Long-term relationship and Reciprocity in Credit Market Experiment: Implications for Microfinance

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1 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 sociodemographic 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 longterm 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 RENNES Cedex 7. simon.cornee@etudiant.univ-rennes1.fr. Telephone: CREM (CNRS University Rennes 1), 7, place Hoche Rennes, France. david.masclet@univrennes1.fr. # CREM-IGR (University Rennes 1), 11 rue Jean Macé CS RENNES Cedex 7. gervais.thenet@univ-rennes1.fr 1

2 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 areas 1. The contemporary movement of microfinance 2, 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 mechanism 4 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 coguarantor (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 In Europe, popular credit granting in the 19 th 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. 2

3 (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 longterm 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 clients 7. 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 longterm and personalized relationships that involve physical proximity and regular face-to-face, which is generally essential for interacting with vulnerable populations 9. Building up longterm 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. 3

4 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 longterm 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 market 12. 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. 4

5 borrower a borrower may be strongly incited to choose an inefficient high-risk project to reimburse too onerous a loan 13 (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 market 14. 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 strangermatching 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 reallife 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. 5

6 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 endogenously 15. 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). 6

7 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 sociodemographic variables. In addition, it also allows to measure the relative influence of sociodemographic 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 7

8 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 d offer in which two pieces of information must be stipulated: a desired project ( p { A, B} and a desired repayment ( r d ). ) 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 return 17. 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 ( r = 0 ). If the project turns out to be successful, the borrower is free to repay the amount she desires ( r 0, R p ). Finally, at the end of the period, each participant is informed about her final payoff given as: ( π Lender ) = r if the lender grants a credit of 32 ECUs to the borrower (1) = S if the lender chooses the outside option. ( π Borrower ) = 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. 8

9 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 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 banks 18 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 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 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. 9

10 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. 10

11 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 partnermatching 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). 11

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. 12

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. 13

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: Y i, t α + β1nip + β 2INP + β 3Rt 1 + β 4 period + β5 X i + ε i, t = (3) Where Y it 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. R t-1 corresponds to the repayment received by subject i in t-1. We also control for time effects by including the variable Period. X i is the vector of personal characteristics including gender, a measure for risk aversion 25, 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 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. 14

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 A Mann- Whitney test confirms that reimbursements are significantly higher in NIP than in NIS either considering all repayments (p = ) or repayments after project success (p = ). 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). 15

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 twostep 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. 16

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 efficiencyenhancing 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 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: RATE i, t = α + β1nip + β 2INP + β3rt 1 + β 4shirkt 1 + β 5Rt 1 partner + β 6 X i + ε i, t (4) RATE i,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: R t-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 17

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= for NIP treatment; p= for INP treatment; p= 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 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. 18

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 19

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 exante 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 holdup 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. 20