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

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.. The results have als

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

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 INSTITUTE OF SOCIAL STUDIES

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

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

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

ABBREVIATIONS

SME Small and Medium Enterprise

CDM Crepon Duguet Model

CIEM Central Institute of Economic Management

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

SFA Stochastic frontier analysis

TFP Total Factor Productivity

WTO World Trade Organization

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

TABLE OF CONTENTS

LIST OF TABLES VIII LIST OF FIGURES IX

CHAPTER 1 INTRODUCTION 1

1.1 PROBLEM STATEMENT 1

1.2 R ESEARCH OBJECTIVES 2

1.3 R ESEARCH Q UESTIONS 2

1.4 S COPE OF THE STUDY 3

1.5 S TRUCTURE 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 R ELATIONSHIP OF INNOVATION AND PRODUCTIVITY 8

2.3.3 EMPIRICAL REVIEW OF INNOVATION AND PRODUCTIVITY RELATIONSHIP 10

2.3.4 D ETERMINANTS OF THE INNOVATION IMPACT 13

2.3.5 I NNOVATION AND FIRM PRODUCTIVITY IN V IET N AM 16

2.3.6 C HAPTER REMARK 16

CHAPTER 3 RESEARCH METHODOLOGY AND DATA 17

3.1 A N OVERVIEW OF SMES IN V IET N AM 17

3.2 CONCEPTUAL FRAMEWORK AND MODEL SPECIFICATION 23

3.2.1 C ONCEPTUAL FRAMEWORK 23

3.1.2 MODEL SPECIFICATION 23

3.3 R ESEARCH HYPOTHESES 26

3.4 D EFINITIONS OF VARIABLES AND CONCEPTS 26

3.5 D ATA COLLECTION 29

3.6 METHODOLOGY 29

3.6.1 R ANDOM E FFECT R EGRESSION M ODEL (RE) 30

3.6.2 F IXED E FFECT R EGRESSION M ODEL 30

3.6.3 S ELECTION BETWEEN RE AND FE MODEL BY H AUSMAN T EST 30

3.7 M EASUREMENTS OF V ARIABLES 31

CHAPTER 4 DATA ANALYSIS 34

4.1 E MPIRICAL R ESULTS 34

CHAPTER REMARK 41

CHAPTER 5 CONCLUSIONS 42

5.1 CONCLUSION AND POLICY IMPLICATION 42

5.2 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

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

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

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

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 R ESEARCH 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 R ESEARCH Q UESTIONS

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

(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 S COPE 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 S TRUCTURE 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

C HAPTER 2 L ITERATURE R EVIEW

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

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

Output

A

A

Figure 1: Production Frontier

And Technical Change

D

C

A

B Production frontiers

technical change

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

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

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

Research & Development

(new idea, research, design)

Innovation Investment

(product or process)

Market Diffusion

(adoption, market penetration, improvement)

Source: Author’s analysis

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 1992-

1996 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 non-R&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 R ELATIONSHIP OF INNOVATION AND PRODUCTIVITY

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-

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

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

Figure 3: Crepon Duguet and Mairesse Model - CDM Model

Research &

Development

Knowledge Capital

Innovation

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

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

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 D ETERMINANTS 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

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 medium-sized 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

closely correlated (Baily, Chakrabarti et al 1985) In chemical industry, if there is a new physical or structural product then that is considered as the new products New process is when there is the new equipment or instrument innovation to produce the new materials, help changing environment, saving energy… In textile industry, the product innovation is new yarns or fabrics, while process innovation is the new equipment or technology for improving the speeding of operation in dyeing or finishing, reducing the input requirements In paper industry, Ghosal and Nair-Reichert (2009) found that the greater investment in modernization or innovation, the higher productivity that the firm can achieve, especially the investment in information technology and digital monitoring devices In another research of German industry, Fritsch and Meschede (2001) presented that low-technology manufacturing firms lag behind their medium and high-technology counterparts in product innovation performance, but they appears to perform well and even better in process innovation For Italy manufacturing firms, Hall, Lotti et al (2009) found that the impact of product innovation on productivity was positive and slightly stronger for firms in high-tech industry than low-tech sector However, the larger and

older firms seemed to be less productive than smaller firms, ceteris paribus

d Research & Development

In the innovation process, research and development is considered as the innovation step, which attributes to developing new products or processes for future growth Many previous studies have measured innovation by patents and R&D expenditures of the firm Using data for French manufacturing firms and interval data for innovative sales, Crepon, Duguet et al (1998) estimated that a 10% increase in R&D intensity had an impact of almost 5% on innovative sales In addition, based on the 1993 Canadian Survey of Innovation in manufacturing firms and using binary variables as innovation output indicators, (Baldwin and Branch 2000) estimated that the probability of introducing a product (process) innovation increased 24% (15%) in firms that engaged in R&D activities Besides that, R&D activity and firm size have the greatest impact on innovation, regardless of the model used Firms not performing R&D have only an 11% probability of innovating, while firms conducting R&D have a 41% probability of innovating

pre-2.3.5 I NNOVATION AND FIRM PRODUCTIVITY IN V IET N AM

As mentioned in the introduction part, there are very few researchers studied about SME in Viet Nam, especially in the topics related to the innovation and productivity Recently, there are two papers discussing about innovation and its impact on the SME’s performance in Viet Nam The first research was conducted by Nguyen, Quang Pham et

al (2008), using Viet Nam SME survey in 2005 With the logit and probit models, the authors have found that three measures of innovation, which are understood as the investment in new products or new production process or improvement of existing products, are significant determinants of exporting

And the second one, Lang, Lin et al (2012) used survey questionnaires and collected data from Vietnamese high-tech manufacturing firms The research has found that investment capability had positive effects on their technology innovation capabilities and positively correlated to their firm competitive performance Thus, investment in technology innovation capabilities was helpful to business competitiveness in Viet Nam enterprises

In fact, from the statistics in SME survey, most of SMEs in Vietnam still use the out-of-date technology which might be 3 to 4 generation older than the world’s technologies Besides that, they even do not put adequate investment in human capital for improving the technology management skills

2.3.6 C HAPTER REMARK

In the short words, it is very important and useful to review the key studies in the relationship between innovation and productivity for modeling the research hypotheses Positive relationships have been found in many previous studies (Lööf and Heshmati 2006), (Chudnovsky, López et al 2006), (Cassiman and Martinez-Ros 2007) The determinants of this relationship are also investigated: firm size, location and industrial sector

CHAPTER 3 RESEARCH METHODOLOGY AND DATA

The research methodology of this thesis will be presented in this following part Firstly, it provides the empirical models which are employed to answer the research questions in the first chapter and the measurements of all variables Secondly, there are four hypotheses to be tested and they are connected to relevant research questions The estimation methods models and the selection among the alternative regression models are showed in the final part However, before presenting those contents, it is helpful to start with the overview of SMEs in Viet Nam

3.1 A N OVERVIEW OF SMES IN V IET N AM

3.1.1 Small Medium Enterprises (SMEs)

SMEs’ definitions differ widely among the regions and countries Basically, SMEs are defined by the number of employees, invested capital, total amount of assets, sales volume or production capability However, the most common used index is the number of

employees and total invested capital Table 1 below has shown three different definitions

of European Commission – as an example for developing countries, Cambodia – one of our neighbor country and current used definition in Viet Nam Generally, the classification seems to be similar with the size of employees, while it is quite different in size of capital

or assets due to the economic scale From this comparison, we can see that Viet Nam has bigger firm scale than Cambodia and smaller than European in terms of the size of invested capital

Table 1: Summary of main definitions of SME in selected economies

Country Company category Employees Turnover (€)

between 10 – 50

≤ 1,000,000

between 200 - 300 Trade & services:

between 50 – 100

Manufacturing: between 1,000,000 – 5,000,000 Trade & services: between 500,000 – 2,000,000

Source: Author's collection

Since 2005 - 2006, when the enterprise law has been effective and Viet Nam has become the member of World Trade Organization (WTO), the numbers of enterprises in

Viet Nam has kept increasing over years From Figure 4 below, we can see the total

numbers of enterprises in 2011 had grown by 2.6 times compared with that in 2006 It averagely increased 21 percent per year, especially the rise of non-stated firms According

to the classification in Decree 56/2009/ND-CP, the number of SMEs has accounted for 97% - 98% of total numbers of enterprises Among that, micro and small firms keep the significant numbers while medium and large enterprises only have a small percentage

Figure 4: Number of Enterprises by Size of Employees 2006 - 2011

Source: Authors’ calculation based on Enterprise Census 2012 - GSO Viet Nam

50.000

According to SMEs survey report (CIEM 2010), 30% of manufacturing SMEs are

located in ten major provinces including: Ha Noi, Phu Tho, Ha Tay, Hai Phong, Nghe An,

Quang Nam, Khanh Hoa, Lam Dong, Ho Chi Minh City and Long An In addition, the

manufacturing activities are divided into around 18 various industries or sectors (2011) In

the whole economy, number of enterprises in trade and service business accounts for

nearly 68%, while industry and construction enterprises contribute 31.2 % and a very

small part of agriculture, forestry and fishing (1%) The GSO report has also analyzed that

manufacturing and construction enterprises provided a significant contribution to the

national budget in the same period, with high investment returns, technology intensive and

attract a lot of labors Table 2 below provides the number of enterprises by kind of

economic sectors from 2006 – 2011 and the percentage sector for the year 2011

Table 2: Number of Enterprises by Sector 2006 – 2011

324,691

279,360

236,584

192,179

149,069

125,092 Agriculture, Forestry

3,308

2,569

2,408

2,259

2,395

2,372 Industry, construction 31.2%

101,288

92,357

81,905

67,676

52,472

44,703

Mining and quarrying 0.8%

2,545

2,224

2,191

1,912

1,396

1,109 Manufacturing 16.2%

52,587

45,472

42,894

36,459

29,182

25,086 Electricity, gas, stream and

air conditioning supply 0.3%

1,045

waste management and

44,183

42,901

35,178

27,867

20,682

17,485

Trade and services 67.8%

220,095

184,434

152,271

122,244

94,202

78,107

Source: Authors’ calculation based on Enterprise Census 2012 - GSO Viet Nam

3.1.2 Productivity and Innovation of SMEs in Viet Nam

a Labor Productivity Characteristics

Table 3 shows that average real revenue per full-time employee was 73.0 million VND in 2011, while real value added per full-time employee was 20 million VND The medium-sized firms have the highest labor productivity In addition, the labor productivity

in the urban area and southern Vietnam is higher than the rest area In CIEM (2010) report, urban area includes Ha Noi and HCMC

Table 3: Labor Productivity by Firm size and Location

Unit: million VND, Source: CIEM (2010)

Table 4: Labor Productivity by Sector

Labor Productivity ISIC

From the SMEs survey data, the innovation rates could be calculated for the

manufacturing SMEs (Table 5) Innovation rates in 2005 are pretty high for all categories,

compared with 2007 and 2009 It could be explained by the effectiveness of Enterprise Law in 2005 when most of Vietnamese enterprises have set up their new businesses and hence putting much investments on the innovation and new market penetration However, they tend to improve their existing products rather than introducing new products as it is less expensive and resource consuming In 2007 and 2009, there was a sharp decrease in innovation rates down to 4.9% and 2.7%, while it was 40.8% in 2005 Similarly, the rates

of product improvement and new production technology introduction also declined significantly in 2007 and 2009 The main reasons for this is the lack of capital and market outlet, as well as the fall of new technology usage in business environment (CIEM 2008); (CIEM 2010) Diversification is a bit different, a firm is considered as diversifying enterprise if it produces more than one 4-digit ISIC product This activity seems to be similar among the years from 2005 – 2009 with a slight decrease in 2007 and recovered in

2009 But in general, firms have put more investments in new technology and improvement of existing products

Table 5: Innovation Rates in Manufacturing SMEs

Introduced new production technology 29.5% 15.4% 13.9%

Source: (CIEM 2008), (CIEM 2010) and author’s calculation

If we take a look into the firm size, location and firm age, the SMEs report in 2009 has shown some interesting results Larger firms were more likely to introduce the new product line and improve the existing products, compared with micro firms Urban enterprises (in Ha Noi and HCMC) appeared to be more innovative than rural areas Furthermore, older firms improve their existing product more regular in 2009 than 2007

Table 6: Diversification and Innovation Rates (%)

Diversification Introduced new

product

Improved existing product

Source: SMEs Survey 2009

In terms of sector, Table 7 looks at the diversification and innovation rates in some selected industries Firms in foods and beverages have lower diversification and innovation rate than firms in other sectors Most of the sectors tend to improving existing products than introducing new product

Table 7: Diversification and Innovation Rates, by Sector (%)

3.2 CONCEPTUAL FRAMEWORK AND MODEL SPECIFICATION

3.2.1 C ONCEPTUAL FRAMEWORK

From the theoretical and empirical review in previous section, the interrelationship between innovation and productivity concepts and other controlled factors should be described in the following framework In which, innovation and productivity are highlighted as the main focus in this thesis Labor, capital and innovation are independent variable and productivity is the dependent variable (Cobb and Douglas 1928)

3.2.2 MODEL SPECIFICATION

In order to test the correlation between innovation and firm’s productivity and other influencing factors, I am going to run various models which apply Cobb Douglas production function:

physical assets; M is total material inputs; A is total factor productivity In this equation α,

β and γ are the output elasticity of capital, labor and materials respectively

Productivity can be calculated as the ratio of output to a specific factor or to all relevant factors of production In this paper, the author applies the non-parametric measure

of productivity – labor productivity as it gives a simple and full meaning of firm productivity performance By dividing both sides by L, we have the new productivity function:

I it : Innovation of firm i in 2007 and 2009 (proxied by the investment in the new product or

new production technology or improvement of existing products – dummy variable)

ln(K it /L it ) is log of physical capital per employee

ln(M i /L i ) is log of total inputs per employee

ln(L i ) is log of number of employees

ε it is error terms or presenting the unobserved variables in the models in 2007 and 2009

α, β, γ are the productivity elasticity of capital, labor and materials respectively

(α+β+γ–1) is the coeffient for lnLit, measures the deviation from constant returns to scale

δ is the productivity elasticity of innovation

Furthermore, for testing the impacts of firm size, firm location and manufacturing sector on the relationship of innovation and productivity, we are going to have following models:

Model 2 - Firm size

ln(Y it /L it ) = ln A + α ln(K it /L it ) + γ ln(M it /L it ) + (α+β+γ–1) ln(L it ) + δ I it +

δ 1 I it *dLsize + ε it (2)

where

dLsize is dummy variable for micro and small-sized firms by employee dLsize=1 if the firm size

has less than or equals to 10 employees

δ 1 is the productivity elasticity of innovation of firms less than or equals to 10 employees

δ+δ 1 is the productivity elasticity of innovation

Model 3 - Firm location

ln(Y it /L it ) = ln A + α ln(K it /L it ) + γ ln(M it /L it ) + (α+β+γ–1) ln(L it ) + δ I it +

δ 1 I it *dlocation + ε it (3)

where

dlocation is dummy variable for the big cities dlocation=1 if the firm is located in Ha Noi or Ho

Chi Minh City

δ 1 is the productivity elasticity of innovation of firms in the big cities

δ+δ 1 is the productivity elasticity of innovation

Model 4 –Manufacturing sector

ln(Y it /L it ) = ln A + α ln(K it /L it ) + γ ln(M it /L it ) + (α+β+γ–1) ln(L it ) + δ I it +

δ 1 I it *In_High + ε it (4)

where

In_High is dummy variable for High-tech industry In_High=1 if the firm is high-tech industry

δ 1 is the productivity elasticity of innovation of firms in high-tech industry

δ+δ 1 is the productivity elasticity of innovation

3.3 R ESEARCH HYPOTHESES

Following hypothesis is determined basing on the theoretical and empirical reviews which are discussed in above

(i) Hypothesis 1: innovation influences positively in firm’s productivity

(ii) Hypothesis 2: the higher firm size, innovation has more impact on the productivity

(iii) Hypothesis 3: impact of innovation on productivity in the big cities is higher than

other area

(iv) Hypothesis 4: innovation in the high-tech sector has higher impact on the

productivity than the other sector

Innovation can promote the productivity, this argument was supported by many researches in this field, such as Chudnovsky, López et al (2006), Griffith, Huergo et al (2006), Masso and Vahter (2008) or Huergo and Jaumandreu (2004) It is why the first hypothesis is positive relationship between innovation and firm’s productivity In terms of the firm size, as Dhawan (2001) stated, the large firms tend to be more productive and efficient than the smaller firms Therefore, the second hypothesis is the higher firm size, the more impact of innovation on productivity Location effect is a bit different in Vietnam since the enterprises are established along the countries and clustered by economics and industrial areas CIEM (2010) showed that the innovation rate and higher productivity in the urban area (Ha Noi, HCMC) is higher than in the rural area of Vietnam The third hypothesis, therefore, is the impact of technology innovation on productivity in the Ha Noi and HCMC is higher than other regions Last but not least, as always being understood along history, the high-tech industry seems to be more intensive technological innovation This is why we assume the impact of innovation is higher in this sector like it stated in the final hypothesis

3.4 D EFINITIONS OF VARIABLES AND CONCEPTS