RATIONALE OF THE RESEARCH
In today's modern economy, technological progress plays a crucial role in driving economic growth and enhancing the competitiveness of firms in both domestic and global markets Research and Development (R&D) is considered the foundation of technological advancement, with a firm's innovative capacity closely linked to its R&D expenditure levels and growth rates Countries within the Organization for Economic Cooperation and Development (OECD) invest substantial resources in R&D activities, highlighting its importance in fostering innovation and economic development.
On average, OECD countries have spent more than 2 percent of GDP on annual public and private R&D investments during the last two decades (OSTP 1 , 1997)
Traditionally, firms prioritize research and development (R&D) due to its potential to enhance productivity, thrive in competitive markets, and comply with environmental regulations R&D contributes significantly to the creation of new products and the emergence of new markets Economic returns are a key factor in determining the significance and nature of R&D efforts, with companies engaging in R&D activities primarily when the expected outcomes yield higher returns compared to other investment options like acquiring new machinery, advertising, or purchasing speculative assets.
One effective strategy for boosting productivity growth is to enhance the stock of knowledge within an organization This can be achieved through formal investments in training and development programs, which equip employees with essential skills and information By prioritizing knowledge enhancement, companies can foster innovation and improve overall efficiency, leading to sustainable productivity improvements.
The Office of Science and Technology Policy (OSTP) plays a crucial role in shaping national science and technology policies It provides guidance and support for scientific research and innovation, ensuring that these areas align with the nation's goals For the latest updates and resources, including access to comprehensive research documents, you can visit their official website or contact them via email.
R&D activities In the private and public sectors, the allocation of resources toward the investment to generate new knowledge must be decided carefully
Despite the critical role of research and development (R&D) in enhancing productivity, Vietnam's investment in R&D remains significantly low, particularly within the business sector While OECD countries and China allocate approximately 2% of their GDP to R&D, Vietnam invests only 0.5% (Nguyen and Tran, n.d.) In 2002, Vietnamese enterprises contributed merely 20% of the nation's total R&D expenditure (Nguyen, n.d.), contrasting sharply with OECD companies that fund over 50% of all R&D spending and carry out two-thirds of R&D activities (OSTP, 1997) Notably, small and medium-sized enterprises (SMEs) represent 96.5% of registered companies in Vietnam, highlighting the need for increased R&D investment to boost innovation and competitiveness.
The technology level in Vietnam's SME sector is significantly lagging, being evaluated as two to three times lower than global and regional standards (Bezanson et al., 2000).
The Ministry of Industry highlights that a primary challenge in Vietnam's technological advancement is the lack of skilled labor necessary for supporting upgrades, coupled with minimal relevant R&D activities A mere fraction of the country's R&D scientists and engineers are engaged in industrial enterprises, while most work in national R&D centers, government agencies, or universities Additionally, there is a weak market-oriented relationship among firms, R&D institutions, and universities Financial resource limitations also significantly hinder Vietnamese enterprises' investment in R&D activities.
The case of Vietnam brings into question the connection between R&D and the productivity of manufacturing firms Numerous empirical studies at the firm level have highlighted the significance of technological and knowledge capital in driving productivity growth.
Early research emphasized the significance of R&D investment, revealing that in many countries, the allocation of resources toward research and development is crucial for innovation and economic growth.
Research and development (R&D) plays a crucial role in enhancing the productivity growth of manufacturing firms in Vietnam, particularly when analyzed across different sectors Despite its significant contributions, the impact of R&D on productivity growth remains unverified within the Vietnamese context, highlighting the need for further investigation into this relationship.
OBJECTIVE OF THE RESEARCH
Investment in research and development (R&D) is crucial for enhancing productivity levels in firms, a fact supported by numerous empirical studies across various countries However, this relationship has been overlooked in the context of Vietnam.
Therefore, based on previous studies, the research is going to examine the relationship between R&D activities and productivity growth of manufacturing firms in Vietnam to answer the following questions:
Is there a positive impact of R&D on productivity growth in Vietnamese manufacturing firms?
What should those firms do to increase their productivities? and What policies should be recommended to support them m improving productivity by increasing R&D expenditure?
RESEARCH METHODOLOGY
The thesis studied the impacts of R&D expenditure to productivity growth of Vietnamese manufacturing firms ~y using data from the Vietnam Enterprise Survey
2004 The thesis used such methods as descriptive statistics, quantitative analysis and OLS regression to deal with the research questions.
THESIS STRUCTURE
The thesis is organized into six chapters, starting with an Introduction that outlines the research rationale, objectives, hypotheses, methodology, and structure The second chapter, Literature Review, delves into theories and empirical studies on the relationship between R&D expenditure and productivity growth in manufacturing firms Chapter three focuses on the Overview of R&D and its impact on firm performance in Vietnam.
Chapter 4 of the article outlines the research methodology, emphasizing the justification for model specification and variable selection In Chapter 5, the practical results are examined through descriptive statistics and regression analysis The article concludes with a chapter dedicated to conclusions and policy recommendations.
LITERATURE REVIEW
INTRODUCTION
This chapter reviews relevant literature to establish a scientific foundation for the research It is divided into three main sections: the first discusses key concepts such as R&D, productivity, and manufacturing; the second outlines the economic theories that support the study; and the final section presents a research model illustrating the factors that influence productivity.
The final section of the article examines empirical studies on the impact of research and development (R&D) expenditures on the productivity growth of manufacturing firms across various countries This chapter highlights how R&D investment influences productivity growth, supported by relevant economic theories and empirical findings.
CONCEPTS
Research and experimental development (R&D) is defined by the OECD (1994) as systematic creative work aimed at enhancing knowledge across various domains, including human culture and society, with the goal of developing new applications R&D is categorized into three types: basic research, applied research, and experimental development Basic research focuses on advancing knowledge without immediate practical benefits, involving the analysis of characteristics and relationships to formulate and test hypotheses, typically resulting in publications rather than commercial products In contrast, applied research seeks to acquire new knowledge with specific applications in mind, often leading to patents or proprietary information, and aims to find practical uses for the findings of basic research Lastly, experimental development utilizes existing knowledge and practical experience to create new materials, products, or processes, or to significantly enhance existing ones.
Basic research explores the theoretical factors affecting regional differences in economic growth, while applied research focuses on investigations aimed at shaping government policy.
Experimental development is the development of operational models based on laws with the purpose of modifying regional variations
"Expenditure on R&D may be made within the statistical unit or outside it" (OECD,
Measuring R&D expenditures is complex due to the various costs that must be included or excluded This thesis utilizes R&D expenditure data from the Vietnam Enterprise Survey to analyze its impact on the productivity growth of Vietnamese manufacturing firms.
Scientific and technological innovation involves transforming ideas into new or enhanced products, operational processes, or social services The term "innovation" varies in meaning based on specific measurement or analysis objectives Technological innovations are characterized by new products, processes, and significant technological advancements An innovation is recognized when it is introduced to the market or utilized in production This process encompasses various activities related to science, technology, organization, finance, and commerce, with research and development (R&D) playing a crucial role R&D serves as a source of inventive ideas and problem-solving, making it a vital input for measuring innovation (OECD, 1994; Rogers, 1998).
According to the OECD (2001), productivity is defined as the ratio of output volume to input volume, a widely accepted concept that can be applied in various contexts This flexibility allows for multiple purposes and methods of measuring productivity, highlighting its importance in evaluating efficiency across different sectors.
A frequently stated objective of measuring productivity growth IS to trace technical change
Productivity growth is assessed to track efficiency changes, distinct from technical advancements Achieving full efficiency in engineering terms signifies that a production process reaches the highest possible output with existing technology and a set quantity of inputs.
A real way to describe the essence of measured productivity change IS to identify real cost savings in production
In the field of business economics, comparisons of productivity measures for specific production processes can help to identify inefficiencies
Measurement of productivity is a key element to assess the standard of living
The innovation process is detailed in Appendix 1, which outlines the steps involved in developing new ideas and solutions For the latest version of the thesis download, please refer to the provided email for access.
Productivity can be measured in various ways, influenced by the measurement's purpose and data availability There are two main types of productivity measures: single factor productivity, which compares output to a single input, and multifactor productivity, which assesses output in relation to multiple inputs Additionally, at the industry or firm level, productivity measures can differentiate between those that relate gross output to one or more inputs and those that utilize value-added metrics to reflect output changes.
Table 2.1: Overview of main productivity measures
Type of Capital, labor and output
Labor Capital Capital and labor intermediate inputs measure (energy, materials, services)
Gross productivity productivity KLEMS multifactor output (based on gross (based on gross (based on gross productivity output) output) output)
Capital-labor MFP Value productivity productivity added (based on value (based on value (based on value added) added) added)
Single factor productivity measures Multifactor productivity (MFP) measures Source: OECD, 2001
This thesis utilizes gross-output based labor productivity, defined as the ratio of the quantity index of gross output to the quantity index of labor input, as a key metric for measuring productivity Labor productivity is significant as it directly relates to the essential factor of production—labor—and is relatively straightforward to assess.
The US Census Bureau defines the manufacturing sector as encompassing establishments that engage in the physical or chemical transformation of materials, substances, or components into new products This sector includes not only the assembly of manufactured product parts but also the blending of materials and various related activities, excluding those in the construction industry.
Manufacturing establishments, commonly referred to as plants, factories, or mills, play a crucial role in transforming raw materials from agriculture, forestry, fishing, and mining into new products These facilities may either process materials independently or outsource processing through contracts The output of manufacturing can include finished products that are ready for consumer use or semi-finished products that serve as inputs for further manufacturing processes.
The manufacturing sector is categorized into various sub-sectors based on distinct production processes, material inputs, production equipment, and employee skills Assembling activities, where parts and accessories are created for separate sale, are classified under the industry of the finished product; for instance, manufacturing a replacement refrigerator door falls under refrigerator manufacturing Conversely, the classification of components intended for other manufacturing processes is determined by the production function of the component manufacturer, such as electronic components being classified under Computer and Electronic Product Manufacturing, while stamps are categorized within Fabricated Metal Product Manufacturing.
ECONOMIC THEORIES
The Cobb-Douglas production function, introduced by Knut Wicksell between 1851 and 1926, is a fundamental concept in microeconomics that illustrates the relationship between output and inputs, as highlighted by Pindyck and Rubinfeld (1992).
- - - - - - - - and then in 1928 Paul Douglas and Charles Cob tested it against statistical evidence
The production function has the form as follows:
• Q denotes output, L: labor input, K: capital input
• A is a constant depending on the units in which inputs and output are measured
In economic analysis, the output elasticity of labor (a) and capital (p) are crucial constants, typically less than one This reflects the principle that the marginal product of each input decreases as the quantity of that factor increases, highlighting the diminishing returns associated with labor and capital in production processes.
Output elasticity quantifies how output responds to changes in labor or capital inputs, assuming other factors remain constant For instance, with an elasticity of 0.15, a 1% increase in labor would result in an approximate 0.15% increase in output.
The production function demonstrates different returns to scale based on the sum of the parameters a and p When a + p equals 1, it indicates constant returns to scale In contrast, if a + p is less than 1, the production function experiences decreasing returns to scale, while a + p greater than 1 signifies increasing returns to scale For instance, when both labor (L) and capital (K) are increased by 20%, output (Y) will also rise by 20% if a + p equals 1, whereas Y will change by more or less than 20% if a + p deviates from 1.
P < 1 and a + p < 1, respectively The Cobb-Douglas production function is sometimes written in logarithmic form: log Q = log A + a log L + p log K This form is useful when performing a regression analysis
According to Pindyck and Rubinfeld (1992), the general production function Q = F(K, L) reflects a specific technology, indicating that a certain level of knowledge is utilized to convert inputs into outputs When technological advancements occur, the production function evolves, allowing firms to generate increased output with the same input levels For example, the introduction of a faster computer chip can empower hardware manufacturers to produce computers more efficiently within a set timeframe.
The Cobb-Douglas production function is a valuable tool for measuring production functions; however, it is frequently supplanted by more intricate models in industrial studies According to Pindyck and Rubinfeld (1992), one limitation of the Cobb-Douglas function is its inability to account for real-world scenarios where a firm's production process may exhibit increasing returns at low output levels, constant returns at intermediate output, and decreasing returns at high output levels.
2.3.1.2 The Law of Diminishing Returns
According to Pindyck and Rubinfeld (1992), the law of diminishing returns states that as the quantity of one input, such as labor, increases while keeping other inputs fixed, there comes a point where the additional output gained from each new unit of input begins to decline Initially, a small increase in labor can significantly boost output due to specialization However, when the workforce becomes too large relative to fixed capital, worker effectiveness decreases, leading to a reduction in the marginal product of labor, illustrating the principle of diminishing returns.
The law of diminishing returns is primarily relevant in short-run analyses where at least one input remains fixed, though it can also apply to long-run scenarios It's essential to distinguish this law from a decrease in output caused by variations in labor quality when increasing labor inputs For example, hiring the most qualified workers initially leads to significant output gains, while subsequent hires of less qualified workers may yield minimal or no increases in output In production analysis, it is assumed that all labor inputs are of equal quality Diminishing returns arise from constraints on the use of fixed inputs, such as machinery, rather than from a decline in worker quality Additionally, it is crucial to differentiate diminishing returns, which indicate a declining marginal product, from negative returns.
The law of production assumes a specific technology, but advancements can shift the total product curve upward, enabling greater output with the same inputs While diminishing returns to labor exist in any production process, enhancements in technology can boost labor productivity As illustrated in Figure 2.1, technological improvements can elevate the output curve, demonstrating the potential for increased efficiency and production.
Figure 2.1: The effect of technology improvement
Labor per time Source: Pindyck and Rubinfeld, 1992
The R&D capital model, highlighted by Griliches (2000), remains a crucial research method for assessing the impact of R&D on productivity growth, despite its limitations This straightforward model allows for the estimation of R&D returns and their contribution to productivity enhancement, forming the foundation of numerous applied studies It encompasses various forms of R&D capital, including private, public, and industry-specific research efforts.
Y denotes some measures of output at the firm, industry, or national level;
X is a vector of standard economic inputs such as man-hours, structures and equipment, energy use, and so on;
K represents the cumulative research effort or "knowledge capital," while a(t) signifies various factors that systematically influence output over time Additionally, u encompasses all other random fluctuations affecting output.
The logarithmic form of the Cobb-Douglas production function serves as a foundational approximation of a more intricate relationship This equation emphasizes estimating the elasticity of output concerning research capital (y) Research and development (R&D) capital is typically quantified as a weighted sum of historical R&D expenditures, with weights accounting for potential delays in R&D's effect on output and the likelihood of eventual depreciation.
In the second approach, growth rates are used to replace levels and the above equation becomes as follows:
The term y~log K is simplified as follows: p = dY/dK = y(YIK), ~log K = RIK, y~log K = RIK*p*(KIJ)
R represents the net investment in knowledge (K), adjusted for the depreciation of previously accumulated research and development (R&D) capital.
- p is interpreted as the gross rate of return to investment m K, gross of depreciation and obsolescence;
The growth rate of output or productivity is directly linked to the intensity of investment in research and development (R&D) or broader investments in science and technology.
The application of this model presents several conceptual challenges, particularly in accurately measuring output and growth in the science and technology sectors Additionally, constructing the R&D capital variable encounters issues related to timing, depreciation, and coverage A significant limitation of this model is its treatment of R&D and science as mere investments, akin to purchasing machinery or constructing facilities, which overlooks the complexities of knowledge creation and its measurement Despite these difficulties, this straightforward model serves as a useful starting point for exploring empirical research in this field, provided we acknowledge its conceptual and data-related issues.
EMPIRICAL STUDIES
Numerous analysts have explored the crucial link between research and development (R&D) expenditure and productivity growth at the firm level, leading to a wealth of empirical studies on this topic The Congressional Budget Office (CBO) reported in 2005 that findings vary widely; some studies indicate R&D has little to no impact on productivity, while others reveal its effects are substantial and exceed those of other investments Despite these differing views, most estimates suggest a consensus that there is a positively significant relationship between R&D spending and productivity growth.
Mairesse and Sassenou (1991) conducted a study that reviews econometric research on the link between R&D and firm-level productivity, highlighting both findings and challenges faced Their analysis reveals that the Cobb-Douglas production function serves as the primary framework for most studies assessing R&D's impact on productivity growth This function incorporates traditional production factors—such as labor, physical capital, and materials—while also including R&D capital as a key explanatory variable.
The Cobb-Douglas production function has an advantage that it can be estimated as a linear regression if all variables are transformed into logarithmic forms
On viewing problems encountered as mentioned above, Mairesse and Sassenou
Econometricians aim to simplify complex phenomena, particularly in the context of R&D activities and their effects on productivity According to research from 1991, the impacts of R&D are inherently uncertain, often occur with long delays, and can vary significantly across different firms and sectors over time Additionally, simultaneous influences on productivity may obscure the effects of R&D Despite challenges in measuring variables and collecting reliable data, many studies surprisingly find statistically significant and plausible estimates of R&D elasticity and rates of return.
CBO (2005) and Mairesse and Sassenou (1991) conducted a review of related studies on the relationship between R&D and productivity To further explore this connection, three case studies will be examined in detail.
2.4.2 R&D and Productivity in French manufacturing firms
Cuneo and Mairesse (1983) examined the relationship between R&D expenditures and productivity performance in the French manufacturing sector from 1972 to 1977 Their study analyzed a sample of 182 firms, categorizing them into two groups: R&D-intensive firms in industries such as chemicals, drugs, and electronics, and other manufacturing firms The research utilized an extended Cobb-Douglas production function model, expressed in logarithmic form, to assess the impact of R&D on productivity.
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In the equation, "i" represents the firm and the current year, while "e" denotes the error term The variables "v," "c," "l," and "k" correspond to production value added, physical capital, labor, and R&D capital, respectively The coefficient of returns to scale is expressed as "Jl = a + ~ + y," and "A" signifies the rate of disembodied technical change.
This study evaluates production using deflated value-added (V) instead of deflated sales, while labor (L) is quantified by employee count Physical capital stock (C) is assessed through gross-plant figures adjusted for inflation, and R&D capital stock (K) is derived from the weighted sum of historical R&D expenditures, applying a constant obsolescence rate.
15 percent per year Two variables, labor and physical capital stock are corrected for the double counting because they are already included in the R&D capital stock
In Vietnam, accurately determining the number of R&D employees is challenging, as it involves subtracting these employees from the total workforce Additionally, calculating the portion of physical capital used in R&D relies on the average ratio of physical investment to total R&D expenditures, which is also subtracted However, due to the difficulty in obtaining complete financial statements from firms, it remains impossible to isolate the physical capital component within R&D expenditures from the overall physical capital stock.
The authors identify discrepancies between total and within-firm estimates for two key parameters: the elasticity of physical capital stocks (a) and R&D capital stocks (y) However, the impact of these discrepancies is mitigated by accurate variable measurements, resulting in generally statistically significant and likely high estimates To explore additional results, the authors substituted sales for value added and varied the inclusion of materials (M) in the production function The total estimates derived from sales, excluding materials, are comparable to those based on value added Similarly, within-firm estimates using sales align closely with those from value added when constant returns to scale are applied However, significant discrepancies arise when constant returns to scale are not enforced Notably, within-firm estimates improve substantially when materials are included, indicating that omitting materials in the sales specification particularly influences these estimates.
2.4.3 R&D and Productivity Growth in Japanese manufacturing firms
Kwon and Inui (2003) investigated the link between research and development (R&D) and productivity enhancement in Japanese manufacturing companies Their study analyzed a Cobb-Douglas production function utilizing three key inputs—labor, physical capital, and knowledge capital—across over 3,000 Japanese firms during the years 1995 to 1998.
The data used in this research is drawn from the Basic Survey of Business Structure and Activities conducted by Japanese Ministry of Economy, Trade and Industry
From this data set, the authors selected 3,830 firms in the manufacturing sector which had positive R&D expenditures from 1995 to 1998 Those firms all have no less than
50 employees and 30 millions yen of capital and are grouped into 22 manufacturing industries based on their main business activities
Kwon and Inui (2003) explored the impact of R&D on productivity growth in Japanese manufacturing firms using two methods: the Production Function Approach and the Rate of Return to R&D Approach While the Production Function Approach allows for potential bias due to simultaneous output and input decisions, it benefits from avoiding assumptions related to competitive factor markets, cost minimization, and constant returns to scale This method illustrates the relationship between R&D and productivity growth through a regression function utilizing first-differences.
In the given model, Y represents the value added, K denotes the physical capital stock, L signifies labor input, and R indicates knowledge capital stock The variable A-t reflects time-specific factors and the rate of disembodied technical change The subscripts i and t correspond to the firm and the year, respectively To ensure a positive marginal product of labor, the condition 1 - a > f3 is assumed The scale parameter r indicates increasing returns to scale when positive and decreasing returns to scale when negative.
In their second approach, K and Inui assessed the impact of R&D on productivity by calculating the rate
The study reveals that R&D expenditure positively influences productivity growth, with varying effects based on firm size and technology characteristics Larger and high-tech firms exhibit higher R&D elasticities compared to their smaller counterparts Additionally, physical capital stock plays a significant role in enhancing labor productivity growth However, industry effects are not significant in accounting for productivity disparities among firms.
2.4.4 The effect of R&D Capital on Danish Firm Productivity
This study, conducted by Graversen and Mark (2005), examines the impact of R&D on Danish private firm productivity using cross-section data, differing from previous time-series analyses The research aims to identify the return on R&D capital and other factors influencing firm productivity growth, utilizing data from the 2001 Danish R&D Statistics compiled by the Danish Centre for Studies in Research and Research Policy The sample comprises over 2,200 firms with positive R&D expenditures, representing a broad spectrum of Danish private sector firms with more than 9 employees By applying a logarithmic Cobb-Douglas production function, the analysis estimates productivity as a function of various independent variables.
Productivity= f (R&D Capital, Assets, Labour, Business Sector, Size)
OVERVIEW OF R&D AND FIRM PERFORMANCE IN
INTRODUCTION
This chapter provides an overview of research and development (R&D) activities in Vietnam, focusing on both the general landscape and specific firm-level initiatives to ground the analysis in real-world context It is divided into three key sections: the first section examines R&D activities in Vietnam and compares them with those of other countries; the second section outlines the structure of Vietnam's R&D system; and the final section explores the connections between R&D institutions and the productive sector.
R&D ACTIVITIES IN VIETNAM
Vietnam's investment in research and development (R&D) has been relatively low compared to OECD countries and its neighbors In 1996, Vietnam allocated about 0.3% of its GDP to R&D, which increased to approximately 0.5% by 2003 Notably, a significant portion of this funding, around 80% in 2002, came from the government, contrasting sharply with OECD nations where private companies contributed about 70% of R&D financing in the same year.
Figure 3.1: Percentage ofGDP spent on R&D in 1996
Figure 3.2: Expenditure on R&D by Government and Business sector in 2002
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Figure 3.3: Sector-wise R&D Expenditure in Vietnam in 2002
• Direct Government • Business enterprises o Funds from abroad
0 Higher Education • Private non-profit C others
Research and development (R&D) spending per full-time researcher in Vietnam has sharply declined since the introduction of "doi moi" reforms, with expenditures dropping from US$687 in 1987 to US$289 in 1990 (Bezanson et al., 2000) These figures are significantly lower than those of other East Asian nations, such as Japan at US$135,000 and Korea and Singapore at over US$50,000 Additionally, annual expenditure on research facilities per full-time researcher was only around US$50 in the early 1990s, and only about 10% of Vietnamese researchers had access to experimental equipment comparable to that of other East Asian countries Furthermore, the number of R&D personnel per thousand employees in Vietnam was 0.59 in 2002, which, while higher than Thailand and India, still lags behind Japan, Korea, Singapore, and China (Nguyen, n.d.).
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Figure 3.4: R&D Personnel per Thousand of Total employees in 2002
STRUCTURE OF THE R&D SYSTEM IN VIETNAM
According to Bezanson et al (2000), Vietnam's R&D system comprises three key components The first component includes around 180 laboratories and R&D institutes operated by various line ministries and government agencies across the country While some large state-owned corporations like Petro Vietnam maintain their own laboratories, most Vietnamese industrial enterprises lack the capability and experience to conduct independent research and development The second component of the R&D system consists of universities and colleges.
The majority of universities and colleges in Vietnam face significant challenges in conducting research and development (R&D) activities due to a lack of essential resources Most institutions are insufficiently equipped with qualified personnel, necessary equipment, and adequate library facilities, hindering their ability to engage effectively in R&D initiatives.
The National Center for Natural Science and Technology, now known as the Vietnamese Academy of Science and Technology, is the most prominent among the national research centers controlled by the Government Office.
The three main components are closely interconnected, each serving distinct functions Research institutes under various ministries focus on applied research and experimental development, while universities and colleges play a crucial role in supplying R&D human resources.
Vietnamese Academy of Science and Technology is mainly responsible for performing advanced basic research
Research infrastructure in Vietnam is reportedly below international standards, with a focus on theoretical and supply-driven studies that fail to address the needs of the manufacturing sector (Nguyen and Tran, n.d.) Most research and development (R&D) activities occur in government research institutes and national centers rather than universities, with limited public funding for R&D Consequently, the national R&D system is structured in a manner that complicates and raises the costs of technology transfer (Bezanson et al., 2000, cited in Nguyen and Tran, n.d.).
Table 3.1: Science & Technology Organizations in Vietnam by 31 Dec 2003
Administration Num % Num % Num % Num %
Source: MOST 5 (2004), cited in Nguyen and Tran (n.d.)
4 The definition of basic research, applied research and experimental development are discussed in the chapter
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The impact of research and development (R&D) on the productivity growth of manufacturing firms in Vietnam is significant R&D investments enhance innovation, leading to improved production processes and product quality As Vietnamese manufacturing firms adopt advanced technologies and practices, they experience increased efficiency and competitiveness in the market This growth in productivity not only boosts individual firm performance but also contributes to the overall economic development of Vietnam Therefore, fostering a robust R&D environment is crucial for sustaining long-term productivity growth in the manufacturing sector.
3.4 LINKAGE BETWEEN THE PRODUCTIVE SECTOR AND R&D INSTITUTIONS
The relationship between the productive sector and R&D institutions in Vietnam is notably weak, with SMEs and large corporations alike relying primarily on external sources for research and development rather than collaborating with local R&D institutions and universities Most foreign firms conduct their R&D activities in their home countries, limiting opportunities for Vietnamese institutions to contribute meaningfully Despite firms expressing a clear demand for technology and training services from R&D institutes and universities, this need remains largely unmet, resulting in support levels that fall below expectations.
To grasp the innovation landscape in Vietnam, it's essential to recognize that research and development (R&D) is a critical component of this process According to Nguyen and Tran, Vietnam's status as a transitional and developing country creates a unique innovation environment that contrasts with those of developed nations Furthermore, external factors play a significant role in shaping the innovations of Vietnamese firms, highlighting key characteristics of the country's innovation ecosystem.
Competition among Vietnamese firms primarily hinges on the availability of natural resources and access to inexpensive labor, with only a limited number of companies leveraging new technology or offering differentiated products.
Innovations of those firms who make components or operate as subcontractors for foreign companies are under determination of foreign customers
Innovation system is weak at both ãnational and local levels There are limited public resources for R&D and supports for innovation
The OECD, established in 1994, plays a crucial role in promoting economic growth and stability among its member countries This organization focuses on various aspects of economic policy, ensuring that nations collaborate to enhance their economic performance For more detailed insights, please refer to Chapter 2 of the relevant document.
Markets for technical and innovation services are under development; and so on
Nguyen and Tran (n.d.) highlighted that innovations in Vietnam are primarily classified as either incremental or new to the firms Incremental innovations arise when companies address specific technical challenges related to imported production lines or develop new products using existing machinery In contrast, "new to firms" innovations occur when businesses acquire entire production lines or components to create new products.
Research institutions often struggle to align with the production sector in supporting innovation, largely due to the nature of Vietnamese firms' innovations being problem-solving rather than science-based As a result, academic institutes are expected to play a key role in helping firms address their technical problems, rather than driving innovation through scientific research.
Academic institutions often struggle to meet the expectations of businesses due to differing organizational priorities There is a significant gap between what firms seek from these institutions and the actions that academic leaders believe they should take to fulfill those needs.
Table 3.2: Ranking of most wanted services (for firms) and most capable activities (for academic institutions) of enterprises
Type of services Firms' Rank Academic Insts'rank
Installation of new machines and equipment 2 10
Maintenance and fixing production machines 3 9
Analyzing, testing product/material sample 1 4
Modify product design or material specifications 8 7
Manufacture production machines or components 10 6
Advice in buying production machines 9 3
The Innovation Survey 2002 and the PROs Survey from 2000, conducted by NISTPASS, provide valuable insights into innovation trends and practices These surveys, referenced by Nguyen and Tran, highlight key findings that can inform future research and development strategies For further information and access to the full report, interested parties can reach out via email.
RESEARCH METHODOLOGY
INTRODUCTION
This chapter aims to develop an econometric model to analyze the factors influencing productivity growth and specifically assess the impact of R&D activities on this growth It begins by specifying the econometric model grounded in relevant theories and empirical research, along with definitions and measurements for the variables utilized in the regression analysis Next, it addresses the analytical framework, methods, and key challenges associated with data collection The chapter concludes with a summary of the research methodology employed.
MODEL SPECIFICATION
The relationship between R&D and productivity in Vietnamese manufacturing firms is modeled using a logarithmic version of the Cobb-Douglas production function, which incorporates traditional factors like capital and labor alongside knowledge capital This model aims to link productivity growth directly to increases in these inputs, with a focus on labor productivity as it is the most significant production factor and easily measurable Additionally, the assumption of constant returns to scale is rigorously tested within this framework.
The Cobb-Douglas specification can be written as:
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The impact of research and development (R&D) on the productivity growth of manufacturing firms in Vietnam is significant R&D activities enhance innovation, leading to improved processes and products As a result, manufacturing firms experience increased efficiency and competitiveness in the market Investing in R&D not only boosts productivity but also contributes to sustainable economic growth in Vietnam's manufacturing sector Emphasizing R&D can help firms adapt to changing market demands and technological advancements, ultimately driving long-term success.
Dividing both sides by the labor inputs, taking the logs of both sides and adding some interaction variables, we have the regression equation as follows:
Log(-)= a 0 + a 1 1og(-)+ a 2 1og(-)+ a 3 logL + a 4 SIZE + a 5 0WNS + u (4.2)
K denotes the stock of physical capital
Log( Y) denotes natural log of labor productivity based on output
L log(K) denotes natural log of physical capital per labor
L log(R) denotes natural log of R&D expenditure per labor
L log L denotes natural log of people employed SIZE denotes firm size, dummy variable
The variable OWNS represents different types of ownership as a dummy variable, while 'u' denotes the error term, and 'a3' acts as a scale parameter If the coefficient 'a3' is statistically significant and differs from zero, it allows us to reject the assumption of constant returns to scale concerning the three inputs A positive value for 'a3' indicates increasing returns to scale, whereas a negative value suggests decreasing returns to scale.
In this study, due to the limitation of the data material, the cross sectional data is employed to test the relationship between R&D expenditure and productivity
To enhance the validity of testing procedures and mitigate heteroskedasticity, it is essential to transform key variables in the multiple regression model into log and ratio terms Additionally, utilizing cross-sectional data is advantageous because the elasticity of R&D is significantly higher in this dimension compared to the time series dimension, where it tends to be smaller and often statistically insignificant (Matteucci and Sterlacchini, 2004).
In empirical studies, output is typically measured by value-added, which includes operating profit, depreciation allowances, employee compensation, taxes, and rent (Kwon and Inui, 2003) However, due to data limitations, this study measures output (Y) by total sales or revenues instead Labor (L) is represented by the number of employees, as data on working hours is unavailable To avoid double counting, R&D employees were excluded from the total employee count since their contributions are reflected in R&D expenditures Physical capital (K) is defined as total fixed gross assets, although these assets are recorded at nominal (book) value While Cuneo and Mairesse (1983) suggest that physical capital used for R&D should be deducted from total capital stock to prevent double counting, this adjustment is not feasible due to data constraints.
R&D capital measurement relies on available R&D expenditure data due to the complexities of calculating it directly Literature indicates that R&D capital is typically derived from historical R&D spending, which is accumulated, deflated, and depreciated over time Griliches (1979) emphasized that R&D capital reflects the current state of technical knowledge and is influenced by both current and past R&D expenditures.
Current R&D capital incorporates both present and historical R&D expenditures, reflecting their cumulative impact on technical knowledge Ideally, researchers should estimate the lag structure connecting past R&D investments to current advancements in technology using available data However, this study faces limitations as R&D expenditure data is only accessible in a single instance.
The impact of research and development (R&D) on the productivity growth of manufacturing firms in Vietnam is a critical area of study However, measuring R&D effectively poses challenges, as traditional metrics may not capture its true influence This limitation highlights the complexities involved in assessing the relationship between R&D and productivity, a topic that will be further explored in the conclusion of the research.
With reference to dummy variables, firm size (SIZE) is categorized by number of employees into three kinds of size: large scale, medium scale and small scale
Moreover, firms are also categorized into three kinds of ownership (OWNS) such as state-owned, foreign-owned and others
KIL is a crucial production input that significantly contributes to productivity growth, as evidenced by numerous empirical studies indicating a positive relationship with productivity functions Research and development (R&D) investments enhance firm productivity, with higher R&D spending correlating to increased productivity levels Consequently, log(RJL) is anticipated to have a positive effect Additionally, log(L) is expected to be positive, reflecting that greater labor employment typically leads to higher productivity; however, this may reverse if labor reaches the point of diminishing returns It is also posited that large-scale firms exhibit higher productivity than smaller counterparts, and similarly, foreign-owned firms are expected to outperform state-owned firms in productivity metrics.
In conclusion, the regression equation can be fully rewritten as follows:
Log(-) = a 0 + a 1 log(-) + a2log(-) + a 3 log L + a 4 LARGESCL + a 5 MEDIUMSCL
LARGESCL and MEDIUMSCL refer to large and medium scale firms, respectively, while STATE indicates state-owned enterprises and FOREIGN denotes foreign-owned companies.
The coefficients in version (4.3) are determined by the arguments presented in version (4.2), with all signs reflecting the empirical findings from prior studies Specifically, the values indicate that a1, a2, a3, a4, a5, and a7 are positive, while a6 is negative.
The disturbance term u plays a significant role in a firm's productivity, as it encompasses unobservable factors like managerial capabilities (Wang and Tsai, 2003) These factors differ among firms, leading to potential heteroskedasticity in the variances of u Addressing this issue in estimations is crucial, as neglecting it could lead to biased or inefficient estimates.
DATA TRANSFORMATION
This study utilizes data from the Vietnam Enterprise Survey (VES) conducted by the General Statistics Office in 2004, encompassing over 91,750 enterprises across various sectors, including manufacturing, mining, construction, and commerce, throughout all provinces in Vietnam The focus is narrowed to the manufacturing sector, specifically selecting 450 firms that reported positive R&D expenditure in 2004 Industries are categorized based on the NACE classification system, facilitating the extraction of manufacturing firms Ultimately, the analysis is based on a refined sample of 264 observations suitable for regression analysis.
However, the data must be transformed into an appropriate form before inputting into the model This section describes how variables in version (4.3) are computed based on some assumptions
4.3.1 Labor productivity based on output (Y/L)
It is challenging to differentiate between companies that did not disclose their R&D expenditures and those that reported no R&D spending at all.
This variable is the ratio of total output of a manufacturing firm to its total labor in
In 2004, a firm's total output (Y) was assessed based on the turnover from goods and services, as indicated in the questionnaire Total labor input (L) was calculated by subtracting the number of R&D employees from the total labor count at the year's end Labor productivity was represented as the natural logarithm of its original value, measured in million VND.
This variable represents the physical capital per employee within a firm, measured in million VND The natural logarithm of KIL was utilized in the analysis Physical capital (K) is quantified by the total fixed assets and long-term investments recorded at the end of the year, sourced directly from the VES-2004 data.
The ratio of R&D expenditures to total labor input, denoted as R/L, is measured in million VND and expressed as a natural logarithm R&D expenditures (R) were sourced directly from the VES-2004 dataset.
This thesis categorizes firm sizes into small, medium, and large using three dummy variables: LARGESCL, MEDIUMSCL, and SMALLSCL According to the EU's definition of Small and Medium Enterprises (SMEs), a medium-sized enterprise has fewer than 250 employees, while a small enterprise has fewer than 50 employees, and a micro enterprise has fewer than 10 employees Consequently, LARGESCL represents firms with 250 or more employees, MEDIUMSCL includes firms with 50 to 249 employees, and SMALLSCL encompasses firms with 1 to 49 employees.
49) is regarded as SMALLSCL However, only two dummies, LARGESCL and MEDIUMSCL are included in the regression model
4.3.5 Types of ownership (STATE, FOREIGN)
STATE refers to companies where the government holds over 50% of the shares, while FOREIGN indicates companies owned by foreign entities, which includes fully foreign-owned enterprises and joint ventures with at least one foreign partner.
RESULT ANALYSIS
INTRODUCTION
This chapter aims to analyze the findings from the regression model to determine if a positive relationship exists between R&D expenditure and productivity growth in Vietnamese manufacturing firms It is structured into three sections: the first section outlines the key characteristics of the sampled firms using descriptive statistics; the second section involves running and testing the specified regression model, followed by an analysis of the model results, empirical findings, and their statistical validity; the final section provides a summary of the result analysis.
FIRMS CHARACTERISTICS
The study analyzes a data set comprising 91,755 observations, revealing that only 450 firms (0.49%) across all business sectors report positive R&D expenditures From this group, 264 firms in the manufacturing sector were selected, indicating that just 0.29% of manufacturing firms engage in R&D activities While it's challenging to differentiate between firms that did not disclose their R&D spending and those with negligible investments, these figures highlight that a small percentage of firms in Vietnam prioritize and invest in research and development.
In the analyzed sample, state ownership dominates, comprising 59.47% of observations, while foreign or joint-venture ownership is significantly lower at 11.74% Additionally, according to the EU's definitions of SMEs, large firms, defined as those with 250 or more employees, represent over 60% of the sample.
The next is the medium (50- 249 employees) and the small (1- 49 employees) with 25.38% and 13.26%, respectively
Figure 5.1: Structure of firms by ownership
Figure 5.2: Structure of firms by size
Source: Author's calculation based on the data ofVES-2004
Based on the industrial classification of NACE, the number of firms in each industry and its percentage in the sample have been calculated and presented in the table 5.1
The manufacturing sector is dominated by 47 firms producing Chemicals and Chemical Products, which represent the largest share at 17.80% Following closely, 45 firms in the Food Products and Beverages category account for 17.05% of the sample Additionally, 26 firms in the Other Non-Metallic Mineral Products sector contribute 9.85%, securing the third position after Food Products and Beverages.
Machinery and apparatus manufacturing represent significant portions of the sample, with shares of 7.95% and 6.82%, respectively In contrast, other industries contribute only a small fraction to the overall sample Notably, the technology utilized by most manufacturing firms in this sample is generally classified as low to medium level.
Table 5.1: Industrial Classification of the Sample
:No~,:: , lndustl)' ã ã NC) or firms Percent (o/0)
4 Wearing Apparel, except fur apparel 9 3.41%
5 Manufacture of luggage, handbags, saddlery, harness and
6 Wood and Products of wood and cork; Articles of straw and
11 Other non-metallic mineral products 26 9.85%
13 Fabricated metal products, except machinery and equipment 10 3.79%
17 Radio, Television and Communications Equipment and
18 Medical, Precision and Optical Instruments, Watches &
19 Motor Vehicles, Trailers & Semi-Trailers 7 2.65%
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State-owned enterprises employ a higher average number of workers compared to other business types, with the largest firm boasting 8,993 employees However, despite their larger workforce, state-owned enterprises demonstrate lower efficiency than foreign firms The average revenue of foreign companies stands at VND 381,314 million, surpassing the VND 327,332 million average of state-owned enterprises.
Foreign firms outperform state-owned and other types of firms in capital resources, emphasizing innovation and technological research and development On average, foreign firms hold total fixed assets and long-term investments of VND 193,764 million, compared to VND 113,795 million for state-owned firms Additionally, foreign firms invest significantly more in R&D, with an average expenditure of VND 7,143 million, which is seven times higher than the VND 1,076 million spent by state-owned firms.
Large firms significantly outpace medium and small firms in terms of mean turnover and capital, with large firms averaging VND391,028 million compared to VND87,114 million for medium firms and VND10,500 million for small firms, representing differences of approximately four times and thirty-five times, respectively Interestingly, medium firms invest slightly more in R&D, averaging VND1,783 million, while large firms spend VND1,749 million Notably, the highest R&D expenditure recorded is VND55,210 million, attributed to a foreign-owned large-scale firm.
The impact of research and development (R&D) on the productivity growth of manufacturing firms in Vietnam is significant R&D activities foster innovation, enhance operational efficiency, and improve product quality, leading to increased competitiveness in the market By investing in R&D, manufacturing firms can better adapt to changing consumer demands and technological advancements This strategic focus on R&D not only boosts productivity but also contributes to the overall economic development of Vietnam's manufacturing sector.
142,518.0 52,148.0 676.5 300.0 Source: Author's calculation based on the data ofVES-2004
Note: The unit of Y, K and R is VND million
According to figures 5.3 and 5.4, manufacturing firms in the sample primarily fund their research and development (R&D) activities through their own investments, which account for 82% of total R&D costs In contrast, only 7% is financed by the state budget and a mere 1% from foreign sources This indicates that enterprises are largely responsible for financing their technological advancements independently Notably, 81% of the R&D expenditure is directed towards technology development, while only 17% is allocated for research purposes This trend reflects the problem-solving nature of innovation in Vietnamese firms, which often focuses on practical applications such as installing new production lines, adopting new technologies, or enhancing existing technologies to create new products.
Figure 5.3: Total cost for research & development of technology by resources
Source: Author's calculation based on the data ofVES-2004
Figure 5.4: Total cost for research & development of technology by purposes
The analysis presented is based on the author's calculations derived from the VES-2004 data This study aims to provide insights into the latest trends and findings relevant to the subject matter For comprehensive information, please refer to the full thesis available for download.
REGRESSION ANALYSIS
This section addresses two key topics: the analysis of the correlation matrix to determine the appropriateness of independent variables for inclusion in the regression model, and the presentation of model estimation, empirical results, and the statistical validity of the model.
5.3.1 Correlation matrix Table 5.3: Correlation matrix from the variables in the function
FOREIGN 0.38 0.35 0.33 -O.IO ã Source: Author's estimate based on the data ofVES-2004
All Ln denote natural log of variables;
Y _ L denotes labor productivity based on output;
K _ L denotes physical capital per labor;
L denotes the total number of labor;
FOREIGN denotes type of ownership (Foreign firm= I, if not= 0);
STATE denotes type of ownership (State-owned firm= I, if not= 0);
LARGESCL denotes firm size based on the number of labor (Large = I, if not = 0);
MEDIUMSCL denotes firm size based on the number of labor (Medium = 1, if not = 0);
The analysis of the productivity model reveals that the three key independent variables—lnK_L, lnL, and lnR_L—exhibit a positive correlation with labor productivity, indicating their essential role in the model Notably, physical capital per labor demonstrates a stronger relationship with productivity compared to other variables Furthermore, the correlation between foreign firms and productivity surpasses that of state-owned firms, highlighting the impact of firm ownership on productivity levels.
5.3.2 Model estimation and empirical results
Labor productivity is significantly affected by factors such as physical capital per labor, the number of laborers, R&D expenditure per labor, and the size and ownership of institutions Analyzing these key institutional factors is essential for understanding their impact on labor productivity According to the empirical results presented in Table 5.4, all variables, except for STATE, demonstrate statistical significance at the 1%, 5%, and 10% levels.
This analysis explores the impact of ownership and firm size on productivity growth The findings reveal that foreign firms experience a mean productivity growth rate that is 1.3% higher than that of state-owned enterprises, with the FOREIGN variable being statistically significant at 10% This difference is attributed to the generally lower efficiency of state-owned firms Additionally, there is no notable difference in productivity growth between state-owned firms and other types, such as private or limited companies Regarding firm size, both medium-sized (50-249 employees) and large-sized firms (over 249 employees) demonstrate higher productivity growth rates compared to small-sized firms, with medium-sized firms slightly outperforming large firms.
The impact of research and development (R&D) on the productivity growth of manufacturing firms in Vietnam is significant R&D activities enhance innovation, leading to improved processes and products This, in turn, boosts operational efficiency and competitiveness in the market Investing in R&D allows Vietnamese manufacturing firms to adapt to changing consumer demands and technological advancements Consequently, the integration of R&D strategies is essential for sustainable growth and development in the manufacturing sector.
Table 5.4: Coefficients and statistics for the productivity model
Unstandadized Coef Standadized Level of t Prob
Ln of Physical capital per labor (LnK_L) 0.349 0.005 0.49 72.326 0.000 1%
Ln ofR&D expenditure per labor (LnR_L) 0.102 0.009 0.16 11.859 0.000 1%
Source: Author's estimate based on the data ofVES-2004
Standadized coefis the correlation coefbetween each independent variable and dependent variable
The Cobb-Douglas production function and R&D capital model highlight that productivity is directly influenced by physical capital per labor (K/L), R&D expenditure per labor (R/L), and total labor (L).
These factors were taken as the natural log to represent proxy variables in the model
The regression analysis indicates that the elasticity of labor productivity is positively influenced by three key variables, aligning with expectations Notably, physical capital per labor demonstrates the most significant effect on productivity, with elasticities of approximately 0.35 for physical capital per labor and 0.15 for total labor This implies that, when other inputs remain constant, a 1% increase in physical capital per labor results in a 0.35% rise in productivity, while a similar increase in total labor yields a 0.15% boost in productivity.
5.3.2.2 Analysis of the elasticity of productivity with respect to R&D
The elasticity of productivity concerning R&D expenditure per labor is relatively low, measured at approximately 0.1, indicating a lesser impact compared to other variables.
A 1% increase in R&D expenditure per labor results in only a 0.1% increase in productivity, indicating that physical capital and labor significantly influence productivity more than R&D investment Griliches (1995) found that the output elasticity of R&D capital ranges from 0.09 to 0.14, supporting this conclusion (Wang and Tsai, 2003) This trend is particularly evident in Vietnamese enterprises, which tend to prioritize inexpensive and readily available labor over R&D investments.
While research and development (R&D) expenditure is generally believed to positively impact productivity, Wang and Tsai (2003) caution that this conclusion may be overly optimistic due to issues like the 'file-drawer' problem and challenges in measuring R&D capital This study's limitations in data hinder a comprehensive assessment of R&D capital, overlooking factors such as past expenditure accumulation, lag effects, deflation, and obsolescence Surprisingly, the regression results indicate a positive relationship, which may be attributed to the nature of R&D activities in Vietnamese enterprises, which focus more on problem-solving and technology development rather than pure scientific research Consequently, these technology-developing efforts tend to enhance productivity more rapidly than traditional research initiatives.
5.3.2.3 Statistical validity of the model
The model tests the hypothesis of constant returns to scale concerning three inputs: capital (K), labor (L), and resources (R) The coefficient of the natural logarithm of total labor is approximately 0.15, serving as a scale parameter This coefficient is statistically significant at the 1% level, leading to the rejection of the assumption of constant returns to scale and the acceptance of increasing returns to scale.
The impact of research and development (R&D) on the productivity growth of manufacturing firms in Vietnam is significant R&D activities enhance innovation, leading to improved production processes and higher efficiency As firms invest in R&D, they are better equipped to adapt to market changes and consumer demands, ultimately driving competitiveness The relationship between R&D investment and productivity growth underscores the importance of fostering a supportive environment for innovation within the manufacturing sector in Vietnam Prioritizing R&D can lead to sustainable economic growth and bolster the overall performance of the manufacturing industry.
There are two important tests in this model: multicollinearity and heteroscedasticity
The tests conducted ensure the statistical validity of the model, revealing that multicollinearity is not a concern, as most correlation coefficients between regressors remain below 0.5, with exceptions found in certain dummy variable correlations (see Table 5.3) Additionally, all independent variables included in the model were statistically significant at the 1% level, and the R-squared value was below 0.8 (refer to Table 5.4 and Appendix 2) Furthermore, the model was assessed for heteroscedasticity and confirmed to be free of this issue, with detailed results from the White Test available in Appendix 3.
CONCLUSIONS AND RECOMMENDATIONS
CONCLUSION
This study examines the correlation between R&D expenditure and the productivity of manufacturing firms, utilizing a sample of 264 firms that reported positive R&D activities The sample is derived from a comprehensive dataset of 91,755 observations from the Vietnam Enterprise Survey conducted in 2004 Notably, state-owned firms represent 59.47% of the sample, whereas foreign firms constitute only 11.74%.
Large-scale firms with over 249 employees constitute over 60% of the sample, while medium-sized firms (50-249 employees) and small firms (1-49 employees) represent 25.38% and 13.26%, respectively In terms of industrial classification, companies in the Chemicals and Chemical Products sector account for the largest portion of the sample at 17.08%, closely followed by those in the Food Products and Beverage manufacturing sector at 17.05%.
State-owned enterprises generally exhibit lower efficiency compared to foreign firms, which demonstrate superior capital resources and greater investment in innovation and R&D activities In the sample analyzed, a significant 82% of the total cost for research and development in manufacturing firms is self-financed, with 81% of this expenditure allocated to technology development and only 17% dedicated to research.
The research utilized an analytical framework based on the Cobb-Douglas production function and the R&D capital model, estimating a regression equation in logarithmic form that demonstrated statistical validity.
The impact of research and development (R&D) on the productivity growth of manufacturing firms in Vietnam is significant R&D investments enhance innovation, leading to improved processes and products As manufacturing firms adopt advanced technologies and practices through R&D, they experience increased efficiency and competitiveness in the market This growth in productivity not only boosts individual firm performance but also contributes to the overall economic development of Vietnam Therefore, fostering a strong R&D environment is crucial for sustaining the growth of the manufacturing sector in the country.
Log(y) = 2.24 + 0.351og(K) + O.IOlog(R) + 0.15log L + 0.26LARGESCL +
The research highlights that R&D investment is a crucial factor in enhancing firm productivity, with a 1% increase in R&D expenditure per labor correlating to approximately 0.1% growth in labor productivity Despite using a simpler measurement of R&D capital compared to other studies, the findings are notable due to the statistically significant and positive R&D elasticity coefficient This may be attributed to the fact that many Vietnamese enterprises focus their R&D activities on practical problem-solving rather than scientific research, with a majority of R&D expenditures directed towards technology development rather than pure research initiatives.
Research and development (R&D) capital is not the sole contributor to productivity growth; other factors also play a significant role Specifically, the elasticities of productivity in relation to physical capital per labor and total labor are approximately 0.35 and 0.15, indicating that these elements have a more substantial impact on productivity growth compared to R&D expenditure per labor.
Ownership and firm size significantly influence productivity growth rates Research indicates that foreign firms experience a higher productivity growth rate compared to state-owned and other firms Additionally, the analysis reveals no notable difference between state-owned firms and their counterparts, as the STATE variable shows insignificance.
Medium-sized firms, employing 50 to 249 workers, experience a productivity growth rate that slightly surpasses that of large firms with over 249 employees, and significantly exceeds that of small firms.
In summary, research and development (R&D) activities significantly contribute to the productivity growth of manufacturing firms in Vietnam, alongside traditional inputs like physical capital and labor To enhance productivity, manufacturing firms must prioritize investments in R&D The following section will outline policy recommendations to support these firms in their endeavors.
POLICY RECOMMENDATIONS
To boost R&D activities in the industrial sector, the Korean Government implemented various flexible policies aligned with different national development strategies In the 1960s and 1970s, the focus was primarily on tax incentives and preferential treatment for R&D initiatives However, these efforts fell short as industries lacked a clear demand for R&D investments, believing that existing technologies were easily accessible By the 1980s, the government introduced additional incentives, such as reduced tax rates on imported R&D equipment and allowing R&D expenditures and the development of human resources to be tax-deductible, alongside tax exemptions for fixed assets related to R&D.
The Korean Government has implemented various indirect programs to stimulate research and development (R&D) activities, including the International Standard Korean Products Program, which supports 21 products from 59 manufacturers in the industrial sector In addition to tax incentives, financial support policies have been established to encourage enterprises to invest in R&D Small businesses, lacking the resources to create their own R&D centers, are motivated to collaborate with other firms As a result of these initiatives, the number of R&D institutes and associations surged significantly during the 1980s and 1990s.
In September 1999, the Korean Industrial Property Office initiated a campaign to support small and medium-sized enterprises (SMEs) by encouraging them to innovate and leverage new technologies as essential business assets The program aimed to enhance community awareness of intellectual property, connect research and development activities with intellectual property, streamline the patent issuance process, and facilitate the usage and transaction of patented technologies.
To bolster financial resources for the commercialization of new technologies, the Government has actively promoted private venture capital and established government venture funds Additionally, it has strengthened capital markets for newly-established and science and technology firms while developing a secondary stock market to support their growth.
Most Vietnamese firms are small to medium-sized and face limitations in financial resources, resulting in restricted investment in R&D activities The domestic market for technology services remains underdeveloped, and companies lack access to information about local research and innovations Consequently, many firms focus primarily on acquiring new equipment while neglecting research and technology transfer Additionally, SMEs often depend on external sources for their R&D due to insufficient connections with local research institutions.
For many reasons, R&D activities have not been invested at a proper level
In today's globalized economy, businesses must prioritize investment in research and development (R&D) and technological innovation to thrive and grow These investments enhance production capabilities, leading to increased productivity, reduced production costs, lower product prices, and improved competitiveness Although R&D initiatives can be time-consuming, capital-intensive, and risky, they represent a crucial long-term investment for firms Moreover, support from authorities is essential to encourage R&D investment This research outlines policy recommendations inspired by South Korea's experience and tailored to Vietnam's context to promote R&D activities among firms.
To enhance the effectiveness and accountability of R&D institutions in Vietnam, it is essential to equitize these entities, fostering greater creativity and responsiveness to the needs of the productive sector Currently, Vietnam's research infrastructure falls short of international standards, and the connection between local enterprises and research institutions is notably weak Many local businesses rely heavily on external sources for R&D, while foreign companies depend on their parent organizations in their home countries Overall, the support provided by R&D institutions and universities to local firms is below optimal levels, necessitating significant improvements.
Equitization may enable them to operate in a more market-oriented and efficient way Through this, R&D institutions may have better contribution in supporting enterprises
Tax incentives play a crucial role in promoting research and development (R&D) activities, including reduced tax rates on imported R&D equipment Additionally, expenses related to R&D and the development of skilled human resources in this field are eligible for tax deductions Furthermore, fixed assets associated with R&D or Technology Development Funds benefit from tax exemptions, fostering a supportive environment for innovation and technological advancement.
Establishing R&D venture funds of the government to support financial resources for firms and stimulate the development of private venture capital
These funds must be managed and supervised strictly to make sure that the capital is used in right places, at right time and for right purposes
Encouraging companies to develop their own research and development (R&D) departments or collaborate with other businesses is essential This initiative should be implemented through long-term national programs or campaigns that promote innovation and cooperation within the industry.
LIMITATIONS OF THE RESEARCH
The research acknowledges certain limitations, particularly the common issue of double counting in studies examining the impact of R&D on productivity growth This problem arises when R&D labor and physical capital are included in both labor and capital measurements, leading to inflated assessments of their contributions.
The impact of research and development (R&D) on the productivity growth of manufacturing firms in Vietnam is significant R&D investments enhance innovation and efficiency, driving competitive advantage in the manufacturing sector By measuring R&D capital, as highlighted by Cuneo and Mairesse (1983), firms can better understand the correlation between R&D activities and productivity improvements This relationship is crucial for fostering economic growth and sustaining industrial development in Vietnam.
The research excluded only R&D labor from the total labor count but did not address the issue of double counting physical capital Due to data limitations, accurately correcting these discrepancies proves to be an impossible task.
The research conducted in 2004 faced limitations due to its reliance on a single year's data, which hindered an accurate measurement of R&D capital It primarily assessed R&D through available expenditure, neglecting factors such as the accumulation of past R&D investments, their lag effects, and depreciation Additionally, the study's examination of the relationship between R&D spending and productivity growth over just one year raises questions about the reliability of the R&D elasticity results This indicates a need for future research utilizing panel or time-series data, as a multi-year analysis of R&D expenditure could yield more dependable outcomes than the current study.
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The impact of research and development (R&D) on the productivity growth of manufacturing firms in Vietnam is significant R&D activities enhance innovation, leading to improved production processes and product quality This, in turn, increases efficiency and competitiveness within the manufacturing sector As firms invest in R&D, they are better positioned to adapt to market changes and technological advancements, ultimately driving economic growth in Vietnam Emphasizing R&D can thus play a crucial role in boosting the overall productivity of manufacturing firms in the country.
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Wang and Tsai (2003) explore the relationship between productivity growth and research and development (R&D) expenditure in Taiwan's manufacturing sector Their findings highlight the significant impact of R&D investments on enhancing productivity within these firms, suggesting that increased funding in innovation is crucial for competitive advantage The study underscores the importance of strategic R&D allocation to foster economic growth in Taiwan's manufacturing industry For further details, the full paper is accessible online.
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A System Model for Technological Innovation
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