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

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UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

!

INSTITUTE OF SOCIAL STUDIES
THE HAGUE
THE NETHERLANDS

VIETNAM- NETHERLANDS PROJECT ON DEVELOPMENT ECONOMICS

IMPACT OF R&D ON THE PRODUCTIVITY
GROWTH OF MANUFACTURING FIRMS
IN VIETNAM

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By

Duong Thi Phuong Ngoc

Academic supervisor: Dr. Vo Van Huy

TRUONG £),b,l HOC I"INH TE TP.HCM

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CHUONG TRINH HQ~ 1A~ £)~O }~ 0
CAO HQC KINH TE PHAT TRIEN
VI~T NAM- Hf> LAN (UEH-ISS!

Ho Chi Minh City, November, 2008


CERTIFICATION
I certify that the substance of this thesis has not already been submitted for any degree
and is not being currently submitted for any other degrees.

I also certify that, to the best of my knowledge, any help received in preparing this
thesis, and all sources used, have been acknowledged in the thesis.

Signature

Duong Thi Phuong Ngoc
i


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.


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
i

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

i

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
Production theories ...................................................................................... 9
2.3.1.
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
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

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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

•


ACRONYMS

CBO

Congressional Budget Office

EU

European Union

GDP

Gross Domestic Product

MFP

Multifactor Productivity

MOST

Ministry of Science and Technology

NACE

Classification of Economic Activities in the European Community

NISTPASS National Institute for Science and Technology Policy & Strategy Studies
OECD

Organization for Economic Cooperation and Development

OSTP

Office of Science and Technology Policy

R&D

Research & Development

SME

Small and Medium Enterprise

VES

Vietnam Enterprise Survey

VND

Vietnamese Dong


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
1

public and private R&D investments during the last two decades (OSTP , 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

1


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

2


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?

1.3.

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.

1.4.

THESIS STRUCTURE

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

3


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.

4

CHATPER2
LITERATURE REVIEW
2.1.

INTRODUCTION

This chapter aims at reviewing literature related to the topic to make sure that the
research is conducted based on a scientific background. The chapter will be presented
in three main parts. In the part one, key concepts related to the topic such as R&D,
productivity as well as manufacturing will be discussed. Economic theories
supporting for the study are found out and stated in the next part. At the end of this
part, a research model which represents factors affecting productivity is suggested.
Finally, empirical studies regarding effect of R&D on productivity growth of
manufacturing firms in some countries are discussed in the last part. Through this
chapter, impact of R&D expenditure on productivity growth is generally figured out
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

5


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

6


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 process 2 • 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.

7


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
Type of
output
measure

Gross
output

Value
added

Labor

Capital

Labor
productivity
(based on gross
output)
Labor
productivity
(based on value
added)

Capital
productivity
(based on gross
output)
Capital
productivity
(based on value
added)

Single factor productivity measures

Capital and labor

Capital, labor and
intermediate inputs
(energy, materials,
services)

Capital-labor MFP
(based on gross
output)

KLEMS multifactor
productivity

Capital-labor MFP
(based on value
added)

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

8


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,

9


------------------- -- - -

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

Q=ALaJ
(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

10


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.

11


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
0 2 and then 03.

Figure 2.1: The effect of technology improvement

c

Output
per
time
period

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

12


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;

13


-

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)

14


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.

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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)

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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

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