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: ceb@admin.ulb.ac.be Tel. : +32 (0)2/650.48.64 Fax : +32 (0)2/650.41.88

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 micro�nance

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, DenialRate, 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:

isabelleagier@gmail.com‡Université Libre de Bruxelles (ULB), SBS-EM, Centre Emile Bernheim, and CERMi,

Email: aszafarz@ulb.ac.be

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�It is extremely important (...) to conduct research into the social processes ofdiscrimination and the politics of access, control, agency, and empowerment.Little can be assumed about gendered relations of disadvantage. They requireempirical speci�cation which in turn requires micro-level research" Saith andHarriss-White (1999, p. 492).

1 Introduction

Women-owned businesses are taking an increasing importance in the econ-omy. According to Jalbert (2000), the percentage of female business ownersin 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 concernfor policymakers and researchers (Greene et al., 2003; Gatewood et al., 2004;Jamali, 2009). Although women tend to create smaller �rms, lack of capitalis still a major obstacle to them. Indeed, several studies show that, on aver-age, female entrepreneurs are less �nanced than male ones (see, e.g., Ridingand Swift (1990) for Canada, Verheul and Thurik (2001) for the Netherlands,Alsos, Isaksen and Ljunggren (2006) for Norway, Alesina, Lotti and Mistrulli(2008) for Italy).2

By focusing on poor female entrepreneurs in developing countries, microcre-dit has brought to light the underestimated potential of female self- employ-ment. Notably, the microcredit industry has proved on a large scale thatwomen are more trustworthy than men in terms of repayment conduct (Ar-mendáriz and Morduch, 2000). Still, Buvinic and Berger (1990); Fletschner(2009) and Agier and Szafarz (2010) show that women keep being morecredit-rationed than men3 by micro�nance institutions (MFIs).

At �rst sight, one might be puzzled by the combination of women beingmore reliable and receiving smaller loans. However, this combination doesnot per se imply the presence of gender discrimination. Indeed, genderedrepayment rates are established irrespectively of the personal and business

1More precisely, the percentage of women business owners rose between 1970 and 1990from 17.5% to 25% in Africa, from 8% to 11% in Asia and the Paci�c, from 33.5% to 28%in Eastern Europe, from 11% to 24% in Latin America and the Caribbean, from 11% to19% in Western Europe and other. For the US, Gatewood et al. (2004) state that: �From1997 to 2004, the number of women-owned �rms grew at a rate of 17 percent (...) incomparison to a 9 percent growth in the number of �rms overall�.

2Some papers do not share this conclusion (Haines, Orser and Riding, 1999).3Credit 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 Agierand Szafarz (2010).

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characteristics of the borrowers. Moreover, men and women entrepreneursdi�er in at least two respects: 1) women are poorer than men on average,4

and 2) women have smaller-scope business projects. Besides, smaller loanstypically generate higher operational and monitoring costs for the lender(Morduch, 1999; Armendáriz and Szafarz, 2011). Therefore, unconditionalstatistics might be misleading. A deeper approach is required to reach robustconclusions. This is precisely the aim of this paper.

In a companion paper (Agier and Szafarz, 2010) based on an exceptionaldatabase including 34,000 loan applications from a Brazilian MFI, we haveshown that fair access to credit is compatible with the presence of a glass ceil-ing in loan size (larger female projects are more credit-rationed than compa-rable male projects). Complementing this analysis thanks to reimbursementrecords from the same institution, the present paper investigates whether theglass-ceiling e�ect is economically justi�ed.

Existing evidence pertaining to access to credit in developing countries ismostly based on household surveys. This approach provides valuable infor-mation on the demand side of the market but is unable to re�ect the supply-side 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 unfavorabletreatment not because of discriminatory treatment per se, but rather becauseof gender di�erences in individual characteristics that are relevant for loanquali�cation� (p. 39). However, because the body of evidence is demand-sided, we argue that this conclusion is premature. Indeed, as emphasizedby Diagne, Zeller and Sharma (2000), credit limits typically emanate fromthe lenders. Unfortunately, due to data unavailability, the way MFIs assesscreditworthiness and grant loans has hardly been investigated yet, let alonethe gender issue.5 Bene�ting from exhaustive information gathered by anMFI on its loan applicants and borrowers over an eleven-year period, ourcontribution aims at �lling this gap.

Discrimination in the lending industry has been scrutinized in various coun-tries, notably in the US where it is a legal o�ense.6 Unfortunately, no con-sensus has emerged so far regarding the methodology to be used (Dymski,

4According to ILO (2009), 75% of worldwide poverty a�ects women.5Exceptions include Buvinic and Berger (1990) who obtained data from the Urban

Small Enterprise Development Fund in Peru, and Marrez and Schmit (2009) who analyzethe credit risk of a leading Maghrebian MFI.

6The 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 DisclosureAct. 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

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2006). This is likely due to data-driven limitations. Indeed, authors tend toadapt methodology to data, rather than the reverse.

Empirical tests for credit discrimination may be split into two approaches ac-cording to their underlying assumptions on gendered creditworthiness (Blan-chard, Zhao and Yinger, 2008). The �rst approach postulates that men andwomen with identical personal and business characteristics are equally cred-itworthy. Hence, gender discrimination is assessed by testing whether genderin�uences the probability of loan denial (or the credit conditions). The sec-ond approach, used in this paper, avoids any prior assumption on genderedcreditworthiness. It is more general but it necessitates data on individualreimbursement records.7 Discrimination is detected if a lower credit risk isassociated 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 welladapted to microcredit. This method is based on the comparison of gendercoe�cient in loan size regression, on the one hand, and on the regressionof loss-over-loan-size ratio (hereinafter �relative loss�), on the other hand.Secondly, this paper provides an original application. Exhaustive data helprobustifying the estimation with respect to the missing-variable problem8

that often plagues studies on discrimination.9

The empirical results point to gender discrimination. Indeed, all other thingsequal, women face signi�cantly worse credit conditions, while being credit-worthier. Additionally, it is shown that reducing the information asymmetrythrough 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 dis-crimination and section 5 shows that it is not tempered by existing relation-ship. Section 6 concludes.

and Ladd (1982); Cavalluzzo and Cavalluzzo (1998); Ross and Yinger (1999, 2002); Blanch-�ower, Levine and Zimmerman (2003); Han (2004); Cavalluzzo and Wolken (2005); Blan-chard, Zhao and Yinger (2008). This empirical literature in surveyed in Agier and Szafarz(2010).

7Detailed characteristics of small-business borrowers are scarcely disclosed by thelenders (GAO, 2008).

8Admittedly, our results may still su�er from the self-selection bias put forward byCavalluzzo (2002).

9In that way, we follow Ross and Yinger (2002)'s recommendation; "(...) well knownmethodological problems, such as selection and endogeneity bias, could lead to disparate-impact discrimination even when the designers (...) are trying hard to avoid it. Scholarlyaccess to loan performance data and careful research are needed to shed further light onthese issues" ( p. 298).

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2 Data

Our unique database comes from Vivacred, a Brazilian MFI. Vivacred pro-vides credit to micro-entrepreneurs located in the Rio de Janeiro low incomecommunities 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, Co-pacabana (now in Gloria) in 1999, Maré in 2000, Santa Cruz in 2002 andin the city of Macaé (Rio state) in 2004. Until 2009, Vivacred was mostlyfunded by the Brazilian Development Bank (BNDES). Then, Vivacred inte-grated the national CrediAmigo program �nanced by Banco do Nordeste, aBrazilian public bank.

Vivacred's loans are accessible to businesses with at least six months of ac-tivity. For each application, the credit o�cer in charge collects detailedinformation10 on the applicant and the guarantor, if any, and on the charac-teristics of the business.11 The credit o�cer then provides a recommendationto 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

Vivacred charges the same interest rate to all its clients (3.9% per month).13

Its lending methodology is based on credit rationing, rather than on adjustingthe interest rate to perceived credit risk. Although this way of doing isstandard for MFIs, it raises ethical concerns (Hudon, 2009).

The data have been collected by the six branches of Vivacred. For the pe-riod under consideration (1997-2007), about 41,000 loans were solicited by15,400 applicants, and about 32,000 loans were granted to 11,400 borrowers.However, we removed the applications canceled by the clients, the contractswith incomplete speci�cations, the loans to Vivacred's employees, and thefew group loans. Therefore, the study is based on exhaustive data of 34,000applications and 32,000 actual loans.

10Private and professional addresses, birth date, birth state, marital status, gender,dependent(s), profession, bank references, spouse's ID, current account, family consump-tion, family external income, full credit history (as a borrower, a borrower's spouse, or aguarantor).

11Location, sector, legal status, number of employees.12Depending on the requested amount, this person is either the branch manager, or a

senior credit o�cer.13Banco da Mulher, a comparable non-pro�t institution, provides loans with rates be-

tween 3% and 5% a month, while Fininvest, a for-pro�t institution, proposes consumptioncredit with a monthly 12% rate.

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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 isthe client's name inclusion within the SPC register,14 which is available forconsultation by any institution supplying credit, including shops. Beyondlosing access to credit, those who are registered in SPC face serious troublegetting a cell phone contract or buying household appliances, for example.

Table 1 gives the descriptive statistics, globally and then split by applicant'sgender, with t-tests for equality of means.

Vivacred claims no special commitment to serve women. Its clientele is bal-anced, with 49.6% of women over the period 1997-2007. About the sameshare (47.41%) is observed for female credit o�cers, and these are more of-ten 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),15 and logically receive smalleramounts (BRL 891 against BRL 1,136). Additionally women are slightlymore credit-rationed than men as they get, on average, 21.4% less than theyrequest, against 20.7% for men. Nevertheless, men and women face similarapproval rates around 95%.

Women entrepreneurs are two years older than males (43 versus 41), lesslikely to be married (43% versus 52%), and less likely to have dependents(51% versus 53%).

Male and female applicants also di�er in business characteristics. Female-owned businesses are smaller, in terms of both pro�ts and sta� size.16 Theexternal income (i.e., income earned by any household member and unrelatedto business activity) is similar for men and women (around BRL 213 permonth).

Regarding the credit characteristics, the purpose of capital investment (asopposed to liquidity) is present in 34% and 29% of applications from men andwomen, respectively. Women need loans for both liquidity issues and loanrepayment more often than men. Finally, the guarantor's and the client'sgenders 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

14SPC is a national database recording bad payers.15The average requested amount for all applications, including the denied ones, is BRL

1,250 for women and BRL 1,524 for men.16Kevane and Wydick (2001) observe similar characteristics for micro-entreprises in

Guatemala.

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Table 1: Global and Gender-speci�c Descriptive Statistics: All ApplicantsAll applicants M app. F app.

Mean S.D. Mean Mean t-testb

Applicant's, o�cer's and guarantor's gendersFemale applicantc 0.496 0.500Female credit o�cerc 0.474 0.499 0.459 0.490 −0.0311∗∗∗Female guarantorc 0.430 0.501 0.429 0.430 −0.00106

Request, loan size, and repayment recordRequested Amount (BRL)a 1,380 1,242 1,518 1,237 280.7∗∗∗Loan size (BRL)a 1,015 996 1,136 891 245.3∗∗∗Rationing factor (RA−LS

RA) (%) 21.38 24.16 20.73 21.98 −1.263∗∗∗

Delayc (30 days) 0.086 0.281 0.094 0.078 0.0165∗∗∗Defaultc (180 days) 0.029 0.167 0.030 0.027 0.003Loss (BRL)a 18.6 156.0 21.4 15.5 5.888∗∗∗Relative loss (%) 2.52 15.27 2.75 2.29 0.465∗∗

Applicant's characteristicsAge (years) 42.2 12.0 41.2 43.2 −1.925∗∗∗Marriedc 0.47 0.50 0.52 0.43 0.0962∗∗∗At least one dependentc 0.52 0.50 0.53 0.51 0.0169∗∗Mth. ext. income (X100 BRL)a 2.13 3.76 2.11 2.16 −0.04# former loans 2.25 3.27 2.35 2.15 0.202∗∗∗# former loans with delay 0.04 0.21 0.04 0.04 0.0077∗∗∗# times as a guarantor 0.74 2.11 0.89 0.60 0.282∗∗∗

Business characteristicsBusiness pro�t (X100 BRL)a 9.19 13.44 10.26 8.09 2.177∗∗∗Sector (trade = 1, other = 0) 0.53 0.50 0.49 0.56 −0.0760∗∗∗O�cial businessc 0.06 0.23 0.07 0.05 0.0165∗∗∗# employees 0.63 2.20 0.72 0.54 0.175∗∗∗

Credit characteristics# installments 9.03 4.39 9.10 8.97 0.128∗∗Capital investment purposec 0.32 0.47 0.34 0.29 0.0518∗∗∗Loan repayment purposec 0.09 0.29 0.08 0.10 −0.0171∗∗∗Guarantor's involvementc 0.92 0.27 0.93 0.92 0.00756∗∗

Observations 33,530 16,899 16,631aAll �nancial values are in de�ated BRL (Real), the Brazilian currency. Over the period,the Real �uctuated between 0.270 and 0.588 USD.

bT-test for equality of means between male and female applicants; *** p<0.01, ** p<0.05cDummy variables

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to signi�cantly smaller losses for the MFI, in absolute and relative terms.Vivacred's average relative loss is 2.8% for male borrowers and 2.3% forfemale ones. These numbers are consistent with those reported by otherMFIs.17

In sum, irrespectively of their characteristics women receive smaller loansand reimburse better than men. Section 4 will examine whether this evidenceresists multivariate analysis.

3 Methodology

Assessing discrimination in lending is complex for reasons pertaining to boththe 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 theoreticalde�nitions of discrimination; the inescapability of the point of view of theobserver and observed in empirical studies of racial discrimination; and theway in which empirical methodologies require research questions to be framed �(p.215).

In this paper, we adopt a narrow de�nition. Namely, we de�ne gender dis-crimination in lending as the economically unjusti�ed awarding of inferiorcredit conditions to female borrowers. This de�nition corresponds to theintuition of a double-standard lending practice. It therefore excludes the so-called �rational discrimination� where unequal credit conditions result frombusiness needs.18

Following our de�nition, �disparate treatment� (i.e., a harsher applicationprocess for women) is a necessary � but not su�cient � condition for gen-der discrimination. Indeed, disparate treatment could sometimes be econom-ically rationalized by objective credit risk characteristics. For instance, let usimagine just for the sake of the argument that women exhibit higher credit

17For instance, reported default rates are: below 2.2% for CrediAmigo in NortheastBrazil (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 of12% (Robinson, 2002).

18Rational discrimination can arise because of information costs (Lang and Nakamura,1993). Also, when some variables a�ecting creditworthiness are not observable (e.g., busi-ness 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, someempirical papers in micro�nance use gender as a proxy for poverty. If gender were used inthe same way by lenders, this could lead to statistical discrimination.

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risk than men, all other things equal. In such a situation, disparate treatmentcould be a rational reaction from the lender. Under such circumstances, wewould not characterize the lender's attitude as gender discrimination.

Obviously, the de�nition of discrimination used here is based on economics,and not on ethics. Actually, whether rationalizable or not, disparate treat-ment is highly questionable on ethical, and even legal, grounds. Pragmat-ically, our motivation for choosing such a narrow de�nition for gender dis-crimination in lending is linked to its empirical testability and the level ofconclusiveness it allows to reach. Indeed, detecting narrowly de�ned discrim-ination brings stronger conclusions on the lender's practice.

Besides, this narrow de�nition is close in spirit to Becker's de�nition of �taste-based� discrimination (Becker, 1971). However, the quali�cation 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 andSpencer, 1997; Kunda and Sinclair, 1999) to be a common human feature.Indeed, Buttner and Rosen (1988) emphasize that women entrepreneurs stillsu�er from gender stereotypes related to their ability to e�ciently run a �rm(in terms of leadership, autonomy, lack of emotionalism, etc.).

On top of de�nitional complexity, empirical studies on discrimination in lend-ing are often plagued by technical problems. First and foremost, because datamade available to researchers are generally insu�cient to trustfully reproducethe lender's scoring process, the sources of gender gap in credit conditions, ifany, are hard to identify empirically. This paper will circumvent this seriousidenti�cation problem by using an exhaustive database.

Other challenging issues go beyond data availability (Ross, 2000). Firstly,discrimination in lending may take di�erent forms. Indeed, it may be ob-served with regard to access to credit (higher denial probability), and/orto credit conditions (higher interest rates, smaller loans, more collateral re-quired, 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 approvedloans solely. As a consequence, loan allocation and credit conditions do notconcern the same pool of applicants.

Thirdly, the lender's assessment of creditworthiness is generally unknown.Therefore, researchers commonly use a surrogate for creditworthiness built

19Some authors, like Blanch�ower, Levine and Zimmerman (2003) and Weller (2009),combine the two perspectives.

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from a set of relevant variables (ideally, the ones used as screening devicesby the lender), referred to as �controls�, which aim at capturing all gender-unrelated relevant variables. This approach may su�er from several draw-backs, notably omitted variables. Inevitably, researchers are confronted tosome degree of uncertainty regarding the lender's screening process.

Fourthly, ex ante creditworthiness is, by nature, unobservable. It is typicallyproxied by ex post variables like delay, default, and loss. However, thesevariables are to some extent endogenous because they are a�ected by thecredit conditions. For instance, default might be more frequent for larger �and therefore presumably riskier � loans (Stiglitz and Weiss, 1981). Alter-natively, more rationed borrowers could �nd it harder to reimburse.20 In anycase, endogeneity prevents ex post outcomes from being straightforward ex-planatory variables for the probability of approval and the credit conditions.

Given all these methodological limitations, how should we test for genderdiscrimination in lending? We address this question by referring to the theo-retical approach proposed by Ferguson and Peters (1995), who de�ne discrim-ination as �the use of di�erent credit standards across the two components ofthe population� and state that discrimination happens when a lower or equaldefault rate is associated to a higher or equal denial rate, provided that atleast one inequality is strict. The remaining of this section is devoted tomaking this rule econometrically operational, and applicable to microcredit.

The lending methodology of the microcredit industry is based on standard-ized contracts, with typically the same interest rate for all borrowers. In thatframework, loan size is the sole credit condition that is tailored to the client'sneeds by the MFI. Hence, the lender's problem may be represented as:

MaxLS>0

{(1 + r)LS − E [Loss (LS)]} (1)

where r is the �xed interest rate, LS is the loan size (denial correspondingto a zero loan size) that is the lender's decision variable, and E [Loss (LS)]is the expected loss that depends on loan size. Equivalently, this problemwrites:

MinLS>0

E [Loss (LS)]

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

20The results in Appendix A reveal that, in Vivacred, loan size negatively impacts theprobabilities of delay and default.

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function). Expectations being unobservable, we will take realized loss as aproxy for expected loss.21

In order to test for gender discrimination, we introduce the following nota-tions. The loan applications are indexed by i.22 Each application involvesseveral variables. First, the applicant's ex ante characteristics are:

• Applicant's gender represented by a dummy variable:

Fi =

{1 if the applicant is female0 if the applicant is male

• Vector (z1i, ..., zni) summarizing all other characteristics, including theapplicant's requested amount RAi.

Second, the lender's decision variables are:

• Loan approval represented by a dummy variable:

Ai =

{1 if the loan is approved0 if the loan is denied

• Loan size: LSi

We have explicitly split the decision variable in two parts (approval andloan size) in order to make the impact of the selection process visible, andsubsequently 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 signi�cantly di�erent between men andwomen, but female borrowers receive signi�cantly smaller loans than men.

21As loss is endogenous, the expectation error will simply be absorbed in the error termof the regression without introducing any bias in the estimated coe�cients.

22A person who introduces several loan applications will thus appear once for eachapplication.

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Given these results, the current econometric model concentrates on loan size,and not on denial probability:

LSi = βFFi+n∑

k=1

βkzki + ε1i ∀i s.t. Ai = 1 (3)

Lossi/LSi = ϕFFi+n∑

k=1

ϕkzki + ε2i ∀i s.t. Ai = 1 (4)

Equations 3 and 4 make it possible to operationalize the Ferguson and Peters(1995) rule. Indeed, gender discrimination corresponds to the situation whereβF ≤ 0 and ϕF ≤ 0, with at least one strict inequality. Moreover, theselection 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, andnot a pro�t-oriented lender. Does it make a di�erence when it comes totesting for discrimination? We argue that it does not, so that discriminationin social lending may be addressed like in pro�t-based lending.

Our argument is the following. For the sake of self-sustainability, socially-oriented lenders are bound to assess their applicants' creditworthiness. Inpractice, MFIs select their clients in two steps. Firstly, they de�ne their tar-get pool of borrowers according to their social mission (typically, the poorand/or unbanked entrepreneurs in a given area). Secondly, they assess cred-itworthiness of the applicants from this target pool basically in the same wayas pro�t-oriented institutions do.23 Therefore, gender discrimination mayshow o� in the same way too, provided that the target pool is de�ned in-dependently from gender considerations,24 which is indeed the case for theMFI under study.

23This 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 alsoKarlan and Zinman (2008) on the credit elasticities of the poor.

24This restriction is important since some MFIs, like the Grameen Bank, serve womensolely or majoritarily. Our approach would not make sense for such MFIs.

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4 Estimation Results

4.1 Testing for Gender Discrimination

In this section, we compare the gender dummy coe�cients in loan size andrelative loss regressions, along the lines of the econometric methodology ex-posed in section 3. In other words, we check whether the harsher creditrationing imposed by Vivacred to female entrepreneurs is, at least partially,attributable to repayment conduct. We address this issue by estimatingequations (3), and (4).

In the �rst regression (equation (3)) loan size is explained by the borrower'sgender, the amount requested by the borrower, and control variables. Thesecond regression (equation (4)) explains relative loss with the same vari-ables. Additionally, two alternative speci�cations for equation (4) open thepossibility of capturing the impact of credit rationing.

The borrower's gender is our explanatory variable of interest. In both equa-tions, we control for all variables collected by Vivacred's credit o�cers as wellas for this credit o�cer'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 (pro�ts, sec-tor, o�cial status, number of employees), the loan characteristics (requestedamount, installments, loan renegotiation), and the guarantor's existence andgender, if any. The relationship with Vivacred is accounted for by three vari-ables: the number of former loans as a client and as a guarantor, and thenumber of former loans repaid with delay (as a client, solely). Year dummiesare added to capture time heterogeneity.

Table 4.1 presents the regression results including one speci�cation for loansize and three speci�cations for relative loss.25 Column (2) displays theresult for the basic formulation of equation (4). In columns (3) and (4),the requested amount (RA) is replaced respectively by the loan size26 (LS),and the rationing factor (RA−LS

RA).

25Because, the Ferguson and Peters (1995) framework is purely theoretical (and onlyconsiders, in its original form, denial and default), it does not state which variables arerelevant in the estimation.

26Loan 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'scharacteristics, among which gender. However, ignoring this variable could create anomitted-variable problem. Alternatively, we could use simultaneous-equation estimationto account for endogeneity. However, combining such estimation with Heckman's proce-dure is tedious.

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Table 2: Loan Size and Relative Loss: Heckman's Regressions(1) (2) (3) (4)LS Loss/LS Loss/LS Loss/LS

Female borrower (F) -32.48*** -0.832*** -0.881*** -0.870***(5.116) (0.174) (0.175) (0.173)

Requested amount (RA) 0.623*** -0.000112(0.00265) (9.03e-05)

Loan size (LS) -0.000683***(0.000116)

Rationing factor (RA−LSRA

) 0.0460***(0.00379)

Female guarantor -26.58*** -0.0981 -0.165 -0.0745(5.280) (0.180) (0.180) (0.178)

Female credit o�cer -50.45*** 0.364** 0.312* 0.253(5.146) (0.175) (0.176) (0.174)

Married client 16.38*** -1.128*** -1.113*** -1.087***(5.262) (0.179) (0.180) (0.178)

Client with dependent(s) 13.29** -0.146 -0.107 -0.148(5.334) (0.181) (0.182) (0.180)

Client's age 0.613*** -0.0446*** -0.0447*** -0.0399***(0.219) (0.00748) (0.00749) (0.00744)

No Guarantor 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 pro�t 0.0576*** 9.23e-05 0.000226*** 6.98e-05(0.00219) (7.46e-05) (7.52e-05) (6.93e-05)

Trade (sector) -24.10*** 0.448** 0.445** 0.314*(5.347) (0.182) (0.183) (0.182)

O�cial business 179.9*** 0.270 0.653 0.209(11.70) (0.399) (0.401) (0.390)

# employees 9.331*** 0.00708 0.0307 0.00311(1.213) (0.0414) (0.0415) (0.0408)

# former loans 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)

Mills -118.5*** 3.840*** 4.948*** 1.671*(30.91) (1.053) (1.030) (1.004)

Observations 33,530 33,530 33,530 33,530Censored obs. 1,860 1,860 1,860 1,860Wald Chi2 128,127 822.1 852.1 977.1Degrees of freedom 29 26 26 26

Year dummies as controls (not reported). Heckman's selection: Approval by the committee.Selection instruments: Kind of premises, source of funds, credit o�cer's family status.favela resident, and seniority, credit o�cer turnover faced by the client, Rocinha branch.

Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

14

The requested amount acts as a proxy for the entrepreneur's project size. Inparticular, it allows to take into account the fact that women typically askfor smaller loans. By controlling for this rare piece of information, we intendto clean the regression from the e�ect of gender-speci�c request.27 Whenincluding loan size,28 we control for the level of indebtedness irrespectivelyof the source of the gender gap.

The correlation between the requested amount and the loan size is high (equalto 0.667). For this reason, we avoid putting both variables simultaneouslyin the second regression. Instead, we opt for a third speci�cation using therationing factor that measures the fraction of the requested amount that hasactually been granted to the applicant.

Column (1) of table 4.1 con�rms that women su�er from harsher credit ra-tioning than men. Indeed, even when accounting for the selection bias andthe di�erences in requested amounts, women receive signi�cantly smallerloans than men. As detailed in section 3, this result can be due to either(economically unjusti�ed) discrimination, or economically justi�ed lendingpractice.

The remaining columns of table 4.1 allow to disentangle these two possibil-ities unambiguously. Indeed, in all speci�cations the gender dummy has asigni�cant negative impact on relative loss, meaning that, all things equal,women are creditworthier than men.The requested amount in itself has nosigni�cant impact (column (2)). On the other hand, the loan size a�ectsrelative loss negatively (but a�ects absolute loss positively, see Appendix A)while the rationing factor has a positive impact. More rationed loans areharder to repay.

Remarkably, despite the handicap of being more credit-rationed, women man-age to reimburse their loans better than men. In other words, if men andwomen were equally rationed, the female repayment conduct would be evenbetter than it actually is.

Globally, the results are robust. The coe�cient of the gender dummy is aboutthe same in the three speci�cations of the relative loss equation. AppendixA proves that the same result applies to the absolute loss, the probability of

27Still, we cannot exclude that women try to maximize their chances of getting a loanby intentionally introducing smaller requests. If this is the case, then the request e�ect ispartly driven by the borrower's strategy. More generally, the identi�cation of demand andsupply e�ects in credit markets is discussed by, e.g., Kanoh and Pumpaisanchai (2006);de Janvry, McIntosh and Sadoulet (2010).

28The inclusion of loan size in the second equation is tricky because loan size is thedependent variable of the �rst equation. However, as a matter of fact, the coe�cient ofthe gender dummy is not much a�ected by such inclusion.

15

delay, and the probability of default. In addition, although the legitimacy ofusing Heckman's estimation method is exhibited by a signi�cant Mills ratio,OLS estimation brings similar features (results not reported here).

At this point, the �rst conclusion of our empirical study emerges: Womenentrepreneurs are trustworthier borrowers than men, but do not bene�t fromthis quality. On the contrary, they face harsher credit conditions. Conse-quently, the Ferguson and Peters (1995) rule leads to asserting the presenceof discrimination.

Does the same conclusion apply to women involved as guarantors? In Viva-cred, 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 incase of default. Indeed, they all bear the risk of being registered in SPC (theBrazilian insolvency register) and, consequently, experiencing serious troublein future �nancial transactions.29 From the lender's viewpoint, having morepeople involved in a credit contract is always better.30 This is likely thereason why married borrowers repay better (and receive larger loans).

The coe�cient of the guarantor's gender reveals that female guarantors asopposed to male guarantors have a negative impact on loan size, but nosigni�cant 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 o�cer's gender is signi�cant forloan size, but not for relative loss. Female o�cers typically o�er smaller loans,but obtain similar relative losses. Thus, viewed from the MFI's perspective,male and female credit o�cers are equally pro�table, although using di�erentscreening processes. Moreover, in Agier and Szafarz (2010), we show thatloan allocation by both male and female o�cers leads to disparate treatment.If, as conjectured, discrimination is attributable to gender stereotyping, thenthe stereotypes are shared by male and female credit o�cers.

The signs of the coe�cients associated to gender-neutral characteristics of theborrowers match well with the intuition that lower loan size is associated tohigher relative loss, and vice versa. This means that the credit o�cers grantloans rationally in all respects except the applicant's gender. For instance,married clients and older clients receive larger loans and repay better. Thesame is true for borrowers with larger extra income. Applications from the

29All guarantors provide their �scal identity number (CPF), which is the code requiredfor registering them in SPC.

30However, Alesina, Lotti and Mistrulli (2008) mention that the presence of a guarantormight signal a borrower's higher credit risk.

16

trade sector (as opposed to services) bring smaller loans and generate higherrelative losses.

Mechanically, the loan size increases with the number of installments. For agiven loan size, the higher the number of installments, the worse the repay-ment conduct. As expected, all indicators of the borrower's credit history aresigni�cant: existing relationship (as a borrower and/or a guarantor) leads tolarger and better repaid loans. Former delays act in the opposite way.

To summarize, we have shown that gender discrimination is present in thedata. In line with the �glass-ceiling� theory (Agier and Szafarz, 2010), thenext section will examine in greater details the interaction between the ap-plicant'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. Nev-ertheless, loan size is likely a non-linear function of the requested amount.Indeed, for tiny requests, it would be cost-ine�cient for credit o�cers todevote much attention to the speci�cities of the request �le. Instead, thecredit o�cers may roughly examine some basic creditworthiness characteris-tics, and make a �yes-or-no� decision. They would either approve the loan assuch and o�er 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 requestedamount is very small. Moreover, if the observed gender discrimination isassociated with stereotyping (�women are less able to run large projects�),then the gender gap in loan size should be increasing with the requestedamount. 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 con�rm the basic intuitions. The loan sizeequation in column (1) features a positive coe�cient for the gender dummyand a negative coe�cient for the interaction term. In theory, this shouldmean that women are favored for tiny loans (below BRL 100), but in practiceno actual loan lies below this limit. Thus, except for tiny loans (around BRL100), there always exists a gender gap, and this gap is increasing with thescope of the project. This result is consistent with the glass-ceiling e�ectunveiled by Agier and Szafarz (2010).

In contrast, column (2) shows that the relative loss is not related to therequested amount, even when men and women are considered separately.

17

Table 3: Gender Gap and Project Scope: Heckman's Regressions(1) (2) (3) (4)LS Loss/LS Loss/LS Loss/LS

Female borrower (F) 91.49*** -0.995*** -0.938*** -0.494**(7.561) (0.260) (0.250) (0.230)

Requested Amount (RA) 0.656*** -0.000156(0.00303) (0.000104)

RA * F -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−LSRA

) 0.0548***(0.00520)

Rationing factor * F -0.0174**(0.00702)

Mills -129.0*** 3.854*** 4.954*** 1.677*(30.69) (1.053) (1.030) (1.004)

Observations 33530 33530 33530 33530Censored obs. 1860 1860 1860 1860Wald Chi2 130358 822.8 852.1 983.5DF 30 27 27 27Same controls as in table 4.1; Heckman selection: Approval by the committee.

Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

Despite being more heavily penalized, women with larger projects do notincur higher losses. The negative impact of loan size on relative loss doesnot interact with gender (column (3)). On the contrary, the worsening ofrelative loss incurred by rationing is less pronounced for female borrowers.This is perhaps attributable to higher female adaptability to bad circum-stances. Women cope with restricted loans better than men under similarcircumstances. In conclusion, gender discrimination is stronger for more am-bitious projects. The next section examines whether relationship mitigatesdiscrimination.

5 Impact of Relationship

We now address the resilience of the gender gap in loan size by examiningthe dynamics of the gender-speci�c treatment along the borrower's credithistory. Relationship between the lender and the borrower typically reducesinformation asymmetry in lending(Tra and Lensink, 2007). Indeed, timelyrepayments demonstrate the borrower's creditworthiness. As a consequence,

18

a borrower who has successfully reimbursed a �rst loan will more easily obtaina second � and often larger � loan, and so on. This is the basic principledriving progressive lending (Egli, 2004).31

Figure 1: Evolutions of the gender-speci�c requested amounts and loan sizeswith respect to the length of relationship

Chakravarty and Scott (1999) show that the duration of relationship lowersthe probability of credit rationing in consumer loans. Our previous estima-tions (table 4.1) con�rm that former loans have a positive impact on loansize, and a negative impact on relative loss. However, as �gure 1 exhibits,after the second loan, the spread between the requested amount and the loansize seems to stabilize.32 Actually, successful second-time applicants requestsmaller amounts than in their �rst applications, but then bene�t from sec-ond loans higher than their �rst loans. Later on, regular borrowers do not

31Copestake (2002) emphasizes that progressive lending may also induce an increasinginequality e�ect.

32This constant spread may be seen as a steady-state equilibrium of the lending gameunder credit rationing. Indeed, the borrower knows that the lender is going to exertcredit rationing and rationally in�ates his/her request accordingly. Therefore, the optimalresponse of the lender is to keep applying credit rationing, but in a constant - and thuspredictable - way to allow their regular borrowers accurately size their requests. If thisscenario holds, no player has any advantage of moving to an equilibrium without creditrationing.

19

downscale their requests with respect to the previous one anymore.33

Table 4 provides additional descriptive statistics. The left side of the tableconcerns the new applicants, whereas the right side concerns the known ap-plicants. In each case, the overall and gender-speci�c means are displayedfor the following variables: requested amount (RA), loan approval rate, loansize (LS), and rationing factor (RA−LS

RA), with the corresponding t-tests for

equal means between genders. While requesting more on average (BRL 1,440versus BRL 1,366), new applicants face more denial (9% versus 5%), receivesmaller loans (BRL 772 versus BRL 1,059), and are more rationed (38.9%versus 18.2%). The global statistics are thus consistent with the asymmetricinformation story.

Table 4: Descriptive Statistics for New and Known ApplicantsNew applicants Known applicants

All M F t-test All M F t-testRequested amounta (RA) 1,440 1,545 1,334 10.8∗∗∗ 1,366 1,519 1,209 17.5∗∗∗Loan approval (%) 90.9 91.2 91.3 −0.18 95.0 95.1 95.3 −0.61Loan sizea (LS) 772 849 694 11.9∗∗∗ 1,059 1,190 925 17.8∗∗∗Rationing factor (%) 38.9 37.8 39.9 −3.8∗∗∗ 18.2 18.1 18.4 −0.81Observations 12,190 6,115 6,075 21,367 10,815 10,552a in BRL, *** p<0.01

Let us now examine whether discrimination tends to scale down with rela-tionship. Table 4 shows that, regardless of their credit history, women areleft with identical opportunity to obtain a loan.

However, con�rming the observations from �gure 1, the gender gaps in bothrequested amount and loan size widen with relationship. The female-over-male mean value ratios for new applicants are 86.3% for requested amountand 81.7% for loan size, while the corresponding ratios for known applicantsare 79.6% and 77.7%, respectively. Contrastingly, only for �rst loans is therationing factor signi�cantly higher for women. Perhaps with time, womenlearn about the endured gender gap, and revise the scope of their projectsaccordingly.

If gender discrimination were due to prejudice and/or stereotyping, relation-ship could reveal insu�cient to mitigate it. In such a case, repayment historywould not matter, and disparate treatment would subsist despite the revela-tion of women's creditworthiness.

33Interestingly though, after two loans men start to progressively increase their requestswhile, under similar circumstances, women seem to keep more or less the same requestedamount.

20

Alternatively, if gender discrimination were due to informational de�ciencies(attributable to cultural reasons, for instance), credit o�cers would learnfrom experience and adapt their practice to the facts. In such a case, theintensity of discrimination would be decreasing with the number of previousloans.34 Relationship would then exhibit a stronger (positive) impact onloan size for female borrowers than for male ones, allowing the former to betreated in a progressively fairer way with time.

In order to disentangle these two possible scenarios, we add a gender-speci�c�relationship factor� into the regressions by means of an interaction term be-tween the number of former loans and the gender dummy. The coe�cientassociated to that new variable will indicate whether the impact of relation-ship di�ers across genders.

Table 5: Gender Gap and Relationship: Heckman's Regressions(1) (2) (3) (4)LS Loss/LS Loss/LS Loss/LS

Female borrower (F) -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)

# Former loans 10.51*** -0.141*** -0.130*** -0.116***as a guarantor (1.247) (0.0425) (0.0426) (0.0423)

# Former loans -38.58*** 0.360** 0.275* 0.370***with delay (4.246) (0.145) (0.145) (0.143)

Mills -121.8*** 3.850*** 4.954*** 1.689*(30.92) (1.054) (1.030) (1.004)

Requested Amount X XLoan Size XRationing factor XObservations 33,530 33,530 33,530 33,530Censored obs. 1,860 1,860 1,860 1,860Wald Chi2 128,113 822.1 852.0 977.4DF 30 27 27 27

Same controls as in table 4.1; Heckman selection: got at least a 2nd loanStandard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

Our database includes 11,422 di�erent borrowers, among which 63.31% ben-e�ted from a second loan. About one third of the newcomers dropped outafter their �rst loan. This second selection issue leads to a second use ofHeckman's estimation procedure.35

34For instance, Beaman et al. (2009) show on Indian data that female political leadershipweakens stereotypes about gender roles.

35As Heckman's procedure allows one selection solely, we perform two separate exercises.

21

The results are presented in table 5. Expectedly, the number of former loanshas a positive impact on loan size and a negative impact on relative loss. Moretroubling is the negative e�ect of the interaction term on loan size. Whilemen bene�t of an average extra BRL 39.57 for each former loan, women seethis bonus in loan size reduced by BRL 4.95, thus amounting BRL 34.62only. Credit restrictions are progressively relaxed with relationship, but at aslower pace for women. Relationship is thus less valued for females, diggingthe gender gap instead of reducing it.

Loan size is also increasing with relationship as a guarantor, but the e�ect ismilder. A former loan as a borrower brings a bonus almost four time largerthan as a guarantor.36 Moreover, former loans with delays have a negativeimpact on loan size and, consistently, lead to higher relative losses.

6 Conclusion

The empirical approach to discrimination in the lending industry is less clear-cut than in the labor market. Indeed, the literature exhibits large method-ological variations, mainly data-driven, which plague the comparability ofresults. For this reason, we have adopted a restrictive de�nition of dis-crimination in lending, embodying double standards solely. Using such arestrictive de�nition strengthens our conclusion that discrimination is indeedpresent in the MFI under scrutiny.

In a nutshell, we have shown that, all things equal, women entrepreneursreceive smaller loans and induce smaller losses for the lender. This resultis consistent with the stylized facts reported by Armendáriz and Morduch(2010). Nonetheless, our �ndings are more reliable than rough descriptivestatistics since the regressions take into account all variables actually reportedby the credit o�cers, including the required amount.

Furthermore, the gender gap in loan size increases with relationship andsubsequent asymmetric information dwindling. Although being trustworthierthan 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 thesame regressions to the pool of applicants who bene�ted from one former loan at least. Inboth cases, the selected clients are the ones who obtained a second loan. As both exercisesbrought similar �gures, we only present the results concerning the pool of all applicants.

36Nonetheless, the impact of relationship as a guarantor is gender-insensitive (regressionnot reported).

22

initial handicap. This �nding strongly advocates for external intervention tocombat gender discrimination.

It is worth stressing that Vivacred, the MFI under scrutiny in this paper, is avery well-run organization. The release of its remarkable database also indi-cates that good governance practices are in place.37 We therefore conjecturethat our �ndings underestimate the global level of gender discrimination insmall-business lending.

As a matter of fact, the microcredit industry is highly subsidized internation-ally, notably by donors having a women empowerment agenda.38 Given thelack of anti-discrimination regulations in many developing countries, donors'concern could appear as an alternative disciplining device. The main obstaclethereto is data unavailability. The �rst step should therefore be to requestmore transparency in the screening processes put in place in MFIs, and moregenerally in lending institutions. The need for critical assessments of thefairness of micro�nance 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 haveexploited all screening variables collected by the MFI itself, we cannot excludethat face-to-face interviews bring unobservable but relevant gender-relatedpieces of information, linked for instance to education, �nancial literacy, andattitude toward risk. Moreover, despite the exceptional exhaustivity of ourdatabase, we do not possess information on the steps that predate the formalloan application. For instance, an informal contact with a credit o�cer mightdiscourage some entrepreneurs to pursue the application process. However,it seems highly unlikely that such unobservable elements could challenge theconclusion pointing to discrimination. More plausibly, they would reinforceit.

The origins and consequences of prejudice and stereotyping, and the means todeter them, are widely discussed in the socio-economic literature. We do notelaborate further on these issues. Nevertheless, our �ndings raise additionalunanswered questions.

Firstly, why are women entrepreneurs trustworthier than their male coun-terparts? Do they fear penalties more, a hypothesis compatible with theevidence that women are more risk-averse than men (Borghans et al., 2009)?Or are they acting strategically in order to obtain better credit conditions

37One of the authors has had the opportunity to observe Vivacred's day-to-day businesspractices in details, and her �ndings con�rm that statement.

38Isserles (2003) and Berkovitch and Kemp (2010) discuss the underlying ideology ofthis agenda.

23

in the future? Anyhow, the facts documented in this paper contradict theincentive-based argument stating that borrowers who accept less favorablecredit conditions (all other things equal) are more likely to default.39

Secondly, why do women ask for smaller loans? Do they expect to be dis-criminated against40 and refrain from applying for riskier projects therebycreating a self-selection e�ect? The results in section 5 are consistent withthis scenario. Moreover, if this explanation holds, it means that a consid-erable amount of hidden entrepreneurial talent is wasted through rationallyexpected discrimination.

Thirdly, how do women manage to reimburse better than men while beingmore credit-rationed (which is detrimental to repayment conduct)? How dohousehold constraints interfere with female business projects? Recent studieson 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 eco-nomic reasons. Indeed, the potential for female-driven economic develop-ment is far from being exhausted. Better knowing the needs and aspirationsof women entrepreneurs will help designing gender-conscious �nancial prod-ucts, as emphasized for the micro�nance industry by Johnson (2004); Corsiet al. (2006); Guérin (2010) and many others.

By demonstrating that even well-run socially-oriented MFIs are not immuneto gender discrimination, this paper has stressed the importance of �ndingcreative solutions to the lack of capital endured by women entrepreneurs.

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31

Appendix A Robustness check: Repayment Behavior

Table 6: Absolute Loss, and Probabilities of Delay and Default(1) (2) (3) (4) (5) (6) (7) (8) (9)Loss Loss Loss Delay Delay Delay Default Default Default

Female borrower (F) -6.340*** -6.351*** -7.011*** -0.0107*** -0.0118*** -0.0123*** -0.00332*** -0.00362*** -0.00359***(1.749) (1.753) (1.752) (0.00252) (0.00251) (0.00263) (0.000900) (0.000905) (0.000953)

Requested Amount (RA) 0.00559*** 2.94e-06** 6.97e-07(0.000906) (1.22e-06) (4.92e-07)

Loan Size (LS) 0.00542*** -8.60e-06*** -3.32e-06***(0.00116) (1.73e-06) (8.51e-07)

Rationing Factor 0.191*** 0.000861*** 0.000206***(0.0382) (6.06e-05) (2.55e-05)

Married client -8.568*** -8.628*** -8.318*** -0.0244*** -0.0237*** -0.0249*** -0.00556*** -0.00544*** -0.00579***(1.800) (1.803) (1.805) (0.00261) (0.00259) (0.00272) (0.000977) (0.000973) (0.00104)

Female Guarantor -0.999 -1.187 -1.826 0.00310 0.00196 0.00271 -0.000194 -0.000446 -0.000294(1.805) (1.808) (1.803) (0.00259) (0.00257) (0.00270) (0.000891) (0.000888) (0.000931)

Female Credit O�cer -0.154 0.00632 -0.934 0.0128*** 0.0123*** 0.0103*** 0.00205** 0.00185** 0.00157*(1.758) (1.763) (1.764) (0.00257) (0.00253) (0.00269) (0.000888) (0.000884) (0.000932)

Client with dependent(s) -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)

Client's age -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)

No Guarantor 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)

# Installments 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***(0.00240) (0.00240) (0.00236) (3.35e-06) (3.34e-06) (3.46e-06) (1.69e-06) (1.69e-06) (1.77e-06)

Business pro�t 0.00250*** 0.00285*** 0.00422*** 6.47e-07 2.86e-06*** 1.53e-06 2.38e-07 8.08e-07** 3.98e-07(0.000749) (0.000755) (0.000700) (8.83e-07) (9.60e-07) (9.34e-07) (3.15e-07) (3.78e-07) (3.60e-07)

Trade (sector) 0.556 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)

O�cial business 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)

# Employees 0.390 0.462 0.709* 0.000556 0.000979** 0.000844* -1.96e-05 0.000182 5.50e-05(0.415) (0.416) (0.413) (0.000493) (0.000462) (0.000495) (0.000295) (0.000235) (0.000302)

# Former loans -1.742*** -1.780*** -1.064*** -0.0144*** -0.0131*** -0.0120*** -0.00360*** -0.00325*** -0.00308***(0.343) (0.351) (0.341) (0.000810) (0.000786) (0.000845) (0.000343) (0.000329) (0.000367)

# Times as a guarantor -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)

# Former loans with delay 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)

Observations 33,530 33,530 33,530 33,530 33,530 33,530 33,530 33,530 33,530Censored obs. 1,860 1,860 1,860 1,860 1,860 1,860 1,860 1,860 1,860Wald Chi2 582.8 564.9 567.3 1,560 1,618 1,744 659.3 679.7 727.9DF 26 26 26 26 26 26 26 26 26

For all regressions, Heckman selection: Approval by the credit committee. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1The coe�cients of the year dummies and constants are not reported. The probabilities of delay and default are estimated by Heckman-probit (marginal e�ect reported).