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  • 1. Credit to Women Entrepreneurs: The Curse of the Trustworthier Sex Isabelle Agier and Ariane Szafarz Women entrepreneurs are known not only to reimburse loans swifter than men, but also to receive smaller loans. However, on average women have smaller-scope business projects and are poorer than men. A deeper investigation is thus required in order to assess the existence of gender discrimination in small-business lending. This is precisely the aim of this paper. Its contribution is twofold. Firstly, it proposes a new estimation method for assessing discrimination in loan allocation. This method operationalizes the theoretical “double standard” approach developed by Ferguson and Peters (1995, Journal of Finance). Secondly, this paper applies the new methodology to an exceptionally rich database from a Brazilian microfinance institution. The empirical results point to gender discrimination. Additionally, it is shown that reducing the information asymmetry through relationship brings no remedy to the curse of the trustworthier sex. Keywords: Small Business, Microcredit, Gender, Loan Size, Denial Rate, Default JEL Classifications: G24, L26, O16, M13 CEB Working Paper N° 11/005 February 18, 2011 Université Libre de Bruxelles - Solvay Brussels School of Economics and Management Centre Emile Bernheim ULB CP114/03 50, avenue F.D. Roosevelt 1050 Brussels BELGIUM e-mail: Tel. : +32 (0)2/650.48.64 Fax : +32 (0)2/650.41.88
  • 2. Credit to Women Entrepreneurs: The Curse of the Trustworthier Sex∗ Isabelle Agier† Ariane Szafarz‡ This version: February 18, 2011 Abstract Women entrepreneurs are known not only to reimburse loans swifter than men, but also to receive smaller loans. However, on average women have smaller-scope business projects and are poorer than men. A deeper investigation is thus required in order to assess the existence of gender discrimination in small-business lending. This is precisely the aim of this paper. Its contribution is twofold. Firstly, it proposes a new estimation method for assessing discrimination in loan allocation. This method operationalizes the theoretical double standard approach developed by Ferguson and Peters (1995, Journal of Finance ). Secondly, this paper applies the new methodology to an exceptionally rich database from a Brazilian micronance institution. The empirical results point to gender discrimination. Additionally, it is shown that reducing the information asymmetry through relationship brings no remedy to the curse of the trustworthier sex. Keywords: Small Business, Microcredit, Gender, Loan Size, Denial Rate, Default JEL codes: G24, L26, O16, M13 ∗ The authors thank Cécile Abramowicz, Marie Brière, Valentina Hartaska, Marc Labie, Bruce Wydick, and the participants to the CERMi Research Day (Mons, October 2010) for helpful discussions and suggestions. † UMR 201 - Développement et Sociétés (Paris I Sorbonne / IRD) and CERMi, Email: ‡ Université Libre de Bruxelles (ULB), SBS-EM, Centre Emile Bernheim, and CERMi, Email: 1
  • 3. It is extremely important (...) to conduct research into the social processes of discrimination and the politics of access, control, agency, and empowerment. Little can be assumed about gendered relations of disadvantage. They require empirical specication which in turn requires micro-level research Saith and Harriss-White (1999, p. 492). 1 Introduction Women-owned businesses are taking an increasing importance in the economy. According to Jalbert (2000), the percentage of female business owners in the world passed from 13% in 1970 to 20% in 1990. 1 Despite this favor- able evolution, access to credit for female entrepreneurs remains a concern for policymakers and researchers (Greene et al., 2003; Gatewood et al., 2004; Jamali, 2009). Although women tend to create smaller rms, lack of capital is still a major obstacle to them. Indeed, several studies show that, on average, female entrepreneurs are less nanced than male ones (see, e.g., Riding and Swift (1990) for Canada, Verheul and Thurik (2001) for the Netherlands, Alsos, Isaksen and Ljunggren (2006) for Norway, Alesina, Lotti and Mistrulli 2 (2008) for Italy). By focusing on poor female entrepreneurs in developing countries, microcredit has brought to light the underestimated potential of female self- employment. Notably, the microcredit industry has proved on a large scale that women are more trustworthy than men in terms of repayment conduct (Armendáriz and Morduch, 2000). Still, Buvinic and Berger (1990); Fletschner (2009) and Agier and Szafarz (2010) show that women keep being more 3 credit-rationed than men by micronance institutions (MFIs). At rst sight, one might be puzzled by the combination of women being more reliable and receiving smaller loans. However, this combination does not per se imply the presence of gender discrimination. Indeed, gendered repayment rates are established irrespectively of the personal and business 1 More precisely, the percentage of women business owners rose between 1970 and 1990 from 17.5% to 25% in Africa, from 8% to 11% in Asia and the Pacic, from 33.5% to 28% in Eastern Europe, from 11% to 24% in Latin America and the Caribbean, from 11% to 19% in Western Europe and other. For the US, Gatewood et al. (2004) state that: From 1997 to 2004, the number of women-owned rms grew at a rate of 17 percent (...) in comparison to a 9 percent growth in the number of rms overall. 2 Some papers do not share this conclusion (Haines, Orser and Riding, 1999). 3 Credit rationing is to be understood here as lower loans granted to women, and not higher loan denial like in Stiglitz and Weiss (1981). This point is further discussed in Agier and Szafarz (2010). 2
  • 4. characteristics of the borrowers. Moreover, men and women entrepreneurs dier in at least two respects: 1) women are poorer than men on average, 4 and 2) women have smaller-scope business projects. Besides, smaller loans typically generate higher operational and monitoring costs for the lender (Morduch, 1999; Armendáriz and Szafarz, 2011). Therefore, unconditional statistics might be misleading. A deeper approach is required to reach robust conclusions. This is precisely the aim of this paper. In a companion paper (Agier and Szafarz, 2010) based on an exceptional database including 34,000 loan applications from a Brazilian MFI, we have shown that fair access to credit is compatible with the presence of a glass ceiling in loan size (larger female projects are more credit-rationed than comparable male projects). Complementing this analysis thanks to reimbursement records from the same institution, the present paper investigates whether the glass-ceiling eect is economically justied. Existing evidence pertaining to access to credit in developing countries is mostly based on household surveys. This approach provides valuable information on the demand side of the market but is unable to reect the supplyside perspective. In their literature review, Morrison, Raju and Sinha (2007) state that: The existing research on credit markets in developing countries admittedly scarce suggests that by and large women receive unfavorable treatment not because of discriminatory treatment per se, but rather because of gender dierences in individual characteristics that are relevant for loan qualication (p. 39). However, because the body of evidence is demandsided, we argue that this conclusion is premature. Indeed, as emphasized by Diagne, Zeller and Sharma (2000), credit limits typically emanate from the lenders. Unfortunately, due to data unavailability, the way MFIs assess creditworthiness and grant loans has hardly been investigated yet, let alone the gender issue. 5 Beneting from exhaustive information gathered by an MFI on its loan applicants and borrowers over an eleven-year period, our contribution aims at lling this gap. Discrimination in the lending industry has been scrutinized in various countries, notably in the US where it is a legal oense. 6 Unfortunately, no con- sensus has emerged so far regarding the methodology to be used (Dymski, 4 According to ILO (2009), 75% of worldwide poverty aects women. 5 Exceptions include Buvinic and Berger (1990) who obtained data from the Urban Small Enterprise Development Fund in Peru, and Marrez and Schmit (2009) who analyze the credit risk of a leading Maghrebian MFI. 6 The US legal framework against discrimination in lending includes the 1968 Fair hous- ing Act, the 1974 Equal Credit Opportunity Act, and the 1975 Home Mortgage Disclosure Act. Since 1989, the lenders must report the race and ethnicity of their loan applicants. Race and gender discrimination has been scrutinized by, e.g., Munnell et al. (1996); Schafer 3
  • 5. 2006). This is likely due to data-driven limitations. Indeed, authors tend to adapt methodology to data, rather than the reverse. Empirical tests for credit discrimination may be split into two approaches according to their underlying assumptions on gendered creditworthiness (Blanchard, Zhao and Yinger, 2008). The rst approach postulates that men and women with identical personal and business characteristics are equally creditworthy. Hence, gender discrimination is assessed by testing whether gender inuences the probability of loan denial (or the credit conditions). The second approach, used in this paper, avoids any prior assumption on gendered creditworthiness. It is more general but it necessitates data on individual 7 reimbursement records. Discrimination is detected if a lower credit risk is associated to a higher probability of denial (or worse credit conditions). The contribution of this paper is twofold. Firstly, it proposes a new esti- mation method for assessing discrimination in loan allocation, which is well adapted to microcredit. This method is based on the comparison of gender coecient in loan size regression, on the one hand, and on the regression of loss-over-loan-size ratio (hereinafter relative loss), on the other hand. Secondly, this paper provides an original application. Exhaustive data help robustifying the estimation with respect to the missing-variable problem 8 9 that often plagues studies on discrimination. The empirical results point to gender discrimination. Indeed, all other things equal, women face signicantly worse credit conditions, while being creditworthier. Additionally, it is shown that reducing the information asymmetry through relationship brings no remedy to the handicap of being female. The rest of the paper is organized as follows. Section 2 describes the database. Section 3 discusses methodological issues. Section 4 provides evidence of discrimination and section 5 shows that it is not tempered by existing relationship. Section 6 concludes. and Ladd (1982); Cavalluzzo and Cavalluzzo (1998); Ross and Yinger (1999, 2002); Blanchower, Levine and Zimmerman (2003); Han (2004); Cavalluzzo and Wolken (2005); Blanchard, Zhao and Yinger (2008). This empirical literature in surveyed in Agier and Szafarz (2010). 7 Detailed characteristics of small-business borrowers are scarcely disclosed by the lenders (GAO, 2008). 8 Admittedly, our results may still suer from the self-selection bias put forward by Cavalluzzo (2002). 9 In that way, we follow Ross and Yinger (2002)'s recommendation; (...) well known methodological problems, such as selection and endogeneity bias, could lead to disparateimpact discrimination even when the designers (...) are trying hard to avoid it. Scholarly access to loan performance data and careful research are needed to shed further light on these issues ( p. 298). 4
  • 6. 2 Data Our unique database comes from Vivacred, a Brazilian MFI. Vivacred provides credit to micro-entrepreneurs located in the Rio de Janeiro low income communities and neighborhoods. It focuses on urban (formal and informal) micro-businesses such as storekeepers, craftspersons, and service providers. Vivacred started its activity in 1996 in Rocinha, the largest favela in Rio. Five other branches were created since then: Rio das Pedras in 1998, Copacabana (now in Gloria) in 1999, Maré in 2000, Santa Cruz in 2002 and in the city of Macaé (Rio state) in 2004. Until 2009, Vivacred was mostly funded by the Brazilian Development Bank (BNDES). Then, Vivacred integrated the national CrediAmigo program nanced by Banco do Nordeste, a Brazilian public bank. Vivacred's loans are accessible to businesses with at least six months of activity. For each application, the credit ocer in charge collects detailed information 10 on the applicant and the guarantor, if any, and on the charac- teristics of the business. 11 The credit ocer then provides a recommendation to the credit committee that makes the nal decision (acceptance or denial, and loan size). Actually, the term credit committee, used by Vivacred itself, is misleading since it refers to a single person. 12 13 Vivacred charges the same interest rate to all its clients (3.9% per month). Its lending methodology is based on credit rationing, rather than on adjusting the interest rate to perceived credit risk. Although this way of doing is standard for MFIs, it raises ethical concerns (Hudon, 2009). The data have been collected by the six branches of Vivacred. For the period under consideration (1997-2007), about 41,000 loans were solicited by 15,400 applicants, and about 32,000 loans were granted to 11,400 borrowers. However, we removed the applications canceled by the clients, the contracts with incomplete specications, the loans to Vivacred's employees, and the few group loans. Therefore, the study is based on exhaustive data of 34,000 applications and 32,000 actual loans. 10 Private and professional addresses, birth date, birth state, marital status, gender, dependent(s), profession, bank references, spouse's ID, current account, family consumption, family external income, full credit history (as a borrower, a borrower's spouse, or a guarantor). 11 Location, sector, legal status, number of employees. 12 Depending on the requested amount, this person is either the branch manager, or a senior credit ocer. 13 Banco da Mulher, a comparable non-prot institution, provides loans with rates be- tween 3% and 5% a month, while Fininvest, a for-prot institution, proposes consumption credit with a monthly 12% rate. 5
  • 7. Our dataset contains the full credit history (number of former loans, delays, defaults, and losses) of all borrowers. A repayment is considered delayed after 30 days, and defaulted after 180 days. The penalty for default is 14 the client's name inclusion within the SPC register, which is available for consultation by any institution supplying credit, including shops. Beyond losing access to credit, those who are registered in SPC face serious trouble getting a cell phone contract or buying household appliances, for example. Table 1 gives the descriptive statistics, globally and then split by applicant's gender, with t-tests for equality of means. Vivacred claims no special commitment to serve women. Its clientele is balanced, with 49.6% of women over the period 1997-2007. About the same share (47.41%) is observed for female credit ocers, and these are more often in charge of dealing with female entrepreneurs. Female applicants request smaller loans, to be paid back in less installments, than men (BRL 1,237 against BRL 1,518), amounts (BRL 891 against BRL 1,136). 15 and logically receive smaller Additionally women are slightly more credit-rationed than men as they get, on average, 21.4% less than they request, against 20.7% for men. Nevertheless, men and women face similar approval rates around 95%. Women entrepreneurs are two years older than males (43 versus 41), less likely to be married (43% versus 52%), and less likely to have dependents (51% versus 53%). Male and female applicants also dier in business characteristics. Female- owned businesses are smaller, in terms of both prots and sta size. 16 The external income (i.e., income earned by any household member and unrelated to business activity) is similar for men and women (around BRL 213 per month). Regarding the credit characteristics, the purpose of capital investment (as opposed to liquidity) is present in 34% and 29% of applications from men and women, respectively. Women need loans for both liquidity issues and loan repayment more often than men. Finally, the guarantor's and the client's genders are unrelated. Women exhibit a lower probability of delay than men (7.8% against 9.4%), but a similar probability of default (2.9%). Most importantly, women lead 14 SPC is a national database recording bad payers. 15 The average requested amount for all applications, including the denied ones, is BRL 1,250 for women and BRL 1,524 for men. 16 Kevane and Wydick (2001) observe similar characteristics for micro-entreprises in Guatemala. 6
  • 8. Table 1: Global and Gender-specic Descriptive Statistics: All Applicants All applicants M app. F app. Mean S.D. Mean Mean t-testb Applicant's, ocer's and guarantor's genders c Female applicant 0.496 Female credit ocer c Female guarantor 0.500 0.474 0.499 0.459 0.490 0.430 c 0.501 0.429 0.430 −0.0311∗∗∗ −0.00106 Request, loan size, and repayment record a Requested Amount (BRL) a Loan size (BRL) RA−LS Rationing factor ( ) (%) RA c Delay (30 days) c Default (180 days) a Loss (BRL) 1,242 1,518 1,015 996 1,136 891 21.38 24.16 20.73 21.98 0.086 0.281 0.094 0.078 0.029 0.167 0.030 0.027 18.6 156.0 21.4 15.5 2.52 Relative loss (%) 1,380 1,237 15.27 2.75 2.29 280.7∗∗∗ 245.3∗∗∗ −1.263∗∗∗ 0.0165∗∗∗ 0.003 5.888∗∗∗ 0.465∗∗ Applicant's characteristics Age (years) c 42.2 12.0 41.2 43.2 Married 0.47 0.50 0.52 0.43 c At least one dependent 0.52 0.50 0.53 0.51 a Mth. ext. income (X100 BRL) 2.13 3.76 2.11 2.16 # former loans 2.25 3.27 2.35 2.15 # former loans with delay 0.04 0.21 0.04 0.04 # times as a guarantor 0.74 2.11 0.89 0.60 −1.925∗∗∗ 0.0962∗∗∗ 0.0169∗∗ −0.04 0.202∗∗∗ 0.0077∗∗∗ 0.282∗∗∗ Business characteristics a Business prot (X100 BRL) 9.19 13.44 10.26 8.09 Sector (trade = 1, other = 0) c Ocial business 0.53 0.50 0.49 0.56 0.06 0.23 0.07 0.05 # employees 0.63 2.20 0.72 0.54 2.177∗∗∗ −0.0760∗∗∗ 0.0165∗∗∗ 0.175∗∗∗ Credit characteristics # installments 9.03 Capital investment purpose c Loan repayment purpose c Guarantor's involvement Observations a All c 4.39 9.10 8.97 0.32 0.47 0.34 0.29 0.09 0.29 0.08 0.10 0.92 0.27 0.93 0.92 16,899 0.128∗∗ 0.0518∗∗∗ −0.0171∗∗∗ 0.00756∗∗ 16,631 33,530 nancial values are in deated BRL (Real), the Brazilian currency. Over the period, the Real uctuated between 0.270 and 0.588 USD. b T-test for equality c Dummy variables of means between male and female applicants; *** p0.01, ** p0.05 7
  • 9. to signicantly smaller losses for the MFI, in absolute and relative terms. Vivacred's average relative loss is 2.8% for male borrowers and 2.3% for female ones. MFIs. 17 These numbers are consistent with those reported by other In sum, irrespectively of their characteristics women receive smaller loans and reimburse better than men. Section 4 will examine whether this evidence resists multivariate analysis. 3 Methodology Assessing discrimination in lending is complex for reasons pertaining to both the underlying economic theory and intrinsic econometric issues. As sum- marized by Dymski (2006), the inconclusiveness of the academic literature [can be attributed] to several factors: the ambiguity of legal and theoretical denitions of discrimination; the inescapability of the point of view of the observer and observed in empirical studies of racial discrimination; and the way in which empirical methodologies require research questions to be framed (p.215). In this paper, we adopt a narrow denition. Namely, we dene gender discrimination in lending as the economically unjustied awarding of inferior credit conditions to female borrowers. This denition corresponds to the intuition of a double-standard lending practice. It therefore excludes the socalled rational discrimination where unequal credit conditions result from business needs. 18 Following our denition, disparate treatment (i.e., a harsher application process for women) is a necessary but not sucient condition for gender discrimination. Indeed, disparate treatment could sometimes be economically rationalized by objective credit risk characteristics. For instance, let us imagine just for the sake of the argument that women exhibit higher credit 17 For instance, reported default rates are: below 2.2% for CrediAmigo in Northeast Brazil (CrediAmigo, 2009), below 5% for the Grameen Bank in Bangladesh (Morduch, 1999), and between 1 and 5.5% for rural MFIs in Indonesia, with a single exception of 12% (Robinson, 2002). 18 Rational discrimination can arise because of information costs (Lang and Nakamura, 1993). Also, when some variables aecting creditworthiness are not observable (e.g., business abilities, social connections, etc.), lenders could use gender as a proxy for credit risk. Such a practice leads to statistical discrimination (Arrow, 1971, 1998). For instance, some empirical papers in micronance use gender as a proxy for poverty. If gender were used in the same way by lenders, this could lead to statistical discrimination. 8
  • 10. risk than men, all other things equal. In such a situation, disparate treatment could be a rational reaction from the lender. Under such circumstances, we would not characterize the lender's attitude as gender discrimination. Obviously, the denition of discrimination used here is based on economics, and not on ethics. Actually, whether rationalizable or not, disparate treatment is highly questionable on ethical, and even legal, grounds. Pragmatically, our motivation for choosing such a narrow denition for gender discrimination in lending is linked to its empirical testability and the level of conclusiveness it allows to reach. Indeed, detecting narrowly dened discrimination brings stronger conclusions on the lender's practice. Besides, this narrow denition is close in spirit to Becker's denition of tastebased discrimination (Becker, 1971). However, the qualication of taste- based might look too restrictively connected to intentional prejudice. In- stead, we tend to view gender discrimination in lending as resulting from mostly unintentional stereotyping shown by social psychologists (Fein and Spencer, 1997; Kunda and Sinclair, 1999) to be a common human feature. Indeed, Buttner and Rosen (1988) emphasize that women entrepreneurs still suer from gender stereotypes related to their ability to eciently run a rm (in terms of leadership, autonomy, lack of emotionalism, etc.). On top of denitional complexity, empirical studies on discrimination in lending are often plagued by technical problems. First and foremost, because data made available to researchers are generally insucient to trustfully reproduce the lender's scoring process, the sources of gender gap in credit conditions, if any, are hard to identify empirically. This paper will circumvent this serious identication problem by using an exhaustive database. Other challenging issues go beyond data availability (Ross, 2000). discrimination in lending may take dierent forms. Firstly, Indeed, it may be ob- served with regard to access to credit (higher denial probability), and/or to credit conditions (higher interest rates, smaller loans, more collateral required, etc.). 19 Secondly, the lender's decision making is sequential: in the selection phase, loans are approved or denied, and then credit conditions are set for approved loans solely. As a consequence, loan allocation and credit conditions do not concern the same pool of applicants. Thirdly, the lender's assessment of creditworthiness is generally unknown. Therefore, researchers commonly use a surrogate for creditworthiness built 19 Some authors, like Blanchower, Levine and Zimmerman (2003) and Weller (2009), combine the two perspectives. 9
  • 11. from a set of relevant variables (ideally, the ones used as screening devices by the lender), referred to as controls, which aim at capturing all genderunrelated relevant variables. This approach may suer from several draw- backs, notably omitted variables. Inevitably, researchers are confronted to some degree of uncertainty regarding the lender's screening process. Fourthly, ex ante creditworthiness is, by nature, unobservable. It is typically proxied by ex post variables like delay, default, and loss. However, these variables are to some extent endogenous because they are aected by the credit conditions. For instance, default might be more frequent for larger and therefore presumably riskier loans (Stiglitz and Weiss, 1981). Alternatively, more rationed borrowers could nd it harder to reimburse. 20 In any case, endogeneity prevents ex post outcomes from being straightforward explanatory variables for the probability of approval and the credit conditions. Given all these methodological limitations, how should we test for gender discrimination in lending? We address this question by referring to the theoretical approach proposed by Ferguson and Peters (1995), who dene discrimination as the use of dierent credit standards across the two components of the population and state that discrimination happens when a lower or equal default rate is associated to a higher or equal denial rate, provided that at least one inequality is strict. The remaining of this section is devoted to making this rule econometrically operational, and applicable to microcredit. The lending methodology of the microcredit industry is based on standardized contracts, with typically the same interest rate for all borrowers. In that framework, loan size is the sole credit condition that is tailored to the client's needs by the MFI. Hence, the lender's problem may be represented as: M ax {(1 + r) LS − E [Loss (LS)]} LS 0 where r is the xed interest rate, LS (1) is the loan size (denial corresponding to a zero loan size) that is the lender's decision variable, and is the expected loss that depends on loan size. E [Loss (LS)] Equivalently, this problem writes: E [Loss (LS)] 0 LS M in LS (2) On the empirical side, two variables are going to be explained: the loan size (i.e., the decision variable), and the expected relative loss (i.e., the objective 20 The results in Appendix A reveal that, in Vivacred, loan size negatively impacts the probabilities of delay and default. 10
  • 12. function). Expectations being unobservable, we will take realized loss as a proxy for expected loss. 21 In order to test for gender discrimination, we introduce the following notations. The loan applications are indexed by i. 22 Each application involves several variables. First, the applicant's ex ante characteristics are: • Applicant's gender represented by a dummy variable: Fi = • Vector (z1i , ..., zni ) 1 0 if the applicant is female if the applicant is male summarizing all other characteristics, including the applicant's requested amount RAi . Second, the lender's decision variables are: • Loan approval represented by a dummy variable: Ai = • Loan size: 1 0 if the loan is approved if the loan is denied LSi We have explicitly split the decision variable in two parts (approval and loan size) in order to make the impact of the selection process visible, and subsequently apply the Heckman procedure. Third, the ex post outcome variable is: • Relative loss: Lossi /LSi A companion paper (Agier and Szafarz, 2010) shows that, all things equal, Vivacred's denial probability is not signicantly dierent between men and women, but female borrowers receive signicantly smaller loans than men. 21 As loss is endogenous, the expectation error will simply be absorbed in the error term of the regression without introducing any bias in the estimated coecients. 22 A person who introduces several loan applications will thus appear once for each application. 11
  • 13. Given these results, the current econometric model concentrates on loan size, and not on denial probability: n βk zki + 1i ∀i s.t. Ai = 1 (3) ϕk zki + LSi = βF Fi + 2i ∀i s.t. Ai = 1 (4) k=1 n Lossi /LSi = ϕF Fi + k=1 Equations 3 and 4 make it possible to operationalize the Ferguson and Peters (1995) rule. Indeed, gender discrimination corresponds to the situation where βF ≤ 0 and ϕF ≤ 0, with at least one strict inequality. Moreover, the selection issue will be addressed by using the Heckman estimation method (Heckman, 1976, 1979). Lastly, it is worth mentioning that Vivacred is a socially-oriented MFI, and not a prot-oriented lender. Does it make a dierence when it comes to testing for discrimination? We argue that it does not, so that discrimination in social lending may be addressed like in prot-based lending. Our argument is the following. For the sake of self-sustainability, sociallyoriented lenders are bound to assess their applicants' creditworthiness. In practice, MFIs select their clients in two steps. Firstly, they dene their target pool of borrowers according to their social mission (typically, the poor and/or unbanked entrepreneurs in a given area). Secondly, they assess creditworthiness of the applicants from this target pool basically in the same way 23 as prot-oriented institutions do. Therefore, gender discrimination may show o in the same way too, provided that the target pool is dened in- 24 dependently from gender considerations, which is indeed the case for the MFI under study. 23 This way of doing partly explains why MFIs do not reach the very poor (Rhyne, 2001). Nevertheless, Hartarska (2005) nds evidence that in Eastern Europe and Central Asia, MFIs with higher proportion of women on their board reach poorer borrowers. See also Karlan and Zinman (2008) on the credit elasticities of the poor. 24 This restriction is important since some MFIs, like the Grameen Bank, serve women solely or majoritarily. Our approach would not make sense for such MFIs. 12
  • 14. 4 Estimation Results 4.1 Testing for Gender Discrimination In this section, we compare the gender dummy coecients in loan size and relative loss regressions, along the lines of the econometric methodology exposed in section 3. In other words, we check whether the harsher credit rationing imposed by Vivacred to female entrepreneurs is, at least partially, attributable to repayment conduct. We address this issue by estimating equations (3), and (4). In the rst regression (equation (3)) loan size is explained by the borrower's gender, the amount requested by the borrower, and control variables. The second regression (equation (4)) explains relative loss with the same variables. Additionally, two alternative specications for equation (4) open the possibility of capturing the impact of credit rationing. The borrower's gender is our explanatory variable of interest. In both equations, we control for all variables collected by Vivacred's credit ocers as well as for this credit ocer's gender. More precisely, the control variables in- clude the borrower's characteristics (marital status, existence of dependents, age, and household's extra income), the business characteristics (prots, sector, ocial status, number of employees), the loan characteristics (requested amount, installments, loan renegotiation), and the guarantor's existence and gender, if any. The relationship with Vivacred is accounted for by three variables: the number of former loans as a client and as a guarantor, and the number of former loans repaid with delay (as a client, solely). Year dummies are added to capture time heterogeneity. Table 4.1 presents the regression results including one specication for loan size and three specications for relative loss. 25 result for the basic formulation of equation (4). Column (2) displays the In columns (3) and (4), 26 the requested amount (RA) is replaced respectively by the loan size RA−LS ). and the rationing factor ( RA (LS ), 25 Because, the Ferguson and Peters (1995) framework is purely theoretical (and only considers, in its original form, denial and default), it does not state which variables are relevant in the estimation. 26 Loan size inclusion is tricky because it is the dependent variable of the rst equa- tion. Making it appear in the second equation could distort the impact of the borrower's characteristics, among which gender. However, ignoring this variable could create an omitted-variable problem. Alternatively, we could use simultaneous-equation estimation to account for endogeneity. However, combining such estimation with Heckman's procedure is tedious. 13
  • 15. Table 2: Loan Size and Relative Loss: Heckman's Regressions (1) (4) Loss/LS Loss/LS -32.48*** -0.832*** -0.881*** -0.870*** (0.174) (0.175) (0.173) 0.623*** -0.000112 (0.00265) Requested amount (RA) (3) Loss/LS (5.116) Female borrower (F) (2) LS (9.03e-05) Loan size (LS) -0.000683*** (0.000116) Rationing factor ( RA−LS ) RA 0.0460*** (0.00379) Female guarantor -26.58*** -0.0981 -0.165 -0.0745 (5.280) (0.180) (0.180) (0.178) -50.45*** 0.364** 0.312* 0.253 (5.146) (0.175) (0.176) (0.174) 16.38*** -1.128*** -1.113*** -1.087*** (5.262) (0.179) (0.180) (0.178) 13.29** -0.146 -0.107 -0.148 (5.334) (0.181) (0.182) (0.180) 0.613*** -0.0446*** -0.0447*** -0.0399*** (0.219) (0.00748) (0.00749) (0.00744) 13.98 0.0280 -0.258 0.785** (11.13) (0.379) (0.376) (0.372) # installments 24.65*** 0.110*** 0.144*** 0.129*** (0.619) (0.0211) (0.0216) (0.0205) Capital investment 31.00*** -0.401** -0.283 -0.502** (5.811) (0.198) (0.198) (0.195) Loan repayment 81.71*** 3.053*** 3.142*** 3.263*** (9.181) (0.313) (0.314) (0.312) External income 0.0943*** -0.000753*** -0.000528** -0.000799*** (0.00701) (0.000239) (0.000240) (0.000233) Business prot 0.0576*** 9.23e-05 0.000226*** 6.98e-05 (0.00219) (7.46e-05) (7.52e-05) (6.93e-05) -24.10*** 0.448** 0.445** 0.314* (5.347) (0.182) (0.183) (0.182) Female credit ocer Married client Client with dependent(s) Client's age No Guarantor Trade (sector) Ocial business 0.270 0.653 0.209 (11.70) # employees 179.9*** (0.399) (0.401) (0.390) 9.331*** 0.00708 0.0307 0.00311 (1.213) (0.0414) (0.0415) (0.0408) 37.68*** -0.292*** -0.243*** -0.238*** (1.003) (0.0342) (0.0350) (0.0337) # former loans with delay -39.53*** 0.363** 0.276* 0.376*** (4.236) (0.144) (0.145) (0.143) # times as a guarantor 10.69*** -0.141*** -0.130*** -0.117*** (1.246) (0.0425) (0.0426) (0.0423) Constant -218.5*** 2.032*** 2.081*** 0.583 (24.57) (0.469) (0.468) (0.478) -118.5*** 3.840*** 4.948*** 1.671* (30.91) (1.053) (1.030) (1.004) 33,530 33,530 33,530 33,530 1,860 1,860 1,860 1,860 128,127 822.1 852.1 977.1 29 26 26 26 # former loans Mills Observations Censored obs. Wald Chi2 Degrees of freedom Year dummies as controls (not reported). Heckman's selection: Approval by the committee. Selection instruments: Kind of premises, source of funds, credit ocer's family status. favela resident, and seniority, credit ocer turnover faced by the client, Rocinha branch. Standard errors in parentheses; *** p0.01, ** p0.05, * p0.1 14
  • 16. The requested amount acts as a proxy for the entrepreneur's project size. In particular, it allows to take into account the fact that women typically ask for smaller loans. By controlling for this rare piece of information, we intend to clean the regression from the eect of gender-specic request. including loan size, 28 27 When we control for the level of indebtedness irrespectively of the source of the gender gap. The correlation between the requested amount and the loan size is high (equal to 0.667). For this reason, we avoid putting both variables simultaneously in the second regression. Instead, we opt for a third specication using the rationing factor that measures the fraction of the requested amount that has actually been granted to the applicant. Column (1) of table 4.1 conrms that women suer from harsher credit rationing than men. Indeed, even when accounting for the selection bias and the dierences in requested amounts, women receive signicantly smaller loans than men. As detailed in section 3, this result can be due to either (economically unjustied) discrimination, or economically justied lending practice. The remaining columns of table 4.1 allow to disentangle these two possibilities unambiguously. Indeed, in all specications the gender dummy has a signicant negative impact on relative loss, meaning that, all things equal, women are creditworthier than men.The requested amount in itself has no signicant impact (column (2)). On the other hand, the loan size aects relative loss negatively (but aects absolute loss positively, see Appendix A) while the rationing factor has a positive impact. More rationed loans are harder to repay. Remarkably, despite the handicap of being more credit-rationed, women manage to reimburse their loans better than men. In other words, if men and women were equally rationed, the female repayment conduct would be even better than it actually is. Globally, the results are robust. The coecient of the gender dummy is about the same in the three specications of the relative loss equation. Appendix A proves that the same result applies to the absolute loss, the probability of 27 Still, we cannot exclude that women try to maximize their chances of getting a loan by intentionally introducing smaller requests. If this is the case, then the request eect is partly driven by the borrower's strategy. More generally, the identication of demand and supply eects in credit markets is discussed by, e.g., Kanoh and Pumpaisanchai (2006); de Janvry, McIntosh and Sadoulet (2010). 28 The inclusion of loan size in the second equation is tricky because loan size is the dependent variable of the rst equation. However, as a matter of fact, the coecient of the gender dummy is not much aected by such inclusion. 15
  • 17. delay, and the probability of default. In addition, although the legitimacy of using Heckman's estimation method is exhibited by a signicant Mills ratio, OLS estimation brings similar features (results not reported here). At this point, the rst conclusion of our empirical study emerges: Women entrepreneurs are trustworthier borrowers than men, but do not benet from this quality. On the contrary, they face harsher credit conditions. Conse- quently, the Ferguson and Peters (1995) rule leads to asserting the presence of discrimination. Does the same conclusion apply to women involved as guarantors? In Vivacred, each contract involves at most three people from the borrowing side: the client, the guarantor, and the client's spouse. Each of them is at risk in case of default. Indeed, they all bear the risk of being registered in SPC (the Brazilian insolvency register) and, consequently, experiencing serious trouble 29 in future nancial transactions. From the lender's viewpoint, having more people involved in a credit contract is always better. 30 This is likely the reason why married borrowers repay better (and receive larger loans). The coecient of the guarantor's gender reveals that female guarantors as opposed to male guarantors have a negative impact on loan size, but no signicant impact on relative loss. According to the Ferguson and Peters (1995) rule, this again should be viewed as a stigma of gender discrimination, in a milder form though. Incidentally, table 4.1 shows that the credit ocer's gender is signicant for loan size, but not for relative loss. Female ocers typically oer smaller loans, but obtain similar relative losses. Thus, viewed from the MFI's perspective, male and female credit ocers are equally protable, although using dierent screening processes. Moreover, in Agier and Szafarz (2010), we show that loan allocation by both male and female ocers leads to disparate treatment. If, as conjectured, discrimination is attributable to gender stereotyping, then the stereotypes are shared by male and female credit ocers. The signs of the coecients associated to gender-neutral characteristics of the borrowers match well with the intuition that lower loan size is associated to higher relative loss, and vice versa. This means that the credit ocers grant loans rationally in all respects except the applicant's gender. For instance, married clients and older clients receive larger loans and repay better. The same is true for borrowers with larger extra income. Applications from the 29 All guarantors provide their scal identity number (CPF), which is the code required for registering them in SPC. 30 However, Alesina, Lotti and Mistrulli (2008) mention that the presence of a guarantor might signal a borrower's higher credit risk. 16
  • 18. trade sector (as opposed to services) bring smaller loans and generate higher relative losses. Mechanically, the loan size increases with the number of installments. For a given loan size, the higher the number of installments, the worse the repayment conduct. As expected, all indicators of the borrower's credit history are signicant: existing relationship (as a borrower and/or a guarantor) leads to larger and better repaid loans. Former delays act in the opposite way. To summarize, we have shown that gender discrimination is present in the data. In line with the glass-ceiling theory (Agier and Szafarz, 2010), the next section will examine in greater details the interaction between the applicant's gender and the scope of his/her project. 4.2 Interaction Between Gender and Project Scope Up to now, we have assumed that the estimated model is fully linear. Nevertheless, loan size is likely a non-linear function of the requested amount. Indeed, for tiny requests, it would be cost-inecient for credit ocers to devote much attention to the specicities of the request le. Instead, the credit ocers may roughly examine some basic creditworthiness characteristics, and make a yes-or-no decision. They would either approve the loan as such and oer the requested amount, or simply deny the loan. Given that no gender gap would be observed on the loan allocation decision, one can conjecture that gender discrimination is absent when the requested amount is very small. Moreover, if the observed gender discrimination is associated with stereotyping (women are less able to run large projects), then the gender gap in loan size should be increasing with the requested amount. In order to investigate whether these conjectures hold in the data, we now add a gendered interaction term in each estimated equation. The empirical results in table 4.2 conrm the basic intuitions. The loan size equation in column (1) features a positive coecient for the gender dummy and a negative coecient for the interaction term. In theory, this should mean that women are favored for tiny loans (below BRL 100), but in practice no actual loan lies below this limit. Thus, except for tiny loans (around BRL 100), there always exists a gender gap, and this gap is increasing with the scope of the project. This result is consistent with the glass-ceiling eect unveiled by Agier and Szafarz (2010). In contrast, column (2) shows that the relative loss is not related to the requested amount, even when men and women are considered separately. 17
  • 19. Table 3: Gender Gap and Project Scope: Heckman's Regressions (1) RA * F (4) Loss/LS Loss/LS 91.49*** -0.995*** -0.938*** -0.494** (0.260) (0.250) (0.230) 0.656*** -0.000156 (0.00303) Requested Amount (RA) (3) Loss/LS (7.561) Female borrower (F) (2) LS (0.000104) -0.0921*** 0.000121 (0.00416) (0.000143) Loan size (LS) -0.000701*** (0.000128) LS * F 5.86e-05 (0.000183) Rationing factor ( RA−LS ) RA 0.0548*** (0.00520) Rationing factor * F -0.0174** (0.00702) Mills -129.0*** Wald Chi2 DF 1.677* (1.053) (1.030) (1.004) 33530 33530 33530 33530 1860 1860 1860 1860 130358 822.8 852.1 983.5 30 Censored obs. 4.954*** (30.69) Observations 3.854*** 27 27 27 Same controls as in table 4.1; Heckman selection: Approval by the committee. Standard errors in parentheses; *** p0.01, ** p0.05, * p0.1 Despite being more heavily penalized, women with larger projects do not incur higher losses. The negative impact of loan size on relative loss does not interact with gender (column (3)). On the contrary, the worsening of relative loss incurred by rationing is less pronounced for female borrowers. This is perhaps attributable to higher female adaptability to bad circumstances. Women cope with restricted loans better than men under similar circumstances. In conclusion, gender discrimination is stronger for more ambitious projects. The next section examines whether relationship mitigates discrimination. 5 Impact of Relationship We now address the resilience of the gender gap in loan size by examining the dynamics of the gender-specic treatment along the borrower's credit history. Relationship between the lender and the borrower typically reduces information asymmetry in lending(Tra and Lensink, 2007). Indeed, timely repayments demonstrate the borrower's creditworthiness. As a consequence, 18
  • 20. a borrower who has successfully reimbursed a rst loan will more easily obtain a second and often larger loan, and so on. This is the basic principle driving progressive lending (Egli, 2004). Figure 1: 31 Evolutions of the gender-specic requested amounts and loan sizes with respect to the length of relationship Chakravarty and Scott (1999) show that the duration of relationship lowers the probability of credit rationing in consumer loans. Our previous estimations (table 4.1) conrm that former loans have a positive impact on loan size, and a negative impact on relative loss. However, as gure 1 exhibits, after the second loan, the spread between the requested amount and the loan size seems to stabilize. 32 Actually, successful second-time applicants request smaller amounts than in their rst applications, but then benet from second loans higher than their rst loans. Later on, regular borrowers do not 31 Copestake (2002) emphasizes that progressive lending may also induce an increasing inequality eect. 32 This constant spread may be seen as a steady-state equilibrium of the lending game under credit rationing. Indeed, the borrower knows that the lender is going to exert credit rationing and rationally inates his/her request accordingly. Therefore, the optimal response of the lender is to keep applying credit rationing, but in a constant - and thus predictable - way to allow their regular borrowers accurately size their requests. If this scenario holds, no player has any advantage of moving to an equilibrium without credit rationing. 19
  • 21. 33 downscale their requests with respect to the previous one anymore. Table 4 provides additional descriptive statistics. The left side of the table concerns the new applicants, whereas the right side concerns the known applicants. In each case, the overall and gender-specic means are displayed for the following variables: requested amount (RA), loan approval rate, loan RA−LS ), with the corresponding t-tests for size (LS ), and rationing factor ( RA equal means between genders. While requesting more on average (BRL 1,440 versus BRL 1,366), new applicants face more denial (9% versus 5%), receive smaller loans (BRL 772 versus BRL 1,059), and are more rationed (38.9% versus 18.2%). The global statistics are thus consistent with the asymmetric information story. Table 4: Descriptive Statistics for New and Known Applicants Requested amount a All (RA) New applicants M F t-test 1,440 1,545 1,334 Loan approval (%) a Loan size (LS) 90.9 91.2 91.3 772 849 694 Rationing factor (%) 38.9 37.8 39.9 12,190 6,115 10.8∗∗∗ −0.18 11.9∗∗∗ −3.8∗∗∗ 6,075 Observations a in BRL, *** p0.01 All Known applicants M F t-test 1,366 1,519 1,209 95.0 95.1 95.3 1,059 1,190 925 18.2 18.1 18.4 21,367 10,815 17.5∗∗∗ −0.61 17.8∗∗∗ −0.81 10,552 Let us now examine whether discrimination tends to scale down with relationship. Table 4 shows that, regardless of their credit history, women are left with identical opportunity to obtain a loan. However, conrming the observations from gure 1, the gender gaps in both requested amount and loan size widen with relationship. The female-overmale mean value ratios for new applicants are 86.3% for requested amount and 81.7% for loan size, while the corresponding ratios for known applicants are 79.6% and 77.7%, respectively. Contrastingly, only for rst loans is the rationing factor signicantly higher for women. Perhaps with time, women learn about the endured gender gap, and revise the scope of their projects accordingly. If gender discrimination were due to prejudice and/or stereotyping, relationship could reveal insucient to mitigate it. In such a case, repayment history would not matter, and disparate treatment would subsist despite the revelation of women's creditworthiness. 33 Interestingly though, after two loans men start to progressively increase their requests while, under similar circumstances, women seem to keep more or less the same requested amount. 20
  • 22. Alternatively, if gender discrimination were due to informational deciencies (attributable to cultural reasons, for instance), credit ocers would learn from experience and adapt their practice to the facts. In such a case, the intensity of discrimination would be decreasing with the number of previous loans. 34 Relationship would then exhibit a stronger (positive) impact on loan size for female borrowers than for male ones, allowing the former to be treated in a progressively fairer way with time. In order to disentangle these two possible scenarios, we add a gender-specic relationship factor into the regressions by means of an interaction term between the number of former loans and the gender dummy. The coecient associated to that new variable will indicate whether the impact of relationship diers across genders. Table 5: Gender Gap and Relationship: Heckman's Regressions (1) (2) (3) (4) LS Loss/LS Loss/LS Loss/LS -21.38*** -0.861*** -0.896*** -0.935*** (6.182) (0.211) (0.211) (0.210) # Former loans 39.57*** -0.297*** -0.245*** -0.249*** (1.166) (0.0397) (0.0405) (0.0391) # Former loans * F -4.946*** 0.0128 0.00666 0.0289 (1.547) (0.0527) (0.0529) (0.0524) 10.51*** -0.141*** -0.130*** -0.116*** (1.247) (0.0425) (0.0426) (0.0423) -38.58*** 0.360** 0.275* 0.370*** (0.143) Female borrower (F) # Former loans as a guarantor # Former loans with delay (0.145) (0.145) 3.850*** 4.954*** 1.689* (30.92) Requested Amount (4.246) -121.8*** Mills (1.054) (1.030) (1.004) X X Loan Size X Rationing factor Observations Censored obs. Wald Chi2 DF X 33,530 33,530 33,530 33,530 1,860 1,860 1,860 1,860 128,113 822.1 852.0 977.4 30 27 27 27 2nd loan p0.1 Same controls as in table 4.1; Heckman selection: got at least a Standard errors in parentheses; *** p0.01, ** p0.05, * Our database includes 11,422 dierent borrowers, among which 63.31% beneted from a second loan. About one third of the newcomers dropped out after their rst loan. This second selection issue leads to a second use of 35 Heckman's estimation procedure. 34 For instance, Beaman et al. (2009) show on Indian data that female political leadership weakens stereotypes about gender roles. 35 As Heckman's procedure allows one selection solely, we perform two separate exercises. 21
  • 23. The results are presented in table 5. Expectedly, the number of former loans has a positive impact on loan size and a negative impact on relative loss. More troubling is the negative eect of the interaction term on loan size. While men benet of an average extra BRL 39.57 for each former loan, women see this bonus in loan size reduced by BRL 4.95, thus amounting BRL 34.62 only. Credit restrictions are progressively relaxed with relationship, but at a slower pace for women. Relationship is thus less valued for females, digging the gender gap instead of reducing it. Loan size is also increasing with relationship as a guarantor, but the eect is milder. A former loan as a borrower brings a bonus almost four time larger than as a guarantor. 36 Moreover, former loans with delays have a negative impact on loan size and, consistently, lead to higher relative losses. 6 Conclusion The empirical approach to discrimination in the lending industry is less clearcut than in the labor market. Indeed, the literature exhibits large methodological variations, mainly data-driven, which plague the comparability of results. For this reason, we have adopted a restrictive denition of dis- crimination in lending, embodying double standards solely. Using such a restrictive denition strengthens our conclusion that discrimination is indeed present in the MFI under scrutiny. In a nutshell, we have shown that, all things equal, women entrepreneurs receive smaller loans and induce smaller losses for the lender. This result is consistent with the stylized facts reported by Armendáriz and Morduch (2010). Nonetheless, our ndings are more reliable than rough descriptive statistics since the regressions take into account all variables actually reported by the credit ocers, including the required amount. Furthermore, the gender gap in loan size increases with relationship and subsequent asymmetric information dwindling. Although being trustworthier than men, women entrepreneurs thus seem to undergo a never-ending curse. Starting with smaller rst loans than men, they never recover from their First, we apply Heckman's regressions to the pool of all applicants. Second, we apply the same regressions to the pool of applicants who beneted from one former loan at least. In both cases, the selected clients are the ones who obtained a second loan. As both exercises brought similar gures, we only present the results concerning the pool of all applicants. 36 Nonetheless, the impact of relationship as a guarantor is gender-insensitive (regression not reported). 22
  • 24. initial handicap. This nding strongly advocates for external intervention to combat gender discrimination. It is worth stressing that Vivacred, the MFI under scrutiny in this paper, is a very well-run organization. The release of its remarkable database also indi- 37 cates that good governance practices are in place. We therefore conjecture that our ndings underestimate the global level of gender discrimination in small-business lending. As a matter of fact, the microcredit industry is highly subsidized internationally, notably by donors having a women empowerment agenda. 38 Given the lack of anti-discrimination regulations in many developing countries, donors' concern could appear as an alternative disciplining device. The main obstacle thereto is data unavailability. The rst step should therefore be to request more transparency in the screening processes put in place in MFIs, and more generally in lending institutions. The need for critical assessments of the fairness of micronance practices is advocated by many authors, including, e.g., Servet (2005); Rossel-Cambier (2008); Labie et al. (2010). Data limitations are still present in our study. Although the regressions have exploited all screening variables collected by the MFI itself, we cannot exclude that face-to-face interviews bring unobservable but relevant gender-related pieces of information, linked for instance to education, nancial literacy, and attitude toward risk. Moreover, despite the exceptional exhaustivity of our database, we do not possess information on the steps that predate the formal loan application. For instance, an informal contact with a credit ocer might discourage some entrepreneurs to pursue the application process. However, it seems highly unlikely that such unobservable elements could challenge the conclusion pointing to discrimination. More plausibly, they would reinforce it. The origins and consequences of prejudice and stereotyping, and the means to deter them, are widely discussed in the socio-economic literature. We do not elaborate further on these issues. Nevertheless, our ndings raise additional unanswered questions. Firstly, why are women entrepreneurs trustworthier than their male counterparts? Do they fear penalties more, a hypothesis compatible with the evidence that women are more risk-averse than men (Borghans et al., 2009)? Or are they acting strategically in order to obtain better credit conditions 37 One of the authors has had the opportunity to observe Vivacred's day-to-day business practices in details, and her ndings conrm that statement. 38 Isserles (2003) and Berkovitch and Kemp (2010) discuss the underlying ideology of this agenda. 23
  • 25. in the future? Anyhow, the facts documented in this paper contradict the incentive-based argument stating that borrowers who accept less favorable credit conditions (all other things equal) are more likely to default. 39 Secondly, why do women ask for smaller loans? Do they expect to be discriminated against 40 and refrain from applying for riskier projects thereby creating a self-selection eect? The results in section 5 are consistent with this scenario. Moreover, if this explanation holds, it means that a consid- erable amount of hidden entrepreneurial talent is wasted through rationally expected discrimination. Thirdly, how do women manage to reimburse better than men while being more credit-rationed (which is detrimental to repayment conduct)? How do household constraints interfere with female business projects? Recent studies on intra-household relations in India (Garikipati, 2008; Guérin et al., 2009) have shown that access to credit may increase female nancial vulnerability. Seriously addressing these questions is necessary, at the very least for economic reasons. Indeed, the potential for female-driven economic develop- ment is far from being exhausted. Better knowing the needs and aspirations of women entrepreneurs will help designing gender-conscious nancial products, as emphasized for the micronance industry by Johnson (2004); Corsi et al. (2006); Guérin (2010) and many others. By demonstrating that even well-run socially-oriented MFIs are not immune to gender discrimination, this paper has stressed the importance of nding creative solutions to the lack of capital endured by women entrepreneurs. References Agier, Isabelle, and Ariane Szafarz. 2010. Micronance and Gender: Is There a Glass Ceiling in Loan Size? Université Libre de Bruxelles CEB Working Papers 10-047. Alesina, Alberto F., Francesca Lotti, and Paolo Emilio Mistrulli. 2008. Do Women Pay More for Credit? Evidence from Italy. National Bureau of Economic Research Working Paper Series 14202. 39 Actually, the original argument, as summarized by Banerjee and Duo (2010), is phrased in terms of interest rates, and not loan size. However, the same logics should be applicable to any credit condition, including loan size. 40 Blumberg and Letterie (2007) argue that applicants foresee pretty well the decision procedure of the lender. 24
  • 26. Alsos, Gry Agnete, Espen John Isaksen, and Elisabet Ljunggren. 2006. New Venture Financing and Subsequent Business Growth in Men- and Women-Led Businesses. Entrepreneurship Theory and Practice, 30: 667686. Armendáriz, Beatriz, and Ariane Szafarz. 2011. On Mission Drift In Micronance Institutions. In The Handbook of Micronance, B. Armendariz and M. Labie ed. London-Singapore:World Scientic Publishing (forthcoming). Armendáriz, Beatriz, and Jonathan Morduch. 2000. Micronance Beyond Group Lending. Economics of Transition, 8(2): 401420. Armendáriz, Beatriz, and Jonathan Morduch. 2010. The Economics of Micronance. 2 ed. Cambridge, MA:The MIT Press. Arrow, Kenneth. 1971. The Theory of Discrimination. Princeton Uni- versity, Department of Economics, Industrial Relations Section. Arrow, Kenneth J. 1998. What Has Economics to Say about Racial Discrimination? Journal of Economic Perspectives, 12(2): 91100. Banerjee, Abhijit V, and Esther Duo. 2010. Giving Credit Where it is Due. Journal of Economic Perspectives, 24(3): 6180. Beaman, Lori, Raghabendra Chattopadhyay, Esther Duo, Rohini Pande, and Petia Topalova. 2009. Powerful Women: Does Exposure Reduce Bias? Quarterly Journal of Economics, 124(4): 14971540. Becker, Gary S. 1971. The Economics of Discrimination. 2nd ed. Chicago, IL:University of Chicago Press. Berkovitch, Nitza, and Adriana Kemp. 2010. Economic Empowerment of Women' as a Global Project: The Limits of Social Change in the Neo Liberal Era. In Confronting Global Gender Justice: Women's Lives, Hu- man Rights, D. Bergoen, P. R. Gilbert, T. Harvey, and C. L. McNeely ed. UK: Routledge (forthcoming). Blanchard, Lloyd, Bo Zhao, and John Yinger. 2008. Do Lenders Discriminate Against Minority and Woman Entrepreneurs? Journal of Urban Economics, 63(2): 467497. Blanchower, David G., Phillip B. Levine, and David J. Zimmerman. 2003. Discrimination in the Small-Business Credit Market. Review of Economics and Statistics, 85(4): 930943. 25
  • 27. Blumberg, Boris F., and Wilko A. Letterie. 2007. Business Starters and Credit Rationing. Small Business Economics, 30(2): 187200. Borghans, Lex, Bart Golsteyn, James J. Heckman, and Huub Meijers. 2009. Gender Dierences in Risk Aversion and Ambiguity Aversion. IZA Discussion Paper 3985. Buttner, E. Holly, and Benson Rosen. 1988. Bank Loan Ocers' Perceptions of the Characteristics of Men, Women, and Successful Entrepreneurs. Journal of Business Venturing, 3(3): 249258. Buvinic, Mayra, and Marguerite Berger. 1990. Sex Dierences in Access to a Small Enterprise Development Fund in Peru. World Develop- ment, 18(5): 695705. Cavalluzzo, Ken. Discrimination: 2002. Competition, Small Business Financing, and Evidence from a New Survey. Journal of Business, 75(4): 641680. Cavalluzzo, Ken, and John Wolken. 2005. Small Business Loan Turndowns, Personal Wealth, and Discrimination. Journal of Business, 78(6): 21532178. Cavalluzzo, Ken, and Linda Cavalluzzo. 1998. Market Structure and Discrimination: The Case of Small Businesses. Journal of Money, Credit and Banking, 30(4): 77192. Chakravarty, Sugato, and James S. Scott. 1999. Relationships and Rationing in Consumer Loans. Journal of Business, 72(4): 523544. Copestake, James. 2002. Inequality and the Polarizing Impact of Mi- crocredit: Evidence from Zambia's Copperbelt. Journal of International Development, 14(6): 743755. Corsi, Marcella, Fabrizio Botti, Tommaso Rondinella, and Giulia Zacchia. 2006. Women and Micronance in Mediterranean Countries. Development, 49(2): 6774. CrediAmigo. 2009. CrediAmigo Annual report - 2009. de Janvry, Alain, Craig McIntosh, and Elisabeth Sadoulet. 2010. The Supply- and Demand-Side Impacts of Credit Market Information. Journal of Development Economics, 93(2): 173188. 26
  • 28. Diagne, Aliou, Manfred Zeller, and Manohar Sharma. 2000. Em- pirical Measurements of Households' Access to Credit and Credit Constraints in Developing Countries: Methodological Issues and Evidence. IFPRI Food Consumption and Nutrition Division Discussion Paper 90, Washington, D.C. Dymski, Gary A. 2006. Discrimination in the Credit and Housing Markets: Findings and Challenges. In Handbook on the Economics of Dis- crimination, W.M. Rodgers III ed., 215259. Cheltenham:Edward Elgar Publishing. Egli, Dominik. 2004. Progressive Lending as an Enforcement Mechanism in Micronance Programs. Review of Development Economics, 8(4): 505 520. Fein, S, and S.J. Spencer. 1997. Prejudice as Self-Image Maintenance: Arming the Self Through Derogating Others. Journal of Personality and Social Psychology, 73: 44, 31. Ferguson, Michael F, and Stephen R Peters. 1995. What Constitutes Evidence of Discrimination in Lending? Journal of Finance, 50(2): 739 748. Fletschner, Diana. 2009. Rural Women's Access to Credit: Market Imper- fections and Intrahousehold Dynamics. World Development, 37(3): 618 631. GAO, (U.S. Government Accountability Oce). 2008. Fair Lending: Race and Gender Data Are Limited for Nonmortgage Lending. Report to Congressional Requesters 08-1023 T, Washington, D.C. Garikipati, Supriya. 2008. The Impact of Lending to Women on Household Vulnerability and Women's Empowerment: Evidence from India. World Development, 36(12): 26202642. Gatewood, Elizabeth J., Candida G. Brush, Nancy M. Carter, Patricia G. Greene, and Myra Hart. 2004. Women Entrepreneurs, Growth, and Implications for the Classroom. Coleman Foundation White Paper Series for the United States Association for Small Business and Entrepreneurship. Greene, Patricia G., Myra M. Hart, Elizabeth J. Gatewood, Candida G. Brush, and Nancy M. Carter. 2003. Women Entrepreneurs: 27
  • 29. Moving Front and Center: An Overview of Research and Theory. US- ASBR White Papers. Guérin, Isabelle. 2010. The Gender of Finance and Lessons from Micronance. In The Handbook of Micronance, B. Armendariz and M. Labie ed. London-Singapore:World Scientic Publishing. Guérin, Isabelle, Marc Roesch, Venkatasubramanian, Mariam Sangare, and Santosh Kumar. 2009. Micronance and the Dynamics of Financial Vulnerability. Lessons from Rural South India. RuMe Working Paper. Haines, G.H., B.J. Orser, and A.L. Riding. 1999. Myths and Realities: An Empirical Study of Banks and the Gender of Small Business Clients. Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration, 16(4): 291307. Han, Song. 2004. Discrimination in Lending: Theory and Evidence. Jour- nal of Real Estate Finance and Economics, 29(1): 546. Hartarska, Valentina. 2005. Governance and Performance of Micronance Institutions in Central And Eastern Europe and the Newly Independent States. World Development, 33(10): 16271643. Heckman, James J. 1976. The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models. Annals of Economic and Social Mea- surement, 5(4): 120137. Heckman, James J. 1979. Sample Selection Bias as a Specication Error. Econometrica, 47(1): 15361. Hudon, Marek. 2009. Should Access to Credit be a Right? Journal of Business Ethics, 84(1): 1728. ILO. 2009. Small Change, Big Changes: Women and Micronance. Inter- national Labour Organization, Geneva. Isserles, Robin G. 2003. Microcredit: The Rhetoric of Empowerment, the Reality of Development As Usual. Women's Studies Quarterly, 31(3/4): 3857. Jalbert, Susanne E. 2000. Women Entrepreneurs in the Global Economy. Center for International Private Enterprise, Washington, D.C. 28
  • 30. Jamali, Dima. 2009. Constraints and Opportunities Facing Women En- trepreneurs in Developing Countries: A Relational Perspective. Gender in Management: An International Journal, 24(4): 232251. Johnson, Susan. 2004. Gender Norms in Financial Markets: Evidence from Kenya. World Development, 32(8): 13551374. Kanoh, Satoru, and Chakkrit Pumpaisanchai. 2006. Listening to the Market: Estimating Credit Demand and Supply from Survey Data. Institute of Economic Research, Hitotsubashi University Hi-Stat Discussion Paper Series d05-137, Tokyo. Karlan, Dean S., and Jonathan Zinman. 2008. Credit Elasticities in Less-Developed Economies: Implications for Micronance. American Eco- nomic Review, 98(3): 104068. Kevane, Michael, and Bruce Wydick. 2001. Microenterprise Lending to Female Entrepreneurs: Sacricing Economic Growth for Poverty Alleviation? World Development, 29(7): 12251236. Kunda, Ziva, and Lisa Sinclair. 1999. Motivated Reasoning with Stereotypes: Activation, Application, and Inhibition. Psychological Inquiry, 10(1): 1222. Labie, Marc, Pierre-Guillaume Méon, Roy Mersland, and Ariane Szafarz. 2010. Discrimination by Microcredit Ocers: Theory and Evidence on Disability in Uganda. Université Libre de Bruxelles CEB Working Papers 10-007. Lang, William W., and Leonard I. Nakamura. 1993. A Model of Redlining. Journal of Urban Economics, 33(2): 223234. Marrez, Helena, and Mathias Schmit. microcredit: How does gender matter? 2009. Credit risk analysis in Université Libre de Bruxelles CEB Working Papers 09-053. Morduch, Jonathan. 1999. The Micronance Promise. Journal of Eco- nomic Literature, 37(4): 15691614. Morrison, Andrew, Dhushyanth Raju, and Nistha Sinha. 2007. Gender Equality, Poverty and Economic Growth. The World Bank 4349. Munnell, Alicia H., Georey M. B. Tootell, Lynn E. Browne, and James McEneaney. 1996. Mortgage Lending in Boston: Interpreting HMDA Data. American Economic Review, 86(1): 2553. 29
  • 31. Rhyne, E. 2001. The Yin and Yang of Micronance: Reaching the Poor and Sustainability. Microbanking Bulletin, 2. Riding, Allan L., and Catherine S. Swift. 1990. Women Business Owners and Terms of Credit: Some Empirical Findings of the Canadian Experience. Journal of Business Venturing, 5(5): 327340. Robinson, Marguerite S. 2002. Micronance Revolution Volume 2: Lessons from Indonesia. World Bank Publications. Rossel-Cambier, Koen. 2008. Combined Micro-Finance: Selected Re- search Questions from a Stakeholder Point of View. Université Libre de Bruxelles CEB Working Papers 08-004. Ross, Stephen L. 2000. Mortgage Lending, Sample Selection and Default. Real Estate Economics, 28(4): 581621. Ross, Stephen L, and John Yinger. 1999. Other Evidence of Discrimination: Recent Studies of Redlining and of Discrimination in Loan Approval and Loan Terms. In Mortgage Lending Discrimination: A Review of Existing Discrimination, Margery Austin Turner, Felicity Skidmore ed., 1742. Washington, D.C.:The Urban Institute. Ross, Stephen L, and John Yinger. 2002. The Color of Credit: Mort- gage Discrimination, Research Methodology, and Fair-Lending Enforcement. Cambridge, MA:MIT Press. Saith, Ruhi, and Barbara Harriss-White. 1999. The Gender Sensitivity of Well-being Indicators. Development and Change, 30(3): 465497. Schafer, Robert, and Helen F Ladd. 1982. Discrimination in Mortgage Lending. Cambridge, MA:MIT Press. Servet, Jean Michel. 2005. Le besoin d'objectifs principaux nouveaux pour la micronance : lutter contre les inégalités et faire face aux risques. Techniques Financières Développement, 78: 1220. Stiglitz, Joseph E, and Andrew Weiss. 1981. Credit Rationing in Markets with Imperfect Information. American Economic Review, 71(3): 393 410. Tra, Pham Thi Thu, and Robert Lensink. 2007. Lending policies of informal, formal and semiformal lenders. Economics of Transition, 15(2): 181209. 30
  • 32. Verheul, Ingrid, and Roy Thurik. 2001. Start-Up Capital: Does Gen- der Matter?. Small Business Economics, 16(4): 32945. Weller, Christian E. 2009. Credit Access, the Costs of Credit and Credit Market Discrimination. Review of Black Political Economy, 36(1): 728. 31
  • 33. Appendix A Robustness check: Repayment Behavior Table 6: Absolute Loss, and Probabilities of Delay and Default (1) (4) (5) (6) (7) (8) (9) Loss Loss Delay Delay Delay Default Default Default -6.340*** -6.351*** -7.011*** -0.0107*** -0.0118*** -0.0123*** -0.00332*** -0.00362*** -0.00359*** (1.749) Requested Amount (RA) (3) Loss Female borrower (F) (2) (1.753) (1.752) (0.00252) (0.00251) (0.00263) (0.000900) (0.000905) (0.000953) 0.00559*** 2.94e-06** 6.97e-07 (1.22e-06) (0.000906) (4.92e-07) 0.00542*** -8.60e-06*** (0.00116) Loan Size (LS) (1.73e-06) Rationing Factor -3.32e-06*** (8.51e-07) 0.000861*** (0.0382) Married client 0.191*** (6.06e-05) 0.000206*** (2.55e-05) -8.568*** -0.0237*** -0.0249*** -0.00556*** -0.00544*** (1.803) (1.805) (0.00261) (0.00259) (0.00272) (0.000977) (0.000973) (0.00104) -0.999 -1.187 -1.826 0.00310 0.00196 0.00271 -0.000194 -0.000446 -0.000294 (1.808) (1.803) (0.00259) (0.00257) (0.00270) (0.000891) (0.000888) (0.000931) -0.154 0.00632 -0.934 0.0128*** 0.0123*** 0.0103*** 0.00205** 0.00185** 0.00157* (1.758) Client with dependent(s) -0.0244*** (1.805) Female Credit Ocer -8.318*** (1.800) Female Guarantor -8.628*** -0.00579*** (1.763) (1.764) (0.00257) (0.00253) (0.00269) (0.000888) (0.000884) (0.000932) -2.938 -2.814 -2.396 -0.00421 -0.00378 -0.00429 -0.000179 -0.000149 -0.000216 (1.821) (1.823) (1.823) (0.00262) (0.00261) (0.00274) (0.000903) (0.000903) (0.000948) -0.229*** -0.237*** -0.221*** -0.000885*** -0.000894*** -0.000866*** -0.000191*** -0.000194*** -0.000187*** (0.0750) (0.0751) (0.0753) (0.000110) (0.000109) (0.000116) (4.00e-05) (3.98e-05) (4.25e-05) 0.458 -1.699 -2.104 -0.000676 -0.00655 0.00892 0.00181 0.000314 0.00552* (3.807) (3.773) (3.755) (0.00579) (0.00526) (0.00671) (0.00218) (0.00192) (0.00293) 1.524*** 1.513*** 1.936*** 0.00181*** 0.00237*** 0.00257*** 0.000526*** 0.000673*** 0.000725*** (0.211) (0.217) (0.207) (0.000286) (0.000289) (0.000297) (0.000102) (0.000107) (0.000114) Capital investment -5.118** -4.609** -3.682* -0.00722*** -0.00543** -0.00707** -0.00231** -0.00178* -0.00248** (1.988) (1.988) (1.974) (0.00270) (0.00269) (0.00281) (0.000956) (0.000964) (0.00101) Loan repayment 27.29*** 27.07*** 28.78*** 0.102*** 0.0991*** 0.120*** 0.0241*** 0.0240*** 0.0293*** (3.140) (3.148) (3.152) (0.00831) (0.00817) (0.00905) (0.00346) (0.00344) (0.00400) External income -0.00366 -0.00305 -0.000757 -1.20e-05*** -8.30e-06** -1.09e-05*** -6.74e-06*** -5.44e-06*** -6.31e-06*** (1.77e-06) Client's age No Guarantor # Installments (0.00240) (0.00236) (3.35e-06) (3.34e-06) (3.46e-06) (1.69e-06) (1.69e-06) 0.00285*** 0.00422*** 6.47e-07 2.86e-06*** 1.53e-06 2.38e-07 8.08e-07** 3.98e-07 (0.000749) Trade (sector) (0.00240) 0.00250*** Business prot (0.000755) (0.000700) (8.83e-07) (9.60e-07) (9.34e-07) (3.15e-07) (3.78e-07) (3.60e-07) 0.556 Ocial business 0.774 0.250 -6.67e-05 -0.000752 -0.00222 0.00193** 0.00176* 0.00150 (1.829) (1.831) (1.836) (0.00259) (0.00258) (0.00272) (0.000903) (0.000904) (0.000945) # Employees 2.788 3.644 7.551* 0.00855 0.0195*** 0.0131** -0.000190 0.00275 0.000371 (4.002) (4.019) (3.940) (0.00575) (0.00644) (0.00611) (0.00222) (0.00276) (0.00239) 0.390 # Times as a guarantor 0.709* 0.000556 0.000979** 0.000844* -1.96e-05 0.000182 5.50e-05 (0.416) (0.413) (0.000493) (0.000462) (0.000495) (0.000295) (0.000235) (0.000302) -1.742*** -1.780*** -1.064*** -0.0144*** -0.0131*** -0.0120*** -0.00360*** -0.00325*** -0.00308*** (0.343) # Former loans 0.462 (0.415) (0.351) (0.341) (0.000810) (0.000786) (0.000845) (0.000343) (0.000329) (0.000367) -1.243*** -1.272*** -1.067** -0.00588*** -0.00543*** -0.00508*** -0.00146*** -0.00134*** -0.00128*** (0.426) (0.427) (0.427) (0.00104) (0.00103) (0.00107) (0.000359) (0.000356) (0.000369) 3.214** 3.014** 2.128 0.0228*** 0.0205*** 0.0217*** 0.00494*** 0.00441*** 0.00471*** (1.448) (1.452) (1.441) (0.00233) (0.00229) (0.00241) (0.000862) (0.000847) (0.000923) 33,530 33,530 33,530 33,530 33,530 33,530 33,530 33,530 33,530 Censored obs. 1,860 1,860 1,860 1,860 1,860 1,860 1,860 1,860 1,860 Wald Chi2 582.8 564.9 567.3 1,560 1,618 1,744 659.3 679.7 727.9 26 26 26 26 26 26 26 26 26 # Former loans with delay Observations DF For all regressions, Heckman selection: Approval by the credit committee. Standard errors in parentheses; *** p0.01, ** p0.05, * p0.1 The coecients of the year dummies and constants are not reported. The probabilities of delay and default are estimated by Heckman-probit (marginal eect reported).