The relationship between financial constraint and SMEs total factor productivity

ERASMUS UNIVERSITY ROTTERDAM
INSTITUTE OF SOCIAL STUDIES
THE NETHERLANDS

UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

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

THE RELATIONSHIP BETWEEN
FINANCIAL CONSTRAINT
AND SMEs TOTAL FACTOR PRODUCTIVITY

By
HỒ BẢO TRÂN

MASTER OF ART IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, JANUARY 2016


UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

ERASMUS UNIVERSITY ROTTERDAM
INSTITUTE OF SOCIAL STUDIES
THE NETHERLANDS

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

THE RELATIONSHIP BETWEEN
FINANCIAL CONSTRAINT
AND SMEs TOTAL FACTOR PRODUCTIVITY
By Hồ Bảo Trân

A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of
Master of Art in
Development Economics

Academic Supervisor Prof. Dr. Nguyễn Trọng Hoài

Ho Chi Minh City, January 2016


DECLARATION

This is to certify that this thesis entitled “The relationship between financial
constraint and SMEs total factor productivity”, which 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.”
Hồ Bảo Trân


ACKNOWLEDGMENTS
I would not be possible to write this master thesis without the support and the

encouragement from many people.
I would like to express special appreciation to my supervisor, Prof. Dr.
Nguyen Trong Hoai, 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
programmme. In particular, I am grateful to Dr. Tran Tien Khai, Dr. Pham Khanh
Nam, and Dr. Truong Dang Thuy for their valuable comments and suggestions for
my concept note, thesis research design as well as in the thesis writing process.
Next, I would like express my thank you to all my friends at VNP. They have
spent time learning and sharing memorable moments with me.
Finally, I dedicate my thesis to my parents and my sister, who always love,
take care and support me.


ABBREVIATIONS
CIEM:

Central Institute for economic management

GSO:

General Statistic Office of Vietnam

R&D:

Research and development

SMEs:

Small and medium enterprises

TFP :

Total factor productivity

UK:

The United Kingdom


ABSTRACT
Using panel data from Vietnamese Small and Medium Enterprises Survey
from 2004 to 2010, this study aims to examine the relationship between financial
constraint and firm total factor productivity of small and medium enterprises
(SMEs) in Vietnam. To achieve this objective, this study measures the total factor
productivity of the SMEs. Then, this study identifies the relationship between
financial constraint and total factor productivity of SMEs. Levinson-Petrin approach
is used in order to calculate the total factor productivity of firm in the first step. In
the second step, an estimation model is built to examine the linkage between
financial constraint and firm total factor productivity. The research finds that there
is relationship between financial constraint and firm productivity. For example, the
relationship between firm leverage and total factor productivity of firm is positive.
It means that access to large external finance can help a firm ease the degree of
credit constraints, thereby increasing its capacity and survival in the market.
Secondly, liquidity ratio does not have significant impact on firm productivity in
Vietnam. Thirdly, firm size and firm age have positive relationship with firm
productivity. In other word, firms with larger size and older will have higher
productivity. Finally, firms join in export activities are found to get higher
productivity than the others.

TABLE OF CONTENTS
Chapter 1: INTRODUCTION ................................................................................................. 1
1.1

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

1.2

Research objective: ................................................................................................... 2

1.4

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

1.5

Scope of the study: .................................................................................................... 3

1.6

The structure of the study:......................................................................................... 3

Chapter 2: LITERATURE REVIEW ...................................................................................... 4
2.1

Productivity: Concepts and measurements ............................................................... 4

2.2

Financial constraint and firm productivity ................................................................ 5

2.2.1

Financial constraint definition ........................................................................... 5

2.2.3

Financial constraint and firm’s productivity ...................................................... 6

2.2.4

Factors impact productivity ............................................................................. 10

2.2.5

Conceptual framework ..................................................................................... 12

CHAPTER 3: RESEARCH METHODOLOGY AND DATA ............................................. 14
3.1

An overview of SMES in Vietnam ......................................................................... 14

3.2

Model specification ................................................................................................. 18

3.3

Research hypotheses and concept measurements ................................................... 20

3.3.1

Research hypotheses: ....................................................................................... 20

3.3.2

Measurement of variables ................................................................................ 20

3.4

Data collection ........................................................................................................ 21

3.5

Estimation methodology ......................................................................................... 22

CHAPTER 4: EMPIRICAL RESULT .................................................................................. 24
4.1

Total factor productivity of SMES .......................................................................... 24

4.1.1

Data descriptions.............................................................................................. 24

4.1.2

Total factor productivity of SMEs in Vietnam ................................................ 25

4.2

The relationship between financial development and total factor productivity ...... 26

4.2.1

Data description ............................................................................................... 26

4.2.2

Regression result .............................................................................................. 29

Chapter 5: CONCLUSION .................................................................................................... 32
5.1

Main finding: ........................................................................................................... 32


5.2

Policy implications .................................................................................................. 33

5.3

Limitations and further research ............................................................................. 34

REFERENCES ...................................................................................................................... 35
APPENDIX ............................................................................................................................ 39

LIST OF TABLES
Table 3.1: Definition of SMEs in Vietnam ............................................................................ 14
Table 3.2: Concepts and measurements of variables in the study ......................................... 21
Table 4.1: Descriptive statistic of production factor variables .............................................. 24
Table 4.2: Levinsohn-Petrin Estimation of Production Technology ..................................... 26
Table 4.3: Summary of Statistics ........................................................................................... 26
Table 4.4: The correlation matrix of Structural variables ...................................................... 28
Table 4.5.1: Regression result................................................................................................ 29
Table 4.5.2: Regression result................................................................................................ 30


LIST OF FIGURES
Figure 2.1 Conceptual framework .................................................................................................. 12
Figure 3.1: Number of Small, Medium and Large Enterprises by Size of Capital resources 16
Figure 3.2: Number of Large, Medium and Small Enterprises by Size of Employees ........... 17
Figure 4.1: A Fitted Plot of Total Factor Productivity versus Financial Indicators ................ 27


Chapter 1: INTRODUCTION
1.1 Problem statement:
The importance of financial development in raising productivity and promoting
economic growth has been discussed in many reports (e.g., Goldsmith 1969;
McKinnon 1973; Greenwood and Jovanovic 1990; Bencivenga and Smith 1991).
The financial system pressure handicaps financial development and results in
misallocation of resources, then reduce productivity and economic growth. As the
financial systems develop well, overall economic productivity will be improved
through the efficient reallocation of resources. At firm level, financial development
of a firm allows a firm to appropriate new business opportunities, conduct
investment and research activities, make a defense against financial and nonfinancial shocks and achieve higher productivity. More importantly, firm

productivity is an essential indicator in transforming financial market development
to economic growth at macro level.
Financial constraints are prevalent problems that firms have to face in most
developing countries, where finance resources are inadequate and financial
institutions are underdeveloped. According to the investment climate surveys of the
World Bank, including more than 26,000 firms in 53 developing countries,
Hallward-Driemeier and Smith (2005) found that the cost and access to external
finance was regarded as one of the top 5 problems that firms faced. The operation of
financial market and the availability of credit influence the performance of firms. It
prevents firms from funding all desired investments. Levine (2005), Beck et al.
(2005) point out that financial constraints, including low liquid and limited access to
financial resources, make the growth prospect of firms worse. Using a large panel
data of firms in 47 developing countries, Ayyari et al. (2007) indicated that external
finance increased innovation significantly. Similarly, Gatti and Love (2008) found
that access to credit had a positive effect on TFP in Bulgaria.

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In Vietnam, manufacturing firms play an important role in the decade of
economic growth. In 2014, there are nearly 70,000 businesses closed. There are
many reasons such as long loss profit, management capacity, operating restrictions
and lack of funds business… In which, financial market development offers a
crucial impetus for enhancing firm competitiveness and catalyzing industrialization.
As mentioned above, financial constraints are a prevailing problem facing firms in
developing countries where capital is scarce and financial institutions are
underdeveloped. The research of Minjia Chen (2010), Badia et al. (2008) suggested
that financial constraints have significant effect on productivity in China and
Estonia.
In Vietnam, the scientific research using a panel data to find out the link

between financial constraints and total factor productivity of Vietnam’s
manufacturing sector is limited. This study will present the evidence of this linkage
using the panel data for manufacturing firms from 2004 to 2010.
1.2

Research objective:
The study aims to examine the linkage between financial constraint and firm

productivity of SMEs in Vietnam. Therefore, it has three main objectives, which
can be stated as following:
 To estimate the total factor productivity of SMEs in Vietnam.
 To find the relationship between financial constraint and total factor
productivity of SMEs in Vietnam.
 To give policy implication for improving firm productivity through financial
constraint.

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1.4

Research questions:
The main research question this study aims to answer is:
1. Is there any relationship between financial constraint and productivity of
SMEs in Vietnam?
2. What are the policy implications to improve the productivity of SMEs?

1.5

Scope of the study:

The study will examine the link between financial constraint and total factor

productivity of SMEs in Vietnam using the panel data of 2004, 2006, 2008, 2010.
1.6

The structure of the study:
This study contains 5 main chapters, which are constructed by following:

Chapter 2 reviews the literature as well as empirical study about the relationship
between financial constraint and firm productivity. It begins with the definition of
productivity and its measurement. Then, the linkage between financial constraint
and firm productivity at firm level is discussed. Chapter 3 presents the methodology
and data. This chapter provides an overview about SMEs in Vietnam, conceptual
framework and model specification. Chapter 4 provides the empirical result.
Finally, chapter 5 concludes the main finding, limitation and further researches.

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Chapter 2: LITERATURE REVIEW
This chapter will review the literature about the relationship between financial
constraint and total factor productivity. Initially, the concept of total factor
productivity is introduced. Then, the empirical studies of the impact of financial
constraint and other factors on total factor productivity are examined. Finally, the
conceptual framework is presented.
2.1 Productivity: Concepts and measurements
Productivity is normally defined as a ratio of the output volume to the input
volume. In other words, it measures how much of output is acquired from a given
level of inputs.
Productivity = Quantity of outputs produced/ Quantity of inputs consumed

Productivity measurement efforts to point out improvements in using the capital
resources, that is, to motivate and evaluate efforts to produce more outputs with
fewer inputs while maintaining quality.
Total Factor Productivity (TFP) is the fraction of output, which not explained
by the amount of inputs used in production process. Thus, its level is defined by the
efficiency of inputs used in production process.
Total Factor Productivity (TFP) is normally defined as the contribution of
‘third factor” input other than capital and labour input. The third factor is normally
refer to factors including the improvement of technology and knowledge,
innovation, superior management technique, as well as workers education, skills
and experience.
Total factor productivity cannot be measured directly. Instead, it is a residual
which accounts for effects on total output not caused by inputs. In the CobbDouglas production function, total factor productivity is captured by the variable A.
Consider the simple Cobb-Douglas function:
Y = ALα Kβ , α + β = 1
Y is total aggregate output
L is an index of aggregate labour input

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K is an index of aggregate capital input
A is an index of aggregate of technology or called total factor productivity
α and β are the output elasticities of labor and capital, respectively.
The TFP index is calculated by diving both sides of production function by LαKβ
TFP = A = Y/Lα Kβ
2.2 Financial constraint and firm productivity
2.2.1 Financial constraint definition
“Financial constraints” means that obstacles prevent firms from funding
profitable investment projects. This barrier can come from credit constraints,

dependence on bank loans, incapability to issue equity, or illiquidity assets.
Financial constraints caused by information asymmetries and agency problems
have been well demonstrated by literature to affect firm’s activities, including fixed
investment, inventories, employment, and wages (e.g. Benito and Hernando, 2007;
Nickell and Nicolitsas, 1999). Firms facing financial constraints have difficulties in
raising external finance. Due to the pecking order of financing costs (Myers and
Majluf, 1984) financially constrained firms have to mainly rely on their own
internal finance and pay premium to obtain debt and, less likely, equity finance.
Therefore, they cannot choose their optimal capital structure and consequently
cannot make optimal decisions on other real activities. Financially constrained firms
may have to forego profitable investment opportunities when they are short of
funds. This certainly distorts the efficient allocation of resources and reduces firm’s
efficiency and productivity. Further, if firms want to improve productivity by
carrying out research and development activities, without a supportive financial
system, they will find it extremely difficult to finance the highly risky research and
development projects due to the classical concerns of external lenders.

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2.2.3 Financial constraint and firm’s productivity
Nickell and Nicolitsas (1999) used a panel data of UK’s firms to analyze the
impact of increasing financial pressure, which is measured by borrowing ratio, on
firm’s productivity, as well as employment and wage rises. It found that financial
pressure has a positive but small effect on productivity. In their approach, TFP is
measured by the logarithm of the output to capital and the model is estimated by
using production function with financial variables. Their results are consistent with
the bankruptcy theory. When financial pressure increases, bankruptcy risks
intensify. Firms’ managers and employees have strong motivation to reduce
bankruptcy risks in order to keep their jobs. Therefore, it is believed that they will

increase their efforts to enhance productivity.
Gatti and Love (2008) used data from a cross section of Bulgarian firms to
estimate the impact of access to credit on productivity. First, they estimate TFP in
cross section of Bulgarian firms and then construct models to identify financial
effects on firms’ productivity. The explanatory are mainly dummies to indicate firm
size, industry types, ownerships and other firm characteristic variables. Their result
suggests that access to credit to be strongly and positive associated with firms’
productivity.
Badia and Slootmaekers (2008) gave evidence on the relationship between
financial constraints and firm’s productivity in case of Estonia. Their estimation
methods combine two steps, a production function with financial variables and a
process with an estimation TFP variables first and a model to estimate TFP second.
Their result shows that young and highly indebted firms tend to be more financially
constrained. A large number of firms show some degree of financial constraints,
with firms in the primary sector are the most constrained. However, financial
constraints do not decrease firms’ productivity in most of sectors excepting R&D
sector. The negative impacts of financial constraints on productivity are remarkably
large for R&D firms. Their results are consistent with the argument that financial

6


development can enhance firms’ innovation, through which influence firms’
productivity. The impact can be transmitted to growth at the firm level first and at
aggregate level finally.
At micro level financial development can affect productivity by improving firm
innovation and then productivity. Beck et al (2005) suggested that firm with high
liquidity is expected to be resilient to financial and non-financial shocks. Similarly,
entrance to large external finance can help a company reduce the level of credit
constraints, therefore increase their ability and existence in the market (Aghion et al

2007; Levine 2005).
In this study, I will use two variables including leverage and liquidity as proxies
of financial constraints.
2.2.3.1 Leverage
The first proxy is Leverage, the ratio of debt to total assets, which denotes two
opposite effects. On the one hand, high debt implies that firms had access to
external finance in the past, which may be a signal that the firms do not face with
financial constraint. However, it does not certainly mean that firms can obtain as
much finance as it would like to or receive the loan as much as the optimal value.
On the other hand, leverage can have negative impact on investment expenditure
through (1) a liquidity effect (according to Lang et al., 1996) and (2) firms can face
bigger barriers in accessing external sources of capital because of high leverage.
Nucci, Pozzolo and Schvardi (2005) used a dataset for panel of Italian firms in
order to study the relationship between firms’ capital structure and TFP. They
argued that firms undertaking innovative activities hold a large share of immaterial
and thus, more innovative firms tends to have a different capital structures from less
innovative ones. Ultimately, differences in propensity to innovate are likely to
convert into different TFP levels. They built a TFP model on firms’ leverage,
immaterial assets ratio and other control variables such as firms’ size, firms’ cash
flow, long-run bank debt ratio, liquid assets ratio, time dummy, and geography

7


dummy. The results suggested that the negative relationship between firms’
leverage and productivity is non-linear and it depended on some firm-specific
characteristics. In particular, they found that the negative relationship is stronger for
firms, which have a lower share of short-term debt and lower liquidity.
Ghosh (2009) utilized data on high-tech Indian firms to study the association
between leverage and productivity. The results showed that firms with low leverage

tend to have higher productivity, on average. Lower leverage may infer higher
accumulation of immaterial assets, for example knowledge capital, and result in
higher TFP.
Nunes, Sequeira and Serrasqueiro (2007) used a large sample of firms in
Portunal to examine the link between leverage and labour productivity, and find the
relationship is non-linear. They used quantile regressions to estimate labor
productivity on firms’ leverage, tangibility, size, growth and ownership. They
showed that leverage of Portuguese firms tended to negative affect labour
productivity for the majority of firms with relatively lower productivity but to
positive affect this variables for firms with relative higher productivity.
Pushner (1995) studied the relationship between leverage and firm’s TFP in
Japan, and find the relationship is strong negative. On one hand, a positive
relationship between leverage and firms’ productivity can be supported by
bankruptcy theory mentioned above. A high degree of leverage increases
bankruptcy risks and hence stimulates managers as well as employees to increase
their efforts to improve productivity. On the other hand, a negative relationship can
come from an agency problem. Banks usually issue loan collateralized. R&D
activities positively related with productivity, but negatively related to leverage
because of its negative relationship with collateral. Therefore, leverage has negative
relationship with productivity perhaps through the linkage of research and
development. The findings above point that the effect of leverage on productivity
may depend on the dominant impact of leverage and other firm characteristics.

8


Consequently, policies in order to increase borrowing may not help to improve
productivity.
Hypothesis 1: Leverage has a significant and negative relationship with firm
productivity.

2.2.3.2 Liquidity
According to Keynes ((1930), p.67), one asset is considered as more liquid
than another “if it is more certainly realizable at short notice without loss”. Liquid
asset can be converted into cash quickly and at a low cost. Keeping liquid assets can
result in high opportunity cost to firms, but firms have more liquid asset can be seen
as less risky to lenders. Theories imply that the more liquid assets increase firms’
capacity to raise cash on short notice. Myers and Rajan (1998) argued that the
liquidity of assets can open up various business strategies, which may be
disadvantageous to lenders’ benefits, and hence excessive liquid assets may reduce
the reliability of firms to their lenders. As a result, excessive liquidity can reduce
the ability of firms in raising external finance. However, liquidity is still an aspect
that the lenders will consider when they make lending decisions.
Firms with high share of liquid assets may be more able to implement
activities for enhancing productivity than firms with low share of liquid assets and
depend less on their floating cash flow for these activities. This is acceptable that
liquid firms can quickly liquidize some of their assets in case they need extra funds
for activities to improve productivity. Illiquid firms may not be able to do the same
and, thus, more depend on their current cash flow for productivity enhancing
activities. In fact, Nucci, Pozzolo and Schivardi (2005) discovered that low liquidity
Italian firms suffer stronger negative effects from leverage than high liquidity firms.
Hypothesis 2: Liquidity will have positive influence on firm productivity.

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2.2.4 Factors impact productivity
2.2.4.1 Firm’s Size
Palangkaraya, Stierwald and Yong (2009) used a panel data to study the
relationship between productivity, size, and age of Australian firms. They estimated
a translog cost function to construct productivity index of firms. The results showed

that firms’ size and firms’ age relate to firms productivity. In particular, larger firms
are on average more productive, but older firms are less productive.
Castany, López-Bazo, and Moreno (2005) studied the differences in TFP
across firm size in Spanish manufacturing sector. They showed that some factors
including innovative activities, using of skill labor has promoted productivity,
especially, firm size plays an important role in explaining differences in
productivity between firms. Firm size affects research and development activities
and employees’ qualification, which impact on productivity, and thus firm size
relate to firm productivity. Firm’s size is also used to control for the effect of
economies of scale on firm’s productivity (Balk, 2001).
2.2.4.2 Firm’s Age
Several empirical studies about the relationship between firm age and firm
productivity pointed out that productivity was not constant over lifetime of firm.
Taymaz (2002) stated that new firms awaked to their actual productivity after
observing their performance in the industry. If their performance is ineffective, they
either grow or exit. New firms, which survive, attaint higher productivity than
existing firms. The results also suggested that productivity growth rates have
negative correlation with age and size of firms.
Similarly, Power (1998) studied the linkage between productivity and age of
firms in the manufacturing sector of the U.S in the period from 1972 to 1988 and
found that the productivity growth rates are negative correlated with firms age at a
specific time in the lifetime of firms.

10


In contrast, Celikkol (2003) showed that the oldest firms within a given
industry performed higher productivity growth rate than the average rates. This
study focused on the food and kindred products industry in the U.S and found that
older firms achieved higher productivity growth rate than younger firms.

Huyghebaert & Van de Gucht (2007) indicated that the access to external
finance is often limited for young companies. Even if they can obtain external
financing, they have to pay expensive costs for it. Consequently, the chances for
young firms to develop are often restricted.
Palangkaraya, Stierwald and Yong (2009) used a panel data to study the
relationship between productivity, size, and age of Australian firms. The results
show that firms’ size and firms’ age relate to firms productivity. In particular, larger
firms are on average more productive, but older firms are less productive.
2.2.4.3 Export status
Firms’ export status is included to capture the role of exporting on firms’
productivity. In the economics literature, exporters are often recognized to be more
productive than non-exporters. This finding is cited as a reason for export
promotion in many developing countries. There are two theories to explain for the
positive link between firm’s export status and its productivity. One is self-selection:
only productive firms have ability to take part in export activities and compete in
international markets (Bernard and Jensen, 1999). The other is “learning by doing”:
engaging in export market allows firms to achieve new knowledge, and access to
new technology which contribute to enhance their productivity (Van Biesebroeck
(2005), Clerides et al., 1998; Evenson and Westphal, 1995). In this model, I use
export status dummy XMit to identify the effect of export market on productivity. It
takes value of 1 if the firms take part in export/ import market and 0 otherwise.

11


2.2.5 Conceptual framework
Figure 2.1 Conceptual framework
Inputs
-

Outputs

Physical capital
Labors
Materials

( Revenue )

Total Factor Productivity

Control variables

Financial constraint
-

-

Leverage
Liquidity

Firm size
Firm age
Export Status

The relationship between financial constraints and firm productivity will be
examined in two steps:
(1) TFP measurement: productivity is proxy by total factor productivity (TFP). It
captures the growth, which could not be explained by changes in production
inputs, thus it can serve as a traditional proxy of productivity improvement.
It is measured by Levinson and Petrin (2003) method.

(2) Relationship between financial constraint and TFP: the total factor
productivity index will be regressed again by financial variables and control
variables. Financial constraint variables are mentioned in this study including
leverage and liquidity. At micro level finance can affect productivity by

12


improving firm innovation and then productivity (Nickell and Nicolitsas,
1999; Beck et al, 2005; Ghosh, 2009; Pushner, 1995; Nucci, Pozzolo and
Schivardi, 2005)
This step will also employ control variables, which have impact on firm
productivity, such as: firm size (Palangkaraya, Stierwald and Yong, 2009; Castany,
López-Bazo, and Moreno, 2005; Balk, 2001), firm age (Taymaz, 2002; Celikkol,
2003; Power, 1998; Palangkaraya, Stierwald and Yong, 2009) and export status
(Bernard and Jensen, 1999; Van Biesebroeck, 2005; Clerides et al., 1998; Evenson
and Westphal, 1995).

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CHAPTER 3: RESEARCH METHODOLOGY AND DATA
Firstly, this chapter will present an overview of SMEs in Vietnam. Then, the
model specification, and variables measurement will be constructed based on the
literature. Finally, the using data and estimation methodology will be mention at the
end of this chapter.
3.1 An overview of SMES in Vietnam
Every country has its own definition of small and medium-sized enterprise.
Basically, the definition include aspects such as number of employees, invested
capital, size of annual turnover, and total assets. However, the most common used

indexes are number of employees and total invested capital. In Vietnam, the
definition of SMEs is defined by the government through the Decree number
90/2001/ND-CP in November 2001, and updated by 56/2009/ND-CP in June 2009.
According to the degree 56, a firm is defined as a SME when it has equal or less
than 300 persons or maximum total capital is 100 billion. The definition of SMEs is
defined more clearly in table 1 below:
Table 3.1: Definition of SMEs in Vietnam
Micro enterprises
Type of industry

Number of
employees

I. Agriculture,
forestry and fishery
II. Industry and
construction
III. Trade and
services

Small enterprises

Total capital

Number of
employees

Medium enterprises
Number
Total capital

of
employees

≤10

≤ VND 20 billion

10-200

VND 20-100 billion

200-300

≤10

≤ VND 20 billion

10-200

VND 20-100 billion

200-300

≤10

≤ VND 10 billion

10-50

VND 10-50 billion

50-100

Source: Government's Decree No. 56/2009/ND-CP

In recent year, promotion of small and medium enterprises (SMEs) has been given
more attention. Many laws such as company law, enterprises law, home investment

14


law, civil law, and commercial law have been passed to create favorable
environment for the development of small and medium enterprises. Since 20052006, when enterprises law has been effective and Vietnam has become the member
of World Trade Organization (WTO), the number of enterprises has been increasing
significantly.
Small and medium enterprises have played an important role in Vietnam’s economy
by contributing into GDP, creating job, raising fund for business and solving social
problems. According to survey in 2011, SMEs has accounted for 97.6 percent of
total numbers of enterprises. The number of small and medium enterprises has
increased significantly from 2006 to 2011. From figure 3.1, we can see that along
with the increasing trend in the number of total enterprises, the total number of
SMEs in 2011 had grown by 2.64 times compared with that in 2006; average
increased 21.4 percent per year.

15