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 national and international markets Research and Development (R&D) is considered the backbone of technological advancement, with a firm's innovative capacity often reflected in its R&D expenditure levels and growth rates Countries within the Organization for Economic Cooperation and Development (OECD) allocate substantial resources to R&D activities, underscoring 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, companies prioritize research and development (R&D) due to its potential to enhance productivity, achieve success in competitive markets, and comply with environmental and regulatory standards R&D plays a crucial role in developing new products and often leads to the emergence of new markets When evaluating R&D performance, firms consistently consider economic returns, typically engaging in R&D activities only when they promise higher returns compared to other investment options, such as acquiring new machinery, advertising, or investing in speculative assets.
Enhancing productivity growth is widely recognized as being linked to increasing the stock of knowledge This can be achieved through formal investments aimed at expanding knowledge resources.
1 OSTP is Office of Science and Technology Policy
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 in the business sector, with only 0.5% of its GDP allocated to R&D compared to around 2% in most OECD countries and China In 2002, Vietnamese enterprises contributed merely 20% of the nation's total R&D expenditure In contrast, companies in OECD nations fund over 50% of all R&D spending and carry out two-thirds of R&D activities Furthermore, small and medium-sized enterprises (SMEs), which constitute 96.5% of registered companies in Vietnam, generally exhibit a technology level that is two to three times lower than global and regional standards.
The Ministry of Industry highlights that a significant barrier to technological upgrading in Vietnam is the lack of skilled labor and insufficient R&D activities tailored to meet these needs Currently, only a small percentage of the nation's R&D scientists and engineers are employed in industrial enterprises, with most working in national R&D centers, ministries, universities, and other research institutions Additionally, there is a weak market-oriented collaboration among firms, R&D institutions, and universities A major contributing factor to the low investment in R&D by Vietnamese enterprises is their financial resource limitations.
The relationship between research and development (R&D) and productivity in Vietnamese manufacturing firms remains uncertain Numerous empirical studies at the firm level highlight the importance of technological and knowledge capital in driving productivity growth While early research indicates that R&D investment significantly contributes to productivity growth across various countries, this conclusion has yet to be validated in the context of Vietnam.
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 not been thoroughly explored in the context of Vietnam This research aims to investigate the connection between R&D activities and productivity growth within Vietnamese manufacturing firms, addressing key questions regarding their impact.
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 structured into six chapters, beginning with an Introduction that outlines the research rationale, objectives, hypothesis, methodology, and overall structure The second chapter, Literature Review, explores theories and empirical studies on the relationship between R&D expenditure and productivity growth in manufacturing firms Chapter 3, Overview of R&D and Firm Performance in Vietnam, delves into the R&D activities of firms Chapter 4, Research Methodology, details the model specification and justifies the choice of variables The analysis of practical results is presented in Chapter 5, Result Analysis, utilizing descriptive statistics and regression analysis Finally, the Conclusions and Recommendations chapter offers key conclusions and policy suggestions based on the research findings.
LITERATURE REVIEW
INTRODUCTION
This chapter reviews relevant literature to ensure the research is grounded in scientific principles, structured into three main sections The first section explores key concepts such as R&D, productivity, and manufacturing The second section identifies and discusses economic theories that support the study, culminating in a proposed research model illustrating the factors influencing productivity The final section examines empirical studies on the impact of R&D on productivity growth in manufacturing firms across various countries Overall, this chapter elucidates the relationship between R&D expenditure and productivity growth, informed by both economic theories and empirical evidence.
CONCEPTS
Research and experimental development (R&D) is defined by the OECD (1994) as systematic creative work aimed at expanding knowledge, including insights into humanity, culture, and society, ultimately leading to 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, analyzing characteristics and relationships to formulate and test theories, with results typically published in scientific journals or kept confidential for security In contrast, applied research seeks to acquire new knowledge for specific applications, often determining uses for basic research findings and resulting in innovations that may be patented or kept secret Lastly, experimental development utilizes existing knowledge and practical experience to create new materials, products, or processes, significantly enhancing previous developments.
Basic research explores the theoretical factors influencing regional economic growth variations, while applied research focuses on investigations aimed at shaping government policy Additionally, experimental development involves creating operational models grounded in established laws to address and modify these regional disparities.
"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 considered for inclusion or exclusion This thesis utilizes R&D expenditure data from the Vietnam Enterprise Survey to analyze its impact on productivity growth within Vietnamese manufacturing firms.
Scientific and technological innovation refers to transforming ideas into new or enhanced products, processes, or social services The term "innovation" varies in meaning across different contexts, depending on specific measurement objectives Technological innovations encompass new products, processes, and significant technological changes An innovation is recognized when it is introduced to the market or utilized in production This process involves various activities related to science, technology, organization, finance, and commerce, with research and development (R&D) playing a crucial role R&D can generate inventive ideas or solve problems and is considered a key input measure of innovation.
Productivity is generally defined as the ratio of output volume to input volume, a concept widely accepted and applicable in various contexts, according to the OECD (2001) This definition underscores the multiple purposes and methods for measuring productivity, highlighting its versatility in different scenarios.
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 Full efficiency, in an engineering context, indicates that a production process has reached 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
2 See Appendix 1 for explanation of innovation process
Productivity can be measured in various ways, influenced by the measurement's purpose and available data There are two main types of productivity measures: single factor productivity, which compares output to a single input, and multifactor productivity, which evaluates output against multiple inputs At the industry or firm level, productivity measures can focus on gross output in relation to one or more inputs or utilize value-added metrics to assess 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 labour productivity, defined as the ratio of the quantity index of gross output to the quantity index of labour input, to assess productivity This measure is valuable as it directly relates to labour, the most crucial factor of production, and is straightforward to quantify.
The manufacturing sector, as defined by the US Census Bureau, encompasses establishments involved in the physical or chemical transformation of materials, substances, or components into new products This sector includes activities such as assembling parts of manufactured goods, blending materials, and other related processes, 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 establishments may process materials independently or contract with others for processing The end products can either be finished goods ready for consumption 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 involving parts and accessories sold separately are classified under the industry of the finished product; for example, manufacturing a replacement refrigerator door falls under refrigerator manufacturing Conversely, the classification of components produced for use in other manufacturing processes depends on the production function of the component manufacturer, such as electronic components classified under Computer and Electronic Product Manufacturing and stamps categorized under Fabricated Metal Product Manufacturing.
ECONOMIC THEORIES
The Cobb-Douglas production function, introduced by Knut Wicksell between 1851 and 1926, is a fundamental model in microeconomics that illustrates the relationship between output and various inputs, as noted 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 terms, the output elasticity of labor (a) and capital (p) are typically constants that are less than one This is due to the principle that the marginal product of each input decreases as the quantity of that input increases.
Output elasticity quantifies how output responds to changes in labor or capital, holding other factors constant For instance, with an elasticity of a = 0.15, a 1% rise in labor results in a 0.15% increase in output The relationship between labor and capital in the production function indicates returns to scale: if a + p = 1, it shows constant returns; if a + p < 1, it reveals decreasing returns; and if a + p > 1, it indicates increasing returns For example, when both labor (L) and capital (K) are increased by 20%, the output (Y) increases by 20% if a + p = 1, while the output change varies when a + p is different 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 the relationship between inputs and outputs based on a specific technology This indicates that advancements in technology can enhance the efficiency of transforming inputs into outputs, allowing firms to increase production without additional resources For example, the introduction of a faster computer chip can enable hardware manufacturers to produce higher-speed computers within the same timeframe.
The Cobb-Douglas production function serves as a foundational tool for measuring production functions, yet it is frequently supplanted by more intricate models in industry research According to Pindyck and Rubinfeld (1992), one key limitation of the Cobb-Douglas function is its inability to account for real-world scenarios where a firm's production process exhibits increasing returns at low output levels, constant returns at intermediate levels, and decreasing returns at high output levels.
2.3.1.2 The Law of Diminishing Returns
The law of diminishing returns, as described by Pindyck and Rubinfeld (1992), states that increasing an input while keeping others fixed will eventually lead to reduced output gains Initially, when labor input is low and capital remains constant, a slight increase in labor can significantly boost output due to task specialization However, as more workers are added, inefficiencies arise, causing the marginal product of labor to decline This phenomenon illustrates the law of diminishing returns in production.
The law of diminishing returns is primarily relevant in short-run analyses, where at least one input remains fixed; however, it can also apply to long-run scenarios It's essential to distinguish this law from output decreases caused by variations in labor quality when increasing labor input For example, hiring highly qualified workers initially leads to significant output increases, while bringing in less qualified workers later may result in minimal or no output growth In production analysis, it is assumed that all labor inputs are of equal quality, with diminishing returns stemming from limitations in fixed inputs like machinery, rather than declines in worker quality Furthermore, diminishing returns should not be confused with negative returns, as the former indicates a declining marginal product rather than a negative one.
The law of production assumes a specific production technology, but advancements in inventions and improvements can shift the total product curve upward, enabling higher output with the same inputs While diminishing returns to labor are inherent in any production process, labor productivity can increase through technological enhancements As illustrated in Figure 2.1, these technological improvements can elevate the output curve, demonstrating the potential for greater efficiency in production.
Figure 2.1: The effect of technology improvement
Labor per time Source: Pindyck and Rubinfeld, 1992
The R&D capital model remains a crucial research method for estimating the impact of R&D on productivity growth, despite its limitations (Griliches, 2000) This straightforward and accessible model allows for the calculation of the rate of return on R&D investments and their contribution to productivity advancements Most applied studies rely on this model, enabling the examination of various forms of R&D capital, including private, public, and research conducted by adjacent firms or industries.
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) denotes other systematically changing factors influencing output over time Additionally, u accounts for all random fluctuations affecting output.
The logarithmic form of the Cobb-Douglas production function serves as an initial approximation of a more intricate relationship, specifically aimed at estimating the elasticity of output concerning research capital Research and development (R&D) capital is typically measured as a weighted sum of historical R&D expenditures, where the weights account for potential delays in R&D's impact on output as well as its 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 is the net investment in K, net of the depreciation of the previously accumulated 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 linked to the intensity of investment in research and development (R&D) and 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 can encounter issues related to timing, depreciation, and coverage A significant concern is that this model equates R&D and scientific endeavors with traditional investments, whereas knowledge creation differs fundamentally from purchasing machinery or constructing facilities, making it difficult to assess the outcomes of such activities Despite these limitations, this straightforward model serves as a useful starting point for exploring empirical research in this field, provided we address the associated conceptual and data-related issues.
Griliches (2000) highlighted the simultaneity problem in econometrics, noting that causality can be confusing; specifically, future output and profitability rely on past R&D, while R&D is influenced by past output This creates a complex system of equations where current output is dependent on past R&D, and past R&D is contingent on past output However, distinguishing these relationships becomes significantly more challenging when using cross-sectional data.
2.3.3 Suggested research model from economic theories
Based on the above economic theories, the relationship between firm productivity and its determinants can be described in a function with dependent and independent variables as follows:
Y denotes measures of output of firms It is expressed in the form that representing labor productivity of firm
L denotes labor input of finn
K is physical capital of firm
CHAR is considered as some characteristics of firm which affect its productivity such as size of labor or type of ownership.
EMPIRICAL STUDIES
Numerous analysts have recognized the significance of research and development (R&D) and explored its connection to productivity growth at the firm level A multitude of empirical studies has been conducted to assess the impact of R&D investment on this growth According to the Congressional Budget Office (CBO, 2005), findings vary widely; some research indicates that R&D has little to no effect on productivity, while other studies reveal that its impact is substantial and surpasses that of other investments However, most estimates fall between these extremes, leading to a consensus that there is a positively significant relationship between R&D spending and productivity growth.
Mairesse and Sassenou (1991) conducted a study examining the link between R&D and productivity at the firm level, analyzing both the results and challenges faced in econometric research They identified the Cobb-Douglas production function as the primary analytical framework utilized in these studies to estimate 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 an 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 realm of R&D activities and their influence on productivity As noted in 1991, the effects of R&D are inherently uncertain, often manifesting with long delays and varying significantly across different firms and sectors over time Additionally, simultaneous factors impacting productivity can obscure the effects of R&D Despite challenges in measuring variables and gathering accurate data, most studies surprisingly reveal statistically significant and plausible estimates of R&D elasticity and rates of return.
CBO (2005) and Mairesse and Sassenou (1991) conducted comprehensive reviews of existing studies on the link between research and development (R&D) and productivity To deepen the understanding of this relationship, the following three case studies will be examined.
2.4.2 R&D and Productivity in French manufacturing firms
Cuneo and Mairesse (1983) conducted a study to examine the relationship between R&D expenditures and productivity performance in the French manufacturing industry from 1972 to 1977, analyzing a sample of 182 firms The firms were categorized into two sub-samples: R&D intensive firms in sectors such as chemicals, drugs, and electronics, and other manufacturing firms The research utilized an extended Cobb-Douglas production function, expressed in logarithmic form, to model the data.
In the equation, where 'i' represents the firm and the current year, 'e' denotes the error term, while '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 the number of employees Physical capital stock (C) is assessed through gross-plant figures adjusted for inflation Additionally, R&D capital stock (K) is determined by the weighted sum of historical R&D expenditures, applying a consistent obsolescence rate.
To accurately assess R&D capital stock, it is essential to adjust for double counting of labor and physical capital This involves subtracting R&D employees from the total workforce and calculating the portion of physical capital allocated to R&D based on its average ratio to total R&D expenditures However, in Vietnam, the lack of complete financial statements for firms complicates the separation of physical capital used in R&D from overall physical capital stock, making precise adjustments challenging.
The authors identify discrepancies between total and within-firm estimates of two key parameters: the elasticity of physical capital stocks (a) and R&D capital stocks (y) However, due to accurate variable measurements, these issues are less severe than anticipated, with estimates generally being statistically significant and likely high To explore additional results, the authors utilized sales instead of value added and varied the inclusion of materials in the production function The total estimates derived from sales, excluding materials, are similar to those obtained from value added When constant returns to scale are applied, within-firm estimates using sales also show comparable results However, without this assumption, significant discrepancies arise between total and within-firm estimates Notably, including materials greatly enhances the within-firm estimates, indicating that omitting materials in the sales specification particularly impacts these estimates.
2.4.3 R&D and Productivity Growth in Japanese manufacturing firms
Kwon and Inui (2003) explored the link between research and development (R&D) and productivity enhancement in Japanese manufacturing companies Their study estimated a Cobb-Douglas production function utilizing three key inputs: labor, physical capital, and knowledge capital, analyzing data from over 3,000 Japanese firms during the years 1995 to 1998.
This research utilizes data from the Basic Survey of Business Structure and Activities conducted by Japan's Ministry of Economy, Trade and Industry The study focuses on 3,830 manufacturing firms that reported positive R&D expenditures between 1995 and 1998, ensuring that each selected firm met specific criteria for inclusion.
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) examined the impact of R&D on productivity growth in Japanese manufacturing firms through two methods: the Production Function Approach and the Rate of Return to R&D Approach While the Production Function Approach allows for potential bias from simultaneous output and input decisions, it benefits from avoiding assumptions related to competitive factor markets, cost minimization, and constant returns to scale This approach illustrates the connection between R&D and productivity growth using a regression function based on first-differences.
In the context of economic production, the value added (Y) is influenced by physical capital stock (K), labor input (L), and knowledge capital stock (R), with A-t representing a time-specific variable and the rate of disembodied technical change The notation indicates that i and t correspond to the firm and the year, respectively To ensure a positive marginal product of labor, it is assumed that 1 - a > f3 The scale parameter r indicates the nature of returns to scale; a positive r signifies increasing returns, while a negative r indicates decreasing returns.
K and Inui's second approach assessed the impact of R&D on productivity by estimating the rate of return on R&D investments instead of measuring the elasticity of value added related to R&D This method effectively circumvents the challenges associated with quantifying R&D capital stock The relationship between labor productivity growth and R&D intensity is represented by an equation where E denotes the R&D expenditures of firm i during period t.
The study reveals that R&D expenditure significantly boosts productivity growth, with the impact varying based on firm size and technological characteristics Notably, larger and high-tech firms exhibit greater R&D elasticities compared to their smaller counterparts Additionally, the analysis highlights that physical capital stock plays a crucial role in enhancing labor productivity growth Interestingly, industry effects are found to be less significant in accounting for productivity disparities among firms.
2.4.4 The effect of R&D Capital on Danish Firm Productivity
OVERVIEW OF R&D AND FIRM PERFORMANCE IN
INTRODUCTION
This chapter aims to provide a comprehensive overview of research and development (R&D) activities in Vietnam, focusing on both the national context and the specific contributions of firms It is structured into three key sections: the first section compares Vietnam's R&D activities with those of other countries; the second outlines the structure of Vietnam's R&D system; and the final section examines the connections between R&D institutions and the productive sector.
R&D ACTIVITIES IN VIETNAM
Vietnam's investment in research and development (R&D) is significantly lower than that of OECD countries and its neighbors, with expenditures rising from approximately 0.3% of GDP in 1996 to about 0.5% in 2003 Notably, in 2002, the Vietnamese government financed around 80% of R&D spending, contrasting sharply with OECD nations, where private companies accounted for about 70% of R&D funding.
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
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 seen a significant decline since the implementation of the "doi moi" reforms, with expenditures dropping from US$687 in 1987 to US$289 in 1990, according to Bezanson et al (2000) Despite potential estimation errors, these figures remain considerably lower than those of other East Asian nations, such as Japan's US$135,000 and Korea and Singapore's over US$50,000 Additionally, NISTPASS reported that in the early 1990s, annual spending on research facilities per researcher was only around US$50, and only about 10% of Vietnamese researchers had access to experimental equipment comparable to that found in 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 fell short compared to Japan, Korea, Singapore, and China.
3 NISTPASS is National Institute for Science and Technology Policy & Strategy Studies
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: first, approximately 180 laboratories and R&D institutes operated by line ministries and government agencies, with state-owned corporations like Petro Vietnam conducting their own research; second, universities and colleges, which face significant resource limitations, including a shortage of personnel and equipment for effective R&D; and third, national research centers, such as the National Center for Natural Science and Technology (now the Vietnamese Academy of Science and Technology), which are managed by the Government Office and play a crucial role in the country's research landscape.
The three key components of the research ecosystem are interconnected, each serving distinct roles Research institutes within various ministries focus on applied research and experimental development, while universities and colleges are the primary sources of R&D human resources Additionally, the Vietnamese Academy of Science and Technology is tasked with conducting advanced basic research.
Vietnam's research infrastructure quality falls below international standards, as noted by Nguyen and Tran (n.d.) Research efforts are primarily theoretical and supply-driven, failing to address the needs of the manufacturing sector Most R&D activities occur within government research institutes and national centers rather than universities Publicly funded research is predominantly conducted by government entities, with minimal state budget allocation for R&D Overall, the national R&D system is structured in a manner that complicates and increases the cost 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
5 MOST: Ministry of Science and Technology
3.4 LINKAGE BETWEEN THE PRODUCTIVE SECTOR AND R&D
The relationship between the productive sector and R&D institutions in Vietnam is notably weak, with SMEs and large corporations alike relying heavily on external R&D sources rather than local universities and research facilities Most R&D activities conducted by foreign firms occur in their home countries, limiting opportunities for Vietnamese institutions to contribute meaningfully Despite a clear demand from firms for technology and training services offered by R&D institutes and universities, this need remains largely unmet, resulting in insufficient support for businesses from these institutions Overall, the collaboration between research entities and enterprises is below desirable levels, highlighting significant challenges in fostering effective partnerships.
To comprehend the innovation landscape in Vietnam, it is essential to recognize that research and development (R&D) is a crucial component of this process According to Nguyen and Tran (n.d.), Vietnam's status as a transitional and developing nation results in a unique innovation environment that contrasts with that of developed countries Additionally, external factors significantly impact the innovation efforts of Vietnamese firms Key characteristics of Vietnam's innovation environment include its transitional nature and the substantial influence of external elements on local businesses.
Competition among Vietnamese firms primarily hinges on the availability of natural resources and access to inexpensive labor Only a limited number of enterprises differentiate themselves through innovative technology or unique 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
6 Definition ofOECD, 1994 This was discussed in chapter 2
Markets for technical and innovation services are under development; and so on
According to Nguyen and Tran (n.d.), innovations in Vietnam can be categorized as either incremental or "new to the firms." Incremental innovations arise when companies address technical issues related to their imported production lines or when they develop new products using existing machinery In contrast, "new to firms" innovations occur when businesses acquire new or partial production lines to create different products.
Research institutions in Vietnam often fall short in supporting innovation within the production sector According to Nguyen and Tran (n.d.), Vietnamese firms primarily seek problem-solving innovations rather than science-based solutions, leading them to expect academic institutions to assist with their technical challenges However, the organizational structure and priorities of these academic institutes hinder their ability to meet these expectations effectively Furthermore, there is a significant disconnect between what firms desire from academic institutions and what these institutions believe their role should be in supporting those firms.
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
Source: Innovation Survey 2002 & PROs Survey in 2000 ofNISTPASS, cited in Nguyen and Tran (n.d)
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 an econometric model grounded in pertinent theories and empirical research, including definitions and measurements of the variables utilized in the regression model Next, it addresses the analytical framework, methodologies, 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 the productivity of Vietnamese manufacturing firms is modeled using a logarithmic version of the Cobb-Douglas production function, which incorporates standard factors like capital and labor, along with an additional factor, knowledge capital This model aims to link productivity growth directly to increases in these inputs, with productivity specifically measured in terms of labor productivity, as it is the most significant production factor and easy to quantify Additionally, the model explicitly tests the assumption of constant returns to scale.
The Cobb-Douglas specification can be written as:
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
OWNS represents a type of ownership as a dummy variable, where 'u' denotes the error term and 'a3' serves as a scale parameter If the coefficient 'a3' is statistically significant and differs from zero, it indicates a rejection of the assumption of constant returns to scale concerning the three inputs A positive value of 'a3' suggests increasing returns to scale, while a negative value indicates decreasing returns to scale.
This study utilizes cross-sectional data to examine the relationship between R&D expenditure and productivity, addressing data limitations To enhance the validity of the multiple regression model and reduce heteroskedasticity, key variables are transformed into logarithmic and ratio terms Notably, R&D elasticity is found to be significant and greater in cross-sectional analysis, contrasting with its smaller and often statistically insignificant presence in time series analysis (Matteucci and Sterlacchini, 2004).
In empirical studies, output is typically measured by value-added, which includes operating profit, depreciation, employee compensation, taxes, and rent (Kwon and Inui, 2003) However, this study cannot calculate value-added due to data limitations; instead, output is assessed through total sales or revenues Labor is quantified by the number of employees, as information on working hours is unavailable To avoid double counting, R&D employees are excluded from the total employee count, since their contributions are reflected in R&D expenditures Physical capital is represented by total fixed gross assets, though these are recorded at nominal (book) value in financial statements While Cuneo and Mairesse (1983) suggest subtracting R&D-related physical capital 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 involved in calculating it directly As highlighted in the literature, 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 is influenced by both current and historical R&D expenditures, as highlighted by Wang and Tsai (2003) To accurately assess the relationship between past R&D spending and the present growth in technical knowledge, it is essential to estimate the lag structure from available data Unfortunately, this research is limited by the availability of R&D expenditure data from only one specific time frame.
- - - , - - - - - - - year, therefore, it is impossible to measure R&D in such way This is one of the limitations of this research, which will be discussed in the end
In the context of dummy variables, firm size (SIZE) is classified into three categories based on the number of employees: large scale, medium scale, and small scale Additionally, firms are categorized by ownership type (OWNS) into three groups: state-owned, foreign-owned, and other ownership types.
KIL is a crucial production input that significantly contributes to productivity growth, with its natural log generally showing a positive relationship in empirical studies Consequently, the log of KIL is anticipated to have a positive effect on productivity functions Additionally, research and development (R&D) and technical advancements enable firms to enhance their productivity, with studies indicating that firms investing more in R&D tend to achieve higher productivity levels, thus suggesting a positive expectation for log(RJL) Similarly, the log of labor (log(L)) is also expected to be positive, as increased employment typically leads to greater productivity; however, this may reverse if the firm experiences diminishing returns It is assumed that larger firms exhibit higher productivity levels compared to smaller ones, aligning with the positive sign of log(L) Furthermore, foreign-owned firms are predicted to demonstrate greater productivity than state-owned and other types of firms.
In conclusion, the regression equation can be fully rewritten as follows:
Log(-) = a0 + a 1log(-) + a2log(-) + a3log L + a4LARGESCL + a5MEDIUMSCL
Where: LARGESCL and MEDIUMSCL denote large or higher scale and medium scale firms respectively STATE represents state-owned firms FOREIGN represents foreign-owned firms
In version (4.3), the coefficients indicate that a1, a2, a3, a4, and a5 are positive, while a6 is negative, and a7 is positive These sign assignments are derived from the arguments presented in version (4.2) and are supported by empirical findings from prior studies.
The disturbance term u includes unobservable factors, such as managerial capabilities, which significantly influence a firm's productivity (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 it can 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 analysis focuses specifically on the manufacturing sector, selecting 450 firms that reported positive R&D expenditure in 2004 Industries are classified according to the NACE (Classification of Economic Activities in the European Community), facilitating the extraction of relevant manufacturing firms Ultimately, the final sample comprises 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)
7 It cannot be distinguished between firms that did not report their R&D expenditw-es and those that had no R&D expenditures
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 reported in a questionnaire Total labor input (L) was calculated by subtracting the total number of R&D employees at the end of the year from the overall labor count Labor productivity was measured using the natural logarithm of the original unit, expressed in million VND.
This variable represents the physical capital per employee within a firm, measured in million VND Additionally, KIL is expressed as a natural logarithm The measurement of physical capital (K) includes total fixed assets and long-term investments, with data sourced directly from the VES-2004 at the end of the year.
The ratio of R&D expenditures to total labor input, denoted as R/L, is expressed in million VND and is analyzed in its natural logarithmic form R&D expenditure data was sourced directly from the VES-2004 dataset.
In this thesis, firms are categorized into small, medium, and large sizes using three dummy variables: LARGESCL, MEDIUMSCL, and SMALLSCL According to the EU definition of Small and Medium Enterprises (SMEs), a medium-sized enterprise is defined as having fewer than 250 employees, while small enterprises have fewer than 50 employees, and micro enterprises have fewer than 10 employees Consequently, LARGESCL represents firms with 250 or more employees, MEDIUMSCL includes firms with 50 to 249 employees, and SMALLSCL encompasses those with fewer than 50 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 over 50% of the shares are owned by the government FOREIGN indicates companies that are either fully owned by foreign investors or are joint ventures in which at least one partner is foreign.
RESULT ANALYSIS
INTRODUCTION
This chapter aims to analyze the findings from the regression model to determine the positive relationship between R&D expenditure and productivity growth in Vietnamese manufacturing firms It is structured into three sections: the first provides an overview of the main characteristics of the sampled firms using descriptive statistics; the second involves running and testing the specified regression model, followed by an analysis of the model results, empirical findings, and their statistical validity; and the final section summarizes the results of the analysis.
FIRMS CHARACTERISTICS
The study's data set comprises 91,755 observations, revealing that only 450 firms (0.49%) across all business sectors report positive R&D expenditures Among these, 264 firms from the manufacturing sector are selected for analysis, representing 0.29% of manufacturing firms While it is difficult to differentiate between firms that did not report R&D expenditures and those with negligible investments, the findings highlight that a small percentage of Vietnamese firms prioritize and invest in R&D activities.
In the analyzed sample, state ownership dominates, comprising 59.47% of observations, while foreign or joint-venture ownership accounts for only 11.74% According to EU definitions of SMEs, large firms with 250 or more employees represent over 60% of the sample, followed by medium-sized firms (50-249 employees) at 25.38% and small firms (1-49 employees) at 13.26%.
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
According to the NACE industrial classification, Table 5.1 illustrates the distribution of firms across various industries within the sample The Chemicals and Chemical Products sector leads with 47 firms, representing 17.80% of the total sample Following closely, the Food Products and Beverages industry comprises 45 firms, accounting for 17.05% Additionally, the Other Non-Metallic Mineral Products sector includes 26 firms, contributing 9.85% to the sample, ranking third after Food Products and Beverages.
Machinery and apparatus manufacturing represent significant portions of the sample, with shares of 7.95% and 6.82%, respectively, while other industries contribute only a small fraction Notably, the technology levels of most manufacturing firms in the sample are generally categorized as low to medium.
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%
Source: Author's calculation based on the data ofVES-2004
State-owned enterprises have a larger average workforce compared to other types of firms, with the largest having 8,993 employees However, they exhibit lower efficiency than foreign enterprises, which have an average turnover of VND381,314 million, surpassing the VND327,332 million of state-owned firms Foreign firms also outperform state-owned enterprises in terms of capital resources and prioritize innovation, research, and technological development On average, foreign firms possess total fixed assets and long-term investments of VND193,764 million, significantly higher than the VND113,795 million of state-owned firms Furthermore, foreign firms' average R&D expenditure is VND7,143 million, which is seven times greater than the VND1,076 million spent by state-owned enterprises.
Large firms significantly outperform 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 about four times and thirty-five times, respectively Interestingly, medium firms slightly surpass large firms in mean R&D expenditure, with VND1,783 million compared to VND1,749 million Notably, the highest R&D expenditure recorded is VND55,210 million, attributed to a foreign-owned large-scale firm.
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
The data presented in figures 5.3 and 5.4 reveals that a significant majority, 82 percent, of the research and development costs for manufacturing firms in the sample are funded by the firms' own investments In contrast, only 7 percent comes from state budgets and a mere 1 percent from foreign sources This indicates that enterprises are primarily responsible for financing their technology improvements through R&D Notably, 81 percent of the total R&D expenditure is allocated towards technology development, while only 17 percent is dedicated to research activities This trend can be attributed to the nature of innovation among Vietnamese firms, which tends to be problem-solving rather than science-based Consequently, R&D efforts are predominantly focused on practical applications, such as installing new production lines, adopting new technologies, or enhancing existing processes 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
Source: Author's calculation based on the data ofVES-2004
REGRESSION ANALYSIS
This section focuses on two key aspects: the analysis of the correlation matrix to assess the suitability of independent variables for inclusion in the regression model, and the presentation of model estimation along with its empirical results and statistical validity.
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 productivity model presented in Table 5.3 highlights a positive correlation between labor productivity and three key independent variables: lnK_L, lnL, and lnR_L, which are essential for the model Notably, physical capital per labor exhibits a stronger relationship with productivity compared to the other variables Additionally, the analysis reveals that foreign firms demonstrate a higher correlation with productivity than state-owned firms.
5.3.2 Model estimation and empirical results
Labor productivity is affected by several key factors, including physical capital per worker, the number of workers, research and development expenditure per worker, as well as the size and ownership of the organization An analysis of these institutional factors is essential for understanding their impact on labor productivity The empirical results presented in Table 5.4 indicate that, aside from the STATE variable, all other variables demonstrate statistical significance at the 1%, 5%, and 10% levels.
The analysis of dummy variables reveals that productivity growth varies based on ownership and firm size Notably, the FOREIGN variable is significant at the 10% level, indicating that foreign firms experience a mean productivity growth rate that is 1.3% higher than state-owned enterprises and others, likely due to the typically less efficient operations of state-owned firms There is no significant difference in productivity growth between state-owned firms and other types, such as private or limited companies Furthermore, regarding firm size, the regression results show that 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 outpacing large firms in this regard.
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 the significant impact of physical capital per labor (K_L), R&D expenditure per labor (R_L), and total labor (L) on productivity By utilizing the natural logarithm of these factors as proxy variables, the model reveals that each coefficient represents the elasticity of labor productivity The regression analysis indicates that all three variables positively influence productivity, with physical capital per labor demonstrating the most substantial effect Specifically, the elasticities for physical capital per labor and total labor are approximately 0.35 and 0.15, respectively, suggesting that a 1% increase in physical capital per labor and total labor results in a 0.35% and 0.15% rise in productivity, respectively, when other inputs are held constant.
5.3.2.2 Analysis of the elasticity of productivity with respect to R&D
Being lower than the elasticities of productivity with respect to the two other variables, the one with respect to R&D expenditure per labor was about 0.1 This
A 1% increase in R&D expenditure per labor results in only a 0.1% increase in productivity, which aligns with Griliches' (1995) findings that the output elasticity of R&D capital ranges from 0.09 to 0.14 (Wang and Tsai, 2003) This suggests that physical capital and labor contribute more significantly to productivity than R&D investments The limited focus on R&D among Vietnamese enterprises can be attributed to their preference for utilizing labor, which is a more affordable and readily available resource.
While the findings indicate a positive relationship between R&D expenditure and productivity, it is essential to address certain concerns Wang and Tsai (2003) suggest that the commonly held belief in the significant impact of R&D investment on productivity may be overly optimistic due to issues like the 'file-drawer' problem and challenges in measuring R&D capital This study, constrained by data limitations, does not account for the accumulation of past R&D expenditure, its lag effects, deflation, and obsolescence Interestingly, the regression results surprised the author, potentially because Vietnamese enterprises primarily engage in problem-solving R&D rather than science-based research Consequently, most R&D spending focuses on technology development, which tends to yield quicker impacts on productivity compared to traditional research activities.
5.3.2.3 Statistical validity of the model
The model examines 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, approximately 0.15, serves as a scale parameter This coefficient is statistically significant, leading to the rejection of the constant returns to scale assumption at the 1% significance level, thereby supporting the acceptance of increasing returns to scale.
In this model, two key tests are conducted: multicollinearity and heteroscedasticity, which ensure the statistical validity of the results Testing for multicollinearity is deemed unnecessary, as most correlation coefficients among regressors remain below 0.5, with the exception of certain dummy variable correlations (Table 5.3) Additionally, the independent variables demonstrate statistical significance at the 1% level, and the model's R-squared value is below 0.8 (Table 5.4 and Appendix 2) Regarding heteroscedasticity, the model has been adjusted to eliminate this issue, with detailed results from the White Test provided in Appendix 3.
CONCLUSIONS AND RECOMMENDATIONS
CONCLUSION
This study examines the correlation between R&D expenditure and productivity among 264 manufacturing firms in Vietnam, all of which reported positive R&D The sample was derived from a comprehensive dataset of 91,755 observations from the Vietnam Enterprise Survey conducted in 2004 Among the firms analyzed, state-owned enterprises represent 59.47%, while foreign firms comprise only 11.74% Notably, large-scale firms with over 249 employees constitute over 60% of the sample, with medium-sized (50-249 employees) and small firms (1-49 employees) making up 25.38% and 13.26%, respectively In terms of industry classification, firms in the Chemicals and Chemical Products sector account for the largest proportion at 17.08%, closely followed by those in Food Products and Beverage manufacturing at 17.05%.
State-owned enterprises generally exhibit lower efficiency compared to foreign firms, which also demonstrate greater capital resources and higher investments in innovation and R&D activities In the analyzed sample of manufacturing firms, a significant 82% of total research and development costs are self-financed by the firms Notably, 81% of these costs are allocated towards technology development, while only 17% is dedicated to research efforts.
The research utilized an analytical framework grounded in the Cobb-Douglas production function and the R&D capital model, estimating the regression equation in logarithmic form to ensure statistical validity The regression model is articulated as follows:
Log(y) = 2.24 + 0.351og(K) + O.IOlog(R) + 0.15log L + 0.26LARGESCL +
Research indicates that R&D investment is a crucial factor in enhancing firm productivity Specifically, a 1% increase in R&D expenditure per employee correlates with approximately 0.1% growth in labor productivity Despite using a simpler measurement of R&D capital compared to other studies, the findings are noteworthy 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 efforts on problem-solving rather than fundamental scientific research, with the majority of expenditures directed towards technology development rather than traditional research activities.
Research indicates that, alongside R&D capital, other factors significantly influence productivity growth Specifically, the elasticities of productivity concerning physical capital per labor and total labor are approximately 0.35 and 0.15, respectively, suggesting that these variables have a greater impact on productivity growth than R&D expenditure per labor Additionally, ownership type and firm size play crucial roles in productivity rates; foreign firms demonstrate higher productivity growth compared to state-owned and other firms, with no significant difference between state-owned and other types Furthermore, medium-sized firms (50-249 employees) exhibit slightly higher productivity growth than large firms (over 249 employees) and substantially more than small firms.
In conclusion, 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 address the second research question, it is crucial for these firms to prioritize investment in R&D to enhance their productivity The following section will outline policy recommendations to support this initiative.
POLICY RECOMMENDATIONS
To enhance R&D activities in the industrial sector, the Korean Government implemented various flexible policies aligned with different national development strategies During the 1960s and 1970s, the focus was primarily on tax incentives and preferential treatment for R&D, but these measures yielded disappointing results due to a lack of clear demand for R&D investment among firms, which often relied on easily accessible existing technologies In the 1980s, the government introduced additional incentives, such as reduced tax rates for importing R&D equipment, tax deductions for R&D spending and human resource development, and tax exemptions for fixed assets related to R&D.
The Korean Government implemented various indirect programs to boost R&D activities, including the International Standard Korean Products Program and a support initiative for 21 products linked to 59 manufacturers in the industry sector In addition to tax incentives, the government introduced financial support policies to encourage investment in R&D Small enterprises lacking their own R&D centers were motivated to collaborate with other firms As a result of these initiatives, the number of R&D institutes and associations saw significant growth, particularly 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 promoting innovation and the development of new technologies as essential business assets The program focused on enhancing community awareness of intellectual property rights to achieve its objectives.
The government is actively enhancing the connection between research and development (R&D) activities and intellectual property by improving patent issuance procedures and facilitating the use and transaction of patented technologies To bolster financial resources for the commercialization of new technologies, it has stimulated private venture capital development and established government venture funds Additionally, the government is enhancing capital markets for newly established firms, particularly in science and technology, and has developed a secondary stock market to support their growth.
The majority of Vietnamese firms are small to medium-sized and lack sufficient financial resources, resulting in limited investment in research and development (R&D) activities Additionally, the domestic technology services market remains underdeveloped, leaving firms uninformed about local research and innovations Consequently, many companies focus solely on acquiring new equipment while neglecting research and technology transfer Small and medium-sized enterprises (SMEs) often depend on external sources for their R&D, as local research institutions are not well-connected with businesses As a result, R&D activities have not received adequate investment.
In today's globalized economy, companies must prioritize research and development (R&D) and technology innovation to thrive and grow These efforts enhance production capabilities, leading to increased productivity, reduced production costs, lower product prices, and improved competitive advantage Despite the time, capital, and risks involved, firms should view R&D as a crucial long-term investment Additionally, government support is essential to encourage R&D investment among businesses This research provides policy recommendations inspired by Korea's experience and tailored to Vietnam's context to promote R&D activities among firms.
Equitizing research and development (R&D) institutions in Vietnam can enhance their activity, creativity, and accountability, aligning them more closely with 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 weak Many local companies rely heavily on external R&D sources, while foreign firms depend on their parent companies for support Overall, the collaboration between R&D institutions, universities, and businesses is insufficient By adopting a more market-oriented approach through equitization, R&D institutions could significantly improve their contributions to supporting enterprises.
Tax incentives play a crucial role in promoting R&D activities by offering preferential treatment, such as 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 can be exempted from taxation, thereby encouraging investment in innovative projects.
The government should establish R&D venture funds to provide financial support for firms and encourage the growth of private venture capital It is essential that these funds are managed and supervised rigorously to ensure that the capital is allocated effectively, at the appropriate times, and for the intended purposes.
Encouraging companies to develop their own research and development (R&D) departments or collaborate with other businesses is essential This initiative should be supported by long-term national programs or campaigns aimed at fostering innovation and cooperation within the industry.
LIMITATIONS OF THE RESEARCH
The research acknowledges several limitations, particularly in the assessment of R&D's contribution to productivity growth A significant issue is the double counting of R&D labor and physical capital, which are included both in labor and physical capital measurements as well as in R&D capital evaluations (Cuneo and Mairesse, 1983) While the study did subtract R&D labor from total labor, it did not address the double counting of physical capital This oversight is largely due to the constraints of available data, making it a challenging task to resolve.
The research conducted in 2004 faced limitations due to its reliance on data from a single year, which hindered a comprehensive assessment of R&D capital By measuring R&D solely based on available expenditure, the study overlooked critical factors such as the accumulation of past spending, lag effects, deflation, and depreciation Additionally, examining the relationship between R&D expenditure and productivity growth for only one year raises questions about the validity of the R&D elasticity results This highlights the need for future research to utilize panel or time-series data, as analyzing R&D expenditure over multiple years could yield more reliable insights than the current study.
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