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

UNIVERSITY OF ECONOMICS

INSTITUTE OF SOCIAL STUDIES

HO CHI MINH CITY

THE HAGUE

VIETNAM

THE NETHERLANDS

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

HCMC, NOVEMBER 2016


University of Economics

International Institute of Social Study

Ho Chi Minh City

The Hague

Vietnam

The Netherlands

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

HCMC, NOVEMBER 2016


DECLARATION
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 Netherlands Programme. This
is my original work and all sources of knowledge carried in this thesis have been duly
acknowledged.
HCMC, November 2016

PHẠM ĐỖ TƯỜNG VY


ACKNOWLEDGEMENT

I would like to take this opportunity to express my deepest gratitude for the
help, support and encouragement of the following people, who have contributed to
the completion of this thesis in their very own ways.
Above all, I would like to express my immeasurable appreciation to my
supervisor – Dr. Võ Hồng Đức for his precious time, support and advices to make
this thesis completed.
Furthermore, I would like to send my great thanks to all the lecturers and staffs
at the Vietnam – The Netherlands Programme for their knowledge and supports
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 guidance and
support in the courses and thesis writing process.
To all of my friends in Class 21 and my colleagues at TPF, I could never
thankful 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 have always
been beside me. Without their unconditional love, none of this would have been
possible.


ABBREVIATION

2SLS

Two Stage Least Squares.

CDM

Crépon, Duguet and Mairesse.

FE

Fixed Effect.

GMM

Generalized Method of Moments.

GSO

General Statistic Office.

IV

Instrument Variables.

LP

Levinsohn and Petrin.

OLS

Ordinary Least Squares.

OP

Olley and Parker.

R&D

Research and Development.

SMEs

Small and Medium-sized Enterprises.

TFP

Total Factor Productivity.


ABSTRACT

The majority of enterprises in Vietnam is categorized as small and medium
sized (SMEs) firms which play an important role to the sustainable growth of the
Vietnamese economy. As such, improving the productivity of the SMEs is essentially
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 well-acknowledged by
the SMEs in Vietnam even though their huge contribution to firm’s productivity is
unarguable.

This study aims to examine the relationship between innovation and
productivity in the Small and Medium-sized Enterprises (SMEs) in Vietnam. To
establish 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 productivity
shock. As such, a potential endogeneity caused by possible relationship between input
decision and productivity shocks (unobserved productivity shock) might exist. To
deal with this problem of endogeneity, an approach developed by Levinsohn and
Petrin is applied to estimate firm’s total productivity. In the second stage, the systemGMM approach is adopted to examine the relationship between innovation and
productivity.
An unbalanced panel dataset from five Small and Medium-sized Enterprises
surveys 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
expenditure intensity and high-quality labor share in total firm’s labor force,
innovation activities provide positive and significant impact on firm’s productivity.
In addition, past value of firm’s productivity also has significant relationship with its
current level. This finding implies that higher (lower) level of current productivity


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

3.1.5.

Research hypotheses .............................................................................34

3.1.6.

Concept and variable 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 Plastics

Table 4.3:

Comparison of OLS, Fixed Effect and LP estimators in Non-metallic
mineral, Fabricated metal and Furniture

Table 4.4:

Descriptive statistics of TFP and its determinants

Table 4.5:

Regression results of innovation expenditure intensity and firm’s
productivity

Table 4.6:

Regression results of high-quality labor share in total labor force and
firm’s productivity

Figure 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 are
discussed in this chapter.

1.1.

Problem statement
In line with Decree No. 56/2009/ND-CP regarding assistances for the

development of small and medium – sized enterprises (SMEs) in Vietnam, the SMEs
defined as firms with total employee between 10 and 300, and total equity less than
100 billion dong. Following these criterion, up to Mar 2015, total SMEs in Vietnam
account for over 90% of all enterprises. These firms have created more than 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 this need
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 in their
operations and therefore lead to inefficiency. One of the obstacles facing SMEs in
Vietnam is the process of acknowledging the important role of creating innovation
and applying new technology in production to increase their productivity. Innovation
has not attracted great attention from the SMEs themselves even though huge
contribution to firms’ productivity is widely confirmed.

The common measurement for innovation in empirical studies is R&D
expenditure of a particular firm. Various empirical studies have been conducted to
quantify the relationship between R&D expenditure and firm’s performance.
1


Conclusions vary from these studies including strong correlation between the two
(Siedschlag, Zhang and Cahill (2010); Belderbos, Carree and Lokshin (2004); Crespi
and Pianta (2009)). However, in the Vietnamese context, small and medium-sized
firms have not widely reported their spending on research and development activities.
In addition, innovation activities of SMEs is less formal and involved in many
different exercises 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 SMEs
productivity in Vietnam has opened up the interest of deep investigation. It is
especially essential in the context of the economy dominated by SMEs and
technology level is still low. Therefore gaining further knowledge in this field is
needed for policy makers to orient the development creation and application of
innovative 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 relationship

between 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 of SMEs
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’

productivity using the approach of Levinsohn and Petrin (2003), how it could be
changed due to changes in the level of innovation making. Despite the fact that
innovation play a crucial role in the development, the outcome of innovation activities
are uncertainty. We do not know beforehand whether these activities would success
2


in creating value added to the firms. Research results provide policy makers some
evidences on how to appropriately allocate the available resources to obtain the target
productivity. This topic is interesting in the context of developing countries such as
Vietnam for two reasons as suggested by Indjikian and Siegel (2005). Firstly the
benefit of innovation might not be fully exploited in developing countries. Secondly,
in these countries, national resources allocated to creating new innovation still are
restricted 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 and

productivity 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
reason why these six insuctries are selected in the study is that data of these industries
is biggest and 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 in this 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 relationship between

innovation and productivity. Chapter 2 begins with Schumpeter Theory of Innovation
that explains the role of innovation to economic growth. Then this chapter reviews
the concept of productivity and the methods of how productivity can be estimated as
well as its determinants. In addition, the definition of innovation and how it is
measured are discussed in the chapter. The relationship between these two 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, the
conceptual framework is constructed. The measurement of relevant variables and
3

regression techniques are described. In addition, this section also includes the process
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 and
discussed. The results of regression in relation to the relationship between innovation
and productivity are presented in this chapter.
Chapter 5 provides the summary of the main results and proposes some policy
implications based on the results described in Chapter 4. This Chapter also includes
research limitation and suggests some further research direction in the future.

4


CHAPTER 2
LITERATURE REVIEW

This chapter provides the literature review on the relationship between
innovation and productivity. At first, Schumpeter Theory of Innovation that explain
the role of innovation in the economic growth is presented. Then the concept,
calculation and determinant of productivity is reviewed. After that, this chapter
presents the definition and measurement methods of concept innovation. In the end
of the chapter, the relationship between innovation and productivity has been
reviewed 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 an
important 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 and
resulted in the upward trend of the business cycle. If that new technology has been
introduce at the time the economy is saturated and became more vulnerable to the any
negative shock and easily get into depression then only new technology might not
help out the whole economy. Schumpeter also argued that firms should willing to
take risk and invest in new technologies to take advantage of the profit at the early
stage of these new 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 economic
development. He believed that innovation is the core driver of development as well
5


as emphasized the role of entrepreneur of smoothing the mechanism in which
revolutionarily technical changes occurs via innovation and push the economy out of
its steady state.
Schumpeter explained the development of the economy is mainly driven by
innovation 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 to
Schumpeter, innovation can be expressed in a process of four steps: invention,
innovation, diffusion and imitation (Schumpeter, 1942) in which the first two steps
have less impact while the last two have much more influence on the economic
growth. His arguments relied on the vague of economic achievements in the early
stage of innovation, after that economies would realize the potential of increasing
sales 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 not
alone play the whole game, they need a power to implement these ideas. At that
necessarily, entrepreneur play important role of allocating the resources to the process
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.

6


2.2.
2.2.1.

Productivity: concept and measurements
Concept

Productivity is the efficiency of the process in which firm, industry and

country convert input factors in to output. Therefore productivity is generally defined
as the ratio between output and inputs in the manufacturing process. Productivity is
a good indicator to economic performance of firm, industry or country as a whole.
There are two things could affect productivity: through the availability of input
resources and through value adding to the products in producing process. In a further
details, firm’s productivity could be decreased in the circumstances of lacking inputs
or inputs were not used efficiently. However through creating value added with
available inputs and certain activities in manufacturing process helps to improve
productivity.

2.2.2.

Measurements
There are many ways to measure productivity, but they could be classified into

two groups: single factor productivity measures (in which productivity is the ratio of
output over single input) and multifactor productivity/total factor productivity
measures (a measure of output to several inputs).
In the group of single factor productivity, there are two ways to measure
productivity: labor productivity and capital productivity. In both ways, productivity
has been expressed as quantity index of labor input/capital input over an index of
gross output or value added. These measures are easy to calculate but they only reflect
the partial productivity of workers’ capacity or capital intensity, how efficiency they
are in combine with other input factors in production process. To have a better index
of productivity in which take into account contribution of more than an input,
multifactor productivity/total factor productivity turns out to be more efficient
measure. Therefore in this research, total factor productivity has been used to estimate
firms’ productivity.

Estimating total factor productivity through Production function estimators
have been regularly used to address many relevant issues in the literature: the
7


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

𝛽

𝑌𝑗 = F(𝐴𝑗 , 𝐾𝑗 , 𝐿𝑗 ) = 𝐴𝑗 𝐾𝑗 𝑘 𝐿𝑗 𝑙

(1)

Where 𝑌𝑗 represents firm j’s output, 𝐾𝑗 is physical capital stock, 𝐿𝑗 is labor
input and 𝐴𝑗 denotes for firm’s level of efficiency, 𝛽𝑘 and 𝛽𝑙 are output elasticities
with respect to capital and labor.
Based on the definition of productivity above, 𝐴𝑗 is referred to Total Factor
Productivity and could be derived by taking natural logs of (1):
𝑦𝑗𝑡 = 𝛽0 + 𝛽𝑘 𝑘𝑗𝑡 + 𝛽𝑙 𝑙𝑗𝑡 + 𝜀𝑗𝑡

(2)

Where t subscript denotes time series and lower case letters are represented
for log value. In equation (2), Total Factor Productivity has two components: 𝛽0
and 𝜀𝑗𝑡 , in which 𝛽0 is average productivity for all firms across time and 𝜀𝑗𝑡 captures
firm’s deviation productivity from that average caused by unobserved factors affect

firms’ output outside of inputs. 𝜀𝑗𝑡 then can be separated in two components: firmlevel productivity 𝑤𝑗𝑡 and i.i.d component 𝑣𝑗𝑡 :
𝑦𝑗𝑡 = 𝛽0 + 𝛽𝑘 𝑘𝑗𝑡 + 𝛽𝑙 𝑙𝑗𝑡 + 𝑤𝑗𝑡 + 𝑣𝑗𝑡

(3)

Therefore researchers can get firm’s productivity from estimating (3) and
solving for 𝑤𝑗𝑡 :
̂
̂
𝑤
̂
𝑗𝑡 = 𝑦𝑗𝑡 − 𝛽𝑘 𝑘𝑗𝑡 − 𝛽𝑙 𝑙𝑗𝑡

(4)

Then, the exponential of 𝑤
̂
𝑗𝑡 is the result of firm-level productivity.
Mainly there are two trends of approach of research in how to calculate total
factor productivity, non-parametric and parametric. With non-parametric technique,
growth accounting is the most used based on a paper of Robert Solow in 1957 about
technical changes and production function. Under the assumptions of constant return
to scales and competitive factor markets, growth accounting method expresses how
8


much changes in output growth can be explained by changes in different types of
input and changes in total factor productivity. Although growth accounting technique
is well–established and consistent, it cannot address the problem of causality, which
is investment in technological changes can be driven and resulted of productivity

growth at the same time. With parametric technique, econometric method has been
applied to estimate total factor productivity in the relationship between production
inputs and output (production function estimators). There are several benefits by
using econometric techniques: the parameters can be check for the 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 production
function (Solow, 1957). Growth accounting approach aims to determine how much
economic growth was due to contribution of inputs (growing by the movement along
the production function) and how much growth was due to the improvement in
technology (shift the production function) (Nelson, 1973). This approach has the
assumption of constant return to scales, which means total elasticities of all input
factors in production function equal one (from the equation (1), 𝛽𝑘 + 𝛽𝑙 = 1).
Typically these input factors are weighted by their income shares (in case of
calculating productivity at country level) (Cardona et al., 2013), or by their cost shares
(when calculating firm’s productivity).
Productivity is calculated by solving equation (4) without econometric sense.
𝑤
̂
𝑗𝑡 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 reflects
growth by changes in technological progress but also other factors that affect the
efficiency 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 by
possible relationship between input decision and productivity shocks (unobserved
9


productivity - 𝑤𝑗𝑡 ), which means firms might adjust their input level according to
productivity shocks. For example, firms tend to increase their investment if they
observe a lucrative productivity shock, in another way, if an unfavorable shock occur,
firms might reduce their level of workforce. Therefore the result of input coefficients
in the OLS regression might be biased and inconsistent (Eberhardt and Helmers,
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 by
Arellano and Bond (1991) and Blundell and Bond (1998), commonly known as
Generalized Method of Moment (GMM) approach and the works of Olley and Pakes
(1996) which is categorized as ‘structural estimators’, then been further developed by
Levinsohn and Petrin (2003).
In the standard IV regression, to generate the consistent and unbiased
coefficients, independent variables that causing endogeneity (in this case is input
quantities - K and L) need to be instrumented by variables that satisfy two conditions:
these variables have relationship with input quantities, but are exogenous with
unobserved 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 and Helmers (2010) and Van Beveren (2012) for the following reasons. (i)
Lack of information about input prices in most of dataset. Even those information

exist, they do not 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 argument that productivity shocks might create market power for firms, then
in turns affect to input prices, causing the relationship between instrument variables
and error term. (iii) Even the perfectly competitive inputs market assumption is
strictly hold, input prices might correlate with unobserved productivity in other ways.
That is the changes in ‘input price’ wages might be because of the unobserved labor
quality, and this unobserved labor quality become a part of unobserved productivity,
then wages could not act as valid instrument for labor input in production function
10


estimation. Similar mechanism with rental rate and capital stock and unobserved
productivity. Because of the above reasons, standard IV regression using input prices
as instruments for input quantity could not yield consistent results.
It seems hard to find a strong instrument for input quantity in production
function regression to yield satisfactory results, Arellano and Bond (1991) and
Blundell and Bond (1998) have contributed to the literature by proposing Generalized
Method of Moment (GMM) estimator. In this approach, past values of dependent and
independent variables have been used as instruments to correct endogeneity
problems. These instruments also are valid with the argument that input choices
before time t are uncorrelated with productivity shock at time t, 𝑤𝑗𝑡 .
Although GMM approach was an efficient tool in addressing endogeneity
problem and yield satisfy results, this approach are not constructed from structural
model based on firm’s behaviors (Eberhardt and Helmers, 2010). Olley and Pakes
(1996) (OP) has developed a new approach that explains firm’s production function
using 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 𝑤𝑗𝑡 . They have made two
assumptions when building this approach, one is known as “monotonicity

assumption”, in which stating that firm investment has strong positive relationship
with capital stock and (unobserved) productivity, 𝑖𝑗𝑡 (𝑘𝑗𝑡 , 𝑤𝑗𝑡 ) and this relationship is
continuous in 𝑘𝑗𝑡 and 𝑤𝑗𝑡 . Therefore productivity 𝑤𝑗𝑡 can be determined by inverting
investment function: 𝑤𝑗𝑡 = 𝑓𝑡 (𝑖𝑗𝑡 , 𝑘𝑗𝑡 ) . Labor is not included in this function because
it is assumed to be fully flexible that can proper alter immediately at the time of
observing 𝑤𝑗𝑡 . Another assumption is that only unobserved productivity 𝑤𝑗𝑡 is the
factor that can affect firm’s investment decision. This assumptions is called “scalar
unobservable” condition. OP “structural estimator” determined through two steps.
Details are below:
Firstly, from (3) output 𝑦𝑗𝑡 has been regressed on labor input 𝑙𝑗𝑡 and a proxy
of firm-specific productivity:
𝑦𝑗𝑡 = 𝛽𝑙 𝑙𝑗𝑡 + 𝜑𝑡 (𝑖𝑗𝑡 , 𝑘𝑗𝑡 ) + 𝑣𝑗𝑡
11

(5)


where:
𝜑𝑡 (𝑖𝑗𝑡 , 𝑘𝑗𝑡 ) = 𝛽0 + 𝛽𝑘 𝑘𝑗𝑡 + 𝑓𝑡 (𝑖𝑗𝑡 , 𝑘𝑗𝑡 )

(6)

Equation (5) is in partially – linear form and 𝛽𝑙 is assumed to be exogenous
with error term 𝑣𝑗𝑡 . OP suggested a method based on third-order polynomial
expansion to estimate equation (5) then get the unbiased and consistent result on labor
coefficient 𝛽𝑙 and non-parametric part 𝜑𝑡 (𝑖𝑗𝑡 , 𝑘𝑗𝑡 ).
In the second step, OP has regressed 𝑦𝑗𝑡 - 𝛽𝑙 𝑙𝑗𝑡 on 𝑘𝑗𝑡 and 𝜑𝑡 (𝑖𝑗𝑡 , 𝑘𝑗𝑡 ) then yield
unbiased and consistent result for capital input coefficient 𝛽𝑘 .
“Structural estimator” has been further developed from OP method by
Levinsohn and Petrin (2003) (LP). Instead of using investment as proxy for

productivity, 𝑤𝑗𝑡 , Levinsohn and Pertrin proposed to used intermediate inputs. They
have pointed out that intermediate inputs as proxy might be better satisfy with the
“monotonicity assumption” made in OP approach since the argument that firms with
higher level of capital and productivity would consume more intermediate inputs is
more reasonable than investment decision. Furthermore data on intermediate inputs
are generally available in most of firm level datasets while data on firm investment
might either been missing or reported at nil value thus eliminate the situation of drop
many observations in OP approach. LP method still rely on two assumptions were
made from OP (“monotonicity assumption” and “scalar unobservable”) and
employed the similar procedure with OP to determine firm productivity.

2.2.3.

General productivity determinants
It is important to know about the sources as well as determinants of

productivity. From the definition of productivity, inputs of production (such as
capital, labor, material, energy, etc.) are general well-known as direct factors
affecting firm’s productivity. In addition, there are other factors also have significant
effect on productivity such as: firm age, firm size, ownership status, credit
accessibility, export intensity. These factors could be allocated into two groups: (i)
exogenous factors including firm age, firm size, ownership status and (ii) endogenous
factors: credit accessibility, export intensity.

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This section provides the empirical studies on how exogenous factors (firm
age, firm size) and endogenous factors (credit accessibility) could affect firm
productivity. Because the research scope is limited in small and medium-sized firms

which mostly operating as private enterprises therefore the ownership might have less
impact on firm performance in Vietnamese context despite the fact that ownership
does matter for productivity as confirmed by Cucculelli et al. (2014), Margaritis and
Psillaki (2010) and Kim (2006). Likewise, not many small and medium-sized firms
in Vietnam have international transactions so export intensity might 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 in this
section is firm age, firm size and ownership status. There are many empirical study
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) has applied twostage approach to determine the sources of productivity in Italia using data from
manufacturing firms. In the first stage, they estimated firm’s total factor productivity
by applying Levinsohn and Petrin (2003) approach. In the next stage, other variables
(such as firm age, firm size, family-managed status, ownership concentration, capital
intensity) are included in the regression of total factor productivity obtained from first
stage to examine the impact of these factors on firm productivity. They have
concluded that family-managed firms are less productive than non-family-managed
firms, and this relationship is significantly and robust. In addition, they have found
the evidence for the increasing relationship between firm age and family-managed
firm productivity, but no relationship between age and non-family firm productivity.
Huergo and Jaumandreu (2004) studied on over 2,300 Spanish manufacturing firms
from 1990 to 1998 and found that firms at early stage of operation enjoy high
productivity growth (at 5%), and this rate decreases continuously for 8 years until
equal the average productivity (at 2%).

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In term of firm size, Dhawan (2001) when doing the study for US firms for
the period 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 by Cucculelli et al. (2014) and Margaritis and Psillaki (2010). Tovar et al.
(2011) have analysed data of 17 Brazilian electricity distribution firms from 1998 to
2005 to examine 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 in
productivity.

2.2.3.2.

Endogenous factors

Factors that classified as endogenous because they are related to firm decision
that affect their productivity and lead to the different in those among firms.
Endogenous factors reviewed in this section are firm capital structure and
international trade decision.
The relationship between firm capital structure and productivity has been
analysed carefully in both theories and empirical studies. The theory of agency
suggests the negative effect of debt on firm performance. Agency theory assumes that
there are conflicts between owners and managers, both who are motivated by selfinterest, these conflicts lead to agency cost of equity (Jensen and Meckling, 1976).
The agency cost could come from default risk in which firms are under the pressure

of paying high interest 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 leverage induces firms to avoid misusing free cash flow and put firms under
the pressure of generate more cash to service their debt. In this case, debt has positive
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influence on firm’ performance. Margaritis and Psillaki (2010) using data from three
industries in France: chemicals, computers and textiles from 2002 to 2005 have found
that firm’s capital structure is positive correlated with their efficiency and this effect
is stronger with firms in chemicals and textiles industries. They proposed two-stage
approach to estimate this relationship. First, Data Envelopment Analysis and distance
function approach are applied to estimate firm efficiency. In the next stage, firm’s
efficiency obtained from first stage is regressed against leverage and other control
variables (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 significant negative effect of debt ratio on firm productivity. However they also
found the positive impact of debt ratio in Chaebols firms (which defined as familymanaged, debt-dependent, diverse business activities firm).
In term of international trade decision, many studies have confirms that
involving in international activities does impact on firm’s productivity and could be
a source of productivity. Bernard and Jensen (1999) and Bernard and Wagner (1997)
suggested two hypotheses about the reasons why productivity of exporters could
higher than non-exporters. The first hypothesis about self-selection mechanism in
which higher productivity firms are more likely to export because of additional costs
(transportation, distribution, marketing, human, etc.) that creating barriers to the
export market for less productive firms. In addition, firms desiring to export tend to
improve their productivity themselves today in order to have the ability to export in

the future. The second hypothesis emphasized the important of learning-byexporting. Through exports, firms could learn about new process or technology from
their importers or competitors, then they could improve their performance. In
addition, exporters have to compete against a lot more competitors in severe
environment then they have to push themselves harder and enjoy higher productivity
than firms which only trade domestically. Aw, Roberts and Winston (2007) have
confirmed the relationship between export behaviour and firm’s productivity: export
experience have significant positive relationship with firm productivity.

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