Technical efficiency of small and medium enterprises in manufacturing sector in viet nam

UNIVERSITY OF ECONOMICS

INSTITUTE OF SOCIAL STUDIES

HO CHI MINH CITY

THE HAGUE

VIET NAM

THE NETHERLANDS
VIET NAM – NETHERLANDS

PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
-------------------------------------------

TECHNICAL EFFICIENCY OF SMALL AND MEDIUM ENTERPRISES IN
MANUFACTURING SECTOR IN VIET NAM
By
NGUYEN THI THU HUONG

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

Ho Chi Minh City, December 2013


UNIVERSITY OF ECONOMICS

INSTITUTE OF SOCIAL STUDIES

HO CHI MINH CITY

THE HAGUE

VIET NAM

THE NETHERLANDS

VIET NAM – NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
------------------------------------------TECHNICAL EFFICIENCY OF SMALL AND MEDIUM ENTERPRISES IN
MANUFACTURING SECTOR IN VIET NAM
A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
By
NGUYEN THI THU HUONG

Academic supervisor
Dr. PHAM KHANH NAM

Ho Chi Minh City, December 2013


ACKNOWLEDGEMENTS
This paper has could not be started and completed without the help of several
individuals who supported me directly and indirectly. First of all, I appreciate my
supervisor Dr. Pham Khanh Nam so much for his enthusiastic assistance. He has not
only given me genuine intellectual guidance in academy but also encouraged me a lot
through the analysis process. It is so hard for me to complete this research without his
profound advice. Thus, I am very grateful to him. I am also thankful to Dr. Nguyen
Trong Hoai for sharing his knowledge and practice experiences in researching which

are very useful for this study. I am very grateful thank Dr. Truong Dang Thuy for his
comment and advice about thesis research design. I also thank my friend Le Minh Tri
for sharing his advantage discussions on econometric techniques. Last but not least, I
would like to thank my parents, and all my other family members for their concern and
invaluable moral support.


ABSTRACT
Most enterprises in Viet Nam is Small and medium enterprises (SMEs). In 2009 SMEs
account for 98,54 percent in total enterprises in economy. So, this paper examines the
technical efficiency of small and medium enterprises in manufacturing sector in Viet
Nam. The paper concentrated on evaluating the technical efficiency of the firms. Then
it identifies the determinant of technical efficiency in these small and medium
enterprises. The data set collected from survey of Central Institute for Economic
Management (CIEM), Department of education (DoE), and Institute of Labor Science
and Social Affairs (ILSSA) in 2009 with 1.968 observations. This paper resulted in a
high technical efficiency in 2009. Levels of technical efficiency ranged from 0.94
percent to 0.98 percent. Regarding factors influencing technical efficiency, the paper
shows that only firm age and area have a positive relationship with technical efficiency.
Others factor such as firm size, sub-contractor, competitor, enterprise launched new
product and improve product have a negative relationship.

Keywords: small and medium enterprise in manufacturing sector, technical efficiency,
stochastic frontier production function, determinant, Viet Nam.


TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION…………………………………………………….1
1.1

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

1.2

Research objective………………………………………………………3

1.3

Scope of study…………………………………………………………...3

CHAPTER 2: LITERATURE REVIEW…………………………………………….5
2.1

Theoretical framework for technical efficiency……………………….5

2.2

Empirical literature on technical efficiency…………………………...9

2.3

Conceptual framework………………………………………………..19

CHAPTER 3: OVERVIEW VIET NAM SMEs AND DOMESTIC
MANUFACTURING SECTOR……………………………………………………..21
3.1

Overview of Vietnam SMEs…………………………………………..22

3.2

Domestic manufacturing sector………………………………………26

CHAPTER 4: RESEARCH METHODOLOGY…………………………………...28
4.1

Empirical model……………………………………………………….28

4.2

Data and variables……………………………………………………..31

CHAPTER 5: RESEARCH RESULT………………………………………………34
5.1

Descriptive statistic……………………………………………………34

5.2

Regression results……………………………………………………...36

CHAPTER 6: CONCLUSION AND POLICY RECOMEMDATON…………….45
REPERENCE
APPENDIX


List of Tables
Table 1:

Definition for Small and Medium Enterprises in Viet Nam………..21

Table 2:

Enterprises by labor scale and type in 2009…………………………23

Table 3:

Enterprise by capital size and economic sector and type in
2009..........................................................................................................23

Table 4:

Contributions

to

State

budget

of

SME

in

the

2007-2009

periods………………………………………………………………….25
Table 5:

Variables and their descriptive……… ...……………………………32

Table 6:

Deterministic statistic of main variables……………………………..34

Table 7:

Correlation coefficient of variables…………………………………..37

Table 8:

Generalized Likelihood Ratio Test…………………………………...38

Table 9:

Distribution of Efficiency Level of Sample Firms……………. ……38

Table 10:

Average Efficiency Level by Firm Size………………………………39

Table 11:

Estimate frontier production function using Cob-Douglas production
function…………………………………..…………………………….40

Table 12:

Determinant of technical efficiency………………………..…………42

Table 13:

Number of enterprises by labor scale in 2000 to 2009……………...52

Table 14:

Laborers by sector.………………………………………...………….52

Table 15:

Gross output of industry at constant 1994 prices……………………53

Table 16:

Manufacturing SMEs in Operation (2006-2009)…………………….54


List of Figures

Figure 1: Technical, allocative, and overall efficiency…………………………………5
Figure 2: Production frontier and technical efficiency………………………………….6


CHAPTER 1:
1.1

INTRODUCTION

Problem statement
Since Innovation 1986, private ownership has been encouraged in industries,
commerce and agriculture. Thanks largely to these reforms, Vietnam achieved around
8% annual GDP growth from 1990 to 1997, and the economy continued to grow at an
annual rate of around 7 percent from 2000 to 2005, making Vietnam become one of the
world's fastest growing economies. Growth still remained strong during the late 2000s
global recession, holding at 6.8 percent in 2010. The development is still stable until
now, especially in small and medium enterprises (SMEs).
SMEs are the most important part of Viet Nam economy. They play a vital role
in promoting economic growth, increasing national income, creating jobs for workers
and contributing to the economic restructuring towards industrialization.
According to the Vietnam Chamber of Commerce and Industry (VCCI), until the date
of 31/12/2011, Viet Nam has 543,963 enterprises. In that, there are nearly 97 percent of
small and medium enterprises, mainly private businesses. SMEs use 51 percent of the
workforce and contribute more than 40 percent of the national GDP. If we take 133,000
cooperatives, farms and about 3 million individual businesses, this region contributes
up to 60 percent of GDP. SMEs not only contribute significantly to the economic
development of the country, but also create more than one million new jobs per year
for the most untrained labor, contribute to poverty reduction, enhance security society.
Manufacturing is one of the main fields of SMEs. Output of manufacturing
accounted for 51.3 percent in total gross output of industry (2009). It has also made
significant contributions to the Gross domestic product (GDP). However this figure has
gone down through several years: 21.13 percent in 2007; 20.22 percent in 2008 and
reduced to 19.96 percent in 2009. The question is: why did this figure reduce? Because
productivity growth is contributed by many factors such as scale efficiency change,

1

technical efficiency change and technical progress. So, the reduction bases on this
factor? Many papers in previous time showed that technical efficiency is the important
factor effecting to productivity growth: Viet Le (2011), Viet Le and Charles Harvies
(2010), Hung T. Pham, Thanh L. Dao and Barry Reilly (2010); Nguyen Khac Minh and
Giang Thanh Long (2007); Nguyen Thang (2005); Tran Thị Bich (2008); Pham (2009);
Nguyen Khac Minh (2007). Most of these studies used firm level data from surveys in
1997, 2002, 2005, 2007. Their technical efficiency reached more than 30 percent to
nearly 90 percent. It means that the firm can increase their current level of output with
the same level of input to reach maximum efficiency.
Furthermore, the determinant of technical efficiency is also an import matter to
concern. Nunes (2011) shows that age and size as well as cash flow is restrictive
factors of the growth in SMEs. In Viet Nam, number of labor in manufacturing sector
of the SMEs is 5,665 thousand people in 2007 and increase up to 6,449 people in 2009.
The number of enterprise also increases from 155,771 enterprises to 248,842
enterprises in 2009 (nearly 63 percent increasing). Other factor contributing to
productivity is material. Material is the most important to industry, especially in
manufacturing. That is essential input in production process. By using inputs
effectively the enterprises can raise output. Besides, there are many factors related to
technical efficiency include internal factors and external factors.
So, it is necessary to continue researching technical efficiency in firm, especially in
manufacturing SMEs in Viet Nam. The question is which level of technical efficiency
in manufacturing SMEs is and what factors affect technical efficiency? Which policy
we can use to improve technical efficiency? So, this paper will estimate the technical
efficiency of Vietnamese manufacturing SMEs to evaluate firm performance and
answer above question.

2

1.2

Research objective
General research objective of this thesis is to investigate technical efficiency of
small and medium enterprise in manufacturing sector in Viet Nam.
Specific of objectives are:

a. To measure technical efficiency of manufacturing SMEs in Vietnam
b. To examine determinants of technical efficiency of manufacturing SMEs in Vietnam
1.3

Scope of study
The analytical unit in this thesis is small and medium enterprises in
manufacturing sector in Viet Nam. The reason for choosing manufacturing industry is
this industry uses the same production function. So, there will be an overview of the
effectiveness of this sector without bias. Thereby overall assessment of the
effectiveness of the sector and make appropriate policy.
The year was selected for this study is 2009. The previous years there have been
a lot of studies evaluating the effectiveness of TE in SMEs: 2002, 2005, and 2007. In
2009 SMEs are more variable, increasing both the number of employees and the size of
the enterprises. So assessing the increasing or decreasing of TE is necessary. Thereby
the Government will have the appropriate policies in order to maintain and increase the
TE next years.
The methodology chosen is stochastic frontier approach. This paper used
stochastic frontier model to investigate technical efficiency in manufacturing sector by
using Cobb-Douglas production function and Transcendental-logarithm (Translog)
production function. And the technique is running software Frontier 4.1 developed by
Collie (2005).

3

The paper is structured as follows. The next section is an overview of Vietnamese
SMEs and manufacturing sector. Then, the paper uses data collected from the survey of
2009 in manufacturing SMEs to evaluate the technical efficiency. I bases on stochastic
frontier production function to use methodology parametric approach. I also find down
some empirical that affects the technical efficiency. Then the paper identifies several
policy recommendations to improve the technical efficiency.

4


CHAPTER 2:
2.1

LITERATURE REVIEW

Theoretical framework for technical efficiency
Many researchers have analyzed performance of firm, especially manufacturing SMEs.
Kalirajan and Shand (1999) and Farrell (1957) defined technical efficiency in the i-th
firm is:
TE = Yi/Y*i

(1)

Where:
TE: Technical efficiency of firm
Yi: Actual output
Y*i: Maximum possible output
That is a basis model to estimate. But, maximum possible output is imperceptible and

must be estimated.
Figure 1: Technical, allocative, and overall efficiency

Source: Farrell (1957)

5


Figure 2: Production frontier and technical efficiency

Source: Kalirajan and Shand (1999)
Figure 1 shows the classic framework to define overall efficiency in two technical
efficiency and allocative efficiency. By using two inputs (X1, X1) the enterprises
produce Output (Y). In this figure the efficiency unit isoquant is Y=1. This equation
shows all technical efficiency combinations. The figure 1, P present that the enterprises
uses high levels of input to produce output at Y=1. At that level of input the enterprise
do not reach high technical efficiency. The ratio between optimal and actual resource
use (OR/OP) is magnitude of technical efficiency. Any point on the line Y=1 is
technical efficiency. At point Q, the enterprise use the minimum cost. Besides, if
efficiency includes relative factor price, it is indentified allocative efficiency OS/OR.
So, the overall efficiency is OS/OP.

6


Figure 2 shows the approach of production frontier and technical efficiency. Enterprise
uses input x to produce output Q. At point A, a technical efficiency firm lies on the
production frontier. At point B, the firm is technical inefficiency.
Several authors classified measurement of TE into two main group: parametric
(Deterministic Frontier Model and Stochastic Frontier Model) and non-parametric

approach (Data Envelope Analysis) (Coelli et, 2005; Murillo-Zamorano, 2004;
Kumbhakar and Lovell, 2003). The non-parametric is associated with Data envelope
analysis (DEA). Murillo-Zamorano (2004) showed that DEA is based on mathematical
programming model to estimate optimal level of output and mix inputs. The parametric
method specifies stochastic production function using econometric technique in which
error term in the regression is composed of two elements. The stochastic production
function model was developed by Aigner, Lovell and Schmidt (ALS) (1977), Meeusen
and Van den Broeck (MB) (1977), and Battese and Corra (1977). It was largely used in
econometric. There is a composed error term in this model (Murillo-ZAmorano, 2004).
The general functional form of the stochastic production function (SPF) is in the CobbDouglas function. Follow the Cobb-Douglass:
Y = β1Kβ2Lβ3eut (2)
LnY = β1 + β2lnK + β3LnL + Ut

(3)

In the production function, there are some important factors of production: total labor
force (L); tools machines and raw materials (capital, K); qualified scientific, technical,
organizational capabilities of the enterprise in particular and society in general (the
general factor, β1). Output growth is fast or slow depending on the use of labor and
capital, and also sub-of synthetic elements. In addition, economic factors show overall
production efficiency. This model has some advantages: Among the described model
of production process, the model is the simplest type; despite of a simple model, it
allows reviewing actual production and estimating parameters of the model.

7


However, it is necessary to clearly distinguish the purpose mentioned in this
study, which is technical efficiency instead of allocative efficiency. According to
Koopmans and Debreu-Farrell (1951), allocative efficiency refers to the efficiency of

combining inputs and outputs in optimal proportions, including a price factor. While,
technical efficiency refers the ratio between the observed output and optimal output or
the efficiency of using minimum input to give maximum output. So, instead of
mentioning the relationship between input and output, technical efficiency only refers
to the ratio between the observed output and the maximum output under the fixed
input.
Collie et (2005) developed the software Frontier 4.1 to evaluate enterprises
performance. The model specification may be expressed as:
Yit = xit + (Vit + Uit)

,i=1,...,N, t=1,...,T,

Where:
Yi is production of i-th firm;
Xi is a k x 1 vector of the input quantities of the i-th firm;
β is a vector of unknown parameters
Vi are random variables assumed to be independently and identically distributed as N
(0,б2v)
Ui is non-negative random variable assumed to account for TE in production
and to be independently distributed as truncations at zero of the N(μ, б2) distribution
In addition, Greene (2005) indicated that beside of elements of K, L there are other
factors will also directly affect the efficiency of business operations as follows.

lnyit = αi + lnXit + ZitY + vit - uit

8


Thus, the variable Zity represents other factors such as location of business, main
contractor or sub contractor, competitors, or changes in product manufacturing or

materials to suit the actual operation of the business.
2.2

Empirical literature on technical efficiency
Because of the important of Technical efficiency in firm performance, so there
are many discussion of efficiency measurement based on an efficient production
frontier representing the maximum level of output that a firm can obtain from any
given combination of input. This chapter mentions on two main methods for efficiency
are parametric and non-parametric methods. Parametric method is based on a
mathematical programming technique while non-parametric bases on an econometric
technique. There are some key differences of those methods. A non-parametric
technique is inherently an enveloping technique and requires little or no modification
for efficiency analysis (Fried et. al., 2008). It is not considered a statistical technique,
as inefficiency scores and the envelopment surface are “calculated” and not estimated.
This is also called deterministic because all deviations from the frontier are attributed
to inefficiency. There is no random error in this approach. This also makes the
assumption that all firms have the same technology and production frontier. Thus, all
discrepancies in production are dericed entirely from business management or
inappropriate technology (Nguyen Khac Minh et. al., 2007). The mathematical
programming technique is flexible in terms of the structure of production technology.
This method does not require the specification of a functional form and can avoid the
effects of mis-specific for both technology and efficiency factors (Fried et. al., 2008).
The disadvantage of the technique is that it can be extremely sensitive to variable
selection and data errors (Kalirajan and Shand, 1999).

9


The parametric approach is also called the stochastic frontier approach breaks
down the random error into component. That is due to random in the model and due to

efficiency. Some empirical are list below:
Nguyen Khac Minh, Giang Thanh Long and Bach Ngoc Thang (2007) analyzed
the efficiency using data of 1,492 firms. The data were collected from the Economic
Census for Enterprises conducted by The General Statistic Office of Viet Nam (GSO)
during the period 2000-2003. The methodology which the authors used is parametric
approach (base on stochastic production function) and non-parametric approach (base
on DEA). The result showed that there is difference between two methodologies: the
mean technical efficiency of SMEs was about 50 percent under SFP function and about
40 percent under the DEA approach.
The technical efficiency in the Viet Nam manufacturing was also studied by
Hung T. Pham, Thanh L. Dao, and Barry Reilly in 2010. The authors used SPF
approach to estimate the data from the Viet Nam enterprises survey for 2003 and gave
the result that an average manufacturing enterprises is operating at nearly 62 percent of
its technical efficiency.
Viet Le and Charles Harvie (2010) also evaluated technical efficiency of firm by using
SPF function to analyze 5,204 observation of manufacturing SMEs from the survey
conducted in 2002, 2005, 2007. The estimation give result that manufacturing in
Vietnam SMEs have average technical efficiency from 84,2 percent to 92,5 percent.
This figure is relatively high. The paper also provided the factors which influence
efficiency include firm age, size, location, ownership, cooperation with a foreign
partner Government assistant, innovation and subcontracting.
Rand and Tarp (2006) shows that technical efficiency in Viet Nam manufacturing
SMEs about 61 percent. The data includes 1,128 observations collected from GSO
during period 1991-2001. It is likely that the productivity of enterprises is inefficient.

10


Nunes (2011) tested hypothesis about relationship between young firm, old firm
with high technology and some variables: age, size, labor, internal financing, and

external financing. The research’s results shows that age and size affect the young
SMEs, not old firms; cash flow and debt are determinant of not only young SMEs but
also old SMEs; R&D intensity and labor productivity are possible to all SMEs growth;
R&D intensity helps firms perform better, but the main is high tech firm; interest and
debt are important to firm development.
Bruno and Ricardo (1989) investigated a sample of Italian manufacturing firms. They
compare with the previous research and get the same result. The labor demand is the
process of the firm growth, in detail in small firm.
Browyn H (2008) after present an imperial analysis from Italian SMEs with 10-50
labor by control for heteroskedasticity, serial correlation and survivorship bias, showed
that: Firm growth not only effect by age and size but also by external finance. That is
law, export capacity, State subsidies and ration credit. The empirical also give the
differences between firm with financial system is better than firm independent external
factor.
Havie and Lee (2005) shows that almost employments were created by SMEs when
studying some developing countries in East Asian such as China, Indonesia, and Viet
Nam.
Kokko and Sjöholm (2004) showed the role of internationalization of Viet Nam SMEs
and give a conclusion that openness is the important key to maintain and increase
firm’s growth.

11


Data &
Title

Researcher

Methodolo

Year

Result

gy
A

Guide

to

1996

Non-

Guide

FRONTIER

Develop

parametric

Frontier version 4.1

Version

ed

model

Colli

4.1: A

Computer
Program

in

to

use

2005
for

Frontier
Production
Function
Estimation
Technical

Viet Le

2010

efficiency
performance

of

Tested

a

Technical efficiency

sample

of

from 84,2 percent to

SMEs

in

92,5 percent

Vietnamese

2002, 2005,

manufacturing

2007

small

and

medium
enterprises
Stochastic

Kumbhakar

2003

Frontier Analysis

12

Examine

Provide a gateway

stochastic

to the professional

frontier

literature as well as

model as an

practical application

econometri

of the methods

c extension

Factor

Nguyen,

productivity and

Giang

efficiency of Viet

Bach

DEA

2007
and

and

The mean technical

SPF.

efficiency of SMEs

Survey

was

Nam economy in

from 1.492

percent under SFP

transaction

firms

function and about

in

2000-2003

about

50

40 percent under the
DEA approach

How

do

Vietnamese
SMEs

Viet Le, and

2010

Harvie

Perform?

Technical
Efficiency
SMEs

in

of
the

Manufacturing
Sector

and

Its

Sub-sectors

use SPF to

-

Technical

analyze

efficiency from 84,2

5,204

percent

observation

percent

of

- firm age, size,

manufacturi

location, ownership,

ng SMEs in

cooperation with a

survey

foreign

2002, 2005,

Government

2007

assistant, innovation

to

92,5

partner

and subcontracting
affect to TE

13


Productivity,

use SPF to

TE is from 79,6

analyze

percent

Institutions:

608

percent

Economic

observation

Efficiency

Tran

2008

and

Reforms

and

s

Non-state

to

86,7

from

1996-2001

Manufacturing
Firm
Performance

in

Vietnam
The influence of
age

on

SMEs

Nunes

2011

Use

SABI:

System

- Age and size are
the factor effect the

growth

Analysis

of

young

SMEs

determinant:

Iberian

although not to the

Empirical

Balance

old firms

evidence

Sheets

cash flow and debt
are determinant not
only young SMEs
but also old SMEs
R&D intensity and
labor

productivity

are possible to all
SMEs growth.

14


Relationship
between

Bruno
labor

Ricardo

and

1989

Tested

a

The labor demand is

sample

of

the process of the

demand and firm

Italian

firm

growth

growth

manufacturing

specially in small

firms Gibrat's

firm

Law

In the previous paper, both firm age and firm size are the main variables affect
to technical efficiency of the firms, especially in SMEs. Most studies have found
technical efficiency and firm size are positively related (Le, 2010; Nunes, 2011; Brada
et. al., 1997). Analysis of firm level data of Taiwan SMEs leads to the observation that
the productivity-size relationship has a virtuous cycle built-in. More productive firms
get larger and in the process, optain access to resources and information which enables
them to become more productive. Nevertheless, firm age and other factors is affected
to technical efficiency such as: openness, sub-contractor, location…They can have
positive, negative or no correlation with efficiency. There is no stylized patter of the
relationship between efficiency and the above factors.
A key economic aspect of firm performance in general and small firm performance in
particular is technical efficiency. This chapter showed that small firm efficiency can be
viewed positively for several reasons. Although small firms are often believed to be
less efficiency than larger firms, this is often not the case. The measurement of
efficiency which was made easier with the introduction of an efficiency production
frontier by Farrell in 1957 is discussed. In this methodology a firm’s actual output is
measured against the efficient frontier, representing the best practice frontier.
In general, all the papers above use two main methods are parametric approach
(base on stochastic production function) and non-parametric approach (base on DEA).

15


The main goal of researches is to estimate the technical efficiency of manufacturing
enterprises (specially is manufacturing SMEs). The key difference of the two methods
is the accommodation of statistical noise in the econometric technique. DEA do not
require any detail production function, while parametric approach needs the stochastic

production function. However, the parametric approach is more popular because of
controlling statistical noise.
There is a little diffidence in the result when using two methods. The authors use
parametric approach usually gave the higher level of technical efficiency in
manufacturing firm. The result when using DEA is lower, insignificantly. But, the
author research two key: estimate technical efficiency and determinant of technical
efficiency usually parametric approach. The result of technical efficiency is relatively
high. Firm age and firm size is two main factors affect to TE. Beside, following time
research and correlation of variables, the other factors have positive relationship or
negative relationship with TE.
2.3

Conceptual framework

Output (Y)

Capital
(K)

Labor
(L)

Technical efficiency

Material
(M)

Age

Objective 1

Size

Area,
competition,
Sub-contracting,
improve product,
launch product

Objective 2

16


The level of technical efficiency of a particular company is characterized by the
relationship between observed production and some excellent products or potential
(Greene 1993). Measurement of firm-specific technical efficiency based on the
deviation of the observed output from the best production or efficient production
frontier. The actual production of the company is located on the border that is
completely effective. If it is below the frontier then it is technically inefficient, with the
actual rate to determine the potential production level of each business.


Technical efficiency: Technical efficiency and production efficiency are the

main points of economic growth. Technical efficiency is the effectiveness of a given
set of inputs, which used to produce an output. It concern about productive efficiency.
If company wants to get productive efficiency, need also technical efficiency.
Technical efficiency is necessary for contribute input, human resources. A company
has been technical efficiency if this firm is producing the maximum output from the

minimum quantity of inputs. The input may be capital, labors, technology…


Output (Y) is the amount of energy, work, goods or services produced by a

machine, factory, company or an individual in a period. The more technical efficiency,
the more output quality.


Capital (K) is the physical capital includes: Machinery, equipment, factories,

vehicles, inventory is necessary factor for direct manufacture. Structural system of
socio-economic infrastructure (roads, bridges, warehouses, beach, air port, information
technology, transport, oil, gas…) facilitates and link economy action each others.
Investing more capital is increasing potential output, increasing real output. It directly
impacts to the economy development. In developing countries, capital is the most scale
input, while it is the source to be able to mobilize and effectively use other sources for

17


efficiency. So, capital is important role in economy development in developing
countries.


Labors (L) is input factor in manufacturing. It is the necessary role for

economic growth. Labor is not only reflected in quantity but also in quality of labor.
That is particularly in the knowledge, skills and experience accumulated. In the current
modern

economic

theory,

people

appreciate

the

role

of this knowledge and skill and considered as a kind of human capital to increase the
production capacity of whole countries. In developing countries low quality labors is
usually surplus but lack of skilled, high technical labor.


Material (M) is the last input component in the production function. Those are

total expenditure on raw materials and energy. The energy cost includes gas, fuel,
petrol, coal, diesel and electricity.


Firm size (size) is the number of wage worker. It calculates the number of

employees working in the firm. The number of worker can increase follow increase
standard capital or not. Large firms usually have more workers than small firms. This is
also a basis for classification of enterprises in different level such as: micro enterprise,
small, medium or large enterprise.



Firm age (age) mentions the number of year since establishment. In this paper,

I mention the firm in time from established up to the survey year. The establishment
bases on the date registration on business under the business registration license.


Area is dummy variable, which is used to show the location the firm located.

The dummy variable takes the value of 1 if the firm is located in Industrial Park, Hightech Park or Exporting processing Park. Enterprises in center of industry have better
access to resources. They can get intensive labor, capital and information. Furthermore,
they have benefit from issues law of Government. Nevertheless, enterprises located in

18