Impact of rd on the productivity growth of manufacturing firms in vietnam

i ABSTRACT This study exammes the relationship between R&D expenditure and productivity growth of manufacturing firms in Vietnam.. A regression model is estimated based on the Cobb-Doug

VIETNAM- NETHERLANDS PROJECT ON DEVELOPMENT ECONOMICS

IMP ACT OF R&D ON THE PRODUCTIVITY

IN VIETNAM

By

Duong Thi Phuong Ngoc

Academic supervisor: Dr Vo Van Huy

TRUONG £),b,l HOC I"INH TE TP.HCM CHUONG TRINH HQ~ 1 A~ £)~O }~ 0

CAO HQC KINH TE PHAT TRIEN

VI~T NAM- Hf> LAN (UEH-ISS!

Ho Chi Minh City, November, 2008

ACKNOWLEDGMENT

In completing this thesis, I am indebted to numerous individuals but I cannot name them all here First of all, I would like to thank all the staff and teachers of the Project for their valuable lessons, good learning facilities and warm attitudes during my school time My deepest gratitude goes to my supervisors, Dr Vo Van Huy, and Dr Nguyen Trong Hoai for their valuable comments and instructions concerning my thesis I am also very grateful to Mr Luong Vinh Quoc Duy, a teacher of the Project, for his support and lectures in econometrics Finally, I would like to thank my friends,

my family who have been always behind me, given me moral support, encouragement, and sympathy that have helped me gain more strength to complete this work

i

ABSTRACT

This study exammes the relationship between R&D expenditure and productivity growth of manufacturing firms in Vietnam Data on 264 manufacturing firms having positive R&D, which was drawn from the data set of Vietnam Enterprise Survey conducted by the General Statistics Office in 2004, is used for analysis A regression model is estimated based on the Cobb-Douglas production function and the R&D capital model with three main independent variables: physical capital, labor and R&D capital, and dummy variables reflecting type of ownership and size of labor

R&D capital is measured in a simple way by using available R&D expenditure in the survey and ignoring the accumulation of R&D expenditure in the past, its deflation and obsolescence However, a positive and significant impact of R&D expenditure on productivity is found with the elasticity of productivity with respect to R&D expenditure per labor is about 0.1 Moreover, the effects of physical capital and labor

on productivity are also positively and statistically significant The elasticities of productivity with respect to physical capital per labor and total labor are around 0.35 and 0.15, respectively

TABLE OF CONTENT

CHAPTER!: INTRODUCTION 1

1.1 RATIONALE OF THE RESEARCH 1

1.2 OBJECTIVE OF THE RESEARCH 3

1.3 RESEARCH METHODOLOGY 3

1.4 THESIS STRUCTURE 3

CHATPER 2: LITERATURE REVIEW 5

2.1 INTRODUCTION 5

2.2 CONCEPTS 5

2.2.1 Research and experimental development (R&D) 5

2.2.2 Productivity 7

2.2.3 Manufacturing sector 8

2.3 ECONOMIC THEORIES 9

2.3.1 Production theories 9

2.3.1.1 Cobb-Douglas Production Function 9

2.3 1.2 The Law of Diminishing Returns 11

2.3.2 R&D Capital Model 12

i 2.3 3 Suggested research model from economic theories 14

2.4 EMPIRICAL STUDIES 15

2.4.1 Overview 15

2.4.2 R&D and Productivity in French manufacturing firms 16

2.4.3 R&D and Productivity Growth in Japanese manufacturing firms 18

2.4.4 The effect of R&D Capital on Danish Firm Productivity 19

2.5 SUMMARY 20

CHAPTER 3: OVERVIEW OF R&D AND FIRM PERFORMANCE IN VIETNAM 22

3.1 INTRODUCTION 22

3.2 R&D ACTIVITIES IN VIETNAM 22

3.3 STRUCTURE OF THE R&D SYSTEM IN VIETNAM 25

3 4 LINKAGE BETWEEN THE PRODUCTIVE SECTOR AND R&D INSTITUTIONS 27

3.5 SUMMARY 29

CHAPTER 4: RESEARCH METHODOLOGY 30

4.1 INTRODUCTION 30

4.2 MODEL SPECIFICATION 30

4.3 DATA TRANSFORMATION 34

4.3.1 Labor productivity based on output (Y/L) 34

4.3.2 Physical capital per labor (K/L) 35

4.3.3 R&D expenditures per labor (RIL) 35

4.3.4 Firm sizes (LARGESCL, MEDIUMSCL) 35

4.3.5 Types of ownership (STATE, FOREIGN) 36

4.4 SUMMARY 36

CHAPTER 5: RESULT ANALYSIS 37

5.1 INTRODUCTION 37

5.2 FIRMS CHARACTERISTICS 37

5.3 REGRESSION ANALYSIS 43

5.3 SUMMARY 47

CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS 48

6.1 CONCLUSION 48

6.2 POLICY RECOMMENDATIONS 50

6.2.1 Experience of Korea 50

6.2.2 Policy Recommendations 51

6.3 LIMITATIONS OF THE RESEARCH 52

REFERENCE 54

APPEND IX 57

Appendix 1: A System Model for Technological Innovation 58

Appendix 2: Regression results 59

Appendix 3: White Heteroskedasticity Test 59

j

• LIST OF TABLES

Table 2.1: Overview of main productivity measures 8

Table 3.1: Science & Technology Organizations in Vietnam by 31 Dec 2003 26

Table 3.2: Ranking of most wanted services (for firms) and most capable activities (for academic institutions) of enterprises 28

Table 5.1: Industrial Classification of the Sample 39

Table 5.2: Statistics Summary 41

Table 5.3: Correlation matrix from the variables in the function 43

Table 5.4: Coefficients and statistics for the productivity model 45

LIST OF FIGURES • Figure 2.1: The effect oftechnology improvement 12

Figure 3.1: Percentage ofGDP spent on R&D in 1996 23

Figure 3.2: Expenditure on R&D by Government and Business sector in 2002 23

Figure 3.3: Sector-wise R&D Expenditure in Vietnam in 2002 24

Figure 3.4: R&D Personnel per Thousand of Total employees in 2002 25

Figure 5.1: R&D firms by ownership 38

Figure 5.1: Structure of firms by size 38

Figure 5.3: Total cost for research & development of technology by resources 42

Figure 5.4: Total cost for research & development of technology by purposes 42

•

OSTP Office of Science and Technology Policy

R&D Research & Development

SME Small and Medium Enterprise

VES Vietnam Enterprise Survey

CHAPTER!

INTRODUCTION

1.1 RATIONALE OF THE RESEARCH

In the modem economy today, technological progress has a quite central role It contributes importantly to growth of economy and is a key factor to determine the competitiveness of firms in both national and international marketplace Research and Development (R&D) is widely regarded as the core of technological advance, and innovative capacity of firms are reliably indicated by levels and rates of R&D expenditures growth Countries belonging to the Organization for Economic Cooperation and Development (OECD) spend significant amounts on R&D activities

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 (OSTP1, 1997)

In a traditional way, firms have paid attention to R&D because the technical advances resulting from innovation may allow them to improve productivity, succeed in competitive markets, and meet environmental and regulatory requirements Besides, R&D has also had contribution to the development of new products and, in many cases, the creation of new markets Within firms, economic returns are always taken into consideration on deciding the importance and nature of R&D performance Firms usually take part in R&D activities only when the results are appropriate and offer higher rates of return than that of other available investment alternatives such as acquisition of new machinery, advertising, or purchase of speculative assets

There are many sources for productivity improvements, but one strategy for enhancing productivity growth which is widely acknowledged is increasing the stock

of knowledge This stock of knowledge can be increased by formal investment in

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

In spite of the importance of R&D in firms' productivity, R&D activities have not been taken into consideration for much investment in Vietnam, especially in business sector While most OECD countries and China devoted around 2% of their GDP to R&D activities, Vietnam spent only 0.5% of its GDP for this purpose (Nguyen and Tran, n.d.) R&D expenditure of Vietnamese enterprises accounted for only about 20% of the total R&D expenditure of the country in 2002 (Nguyen, n.d.) Whereas, according to OSTP (1997), companies in OECD countries finance more than 50% of all R&D expenditure and they conduct two-thirds of all R&D activities SMEs make

up the vast majority of registered companies in Vietnam, namely 96.5% Nevertheless, the technology level across the SME sector in Vietnam is generally assessed as being two, three or even more times lower than both world and regional levels (Bezanson et al., 2000)

One of main reasons under the assessment of the Ministry of Industry is that the labor currently lack of necessary skills to support technological upgrading and there are very little R&D activities appropriate for such upgrading Indeed, only a small fraction of the country's R&D scientists and engineers are working in industrial enterprises The rest are working in national centers for R&D, ministries and government agencies, universities or other institutions that perform research Another reason is that there is little market-oriented relationship between firms, R&D institutions and universities (Bezanson et al., 2000) Moreover, the most important reason for a little investment in R&D activities of Vietnamese enterprises may be their limitations in financial resources

The case of Vietnam raises a doubt if R&D has any relationship with productivity of manufacturing firms Practically, there are many empirical studies at firm level that has emphasized the role of technological or knowledge capital in productivity growth Early studies focused on R&D investment and found that in most countries, R&D has

a significant contribution to productivity growth, especially in the cross sectional dimension However, the conclusion has not been verified in Vietnam

1.2 OBJECTIVE OF THE RESEARCH

Research and development (R&D) investment has been regarded as an important factor in the improvement of productivity levels of firms This has been proved true

by many empirical studies for many countries but neglected for Vietnamese case 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?

The thesis consists of six chapters The first chapter is Introduction, which presents

the rationale of the research, the objective of the research, research hypothesis as well

as methodology, and the thesis structure The next one is Literature Review This

chapter examines theories and empirical studies relating to the impact of R&D

expenditure on productivity growth of manufacturing firms R&D activities of firms

are discussed in the chapter 3: Overview of R&D and firm performance in Vietnam Chapter 4: Research Methodology focuses on model specification and variables

choices justification The practical results are analyzed via descriptive statistics and

regression analysis in chapter 5: Result Analysis Finally, conclusions and policy recommendations are provided in Conclusions and Recommendations chapter

CHATPER2 LITERATURE REVIEW

on the basis of economic theories and empirical studies

2.2 CONCEPTS

2.2.1 Research and experimental development (R&D)

OECD (1994) defined that "Research and experimental development (R&D) comprise creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock

of knowledge to devise new applications" R&D has been divided into three categories: basic research, applied research and experimental development

o Basic research is experimental or theoretical work that is undertaken not to obtain long-term benefits but to advance the state of knowledge (CBO, 2005) In basic research, characteristics, structures and relationships are analysed with a view to formulate and test hypotheses, theories and laws The results of basic

research are not for sale but usually for publishing in scientific journals or usage

of interested people Sometimes, it may be kept secret for security reasons

o Applied research is also original work that is undertaken to acquire new knowledge with a specific application in view Its' aims are determining possible uses for results of basic research or determining new ways to achieve specific objectives Results of applied research are mainly valid for a limited number of products, operations, methods or systems The knowledge or information resulting from applied research is often applied for patent or may be kept secret

o Experimental development is systematic work usmg existing knowledge gained from research and practical experience These research and experience is directed toward producing new materials, products and devices; installing new processes, systems or services; or substantially improving what has been produced or installed in the past

For example, basic research is the theoretical investigation of factors which have influence on regional variations in economic growth Applied research is the investigation that is performed for the development of 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,

1994 ) The measurement of such two kinds of R&D expenditures is so complicated with many costs should be included or excluded However, in this thesis, R&D expenditure used to examine its effects on productivity growth of Vietnamese manufacturing firms is available in the Vietnam Enterprise Survey

Scientific and technological innovation is known as the transformation of an idea into

a new or improved product, a new or improved operational process or a new approach toward a social service There are different meanings in different contexts for the

word "innovation" depending on certain objectives of measurement or analysis New products or processes and significant technological changes in products or processes are considered as technological innovations An innovation is performed if it is brought out to the market or used in a production process Thus, innovations include dozens of activities relating to science, technology, organization, finance and commerce R&D is one of such activities and it may be done at different stages of the innovation process2• R&D can act as the origin of inventive ideas or a form of problem-solving (OECD, 1994) According to Rogers (1998), R&D is an important input measure of innovation

2.2.2 Productivity

According to OECD (200 1 ), "productivity is commonly defined as a ratio of a volume measure of output to a volume measure of input use" This general concept has received no disagreement and can be applied in various ways That means there are many purposes and many ways to measure productivity The objectives of productivity measurement can be stated as follows:

A frequently stated objective of measuring productivity growth IS to trace

technical change

Productivity growth is also measured to identify changes in efficiency which is conceptually different from technical change Full efficiency in an engineering sense means that a production process has achieved the maximum amount of output that is physically achievable with current technology, and given a fixed amount 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

There are many different ways to measure productivity, which depend on the purpose

of productivity measurement or the availability of data Productivity measures can be divided into two kinds: single factor productivity measures and multifactor productivity measures Single factor productivity relates a measure of output to a single measure of output, whereas, multifactor productivity relates a measure of output to a bundle of inputs At the industry or firm level, there is a distinction between productivity measures that relate some measures of gross output to one or several inputs and those which use value-added to capture output movements

Table 2.1: Overview of main productivity measures

Type of input measure

output

services)

Capital-labor MFP

output (based on gross (based on gross (based on gross productivity

Capital-labor MFP

added (based on value (based on value (based on value

Single factor productivity measures Multifactor productivity (MFP)

measures Source: OECD, 2001

In this thesis, gross-output based labour productivity, which is a ratio of quantity index of gross output to quantity index of labour input, is used to measure productivity Labour productivity is a useful measure because it relates to the most important factor ofproduction_labour and is relatively easy to measure

2.2.3 Manufacturing sector

According to the US Census Bureau, the manufacturing sector includes establishments which are used in the physical or chemical transformation of materials, substances, or components into new products Except activities in the Construction

sector, manufacturing is also considered as the assembling of parts of manufactured products, the blending of materials, and some other related activities

Manufacturing establishments are often known as plants, factories or mills They may process materials by themselves or sign contracts with others to process their materials for them Manufacturing establishments transform materials, substances or components which are raw products of agriculture, forestry, fishing, mining and so

on The new products of manufacturing establishments may be finished products, which are ready for use or consumption, or semi-finished products, which become inputs for other establishments to use in further manufacturing

The manufacturing sector is divided into sub-sectors depending on different production processes with different kinds of material inputs, production equipment and employee skills In assembling activities, when parts and accessories of manufactured products are made for separate sale, they belong to the industry of the finished manufactured item For example, the manufacturing of replacement refrigerator door is classified in the refrigerators manufacturing However, the classification of components, which are input for other manufacturing establishments,

is based on the production function of the component manufacturer For instance, electronic components belong to Computer and Electronic Product Manufacturing and stamps belong to Fabricated Metal Product Manufacturing

2.3 ECONOMIC THEORIES

2.3.1 Production theories

2.3.1.1 Cobb-Douglas Production Function

According to Pindyck and Rubinfeld (1992), the Cobb-Douglas production function

is a widely-used approach to represent the relationship between an output and inputs

in microeconomics Knut Wicksell proposed the function in the period 1851-1926,

- - -

-and then in 1928 Paul Douglas -and Charles Cob tested it against statistical evidence The production function has the form as follows:

Q=ALaJ<f' (2.1), where:

• Q denotes output, L: labor input, K: capital input

• A is a constant depending on the units in which inputs and output are measured

• a and p are the output elasticity of labor and capital, respectively These values are constants and ordinarily smaller than one because the fact that the marginal product of each input diminishes when that factor increases

Output elasticity measures the responsiveness of output to a change in levels of either labor or capital used in production, ceteris paribus For example, if a= 0.15, a 1% increase in labor would lead to approximately a 0.15% increase in output Furthermore, if a + p = 1, the production function exhibits constant returns to scale If

a + P < 1, there are decreasing returns to scale, and if a + p > 1, then there are increasing returns to scale For example, if L and K each are increased by 20%, Y increases by 20% when a + p = 1 Y increases more than and less than 20% when a +

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

Pindyck and Rubinfeld (1992) stated that a general production function, Q = F(K, L), applies to a given technology This means a given state of knowledge might be used

in the transformation of inputs into output When technology is improved and the production function changes, a firm can obtain more output with a given number of inputs For instance, a new and faster computer chip may enable a hardware manufacturer to produce computers with higher speed in a given period of time

The Cobb-Douglas production function helps to illustrate a way to measure production functions However, it is often replaced by other more complex production

functions in industry studies for some reasons One of the reasons according to Pindyck and Rubinfeld (1992) is that the Cobb-Douglas function does not allow a possibility happening in the reality The possibility is that the firm's production process shows increasing returns at low output levels, constant returns at intennediate output, and decreasing returns at high output levels

2.3.1.2 The Law of Diminishing Returns

Pindyck and Rubinfeld (1992) stated the law of diminishing retums that "as the use of

an input increases (with other input fixed), a point will eventually be reached at which the resulting additions to output decrease" When the labor input is small and capital input is fixed, a small increase in labor input will lead to a substantial increase in output because workers are allowed to develop specialized tasks However, when too many workers are used in the production, some of them become ineffective and therefore the marginal product of labor falls That is called the law of diminishing retums

The law of diminishing retums is often applied in short-run analyses because according to the definition, at least one input is fixed However, it sometimes can be applied to long-run analyses There is one point needed to pay attention to is that the law of diminishing retums differs from decrease in output due to changes in the quality of labor when labor input are increased For instance, when the most qualified workers are hired first, the output will increase much accordingly However, the output may not go up or go up at a low level when the least qualified workers are hired last In the analysis of production, we have to assume that the quality of all labor input are the same Diminishing retums result from limitations on the use of other fixed inputs such as machinery, not from declines in worker quality Moreover, we should not confuse diminishing retums with negative returns In the law of diminishing return, a declining marginal product is described, not a negative one

In this law, a given production technology is also assumed However, over time, inventions and technology improvements may allow the entire total product curve to shift upward, thus, more output can be obtained with the same inputs Although any given production process has diminishing returns to labor, labor productivity can increase if there are improvements in the technology According to the figure 2.1, improvements in technology may allow the output curve to shift upward from 0 1 to

02 and then 03

Figure 2.1: The effect of technology improvement

Output per time period

c

Labor per time Source: Pindyck and Rubinfeld, 1992

2.3.2 R&D Capital Model

According to Griliches (2000), the R&D capital model is still the most important research method today in estimating the effects of R&D on productivity growth, in spite of its many weaknesses It is a simple and easily-applied model that enables us

to estimate the rate of return to R&D and then to measure its contribution to productivity growth Most of applied studies are based on it We can study many different forms of R&D capital such as private, public, and R&D done by neighboring

firms or industries The first, direct approach IS represented by the equation as follows:

LogY= a(t) + fJlog X+ y log K + u (2.2)

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 is one or more measures of cumulated research effort or "knowledge capital";

a(t) indicates other factors that affect output and change systematically over time;

u reflects all other random fluctuations in output

This equation is taken in logarithmic form from the Cobb-Douglas production function It is the first approximation to represent a potentially much more complex relationship In this first equation, y, the elasticity of output with respect to research capital, is focused to be estimated R&D capital is often calculated by a weighted sum

of past R&D expenditures with the weights reflecting both the potential delays in the impact of R&D on output and its possible eventual depreciation

In the second approach, growth rates are used to replace levels and the above equation becomes as follows:

~Log Y = a(t) + f3 Mog X + p (RJY) + ~u (2.3), where

~ denotes a time difference;

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;

In this form, the growth rate of output or productivity is related to the intensity (R/Y) of the investment in R&D or some more general measure of investment

in science and technology

In the application of this model, there are a number of conceptual difficulties First, it

is difficult to measure output and output growth accurately in science and technology sectors conceptually Second, the construction of R&D capital variable may also face issues of timing, depreciation and coverage and others The biggest problem with this model may be that it treats R&D and science as another kind of investment However, investing in the creation of knowledge is not similar to buying a machine or building a plant It is quite difficult to measure the results of such activities Nevertheless, this simple model is conveniently a starting point to examine empirical works in this area and applicable to our problem if we are able to consider their conceptual and data problems

With reference to econometric issue on applying this model, Griliches (2000) stated that there is simultaneity problem referring to possible confusion in causality: "future output and its profitability depend on past R&D, while R&D, in turn, depends on both past output and able to build a system of equations in which current output depends

on past R&D, and past R&D depends on past output" However, with cross-sectional data, it is much more difficult to make such distinctions

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 = f [L, K, R, CHAR(SIZE, OWNERSHIP)] (2.4)

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

R denotes measures of R&D capital

CHAR is considered as some characteristics of firm which affect its

productivity such as size of labor or type of ownership

2.4 EMPIRICAL STUDIES

2.4.1 Overview

Being aware of the importance of research and development, many analysts have examined the relationship between R&D expenditure and productivity growth at firm level As a result, there is a great number of empirical studies estimating the impact of R&D investment on such growth According to CBO (2005), the results of such relationship spread a wide range Some researches have found that R&D virtually has

no effect on productivity Whereas, other studies have discovered that R&D's effect is substantial and larger than effect of other kinds of investment However, most of the estimates lie somewhere between the two extremes, therefore, there is an agreement with the view that the relationship between R&D spending and productivity growth is positively significant

Mairesse and Sassenou (1991) conducted a research which surveys econometric studies examining the relationship between R&D and productivity at the firm level and assesses the results as well as problems encountered According to those authors, the Cobb-Douglas production function is the basic analytical framework used by most econometric studies that estimate the contribution of R&D on productivity growth In addition to such usual factors of production as labor, physical capital, materials and so

on, a measure of R&D capital is also included in the function as 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 ( 1991) stated that econometricians try to simplify phenomena which are often highly complex ones This is especially true with R&D activities and their impacts on productivity They said that "R&D effects are intrinsically uncertain, they often happen with long lags, they may vary significantly from one firm or sector to another and change over time" The effects of other factors of productivity that happen simultaneously and have domination may make R&D effects to be hidden If serious problems in measuring variables and collecting good data are ignored, it is difficult to build up a production function between R&D and productivity Therefore, the authors were surprised to find out that in most studies, estimates of the R&D elasticity or R&D rate of return are statistically significant and frequently plausible

The above are what CBO (2005) and Mairesse and Sassenou ( 1991) found out when reviewing and synthesizing related studies However, the three case studies below will help to investigate further the relationship between R&D and productivity in practice

2.4.2 R&D and Productivity in French manufacturing firms

Cuneo and Mairesse (1983) investigated if there is a significant relationship between R&D expenditures and productivity performance at the firm level in French manufacturing industry for the period 1972 - 1977 The sample including 182 firms is divided into two sub-samples: scientific firms which belong to the R&D intensive industries such as chemicals, drugs, electronics and electrical equipment and other firms in other manufacturing industries The basic model used in this research is the simple extended Cobb-Douglas production function, which can be written in logarithmic form as follows:

(2.5)

Where i, t refer to the firm and the current year; e is the error tenn in the equation; v,

c, 1 and k stand for production (value added), physical capital, labor, and R&D capital, respectively; Jl = a + ~ + y is the coefficient of returns to scale; and 'A is the rate of disembodied technical change

In this study, production is measured by deflated value-added V rather than by deflated sales Labour L is measured by the number of employees, physical capital stock C by gross-plant adjusted for inflation R&D capital stock K is calculated by the weighted sum of past R&D expenditure which use a constant rate of obsolescence of

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 Thus, the available number of R&D employees is simply subtracted from the total number of employees Whereas, the part of physical capital stock used in R&D is calculated based on the average ratio of the physical investment component of R&D expenditures to total R&D expenditures and is also subtracted However, in the practice of Vietnam, because having full financial statements of examined firms is very difficult, it is impossible to separate the part of physical capital in R&D expenditure from the total physical capital stock

The authors finally come up with discrepancies between the total and within-firm estimates of the two main parameters: the elasticity of physical capital stocks (a) and R&D capital stocks (y) However, due to good measures of the variables, the problem

is much less serious than it could have been, and in general the estimates are statistically significant and likely high Besides, in order to find out further results, the authors used sales instead of value added and included and excluded materials M in tum in the production function The total estimates using sales and omitting materials

do not differ much from those obtained with value added The within-firm estimates with sales instead of value added are also similar when constant returns to scale is imposed However, if constant returns to scale is not imposed, large discrepancies between the total and within-firm estimates occur The within-firm estimates are

much improved when materials are taken into consideration Hence, the omission of materials in the sales specification affects especially the within-firms estimates

2.4.3 R&D and Productivity Growth in Japanese manufacturing firms

Kwon and Inui (2003) conducted a research to examine the relationship between the R&D and the productivity improvement in Japanese manufacturing firms In this research, they estimated a Cobb-Douglas production function with three inputs: labor, physical capital and knowledge capital for more than 3,000 Japanese firms for the period 1995-1998

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

On investigating the contribution of the R&D to the productivity growth of Japanese manufacturing firms, Kwon and Inui (2003) used two approaches: Production Function Approach and The Rate of Return to R&D Approach In the production function framework, the disadvantage is that possible bias is allowed due to simultaneous output and input decisions, and the advantage is that the assumptions of competitive factor markets, cost minimization, and constant returns to scale are avoided In this approach, the relationship between R&D and productivity growth is represented in the regression function using first-differences as follows:

Where: Y denotes the value added, K as the physical capital stock, L as the labor input, and R as the knowledge capital stock A-t is the time-specific variable and the rate of disembodied technical change The subscripts i and t denote the firm and the year respectively Here, 1 - a > f3 is assumed to maintain a positive marginal product

of labor r is a scale parameter r implies increasing returns to scale if it has a positive

value, and decreasing returns to scale if it has a negative value

In the second approach, K won and Inui estimated the contribution of R&D to productivity by estimating the rate of return to the R&D rather than the elasticity of value added with respect to R&D The advantage of this approach is that it can avoid the measurement problem of the R&D capital stock The relation between the growth

of labor productivity and the level of R&D intensity can be written as the following equation:

where E is the R&D expenditures of firm i in period t

In both approaches, the study found a positive and significant effect of R&D expenditure on productivity growth and this effect is different by the firms' sizes and characteristics of technology The R&D elasticities are higher for the large sized and high-tech firms than they are for other types of firms Besides R&D capital, the physical capital stock also significantly affects labor productivity growth Moreover,

an industry effect is found not important in explaining productivity differences among firms

2.4.4 The effect of R&D Capital on Danish Firm Productivity

Unlike the two above studies using time series data on analysis, this paper analyses the importance of R&D for Danish private firm productivity on the basis of cross-section data It was conducted by Graversen and Mark (2005) This report aims to

identify the return to R&D capital rate and other related factors that have influence on firm productivity growth The data used in this research is drawn from the official Danish R&D Statistics 200 1 conducted by the Danish Centre for Studies in Research and Research Policy The sample contains more than 2200 firms with positive R&D among 18.381 firms in 2001 and it represents broadly all Danish private sector firms with more than 9 employees The analysis is based on a logarithmic version of the Cobb-Douglas production function where productivity is estimated as a function of independent variables as follows:

Productivity= f (R&D Capital, Assets, Labour, Business Sector, Size)

Even though other variables are measured in 2001, R&D capital is calculated from the firms' R&D cost in the past, and R&D costs are accumulated, deflated and depreciated Like earlier researches, the study also shows the major results that the return to R&D capital is highly and positively significant and it increases when the firms have research educated employees The value added per employee of R&D active firms is 40 percent higher than that of R&D inactive firms Moreover, each 10 percent increase in R&D capital among the R&D active firms leads to 1 percent increase in the value added

2.5 SUMMARY

There are many analysts so far have attempted to assess the contribution of R&D to firm productivity growth and their analytical framework is mainly based on the Cobb-Douglas production function However, a more complex equation in which R&D capital and some characteristics regarding technology, size, ownership are added as an explanatory variable and dummy variables, respectively is widely used According to Mairesse and Sassenou ( 1991 ), it is a difficult task to formulate the relationship between R&D and productivity due to problems in measuring variables and limitation

of the data collected Nevertheless, the result in most studies is quite surprising that

the impact of R&D expenditure on productivity growth is positively and statistically significant

On the basis of economic theories, this research is going to estimate the relationship between productivity of manufacturing firms in Vietnam and its main determinants such as labor, physical capital, R&D capital, and some characterilstics of firm: namely size of labor and type of ownership Like many other studies, the regression equation

in this research is mainly based on the Cobb-Douglas production function and the R&D capital model Moreover, the production function approach stated in the empirical study of K won and lnui (2003) is also applied in this research

CHAPTER3 OVERVIEW OF R&D AND FIRM PERFORMANCE IN

3.2 R&D ACTIVITIES IN VIETNAM

As mentioned in the rationale of the research, in comparison with OECD countries and neighbors, Vietnam spent a rather little amount on R&D activities In 1996, Vietnam spent approximately 0.3% of its GDP on R&D (figure 3.1) and this number increased to about 0.5% in 2003 But most of such expenditure was financed by the Government, namely 80% in 2002 This is different from OECD countries, where most of R&D expenditure was financed by companies, say about 70% in 2002 (figure 3.2)

Figure 3.2: Expenditure on R&D by Government and Business sector in 2002 100%

Figure 3.3: Sector-wise R&D Expenditure in Vietnam in 2002

3% 1%1%

Source: Nguyen (n.d.)

According to Bezanson et al., (2000), NISTPASS3 provided statistic nwnbers which show that total R&D spending per full-time researcher has been declining sharply since reforms under "doi moi" was introduced For instance, the per labor R&D expenditure decreased from US$687 in 1987 to US$289 in 1990 Even though these estimates may encounter some error, such figures were much lower than that of other East Asian countries, namely US$135,000 in Japan or more than US$50,000 in Korea and Singapore NISTP ASS also said that in the early of 1990s, the annual expenditure

on research facilities per full-time researcher was around US$50 Moreover, the number of Vietnamese researchers having opportunities to do with experimental equipment, which is in the same quality with those of most East Asian countries, is approximately 10% Regarding R&D personnel per thousand of total employees, according to Nguyen (n.d.), that number of Vietnam was 0.59 in 2002, higher than Thailand or India, but still much lower than other East Asian countries such as 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

3.3 STRUCTURE OF THE R&D SYSTEM IN VIETNAM

Bezanson et a!., (2000) stated that the structure of the R&D system in Vietnam

consists of three main components The first one is laboratories and other R&D institutes of line ministries or government agencies There are approximately 180 R&D units of this sort in many provinces of Vietnam Except for some state-owned large corporations such as Petro Vietnam, which runs its own labs, Vietnamese industrial enterprises seldom do research & development work by themselves and have little experience on it The second component is universities and colleges However, the number of university faculties which have sufficient resources for performing R&D activities is rather tiny Most Vietnamese universities and colleges lack of necessary personnel, equipment, libraries and other resources to do the task And the last one is national research centers (or academies) that are under control of the Government Office Of which, the National Center for Natural Science and Technology (now is Vietnamese Academy of Science and Technology) is the most significant

It is expected that the three main above components have close connections with each other Each one is assigned different function Research institutes of individual ministries are assigned to do applied research and experimental development4;

whereas, universities and colleges are the main providers of R&D human resources Vietnamese Academy of Science and Technology is mainly responsible for performing advanced basic research

However, according to Nguyen and Tran (n.d.), the quality of research infrastructure

of Vietnam is lower than international standards The tendency of researches carried out is theoretical, supply-driven and does not meet the manufacturing sector's demands Most of R&D activities in Vietnam are performed in research institutes of ministries and national research centers, instead of universities Moreover, most R&D, which is publicly funded, is carried out in research institutes of the government In Vietnam, there is a small fraction of R&D activities financed by the state budget In general, "the national R&D system is organized, financed and managed in such a way

that technology transfer is difficult and expensive" (Bezanson et al., 2000 cited in

Nguyen and Tran, n.d.)

Table 3.1: Science & Technology Organizations in Vietnam by 31 Dec 2003

Source: MOST5 (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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5

MOST: Ministry of Science and Technology