Gender differences in the repayment of microcredit: The mediating role of trustworthiness

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Gender Differences in the Repayment of Microcredit: The Mediating Role of Trustworthiness Abu Zafar M. Shahriar , Luisa A. Unda , Quamrul Alam PII: DOI: Reference:

S0378-4266(19)30259-6 https://doi.org/10.1016/j.jbankfin.2019.105685 JBF 105685

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Journal of Banking and Finance

Received date: Accepted date:

3 May 2018 22 October 2019

Please cite this article as: Abu Zafar M. Shahriar , Luisa A. Unda , Quamrul Alam , Gender Differences in the Repayment of Microcredit: The Mediating Role of Trustworthiness, Journal of Banking and Finance (2019), doi: https://doi.org/10.1016/j.jbankfin.2019.105685

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Gender Differences in the Repayment of Microcredit: The Mediating Role of Trustworthiness Abu Zafar M. Shahriar Department of Banking and Finance Monash Business School, Monash University 900 Dandenong Road, Caulfield East, VIC 3145, Australia [email protected] Phone: (61) 3 9903 2652 Luisa A. Unda Department of Accounting Monash Business School, Monash University 900 Dandenong Road, Caulfield East, VIC 3145, Australia [email protected] Phone: (61) 3 9905 5847 Quamrul Alam School of Business and Law Central Queensland University Melbourne 120 Spencer St, Melbourne VIC 3000, Australia [email protected] Phone: (61) 3 96160676 ACKNOWLEDGEMENTS We would like to thank Elaine Hutson, Abe de Jong, and two anonymous reviewers for their detailed and insightful comments. The Centre for Global Business of Monash Business School provided financial support. We would like to thank our team of field researchers and all the participants of the experiments. Afroza Ferdous provided excellent research assistance. We are responsible for any remaining errors.

ABSTRACT Growing evidence suggests that women are more likely to repay collateral-free microloans than men. However, we know little about what explains such gender differences. We hypothesize that better repayment performance of women microcredit borrowers can largely be explained by gender differences in innate trustworthiness. We conduct a trust game and a microloan repayment game in rural Bangladesh. We find that women are more trustworthy than men and that they are more likely to repay their loans irrespective of any control mechanisms, such as joint liability or dynamic repayment incentives. The results of a mediation test suggest that the gender effect on loan repayment is significantly mediated by differences in innate trustworthiness. We conduct a sensitivity test to check the extent to which unobserved confounders might have influenced the mediation effect, and find no evidence of significant omitted variables bias.

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JEL classification: C9; G21; O16; O17; O21 Keywords: Gender; Trust; Repayment; Microcredit; Experiments.

1.

Introduction Growing evidence suggests that women are more likely to repay collateral-free

microloans than men. Women have shown better repayment performance in Bangladesh, Guatemala, Malawi, Malaysia, and Mexico (Armendáriz and Morduch, 2010). Multi-country studies by D‘Espallier et al. (2011) and Gul et al. (2017) suggest that a higher percentage of female clients in microfinance institutions (MFI) is associated with lower portfolio risk, fewer write-offs, and fewer provisions. However, we know little about what explains such gender differences. Based on anecdotal evidence, some scholars argue that women have better repayment records simply because of the role they play in a male-dominated society (e.g., Hashemi et al., 1996; Morduch, 1999). For example, because of lower mobility and fewer alternative borrowing possibilities, women in developing countries are less likely to ‗take the bank‘s money and run‘. In contrast to this popular belief, we test the hypothesis that gender differences in the repayment of microloans can largely be explained by gender differences in innate trustworthiness. Trust refers to the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will not behave opportunistically (Berg et al., 1995; Mayer et al., 1995; James, 2002). Trustworthiness, on the other hand, refers to the innate personal characteristics of an individual reflecting her or his preference to reciprocate to the act of trusting in the absence of any economic incentives. In a lending relationship, the lender shows trust by accepting the risk that the borrower may strategically default even if the project yields adequate return to repay (Jaffee and Russell, 1976). Under asymmetric information, the borrower shows trustworthiness by repaying a loan (Saparito et al., 2004; 2

Becchetti and Conzo, 2011). The trust-repayment relationship in microcredit has been investigated by several authors. Karlan (2005) conducts a trust game with the borrowers of FINCA (Foundation for International Community Assistance) in Peru and finds that trustworthy individuals are more likely to repay their loans. Cassar et al. (2007) and Cassar and Wydick (2010) conduct laboratory experiments in Armenia, Guatemala, India, Kenya, South Africa, and the Philippines. They also find a positive association between trustworthiness and microloan repayment decision. There are, however, two important gaps in this literature. First, the role of borrowers‘ gender in the trust-repayment relationship has not yet been studied. Biological research suggests that trustworthiness is positively associated with the level of oxytocin release in a person (Kosfeld et al., 2005; Zak et al., 2005), and that the magnitude of oxytocin release is significantly higher among women than men (Carter, 2007). Evolutionary psychologists argue that gender-differentiated behaviors have roots in the evolutionary past (Wood and Eagly, 2002). Ancestral women competed with other women to attract long-term mates to protect the future of their offspring, which evolved dispositions such as risk-aversion and trustworthiness. It is therefore not surprising that women have shown more trustworthiness than men in most of the experimental trust games (Croson and Gneezy, 2009). We argue that higher trustworthiness translates into better repayment behavior by women borrowers. The study that is closest to ours is by Aggarwal et al. (2015), who report that MFIs are more likely to target female clients in countries with a low level of social trust. Social trust refers to the extent to which the members of a society can be trusted (Guiso et al., 2004). Thus, the targeting of female clients by MFIs is a substitute for low levels of trust in society. This conclusion is based on an untested premise that better repayment performance of female microcredit borrowers can largely be attributed to their innate trustworthiness. We formally test this proposition. 3

Second, when examining the trust-repayment relationship, prior studies have ignored the differences in lending method. Karlan (2005), Cassar et al. (2007), and Cassar and Wydick (2010) all investigated the role of trustworthiness in joint liability-based group loans with dynamic repayment incentives, whereas we investigate loan repayment decision with, and without, joint liability and dynamic incentives—the two control mechanisms widely used by MFIs to ensure prudent behavior by borrowers.1 We conducted laboratory experiments in rural Bangladesh with male and female subjects. A subject first participated in a trust game, which measures trust and trustworthiness in individuals (Berg et al., 1995; Karlan, 2005), and then a loan repayment game, which captures an individual‘s willingness to repay microloans (Abbink et al., 2006; Cassar et al., 2007; Shahriar, 2016). We conducted four treatments of the repayment game. In the benchmark treatment, subjects repaid individual loans without dynamic incentives. In the second treatment, we added dynamic incentives to individual loans. In the third treatment, subjects repaid joint liability loans without dynamic incentives. In the fourth treatment, we added dynamic incentives to joint liability loans. Our experimental design sheds light on two important issues. First, it enables us to examine whether women are more (or less) responsive than men to joint liability and dynamic incentives. Some scholars have argued—without formal investigation—that women are more responsive to peer pressure induced by joint liability loans (Rahman, 1998; Morduch, 1999). However, if women have better repayment records because of their innate attitude, as we propose, they will show better repayment performance than men irrespective

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Under joint liability, a group of borrowers remains collectively responsible for repayment, which introduces peer monitoring (Stiglitz, 1990). MIX Market provided information on the lending method of 972 MFIs in 2009 (MicroBanking Bulletin, 2010). Of these MFIs, 364 provided individual loans, 97 provided group loans, 426 offered both types of loans, and 85 MFIs lent through self-help groups. Cull et al. (2009) provided similar information on 315 MFIs over 2002-2004. Of these MFIs, 105 provided individual loans, 157 provided group loans, and 53 MFIs lent through self-help groups. Dynamic incentives, on the other hand, refer to the practice of extending repeat loans upon successful repayment and terminating lending relationships upon default. Although most microcredit contracts are dynamic (Banerjee, 2013), there are exceptions. For example, the Grameen Bank of Bangladesh offer housing loans and Bangladesh Rural Advancement Committee (BRAC) offer migration loans without joint liability or dynamic incentives.

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of peer pressure or dynamic incentives. Second, our experimental treatments allow us to examine whether trust and control are substitute, or parallel, to each other. There is a debate on the role of trust and control in exchange relationships. Some scholars argue that in the presence of effective control mechanisms, trustworthiness is redundant to ensure reciprocity, that is, trust and control are substitute to each other (Leifer and Mills, 1996; Inkpen and Currall, 2004). Others argue that trust and control have independent effects on decisionmaking, that is, they are parallel to each other (Das and Teng, 1998, 2001; Coletti et al., 2005). If trust and control are substitutes, trustworthiness will have a weaker impact on the repayment of joint liability-based loans compared to that of individual loans. Similarly, when loans are provided with dynamic incentives, trustworthiness will have a weaker impact on borrowers‘ repayment decision compared to loans without dynamic incentives. In contrast, if trust and control are independent to each other, trustworthiness will influence a borrower‘s repayment decision irrespective of joint liability or dynamic incentives. To examine whether trustworthiness mediates gender differences in the repayment of microloans, we conduct a mediation test proposed by Baron and Kenny (1986). According to this method, the mediation-effect is supported if three conditions hold: (a) the treatment variable has a significant relationship with both the mediator and the outcome, (b) the relationship between the mediator and the outcome is significant, and (c) after statistically controlling for the mediator, a previously significant relationship between the treatment and the outcome is substantially weakened. The results of our trust game suggest that women are more trustworthy than men. In the loan repayment game, women have shown better repayment performance than men in every treatment, that is, irrespective of joint liability or dynamic incentives. We find that trustworthiness is positively associated with loan repayment rates irrespective of the lending method, that is, trustworthiness explains repayment decisions independent of the control mechanisms. Finally, we find that after controlling for the effects 5

of trustworthiness, the association between gender and repayment is substantially weakened. Together, these findings support our mediating hypothesis. There is, however, a limitation of our study. Mediation analysis relies on the assumption that the treatment is independent of the mediator (MacKinnon, 2008), whereas in the present context, a number of factors are simultaneously associated with gender, trustworthiness, and repayment behavior. In our empirical analyses, we control for the effects of a wide array of socioeconomic and demographic characteristics of the subjects. We control for factors that proxy for subjects‘ attitude toward risk, social connectedness, and cultural proximity. We control for subjects‘ perception of trust, fairness, and benevolence. We also control for the effects of human capital endowment at the community level and urbanization. There are, however, many unobserved factors that we cannot control for. We conduct a sensitivity analysis (Imai et al., 2010a) to check the extent to which unobserved confounders might have influenced the mediation effect, and find no evidence of significant omitted variables bias in our results. However, given the limited set of observed characteristics that can be controlled for, the findings we report are indeed suggestive. Future research can build on these findings and draw more reliable evidence from experimental manipulation of the mediator. MFIs have served millions of ‗non-bankable‘ clients in low-income communities (Islam et al., 2015; Mersland and Strøm, 2009; Servin et al., 2012; Strøm et al., 2014). Muhammad Yunus, the founder of the Grameen Bank, attributes this success to the trustbased relationships between MFIs and their clients (Yunus, 1999). However, because of rapid commercialization in recent years, many MFIs have deviated from trust-based lending and adopted an aggressive approach to expanding the borrowing base, accompanied by coercive loan collection practices, which created debt crises in countries, such as India (Haldar and Stiglitz, 2014, 2016). At the same time, many MFIs have curtailed outreach to women 6

because they are expensive to reach (Cull et al., 2007, 2009, 2011). Against this backdrop, our findings offer two important insights for MFIs. First, trust-based relationships are the key to sustained success of microfinance. Second, MFIs would do better by not scaling back on their outreach activities to women. Women are perhaps inherently better than men at repaying microloans. Thus, targeting women as borrowers would help MFIs maintain low default costs. We organize the remainder of the paper as follows. We explain the experimental design in section 2. We discuss our findings in section 3. Section 4 concludes the paper. 2. Experimental design 2.1. Lab setting and subjects We conducted our experiments in rural Bangladesh. Using simple lotteries, we first chose 10 (out of 64) districts, and then chose five villages from each district. In each village, we prepared a list of 20 households with the help of the members of the local Union Council, which is the lowest tire of the local government system in rural Bangladesh. The selected households owned less than half an acre of arable land. Many prominent MFIs in Bangladesh—such as Grameen Bank, Association for Social Advancement (ASA), Bangladesh Rural Advancement Committee (BRAC), and Bangladesh Extension Education Services (BEES)—use this threshold of land-ownership as an eligibility criterion for providing microloans (Hossain, 1988; Sharma and Zeller, 1997; Shahriar and Shepherd, 2019). From the odd (even) numbered households of the list, we randomly selected an adult female (male) member and invited her (him) to participate in the experiments. However, we deliberately excluded individuals who had loans from an MFI to avoid potential confounding effects (Chakravarty and Shahriar, 2015). To conduct the experiments, we set up a simple laboratory in every village either in a community center or in a school building. Out of the 1,000 individuals we invited, 894 showed up at the lab at a pre-specified time. They all

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participated in the trust game. Two to four weeks later, a subsample of these subjects participated in the loan repayment game. 2.2. The trust game The experimental procedure of the trust game is similar to that of Karlan (2005). Six to ten subjects of the same gender participated in a session. At the beginning of a session, all subjects sat in the same room, where we provided experimental instructions in Bengali. The English version of the instructions is provided in Appendix A. We asked subjects specific questions to ensure that they understood the rules and the pay-off allocation of the game. Subjects played the trust game in pairs. Each pair consisted of a Player 1 (the sender) and a Player 2 (the recipient). Pairing and role-assignment (either as Player 1 or Player 2) were done randomly by the experimenters. When the pairing was announced, subjects observed the identity of their partner but were separated immediately. Those who played as Player 2 were taken to another room. Thus, subjects had no opportunity to communicate with their partners.2 Each subject received three tokens from the experimenter. Subjects in the role of Player 1 had the opportunity to pass their partner zero, one, two, or three tokens. The ratio of the amount sent to the initial endowment is considered a measure of trust. If Player 1 passed no token, the game ended. If at least one token was passed, the experimenter matched the amount passed and transferred it to the partner. For example, if Player 1 passed two tokens, the receiving partner (i.e., Player 2) received four tokens from the experimenter. Player 2 then had the opportunity to pass back any number of tokens to the sender. The ratio of the amount returned to the amount available to return is considered a measure of trustworthiness. For example, if Player 2 received four tokens from the experimenter and returned three of them to

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This approach is consistent with Glaeser et al. (2000) and Karlan (2005). The lack of anonymity between the partners enabled us to determine how different levels of social connection influence trustworthiness.

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Player 1, her/his trustworthiness measure is ¾. The game ended after Player 2 had revealed her or his decision. At the end of the experiment, a subject received TK100 from the experimenter for each of the tokens she/he had at possession. In addition, each subject received TK100 for participation. 3 Given the finite end of the game, and assuming no post-game consequences, the subgame perfect equilibrium for Player 2 was to pass back nothing to Player 1, and knowing this, for Player 1 to pass nothing to Player 2. However, the basic results of the trust game— reported in Table 1—suggest that 80% of the senders (Player 1) passed at least one token to their partners, and 64% of the recipients (Player 2) passed back at least one token. These findings are consistent with those of Glaeser et al. (2000) and Karlan (2005). [Table 1] 2.3. The post-experiment survey Following the trust game, we conducted a survey. We collected information on the demographic and socioeconomic characteristics of the subjects, such as age, education, religious affiliation, occupation, business-ownership, ownership of household assets, and exposure to income shocks in the recent past. We asked subjects three questions from the General Social Survey (GSS), which measure an individual‘s perception of trust, fairness and helpfulness.4 These questions have been widely used in empirical research on trust (Glaeser et al., 2000). Furthermore, we asked subjects to report the level of intensity of contact with their partner in the trust game on a scale of 1 (no contact) to 7 (frequent contact).

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Taka (TK) is the official currency of Bangladesh. One US dollar is equivalent to TK84. The per capita daily income in Bangladesh is TK262 (http://data.worldbank.org/indicator/NY.GDP.PCAP.CD?year_high_desc). 4 The General Social Survey is a sociological survey—conducted by the National Opinion Research Center and the University of Chicago—used to collect data on demographic characteristics and attitudes of residents of the United States. The exact wording of the GSS questions can be found in Table 3.

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2.4. The loan repayment game Subjects who played as a recipient in the trust game (i.e., Player 2, who revealed trustworthiness), participated in the microloan repayment game. Of the 447 recipients, however, 91 did not receive any tokens from their partner, and hence were unable to reciprocate. We did not consider them for further analysis because we could not measure their innate trustworthiness. The remaining 356 subjects (182 female and 174 male) participated in the loan repayment game. This game consisted of multiple rounds of borrowing and repayment. In each round, a subject received a ―loan‖ of TK50 from the experimenter, and ―invested‖ the loan in a risky venture. To determine the outcome of the risky venture, she/he played a simple ball-drawing game. Specifically, she/he drew a ball at random from a nontransparent jar that contained five green balls and one red ball. Drawing the red ball implied a negative shock to the borrower‘s project. In this case, the subject lost her/his investment and could not repay the loan. Drawing a green ball, on the other hand, implied a successful investment, from which the subject earned a positive return of TK120. A subject with positive project return either repaid the loan with 20% interest or decided not to repay. There were four treatments of the loan repayment game, summarized in Table 2. Each subject participated in one treatment. There were two treatments of individual loans and two treatments of joint liability-based loans. In the treatments of individual loans, a subject was individually responsible for repaying TK60. A subject whose project failed could not repay. A subject with a successful project either repaid the loan or decided not to repay. The benchmark treatment was individual loan without dynamic incentives. In this treatment, a subject received a repeat loan even if she/he was unable or unwilling to repay in the current round. The second treatment was individual loan with dynamic incentives. In this treatment, a subject received a repeat loan only if she/he repaid her/his loan in the current round. The 10

structure of a subject‘s payouts in a given round of the game is summarized in Table 2. To operationalize the treatments of individual loans, we took each subject individually to a private room, where she/he played the ball-drawing game and made repayment decisions. Thus, a subject‘s repayment decision was not influenced by that of the other subjects in the same session. [Table 2] In the treatments of joint liability-based loans, each subject was randomly matched with a borrowing partner, and the pair (or group) was collectively responsible for repaying TK120. A subject whose project failed could not repay. A subject with a successful project either contributed to group-repayment or decided not to contribute. If both subjects repaid, the experimenter collected TK60 from each of them. If only one member of the group repaid, the experimenter collected TK120 from the contributing member. Thus, at least one member had to repay to fulfill the group-repayment obligation. The third treatment of the game was joint liability loans without dynamic incentives. In this treatment, both members of a group received repeat loans at the end of a loan cycle irrespective of their repayment decisions. The fourth treatment was joint liability loans with dynamic incentives. In this treatment, subjects received repeat loans only if the group-repayment obligation had been fulfilled; otherwise, the game ended for the group. To operationalize the treatments of joint liability loans, we took each pair at a time to a private room, where subjects played the ball-drawing game and made repayment decisions. Thus, a subject‘s project outcome and repayment decision were observed by her/his partner.5 Special care was taken so that both of the subjects revealed their

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In prior joint liability loan treatments (e.g., Abbink et al., 2006; Cassar et al., 2007; Cassar and Wydick, 2010; Chakravarty et al., 2014; Shahriar, 2016), subjects could not monitor their group members‘ loan outcome or repayment decision. Gine et al. (2010) and Cason et al. (2012) allowed peer monitoring, but actual loan repayment decisions were not observed in their experiments. Rather, subjects revealed their investment decision with microloans.

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repayment decisions simultaneously (see Appendix B for experimental instructions of the loan repayment game). We did not allow subjects to communicate with each other. A potential concern with the loan repayment game is that in treatments with dynamic incentives, subjects‘ repayment decision may deteriorate toward the end of the experiments if they know the end point of the game (see, for example, Cassar et al., 2007). It is thus important to conduct these treatments without a predetermined end point. Following this intuition, and in order to be consistent across all the treatments of the loan repayment game, we conducted all four treatments without a predetermined end point. Specifically, after the third round of play, we continued the game only with a probability of one-half. A coin was tossed by the experimenter to determine whether to continue, but subjects were never informed of this. 6

3.

Empirical estimation and results

3.1.

Summary statistics Table 3 reports the definition of the variables used in regression analyses along with

their summary statistics. We observe a subject‘s repayment decision in the loan repayment game. We define loan repayment rate as the number of times a subject repaid her or his loan divided by the number of times a project was successful. Suppose a subject played the loan repayment game for three rounds. In two out of the three rounds, her project was successful but she repaid her loan only once. Her loan repayment rate is 0.5. This measure of repayment rate is similar to those of Cassar et al. (2007), Cassar and Wydick (2010), and Shahriar (2016).7 The average loan repayment rate in our sample is 0.48. We observe trustworthiness

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In empirical analyses, we used data from the first three rounds of every treatment. Our main results hold if we include the small amount of data from later rounds. 7 A total of 11 subjects had no success with their project. Following Cassar et al. (2007) and Shahriar (2016), we considered their loan repayment rates to be zero. As a robustness check, we removed these observations from our analyses. The results—not reported here for brevity—are not affected by this removal.

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of the subjects who played as a receiver (i.e., Player 2) in the trust game. The variable, trustworthiness, refers to the number of tokens returned by Player 2 to Player 1 divided by the number of tokens available to return. The average level of trustworthiness is 0.43. The dummy variable, female, equals one for female and zero for male subjects. A subject‘s age is measured in actual years, and education is measured in terms of years of school attended. The value of a subject‘s household assets is measured as the market value of arable land, dwelling house, cattle, and other valuables such as ornaments, furniture, personal vehicles, and other household goods. In regression analyses, we take the natural logarithm of household assets. The dependency ratio of a subject‘s household is measured as the ratio of the economically inactive (aged under 15 or over 64) household members to the economically active (aged between 15 and 64) household members. Siblings equals one if the subject has siblings (either alive or deceased), and zero otherwise. Debt equals one if the subject has any outstanding loans, and zero otherwise. Shocks equals one if—in the last 12 months—a subject experienced natural disasters (such as flood or river erosion), or the death of an earning member of the family; otherwise, shocks equals zero. The variable, farm household, equals one if the subject reported that agriculture (including wage labor in agriculture) is the main source of household income; otherwise it equals zero. Subjects who started a new business in the past, alone or with partners, are characterized as entrepreneurs. New venture creation serves as a proxy for attitude toward risk because risk-averse individuals are less likely to start a new business (Cameron and Shah, 2015; Cramer et al., 2002). The variable, acquaintanceship, measures a subject‘s intensity of contact—on a scale of 1 (no contact) to 7 (frequent contact) —with the playing partner in the trust game. It is worth mentioning that in the trust game, a subject was randomly matched with a playing partner who lives in the same village. As such, acquaintanceship serves as a proxy for a 13

subject‘s average level of social connectedness with neighbors. This measure of social connectedness is consistent with those of Abbink et al. (2006) and Chakravarty and Shahriar (2015). The variable, same religion, equals one if both partners in the trust game have the same religious affiliation; otherwise it equals zero. This variable serves as a proxy for cultural proximity between playing partners in the trust game. As mentioned in section 2.3, we asked subjects three questions from the General Social Survey, which measure an individual‘s perception of trust, fairness and helpfulness. GSS_trust equals one if the participant believes that generally people can always or almost always be trusted, and zero otherwise. GSS_fairness equals one if the participant believes that people try to be fair almost all or most of the time, and zero otherwise. GSS_helping equals one if the participant believes that most of the time people try to be helpful, and zero otherwise. We collected information on the level of human capital endowment and urbanization in the districts where we conducted the experiments from the latest population census report (Bangladesh Bureau of Statistics, 2015). School attendance rate refers to the percentage of the population aged 5-24 in the survey district who attended school. Rural population refers to the percentage of the population in the survey district who live in rural areas. [Table 3] The mean age of the subjects was 29 years. On average, they had spent three years at school. The fraction of female subjects was 0.51. Of all the subjects, 74% reported that the primary source of their household income was agriculture, and 26% reported that they had experience in starting a new business in the past, such as running small handicrafts or grocery stores, selling prepared foods, rearing livestock, tailoring etc. Of all the subjects, 21% experienced negative income shocks in the last 12 months because of natural disasters or the death of an earning member of the family, and 30% reported having an outstanding loan from 14

formal or informal sources. In our sample, 67% of the subjects believe that people can always, or almost always, be trusted, 61% believe that people try to be fair most of the time or almost all of the time, and 72% believe that most of the time people try to be helpful. On average, 81% of the population in the survey districts live in rural areas, and 55% of the population aged 524 attended school. Table 4 presents gender differences in summary statistics. We use Welch's (1938) two-sample t-test (assuming unequal samples and variances) to check whether these differences are statistically significant. It appears that there is no significant gender difference in age, education, ownership of household assets, and source of household income. However, more men than women reported that they had an outstanding loan (p < 0.05) and that they had started a small business in the past (p < 0.01). These findings are consistent with extant evidence that women are more likely to be credit-constrained and less likely to be entrepreneurs than men in male-dominated societies (Jennings and Brush, 2013; Shahriar, 2018). Compared to male subjects, female subjects reported having more frequent contact with their partners in the trust game (p < 0.05). As mentioned above, the variable, acquaintanceship, also serves as a proxy for a subject‘s social connectedness with neighbors. This finding, therefore, implies that compared to men, women have stronger social ties with their neighbors. Women were more likely than men to perceive others as trustworthy (p < 0.01) and fair (p < 0.01). More female than male subjects in our sample have siblings (p < 0.01). This is not surprising because in many developing countries, including Bangladesh and India, parental son preference (i.e., the attitude that sons are more important and more valuable than daughters) is a major determinant of high fertility rates among women (e.g., Chowdhury and

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Bairagi, 1990; Clark, 2000). We also find that more male than female subjects were exposed to income shocks in the recent past (p < 0.10).8 [Table 4] We compare the mean values of the key observable characteristics of our subjects across the four treatments. The idea is to check whether the experimental groups we created are balanced along those characteristics. The results of this randomization check are reported in Appendix D. We use Welch's (1938) two-sample t-test to check whether the mean values of observable characteristics in the treatments of joint liability, dynamic incentives, and combined control mechanisms are significantly different from those in the benchmark treatment. Glennerster and Takavarasha (2013, p. 151) suggest that if one out of 10 variables is unbalanced at the 10% level, or two out of 20 variables are unbalanced at the 5% level, a researcher need not be worried about unbalanced treatment assignment. It appears that in the treatment of individual loans with dynamic incentives, three out of 14 variables are unbalanced at the 5% level. However, in the treatment of joint liability loans without dynamic incentives, one variable is unbalanced at the 5% level and one is unbalanced at the 10% level. Finally, in the treatment of joint liability loans with dynamic incentives, only one variable is unbalanced at the 10% level. Thus, the treatments are reasonably balanced along observed characteristics. 3.2.

Results of the trust game In this section, we identify the determinants of trustworthiness. Following Karlan

(2005), we run an ordinary least squares (OLS) regression based on Equation (1). Standard errors are corrected for heteroscedasticity. (1) 8

In regression analyses, we control for the effects of all the covariates, for which we have data. As a robustness check, we re-run these regressions only with control variables, for which there are significant gender differences. The results—available upon request—are consistent with our main findings reported in Table 6.

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The dependent variable, trustworthiness, has been defined in section 3.1. The determinants of trustworthiness include basic demographic characteristics of the subjects (Di), pair-specific factors in the trust game (Pi), subjects‘ perception of social capital (Si), and communityspecific factors (Ci). First, we regress trustworthiness on basic demographic and socioeconomic characteristics of the subjects and report the findings in Column A of Table 5. In Column B of Table 5, we add two pair-specific factors: acquaintanceship and same religion. We also control for the number tokens (amount received) received from Player 1 in the trust game. In Column C, we add the three control variables for social capital: GSS_trust, GSS_fairness and GSS_helping. Finally, in Column D, we add two control variables that capture the level of human capital endowment and urbanization in the districts where we conducted the experiments: school attendance rate and rural population. [Table 5] The results (across the four specifications) show that compared to men, women return 16-21 percentage points more to the sender (p < 0.01). Thus, consistent with the findings of most prior studies, we find that women are more trustworthy than men, and this gender difference is not influenced by the inclusion of a wide array of control variables. This suggests that gender is a fairly stable predictor of trustworthiness. Other demographic characteristics, such as age, education, or household assets, are statistically insignificant. However, individuals with siblings returned 17-24 percentage points more in the trust game compared to those with no siblings. The behavioral consequences of not having siblings has drawn attention of the researchers. Cameron et al. (2013) conducted a series of laboratory experiments in China and found that the one child policy has produced significantly fewer trusting and fewer trustworthy individuals—a generation of ―little emperors‖ with low levels of social skills. Glaesser et al. (2000) conducted a trust game with the undergraduate students

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of Harvard University and found that subjects with siblings were more trustworthy than subjects who were only children. The level of acquaintanceship among playing partners is positively associated with trustworthiness. A one-unit increase in the level of acquaintanceship is associated with four to six percentage points increase in the return ratio (p < 0.01). Prior research has shown that acquaintanceship, or familiarity, breeds trust in exchange relationships (Abreu, 1988; Greif, 1993; Ruef et al., 2003). Thus, it is not surprising that subjects in the trust game have shown more trustworthiness to partners they know well. Having the same religious affiliation with a partner enhances trustworthiness. Everything else being constant, Player 2 passes six to ten percentage points more to Player 1 if both have the same religious affiliation (p < 0.5). This finding is consistent with Karlan (2005) who found that cultural proximity enhances trustworthy behavior. Our results further suggest that the amount received from Player 1 is not significantly correlated with the return ratio of Player 2. While this finding is consistent with that of Karlan (2005), Glaeser et al. (2000) reports a statistically significant but economically small positive correlation. Thus, the amount received from the sender is not a stable predictor of trustworthy behavior in the trust game. Finally, we find that the GSS measures are correlated with trustworthiness. In particular, subjects who responded positively to the GSS-trust question returned 17 percentage points more (p < 0.01), and those who responded positively to the GSS-helping question returned five percentage points more to Player 1 (p < 0.10). A plausible explanation is that trustworthy people are more likely to consider others as trustworthy and helpful (Glaeser et al., 2000). None of the community-level factors is significantly associated with trustworthiness. 3.3.

Gender differences in loan repayment 18

We regress loan repayment rate on gender using Equation (2). We use an OLS technique. Standard errors are corrected for heteroscedasticity.

∑

(2)

On the right-hand side of Equation (2), we introduce three dummy variables for the four experimental treatments. The base category is the treatment of individual loans without dynamic incentives. IL_DRI is a dummy variable for the treatment of individual loans with dynamic repayment incentives, JL is a dummy variable for the treatment of joint liability loans without dynamic incentives, and JL_DRI is a dummy variable for the treatment of joint liability loans with dynamic repayment incentives. We use three treatment-gender interaction terms, which allows us to compare the repayment rates of men and women in each of the four treatments. Xik is the full set of control variables, listed in Column D of Table 5 (i.e., the full specification).

is the normally distributed zero-mean error term.

The results are reported in Column A of Table 6. In every treatment, women have shown better repayment performance than men. The average repayment rate of female borrowers is 21 to 38 percentage points higher than that of male borrowers (p < 0.01).9 Both joint liability and dynamic repayment incentives have positive impact on loan repayment. The interaction effects in equation (2) allow us to investigate whether the impact of joint liability and dynamic incentives on loan repayment varies across genders. It appears that joint 9

Several authors have reported better repayment performance by female borrowers using field-level data from Bangladesh and other developing countries. For example, Khandker et al. (1995) examined the repayment behavior of the borrowers of Grameen Bank over 1984 to 1991. They found that more men than women missed the weekly repayment of their loans. The overall loan default rate (i.e., failure to repay a loan within 52 weeks) was higher among men. In 1994, the International Food Policy Research Institute conducted a survey among the borrowers of three large MFIs in Bangladesh, namely, ASA, BRAC, and RDRS (Rangpur-Dinajpur Rural Service) (Sharma and Zeller, 1997). Both default and delinquencies were higher among men than women borrowers. Hulme (1991) used survey data on 326 borrowers of the Malawi Mudzi Fund, and Kevane and Wydick (2001) used survey data on 260 borrowers of FUNDAP in Guatemala. In both studies, women had shown better repayment performance than men.

19

liability improves the average repayment rate by 25 percentage points (p < 0.01) for male borrowers and 11 percentage points for female borrowers (p < 0.01). Dynamic incentives improve the average repayment rate by 28 percentage points (p < 0.01) for male borrowers and 18 percentage points for female borrowers (p < 0.01). Together, joint liability and dynamic incentives improves the average repayment rate by 39 percentage points (p < 0.01) for male borrowers and 22 percentage points for female borrowers (p < 0.01). However, none of the interaction terms between gender and the type of loan contract (Column A of Table 6) is statistically significant. A plausible explanation of why such large gender differences are not statistically significant is that we have introduced three interaction terms in a relatively small sample, and as a result, the regression analysis lacks statistical power. We therefore categorize subjects into two groups—those who repaid loans without any control mechanism (i.e., those who participated in the benchmark treatment) and those who repaid loans with dynamic incentives or joint liability or both of the control mechanisms. We regress loan repayment rate on female, control mechanism (equals one if the borrower repays loan with a control mechanism, zero otherwise), and their interaction term. We control for all the observable characteristics introduced above. The results are reported in column B of Table 6. Control mechanisms improve the average repayment rate by 30 percentage points (p < 0.01) for male borrowers and 13 percentage points for female borrowers (p < 0.01). The interaction between female and control mechanism is negative and statistically significant at the 5% level. Thus, it appears that male borrowers are more responsive to behavioral controls used by MFIs. [Table 6] Results reported in Columns A and B of Table 6 further suggest that subjects who experienced an income shock in the recent past have poorer repayment performance than those who did not experience such shocks. Having an outstanding loan also deteriorates 20

repayment performance. The variable, acquaintanceship, is positively associated with loan repayment rate, which implies that individuals with strong social ties with neighbors are more likely to repay their microloans. Finally, subjects with siblings have shown better repayment performance. 3.4.

Does trustworthiness predict repayment behavior? In this section, we examine whether innate trustworthiness is associated with

repayment behavior. We run the following OLS model. Standard errors are corrected for heteroscedasticity.

∑

(3)

As mentioned in section 3.1, trustworthiness is measured as the return ratio (by Player 2) in the trust game. In Equation (3), we multiply this variable by 100, which allows us to quantify the change in repayment rates due to a one-percentage point increase in the return ratio. On the right-hand side, we introduce three dummy variables for the four treatments, and their interaction with trustworthiness. Xik is the full set of control variables introduced above, and is the normally distributed zero-mean error term. The results are reported in Column C of Table 6. We find that trustworthiness is positively associated with loan repayment rate irrespective of the type of loan. On average, a one-percentage point increase in the return ratio is associated with 0.6 to 0.8 percentage point increase in the repayment of microloans (p < 0.01). After controlling for the effects of trustworthiness, both dynamic incentives and joint liability have positive impact on loan repayment rates. As before, we categorize subjects into two groups—those who repaid loans without any control mechanism and those who repaid with joint liability or dynamic incentives or both of the control mechanisms. We regress loan repayment rate on 21

trustworthiness, control mechanism, and their interaction term. The results are reported in column D of Table 6. It appears that both trustworthiness and control mechanism are positively associated with loan repayment rates. The interaction between trustworthiness and control mechanism, however, is not statistically significant. One of the key questions in an exchange relationship, such as a lending relationship, is whether to trust the partner or to control (Coletti et al., 2005; Das and Teng, 1988; Saparito et al., 2004). To ensure prudent behavior by borrowers, MFIs use joint liability and dynamic repayment incentives as control mechanisms (Banerjee, 2013). At the same time, they develop trust-based relationships with their clients (Behr et al., 2011; Berg and Schrader, 2012; Shahriar and Garg, 2017; Yunus, 1999). Our findings suggest that borrowers‘ trustworthiness and the control mechanisms imposed by MFIs have independent impact on the repayment of microloans. Trust and control are therefore parallel concepts and their relationship is of a supplementary character in the context of microlending. 3.5.

Test of mediating relationship In this section, we test the hypothesis that gender differences in the repayment of

microcredit are mediated by gender differences in trustworthiness. As outlined in the introduction, we will fail to reject the mediating hypothesis if three conditions hold: (a) women are more trustworthy, and more likely to repay, than men; (b) trustworthiness is positively associated with repayment rate; and (c) after statistically controlling for the effects of trustworthiness, the association between gender and repayment rate is substantially weakened. In section 3.2, we show that women are more trustworthy than men. In section 3.3, we show that women are more likely to repay than men. In section 3.4, we show that trustworthiness is positively associated with loan repayment decision. As the final condition of mediation, we investigate whether the association between gender and repayment rate is affected when we control for the effects of trustworthiness. For this, we first regress 22

repayment rate on female and the other control variables except trustworthiness without introducing any interaction-term (Column E of Table 6). Next, we introduce trustworthiness as a control variable (Column F of Table 6). When we control for the effects of trustworthiness, the magnitude of the coefficient on female decreases by nine percentage points. Thus, we find support to all three conditions of a mediation effect. According to MacKinnon (2008), the total effect of gender on the repayment of microloans is given by the coefficient on female in Column E of Table 6. The direct effect of gender, holding trustworthiness constant, is given by the coefficient on female in Column F of Table 6. Finally, the magnitude of the mediation effect (i.e., the indirect effect of gender through trustworthiness) is given by the difference between the coefficients on female in Columns E and F. The results reported in Table 6 suggest that the total effect of gender is 0.25, whereas the direct effect, holding trustworthiness constant, is 0.16. Therefore, the magnitude of the mediation effect is 0.09, which means 36% of the gender differences in the repayment of microloans can be explained by gender differences in trustworthiness. We conducted a Sobel test (Sobel, 1982) to estimate the standard error of the mediation effect, and the results suggest that it is statistically significant at the 1% level. 10 3.6.

Sensitivity test of mediation effect As mentioned in the introduction, mediation analysis relies on the key identification

assumption that the mediator is independent of the treatment. In practice, however, the treatment is often randomly assigned but the mediator is not. In such a case, pretreatment variables may confound the relationship between the mediator and the outcome. In the 10

Sobel (1982) provides a significance test for the mediation effect, which has been widely used in the literature (e.g., Baron and Kenny, 1986; Bauer and Smeets, 2015; Van Hoorn, 2014; Wood et al., 2008). In Sobel test, a zstatistic is measured as the magnitude of the mediation effect divided by its standard error. The z-statistic is compared to the standard normal distribution to test for significance. z = . Here, a denotes the path √

from the independent variable to the mediator, and its standard error is sa. The path from the mediator to the dependent variable is denoted as b, and its standard error is sb. The magnitude of the mediation effect is ab. The results of the Sobel test are not reported in the paper for brevity, they are available on request.

23

present context, innate trustworthiness is affected by a host of observed and unobserved factors that are simultaneously associated with gender and loan repayment decisions. For example, it is possible that women are more altruistic and more averse to inequality than men (Andreoni and Vesterlund, 2001; Croson and Gneezy, 2009; Eckel and Grossman, 1998), both of which may influence trustworthiness and repayment decision in an experimental setup. Furthermore, prior research has shown that women are more sensitive to religious beliefs (Miller and Hoffman, 1995), and that the degree of religiosity is positively associated with both trustworthiness (Guiso et al., 2006) and loan repayment decision (Al-Azzam et al., 2012). In this section, we conduct a sensitivity analysis (Imai et al., 2010a) to check the extent to which unobserved confounders might have influenced our results. This test is based on the correlation between the error for the mediation model (i.e., the model that identifies the determinants of trustworthiness) and the error for the outcome model (i.e., when loan repayment rate is regressed on female, trustworthiness, and other control variables). Let us denote this correlation across the two error terms as ρ. If there are no unobserved confounders, ρ equals zero because the effects of such unmeasured covariates are part of the error terms. Nonzero values of ρ imply departures from the key identification assumption. The sensitivity analysis is conducted by varying the value of ρ and examining how the average mediation effect changes. It tells us how large the value of ρ needs to be for the mediation effect to go away. In the present context, our original conclusion about the mediating effect of trustworthiness would be maintained unless the value of ρ is greater than 0.32 (Figure 1). Thus, a reasonably large violation of the identification assumption is required for our main findings to be reversed. While there is no threshold level for the acceptable value of ρ, prior research, which used sensitivity analysis (e.g., Imai et al., 2010b; Imai and Yamamoto, 2013), suggests that ρ greater than 0.3 implies a moderate degree of robustness. Thus, the mediation 24

effect of trustworthiness on loan repayment rate is moderately robust to the violation of the key identification assumption due to unobserved confounders. [Figure 1] 3.7.

Subjects’ attitude toward risk and loan-repayment decision In the loan repayment game, subjects make decisions involving risk (Shahriar, 2016).

For example, by repaying loans with dynamic incentives, a subject is exposed to a risky investment project. If that project fails, the subject loses the initial endowment (i.e., the loan) of TK50. Furthermore, by repaying a joint liability loan, a subject is exposed to the risk that her/his partner will not repay and she/he will lose TK120. Thus, one may argue that a subject strategically defaults in the repayment game simply because she/he wants to avoid being exposed to a risky bet, and thus, we observed gender differences in attitude toward risk rather than gender differences in repayment behavior. To formally examine this issue, we conducted a separate risk-elicitation game. The purpose was to examine whether men and women show different attitudes toward risk over similar monetary stakes involved in the loan repayment game. Of the 356 participants of the loan repayment game, we randomly selected 60 men and 60 women and invited them to participate in this experiment. A total of 109 subjects (52 female and 57 male) played the risk elicitation game two to four weeks following the completion of the loan repayment game. 11 The risk-elicitation game was inspired by Gneezy et al. (2009) and Shahriar (2016). A subject received TK120 and decided what portion of this endowment [0, 120] she or he wanted to bet in a lottery that returned three times the bet with a one-half probability and nothing with a

11

We did not conduct the loan repayment game and the risk-elicitation game in the same session because subjects' decision in the second game could have been influenced by the outcome of the first game. We did not have sufficient resources to run the risk-elicitation game with all the subjects at a later time. Experimental instructions for the risk-elicitation game, adopted verbatim from Shahriar (2016), are provided in Appendix C.

25

one-half probability. Thus, the monetary stakes involved in this game were similar to those of the loan repayment game.12 The basic results of our risk elicitation game suggest that, on average, women bet TK62.2 (standard deviation 0.216) whereas men bet TK64.4 (standard deviation 0.227) of their endowment of TK120. A two-sample t-test (assuming unequal variances and unequal sample sizes) rejects the hypothesis that women have different attitude toward risk than men over similar monetary stakes involved in our original loan repayment game. 13 Thus, a subject‘s attitude toward risk can be excluded as an alternative explanation of our findings. 4.

Conclusion Female microcredit-borrowers have better repayment records than male borrowers, but

we know little about what explains such gender differences. The present study hypothesizes that gender differences in the repayment of microloans are mediated by gender differences in innate trustworthiness. This conjecture is based on two streams of literature. First, a growing body of work shows that in the absence of formal legal institutions, a trust-based system effectively regulates small transactions between microfinance institutions and their clients, resulting in high repayment rates (e.g., Karlan, 2005; Haldar and Stiglitz, 2014). Second, studies on gender differences in human behavior indicate that women can be inherently more trustworthy than men (Wood and Eagly, 2002; Kosfeld et al., 2005; Carter, 2007). We recruited male and female subjects from rural Bangladesh, who participated in a trust game and a microloan repayment game. Unlike most of the repayment games, we allowed our subjects to repay loans with and without joint liability and dynamic repayment incentives. We find that women are more trustworthy than men and that they are more likely 12

In our loan repayment game, a subject risks a maximum of TK120 by repaying in a given round. In contrast, if the game continues up to the third round, a subject earns a maximum of TK360. 13 In risk-elicitation games conducted in Western and industrialized societies, women have shown more risk aversion than men. However, studies conducted in rural and traditional societies do not report systematic gender differences in attitude toward risk (e.g., Binswanger, 1980; Gneezy et al., 2009). See Eckel and Grossman (2008) for a comprehensive review.

26

to repay irrespective of joint liability or dynamic repayment incentives. In contrast to popular beliefs, we find that male borrowers are more responsive than female borrowers to the control mechanisms imposed by MFIs. Trustworthiness is positively associated with loan repayment rates irrespective of the lending methods. After controlling for the effects of trustworthiness, both joint liability and dynamic incentives have positive impact on repayment decision. Thus, trust and control have independent impact in the context of microloan repayment. Finally, our findings suggest that the widely observed gender differences in the repayment of microcredit can be explained by differences in innate trustworthiness. The magnitude of this mediation effect is statistically significant and economically substantial. Female entrepreneurs, compared to their male counterparts, are systemically deprived of vital resources, such as financing (Fischer et al., 1993; Kallerberg and Leicht, 1991). Microfinance is committed to reducing the extent of gender-based financial exclusion at the bottom of the income pyramid (Yunus, 1999). In recent years, however, rapid commercialization has changed the institutional logic of many lenders. The more recent entrants in this arena have made profit maximization their dominant strategy, which influenced their goals, client selection, and other management principles, all geared toward generating profits at the expense of the original vision of microfinance (Cull et al., 2009; Battilana and Dorado 2010; Shahriar et al., 2016). As part of this transition, many MFIs have curtailed outreach to female borrowers. Our findings suggest that women are perhaps inherently better at repaying microloans, and as such, MFIs would do better by not compromising their outreach activities to women as this would help them maintain low default costs. We acknowledge that our study has limitations. We were unable to control for the effects of many unobserved factors that are simultaneously correlated with gender, trustworthiness, and the loan repayment decision of a borrower. Although we have conducted 27

a sensitivity test, which suggests no evidence of significant omitted variables bias in our results, such tests are not substitute for random assignments of the treatment and the mediator. Given such limitations, our findings should be interpreted with caution. We hope future research will be devoted to find more reliable results based on experimental manipulation of the mediator.

Table 1 Basic results of the trust game Coins passed 0 1 2 3 4 5 6 Total

Player 1 (sender) Frequency 91 170 88 98

Percent 20.4 38.0 19.7 21.9

447

100

Player 2 (recipient) Frequency 159 121 107 40 11 8 1 447

Percent 35.6 27.1 23.9 8.9 2.5 1.8 0.2 100

28

Table 2 Payoff allocation of the loan repayment game Repayment Subject‘s net payout Proceed to the next decision of the from a given round of round? subject play Treatment 1: Individual loans without dynamic incentives (n = 86) Default 0 Yes Default 120 Yes Repay 60 Yes Treatment 2: Individual loans with dynamic incentives (n = 90) Default 0 No Default 120 No Repay 60 Yes Treatment 3: Joint liability-based loans without dynamic incentives (n = 90)

Outcome of the project

Failure Success Success Failure Success Success Failure

Default

0

Yes

Success

Default

120

Yes

Success Repay If the partner repays 60 Yes If the partner does not repay 0 Yes Treatment 4: Joint liability-based loans with dynamic incentives (n = 90) Failure Default If the partner repays If the partner does not repay

0

Success Default If the partner repays If the partner does not repay

120

Success Repay If the partner repays If the partner does not repay

Yes No

Yes No

60 0

Yes Yes

29

Table 3 Descriptive statistics and definition of variables Variables Loan repayment rate

Trustworthiness

Female Age Education Household assets Dependency ratio

Siblings Debt Shocks

Farm household Entrepreneur Same religion Acquaintanceship

GSS_trust

GSS_fairness

Description Measured from loan repayment game: the number of times a subject repaid the loan divided by the number of times the project was successful (Min:0; Max:1) Measured for Player 2 in the trust game: the number of tokens returned to Player 1 divided by the number of tokens available to return (Min:0; Max:1) 1 if the subject is female; 0 if male Age of a subject (Min:18; Max:42) Number of years of school attended by a subject (Min:0; Max:12) Natural logarithm of the market value of subject‘s household assets (Min:0; Max:13.7) Number of economically inactive (age <14 or >65) household members divided by the number of economically active (age 1564) household members (Min:0; Max:2) 1 if the subject has any siblings (alive or deceased); 0 otherwise 1 if the subject has an outstanding loan from formal or informal sources; 0 otherwise 1 if a subject answered ―yes‖ to any of the two questions; 0 otherwise: (a) In the last 12 months, did you evacuate your home due to flood or river erosion? (b) In the last 12 months, did an earning member of your household die? 1 if agriculture is the subject‘s primary source of household income; 0 otherwise 1 if the subject, alone or with others, started a new business in the past; 0 otherwise 1 if both partners in the trust game have the same religious affiliation; 0 otherwise A subject‘s intensity of contact—on a scale of 1 (no contact) to 7 (frequent contact) —with the partner in the trust game (Min:1; Max:7) 1 if a subject answered either (a) or (b) in response to the following question; 0 otherwise: ―Generally speaking, would you say that most people can be trusted or that you can‘t be too careful in dealing with people?‖ (a) People can almost always be trusted (b) People can always be trusted (c) You usually can‘t be too careful in dealing with people (d) You almost always can‘t be too careful in dealing with people 1 if a subject answered either (c) or (d) in response to the following question; 0 otherwise: ―Do you think most people would try to take advantage of you if they got a chance, or would they try to be fair?‖ (a) Try to take advantage almost all of the time (b) Try to take advantage most of the time (c) Try to be fair most of the time

Mean(std.dev.) 0.48 (0.43)

0.43 (0.28)

0.51 (0.50) 29.12 (7.19) 3.47 (3.41) 7.52 (5.30) 0.37 (0.37)

0.89 (0.31) 0.30 (0.46) 0.21 (0.41)

0.74 (0.44) 0.26 (0.44) 0.87 (0.34) 4.55 (1.44)

0.67 (0.47)

0.61 (0.49)

30

(d) Try to be fair almost all of the time Table 3 (continued) Variables

Description

GSS_helping

1 if a subject answered (a) in response to the following question; 0 otherwise: ―Would you say that most of the time people try to be helpful, or that they are mostly just looking out for themselves?‖ (a) Try to be helpful (b) Just look out for themselves Measured for Player 2 in the trust game: the number of tokens received from Player 1 (Min:0; Max:3) Percentage of the population of age 5-24 in the survey districts who ever attended school. Percentage of the population living in rural areas in the survey districts

Amount received School attendance Rural population

Mean (std. dev.) 0.72 (0.45)

1.79 (0.85) 55.08 (2.89) 81.16 (8.86)

31

Table 4 Gender differences in summary statistics t-statistics for gender differences

Male subjects

Female subjects

Mean (standard deviation)

Mean (standard deviation)

Age

29.65 (7.24)

28.62 (7.12)

1.34

Education

3.67 (3.38)

3.25 (3.43)

1.17

Household assets

7.67 (5.26)

7.35 (5.34)

0.06

Dependency ratio

0.39 (0.41)

0.35 (0.32)

1.42

Siblings

0.84 (0.37)

0.94 (0.24)

-3.04

Debt

0.35 (0.48)

0.25 (0.43)

2.25

Shocks

0.24 (0.43)

0.17 (0.38)

1.65

Farm household

0.75 (0.43)

0.73 (0.44)

0.47

Entrepreneur

0.39 (0.48)

0.14 (0.34)

5.62

Same religion

0.85 (0.35)

0.87 (0.33)

-0.47

Acquaintanceship

4.32 (1.34)

4.75 (1.50)

-2.81

GSS_trust

0.60 (0.49)

0.74 (0.44)

-2.79

GSS_fairness

0.53 (0.50)

0.69 (0.46)

-3.08

GSS_helping

0.71 (0.45)

0.74 (0.44)

-0.73

Loan repayment rate

0.31 (0.41)

0.64 (0.38)

-7.78

Trustworthiness

0.31 (0.23)

0.54 (0.27)

-8.61

Amount received

1.70 (0.85)

1.89 (0.82)

-2.17

32

Table 5 Determinants of trustworthiness (Dependent variable: trustworthiness) Variables Column A Female 0.208*** (0.028) Age -0.001 (0.002) Education 0.003 (0.003) Household assets -0.003 (0.002) Dependency ratio -0.050 (0.034) Siblings 0.243*** (0.054) Debt -0.019 (0.028) Shocks -0.024 (0.029) Farm household 0.014 (0.027) Entrepreneur 0.031 (0.031) Acquaintanceship Same religion Amount received

Column B 0.185*** (0.027) -0.001 (0.002) 0.003 (0.003) -0.002 (0.002) -0.039 (0.048) 0.229*** (0.048) -0.029 (0.027) -0.033 (0.027) -0.001 (0.025) 0.032 (0.029) 0.056*** (0.009) 0.099*** (0.035) 0.005 (0.014)

Column C 0.164*** (0.024) 0.001 (0.002) 0.002 (0.003) -0.003 (0.002) -0.045 (0.029) 0.168*** (0.049) -0.036 (0.025) -0.022 (0.026) -0.003 (0.024) 0.042 (0.027) 0.046*** (0.009) 0.064** (0.031) -0.012 (0.013) 0.167*** (0.028) 0.040 (0.025) 0.051* (0.026)

-0.158* (0.093) 0.346 356

-0.184** (0.088) 0.446 356

GSS_trust GSS_fairness GSS_helping School attendance Rural population Constant R-squared N

0.147* (0.069) 0.255 356

Column D 0.165*** (0.025) -0.001 (0.021) 0.001 (0.003) -0.002 (0.002) -0.046 (0.029) 0.167*** (0.049) -0.034 (0.025) -0.018 (0.026) -0.001 (0.025) 0.044 (0.028) 0.046*** (0.009) 0.063** (0.031) -0.011 (0.013) 0.169*** (0.028) 0.037 (0.025) 0.049* (0.026) -0.005 (0.004) -0.001 (0.001) -0.126 (0.318) 0.449 356

Trustworthiness is measured based on data from the trust game. It is the number of tokens returned by Player 2 to Player 1 divided by the number of tokens available to return. Female is a dummy variable that equals one for female subjects, and zero for male subjects. All other variables are defined in Table 3. Results are based on OLS regression. Standard errors, corrected for heteroscedasticity, are in parentheses. *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level.

33

Table 6 Determinants of loan repayment rate (Dependent variable: loan repayment rate) Variables

Column A

Column B

Female Trustworthiness Age Education Household assets Dependency ratio Siblings Debt Shocks Farm household Entrepreneur Acquaintanceship Same religion Amount received GSS_trust GSS_fairness GSS_helping School attendance Rural population IL_DRI JL JL_DRI Female*IL_DRI Female*JL Female*JL_DRI Trustworthiness*IL_DRI Trustworthiness *JL Trustworthiness*JL_DRI

0.386*** (0.083)

0.379*** (0.082)

-0.001 (0.003) -0.004 (0.006) -0.003 (0.004) -0.074 (0.053) 0.237*** (0.066) -0.149*** (0.045) -0.089* (0.053) 0.065 (0.049) 0.058 (0.048) 0.024* (0.014) -0.002 (0.059) -0.023 (0.024) 0.052 (0.047) 0.010 (0.045) 0.047 (0.047) -0.014 (0.017) -0.003 (0.002) 0.278*** (0.079) 0.253*** (0.077) 0.395*** (0.084) -0.107 (0.112) -0.140 (0.119) -0.177 (0.118)

0.001 (0.003) -0.004 (0.006) 0.001 (0.004) -0.069 (0.053) 0.235*** (0.068) -0.150***(0.045) -0.090* (0.052) 0.059 (0.048) 0.052 (0.047) 0.028** (0.014) -0.004 (0.059) -0.027 (0.024) 0.051 (0.047) -0.005 (0.045) 0.048 (0.047) -0.014* (0.008) -0.003 (0.003)

Column C 0.007*** (0.001) -0.001 (0.003) -0.005 (0.006) -0.002 (0.004) -0.056 (0.048) 0.149** (0.054) -0.151***(0.045) -0.095* (0.50) 0.057 (0.049) -0.017 (0.046) 0.001 (0.014) -0.039 (0.058) -0.030 (0.023) 0.071 (0.047) 0.021 (0.045) 0.002 (0.046) -0.009 (0.017) -0.001 (0.003) 0.261*** (0.091) 0.220*** (0.093) 0.275*** (0.095)

Column D 0.007*** (0.001) -0.001 (0.003) -0.005 (0.006) 0.003 (0.004) -0.046 (0.049) 0.150*** (0.054) -0.150***(0.046) -0.095* (0.050) 0.049 (0.047) -0.023 (0.046) 0.003 (0.014) -0.042 (0.058) -0.027 (0.024) -0.050 (0.047) -0.013 (0.045) 0.005 (0.047) -0.009 (0.007) -0.001 (0.003)

Column E

Column F

0.251*** (0.047)

0.160*** (0.049) 0.006*** (0.001) -0.001 (0.003) -0.005 (0.006) 0.002 (0.003) -0.063 (0.048) 0.135** (0.056) -0.131***(0.044) -0.091* (0.049) 0.062 (0.047) 0.034 (0.046) -0.001 (0.013) -0.041 (0.056) -0.021 (0.023) 0.063 (0.044) 0.037 (0.043) 0.011 (0.046) 0.013 (0.017) -0.002 (0.002) 0.205*** (0.057) 0.155*** (0.058) 0.272*** (0.055)

-0.001 (0.003) -0.004 (0.006) -0.001 (0.004) -0.082 (0.053) 0.234*** (0.068) -0.151***(0.048) -0.101** (0.050) 0.053 (0.048) 0.059 (0.048) 0.027* (0.014) -0.004 (0.060) -0.025 (0.024) 0.044 (0.047) -0.012 (0.045) 0.039 (0.047) -0.015 (0.017) -0.002 (0.002) 0.207*** (0.061) 0.197*** (0.060) 0.271*** (0.059)

-0.001 (0.002) 0.001 (0.002) -0.001 (0.002)

34

Table 6 (continued) Variables Control mechanism Control mechanism*Female Control mechanism*Trustworthiness

Column A

Column B 0.302*** (0.061) -0.167** (0.083)

Column C

Column D

Column E

Column F

0.271*** (0.073)

-0.001 (0.002) Constant 0.943* (0.563) 0.803 (0.555) 0.615 (0.570) 0.938** (0.561) 0.978* (0.524) 0.557 (0.550) R-squared 0.317 0.310 0.363 0.352 0.308 0.384 N 356 356 356 356 356 356 Loan repayment rate is measured based on data from the loan repayment game. It is the number of times a subject repaid the loan divided by the number of times the project was successful. Female equals one for female subjects and zero for male subjects. Trustworthiness is measured based on data from the trust game. It is the number of tokens returned by Player 2 to Player 1 divided by the number of tokens available to return. IL_DRI is a dummy variable for the treatment of individual loans with dynamic repayment incentives, JL is a dummy variable for the treatment of joint liability loans without dynamic incentives, and JL_DRI is a dummy variable for the treatment of joint liability loans with dynamic repayment incentives. Control mechanism equals one if the borrower repays loan with joint liability or dynamic incentives or both of the control mechanisms, otherwise it equals zero. All other variables are defined in Table 3. Results are based on OLS regression. Standard errors, corrected for heteroscedasticity, are in parentheses. *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level.

35

-.5

0

.5

1

ACME(p)

-1

-.5

0 Sensitivity parameter: p

.5

1

Fig. 1. Sensitivity analysis of the mediation effect of trustworthiness The solid line represents the estimated average mediation effect at different values of ρ. The value of ρ at which the average causal mediation effect (ACME) equals zero is 0.3188. The grey areas represent the 95% confidence interval for the mediation effects at each value of ρ.

36

Appendix A. Experimental instructions for the trust game (General instructions. Read loudly by the experimenter at the beginning of the experiments to all participants.) Good afternoon everyone. Thanks for taking the time to come today. We would like you to participate in a simple activity and a post-activity survey. You will receive TK100 for participation. Based on the decisions you will make in this activity, you can earn more money in addition to the show-up fee. You will participate in this activity in pairs. That means, you will have a partner throughout this activity. Each pair is made up of a Player 1 and a Player 2. We will use a simple lottery to assign your specific role (i.e., Player 1 or Player 2) in the pair. You cannot choose your partner. I will shortly announce who your partner is. After the announcement, you will be separated from your partner. If you are Player 1, you will stay in this room. If you are Player 2, you will be taken to another room. You are not allowed to talk to your partner or anyone else participating in this activity. If you have any questions, please raise your hand and you will be personally attended to. At the beginning of this activity, I will give three tokens to each Player 1 and another three tokens to each Player 2. Player 1 will have the opportunity to give a portion of her/his three tokens to Player 2. She/he may give one, two, or three of her/his tokens, or nothing. Whatever amount Player 1 decides to give to Player 2 will be matched and passed on to Player 2. For example, if Player 1 gives two tokens, I will give another two, and Player 2 will receive four tokens in total. Then Player 2 will have the option of returning any number of tokens she/he will receive from me. For example, if Player 2 receives four tokens from me, she/he may keep one, two, three, or four tokens and return the rest to Player 1. This activity will be done only once. At the end of this activity, you will exchange your tokens with real money. For every token you have, you will receive TK100.

37

(Instructions for Player 1. Read loudly by the experimenter.) You are Player 1. Here are your tokens. Your task is simple. There is an envelope on your table. Please keep a portion of your tokens in the envelope that you want to give to your partner. I will match that amount and pass it on to Player 2. Remember the more you give to Player 2 the greater the amount of money at her or his disposal. While Player 2 is under no obligation to give anything back, we will pass onto you whatever she or he decides to return. (Instructions for Player 2. Read loudly by the experimenter.) You are Player 2. Here are your three tokens. Now I will show you how much your partner— Player 1—decided to give to you. I will double Player 1‘s pass and give it to you. Your task will be to return any number of tokens received from me to Player 1. Remember, you can choose to give something back or not. There is an envelope on your table. Please keep any number of tokens in the envelope that you want to return to your partner.

Appendix B. Experimental instructions for the microloan repayment game (General instructions. Read loudly by the experimenter at the beginning of every session.) Good afternoon everyone. We are a group of students carrying out research to understand how people in rural Bangladesh repay microcredit. As a part of our study, we would like you to participate in a simple loan repayment activity. You will receive TK100 for your participation. Based on the decisions you will make in this activity, you can earn more money in addition to the show-up fee. You are not allowed to talk to each other. If you have any questions, please raise your hand and you will be personally attended to. This activity consists of multiple rounds. At the beginning of the first round, you will receive a loan of TK50. The loan has to be repaid—with 20% interest—in one installment. You will invest your loan in a risky business. Businesses sometimes fail to generate income. In this activity, your business may 38

fail as well. If your business fails, you will lose the TK50. If your business succeeds, you will earn TK120. To determine whether your business is successful or not, you have to pick a ball from a non-transparent jar. There are one red and five green balls in the jar. If you draw the red ball, your business fails. If you draw a green ball, your business succeeds. (Treatment-specific instructions. Read loudly before a specific treatment.) Individual loans without dynamic incentives After the disbursement of loan, you will be individually taken to another room, where you will play the ball-drawing game to determine the status of your business. If your business fails, you will not be able to repay the loan because you have no money. If your business succeeds, you have two choices. You may repay TK60 (TK50 of principal plus TK10 of interest). Or, you may decide not to repay. The excess monies after repaying, or not repaying, the loan will be converted to real currency at the end of this activity. Once you make your repayment decision, you will receive a new loan of TK50, and the activity will proceed to the next round. The same process will be repeated in the new round. Individual loans with dynamic incentives After the disbursement of loan, you will be individually taken to another room, where you will play the ball-drawing game to determine the status of your business. If your business fails, you will not be able to repay the loan because you have no money. If your business succeeds, you have two choices. You may repay TK60 (TK50 of principal plus TK10 of interest). Or, you may decide not to repay. The excess monies after repaying, or not repaying, the loan will be converted to real currency at the end of this activity. If you do not repay your loan in this round—either because your business had failed or because you do not want to repay—the activity will end for you. In contrast, if you repay,

39

the activity will proceed to the next round, you will receive a new loan of TK50, and the same process will be repeated.

Joint liability loans with dynamic incentives You have a borrowing partner in this activity. Using a simple lottery, one of the participants in this room has been selected as your partner. I will shortly announce who your partner is. Loan repayment is a joint responsibility of your group. That means, you and your partner are collectively expected to repay TK120—the total amount disbursed to your group plus interest payment. Please note that you are not allowed to talk to your partner. After the disbursement of loan, you will be taken to another room with your partner. First, both of you will play the ball-drawing game. You and your partner will monitor each other‘s business-outcome (i.e., success or failure). Next, you will make your repayment decisions. It is important that both of you simultaneously reveal your repayment decisions. To ensure this, I will ring a bell. As soon as you hear the bell ringing, you will pick a card from your desk and show it to us. There will be a ‗yes‘ (with a

sign), and a ‗no‘ (with a

sign) card on the desk. If you want to repay the loan, you will show the ‗yes‘ card. If you do not want to repay, you will show the ‗no‘ card. You and your partner will monitor each other‘s repayment decision. Business-outcome, loan repayment, and expected payoff If your business fails, you will not be able to contribute to the group repayment because you have no money. If your business succeeds, you have two choices. You may contribute to the group repayment, or you may decide not to contribute. The excess monies after repaying, or not repaying, the loan will be converted to real currency at the end of this activity. 

Suppose your business succeeds and you decide to contribute to the group repayment. If your partner also contributes, we will collect TK60 from you, and another TK60 40

from your partner. If your partner does not contribute, we will collect TK120 from you. In either case, the group repayment obligation of TK120 will be fulfilled. Each of you will receive another loan of TK50, and the whole process will be repeated. 

If your business fails, you have to rely on your partner for repayment. If your partner decides to contribute to the group repayment, we will collect TK120 from her or him. Each of you will receive another loan of TK0, and the whole process will be repeated. If your partner does not contribute, the group repayment obligation will not be fulfilled. So, none of you will receive a new loan, and the activity will end.



Suppose your business succeeds but you decide not to contribute to the group repayment. As before, you have to rely on your partner for repayment. If your partner decides to contribute, we will collect TK120 from her or him. Each of you will receive another loan of TK50, and the whole process will be repeated. If your partner does not contribute, the group repayment obligation will not be fulfilled. None of you will receive a new loan, and the activity will end.

Joint liability loans without dynamic incentives You have a borrowing partner in this activity. Using a simple lottery, one of the participants in this room has been selected as your partner. I will shortly announce who your partner is. Loan repayment is a joint responsibility of your group. That means, you and your partner are collectively expected to repay TK120—the total amount disbursed to your group plus interest payment. Please note that you are not allowed to talk to your partner. After the disbursement of loan, you will be taken to another room with your partner. First, both of you will play the ball-drawing game. You and your partner will monitor each other‘s business-outcome (i.e., success or failure). Next, you will make your repayment decisions. It is important that both of you simultaneously reveal your repayment decisions. To ensure this, I will ring a bell. As soon as you hear the bell ringing, you will pick a card 41

from your desk and show it to us. There will be a ‗yes‘ (with a

sign), and a ‗no‘ (with a

sign) card on the desk. If you want to repay the loan, you will show the ‗yes‘ card. If you do not want to repay, you will show the ‗no‘ card. You and your partner will monitor each other‘s repayment decision. Business-outcome, loan repayment, and expected payoff If your business fails, you will not be able to contribute to the group repayment because you have no money. If your business succeeds, you have two choices. You may contribute to the group repayment, or you may decide not to contribute. The excess monies after repaying, or not repaying, the loan will be converted to real currency at the end of this activity. 

Suppose your business succeeds and you decide to contribute to the group repayment. If your partner also contributes, we will collect TK60 from you, and another TK60 from your partner. If your partner does not contribute, we will collect TK120 from you. In either case, the group repayment obligation of TK120 will be fulfilled.



If your business fails, you have to rely on your partner for repayment. If your partner decides to contribute to the group repayment, we will collect TK120 from her or him. If your partner does not contribute, the group repayment obligation will not be fulfilled.



Suppose your business succeeds but you decide not to contribute to the group repayment. As before, you have to rely on your partner for repayment. If your partner decides to contribute, we will collect TK120 from her or him. If your partner does not contribute, the group repayment obligation will not be fulfilled.

Please note that irrespective of your and your partner‘s repayment decisions, both of you will receive another loan of TK50, and the whole process will be repeated.

42

Appendix C. Experimental instructions for the risk-taking game Thank you for joining us today to participate in this activity. You will receive TK100 for participation. Based on the decisions you make, you may earn more money in addition to the show-up fee. At the beginning of this activity, you will receive TK120. You will be asked to choose any portion of this amount (between 0 and 120) that you wish to invest in a risky option. The rest of the money will be accumulated to your total balance. The risky investment: there is an equal chance that the investment will fail or succeed. If the investment fails, you lose the amount you invested. If the investment succeeds, you receive three times the amount invested. How do we determine if you win? After you have chosen how much you wish to invest, you will toss a coin to determine whether you win or lose. If heads appears, you win three times the amount you chose to invest. If tails appears, you lose the amount invested. Examples 1. If you choose to invest nothing, you will get TK120 for sure. That is, the coin flip would not affect your earnings. 2. If you choose to invest all of the TK120, then if heads appears in the coin-toss, you win TK 360. If tails appears, you win nothing. 3. Suppose you choose to invest TK20. You will receive TK 100 for sure, and the rest (TK20) will be invested in the risky option. Then if heads appears in the coin-toss, you win TK60 from the investment, and if tails appears, you win nothing. If you have any questions, please raise your hand and you will be personally attended to. 43

Appendix D Appendix Table: Randomization check of experimental treatments A: Benchmark treatment of individual loans without dynamic incentives (n=86)

Female Age Education Household assets Dependency ratio Siblings Debt Shocks Farm household Entrepreneur Same religion Acquaintanceship GSS_trust GSS_fairness GSS_helping

B: The treatment of individual loans with dynamic incentives (n=90)

Mean

Std. dev.

Mean

Std. dev.

0.46 28.44 2.99 7.84 0.43 0.94 0.33 0.30 0.74 0.26 0.86 4.36 0.66 0.59 0.76

0.49 6.7 3.25 5.3 0.47 0.24 0.47 0.46 0.44 0.44 0.35 1.47 0.39 0.49 0.43

0.52 28.97 3.74 6.51 0.3 0.83 0.28 0.17 0.84 0.27 0.83 4.44 0.60 0.52 0.67

0.5 7.18 3.6 5.44 0.28 0.37 0.45 0.37 0.36 0.44 0.37 1.51 0.5 0.5 0.47

Differences in mean values reported in A and B

C: The treatment of joint liability loans without dynamic incentives (n=90)

-0.06 -0.53 -0.75 1.33 0.13** 0.11** 0.05 0.13** -0.1 -0.01 0.03 -0.08 0.06 0.07 0.09

Mean

Std. dev.

0.54 30.79 3.6 7.97 0.4 0.89 0.30 0.19 0.69 0.21 0.86 4.73 0.70 0.70 0.77

0.5 7.12 3.29 5.18 0.38 0.32 0.46 0.39 0.47 0.41 0.35 1.32 0.46 0.46 0.43

Differences in mean values reported in A and C

-0.08 -2.35** -0.61 -0.13 0.03 0.05 0.03 0.11* 0.05 0.05 0.00 -0.37* -0.04 -0.11 -0.01

D: The treatment of joint liability loans with dynamic incentives (n=90)

Mean

Std. dev.

0.51 28.28 3.52 7.75 0.37 0.9 0.3 0.19 0.69 0.31 0.91 4.63 0.72 0.62 0.71

0.5 7.56 3.49 5.22 0.31 0.3 0.46 0.39 0.47 0.47 0.29 1.45 0.45 0.49 0.46

Differences in mean values reported in A and D

-0.05 0.16 -0.53 0.09 0.06 0.04 0.03 0.11* 0.05 -0.05 -0.05 -0.27 -0.06 -0.03 0.05

*** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level.

44

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