Determinants on households’ partial credit rationing an analysis from VARHS 2008

UNIVERSITY OF ECONOMICS

INSTITUTE OF SOCIAL STUDIES

HO CHI MINH CITY

THE HAGUE

VIETNAM

THE NETHERLANDS

VIETNAM-NETHERLANDS
PROGRAMME FOR M.A. IN DEVELOPMENT ECONOMICS

DETERMINANTS ON HOUSEHOLDS’ PARTIAL CREDIT RATIONING
AN ANALYSIS FROM VARHS 2008

By
NGUYEN VAN HOANG

Academic Supervisor:
Dr. TRAN TIEN KHAI

HO CHI MINH CITY, NOVEMBER 2013


AKNOWLEDGEMENT
I am indebted to many individuals for their enthusiastic support and guidance while doing this
thesis. This paper would be impossible to accomplish without their unlimited support.
Firstly of all, I would like to express my great appreciation with Dr. Tran Tien Khai, my

supervisor, for the whole support to help me constructing ideas, structure, advices and
comments, from the beginning to the end.
May thanks for Prof. Dr. Nguyen Trong Hoai, Dean of Vietnam – The Netherlands Programme
who has provided necessary assistance and motivation for me to achieve this thesis.
I would like give my special thanks for Dr. Pham Khanh Nam, Academic Director of Vietnam –
The Netherlands Programme. Without his introduction for VARHS 2008, an important data set
used in this research, this paper would be impossible to complete.
Many thanks for Dr. Truong Dang Thuy, Chair of the Department of Economics - Faculty of
Development Economics, who has passionately cooperate with me to solve issues related to
econometric techniques.
Last but not least, I must express my most gratitude to my parents and my aunt’s family for
providing comfortable condition during hard time, so that I can finish this thesis.

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List of Tables and Figures
LIST OF TABLES
Table 1 - VARHS 2008 Survey Questions ................................................................................... 27
Table 2 - Model Specification....................................................................................................... 44
Table 3 - Determinants of Credit Accessibility ............................................................................ 54
Table 4 - Determinants of Partial Credit Rationing Probability ................................................... 56
Table 5 - Determinants of Partial Credit Rationing Degree ......................................................... 60

LIST OF FIGURES
Figure 1 – Credit Supplier Expected Return ................................................................................. 15
Figure 2 - Rationing in Credit Market .......................................................................................... 17
Figure 3 - Identify Case of being Credit Rationed ........................................................................ 20
Figure 4 - Survey Site Mapping for VARHS 2008 Source: IPSARD (2006-2008) ..................... 26
Figure 5 - Sample Distribution Source: Author Calculation from VARHS 2008 ........................ 29

Figure 6 - Analytical Framework .................................................................................................. 38
Figure 8 - Credit Access & Credit Ration Source: Author’s calculation from VARHS 2008 ..... 45
Figure 9 - Credit Access & Household Head Age Source: Author’s calculation from VARHS
2008............................................................................................................................................... 46
Figure 10 - Household Head Age & Credit Ration Source: Author’s calculation from VARHS
2008............................................................................................................................................... 47
Figure 11 - Credit Access & Household Head Education Level Source: Author’s calculation
from VARHS 2008 ....................................................................................................................... 48

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Figure 12 - Household Head Education Level & Credit Ration Source: Author’s calculation from
VARHS 2008 ................................................................................................................................ 49
Figure 13 - Credit Access & Loan Purposes Source: Author’s calculation from VARHS 2008 . 50
Figure 14 - Loan Purposes & Credit Ration Source: Author’s calculation from VARHS 2008 .. 51
Figure 15 - Credit Access & Credit Institutions Source: Author’s calculation from VARHS 2008
....................................................................................................................................................... 52
Figure 16 - Partial Ration & Credit Institutions Source: Author’s calculation from VARHS 2008
....................................................................................................................................................... 53

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Abstract
This study aim to identify key factors affected the partial credit ration’s probability and its
degree in rural area of 12 provinces in Vietnam including Ha Tay, Nghe An, Khanh Hoa, Lam
Dong, Phu Tho, Quang Nam, Long An, Dac Lac, Dac Nong, Lao Cai, Dien Bien, Lai Chau
period 2006-2008. Based on VARHS 2008 data set, the research has employed Heckman sample
selection bias model to investigate the determinants of partial ration’s degree, and bivariate

probit with sample selection model to examine the determinants of partial ration’s probability.
Besides that, the impact of credit accessibility’s determinants, as a supplement outcome from the
two regression models, were also revealed.
The result showed that households who have following characteristics - Kinh ethnicity, large
household size, high land value, suffering shock at household level (economic shock, illness,
unemployment, etc.), holding social position (at least one member working for government, local
authority unit) tend to have higher chance of credit access, while those who have high
dependency ratio, and older household, tend to have negative correlation with credit
accessibility.
Formal credit institutions appeared to have higher rate of partial credit rationed than the informal
sector, and those who requested a large size of loan were likely to be partial rationed as well. In
contrast, households who own larger house, borrowed for investment purposes (build/buying
house, land and other assets) or holding social position had a lower chance of being partial
rationed.
The finding also uncovered the negative correlation between the degree of partial credit ration
and following factors - Household head age, dependency ratio, house size and collateral value.
On the contrary, household size, loan size applied, loan for consumption purposes negatively
affect the degree of partial credit ration.
The regression result also shown that unless treatments such as bivariate probit with sample
selection bias or Heckman two stages regression are applied, the regression result might be bias
due to inherent sample selection problem in the data set.
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Table of Contents
Chapter 1. Introduction ................................................................................................................... 9
1.1. Research Context ................................................................................................................. 9
1.2. Research Problem .............................................................................................................. 10
1.3. Research Objectives ........................................................................................................... 11
1.4. Research Questions ............................................................................................................ 11

1.5. Scope of Study ................................................................................................................... 11
1.6. Thesis Structure ................................................................................................................. 12
Chapter 2. Literature Review ........................................................................................................ 12
2.1. Rural credit......................................................................................................................... 12
2.1.1. Definition .................................................................................................................... 12
2.1.2. Characteristics of rural credit market .......................................................................... 12
2.1.3. Types of rural credit .................................................................................................... 13
2.2. Asymmetric Information and Credit Rationing ................................................................. 14
2.2.1. Asymmetric information ............................................................................................. 14
2.2.2. Problems of lenders in context of asymmetric information ........................................ 15
2.2.3. Screening mechanism in lending ................................................................................ 17
2.3. Credit Rationing ................................................................................................................. 18
2.3.1. Types of Credit Rationing ........................................................................................... 18
2.3.2. Identify Credit Rationing ............................................................................................ 19
2.3.3. Impact of Credit Rationing in Rural Area................................................................... 21
2.4. Empirical Studies ............................................................................................................... 21
2.4.1. Factors of Credit Demand ........................................................................................... 22

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2.4.2. Factor of Credit Supply............................................................................................... 23
Chapter 3. Methodology ............................................................................................................... 25
3.1. Data Source and Features................................................................................................... 25
3.2. Issue of Data Bias (Sample Selection Problem) ................................................................ 28
3.3. Heckman Two-Stages Model ............................................................................................. 30
3.3.1. Sample Selection Bias vs. Omitted Variables Bias .................................................... 30
3.3.2. Heckman Two Stages Procedures ............................................................................... 32
3.3.3. Application to study & Model Specification .............................................................. 33
3.4. Bivariate Probit with Sample Selection Model .................................................................. 34

3.4.1. Model Review ............................................................................................................. 34
3.4.2. Application to study & Model Specification .............................................................. 36
3.5. Multicollinearity Test......................................................................................................... 37
3.6. Analytical Framework ....................................................................................................... 38
3.7. Hypothesis.......................................................................................................................... 40
3.7.1. Hypothesis for the probability and degree of partial credit rationing ......................... 40
3.7.2. Hypothesis for the probability of access to credit ....................................................... 42
3.8. Model Specification ........................................................................................................... 44
Chapter 4. Results and Discussion ................................................................................................ 45
4.1. Characteristics of Borrowers by Credit Rationing ............................................................. 45
4.2. Determinants of Credit Accessibility ................................................................................. 54
4.3. Determinants of Partial Credit Rationing Probability ........................................................ 56
4.4. Determinants of Partial Credit Rationing Degree .............................................................. 58
4.5. Multicollinearity Test......................................................................................................... 61
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Chapter 5. Conclusions and Policy Implications .......................................................................... 62
5.1. Conclusions ........................................................................................................................ 62
5.1.1. Findings, answers for research questions.................................................................... 62
5.1.2. Conclusions on degree of solving research objectives ............................................... 62
5.1.3. Limitation of the study ................................................................................................ 63
5.2. Policy implications............................................................................................................. 63
5.2.1. Policy implications...................................................................................................... 63
5.2.2. Research perspectives ................................................................................................. 65

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Chapter 1. Introduction

“Credit rationing is not necessary the source of poverty trap, but it reinforce them ”
(Ping, Heidhues, & Zeller, 2010)

1.1. Research Context
Since 1986, Vietnam Government has initiated the economic reform that has transformed the
nation from the central planning to market oriented economy. Major achievements in terms of
economic growth and poverty reduction have been attained as a result of the reform. However,
the large gap between rural and urban areas has still existed. To ensure the sustainability of
economic development and the stability of political environment, rural and agriculture
development are therefore considered as a priory goal in the nation development strategy.
Providing access to finance to the poor or microfinance has been considered as a tool for
economic development and poverty reduction (Morduch & Haley, 2002; Khandker, 2003). It is
the interest of many policy makers and researchers in recent years. Thus, in this strategy, rural
credit, which aims at ensuring rural households having access to financial services, is regarded as
an important component. The Government has launched many credit programs supporting the
development of rural area such as preferential credit for the poor, agriculture forestry and fishery
encouragement through special state own banks or government agencies. The credit program has
some significant impact to economic development of Vietnam rural areas; however there still
exist some issues such credit rationing in the program. When credit rationing occurs, credit
suppliers ignore to offer loan to some borrowers to avoid the risk of default, thus it limit the
credit accessibility of the poor. Due to it implication to the economic development in general,
and to the effectiveness of Government credit for the poor program in particular, this paper will
aim at examining the factors that affect to credit rationing, particularly focus on the cases of
partial credit ration, with the hope of revealing some potential implications for policy makers.

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1.2. Research Problem
Rural credit market is an importance factor that helps to foster the economic development in

rural areas, thus improving the poor living standard and supporting poverty alleviation. One of its
function is funding household’s credit demand.
However, the degree to which rural credit impacts on the rural area welfare depends on how well
the rural credit market operates, however the problem of credit rationing could have negative
effect on the performance of rural credit market. Credit rationing could be described as the cases
in which credit lenders refuse to offer loan to borrowers, or offer an amount of loan that is less
than borrower’s request, even though the borrowers willing to accept higher level of interest rate
to help the lender to cover the default risk (Barham, Boucher, & Cater, 1996; Buchenrieder,
1996; Heidhues & Schrieder, 1998; Zeller, 1993). The higher the probability of credit rationing,
the more difficulties for household to satisfy their credit demand. In other word, if credit
rationing is a common practice in the area, it may lead to the inefficiency of the credit market in
the area as a consequence.
As Stiglitz, J. and A. Weiss (1981) pointed out, asymmetric information is an explanation for the
problem of the credit rationing. In rural credit market, which is characterized by numbers of poor
households and the difficulties to evaluate their credit worthiness, is concealed by a fog of
asymmetry information between lenders and borrowers. In other word, lenders are reluctant to
lend as they are uncertain about the loan repayment probability. To overcome this problem,
lenders require different kinds of information about their borrowers such as household’s
dependency ratio, household size, land value, social position, etc., to assess their repayment
ability and make a basis for lending decision.
As such kinds of information may affect to the probability and the degree to which a borrower be
credit rationed, a question has been raised by many researchers is that what factors determined
lenders’ decision of credit ration. The answers are different depending on the period and location
under examination of a study. For this paper, the research aim to examine the determinants of
credit rationing – especially for the case of partial credit ration, concentrating on 12 provinces Ha

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Tay, Nghe An, Khanh Hoa, Lam Dong, Phu Tho, Quang Nam, Long An, Dac Lac, Dac Nong,

Lao Cai, Dien Bien, Lai Chau period 2006-2008.

1.3. Research Objectives
The general objective of this study is to examining the relationship between partial credit
rationing and its determinants in rural area of Vietnam in period 2006-2008.
The general objective could be archived by meeting following sub-objectives:


Identify key factors that affects the household’s credit accessibility



Identify key factors that affects the degree of partial credit rationing



Identify key determinants on the probability of partial credit rationing



Suggest policy implication to reduce partial credit rationing practice

1.4. Research Questions
The research aims at answering the following questions:


What are the key determinants on the credit accessibility of rural households?



What are the key determinants on the partial credit ration probability of rural households?



What are the key determinants on the degree of partial credit rationing of rural
households?

1.5. Scope of Study
This study focuses on the issue of partial credit rationing (the case in which credit lenders
constraint the amount of loan, thus borrowers do not fully satisfy their credit demand) at
household level for 12 provinces in Vietnam including Ha Tay, Nghe An, Khanh Hoa, Lam
Dong, Phu Tho, Quang Nam, Long An, Dac Lac, Dac Nong, Lao Cai, Dien Bien, Lai Chau
period 2006-2008 using Vietnam Access to Resources Household Survey (VARHS) 2008 data
set.

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1.6. Thesis Structure
The remainder of this paper is organized into 4 chapters. Chapter two is literature review which
aims to provide a basis for rural credit understanding, theoretical and empirical framework for
the study. Chapter three describes VARHS 2008 data source, discusses about the issue of sample
selection of the data set and methodologies, which including Heckman two-stage probit model
and Bivariate probit with sample selection model, applied in this study. Chapter four presents
major finding revealed by methods of descriptive statistics and econometric regression, as well
as providing results discussion. Chapter five, the last one, is for concluding remarks, limitation of
the study and policy implication.

Chapter 2. Literature Review
The section firstly discusses concepts related to rural credit such as what is rural credit,

characteristics of rural credit, problem of asymmetric information in rural credit, and credit
rationing as well as theoretical background of credit rationing, to provide basic understanding
about the study.
Secondly, the determinants of credit supply and credit demand are summarized from earlier
empirical researches, to provide empirical framework for the study of credit rationing.

2.1. Rural credit
2.1.1. Definition
Rural Credit is referred to the credit offered to farmers to fund their agricultural and other rural
relating activities. It is estimated that 90 percent of rural finance activities is rural credit (Pham,
T., 2010).
2.1.2. Characteristics of rural credit market
Rural credit market has some distinct characteristics:
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High transaction costs:
Transaction costs in rural credit market is high due to several reasons: the dispersion of local
users, wide segments of the farming community, small loan value, the value of time lost, travel
costs, and other noninterest costs in getting and repaying loans and making deposits, high
information and marketing costs due to low developed infrastructure for transport and
communication.
High risks:
Another characteristic of rural credit market is high risk, for the reasons of vulnerability due to
unfavorable climate and weather, low return on investment of agricultural activities, need of
household consumption, chain effect due to concentrated in small geographic rural areas, price
changes causing further variability in farmers' income and the related repayment capacity, high
probability of default, little acceptable loan collateral, property rights to mortgaged land may be
uncertain and hard to enforce, the weak legal system and the ineffective reinforcement
arrangements.

2.1.3. Types of rural credit
Rural credit providers can be categorized into three sectors – formal, semi-formal and informal
credit suppliers.
Formal credit providers
Including commercial banks, branches of foreign banks, joint-stock banks and joint venture
banks. Some examples are Vietnam bank of agriculture and rural development (VBARD) is the
major commercial source of credit for rural households. Bank of social policy (BSP) is
government-owned and non-profit bank, providing credit mainly to poor, ethnic minority, social
policy households.
Semi-formal credit providers
Providing loans through socio-political unions in rural areas and the level of activity of this
sector in a region is related to priority programs of the government, consignment services of the
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bank and the activity of unions, for example: PCF, Women’s Union, Farmers’ Association etc.
Their loan is often low interest rates, small loan amounts, short-term
Informal credit providers
Informal sources have been traditional providers of credit in rural areas and are the result of an
underdevelopment formal credit market. Four forms of informal credit sources: mutual lending
among friends and neighbors, rotating savings and credit associations, specialized moneylenders
including pawnbrokers, and traders. Loan from informal sector is often high variety in interest
rates and loan amounts.

2.2. Asymmetric Information and Credit Rationing
2.2.1. Asymmetric information
Asymmetric information is a common problem in rural credit market. The issue could be
understood as a situation in which one party has more information than the other in a transaction.
For instance, in credit market, borrowers may know their credit worthiness better than their
lenders, as the information such as income for repayment or loan use is on hands of the

borrowers.
Two main problems related to information asymmetry:
1. Adverse selection- immoral behavior that takes advantage of asymmetric information before a
transaction. For example, credit borrowers know their project is high risk and high return, so they
may readily accept a high level of interest rate that the lenders offer them.
2. Moral hazard - immoral behavior that takes advantage of asymmetric information after a
transaction. For instance, when the loan has been disbursed to borrowers, lenders may have
difficulty in monitoring on how their lending money is used, and the borrowers may use the loan
for purposes other than the one they stated in the loan contract. The purposes may earn more
return to the borrowers, but riskier for the lenders.

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2.2.2. Problems of lenders in context of asymmetric information
To understand the behaviour of credit lenders in context of asymmetric information, it is
necessary firstly to understand credit lenders expected return function. According to Jafee, D. &
Stiglitz, J.E. (1990), the expected return to the bank is a function of quoted interest rate,
graphically represented by an concave curve.
Expected return to
lender

r*

Quoted interest rate

Figure 1 – Credit Supplier Expected Return

Credit lenders reach the highest level of expected return when it charges the loan at interst rate r*.
At the level of interst rate higher or lower than r*, the expected return to the lenders will fall, thus

it is reluctant for them to variate their interst rate away from the optimum level of r*.
An important question arises is that what is the basis underlying the assumption of the concave
curve of the credit lenders’ expected return, which is the key building block of the whole Jafee &
Stiglitz’s (1990) explanation for the behavior of credit lenders. In other words, why the
increasing in level of interest rate may lead to a fall in the expected return to the lenders. The
answer is imperfect information problem in credit market, in particular it is the result of adverse
selection and adverse incentive effects.

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Adverse selection effect
The adverse selection effect could shape credit suppliers’ expected return curve into concave
form in which as interest rate raises above the optimum level, the expected return begins to fall.
As the interest rate is increased, the lending portfolio of lenders will change adversely, along
with the risk of default of the portfolio. That is due to the fact that safe potential borrowes who
need credit to undertake the projects with low risk - low return, are unable to pay the high interst
rate and consequently drop out of the market. In constrast, the number of risky borrowers, who
need fund to finance their high risk – high return projects, will increasingly take place in the
lending portfolio. Consequently, risk of default in their lending will increase, thus decreasing
their expected return (Stiglitz, J. & A. Weiss, 1981).
Adverse incentive effect
Adverse incentive (or moral hazard) is another effect that could shape credit suppliers’ expected
return curve into concave form. In this case, the action of borrowers tends to change in response
to high interest rate after getting lending contracts approval. That is the applicants do not follow
their commissions in the lending contract in term of undertaking riskier projects rather than the
ones stated in the contract, so that they can seek higher return to offset the high interest.
However, it is in turn increasing the risk of lending portfolio in an unexpected way, and therefore
lowering lenders’ expected return. Although it is the function of lenders’ monitoring practices to
keep borrowers on track with their contract obligation, it is costly and never perfect.

Thus, for those reasons of information asymetry, it is reluctant for credit suppliers to variate their
interst rate away from the optimum level r*; and it is this rational behavior of lenders that lead to
a situation that although the high demand for credit may lead to a raise in the level of interest rate
from credit suppliers due to the law of supply and demand, the equilibrium interest rate in the
market do not adapt to change away from the credit lenders’ optimum level of interst rate r *. In
this case a proportion of credit borrowers, who willing to pay a high level of interest rate in order
to satisfy their credit demand, will be unable to raise the amount of credit supply in the market,
and therefore getting unapproval for their loan application. In other word, they are credit rationed
by the credit suppliers.
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Interest
Rate
Supply

r
r*

Demand

Q*

Qe

Credit Amount

s

Q

Amount of Credit Rationed
Figure 2 - Rationing in Credit Market

2.2.3. Screening mechanism in lending
Lending in context of asymmetric information, credit suppliers face three main issues: (1) how to
identify high risk borrowers and put credit constraint on them (screening), (2) how to monitor
and prevent the loan from miss-uses (incentives), and (3) enforce them to repay the loan when
they have ability (enforcement). Thus, to help credit lenders solve those issues, two screening
mechanisms, i.e. indirect and direct screening mechanism are frequently applied
Indirect screening
Credit suppliers may charge high interest to cover the risk of default on borrowers. However this
type screening may lead to the issue of moral hazard and adverse selection.
Credit lenders may give threat of cutting off credit or contractual terms in other exchanges to
monitoring the loan use and enforce repayment of borrowers.

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Direct screening
In context of asymmetric information, credit lenders can adopt direct screening mechanism to
decide whether to approve a loan by ensuring their clients’ repayment probability. Credit
suppliers can control the risk of default by three following approaches:
Firstly, collecting and evaluate necessary information about risk of their clients such as income,
education level, ages, etc. In this type of screening, the lenders can directly ration borrowers
when they do not have enough required information to evaluate risk of the loan.
Secondly, credit suppliers can enforce borrowers to inter-linkages with other markets such as
input and output market to ensure the loan is used for right purposes. Or limiting the range of
lending in a particular geography and kinship group residents in a given region, or individuals
with whom they trade.

Finally, using collateral such as land, livestock, or other kinds of asset to back the risk of default
is often required by lenders. If the collaterals are not secured enough to back the loan, the
borrowers could be evaluated as unqualified to get loan approval.

2.3. Credit Rationing
According to Hoff and Stiglitz (1990) – “Credit rationing is broadly defined as a situation in
which there exists an excess demand for loans because quoted loan rates are below the Walrasian
market clearing level”. In other words, when credit borrowers are credit rationed, loan demand of
those credit borrowers in the market cannot be fulfilled as credit lenders limit their lending to
them even though the borrowers willing to accept a higher level of interest rate than the one that
credit lenders set.
2.3.1. Types of Credit Rationing
There could be various types of credit rationing depend on how the term - “excess demand for
loan” is defined.
It could be excess demand in term of a borrower receives a smaller loan size than the one
requested at a given loan rate; and the borrower has to accept a higher rate in order to obtain a
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larger loan. The case is classified as interest rate – or price ration (Jafee, D. & Stiglitz, J.E.,
1990).
In another case, the excess of loan demand comes to exist in the circumstance that some
individuals are unable to get their loan application approved at the level of interest rate that they
supposed to be appropriate with their risk of default. It is the case of divergent views rationing
(Jafee, D. & Stiglitz, J.E., 1990).
Redlining is also a type of credit rationing in which “given the risk classification, a lender will
refuse to grant credit to a borrower when the lender cannot obtain its required return at any
interest rate. Moreover, loans which are viable at one required rate of return (as determined by
the deposit rate) may no longer be viable when the required return rises.” (Jafee, D. & Stiglitz,
J.E., 1990).

Pure Credit Rationing: This is a form of credit rationing that arises as an effect of imperfect
information problem. In this instance, there is discrimination in the credit lenders’ loan approval
decision between two apparently identical groups of borrowers, although they have precisely the
same terms in loan contracts. One group is accepted for loan, while the other one do not.
According to Jafee and Stiglitz (1990) when it is the case, “changes in the availability of credit,
not change in the interest rate, may determine the extent of borrowing”.
2.3.2. Identify Credit Rationing
According to Barham, Boucher and Cater (1996), Buchenrieder (1996), Heidhues and Schrieder
(1998), Zeller (1993), the case of being credit rationed can fall into three situations:

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Figure 3 - Identify Case of being Credit Rationed

Discourage
For those who has demand for loan but don’t contact with credit lenders because for some
reasons, they know that their credit worthiness are not qualify enough so that their loan
applications are unable to get approval.
Completely Credit Ration
For those who has demand for credit and make loan applications, but their applications are fully
rejected by credit lenders, so they cannot get any amount of credit that they requested.
Partially Credit Ration
In this case, loan applications from credit borrowers are accepted, but loan size is not fully
granted. In other words, the credit borrowers receive an amount of credit less than the one they
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requested. Petric (2003) said that - “farm household are credit rationed by formal lenders in the
sense that they cannot borrow as much as needed to finance inputs, investment and indispensable

consumption expenditure.”
2.3.3. Impact of Credit Rationing in Rural Area
Many studies have investigated the impact of credit rationing in rural area welfare and showed
that credit rationing has been negatively affecting the efficiency function of credit in term
boosting the economic performance supporting social welfare in rural areas.
Firstly, efficient credit market can improve the productivities in rural area. Pham and Izumida
(2002) found that credit had a considerable impact on household production. For the case of rural
area in Ethiopia, it was estimated to increase agricultural productivity in high potential, favorable
condition producing areas by 11% if credit constraint was eliminated (Ali, D., & Deininger, K.,
2012).
Secondly, a well-functioning rural credit market may help reduce poverty and contribute to rural
household income growth. As Józwiak (2001) shown, in general higher income growth and a
greater extent of increasing family labors used tend to be the case of farmers who could get
borrowing. Krandker and Faruqe (2003) also gave proof on the contribution of credit on the farm
welfare improvement.
In contrast, in a reasearch of Li et al. (2013), credit rationing was found to cause a loss in net
income of 15.7% and a loss in consumption expenditure of 18.2% for households in China rural
areas. Feder et al. (1990) also shown negative impact of credit rationing on farm profitability as
well.

2.4. Empirical Studies
To understand behavior of lenders and borrower such as credit rationing or credit accessibility in
credit market, it is essential to examine the forces of credit demand and credit supply. This
section aims to review earlier studies about determinants of credit demand and credit supply,
providing an empirical framework for the factors of partial credit rationing.

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2.4.1. Factors of Credit Demand

For various studies, age is essentially a factor that may affect credit demand. Mpuga (2004)
showed that the youngs have higher demand for credit than the olds for the reason that the
youngs are more active engaging in doing business and need credit as a source of funding, while
the old are more rely on their saving. However, Tang et al. (2010) revealed a contradict finding
in which the old farmers, with wide social network and social capital, are more likely to get
borrow than the youngs. Okurut et al. (2005) also found the same result with Tang et al. (2010)
in Uganda.
Credit demand may depend on which gender of borrower is. Women in rural area are oftern seen
as responsible for housework rather than market-oriented activities, thus their demand for credit
is not much necessary as man’s (Nwaru, 2011).
Education is realized as having effect on credit demand. Tang et al. (2010) study indicated that
highly educated individuals are more likelly to borrow, especially in formal credit sectors.
However, it may not be the case at higher education level such as four year universtiy level, as
upper level educated people tend to rely more on their high income rather than credit (Chen &
Chiivakul, 2008).
Labor structure of household may affect their credit demand. For instance, number of adults
normally positively related with loan amount borrowed (Barslund & Tarp, 2008). Pham and
Izumida (2002) argued that the need for expanding production in households with large number
of adults leads them find credit market as source of funding. Higher dependency households may
also demand more for credit (Pham & Izumida, 2002; Okurut et al., 2005), for the reason that
household seek more credit to smooth economic burden bore by large number of dependents in
their family.
Household assets such as livestock, farming area were found to have positive impact on credit
demand (Pham & Izumida, 2002). This is due to the need of working capital to raise livestock or
farming. The finding was also confirmed in the work of Hussain and Khan (2011) However,
study of (Dulflo et al., 2008) found a contradict result that households who had large number of

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livestock demanded less for credit as those who has large livestock are normally in better
economic postion and thus not relying much on credit.
Other factors such as household shock (illness), or social position (household having social
responsibility in community) were also found to have positive effect on credit demand (Zeller,
1994).
2.4.2. Factor of Credit Supply
Empirical studies showed that major credit rationing determinants are demographic
characteristic, i.e. individual characteristics, borrowers’ skill, credit history, household head’s
reputation, dependency ratio, gender, education, and collateral (Petric, 2003; Bester, 1987;
Diamond, 1989; Pham & Izumida, 2002; Craigwell, 1992; and McKee, 1989). Borrowing
purposes and loan size also affect the chance of being credit ration of households (Pham &
Izumida, 2002). Political and social network was found to have affect credit rationing behavior
(Ali & Deininger, 2012). Land holding, livestock and durable goods possession, and village
infrastructure level may also determine whether household being credit rationed or not
(Chaudhuri & Cherical, 2012).
Collateral plays an important role in rural credit market. It acts as a signaling mechanism in
which low risk borrowers are identified as those who willing to secure their loan contracts with a
high amount of collateral. Moreover, rationing due to problem of moral hazard is limited as
higher degree of collateralization can induce investment in safer projects. In either case,
rationing occurs in case of lacking collateral (Bester, 1987). Land, livestock, and asset are
normally supposed relating the credit constraint. Land is conventional collateral used in credit
market, and the investigation often concentrated on this kind of collateral (Petric, 2003; Barslund
& Tarp, 2008; Vuong et al., 2012; Aguilera, 1990; Ali & Deininger, 2012; Li, Huang & Zhu,
2013; Ping, Heidhues & Zeller, 2010). Land was confirmed as a significant factor of credit
rationing probability in various researches. According to Petric (2003), household whose farming
with more rented land would be likely to be credit rationed; as rented land, unlike owned land,
was not qualified collateral to secure the loans. Livestock was also considered as collateral in
rural credit market (Okurut, 2005; Barslund, & Tarp, 2008).
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Regarding to human capital, the effect of education or farming experience on credit constraint
were normally examined (Petric, 2003; Chaudhur & Cherical, 2012; Pham & Izumida, 2002;
Vuong et al, 2012; Barslund & Tarp, 2008; Zeller, 1994, Ping, Heidhues & Zeller, 2010). As
education was supposed to be related to productivity of a person and educated people tend to get
respect from society, higher education level means higher loan repayment ability and higher
credit worthiness.
Concerning to household characteristics, Petric (2003) found that gender had a significant
influence on the chance of being credit rationed. As women tended to be responsible more for
housework rather than income generating activities, credit repayment ability was low in those
households with more women and credit was more constraint to them as a consequence. Oppose
to the result of Petric (2003), Chaudhur and Cherical (2012) showed that female head households
had a higher chance of loan approval. However, larger familiy size reduce the probability of
receiving in case of loans from banks.
Kereta (2007) found that young and old people in Ethiopia are less likely to access credit than
the middle age. Chaudhur and Cherical (2012) showed that age factor was positive relate to the
chance of getting credit approval while the research of Pham and Izumida (2002) resulted in
negative relationship; however those two researches are not showed a significant impact of age
on lenders’ rationing decision.
For various researches, dependency ratio, which measures the ratio between numbers of
dependent over household size, has appeared to be a key determinant of ration decision by credit
lenders. Higher dependent ratio may lead to higher rate of credit ration. (Pham & Izumida,
2002). The interpretation was that households with large number of dependents are normally
poor as the more dependents means the more economic burden for the households.
Lenders’ rationing decision may also be influenced by the factor of household reputation. Pham
and Izumida (2002) found that those households with low reputation are likely to be rationed.
Another argument of Diamond (1989) that reputation has effect on interest rate between lenders
and borrowers. Political and social network was found to have affect credit rationing behavior as
well in the study of Ali and Deininger (2012).
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Regarding to the factor of loan contract, Pham and Izumida (2002) confirmed that households
who requested large loan size are likely to be rationed for reasons such as low repayment ability
in case of large loan. For loan purposes, Chaudhur and Cherical (2012), and Diagne (1999) found
that loan for production and farming were less likely rationed, while it was contrast in the study
of Kedir, Abbi, Gemal and Torres (2007).

Chapter 3. Methodology
3.1. Data Source and Features
The research will employ Vietnam Access to Resources Household Survey (VARHS 2008) as
primary source for its sample data set to econometrically conduct the investigation on
determinants of rural credit rationing in Vietnam. Started in 2002, Vietnam Access to Resources
Household Survey (VARHS), began to survey with around 1000 households in 4 provinces of
Ha Tay, Phu Tho, Quang Nam and Long An. Then, the survey is repeated each 2 years with
expanded surveyed samples. In 2006 VARHS implemented in 12 provinces with 2,324
households and in 2008 VARHS implemented in 12 provinces with 3,223 households. The next
survey will be implementing in 2010 in the framework of this project.
VARHS has been supported by Vietnamese Government to conduct nationwide investigation in
order to provide detail information on situation of rural household access to resources such as
land, credit, S&T, market information as well as other material resources for economic and
livelihood development. Started in 2002, Vietnam Access to Resources Household Survey
(VARHS) implemented a survey with around 1000 households in 4 provinces of Ha Tay, Phu
Tho, Quang Nam and Long An; and was repeated every 2 years with expanded the scope of
survey. In 2006, VARHS conducted the investigation in 12 provinces with 2,324 households and
increased survey sample to 3,223 households in 2008. The 12 provinces in VARHS 2008 was
area including Ha Tay, Nghe An, Khanh Hoa, Lam Dong, Phu Tho, Quang Nam, Long An, Dac
Lac, Dac Nong, Lao Cai, Dien Bien and Lai Chau.

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