Innovation and productivity in small and medium enterprises, a case study of vietnam

Toestablish and quantify this relationship, this study employs the two-stage process: ithe estimation of total factor productivity for each firm; and ii a determination of an innovation

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

VIETNAM – THE NETHERLANDS

PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

INNOVATION AND PRODUCTIVITY IN SMALL AND MEDIUM ENTERPRISES:

A CASE STUDY OF VIETNAM

By PHAM DO TUONG VY

MASTER OF ART IN DEVELOPMENT ECONOMICS

University of Economics International Institute of Social Study

VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

INNOVATION AND PRODUCTIVITY IN SMALL AND MEDIUM ENTERPRISES:

A CASE STUDY OF VIETNAM

by Pham Do Tuong Vy

A Thesis Submitted in Partial Fulfilment of the Requirements for

the Degree of

Master of Art in Development Economics Academic Supervisor: Dr Vo Hong Duc

I hereby declare that this thesis entitled “Innovation and Productivity in

Small and Medium sized Enterprises: A case study of Vietnam”, which is

written and submitted by me in accordance with the requirement for the degree of

Master of Art in Development Economics to the Vietnam – The NetherlandsProgramme This is my original work and all sources of knowledge carried in thisthesis have been duly acknowledged

HCMC, November 2016

PHẠM ĐỖ TƯỜNG VY

I would like to take this opportunity to express my deepest gratitude for thehelp, support and encouragement of the following people, who have contributed tothe completion of this thesis in their very own ways

Above all, I would like to express my immeasurable appreciation to mysupervisor – Dr Võ Hồng Đức for his precious time, support and advices to makethis thesis completed

Furthermore, I would like to send my great thanks to all the lecturers andstaffs at the Vietnam – The Netherlands Programme for their knowledge andsupports during my time joining in the program In specific, I am extremely grateful

to Dr Phạm Khánh Nam and Dr Trương Đăng Thụy for their valuable guidanceand support in the courses and thesis writing process

To all of my friends in Class 21 and my colleagues at TPF, I could neverthankful enough for your encouragement and support until the very end of this thesis

Last but not least, my deepest thanks and love to my parents, who havealways been beside me Without their unconditional love, none of this would havebeen possible

Two Stage Least Squares.

Crépon, Duguet and Mairesse

Fixed Effect

Generalized Method of Moments.General Statistic Office

Instrument Variables

Levinsohn and Petrin

Ordinary Least Squares

Olley and Parker

Research and Development

Small and Medium-sized Enterprises.Total Factor Productivity

The majority of enterprises in Vietnam is categorized as small and mediumsized (SMEs) firms which play an important role to the sustainable growth of theVietnamese economy As such, improving the productivity of the SMEs isessentially needed and this request becomes a crucial mission for the governments

It is generally accepted that innovation and technology improvement are key drivers

of productivity (Bartelsman & Doms, 2000) However, they have not been acknowledged by the SMEs in Vietnam even though their huge contribution tofirm’s productivity is unarguable

well-This study aims to examine the relationship between innovation andproductivity in the Small and Medium-sized Enterprises (SMEs) in Vietnam Toestablish and quantify this relationship, this study employs the two-stage process: (i)the estimation of total factor productivity for each firm; and (ii) a determination of

an innovation – productivity relationship In the first stage, total factor productivity

is estimated based on production function using the input and output approach.However, firms might adjust their input level according to expected productivityshock As such, a potential endogeneity caused by possible relationship betweeninput decision and productivity shocks (unobserved productivity shock) might exist

To deal with this problem of endogeneity, an approach developed by Levinsohn andPetrin is applied to estimate firm’s total productivity In the second stage, thesystem-GMM approach is adopted to examine the relationship between innovationand productivity

An unbalanced panel dataset from five Small and Medium-sized Enterprisessurveys from 2005 to 2013 is used in this study Findings from this study indicate that,

in the context of Vietnam, when innovation is measured as innovation expenditureintensity and high-quality labor share in total firm’s labor force, innovation activitiesprovide positive and significant impact on firm’s productivity In addition, past value of

could lead to higher (lower) level of productivity in the future The study also providesempirical evidence to confirm that larger firms might perform better than the relativelysmaller firms In contrast, capital structure provides negative impact on firm’sproductivity However, this study fails to provide any evidence to support the view thatlongevity of firm does provide significant impact on productivity of firms.

Key words: Vietnam SMEs; Total factor productivity; Productivity Shock;

Innovation, GMM.

TABLE OF CONTENTS

CHAPTER 1 1

INTRODUCTION 1

1.1 Problem statement 1

1.2 Research objectives 2

1.3 Research questions 2

1.4 Research motivations 2

1.5 Research scope and data 3

1.6 The structure of this study 3

CHAPTER 2 5

LITERATURE REVIEW 5

2.1 Schumpeter Theory of Innovation – How does Innovation play its role in economic development? 5

2.2 Productivity: concept and measurements 7

2.1.1 Concept 7

2.1.2 Measurements 7

2.1.3 General productivity determinants 12

2.3 Innovation: concept and measurements 16

2.1.4 Concept 16

2.1.5 Measurements 17

2.4 How has the relationship between innovation and firms’ performance been analysed in the literature? 18

CHAPTER 3 23

RESEARCH METHODOLOGY 23

3.1 An overview of Vietnamese Small and Medium-sized Enterprises 23

3.1.1 Statistic overview 23

3.1.2 Difficulties 26

3.2 Methodology 27

3.1.3 Conceptual framework 27

3.1.4 Model identification 29

3.3 Research hypotheses and concept measurements 34

3.4 Data sources 36

CHAPTER 4 39

EMPIRICAL RESULTS 39

4.1 Total Factor Productivity of Vietnamese SMEs 39

4.1.1 Data descriptions 39

4.1.2 Total factor productivity from production function estimation of Vietnamese SMEs 42

4.2 Innovation – Firm’s productivity relationship 45

4.1.3 Data descriptions 45

4.1.4 The relationship between innovation expenditure intensity and firm’s productivity 46

4.1.5 The relationship between high-quality labor share in total firm’s labor force and their productivity 49

CHAPTER 5 52

CONCLUSION AND POLICY IMPLICATION 52

5.1 Conclusion remarks 52

5.2 Policy implications 54

5.3 Limitation and potential future research 54

REFERENCES 56

APPENDIX 1: Empirical studies on general productivity determinants 62

APPENDIX 2: Empirical studies on relationship between innovation and firm’s performance 65

APPENDIX 3: Durbin – Wu Hausman test for endogeneity 69

APPENDIX 4: Durbin – Wu Hausman test for endogeneity 71

LIST OF TABLES AND FIGURES

Table 3.1: Classification of SMEs in Vietnam

Table 3.2: Concepts and measurements of variables used in the study

Table 3.3: Number of observation in selected industries in dataset

Table 3.4: Number of observation after filtering

Table 3.5: Number of observation after filtering in stage 2

Table 4.1: Descriptive statistics of production function variables

Table 4.2: Comparison of OLS, Fixed Effect and LP estimators in Foods, Woods

and Rubber and PlasticsTable 4.3: Comparison of OLS, Fixed Effect and LP estimators in Non-metallic

mineral, Fabricated metal and FurnitureTable 4.4: Descriptive statistics of TFP and its determinants

Table 4.5: Regression results of innovation expenditure intensity and firm’s

productivityTable 4.6: Regression results of high-quality labor share in total labor force and

firm’s productivityFigure 3.1: Number of enterprises at 31/12 (by size of employees)

Figure 3.2: Conceptual framework

CHAPTER 1 INTRODUCTION

This chapter introduces the research topic and presents research objectives,research questions and motivation Research scope and data requirement also arediscussed in this chapter

1.1. Problem statement

In line with Decree No 56/2009/ND-CP regarding assistances for thedevelopment of small and medium – sized enterprises (SMEs) in Vietnam, theSMEs defined as firms with total employee between 10 and 300, and total equityless than 100 billion dong Following these criterion, up to Mar 2015, total SMEs inVietnam account for over 90% of all enterprises These firms have created morethan half a billion of jobs every year These firms also contribute appropriately 40%overall GDP

SMEs play an important role to the sustainable growth of the economy.Improving the productivity of SMEs is essentially and urgently needed and thisneed becomes a crucial mission of the Vietnamese Government because the growth

of the economy depends significantly on the productivity of firms in the economy.Key drivers of firm’s productivity are innovation and technology improvement(Bartelsman & Doms, 2000) However SMEs in Vietnam have still struggled intheir operations and therefore lead to inefficiency One of the obstacles facing SMEs

in Vietnam is the process of acknowledging the important role of creatinginnovation and applying new technology in production to increase theirproductivity Innovation has not attracted great attention from the SMEs themselveseven though huge contribution to firms’ productivity is widely confirmed

The common measurement for innovation in empirical studies is R&Dexpenditure of a particular firm Various empirical studies have been conducted toquantify the relationship between R&D expenditure and firm’s performance

Conclusions vary from these studies including strong correlation between the two(Siedschlag, Zhang and Cahill (2010); Belderbos, Carree and Lokshin (2004); Crespiand Pianta (2009)) However, in the Vietnamese context, small and medium-sized firmshave not widely reported their spending on research and development activities Inaddition, innovation activities of SMEs is less formal and involved in many differentexercises as compared with larger firms As such, research on the impact of innovation

on SMEs productivity faces many obstacles in the Vietnamese context

A lack of interest in relation to the relationship between innovation and SMEsproductivity in Vietnam has opened up the interest of deep investigation It isespecially essential in the context of the economy dominated by SMEs andtechnology level is still low Therefore gaining further knowledge in this field isneeded for policy makers to orient the development creation and application ofinnovative activities toward the growth of firms as well as country

1.2 Research objectives

This study is conducted to provide an additional evidence on the relationshipbetween innovation and firms’ productivity for the Vietnamese SMEs The main

objective of this study can be summarized as Defining and quantifying the

relationship between innovation and productivity in firm level in the context of Vietnamese SMEs.

1.3. Research questions

The study aims to provide empirical evidence for the main questions emerged:

Is there any relationship between innovation and productivity in the context ofSMEs in Vietnam? If yes, then how does innovation can affect SMEs productivity?

1.4. Research motivations

This study aims to provide the closer look at the Vietnamese SMEs’ productivityusing the approach of Levinsohn and Petrin (2003), how it could be changed due tochanges in the level of innovation making Despite the fact that innovation play acrucial role in the development, the outcome of innovation activities are uncertainty

We do not know beforehand whether these activities would success

in creating value added to the firms Research results provide policy makers someevidences on how to appropriately allocate the available resources to obtain the targetproductivity This topic is interesting in the context of developing countries such asVietnam for two reasons as suggested by Indjikian and Siegel (2005) Firstly thebenefit of innovation might not be fully exploited in developing countries Secondly, inthese countries, national resources allocated to creating new innovation still arerestricted despite the fact that innovation plays an important role in global growth.

1.5. Research scope and data

The study aims to determine the relationship between innovation andproductivity in Vietnam SMEs from 2005 to 2013 in six selected industries include:(1) foods; (2) wood and wood-related products; (3) rubber and plastic products; (4)non-metallic mineral products; (5) fabricated metal products and (6) furniture The reasonwhy these six insuctries are selected in the study is that data of these industries is biggestand have accounted for nearly 70% of total SMEs in the five-round survey, therefore can

be representative for the whole dataset At the time data used in this study was collected,the dataset of 2015 survey was not fully gathered and published As such, data used inthis study only ends in 2013

1.6. The structure of this study

This study contains five chapters which can be presented as follow:

Chapter 2 provides theoretical and empirical studies on the relationshipbetween innovation and productivity Chapter 2 begins with Schumpeter Theory ofInnovation that explains the role of innovation to economic growth Then thischapter reviews the concept of productivity and the methods of how productivitycan be estimated as well as its determinants In addition, the definition of innovationand how it is measured are discussed in the chapter The relationship between thesetwo concepts has been reviewed through literature

Chapter 3 presents the methodology which is utilised in the study An overview

of Vietnam SMEs is discussed On the ground of literature review in Chapter 2, theconceptual framework is constructed The measurement of relevant variables and

regression techniques are described In addition, this section also includes theprocess of how to filter data.

Chapter 4 presents empirical results Statistical descriptive of data is presented

in this chapter Then, the findings on Vietnam SMEs’ productivity are described anddiscussed The results of regression in relation to the relationship betweeninnovation and productivity are presented in this chapter

Chapter 5 provides the summary of the main results and proposes some policyimplications based on the results described in Chapter 4 This Chapter also includesresearch limitation and suggests some further research direction in the future

CHAPTER 2 LITERATURE REVIEW

This chapter provides the literature review on the relationship betweeninnovation and productivity At first, Schumpeter Theory of Innovation that explainthe role of innovation in the economic growth is presented Then the concept,calculation and determinant of productivity is reviewed After that, this chapterpresents the definition and measurement methods of concept innovation In the end

of the chapter, the relationship between innovation and productivity has beenreviewed through empirical studies in the past

2.1 Schumpeter Theory of Innovation – How does Innovation play its

role in economic development?

Schumpeter was seen as a person who built very first basic foundation to the

theory of innovation and economic development In his famous book The Theory of Economic Development (published first time in 1912), he has argued that the whole

economy has its own business cycle in which technological innovations play animportant role When a new technology has been introduced and the economy is ready

to adapt then the economy would alter itself to fully employ the new technology andresulted in the upward trend of the business cycle If that new technology has beenintroduce at the time the economy is saturated and became more vulnerable to the anynegative shock and easily get into depression then only new technology might not helpout the whole economy Schumpeter also argued that firms should willing to take riskand invest in new technologies to take advantage of the profit at the early stage of thesenew technological innovations when the other firms haven’t applied

Together with The Theory of Economic Development (1934), Capitalism, Socialism and Democracy (1942) and Business Cycles: A Theoretical, Historical and Statistical Analysis of a Capitalist Process (1939), Schumpeter has contributed to the

economics theories with the role of innovation and entrepreneurship in the economicdevelopment He believed that innovation is the core driver of development as well

as emphasized the role of entrepreneur of smoothing the mechanism in whichrevolutionarily technical changes occurs via innovation and push the economy out

of its steady state

Schumpeter explained the development of the economy is mainly driven byinnovation which he categorized into five types:

(i) launching new products, whether these are about improving a part of products or totally new to the market,

(ii) introducing new method of production,

(iii) opening new markets which have not entered in the past yet,

(iv) searching/discovering new sources/suppliers for raw material and other inputs in production process,

(v) acquiring new market structures in any industry (i.e changing the

monopoly position)

How innovation could become driven to the growth of economy? According toSchumpeter, innovation can be expressed in a process of four steps: invention,innovation, diffusion and imitation (Schumpeter, 1942) in which the first two stepshave less impact while the last two have much more influence on the economicgrowth His arguments relied on the vague of economic achievements in the earlystage of innovation, after that economies would realize the potential of increasingsales or cost deduction when they come to the period of diffusion and imitation and

at that time they invest more in these innovation However purely ideas could notalone play the whole game, they need a power to implement these ideas At thatnecessarily, entrepreneur play important role of allocating the resources to theprocess of replacing old technologies with new ones which Schumpeter labelled as

creative destruction In other words, Schumpeter explained the economic

development through the process of creative destruction driven by innovations.

2.2. Productivity: concept and measurements

2.2.1. Concept

Productivity is the efficiency of the process in which firm, industry and countryconvert input factors in to output Therefore productivity is generally defined as theratio between output and inputs in the manufacturing process Productivity is a goodindicator to economic performance of firm, industry or country as a whole

There are two things could affect productivity: through the availability ofinput resources and through value adding to the products in producing process In afurther details, firm’s productivity could be decreased in the circumstances oflacking inputs or inputs were not used efficiently However through creating valueadded with available inputs and certain activities in manufacturing process helps toimprove productivity

2.2.2. Measurements

There are many ways to measure productivity, but they could be classifiedinto two groups: single factor productivity measures (in which productivity is theratio of output over single input) and multifactor productivity/total factorproductivity measures (a measure of output to several inputs)

In the group of single factor productivity, there are two ways to measureproductivity: labor productivity and capital productivity In both ways, productivityhas been expressed as quantity index of labor input/capital input over an index ofgross output or value added These measures are easy to calculate but they onlyreflect the partial productivity of workers’ capacity or capital intensity, howefficiency they are in combine with other input factors in production process Tohave a better index of productivity in which take into account contribution of morethan an input, multifactor productivity/total factor productivity turns out to be moreefficient measure Therefore in this research, total factor productivity has been used

to estimate firms’ productivity

Estimating total factor productivity through Production function estimatorshave been regularly used to address many relevant issues in the literature: the

relationship between foreign direct investment and domestic firms’ productivity(Javorcik, 2004), impact of R&D (Hall et al., 2009), impact of informationtechnology (Chun et al , (2015) These relationships are mostly estimated based onsimple Cobb-Douglas production function regression.

=F(,,)=

Where represents firm j’s output, is physical capital stock, is labor input anddenotes for firm’s level of efficiency, and are output elasticities with respect tocapital and labor

Based on the definition of productivity above, is referred to Total FactorProductivity and could be derived by taking natural logs of (1):

= 0+++

Where t subscript denotes time series and lower case letters are represented for log

value In equation (2), Total Factor Productivity has two components: 0 and , in which 0 isaverage productivity for all firms across time and captures firm’s deviation productivityfrom that average caused by unobserved factors affect firms’ output outside of inputs thencan be separated in two components: firm-level productivity and i.i.d component :

Then, the exponential of ̂ is the result of firm-level productivity.

Mainly there are two trends of approach of research in how to calculate totalfactor productivity, non-parametric and parametric With non-parametric technique,growth accounting is the most used based on a paper of Robert Solow in 1957 abouttechnical changes and production function Under the assumptions of constant return toscales and competitive factor markets, growth accounting method expresses how

much changes in output growth can be explained by changes in different types ofinput and changes in total factor productivity Although growth accountingtechnique is well–established and consistent, it cannot address the problem ofcausality, which is investment in technological changes can be driven and resulted

of productivity growth at the same time With parametric technique, econometricmethod has been applied to estimate total factor productivity in the relationshipbetween production inputs and output (production function estimators) There areseveral benefits by using econometric techniques: the parameters can be check forthe statistical significance; solving problem of endogeneity

2.2.2.1 Growth accounting method

Non - parametric growth accounting method was developed by Robert Solow

in his paper about the technical change through analyzing aggregate productionfunction (Solow, 1957) Growth accounting approach aims to determine how mucheconomic growth was due to contribution of inputs (growing by the movementalong the production function) and how much growth was due to the improvement

in technology (shift the production function) (Nelson, 1973) This approach has theassumption of constant return to scales, which means total elasticities of all inputfactors in production function equal one (from the equation (1), + = 1) Typicallythese input factors are weighted by their income shares (in case of calculatingproductivity at country level) (Cardona et al., 2013), or by their cost shares (whencalculating firm’s productivity)

Productivity is calculated by solving equation (4) without econometric sense. ̂ is called “Solow residual”, has positive value whenever the growth rate of output

rises faster than the growth rate of input factors “Solow residual” not only reflectsgrowth by changes in technological progress but also other factors that affect theefficiency in general outside of input factors (Schreyer, 2001)

2.2.2.2 Production function estimator methods

As mentioned above, there could exist problem of endogeneity caused bypossible relationship between input decision and productivity shocks (unobserved

productivity - ), which means firms might adjust their input level according toproductivity shocks For example, firms tend to increase their investment if theyobserve a lucrative productivity shock, in another way, if an unfavorable shockoccur, firms might reduce their level of workforce Therefore the result of inputcoefficients in the OLS regression might be biased and inconsistent (Eberhardt andHelmers, 2010).

After the problem of endogeneity arises in production function estimation,there are several solutions have been developed and applied in the literature:Instrumental Variables (IV) regression; ‘dynamic panel estimators – developed byArellano and Bond (1991) and Blundell and Bond (1998), commonly known asGeneralized Method of Moment (GMM) approach and the works of Olley andPakes (1996) which is categorized as ‘structural estimators’, then been furtherdeveloped by Levinsohn and Petrin (2003)

In the standard IV regression, to generate the consistent and unbiasedcoefficients, independent variables that causing endogeneity (in this case is inputquantities - K and L) need to be instrumented by variables that satisfy two conditions:these variables have relationship with input quantities, but are exogenous withunobserved productivity With the assumption of perfectly competitive input and output

markets, input prices (r, w) have been introduced as instruments for input quantities.

However input prices seem not to be good instruments as summary by Eberhardt andHelmers (2010) and Van Beveren (2012) for the following reasons (i) Lack ofinformation about input prices in most of dataset Even those information exist, they donot vary across firms enough to estimate the production function (ii) The assumption

of perfectly competitive inputs market seems hard to be hold because of the argumentthat productivity shocks might create market power for firms, then in turns affect toinput prices, causing the relationship between instrument variables and error term (iii)Even the perfectly competitive inputs market assumption is strictly hold, input pricesmight correlate with unobserved productivity in other ways That is the changes in

‘input price’ wages might be because of the unobserved labor quality, and thisunobserved labor quality become a part of unobserved productivity, then wages couldnot act as valid instrument for labor input in production function

estimation Similar mechanism with rental rate and capital stock and unobservedproductivity Because of the above reasons, standard IV regression using inputprices as instruments for input quantity could not yield consistent results.

It seems hard to find a strong instrument for input quantity in productionfunction regression to yield satisfactory results, Arellano and Bond (1991) andBlundell and Bond (1998) have contributed to the literature by proposingGeneralized Method of Moment (GMM) estimator In this approach, past values ofdependent and independent variables have been used as instruments to correctendogeneity problems These instruments also are valid with the argument that input

choices before time t are uncorrelated with productivity shock at time t,

Although GMM approach was an efficient tool in addressing endogeneityproblem and yield satisfy results, this approach are not constructed from structuralmodel based on firm’s behaviors (Eberhardt and Helmers, 2010) Olley and Pakes(1996) (OP) has developed a new approach that explains firm’s production functionusing observed firm’s behaviors Put in the simpler explanation, OP solved the

problem of endogeneity in the production function by using observed firm’s

investment decision to proxy for unobserved productivity They have made two

assumptions when building this approach, one is known as “monotonicityassumption”, in which stating that firm investment has strong positive relationshipwith capital stock and (unobserved) productivity, ( , ) and this relationship iscontinuous in and Therefore productivity can be determined by invertinginvestment function: = ( , ) Labor is not included in this function because it isassumed to be fully flexible that can proper alter immediately at the time ofobserving Another assumption is that only unobserved productivity is the factorthat can affect firm’s investment decision This assumptions is called “scalarunobservable” condition OP “structural estimator” determined through two steps.Details are below:

Firstly, from (3) output has been regressed on labor input and a proxy offirm-specific productivity:

=+ ( , )+

11

( , )= 0 + + ( , )

Equation (5) is in partially – linear form and is assumed to be exogenous with errorterm OP suggested a method based on third-order polynomial expansion to estimateequation (5) then get the unbiased and consistent result on labor coefficient and non-parametric part ( , )

In the second step, OP has regressed - on and ( , ) then yield unbiased and consistent result for capital input coefficient

“Structural estimator” has been further developed from OP method byLevinsohn and Petrin (2003) (LP) Instead of using investment as proxy forproductivity, , Levinsohn and Pertrin proposed to used intermediate inputs They havepointed out that intermediate inputs as proxy might be better satisfy with the

“monotonicity assumption” made in OP approach since the argument that firms withhigher level of capital and productivity would consume more intermediate inputs ismore reasonable than investment decision Furthermore data on intermediate inputs aregenerally available in most of firm level datasets while data on firm investment mighteither been missing or reported at nil value thus eliminate the situation of drop manyobservations in OP approach LP method still rely on two assumptions were made from

OP (“monotonicity assumption” and “scalar unobservable”) and employed the similarprocedure with OP to determine firm productivity

2.2.3. General productivity determinants

It is important to know about the sources as well as determinants ofproductivity From the definition of productivity, inputs of production (such ascapital, labor, material, energy, etc.) are general well-known as direct factorsaffecting firm’s productivity In addition, there are other factors also havesignificant effect on productivity such as: firm age, firm size, ownership status,credit accessibility, export intensity These factors could be allocated into twogroups: (i) exogenous factors including firm age, firm size, ownership status and (ii)endogenous factors: credit accessibility, export intensity

This section provides the empirical studies on how exogenous factors (firmage, firm size) and endogenous factors (credit accessibility) could affect firmproductivity Because the research scope is limited in small and medium-sized firmswhich mostly operating as private enterprises therefore the ownership might haveless impact on firm performance in Vietnamese context despite the fact thatownership does matter for productivity as confirmed by Cucculelli et al (2014),Margaritis and Psillaki (2010) and Kim (2006) Likewise, not many small andmedium-sized firms in Vietnam have international transactions so export intensitymight be not the sources of productivity differentials in Vietnamese SMEs.

2.2.3.1 Exogenous factors

Exogenous factors which are related to firm characteristic are reviewed inthis section is firm age, firm size and ownership status There are many empiricalstudy in which mention about the effect of these factors on firm’s productivity such

as De Kok et al (2006), Cucculelli et al (2014), Huergo and Jaumandreu (2004),Dhawan (2001), Tovar et al (2011) and Kim (2006) Cucculelli et al (2014) hasapplied two-stage approach to determine the sources of productivity in Italia usingdata from manufacturing firms In the first stage, they estimated firm’s total factorproductivity by applying Levinsohn and Petrin (2003) approach In the next stage,other variables (such as firm age, firm size, family-managed status, ownershipconcentration, capital intensity) are included in the regression of total factorproductivity obtained from first stage to examine the impact of these factors on firmproductivity They have concluded that family-managed firms are less productivethan non-family-managed firms, and this relationship is significantly and robust Inaddition, they have found the evidence for the increasing relationship between firmage and family-managed firm productivity, but no relationship between age andnon-family firm productivity Huergo and Jaumandreu (2004) studied on over 2,300Spanish manufacturing firms from 1990 to 1998 and found that firms at early stage

of operation enjoy high productivity growth (at 5%), and this rate decreasescontinuously for 8 years until equal the average productivity (at 2%)

In term of firm size, Dhawan (2001) when doing the study for US firms for theperiod of 1970 to 1989 found that small firms are more productive than large firms.The positive relationship between firm size and their productivity is then confirmed byCucculelli et al (2014) and Margaritis and Psillaki (2010) Tovar et al (2011) haveanalysed data of 17 Brazilian electricity distribution firms from 1998 to 2005 toexamine the effect of firm size on the productivity of Brazilian Electricity Industry.They determined productivity by using Stochastic Frontier Analysis and then TPF is

decomposed into three components: (i) pure technical efficiency change,

(ii) scale efficiency change and (iii) technical change They argued that through scale efficiency change and technical change, different in firm size can explain

productivity differential Because firm size is proved to have positive impact on TFP,

they suggested that mergers of small electricity distribution firms could lead to gain inproductivity

2.2.3.2 Endogenous factors

Factors that classified as endogenous because they are related to firmdecision that affect their productivity and lead to the different in those among firms.Endogenous factors reviewed in this section are firm capital structure andinternational trade decision

The relationship between firm capital structure and productivity has beenanalysed carefully in both theories and empirical studies The theory of agency suggeststhe negative effect of debt on firm performance Agency theory assumes that there areconflicts between owners and managers, both who are motivated by self-interest, theseconflicts lead to agency cost of equity (Jensen and Meckling, 1976) The agency costcould come from default risk in which firms are under the pressure of paying highinterest rate As a result, firms with higher debt ratio appear to perform less efficient

than firms with lower debt ratio (Myer, 1977) In another hand, free cash flow theory suggests that free cash flow in companies should be paid out to investors as it would

prevent managers from using it improperly (Jenson, 1986) Therefore higher leverageinduces firms to avoid misusing free cash flow and put firms under the pressure ofgenerate more cash to service their debt In this case, debt has positive

influence on firm’ performance Margaritis and Psillaki (2010) using data from threeindustries in France: chemicals, computers and textiles from 2002 to 2005 have foundthat firm’s capital structure is positive correlated with their efficiency and this effect isstronger with firms in chemicals and textiles industries They proposed two-stageapproach to estimate this relationship First, Data Envelopment Analysis and distancefunction approach are applied to estimate firm efficiency In the next stage, firm’sefficiency obtained from first stage is regressed against leverage and other controlvariables (ownership status, profitability, asset structure, growth opportunities and size)using dynamic OLS estimation to examine the impact of leverage on firm’s efficiency.

In contrast, studying on Korean manufacturing firms, Kim (2006) found the significantnegative effect of debt ratio on firm productivity However they also found the positiveimpact of debt ratio in Chaebols firms (which defined as family-managed, debt-dependent, diverse business activities firm)

In term of international trade decision, many studies have confirms thatinvolving in international activities does impact on firm’s productivity and could be asource of productivity Bernard and Jensen (1999) and Bernard and Wagner (1997)suggested two hypotheses about the reasons why productivity of exporters could higherthan non-exporters The first hypothesis about self-selection mechanism in whichhigher productivity firms are more likely to export because of additional costs(transportation, distribution, marketing, human, etc.) that creating barriers to the exportmarket for less productive firms In addition, firms desiring to export tend to improvetheir productivity themselves today in order to have the ability to export in the future.The second hypothesis emphasized the important of learning-by-exporting Throughexports, firms could learn about new process or technology from their importers orcompetitors, then they could improve their performance In addition, exporters have tocompete against a lot more competitors in severe environment then they have to pushthemselves harder and enjoy higher productivity than firms which only tradedomestically Aw, Roberts and Winston (2007) have confirmed the relationshipbetween export behaviour and firm’s productivity: export experience have significantpositive relationship with firm productivity

As mentioned before, for the reason of not many Vietnam SMEs involving ininternational trade, this study does not take international trade as an endogenousdeterminant of Vietnam SMEs productivity.

Appendix 1 provides the summaries on the empirical studies about generaldeterminants of productivity

2.3. Innovation: concept and measurements

2.3.1. Concept

Oslo Manual (OECD 2005, p.46) suggested the definition for innovation “isthe implementation of a new or significantly improved product (good or service), orprocess, a new marketing method in business practices, workplace organization orexternal relations” This definition has been widely used in studies and referred bymany institutions when conducting surveys on innovation aspects

There are four type of innovations which proposed in Oslo Manual (OECD2005):

(i) Product innovation in which new goods or services are introduced to the

market and their characteristics are much improved in compared with current goods orservices in term of appearance, technical identification, new function, or userfriendliness, etc

(ii) Process innovation implies improvement in the way of how goods and

services are produced and distributed which help to reduce costs or raise product’squality Process innovation directly relates to technique applied, supporting equipmentand software in the process of producing goods and services

(iii) Marketing innovation refers to improvements in product design, pricing

determination, promotion campaign, etc through new marketing method objecting tobest suit to the client need, occupy the market and increase sales

(iv) Organizational innovation refers to changes in organizational structure,

business environment, etc that helping in reducing administrative cost,

transaction cost, improving work efficiency In addition, organizationalinnovation not only involves internal activities but also external relationsimprovements (with suppliers, clients, state agencies, etc.)

It is necessary to clearly distinguish among these types of innovation becausethere are many innovations which their characteristics can be belong to more thanone type of innovation For instant, introducing new product which it needs a new

production process to be produced, therefore it can be categorized both product

innovation and process innovation Oslo Manual (2005) provided detail guidelines

on how to distinguish these types of innovation

2.3.2. Measurements

Different types of innovation can be easily represented by dummy variableswhich applied by many studies such as Hall, Lotti, & Mairesse (2008), Griffith,Huergo, Harrison, & Mairesse (2006), Mairesse, Mohnen, & Kremp (2005), Mairesse

& Robin (2009), Polder, Van Leeuwen et al (2009) However this measurementcould not adequately reflect differential in innovation intensity among firms (Mohnenand Hall, 2013) It could generate misleading results when examine innovation activitiesacross firms in different sizes that large firms could more innovative than small firms Infact, it is argued that large firms might involve in many activities thus these activitiescould fall into one of four innovation types Therefore innovation dummy variables mightnot suitable to come up with the conclusion that large firms are more innovative thansmall firms

Innovation can be measured in term of both input and output approaches.Input approaches refer to firm’s effort to introduce new products, improve theirproduction process, open new markets or raise firm’s efficiency In the output sides,innovation are reflected through new products introduced, successfully improvedproduction process, costs deduction or gain in efficiency (Mohnen and Hall, 2013)

With the input approached, innovation is commonly measured by Research andDevelopment (R&D) expenditures which involved in developing process of introducingnew products or production methods However, there are many non-R&D activities thatfirms are involved in and considered as innovation Oslo Manual (2005)

has pointed down several non-R&D innovation activities that firms might have.They can buy patents, pay royalties or scientific information then modify incompliance with their need They can improve their labor knowledge and skill viainternal training They can buy new equipment, software or improve their facilitiesthat affect innovative process They can improve their current management structure

or creating new method to introduce products to the market Together with purelyR&D activities, these above non-R&D activities also have the common objective ofinnovation to improve firm’s efficiency Therefore expenditures of these non-R&Dactivities should be taken into account in measuring innovation together with R&Dexpenses Another suggestion from Oslo Manual (2005) about measuringinnovation with the input approach is by the skilled employees of firm As skilledemployees is considered as key assets for innovation activities in which they help tofacilitate the process of adopt new technologies, manage manufacturing operationsand resolve the technological problems might occur

In the output side, innovation is generally measured based on its qualitywhich can be represented through revenue The percentage revenue share of newproducts or current products with added value through innovation process is aproper indicator for showing the results of innovation in overall firm’s performance.This indicator has been used to proxy for innovation in many studies such as MiguelBenavente (2006), Jefferson et al (2006), Siedschlag et al (2010) Anothermeasurement of innovation effect on firm’s performance is cost deduction due toprocess innovation (Peters, 2008)

2.4 How has the relationship between innovation and firms’

performance been analysed in the literature?

The relationship between innovation and firm’s performance has been wellanalysed in the literature However the conclusion about this relationship has notcome to the consensus In one hand, innovation can positive influence firm’sperformance, which can be measured by several indicators In another hand, manystudies pointed out negative impact caused from innovation

How innovation affect firm’s performance? From innovation to firm’sperformance is argued as process in which innovation inputs generate innovation outputand then these output contributes to the overall firm’s performance (Crépon, Duguetand Mairessec, 1998) Crépon, Duguet and Mairessec (1998) proposed a method toestimate the relationship between innovation and performance called CDM modelwhich is applied and developed by many researchers over the world such as MiguelBenavente (2006), Janz, Lööf, and Peters (2003), Mairesse, et al (2005) In theresearch of Siedschlag, Zhang and Cahill (2010), CDM model also is employed withpanel data of 723 firms from Community Innovation Survey of Ireland in period of2004-2008 and control for foreign ownership and international trade activities CDMmodel with three stages of estimation: (i) firm's decision to invest in innovation;

(ii) determine innovation output using innovation inputs and (iii) innovation outputand other production inputs in the relationship with final output production Throughthese three step, they concludes that: (i) foreign owned firms and domestic firms involved

in export activities are more likely to invest in innovation than firms with domesticactivities only; (ii) foreign owned firms and domestic firms involved in export activitiesare more likely to have innovation output Innovation expenditure have no significanteffect on innovation output; and (iii) innovation outputs have positive relationship withlabor productivity

The innovation – firm’s performance relationship is argued as causalrelationship (Belderbos, Carree and Lokshin (2004); Lokshin, Belderbos and Carree(2008); Parisi, Schiantarelli and Sembenelli (2006); Santos, Basso, Kimura and Kayo(2014)) In one hand, innovation creates new products, improves production process

or business practices then leads to an improvement in firm’s efficiency and theirperformance as well In another hand, firms with better performance tend to putmore effort on innovation creation Because of the causal relationship betweeninnovation and firm’s performance, IV or GMM estimation have been applied inmany studies to correct the endogeneity problem arises

Belderbos, Carree and Lokshin (2004) using data from Community Innovationsurvey in 1996 and 1998 for 2056 manufacturing firms to analyse the impact ofinnovation and firm performance in Netherlands Not only internal innovation

activities but also external innovative collaboration have taken into account indetermining this relationship They measure firm’s innovation by two set of variables:(i) internal innovation activities represented by internal innovation expenditure persales and (ii) external innovation collaboration through R&D cooperation dummies withcompetitors, suppliers, customers and universities or other research institutions.

Firm’s performance is expressed in labor productivity growth and sales of theproducts that new to the market growth IV regression has been applied to identifythe causal relationship between innovation and productivity, control for firm size, 2-digit industry dummies, ownership status, demand-pull and cost-push Productivity

in the previous period also is included in the model because of the argument thatpast level of productivity can impact on current productivity growth They foundthat different types of R&D collaboration and innovation intensity significantly andpositively affect productivity growth, but no significant effect of innovationintensity on innovation sales growth

Also using internal and external innovative activities to represent forinnovation, Lokshin, Belderbos and Carree (2008) found the significant positiverelationship between internal innovation and labor productivity Internal innovation

is defined in-firm’s R&D expenditure while external innovation is expenditure oncontracted R&D with other firms They applied GMM estimation for the dynamicpanel equation from augmented Cobb-Douglas production function for 304Netherlands manufacturing firms from 1996 to 2001 Innovation variables included

in the model beside internal and external R&D expenditure are their quadratic formsand interaction form The authors concluded that internal and external R&D arecomplement in the relationship with productivity with decreasing returns to scaleseffect, internal R&D plays important role in firm’s productivity and external R&Donly have significant impact in the circumstance in which firm has invested enough

in internal R&D

Parisi, Schiantarelli and Sembenelli (2006) did the research on 941manufacturing firms in Italy from two surveys in 1995 and 1998 to examine theinnovation – firm’s performance relationship They used product and process

innovation effort The study use two approaches to identify impact of innovation onfirm performance At first, Cobb-Douglas production function is applied againstoutput growth and innovation variables, instrumented by lag of ln(output/labor),ln(material/labor), ln(capital/labor), R&D intensity, size) In the second approach,TFP (calculated using Levinsohn and Petrin (2003)) is regressed against innovationvariables, instrumented by the same set of variables in the first approach The resultsshowed positive impact of process and product innovation on productivity Indetails, the impact of process innovation is bigger than product innovation Theseresults are robust in both approaches: TFP growth and Cobb-Douglas productionfunction estimation.

In contrast, many papers found no significant effect of innovation onproductivity Santos, Basso, Kimura and Kayo (2014) concluded that innovationefforts from innovative investment do not significant explain firm's performance Inaddition, Li and Atuahene-Gima (2001) explained the insignificant impact ofinnovation on firm’s performance through the uncertainty characteristic ofinnovation They argued that innovation activities are risky while consumingconsiderable resources but we do not know beforehand whether these activitiescreates value added to the firms Furthermore, it is required special resources andcapabilities in terms of organizational structure for innovation activities to generatepositive outcome to the firms as suggested by Branzei and Vertinsky (2006)

In Vietnam context, there is an emerging interest in studying about innovation,productivity and related concepts Nguyen et al (2007) has studied about therelationship between innovation and export of Vietnam SMEs in 2005, they have useddummy of whether firm introduces new product, new process or improves existingproduct to represent for innovation Vu and Doan (2015) also used these dummy aswell as marketing changes to proxy for innovation when investigate the relationshipbetween innovation and performance of Vietnam SMEs Firm’s performance ismeasured as gross profit within a year In their research, they also detected the problem

of endogeneity in innovation – firm’s performance relationship and have used 2SLSmodel to deal with that problem This research found that innovation

efforts in product, production process or marketing do have positive impact onfirm’s performance.

Productivity for Vietnamese firms have been analysed widely in theliterature Ha and Kiyota (2014) used data from Annual Survey on Enterprises from

2000 to 2009 and non-parametric method to estimate firm’s TFP when analysing therelationship between firm productivity and turnover Yang and Huang (2012) hasapplied Levinsohn and Petrin approach to estimate firm’s TFP for VietnameseSMEs However they focused on the effect of trade liberalization on productivitywhich is different with this study

This study expects to have a closer look at the important of innovation tofirm productivity of SMEs in Vietnam – a transition economy Innovation effort offirm is represented in broader measurements including: innovative expenditureintensity, dummy for innovation and share of high-quality employee in total laborforce Productivity in the study is measured using Levinsohn and Petrin approachwhich can solve the problem of endogeneity arisen due to the potential relationshipbetween input decision and productivity shock This method of estimating TFP israrely employed in the literature of Vietnam context

Appendix 2 provides the summaries on the related empirical studies on therelationship between innovation and firm’s performance

CHAPTER 3 RESEARCH METHODOLOGY

This chapter begins with the overview of Vietnam small and medium-sizedenterprises After that, based on the literature reviews in Chapter 2, the study’sconceptual framework is constructed and described The regression techniques thenare discussed together with construction of related variables employed in the model.The hypotheses of the relationship between innovation and productivity aredetermined In the end of the chapter, source and process of filter data are presented

3.1. An overview of Vietnamese Small and Medium-sized Enterprises3.1.1. Statistic overview

The common classifications about Small and Medium-sized enterprises of mostinternational institutions and country over the wall are usually based on number ofemployees, total revenues and/or total assets or equity In line with The Decree No.56/2009/ND-CP regarding assistances for the development of small and medium

– sized enterprises in Vietnam, small and medium – sized enterprises have beendefined as firms with total employee between 10 and 300, and total equity less than

100 billion dong The details of the classification following Decree No.56/2009/ND-CP is presented in Table 3.1 below:

Table 3.1: Classification of SMEs in Vietnam

Micro

enterprises

Source: Government’s decree No 56/2009/ND-CP

The number of enterprises in Vietnam has been increased significantly afterimplementing Enterprises Law in 2005 (Figure 3.1) Together with the increase oftotal enterprises, total SMEs also gone up dramatically, from 2006 to 2013 totalVietnamese SMEs has been doubled up, from 120,074 (in 2006) to 367,300 (in2013) In Vietnam context, the number of total micro, small and medium – sizedfirms is the largest and accounted for approximately 96% to 98% total firms everyyear from 2006 to 2013 The number of total enterprises has been increased year toyear, but the size of enterprises has been gradually tighten down In further details,

in 2006, 61% of total firms are micro firms (which have 1-10 employees) comparedwith 66% in 2006, in the second rank is small firms with 35% in 2006 and 32% in

2013, and large firms decreased from 4% in 2006 to only 1.6% in 2013 Thedownsizing trend of Vietnam enterprises leads to the insufficient medium and large– sized enterprises to guide the economy in the international integration

24

Figure 3.1: Number of enterprises at 31/12 (by size of employees)

Source: General Statistic Office (2006-2011, 2012, 2013)

SMEs has been growing significantly, accounted for the biggest part of total

enterprises and contributed significantly for the economy According to the Mid-term

Evaluation Report of the Implementation of Developing Small and Medium – sized

enterprises Plan in the period of 2011-2015, SMEs has four main contributions to the

economy: GDP, government budget, total investment and employment

In the structure of GDP, non-state owned enterprises (which SMEs account

up to 98.6%) has been contributed the most to the total GDP of the whole economy

in the period of 2009-2012, at around 48-49% In the second rank, the contribution

of state owned enterprises, in which 59.3% are SMEs, has been decreased from

37.32% (2009) to 32.57% (2012) due to the government privatization plan applied

for this kind of firm Foreign direct invested enterprises (in which 78,8% are SMEs)

is in the third rank with the stable contribution to total GDP at around 17-18%

during 2009 to 2012 Because of this GDP structure and the privatization process, it

is expected that SMEs in the non-state owned enterprises sector would become

more important in the growth of GDP in the future

The total amount of taxes and fees that SMEs has contributed accounting for

a large part in government budget since they are the key sector in the economy In

2010, SMEs contributed over VND 181,060 billion dong to the state budget,accounting for 41% total collected taxes and fees from all enterprises In 2011 and

2012, these figures are VND 181,210 billion dong (34%) and VND 205,260 billiondong (34%)

In term of the contribution to the total national investment, SMEs accountedfor a large part of total investment of the whole economy For further details, in

2010 total investment of SMEs was VND 236,119 billion dong, accounting for 32%total investment of enterprise sector In 2011, this amount of investment increasedsignificantly to VND 699,690 billion dong (57%) (almost from micro and small –sized enterprises) then decreased to VND 235,463 billion dong in 2012 (29%) Inthe structure of total investment from enterprise sector, investment from small-sizedfirms accounts for the largest proportion, 61%-68% from 2010 to 2012

Another contribution of SMEs to Vietnam economy is maintaining andcreating jobs for employees In 2010 Vietnamese SMEs had provided more than4.35 million jobs, accounting for nearly 45% total jobs in enterprise sector After 2years, in 2012 total jobs created by SMEs has increased 16.83% to 5.09 million,accounting for 47% total jobs The worker’s income in SMEs sector also has beenimproved overtime In 2010, the average income of an employees working in SMEswas VND 42 million dong/employee/year and this figure reaches to VND 46million in 2011 and continuous increases to VND 61 million in 2012, approximately90% average income of employee in the whole enterprise sector

It is obvious that SMEs play important roles in the economic growth ofVietnam They have had huge contributions in national income, government budget,and total investment and creating more jobs, stabilize worker’s income and improvingtheir living standards However Vietnamese SMEs are still facing a lot of difficulties

3.1.2. Difficulties

Insufficient internal capital, hard to access external capital: because these

firms are in small and medium – sized, so their internal capital stock are limited and

usually in shortage In addition they found it is hard to access to external capitalsources like banks and other financial institutions due to lack of collateral,untrustworthy financial operation or complex procedures or lack of information.Therefore they usually fail to raise capital when they want to expand the market,change technology or invest in new projects.

Outdate applied technology: technology used in most of these firms is not

updated as compared with other countries over the world A survey of UNDP haspointed out that technology import percentage of Vietnam only at 10% total exportand import while this average figure for the others country is 40% The low level oftechnology applied in Vietnamese SMEs could be explained by three reasons: (i)relatively low profit as compared with large firms so the accumulate capital forrenew technology is still limited; (ii) the ability of accessing to governmentpreferential programmes or supporting policies; (iii) Vietnamese SMEs are stillstanding out of supply chain, in-developed sub-industries Low level of appliedtechnology in Vietnamese SMEs could affect labor productivity, product quality andtheir competitiveness

Low level of management and labor force: in 2012, 75% workers are not

going through technical training process in official facilities In term of managers,

only 40% SMEs owners have university degree Moreover not many managers havebeen trained in economic knowledge, management business and law; managementdecisions are mostly based on experience Therefore Vietnamese SMEs’ operationalefficiency is still lower than other country in the region and over the world

3.2. Methodology

3.2.1. Conceptual framework

From the theories and empirical studies, the conceptual framework for thisstudy is built and illustrated as Figure 3.2 below:

Figure 3.2: Conceptual framework

The impact of innovation on productivity can be estimated through two stages:(i) Total factor productivity estimation: at this stage, total factorproductivity for each firm will be determined using Levinsohn and Petrin (2003)approach applied in production function Input variables is labor, capital and intermediateinputs Output variables is value added

(ii) Innovation and productivity relationship examination: the result of totalfactor productivity for each firm generated from stage (1) then will be regressed againstinnovation proxies: dummy variable of invest in innovation activities; innovationexpenditure intensity; share of high-quality employees in total labor force Thisrelationship are built on the ground of Schumpeter Theory of Innovation and empiricalstudies such as Siedschlag, Zhang and Cahill (2010); Belderbos, Carree and Lokshin(2004); Crespi and Pianta (2009); Santos, Basso, Kimura and Kayo (2014) and Lokshin,Belderbos and Carree (2008)

Beside the innovation variables, other control variables also will be added tothe regression at this stage, which are firm age, firm size, capital structureand past value of firm productivity These variables are suggested to haverelationship with firm’s productivity by many empirical

studies: Cucculelli et al (2014); De Kok et al (2006); Huergo and

Jaumandreu (2004); Dhawan (2001); Margaritis and Psillaki (2010) and

Aw, Roberts and Winston (2007)

The detail methodology for above two stages are presented in the nextsections

3.2.2 Model identification

3.2.2.1 First stage: Total factor productivity estimation using Levinsohn

and Petrin (2003) approach

Levinsohn and Petrin (2003) built up a new approach to calculate TFP in theframework of Olley and Pakes (1996) As mentioned in the Literature review,instead of using investment, LP used intermediate inputs to control for unobservedproductivity (part of error term that correlated with input) and proved that usingsuggested proxy would generate more consistent results in coefficients estimated

LP developed the estimation in two cases: one with output variable is grossrevenue and one with output variables is value added This study will employ LPapproach in value added case to estimate firm’s productivity The details of thismethod are described following LP method in value-added case starts with Cobb-Douglas production function in logs with two inputs: the freely variable input labor

(l) and the state variable capital (k) and output: value added (y) of firm i in year t:

= 0 + ++

Error term can be divided by two components: unobservable productivity(productivity shocks that correlated to inputs) and an i.i.d component which doesnot affect inputs choice decision Rewrite (7) we have:

= 0 + +++

LP made an assumption of perfect market competition in which inputs andoutputs price are identical for all firms in the market Therefore intermediate inputs

(i) demand can be expressed through capital and unobservable productivity without

presentation of inputs and output prices: = ( , ), which means that intermediateinputs have correlation with capital stock and unobservable productivity