Networks and bank financing the study of SMEs in vietnam

VIETNAM-NETHERLANDS PROGRAM
FOR MASTER OF ARTS IN DEVELOPMENT ECONOMICS

Networks and Bank Financing:
The Study of SMEs in Vietnam

A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By
DANG NGUYEN KHANG

Academic Supervisor:
DR. DINH CONG KHAI

HCM, December, 2013


ABSTRACTS

Small and medium-size enterprises (SMEs) play crucial roles in the economy. They
generate over 60% employment in many countries and are the key factor for the economic
growth. SMEs, however, face constraints to access external finance, which negatively impacts
on their business performance and growth. Therefore, a research on SMEs’ ability to access to
external finance poses a significant important issue for academic scholars and policy makers.
In such emerging countries as Vietnam, bank financing tends to be the most importance
financial resources for SMEs. However, the bank employs credit rationing because of
asymmetric information between banks and SMEs. Networks may be the most effective
channels for SMEs to overcome information asymmetries, thus enabling them to gain access
to external financial resources. The objective of this study is to investigate the effects of
supporting networks and network diversity on banking financial accessibility of SMEs in

Vietnam. Particularly, four types of networks including networking with government officials,
bank officials, business associations and network diversity will be examined in depth.
Longitudinal data set of more than 1500 manufacturing SMEs in Vietnam from 2007 to 2011,
random effect estimator and Stata program will be employed in this research.
Key words: Networks, Bank financing, SMEs

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ACKNOWLEDGMENT

Foremost, I would like to gratefully and sincerely thank my supervisor, Dr. Dinh Cong
Khai, for his valuable guidance, insightful comments and supports in all the time of research
and writing of this thesis.
Besides, I would also like to thank Dr. Truong Dang Thuy and Dr. Pham Khanh Nam,
who gave me assistance and guidance through my thesis process. Another special thank goes
to all the lecturers for their wonderful knowledge, and program administrator and technical
staffs at the Vietnam – Netherlands Program for their help during the time I studied in the
program.
Last but not least, I would like to thank my family and friend for their support and
encouragement not only through my thesis process but also throughout my life.

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CONTENTS
ABSTRACTS ....................................................................................................................................... i
ACKNOWLEDGMENT ................................................................................................................... ii
LIST OF TABLES ............................................................................................................................ vi
LIST OF FIGURES ........................................................................................................................ viii

ABBREVIATIONS ........................................................................................................................... ix
1.

INTRODUCTION ................................................................................................................. 1

1.1.

Problem Statements............................................................................................................... 1

1.2.

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

1.3.

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

1.4.

Scope of the research ............................................................................................................. 4

1.5.

Methodology ........................................................................................................................... 5

1.6.

The structure of the research ............................................................................................... 6

2.

LITERATURES REVIEW .................................................................................................. 7

2.1.

SMEs and Networks .............................................................................................................. 7

2.1.1. Overview of SMEs ................................................................................................................. 7
2.1.2. Networks concepts ............................................................................................................... 10
2.2.

Theoretical review ............................................................................................................... 10

2.2.1. Credit rationing theory by Stiglitz and Weiss (1981)..................................................... 11
2.2.2. Strength of weak ties theory by Granovetter (1973) ...................................................... 13

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2.2.3. Resources Dependency Theory (RDT) by Pfeffer and Salancik (2003) ...................... 14
2.3.

Empirical review .................................................................................................................. 15

3.

RESEARCH METHODOLOGY ...................................................................................... 25

3.1.

Estimating Model ................................................................................................................. 25

3.2.

Variables and measurement............................................................................................... 26

3.3.

Data description ................................................................................................................... 28

3.4.

Selection estimators ............................................................................................................. 30

3.4.1. Estimating fixed effects ....................................................................................................... 30
3.4.2. Estimating Random effects ................................................................................................ 31
3.4.3. Pool OLS, Fixed effects or Random effects ..................................................................... 32
3.4.4. Choosing Pool OLS and Fixed effects by F-test .............................................................. 33
3.4.5. Choosing the Pool OLS and Random effects by LM-test .............................................. 34
3.4.6. Choosing Fixed effect and Random effects by Hausman test ....................................... 34
4.

EMPIRICAL RESULTS .................................................................................................... 36

4.1.

Data analysis ......................................................................................................................... 36

4.1.1. Data description ................................................................................................................... 36
4.1.2. Correlation matrix ............................................................................................................... 37

4.1.3. Network over time ............................................................................................................... 38
4.1.4. Networking with government officials and bank loan ................................................... 40
4.1.5. Networking with bank officials and bank loan ............................................................... 41

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4.1.6. Networking with business associations and bank loan .................................................. 42
4.1.7. Network diversity and bank loan ...................................................................................... 43
4.2.

Selection estimator results .................................................................................................. 44

4.2.1. F-test: Choosing between FE and Pool OLS .................................................................. 44
4.2.2. LM-test : Choosing between RE and Pool OLS.............................................................. 44
4.2.3. Hausman test : Choosing between FE and RE ............................................................... 45
4.3.

Regression results ................................................................................................................ 46

5.

CONCLUSIONS AND POLICY IMPLICATION ........................................................ 51

5.1.

Conclusions ........................................................................................................................... 51

5.2.

Policy Implications .............................................................................................................. 52

5.3.

Limitation and Future research ........................................................................................ 53

REFERENCES ................................................................................................................................. 54
APPENDIX A: CHOOSING APPROPRIABLE ESTIMATOR .............................................. 59
APPENDIX B: ESTIMATION RESULTS .................................................................................. 61

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LIST OF TABLES

Table 2.1: Classification of SMEs in Vietnam ........................................................................... 8
Table 2.2: Share of SMEs in total Enterprises .......................................................................... 8
Table 2.3: The Ownership structure of SMEs ........................................................................... 9
Table 3.1: Variables and measurement .................................................................................... 28
Table 3.2: Size of SMEs in the sample ...................................................................................... 29
Table 4.1: Description of the sample ......................................................................................... 37
Table 4.2: Correlation matrix between variables of the sample ........................................... 38
Table 4.3: Network over time (mean value) ............................................................................. 39
Table 4.4: Level changed of network over time (%) .............................................................. 39
Table 4.5: F-test results ............................................................................................................... 44
Table 4.6: LM-test results ........................................................................................................... 45
Table 4.7: Hausman-test results................................................................................................. 45
Table 4.8: Regression results ...................................................................................................... 48
Table A.1: Fixed Effects result ................................................................................................... 59
Table A.2: LM-test result ............................................................................................................ 59

Table A.3: Hausman-test result ................................................................................................. 60
Table B.1: The estimated result ................................................................................................. 61

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vii


LIST OF FIGURES

Figure 2.1: Conceptual framework ........................................................................................... 24
Figure 3.1: Process of choosing the appropriate estimator ................................................... 33
Figure 4.1: Networking with government and bank loan rate.............................................. 40
Figure 4.2: Networking with bank and bank loan rate .......................................................... 41
Figure 4.3: Networking with business associations and bank loan rate .............................. 42
Figure 4.4: Network diversity and bank loan rate .................................................................. 43

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ABBREVIATIONS

FE

Fixed effect

GDP

Gross Domestic Product

Pool OLS

Ordinary Least Squares

RDT

Resources Dependency Theory

RE

Random effect

SMEs

Small and medium size enterprises

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1. INTRODUCTION
Firstly, this section presents problem statements in which the author will explain the
significance of networks and bank financing topic. Based on the problem of this topic,
research objective and research questions will be formed in the second part. Subsequently, the
scope of this research and methodology of solving topic problems are depicted. Finally, the
structure of the research is presented.
1.1.

Problem Statements
Small and medium-size enterprises (SMEs) play crucial roles not only in developing

economies but also in developed economies. In many countries they generate over 60%
employment (Beck, 2007) and are the key factor for economic growth (Ganbold, 2008).
SMEs, however, face constraints to access external finance resources because of high
uncertainty (Beck & Demirguc-Kunt, 2006). Pissarides (1999) argues that lack of financial
resources is one of the three-most central obstacles which obstructs business performance and
growth of small entrepreneurs all over the world. Therefore, research on the ability to access
external financial resource for SMEs raise a significant issue for academic scholars and policy
makers over the world (Berger & Udell, 2006).
In emerging countries like Vietnam, the capital market, angel investors and venture
capital are incipient, so bank financing seems to be significantly formal external financial
sources for SMEs (N. T. Le & Nguyen, 2009). In addition, according to CIEM (2012), formal
credit is the most importance financial source for SMEs investments. Formal-credit accounts
for 46.3% of total SMEs’ financial investment. However, over 39% of firms have limited

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accessibility or are rationed to bank financing (CIEM, 2012). Nguyen, Le , and Freeman
(2006) explain that banks face uncertainty and risks when they lend SMEs because of
asymmetric information between them and SMEs. Stiglitz and Weiss (1981) argue that credit
rationing is mostly rooted from asymmetric information. In addition, emerging countries
usually and currently lack of formal information institutions (Ahlstrom & Bruton, 2006). To
survive and grow SMEs must, therefore, find solutions to overcome information asymmetries
between banks and SMEs. Granovetter (1973) points out that network can solve asymmetric
information, moreover, increase firms’ reputation and approach external resources.
In Vietnam, most previous studies about the effects of networks on accessibility of
bank financing issue use cross-section data, which limits the validity of implied causes of
networks and bank financing relationship (N. T. Le, Venkatesh, & Nguyen, 2006). In the
expectations of reducing the limit of cross-section data, this research uses longitudinal data

sets from 2007 to 2011 to investigate the effects of networks on bank financing. Baltagi (2008)
argues that panel data provides many advantages such as minimizing the heterogeneity among
individuals; providing more information, less collinearity, more degree of freedom and more
efficiency than cross section and time-series data. Moreover, panel data is widely recognized
for its ability to control variables, which cannot detect in pure cross section and time-series,
and is more efficient in research dynamics of adjustment.
This study focuses on investigating the role of supporting networks with the ability to
access bank financing of SMEs in Vietnam. Particularly, three types of networks, which
embrace networks with government officials, bank officials, and business associations, will be

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closely examined. Another primary objective of this study is to investigate the impacts of
network diversity on bank financing of SMEs.
This research is inspired by two studies about networks and bank financing of N. T.
Le, Venkatesh, & Nguyen (2006) and N. T. Le & Nguyen (2009). However, compared with
two these articles, this research have some differences which are expected to create additional
values. Firstly, this study uses updated panel data, enabling it to fill the limitation of crosssection data which were used in the two previous researches. Secondly, in the way of
measuring networks, the two previous researches use qualitative measurement while this study
uses quantitative measurement which is more detailed and accurate. Finally, this study
investigates two different networks including networking with bank officials and network
diversity, which may have significant effects on bank financing but are not considered in the
two former studies.
1.2.

Research objectives
This study aims to examine the effects of networks on bank financing accessibility of

SMEs, which facilitate SMEs to develop policies to access the bank financing based on their

networks.
1.3.

Research questions
This paper attempts to answer following research questions:

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+ Main question
Do networks affect bank financing accessibility of SMEs?
+ Specific questions
Does networking with government officials affect bank financing accessibility of
SMEs?
Does networking with bank officials affect bank financing accessibility of SMEs?
Does networking with business associations affect bank financing accessibility of
SMEs?
Does network diversity affect bank financing accessibility of SMEs?
1.4.

Scope of the research
This research investigates impacts of networks on bank financing accessibility of

manufacturing SMEs in Vietnam from 2007 to 2011.
Manufacturing SMEs is the main focus of this research because of two reasons. Firstly,
manufacturing sector plays important roles in the economy; therefore, understanding more
about this sector is significant. Secondly, data of manufacturing SMEs is available.
According to Naudé and Szirmai (2012), the manufacturing sector holds a crucial role
in economic development. Especially in developing countries, it is a trigger for economic
growth and catch-up. Manufacturing sector provides a country with opportunities to gain

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benefits from economics of sale, aborting and approaching technology, providing jobs at
different skill levels.

In Vietnam, according to GSO (2012), the manufacturing sector

accounted for over 19% of whole GDP, and occupied over 87% GDP of the industry sector in
2011. In addition, it used 13.8% employed population, and was ranked second after
Agriculture, forestry and fishing sector (48.4%) in 2011. Additionally, nearly 90% of
manufacturing enterprises are SMEs (Hakkala & Kokko, 2007).
Data of manufacturing SMEs is available in Vietnam. According to CIEM (2012),
SMEs are central in the development process in Vietnam. They have important contributions
in economic growth and employment. However, these enterprises face many constraints. For
the purpose of providing deeper insights into SMEs sector and assisting policy research, CIEM
surveyed more than 2.500 manufacturing SMEs in 10 different provinces from 2007 to 2011.
These data are publicized and are available in CIEM website.

1.5.

Methodology
This research adopts the quantitative method. Based on network theory and some

empirical studies, the author builds up the bank loan rate-networks relationship model to
measure the effects of specific types of networks on SMEs’s bank loan accessibility. In
addition, selected panel data of more than 1500 SMEs from 2007 to 2011, random effect
estimator, and Stata program would be employed.

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1.6.

The structure of the research
This thesis includes five sections, and the four remain sections are structured as

bellow:
Section two, literatures review, clarifies some key concepts. Moreover, the overview of
SMEs in Vietnam will be presented, and some related theories and empirical studies are
reviewed. This section ends by building up four hypotheses and a conceptual framework.
Section three presents methodology to reach this thesis’ objectives. It includes:
conducting models, process of collecting data and choosing the appropriate estimator to run
the regression.
Section four reports empirical results, including data analysis, selection appropriate
estimator results and regression results.
Section five presents conclusion, policy recommendations as well as limitations and
future research of this study.
This thesis also contains two appendixes:
Appendix A describes results of three kinds of test which are used to choose the
appropriate estimator.
Appendix B indicates final estimation results.

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2. LITERATURES REVIEW
This section focuses on literature, which investigates the relationship of network and
bank financing. Firstly, two key concepts, SMEs and network, will be explored. The role of

SMEs in the Vietnamese economy and the problem of constraints to access bank financing
will also be presented. Secondly, the credit rationing and strength of weak ties theory are
mentioned for explaining of the causes of SMEs’s credit constraint and the trend to overcome
this problem. Finally, some empirical studies are reviewed. Based on these studies, this thesis
conducts four hypotheses and a conceptual framework for examining networks-bank financing
relationship.
2.1.

SMEs and Networks

2.1.1. Overview of SMEs
According to the definition of the government decree no. 90/2001/CP-ND in 2001,
small and medium enterprises are independent production facilities business, which are
registered and have the business capital of up to 10 billion VND or annual labor not more than
300 people. In the government decree no. 56/2009/CP-ND in 2009, SMEs is classified in three
groups: micro, small and medium scale enterprise. SMEs are classified based on below table:

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Table 2.1: Classification of SMEs in Vietnam
Very small
enterprises

Small –sized enterprises

Number of
employees

Total

capital

I. Agriculture,
forestry and
fishery

Medium-sized enterprises

Number of
employees

Total
capital

Number of
employees

10 persons
or less

VND 20
billion or
less

11-200
persons

Over VND
20 billion to
VND 100

billion

201-300
persons

II. Industry and
construction

10 persons
or less

VND 20
billion or
less

11-200
persons

Over VND
20 billion to
VND 100
billion

201-300
persons

III. Trade and
service

10 persons

or less

VND 10
billion or
less

11-50
persons

Over VND
10 billion to
VND 50
billion

51-100
persons

Source: The government decree no. 56/2009/CP-ND
The numbers of SMEs have dominated in Vietnamese enterprises. According to C. L.
V. Le (2010), the share of SMEs (classified by number of employees) accounted for over 94%
of total enterprises in operation and continually increased from 2000 to 2007. In 2007, the
share of SMEs reached 97.4 % of whole enterprises (Table 2.2).
Table 2.2: Share of SMEs in total Enterprises
Year
Total Enterprises in
Operation
Number of SMEs
(classified by employees)
Share of SMEs (%)

2000

2001

2002

2004

2005

2006

2007

42,297 51,680 62,908 72,012 91,756

112,950

131,318

155,771

39,897 49,062 59,831 68,687 88,222
94.3
94.9
95.1
95.4
96.1

109,338

96.8

127,593
97.2

151,780
97.4

Source: C. L. V. Le (2010)

8

2003


Considering the ownership structure of SMEs, the number of non-state domestic SMEs
dominated and increased over time from 86.5% in 2000 to 95.9% in 2007, while the figures of
state-owned SMEs and foreign invested SMEs consistently decreased from 10.5 % and 3% in
2000 to 1.5 % and 2.5% in 2007 (Table 2.3).
Table 2.3: The Ownership structure of SMEs
Year
Total (%)
State owned enterprise
Non-state domestic enterprise
Foreign invested enterprise

2000
100
10.5
86.5

3

2001
100
7.6
89
3.4

2002
100
6.1
90.9
3

2003
100
4.6
92.5
2.9

2004
100
3.4
93.9
2.7

2005
100
2.4
94.9

2.6

2006
100
1.9
95.5
2.6

2007
100
1.5
95.9
2.5

Source: C. L. V. Le (2010)
SMEs have a significant contribution in generating employment and GDP. According
to Cuong (2007), in 2007, SMEs’ employees accounts for 26% of entire employees and
together they generate about 26% of whole GDP. In the manufacturing sector, according to
GSO (2012), manufacturing SMEs accounted for over 19% of GDP, and occupied over 87%
GDP of the industry sector in 2011. In addition, manufacturing sector employed 13.8% of total
employee population, standing at the second position after Agriculture, forestry and fishing
sector (48.4%) in 2011.
Although SMEs has important roles in the Vietnamese economy, they face many
constraints on development. One of the most serious obstacle for SMEs growth in the future is
the credit constraint, which has existed for years (CIEM, 2012). In the research about credit
constraints of the Vietnamese manufacturing enterprises, Rand (2007) argues that
approximately 14-25% manufacturing enterprises are constrained to access formal credit.

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CIEM reaches the same conclusion with Rand (2007) when they show the evidence that a
large proportion of SMEs are obstructed. According to CIEM (2012), formal credit provides
the most important financial resource for manufacturing SMEs’ investment. Formal-credit
accounts for 46.3% in SMEs’ total financial investment, while retained earnings and informal
credit represent 45.3% and only 8.4% respectively. However, over 39% of firms have limited
accessibility or are rationed on bank financing.
2.1.2. Networks concepts
This study uses SMEs data set of CIEM; therefore, conceptual networks are perceived
from the perspective of networks of CIEM, which operate SMEs’ surveys in Vietnam.
According to CIEM (2012), networks are the regular contact between enterprises’
owners/managers with other individuals or organizations, which are useful for business
operation. Networks are seen as an entrepreneurial asset which can support them to gain
access to information, new technologies as well as external resources, which create profits for
the enterprise.
2.2.

Theoretical review
This thesis refers to three theories. The first one is credit rationing theory, which

explains the reasons why SMEs face constraints in bank financing. The two others are strength
of weak tie theory and resources dependency theory, which recommend that networks can
mitigate credit rationing through asymmetric information reduction.

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2.2.1. Credit rationing theory by Stiglitz and Weiss (1981)
Credit rationing theory is the most well-known theory which explains the failure of the
loans market after loan applications had been evaluated by banks. Credit rationing theory

based on asymmetry information was, firstly, introduced by Jaffee and Russell (1976) and
then expanded by Stiglitz and Weiss (1981). The Stiglitz-Weiss model indicates a
circumstance in which borrowers would not receive a loan, at the prevailing interest rate, even
if they are willing to pay higher interest rates because of rejection from the lender. It means
that loan market would not reach the equilibrium point at which quantity demanded equals
quantity supplied. Instead of that, loan markets establish a different equilibrium point, the
bank-optimal interest rate, with which banks reach to maximize profit. At the bank-optimal
point, quantity demanded excesses quantity supplied.
In bank's perspectives, under asymmetric information condition, increasing the loan
interest rate may conduct adverse selection and moral hazard effects. Asymmetric information
refers the condition in which firms understand their expected risk and return when they
operate projects, while banks cannot or only know to base on the expected risk and return of
the average projects in the economy. Because banks have difficulties to identify the qualified
borrowers, so they employ interest rate as a screening device. The borrower, who offers a high
interest rate, may be at worse risks. Therefore, if banks increase the lending interest rate, they
may attract higher risk borrowers and abandon lower risk borrowers. This situation is called
adverse selection effect. Moreover, loan interest rate influences on the project selection of
borrowers. Those who offer a high interest rate may choose riskier projects in order to gain

11


high return. This situation is called moral hazard effect. Both effects may reduce banks’ profit.
To avoid benefit losing, banks will not charge the interest rate which is higher than the bankoptimal interest rate. Borrowers offer any interest rates, which are higher bank-optimal interest
rate, may not receive loans. This is called credit rationing. In conclusion, the Stiglitz-Weissmodel suggests that asymmetric information leads to credit rationing. A part of the borrowers,
therefore, are constrained to access bank financing.
The credit rationing model also implies that the loan rate may not be the efficient
mechanism to allocate credit. However, banking systems apply this price mechanism for
lending credit not only in Vietnam but also in many countries over the world. Banks may need
non-price mechanism to solve the problem of asymmetric information and evaluate

creditworthy of borrowers. The non-price mechanism includes four technologies namely:
asset-based lending, credit scoring, financial statement lending, and relationship lending and
these techniques can mintage the effect of asymmetric information. Berger & Udell (1995,
2002) suggest that lending relationship provides the most powerful solution to resolve
asymmetric information problem and evaluate firms’ financial health. In lending relationship
condition, banks collect soft information by contacting in a variety of dimensions with
entrepreneurs, firms, the local community, and use that information to judge firms’ financial
abilities and make loan decisions.
In the next part, the author will present two theory which was introduced by
Granovetter (1973) and Pfeffer and Salancik (2003). These theories argue that networks are a

12


powerful channel to overcome asymmetric information situations which are the main reason to
cause credit rationing.
2.2.2. Strength of weak ties theory by Granovetter (1973)
Granovetter (1973) argues that networks offer a crucial channel for sharing,
transferring information, and spreading the existence and practice of firms. Therefore,
networks are an efficient mechanism to overcome the asymmetric information problem and
increase firms’ reputation. Moreover, networks are also an important source to gain external
resources. According to the strength of weak ties theory, networks consist two kinds of ties
which are strong and weak ties. The impacts of these ties on individuals’ actions are different.
Strong ties offer information and support with many beneficial characteristics, which are
cheap, more detailed, accurate and reliable. However, Granovetter (1973) recommends that
weak ties are more beneficial than strong ties because strong ties offer redundant information,
so they have limited access to information and are less efficient. By contrast, weak ties have
access to non-redundant information and recourses, therefore, weak ties are more superior to
strong ties. This theory emphasizes mastery of weak ties on the process of seeking information
and resources of individuals.

In the entrepreneurship term, this hypothesis indicates that the strength of network s
determines a kind of resource. Strong ties mainly provide motivation and support to make
choice and solve problems. Weak ties offer the opportunities to access diverse information and
resources. Entrepreneurship may gain benefits from both strong and weak ties.

13


This theory also implies that entrepreneurs should join networks to share knowledge,
information as well as resources, given the fact that networks help entrepreneurs develop
relationships, make trust, as well as reduce asymmetric information among themselves and
other people who have positive influences on their enterprises’ business performance and
growth. Moreover, through networks, entrepreneurs have to learn from knowledge and
experience of other individuals, as well as obtain superior information, external resources and
reduce the transaction cost.
Granovetter (1985) argues that entrepreneurs, who have extensive networks, have an
advantage in obtaining information, new technologies as well as accessing such external
resources as credit.
2.2.3. Resources Dependency Theory (RDT) by Pfeffer and Salancik (2003)
RDT explains the behavior of enterprises to relation on external environment.
According to RDT, the enterprises cannot self-sufficient all resources (information, money or
physical resources). The survival and performance of enterprise depend on resources and
supporting networks which are controlled by outside actor of the firm. Therefore, the
enterprises reply to external environment to get supports. However, the interdependence
between organizations and external environment is the cause for uncertainty and unpredictable
of organizations because of lack coordination between them. The solution for these problems
is a coordination relationship on inter enterprises and between enterprises and external
environment. Networks assist organizations gain three benefits. Firstly, networks provide the
existent and activities information of organizations for other organizations. Secondly,

14


networks provide a channel for communication with other organizations. Therefore, networks
have an important role in reducing the asymmetric information. Finally, networks provide an
essential condition to ensure for support commitment from other organizations. In brief,
networks with external environment may support not only resources but also reduce
asymmetric information for enterprises.
2.3.

Empirical review
There are many studies argue that networks are useful for both lenders and borrowers,

as they play a crucial role in solving the asymmetric information problem between lender and
borrower. Therefore, networks assist borrowers can access financing, and assist lenders invest
in the right projects.
Shane and Cable (2002) investigate the relationship of network ties, reputation and
new venture financing. They show that, entrepreneurs are obstructed in mobilizing financial
capital because of imperfect information. In addition, they attempt to research how
entrepreneurs can overcome asymmetric information problem among themselves and potential
investors to access financing. They examine the influence of direct and indirect ties of
entrepreneurs on financial decisions of 202 investors. As a result, they found that networks
can mitigate asymmetric information effects on lender-borrower relationship through the
information transferring process. Moreover, they also find that networks may increase firms’
reputation, which impacts directly on the firms’ ability to access external financing.
According to Ahlstrom and Bruton (2006), in the study of the function of networks in
venture capital in East Asian emerging economy, after analyzing the results of semi-structured

15