Determinants of access to formal credit by small and medium enterprises in Vietnam.pdf

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES
HO CHI MINH CITY THE HAGUE
VIETNAM THE NETHERLANDS
VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
DETERMINANTS
OF ACCESS TO FORMAL CREDIT
BY SMALL AND MEDIUM ENTERPRISES
IN VIETNAM
By
TRAN NGUYEN THUY BAO ANH
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
Ho Chi Minh City, April, 2014

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES
HO CHI MINH CITY THE HAGUE
VIETNAM THE NETHERLANDS
VIETNAM – NETHERLANDS
PROGRAMME FOR M.A. IN DEVELOPMENT ECONOMICS
DETERMINANTS
OF ACCESS TO FORMAL CREDIT
BY SMALL AND MEDIUM ENTERPRISES
IN VIETNAM
A thesis
submitted in partial fulfillment of the requirements for the degree of
Master of Arts in Development Economics
By
Academic supervisor
Dr. PHAM KHANH NAM
Ho Chi Minh City, April, 2014

i
DECLARATION
I declare that: "Determinants of access to formal credit by small and medium enterprises
in Vietnam" is my own work; it has not been submitted to any degree at other universities.
I confirm that I have made all possible effort and applied all knowledge for finishing this
thesis to the best of my ability.
Ho Chi Minh City, April 2014

ii
ACKNOWLEDGEMENT
This thesis would not have been accomplished without the kind assistance and
enthusiastic guidance of several individuals who have in one way or another contributed
toward to the formation and fulfillment of this paper.
First of all, I would like to express our deepest gratitude to my supervisor Dr. Pham
Khanh Nam for invaluable comments, guidance and engagement through the learning
process of the thesis.
I would like to express my special thanks Dr. Truong Dang Thuy for his comment
and advice about thesis research design.
Another special thank goes to Nguyen Quang, from whom I have a lot of things to
learn. I am thankful for Phan Thach Truc for all your kind help during my time in class 17.
I sincerely would like to thank all my loved classmates in class MDE17 and staff in
the VNP office, who always give me their restless assistance when I was in trouble.
Last but not least, I must express my most gratitude to my family members for all the
kind understanding and spiritual support.

iii
ABSTRACT
The shortage of capital and difficulties in accessing bank loans were the most challenging
issues for SMEs. According to a survey of SMEs Development Department - Ministry of
Planning and Investment, only one-third of SMEs can access to bank funds; one-third has
obstacles to reach the loans; and one-third cannot access. Among businesses in VN which
could not access to bank loans, the 80% does not meet loan conditions
The descriptive statistic result shows that State Owned Commercial Bank (SOCB) is the
most important formal source for SMEs. The banks appreciate the Certificate of Land Use
Right or housing which can be used as collateral for the most important formal loans. The
enterprises which applied for formal loans may be have problems getting loans. The main
reasons are difficulties in obtaining clearance from bank authorities and lack of collateral.
Enterprises in credit constrained group have the option of accessing to the informal credit
market. The proportion of credit constrained group applied for informal credit is always
higher than non- credit constrained. These proportions have tended to increase for both
groups.
Asymmetric information is the main theory of the research to classify the factors
determining access to credit of SMEs into three main groups: (i) a grouped factor
representing for Owner’s characteristics comprises education, ethnicity, (ii) a grouped factor
representing for firm’s characteristics consists of firm age, firm size, type of firm, (iii) a
grouped factor representing for relationship between banks and borrowers includes
previously borrowed, overdue debt.
Based on the data set of 1427 enterprise from “Characteristics of the Vietnamese business
environment: evidence from a SME survey in 2009”, the research has applied probit model to
identify determinants of access to formal credit by small and medium-sized enterprises
(SMEs) in Vietnam.
The result shows that Education (negative), Employee, Equipment, Liabilities and
Borrow (positive) which are significant on probabilities of access to credit. The research
finds that 50% of enterprises have probability of access to credit higher than 75.4%. The
paper finds that Ethnicity, Year, From, Revenue, Ap, Ar, Overdue debt do not contribute to
credit access of SMEs and are not significant at 10% level.

iv
In conclusion, the formal credit market plays a very important role for capital of SMEs.
However, access to this source is still a challenge for SMEs. The barriers, difficulties in
accessing credit from formal sources have forced the SMEs to involve in the informal credit
market.

v
CONTENT
DECLARATION....................................................................................................................... i
ACKNOWLEDGEMENT........................................................................................................ii
ABSTRACT.............................................................................................................................iii
CONTENT................................................................................................................................ v
LIST OF FIGURES ................................................................................................................vii
CHAPTER 1: INTRODUCTION............................................................................................. 1
1.1.Problem statement........................................................................................................... 1
1.2.Research objectives......................................................................................................... 2
1.3.Research questions.......................................................................................................... 3
1.4.Organization of the study................................................................................................ 3
CHAPTER 2 LITERATURE REVIEW ................................................................................... 4
2.1 SME definition................................................................................................................ 4
2.2 Theoretical literature....................................................................................................... 5
2.2.1 Theory of monopoly ................................................................................................ 5
2.2.2 Theory of asymmetric information .......................................................................... 6
2.2.3 BARRIERS TO FINANCE FOR SMEs.................................................................. 7
2.3 EMPIRICAL STUDIES.................................................................................................. 8
2.3.1 International empirical studies................................................................................. 8
2.3.2 Vietnamese empirical studies .................................................................................. 9
2.4 Conceptual framework.................................................................................................. 15
CHAPTER 3: DATA AND RESEARCH METHODOLOGY .............................................. 21
3.1 Background of SME Financing in Vietnam.................................................................. 21
3.2 Data............................................................................................................................... 28
3.3 Research methodology.................................................................................................. 28
3.3.1 Descriptive analysis ............................................................................................... 28
3.3.2 Econometric model ................................................................................................ 28
CHAPTER 4: EMPIRICAL RESULTS ................................................................................. 31

vi
4.1 Descriptive Statistics..................................................................................................... 31
4.2 Empirical results ........................................................................................................... 34
CHAPTER 5: CONCLUSIONS AND POLICY IMPLICATION ......................................... 44
5.1 Conclusion .................................................................................................................... 44
5.2 Policy Implication......................................................................................................... 45
5.3 Limitations and directions for further studies............................................................... 45
REFERENCES ......................................................................................................................viii
APPENDIS .............................................................................................................................. xi

vii
LIST OF FIGURES
Figure 2.1: Monopoly & competitive markets.......................................................................... 6
Figure 2.2: Access to credit: determinants and channels of influence................................... 16
Figure 3.1: Capital for investment of SMEs........................................................................... 21
Figure 3.2: The main purpose of investment of SMEs........................................................... 22
Figure 3.3: Problems getting the bank loan of SMEs ............................................................. 24
Figure 3.4: Why don’t Enterprises apply for loans? (%)....................................................... 25
Figure 3.5: Source of formal loan........................................................................................... 26
Figure 3.6: Type of Collateral................................................................................................ 27

viii
LIST OF TABLES
Table 2.1: Definition for Small and Medium Enterprises in Viet Nam.................................... 4
Table 2.2 Summary of empirical studies ................................................................................ 11
Table 2.3: Variable summary.................................................................................................. 19
Table 3.1 Access to Credit...................................................................................................... 23
Table 3.2: Informal Loans and Credit Constraints (%)........................................................... 27
Table 4.1: The reason Why enterprises did not apply for formal loan ................................... 31
Table 4.2: Access to credit...................................................................................................... 32
Table 4.3: Summary statistics of explanatory variables ......................................................... 32
Table 4.4: Correlation matrix.................................................................................................. 34
Table 4.5: Regression result.................................................................................................... 35
Table 4.6: Detail of Pr(access)................................................................................................ 39
Table 4.7: Marginal effects at means...................................................................................... 41
Table 4.8: Average Marginal Effects...................................................................................... 42

1
CHAPTER 1: INTRODUCTION
1.1.Problem statement
The shortage of capital and difficulties in accessing bank loans were the most
challenging issues for SMEs. According to a survey of SMEs Development Department -
Ministry of Planning and Investment, only one-third of SMEs can access to bank funds; one-
third has obstacles to reach the loans; and one-third can not access. Among businesses in VN
which could not access to bank loans, the 80% does not meet loan conditions. For example,
in Quang Binh, only about 30% of SMEs access to loans banks and interest rates up to 25%.
In the crisis, bank credit for small firms is reduced more than bank credit for the large
ones (Gertler and Gilchrist, 1994; Gilchrist and Zakrajsek, 1995). The main reason is that
small firms are more dependent on bank credit as they hardly have access to alternative
financing sources, such as financial markets and money markets. Cao Sy Kiem, chairman of
the Viet Nam Small and Medium-Sized Enterprises Association, said lack of funds and
difficulties in access to capital is the central difficulty of SMEs. Because of small own
capital, 90% of SMEs loans for business, of which 70% is bank loans. However, SMEs find
difficulty to access loans, due to small scale production, weak business management, lack of
collateral, etc.
Survey of Vietnam Chamber of Commerce and Industry (VCCI) indicated that lack of
capital is one of the biggest reasons that forced businesses to stop operating in 2013. It is the
cause of 38.1 % of business’s narrow. One of the SMEs’ major financing sources for
investment is bank loans (41.9%) and more than 50% SMEs have interest rates higher than
they can afford. Only about 20 % of businesses were able to access loans in spite of their
small production scale and lack of financial transparency. 63.1% SMEs does not apply for
bank loan because of inadequate collateral, high interest rate, complexities in application
process, etc
Paradoxically, the banking system is falling into a "capital inventory". Concern of banks,
in lending process is the risk of bad debt especially in the period in which the bad debt
reaches an alarming rate in the whole of banking system.
So the difficulties those enterprises face when borrowing form bank are what. There is a

2
lot of researches try to find out the answer to that question. According to theory of
asymmetric information between borrowers and banks, the factors determining access to
credit of enterprises can be classified into three main groups:
(i) a grouped factor representing for Owner’s characteristics (Biggs et al ,2001),
(Gartner et al, 2011), (Nguyen & Luu,2013), comprises education, ethnicity .
(ii) a grouped factor representing for Firm’s characteristics (Biggs et al ,2001),
(Bebczuk, 2004),(Gartner et al, 2011), (Vo et al, 2011), (Said et al ,2013), (Le,2013) consists
of firm age, firm size , type of firm, asset, liabilities…
(iii) a grouped factor representing for Relationship between banks and borrowers
(Biggs et al ,2001), (Bebczuk, 2004), Vo et al, 2011)includes previously borrowed, overdue
debt.
However, the previous studies were heterogeneous definitions of the variables. For
example, revenue (Gartner et al, 2011) and number of employee (Vo et al, 2011), (Said et al,
2013) were used as representing firm size. Therefore, the impact of factors on credit access is
different between studies.
According to above problems, this paper aims to indicate Determinants of access to
formal credit by small and medium enterprises (SME) in Vietnam. Based on the data set
of 1427 enterprise from “Characteristics of the Vietnamese business environment: evidence
from a SME survey in 2009”, the research has applied probit model to identify determinants
of access to formal credit by small and medium-sized enterprises (SMEs) in Vietnam.
1.2.Research objectives
General research objective is to examine determinants of access to formal credit by
SMEs in Vietnam.
Specific objectives are:
a. To investigate factors that effect of probabilities of access to formal credit by SMEs
in Vietnam.
b. To recommend policy implications in order to improve SMEs’s access to formal
credit

3
1.3.Research questions
The research’s main question is what are relationship between determinants and
probability of access to formal credit?
1.4.Organization of the study
The rest of the paper is organized into four chapters. Chapter 2 presents Literature
review of SMEs, theoretical review, and empirical studies which were carried out inside and
outside of Vietnam. Chapter 3 describes SMEs credit market in Vietnam, data, research
methodology and analytical framework. Chapter 4 analyses the empirical results, identifies
determinants of SMEs access and gives some quantitative analysis of those factors. Chapter 5
concludes, suggests some practical policy implications; limitation and direction for further
studies are also discussed in this chapter.

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CHAPTER 2 LITERATURE REVIEW
This chapter is to review the theoretical and empirical literature
2.1 SME definition
The term "SME" has a wide range of definitions. Most of organizations and countries
determine small businesses based on the number of employees, revenue and assets.
While World Bank defines SMEs as the companies have not more than 300 employees, $15
million in annual revenue, and $15 million in assets. European Union defines SMEs as those
enterprises with between 10 and 250 employees, and more than 10 million euro turnover or
annual balance sheet total. American, meanwhile, described SMEs is a maximum of 100
employees and less than $3 million revenue. Egypt defines small businesses as firms have
more than 5 and less than 50 employees.
In periods 2001-2009, based on Government Decree 90/2001 ND- CP, SMEs in Vietnam was
identified as follows:
 The business establishments are independent.
 The registered capital is no more than 10 billion VND.
 The average annual number of permanent employees is no more than 300.
Today, according to the Decree 56/2009/ND-CP, SMEs is differently categorized based
on the total capital (must equal the total assets in balance sheet of enterprises) and The
average yearly number of workers.
The SME in three major sectors were divided into small and medium enterprises
Table 2.1: Definition for Small and Medium Enterprises in Viet Nam
Small-sized enterprises Medium-sized enterprises
Total capital Number of
laborers
Total capital Number of laborers
I. Agriculture,
forestry and fishery
VND 20
billion or less
Between over 10
persons and 200
persons
Between over
VND 20
billion and
VND 100
billion
Between over 200
persons and 300
persons

5
II. Industry and
construction
VND 20
billion or less
Between over 10
persons and 200
persons
Between over
VND 20
billion and
VND 100
billion
Between over 200
persons and 300
persons
III. Trade and
service
VND 10
billion or less
Between over 10
persons and 50
persons
Between over
VND 10
billion and
VND 50
billion
Between over 50
persons and 100
persons
Source: Government‘s Decree No.r 56/2009/NĐ-CP date 30, June 2009
2.2 Theoretical literature
2.2.1 Theory of monopoly
Banks in countries with immature financial systems often face little competition and low
threat of entry and can therefore earn handsome returns by lending almost public and private
players (USAID, 2004). Bank credit to small firms is reduced more than bank credit to large
firms (Gertler & Gilchrist, 1994); (Gilchrist & Zakrajsek, 1995).However, small firms are
more dependent on bank credit and they hardly have access to alternative financing sources,
such as financial markets.
In this view, the banks characterized as a monopolist. The banks with monopoly power
manipulate the interest rate and contracts to gain maximize profits. Therefore, they usually
charge SMEs higher interest rate and collateral requirements (Beck, 2008).
Monopoly lenders reduce welfare of SMEs because credit costs more and their living
standards fluctuate more and more (because costly credit reduces their demand for credit).
However, they must get loans from the monopolist for their operation. The monopolist raises
interest rates until the marginal revenue from higher rates equals the marginal cost from
lower loan demand.
The existence of monopoly profit or usurious interest rate can be illustrated with the help

6
of a simple diagram
Figure 2.1: Monopoly & competitive markets
2.2.2 Theory of asymmetric information
Information asymmetry is uneven distribution between sellers and buyer. It can have
effect on decision making. In the financial market, asymmetric information between
borrowers and lenders increase obstacle of trade (Ray (1998). Borrowers always have better
information about their projects than lenders. According to the bank lending view, financial
markets are characterized by imperfections and bank assets (loans, securities) are imperfect
substitutes (Bernanke and Gertler, 1995). Stiglitz and Weiss (1981) show that interest rate is
determined not only the demand for capital but also the riskiness of the borrowers.
Therefore theories of credit market focus on asymmetric information which implies adverse
selection (before the agreement is made) and moral hazard (after the agreement is made)
(Stiglitz & Weiss, 1981).
Adverse selection exists when the probability of repaying loan of borrowers is not
estimated correctly. In this case, lower risk borrowers may incur higher interest rate (Bester,

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1987). Therefore, they stop borrowing because the high rates decrease their credit profile and
profit. On the other hand, higher risk enterprises can gain loans with lower interest rate.
Finally, the lenders have a loan portfolio of almost higher risk enterprises.
In developing countries, beside adverse selection, moral hazard is a controversial factor
on credit markets. Moral hazard appears when the loans are not used for initial purpose. The
lenders find it difficult controlling borrowers’ loan utilization. In order to reduce higher
interest payments, they are pressed to seek high profitable projects despite of risk increase
(Bester,1987).
Informational asymmetry, high transaction costs and uncertainty are specific
characteristics of credit markets. These characteristics typically lead to problems of adverse
selection and moral hazard.
This is in line with the literature since, in order to reduce the anticipated risk and moral
hazard associated with lending, banks use collateral as one of their instruments. Therefore,
the larger the capital, the more a firm is able to obtain a loan since it has enough collateral.
For this reason, Berger and Udell (1994) found that smaller and younger firms are more
likely to face higher cost of financing since they are required to offer more collateral than
larger firms.
2.2.3 BARRIERS TO FINANCE FOR SMEs
Access to credit is necessary to create an economic environment that enables firms to
grow and prosper (Thorsten, 2011), improves firm performance, facilitates market entry,
growth of companies and risk reduction (Beck, 2008) and promotes innovation,
entrepreneurial activity (Klapper, 2006). According Beck (2008) the firms with greater
access to credit are more able to exploit growth and investment opportunities. Increasing
access to credit will foster efficient growth in the SME sector. Credit might be needed for
SMEs to make the jump to the next step of production technologies (e.g. move from manual
to automatic production) (Abhijit, 2011).
It’s a fact that SMEs have been found it difficult to approach external finance to be more
constrained in their operation and growth (Berger & Udell, 1998); (Galindo & Schantiarelli,
2003). SMEs face disproportionate barriers to finance, especially in developing countries.
Financing for SMEs is limited, particularly when compared to commercial debt for large

8
firms and microfinance. Based on World Bank, 2010, one of the most-severe obstacle to
growth of SMEs is financing constrains. They are result of high cost such as administration,
collateral and lack of experience. On the other hand, commercial finance is too difficult to
support SMEs due to high cost and risks. SMEs capital needs are not satisfied by microloans
(Karlan, 2011).
In developing countries, the shortage of information and regulatory hinder banks from
lending SMEs. The reasons why regular banks provide insufficient debt to SMEs including
lower returns (Beck, 2008), higher administrative costs (David, 2007), higher risk
perceptions (Paul Collier, 2009), an uninspiring regulatory environment (Brian, 2008), and a
lack of intermediary skills, information, experience and capacity (USAID, 2004). In other
hand, Banks have difficulty providing long-term capital. Therefore, Banks are challenged in
providing long-term capital to SMEs. As a result, SME lending market does not meet capital
needs.
Because of the higher costs, lack of skills and higher (perceived) risks of investment in
SMEs translate, Banks charge more than interest rates and collateral requirements (Bech,
2008). However, posting collateral is complicated by the fact that most SMEs operate in
environments with weak property rights and poor contract enforcement, in which borrowers
do not have legal titles to house or land, and therefore cannot use these as collateral
(Hernando, 2000)
2.3 EMPIRICAL STUDIES
2.3.1 International empirical studies
Bebczuk (2004) use data of Argentina 140 companies in 1998 to run a logit regression
analysis to identify the determinants of SME access to a credit loan. There were three
exciting findings in their study. Firstly, the firm size, tangibility and the length of the lending
relationship are not significant on the probability of obtaining a loan. Secondly, the profit, the
debt ratio and the use of overdraft credit have positive relationship with the probability of
obtaining a loan. Finally, the probability of obtaining a loan decrease when liquidity is
higher.
An interesting paper of Biggs at el (2002), they identified the characteristics that
influence access to credit in Kenyan. They used data of 182 Kenya businesses which account

9
for 72% output in 4 industries (metal working, food processes, textile and wood). They found
that the main factors which affect on access to bank overdrafts included education of owner,
company size, availability of collateral and length of relationship with banks. Borrower’s
ethnicity has little effect on supplier credit. Meanwhile, it does not influence access to
overdraft.
Another view of Gartner at el (2011), they use data from the Panel Study of
Entrepreneurial Dynamics II (PSED II) which was collected between October 2005 and
January 2006 to identify the financing behaviors of companies during in the USA. They
found that Firm characteristics, such as potential sales revenue, legal form of the business,
and whether it is registered, affect the acquisition of external sources of financing. On other
hand, owner education and the company’s net worth also impact the acquisition of certain
types of financing. They perceive in nascent ventures, relationship between expected
revenues and financing amount is positive; the firm size is not significant for the selection
decision of funding source.
In the study of Said (2013), they examined the determining factors which impact of
benefiting from banking facilities of 36,492 firms in Egypt. They applied the Heckman two-
stage selection model. First, they examine the determinants of having banking facilities.
Then, we analyze the factors that explain banking problems. They found that the smaller the
company, the higher the probability of having banking problems. Some findings of this study
show that the age of the firm has not significant effect on having banking facilities white
sales turnover, economic activity, labor, capital, and legal form have a significant.
Similarly, Le (2013) attempted to identify determinants of credit access by Chinese firms.
She used the logit model to analyze data which were collected from 12,400 enterprises
surveyed around China in 2005. She found that firm age, type of ownership, loan quota, sale,
profit and region are determinations of access to credit. All variables have positive
relationship with probability of access to credit. The highest significant variable is loan
quota.
2.3.2 Vietnamese empirical studies
According Le (2012), she used cross sectional enterprise survey data and logit model to
examine the participation of Vietnamese SMEs in the credit market. Data were collected

10
from the survey of 1,024 enterprises and conducted in five representative regions of Vietnam:
Red River Delta, the North Centre Coast, Mekong River Delta, South Centre Coast and
South East. The results showed that value of machinery, proportion of loan from bank,
percent of national sales, overdraft facility, industry and regions have significance to
probability of access to credit. The relationship between value of machinery, proportion of
loan from bank, percent of national sales with probability of access to credit is positive.
However, probability of access to credit has negatively related with overdraft facility.
Industries have different the probability of access credit and the highest one is service. The
businesses in Red River Delta and Central North have higher probability to obtain bank loans
than other regions.
In the other study, Nguyen and Luu (2013) collected a panel dataset and applied the
Unordered-Multinomial Logistic. The dataset includes 7900 observations of 2200 firms in
2005, 2007, and 2009. They categorized independent variables into four groups: owner’s
characteristics, firm’s characteristics, network and regions. The result showed that owner’s
characteristics including age, experience, ethnic do significantly impact the ability to borrow
from formal sources. However, among firm’s characteristics variables including: types of
ownership, age of firm, firm size, profit, export …. only firm size impact on probability of
access to formal finance. The companies have diversity networking tend to have higher
probability to access to bank. The rural- based firms seem access more the bank debts than
firms located in big cities like Hanoi, Ho Chi Minh or Haiphong
In the study of Le (2013), she identified the characteristics that influence access to credit
in Vietnam. The dataset was conducted in five regions containing 14 provinces and had 1,150
observations in 2005. She applied logit model and found four factor impacts on probability of
access to credit. Four variables are type of ownership, export, profit, new fixed asset. They
have positive sign with ability access to credit.
Another view of Vo at el (2011), they used data of 169 firms were collected in six
provinces Vietnam. They run the logistic regression to find the relationship between the
chances of getting loan with firm age, size firm, owner’s experience and production network.
They found that the ability of getting loan increased for older firms, larger firms, more
experience and participation in production networks. The lenders seem prefer enterprises

11
which have collateral, and quantity business plans.
The following table summarizes the empirical studies above in a more intuitive way
Table 2.2 Summary of empirical studies
No Author Data Methodology Finding
1 Ricardo
N.
Bebczuk
(2004)
140 Argentina
firms in 1998
Logit model There were three exciting findings in
their study. Firstly, the firm size,
tangibility and the length of the
lending relationship have not
significances on the probability of
obtaining a loan. Secondly, the
profit, the debt ratio and the use of
overdraft credit have positive
relationship with the probability of
obtaining a loan. Finally, the
probability of obtaining a loan
decrease when liquidity is higher.
2 Tyler
Biggs,
Mayank
Raturi ,
Pradeep
Srivastava
(2001)
182 Kenyan
firms in four
sectors :
textile, wood,
food, metal in
1993
Probit model Firm size, length of relationship with
the lender, education of
owner/manager and availability of
collateral are important determinants
of access to bank overdrafts. The
ethnicity of borrower has not impact
on access to overdrafts but it has
little impact on access to credit
supplier.

12
3 William
B.
Gartner,
Casey J.
Frid,
& John C.
Alexander
(2011)
Panel data of
1,214 USA
nascent
entrepreneurs
was collected
between
October 2005
and January
2006.
The logit and
OLS
regression
models
There is positive relationship
between expected revenue and
financing amount.
The firm size is not significant for
the decision of selecting source.
Firm characteristics, such as
potential sale revenue, legal form of
the business affect the acquisition of
personal and external sources of
financing. Owner’s education, and
the entrepreneur’s net worth, also
affect the acquisition of certain types
of financing
4 Hala El-
Said,
Mahmoud
Al-Said
and
Chahir
Zaki
(2013)
36,492 Egypt
firms in 2008
Heckman
two- stage
selection
model
The smaller the companies are, the
higher the probability of having
banking problems is. The age of the
firm has not significant effect on
obtaining bank loans while sales
turnover, economic activity, labor,
capital, and legal form are
significant.
5 Phuong
Nu Minh
Le (2013)
12,400
Chinese
enterprises in
2005
Logit model Firm age, type of ownership, loan
quota, sale, profit and region are
determinations of access to credit.
All variables have positive
relationship with probability of
access to credit. The highest
significant variable is loan quota.

13
6 Phuong
Nu Minh
Le (2012)
1,024
Vietnamese
enterprises in
five
representative
region of
Vietnam: Red
River Delta,
the North
Centre Coast,
Mekong River
Delta, South
Centre Coast
and South
East
Logit model Value of machinery, proportion of
bank loan, percent of national sales,
overdraft facility, industry and
region have effects on probability of
access to credit. The relationships
between value of machinery,
proportion of loan from bank,
percent of national sales with
probability of access to credit are
positive. However, probability of
access to credit has negatively
related with overdraft facility.
Industries have the different
probability of access credit and the
highest one is service. The
businesses in Red River Delta and
Central North have higher
probability to obtain bank loans than
other regions.

14
7 Nhung
Nguyen,
Nhung
Luu
(2013)
Panel data
7900
observations
of 2200
Vietnamese
firms in 2005,
2007, and
2009.
Owner’s characteristics including
age, experience, ethnic do
significantly impact the ability to
borrow from formal sources.
Firm size impact on probability of
access to formal finance.
The companies which have diversity
networking tend to have higher
probability to access to bank.
Rural- base the firms seem access
more the bank debts than firms
located in big cities like Hanoi, Ho
Chi Minh or Haiphong
8 Phuong
Nu Minh
Le (2013)
1,150
Vietnamese
firms
in five regions
containing 14
provinces in
2005.
Logit model Type of ownership, export, profit,
new fixed asset impact on
probability of access to credit. They
are positively related with ability of
access to credit.
9 Vo, T. T.,
T.C. Tran,
V. D. Bui
and D. C.
Trinh
(2011)
169 firms
were collected
in six
Vietnam
provinces
Logistic
regression
The ability of getting loan increases
for older firms, larger firms, more
experience and active in production
networks. The lenders seem prefer
enterprises which having collateral,
good credit profiles and quantity
business plans.

15
2.4 Conceptual framework
As a result of asymmetric information, banks are unable to grant loans for SMEs. In order
to minimize negative impacts of asymmetric information, the banks rely on private
information on borrowers collected through repeated interaction. In addition, public
information is one of the most important channels for the banks to approve of credit
application. Therefore, the banks always prefer such older and larger enterprises. Moreover,
the businesses which have longer relationships with the banks are also more likely to being
granted loans.
The factors determining access to credit of enterprises can be categorized into three main
groups: (i) Group 1 concerns for Owner’s characteristics comprises education, ethnicity, (ii)
Group 2 concerns for Firm’s characteristics consists of firm age, firm size, type of firm, (iii)
Group 3 concerns for Relationship between banks and borrowers includes previously
borrowed, overdue debt.

16
Figure 2.2: Access to credit: determinants and channels of influence
Education
Ethnicity
Owner’s
characteristics
Accounts
receivable
Total liabilities,
Account payable
Ratio debt
Firm size
Land,
Building,
Equipment,
Inventory
Year
Collateral
Form
Revenue
Employee
Firm’s
characteristics
Liquidity
Borrow
Overdue debt Credit profile
Relationship
with banks
Relationship
between lenders
and borrowers
Access
to
credit
2,7
4,5,9
3,4,5,8
1,2,3,4,7,9
2,6,9
1,6
1
1,2
9
2,3,7

17
The specific hypothesis for each factor is as follows:
1. Owner’s ethnicity (Biggs et al ,2001), (Nguyen & Luu,2013)
This factor could be a positive coefficient. However, some studies found that this factor is
not statistically significant on probability of access to credit. In the model, ethnicity of owner
is a dummy variable. It equals 1 if owner’s ethnicity is Kinh.
2. Owner’s education (Biggs et al ,2001), (Gartner et al, 2011)
Education of owner may also assist in managing the business. Owner with good educational
background can give the good idea, right decision to improve productivity. Therefore, the
banks may prefer to lend to enterprises with educated owner. In the model, education of
owner is a dummy variable. It equals 1 if owner completed College/University/post-graduate.
3. Number of years of operation (Vo et al, 2011) (Le, 2013), (Said et al ,2013)
The older enterprises may have more experiences of access to credit. They improve their
reputation and relationship with banks. Therefore, the negative effects of asymmetric
information on the probability of access to credit are minimized. So we expect positive
relationship between number of years of operation and probability of access to credit.
4. Type of firm (Gartner et al, 2011), (Said et al ,2013), (Le,2013)
This factor could be a statistically significant coefficient. In the model, this variable equals 1 if
enterprise is Private (sole proprietorship)/ Limited Liability Company. This coefficient is
expected to have positive sign.
5. Revenue (Gartner et al, 2011)
The higher-revenue enterprises may have more profit and faster turnover sale. Therefore,
they may have higher financial capacity and probability repayment. Therefore, banks can feel
secure for their loans. In addition, revenue is one of the most important factors when the
banks issued credit quota to enterprises. Positive relationship between revenue and
probability of access to credit was expected. It is measured by VNĐ billion.
6. Number of employee (Said et al 2013), (Vo et al, 2011)
Number of employee is one of the determining factors of company size. The smaller the
companies are, the higher probability of having banking problems is. Therefore, the bigger
firm may be easier to access bank credit. Therefore, we expect this coefficient has positive
sign.

18
7. Value of land asset, building asset, equipment asset, inventory (Bebczuk, 2004),
(Biggs et al ,2001), (Vo et al, 2011)
These variables represent availability of collateral or liquidity. The higher availability of
collateral or lower liquidity enterprises will have more probability of access to credit. These
coefficients are expected to have positive sign. They are measured by VNĐ billion.
8. Total liabilities, account payable (Bebczuk, 2004), (Le, 2012)
Total liabilities include the formal and informal debts. The formal debts may be include bank
loans and account payable. The enterprises have bank loans that mean the banks have
information of enterprises. The banks will decrease the negative impact of asymmetric
information and adverse selection. Therefore, we expect this coefficient has positive sign.
Total liabilities, account payable are measured by VNĐ billion.
9. Accounts receivable (Le, 2013)
This variable represents liquidity. The higher liquidity enterprises will have lower probability
of obtaining a loan. These coefficients are expected to have negative sign. They are measured
by VNĐ billion.
10. Borrow, overdue debt (Bebczuk, 2004), (Biggs et al ,2001), (Vo et al, 2011)
The “borrow” variable represents the companies which had bank loans in the past. That
means the enterprises have the relationship with banks. The “overdue debt” represents credit
profile of companies. This is the dummy variable. It equals 1 if company fails to service its
debt on time in 2008. The expected sign of “Borrow” is positive and negative for “overdue
debt”.
In order to test hypotheses based upon the relationship between an explanatory variable
and independent variables, the explanatory variable is considered as probability of SMEs
access to formal credit.
The equation is formulated as follows:
Accessi =  + i Xi + i
The explanatory variable of obtains two values:
Accessi =1, the ith SME is selected to lend
Accessi =0, if SME still in need of a loan but being reject
The study combines 3 questions to specify the value of the explanatory variable

19
99. Has your firm applied
for bank loans or other
formal credit since August
2007 (last survey)?
Yes Yes No No
104 b. Amount originally
borrowed (1,000 VND).
> 0 0
106. Why has the firm not
applied for formal loans since
August 2007 (last survey)?
Inadequate
collateral
Don’t want
to incur debt
Process too
difficult
Didn’t need
one
Interest rate
too high
Other
Already
heavily indebted
Access 1 0 0
Not include
in sample
The Xi vector includes education, ethnicity, firm age, firm size, type of firm, previously
borrowed, overdue debt…
Table 2.3: Variable summary
Variable Description Measurement unit
Expected
sign
Response variable
Access Access to credit
=1 if has bank loans
=0 if being reject
Explanatory variables
Owner’s characteristics
Ethnicity Owner’s Ethnicity = 1 if Kinh, 0 otherwise N/A

20
Education Owner’s Education
= 1 if
College/University/post-
graduate,
= 0 otherwise
+
Firm’s characteristics
Year Number of years of operation Year +
Form Type of firm:
= 1 if Private (sole
proprietorship) /Limited
liability company,
= 0 otherwise
N/A
Revenue Revenue of 2008 Billion +
Employee Number of employee Employee +
Land Value of Land asset Billion +
Building Value of Building asset Billion +
Equipment Value of Equipment Billion +
Inventory Value of Inventory Billion +
Liabilities Total liabilities of 2008 Billion +
AP Account payable Billion +
AR Accounts receivable Billion -
Relationship with lenders
Overdue debt
fail to service its debt on time
in 2008,
= 1 if yes, 0 otherwise -
Borrow
Had borrowed from banks in
the past,
= 1 if yes, 0 otherwise +

21
CHAPTER 3: DATAAND RESEARCH METHODOLOGY
3.1 Background of SME Financing in Vietnam
Since 2000, SME sector has grown rapidly, especially after becoming WTO membership
in 2007. Total registered enterprise increased from 14.453 enterprises in 2000 to 499.519
enterprises in 2010. On the other hand, Vietnamese financial market has opened and
liberalized since 2000. It witnessed the accession of the domestic and foreign. They provide
capital for businesses including SMEs.
The availability of credit resources for SMEs is one of the main factors for
entrepreneurial activity. According Beck (2008) the firms with greater access to capital are
more able to exploit growth and investment opportunities. Most of enterprises have new
investments during 2005-2009, however it has tend decline. The proportion of the investment
decreases from 62.42% in 2005 to 60.7% in 2009. One of the main reasons of decrease is the
change of the source capital. Before 2009, internal funds were the main capital for
investment, however, in 2009; banks or credit institutions become the main capital for
investment.
Figure 3.1: Capital for investment of SMEs
0
10
20
30
40
50
60
70
80
2005 2007 2009
CAPITAL FOR INVESTMENT
internal funds/ own capital Borrowed from bank/ credit institution
Source: Author calculated from Characteristics of the Vietnamese business environment:
evidence from a SME survey in 2005, 2007, 2009

22
Increasing access to credit will promote efficient growth in the SME sector. Access to
credit is necessary to create an economic environment that enables firms to grow and prosper
(Thorsten, 2011), improves firm performance, facilitates market entry, growth of companies
and risk reduction (Beck, 2008) and promotes innovation, entrepreneurial activity (Klapper,
2006). The enterprises invest to add to capacity, replace old equipment, improve
productivity, improve quality of output, and produce a new output, safety, environmental
requirements and other purpose. “Add to capacity” is the most important reason which
accounts for above 50% of total.
Figure 3.2: The main purpose of investment of SMEs
THE MAIN PURPOSE OF THE INVESTMENT
0
10
20
30
40
50
60
70
Add
to
capacity
Replace
old
equipment
Improve
productivity
Improve
quality
of
output
Produce
a
new
output
Safety
Environmental
requirements
Other
purpose
2005 2007 2009
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23
Access to credit is one of the important factors for SMEs growth and development.
However, SMEs often face the credit constrained situation. The number of enterprises
applying and obtaining bank loans or other forms of formal credit in 2005, 2007 and 2009 are
shown in table 3.1.
Table 3.1 Access to Credit
2005 2007 2009
Enterprise
applied for
formal loan
Yes No Yes No Yes No
(1108) (1713) (970) (1665) (998) (1660)
39.28 60.72 36.81 63.19 37.55 62.45
Problems getting
loans
Yes No Yes No Yes No
(214) (894) (200) (770) (221) (777)
19.31 80.69 20.62 79.38 22.1 77.8
Are you still in
need of a loan
82.1 60.4 79.8 54.4 75.3 57.0
Note: All numbers in percentages (observations in parenthesis)
The numbers of enterprises have applied for bank loans which decrease in the period
2005-2009. The enterprises applied for formal loan which may be have problems getting
loans, so their loan application were denied. The proportion of enterprises has problems
getting loans increase from 19.31% in 2005 to 22.1% in 2009. The main reasons are
difficulties in obtaining clearance from bank authorities and lack of collateral.
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24
Figure 3.3: Problems getting the bank loan of SMEs
Problems gettinh the loan
0
10
20
30
40
50
60
Lack
of
collateral
Did
not
deliver
a
proper
description
of
the
potential
of
the
enterprise
Complicated
government
regulations
Administrative
difficulties
in
obtaining
clearance
from
bank
authorities
Other
2005 2007 2009
Whether or not credit applicants have problems in obtaining loans, they still consider
themselves in need of a loan. They can be classified as credit constrained. Credit constrained
group may be include some of the enterprises which did not apply for formal loans. Around
70 percent of these enterprises do not want in need of loan or incur debt. The rest of non-
applicant group did not apply because they did not have adequate collateral, difficult in lend
processes or high interest rates. These enterprises can be considered as having limited access
to credit. Adding these enterprises into the credit constrained group, the proportion of group
is 44.7% in 2005, 40, 4 in 2007 and 39% in 2009.
6675826

Determinants of access to formal credit by small and medium enterprises in Vietnam.pdf

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