Joint-liability lending and asymmetric information
1. Joint-liability lending and asymmetric information
problems: An experimental case study from Bolivia
Key Words: Joint-liability Lending, Microfinance, Asymmetric Information, Adverse Selection,
Moral Hazard, Social Capital, Free Riding, Experimental Economics
Arturo Rodriguez Trejo
Master’s Candidate in International and Development Economics
Department of Economics
University of San Francisco
2130 Fulton St.
San Francisco, CA 94117
E-mail: arturo.rdgz.trejo@gmail.com
May 2010
Abstract: Are there asymmetric information problems with joint-liability loans?
If so, how does social capital curb these problems? Do borrowers use group
lending as a way to free ride on their peers or just as a way to diversify risk?
These are three questions addressed in this research paper by using the results
from an artefactual experiment carried out in Bolivia where five treatments to
test for adverse selection, moral hazard and risk preferences were carried out.
Results show evidence of adverse selection but not of moral hazard. Free riding
behavior, rather than a risk diversifying motivation, drives these results.
This work could not be possible without the support of the University of San Francisco and especially without the
contributions and guidance from Bruce Wydick, Alessandra Cassar and Michael Jonas. I owe a special “thank you”
to my friend, colleague and co-researcher Eliana Zeballos for her ongoing advice both on and off the field. I also
wish to recognize the support of Porvenir for allowing its clients to be part of this study; Giorgia Barboni and the
rest of the Bolivian team for making the experiment possible; Travis Lybbert for his technological assistance; and
my student colleagues for their constant constructive comments. Of course, this work would not exist if it weren’t
for the unconditional support of my parents and my life partner.
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1. Introduction
Understanding how credit mechanisms operate in underdeveloped settings is imperative
in the creation of policies and self-sustaining programs that aim to break the vicious cycle of
poverty. Credit markets, by nature, are plagued with problems of information asymmetries as
first noted by Stiglitz and Weiss (1981). The primary problem arises from the fact that incentives
between the lender and the borrower are not necessarily aligned. As detailed by Ghatak and
Guinnane (1999), this unsynchronized set of incentives creates four major problems in credit
markets: adverse selection, ex-ante moral hazard, high auditing costs and ex-post moral hazard.
The purpose of this paper is to explore the first two. Adverse selection occurs when the lender
cannot ascertain the type of borrower that is asking for a loan; that is, if the borrower is either
risky or safe. Ex-ante moral hazard, on the other hand, happens when the lender cannot make
sure that, once the loan has been made, it is utilized in a way that maximizes the probability of
repayment (hereinafter ex-ante moral hazard will be referred to just as moral hazard unless
otherwise stated).
Imperfect information forces safe individuals to cross-subsidize risky borrowers and,
moreover, it leads to credit rationing. In a world of perfect information, a lender would be able
to price discriminate among the two types of borrowers by charging higher interest rates to risky
individuals and lower interest rates to safe borrowers. However, in the context of asymmetric
information, a bank will not have all the necessary information to assess the nature of a potential
borrower. As a result, it will be forced to offer loans to all borrowers at the same nominal
interest rate. Under these circumstances, both types of borrowers will have the same cost of
capital even when the probability of success across types varies. Herein, lies the problem. Safe
borrowers, that could otherwise be paying lower interest rates, are forced to pay more for their
loans effectively cross-subsidizing risky individuals. Credit rationing occurs if there are enough
risky borrowers in a lender’s portfolio. If this is the case, the equilibrium interest rate will be
pushed upwards just to the point where safe borrowers will be driven away from the market
(Ghatak and Guinnane (1999)).
Institutional and information innovations, such as the use of collateral, the emergence of
credit agencies and the appearance of joint-liability group lending schemes, have spurred the
reduction of the negative effects caused by imperfect information. The joint-liability lending
condition is simple, yet extremely powerful, since it changes the incentive structure that
borrowers face. Group lending under joint-liability is often pegged to the microfinance
movement since actual loans provided under this scheme are, on average, smaller in scale than
conventional loans. Both the scale of the microfinance movement (more than 110 million clients
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worldwide (Microcredit Summit Campaign Report (2006)) and the need to understand why some
institutions have succeeded while others have failed (as detailed by Armendáriz and Morduch
(2005)) are two of many reasons why additional research on this field, especially empirical work
like this effort, is still important.
This paper is structured as follows. Section 1 continues on touching upon the literature
of asymmetric information, focusing on the ways in which joint-liability lending and social capital
help reduce adverse selection and moral hazard problems. Section 2 provides a detailed overview
of the experimental work carried out on the field and of the subjects that partook in the
experiment. Section 3 looks at the methodology used to construct the econometric models. In
Section 4 results from these models are presented. These findings are further analyzed in Section
5 where the line between free riders and risk diversifiers is set; and finally, Section 6 presents the
concluding remarks and policy implications.
1.1 Adverse selection literature
Adverse selection occurs when a potential borrower’s type is unobservable to the lender,
making him unable to distinguish between inherently risky and safe individuals. This is a problem
because the probability of loan repayment is a function of a borrower’s type. Van Tassel (1999)
constructs a model to show that joint-liability contracts induce endogenous group formation and
self-selection among borrowers. Members of a specific village are much more likely to know the
character of their neighbors than the actual lender. A risky borrower would then find it in her
best interest to be part of a group formed by safe individuals since, in case of loan default, they
will be made jointly liable for her share. However, a safe individual has no incentive to be part of
the same group. Given the knowledge that they have about each other, an endogenous process
of assortative matching takes place. In equilibrium, borrowers end up with partners of the same
type, full efficiency in the market is restored and credit rationing is no longer a problem (Ghatak
(1999)). Under these conditions, Van Tassel goes to show that risky types will actually prefer to
take out individual loans (they simply lack incentives to be part of a group formed by risky
individuals now that default by any member is much more likely). The joint-liability condition is
thus transferring borrowing costs to the risky types since they are now faced with higher cost of
capital. Ultimately, the condition enables the bank to screen borrowers by offering different
types of contracts on the basis of interest rates and various degrees of joint-liability.
Some authors have even suggested that, innately, group lending with joint-liability can
help reduce interest rates even if the lender and the borrower remain ignorant about who is safe
and who is risky (see Armendáriz and Gollier (2000), Karlan (2003)). Successful risky borrowers
can always repay the loans of their defaulting partners, whether they are safe or risky. However,
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this is not necessarily the case for safe borrowers whose investments’ returns are on average
lower. Thus, defaults are de facto shouldered by risky types only (Armendáriz and Morduch
(2005)). This is particularly important since peer screening is not always done before group
formation. As Wydick (1999) explains, much of the screening of borrowers actually takes place
ex-post to group formation in the form of group expulsions. Under group liability, clients have
an incentive to screen other clients so that only trustworthy individuals are allowed into the
program (Giné & Karlan 2008).
A collection of empirical studies has tried to discover if the mitigation of adverse
selection by joint-liability group lending schemes actually translates into higher repayment rates.
The results have been, for the most part, in favor of this idea. Cassar and Wydick (2010), for
example, use experimental data collected in five different countries to show that self-selection
among acquaintances has a significant and positive effect on contribution rates. Giné and Karlan
(2009) also find evidence of the existence of peer screening mechanisms like the one mentioned
above; they do, however, find that it does not add up in an economically meaningful way to
changes in default.
1.2 Moral Hazard literature
Moral hazard occurs because the lender is unable to dictate, or even observe, what
actions the borrower is taking with a loan that has already been granted and whose benefits have
not been realized. Given a loan, and regardless of whether the borrower’s actual project-choice is
safe or risky, a lender’s benefits will only be equal to the contract’s interest rate (Stiglitz and Weis
(1981)). Thus, the bank would much rather prefer the borrower to invest her loan in the safe
project since probability of repayment is higher. Nonetheless, in the absence of collateral, the
bank cannot force the borrower to play safe. Herein lies the problem; the borrower is
recompensed by the additional monetary benefits from the risky project, but the lender is not.
Since the borrower has no incentives to fully internalize the costs of project failure, it is the
lender that will bear the risk of a borrower’s actions.
Group lending with joint liability helps reduce the moral hazard problem. The seminal
works of Stiglitz (1990) and Stiglitz and Arnott (1990) on this matter point to peer monitoring as
the main reason why this is so. Under a joint-liability contract a borrower’s expected utility
depends not only on her own actions but also on those of her peers. If all members of a group
are successful, utility is higher for everybody involved. This is true even as all individual
borrowers are exposed to greater risk – caused by what Stiglitz calls “artificially created
interdependence” – relative to an individual contract. If a member’s project fails, however,
expected utility will be lower for those whose project was successful. This scenario induces
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borrowers to monitor each other’s actions and to punish, if necessary, those group members that
have undertaken unnecessary risks. Stiglitz’ model assumes that monitoring is costless, but
several authors after him have expanded the literature. They have proven that even when there
are costs to monitoring, joint liability contracts help reduce moral hazard; at least in principle (see
Banerjee et al (1994), Conning (1999), Zeller (1998), and Ghatak and Guinnane (1999)).
As with adverse selection, these findings have been put to the test empirically. For the
most part, studies show strong support for the fact that peer monitoring helps reduce moral
hazard problems and increase repayment rates (see Wydick (1999)) although there are also some
studies that show insignificant (see Giné and Karlan (2003)) or even negative effects of
monitoring treatments (see Cassar and Wydick (2010)). This paper is another attempt at
understanding how moral hazard, and information asymmetries in general, are affected by joint-
liability lending in different economic settings. Its results will fit among this collection of
empirical papers.
1.3 Social capital and the future of joint-liability group lending
As discussed earlier, a major component to the success of joint-liability lending is its
reliance on closed networks of information within groups where social capital is strong. As
Cassar and Wydick (2010) explain “social capital may facilitate a general sense of trust and
goodwill surrounding economic exchange”. Borrowing groups that are formed mainly by people
that know and constantly interact with each other are much more likely to: (1) posses valuable
information about themselves that helps in the screening process that mitigates adverse selection,
and (2) be more able to monitor the actions of their peers to curtail moral hazard.
A strong feeling of group solidarity is vital for joint-liability lending schemes to work.
When social connections are weak among borrowing partners, repayment rates suffer. This has
been widely documented in the field (for a comprehensive review on the topic see Ghatak and
Guinnane (1999)). However, social capital doesn’t always come without its complications.
Evidence from different authors has showed that too much social capital might actually be a bad
thing for the lender. Alhin and Townsend (2003) show that when there is collusion against the
bank repayment rates actually fall. Giné and Karlan (2003) find similar results in the Philippines;
they conclude that the depth of family relations within a group is correlated with default. Finally,
using data from Guatemala, Wydick (1999) also reaches similar results. Reasons as to why social
capital has this two-way effect vary from collusion-based arguments to the unwillingness of
individuals to sanction those closer to them.
As it has been noted, joint-liability group lending does not come without its costs. Besley
and Coate (1995) show that lending to groups that are jointly liable has both positive and
6. 6
negative effects on repayment rates. A successful borrower faces two alternatives when being
part of a group that has defaulting members. She might find it in her best interest to repay the
entire group loan, especially if that ensures her repeated access to future loans (positive effect).
However, she might also decide not to repay her own share and create group default even when
she would have been able to pay her share individually (negative effect).
Additional problems with joint-liability lending have been documented and tested
empirically (see Giné and Karlan (2009), Armendáriz de Aghion and Morduch (2000)). It has
been shown that default rates increase when group size increases, for example. This might be
because coordination between the group members is more difficult or simply because the “free
rider” problem intensifies. Another problem with joint-liability group lending arises if credit
needs among borrowers are no longer homogeneous. When demand for credit by a single
borrower increases additional pressure is put on the other members that will be made jointly
liable for the new and increased loan. If the group is not willing to shoulder this new contract the
diverging ambitions of a successful borrower might be constrained. Some other problems
include an increased sense of pressure among borrowers caused mainly by high social sanctions,
individuals growing frustrated at the costs of attending group meetings and loan officers refusing
to sanction good borrowers who happen to be in a “bad” group (Ghatak and Guinnane (1999)).
A recent movement in credit markets in developing areas has taken these problems into
account and relaxed the joint-liability clause in the group lending scheme. In fact, group lending
without joint-liability is now an alternative in some areas, as documented by Giné and Karlan
(2009). In their words: “some micro lenders have expanded rapidly using individual liability loans
but still maintaining group meetings for the purpose of coordinating transactions. While liability
is individualized, the group process helps lenders lower their transaction costs while possibly
maintaining some but not all of the peer screening, monitoring, or enforcement elements due to
reputation and shame.” Additionally, some microfinance institutions have started offering
individual loans as one of their products. Armendáriz and Morduch (2000) document the
advantages of employing individual-based contracts instead of only group lending schemes in
transition economies like China, Russia and some Eastern European countries. These new trends
in microfinance show that the joint-liability clause in group lending is just one component of an
array of mutually enforcing mechanisms that help reduce information asymmetries. These new
movements are a great area for future research, especially empirical endeavors.
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2. The Experiment
The experiment included four treatments to test the existence of asymmetric information
problems in joint-liability lending contracts and one treatment to measure risk preferences. The
first two treatments were designed to test the presence of adverse selection and ultimately answer
the question: does facing a risky project make borrowers more likely to choose a joint-liability contract?
Treatments 3 and 4, on the other hand, sought to test the existence of moral hazard behavior.
Basically, the hypothesis being tested here is the following: being part of group leads borrowers to choose
riskier investments. Finally, Treatment 5 was used to classify subjects as risky or safe individuals
based on their risk preferences.
Each experimental session was followed by a 37-question confidential survey where
individual- and group-level characteristics were collected. Two questions intended to measure
social capital were included in the survey. Subjects were asked how many members of the group
(with whom they had played the experiment) they would be willing to (1) invite to a social
gathering at their house, and (2) help out financially if they faced loan-repayment problems. An
index for social capital was constructed by adding the answers to these questions and with the
purpose of testing whether or not social capital helps in curbing asymmetric information problems.
2.1 Subjects
The experiment was carried out mostly in urban areas within La Paz, Bolivia. The South
American country, alongside Bangladesh, has been a benchmark for microfinance practices and a
site for several studies on the subject. Estimations from 2006 indicate that between 568,000 and
650,000 clients have been reached by microfinance institutions (González-Vega & Villafani-
Ibarnegaray, 2007). These figures represent a high per capita coverage when compared to other
countries (Christen, 2000).
The subjects were recruited with the collaboration of PORVENIR, a local microfinance
institution. The 200-subject sample is comprised by actual microfinance borrowers and a share
of non-borrowers: 83% are real clients. Not surprisingly, the subjects fit the standard profile of a
microfinance borrower: average age is 37 years old, 87% are female, formal schooling levels are
low (8.5 years on average) and 53% either own or work in a family business. Also, most of the
experimental sessions were carried out in poor neighborhoods in the outskirts of the capital city.
Average monthly household income is 1350 bolivianos (USD $190) for households that, on
average, are formed by 5 members. Additionally, information about the groups was collected
from their credit officers. This information included group loan sizes (between USD $145 and
$571) and group repayment performance (61% of the subjects were members of a group that
8. 8
had had some sort of difficulty with loan repayment). Table 1 shows the description, nature and
summary statistics for the main variables collected from the survey.
2.2 Experimental Design
Each session was carried out with a group of either 10 or 15 subjects who participated at
the same time. At the beginning of the session subjects were randomly divided into two (or
three) groups of 5 members each. Subjects played both the adverse selection and the moral
hazard treatments with the same group but their individual choices were never disclosed to other
group members. The risk game was played individually. At the beginning of the session the
experimenter read directions and carried out three test runs for each treatment with the help of
visual aids. After each trial, subjects were asked questions to ascertain their knowledge on the
game’s dynamics. If doubts remained the experimenter read directions again until they were
clarified. A total of 17 sessions were conducted as part of the data collection efforts.
To begin with, all group members had a 500 boliviano guarantee that was used to take up
a hypothetical loan of 1000 bolivianos (USD $140). The loan had to be repaid at 20 percent
interest at the end of the agreement for a total payment of 1200 bolivianos. In a joint-liability
contract these conditions would create a group obligation with a principal of 5000 bolivianos and
a net disbursement of 6000 bolivianos after interest.
Once the experiment started, the subjects had to decide on the type of projects or on the
type of contracts under which they would invest their “loans”. Depending on their own decisions
and on chance, subjects could earn from 0 to 43 bolivianos (USD $0 to $6) at the end of the
experiment; this in addition to the 30 bolivianos that were given as a show-up fee. For most of
our subjects, the maximum possible earnings represented more than a day’s wage in order to
guarantee behavioral-truthfulness in their choices.
2.2.1 Adverse Selection Experiment
The adverse selection experiment consisted of two separate treatments: Treatment 1 (T1)
and Treatment 2 (T2). In both, the subjects had to choose whether to sign their hypothetical
loans under an individual contract or a joint-liability group contract. The difference between the
treatments lied in the riskiness of the project being exogenously faced. In T1 the subjects faced a
safe project that generated a 3000 boliviano profit with 5/6 probability and a zero profit
otherwise. On the other hand, in T2 the subjects faced a riskier project that, if successful, resulted
in a higher profit of 5000 bolivianos and a zero-profit otherwise; however, the probability of
success was only 1/2. The success (or failure) of the investments was determined randomly with
a roll of a die after the subjects had made their contract-selection.
9. 9
The subjects’ actual earnings depended on the type of contract chosen. Under an
individual contract, in both T1 and T2, the investment’s gross profits remained as mentioned
before (3000 or 5000 bolivianos respectively if successful); however, if the subject played group
contract in either treatment, her payoff depended on the amount of successful projects within
her group, including her own. To illustrate the experiment’s payoff system let us suppose a
subject chooses to play group contract in T2 (when faced with a risky project). Additionally, let us
imagine that when she rolls the die it is determined that her investment has been successful. At
this point our borrower’s gross profit is 5000 bolivianos. However, since she has decided to face
the risky project under a joint-liability group contract, she has to wait and see if her peers are as
successful as she was. If all members of the group have successful projects, her payoff remains
unchanged; however, if any of them have unfavorable outcomes her original gross profit will be
reduced as she faces her joint-liability obligations. Now, let us assume that our subject’s project
was deemed a failure. Since she played “group contract” her profit is not automatically zero as it
would have been if she had played “individual contract”. Again, her final payoff depends on the
amount of successful projects within the group. The best she can do at this point is to keep her
500 boliviano security if the rest of her peers are able to repay the group loan. Tables 2.A and
2.B show a more detailed explanation on the possible outcomes of each treatment.
To determine whether there is a problem of adverse selection, the choices made in both
treatments have to be analyzed. Adverse selection happens when a subject plays individual contract
when faced with a safe project (T1) but changes her choice to group contract when faced with
higher risks (T2). Note that by doing this, the borrower is disseminating the potential negative
externalities of her own riskiness onto her peers.
2.2.2 Moral Hazard Experiment
The moral hazard experiment also consisted in two different treatments: Treatment 3
(T3) and Treatment 4 (T4). In both treatments subjects were exogenously given the type of
contract under which their loans had been signed: borrowers were under an individual loan in T3
and part of a group loan in T4. This time around, the choice was to decide whether to invest in a
safe project or in a risky project. Again, investing in a safe project generated a gross profit of 3000
bolivianos with 5/6 probability, while doing the same in a risky project generated a gross profit
of 5000 bolivianos with 1/2 probability. Success or failure of investments was determined
randomly by rolling a die after subjects had made their project-choice.
In T3 subjects faced an individual loan. Therefore, rewards and risks were borne solely by
the subject. Possible payoffs were constrained to 3000 bolivianos (if the subject played safe and
her investment succeeded), 5000 bolivianos (if she played risky and succeeded) and zero (if
10. 10
investment failed regardless of project choice). In T4, on the other hand, subjects faced a joint-
liability group loan. So now, rewards and risks were spread between the members of the group.
The payoff schedule for our borrower depended on the success or failure of her own project as
well as the amount of successful projects within her group. Tables 3.A and 3.B show all the
possible payoff outcomes for T3 and T4 respectively.
To analyze whether or not there is a moral hazard problem, the choices in both
treatments have to be analyzed. Moral hazard occurs when a subject chooses to play safe project
under an individual loan (T3) but changes her strategy to risky project when she is part of a group.
Note that if a borrower chooses to play risky when in a group, she is imposing additional risk
(potential negative externalities) on her peers.
2.2.3 Risk Game
The risk game was based on the work of Holt and Laury (202). The purpose of this
experiment is to create a measure of risk that ultimately leads to the classification of borrowers
between safe and risky types. The experiment works as follows. The subjects are presented with
two different kinds of lotteries: Lottery A and Lottery B. By choosing Lottery A the subject can
either make a 2000-boliviano or a 1600-boliviano profit. On the other hand, Lottery B can
produce a higher profit of 3850 bolivianos but it can also imply a 100-boliviano gain. Lottery B
has a higher payoff but is more risky than Lottery A. The actual profit that subjects make
depends on two factors: (1) the lottery that they have chosen and (2) the color of a ball that is
randomly drawn from a 10-ball bag. Green balls represent the higher payoff in each lottery (2000
and 3850 bolivianos for A and B respectively) and red balls represent the lower figure (1600
bolivianos for A and 100 for B).
Subjects have to decide whether to go with Lottery A or Lottery B in ten different
rounds. The probabilities of getting a green or a red ball in each round are known by the players
before they make their lottery-choice. Moreover, the chances of getting a green ball increase
between rounds. In round one, for example, subjects know that the bag from which the ball is
drawn contains 1 green ball and 9 red ones. In the second scenario the bag contains 2 green balls
and 8 red ones. The third bag has 3 green balls and 7 red ones, and so on. This pattern continues
until the last round where the bag has 10 green balls and no red ones. At this point it is
important to underline that in scenario 10 there is a 100% probability of getting a green ball, so
rational subjects are expected to choose Lottery B (since 3850 bolivianos is higher than 2000
bolivianos). For a visual depiction of how the experiment works refer to Table 4.
The risk index is constructed by looking at the point at which subjects switch from
Lottery A to Lottery B. Subjects that switch in an early round are riskier than those that switch in
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a latter round. A person that chooses Lottery B in all the 10 rounds is extremely risky. A subject
that chooses Lottery A in all rounds is extremely safe (and irrational too, as we have explained
using round 10 as an example). On average, subjects changed from Lottery A to Lottery B
between rounds 5 and 6.
3. Methodology
To construct the econometric model a panel data approach was utilized by using the
treatments as the “time” variable. After a Hausman Test was conducted the use of random
effects was deemed more efficient for both the adverse selection and the moral hazard
hypotheses. The variables of interest for this study are the treatment dummies and the social
capital variable. Also, the same set of variables was used for both models to control for
individual and group level characteristics (refer to Table 1 for a complete list of these
characteristics). A simple linear probability model was selected over a logit regression since the
panel data is, by nature, wide and using a logit caused problems with coefficient interpretation.
3.1. Adverse Selection Model
The adverse selection model was constructed as follows. The dependent variable, group, is
a dummy variable that takes the value of one if the subject chose to play “group contract” and
zero if her choice was “individual contract”. The values for group in “time” 1 are the loan choices
made by subjects in T1. The contract choices made in T2 are the values for “time” 2.
Econometrically, the model has the following linear structure:
groupit = α0 + δ1 riskyprojectit + δ2 socapi + α2Xi + α3Zi + uit EQ (1)
where Xi is a vector of individual characteristics and Zi is a vector of group characteristics. The
treatment dummy, riskyproject, identifies the treatment in which subjects exogenously faced a risky
project (T2) and the variable socap is the social capital index.
The presence of adverse selection would imply the coefficient of riskyproject to be positive
(δ1 > 0): facing a risky project increases the likelihood of signing a joint-liability group contract.
Likewise, social capital literature would suggest the coefficient of socap to be positive as well (δ2 >
0): increased social capital within a community increases the likelihood of joining a group
contract.
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3.2. Moral Hazard Model
The moral hazard model is similar to the adverse selection model presented above. In
this case, the dependent dummy variable, risky, takes the value of one when subjects chose to
invest in a “risky project” and zero when they invested in a “safe project”. The results from T3
correspond to “time” 1 while the values of T4 are captured in “time” 2. Econometrically, the
model has the following linear form:
riskyit = β0 + φ1 grouploanit + φ2 socapi + β2Xit + β3Zit + uit EQ (2)
where, again, Xi and Zi control for individual- and group-level characteristics and socap is the
social variable. The treatment dummy is now grouploan and it identifies the treatment in which
subjects were exogenously given a group contract to begin with (T4).
Moral hazard literature (Stiglitz (1990), Stiglitz and Arnott (1990), Zeller (1998)) suggests
that being part of a group increases the likelihood of choosing a risky project (free riding), so the
coefficient for grouploan is expected to be positive (φ1 > 0). Conversely, the coefficient for socap is
expected to be negative (φ2 < 0) since as social capital increases the likelihood of going after risky
investments should decrease (Cassar and Wydick (2010)).
4. Experimental Results
4.1 Adverse Selection Hypothesis
As mentioned earlier, adverse selection problems arise when a subject facing a safe
project chooses to go into an individual contract but changes her strategy, deciding to become
part of a group loan, when faced with higher risks. During treatments T1 and T2, 35% of the
subjects chose this individual-group strategic set, 34% played individual-individual, 7% chose
group-individual and 24% went for group-group.
Insightful information can also be obtained by looking at the treatments individually. In
T1, when faced with a safe project, 70% of subjects chose an individual loan; the other 30%
chose to be under a joint-liability group contract. On the other hand, in T2, when facing a riskier
investment, the share of subjects that played “individual loan” decreased to 41% while the
percentage of those who decided to become part of a group almost double-folded (going from
30% to 59%). A simple difference-in-means t-test was conducted to examine whether there
were two underlying distributions driving these results. As Table 5.A shows, the p-value suggests
the rejection of the null hypothesis that the mean for T1 is equal to the mean of T2; hence, the
13. 13
share of individuals that chose group when faced with a safe investment is statistically different
from the share that did the same when facing a risky project.
The results from the linear probability regression shown in EQ (1) are presented in Table
6. The random effect LPM was ran for the entire sample (Column 1) and for two sub-samples:
safe and risky individuals (Columns 2 and 3 respectively). The purpose was to see whether
adverse selection issues were more prevalent in a specific group. Also, running the regression on
sub-groups made it possible to identify if social capital influenced choices differently. Borrowers
were classified as safe or risky borrowers using the results obtained during the risk game and also
based on one risk-related question in the survey.
The results show that there is evidence of adverse selection. The coefficient for the risky treatment
(δ1) is positive and highly significant for the three models estimated. This is in line with the
expectations discussed in Section 3.1. Overall, subjects that face a risky project are 28% more
likely to seek membership in a joint-liability group contract than those who face a safe project.
Adverse selection issues seem to be prevalent for both safe and risky borrowers once the sample
is divided. The coefficients for these regressions indicate that safe and risky individuals are,
respectively, 28% and 25% more likely to join a group contract when facing increased risk. The
low number of risky individuals in the sample (24 subjects) is a concern that should be noted;
however, even if significance were affected by small sample bias, the sign of the coefficient
remains positive.
The results also suggest that social capital increases the likelihood of joining a borrowing group.
This is in line with the expectations discussed earlier and with previous research (Cassar and
Wydick (2010), Zeller (1998), etc.). For safe borrowers, increased societal trust is still positive
and significant, albeit at a lower level; however, it does not seem to have a statistically significant
effect on risky borrowers. This is an interesting result in and of itself. It is even more telling
when coupled with other coefficients. Note that the data also show that the coefficient for group
pressure (a variable measuring the self-reported sense of peer pressure) is negative and significant
in column 3 (risky borrowers); this suggests that for risky borrowers social capital does not
matter and that the additional pressure imposed by group membership actually deters them from
seeking group loans.
4.2 Moral Hazard Hypothesis
Referring back to the experimental treatments, moral hazard behavior occurs when a
subject under an individual obligation chooses to invest in a safe project but changes her
investment decision to a risky endeavor when she is member of a joint-liability borrowing group.
14. 14
During the experiment, 20% of subjects chose this safe-risky strategy; virtually half of the subjects
played safe-safe (49%) and the same share of subjects chose risky-safe and risky-risky (15.5%).
During T3 alone, where the individual contract was exogenously determined, 31% of the
sample invested in a risky project and the rest 69% decided to go after a safe venture. The figures
did not drastically change during T4 when the subjects faced a jointly-liable obligation: 35%
played risky and the remaining 65% chose safe. As with the adverse selection hypothesis, a
difference-in-means test was carried out (see Table 5.B). The null-hypothesis (Ho: mean of T3 is
equal to that of T4) could not be rejected; thus, the difference between the share of subjects that
chose risky between both treatments is not statistically different from zero. This finding seems to
permeate into the econometric model.
Table 7 shows the results from the regression expressed in EQ (2). Again, the results are
presented in three columns, one for the entire sample and the following two for the safe-risky
borrower sub-samples. The group treatment coefficient (φ1) is positive, as expected, but lacks
significance. The data suggests that there is no evidence of moral hazard behavior: being part of a group
does not seem to increase risky conduct. This is true for all of the model specifications.
Moreover, there is no statistical evidence to support that social capital deters moral
hazard activity. Although this might be counterintuitive, some researchers have come to the
same conclusion (Alhin and Townsend (2003) and Giné and Karlan (2003) for example). This is
true for the three models presented in Table 7. What is interesting to note, however, is the fact
that the sign of the coefficient for the social capital variable is negative for risky borrowers. Even
though there is lack of significance, at least the data are capturing the expected sign for this sub-
sample.
5. Free riding versus risk diversifying
Up until this point the results from running regressions EQ (1) and EQ (2) suggest two
points with regards to asymmetric information problems in microcredit loans. First, facing a
risky project increases the probability of joining a group. Second, being in a group has no effect
on the likelihood of choosing a risky project. In a way, the results suggest that it is not that the
borrower is taking riskier investment decisions when in a group, but that she is using joint-liability either as
insurance (when faced with riskier projects) or as means to free ride on others. The following section seeks to
explain why borrowers, when faced with higher risks, choose to be in a group: is it because they
are diversifying risk or is it because they are free-riding?
15. 15
5.1 The fine line between free-riding and risk-diversifying
Before tackling this intriguing question it should be noted that there is a fine line between
free riders and risk diversifiers. The following statements should help in distinguishing the two.
First and foremost, we should note that a borrower that decides to invest in a risky endeavor
when in a lending group is imposing additional risk onto her peers (Zeller (1998)). This
definition would make all of the subjects that played risky in T4 “culprits” of free-riding (35% of
our sample). Let us call this group “static free-riders”. We can go further in identifying free-
riding behavior by using a more dynamic definition. As detailed in Section 2.2.2, a borrower that is
willing to invest in a risky venture when in a group, but that would play safe otherwise, is
incurring in free-riding conduct. Let us brand this set of borrowers as “dynamic free-riders”
(20% of our sample). Both definitions are trying to categorize wrongdoing borrowers, the
difference lies only in the fact that the former group choose risky in only one treatment (T4) and
the latter actually switches from safe to risky between two treatments (T3 and T4). Thus,
dynamic free-riders are a subset of the more generalized definition of static free-riders.
The risk diversifier, on the other hand, will choose to be part of a group when faced with
a risky investment but will not impose additional risk onto her peers by deliberatively choosing to
invest in a risky business. In other words, a risk-diversifier will join a group in T2 but chose a safe
project in T4 (to avoid indirectly hurting her peers).
5.2 Risk diversifying or free-riding?
Two different models were estimated to explain whether borrowers are free-riding or
risk-diversifying. The first model uses only the results from T2 as its dependent variable.
Recalling, the variable takes the value of one if the subject chose to be part of a group in the face
of additional risk and zero if she chose an individual contract. The second model uses the
combined results from T1 and T2 as its dependent variable. This new variable, which will be
labeled individual-group, takes the value of 1 for all those subjects that played individual contract in
T1 but switched to group loan in T2, and zero otherwise. Both models were estimated using the
original 200 observations since there is no need to treat the sample as panel data anymore. A
logit was preferred over a simple linear probability model and the same set of individual and
group level characteristics were used as control variables.
The variable of interest for both estimations is free rider. Moreover, both definitions of
free riding (static and dynamic) were used to run competing models for each of the dependent
variables. Note that if the coefficient for any of the free-rider dummies is significant then the
results found in Section 4 are mainly driven by free riding behavior. However, if the coefficient is
16. 16
insignificant, the results from the adverse selection experiment are being driven by a risk-
diversifying rationale. Table 8 shows the results from these estimations. Column (1) presents the
coefficients from the first model where the dependent variable is only group. Column (2) shows
the results from estimating the second model where the dependent variable is the switching
pattern between individual and group loans.
The coefficient for dynamic free rider is positive and significant for both estimations at the
99% and 90% level respectively. These estimations favor the free-riding hypothesis over the risk-diversifying
one. People that are deliberately free riding (in the moral hazard sense) are more likely to join a
group when faced with increased risks. In fact, the data show that these individuals are almost
19% more likely to sign a joint-liability loan. Both models arrive at almost identical point
estimates, although the second model performs better.
The models where the static free-rider definition was used as the variable of interest show
contradictory results. The coefficients for both, the first and second columns, are positive but
insignificant. This suggests that static free riding behavior has no explanatory power over
choosing (or switching to) group when faced with a riskier investment. Hence, contrarily to what
was stated before, the risk-diversifying hypothesis seems to be driving the results from the
adverse selection experiment.
So, is the data inconclusive? Not necessarily. A case can be constructed to support the
fact that the dynamic free-rider definition is a better depiction of what free-riding behavior is all
about. The static description, on the other hand, is subject to other interpretations. A subject
might have chosen to invest in a risky business because she is, by nature, a risky type, and not
because she was willingly imposing potential negative externalities on her peers. However, no
such case can be constructed for a person that would have invested safely when alone but riskily
when having the safety-net of her group. The dynamic definition is then more consistent and
practical over the static one. Thus, this paper concludes that, indeed, people use joint-liability
contracts as means to free ride and not as a tool to diversify risk.
6. Conclusions
Using the results from an artefactual experiment this paper has found evidence of
asymmetric information problems in microfinance loans, especially adverse selection issues.
Additionally, it has shown that these problems are driven by a free-riding philosophy rather than
by risk diversifying motivations.
17. 17
First, the adverse selection experiment proved that borrowers facing a risky investment
are more likely to seek group membership under a joint-liability contract than those that face a
safe business opportunity. Overall, social capital increases the probabilities of them joining a
group. Moreover, it was shown that safe borrowers will, on average, join a group under these
conditions with a higher probability than risky borrowers.
The underlying reason of this behavior was also put to the test. Evidence of free riding
was found. Borrowers prefer joint-liability loans because these contracts give them the chance to
pass on some of their own riskiness, ex-ante project selection, onto their peers.
Second, the moral hazard treatments found no evidence of such a problem. Borrowers,
on average, are not more likely to invest in a risky project when in a group relative to when they
are under an individual contract. This result holds for both risky and safe borrowers. Moreover,
no significant evidence on the curbing effects of social capital on project selection was found.
The policy implications of these findings go in hand with what other research has
suggested (Ghatak (1999), Wydick(1999), Giné and Karlan (2008)). Increased peer screening, ex-
ante and ex-post group formation, should be incentivized to avoid adverse selection of
borrowers. This is not to say that mechanisms which are aimed at reducing moral hazard
problems, like peer monitoring or further-harnessing social capital within communities, should
stop being used.
All in all, it is not that borrowers chose riskier investment once they are in a group but
that they use the group to face riskier exogenous conditions. This is especially important now
that the microfinance movement is steering away from joint-liability group loans and into
individual contracts and group loans without joint-liability. In fact, these findings seem to provide
a reason as to why this change is happening.
18. 18
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20. 20
TABLE 1: Summary Statistics
Variable Mean (S.E.) Type Description
Female 0.87 Dummy 1 if female
(0.337)
Age 37.27 Continuous years of age
(12.713)
Married 0.65 Dummy 1 if married
(0.478)
Home owner 0.56 Dummy 1 if subject owned her house
(0.498)
People per room 2.88 Continuous no. of people per sleeping room
(1.751)
Entrepreneur 0.54 Dummy
1 if subject owned or worked in
family business
(0.5)
Income 1350.05 Continuous
proxy of monthly based on
expenditure
(1546.52)
Group pressure 4.27 Index
self-reported sense of group
pressure (1-5 index)
(1.077)
Schooling 8.51 Continuous years of formal education
(4.128)
Bad group 0.61 Dummy
1 if evaluated as part of a bad
group by credit officer
(0.489)
Real borrower 0.83 Dummy
1 if subject was part of a real
borrowing group
(0.377)
Risky individual 0.12 Dummy 1 if subject is risky
(0.325)
Social capital 4.96 Index social capital (1-8 index)
(2.377)
EXPERIMENT TREATMENTS
T1 0.305 Dummy
1 if subject chose group when in
safe project
(0.462)
T2 0.588 Dummy
1 if subject chose group when in
risky project
(0.493)
T3 0.312 Dummy
1 if subject chose risky when in
individual loan
(0.464)
T4 0.350 Dummy
1 if subject chose risky when in
group loan
(0.478)
Risk game 5.83 Index
Risk preference index (1 is high to
11 is low)
(1.689)
23. 23
TABLE 4: Risk Game
Lottery A Lottery B
Round
Green
Balls
Red
Balls
if green if red if green if red
1 1 9
2000 1600 3850 100
2 2 8
3 3 7
4 4 6
5 5 5
6 6 4
7 7 3
8 8 2
9 9 1
10 10 0
TABLE 5: Difference in Means Paired Data t-Test
A. Adverse Selection Experiment
Mean Std. Dev.
T1 0.3065 0.462
T2 0.5879 0.493
Difference -0.2814 0.587
Ho: mean difference = 0 p-value 0.000
B. Moral Hazard Experiment
Mean Std. Dev.
T3 0.3115 0.464
T4 0.3517 0.479
Difference -0.0402 0.593
Ho: mean difference = 0 p-value 0.340
24. 24
TABLE 6: Adverse Selection Hypothesis
Linear Probability Model with Random Effects
(1) (2) (3)
Dep. Variable:
Group Loan
Entire
Sample
Safe
Individuals
Risky
Individuals
Risky project 0.282*** 0.286*** 0.25***
(0.042) (0.044) (0.124)
Social capital 0.02* 0.017+ 0.059
(0.011) (0.012) (0.051)
Female 0.024 0.013 -0.032
(0.08) (0.094) (0.254)
Age -0.003 -0.003 0.002
(0.003) (0.002) (0.011)
Married -0.001 0.016 -0.035
(0.062) (0.065) (0.245)
Home owner -0.084+ -0.124** 0.123
(0.055) (0.059) (0.247)
People per room -0.013 -0.012 0.044
(0.015) (0.016) (0.131)
Entrepreneur 0.036 0.045 -0.29
(0.059) (0.062) (0.262)
Income (log) -0.018 -0.032 0.152
(0.022) (0.023) (0.139)
Group pressure -0.004 0.007 -0.214*
(0.027) (0.028) (0.126)
Schooling -0.016* -0.019** -0.015
(0.008) (0.008) (0.029)
Bad group -0.081 -0.083 0.137
(0.06) (0.062) (0.252)
Real borrower 0.051 0.026 0.763
(0.077) (0.079) (0.557)
Risky individual -0.009 - -
(0.094) - -
Constant 0.682*** 0.798*** -0.881***
(0.24) (0.27) (1.052)
Observations 399 351 48
Subjects 200 176 24
*** p<0.01, ** p<0.05, * p<0.1, + p<0.15
Robust SE in (1), SE in (2) and (3)
25. 25
TABLE 7: Moral Hazard Hypothesis
Linear Probability Model with Random Effects
(1) (2) (3)
Dep. Variable:
Risky Project
Entire
Sample
Safe
Individuals
Risky
Individuals
Group loan 0.039 0.027 0.125
(0.042) (0.044) (0.125)
Social capital 0.008 0.014 -0.047
(0.011) (0.011) (0.045)
Female -0.041 0.029 -0.214
(0.084) (0.092) (0.224)
Age 0.001 0.003 -0.01
(0.002) (0.002) (0.009)
Married -0.009 -0.008 -0.368*
(0.059) (0.063) (0.216)
Home owner 0.051 0.054 0.296
(0.055) (0.058) (0.218)
People per room 0.000 0.001 0.168+
(0.017) (0.015) (0.116)
Entrepreneur -0.017 0.017 -0.147
(0.059) (0.06) (0.231)
Income (log) -0.015 -0.02 0.047
(0.024) (0.023) (0.123)
Group pressure 0.028 0.026 0.184*
(0.024) (0.028) (0.111)
Schooling 0.001 0.001 -0.013
(0.007) (0.008) (0.025)
Bad group 0.008 0.028 -0.143
(0.058) (0.061) (0.223)
Real borrower -0.023 -0.036 -0.767+
(0.076) (0.078) (0.492)
Risky individual 0.13+ - -
(0.091) - -
Constant 0.22 0.073 0.827
(0.238) (0.266) (0.929)
Observations 399 351 48
Subjects 200 176 24
*** p<0.01, ** p<0.05, * p<0.1, + p<0.15
Robust SE in (1), SE in (2) and (3)
