles" (1939), highlighted the critical roles of innovation and entrepreneurship in economic development He argued that innovation is the primary catalyst for growth, with entrepreneurs serving as vital agents who facilitate revolutionary technological changes and propel the economy beyond its equilibrium 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,
(ii) introducing new method of production,
(iii) opening new markets which have not entered in the past yet,
(iv) searching/discovering new sources/suppliers for raw material and other inputs in production process,
(v) acquiring new market structures in any industry (i.e changing the monopoly position)
Innovation is a key driver of economic growth, as outlined by Schumpeter's four-step process: invention, innovation, diffusion, and imitation While the initial stages of invention and innovation have limited impact, it is during diffusion and imitation that economies experience significant growth through increased sales and cost reductions For innovation to thrive, it requires effective implementation, which is where entrepreneurs play a crucial role by allocating resources to replace outdated technologies with new ones This dynamic process, termed "creative destruction" by Schumpeter, underscores how innovation fosters economic development.
Productivity: concept and measurements
Productivity measures how efficiently a firm, industry, or country transforms input factors into output, defined as the ratio of output to inputs in the manufacturing process It serves as a key indicator of economic performance for businesses, industries, and entire nations.
Productivity can be influenced by the availability of input resources and the value added during the production process A firm's productivity may decline due to insufficient inputs or inefficient use of resources Conversely, enhancing productivity can be achieved by creating value from available inputs and optimizing activities within the manufacturing process.
Productivity can be assessed through various methods, which are generally categorized into two main types: single factor productivity measures, where productivity is determined by the ratio of output to a single input, and multifactor productivity or total factor productivity measures, which evaluate output in relation to multiple inputs.
In the realm of single factor productivity, productivity can be measured through labor productivity and capital productivity, expressed as a quantity index of labor or capital input relative to gross output or value added While these measures are straightforward to calculate, they only provide a limited view of productivity, focusing on the partial productivity of labor or capital without considering their combined efficiency with other input factors To achieve a more comprehensive understanding of productivity that accounts for multiple inputs, total factor productivity (TFP) is identified as a more effective measure Consequently, this research utilizes total factor productivity to assess the productivity of firms.
Estimating total factor productivity using production function estimators has been a key method in addressing significant issues in the literature, such as the relationship between foreign direct investment and the productivity of domestic firms (Javorcik, 2004), the impact of research and development (Hall et al., 2009), and the effects of information technology (Chun et al., 2015) These analyses are primarily conducted through simple Cobb-Douglas production function regression.
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):
In the context of time series analysis, Total Factor Productivity (TFP) consists of two key components: the average productivity across all firms, denoted as 𝛽 0, and the deviation in a firm's productivity, represented by 𝜀 𝑗𝑡 The latter reflects the impact of unobserved factors on a firm's output beyond the traditional inputs Furthermore, the deviation 𝜀 𝑗𝑡 can be broken down into firm-level productivity, 𝑤 𝑗𝑡, and an independent and identically distributed (i.i.d) component, 𝑣 𝑗𝑡.
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
There are two primary research approaches for calculating total factor productivity: non-parametric and parametric methods The non-parametric technique, particularly growth accounting, is based on Robert Solow's 1957 paper on technical changes and production functions This method, which operates under the assumptions of constant returns to scale and competitive factor markets, illustrates how variations in output growth can be attributed to changes in input types and total factor productivity While growth accounting is well-established, it falls short in addressing causality, particularly regarding how investment in technological changes can simultaneously drive and result from productivity growth In contrast, parametric techniques utilize econometric methods to estimate total factor productivity by examining the relationship between production inputs and outputs The advantages of econometric techniques include the ability to test parameters for statistical significance and to address endogeneity issues.
The non-parametric growth accounting method, introduced by Robert Solow in his 1957 paper on technical change, analyzes aggregate production functions to assess the sources of economic growth This approach distinguishes between growth attributed to input contributions, represented by movements along the production function, and growth driven by technological advancements that shift the production function It operates under the assumption of constant returns to scale, meaning the total elasticities of all input factors equal one Typically, input factors are weighted by their income shares for country-level productivity calculations or by their cost shares for firm-level productivity assessments.
Productivity is calculated by solving equation (4) without econometric sense
The "Solow residual," denoted as 𝑤̂ 𝑗𝑡, represents the portion of output growth that exceeds the growth of input factors, indicating positive value when output growth outpaces input growth This residual captures not only the impact of technological advancements but also encompasses various external factors that influence overall efficiency beyond the input factors themselves (Schreyer, 2001).
Endogeneity issues may arise from the relationship between input decisions and unobserved productivity shocks (𝑤 𝑗𝑡), leading firms to adjust their input levels in response to these shocks For instance, firms are likely to increase investments when they experience positive productivity shocks, while they may reduce their workforce in the face of negative shocks Consequently, the input coefficients derived from OLS regression could be biased and inconsistent (Eberhardt and Helmers).
To address the endogeneity issue in production function estimation, several solutions have been proposed in the literature Key methods include Instrumental Variables (IV) regression, dynamic panel estimators developed by Arellano and Bond (1991) and Blundell and Bond (1998), commonly referred to as the Generalized Method of Moments (GMM) approach, as well as the techniques introduced by Olley and Pakes.
(1996) which is categorized as ‘structural estimators’, then been further developed by Levinsohn and Petrin (2003)
In standard IV regression, consistent and unbiased coefficients require independent variables causing endogeneity, such as input quantities (K and L), to be instrumented by exogenous variables that correlate with these inputs but remain unaffected by unobserved productivity While input prices (r, w) have been proposed as instruments under the assumption of perfectly competitive markets, studies by Eberhardt and Helmers (2010) and Van Beveren (2012) highlight several issues with this approach First, there is often insufficient data on input prices, and when available, they lack sufficient variation across firms to effectively estimate the production function Second, the assumption of perfectly competitive input markets is questionable, as productivity shocks can create market power that influences input prices, thus compromising the relationship between the instruments and the error term Lastly, even under strict assumptions of market competition, input prices may still correlate with unobserved productivity through factors like unobserved labor quality affecting wage changes Consequently, using input prices as instruments for input quantities in standard IV regression fails to produce consistent results.
Finding a robust instrument for input quantity in production function regression can be challenging However, Arellano and Bond (1991) and Blundell and Bond (1998) have advanced the field by introducing the Generalized Method of Moments (GMM) estimator This method utilizes past values of both dependent and independent variables as instruments to address endogeneity issues These instruments are considered valid based on the premise that input choices made prior to time t are uncorrelated with productivity shocks occurring at time t, denoted as 𝑤 𝑗𝑡.
The GMM approach effectively addresses the endogeneity problem and produces satisfactory results; however, it is not built on a structural model that reflects firm behaviors (Eberhardt and Helmers, 2010).
In 1996, OP introduced a novel method for explaining a firm's production function by analyzing observed firm behaviors, particularly addressing the issue of endogeneity in production functions They proposed using a firm's investment decisions as a proxy for unobserved productivity, denoted as \( w_{jt} \) Two key assumptions underlie this approach: the "monotonicity assumption," which posits a strong positive correlation between firm investment, capital stock, and unobserved productivity, allowing productivity to be derived from the investment function \( w_{jt} = f_t(i_{jt}, k_{jt}) \); and the "scalar unobservable" condition, which asserts that only unobserved productivity influences a firm's investment decisions Labor is excluded from this function due to the assumption of its full flexibility, enabling immediate adjustments upon observing \( w_{jt} \) OP's "structural estimator" is determined through a two-step process.
Firstly, from (3) output 𝑦 𝑗𝑡 has been regressed on labor input 𝑙 𝑗𝑡 and a proxy of firm-specific productivity:
Innovation: concept and measurements
The Oslo Manual (OECD 2005, p.46) defines innovation as the implementation of a new or significantly improved product, process, or marketing method within business practices, workplace organization, or external relations This widely recognized definition has been referenced by numerous institutions in their studies and surveys on various aspects of innovation.
There are four type of innovations which proposed in Oslo Manual (OECD 2005):
Product innovation involves the introduction of new goods or services to the market, significantly enhancing their features compared to existing offerings This improvement can manifest in various ways, including superior design, advanced technical specifications, new functionalities, or increased user-friendliness.
Process innovation involves enhancing the methods used in the production and distribution of goods and services, leading to cost reduction and improved product quality It is closely associated with the techniques employed, as well as the supporting equipment and software utilized in the production process.
Marketing innovation involves enhancing product design, pricing strategies, and promotional campaigns by implementing new marketing methods aimed at better meeting client needs, capturing market share, and boosting sales.
Organizational innovation encompasses changes in the organizational structure and business environment that aim to lower administrative and transaction costs while enhancing work efficiency This type of innovation extends beyond internal processes to also include improvements in external relationships with suppliers, clients, and government agencies.
It is essential to differentiate between various types of innovation, as many innovations may share characteristics across multiple categories For example, a new product that requires a novel production process can be classified as both product innovation and process innovation The Oslo Manual (2005) offers comprehensive guidelines for distinguishing between these types of innovation.
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) and Polder, Van Leeuwen et al (2009) highlights that traditional measurements of innovation intensity may not accurately reflect differences among firms According to Mohnen and Hall (2013), such metrics can lead to misleading conclusions, particularly when comparing innovation activities across firms of varying sizes It is often assumed that larger firms are more innovative than smaller ones; however, this perspective overlooks the complexity of innovation activities, which can be categorized into four distinct types Consequently, using innovation dummy variables may not be appropriate for determining the true innovation capacity of large versus small firms.
Innovation can be evaluated through both input and output methods Input approaches focus on a company's initiatives to launch new products, enhance production processes, explore new markets, or increase efficiency Conversely, output measures of innovation are evident in the introduction of new products, successful improvements in production processes, cost reductions, and enhanced efficiency (Mohnen and Hall, 2013).
Innovation is often assessed through Research and Development (R&D) expenditures, which focus on creating new products or production methods However, many non-R&D activities also contribute to innovation, as highlighted in the Oslo Manual (2005) These activities include purchasing patents, paying royalties, enhancing workforce skills through internal training, acquiring new equipment or software, and improving management structures to better introduce products to the market Both R&D and non-R&D activities aim to enhance a firm's efficiency, making it essential to consider non-R&D expenditures when measuring innovation Furthermore, the presence of skilled employees is crucial, as they play a vital role in adopting new technologies, managing operations, and addressing potential technological challenges.
Innovation is often evaluated by its quality, which can be reflected in revenue generation A key indicator of innovation's impact on a firm's overall performance is the percentage revenue share derived from new products or existing products enhanced through the innovation process This metric has been utilized in various studies, including those by Miguel Benavente, to represent the results of innovation effectively.
(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 studied, yet there remains no consensus on the outcome On one hand, innovation is often seen to positively influence performance, as evidenced by various metrics Conversely, numerous studies highlight potential negative impacts stemming from innovation efforts.
Innovation significantly impacts a firm's performance by transforming innovation inputs into outputs that enhance overall performance, as highlighted by Crépon, Duguet, and Mairessec (1998) They introduced the CDM model, which has been widely utilized by researchers, including Miguel Benavente (2006) and Mairesse et al (2005), to analyze this relationship A study by Siedschlag, Zhang, and Cahill (2010) applied the CDM model to a panel of 723 firms from the Community Innovation Survey of Ireland (2004-2008), considering factors like foreign ownership and international trade The model involves three stages: (i) the decision to invest in innovation, (ii) the determination of innovation output from inputs, and (iii) the relationship between innovation output and final production Findings indicate that firms engaged in export activities, especially foreign-owned ones, are more inclined to invest in innovation and achieve higher innovation outputs, while innovation expenditures do not significantly influence these outputs Additionally, there is a positive correlation between innovation outputs and labor productivity.
The relationship between innovation and firm performance is recognized as causal, with studies indicating that innovation leads to the development of new products and enhancements in production processes, ultimately improving a firm's efficiency and overall performance Conversely, firms that demonstrate superior performance are more likely to invest in innovation Due to this interdependent relationship, researchers have utilized Instrumental Variable (IV) or Generalized Method of Moments (GMM) estimations in various studies to address the endogeneity issues that may arise.
Belderbos, Carree, and Lokshin (2004) analyzed data from the Community Innovation Survey (1996 and 1998) involving 2,056 manufacturing firms to assess the impact of internal innovation activities and external innovative collaborations on firm performance They measured innovation through two variables: internal innovation expenditure relative to sales and external collaboration via R&D partnerships with competitors, suppliers, customers, and research institutions Firm performance was evaluated based on labor productivity growth and the sales of new market products Using IV regression to establish causal relationships while controlling for factors such as firm size, industry classification, ownership status, and previous productivity levels, the study revealed that various forms of R&D collaboration and innovation intensity positively influence productivity growth However, the intensity of innovation did not significantly affect sales growth of new products.
Lokshin, Belderbos, and Carree (2008) identified a significant positive relationship between internal innovation and labor productivity by analyzing data from 304 manufacturing firms in the Netherlands between 1996 and 2001 They defined internal innovation as a firm's R&D expenditure and external innovation as spending on contracted R&D with other firms Utilizing GMM estimation within an augmented Cobb-Douglas production function framework, their findings revealed that both internal and external R&D expenditures, along with their quadratic and interaction forms, are complementary in enhancing productivity They concluded that while internal R&D is crucial for a firm's productivity, external R&D significantly impacts productivity only when the firm has sufficiently invested in internal R&D, highlighting a decreasing returns to scale effect.
Parisi, Schiantarelli, and Sembenelli (2006) conducted research on 941 manufacturing firms in Italy, utilizing surveys from 1995 and 1998 to explore the relationship between innovation and firm performance They examined both product and process innovation efforts through two analytical approaches The first approach applied the Cobb-Douglas production function to assess output growth in relation to innovation variables, while the second approach regressed Total Factor Productivity (TFP), calculated using Levinsohn and Petrin (2003), against the same innovation variables The findings revealed a positive impact of both process and product innovation on productivity, with process innovation demonstrating a greater effect than product innovation These results were consistent across both analytical methods, affirming the significance of innovation in enhancing firm performance.
Several studies indicate that innovation does not significantly enhance productivity For instance, Santos et al (2014) found that innovative investments do not meaningfully impact a firm's performance Similarly, Li and Atuahene-Gima (2001) attributed the negligible effect of innovation on performance to its inherent uncertainties, highlighting that innovation is resource-intensive and risky, with uncertain returns Additionally, Branzei and Vertinsky (2006) emphasized the necessity of specific resources and organizational capabilities for innovation activities to yield positive results for firms.
In Vietnam, there is a growing interest in exploring the concepts of innovation and productivity, particularly among small and medium-sized enterprises (SMEs) Research by Nguyen et al (2007) examined the link between innovation and exports in Vietnamese SMEs, utilizing dummy variables to signify the introduction of new products or processes and improvements to existing products Similarly, Vu and Doan (2015) employed these dummies along with marketing changes to assess the impact of innovation on SME performance, measured by annual gross profit Their study identified endogeneity issues in the innovation-performance relationship and applied a two-stage least squares (2SLS) model to address this challenge The findings indicated that innovation in products, production processes, and marketing positively influences firm 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, a non-parametric method was introduced to estimate a firm's total factor productivity (TFP) while examining the relationship between productivity and turnover Yang and Huang (2012) utilized the Levinsohn and Petrin approach to assess TFP specifically for Vietnamese small and medium-sized enterprises (SMEs) However, their research concentrated on the impact of trade liberalization on productivity, which distinguishes it from the focus of this study.
This study aims to explore the significance of innovation for the productivity of SMEs in Vietnam, a transitioning economy It evaluates innovation through various metrics, including the intensity of innovative expenditures, a dummy variable for innovation, and the proportion of high-quality employees within the total workforce Productivity is assessed using the Levinsohn and Petrin approach, which effectively addresses endogeneity issues that may arise from the interplay between input decisions and productivity shocks Notably, this method of estimating Total Factor Productivity (TFP) is seldom utilized in the context of Vietnam's literature.
Appendix 2 provides the summaries on the related empirical studies on the relationship between innovation and firm’s performance.
An overview of Vietnamese Small and Medium-sized Enterprises
Small and Medium-sized Enterprises (SMEs) are typically classified by international institutions and countries based on criteria such as the number of employees, total revenues, and total assets or equity This classification aligns with established regulations, such as The Decree, which provides a framework for understanding the significance and role of SMEs in the economy.
According to No 56/2009/ND-CP, small and medium-sized enterprises (SMEs) in Vietnam are defined as businesses that employ between 10 and 300 individuals and possess total equity of less than a specified amount.
100 billion dong The details of the classification following Decree No 56/2009/ND-
CP is presented in Table 3.1 below:
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 rise in the number of businesses Notably, the total number of small and medium-sized enterprises (SMEs) has dramatically increased, with the figure doubling from 120,074 in 2006 to 367,300 by 2013.
In Vietnam, micro, small, and medium-sized enterprises (MSMEs) represent a significant majority, accounting for approximately 96% to 98% of all firms from 2006 to 2013 While the total number of enterprises has increased annually, the size of these firms has been shrinking Specifically, micro firms, defined as those with 1-10 employees, constituted 61% of total firms in 2006, dropping to 66% in 2013 Small firms also saw a decrease, from 35% in 2006 to 32% in 2013, while large firms plummeted from 4% to just 1.6% over the same period This trend of downsizing among Vietnamese enterprises has resulted in a lack of medium and large-sized firms necessary to effectively drive the economy amid international integration.
Figure 3.1: Number of enterprises at 31/12 (by size of employees)
Small and medium-sized enterprises (SMEs) have experienced substantial growth, representing the largest segment of total enterprises and making significant contributions to the economy The Mid-term Evaluation Report on the Development of SMEs from 2011 to 2015 highlights four key areas of impact: GDP contribution, government budget enhancement, total investment stimulation, and job creation.
From 2009 to 2012, non-state owned enterprises, predominantly comprising small and medium-sized enterprises (SMEs) which represent 98.6%, significantly contributed to the GDP, accounting for approximately 48-49% of the total economy In contrast, state-owned enterprises, with 59.3% being SMEs, experienced a decline in their contribution to GDP, decreasing from 37.32%.
Between 2009 and 2012, the contribution of state-owned enterprises to GDP decreased from 32.57% to 17-18% as a result of the government's privatization plan In contrast, foreign direct investment enterprises, predominantly small and medium-sized enterprises (SMEs), maintained a stable GDP contribution, ranking third overall.
In 2012, the structure of GDP and the ongoing privatization process indicate that small and medium-sized enterprises (SMEs) within the non-state owned sector are poised to play a significant role in driving future GDP growth.
Micro Small and Medium Large
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%)
Small and medium-sized enterprises (SMEs) play a significant role in national investment, contributing a substantial portion to the overall economy In 2010, SMEs accounted for VND 236,119 billion, representing 32% of total enterprise sector investment This figure surged to VND 699,690 billion (57%) in 2011, primarily driven by micro and small-sized enterprises, before dropping to VND 235,463 billion (29%) in 2012 Notably, small-sized firms consistently represented the largest share of total investment from the enterprise sector, contributing between 61% and 68% from 2010 to 2012.
Small and medium-sized enterprises (SMEs) play a crucial role in Vietnam's economy by generating employment opportunities In 2010, Vietnamese SMEs created over 4.35 million jobs, representing nearly 45% of total employment in the enterprise sector By 2012, this number rose by 16.83% to 5.09 million jobs, accounting for 47% of total employment Additionally, the income of workers in the SME sector has seen significant improvements over time.
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-sized enterprises (SMEs) are crucial to Vietnam's economic growth, significantly contributing to national income, government revenue, and overall investment They play a vital role in job creation, stabilizing workers' incomes, and enhancing living standards Despite their importance, Vietnamese SMEs continue to encounter numerous challenges that hinder their development.
Small and medium-sized firms often struggle with insufficient internal capital and face challenges in accessing external funding Their limited internal capital stock frequently leads to shortages, while difficulties in obtaining loans from banks and financial institutions arise from a lack of collateral, unreliable financial practices, complex application processes, and insufficient information As a result, these businesses commonly fail to secure the necessary capital for market expansion, technological upgrades, or new project investments.
Vietnamese SMEs face significant challenges due to outdated technology, with only 10% of their total exports and imports involving technology imports, compared to an average of 40% in other countries, according to a UNDP survey This low technology adoption can be attributed to three main factors: limited profits compared to larger firms, restricted access to government support programs, and a lack of integration into supply chains and developed sub-industries Consequently, the inadequate technology affects labor productivity, product quality, and overall competitiveness of these enterprises.
In 2012, a significant 75% of workers in Vietnamese SMEs lacked access to formal technical training, while only 40% of SME owners held a university degree Additionally, many managers lacked training in essential areas such as economics, business management, and law, leading to decision-making primarily based on experience As a result, the operational efficiency of Vietnamese SMEs remains lower compared to their regional and global counterparts.
Methodology
From the theories and empirical studies, the conceptual framework for this study is built and illustrated as Figure 3.2 below:
The impact of innovation on productivity can be assessed in two stages, starting with the estimation of total factor productivity for each firm using the Levinsohn and Petrin (2003) approach within the production function framework This method incorporates labor, capital, and intermediate inputs as input variables, while value added serves as the output variable.
The examination of the relationship between innovation and productivity involves regressing total factor productivity from each firm against various innovation proxies, including a dummy variable for investment in innovation activities, innovation expenditure intensity, and the proportion of high-quality employees within the total labor force This analysis is grounded in Schumpeter's Theory of Innovation and supported by empirical studies, such as those conducted by Siedschlag, Zhang, and Cahill (2010), Belderbos, Carree, and Lokshin (2004), Crespi and Pianta (2009), Santos, Basso, Kimura, and Kayo (2014), and Lokshin, Belderbos, and Carree (2008).
In addition to innovation variables, the regression analysis will incorporate control variables such as firm age, firm size, capital structure, and historical firm productivity Numerous empirical studies suggest that these factors significantly influence a firm's productivity.
Output (Value added) Total factor productivity
- Share of high-quality labor in total labor force
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 a novel method for calculating Total Factor Productivity (TFP) within the Olley and Pakes (1996) framework Unlike traditional approaches that rely on investment data, their model utilizes intermediate inputs to account for unobserved productivity, addressing the correlation between the error term and inputs This innovative proxy has demonstrated the ability to yield more consistent estimates of the coefficients.
The study utilizes the LP method to estimate firm productivity, specifically focusing on the value-added case This approach is based on the Cobb-Douglas production function expressed in logarithmic form, incorporating two inputs: labor (l) as a freely variable input and capital (k) as a state variable, with the output being the value added (y) of firm i in year t Detailed methodology for this estimation will be provided subsequently.
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:
In a perfectly competitive market, all firms experience identical input and output prices, leading to the conclusion that the demand for intermediate inputs can be modeled as a function of capital stock and unobservable productivity shocks This relationship is represented by the equation 𝑖 𝑗𝑡 = 𝑖 𝑡 (𝑘 𝑗𝑡 , 𝑤 𝑗𝑡), indicating that intermediate inputs are influenced by both capital and productivity changes The model accounts for temporal variations in input and output prices by indexing the function with time variable t.
The monotonicity condition assumes that, given a fixed amount of capital, an increase in productivity or a positive productivity shock will result in higher usage of intermediate inputs This is based on the premise that firms will increase output when the marginal revenue from intermediate inputs rises In a perfectly competitive market, this relationship is clear; however, in an oligopolistic market, firms may refrain from increasing output despite a positive productivity shock, as doing so could lower prices and negatively impact their sales.
Under the assumption of monotonicity, the demand function for intermediate inputs can be inverted, allowing productivity shocks \( w_t \) to be expressed as a function of intermediate inputs and capital: \( w_{jt} = w_t(i_{jt}, k_{jt}) \) In this context, productivity shocks are represented through the use of intermediate inputs.
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 characterized as partially linear, exhibiting linearity in labor while being non-linear in intermediate inputs and capital As outlined by Robinson (1988), a semiparametric estimation approach can be employed to estimate such partially linear equations This method involves predicting 𝑦 𝑗𝑡 and 𝑙 𝑗𝑖 based on given values of 𝑖 𝑗𝑡 and 𝑘 𝑗𝑡, specifically through the expected values 𝐸(𝑦 𝑗𝑡 |𝑖 𝑗𝑡 , 𝑘 𝑗𝑡) and 𝐸(𝑙 𝑗𝑖 |𝑖 𝑗𝑡 , 𝑘 𝑗𝑡) This prediction utilizes weighted least squares within a second-order approximation framework concerning 𝑖 𝑗𝑡 and 𝑘 𝑗𝑡.
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 and: 𝐸(𝜑 𝑡 (𝑖 𝑗𝑡 , 𝑘 𝑗𝑡 )|𝑖 𝑗𝑡 , 𝑘 𝑗𝑡 ) = 𝜑 𝑡 (𝑖 𝑗𝑡 , 𝑘 𝑗𝑡 ))
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 presumed to respond gradually to productivity shocks (𝑤) When 𝑤 is divided into forecastable and non-forecastable components, capital tends to adjust based on the forecastable part rather than the non-forecastable one Furthermore, LP posits a first-order Markov process for the unobservable productivity (𝑤).
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:
𝑤 𝑗𝑡 ̂+ 𝑣 𝑗𝑡 = 𝑦 𝑗𝑡 − 𝛽̂ 𝑙 𝑙 𝑗𝑡 − 𝛽 𝑘 ∗ 𝑘 𝑗𝑡 (14) Estimation of 𝜑 𝑡 got from first stage then can be applied to the following function:
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
The two-stage estimation process created by Levinsohn and Petrin effectively addresses the issue of endogeneity caused by the correlation between input choices and unobservable productivity This methodology yields consistent results for input coefficients, facilitating the calculation of Total Factor Productivity (TFP).
Stata offers a user-friendly command known as levpet, designed to simplify the calculation of Total Factor Productivity (TFP) This study utilized the levpet command for accurate TFP measurement.
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 study examines the impact of various innovation variables, including a dummy for innovation, innovation expenditure intensity, and the proportion of high-quality labor within a firm's total workforce Additionally, it considers control variables such as the firm's age, size, and capital structure to provide a comprehensive analysis of factors influencing innovation outcomes.
Incorporating a lagged dependent variable (𝑇𝐹𝑃 𝑗𝑡−1) into a model may result in biased estimations Achen (2000) indicates that this can lead to an overestimation of the lagged variable and an underestimation of other explanatory variables, primarily due to the combined effects of serial correlation and significant trends in exogenous variables.
Ordinary Least Squares (OLS) estimation can produce biased estimators, as noted by Nickell (1981), and Fixed Effects (FE) estimation may also yield similar issues (Bond, 2002) To address these biases and achieve unbiased and consistent estimators, alternative estimation methods are required In such cases, Instrumental Variables (IV) or Generalized Method of Moments (GMM) estimation is recommended (Keele and Kelly, 2006).
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 productivity in Vietnamese SMEs, focusing on two key components: the intensity of innovation expenditure and the proportion of high-quality labor within the total workforce.
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 below provides the summary of the concepts and measurements for the above variables
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
The age of a firm is calculated by subtracting its established year from the year of the survey If a firm provides varying established years across different surveys, the established year used will be the one reported in the first survey round.
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
Data sources
This study analyzes data from surveys conducted on Small and Medium-sized Enterprises (SMEs) in Vietnam between 2005 and 2013 The surveys were a collaborative effort involving the Institute of Labor Studies and Social Affairs (ILSSA), the Stockholm School of Economics (SSE), and the University of Copenhagen's Department of Economics The research focused on ten selected provinces and cities, including Ha Noi, Ho Chi Minh, and Hai Phong, with each survey sampling 2,500 firms, 80% of which were consistent across the years The remaining 20% of firms were replaced due to operational changes or non-responsiveness, ensuring that the data remains representative of the Vietnamese SME population.
The study analyzes six major industries from approximately fifty reported sectors, specifically focusing on foods, wood and wood-related products, rubber and plastic products, non-metallic mineral products, fabricated metal products, and furniture These six industries represent nearly 70% of total SMEs surveyed in five rounds, making them a significant representation of the overall dataset.
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 employs some basic filters to remove missing data and outliers The details of filter process are described below:
In Stage 1 of estimating Total Factor Productivity (TFP) using the LP approach, it is essential to have complete data on output (revenue) and input factors, including labor, intermediate inputs, and capital Consequently, any firms lacking this information are excluded from the dataset The summary descriptive statistics for the remaining data are displayed in Table 3.3 below.
Table 3.4: Number of observation after filtering
In Stage 2, variables such as a firm's age and total assets, which serve as a proxy for firm size, exhibit a wide range of values To prevent biased results caused by potential measurement errors or outliers, the study implemented a basic filtering process to eliminate these outliers Any observation exceeding the mean value plus two standard deviations was classified as an outlier and removed from the dataset This filtering process was applied to both the firm's age and total assets across each selected industry The summary statistics of the filtered data are presented in Table 3.5 below.
Table 3.5: Number of observation after filtering in stage 2
Industry Total obs No of outliers
Total Factor Productivity of Vietnamese SMEs
In the initial phase of the study, the LP approach is utilized to calculate Total Factor Productivity (TFP) for each firm over the reported years, relying on output and input data detailed in Chapter 3 The output is represented by the firm's net revenue from sales during the fiscal year, while input factors encompass Labor, Intermediate Inputs, and Capital Labor is quantified by the total number of employees at the end of the year, Intermediate Inputs reflect the total expenditure on raw materials and indirect factors in the production process, and Capital is assessed as the average value of physical capital at both the beginning and end of the fiscal year Although this capital measurement may not precisely capture the capital used in production, it serves as a suitable proxy A summary of the variables utilized in this stage—firm's net revenue, labor, capital, and intermediate inputs—is presented in Table 4.1.
Table 4.1: Descriptive statistics of production function variables
Industry Foods Woods Rubber and Plastic
Year Variables Mean Std Dev Max Min Mean Std Dev Max Min Mean Std Dev Max Min
Industry Non-metallic mineral Fabricated metal Furniture
Year Variables Mean Std Dev Max Min Mean Std Dev Max Min Mean Std Dev Max Min
4.1.2 Total factor productivity from production function estimation of
Tables 4.2 and 4.3 present a comparative analysis of production function parameters estimated through OLS, Fixed Effect, and LP regression methods across six industries: Foods, Woods, Rubber and Plastics, Non-metallic minerals, Fabricated Metal, and Furniture, covering the period from 2005 to 2013.
The study’s results of higher estimation on free labor input in OLS regression as compared with LP regression confirmed the concern of Marschak and Andrews
In the context of Levinsohn and Petrin (2003), the presence of correlation between input factors and unobserved productivity shocks leads to upward bias in the estimation of input factors However, the bias in capital input estimation can vary, being either upward or downward depending on its correlation with unobserved productivity shocks When there is little to no correlation, the Ordinary Least Squares (OLS) estimation tends to be biased downward, particularly when labor is treated as a free variable The study's findings indicate a consistent downward bias in OLS capital estimation compared to the Levinsohn-Petrin (LP) regression across six selected industries.
The fixed effects (FE) estimations vary significantly across six selected industries compared to ordinary least squares (OLS) and linear probability (LP) estimations This discrepancy may be attributed to the fluctuating fixed effects caused by unobserved productivity shocks unique to each firm Furthermore, FE estimation proves to be inefficient when there is a correlation between input factors and unobserved productivity.
Table 4.2: Comparison of OLS, Fixed Effect and LP estimators in Foods, Woods and Rubber and Plastics
Foods Woods Rubber and plastics
OLS FE LP OLS FE LP OLS FE LP lnL 0.766*** 0.470*** 0.379*** 0.680*** 0.424*** 0.361*** 0.721*** 0.527*** 0.382***
Table 4.3: Comparison of OLS, Fixed Effect and LP estimators in Non-metallic mineral, Fabricated metal and Furniture
Non-metallic mineral Fabricated metal Furniture
OLS FE LP OLS FE LP OLS FE LP lnL 0.727*** 0.357*** 0.348*** 0.790*** 0.599*** 0.368*** 0.561*** 0.456*** 0.310***
Total factor productivity then be calculated and stored for each firm using the following equation:
𝑤̂ = 𝑦 𝑗𝑡 𝑗𝑡 − 𝛽̂𝑘 𝑘 𝑗𝑡 − 𝛽̂ 𝑙 𝑙 𝑗𝑡 (4) Then the exponential of 𝑤̂ 𝑗𝑡 is the result of firm-level productivity.
Innovation – Firm’s productivity relationship
In this stage, the productivity of firms is analyzed by comparing it to historical values while considering innovation variables, such as dummy indicators for innovation, the intensity of innovation expenditure, and the proportion of high-quality labor within the total workforce Additionally, control variables including the firm's age, size, and capital structure are taken into account Summary statistics for these variables are presented in Table 4.4.
Table 4.4: Descriptive statistics of TFP and its determinants
No of obs Average Std Dev Max Min
The majority of SMEs in the selected sample are micro and small-sized enterprises, with an average log of total assets measuring 13.12, equivalent to nearly 2 billion dong This prevalence aligns with the statistics on Vietnamese SMEs provided by the General Statistics Office (GSO) The average operational lifespan of these firms is 14 years, indicating a relatively long duration in the market Out of 8,374 observations, 4,509, or 54% of the total sample, reported expenditures on innovation activities Notably, 65% of those who reported innovation spending indicated non-zero amounts, highlighting a significant investment in innovation among these SMEs.
4.2.2 The relationship between innovation expenditure intensity and firm’s productivity
This section explores the testing of Hypothesis 1, which posits a positive relationship between innovation expenditure intensity and firm productivity As shown in Table 4.5, the results from estimating model (16) utilize three methods—Pooled OLS, Fixed Effects (FE), and System-GMM Notably, the GMM method incorporates lagged endogenous variables as instruments to enhance the analysis.
Table 4.5: Regression results of innovation expenditure intensity and firm’s productivity
Testing for autocorrelation and validity of instruments in GMM results:
This study addresses the potential endogeneity issue identified in previous chapters, particularly the simultaneous relationship between innovation expenditure intensity and firm productivity (TFP) The Durbin-Wu Hausman test for endogeneity was conducted, with results detailed in Appendix 3, confirming that innovation expenditure intensity is indeed endogenous Consequently, both Ordinary Least Squares (OLS) and Fixed Effects (FE) estimators are deemed biased and inconsistent To effectively address the endogeneity problem, the Generalized Method of Moments (GMM) approach is recommended, utilizing lagged values of the endogenous variable, specifically innovation expenditure intensity (Inv_exp), as instruments.
The system-GMM approach for estimating equation (16) is presented in the final column of Table 4.5 In this model, the innovation variable is represented by innovation expenditure intensity (Inv_exp), while control variables include the firm's age (Age), size (Size), and capital structure (Cap_struc) The dependent variable analyzed is the firm's total factor productivity (TFP), and the model also incorporates the lagged value of the firm's TFP (L.TFP) as an independent variable.
The coefficient of innovation expenditure intensity (Inv_exp) significantly impacts a firm's total factor productivity (TFP), indicating that a 1% increase in innovation spending can lead to a 0.79 increase in TFP, holding other factors constant This positive correlation aligns with Schumpeter's Theory of Innovation and is supported by various empirical studies, including those by Belderbos, Carree, and Lokshin (2004), Crespi and Pianta (2009), Rosenbusch, Brinckmann, and Bausch (2011), and Lokshin, Belderbos, and Carree (2008).
Testing for autocorrelation in AR(1) yielded a p-value of 0.027, indicating significant autocorrelation, as discussed in Chapter 3 In contrast, the AR(2) test produced a p-value of 0.924, suggesting no autocorrelation, which is crucial since detecting autocorrelation in AR(2) could invalidate the use of instrumental variables Thus, a higher p-value in the AR(2) test is preferable Overall, the study confirms the presence of autocorrelation in AR(1) while supporting the validity of instrumental variables in AR(2).
Larger firms, indicated by the logarithm of total assets, tend to exhibit higher Total Factor Productivity (TFP) Conversely, a firm's capital structure has a significant negative impact on TFP Additionally, the lagged value of TFP demonstrates a positive and significant influence on the current TFP level, supporting the notion that previous TFP levels affect present productivity outcomes.
4.2.3 The relationship between high-quality labor share in total firm’s labor force and their productivity
The second hypothesis will be evaluated by incorporating the high-quality labor share (Share_L) into the total labor force of the firm within the innovation variables in equation (16), as utilized in the previous section.
Table 4.6 below presents the comparison among OLS, FE and GMM estimations when firm’s TFP is regressed against innovation variables (Inv_exp,
Share_L), control variables (Age, Size, Cap_struc) and lagged one time of TFP
Table 4.6: Regression results of high-quality labor share in Total labor force and Firm’s productivity
Testing for autocorrelation and validity of instruments in GMM results:
The study utilizes the Durbin-Wu Hausman test to assess the endogeneity of the high-quality labor share within the total labor force (Share_L) The findings indicate that Share_L is indeed endogenous, with detailed results of the Durbin-Wu Hausman test provided in the analysis.
Appendix 4) Therefore GMM estimation would become appropriate solution to the endogeneity issue In the results showed in Table 4.6, lag of endogenous variables (in this case are Inv_exp and Share_L) are used as instruments to deal with the problem of endogeneity in GMM estimation
Both Share_L and Inv_exp positively influence Total Factor Productivity (TFP), with statistically significant results Specifically, a 1% increase in the share of high-quality labor within the total labor force corresponds to a 5.54 unit rise in a firm's TFP, assuming other factors remain constant Likewise, an increase of 1% in innovation expenditure intensity leads to a 1.06 unit increase in TFP, ceteris paribus.
The autocorrelation test results for AR(1) and AR(2) using GMM estimation show p-values of 0.093 and 0.881, respectively The study accepts the null hypothesis of no autocorrelation in AR(2), which reinforces the validity of the instrumental variables used.
The relationship between a firm's leverage and size significantly influences its productivity Higher leverage, indicated by an increased debt-to-total-assets ratio, negatively impacts productivity; specifically, a one-percentage-point rise in this ratio correlates with a 0.5 decrease in total factor productivity Conversely, larger firms tend to exhibit higher productivity levels, all else being equal Notably, a firm's age does not significantly affect its productivity.
The positive and significant coefficient of lag one time Total Factor Productivity (TFP) indicates that the current level of TFP is likely influenced by its past values, aligning with the findings from the previous section.