The relationship between business networking and SMEs production efficiency

UNIVERSITY OF ECONOMICS

INSTITUTE OF SOCIAL STUDY

HO CHI MINH CITY

ERASMUS UNIVERSITY OF ROTTERDAM

VIETNAM

THE NETHERLANDS

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

THE RELATIONSHIP BETWEEN
BUSINESS NETWORKING
AND SMES PRODUCTION EFFICIENCY
By
LE HOANG LONG

MASTER OF ART IN DEVELOPMENT ECONOMICS

HCMC, NOVEMBER 2013


University of Economics

International Institute of Social Study

Ho Chi Minh City, Vietnam

Erasmus University of Rotterdam, The Netherlands

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

THE RELATIONSHIP BETWEEN BUSINESS NETWORKING
AND SMEs PRODUCTION EFFICIENCY

by L H

gL g

A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of

Master of Art in

Development Economics
Academic Supervisor Dr. V H g

Vietnam – Netherlands Programme, November 2013


DECLARATION
This is to certify that this thesis e titled “The relationship between business
networking and SMEs production efficiency”, whi h is submitted by me in fulfillment
of the requirements for the degree of Master of Art in Development Economic to the
Vietnam – The Netherlands Programme. The thesis constitutes only my original work
and due supervision and acknowledgement have been made in the text to all
materials used.

L H

gL

g


ACKNOWLEDGEMENT

I would not be possible to write this master thesis without the help and support
of people surrounding me.
Above all, I w uld li e t th
Ki

Hi , wh

lw ys l ves, t es

yf

ily, es e i lly

e f

d su

ts

e

y

the – H

g Th

the w y I have chosen.

I would like to express special appreciation to my supervisor, Dr. V H

g

,

who I have learned a lot from his guidance, useful recommendations and valuable
comments.
I would like to acknowledge all the lecturers at the Vietnam – Netherlands
Programme for their knowledge of all the courses, during the time I studied at the
program. I
h
Ch

Kh h N

ti ul , I
,

T

g teful t

g

ss

g Th y, M

f
h

Nguy
g Th h

T
h

g H i,
d

L V

, who support me significantly in the courses as well as in the thesis writing

process.

Last, but not least, I would like to thank my friends and colleagues at Banking
University of HCMC for their helps.

HCMC, November 2013
L H

iii

gL

g


ABBREVIATIONS

AE

Allocative efficiency

CIEM

Central Institute for economic mangement

CRS

Constant returns to scale

DEA

Data envelopment analysis

DMU

Decision making unit

GSO

General Statistics Office Of Vietnam

SE

Scale efficiency

SFA

Stochastic frontier analysis

SMEs

Small and medium sized enterprises

TE

Technical efficiency

TFP

Total-factor productivity

VRS

Variable returns to scale

iv

ABSTRACT
This study aims to examine the relationship between business networking and
the technical efficiency of small and medium sized enterprises (SMEs) in Vietnam. To
achieve this objective, this study proposes a framework to measure the production
efficiency of the SMEs; then, the study identifies the relationship between business
networking and their performance efficiency. Data Envelopment Analysis method is
employed in the first stage to measure the efficiency. In the second stage, the study
uses both Tobit and least squared regressions to examine the relationship between the
firm networking and its performance efficiency. The unbalanced data from the four
SMEs surveys, which cover the period of 6 years, from 2004 to 2010, will be
employed in this study. The research finds that the average technical efficiency scores
of SMEs in this period are moderately low, ranging from 48 percent to 70 percent
depending on the industries. Additionally, the relationship between business
networking and firm’s production efficiency appears to be different in different
indutries. For example, in food products and beverages, the network quantity is found
to have positive impact on the technical efficiency. However, network quality as well
as the network diversity might hinder the firms in this industry. The wood and wood
products and fabricated metal product experience a contradictory tendency when the
total network size and cluster size appear to have no impact, or even negative impact
on the technical efficiency. In these industries, the network quality appears to hold a
significantly crucial role than other dimensions of networking when it has positive
correlation with firm efficiency. Finally, the role of official business association
appears to be vague to firm efficiency.

v


TABLE OF CONTENTS
LIST OF TABLES......................................................................................................... ix
LIST OF FIGURES ........................................................................................................ x

Chapter 1: INTRODUCTION ...................................................................................... 1
1.1

Problem statement ............................................................................................. 1

1.2

Research objectives ........................................................................................... 3

1.3

Research questions ............................................................................................ 3

1.4

Research scope and data ................................................................................... 3

1.5

The structure of this study................................................................................. 3

Chapter 2: LITERATURE REVIEW ........................................................................... 5
2.1

Production efficiency: Concepts, measurements and sources .......................... 5

2.1.1

Concepts .................................................................................................. 5

2.1.2

Measurements ......................................................................................... 8

2.1.3

Efficiency measurement methods ........................................................... 9

2.1.4

Sources of technical efficiency ............................................................. 12

2.1.4.1

Exogenous sources ................................................................................ 13

2.1.4.2

Internal sources .................................................................................... 14

2.2

Business networking ....................................................................................... 16

2.2.1

Business networking and related concepts ........................................... 16

2.2.2

Components and roles of business networking .................................... 17

2.2.3

Relationship between business networking and technical efficiency ... 19

Chapter 3: RESEARCH METHODOLOGY ............................................................. 23
3.1

An overview of Vietnamese Small and Medium sized Enterprises................ 23

3.1.1

Growth and contribution of SMEs in Vietnam ..................................... 23

3.1.2

An overview of manufacturing SMEs .................................................. 26

vi


3.2

Conceptual framework and model specification ............................................ 27

3.2.1

The first stage: Efficiency measurement using the DEA method ........... 29

3.2.2

The second stage: Regression model .................................................. 32

3.3

Research hypotheses and concept measurements ........................................... 34

3.3.1

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

3.3.2

Concept and variable measurements .................................................... 35

3.4

Data source and filter process ......................................................................... 34

Chapter 4 EMPIRICAL RESULTS............................................................................ 37
4.1

Production efficiency of SMEs ....................................................................... 37

4.1.1

Data descriptions................................................................................... 37

4.1.2

Production efficiency of SMEs in Vietnam .......................................... 39

4.2

The relationship between business networking and production efficiency .... 41

4.2.1

Data description....................................................................................... 41

4.2.2

Regression results ................................................................................. 43

4.2.2.1

Network quantity .................................................................................. 46

4.2.2.2

Network quality .................................................................................... 49

4.2.2.3

Network diversity ................................................................................. 50

4.2.2.4

Cluster size ............................................................................................ 52

4.2.2.5

Participation in a business association .................................................. 53

Chapter 5: CONCLUSION AND POLICY IMPLICATION .................................... 55
5.1

Conclusion remarks ........................................................................................ 55

5.2

Policy implications .......................................................................................... 57

5.3

Limitations and recommendations for future research ................................... 58

REFERENCES ............................................................................................................ 60
Appendix 1: Empirical studies on the sources of technical efficiency....................... 65

vii


Appendix 2:

Empirical studies on the relationship between business network and

firm performance .......................................................................................................... 68
Appendix 3:

Empirical studies on the technical efficiency measurements of

manufacturing firms in Vietnam .................................................................................. 72

viii


LIST OF TABLES

Table 3.1:

Definition for SMEs in Vietnam ............................................................ 24

Table 3.2:

Main indicators of enterprises as of 01/01/2012, by sizes ...................... 26

Table 3.3:

Number and proportion of manufacturing firms from 2006 to 2011 ..... 26

Table 3.4:

Proportion of three main manufacturing industries ................................ 27

Table 3.5:

Concepts and measurements of variables in the study ........................... 33

Table 3.6:

Number of observations before and after filtering ................................. 35

Table 3.7:

Number of observations before and after filtering in the stage 2 ........... 36

Table 4.1:

Descriptive statistic of production factor variables ................................ 38

Table 4.2:

Average value of technical efficiency scores ......................................... 39

Table 4.3:

Proportion of efficient enterprises in the period 2004-2010 .................. 41

Table 4.4:

Descriptive statistic of efficiency index and its determinants ................ 43

Table 4.5:

The correlation matrix among variables and variance inflation factors . 44

Table 4.6:

Heteroscedasticity test for Pooled OLS model ....................................... 45

Table 4.7:

Regression results of network size and efficiency score ........................ 46

Table 4.8:

Regression results of network quality and efficiency score ................... 49

Table 4.9:

Regression results of network range and efficiency score ..................... 51

Table 4.10: Regression results of cluster size and efficiency score .......................... 52
Table 4.11: Regression results of business association and efficiency score ............ 54

ix


LIST OF FIGURES
Figure 2.1:

Production frontiers and technical efficiency....................................... 6

Figure 2.2:

Technical efficiency measurement ....................................................... 8

Figure 3.1 (a): Number of enterprises at 31/12 (by size of total assets) .................... 25

Figure 3.1 (b): Number of enterprises at 31/12 (by size of employees) ..................... 25
Figure 3.2:

Conceptual framework ....................................................................... 28

Figure 4.1:

CRS frontier and VRS frontier ........................................................... 42

x


Chapter 1:
INTRODUCTION
This chapter introduces the research topic and the problem statement. The
research objectives, the research questions and the research scope and data are also
included in this section. This chapter will end with the introduction of the thesis
organization.

1.1

Problem statement
Small and medium sized enterprises (SMEs) hold a crucial role in the

economic development, especially in developing countries including Vietnam.
Compared to large sized enterprises, SMEs appear to bring more merits to the
economy in terms of generating jobs, meeting the urgent demand immediately and
growing rapidly and efficiently (Assefa, 1997 in Admassie & Matambalya, 2002;
Hallberg, 1999). In the developing countries including Vietnam, SMEs have played
a major role to contribute significantly to reduce the unemployment rate. Often

being labor-intensive, SMEs help creating jobs for low skilled labor, which is
redundant in the developing countries (Schmitz, 1995; Hallberg, 1999). According
to the General Statistic Office of Vietnam, a number of formal SMEs (legally
registered firms) are 305,000 firms, accounting for 97.5 per cent of the total firms in
January 2012. This figure may be underestimated because of the lack of informal
SMEs statistics. These numbers of enterprises generate approximately 5 million
jobs and obtain about VND 4,600 billion revenue annually. In spite of the large
number and sustaintial contribution to the economy, SMEs have to deal with
countless problems to survive and develop. In the developing countries, SMEs often
face to the lack of resources such as capital, information, and knowledge. Hallberg
(1999) stated that information is a more serious problem to the SMEs rather than the
large firms, while Beck & Demirguc-Kunt

1

(2006) advocated the influence of


capital shortage to the SMEs' growth. In this circumstance, business networking can
be a solution when it can help the SMEs overcome problems of resources.

Firms, particularly small and medium sized enterprises (SMEs), can exploit
the business network as a source of information, knowledge and competitive
advantage (Dyer & Singh, 1998). As such, business networking appears to be the
channel of resources. Furthermore, the benefits of business network have been
demonstrated in many empirical studies (e.g. Gulati, 1999; Dyer & Singh, 1998;
Lechner, Dowling & Welpe, 2006). Many scholars presented the positive
relationship between business network and firm growth and development (for
example, Schoonjans, Cauwenberge & Bauwhede, 2011; Lechner et al., 2006).

In Vietnam, network can bring the entrepreneurs many benefits such as
information, knowledge and other substitution resources. There appears to be a
significant correlation between network and firm efficiency in the case of Vietnam.
However, empirical studies to examine the link between business network and
Vietnam SMEs efficiency are limited. This study will present the evidence of this
linkage between business networking and production efficiency of the SMEs using
panel data and the data envelopment analysis (DEA) technique, which is an
effective method for measuring firm efficiency. The thesis deals with the
manufacturing SMEs in three major industries, which include food products and
beverages, wood and wood products and fabricated metal products. These three
industries, which account for over 50% of the total number of SMEs in Vietnam and
often deal with the problems of poor production capacity and the resource
constraint, can represent for the population of Vietnamese SMEs.

2


1.2

Research objectives
The study aims to examine the relationship between business networking

and production efficiency of SMEs in Vietnam. As such, it has two main objectives
which can be stated as follows:
(i)

Estimating and analyzing the production efficiency of SMEs.

(ii)

Investigating the relationship between the business networking and
the efficiency scores obtained from the first--stage. The study
attempts to exam the multi-dimensional impact of business
networking on the production efficiency such as network quantity,
network quality and network diversity.

1.3

Research questions
The main research question this paper attempts to answer is: Is there any

relationship between the business networking and the production efficiency of
SMEs in Vietnam? If yes, then how can business networking can influence the
production efficiency of SMEs?

1.4

Research scope and data
The study will examine the relationship between business networking and

the SMEs efficiency using the panel data for the period from 2004 to 2010. Three
selected industries include: (i) food products and beverages; (ii) woods and wood
products; and (iii) fabricated metal products. Of 18 industries, these three industries
have accounted for over 55 percent of the total number of SMEs in Vietnam (CIEM,
2011; CIEM, 2013); therefore, they can represent for the SMEs population.

1.5

The structure of this study
This study is presented in five chapters, which are constructed as follows:

3


Chapter 2 reviews the literature as well as empirical studies on the
relationship between business networking and firm production efficiency. It begins
with the definitions and determinants of the production efficiency. This chapter then
discusses the networking definition and its crucial role to the firms. Business
networking can influence production efficiency both directly and indirectly. In
addition, its impact on firm production efficiency can be etheir positive or negative
depending on the circumstances.

Chapter 3 presents the research methodology, in which both data
envelopment analysis and regression technique are discussed. This chapter also
provides the conceptual framework as well as the concept measurements. Five
hypotheses to examine the multi-dimensional impact of business networking on the
production efficiency are included. In addition, this chapter introduces the data
source and filter mechanism.
Chapter 4 presents the empirical results. The statistic descriptions of the
data are presented. Then, the findings of production efficiency of the SMEs will be
represented and discussed. This section also produces the regression results that
provide evidence on the relationship between business networking and production
efficiency.

Chapter 5 will summarize the main results along. Some policy implications
are proposed based on the results obtained from Chapter 4. This chapter also
outlines limitations and suggests the directions for future research.

4

Chapter 2:
LITERATURE REVIEW
This chapter will review the literature on the relationship between business
networking and firm production efficiency. Initially,

the

concepts, the

measurements and the determinants of the production efficiency will be analyzed.
This chapter then discusses the definitions of business networking as well as its
functions. The empirical studies on the relationship between business networking
and the production efficiency will be examined at the end of the chapter.

2.1

Production efficiency: Concepts, measurements and sources

2.1.1

Concepts
Production efficiency is one of the most central topics of economics

research at firm’s level. The concept of production efficiency is derived from the
production process, which converts input factors (including labor and capital) into
products (or production outputs). The overall or economic efficiency can be
decomposed into two components: (i) technical efficiency and (ii) allocative
efficiency.

5


Figure 2.1:

Production frontiers and technical efficiency

y
technical change

B
C

A

0

The former component is proposed for long time, accompanied with the
concept of production possibility frontiers (PPF). Production frontiers describe the
maximum possible outputs for given inputs and technology level. In the production
process, due to the limited input factors, firms are only able to just produce on or
below the frontiers. Therefore, firms achieve technical efficiency when they
produce in the production frontiers (point B and point C in Figure 1). In a formal
definition, Koopmans (1951) stated that an efficient point is attained if it is feasible
and if there is no other point higher than it. Accordingly, a technically efficient firm
can increase its output if and only if there is a reduction in another output or at least
an increase in an input. The definition of Farell (1957) is well-accepted and is often
considered the pioneer definition of technical efficiency. Farell (1957, p. 254) stated
th t fi
ut ut f

g i s effi ie y whe it su eeds i “
give sets f i

uts” This defi iti

du ing as large as possible an
is ge e lly

w

s the

output-oriented viewpoint. As a supplement, Coelli et al. (2005) mentions the inputorientated view as an efficient firm could produce a given output with the minimum
of inputs combinations. Derived from the production process, technical efficiency
can be understood as production efficiency.

6


The latter concept (allocative efficiency) reflects how efficient firms control
their costs. Allocative efficiency represents the capability of a firm to combine or
mix the inputs sets to produce the given output within the minimum budget. While
technical efficiency can be measured from the production function, estimation of
allocative efficiency requires cost, revenue or profit function.

Another crucial concept in efficiency is scale efficiency. In Figure 1,
although both firm B and firm C are in the production frontiers, they have different
productivity levels. Productivity is measured by the ratio of output and input
quantities, which is equal to the slope of a ray drawn from the origin through the

point. The productivity gap between firm B and firm C is derived from the impact
of scale. Many studies (Fä e, G ss
Fä e, G ss

f&R

f & L vell, 1983; Banker & Thrall, 1992;

s, 1998; C elli et l , 2005…) represented the measurement

of scale efficiency. Nevertheless, they have not reached the final definition of scale
efficiency. Coelli et al. (2005, p. 58) stated th t: “S le effi ie y is

si

le

concept that is easy to understand in a one-input, one-output case, but it is more
difficult to conceptualize in a multi-input, multi- ut ut situ ti

” I this study,

scale efficiency can be understood as a difference between the firms in the most
technically productive scale and the firm with the remaining scales. It appears to be
a component which is derived from technical efficiency.

In order to identify the relationship between business networking and
production efficiency, this study will consider production efficiency as technical
efficiency in both assumptions: (i) constant returns to scale (pure technical
efficiency); and (ii) variable returns to scale (technical efficiency including scale

efficiency).

7


2.1.2

Measurements
This section will represent the basic measurements of efficiency in a simple

case with two inputs and one output under the assumption of a constant return to
scale. The below-mentioned measurements are from the input-orientated approach,
which will be employed in this study.

Figure 2.2:

Technical efficiency measurement

x2 /y

S

C

AE

Q*

TE

P
Q

R

C’

0

S’
x1 /y

The simple production model with two inputs x1 , x2 and one output y , the
measurements are demonstrated in Figure 2. Let xP , xQ and x* represent the input
vectors associated with point P , Q and Q * respectively. In addition, let w represent
the vector of input prices.

The iso-quant curve SS ' is a collection of many combinations ( x1 , x2 ) ,
which produce same amount of output. Therefore, firms working in this curve (at
point Q and Q * ) are technical efficient, while other firms (like point P ) are not. The
technical efficiency can be calculated by the ratio:

8


TE 

Ratio

0Q

QP w ' xQ
 1

0P
0 P w ' xP

QP
represents the amount of required input reduction to be more
0P

efficient (move form point P to point Q ). Therefore, technical efficiency index (TE
index), which always takes the value between 0 and 1, can reflect the technical
efficiency of a firm.

The iso-cost curve CC ' represents the mix of inputs subject to the same and
minimum cost. Then, the allocative efficiency (AE) can be measured by the ratio:
AE 

0R 0Q * w ' x *


0Q 0Q w ' xQ

Firm producing at point Q * gains both TE and AE. As such, it achieves
overall economic efficiency (OE):
OE  TE  AE 

0Q 0 R 0 R w ' x *




0 P 0Q 0 P w ' xP

The scale efficiency is resulted from the differences between the technical
efficiency in case of constant returns to scale (CRS) and this one in case of varied
returns to scale (VRS) (Fä e et l , 1983; C elli et l , 2005):
SE 

2.1.3

TECRS
SEVRS

Efficiency measurement methods
Production efficiency is such an appealing area of research that many

studies have attempted t fi d ut the “best”

eth d t esti

te C elli et l (2005)

summarized that there are at least four popular methods to calculate these concepts:
1.

Least square econometric production models

2.

Total factor productivity indices (TFP index)

9


3.

Data envelopment analysis (DEA)

4.

Stochastic frontier analysis (SFA)

Four techniques can be classified into two sub-groups based on their
assumptions and applications. Assuming that all firms are technically efficient, the
objectives of the initial two methods are to estimate the technical change rather than
the TE and AE. Without under the assumption that all firms are technically efficient
and taking into account the scale efficiency measurement, DEA and SFA are used
commonly in calculating relative efficiency among firms (Coelli et al., 2005). As
above-mentioned analyses, the technical efficiency can be derived from the concept
of production frontiers, where a firm can belong to the curve (technically efficient)
or stay below the curve (technically inefficient). However, the "true" curve is
unknown; therefore, based on their own assumptions, both methods attempts to
develop the curve by identifying the most efficient firms and forming the
production boundary.

SFA is a parametric method that needs to form a production function based
on some economic theories. When a functional form is specified (for example,
Cobb-Douglas’s production function), the parameters will be estimated. The error
term derived from the regression will contain both noise component and
inefficiency component. The strength of a parametric method is that if the selection

of the

du ti

fu ti

is “t ue”, the

e su e e t

be

l ulated more

accurately. Using a production function, SFA can fix the issue of statistical noise of
non-parametric methods. For example, SFA can include relevant variables into the
function to measure the accurate efficiency indices while DEA cannot. However,
this characteristic is also the drawback of the method. The production function is
difficult to define; even in some cases, it is unreasonable to identify the function.
Because this thesis is aiming to the large number of SMEs in three industries, the
"true" production function form becomes considerably difficult to identify.

10


In a different approach, DEA is a mathematical technique, which compares
the inputs/outputs ratio to identify the "best" firms and form an envelopment curve.
As a non-parametric approach, the weakness of DEA is the statistical noise issue.
However, DEA has some merits that make it better than SFA in many cases. Firstly,
the materials of DEA can be chosen flexibly subject to the object of the researchers.

Shafer & Byrd (2000), for example, can choose three inputs related to investments
and two outputs to identify the efficiency of firm investments in information
technology. Secondly, the result of DEA can be used extensively for many
objectives. In many cases, DEA gives the efficiency indices for each Decision
Making Unit (DMU) and even presents a component that should be adjusted to
achieve efficiency. In other researches, the efficiency indices also can be used as a
variable for the second regression stage. Thirdly, extended DEA can fix some
problems of statistical noise. We can overhaul DEA by adding the environmental
factors as non-discretionary variables into the original DEA (in the case of using
only one-stage DEA) or running an additional regression (in the case of using twostage DEA). Finally, DEA appears to be fairly simple and easy to calculate for both
multi-outputs and multi-inputs. Thanks to these merits of DEA method, this study
will employ it to calculate the efficiency scores of the manufacturing SMEs in
Vietnam.

DEA method was introduced by Farrell (1957) and first applied in an
empirical by Charnes, Cooper & Rhodes (1978). In this first empirical study,
Charnes et al. (1978) proposed an input-orientation approach under the CRS
assumption. DEA also has been used as a formal term since this paper was realized
in a public domain. Contributing to the development of this method, Fä e et l
(1983) constructed it under the assumption of VRS. Since then, this technique has
been widely used in measuring production efficiency in many industries such as:
manufacturing, banking, public and non-profit organizations.

11


In the initial approach to DEA method, Farell (1957) represented a measure
of technical efficiency when he compared all given technology firms and calculated
the relative efficiency scores for each firm. In the input-orientation approach, firm
which produces a given output with minimum sets of input will gain a unity score

of technical efficiency. Inefficient firm's score will be calculated by one minus
maximum proportion of redundant input. In the output-orientation approach, with
given input and technology, firm is technical efficiency and gains unity if it can
produce maximum quantity of output. Meanwhile, score of technically inefficient
firm is calculated as the proportion of its output compared to output of the efficient
firm and, as such, this score is less than one.

This study also uses this technique in the first stage to identify the relative
production efficiency of SMEs in Vietnam.

2.1.4

Sources of technical efficiency
Timmer (1971, p. 777) concluded that "The extent of technical efficiency in

an industry is, then, important. Knowledge of the sources of any inefficiencies is
doubly important". This study is generally considered as a pioneer study using twostage approach to identify the determinants of technical efficiency. Traditional
inputs of production such as capital, labor, material, land and natural resources
influence directly technical efficiency. Additionally, there are also a number of
other factors that have significant impact on firm’s performance. Fried et al. (1999)
and Fried et al. (2002) classified these factors into three categories: (i) managerial
components, (ii) ownership components and (iii) regulatory components. The first
category may also be understood as internal components, while the two latter may
considered as exogenous components. Aiming to identify the relationship between
business networking and technical efficiency, this study organizes these
determinants in only two groups as following: (1) Exogenous factors, which are
related to firm demographic or characteristics such as: age, ownership, size; and (2)

12

Internal factors, which influence firm management ability to translate the inputs into
outputs.

This study will present empirical studies on two exogenous factors (age and
size) and two internal factors (information and credit accessibility). Although many
studies demonstrate that ownership is a crucial determinant of the technical
efficiency, the empirical of SMEs in Vietnam shows that Vietnamese SMEs are
almost in private sector and do business as a household enterprise. Therefore, the
ownership may be not the source of differences in the technical efficiency of
Vietnamese SMEs.

2.1.4.1 Exogenous sources
Empirical studies in the first-group factors such as age and size are plentiful
such as Timmer (1971), Pitt & Lee (1981), Admassive & Matambalya (2002),
Binam et al. (2003). As the pioneer, Timmer (1971) applied his proposal of twostage regression in the case of the US agricultural production at the State level. In
the first stage, Timmer ran a regression for the traditional Cobb-Douglas production
function to investigate the inefficiency of each state. In the next phase, other
variables such as age proportion, education and tenant were employed to examine
their impacts on the inefficiencies. Timmer concluded that higher proportion of
middle age operators have positive impact on technical efficiencies. Pitt & Lee
(1981) also used two-stage regression approach in the case of Indonesian weaving
industry and concluded that age of firm, size and ownership are main resource of
technical efficiency. This study found that age has negative relationship with
efficiency. Studying on small and medium scale firms, Admassie & Matambalya
(2002) based on Tanzanian SMEs survey in three sectors: food, textile and tourism
to identify the linkage between external factors such as age, size and technical
efficiency of firm. They argued that age of firm can positively influence the
technical efficiency according to theory of learning-by-doing. However, learning-

13


by-doing has the decreasing marginal effect when firm is mutual. Furthermore,
young firms tend to have better ability of applying new technology than old firms.
Therefore, firm age can have negative impact on efficiency as the results of
Admassie & Matambalya (2002) and Binam et al. (2004).

In term of firm size, Admassie & Matambalya (2002) argued that both too
small firms and too big firms have trouble with management and supervision. In
case of SMEs, firm size was found to have positive impact on firm efficiency. This
result is in line with Pitt & Lee (1981) and Hallberg (1999). Rios & Shively (2004)
applied non-parametric method (DEA) to identify technical efficiency and cost
efficiency of 209 small farming households in Vietnam. In the second stage, they
employed two-tail Tobit model to regress the efficiencies with some farms'
characteristic factors, which includes farm size. The result also indicated the same
with above-mentioned studies when farm size has positive impact on farm
efficiency. Also objecting to small scale firms, Nikaido (2004) showed opposite
result when firm size influences negatively on technical efficiency. This study
argued that small firms may receive large supports from government rather than the
bigger ones, so they have no incentive to become bigger.

2.1.4.2 Internal sources
Internal sources include factors that influence the firm management ability
and lead to differences in firm efficiency. This section will discuss the impact of
information and credit accessibility on the technical efficiency.

The role of information significantly influences on firm behavior and
performance. As mentioned in many microeconomics textbooks, for example,
Pindyck & Rubinfeld, 7th edition, 2008, asymmetric information can lead to adverse

selection and damage the firm performance as well as social welfare. Raju & Roy
(2000) demonstrated that information is more valuable in a more competitive

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