Innovation and productivity of SMEs in vietnam firm level panel data evidence

UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

INSTITUTE OF SOCIAL STUDIES
THE HAGUE
THE NETHERLANDS

VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

INNOVATION AND PRODUCTIVITY OF
VIETNAMESE SMALL AND MEDIUM
ENTERPRISES
FIRM LEVEL PANEL DATA EVIDENCE
BY

HO THI MAI ANH

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, December 2013


UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

INSTITUTE OF SOCIAL STUDIES
THE HAGUE
THE NETHERLANDS

VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

INNOVATION AND PRODUCTIVITY OF
VIETNAMESE SMALL AND MEDIUM
ENTERPRISES
FIRM LEVEL PANEL DATA EVIDENCE

A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By

HO THI MAI ANH

Academic Supervisor:

Dr. PHAM DINH LONG

HO CHI MINH CITY, December 2013


DECLARATION

This is to certify that this thesis entitled “Innovation and Productivity of
Vietnamese Small and Medium Enterprises: firm level panel data evidence” which is
submitted in fulfillment of the requirements for the degree of Master of Art in
Development Economic to the Vietnam – The Netherlands Programme. The thesis
constitutes only my original work and due supervision and acknowledgement have been

made in the text to all material used.


CERTIFICATION

ii | P a g e


ACKNOWLEDGEMENT

Above all, I would like to thank and gratefully express the special appreciation to
my supervisor - Dr. Pham Dinh Long for all of his guidance, useful recommendations and
valuable comments. I could not be able to complete this thesis without his help and
support.
I would like to acknowledge the great works from the Vietnam – The Netherlands
Programme team, especially all of lecturers who have put their enthusiastic into the
lectures for students. Personally, I would like to thank Dr. Truong Dang Thuy and Dr.
Pham Khanh Nam, who have greatly supported me in the courses and in thesis writing
process.
Special thanks go to my family, friends and colleagues for their motivation and
encouragement during my study in this program.

iii | P a g e


ABBREVIATIONS

SME

Small and Medium Enterprise

CDM

Crepon Duguet Model

CIEM

Central Institute of Economic Management

FE

Fixed effect model

GDP

Gross Domestic Products

GSO

General Statistics Office of Vietnam

HCMC

Ho Chi Minh City

ISIC

International Standard Industrial Classification

ILSSA

Institute of Labor Science and Social Affairs

MFP

Multi Factor Productivity

OECD

Organization for Economic Co-operation and Development

R&D

Research & Development

RE

Random effect model

SFA

Stochastic frontier analysis

TFP

Total Factor Productivity

WTO

World Trade Organization

iv | P a g e


ABSTRACT

Innovation is considered an essential factor for motivating the productivity of
nations and firms. Innovation and productivity are connected by multidimensional
relationships and investigated in many countries. However, there is very limited research
in this field for Viet Nam. This paper examines the relationship between innovation and
productivity of Small and Medium Enterprises (SMEs) by using Viet Nam SMEs survey
balanced panel data in 2007 and 2009. Cobb-Douglas production function and the fixed
effect model are employed throughout the thesis. The author has found that the presence of
innovation has positive effects on a manufacturing firm’s productivity. In addition, this
study also looks at the impact of firm size, firm location and manufacturing sector on the
relationship between innovation and SMEs’ productivity.
Key words: innovation, productivity, SMEs Viet Nam

v|P a g e


TABLE OF CONTENTS
LIST OF TABLES ................................................................................................................................. VIII
LIST OF FIGURES ................................................................................................................................. IX
CHAPTER 1
1.1
1.2
1.3
1.4
1.5

INTRODUCTION .......................................................................................................... 1

PROBLEM STATEMENT ...................................................................................................................... 1

RESEARCH OBJECTIVES .................................................................................................................... 2
RESEARCH QUESTIONS ..................................................................................................................... 2
SCOPE OF THE STUDY ........................................................................................................................ 3
STRUCTURE OF THE STUDY ............................................................................................................... 3

CHAPTER 2

LITERATURE REVIEW ............................................................................................... 4

2.1 PRODUCTIVITY: CONCEPTS AND MEASUREMENTS ............................................................................. 4
2.2 INNOVATION: CONCEPTS AND MEASUREMENTS ................................................................................. 6
2.3 RELATIONSHIP OF INNOVATION AND PRODUCTIVITY ........................................................................ 8
2.3.3 EMPIRICAL REVIEW OF INNOVATION AND PRODUCTIVITY RELATIONSHIP ...................................... 10
2.3.4 DETERMINANTS OF THE INNOVATION IMPACT.................................................................................. 13
2.3.5 INNOVATION AND FIRM PRODUCTIVITY IN VIET NAM ...................................................................... 16
2.3.6 CHAPTER REMARK ............................................................................................................................. 16
CHAPTER 3
3.1
3.2
3.2.1
3.1.2
3.3
3.4
3.5
3.6

3.6.1
3.6.2
3.6.3
3.7

RESEARCH METHODOLOGY AND DATA ............................................................. 17

AN OVERVIEW OF SMES IN VIET NAM ............................................................................................. 17
CONCEPTUAL FRAMEWORK AND MODEL SPECIFICATION ................................................................ 23
CONCEPTUAL FRAMEWORK ......................................................................................................... 23
MODEL SPECIFICATION ................................................................................................................ 23
RESEARCH HYPOTHESES ................................................................................................................. 26
DEFINITIONS OF VARIABLES AND CONCEPTS ................................................................................... 26
DATA COLLECTION ......................................................................................................................... 29
METHODOLOGY .............................................................................................................................. 29
RANDOM EFFECT REGRESSION MODEL (RE)................................................................................... 30
FIXED EFFECT REGRESSION MODEL ................................................................................................ 30
SELECTION BETWEEN RE AND FE MODEL BY HAUSMAN TEST ....................................................... 30
MEASUREMENTS OF VARIABLES ..................................................................................................... 31

CHAPTER 4

DATA ANALYSIS ........................................................................................................ 34

EMPIRICAL RESULTS ...................................................................................................................... 34
CHAPTER REMARK ................................................................................................................................... 41
4.1

vi | P a g e

CHAPTER 5
5.1
5.2

CONCLUSIONS ........................................................................................................... 42

CONCLUSION AND POLICY IMPLICATION ......................................................................................... 42
RESEARCH LIMITATION AND FUTURE STUDY ................................................................................... 44

REFERENCES ....................................................................................................................................... 46
APPENDIX A:DESCRIPTION OF DATASET .................................................................................... 49
APPENDIX B: REGRESSION RESULTS ............................................................................................ 50
APPENDIX C: HAUSMAN TEST RESULTS ...................................................................................... 54
APPENDIX D: INDUSTRY CLASSIFICATION.................................................................................. 57
APPENDIX E - EMPIRICAL STUDIES ON RELATIONSHIP BETWEEN INNOVATION AND
PRODUCTIVITY ................................................................................................................................... 58

vii | P a g e


LIST OF TABLES
Table 1: Summary of main definitions of SME in selected economies ..............................17
Table 2: Number of Enterprises by Sector 2006 – 2011 .....................................................19
Table 3: Labor Productivity by Firm size and Location .....................................................20
Table 4: Labor Productivity by Sector ................................................................................20
Table 5: Innovation Rates in Manufacturing SMEs ............................................................21
Table 6: Diversification and Innovation Rates (%) .............................................................22
Table 7: Diversification and Innovation Rates, by Sector (%)............................................22
Table 8: Definitions & Measurement of Variables and Expected Sign of Coefficients .....27

Table 9: Descriptive Statistic of Variables ..........................................................................34
Table 10: Regression results................................................................................................36
Table 11: Hausman Test Results .........................................................................................37
Table 12: Regression results with regards to employee size ...............................................37
Table 13: Regression results with regards to employee size - 2 groups..............................38
Table 14: Regression results with regards to firm location .................................................38
Table 15: Regression results with regards to firm location - 2 groups................................39
Table 16: Regression results with regards to manufacturing sector ....................................40
Table 17: Regression results with regards to manufacturing sector- 2 groups ...................40

viii | P a g e


LIST OF FIGURES

Figure 1: Production Frontier And Technical Change ..........................................................5
Figure 2: Process of Innovation ............................................................................................7
Figure 3: Crepon Duguet and Mairesse Model - CDM Model ...........................................10
Figure 4: Number of Enterprises by Size of Employees 2006 - 2011 .................................18
Figure 5: Conceptual Framework ........................................................................................23
Figure 6: Shares of enterprises by provinces ......................................................................32
Figure 7: Numbers of enterprises by industries ..................................................................33

ix | P a g e


CHAPTER 1

INTRODUCTION

This chapter introduces the research topic, problem statement, research objectives
and research questions. It also summarizes the research scope and data, and then ends with
the thesis structure.
1.1

PROBLEM STATEMENT

Small medium enterprises (SMEs) have played an important role in economic
development. They are also an essential source of job creation, innovation, increasing the
competitiveness and thus the engine of developed and developing countries (Europeia
2005). In Viet Nam, with more than 300,000 registered enterprises (General Statistics
Office - 2011), SMEs play a crucial role in the economy reform, not only representing the
major percentage (97,6%) of businesses of the country, but also significantly contributing
to Gross Domestic Product (GDP) and achieving sustainable economic development.
How could we motivate the development of this sector by enhancing SME’s
performance? Within a firm’s scale, it is important to foster its operational efficiency and
productivity for increased competitiveness in the global market. Innovation was found to
be essential for increasing productivity. The evidence in Crespi and Zuñiga (2012) shows
that applying technological advances led to a more effective use of productive resources,
and the transformation of new ideas into new economic solutions such as new products,
processes, and services. Innovation will be the basis of sustainable competitive advantages
for firms and the crucial source of permanent increases in productivity. A large amount of
research has been completed in this field for many developed and developing countries
such as:Chudnovsky, López et al. (2006), Griffith, Huergo et al. (2006), Masso and Vahter
(2008), Roper and Love (2002); however, firms in developing countries, especially in
SME sector, do not always properly consider the impact of innovation on their
performance. It is no surprise that there is a lack of studies on this subject in Asian
countries, especially Vietnam. Two authors have studied a similar topic. The first one is
Nguyen, Quang Pham et al. (2008), investigated the relationship between innovation and
export performance by using Viet Nam SME survey in 2005; and another research was

done by Lang, Lin et al. (2012), studied the effects of innovation capabilities on the firm’s

1|P a g e


performance. Therefore, the main purpose of this paper is to contribute the findings of the
relationship between innovation and a firm’s productivity, which does not seem to be
investigated for Vietnamese SMEs before. This paper examines the impact of innovation
on the firm’s productivity using micro data from Vietnam SME survey for the period from
2007 - 2009. The empirical model used Cobb-Douglas production function and fixed
effect model and produced a lot of interesting results. In line with the literature, the author
has found a strong association between innovation and productivity in Vietnamese SMEs.
The results have also highlighted the impacts of other influencing factors: firm size, firm
location and manufacturing sector on the relationship of innovation and a firm’s
productivity. Regarding the firm’s location, the results have shown that impact of
innovation on productivity in the big cities such as Ha Noi and Ho Chi Minh City is lower
than the smaller cities in Vietnam. More interestingly, the author was not be able to find
any effects of firm size and high-tech industry on the relationship between innovation and
firm’s productivity. No significant difference was found between the impact of innovation
on productivity of micro firms, who have less than 10 employees and those who have
more than 10 employees. In addition, the high-tech and low-tech industry seem to have
similar productivity when they are innovative.
1.2

RESEARCH OBJECTIVES
With the above problem statement, this thesis aims to investigate the relationship

between innovation and a firm productivity, and the influencing factors on this
relationship, to assist with a firm’s decision about investing in innovation for productivity
benefits. Specifically, this thesis has two main objectives:

(i)

To identify the role of innovation on a firm’s productivity using Cobb-Douglas
production function model

(ii)

To analyze the impact of innovation on SME’s productivity by firm size, firm
location and manufacturing sector

1.3

RESEARCH QUESTIONS

In order to meet the above objectives, this paper attempts to answer the following two
questions:

2|P a g e


(i)

Is there a positive relationship between technology innovation and productivity of
a firm?

(ii)

What are the roles of firm size, firm location, and manufacturing sector on the
impact of innovation and firm productivity?

1.4

SCOPE OF THE STUDY
To answer above research questions and meet the research objectives, the study

relies on data from the Small and Medium Enterprises (SMEs) in Viet Nam for the year
2007 and 2009. In the survey, there are approximately 2500 enterprises of 10 provinces in
Viet Nam; most of them are micro and small enterprises, as the majority of SMEs
numbers, thus they can represent for SMEs population. The study has also focused on
manufacturing sector, which is considered to have higher technological intensity and
innovation involvements than other sectors.
1.5

STRUCTURE OF THE STUDY
Followed by this introductory chapter, chapter Two provides the literature review

with both theoretical and empirical findings from previous studies. It presents meaning of
key concepts, the measurements, the debates about relationship between innovation and
productivity and other influencing factors at micro level and findings in Viet Nam. Then it
will specify the conceptual framework.
Chapter Three describes the data and research methodology. The first part defines
the variables and concepts which are used in the thesis and their measurements. The
second part introduces the empirical models and research hypotheses that will be tested. It
will finally present the estimation strategy or regression methodology for panel data.
Chapter Four presents the research findings which are obtained from the estimation
results. This chapter also analyzes how the regression results can answer the research
questions, how the research hypotheses in chapter Three will be tested and how it relates
to the previous findings.
The next chapter is the conclusion, which summarizes all of above chapters and
based on the finding results in chapter Four, it also suggests some strategies to Vietnamese

enterprises for the long-term development and growth.
All of appendices and references shall be provided in the final part.

3|P a g e


CHAPTER 2

LITERATURE REVIEW

This chapter reviews the literature on the relationship between innovation and
productivity at firm level. In combination, it provides the definitions of related concepts,
the theories that represent the relationship of these concepts and reviews the findings of
previous studies about innovation and productivity relationship at micro level and the
findings in Viet Nam.
2.1

PRODUCTIVITY : CONCEPTS AND MEASUREMENTS

Productivity is commonly defined as a ratio of a volume measure of output to a
volume measure of input use (Schreyer and Pilat 2001) or in other words, how much of
output which is obtained from a given set of inputs (Syverson 2010).
Productivity = Total outputs/ Total inputs
The purposes of measuring productivity is to identify the changes in innovation,
the efficiency from the technological changes, the real cost savings or benchmarking the
production processes at micro level. For economic growth or at macro level, it is used to
measure the development of living standards. There are many different types of
productivity measures, depending on the purposes of measurement and the data
availability. In general, productivity measures are classified as single factor productivity
measures, which is a measure of output to a single measure of input or multifactor

productivity measures, which is relating to a measure of output to many inputs. Single
factor productivity measures can be based on labor or capital; we normally call labor
productivity or capital productivity. And multifactor productivity measures (MFP) can be
in the combination of capital-labor MFP or capital-labor-materials MFP. Both can be
evaluated on the basis of gross outputs concept or value-added concept. If “gross output”
represents the total value of sales or total production outputs of a firm, industry, or total
output of an economy without deducting intermediate inputs, then “value added” equals
gross outputs minus the purchased value of intermediate inputs.
There are some advantages and disadvantages in using these two concepts. OECD
Productivity Manual mentioned that if technical progress affects all inputs proportionately,
then gross output productivity measures give estimates of underlying technical progress

4|P a g e


and this is not true for the value-added measure. For the value-added measure, because it
depends on the share of value-added in gross output, depends not only on technology but
also on the time paths of outputs, inputs and prices, thus it can be considered as a measure
of the ability of an industry to translate technical change into income and final demand.
Value-added would precisely measure only technical change if technical change, instead
of affecting all inputs equally, affected only primary inputs of capital and labor. However,
this does not always seem to be the case. As the result, these two measures will answers to
different questions, depending on the purposes of measurement and data availability.
As we have known, productivity is a technical concept which measures the
efficiency from the used factors of production of a SME. Higher productivity is likely to
improve profitability and enhance a firm’s competitiveness relative to its rivals. However,
why do firms differ so much in their ability to convert the inputs to outputs? According to
the theory of production (Cobb and Douglas 1928), productivity is basically dependent to
labor, capital and total factor productivity. An increase in labor, capital input or total factor
productivity (TFP) will lead to an increase in output. If capital and labor input are tangible,

TFP appears to be more intangible as it can range from technology to knowledge of
worker or human capital. Since Solow (1957), TFP indicates how efficient that firms turn
inputs into outputs and it has been considered as the major factor in generating growth,
whilst labor and capital investment are just important contributors. As the results, the
difference in firm’s technology innovation will lead to the changes in their ability to
convert the inputs to outputs. Figure 1 has shown the improvement of productivity due to
the contribution of technical change – as a component of TFP. Within the production
frontier or the same inputs (point B & point C), the firm can produce greater outputs (point
D) if they make difference in their innovation activities.
A
A

Production frontiers

Output

D

technical change

B

Figure 1: Production Frontier
And Technical Change

C
A

0

Input

5|P a g e


At micro level, TFP is a measure of elements such as managerial capabilities
research and development, technical innovation. In a TFP survey across the developing
countries during the period 2006-2009 (Enterprise Note No.23 – World Bank Group,
2011), there are five Asian countries Indonesia, Mongolia, Nepal, Philippines and Viet
Nam. The average TFP value of these countries is 0.03. Nepal has the highest aggregate
productivity level (0.38) which is followed by Indonesia (0.27). The lowest aggregate
productivity is observed in Viet Nam (-0.004). Thus, even most of SMEs in Viet Nam
believe that technology innovation is the main factor influencing their competitiveness in
the market, but from above figures, we can easily see most of Vietnamese SMEs currently
have low productivity and competitiveness because of low investment in technological
innovation. The negative aggregate productivity showed that the productivity and
innovative investments are decreasing overtime.
On another hand, Hansen (2006) has found the positive and significant effect of
innovation on the survival of SMEs in his study using data of Vietnamese SMEs from
1990-2000. Thus, it is vital to study the relationship between technology innovation and
firm productivity, which hopefully help SMEs to enhance their productivity through the
technology innovation, increase the competitiveness and efficiency for the better
performance and development.
2.2

INNOVATION : CONCEPTS AND MEASUREMENTS

According to Greenhalgh and Rogers (2010), innovation can be defined as the
application of new ideas to the products, processes, or other aspects of the activities of a
firm that lead to increased value added for the firm and also benefits to consumers or other

firms. There are two important types of innovation, product innovation and process
innovation. Product innovation is the introduction of a new product, new type or design of
good, service, or it could be a significant qualitative change in an existing product. While,
process innovation is developed to introduce the new process, new techniques for making
or delivering goods and services. They are two separated types of innovation but they are
quite correlated to each other. Normally, the new process will allow firms to deliver the
new design and development of new products, and vice versa, the new product would
require firms to change its production process, making it more effective, saving energy

6|P a g e


and most importantly improving productivity. However, creating a new firm or making a
new investment in a plant or factory is also considered as innovative activity (Audretsch,
Santarelli et al. 1999).
The essential effect of product and process innovation is cost reduction in
production, thus enhances the firm’s competitiveness in the global market. This is
understood as the innovation process, which is summarized in Figure 2. The innovation
process normally starts from the research and development activity such as market survey,
demand analysis, developing the new idea, testing it with assessment, designing the new
product. Research & development (R&D) has been found as the very important activity in
the innovation process and of course in economic growth (Crepon, Duguet et al. 1998),
(Baldwin and Branch 2000). This activity helps forming the market needs and the firm’s
responses to the market analysis. After that, the investment for innovation should be
implemented for product, existing product, and technology or for the whole process. At
this stage, we will probably know how much innovation impacts on the productivity, how
efficient of the firm’s performance and how much costs will be potentially saved. Last but
not least, it is the time for market penetration and adaptation. To some circumstances,
adjustments or improvements will be required in this stage.
Figure 2: Process of Innovation

Research & Development
(new idea, research, design)

Innovation
Investment
(product or
process)

Market Diffusion
(adoption, market
penetration,
improvement)
Source: Author’s analysis

Following to innovation process, many surveys and researches have studied the
impact of R&D, innovation and productity. For example, Community Innovation Surveys
(CIS) is the most well-known survey executed by the European Union for measuring the
product and process innovation, innovation activity and expenditure, impacts of
innovation, other sources and findings of innovation of European enterprises. Most of the
firms in Europe and other countries have implemented the surveys of innovation activities
based on R&D expenditures and patent counts as indicators of the input and output of
innovation.Miguel Benavente (2006) used data of Chilean plants to study the relationship

7|P a g e


of research investment (measured by R&D per worker) and innovation (with innovation
sales used as a proxy) on labor productivity (measured by value added per worker). In this
research, innovation sales is measured as a share of sales and used in the tobit model.

Other instrument variables were also investigated such as market share, diversification.
Chudnovsky, López et al. (2006) considered a firm was innovative when it
introduced new or radically modified products and/or processes during the period of 19921996. Importantly, a firm is called an innovator depending on its output of the innovation
process but not on whether it has involved in innovative activities or innovation inputs.
Innovators in this paper were also classified into three groups: product innovation, process
innovation and both product & process innovation.
Lööf and Heshmati (2006) defined innovative firm is when its innovation
investment and innovative sales are positive. Their measure of innovation inputs was more
comprehensive than other researches, as it not only included R&D spending but also nonR&D activities, the outsource services or machinery for innovation activities, all related
expenses in education, marketing, design for new products… Mohnen, Mairesse et al.
(2006) considered innovation as the residual of innovation production function and as part
of innovation intensity due to the improvement and investment in new products.
In summary, many indicators and proxies have been used as a measure of
innovation inputs and outputs, depending on the availability of data, the survey quality and
the purposes of the investigation. But the most popular indicators are R&D expenditures,
patent counts or innovation sales. Despite of which indicators we use or how we define the
innovation process, innovators could be expected to have a better performance or
productivity than non-innovators.
2.3

RELATIONSHIP OF INNOVATION AND PRODUCTIVITY

2.3.1

Production function
Basing on Cobb and Douglas (1928), the productivity is the function of labor (L),

capital (K), materials (M) and total factor productivity (A)
Y = A F (K, L, M) = A Kα LβMγ
Taking the natural logarithm of the function, we have:

8|P a g e


log(Y) = log(A) + β log(L) + α log(K) + γ log(M)
Where Y is outputs in a year, F(·) is a function of observable inputs capital K,
labor L, materials M and A is the total factor productivity (TFP); α, β and γ are the output
elasticity of capital, labor and materials, respectively. If capital, labor and materials input
are tangible, then TFP appears to be more intangible. As mentioned in the introduction
part, firms differ so much in their abilities to convert inputs to outputs due to the
difference in TFP. Innovation or technical change is considered as two sub-sections of
total-factor productivity. Total-factor productivity is often seen as the driving force of
economic growth, up to 87.5% increase of total-factor productivity has contributed to the
doubled gross output per man hour, and the remaining 12.5% was from the increased use
of capital (Solow 1957). At the firm level, how the innovation or technical change
contributes to the firm productivity? It will be discussed in the next part of empirical
review.
2.3.2

Crepon Duguet and Mairesse Model (CDM Model)
Besides Cobb-Douglas production function, there is a well-known model, which

was very popularly used - CDM Model, describing the relationship between innovation
and productivity. It was developed by Crepon, Duguet and Mairesse in Crepon, Duguet et
al. (1998), showed the impact of research and development (R&D) on innovation and
innovation on productivity of firm.
In below model (Figure 2), innovation is considered as a process, which is carried
out from the engagement in R&D activities, investment in technology or knowledge
capital and also affected by other factors such as: market demands, firm size or industries.
The process innovation can improve the production performance and make it more

efficient, thereof enhance the productivity of the firm. In this model, research and
development was strongly emphasized because of its impact onto the rest components.
The square boxes denote the measurable quantity concepts, while the oval boxes represent
the immeasurable factors and we normally need to use the proxies for these factors.

CDM model has been applied in many studies due to its practicality such as: Lööf
and Heshmati (2002), Miguel Benavente (2006), Masso and Vahter (2008)… If Cobb-

9|P a g e


Douglas allows us to consider the relationship between innovation and productivity only,
then CDM model would enable us to model this relationship in the bigger framework and
take into account the impact of other influencing factors. Several links in this structure
will be captured and discussed in analysis section of this paper.

Figure 3: Crepon Duguet and Mairesse Model - CDM Model

Demand pull
Technology push

Research &
Development

Diversification
Market share
Size/ Industry

Knowledge
Capital

Innovation

Physical capital skills

Productivity
Source:Crepon, Duguet et al. (1998)

2.3.3

EMPIRICAL REVIEW OF INNOVATION AND PRODUCTIVITY RELATIONSHIP

At micro level, innovation influences the firm’s productivity with a direct and
indirect impact.
Chudnovsky, López et al. (2006) strongly suggested that innovators attain higher
productivity levels than non-innovators in the study of Argentine manufacturing firms’
behaviors during 1992–2001. Specifically, the estimation results had suggested that the
labor productivity of innovators is 14.1% higher than non-innovators, which was a
significant direct impact to the firm’s productivity. The former performed better than the
latter group in terms of labor productivity. This paper has employed different empirical
methodologies for analyzing the relationship of innovation and productivity based on
CDM approach. Panel data and fixed effect estimators had been used to control for

10 | P a g e


unobservable heterogeneity at the firm level. Additionally, the author included the time
dummy to control the specific time varying unobservable effects of the firms over time
and classify the surveyed firms into four groups: labor intensive, scale intensive, R&D
intensive and natural resources intensive for controlling the changing of sectorial

technological opportunities over time. They are considered as the strengths of this
research.
Griffith, Huergo et al. (2006) also used CDM model and found that product
innovation was associated with higher productivity in France, Spain, and the UK, but not
in Germany. Similarly, Masso and Vahter (2008) suggested that firms, who have more
resources to invest in innovative activities and a higher ability to undertake R&D will get
the improvement in productivity. They have also found the effect of innovation on
productivity not only on the productivity in the last year of the innovation survey, but also
one and two years after the survey. This we normally called the lag of the impact or
spilled-over effect. One more interesting finding in this research was the different results
with different used data set. With Community Innovation Survey4 (CIS4) data, only
process innovation had a positive significant effect on labor productivity, but not product
innovation. On the other hand, when they used CIS3 data, then it provided the opposite
results: product rather than process innovation had a significant impact on productivity.
Organizational innovation was investigated to have a positive impact on productivity. It
seems that CDM model has been popularly used by many economists and researchers to
study the relationship of innovation and productivity at firm level because of its coverage
and practicality.
With another approach, using linear regression model and qualitative
questionnaires, Barlet, Duguet et al. (2000) focused in the impact of product and process
innovation on manufacturing sales of French companies from 1986-1990. The findings
were biased in product improvement as it achieved a high commercial return (4%), even
with moderate technological opportunities. Interestingly, products that are new for the firm
but not for the market never achieve a great gain. The highest contribution to the
manufacturing sales comes from products that are new for the market. While Huergo and
Jaumandreu (2004) stated that process innovations at some point lead to extra productivity
growth.

11 | P a g e

Another strong relationship between innovation (both product and process
innovation) has been asserted by Hall, Lotti et al. (2009) with a significant impact of
innovation outputs on manufacturing firm’s productivity in Italy covering the period from
1995 - 2003. By using the combination of CDM model and Cobb Douglas production
function, this research had developed different models for examining the relationship
between R&D and innovation, innovation and productivity. Labor productivity was
measured by real sales per employee, while product and process innovation was used as a
proxy for innovation input. The results have shown that product innovation has positive
impact on labor productivity, while process innovation has larger effect via associated
capital investment.
However, for a less developed country like Chile, Miguel Benavente (2006) was
not be able to find any significant impact from innovation on the sales and productivity in
the short-run in 1995-1998. This could be explained that the innovation will need
sometimes to wait for market’s responses or really impact on the firm’s productivity,
especially in the long-run period. The study also found the significant effect of labor skills
on the estimation of productivity instead.
When we take a look at the in-direct effect, innovation is likely lead to the
sustainable competitive advantage or firm’s performance. Lengnick-Hall (1992) had said
innovation and competitive advantage are connected and innovative success enabled firms
to broaden its market appeal by cost saving system. It has also been found to have impact
on export performance of the firm (Roper and Love 2002). Specifically, in Germany, the
higher levels of innovation intensity, the lower proportion of sales attributable to new
products. Moreover, the spill-over effects were also discussed in these countries.
Innovative UK plants were more effective in their ability to exploit spill-overs from the
innovation activities of companies in the same sector. By contrast, non-innovators are
more likely to absorb regional and supply chain spill-over effects in Germany.Cassiman,
Golovko et al. (2010) Found that product innovation - not process innovation of Spanish
manufacturing firms, affected productivity and helped small non-exporting firms to enter
the export market. Innovating firms had higher productivity levels and grown faster than

non-innovating firms.
At economics level, innovation or total factor productivity, which is known in
Solow (1957) is the core factor and driving force of economic growth (Greenhalgh and
12 | P a g e


Rogers 2010). If the economy bases merely on capital accumulation without technological
progress, the diminishing returns on capital accumulation will eventually depresses
economic growth to zero. On the other hand, Le Van (2008) has found that the richer a
country is, the more money will be invested in new technology, training and education.
Färe, Grosskopf et al. (1994) used Malmquist index of total productivity growth to
estimate the impact of technology change on productivity growth of 17 OECD
industrialized countries over the period from 1979 – 1988 (Malmquist index was used as a
standard approach in productivity measurement). The study has found that the significant
effect of technology changes on productivity growth for some developed countries like US
and Japan.
There are many different points of views and studies on the impact of innovation
on firm’s productivity, to some certain extent, we can easily see most of the findings have
the similar conclusions with a positive relationship and very few have negative results.
Innovation has impacted not only at firm level in different channels, but also at macro
level. However, within the scale of the thesis, we mostly focus on the firm level to see
what previous studies have found the impact of innovation on productivity and other
related determinants of this correlation.
2.3.4 DETERMINANTS OF THE INNOVATION IMPACT
In analyzing the determinants of the relationship between innovation on
productivity, many papers have usually focused in CDM model (Crepon, Duguet et al.
1998) to evaluate the impacts of influencing factors on the relationship: firm size, firm
location and manufacturing sector. In addition, since research and development plays an
important role as the pre-innovation step, thus the following section will provide the
empirical reviews on these determinants on the relationship between innovation and

productivity and the role of R&D on this impact.
a. Firm size
Firm size is classified based on number of employees or invested capital amount. It
is one of the important factors, which directly affects the firm’s productivity. With
innovative activities, Masso and Vahter (2008) found that the larger firms are more likely
to engage in innovation than small firms. Firm size has an insignificant impact on product

13 | P a g e


innovation but positive impact on the process innovation. More specifically, Chudnovsky,
López et al. (2006) suggested that large firms have a higher probability of engaging in
innovation activities and becoming innovators. Similarly, a study (Dhawan 2001) of US
industrial sector displayed that even smaller firms get a higher profit rate but they will
have lower survival probability and difficulty in accessing the capital market. The study
used a large panel data of US firms for the 1970–1989 periods. The empirical results
indicated that small firms are significantly more productive but also more risky than their
large counterparts. Small firms face market uncertainties, capital constraints and other
challenges which make them more efficient than large firms but might increase their
riskiness. However, the largest firms have a significantly higher probability of being
innovative (68%) than small or medium-sized ones (30%), which was found in (Baldwin
and Branch 2000). And no significant difference was found between small and mediumsized firms in terms of their likelihood of being innovative.
b. Firm location
The impact of location on firm’s efficiency is also considered in Vu (2003),
Glancey (1998), Devereux, Griffith, and Simpson (2007) and many other studies.
Audretsch and Feldman (1996) found that industry localization increased the innovative
activity. Baptista and Swann (1998) used data of 248 manufacturing firms in UK and
concluded that “a firm is considerably more likely to innovate if own-sector employment
in its home region is strong”, which means affirms located in strong clusters; they were
more likely to innovate than other firms. It was explained that the strong clusters tended to

attract more new entrants and also grow faster than other groups. On the other hand, CIEM
(2010) has found a strong evidence of higher labor productivity of firms located in the
urban area or the big cities than rural area and smaller cities, and of course the innovation
rates of these firms are also higher. However, most of their innovation activities are
implemented to satisfy their customer’s requests rather than response’s to the market’s
demands.
c. Manufacturing sector
Manufacturing sector is one of the key determinants of innovation because it is
much related to the technology and production process. There are many investigations for
different industries. In chemical and textile industries, product and process innovation are

14 | P a g e