Determinants of access to formal credit by small and medium enterprises in vietnam

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DETERMINANTS OF ACCESS TO FORMAL CREDIT BY SMALL AND MEDIUM ENTERP

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

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

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

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

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

TRAN NGUYEN THUY BAO ANH

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

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

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

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

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

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

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

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

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

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

CHAPTER 2 LITERATURE REVIEW

This chapter is to review the theoretical and empirical literature

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

Total capital Number of

II Industry and

construction

billion or less

Between over 10 persons and 200 persons

service

billion or less

Between over 10 persons and 50 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

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,

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

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

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

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

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

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

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

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

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

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

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

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

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

Figure 2.2: Access to credit: determinants and channels of influence

Education

Ethnicity

Owner’s characteristics

Liquidity

Borrow

Overdue debt Credit profile

Relationship with banks between lenders Relationship

and borrowers

Access

to credit

2,7

4,5,9 3,4,5,8 1,2,3,4,7,9

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

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

99 Has your firm applied

for bank loans or other

formal credit since August

2007 (last survey)?

104 b Amount originally

106 Why has the firm not

applied for formal loans since

August 2007 (last survey)?

Table 2.3: Variable summary

Education Owner’s Education

= 1 if College/University/post-graduate,

= 0 otherwise

+

Firm’s characteristics

= 1 if Private (sole proprietorship) /Limited liability company,

= 0 otherwise

N/A

Relationship with lenders

Overdue debt fail to service its debt on time

Borrow Had borrowed from banks in

CHAPTER 3: DATA AND 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

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