Effect of rd on the productivity of construction enterprises in vietnam

This study sets out to estimate the relationship between R&D activities and productivity growth of construction firms in Vietnam to answer the following questions:... Table 2.1: Overview

VIETNAM -NETHERLANDS PROGRAMME FOR M.A

IN DEVELOPMENT ECONOMICS

EFFECT OF R&D ON THE PRODUCTIVITY

OF CONSTRUCTION ENTERPRISES IN VIETNAM

IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE DEGREE OF MASTER OF ARTS IN DEVELOPMENT ECONOMICS

BY

DO KHAC THANH

SUPERVISOR

Dr NGUYEN HUU DUNG

Ho Chi Minh City, December 2011

•

-ACKNOWLEDGMENT

I am indebted to numerous individuals but I cannot name them here all First of all,

I would like to thank all the teachers and staff of the Project for their valuable suggestion, good learning facilities and warm attitudes during my school time My deepest gratitude goes to my supervisors, Dr Nguyen Hu Dung, and Dr Nguyen Trong Hoai for their valuable comments and instructions concerning my thesis

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

The study aims to examine the link and relationship between productivity of construction enterprises in Vietnam and R&D expenditure 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 A sample of 264 construction firms was drawn from the data set of Vietnam Enterprise Survey conducted by the General Statistics Office in 2005 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, dummy and R&D capital, and labor variables reflecting type of ownership and size of labor

Research findings show that 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 0 Moreover, the effects of physical capital and labor on productivity are also positively and statistically significant The elasticity of productivity with respect to physical capital per labor and total labor are around 0.35 and 0.15, respectively

ACRONYMS

CBO Congressional Budget Office

GDP Gross Domestic Product

MFP Multifactor Productivity

Ministry of Science and Technology MOST

NACE Classification of Economic Activities in the European Community

National Institute for Science and Technology Policy & Strategy NISTPASS

Research & Development Small and Medium Enterprise Vietnam Enterprise Survey Vietnamese Dong

No Date Variance Inflation Factors

- - - -

-CHAPTER 1 INTRODUCTION

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 Progress of technology has a quite central role in the modem economy today It contributes importantly to growth of economy and is a key factor to determine the competitiveness of firms in both national and international marketplace 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 (OSTP11997)

Those stocks of knowledge can be increased by formal investment in R&D activities In the public and private sectors, the allocation of resources toward the investment to generate new knowledge must be decided carefully There are many sources for productivity improvements, but one strategy for enhancing productivity growth which is widely acknowledged is increasing the stock of knowledge

Following 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 the most important reason for a little investment in R&D activities of Vietnamese enterprises may be their limitations in financial resources Moreover, that there is little market-oriented relationship between firms, R&D institutions and universities (Bezanson et al., 2000)

If R&D has any relationship with productivity of construction finns, the case of Vietnam will increase a doubt Specially, Role of knowledge or technological capital for productivity growth is emphasized by some empirical research at enterprise level Those studies only focused on investment of R&D and appeared that in many countries It proved that R&D has

a contribution to productivity growth significantly, pm1icularly in the cross sectional dimension However, the conclusion is not suitable for Vietnam

Those 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 Neve11heless, 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) R&D expenditure of Vietnamese enterprises accounted for only about 20% of the total R&D expenditure of the country in 2002 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%

(R&D) investment has been regarded as an important factor in the improvement of productivity levels of firms For exainple, the average annual rates ofR&D expenditure relative to gross domestic product in the US and Japan are around 2.64 per cent and 3.04 per cent, respectively (National Science Council, 2001: 195) This has been proved true by many empirical studies for many countries but neglected for Vietnainese case

From 1960s, investment of R&D has played an important role in improvement of

productivity levels Creation and accumulation of that knowledge is rationale factors via the R&D efforts of a firm or industry , this will be available to the production process or product innovation (Mansfield, 1965; 1969) Those investments will lead to promotion of nationwide economic development Therefore, many the advanced countries based on this rationale to invest expenditure on R&D activities This study sets out to estimate the relationship between R&D activities and productivity growth of construction firms in Vietnam to answer the

following questions:

The thesis used such methods as descriptive statistics, quantitative analysis and OLS regression to deal with the research questions The thesis studied the impacts of R&D expenditure to productivity growth of Vietnamese construction firms by using data from the Vietnam Enterprise Survey 2005

Chapter 4: Research Jvfethodology focuses on model specification and variables choices justification Chapter 5 is the practical results are analyzed via descriptive statistics and regression analysis: Result Analysis Finally, conclusions and policy recommendations are provided in Conclusions and Recommendations chapter

CHATPER2 LITERATURE REVIEW

This chapter will make sure that the research is conducted based on a scientific background Through this chapter, impact of R&D expenditure on productivity growth is generally figured out on the basis of economic theories and empirical studies The chapter will be presented in four main parts In the part one, key concepts related to the topic such as R&D, productivity as well as construction 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, there will have empirical studies regarding effect of R&D on productivity growth of construction firms in some countries are discussed in the last part

2.1 CONCEPTS

R&D has been divided into three categories: basic research, applied research and experimental development 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"

>- Experimental development is defined as systematic work that usmg current knowledge That current knowledge gained from practical and research experience These research and experience is directed producing new products and devices, materials; installing new processes, systems or services; or substantially improving at the future in which they has been produced or installed in the past

o Basic research is an experimental or theoretical jobs which ensured not to gain benefits

in long-term However, they must be advance the state of knowledge (CBO, 2005) In those researches, characteristics, structures and relationships are analyzed with a view

to test and formulate theories and laws Moreover, outcome of basic research are not for buy or sale, however usually for publishing in usage of interested people scientific journals Sometimes, it may be kept secret for security reasons

o Applied research is also original work that is unde1iaken 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

Applied research is the investigation that is performed for the development of government policy Basic research is the theoretical investigation of factors which have influence on regional variations in economic growth Experimental development is the development of operational models based on laws with the purpose of modifying regional variations

The measurement of such two kinds of R&D expenditures is so complicated with many costs should be included or excluded "Expenditure on R&D may be made within the statistical unit or outside it" (OECD, 1994, page 20) However, in this thesis, R&D expenditure used to examine its effects on productivity growth of Vietnamese construction firms is available in the Vietnam Enterprise Survey

There are different meanings in different contexts for the word "innovation" depending on certain objectives of measurement or analysis 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 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

2

See Appendix 1 for explanation of innovation process

inventive ideas or a form of problem-solving (OECD, 1994) According to Rogers (1998), R&D is an important input measure of innovation

2.1.2 Productivity

OECD (2001, page 11) defines "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 many different ways That means there are many purposes and many ways to measure productivity The objectives of productivity measurement can be stated as follows:

Productivity growth is also calculated to identify changes in efficiency that is basic different from technical change Full efficiency in an engineering level means that with given a fixed amount of inputs and physically achievable with cunent technology a production process has achieved the maximum amount of output

In the industries of business economics, comparisons of productivity measures for specific

A regularly stated objective of measuring productivity growth is to monitor technical change

There is a real way to show the essence of measured productivity change is to identify

real cost savings in production

In the industries of business economics, comparisons of productivity measures for

specific production processes can help to identify inefficiencies

Measurement of productivity is a key factor to estimate the standard of living

Productivity is measured in many different methods that depend on the purpose of productivity measurement or the availability of data Calculation of productivity can be divided into two ways: single factor productivity measures and multifactor productivity measures Multifactor productivity relates a measure of output to a bundle of inputs, whereas, Single factor productivity relates a measure of output to a single measure of output 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

intermediate output

I

materials, services)

Source: OECD, 2001

In this thesis, labor productivity is used to measure because it will relate to the most important factor of production labor and is relatively easy to measure Gross-output based labor productivity, which is a ratio of quantity index of gross output to quantity index of labor input, is used to measure productivity

2.1.3 Definition of Construction sector

Construction sector is the process by which material, equipment, machinery are assembled into a permanent facility

It reacts rapidly to external economic pressures, tight money or national recesswn Widerange of activities, methods and manufacture, remote site with changing conditions

Construction contractor often enjoys high incomes but due to the competitive nature, result in

a high bankruptcy rate Persoru1el are not permanent and skilled workers earn high wages, but due to interruption of season reduce the annual income of money Compose of large number

of independent suppliers and contractor

2.2 ECONOMIC THEORIES

2.2.1 Production theories

Cobb-Douglas Production Function

Based on Pindyck and Rubinfeld (1992), the Cobb-Douglas production function is a used approach to represent the relationship between inputs in microeconomics and an output Knut Wicksell (1851-1926) proposed the function in the period 1851-1926, and then in 1928 Paul Douglas and Charles Cobb tested it against statistical evidence The production function has the general form as follows:

widely-Q = ALaxfJ (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

and ordinarily smaller than one because the fact that the marginal product of each input diminishes when that factor increases

For example, if a = 0.15, a 1% increase in labor would lead to approximately a 0.15% increase in output Furthermore, if a + B = 1, the production function exhibits constant returns to scale If a + B < 1, there are decreasing returns to scale, and if a + B > 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 + B = 1 Y increases more than and less than 20% when a + B < 1 and a + B < 1, respectively Output elasticity measures the responsiveness of output to a change in levels of either labor or capital used in production, ceteris paribus The Cobb-Douglas production function written in logarithmic form is as follow: log Q = log A + a log

L + ~ log K This form is useful when performing a regression analysis by OLS (linear in log form of parameters)

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

Here, the Cobb-Douglas production function will illustrate a way to measure production functions The possibility is that the firm's production process shows increasing returns at low output levels, constant returns at intermediate output, and decreasing returns at high output levels However, it is often replaced by other more complex production functions in industry studies for some reasons

The Law of Diminishing Returns

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 Pindyck and Rubinfeld (1992, page 1 0) stated the law of diminishing returns 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" 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 returns

In the analysis of production, we have to assume that the quality of all labor input are the same Diminishing returns 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 returns with negative returns In the law of diminishing return, a declining marginal product is described, not a negative one The law of diminishing returns 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 returns 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

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 01 to 02 and then 03 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

Figure 2.1: The effect of technology improvement

Output per

time period

c

Labor per time

Source: Pindyck and Rubinfeld, 1992 (Page 1 0)

2.2.2 R&D Capital Model

We can study many different forms of R&D capital such as private, public, and R&D done

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

Log Y = a(t) + fJ log 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

It is the first approximation to represent a potentially much more complex relationship This equation is taken in logarithmic form from the Cobb-Douglas production function 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

Growth rates are used to replace levels and the above equation in the second approach becomes as follows:

L\Log Y = a(t) + fJ L\log X + p (R/Y) + L\u (2.3), where

p is interpreted as the gross rate of retum to investment in 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

L\ denotes a time difference;

The term yL\log K is simplified as follows:

p = dY/dK = y(Y/K), L\log K = R/K, yL\log K = RIK*p*(KI}j

R is the net investment in K, net of the depreciation of the previously accumulated R&D capital;

There are a number of conceptual difficulties in the application of this model 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

Griliches (2000, page 70) 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

2.2.3 Suggested research model from economic theories

The relationship between firm productivity and its detem1inants can be described in a function with dependent and independent variables based on the above economic theories, 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 firm

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

There are many empirical studies estimating the impact of R&D investment on such influence According to CBO (2005) and (Economic Inquiry, vol 29 (April), pp 203-228), the results of such relationship spread a wide range So 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 Some researchers 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 significant positively

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

Maire sse and Sassenou 1991 (pp 346) said that econometricians try to simplify phenomena which are often complex ones on viewing problems encountered as mentioned above highly; this is especially true with R&D activities and their effects on productivity They talk 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 impacts of other

-factors of productivity that happen simultaneously and have domination may make R&D effects to be hidden It is difficult to build up a production function between R&D and productivity if serious problems in measuring variables and collecting good data are ignored 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

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

This paper analyses the importance of R&D for Danish private firm productivity on the basis

of cross-section data Unlike the two above studies using time series data on analysis, 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 2001 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 production is estimated

as a function of dependent variables as follows:

Productivity = f (R&D Capital, Assets, Labor, 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

2.3.3 R&D and Productivity in French construction firms

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 construction industries Cuneo and Mairesse (1983) investigated if there is a significant relationship between R&D expenditures and productivity performance at the firm level in French construction industry for the period 1972 - 1977 The basic model used in this research is the simple extended Cobb-Douglas production function, which can be written in as follows:

(2.5)

Where i, t refer to the firm and the current year; e is the error term in the equation; v, c, 1 and

k stand for production (value added), physical capital, labor, and R&D capital, respectively;

f.l = a + 0 + 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 Labor 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 uses 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 tind 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 \vithin-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-fi1ms estimates

2.3.4 R&D and Productivity Growth in Japanese construction firms

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 construction sector which had positive R&D expenditures from 1995 to 1998 Those firms all have no less than 50 employees and 30 million yen of capital and are grouped into 22 construction industries based on their main business activities

Kwon and Inui (2003) conducted a research to examine the relationship between the R&D and the productivity improvement in Japanese construction 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

On investigating the contribution of the R&D to the productivity growth of Japanese construction finns, 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 r~presented 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 }.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 y is a scale parameter y

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 measured the contribution of R&D to productivity by estimating the rate of return to the R&D They haven't measure the elasticity of value added with respect to R&D there is a advantage of this approach is that it can avoid the measurement problem of the R&D capital stock The relation between the level of R&D intensity and the growth of labor productivity 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 eflect of R&D expenditure on productivity growth and this effect is diflerent by the firms' sizes and characteristics of technology The R&D elasticity 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

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 according to Mairesse and Sassenou (1991 ) 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 Nevertheless,

the result in most studies is quite surpnsmg that the impact of R&D expenditure on productivity growth is positively and significant statistically

This research is going to estimate the relationship between productivity of firms and its main determinants such as labor, physical capital, R&D capital, and some characteristics of firm, namely size of labor and type of ownership on the basis of economic theories 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 However, because limitation of data, I base on functional form of the production function approach stated in the empirical study of K won and Inui (2003) is also applied in this research with cross sectional data

CHAPTER3 OVERVIEW OF R&D AND FIRM PERFORMANCE IN VIETNAM

The purpose of this chapter is to present the overview of R&D activities in Vietnam in general and R&D activities of firms in particular so that the research is analyzed based on a reality background This chapter consists of three main parts First, part one talks about R&D activities in Vietnam and compared with other countries Structure of the R&D system in Vietnam is stated in the next part The final part will discuss how R&D institutions are linked with the productive sector

In 1996, Vietnam spent approximately 0.3% of its GDP on R&D and this number increased

to about 0.5% in 2003 (figure 3.1) Vietnam spent a rather little amount on R&D activities as mentioned in the rationale of the research, in comparison with OECD countries and neighbors 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 30% in 2002 (figure 3.2)

Figure 3.1: Percentage of GDP spent on R&D in 1996

Countries

Source: Nguyen (2006)

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

· • · GOVERD: ~xpt:n~jture otl.R&Q financed by Government

, ,i·( • · BJ!:RD: expenditure ~n R&D finanCed by B.usiness sector

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

Ell Direct Government • Business enterprises o Funcls from nbroacl

0 Hlgl-1er Eclucation • Private non-profit D ott1ers

·

-under "doi moi" was introduced For instance, the per capita 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

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

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 J apfu1, Korea, Singapore and China

According to Bezanson et a!., (.2000), the structure of the R&D system in Vietnam consists of

three main components There are approximately 1 80 R&D units of this sort in many

-i

provinces of Vietnam The first one is laboratories and other R&D institutes of line ministries

or government agencies Except for some construction 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 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, However, the number of university faculties which have sufficient resources for performing R&D activities is rather tiny

The tendency of researches carried out is theoreticaL supply-driven and does not meet the construction sector's demands However, according to Nguyen and Tran, the quality of research infrastructure of Vietnam is lower than international standards 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)

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

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

Source: MOST5 (2005), cited in Nguyen and Tran (n.d.)

INSTITUTIONS

It is necessary to know the nature of innovation in Vietnam in order to understand more about this issue, R&D is one part of innovation6 In the study of Nguyen and Tran, they said that Vietnam is a transitional and developing country, therefore, its innovation environment differs from that in developed countries External factors have much influence on innovations

of Vietnamese fi1ms Vietnamese innovation environment comprises some main features as follows:

Markets for technical and innovation services are under development; and so on Most Vietnamese firms' competition is mainly based on the availability of natural resources and access to cheap labor There are few enterprises whose competition are based on the background of new technology or ditierentiated 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 national and local levels There are limited public resources for R&D and supports for innovation

Nguyen and Tran (n.d.) also said in their report that "innovations in Vietnam are either incremental or new to the finns" Incremental innovations occur when enterprises attempt to deal with certain technical problems originating from the run of imported produCtion line or when they attempt to make new products by the existing machineries The circumstance when firms purchase a whole or parts of a production line to make new products is called

"new to firms" innovations

5 MOST: Ministry of Science and Technology

6 Definition ofOECD, 1994 This was discussed in chapter 2