Luận Văn The Impact Of Loans T Small And Medium Enterprises.doc

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UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM
ERASMUS UNVERSITY ROTTERDAM
INSTITUTE OF SOCIAL STUDIES
THE NETHERLANDS
VIETNAM – NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
THE IMPACT OF LOANS TO
SMALL AND MEDIUM ENTERPRISES:
THE CASE STUDY OF VIET NAM
By
BUI THI HONG CHINH
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
i

UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM
ERASMUS UNVERSITY ROTTERDAM
INSTITUTE OF SOCIAL STUDIES
THE NETHERLANDS
VIETNAM – NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
THE IMPACT OF LOANS TO
SMALL AND MEDIUM ENTERPRISES:
THE CASE STUDY OF VIET NAM
By
BUI THI HONG CHINH
Supervisor
Dr. NGUYEN THI THUY LINH
ii

ACKNOWLEDGEMENT
This process of writing a thesis is a collaborative experience involving the
support and helps from many people. I want to express my gratitude to those who
give me the tremendous support to complete this thesis.
First of all, I would like to thank gratefully to my supervisor Dr. Nguyen Thi
Thuy Linh. I cannot finish my thesis if do not get the support and the advice from
her.
Beside that, I want to thank to Dr. Pham Khanh Nam who give me many
useful and important advice that help me very much through the time I do the thesis.
Moreover, I want to thank Dr. Truong Dang Thuy who helps me answer
questions when I need it.
And I want to thank the professors and the teacher staff of Viet Nam – Neth-
erlands Program that gave many supports to me to have the knowledge, to solve the
difficult problems in my studying process.
Last, I want to thank my closet friends and my family.
iii

ABBREVIATIONS
SME
RD
PSM
DD
VIF
IV
HET
Small and Medium Enterprise
Regression Discontinuity Design
Propensity Score Matching
Method Difference in difference
Variance inflation factor
Instrumental variable
Heteroscedasticity
SOE State Owned Enterprise
OECD
DNNN
NHNN
NHTM
Organization for Economic Co-operation and Development
State enterprises
State Bank
Commercial Bank
iv

ABSTRACT
After a period of growth and affected by the crisis, Vietnam's economy has de-
creased. Hundreds of thousands of small and medium-sized enterprises went bank-
rupt and shut down. Loan is a solution for business to expand scale, increase sales
and profits, but it can create jobs, increase salaries to improve social welfare or not?
To verify that argument, the author uses the PSM method and combines with DD on
the SME data set from 2009 to 2013 to more accurately assess the impact of the
loans.
The results show that loans do not have the effect of improving employee in-
comes, as well as creating more jobs. In addition, the loans from informal sources
with low cost do not help enterprises to expand their operations because of the small
scale. Loans from official sources are large scale, but the high costs overwhelm
profits. Moreover, the impact of formal loans also causes businesses to reduce their
jobs. The topic also shows other factors such as export, type of ownership, scale,
production technique, entrepreneurial qualification that affects to employment and
wage.
v

TABLE OF CONTENTS
ACKNOWLEDGEMENT......................................................................................................................III
ABBREVIATIONS................................................................................................................................. IV
ABSTRACT ................................................................................................................................................V
CHAPTER 1: INTRODUCTION .......................................................................................................... 1
1.1 PROBLEM STATEMENT..................................................................................................................... 1
1.2.1 Research objectives................................................................................................................ 2
1.2.2 Main research question......................................................................................................... 3
CHAPTER 2: LITERATURE REVIEW ............................................................................................. 4
2.1 REVIEW OF THEORY........................................................................................................................... 4
2.1.1 Definition of small and medium enterprises and types of credits.......................... 4
2.1.2 The impact of loans to employee from producer theory.............................................. 4
2.1.3 Factors affecting the operation of the business ............................................................. 8
2.2 REVIEW OF EMPIRICAL STUDIES ..................................................................................................10
2.2.1 Impact of loan to SMEs in Viet Nam...............................................................................10
2.2.2 Previous researches .............................................................................................................12
2.3 SUMMARY .........................................................................................................................................13
CHAPTER 3: RESEARCH METHODOLOGY .............................................................................14
3.1 ANALYTICAL FRAMEWORK ............................................................................................................14
3.2 ECONOMETRICS MODELS ................................................................................................................15
3.2.1 Impact assessment methodology.......................................................................................15
3.2.2 Research proposal and select model...............................................................................18
3.2.3 Dependent variables ............................................................................................................21
3.2.4 Independent variables..........................................................................................................21
3.3 DATA..................................................................................................................................................24
CHAPTER 4: RESEARCH RESULTS..............................................................................................25
4.1 OVERVIEW OF THE RESEARCH TOPIC ............................................................................................25
4.2 DESCRIPTIVE STATISTICS................................................................................................................28
vi

4.3 REGRESSION RESULTS .....................................................................................................................30
4.3.1 OLS regression results ........................................................................................................30
4.3.2 PSM combined with DD results........................................................................................32
4.3.3 Verification of model stability...........................................................................................41
4.4 DICUSSIONS ......................................................................................................................................42
CHAPTER 5: CONCLUSIONS AND POLICY IMPLICATIONS ..........................................47
5.1 CONCLUSIONS...................................................................................................................................47
5.2 POLICY IMPLICATIONS.....................................................................................................................48
5.3 LIMITS OF THE STUDY......................................................................................................................49
REFERENCES ..........................................................................................................................................50
APPENDICES ...........................................................................................................................................55
APPENDICES 1: DIVIDE THE SIZE OF THE BUSINESS ..........................................................................55
APPENDICES 2. INFLATION AND PRICE INDEX VND (1994=1) .....................................................56
APPENDICES 3. INFLATION AND PRICE INDEX VND (1994=1) (CONT) .......................................56
APPENDICES 4. IMPACT ASSESSMENT BY MATHEMATICAL METHOD..............................................57
APPENDICES 5. GROUPS WERE DEVIDED BY PSM METHOD............................................................58
APPENDICES 6. REGRESSION DISCONTINUITY DESIGN - RD.........................................................59
APPENDICES 7. INSTRUMENTAL VARIABLE - IV...............................................................................60
APPENDICES 8. DEFINITION OF SOME VARIABLES.............................................................................61
APPENDICES 9. DESCRIPTIVE STATISTICS...........................................................................................62
APPENDICES 10. ANALYSIS OF CORRELATION BETWEEN QUANTITATIVE VARIABLES ................64
vii

LIST OF TABLE
TABLE 3.1 DESCRIPTION AND MEASUREMENT VARIABLES .................................................. 22
TABLE 4.1 DATA STATISTICS.......................................................................................................... 25
TABLE 4.2 STATISTICS DESCRIBE THE PARTICIPANTS AND THE CONTROL GROUPS
BEFORE THE LOAN............................................................................................................................. 28
TABLE 4.3 IMPACT OF LOAN TO SMALL AND MEDIUM ENTERPRISES- BASIC MODEL .. 31
TABLE 4.4 REGRESSION MODEL OF THE LOANS TO SME........................................................ 32
TABLE 4.5 TREND POINT OF GENERAL SUPPORT AREA .......................................................... 34
TABLE 4.6 IMPACT OF LOANS TO SME ON THE LABOUR COSTS........................................... 35
TABLE 4.7 IMPACT OF LOANS TO SME ON THE NUMBER OF EMPLOYEES ......................... 37
TABLE 4.8 IMPACT OF EACH TYPE TO SME ON THE LABOUR COSTS................................... 38
TABLE 4.9 IMPACT OF EACH TYPE TO SME ON THE NUMBER OF EMPLOYEES................. 40
TABLE 4.10 INVESTMENT AND LABOUR ..................................................................................... 42
TABLE 4.11 THE SCALE OF THE MOST IMPORTANT LOAN ..................................................... 43
viii

LIST OF FIGURE
Figure 2.1 Illustration of the impact of loan to SMEs………………………………5
Figure 2.2 Optimal coordination of production factors when loan increases…….…6
Figure 3.1 Impact of loans to SMEs when enterprises participate and do not join in
loans………………………………………………………………………….…….15
Figure 3.2 Impact assessment by DD method………………………………...… 18
Figure 3.3 Illustrate the general support area and the observation area discarded
with PSM……………………………………………………………………..……19
Figure 4.1 The supply of formal credit………………………………………….…26
Figure 4.2 The supply of informal credit………………………………………….27
Figure 4.3 The biggest difficulties prevent the development of SMEs……………44
ix

CHAPTER 1: INTRODUCTION
1.1 Problem statement
Vietnam's economy from 2008 to 2015 has more volatile due to the impact of
the financial crisis of the world and the domestic country. High inflation in 2008,
2010 and 2011 are over two figure (Appendices 2, 3); that makes the government
offer the methods to cut public investment, to be macroeconomic stability, to de-
crease inflation … These policies contribute to economic growth mainly relies on
investment. To prevent a recession, the government launched stimulus packages
such as the 4% interest rate subsidy to 17,000 billion in 2009, preferential loans to
buy houses, but not restore the economy, resulting in 600,000 registered businesses
officially only 380,000 active businesses (Truong Tan Sang, 2013). To statistics of
Department of business registration management in 2016, the number of bankrupt
enterprises 1.3 times in 2010, mostly small and medium-sized enterprises (SMEs)
for 10 months in 2015.
The giant state-owned corporations enjoy too many privileges but inefficient op-
erations were restructured, foreign investment (FDI) has not restored as expected
economy, cooperative economy is slow and efficiency is not much. At this time, the
SME is the most emphasis. The SMEs bankrupt, dissolute too much to be explained
by the macro-economy, made SMEs difficult to search their market, difficult to ac-
cess to loans (VCCI, 2015). The dissolution of a series of enterprises affects the so-
cial security issues such as GDP, consumption, unemployment, price indexes ... be-
cause Vietnam has a large stock of human resources, of which SMEs account for
96, 4% of the total number of enterprises in the country (ADB, 2014). By the end of
2013, Viet Nam had a total of 359,794 SMEs, accounting for 5.1 million workers,
accounting for 46.8% of the nation's labor force (ADB, 2014). Vietnam needs more
and more sustainable SMEs in a competitive environment. To do that, the State
needs appropriate support measures, first of all to exist, then to expand, thereby cre-
1

ating jobs and increasing laborers' incomes and solve the macroeconomics of the
country.
According to many economists, loan is very important problem, even the lead-
ing factor to support SMEs (ADB, 2014, Tran Hoang Ngan, 2015). Referring to
SMEs, the first thing to mention is difficult access to credit and the need to remove
barriers to accessing credit (Tran Dinh Thien et al., 2015). Many economists have
traditionally believed that loan is the key to help SMEs better function. The policies
promulgated by the State such as the establishment of the SME Development Fund,
the application of the ceiling lending interest rate, the expectation of SME devel-
opment to increase incomes, create jobs for employees.
In fact, there is little research to quantify the impact of loans to SMEs by exper-
iment. Moreover, the performance evaluation of SMEs does not clearly distinguish
between growth and development. The growth of SMEs is measured by the increase
in revenue and profit. The development of SMEs involves equitable growth and dis-
tribution of results in a relatively fair way, which helps business owners and work-
ers achieved benefit. On the other hand, the development of SMEs is reflected in an
increase in the wage index and employment. This topic explores the impact of loan
on the costs that an enterprise invests in employees and the impact on their wages
and employment from formal or informal loans; after that, provide appropriate poli-
cy recommendations for small and medium enterprises. That is why the study titled
"Impact of loans to employee in small and medium enterprises" in order to clarify
the issue: loan to employee in SMEs is the solution to social security, isn't it? On the
other hand, the topic seeks the impact of loan on development through the wage
index, employment at the enterprise micro level.
1.2 Research objectives
1.2.1 Research objectives
2

This study determines the relationship between loans and number of employees,
wages in small and medium enterprises. From that, research offers the policy impli-
cations to improve the activity of business.
1.2.2 Main research question
Does the loan to SMEs impact on the labour costs?
Does the loan to SMEs impact on the number of employees?
Does the each type of credit to small and medium enterprises impact on the labour
costs and the number of employees?
1.3 Scope of study
This thesis investigates the impact of loan to employee on the number of employees
and the labour costs by using the dataset of Survey of Small and Medium Enterpris-
es which is done by the Central Institute for Economic Management and partner.
The data is collected from 2009 to 2013 due to its availability.
The methods will be applied in this study is descriptive statistics, OLS regression,
Propensity Score Matching Method (PSM) combined with Difference in Difference
(DD), accuracy test of model.
1.4 Structure of the thesis
This thesis consists of five main chapters. Chapter one is introduction. Chapter two
is literature review, including review of theory and review of empirical studies that
related to the impact of loan to employee in SMEs. Chapter three is research meth-
odology; in this chapter will be present about analytical framework and the method-
ology, variable measure, descriptive statistics to dataset. Chapter four is research
results, including overview of the research topic, descriptive statistics, results and
dicussions. Chapter five is conclusions and policy implications in addition that is
limits of the study.
3

CHAPTER 2: LITERATURE REVIEW
2.1 Review of theory
Chapter 2 presents theories concerning the impact of loan capital on labor in
small and medium enterprises. In Section 2.1 is Review of theory, which includes
definitions and the impact of the factors seen from the manufacturer's theory. Sec-
tion 2.2 is a review of empirical studies, including Loans to SMEs in Viet Nam and
other relevant studies. Section 2.4 is summary of chapter 2.
2.1.1 Definition of small and medium enterprises and types of credits.
There are many ways to classify enterprise with the criteria related to the num-
ber of full-time, part-time and non-permanent employees, World Bank definite
about the scales of enterprises such as: microenterprises scale from 1 to 9 employ-
ees; small businesses with 10 to 49 employees; medium-sized businesses with 50 to
299 employees; large enterprises with 300 employees or more. In Viet Nam, there is
distinction by industry. Decree 90/2001/ND-CP and 56/2009/ND-CP said that small
businesses from 10 to 200 employees, medium businesses with 200 to 300 employ-
ees in all industries except trade and services (Appendice 1).
Capital (K) is understood as the inputs for production serves as machinery,
equipment, land .., or financial capital. It is mobilized from various sources. Ac-
cording to this approach, capital and loan are understood to be identical.
Formal credit is the credit that is provided by financial intermediaries who have
the function of lending, mobilizing and lending in a clear and transparent manner.
Informal credit is a credit that is provided by financial intermediaries who do not
have the function of lending. It exists mainly in the form of private loans with high
interest rates and asymmetric information.
2.1.2 The impact of loans to employee from producer theory
Loans do not directly develop businesses. This financial source will be allocated
through investment in inputs such as workshop, equipment, machinery, technology,
4

human resources, then expand production and business activity to increase sales,
profitability and scale expansion (Nguyen Kim Anh et al, 2011). When businesses
invest resources, they will hire more labours, pay more wages (Figure 2.1).
Figure 2.1 Illustration of the impact of loans to SMEs
Increas-
ing of
loans
Investment - Production
+ Many opportunities to invest.
+ Increase Ability to invest
+ Increase investment by using better
technology
+ Create opportunities to expand the
small businesses
+ Diversification of economic activities
+ Enhance profitability from investment
Source: Nguyen Kim Anh et al (2011)
+ Increase
in income
for workers.
+ Increase
Employment
+ Increase
re-sources.
According to producer theory, enterprises operate on the principle of minimizing
production costs. When loans increased, that is described as follows:
Suppose the production function of enterprise is Q = f (K, L) with the output Q.
Q is dependent on the inputs (capital K, labor L and other factors). The underlying
assumption to producer theory is the minimum cost of inputs, denoted C. The next
important assumption is the marginal productivity of capital (MPK=
ginal productivity of labours (MPL=
f
) decrease.
L
f
K
) and Mar-
With capital cost is called “r”, and labor costs (including wages, insurance,
training ...) denoted w. The cost minimization problem is rewritten:
5

Min C (K, L) = r*K + w*L (2.1)
When output Q0 is unchanged:
f (K, L) = Q0 (2.2)
Optimizing the amount of capital and labor input of business, K and L to mini-
mize (2.1) with constraints (2.2). Lagrange multiplier method is applied to solve the
problem, Lagrange equations:
Z = f (K, L) + λ (w*L + r*K - C) (2.3)
Partial of Z to the variables K, L and λ is zero:
With MPK=
f
K
Z

f
r 0
K K
Z

f
w 0
L L
Z
 rK wL C 0

and MPL=
f
, switch side r and w, to score
L
MP  r
K
MP w
L
(2.4)
(2.5)
(2.6)
(2.4): (2.5), we have:
(2.7)
6

Figure 2.2 The optimal coordination of production factors when loan increases
K
Credit C0
r
K2
C0/r
Q1 credit Q2 credit
Long-term scale
Q0
Short-term scale
K0
0 L0
C
0
/w
L2 L1
Credit C0
w
L
Source: Simulation Pindyck, Robert S. and Daniel L. Rubinfeld (2013), Graph
7.8, page 253.
With C0 initial resources, business rearchs maximize productivity at output Q0
with input K0 and L0.
In the short term, the business is difficult to minimize costs because of the fix-
edness of capital elements. So, when the capital increases, resources will be Credit +
C0, production increases into Q1 credit, hiring more workers at L1.
In the long term, businesses will adjust both the K and L, so that the marginal
cost per unit of production increased from the addition of one labour equals margin-
al cost per unit of production increased from the addition of one unit of capital.
From equation (2.7):
MP

MP
K L
r w
(2.8)
7

So, in the long term, the business can invest more capital at K2 and labour at L2,
but the best situation: minimize the cost with optimal output is Q2credit.
Theoretically, businesses will use the loan to expand their production by more
investing in the number of employees; and with fixed wage, the cost of labour is
higher. From the theory above, the subject will focus on two hypotheses:
H1.1: Loans have a positive impact on the labour costs in SMEs
H1.2: Loans have a positive impact on the number of employees in SMEs.
H2.1 Formal and informal credit have a positive impact on the labour costs in
SMEs.
H2.2 Formal and informal credit have a positive impact on the number of employ-
ees in SMEs.
2.1.3 Factors affecting the operation of the business
The subject of the study is the impact of loan to small and medium enterprise in
Viet Nam, in other words, the causal relationship of the loan to the salary and em-
ployment of SMEs based on the producer theory. Businesses that take the initiative
to loan may be different from a business that does not actively loan to expand pro-
duction. Without a loan, businesses will use capital more efficiently, but using more
labour. The number of employees and salaries are influenced by many other factors,
and loan is just one of many factors. The study identifies a number of factors that
influence wages and employment, loan firms that have the potential to bias the im-
pact of loans:
Labour productivity: The economist said that the business want to increase sala-
ries for employees, they must increase labour productivity. If labour productivity
does not increase, but wages rise. That will cause capital deficits because produc-
tion costs increase.
8

Property: It is one of the most visible information about the business. Business
credit is limited by lack of collateral (Gertler and Gilchrist, 1991).
Years of business: In Vietnam, the age of a business influences the viability and
development of that business (Hansen et al., 2009). Bentolila et al. (2013) conclud-
ed that age affects labour in the enterprise. Because businesses connect to many
sources, including financial institutions. Therefore, the ability to loan is easy than
newly established enterprises.
Ownership form: each type of business, salary policy and recruitment will be
different. Type of enterprise affecting the participation of preferential interest rate
package (Dinh Tuan Minh et al., 2010). Foreign ownership influences the efficiency
as well as the ability of SMEs to join the international production network (Harvie,
2010).
Characteristics group of the industry: Each industry produces different products,
so the combination of capital and labor is different (Bentolila et al, 2013). The com-
plex careers require skilled labor, and are highly paid. High profit industries are
more accessible to financial sources. Different technological levels approach differ-
ent credit guarantees (Oh, Inha, et al, 2009).
Market Size: The ups and downs of the macro economy affect the size of the
business. Enterprises operating in the export sector are more likely to have access to
credit than in the domestic market (Dinh Tuan Minh et al., 2010).
Characterized group by size: Large firms that approach banks or formal finan-
cial institutions are easier than small businesses (Harvie, 2010). In Asia, companies
depend on banks are mainly large companies (Claessens et al, 2000). Medium-sized
firms have access to a much higher interest rate subsidy package than microenter-
prises (Signore, 2015). Enterprise size is one of the factors affecting the business
performance of Vietnamese enterprises (Ari Kokko and Fredrik Sjoholm, 2004;
Hansen et al, 2009).
9

Financial capacity: It is also considered by many economists to be characterized
by its size (Ramanathan, 2002).In a market economy, financial capacity is not only
from own capital but also from loan sources. So besides finance, loans also affect
the employee in the business.
Regional characteristics: Input and output markets for enterprises are as near as
possible, the more accessible the business is, the less time and expense for the busi-
ness. Urban enterprises in the South have significantly higher investment probabili-
ties than those in the North and rural enterprises (CIEM, 2014), but urban enterpris-
es are less likely to survive than rural (Hansen et al, 2009).
When studying SME, it is difficult to identify microenterprises with the charac-
teristics of a household (Hulme, 2000). Therefore, the characteristic of the business
owner (sometimes also the household head) has an important role in all household
business activities.
2.2 Review of empirical studies
2.2.1 Impact of loan to SMEs in Viet Nam
With market failures affect the operation of SMEs, the State needs to intervene
to help SMEs develop sustainably, create jobs for workers, increase wages and in-
crease social welfare. The reasons which for the State intervene in supporting SMEs
were summarized by the World Bank in 2004:
Incomplete information: Information asymmetry makes the market failure
and financial institution problems, hindering the development of SMEs. The solu-
tion is to improve institutions, improve financial markets, and provide direct financ-
ing from government for SMEs to promote growth and development.
Positive External: SME loan will help to improve competitiveness and entre-
preneurship. The growth of SMEs makes the economy change about effectiveness
and productivity, and increases in social welfare from the increased benefits of
competition.
10

Poverty Reduction Tool: The rise of SMEs helps to promote employment
more than the growth of large enterprises because of the more labor-intensive
SMEs. Therefore, the policies support SMEs to be linked to social welfare.
The State of Viet Nam has many documents to support SMEs in financial devel-
opment. The first legal document is Decree 90/2001/ND-CP, replacing Decree
56/2009/ND-CP. After that, the Government established the SME Development
Fund (Decision No. 601/QD - TTg) in 2013 with a chartered capital of 2,000 VND
billion. Circular 13/2015/TT-BKHDT promulgates the list of priority areas for sup-
port and criteria for selection of priority beneficiaries of the SME Development
Fund. In 2016, SME will receive a preferential interest rate of 5.5% for short-term
loans and 7% for medium- and long-term loans, but the fund is not enough to sup-
ply the needs of enterprises (Nguyen Xuan Thanh, 2016). That’s right! In total sys-
tem, credit debt for SMEs is more than 977 trillion dong (State Bank, 2015). Ac-
cording to the Ministry of Finance, credit debt of SMEs in the economy over the
years about 25%.
State-owned commercial banks, foreign banks, joint ventures provide finance for
SMEs at a moderate level. The other funds come from donor organizations (World
Bank, 2007). SOEs also loans, that makes the budget to SMEs less (Pham Chi Lan,
2016). Economic stimulus package in 2009 combined with the financial institution
restructuring event in 2012 makes credit growth in 2009 and 2010 higher than 30%.
In this stimulus package, credit guarantees for SMEs accounted for about 29%
(Dinh Tuan Minh et al, 2010; Tran Hoang Nhi, 2009).
In fact, SMEs have low official loaning rates. They must satisfy many criteria,
regulation, collateral assets, credit history from commercial banks (Phan Thi Linh,
2015). Therefore, businesses not only loan from commercial banks but also loan
from other informal sources. In Vietnam, informal loans are the main source of
credit for SMEs, accounting for about 80 percent, and official loans account for 30
percent. Small businesses mainly loan from non-bank sources such as internal funds
11

(savings, retained earnings, family networks) and the informal sector (money lend-
ing) (OECD, 2006).
2.2.2 Previous researches
In terms of business, Wang (2013) uses panel data by collecting the dataset from
2010 to 2011 to look at the impact of microfinance on next year's performance, in-
cluding net profit growth and revenue growth. Data coverage was collected in Tai-
zhou, Zhejiang Province, China. His research shows that microfinance has played
an important role in the growth of revenue and profitability of SMEs. But the limi-
tation of the article is the short time data, so it is impossible to study the impact of
microfinance on the growth of employment and wages.
In terms of employees, Berger (1989) and Petersen (1994) argues that micro-
finance for SMEs tends to stabilize incomes rather than to increase, in other words,
to maintain business activity and not create jobs. Two other studies use random
sampling on the effectiveness of corporate grants for similar results. Micro-
enterprise research, De Mel, McKenzie and Woodruff (2008b) conducted in Sri
Lanka and McKenzie and Woodruff (2008) conducted in Mexico. Random selected
a microenterprise group in each country that was funded from 100 USD to 200
USD. The authors find evidence from grants that increase income for businesses,
whether financed by cash or equipment, the same material gives the same results.
The results also show that one-time donors do not raise poor people's income be-
cause the role of business owners is no longer important. On the other hand, grants
do not increase the income of self-employed women.
In Italy, Europe, Adorno. V and Dg (2007) assessed the impact of subsidy policy
under Law No 488/1992 by nonparametric method and compared it with the con-
ventional parameter method (DD), data from 1996 to 2000. Research used continu-
ous variables. The results show that the impact of the policy was positive, statisti-
cally significant. This means that companies that receive lower subsidies are 12
percent to 9 percent lower, the number of employees is 25 percent to 11 percent,
12

and fixed assets fall 25 percent and opposite. The more capital is subsidized, the
more policy is effective, and then the reduced marginal effectiveness. Therefore,
additional subsidies are not highly effective.
In addition, Signore and Pierfederico Asdrubali (2015) combined PSM and DD
methodology to assess the impact of credit on SMEs in the Middle East. Dataset
collected from 2005 to 2012. Research shows that the youngest and smallest SME
groups were the most beneficiary. And the loan increased labor by 17.3% in the first
five years. In the sixth year, sales increased 19.6%. However, SMEs limited in allo-
cating resources when they creased in short term loans, effectively being solved in
the medium term.
With 9890 businesses from PCI survey, in where 3225 businesses received a 4%
interest rate subsidy. In Viet Nam, Dinh Tuan Minh et al. (2010) studied the effect
of interest rate support policy on enterprises' operations. The results showed that
interest rate subsidy package had had a positive impact on labor change, but very
little, only few workers. Enterprises used this capital to expand short-term produc-
tion by hiring more labor than investing in machinery and equipment for long-term
production. The support was suitable for medium enterprises and mining compa-
nies. Limitations were cross-data, so the author used two multiple regression meth-
ods and PSM only evaluates the impact at the time of the survey.
2.3 Summary
The impact of loan to small and medium enterprises has many different results.
Basing on the assumption that people are rational at the marginal point, firms will
use the loan to invest in the resources which they use the most profitable. For de-
veloped and developing countries, or in the short and long run, the marginal utility
of the plant, equipment and labour are different. They occur the different impact of
loan to employee. Enterprises can use loans to invest in labour or invest in equip-
ment, workshops, technology to expand production. It is even possible to use loans
to invest in other resources not for production.
13

CHAPTER 3: RESEARCH METHODOLOGY
This section presents quantitative methods for assessing the impact of loan capital
and the proposed model. Section 3.1 says about analytical framework. Section 3.2
presents the proposed model, including two models for the two implementation
steps, the variables used for the model. Section 3.3 describes the data used for the
model.
3.1 Analytical framework
To visualize the research objectives and empirical models, it is essential to make the
framework of study on the whole as follows:
Loan
(formal and
informal)
Ability
of loan
(+)
- Loan history
- Age of SMEs
- Age of busi-
ness owner
- Debt to
asset ratio
- Scale
- Labour
produc-tivity
SMALL AND MEDIUM
ENTERPRISE
PSM
DD
- The impact of loans
- Age of business owner
- The sex of business owner
…..
- Qualification of business
owner
- Export
- Technology
- NUMBER OF
EMPLOYEES
- LABOUR COST
(+)
14

3.2 Econometrics models
3.2.1 Impact assessment methodology
The study estimates the impact of loan to employee. The nature of the impact as-
sessment is to see the difference in output after intervention, relative to output in the
absence of intervention.
Figure 3.1 Impact of loans on SMEs when enterprises participate and do not
join in loans.
Output Output
Intervention
Intervention
Impact
Impact
No intervention No intervention
Time Time
Source: Acevedo and Tan (2011). Figure 1.1, Page 3.
In practice, it is not possible to observe the case of no intervention. Therefore, it
is necessary to create a group that is close to the participant group, called the control
group and not affected by the program.
Since the selection variable is not random, it leads to error in sampling, with un-
observable characteristics affecting output and observable characteristics affecting
the outcome or eligibility of the loan. Therefore, the objective of the impact assess-
ment will be to eliminate the impact of sample selection or to find the appropriate
method for treatment. Common methods in impact assessment studies:
15

3.2.1.1 Random sampling, multivariate regression model estimation
By using multivariate regression analysis, the policy variable is one of the factors
influencing the output. The regression models commonly used are:
Y = a1 + a2X2i + a3X3i + . . . +akXki + ak+1Tki+1 + ui (3.1)
With a1 is the root coordinates,, the average value of Y when the
a2X2i = a3X3i = . . . = akXki = 0
ai is the individual regression coefficients
ui is estimated residuals, with expectation 0 and finite variance
Ti is a policy variable, usually using as a binary variable, the value is 1 if the ith
observation has participated in the program. The value is 0 if the ith
observation
does not participate in the program. Policy variables can be continuous treatment.
Y is the dependent variable. That is the output which the research wants to know
how the outcome have been affected when participating to loan.
Xi (i=2…k) is ith
independent variable. That is the observable characteristics of
the business.
Regression to control the differentiation of these observations affect output, so
coefficients ak+1 is impact of joining to loan. This method is often used in terms of
data at a time. The above regression can not show the impact of policy involvement
not related to the different characteristics that affect the dependent variable. This
method strongly assumes the form of regression and may be endogenous. So
applying linear regression will often be inaccurate in estimating the impact of the
policy.
3.2.1.2 Propensity Score Matching Method (PSM)
PSM is the method used to evaluate the impact of a trend point. Assuming that
after controlling the difference of observations, the result of participating groups is
the same of the control group in the absence of intervention. First, regression of the
16

probability model to calculate the Pi probability of the ith
enterprise participating in
the loan. Khandker (2010) and Rubin (1983) prove that the comparison on P (X) is
also approximated on X under certain assumptions. The equation is as follows:
PE(T1|X ) 1
( X )
i i 1 e
1 2 i
(3.2)
At this point, the business group has the same range of Pi values with Xi
attributes not too different. Based on the general support area, establish a control
group (Appendix 5). Since then, the research caculates treatment effect on the treat-
ed - TOT, written in the form of mathematics as follows:
TOT  E
P(X )|T1
E[YT |T1,P(X )] E[Y C |T 0,P(X)]
PSM 
(3.3)
PSM has the advantage of identifying a control group that depends on many varia-
bles. However, this approach is limited in terms of data. For unobserved attributes,
if the error is negligible, PSM should be combined with another method for regres-
sion.
3.2.1.3 Difference in Difference Method (DD)
The difference in difference method compares the impact and control groups based
on differences in the results for each observation period. Original survey on both
non-participants and participants, then investigated both groups after the impact of
loan. From there, we calculated the difference between the median results which
observed in the intervention and control groups before and after the program im-
pact. This method has a parallel assumption: If no policy is involved then the results
of the two groups are equal.
The picture below (Figure 3.2) means (Y3 – Y2) = (Y1 – Y0).. Equation DD = (Y4 –
Y0) - (Y2 – Y1), so by the parallel assumption, the result of the policy effect DD =
(Y4 – Y3)
17

Figure 3.2: Impact assessment by DD method
Output
Intervention
Participating Group
Y4
Y
Y2
Y1
Y0
Impact
Control group
Before intervention After intervention Time
Source: Ruiz and Love (2012). Figure 4, page 23
The advantage of DD is that it eliminates sampling errors and lowers costs.
But if before the intervention, the trend of the two groups was different, the sam-
pling error may not be constant over time as assumed by the DD.
3.2.2 Research proposal and select model
The topic combines PSM with DD method to more accurately compare con-
trol and participation units. This combination can calculate the effect of observed
and unobserved traits under the condition that the parallel hypothesis exists. For
general data, the on-line linear equation DD calculates the median difference in the
outputs between participants i and non-join j belonging to the general support area:
TOT DD
 1  T T
) (i,j)(YC
 Y C
)
 (Y  Y  j 2 j 1 
PSM T i2 i1
 2 
N iT jC
1   1  
TOTPSM
DD
  Yi2
T 

 (i,j)Y j
C
2   Yi1
T
 (i,j)Yj
C
1 
N
T2iT2
jC2 
N
T1iT1
jC1 
(3.4)
(3.5)
18

First, we will prepare the regression of a probability function that calculates the
ability of participants of each enterprise based on pre-programmed features. Then
remove observations that are not in the general support area (Figure 3.3). This step
removes the second kind of deviation from the observable characteristics of the
business.
Figure 3.3: Illustrates the general support area and the observation area dis-
carded with PSM
Density Density
Business does not participate
Density
Business participates
0
The area of remov-
1
The area of removing General support area Trend Point
ing observations
observations
Source: Khandker et al (2010), Figure 4.1, page 59
The next step, regression based on table data by DD method combines POOL-OLS
over time, to calculate the effect of the program. This step removes the first type of
error caused by the unobserved characteristics of the business. At this stage, the
proposed econometric model is:
Yit = β0 + β1Time + β2Treat + β3Treat*Time + β4Zit + εit (3.6)
Yit is the output (the income, the number of employees ...) of enterprise ith
at time T
19

Treat=1 : Businesses participate to loan.
Treat=0 : Businesses depend the control groups
Time=0 : Pre-program time is the end of 2008 in the research paper
Time=1 : Post-program time is the end of 2012 in the research paper
Treat*Time : Interactive variables of Treat and Time dummy variables
Zit are control variables, bearing the characteristics of the business
The OSL regression will result in the following:
^ ^
0 4 : The average value of the control group before the loan with Treat =0 and
Time = 0
^ ^ ^
0 24 : The average value of the participating group before the loan Treat=1
and Time=0
^
2 : The difference between the two groups before the impact of the loan
^ ^ ^
0 
14 : The average value of the control group after the loan Treat = 0 and
Time = 1
^ ^ ^ ^ ^
 012  3 4 : The average value of the participating group after the loan
Treat =1 and Time = 1
^ ^
2 3 : The difference between the two groups after program.
^
 3: The difference between the two groups is created by the impact of the loan.
Here, Time is dummy variable, using to impact assessment of begin and end pro-
gram between control group and participating group. It receives the value “1” if the
time of survey was in 2012, and the value is “0” if time of survey is 2008.
20

3.2.3 Dependent variables
LNWAGE: natural loragit (1 + wageit) with wageit is the total average cost
(thousand) which ith
enterprises in t year to expend for employee (including
salaries, bonuses, allowances, labor outsourcing ...). And other labor costs (social
insurance, training, hiring ...). This cost is the real cost, in other words is already
eliminated inflation over the years, the base year is 1994. The price index of the
year based on information about the inflation of financial announcements, as well as
combined calculated from CIEM (Appendices 2, Appendices 3). In addition,
businesses that do not pay wages (self-employed, self-employed, etc) should
contain a value of 0. Plus 1 to ensure mathematically but do not make distortions to
cost. Using the logarit of the dependent variable to explain the economic
significance as a percentage.
lnLABOR: natural logarit of the total number of employees in the enterprise
including permanent employees and seasonal workers. Regardless of regular
workers were full-time and part-time (as individuals working under 20 hours a
week, or only 5 to 20 days a month) or get paid and unpaid (form self-employed)
are not excluded. Calculate the time taken at the end of the business year,
summarized in the accounting of economic accounts.
3.2.4 Independent variables
The study uses DD combination PSM, so some features may be used for both steps.
But in each step you can use a number of separate variables to increase the fit for
each model.
A number of studies have used the observed characteristics of enterprises to evalu-
ate the ability to receive financial loans. Dinh Tuan Minh et al (2010) showed char-
acteristics of type of ownership. Target market is decisive for participating in the
interest rate support program. X Wang (2013) points out that product innovation, as
well as managerial attitudes and competencies, labor productivity, past credit histo-
ry, retention rates on image revenue affects the ability to receive microfinance. The
21

World Bank (2007) argues that in Vietnam land ownership certificates have re-
stricted SMEs access to credit. Inha Oh et al. (2009), some characteristics affected
the ability to receive credit increases to a point then decreases such as turnover, age,
enterprise size. There are also other characteristics that affect fixed assets, labor
productivity, R & D, ownership, and occupation.
Some studies use enterprise observational characteristics to control labor effects:
Bentolila et al, (2013) use size control variables (assets), age of enterprise, age of
enterprise squared, provincial, sector, short-term bank debt, long-term bank debt.
Pham and Lensink (2008) use household characteristics such as gender, marriage,
ethnicity, education level.The following independent variables will be defined
(Appendix 8) and put into use in the model to explain the dependent variable. But in
the process of regression, there are added or drawn variables as appropriate.
Table 3.1 Description and measurement variable
Sign Variable name Describe and measure
Mark ex-
pectation
Variable joins to
Dummy variable: receive the value is 1 if
TREAT any participating loans and the value is 0 if +/-
loan
not participating loans
Dummy variable: the value is 1 if the time of
Time Time variables survey was in 2012, recognized the value to
0 if time of survey is 2008
The variable interaction between time and
Interact
Interactive varia- group businesses, estimated coefficients of
+/-
bles the variables indicate the impact of loans to
employees in the enterprise
Long-term
The history bor- Dummy variable: the value is 1 if join to bor-
rows of the formal row of the official long-term loan before +
credit
long-term loan program and the value is 0 if not.
Short-term
The history bor- Dummy variable: the value is 1 if they par-
rows of the formal ticipate in the official short-term loan pro- +
credit
short-term loan gram funds and the value is 0 if not.
AGE Years of operations Years of operations since business was +/-
22

founded
MICRO Enterprises Scale1
Dummy variable: the value is 1 if microen-
-
terprises and the value is 0 if other
LNASSET
corporate finance Natural logarithm of total assets which cal-
+
scale culated in money
HOUSE- Type of business Dummy variable: the value is 1 if the house-
-
HOLD ownership hold enterprise, the value is 0 if other
EXPORT Market
Dummy variable: the value is 1 if the export,
+
the value is 0 if no export
OWN-
Dummy variable: the value is 1 if the land
Land ownership use rights, the value is 0 if there is no land +
LAND
use rights
HAND Production tool
Dummy variable: the value is 1 if only hand
-
tools, the value is 0 if other
HAge entrepreneurs age
Measured in years, since business owners
+/-
was born
HGen
Entrepreneurs gen- Dummy variable: the value is 1 if male, the
+/-
der value is 0 if female
Edu Academic level
Dummy variable: the value is 1 if the high
+
school graduation, if the value is 0 if not
Dummy variable: the value is 1 if college
ProEdu Qualification and university qualification, receiving a val- +
ue of 0 if not.
ShareDept Debt to total assets
Liabilities divided by total assets which cal-
-
culated in money.
lnNSLD Labour productivity
Natural logarithm of output divided by the
+
number of full-time employees
Province
Including 10 dummies representing 10 prov-
+/-
inces surveyed
Branch to produce
20 dummies according to industry code level
+/-
2.
1
Classification of enterprises, Clause 1, Article 3 of Decree No. 56/2009 / ND-CP, dated 30/6/2009
23

3.3 Data
Surveys conducted around August, from 2009 to 2013. Direct interviews in 2013 of
about 2,461 non-state SMEs, operating in the processing sector (CIEM 2010, 2012
and 2014). Of these, a further 1,988 respondents were interviewed in 2011. The
2011 survey was similar to 2449 enterprises that had 1999 repeat from the 2009
survey. It was conducted in 10 provinces and cities, including Ha Hai Phong, Ho
Chi Minh City, Ha Tay2
, Phu Tho, Nghe An, Quang Nam, Khanh Hoa, Lam Dong
and Long An. The sample in 10 provinces was selected based on two data sources
from the General Statistics Office of Vietnam (GSO). Survey data includes both
formal and informal household enterprises.
The study focused on the surveyed enterprises in all three surveys, to conduct the
study on the data table. In addition, any business that actually engages in real credit
is considered to have a loan. This step eliminates the "yes" to the question "does the
business have a loan?" But the fact that it can’t be borrowed is not affected by the
impact of the loan.
Also due to some changes in the questions, there are some unobservable traits from
2009 to 2013, which is unfortunately a limitation of the data and accessibility of the
author's data. To apply the PSM and DD combination, the subject will select the
pre-programmed features taken from the 2009 trend-scoring regression. Credit pro-
grams (policy variables) are businesses that are borrowing from August 2009 to
August 2011. Therefore, when applying the DD method of pre-programmed eco-
nomic account is the end of 2008, the economic account after the program is the end
of 2012.
2
Ha Tay was incorporated into Hanoi in early 2009. However, in Ha Tay the data is still considered as a
separate province so that the results of the survey can be compared with previous years.
24

CHAPTER 4: RESEARCH RESULTS
This section presents estimated results. Section 4.1 is overview of the research top-
ic. Section 4.2 shows descriptive statistics. Section 4.3 the research offers regres-
sion results and Section 4.4 is the dicussion about causing of the effects of the loan.
4.1 Overview of the research topic
The topic uses dataset of CIEM from 2009 to 2013 with many statistic number
which concerns to loans, labour, employment, investment, the problem of business.
The aim of paper is to seek the impact of loan to SMEs about employment and
wages and analyze a few the other impacts.
Table 4.1 Data Statistics
Unit: Business
Period 08/2007- 08/2009- 08/2011-
08/2009 08/2011 08/2013
% Loans from formal creditors in total debt 46,02% 46,57% 40,92%
% Loans from non-debt formally in total debt 53,98% 53,43% 59,08%
Age of business leaders 45,70 45,72 46,18
% Workers are male 65,61% 62,65% 59,92%
% Being the Kinh nation 93,29% 92,98% 93,35%
% high school graduation 58,98% 62,02% 70,05%
% Qualified College, University 20,64% 24,05% 25,86%
Age of Business 14,51 16,60 15,57
% Easy to port 45,76% 39,06% 43,61%
% Easy to railway station 57,92% 51,21% 57,63%
% Easy access to highways 78,19% 77,78% 82,44%
Total business 2655 2552 2575
Source: Author of calculations from SME 2009, 2011, 2013.
For the proportion of credit sources: In the debt structure over time, the source
of loans from informal organizations increased from 53% to 59%. Data differed
from the OECD (2006) that informal loans typically about 70% - 80% of the SME
debt structure in Vietnam. This can be explained that SMEs have easily access to
loans from formal sector.
25

Leadership characteristics: the average age of business owners does not change
much, about 45 - 46 years old. Sample survey had trends with the owner of female
(male drop from 65% to 59%). Educational attainment also changed in the sample,
high school graduation from 59% to 70%, and college-level qualifications also in-
creased from 20% to 25%. Data was most of the Kinh-owned enterprise, fluctuate
around 93% and little changed.
Regional Characteristics: There has been a gradual improvement in transporta-
tion over the years.
Figure 4.1: The supply of formal credit
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
3.5% 3.0% 2.3%
3.1% 4.2% 1.2%
8.2%
9.3%
11.6% 1.4%
1.3%
0.8%
17.6%
11.8%
20.0%
68.8% 62.8%
69.3%
08/2007-08/2009 08/2009-08/2011 08/2011-08/2013
Other
Development
Support Fund
Social
policy
bank
Foreign bank
Private equi-
ty bank
State com-
mercial
Bank
Source: Author of calculations from SME 2009, 2011, 2013
Data of enterprises about the source of formal credits interviewed, the majority
of commercial banks, accounting for 62% - 69% of the supply over the period.
While Enterprise Support Fund has limited credit facilities for manufacturing enter-
26

prises, accounting for only 1.2% to 4.4%. Private bank also provided loans ranging
from 11% to 17%. The Social Policy Bank has tended to reduce its role in the for-
mal credit supply structure for enterprises, from 11% to 8%. There is no significant
change in the structure of official capital supply.
Figure 4.2: The supply of informal credit
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
4.3% 4.3% 2.2%
8.3% 4.1% 10.1%
60.3%
63.7%
67.8%
27.9%
27.0% 20.0%
08/2007-08/2009 08/2009-08/2011 08/2011-08/2013
The other
Other
business
Relatives,
friends
Private
Source: Author of calculations from SME 2009, 2011, 2013
When interviewed about sources of informal credits, most of them were from
relatives, relatives, who accounted for 60% - 67% of the supply. This is followed by
other private sources, which are private credit institutions from 27% to 20%. Enter-
prises also borrow from other enterprises with the proportion from 4% - 10%. Other
27

sources of funding are insignificant, ranging from 2% to 4% of the informal loan
structure. As with formal credit, there is no significant change in supply structure.
4.2 Descriptive statistics
Table 4.2 displays the statistic summary for all variables used in the models. The
paper concentrates to analyze the main factors between participating group and con-
trol group.
Table 4.2 Statistics describe the participants and the control groups before the loan
Control Group Participating Difference t-Stat
Group
N Average N Average T=1 – T=0
Labour (person) 1042 11,58 574 25,30 13,718 8,82***
Salary (million dong) 1042 71,32 574 182,2 110,924 7,82***
Total assets (million 1042 989,5 574 1773 783,055 4,5***
VND)
Business features
Operation time (years) 1042 15,99 574 13,10 -2,885 4,92***
Small business 1042 0,798 574 0,589 -0,209 9,2***
Household business 1042 0,752 574 0,592 -0,16 6,78***
Land ownership 1042 0,666 574 0,585 -0,081 3,24***
Hand tools 1042 0,0873 574 0,0383 -0,049 3,71***
Export 1042 0,0422 574 0,0801 0,038 3,19***
South 1042 0,309 574 0,277 -0,032 1,35
Low technology 1042 0,601 574 0,523 -0,078 3,05***
Characteristics of the
business owner
Age (years) 1042 46,71 574 43,96 -2,748 5,13***
Sex 1042 0,670 574 0,676 0,006 0,25
Kinh Nation 1042 0,917 574 0,956 0,04 3,03***
high school graduation 1042 0,530 574 0,636 0,106 4,14***
College degree 1042 0,146 574 0,226 0,081 4,1***
Economic characteris-
tics
Labor productivity 1042 55,810 574 79,426 2,4E+04 4,22***
(million / person/year)
Debt / Total assets 1042 0,0689 574 0,163 0,094 7,34***
There is statistical meaning, meaning level 10%, ** level 5%, *** level 1%.
28

To labour variable, the average of the control group is 11 - 12 laborers, but par-
ticipating group is 25 - 26 people. Total asset of the control group has the average at
989 million, but the participating group is 1773 million. The difference is 784 mil-
lion, near double of the control group.
To salaries, the control group was 71 million VND, but the participating group
is 182 million dong. We saw the participating group invested into assets and labour
very much. The mean age of participation group exists from 13 to 14 years and the
other group is 15-16 years. The scale of the participants is larger than the control
group about 22%. We can see that household enterprise is 75% to the control group,
while the participating is only 59%.
The control group has more exports 4%, hand tools less than 4.9% and low
technology less than 8%. For business owners, the participation group had more ad-
vantages than the control group. Specifically, education variable (Edu) of participa-
tion group graduated high school at 64% compared to 53%, and college qualifica-
tions (ProEdu) was at 23% compared with 15%. The mean age of participating
group (HAge) was 43-44 years, compared with 46-47 of the control group.
The labor productivity (NSLD) of the participating group was 79 million
VND/person/year compared with 55 million VND/person/year of the control group,
an average of 24 million/person/year in a statistically significant.
The debt to equity ratio (ShareDept) of the participation group was 16% while
the control group was 6%.
T-student statistics (Table 4.2) on the average difference between two groups of
enterprises, pre-program period. The results of two groups have different character-
istics. But there are similarities in the gender of the employer, area. The initial find-
ing was that the trend group was better than the control group. Participants differed
and were significantly higher in terms of wages and labour. Other characteristics
show better participation, such as: higher exports, higher labor productivity, more
29

entrepreneurial skills, less age, less micro enterprise, less household enterprise (Ap-
pendices 9).
4.3 Regression results
4.3.1 OLS regression results
After the correlation analysis (Appendices 10), the OLS regression model to es-
timate the impact of credit:
The result (Table 4.3): loans increased by an average of 917% (e2.32
= 10.17) 3
for
wages and 95% (e0.67
= 1.95) for workers with no meaningful loan. After control-ling
for other characteristics, the impact of the loan declined but remained positive at 186%
(e1.05
= 2.86) for salary and 23% (e0.213
= 1.23) for employee. Controlled models also
increased the corrected R2
correction (from 5% to 49%, from 8% to 73%), without the
hyperpolycermia (with vif <10), Differential error (HET) by ro-bust error. In addition,
statistically significant negative (-) variables are expected to be small enterprises,
household enterprises, handicraft producers. Meanwhile, the impacts (+) are the export,
education, labor productivity, debt to total assets ratio.
However, as analyzed in the non-randomized case, the OLS estimation is not
accuracy, the policy variable may be correlated with the estimated excess:
p lim  Cov ( * T ) (4.1)
^
2 2
Var (T )
The baseline model results in a doubling of the OLS estimate (overestimation of
the impact of the loan). In (Table 4.2), the difference between the two groups before
borrowing shows that almost all participants are much better. It is possible that good
businesses are given priority loans, or because they are good to borrow to take ad-
vantage of external resources. Then the formula (4.1) will have positive correlation
^
to 2 > β2. In addition, the magnitude of
^
 2 is too high compared to the expectation
3
(Gujarati, 2004) chapter 3, page 28 (Halvorsen and Palmquist, 1980). Take the logarithmic (anti-
log, base e) of the estimated dummy variable e2.32
= 10.17, then subtracting for 1 being 9.17 or
917%.
30

of the impact of loan consolidation, which doubles the estimate to be biased up-
wards.
Table 4.3 Impact of loan to SMEs - Basic model
(1) (2) (3) (4)
LNWAGE LNWAGE lnLABOUR lnLABOUR
TREAT 2,328*** 1,052*** 0,672*** 0,213***
(0,158) (0,133) (0,0416) (0,0232)
AGE -0,0148** -0,000624
(0,00646) (0,000972)
EXPORT 0,662*** 0,480***
(0,164) (0,0554)
HOUSEHOLD -1,675*** -0,521***
(0,139) (0,0307)
MICRO -1,978*** -1,307***
(0,117) (0,0309)
HAND -1,865*** -0,0432
(0,310) (0,0491)
HAge -0,00367 -0,000931
(0,00585) (0,00108)
HGen 0,195 -0,00296
(0,129) (0,0227)
Edu 0,929*** 0,0440*
(0,157) (0,0236)
ProEdu 0,259* 0,132***
(0,134) (0,0349)
LnNSLD 0,460*** 0,0374***
(0,0668) (0,00949)
ShareDept 0,465** 0,223***
(0,220) (0,0587)
Controls 9 region vari- Yes Yes
ables
Control 19 industry Yes Yes
variables
Constant 7,527*** 5,115*** 1,680*** 2,744***
(0,108) (0,822) (0,0220) (0,126)
Observations 3228 3209 3231 3210
R-squared 0,056 0,498 0,082 0,732
Adj, R-squared 0,0555 0,492 0,0818 0,729
Ftest 217,3 109,9 260,6 175,4
31

* Significant at 10%, ** 5% level, *** 1% level. The number in parentheses is Std
a
The model has overcome the variable variance
4.3.2 PSM combined with DD results
It is difficult to achieve random sampling, and the resulting OLS increases the
doubted estimate of the bias. Therefore, the subject will use the PSM method to
combine DD to test again.
4.3.2.1 Trend point regression results
The first step is to estimate a probable pattern of borrowing by the firm based on the
observed characteristics before borrowing (Table 4.4).
Table 4.4 Regression model of loans to SMEs
Join in the loan Model 1 Model 2 Model 3
Formal credit history
Long-term credit 0,454*** 0,387*** 0,380***
(0,0998) (0,101) (0,102)
Short-term credit 0,810*** 0,679*** 0,681***
(0,0815) (0,0867) (0,0873)
Business features
AGE8 -0,00848** -0,00966*** -0,00968***
(0,00332) (0,00344) (0,00347)
EXPORT8 -0,0994 -0,133 -0,127
(0,156) (0,160) (0,164)
HOUSEHOLD8 0,137 0,144 0,170
(0,110) (0,115) (0,119)
MICRO8 -0,330*** -0,387*** -0,355***
(0,109) (0,114) (0,116)
HAND8 -0,176 -0,147 -0,159
(0,153) (0,156) (0,160)
OWNLAND8 -0,0188 -0,126 -0,114
(0,0772) (0,0806) (0,0819)
Characteristics of business
owners
HAge8 -0,00931*** -0,0112*** -0,0109***
(0,00350) (0,00362) (0,00367)
HGen8 0,0888 0,0433 0,0244
(0,0744) (0,0769) (0,0816)
Edu8 0,0462 0,0433 0,0505
32

Join in the loan Model 1 Model 2 Model 3
(0,0784) (0,0821) (0,0829)
ProEdu8 -0,118 -0,111 -0,105
(0,109) (0,112) (0,114)
Economic characteristics
lnNSLD8 0,0372 0,0661 0,0811*
(0,0465) (0,0489) (0,0493)
ShareDept8 0,389* 0,433** 0,462**
(0,203) (0,210) (0,205)
LNASSET8 0,0361 0,107*** 0,0994***
(0,0285) (0,0335) (0,0341)
Controls 9 regional variables No Yes Yes
Control 19 industry variables No No Yes
Constant -0,928* -2,136*** -2,363***
(0,556) (0,639) (0,652)
Observations 1616 1616 1615
Pseudo R-squared 0,135 0,172 0,182
chi-square test 242,5 311,5 324,3
* Significant at 10%, ** 5% level, *** 1% level. The number in parentheses is
Std a
The third model with the highest Pseudo R-squared resolution, corrected HET,
did not have collinearity. Observed factors do not affect the likelihood of credit par-
ticipation: Market orientation, production tools, ownership type, ownership and use
of land, sex, education level. While the characteristics of credit history, age of en-
terprise, size, age of the business owner, debt-to-asset ratio, asset size, labor produc-
tivity have an impact. Greatly come to the act of participating in the credit of the
business. As follows:
Businesses that have formal credit habits are more likely to continue to engage
in credit in the future. An important point is that the higher the debt-to-asset ratio,
the more likely it will be to continue to engage in credit.
Longer-established businesses tend to reduce their involvement in credit. But the
level of influence is not high, even very small.
Microenterprises tend to take less credit than small and medium enterprises that
participate in the lower end from 1.39 (e0.33
) times to 1.46 (e0.38
) times. As it may
33

be, in practice, the size of the business is also a constraint on the credit side. The
lower the scale, the less likely it is to participate. Similarly, the higher the asset, the
more likely it is to participate in credit.
The age of the business owner influences the participation of credit, bearing
negative signs, but the impact is not great.
After controlling the sector and regional characteristics, the higher the produc-
tivity, the more likely it is to borrow.
The subject will use model 3 to calculate the trend point (probability of loan par-
ticipation) for each observation. The purpose was to find a common support area to
identify the control group.
Table 4.5 Trend point of general support area
Group of average trend points Control group Participating Total
group
0.04 439 65 504
0.2 385 155 540
0.4 134 137 271
0.6 63 153 216
0.8 14 64 78
Total 1035 574 1609
Source: Calculate from data
When the equilibrium conditions are satisfied, the general support area has an aver-
age trend point of 0.04 to 0.8 (Table 4.5). That means the trending points of the two
groups were not significantly different in each group. After removing 7 observa-
tions outside the general support area, we obtained new data.
4.3.2.2 DD regression results after PSM
Impact on wages: using the DD method on the general support area (Table 4.6): the
borrower's participation does not affect wages. Adjusted R2
= 0.05 means that the
model only explains 5% variation of wages. Therefore, the topic continues to com-
bine DD with POOL OLS to control other features.
34

The DD-POOL OLS results (Table 4.6) reinforce that corporate borrowing does not
increase wages. The interpretation of the model increased (49%). In addition, the
model corrects the HET phenomenon and does not have multi-collinearity (vif <10).
Other factors have a statistically and positively positive impact on wages such as
exports, education level and professional qualifications of business owners, labor
productivity. The ratio of debt to total assets also affects, but not high. Negative
variables are expected to be lower than 81% (e-1.67
= 0.18). Similarly, microenter-
prises also paid less than 86% (e = 0,139) as compared to small and medium en-
terprises. Enterprises do not use tools and machinery, they pay 84% lower than (e-
1.87
= 0.154) than enterprises using machinery and equipment. The strongest export
effect was an increase of 93% in wages (e0.66
= 1.93) compared to domestic firms.
Table 4.6 Impact of loans to SMEs on labour costs
LNWAGE
DD DD-POOL Random Fixed
TREAT 2,295*** 0,990*** 1,169***
(0,222) (0,175) (0,180)
Time -0,0805 -0,0733 -0,0349 -0,0359
(0,217) (0,161) (0,113) (0,108)
Treat*Time 0,0651 0,126 0,0732 0,0271
(0,317) (0,238) (0,154) (0,152)
AGE -0,0153** -0,0204**
(0,00666) (0,00818)
EXPORT 0,662*** 0,801*** 0,756***
(0,164) (0,167) (0,275)
HOUSEHOLD -1,679*** -1,763*** -0,512*
(0,140) (0,150) (0,292)
MICRO -1,980*** -1,712*** -1,080***
(0,120) (0,105) (0,133)
HAND -1,881*** -1,669*** -0,982**
(0,316) (0,330) (0,487)
HAge -0,00406 -0,00278 0,000208
(0,00594) (0,00590) (0,00865)
HGen 0,191 0,202 0,157
(0,131) (0,129) (0,168)
Edu 0,936*** 0,519*** -0,0998
35
-1.97

(0,159) (0,151) (0,185)
ProEdu 0,246* 0,249** 0,0367
(0,135) (0,115) (0,139)
LnNSLD 0,463*** 0,286*** 0,0980**
(0,0668) (0,0446) (0,0420)
ShareDept 0,465** 0,217 -0,0157
(0,219) (0,147) (0,118)
Control 9 regional No Yes Yes Fixed
variables
Control 19 industry No Yes Yes Yes
variables
Constant 7,568*** 5,182*** 7,256*** 8,091***
(0,151) (0,828) (0,636) (1,027)
Observations 3214 3195 3195 3195
R-squared 0,056 0,498 0,038
Adj, R-squared 0,0550 0,491 0,0291
a
The model has overcome the variable variance. Interact = Treat*Time
To accurate estimations, the subject continue to use Fixed-Force (FE) regres-
sion, because there may exist different characteristics in each enterprise, constant
over time and do not affect Change of time dependent variable. Controlling these
differences can lead to many binary (N-1) variables entering the model. But the lack
of such features could distort the estimates (Gujarati, 2004). Assuming that the dif-
ferences are not correlated between enterprises, it can be the innate quality of busi-
ness owners, business lines, local …
When using FE (Table 4.6), AGE is in perfect collinearity with Time, thus re-
moving one from the model. As a result, loans have no impact on wages, after over-
coming HET and no more hyperlocal.
In the data, businesses change owners, change the industry and change the pro-
duction techniques ... so the fixed effects may not be appropriate. That is, there is a
correlation between error and independent variable, then random factor regression
should be chosen (Random Effect - RE): With the belief that some characteristics
36

differ in each enterprise, change over time and affect the variability of time depend-
ent variables (Gujarati, 2004).
The results of the RE model (Table 4.6) as well as the previous model, have not
demonstrated that loan capital has an impact on wages in enterprises.
Impact on the number of labours by using DD combined pool OLS after the re-
sulting PSM was performed (Table 4.7): Perform the same model check procedure
as above. All the results do not prove SME loan participation has an impact on la-
bour, even negative results (negative sign) in the models. With RE and OLS, posi-
tive variables (signed +) are exports, entrepreneurial qualification, labor productivi-
ty, debt-to-asset ratio. Negative impact (-) is a household business, small SMEs.
Table 4.7 Impact of loan to SMEs on the number of employees
LnLABOUR
DD DD-POOL Random Fixed
TREAT 0.682*** 0.240*** 0.284***
(0.0600) (0.0320) (0.0328)
Time -0.162*** -0.129*** -0.139*** -0.137***
(0.0440) (0.0253) (0.0198) (0.0188)
Treat*Time -0.0250 -0.0449 -0.0467 -0.0387
(0.0831) (0.0443) (0.0317) (0.0296)
AGE 0.000329 -0.000528
(0.000992) (0.00116)
EXPORT 0.492*** 0.464*** 0.130
(0.0541) (0.0567) (0.0808)
HOUSEHOLD -0.535*** -0.549*** -0.0159
(0.0303) (0.0345) (0.0600)
MICRO -1.284*** -1.144*** -0.790***
(0.0306) (0.0342) (0.0449)
HAND -0.0476 -0.103** -0.116
(0.0500) (0.0490) (0.0721)
HAge -0.000300 0.000510 0.00170
(0.00108) (0.00117) (0.00177)
HGen -0.0178 -0.0174 -0.0205
(0.0227) (0.0225) (0.0285)
Edu 0.0669*** 0.0547** -0.00313
(0.0236) (0.0244) (0.0325)
37

ProEdu 0.142*** 0.144*** 0.0713*
(0.0348) (0.0342) (0.0410)
LnNSLD 0.0346*** 0.0147* -0.0117
(0.00958) (0.00842) (0.0114)
ShareDept 0.214*** 0.124** 0.00199
(0.0559) (0.0531) (0.0505)
Control 9 regional vari- No Yes Yes Fixed
ables
Control 19 industry No Yes Yes Yes
variables
Constant 1,763*** 2,787*** 2,903*** 2,692***
(0,0319) (0,127) (0,124) (0,174)
Observations 3217 3196 3196 3196
R-squared 0,087 0,735 0,267
Adj, R-squared 0,0866 0,732 0,260
F test 93,90 170,8
a
The model has overcome the variable variance. Interact = Treat*Time.
But the results are not yet conclusive in terms of the impact of the loan. For busi-
ness operations, the different nature of the credits may affect differently. Informal
credits, but from more accessible sources, may have a better impact on business.
Are there differences in the impact of different credits? The study will continue to
look at the impact of different credits by separating into two specific borrowing
sources.
Table 4.8 Impact each type of credit on the labor costs in SMEs
LNWAGE
DD-POOL Random Fixed
Formal credit 0,997*** 1,174***
(0,184) (0,188)
Informal credit 0,463* 0,539**
(0,236) (0,240)
Time -0,0892 -0,0527 -0,0480
(0,157) (0,111) (0,105)
Formal credit *Time 0,168 0,136 0,0936
(0,249) (0,160) (0,157)
Informal credit *Time 0,0743 0,0140 -0,0351
38

LNWAGE
(0,331) (0,205) (0,197)
AGE -0,0146** -0,0196**
(0,00669) (0,00823)
EXPORT 0,610*** 0,753*** 0,748***
(0,168) (0,171) (0,276)
HOUSEHOLD -1,663*** -1,751*** -0,517*
(0,141) (0,150) (0,292)
MICRO -1,981*** -1,713*** -1,079***
(0,119) (0,104) (0,133)
HAND -1,892*** -1,675*** -0,981**
(0,316) (0,330) (0,487)
HAge -0,00390 -0,00270 0,000242
(0,00595) (0,00591) (0,00865)
HGen 0,197 0,208 0,161
(0,131) (0,128) (0,168)
Edu 0,948*** 0,527*** -0,0975
(0,159) (0,151) (0,185)
ProEdu 0,251* 0,248** 0,0313
(0,135) (0,116) (0,140)
LnNSLD 0,462*** 0,286*** 0,0988**
(0,0660) (0,0440) (0,0421)
ShareDept 0,453** 0,211 -0,0162
(0,214) (0,146) (0,118)
Control 9 provincial variables Yes Yes Fixed
Control 19 industry variables Yes Yes Yes
Constant 5,155*** 7,256*** 7,914***
(0,818) (0,632) (0,935)
R-squared 0,498 0,039
Adj, R-squared 0,491 0,0289
chi2 2649
a
After isolating the impact of each type of credit (performing the same model verifi-
cation steps), the results (Table 4.8) are similar. There is insufficient evidence of the
impact of formal and informal loans on wages (bold indicators).Follow the same
steps, get results (Table 4.9). Regardless of the regression method, the results show
39

that enterprises that lend from formal sources or informal sources also have no im-
pact on enterprises hiring more workers. Even formal credit has a negative impact
on labor, although the impact is not significant.
Table 4.9 Impact of each type of credit on the number of employees in SMEs
lnLABOUR
Formal credit 0,256*** 0,303***
(0,0353) (0,0361)
Informal credit 0,0557 0,0687
(0,0444) (0,0454)
Time -0,132*** -0,142*** -0,140***
(0,0248) (0,0194) (0,0184)
Formal credit *Time -0,0567 -0,0557 -0,0489
(0,0487) (0,0342) (0,0322)
Informal credit *Time 0,0130 0,00968 0,0174
(0,0626) (0,0450) (0,0419)
AGE 0,000467 -0,000351
(0,000999) (0,00118)
EXPORT 0,480*** 0,452*** 0,131
(0,0546) (0,0573) (0,0810)
HOUSEHOLD -0,529*** -0,543*** -0,0118
(0,0304) (0,0345) (0,0602)
MICRO -1,286*** -1,145*** -0,790***
(0,0306) (0,0342) (0,0450)
HAND -0,0519 -0,107** -0,117
(0,0501) (0,0491) (0,0721)
HAge -0,000321 0,000497 0,00172
(0,00108) (0,00117) (0,00177)
HGen -0,0159 -0,0164 -0,0216
(0,0228) (0,0225) (0,0286)
Edu 0,0693*** 0,0565** -0,00307
(0,0236) (0,0244) (0,0325)
ProEdu 0,145*** 0,146*** 0,0740*
(0,0348) (0,0342) (0,0410)
LnNSLD 0,0342*** 0,0145* -0,0116
(0,00955) (0,00840) (0,0114)
ShareDept 0,215*** 0,124** 0,00263
(0,0574) (0,0542) (0,0503)
Control 9 provincial variables Yes Yes Fixed
40

Control 19 industry variables Yes Yes Yes
Constant 2,780*** 2,903*** 2,691***
(0,126) (0,123) (0,174)
R-squared 0,735 0,268
Adj, R-squared 0,731 0,260
a
4.3.3 Verification of model stability
In order to ensure the sustainability of the results, the subject will reevaluate the
impact of the loan in a number of scenarios where a change is likely to result in a
different outcome. The differences after the change are noted (the unnoticed portion
is unchanged from 4.3.2 above):
Elimination of outlier: The PSM method can remove some but not completely.
The topic eliminates the differences of the dependent variable. LnLABOUR: With
RE, formal credit reduces labor (-7%) in the business statistically. This is appropri-
ate when the burden of debt from formal organizations causes employers to lay off
workers.
Changing the form of a function: Some quantitative variables may be nonlinear
relations with dependent variables, so the second order function change: age of en-
terprise, age of enterprise, labor productivity. lnLABOUR: Similar to the case of
rejecting the difference, in the RE credit model the impact reduces (-6%) labor.
Impact on individual groups: Small and medium-sized enterprises: RE and FE
models, the official credit reduces wage (-15%). This is suitable because this type of
business is sometimes a household, the burden of debt can reduce their pay but less
dismissal because most are relatives. Group of small and medium enterprises: the
impact is unchanged. Medium and high technology: FE credit has a positive impact
(+ 39%) on wages. Low technology: Official credit has a negative impact on labor
(-9%) in RE and FE. South: Informal loans tend to reduce wages, but increase em-
ployment. Northern: The impact of the loan is unchanged.
41

By trend group: The group of enterprises with the same trend point is broken
down (in Table 4.5). Block 3: Positive informal credit (+ 18%), while official credit
negative (-16%) on labor. Block 4: Both formal and informal credit have negative
impacts (-17% and -19%) on labor in the DD-POOL-OLS and RE models. Block 5:
Positive and informal credit on labor force in RE and FE.
4.4 Dicussions
The research results are similar to previous studies by Berger in 1989 and
Woodruff in 2008 and different to study of Signore in 2015. The research of Signo-
re shows that loans to business to make revenues increase and create jobs for em-
ployee. Studies of Berger and Woodruff have shown that loans do not have a posi-
tive effect on workers' incomes. At the same time, loan enterprises do not increase
the number of employees. Large-scale formal loans have high interest rate that
makes the business reduce costs. Small-scale loans are not enough for businesses to
expand the scale.
We see the impact of the loan is studied in the period 2009 - 2013, coincides
with the time of economic fluctuations.
Table 4.2 Investment and labour
PERIOD 08/2007- 08/2009- 08/2011-
08/2009 08/2011 08/2013
There is new investment 60,72% 55,87% 46,83%
% New investment from loans 64,55% 53.93% 65,21%
No reduction of labour force 93,56% 98,04% 98,00%
% Regular labour 90,70% 95,79% 95,91%
% Regular full time labour 87,26% 89,14% 88,09%
% paid worker 64,65% 62,49% 61,95%
% Labor is female 37,53% 37,18% 38,52%
Source: calculated from SME 2009-2013
When the market is in trouble, this makes the business hesitate to invest in new pro-
duction. The decline of new investment in this period from is more than 60% to on-
ly about 47%. In new investments, the average proportion of investment from loans
42

ranges from 54% to 65%. Loans contribute to sustainability. The active enterprises
have contributed to keeping jobs, paying wages to laborers. Most of the enterprises
have no job cuts (over 93% of enterprises). Labor structure has not changed signifi-
cantly, but the wage earners tend to decrease slightly from 64.6% to 61.9%. Busi-
nesses change with the use of more unpaid workers (using family members, busi-
ness owners themselves or wage workers are responsible for more work).
Table 4.3 The scale of the most important loans
Interest under 1% 1% - 1,5% 1,5% - 2% above Total
rate / 2%
month
Informal sources
Quartile 1 under 25M 18.47% 1.35% 1.80% 3.15% 24.77%
Quartile 2 25 – 85M 13.06% 3.60% 2.03% 6.53% 25.23%
Quartile 3 85 – 300M 9.23% 2.48% 4.50% 5.41% 21.62%
Quartile 4 above 11.04% 8.11% 4.95% 4.28% 28.38%
300M
Total 51.80% 15.54% 13.29% 19.37% 100.00%
Formal source
Quartile 1 under 6.95% 7.80% 7.38% 0.99% 23.12%
100M
Quartile 2 100 – 1.70% 6.81% 12.34% 3.12% 23.97%
300M
Quartile 3 300tr – 1B 0.71% 6.24% 15.46% 1.42% 23.83%
Quartile 4 above 1B 0.43% 9.22% 17.16% 2.27% 29.08%
Total 9.79% 30.07% 52.34% 7.80% 100.00%
Source: calculated from SME 2011 (M: Million; B: Billion)
Formal loans were under 300 million VND about 47%. The same amount of
loans accounted for 72% of the structure from the informal sector. The maximum
loan of rural households is 200 million VND (Decree 41 Government). For a work-
er who pays 3 million dong a month, a 300 million loan is not enough to pay 10
workers a year. Enterprises that want to use loans to invest in long-term labor, then
that scale is not enough to implement.
In the context of a troubled economy, credits that are inefficient will become a
burden of debt. 51.8% of informal loans with relatively good interest rates (less than
43

1% per month), 4/5 of them are small (less than 300 million). While, 52.3% of loans
from official sources bear interest rates ranging from 1.5% to less than 2% per
month. And 82% of official loans are subject to interest rates ranging from 1% to
less than 2% per month. The cost of paying too much interest has reduced the size
of the business.
Thus, the enterprise encountered two obstacles: low interest loans but small
scale. In large-scale loans, interest rates are high.
44

Figure 4.3: The biggest difficulties prevent the development of business
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Short of credit capital
Short of credit capital 29.4%
44.9%
Unfair competition
20.9%
Unfair competition
9.2%
Limited demand with orders
Limited demand with orders
25.9%
19.9%
Other, 19.8%
Other, 19.7%
labour shortage, 6.2%
labour shortage, 4.1%
2009-2011 2011-2013
Source: SME 2009-2013
During the period of 2009 - 2011, 44.9% of businesses think that capital is their
biggest obstacle. But after 2 years, only 29.4% of businesses feel that capital is the
biggest obstacle. Rather, competition concerns increased markedly in corporate per-
ceptions (from 9% to 21%). On the other hand, the decrease in the demand for
goods of enterprises increased from 20% to 26%. So the loan is no longer the key to
helping businesses grow as many in the past. Concerns about demand, the competi-
tive market became the highest concern (from 29% to 46%). Survey results from the
PCI data set for 2014 and 2015 also provide similar insights.
45

Summary, the quantitative evidence shows that the loan does not affect workers
and employees in the enterprise in terms of salary and employment. The direct
cause is that enterprises restrict new investment in labor. In the new source of in-
vestment, the share of the loan portfolio declines in stages. The causes that lead to
loans are not included in the investment is due to the size, cost of the loan, and the
poor competitiveness of the business.
46

CHAPTER 5: CONCLUSIONS AND POLICY IMPLICATIONS
5.1 Conclusions
Research shows that loan does not make businesses pay more for labor or em-
ployee, as well as creating jobs. After distinguishing the impact of loans from the
formal or informal sector, it does not affect wages and employment. In addition, the
topic also found a number of factors that positively impacted wages and labor, such
as export markets; the professional qualification of the business owner; production
technology. Another result of the project is identifying the factors that affect SME
participation in the loan, namely: number of years of operation; size; the age of the
business owner; debt ratio of total assets; asset size.
Research background from 2009 to 2013, this time the economy is more vola-
tile. The topic is that loans are not the key to helping SMEs create jobs, increase in-
come for workers. Businesses can use loans to maintain operations rather than ex-
pand production, increase employment. Large-scale loans with high interest rate
become burdensome and make enterprises employ less labour.
Using the dataset from 2009 to 2013, the project has contribution as follows:
Research questions Results
Does the loan to employee impact on the labour costs of
No
SMEs?
Does the loan to employee impact on the number of employees
No
in SMEs?
Does the each type of lending to employee impact on the la-
No
bour costs of SMEs and the number of employees in SMEs?
47

Hypothesis
Support or
No
Loans to employee have a positive impact on the labour costs in
No
SMEs
Loans to employee have a positive impact on the number of em-
ployees in SMEs. No
Formal credit and informal credit have a positive impact on the la-
No
bour costs in SMEs.
Formal credit and informal credit have a positive impact on the
No
number of employees in SMEs.
The results show that loans do not have the effect of improving employee in-
comes, as well as creating more jobs. After analyzing the supplied - source of in-
formal and formal capital, the same conclusion was made. The explanation for this
is that in the context of the crisis and the increasingly competitive market, business-
es are facing many difficulties, leading to the reduced investment, so the enterprises
do not hire more workers. In addition, the loans from informal sources with low cost
do not help enterprises to expand their operations because of the small scale. The
loans from formal sources are large scale, but the high costs overwhelm profits. In
some groups of objects, the impact of formal loans also causes businesses to re-duce
their jobs. The topic also shows other factors such as export, type of owner-ship,
scale, production technique, entrepreneurial qualification that affect to em-ployment
and wages.
5.2 Policy implications
Directly, Enterprise technology change helps businesses improve productivity to
expand production, be ready to compete in new markets, improve wages. The topic
48

offered that enterprises use rudimentary hand tools pay much lower salaries than
those using machinery.
Promote professional education: As a result of the model, the business owner
has a college degree, which employs more workers and pays higher salaries. Be-
cause after training, the management capacity and production skills are better.
Market Access: Enterprises are not only facilitated but also have to be compe-
tent because the business environment is competitive with quality, at least cost.
Businesses need to know more about new markets (administration, law, standards,
culture ...) to change the method of business suit. The evidence from the model is
that exporters pay higher wages and employ more workers.
Indirectly, restraint of market intervention: State-owned enterprises extract large
amounts of capital from the financial market due to preferential treatment and the
remaining capital will be high cost of capital. Therefore, SMEs have to access high
cost capital, so they can’t use effectively.
5.3 Limits of the study
The subject uses a set of survey data available, so it does not fully response the
research needs. Investigative data focuses on certain manufacturing enterprises,
which may be biased and inaccurate for other SMEs.
The focus is on loans, so it will ignore many different causes that cause busi-
nesses not to hire and increase salaries for workers.
The most important thing is that the policy variable of the topic uses binary var-
iables, so it is only possible to determine whether or not there is an impact, not to
know the exact level of effectiveness at each level of the loan.
49

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51

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