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MINISTRY OF EDUCATION AND TRAINING
UNIVERSITY OF ECONOMICS HOCHIMINH CITY
----------

VU DUC CAN

This thesis
is PREFERENCES,
completed at University
Economics
Ho Chi
RISK
SOCIALof
CAPITAL,
AND
Minh City.

MICROCREDIT RISKS: AN EXPERIMENTAL

STUDY
IN THE
DELTA
Supervisor
1: Assoc.
Prof.MEKONG
Dr. Truong
QuangREGION
Thong OF
VIETNAM

Supervisor 2: Dr. Nguyen Duc Quang

Major: Finance – Banking

Reviewer 1:…………………………………………….………
Code: 9340201
Reviewer 2:…………………………………………….………

Reviewer 3:…………………………………………………….

SUMMARY
OF DOCTORAL
IN
The thesis
will be defended
in front of UEHTHESIS
thesis evaluation
ECONOMICS
committee at …………….………………………………........
……………………………………………………………….
…………………………………………………………………
….
At

HOCHIMINH CITY, 2019
(hour) (date) (month) (year)

The thesis can be found at: …………………………..………..
…………………………………………………………………
…..……..

3

ABSTRACT
The aim of this study is to empirically analyze social and
demographic factors related to microfinance borrowers in order to
measure their effects on microcredit risks as undergone by
microfinance institutions (MFIs) in the Mekong Delta Region of
Vietnam. Further, the study looks at risk preferences, social capital,
and others with respect to microfinance borrowers’ behavior to
estimate their impact on microcredit risks facing MFIs.
In this study, a series of economic experiments was conducted
with the participation of microfinance borrowers in six provinces
of the Mekong Delta Region to capture the effects of risk
preferences and social capital on microcredit risks, which was well
justified by the findings. Specifically, those who seek more risk are
less likely to have bad debt, while those being more risk averse
suffer more. Given social capital, mutual support in the community
and trust impact positively on microcredit risk. These results form
a firm basis for devising feasible policies in direct relation to
microfinance lending activities involving MFIs.
Keywords: microfinance, risk preferences, social capital, risk
seeking, risk averse.


Chapter 1: INTRODUCTION
1.1. Problem statement.
Microfinance has come into existence and gone through a long
history of development, thus establishing its significant role and

influence on economic growth in general and poverty alleviation in
particular. Vietnam, however, has seen its advancement only
recently, and activities of formal MFIs are still limited. According
to the State Bank of Vietnam (SBV), up to late 2018, there have
been 16 financial firms, among which six are subsidiaries of a few
banks. The current period has seen a boom in financial companies
in Vietnam to exploit the untapped consumer lending segment,
which has yet to satisfy market needs. According to an SBV’s
report, by the end of 2018, the total outstanding loans of the entire
economy reached around VND7.2 million billion, and the
aggregate informal outstanding loans accounted for more than
20%. Nevertheless, the supply of formal credit has not well met
people’s needs, especially small loans or those without collaterals,
thus resulting in the growing demand for illegal lending, literally
known as ‘black credit’, which made inroads into socioeconomic
well-being. Hence, addressing the issue of risks as well as the
impact of microcredit risks on microfinance activities is urgent and
essential to the current context of Vietnam.
1.1.1. Risk preferences and microcredit risks.
In this study microfinance is understood as small loans (no
larger than VND100 million), and microcredit is one of the
services offered by MFIs. As such, microcredit is a lending product
in credit activities as conducted by credit institutions; it is a
predominant service provided by MFIs in Vietnam. The prospect
theory, developed by Tversky and Kahnerman, suggested that the


value function is determined by gains and losses in relation to the
reference point. Wen et al. (2014) concluded that risk preference is
related to attitudes toward risks. Thus, it is evident that risk

preference is a tendency toward risk decisions as can be made by
individuals and investors to obtain the highest possible
profitability. Handa (1971) argued that risk preference is the choice
between a high-risk asset and low-risk asset to gain higher returns.
Charness et al. (2013) and Eckel et al. (2010) concluded that in
economics emphasis can be put on suggestive methods in
analyzing risk preferences, and the suggested preferences can be
affected by the measures used. According to Stiglitz and Weiss
(1981), borrowers are motivated, and have a tendency, to invest in
risk-ridden projects. This means that borrowers with bad debts are
willing to take high risks. The experiments carried out by Zeballos
et al. (2014) showed that borrowers having no bad debts
demonstrate more risk-seeking behavior than those with bad debts.
Stiglitz and Weiss (1981) hypothesized that people investing in less
risky projects are those suffering bad debts. The poor cannot repay
their loans because they refuse to face risks, so the effectiveness is
low (Zeballos et al., 2014).
In Vietnam, Vieider et al. (2013) concluded that farmers are on
average risk neutral and that income is negatively associated with
risk aversion. The studies of Nguyen et al. (2016) and Tanaka et al.
(2010) in northern and southern villages accentuated the impact of
risk attitudes and risk and time preferences on trust and reliability,
and risk aversion and patience. So, which specific behavioral
characteristic of microfinance borrowers has influence on
microcredit risks? Do risk preferences differ in microfinance


practices between rural and urban regions? These are also the
research gap for this paper to fill.
1.1.2. Social capital and microcredit risks.

To date social capital is regarded as a real form of capital and
thus substantially influences microcredit risks. Trust and reliability
are two major concepts embedded in personal social capital, and
social capital takes a crucial part in different fields in an economy
and society. Still, how does it manifest itself and in what way
would it be measured when it comes to the issue of risk in
microfinance activities? This also receives special attention in this
study.
1.2. Research topic.
In Vietnam there has yet to be any comprehensive, systematic
research that examine risk preferences and social capital with their
effects on microcredit risks as well as the progress of microfinance
activities in Vietnam. Risk is commonplace in decision making
processes, and risk preference is measured by the level of risk
tolerance of a single individual. From this respect, this study is to
be carried out with the title “Risk preferences, social capital, and
microcredit risks: An experimental study in the Mekong Delta
Region of Vietnam”.
1.3. Research objectives.
The principal objective of this study is to examine the effects
of microfinance borrowers’ behavior on microcredit risks in the
Mekong Delta Region. As such, through data collection and
analysis derived from field experiments, the study identifies the
extent to which behavioral factors such as risk preference and
social capital, along with other social and demographic factors
inclusive of the difference between rural and urban borrowers,


have effect on microcredit risks. To obtain the informed objective,
the following questions are to be brought up: (i) How do

borrowers’ risk preferences and other social, demographic factors
affect microcredit risk of MFIs? and (ii) How do borrowers’ social
capital and other social, demographic factors influence microcredit
risks of MFIs?
1.4. Research methods.
Several research hypotheses are proposed based on the review
of related literature and relevant theoretical framework as well as
the results of previous studies. Field experimental method is
adopted to collect the data. Regression analysis with Binary
Logistic is performed to process the data, along with the use of
Probit technique to test the robustness of the results obtained. The
results are then screened, discussed, and interpreted to put forward
policy implications, and limitations are also outlined as a basis for
future research.
The technique proposed by Eckel and Grossman (2002) is
adopted to conduct the experiment of eliciting risk preferences.
Concerning social capital, the study suggests the Game of
contributions to community, and Camerer and Fehr’s (2003)
method is employed to investigate trust and reliability.
1.5. Research participants and scope.
- Participants: microcredit borrowers of microcredit providers,
including both formal and semi-formal institutions.
- Scope: A total of 176 microfinance borrowers residing in both
rural and urban areas in six provinces of the Mekong Delta Region,
namely Kien Giang, Hau Giang, Vinh Long, Tien Giang, Ben Tre,
and Long An. All six surveys and experiments were undertaken
from May through October 2017.


1.6. Contributions of the study.

1.6.1. Theoretical contribution.
Selectively applied in this investigation are the three-Game
experimental technique suited to the reality of Vietnam as well as
the study locations to consider the impact of different factors on
microcredit risks in the Mekong Delta Region, and thus to add
some theoretical basis on behavioral finance as regards risk
preferences and social capital with their diverse effects on
microcredit risks in urban and rural areas.
1.6.2. Contribution to practice.
In this study the author draws critical conclusions on risk
preferences and social capital that influence microfinance
activities. Those who contribute much to the community and who
much willingly donate their money to partners are less likely to
suffer bad debt, and the reverse is also true. When the size of the
loan is high, the level of bad debt abates, and no difference can be
found in bad debt levels between urban and rural areas. Further, the
study provides policy implications regarding microfinance
activities and constructive suggestions as to the advancement of the
Vietnam’s microfinance industry at large.
1.7.

Organization

Chapter

of

the

1:

study.
Introduction.

Chapter 2: Risk preferences, social capital, and microcredit risks.
Chapter

3:

Research

design.

Chapter 4: Analysis of the effects of different factors on
microcredit risks: Surveys and experiments in the Mekong Delta
Region.
Chapter 5: Result discussion and policy implications.


Chapter 2: RISK PREFERENCES, SOCIAL CAPITAL, AND
MICROCREDIT RISKS
2.1. Risk preferences and microcredit risks.
In human society risk is perceived to exist in all kinds of
activities. Attitudes of people toward risk remarkably differ;
therefore, it can be used to speculate on their economic behavior
and decisions. The impact of risk is direct and diverse, from
borrowers’ behavior and activities to investment, manufacture, and
consumption and behavior toward risk. Other effects arise from
demographic, financial, or physical factors and social capital.
2.1.1. Prospect theory.

As the basis for behavioral science, Tversky and Kahnerman
(1979) with the prospect theory posited that people sometimes
show either risk aversion or show risk seeking tendencies
depending on the nature of the prospects (Ackert & Deaves, 2013).
Tversky

and

Kahneman’s

theory

centered

around

these

fundamental points: (i) In the light of the nature of the prospects,
human behavior sometimes implies not just risk aversion (risk
avoidance) but sometimes risk loving (risk seeking). People’s
choices, thus, are made on the basis of gains and losses; (ii) A
person assesses gains and losses against a reference level, which
normally corresponds to his current condition; and (iii) People will
likely lose because the loss impacts more powerfully on their
emotions than the gain.
2.1.2. Risk preferences in microfinance lending activities.
The credit market is imperfect, and always reflects an
asymmetry between borrowers and lenders. Stiglitz and Weiss
(1981) argued that borrowers with bad debts are willing to risk

getting high-interest loans. According to Zeballos et al. (2014),


borrowers without non-performing loans seek more risk than those
who have. Eckel and Grossman (2008) found that female students
are more risk averse than their male counterparts. While
Binswanger (1980) detected no difference in risk compared to the
scope of investment between the rich and poor, Vieider et al.
(2015) showed that unmarried people are less risk averse, whereas
women and the elderly are more risk averse.
In brief, studies on risk preferences concern a wide range of
subjects and areas. The results also indicated many differences in
subjects, areas, and fields of study. However, the impact of risk
preferences

on

risks

involving

microcredit

lending

and

microfinance activities in Vietnam has not been studied at length.
So, how do risk preferences as well as other social, demographic
factors of microfinance borrowers affect the risks in microfinance

lending activities of credit institutions engaging in microcredit
lending and MFIs?
2.2. Social capital and microcredit risks.
2.2.1. The role of social capital.
Social capital is seen as a kind of capital. Social capital is a
comparatively sustainable social network, exhibiting itself with
sympathy, understanding, and interaction among members
(Bourdieu, 1986; Fukuyama, 2001, 2002; Coleman, 1988; Portes,
1998). According to Karlan (2005), social capital of an individual
is the ability to obtain information, their communication, and social
relations to get to grips with imperfect information-related
problems. Economists held that trust is a critical constituent of
social capital. As revealed by Karlan (2005), the more faith people
have in others, the more economical they are; the more reliable
they are, the less credit risk they are exposed to, and the more


people contribute to the community, the less credit risk they face.
According to Knack and Keefer (1997) and Karlan (2005),
countries and cultures with more mutual trust achieve higher
growth rates. Glaeser et al. (2000) maintained that people who are
more trusted are more reliable.
2.2.2. Social capital in microcredit lending activities.
Feigenberg and Field (2010) highlighted lending to the group
of low non-performing loans without mortgage. Akram and
Routray (2013) suggested that social capital index has negligible
influence on microfinance participation. Trust as a measure
facilitates borrowing from a group based on microfinance
programs. Poor households can use their social capital as collateral
for loans. According to Karlan (2005), the higher the social capital,

the better the ability to repay loans and the more economical it is.
Greiner and Wang (2009) exhibited an information asymmetry
between lenders and borrowers.
2.2.3. Studies on social capital in Vietnam.
In a study by Nguyen Tuan Anh and Thomése (2007), social
capital was shown to well handle troubles involving land
consolidation in agriculture. Nguyen Van Ha and Kant (2004)
indicated that social capital has a strong and positive influence on
household income. Tran Huu Dung (2003) pointed out the
relationship between social capital and economic policy; between
social capital and economic growth. Social capital has a profound
impact on the quality and rate of human capital accumulation. Dinh
Hong Hai (2013) noted the dark side of social capital. Ngo Thi
Phuong Lan (2011) believed that social capital helps minimize
risks in the transition from rice cultivation to shrimp farming in the
Mekong Delta. Nguyen Hong Thu (2018) concluded that


microfinance has an impact on the income of poor households,
which, as argued by Mai Thi Hong Dao (2016), is affected by such
factors as age, household size, rate of dependency, total assets,
microcredit, and regions affect the income of poor households.
Phan Dinh Khoi (2013) concluded that working for local
authorities, being members of loan groups, education level, skilled
labor, and inter-commune roads affects the accessibility to
microfinance. Dinh Phi Ho and Dong Duc (2015) asserted that
household characteristics, residential locations, and shocks of
environmental risks have influence on farm households’ income
and expenditure.
Accordingly, it is conceivable that social capital has a

substantial impact on many sectors in an economy or society. Still,
considering the matter of risks in microfinance practices, how does
it represent and how can we measure it? This merits special
attention.
2.3. Measuring risk and effectiveness of microfinance practices.
2.3.1. Definition of microfinance.
The term ‘microfinance’ denotes the models of financial
services provided for the poor that helps them with their own
business development and life improvement.
2.3.2. Measuring and assessing microcredit risks.
2.3.2.1. Effectiveness of microfinance practices.
Indicators of performance and sustainability of a microfinance
institution are varied, including: (1) Institutional self-sustainability
(ISS); (2) Operational self-sustainability (OSS); (3) Financial selfsustainability (FSS) (Ackert & Deaves, 2010; Le Dat Chi et al.,
2013).
2.3.2.2. Risks in microfinance practices.




Objective

risks:

natural

environment,

socio-economic

environment, legal environment.
 Subjective risks:
⁕ MFIs: administration capacity, lending procedure and
policy, loan inspection and supervision, service and ethical quality
of credit staff.
⁕ Microfinance borrowers: education level, production and
business capacity, ethical issues.
2.3.3. Measuring microcredit risks in the study.
2.3.3.1. Definition of bad debt and related views.
Bad debt can be referred to as ‘doubtful debt’ or ‘nonperforming loan’. WB defined it as substandard loans that may be
overdue, or doubts arise over the repayment capacity as well as the
recoverability of capital, frequently occurring in the event that the
debtor has been declared bankrupt or detected with property
dispersion. According to IMF, “a loan is nonperforming when
payments of interest and/or principal are past due by 90 days or
more, or interest payments equal to 90 days or more have been
capitalized, refinanced, or delayed by agreement, or payments are
less than 90 days overdue, but there are other good reasons—such
as a debtor filing for bankruptcy—to doubt that payments will be
made in full.’
Risks in microfinance lending practices
Prospect theory.
- Risk preferences.
- Personal behavior.
- Risk averse.
- Risk seeking.
- Risk neutral.

Bad
debt

Social capital.
- Demographic
characteristics.
- Trust.
- Reliability.
- Contribution to
community.
- Social relations.
- Social network.


Figure 2.2: Research framework.
(Source: from theoretical bases and overview of literature)

2.3.3.2. Bad debt in Vietnam.
Pursuant to SBV’s Circular 02 (2013), bad debts are those from
Group 3 (91 days or more overdue) to Group 5.
2.4. Research framework (Figure 2.2.)
Chapter 3: RESEARCH DESIGN
3.1. Data and methodology.
3.1.1. Qualitative method.
Direct interviews were carried out with 176 microfinance
borrowers on-site, along with consultation provided by experts,
SBV’s senior leaders and other credit institutions.
3.1.2. Quantitative method.
Based on the data collected from the experiments, statistical
description was given, in addition to Binary Logistic regression,
Probit analysis performed to test the robustness of the regression
results in order to analyze the models suggested.

3.1.3. Basis for location and sample selection.
This is a key area of the southern region of Vietnam, full of
distinct characteristics of ecosystems, industries, and ethnic groups.
The author has over 20 years’ experience in the banking industry
for proper selection of participants. Given the total number of 176
participants, 33.5% was selected from Vietnam Bank for Social
Policies (VBSP), and the remaining 66.5% from local commercial
banks.
3.2. Selecting experimental methods in economics.
3.2.1. Eliciting risk preferences.


Several experimental techniques have been developed to delve
into attitudes toward personal risks, including Balloon Analogue
Risk Task (BART), questionnaires, methods as proposed by
Gneezy and Potters, Eckel and Grossman, and selection based on
price lists.
3.2.2. Measuring social capital.
The methods applied comprise Trust Game and Public Goods
Game.
3.2.3. Assessing and selecting methods.
The author employed Eckel and Grossman’s technique, Trust
Game, and Public Goods Game.
3.2.4. Organization and role assignment.
The author first collected demographic information and
decided on the locations for Risk Game (Game 1). In Games 2 and
3, each assigned group is composed of one group leader, one
secretary, and one assistant.
3.2.5. Basis for determining rewards.
Table 3.2: Average income and expenditure per capita per day

Unit: VND

Whole

Urban

Rural

country
Income

78.900

132.134

67.934

Expenditur

62.934

82.034

48.134

e
(Source: Statistical Yearbook of Vietnam 2016 and author’s calculations)

3.3. Methods and steps taken for experiments.
Experiment 1: Eliciting risk preferences.

Each participant received VND100,000 and would have to
make a random selection of one of the six Scenarios as detailed in
Table 3.3. Then, they were instructed to cast either of the two lots
labelled “win” and “lose”.
Table 3.3: Game options
Unit: VND
Scenario
1
2
3
4
5
6

Amount
subtracted
0
-20.000
-40.000
-60.000
-80.000
-100.000

Amount
received

Selection

Result

0
+30.000
+60.000
+90.000
+120.000
+140.000

(Source: Author’s selection).
Experiment 2: Public Goods Game.
Groups of 10–15 participants were formed, and they each
received VND50,000. The Game operator then asked if they
decided to donate or not. A participant’s acceptance means that he
would have to give the operator VND30,000, and the players in the
same group would each receive VND5,000 upon the end of the
Game. His refusal to donate the money means the amount obtained
by the others in the group remained unchanged.
Experiment 3: Trust Game.
The operator asked if the number 1 players would love to give
their money to those with number 2. No money given means the
end of the Game. The money, if given, must be held by the
operator; it was then doubled before received by the number 2
players. Next, the operator asked the number 2 players if they
agreed to give the money back to the number 1 players.
3.4. Research models.


3.4.1. Regression equation for risk Game experiment:
where is a dependent variable, equaling 1 if the participant has bad

debt

and

0

otherwise.

participant’s selection;

Independent
represents age;

variables:

represents

represents gender;

represents education level.
- Z is a control variable denoting living area.
3.4.2. Regression equation for public goods Game experiment:
where is a dependent variable, and is an independent variable
representing the participant’s intention to donate.
3.4.3. Regression equation for trust Game experiment:
where is a dependent variable, is an independent variable denoting
percentage of the amount given to his partner by the participant.
3.4.4. Regression equation for all three experiments (robustness
check):
where is a dependent variable, represents participant’s selection,

represents intention to donate, represents percentage of the amount
given to partner, represents age, represents gender,

represents

education level, and Z is a control variable denoting living area.
3.5. Research hypotheses.
3.5.1. Behavioral hypothesis concerning Risk Game:
H1: Those who seek more risk have less bad debt, while those
being more risk averse suffer more.
3.5.2. Behavioral hypothesis concerning Public Goods Game:
H2: Those who donate are less likely to have bad debt, while it is
more likely for those with no contribution.


3.5.3. Behavioral hypothesis concerning Trust Game:
H3: The larger the percentage of the amount a participant gives his
partner, the less likely it is for him to be burdened with bad debt.
Conversely, the smaller the percentage, the more likely he suffers
bad debt.
3.6. Regression technique.
Primarily employed in this research was Binary Logistic
regression method. Further, Probit analysis was performed to check
the robustness of the results obtained using the regression method.
Chapter 4: ANALYSIS OF EFFECTS OF DIFFERENT FACTORS
ON MICROCREDIT RISKS: RESULTS OF SURVEY,
STATISTICS, AND EXPERIMENTS IN THE MEKONG DELTA
REGION

4.1. Statistical description of data sample.

4.1.1. Overall statistics on participants’ characteristics.
4.1.1.1. Bad debt.
Most of the survey participants have no bad debt (81.8%,
standard loans).
4.1.1.2. Education level.
The general level is rather low. 18.2% are found not to have
completed primary education. While 33% have completed primary
education but not secondary high school education, 29.5% have
finished secondary high school, yet not high school, education.
4.1.1.3. Living areas.
63.1% and 36.9% of the participants live in rural and urban
areas respectively.
4.1.1.4. Mortgages.
37.5% of the households are found with property mortgages
and 62.5% with unsecured loans.


4.1.1.5. Locations of households’ bank loans.
33.5% of the loans are requested at Vietnam Bank for Social
Policies and 66.5% at local joint stock banking institutions.
4.1.1.6. Principal income sources.
There is a relatively even distribution of households’ sources of
income (ranging from 23.3% to 29.5%).
4.1.1.7. Quantitative indicators.
The average age is 46.9 along with the most advanced of 85
and the earliest of 20. Standard deviation is 12.4. Average
household size is 4.5 people (max is 12, and min is 1). The average
percentage of household members with employment is 68.7%. The
average loan rate is VND23.58 million per household. The duration
of loans repayment is about 15.8 months, ranging from 1 to 60

months.
4.1.2. Characteristics of item selection.
4.1.2.1. Risk Game.
The percentages of the number of participants who went for
Scenarios 1, 2, 3, 4, 5, and 6 are 6.3%, 24.4%, 19.3%. 15.9%,
6.3%, and 27.8%, respectively. According to the general view on
risks, risk averse individuals are those picking Scenarios 1-4,
making up 65.9%. A few are risk neutral (6.3% for Scenario 5),
whereas risk seekers make up a high proportion of 27.8%
(Scenario 6). Thus, there is a divergence of opinion given the
sample as typified by: (i) risk aversion; and (ii) risk seeking.
4.1.2.2. Public Goods Game.
81.8% of the participants make donations, and 18.2% do not.
4.1.2.3. Trust Game.
92% agree to give money to their partners.
4.1.3. Detailed statistics on characteristics of experiments.


4.1.3.1. Participants’ characteristics and their options
in Risk Game.
Most opt for Scenario 6 (49 participants, 27.8%), while
Scenarios 2 and 3 are chosen by 24.4% and 19.3%, respectively.
There is little difference in options between males and females.
Urban inhabitants going for Scenario 6 account for 32.3%, whereas
the figures for those for Scenarios 3 and 2 are 24.6% and 15.4%,
respectively. On the other hand, given rural residents, Scenarios 2,
6, and 4 are selected by 29.7%, 25.2%, and 17.1%, respectively.
Most urban dwellers are risk loving in addition to a few risk averse
participants (Scenarios 3 and 4) and very few risk neutral
participants. By the same token, the majority of rural residents are

mostly completely risk averse (Scenario 2), besides the trends
toward risk seeking behavior (Scenario 6) and risk neutral behavior
in rural areas.
4.1.3.2. Participants’ characteristics and their options
in Public Goods Game.
81.1% of the participants choose to contribute; males
and females account for 85.3% and 79.2%, respectively.
Urban and rural citizens with contribution make up 81.5%
and 82%, respectively.
4.1.3.3. Participants’ characteristics and their options in Trust
Game.
The give-money option reaches 92%. Most participants agree to
give money to their partners, but little difference exists between
males and females as well as between urban and rural inhabitants.
4.2. Testing differences in certain criteria based on participants’
characteristics.
4.2.1. Differences in bad debt.


•

Gender: 75 male participants have bad debt, whereas this figure for
females is 101. At 10% significance level, there exists a difference
in characteristics of loans between males and females.

•

Living areas: 65 urban participants have bad debt, whereas this
figure for their rural counterparts is 111. At 10% significance level,
no difference can be detected in characteristics of loans in terms of

living areas.
4.2.2. Differences in certain characteristics in Risk Game.
4.2.2.1. Between risk neutral, risk seeking, and risk averse
participants.
At 10% significance level, the results are as follows:

 Between participants selecting Scenarios 5 and 1: There is no
difference in bad debt.
 Between participants selecting Scenarios 5 and 2: There is no
difference in bad debt.
 Between participants selecting Scenarios 5 and 3: There exists a
difference in bad debt.
 Between participants selecting Scenarios 5 and 4: There exists a
difference in bad debt.
 Between participants selecting Scenarios 5 and 6: There exists a
difference in bad debt.
4.2.2.2. Between risk seeking and risk averse participants.
At 10% significance level, the results are as follows:
 For risk neutral participants: The level of bad debt suffered by risk
neutral participants differ widely from that suffered by those who
are less risk averse and those who are risk seeking. There is no
difference in bad debt between risk neutral participants and highly
or completely risk averse individuals.
 For risk seeking participants: Significant differences are explored


between those who are risk seeking and those who are completely
risk averse, between those who are highly risk averse and those
who are relatively risk averse.
4.2.3. Differences in certain characteristics in Public Goods

Game.
4.2.3.1. Between borrowers’ characteristics and their options in
Public Goods Game.
At 10% significance level, the results are as follows:
 Gender: Participants’ options are different.
 Living areas: Participants’ options are not different.
4.2.3.2. Between borrowers’ options and their bad debt in
Public Goods Game.
A certain difference exists: Most participants with contribution
do not have bad debt, and conversely, those without do have.
4.2.4. Differences in certain characteristics in Trust Game.
4.2.4.1. Intention to give money according to roles of
participants.
A difference exists in decision to return money to partners.
4.2.4.2. Amounts of money given to partners.
Amounts of money given by number 1 participants to their
number 2 partners are larger than those given by number 2
participants to their number 1 partners.
4.2.4.3. Amounts of money given to partners according to bad
debt.
Those having no bad debt give their partners larger amounts of
money than those who have.


4.3. Regression results of the effects of different factors
on bad debt.
4.3.1. Regression results of the effects of different
factors on bad debt in Risk Game.
At 10% significance level, the results are as follows:
 Riskier options have negative associations with bad debt. That is,

the more risk seeking people are, the less likely they are to have
bad debt, and the reverse is also true.
 Considering merely the effects of age and gender, these are not
attributed to bad debt.
 The higher the education level of a participant, the less likely he
bears the burden of bad debt. For young people, more advanced
age is associated with less bad debt. Still, the older a participant
becomes, the more likely he suffers bad debt.
 Larger loans have a link with less bad debt suffered. No substantial
conclusion can be drawn with respect to the difference in bad debt
level between urban and rural areas. The number of household
members with employment history does not make any difference to
bad debt, and neither do loan terms.
4.3.2. Regression results of the effects of different
factors on bad debt in Public Goods Game.
At 10% significance level, the results are as follows:
 Decision to contribute to the community is negatively related to
bad debt, i.e. those who are more ‘generous’ will less likely have
bad debt.
 Age, gender, and education level all have no impact on the
likelihood of having bad debt.
 Larger loans have a link with less bad debt suffered. No substantial
conclusion can be drawn with respect to the difference in bad debt


level between urban and rural areas. The number of household
members with employment history does not make any difference to
bad debt, and neither do loan terms.
4.3.3. Regression results of the effects of different
factors on bad debt in Trust Game.

At 10% significance level, the results are as follows:
•

Those who give more money to their partners will less likely have
bad debt.

•

Considering merely the effects of age and gender, these are not
attributed to bad debt.

•

The higher the education level of a participant, the less likely he
bears the burden of bad debt. For young people, more advanced
age is associated with less bad debt. Still, the older a participant
becomes, the more likely he suffers bad debt.

•

Larger loans have a link with less bad debt suffered. No substantial
conclusion can be drawn with respect to the difference in bad debt
level between urban and rural areas. The number of household
members with employment history does not make any difference to
bad debt, and neither do loan terms.
4.3.4. Regression results of the effects of different
factors on bad debt by combining Risk Game and
Public Goods Game.
At 10% significance level, the results are as follows:

• Age, gender, and education level all have no impact on the
likelihood of having bad debt.
•

Larger loans have a link with less bad debt suffered. No substantial
conclusion can be drawn with respect to the difference in bad debt
level between urban and rural areas. The number of household


members with employment history does not make any difference to
bad debt, and neither do loan terms.
4.3.5. Regression results of the effects of different
factors on bad debt by combining Risk Game and Trust
Game.
At 10% significance level, the results are as follows:
•

Considering merely the effects of age and gender, these are not
attributed to bad debt.

•

The higher the education level of a participant, the less likely he
bears the burden of bad debt. For young people, more advanced
age is associated with less bad debt. Still, the older a participant
becomes, the more likely he suffers bad debt.

•

Larger loans have a link with less bad debt suffered. No substantial

conclusion can be drawn with respect to the difference in bad debt
level between urban and rural areas. The number of household
members with employment history does not make any difference to
bad debt, and neither do loan terms.
4.3.6. Regression results of the effects of different
factors on bad debt by combining Public Goods Game
and Trust Game.
At 10% significance level, the results are as follows:

• Age, gender, and education level all have no impact on the
likelihood of having bad debt.
•

Larger loans have a link with less bad debt suffered. No substantial
conclusion can be drawn with respect to the difference in bad debt
level between urban and rural areas. The number of household
members with employment history does not make any difference to
bad debt, and neither do loan terms.