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Long-term relationship and Reciprocity in Credit Market

Experiment: Implications for Microfinance

Simon Cornée*, David Masclet‡, Gervais Thenet#

Abstract

Microfinance sector is generally associated with high repayment rates. However it is not clear whether such success only results from the use of peer lending or is due to the existence of a long-term relationship between the borrower and her incumbent lender – that also strongly characterizes microcredit reality. Our paper contributes to the existing literature on microcredit market by experimentally examining to what extent long-term lending relationship enables to mitigate moral hazard associated with repayment and project selection in the absence of peer group. Subsequently, the aim of this paper is to investigate to what extend reciprocity and reputation can enhance credit market performance by mitigating moral hazard. The originality of our research lies in the fact that we introduce variability in socio-demographic characteristics by recruiting "real people", including not only students that are typically viewed as the standard subject pool but also bankers both from classical banking and from microcredit institutions. Consistent with previous studies, our findings reveal that the opportunity to engage bilateral long-term relationships strongly improves the market performance by mitigating the repayment problem (ex-post moral hazard) and thus enhancing cooperation between borrowers and lenders. This fact seems to highlight the prominence of reputation as a disciplining devise that conducts selfish borrowers to behave reciprocally. Notwithstanding, our results also seem to indicate that lenders take advantage of their long-term situation by increasing their rates. Credit cost is significantly higher under the partner treatments than under the stranger treatment. As a consequence, the borrowers may be incited to take more risk to reimburse their loan (ex-ante moral hazard). Improving the information disclosure negligibly enhances cooperation and thus market performance but alters the principals’ lending behavioral patterns. Finally, we find that social bankers are more likely to makes fair credit offers to borrowers than classical bankers.

JEL-Codes: C72, C91, G20, G21

Keywords: Credit market, Experimental Economics, Reciprocity, Reputation, Relationship Banking.

Acknowledgements: We gratefully thank the Nef Foundation and IGR foundation for their financial aid. We are indebted to Elven Priour for programming the experiment. We thank Monica Capra, Irene Comeig and the participants of the 2008 ESA meetings for their helpful comment. We are also grateful to the participants of the finance workshop of CREM-CNRS University of Rennes 1, more especially to Patrick Navatte and Franck Moraux. * Corresponding author: CREM-IGR (University Rennes 1), 11 rue Jean Macé CS 70803 35708 RENNES Cedex 7. Email: simon.cornee@etudiant.univ-rennes1.fr. Telephone: + 33 2 99 23 23 77 77. ‡ CREM (CNRS – University Rennes 1), 7, place Hoche 35065 Rennes, France. Email: david.masclet@univ-rennes1.fr. # CREM-IGR (University Rennes 1), 11 rue Jean Macé CS 70803 35708 RENNES Cedex 7. Email: gervais.thenet@univ-rennes1.fr

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Introduction

The year 2005 was declared International Year of Microcredit by the United Nations. One

year later, Muhammad Yunus, the founder of the Grameen Bank, was awarded with the Nobel

Peace Prize. This consecration of microfinance has reflected the recent interest in

microfinance that has rapidly resulted in a flood of investments in the developing countries

but also in the industrialized areas1.

The contemporary movement of microfinance2, which originated in Bangladesh in the mid-

1970s through the Grameen Bank experience, has introduced financial innovations that make

it possible to grant small credits to individuals who cannot meet the minimal criteria required

to obtain a loan from the classical banking system (because of lack of collateral, unstable

employment or insufficient level of resources)3. The prominent innovation coined by

microfinance programs is the group-lending mechanism4 – at least this is the one that has

taken most of the spotlight – that permits to internalize monitoring costs. The group-based

credit approach relies on the use of peer-group pressure, in which each individual acts as a co-

guarantor (Armendariz de Aghion, 1998, 1999; Armendariz de Aghion and Gollier, 2000). “If

one individual is unable to make timely payments, credit for the entire group is jeopardized

which results in heavy peer-group pressure on the delinquent”, (Farnsworth, 1988).

Peer-group lending is often considered as the main factor behind the success of microcredit

systems.5 However several studies have also shed light on the limitations of such peer-group

mechanisms (Morduch, 1999; Diagne, 1997; Besley and Coate, 1995).6 Morduch (1999)

points out the substantial costs inherent to the implementation of peer-group lending. Diagne 1 For example, Grameen Bank started their operation in New York in April 2008. 2 In Europe, popular credit granting in the 19th century was already a form of microfinance (Hollis and Sweetman, 1998). 3 Microfinance system is based on the great promise of alleviating poverty by providing poor people with small loans and financial services (Morduch, 1999). It has successfully helped poor people in Bangaldesh to exit poverty by engaging in micro projects. 4 This mechanism originates from the Grameen Bank and refers to the practice of working with clients in small groups (typically comprised of five neighbor villagers). According to this system, borrowers sort themselves into groups of five. In a first stage, only two members of the group get loans. If they repay on time, the next two get loans. In contrast, when a member defaults, all five are barred from borrowing in the future. Theorists have been particularly interested in group lending for the incentives induced by the joint liability i.e. peer selection and peer pressure (Varian, 1990; Arnot and Stiglitz, 1991; Kandel and Lazear, 1992; Barron and Gjerde, 1997; Rai and Sjöstrom, 2001). Peer pressure relies on the idea that the agents themselves may be in a better position to monitor and sanction each other. Both empirical and theoretical studies have investigated the effects of peer pressure. Arnot and Stiglitz (1991) showed that mutual assistance may be beneficial when nonmarket insurers can observe each other's effort perfectly. Varian (1990) also pointed to the potential effects of peer pressure in mitigating moral hazard. Kandel and Lazear (1992) investigated the effects of peer pressure in the context of a partnership. The use of costly sanctions in social dilemmas has also been frequently observed in a laboratory setting even among unrelated individuals (Fehr and Gätcher, 2000 and 2002, Gintis, 2000; Masclet, Noussair, Tucker and Villeval 2003; Carpenter, Matthews and Ong'ong'a 2004; Gächter and Hermann 2005; Carpenter, 2006). 5 For example, group-lending mechanism is often pointed to be the key determinant to explain China‘s microfinance success. China can claim average repayment rates above 90%. 6 Besley and Coate (1995) show that under certain conditions, borrowers may collude against the bank and undermine the bank's ability to harness “social collateral”.

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(1997) underscores the negative consequences of joint liability. Other studies argue that the

circumstances under which joint liability is optimal may be unlikely to hold in practice (Rai

and Sjöström, 2000). Finally, some authors highlight that peer-group lending may not be

sufficient to ensure contract enforcement and high repayment rates (Sadoulet, 1997;

Armendariz and Morduch, 2000). Sadoulet (1997) argues that “social collateral” induced by

group lending is not a sufficient condition to ensure the success of microfinance. Armendariz

and Morduch (2000) underline the fact that group lending may not be the only way for

microfinance to succeed. In particular, they highlight the key role played by individual long-

term based contracts in microfinance systems implemented for example in Russia and

Albania. Armendariz and Morduch indeed emphasize how dynamic incentives and the use of

“non-refinancing threats” can explain the success of microfinance programs in Asia and Latin

America in absence of peer-group lending. Threats are based on the promise of future loans

over time to good customers while those who default on their debt obligations are not

refinanced. Because borrowers typically desire to finance future projects, the promised

increases enhance the borrowers’ loss from being cut off. These mechanisms substitute for

group lending in developed countries where peer-group lending does not really constitute a

good fit for potential clients7. In particular, efforts to replicate Grameen-style peer-lending

model in developed countries have not generally succeeded.8 These difficulties, coupled with

the limitations of group lending systems, have led several developed countries to focus

instead on individual personalized “lender-borrower” contracts. These contracts rely on long-

term and personalized relationships that involve physical proximity and regular face-to-face,

which is generally essential for interacting with vulnerable populations9. Building up long-

term trust relationship may indeed provide a solution when the use of collateral is not

achievable (Boot and Thakor, 1994). It is the path oft-followed in relationship banking. From

the theoretical stance, reputation mechanism is relatively well established: by interacting

repeatedly with the same borrower and conditioning the credit terms on past behavior a lender

can align the borrower’s interest with hers (Diamond, 1989; Sharpe, 1990). From the

empirical standpoint, a growing body of literature deals with the long-term mechanism.

Evidence shows that long-term relationship facilitates access to credit (Petersen and Rajan,

1994; Elsas and Krahnen, 1998; Cole, 1998; Berger and Udell, 1996, 2002) but its effects on

the term conditions remain controversial (Berger and Udell, 1995; Boot, 2000; Petersen and

Rajan, 1994; Degryse and Van Cayseele, 2005).

7 The Russian program (the microenterprise programs of the Russian Small Business Fund) draws on experiences with individual lending contracts in Peru, El Salvador, Bolivia, and Uganda (Churchill, 1999). 8 For example, the Calmeadow Foundation tested an analogous 'peer lending' model in three locations in Canada: rural Nova Scotia and urban Toronto and Vancouver during the 1990s. It concluded that a variety of factors including their general distaste for the joint liability requirement made solidarity lending unviable without subsidies. 9 As shown by Ghate et al. (1992) microfinance institutions tend to deliver personal service very close to the location of the borrower, operate mostly in a circumscribed area or a specific niche of the market and tend to be much more flexible in respect of maturity periods and debt rescheduling.

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In this paper we seek to contribute to the existing literature on microcredit markets by

experimentally examining to what extent long-term “borrower-lender” relationships, which

are essential for microfinance institutions in the developed countries, allow to mitigate moral

hazard. One of the main advantages of laboratory experiments lies in the possibility to

disentangle phenomena that are difficult to unveil with field data because of issues of variable

measurement and endogeneity. Precisely the aim of this paper is twofold. First, we investigate

to what extent a long-term relationship can enhance credit market performance by improving

repayment rate and mitigating the problem created by asymmetric information. We argue that

the success of microcredit does not solely rest on the use of peer-group lending but may be

also due to the existence of long-term lending relationships. Several authors highlight the key

role played by individual long-term based contracts in the success of microfinance programs,

underlining the fact that group lending may not be the only way for microfinance to succeed

(Armendariz and Morduch, 2000; Bolton and Scharfstein, 1995). We conjecture that the

existence of long-term relationships may be an important factor in explaining the high

repayment rates traditionally associated with microfinance sector. Indeed long-term

relationships may induce more trust, inciting the borrower to abide by the contract, both in

terms of repayment and use of the loan for adequate projects (Guiso et al., 2004).

Furthermore, long-term relationships also rely on the threats of non-refinancing in case of

shirking and on the promise of futures loans to good customers (Armendariz and Morduch,

2000; Bolton and Scharfstein, 1995).10 In the banking relationship, reputation mechanism is

relatively well established: by interacting repeatedly with the same borrower and conditioning

the credit terms on past behavior a lender can align the borrower’s interest with hers

(Diamond, 1989; Sharpe, 1990).

The second aim of the paper is to shed light on the undesirable by-effects induced by long-

term relationship. Precisely we investigate here to what extent long-term based contracts may

have negative consequences for the borrowers by increasing the credit cost. Indeed we

conjecture that the lenders may take advantage of their bargaining power to raise excessively

interest rate because the market is captive (Boot, 2000 and Sharpe, 1990).11 We will refer to

this assumption as the hold-up effect. The underlying idea is that microcredit institutions may

appear, to some extent, as oligopolists enjoying a captive market12. The latter effect may in

turn engender another negative consequence which is the non-optimal project selection by the

10 Armendariz and Morduch emphasize how dynamic incentives and the use of “non-refinancing threats” could explain the success of microfinance programs in Asia and Latin America in absence of peer-group lending. Threats are based on the promise of future loans over time to good customers while those who default on their debt obligations are not refinanced (Bolton and Scharfstein, 1995). Because borrowers typically desire to finance future projects, the promised increases enhance the borrowers’ loss from being cut off. 11 There has been much criticism on the high interest rates charged to borrowers. For example a sample of 704 microfinance institutions that voluntarily submitted reports to the MicroBanking Bulletin in 2006 was 22.3% annually. 12 We indeed contend that real competition has yet to be felt in microfinance by microfinance institutions perhaps so few are actually turning a profit (Morduch, 1999). This may lead to the fact that in many cases borrowers cannot choose their incumbent lender, notably for rural remote areas.

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borrower – a borrower may be strongly incited to choose an inefficient high-risk project to

reimburse too onerous a loan13 (Stiglitz and Weiss, 1981).

Our experimental credit game consists of a finite repeated game involving trading between

lenders and borrowers in which neither loan repayment nor the required choice of project are

third-party enforceable. Each period consists of three stages. In the first stage, the lender

decides whether to keep or transfer her endowment to the borrower. In the second stage, the

borrower chooses the project she would like to invest into between an efficient low-risk

project and an inefficient high-risk project. Finally, in the third stage, after observing the issue

of the project, the borrower decides how much to return to the lender. Two sources of moral

hazard therefore coexist in such credit market14. First, the lender cannot observe the

borrowers’ choice and therefore, borrowers may choose inefficient high-risk projects (ex-ante

moral hazard; Conning, 1998). Second, the absence of legal enforcement of repayment

implies that borrowers may withhold their repayment even if they successfully realized their

projects (ex-post moral hazard; see Fehr et al., 2008). Lenders do not know whether a

defaulting borrower is unable (because the project failed) or unwilling to repay her credit.

The first treatment consists of the game presented above implemented under a stranger-

matching protocol, in which there is no opportunity to establish reputation. This means that a

lender is randomly assigned a different borrower at each period. Our second treatment is

identical to the first treatment except that we implement a partner-matching protocol allowing

opportunities to establish reputation. The comparison between the two matching settings

allows us to investigate whether a long-term relationship improves repayment levels and to

what extent long-term relationship incites the lenders to take advantage of their “captive

market” by increasing their rates. Finally, our third treatment replicates the partner treatment

except that the lender is informed both about the project that has been chosen by the borrower

and about the issue of this project, which allows her to disentangle the two types of moral

hazard presented above. The comparison between the two treatments with and without

information should allow us to investigate whether increasing monitoring costs to obtain such

information improves efficiency by increasing repayment rates.

To our knowledge few laboratory experiments have been carried out in the field of

microfinance and more largely on the credit market. Recent research by Karlan (2005)

suggests that behaviors observed in the laboratory are consistent with those existing in the

field. By comparing the second-movers’ behavior in the trust game and their behavior in real-

life financial decisions (repayment), he shows a positive correlation between these two forms

of behavior. Abbink et al. (2006) provide a good example of such experiment in their analysis

of the role of group size and social ties upon the repayment in microfinance institutions. They

13 Indeed the idealized view of microfinance is that budding entrepreneurs use the loans to start and grow businesses—expanding operations, boosting inventory, and so on. However the reality is more complicated. For example, the borrowers can use microloans to “smooth consumption” or to finance riskier projects than those announced previously to the lender. 14 As Conning (1998), we consider the combination of ex-ante and ex-post moral hazard.

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show that (i) group-lending schemes outperform individual lending in terms of repayment but

only to a certain extent (ii) the effect of social ties remains ambiguous. Furthermore, they also

highlight the fact that dynamic incentives play a major role in determining repayments.

Our work is most akin to the papers by Fehr and Zehnder (2004) and Brown and Zehnder

(2005). Using a credit market experiment, Fehr and Zehnder (2004) investigate how

reputation can endogenously emerge and how it influences the efficiency of a competitive

credit market. They conduct a series of experiments including three different treatments. The

benchmark treatment corresponds to a competitive credit market where debt repayments are

not third party enforceable and participants are anonymous, which prevents opportunities for

the participants to engage in repeated interactions. In a second treatment, participants can

endogenously engage in repeated interactions. This is done by holding fixed the ID numbers

of the participants. Finally the third treatment introduces third party enforcement. The

findings indicate that borrowers’ repayments are significantly higher when bilateral

relationship is feasible than in a one-shot interaction setup. The authors also observe that the

lenders’ credit granting in t is conditioned by a repayment of the previous loan in t-1. Brown

and Zehnder (2005) also stress the importance of endogenously-formed reputation in another

context. Their experiment aims to analyze the effect of information sharing between lenders

in a one-shot interaction protocol and in a scheme in which bilateral relationship is feasible.

Their findings indicate that the incentive effect of information sharing is substantially less

important when bilateral relationships are feasible.

A major difference between these previous experiences and ours lies in the fact that our game

does not imply a competitive market wherein relationships can be established endogenously15.

Rather subjects are exogenously matched by pair, which fits more closely microcredit market

characteristics such as long-term and personalized relationships, the fact that borrowers may

be become a captive market because they may have less opportunities to choose their

partner.16 Moreover, we add an additional treatment where lenders are informed about the

project chosen by the borrower as well as the issue of this project, which allows the lender to

disentangle the two types of moral hazard. The originality of our research also lies in the fact

that we introduce variability in socio-demographic characteristics by recruiting "real people",

including not only students that are typically viewed as the standard subject pool used by

experimenters but also bankers both from classical banking and from microcredit institutions.

Indeed, student samples exhibit limited variability in some key characteristics such as age or

15 Brown and Zehnder (2006) also implement a competitive credit market wherein relationships where endogenously feasible. 16 In Fehr and Zehnder (2004), market is rendered competitive through the implementation of a one-sided continuous posted-offer auction whereas ours is made voluntarily non-competitive by exogenously assigning one lender with one borrower. Compared to Fehr and Zehnder (2008)’s market, we proceed to this simplification so that our experimental setting more closely fits microcredit market characteristics. This assumption is also made by de Aghion and Morduch (2000) when their formalisation follows Bolton and Scharstein (1990) model. This leads to the fact that in many credit granting situations borrowers cannot choice their incumbent lenders (this is especially true in rural/remote areas or in case of interaction with vulnerable populations).

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occupation that may be highly correlated with risk attitude. However, as Harrison and List

(2002) noted, these last years, “several experimenters have deliberately left their reservation:

more and more experimentalists are recruiting subjects in the field rather than in the

classroom.” Introducing variability in socio-demographic characteristics among subjects

allows to investigate whether contextual effects are robust to the introduction of socio-

demographic variables. In addition, it also allows to measure the relative influence of socio-

demographic variables on credit offers.

To anticipate our results, we find that an honor-based market wherein participants cannot

build up reputation is not viable on the long run because borrowers have no repayment

incentives, though a significant fraction of borrowers reciprocate fair offers. In contrast, the

opportunity to engage bilateral long-term relationships strongly improves the market

performance by partially mitigating the repayment problem and thus enhancing cooperation

between borrowers and lenders. This fact seems to highlight the prominence of reputation as a

disciplining devise that conducts selfish borrowers to behave reciprocally. However our

results also indicate that lenders take advantage of this captive market in a long-term situation

by increasing significantly their interest rates. As a consequence, the borrowers may be

incited to take more risk to reimburse their loan. Improving the information disclosure

reduces shirking behaviors, by inciting borrowers to choose the required projects. Finally, we

find that social bankers are more likely to make fair credit offers to borrowers than classical

bankers.

The remainder of the paper is organized as follows. Section 2 presents our experimental

design and the theoretical predictions of the model, with either purely selfish agents or in the

presence of agents with social concerns. The results are detailed in section 3. Finally, section

4 provides a discussion of the results and concludes.

1. Experimental design

1.1. Experimental credit market

Our credit market is based on Berg et al. (1995)’s trust game and inspired from the market

game designed by Fehr and Zehnder. At the beginning of the experiment, each player is

randomly assigned a role of borrower or lender. Each player keeps this role fixed for the

whole duration of the session. Each treatment consists of 15 identical periods of a three-stage

game. In each period, a lender is randomly assigned to a borrower. Our experiment consists of

three different treatments.

The first treatment labeled No Information Stranger (henceforth NIS) is our baseline

condition wherein debt repayment is not enforceable by a third-party and the participants have

no opportunity to engage in repeated interactions with the same partner. Precisely, the lenders

are randomly re-matched with a different borrower at each period. Another key feature of the

NIS treatment is that the lenders are neither informed about the project selected by the

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borrowers nor informed about the project outcome. The only piece of data accessible to the

lenders is the repayment realized by the borrowers. In details, the three stages are as follows:

In the first stage each lender is endowed with 32 ECU and can make use of her endowment

( k ) in two manners. She can either invest her whole endowment in an outside option S that

yields a safe payoff of 32 ECUs (that does not yield any return) or grant a credit of 32 ECUs

to the borrower she is matched with. In the latter case, she can only post one and only one

offer in which two pieces of information must be stipulated: a desired project ( { },dp A B∈ )

and a desired repayment (dr ).

In the second stage of the game, the borrower observes whether she receives (or not) a loan

from the lender. Then, in case of acceptance, she has to choose between two investment

projects: project A and project B. Both investment projects require a financing of 32 ECUs to

be undertaken. This means that to undertake a project (A or B) the borrower is dependent on

the lender’s financing of 32 ECUs. As depicted below in Table 1, project A is an efficient

low-risk project with a high expected return and project B is an inefficient high-risk project

with low expected return17. Once the selection is realized, a random device determines

whether the project selected has experienced a success or a failure.

In the third stage, the borrower decides how much to repay the lender by choosing the size of

a return-transfer. In case of failure of the project, the repayment to the lender is automatically

set to zero ( 0r = ). If the project turns out to be successful, the borrower is free to repay the

amount she desires ( 0, pr R ∈ ). Finally, at the end of the period, each participant is

informed about her final payoff given as:

( Lenderπ ) = r if the lender grants a credit of 32 ECUs to the borrower (1)

= S if the lender chooses the outside option.

( Borrowerπ ) = R r− (2)

[Table 1: about here]

In order to examine to what extend the opportunity of forming long-term relationships enables

to mitigate the incentive problem associated with repayment, a second treatment called No

Information Partner treatment (henceforth NIP) allows participants to engage repeated

interactions. This is rendered by exogenously matching one borrower with the same lender for

the whole duration of the experiment. Apart from that noteworthy change, the NIP treatment

is strictly similar to NIS insofar as the asymmetric information remains the same. Finally, to

17 Diamond (1989)’s model on reputation acquisition in debt market posits the same assumption with type G borrowers and type B borrowers, that have one risky and low-expected-return project.

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test how asymmetric information may distort the credit market functioning, we implement an

additional treatment called Information Partner treatment (henceforth INP). The INP

treatment almost replicates NIP treatment with the notable exception that the lenders are now

informed about the project selected by the borrowers and the project outcome at the end of

each period.

1.2. Procedure and parameters

The experiment was computerized and the scripts were programmed using the z-tree platform

(Fischbacher, 2007). We recruited 126 subjects among students and bankers from lending

institutions. Roughly 32% of our participants that took the role of “lender” were real bankers

10 classical bankers recruited from three well-known French banks18 and 10 social bankers.19

The remaining subjects were students, which constituted our benchmark population in the

experiment. The students were recruited from undergraduate courses in business, literature

and economics at the University of Rennes (France). Some of the subjects had participated in

previous experiments, but all of the subjects were inexperienced in this particular type of

experiment. No subject participated in more than one session of the study.

All sessions were conducted at the LABEX of the University of Rennes, France in 2007. The

experiment was computerized and the scripts were programmed using the z-tree platform

(Fischbacher, 1999).Our overall design consists of 12 sessions.20 Subjects only participated in

one treatment except in sessions 9-12 that involved “real” bankers in which participants were

required to play two successive treatments (NIP and INP). To account for potential order

effects in these sessions, the order of the two treatments was reversed in some sessions. Note

that during each 15-period segment subjects participating in session 9-12 did not know

whether or not the experiment would extend beyond the current segment. Table 2 contains

some summary information about each of the sessions. The first three columns indicate the

session number, the number of subjects that took part in the session and the treatment in

effect. The fourth column indicates the matching protocol in effect in each session. Finally the

two last columns indicate the type of the lenders and borrowers, respectively.

[Table 2: about here]

A session lasted between one hour and one hour and a half including instruction reading and

payment. The average monetary gain amounted to 12.81€ and the exchange rate was set at 40

ECUs for 1€. Upon arrival, all participants were randomly assigned to individual

computerized workplaces. A set of instructions was then given away to each participant and

read aloud by the experimenter. The subjects were then asked to fill out a test with control

18 Banques Populaires, Crédit Agricole and Crédit Mutuel. 19 Social bankers were recruited from the following social banking institutions : Société Financière de la Nef, Fédération des Cigales, PRESOL, ADIE, Bretagne Capital Solidaire. 20 Independent observations consist of each pair in the partner treatments and of an entire session in the stranger treatment.

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questions. The session started after all participants had correctly answered all control

questions.21 The game started after the instructions were read aloud by the experimenter.

At the end of each session, subjects were asked to fill an individual questionnaire. We also

asked them to play a simple lottery choice experiment to determine their degree of risk

aversion. This simple game replicates Holt and Laury (2002)’s design. Precisely, subjects

were confronted with ten choices between two lotteries, one "risky" (with payoffs of €3.85

and €0.1) and one "safe" (with payoffs of €2 and €1.6), with probabilities ranging from 10%

to 100%. In both options the probabilities for the first of the ten sequential decisions are 10%

for the high payoff and 90% for the low payoff. The difference in the expected payoffs

between the two lotteries is such that only an extreme risk-seeker would choose Option B. As

the probability of the high payoff outcome increases B becomes more attractive relative to A,

and at some point subjects will switch their preference. Towards the end of the decision

sequence even the most risk averse subjects should switch over to option B.

2. Theoretical Predictions and behavioral assumptions

2.1. Theoretical predictions for rational and selfish subjects

In this section we derive predictions from our experimental treatments under the assumptions

of common knowledge of rationality, risk neutrality and selfishness. For the three treatments

the theoretical prediction is straightforward: if the game consisted of one period, borrowers

would never repay their debts because of the absence of legal institutions in charge of

enforcing the debt contracts. The lenders would anticipate the borrowers’ behavior and would

deny them credit granting. The same reasoning holds for all of the 15 periods by applying a

backward induction mechanism since it is common knowledge for the subjects that the

experiment lasts for a finite number of periods. Assuming common knowledge of rationality

and selfishness the opportunity to build up partner relationship should therefore not affect the

theoretical predictions of the game since it is finitely repeated. This is stated precisely in H0.

H0 (Pure Self-Interest and Profit Maximizing): Assuming common knowledge of rationality

and selfishness lenders will never offer credits. The opportunity to build up partner

relationship should not affect the theoretical predictions of the game

21 The experimental instructions were phrased in a credit market language. The reason why we use a context specific language is that our experiment was relatively complex. In such a case, too neutral a language may induce that the participants create they own (potentially misleading) interpretations of the decision environment. Thus, an explicit environment enables to have a certain control over what participants have in mind and in so doing strengthen external validity.

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2.2. Behavioral assumptions

One might relax some of the above assumptions and assume that in addition to the purely

selfish subjects there may be also a fraction of the borrowers who are trustworthy. Indeed

several studies have shown that many people are reciprocally motivated and react to unfair

intentions by sacrificing a part of their payoffs in order to punish bad intentions or reward

kind actions (for modeling of reciprocity see Rabin, 1993; Charness and Rabin, 2002; Falk

and Fischbacher, 2006; Dufwenberg and Kirchsteiger, 2004). In the context of our credit

market game reciprocal borrowers could be defined as subjects who would honor the credit

terms proposed by the lender if such proposal is perceived as fair (for example if the lender

asked for the realization of the efficient project and for a fair repayment in case of success of

the project). In addition to reciprocity, repeated interactions in the partner treatments (NIP

and INP treatments) may also give rise to the possibility of reputation effects. Reputation

mechanisms can be defined as follows: albeit selfish a lender may have strong incentives to

enter the credit market if she anticipates the existence of a sufficient fraction of reciprocal

borrowers because she can earn more by entering the market than by choosing the outside

option. Similarly selfish borrowers may also have incentives to imitate the reciprocal

borrowers because they can also benefit from repaying their debt. The intuition is that

borrowers have incentive to build up a reputation as reciprocal borrowers because they

anticipate that lenders will condition the renewal of the contract on past repayments. This in

turn makes it profitable for lenders to enter the credit market. As a consequence both the

realized number of trades and the market performance should be higher in the NIP and INP

treatments than in the NIS treatment. The alternative conjecture to H0 assuming that there

may be a fraction of reciprocal agents is stated precisely in H1.

H1 (Reciprocity and Reputation): The opportunity to engage in repeated interactions (NIP

treatment) should improve cooperation between the lenders and the borrowers. Accordingly,

the average number of credit offers made by the lenders should be higher in NIP treatment

than in NIS treatment. Furthermore the repayment in case of success of the chosen project

should be higher in the NIP treatment.

Considering our credit market wherein debt contracts are not third-party enforceable, we

would expect that the feasibility of reputation building should enhance cooperation between

lenders and borrowers. Indeed, several experimental studies (e.g. Andreoni and Miller, 1993;

Gächter and Falk, 2002) show that cooperation is significantly higher under a partner-

matching protocol than under a stranger-matching protocol by strengthening reciprocity and

reputation. These studies also show a strong endgame effect when no more building

reputation is possible. On a competitive credit market, Fehr and Zehnder (2004) and Brown

and Zehnder (2006) draw the same conclusions and remark that the lenders’ credit granting in

t is conditioned by a repayment of the previous loan in t-1. Empirical studies also show the

positive correlation between long-term relationship and credit availability (Petersen and

Rajan, 1994; Cole, 1998).

12

A possible objection to the expectation of a positive relationship between long-term

relationship and fair credit offers cost is that reciprocity and reputation motives may be

counteracted by the lender’s willingness to take advantage of her bargaining power to raise

excessively interest rate because of the existence of a captive market in long-term

relationships (Boot, 2000 and Sharpe, 1990).This is summarized in assumption H2.

H2 (Hold-up Effect): Under the assumption of hold-up effect, credit cost (i.e. the repayment

rate required by the lender in the credit contract) should be higher under in NIP treatment

than in NIS.

According to H2, we would expect that the lender ask for unfair and very high repayment

rates. Furthermore, because of the forced long-term relationship, we should also observe

higher repayment rates from the borrower than those predicted in H0 because of the fear of

non renewal of the contract in the future in case of non repayment. Such hold-up effect has

been documented in several studies (see for example Bolton and Sharfstein 1990; Sharpe,

1990; Rajan, 1992). According to Bolton and Sharfstein (1990), the high credit cost in such

long-term relationships could be explained by the fear of non renewal of future contracts.

Sharpe (1990) argues that under certain circumstances the lender may hold-up the borrower

by raising excessively the credit cost. In the same vein, Petersen and Rajan (1995) posit that

interest rates are smoothed in the long term to subsidize young enterprise.22

H3: (Credit cost and ex-ante moral hazard). One would expect that high credit cost may

exacerbate ex-ante moral hazard by inciting the borrowers to select the inefficient project (i.e.

project B).

We base our conjecture on Stiglitz and Weiss (1981)’s theoretical model that shows “that

higher interest rates induce firms to undertake projects with lower probabilities of success but

higher payoffs when successful”. Experimental results, notably those of Capra et al.. (2007),

show that higher interest rate demanded by lenders leads borrowers to select riskier projects.

One can easily see from the discussion above that hold-up effect and reciprocity/reputation

mechanisms go in two opposite directions, which leads to a multiplicity of equilibria and

makes the game theoretic prediction indeterminate, leaving it to empirical analysis to identify

links between long-term relationship and credit cost.

H4: (Information disclosure). On should observe less ex-ante moral hazard in the INP

treatment compared to the NIP treatment.

22 Note however that others studies find no such hold-up effect. To underpin our conjecture, we argue that the theoretical analysis offers contradictory views. For example, Diamond (1989) posits that long-term relationship should benefit to the enterprises. Empirical studies are also divergent: Berger and Udell (1995) find a negative correlation between the length of the relationship and the interest rate whereas Petersen and Rajan (1994) show that there is no significant relationship.

13

Referring again to Stiglitz and Weiss (1981), we expect that giving more information to the

lenders about the project selection by borrowers might reduce ex-ante moral hazard. Lenders

might indeed implement a credit granting policy in which the credit renewal in t is

conditioned by the absence of shirking (non optimal project selection) in t-1. But we consider,

in our case, that the role of information would remain marginal in terms of overall credit

market performance. We thus expect that the number of credit offers should not significantly

differ under NIP or INP. By notably referring to Brown and Zehnder (2005), we indeed argue

that long-term relationship permits to bridge the informational gap between the two sides of

the market and disclosing more information only offers little added-value.

3. Results

3.1. Microcredit market performance: credit offers and repayment rates.

Figures 1a and 1b display the time series of average number of offered credits by period,

averaged across treatment. Figure 1c provides similar information distinguishing between

classical and social bankers. The vertical axis indicates the total number of credits offers by

lenders normalized on total number of credit offers possible. The horizontal axis indicates the

period number. As depicted in Figures 1a and 1b, the number of contracts offered tends to

decline overtime for all treatments. In the NIS treatment, the credit market rapidly breaks

down with relatively few trading. In the NIP treatment, the credit market, although declining,

seems to be more resilient until period 13 and then plummets in the two last periods

highlighting a strong end-game effect. The figure also indicates that the NIP treatment

exhibits a higher level in credit offers than the NIS treatment. The effect of long-term

relationships is summarized in Result 1.

[Figure 1a-c: about here]

Result 1: The opportunity to establish a long-term relationship significantly increases the

average number of credit offers per period. In contrast, no significant difference is found

between the NIP and INP treatments. Moreover, the lender conditions the renewal of her offer

in t on repayment of in t-1.

Support for result 1: Our data indicate that having an opportunity of long-term relationship

has a positive effect on the number of credit offers from lenders. One average 79% of the

credits are offered in the NIP treatment whereas only 54% have been offered in the NIS

treatment. A Mann-Whitney rank sum test based on comparison of averages credit offers

indicates that these differences are significant (p=0.0139).23 The credit offers made by lenders

being almost all accepted by the borrowers, we can argue that the credit volume granted to the

23 Each group is considered as an independent observation in both NIP and INP treatments whereas each session is considered as independent observation in the NIS treatment.

14

borrowers is superior in the NIP than in the NIS treatment, highlighting a better general

functioning of the credit market.24 Finally, we find no significant effect of information on the

number of contracts offered by the banks. Indeed a Mann-Whitney rank sum test shows no

significant difference between NIP and INP treatments (p=0.3722).

[Table 3: about here]

To get a clearer picture of lenders’ decision we estimate random effects Probit models that

account for the panel dimension of our data:

tiitti XperiodRINPNIPY ,541321, εβββββα ++++++= − (3)

Where Yit is subject i’s probability to make a credit offer in period t. It takes the value 1 if the

subject i makes a proposal and 0 otherwise. NIP is a dummy variable equal to 1 if subject i is

in the NIP treatment and zero otherwise; INP is a dummy variable equal to 1 if subject i is in

the INP treatment and zero otherwise. Rt-1 corresponds to the repayment received by subject i

in t-1. We also control for time effects by including the variable “Period”. Xi is the vector of

personal characteristics including gender, a measure for risk aversion25, a binary variable

indicating whether the participant is student with prior in economics and two dummy

variables for social bankers and commercial bankers.

Results are reported in Table 3. Columns (1) and (2) report estimates from the NIS/NIP and

NIP/INP data, respectively. Columns (3) and (4) report estimates on the pooled data.

Specification (4) adds several demographics. Finally, column (5) reports the corresponding

marginal effects of the random effect Probit models presented in column (4). The estimates

summarized in Table 3 confirm our previous findings. The first model shows that NIP

treatment influences significantly the probability of offering a contract. In contrast, the

coefficient on the INP variable is not significant in specification (2), which indicates no

significant effect of information on the number of contracts offered. Table 3 also highlights

the lending strategy followed by the bankers. The positive and significant coefficient

associated to the variable “Repayment in t-1” in the four specifications indicates that lenders

are more willing to make an offer in period t when borrowers repaid their debt in the previous

period. This result indicates that lenders manage to implement a credit granting policy in

which they condition the renewal of their credit offer in t upon the past repayment behavior of

the borrowers in t-1. The period variable captures a negative and significant coefficient, which

confirms the decline of credit offers over time. Finally the introduction of demographics does

not affect the experimental variables’ estimated coefficients. The marginal effect 0.114 for

NIP variable shows that lenders who play the NIP treatment have an 11.4 percentage points

higher probability of offering a credit. It amounts to 14.4 percentage points in the INP

24 The percentage of credit offers that were refused by the borrowers were 2.25%, 2.99% and 3.61% in the NIP, INP and NIS treatments, respectively. 25 This score corresponds to the safe choices selected by the subject out of ten choices. If this score is 1, the subject is “highly risk loving”, whereas if the number of safe choices is 8 or above the subject is “(very) highly risk averse”.

15

treatment. An increase of one percentage point in repayment in t-1 is associated with an

increase of the probability of offering a credit by 23.6 percentage points.

Turning next to borrower’s repayment rates (i.e. borrower’s effective repayment upon

repayment required by the lender), we find that long-term relationships improve borrowers’

repayment rate. These observations are summarized in result 2.

Result 2: Borrowers’ repayment rates are significantly improved by long-term relationships.

In contrast, no significant difference is found between the NIP and INP treatments.

Support for result 2: Figures 2a and 2b illustrate the time path of borrowers’ repayment rate

per period, averaged across groups, in the three treatments. Our findings indicate that average

repayment rates are significantly higher in the NIP treatment per individual compared to the

NIS treatment. Comparing the borrowers’ repayment rates under the stranger and the partner

matching protocols, our findings indicate that the repayment ratio nearly doubles both

considering cases where the investment project is successful or not. In the NIS treatment the

repayment rates amount respectively to 0.26 when we consider the average of all repayments

(after project success or failure) and to 0.35 when we only envisage the repayments after

project success. In the NIP treatment, these rates are respectively 0.45 and 0.7226. A Mann-

Whitney test confirms that reimbursements are significantly higher in NIP than in NIS either

considering all repayments (p = 0.0006) or repayments after project success (p = 0.0030). In

contrast no significant difference in found between NIP and INP (p=0.32 and p=0.37, for all

repayment and in case of success only, respectively).

[Figure 2a-c and Table 4: about here]

A more formal proof of Result 2 is given in Table 4 that presents the determinants of

repayment levels. Table 4 consists of two panels. The left panel displays the results of three

regressions in which the dependent variable is the repayment level of subjects who accept a

contract offer and succeed in their chosen project. The right panel shows the results of

alternative specifications that check the robustness of our results. The regressions reported in

columns (1)-(3) are estimated via Generalized Least Squares. Since each subject is observed

up to 15 times, we use panel data methods with random effects. Column (1) displays the

results of the GLS estimation for the pooled data including all treatments. Columns (2) and

(3) provide similar results for treatments NIP/NIS and NIP/INP, respectively. All regressions

include dummy variables for each treatment. The “credit cost” variable captures the following

ratio: repayment desired by the banker / project outcome in case of success. “Project-B” is a

dummy variable that takes 1 if the subject choose project B and 0 otherwise. “Project-A

lender” is a dummy variable that takes 1 if the banker recommended project A and 0

otherwise.

26 The fact that this rate is not equal to 100% may shed light on a strategic behavior which consists of not repaying the totality of the amount demanded by the lender but sufficiently so that the lender will lend to her again in the next period (Mella-Barral and Perraudin, 1997).

16

The parametric analysis confirms our previous results based on nonparametric tests. The

positive and significant coefficient associated to the variables “INP treatment” and “NIP

treatment” highlights the incentive induced by long-term relationship. Besides, the significant

and positive coefficient at 10% level associated to the variable “INP treatment” in

specification (3) indicates that after controlling for several other variables, more information

at the lenders’ disposal seems to ameliorate repayment rate, which contrasts with our previous

non parametric result.

Robustness tests

To check the robustness of our experimental results, we consider a number of alternative

specifications. These additional specifications are reported in the right-hand panel of Table 4.

In column (4) we check to what extent our results are robust to change in the regression

model. We estimate the determinants of repayment rate via a random effects Tobit models.

Indeed Tobit model could be justified by the number of left and right-censored observations

in the sample. Furthermore other specifications seek to take into account the sequential

structure of the credit market. Indeed previous results reported above were based only on

those subjects who accepted a contract. However it may be important to separate offer

acceptance decision from the choice of repayment. We therefore conduct an alternative two-

step estimation procedure that corrects for possible selection bias from the exclusion of the

observations corresponding to the rejected contracts. We first consider the random effects

Probit estimated in column (5) as a selection equation. We then consider effort decisions,

conditional on contract acceptance, corrected for selection bias via the introduction of the

inverse of the Mill’s ratio (IMR) as an explanatory variable. These equations are estimated

via Generalized Least Squares, the results of which are reported in columns (6)-(8); we also

estimate this equation via RE Tobit models (not reported here), that provide very similar

results. We add three additional variables in estimate (8) including a dummy variable for

choosing project B and two interaction variables “projectB*INP treatment” to capture a

potential effect of information and “credit cost*partner”.

The results of the random effects Probit for the decision to accept an offer are shown in

column (5). The probability of accepting an offer depends negatively on the credit cost

offered. The significantly negative coefficient on variable “Credit Cost” in the acceptance

decision suggests that the more equitable is the offer the more likely it is to be accepted.

However the probability of acceptance does not seem to be affected by treatment variables. A

potential explanation is that contract acceptance is a blunt decision, while there is more

latitude in repayment choice. Estimations conditional on contract acceptance are reported in

columns (6)-(8). Specifications (6) and (7) show that treatment variables and “credit cost”

variable continue to affect repayment decision (at the 1% significance level). However the

next specification (8) reveals that credit cost affects repayment decision only in partner

treatments.

17

The main message from the regressions in Table 4 is unaffected by the choice of specification

and models. The robustness checks therefore all deliver the same conclusion: repayment

decision is sensitive to treatment and credit cost. Altogether, results 1 and 2 show that

repeated interactions lead to a strengthening of reciprocity and produced efficiency-

enhancing effects on credit market by improving the debt repayment. This result is in line

with previous findings in the experimental economics literature showing the importance of

reputation in a partner relationship compared to a stranger matching protocol. It is also

consistent with previous findings in the empirical and theoretical literature on microfinance

and relationship lending that shed light on the importance of long-term relationship as a

determinant of the success of microfinance, even in absence of peer lending.

3.2. Hold-up and negative externalities of long-term relationships

Results presented above emphasize the influence of long-term relationships in both improving

repayment rates and increasing the number of credit offers. In this section, we show that

engaging a long-term relationship may also induce negative externalities for both sides of the

market. These observations are summarized and supported in result 3.

Result 3: Credit cost is significantly higher under the partner treatments than under the

stranger treatment. Credit price is lower in the offers specified by the social bankers than in

those made by commercial bankers.

Support for result 3: Figure 3a and 3b illustrate the time path of credit cost per period in the

three treatments. Figure 3a indicates that credit cost for borrowers is higher in the NIP

treatment compared to the NIS treatment. One interpretation of that result lies in the fact that

long bilateral relationship might hold-up borrowers because lenders take advantage of their

bargaining power. To provide a more formal proof of result 3, we estimate the determinants of

credit cost using a Generalized Least Squared model. More precisely, the equation under

estimation is:

tiitttti XpartnerRshirkRINPNIPRATE ,615141321, εββββββα ++×+++++= −−− (4)

RATEi,t corresponds to the credit cost rate (i.e. repayment desired by the banker / project

outcome in case of success). Our independent variables include dummy variables for each

treatment as well as two lag variables: “Rt-1”, which measures the share of previous loan

reimbursed by the borrower in t-1 and “Shirk t-1” which indicates whether the borrower

shirked in t-1 by not selecting the project stipulated by the lender in her offer in t-1. In all

specifications, we control for time effects by including the period variable. We also include

the dummy variables for the two populations of bankers and the usual demographics. Finally

we add two interaction variables “Rt-1*partner” and “Rt-1*social banker” in columns (2) and

18

(3). These variables seek to capture whether “repayment rate in t-1” may affect credit cost

differently depending on social characteristics of the banker and/or whether the treatment

implemented is under a partner matching condition.

Results are presented in Table 5. With respect to the reference treatment that is the NIS

treatment, we observe that credit cost increases when players interact with the same partner

(i.e. NIP and INP treatments). The credit cost increases by almost 15 and 13 percentage points

with respect to the NIS treatment for participants under NIP and INP treatments, respectively.

Moreover, the significant positive coefficient associated to “Period” variable reveals that

credit cost increases along the periods, which is consistent with the idea that the hold-up

effect is produced in the long-term. Finally specification 3 shows that repayment in t-1 has a

positive impact on credit cost in stranger treatment while it significantly reduces the cost of

credit in partner treatments.

[Table 5 and Figure 3a -3c: about here]

Most of demographic are insignificant except “gender” and “social banker” variables. The

positive and significant coefficient associated to the gender variable indicates that males

would be more prone to ask higher credit cost. The negative and significant coefficient at the

10% level associated to “Social bankers” seems to indicate that the credit is less expensive

when granted by social bankers. No significant difference is found between students and

commercial bankers. Mann-Whitney tests indicate that credit cost is significantly more

expensive with commercial bankers than with social bankers (p=0.0963 for NIP treatment;

p=0.0058 for INP treatment; p=0.0156 when strictly independent observations are

considered). Figure 3c also displays the time series of credit per period for field experiment

(partner treatments only). It indicates that credit cost for borrower is higher when made by

commercial bankers compared to social bankers. Subsequently, we argue that commercial

bankers seem to be more prone to take advantage of the captive market, what is not necessary

the case for social bankers whose credit cost does not raise overtime.

3.2. Risky choices and the effect of information disclosure

Our previous results showed that engaging long-term relationships seems to be an efficient

way to deter shirking on repayment, i.e. to mitigate ex-post moral hazard. We focus here on

another form of shirking behavior, i.e. the fact that the borrower may also decide to invest into

a project different from that stipulated in the lender’s offer agreed upon (ex-ante moral

hazard). Since the efficient project is project A, we consider that shirking consists of choosing

the project B while the lender stipulated that the borrower should invest into the efficient

project A. 27 We investigate to what extent long-term relationship affects shirking decision.

27 The reason why we focus our analysis on shirking when project A was stipulated in the lending offer is that A is the project that yields the maximum social benefits. Thus, this is the project that should be favored by the financial system.

19

We also check whether providing the lender with information about which project has been

chosen tends to reduce shirking behavior on the project choice. Before discussing the

determinants of shirking, we briefly describe the project choices realized by lenders and

borrowers. Our findings are summarized in result 4

Result 4: Long-term relationships and high repayment desired by the lender incite the

borrowers to take more risk by choosing inefficient project B, which generally induces more

shirking on the project choice.

Support for result 4: Table 6 contains the Probit estimates for the probability of choosing

project B. It consists of two panels. The left panel shows the results of two regressions in

which the dependent variable is a dummy variable that takes the value 1 when the borrower

invests into the inefficient project B and 0 otherwise. The right panel displays the results of

two additional models in which the dependent variable is a dummy variable for choosing

project B whereas project A was recommended by the lender.

Our independent variables include dummy variables for treatment, a dummy variable “Project

A – lender” that takes 1 if the lender has recommended Project A in her offer and 0 otherwise.

Finally the “desired repayment” variable accounts for the repayment desired by the bankers.

[Table 6: about here]

The significant and positive coefficient associated to the “NIP treatment” variable shows that

partner relationships tend to incite the borrowers to choose project B. The variable “Project

A–lender” attracts a positive and significant coefficient showing that borrowers are less likely

to choose project B when the safer project has been recommended by the lender. The “desired

repayment” is significant in most of estimates, which indicates that high repayment required

by the lender incites the borrower to select the inefficient project B. The estimates reported in

the right panel of table 6 confirm our previous findings. Borrowers in a partner-matching

scheme are more prone to shirk if we refer to the positive and significant coefficients captured

by the “NIP treatment” variable in estimate.

4. Discussion

To date, the success of microfinance has been essentially explained by the recourse to peer

lending. Our experiment sheds light on the existence of another potential explicative factor

which is the long-term relationship between the lending institution and the borrower. Indeed

several previous studies have shown the key role played by individual long-term based

contracts in microfinance systems, in particular when they are implemented in developed

countries in absence of peer lending systems.

Our principal findings are the following. First, we find that the opportunity to engage bilateral

long-term relationships strongly improves the market performance by facilitating access to

credit for borrowers and mitigating the repayment problem (ex-post moral hazard) through

higher repayment rates. Second, our findings indicate that long-term relationships can also

20

induce undesirable by-effects. Precisely we find that lenders tend to take advantage of their

long-term situation by increasing their rates, thereby highlighting a form of hold-up effect that

in turn exacerbates ex-ante moral hazard. Indeed unfair credit offers or too expensive credits

incite borrowers to select non-optimal investment project. In sum, our results highlight that

long-term relationship mitigates ex-post moral hazard – which is the more prejudicial for the

lenders – but tends to aggravate ex-ante moral hazard.

Finally, we find that improving the information disclosure – while maintaining the feasibility

of reputation building between the two parties involved – offers little added-value. More

information negligibly enhances market performance, thereby shedding light on the prominent

role of reputation to mitigate problems created by asymmetric information. Furthermore more

information does not enable to reduce shirking on the project selection. An analysis of the

latter result from another viewpoint may also demonstrate the difficulty of tackling the ex-

ante moral hazard issue and therefore supports the hypothesis of a form of soft-budget

constraint (Boot, 2000; Dewtripoint and Maskin, 1995).

Our findings seem to support our reciprocity assumption according to which long-term

relationships strengthen borrowers’ reciprocal behavior through reputation. Precisely lower

credit rate induces higher repayment levels from the borrower which in turn engender a

reduction in credit cost. This enhanced cooperation between the principal and the agent results

in higher repayment rates and in a facilitated access to credit for borrowers. These findings

are consistent with previous experimental studies showing that long-term relationships have a

powerful disciplinary effect in different contexts including public good games, gift exchange

games or trust games (see for example Andreoni and Miller, 1993; Fehr and Gätcher, 2000;

Gätcher and Falk, 2002; Bohnet and Huck, 2004). However our results also support the hold-

up assumption. Indeed our data indicate that borrowers bear higher credit cost under the

partner treatment condition.

How could we explain those apparently contradictory results? One possible reason is that

reciprocity pattern may not work exactly the same way for both parties. Precisely those who

may benefit more from repeated interactions with the same partner may be the first movers in

the game because they have a bargaining power. For example lenders may take advantage of

their position by threatening the borrower of no-refinancing in case of shirking. This result is

consistent with previous findings in the literature. For example, using a gift exchange game

Gaetcher and Falk (2002) observe that repeated interaction under partner matching induces

higher effort from the second mover (i.e. the employee) and a steeper positive wage-effort

relationship but not higher wages from the first mover (i.e. the employer). In another context

(a trust game experiment), Bohnet and Huck (2004) find that exposure to a partner treatment

makes trustees more trustworthy in the long run while no similar treatment effect is found for

the trustors.

21

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Petersen, M. and Rajan, R. (1995), “The effect of credit market competition on lending relationships”, Quarterly Journal of Economics, 110, pp. 407-443. Rabin, M. (1993), "Incorporating Fairness into Game Theory and Economics", American Economic Review, 83, 1281-1302. Rai, A., and Sjöström, T. (2001), “Is Grameen Lending Efficient?”, Working paper, Harvard University. Rajan, R. (1992), “Insiders and Outsiders: the Choice between Informed and Arm’s-length Debt”, Journal of Finance, 47, pp. 1376-1400. Sadoulet, L. (1997), “The Role of Mutual Insurance in Group Lending”, Working Paper, ECARE, Sharpe, S. (1990), “Asymmetric Information, Bank Lending and Implicit Contracts: a Stylized Model of Customer Relationships”, Journal of Finance, 45(4), 1990, pp. 1069-1087. Stiglitz, J.E. and Weiss, A. (1981), “Credit Rationing in Markets with Imperfect Information, American Economic Review, Vol.71, June, n°3, pp .393-410. Stiglitz, J.E. (1990), “Peer Monitoring and Credit Markets”, World Bank Review, 4(3), pp. 351-366. Varian, H. R. (1990), “Monitoring Agents with Other Agents”, Journal of Institutional and Theoretical Economics, 146, pp. 153-174.

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Table 1: Characteristics of investment projects

Characteristics Project A Project B

Probability of success ( pφ ) 80% 30%

Return in case of success ( epR ) 100 200

Return in case of failure 0 0

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Table 2: Characteristics of the Experimental Sessions

Session

number

Number of

subjects Treatments Matching protocol

1 8 NIS Stranger

2 8 NIS Stranger

3 8 NIS Stranger

4 8 NIS Stranger

5 8 NIS Stranger

6 8 NIS Stranger

7 20 NIP Partner

8 18 INP Partner

9 10 NIP-INP Partner

10 10 INP-NIP Partner

11 10 NIP-INP Partner

12 10 INP-NIP Partner

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Table 3: Probability that a banker makes a credit offer

Regression models Marginal effects

Models RE Probita Dep. Var.: Probability of offering a contract

Treatments: NIS/NIP data

NIP/INP data

Pooled data

Pooled data

Pooled data

1 2 3 4 5

NIS treatment Ref. Ref. Ref. Ref. NIP treatment 0.559** Ref. 0.615*** 0.521* 0.114** (0.238) (0.230) (0.269) (0.055) INP treatment 0.167 0.776*** 0.680** 0.144*** (0.152) (0.232) (0.271) (0.055) Repayment rate t-1 1.363*** 0.934*** 1.048*** 1.037*** 0.236*** (0.202) (0.162) (0.146) (0.146) (0.038) Period -0.033* -0.047*** -0.046*** -0.046*** -0.010*** (0.018) (0.017) (0.015) (0.015) (0.003) All bankers -0.033 -0.007 (0.313) (0.071) Social bankers 0.447 0.092 (0.338) 0.062 Risk -0.036 -0.081 (0.060) (0.014) Gender -0.135 -0.030 (0.223) (0.050) Eco -0.016 -0.003 (0.219) 0.05 Constant 0.241 1.063*** 0.388* 0.665 (0.219) (0.225) (0.201) (0.424) Observations 508 670 849 849 849 Log likelihood -228.27 -247.54 -349.93 -348.21 Wald Chi2 55.25 42.77 73.87 76.12

Notes: a RE Probit=Random Effect Probit; *** Significant at the 0.01 level; ** at the 0.05 level; at the 0.1 level; Standard errors in parentheses. Period corresponds to the time periods (1-15) of the game. Risk aversion is the score which corresponds to the safe choices selected by the subject out of ten choices. If this score is 1, the subject is “highly risk loving”, whereas if the number of safe choices is 8 or above the subject is “(very) highly risk averse”. Banker is a dummy variable which is 1 if the individual is a “real” banker and 0 otherwise. Social bankers is a dummy variable which is 1 if the individual is a “real” social banker and 0 otherwise. Gender is a dummy variable which is 1 when the participant is male and 0 otherwise. Eco is a dummy variable which is 1 when the participant is student with prior in economics and 0 otherwise. To control for “field” effect, the regression models also contain session dummies for field

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Table 4: Probability of offer acceptance and determinants of repayment

Dep. Var.: Repayment rate for accepted contracts Acceptance

Repayment rate for accepted contracts

Models: RE GLSb RE GLSb RE GLSb RE Tobita RE Probitc RE GLSb RE GLSb RE GLSb

Treatments: Pooled data NIS/NIP data

NIP/INP data Pooled data

Pooled data Pooled data Pooled data Pooled data

1 2 3 4 5 6 7 8

NIS treat. Ref. Ref. Ref. Ref. Ref. Ref. Ref.

NIP treat. 0.431*** 0.423*** Ref. 0.825*** 0.776 0.359*** 0.413*** 0.674***

(0.083) (0.088) (0.154) (0.489) (0.077) (0.078) (0.124)

INP treat. 0.481*** 0.053* 0.904*** 0.275 0.411*** 0.469*** 0.714***

(0.083) (0.032) (0.154) (0.405) (0.077) (0.078) (0.125)

Credit cost -0.590*** -0.543*** -0.644*** -1.196*** -2.589*** -0.502*** 0.011

(0.095) (0.123) (0.104) (0.223) (0.807) (0.098) (0.197)

Credit cost*partner

-0.629***

(0.221)

Period -0.018*** -0.016*** -0.021*** -0.040*** -0.019*** -0.018*** -0.018***

(0.003) (0.004) (0.004) (0.007) (0.003) (0.003) (0.003)

Risk -0.031 -0.030 -0.038** -0.082** -0.027 -0.031 -0.033

(0.022) (0.027) (0.016) (0.042) (0.021) (0.021) (0.022)

Gender 0.099 0.102 0.100* 0.338** 0.110 0.099 0.101

(0.073) (0.083) (0.052) (0.149) (0.068) (0.069) (0.070)

Eco -0.101 -0.110 -0.052 -0.229 -0.073 -0.107 -0.108

(0.083) (0.094) (0.060) (0.161) (0.078) (0.078) (0.080)

Project A-lender 1.326***

(0.294)

IMR -1.428*** -1.019*** -1.268***

(0.303) (0.307) (0.310)

Project B 0.046

(0.055)

Project B* INP treat.

0.111

(0.078)

Constant 0.910*** 0.871*** 1.384*** 1.352*** 2.736*** 0.682*** 0.903*** 0.701***

(0.151) (0.178) (0.125) (0.279) (0.520) (0.135) (0.142) (0.158)

Observations 582 353 457 582 917 582 582 582

Left-cens. Obs. 124

Right-cens. Obs 211

R2 0.22 0.22 0.17 0.22 0.24 0.259

Log-Likelihood -503.13 -89.94

Notes: a RE Tobit=Random Effect Tobit; b RE GLS=Random Effects Generalized Least Squares; c RE Probitc =Random Effect Probit ; *** Significant at the 0.01 level; ** at the 0.05 level; * at the 0.1 level; Standard errors in parentheses. Period corresponds to the time periods (1-15) of the game. Risk aversion is the score which corresponds to the safe choices selected by the subject out of ten choices. If this score is 1, the subject is “highly risk loving”, whereas if the number of safe choices is 8 or above the subject is “(very) highly risk averse”. Gender is a dummy variable which is 1 when the participant is male and 0 otherwise. Eco is a dummy variable which is 1 when the participant is student with prior in economics and 0 otherwise. Project-B is a dummy variable that takes 1 if the subject choose project B and 0 otherwise. Project-A lender is a dummy variable that takes 1 if the banker recommended project A and 0 otherwise.

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Table 5: Determinants of credit cost.

Notes: ; *** Significant at the 0.01 level; ** at the 0.05 level; * at the 0.1 level; Standard errors in parentheses.

Model: Random Effect GLS Model Dep. var.: Credit cost Treatment: Pooled data 1 2 3 NIS treatment Ref. Ref. Ref. INP treatment 0.122*** 0.122*** 0.150*** (0.036) (0.036) (0.038) NIP treatment 0.103*** 0.103*** 0.132*** (0.036) (0.036) (0.038) Repayment rate t-1 -0.006 -0.006 0.058* (0.012) (0.012) (0.030) Shirking t-1 0.029** 0.028* 0.029** (0.012) (0.015) (0.015) Period 0.004*** 0.004*** 0.004*** (0.001) (0.001) (0.001) Banker -0.079 -0.079 -0.078 (0.049) (0.048) (0.049) Social banker -0.101** -0.102** -0.100** (0.048) (0.048) (0.048) Risk 0.002 0.002 0.001 (0.008) (0.008) (0.008) Gender 0.069** 0.069** 0.068** (0.031) (0.030) (0.031) Eco 0.005 0.005 0.005 (0.030) (0.030) (0.030) Age 0.001 0.001 0.001 (0.002) (0.002) (0.002)

Shirking t-1* social banker

0.003 0.002 (0.025) (0.025)

Repayment rate t-1*partner treat.

-0.075** (0.032)

Constant 0.349*** 0.349*** 0.335*** (0.068) (0.068) (0.068) Observations 666 666 666 R2 0.167 0.167 0.173 Rho 0.331 0.325 0.330

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Table 6: Probability that borrowers select Project B

Model Random effects Probit Models

dep. var. Prob. of choosing B Prob. of shirking by choosing B

Pooled data NIS/NIP NIP/INP NIS/NIP NIP/INP 1 2 3 4 5 Project A - lender -0.900*** -0.688*** -0.992*** (0.122) (0.152) (0.144) NIP treatment 0.402* 0.507** 0.441* (0.241) (0.252) (0.234) INP treatment 0.570** 0.173 -0.028 (0.242) (0.126) (0.150) Desired Repayment 0.005** 0.004 0.005* 0.007 0.011* (0.002) (0.003) (0.003) (0.006) (0.006) Period -0.003 -0.008 -0.000 0.001 0.020 (0.012) (0.015) (0.014) (0.018) (0.017) Risk -0.000 0.041 -0.008 0.036 0.052 (0.054) (0.061) (0.072) (0.059) (0.068) Gender 0.373** 0.459** 0.402* 0.477*** 0.313 (0.175) (0.178) (0.234) (0.175) (0.229) Eco 0.028 0.016 0.045 0.026 0.055 (0.033) (0.033) (0.041) (0.029) (0.035) Age -0.344* -0.440** -0.192 -0.429** -0.040 (0.207) (0.213) (0.271) (0.202) (0.250) Constant -0.879 -0.924 -0.890 -1.967*** -2.801*** (0.796) (0.826) (1.017) (0.741) (0.939)

Observations 891 534 704 369 485

Log likelihood -467.05 -282.70 -357.08 -184.67 -230.60 Notes: *** Significant at the 0.01 level; ** at the 0.05 level; * at the 0.1 level; Standard errors in parentheses. To control for “field” effect, the regression models also contain session dummies for field sessions

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Figure 1a: Average number of credits offered per period for NIS and NIP treatments

Figure 1b: Average number of credits offered per period for NIP and INP treatments

Figure 1c: Average number of credits offered per period (field data)

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Figure 2a: Average repayment rate after project success per period for NIS and NIP

treatments.

Figure 2b: Average repayment rate after project success per period for NIP and INP treatments

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Figure 3a: Credit cost per period for NIS and NIP treatments

Figure 3b: Credit cost per period for NIP and INP

Figure 3c: Credit cost per period (field data)

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General instructions You are now participating in an economic experiment that aims to analyze decision-making on credit market. Please read carefully the following explanations which you require for participation in the experiment. You can earn Experimental Monetary Units (EMU) over the course of the experiment. This amount depends on your decisions as well as those of the other participants. All EMU you earn during the experiment will be converted to € upon conclusion of the experiment. The following exchange rate applies: 1€ = 47EMU. During the experiment, there is a prohibition of communication. You also have to use only the functions on the computer which are necessary to the completion of the experiment. In case of breach of those rules, we will be obliged to discontinue the experiment. We remain at your disposal should you have any question.

Summary course of experiment Two types of participants are taking part in the game: bankers and borrowers. The bankers’ group as well as the borrowers’ comprises 5 participants. The game is subdivided into 15 individual periods. Each banker is paired with the same borrower for the whole duration of the experiment. At the beginning of each period, all borrowers receive an initial endowment of 10 EMU and they can invest this endowment into two investment projects A or B that present the following characteristics:

� Project A is successful in 8 out of 10 cases. If the project succeeds, the yield amounts to 100 EMU. If it fails, the yield is null (0 EMU).

� Project B is successful in 3 out of 10 cases. If the project succeeds, the yield amounts to 200 EMU. If it fails, the yield is null (0 EMU).

These pieces of information are summarized in the following table: Project A Project B Borrowers’ endowment 10 10 Bank loan 32 32 Probability of success 80% 30% Project yield in case of success 100 200 Probability of failure 20% 70% Project yield in case of failure 0 0 In order to invest into one of those two projects, the borrowers necessitate a bank loan of 32 EMU. The procedure in an individual period includes the following stages:

1. Each banker receives an endowment of 32 EMU at the beginning of each period. She has two possibilities for using her endowment:

� Grant a credit to the borrower she is paired with by making a credit offer. � Not grant a credit. In this case, she retains her initial endowment of 32 EMU as a

guaranteed payout.

2. If the banker decides to make an offer, the borrower she is paired with decides whether to accept the offer. If the borrower rejects the offer, she retains her initial endowment of 10 EMU.

3. All borrowers that have accepted an offer choose the project into which they wish to invest. They freely opt for one project or the other; they may opt for the project requested by the banker or the other one.

4. After all borrowers have chosen their projects into which they want to invest, a random selection will decide whether the project selected was successful or not. The outcome remains confidential to the borrowers insofar the bankers will not have access to the project outcome. The bankers can only observe the repayment realized by the borrowers. If the project yields a positive outcome, borrowers can repay:

� The amount requested by the banker, � A lower amount than that requested by the banker, � Nothing at all.

5. Upon conclusion of each period, the incomes will be calculated both for bankers and borrowers.

Specific instructions for bankers The game is subdivided into 15 individual periods. You are paired with the same borrower for the whole duration of the game.

34

At the beginning of each period, you receive an endowment of 32 EMU. In this first stage, you have two possibilities for using your endowment:

� Grant a credit to the borrower you are paired with. � Retain your initial endowment of 32 EMU and keep it a guaranteed payout.

You can only post one offer per period to the borrower you are paired with. As indicated in the following monitor display, you have to stipulate two pieces of information:

� The project (A or B) which you wish the borrower to invest into. We remind you that the borrower freely chooses the project she wishes to invest into.

� The level of repayment you request. Of course the repayment you request cannot exceed the project outcome in case of success, that is, 100 EMU for project A and 200 EMU for project B. We remind you that the borrower freely determines the amount she wishes to pay back to you.

Once you have specified these two things, you can post your offer by clicking on OK . If you do not wish to make a credit offer, that is, you want to keep your initial endowment, click on NO OFFER.

In the second stage, the screen capture displayed below enables you to check whether your borrower has accepted your offer or not. In the third and last stage, you can observe your income that is calculated as follows: YOUR INCOME FOR THE PERIOD 1) If you have decided not to grant a credit to your borrower or if your offer has not been accepted, you retain your initial endowment and do not earn anything more. [Your income = your initial endowment = 32 UME] 2) If you have decided to grant a credit and your credit offer has been accepted by your borrower, your income for the period equals the repayment realized by your borrower. Thus, the higher the borrower’s repayment is, the higher your income for the period. [Your income = Borrower’s repayment]

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Specific instructions for borrowers The game is subdivided into 15 individual periods. You are paired with the same banker for the whole duration of the experiment. Each period, you can consider two investment projects, A or B. To finance one of those two projects, you have to contract a credit of 32 EMU from your banker. If the banker makes a credit offer to you, you can in the first stage either accept it or refuse it. If you refuse it, you retain your initial endowment of 10 EMU as a certain payout. If your banker does not make any credit offer to you, you also retain your initial endowment. In the second stage, you freely determine whether you wish to invest into project A or project B, that is, you may opt for the project requested by your banker in her offer or, to the contrary, invest into the other project. Your banker will not be informed of this decision.

36

6. In the third stage, the computer randomly determines whether the project you have selected succeeded or not. In the case of success, you freely determine the amount you want to return to the banker, that is, you can repay:

� The amount requested by the banker, � A lower amount than that requested by the banker, � Nothing at all.

In the case of failure, your repayment will be automatically set to 0 EMU.

In the last and fourth period, you can observe your income that is calculated as follows: YOUR INCOME FOR THE PERIOD 1) In the case in which you have accepted an offer from your banker: - If the project has succeeded: [Your income = Project yield – your repayment] - If the project has failed: [Your income = 0 UME] 2) If your banker has not made any credit offer to you, or you have decided not to accept her offer, you retain your initial endowment and do not earn anything more: [Your income = initial endowment = 10 UME]