Are Women Better Loan Officers?

Transcription

1 Are Women Better Loan Offcers? Ths verson: February 2009 Thorsten Beck * CentER, Dept. of Economcs, Tlburg Unversty and CEPR Patrck Behr Goethe Unversty Frankfurt André Güttler European Busness School Abstract What f any s the mpact of the gender of a loan offcer on loan default rsk? Usng a unque data set for a mcrobank n Albana over the perod 1996 to 2006, we fnd that loans handled by female loan offcers show sgnfcantly lower default rates than loans handled by male loan offcers, controllng for a varety of borrower, loan, and loan offcer characterstcs. Ths effect comes n addton to a lower default rate of female borrowers and cannot be explaned by experence dfferences between female and male loan offcer. Our result seems to be drven by dfferences n montorng ntensty, as we do not see sgnfcant dfferences n the acceptance rates of loan offcers of dfferent genders. JEL Classfcaton: G21; J16 Keywords: Loan offcers; gender; loan default; montorng; Albana; mcrocredt * Department of Economcs and European Bankng Center, Tlburg Unversty, P.O. Box 90153, 5000 LE Tlburg, The Netherlands, and CEPR, Emal: [email protected]. Department of Fnance, House of Fnance, Goethe Unversty Frankfurt, Grüneburgplatz 1, Frankfurt, Germany, Emal: [email protected] (correspondng author). HCI Endowed Char of Fnancal Servces, Department of Fnance, Accountng and Real Estate, European Busness School, Rhengaustr. 1, Oestrch-Wnkel, Germany, E-mal: [email protected]. We thank Andreas Madestam and Harry Schmdt for very helpful suggestons and comments, and Annekathrn Entzan for assstance wth the data preparaton.

2 I. Introducton What s the mpact of loan offcers gender and experence on loan default rsk? Whle the role of gender has been explored n a varety of felds n fnance, such as nvestment decsons, mutual fund management or equty analyst performance, and the behavor and mportance of loan offcers n fnancal nsttutons has been studed n several recent papers, the mpact of loan offcers gender on loan default rsk has not been analyzed, yet. Ths paper uses a unque loanlevel data set for an Albanan mcrobank over the perod 1996 to 2006 to assess the relatonshp between borrowers and loan offcers gender and the probablty of loan default, controllng for a vast array of borrower, loan and loan offcer characterstcs. Specfcally, controllng for the borrowers gender, we test whether male or female loan offcers experence a lower default probablty on ther loans and whether ths relatonshp vares wth the experence of loan offcers. Understandng the relatonshp between loan offcers gender and loan default rsk s nterestng and mportant for practtoners and researchers alke. Desgnng ncentves for loan offcers to mnmze loan losses mght have to take nto account loan offcers gender f emprcal fndngs pont to dfferences between male and female loan offcers n ther screenng and montorng qualty and ablty. Explorng the relatonshp between loan offcers gender and experence and loan default rsk also adds to the lterature on borrower-loan offcer relatonshps. Theory provdes ambguous predctons of why the gender of the loan offcer mght matter for the default probablty of ther borrowers. Consder frst the effort exerted by loan offcers n screenng and montorng borrowers. Modelng the relatonshp between loan offcer and bank as prncpal-agent relatonshp can help understand the ncentves of loan offcers to exert effort (Agarwal and Wang, 2008). Female loan offcers have typcally fewer outsde optons 1

3 n the labor market and have therefore stronger ncentves to excel n form of low default rates n ther loan portfolo. 1 Women are typcally less moble, especally f marred, and thus more dependent on the exstng job, agan ncreasng ther ncentves to excel. Especally n developng countres, women are more conservatve and more afrad of socal sanctons, whch ncreases pressure on female loan offcers to perform better than ther male colleagues. These arguments are smlar to arguments of why female borrowers n developng countres are typcally better clents than ther male peers (Armendarz de Aghon and Morduch, 2005). On the other hand, consder the relatonshp between loan offcer and borrower. In patrarchc socetes, male loan offcers mght have a stronger standng vs-à-vs borrowers, be they male or female, n terms of montorng and dscplnng them, thus ensurng loan repayment. In ths case, we would observe lower default probablty of loans approved and montored by male loan offcers. Fnally, loan offcers mght have an easer tme montorng and dscplnng borrowers of ther own gender, hence, we would expect to fnd a lower default probablty of female borrowers f the loan s approved and montored by a female rather than by a male loan offcer, wth the reverse holdng for male loan offcers. We test several alternatve, though not necessarly competng, hypotheses on the relatonshp between loan offcer s gender and experence and loan default probablty. On the one hand, experence mght be negatvely related to loan default rsk, f loan offcers gan expertse on screenng and montorng borrowers over tme (Anderson, 2004). On the other hand, career concerns mght nduce younger and less experenced loan offcers to undertake a greater effort to avod loan losses n order to maxmze ther career progress and thus future ncome perspectves (Agarwal and Wang, 2008). 1 Darty and Mason (1998) provde a comprehensve overvew of gender dscrmnaton n the labor market. 2

4 We explore these hypotheses by analyzng a unque data set on more than 43,000 loans over the years 1996 to 2006 provded by a mcrocredt nsttuton n Albana. For each loan, we can dentfy the loan offcer who screened the borrower and subsequently montored her over the lfetme of the loan. A possble default of the loan,.e. arrears beyond a certan number of days, can thus be drectly lnked to a specfc loan offcer. The data set ncludes extensve nformaton about borrower characterstcs such as the gender or the martal status of the borrower, loan characterstcs such as sze, maturty and nterest rate of the loan, and loan offcer characterstcs such as gender and experence wthn the nsttuton. As Albana s a transton economy and gven that the lender s a typcal mcrocredt nsttuton, we nclude several varables to capture the dfferent lendng technology and dfferent borrower populaton of such a lender. Specfcally, we control for borrower characterstcs lke, for nstance, the number of persons n the household of the borrower or whether a phone s avalable n the household of the borrower, nformaton that s normally not used/avalable when usng data provded by banks n developed countres. Crtcally, we have nformaton on both successful and rejected loan applcants, whch allows us to test whether dfferences n default rsk across loan offcers of dfferent gender are drven by selecton bas to the extent that female or male loan offcers select better performng borrowers ex-ante. Our results ndcate that loans handled by female loan offcers have a sgnfcantly lower default probablty than those of ther male counterparts. Ths result s robust to controllng for borrower s gender and for the correlaton between borrower s and loan offcer s gender. We also fnd only very lttle varaton of women s superor performance vs-à-vs men wth ther experence as loan offcer, suggestng that our results are not drven by women havng harder access to loan offcer postons. Ths result holds over dfferent samples. Specfcally, we confrm our fndng both for frst loans as well as for repeat loans of the same borrowers, wth a stronger 3

5 effect for frst loans. We nterpret ths as supportng for our hypothess that female loan offcers n ths mcrobank face stronger ncentves or have better sklls n dealng wth borrowers, as the agency problems between bank and borrower should be stronger for frst tme borrowers. We also test for dfferences n the approval rates of loan applcants between female and male loan offcers. Controllng for a vast array of borrower characterstcs, we cannot fnd any dfference between female and male loan offcers n ther acceptance of applcants, suggestng that the performance advantage of female loan offcers s n ther montorng of borrowers rather than ther screenng. Ths also confrms that our fndngs are not drven by selecton bas of female loan offcers dealng wth borrowers that have ex-ante a dfferent rsk profle. By nvestgatng gender dfferences n the context of loan offcers, ths paper s related to a growng body of studes on the role of loan offcers n fnancal nsttutons. For nstance, Andersson (2004) fnds that senor loan offcers come to more consstent decsons than nexperenced loan offcers. Berger and Udell (2004) argue that the loan offcers experence wth severe busness envronments decays n boom perods and, as a result, also substandard borrowers get loans. Hertzberg et al. (2008) show that loan offcers are more lkely to reveal negatve nformaton n the case of job rotaton because t seems to be better f the loan offcer reveals ths knd of negatve nformaton herself nstead of havng bad nformaton beng revealed by a successor loan offcer. Lbert and Man (Forthcomng) fnd that the hgher the decson maker s n the bank s herarchy, the lower the mportance of soft nformaton gets because the unverfable soft nformaton looses relablty over herarchy levels. Fnally, n a recent paper, Agarwal and Wang (2008) argue that loan offcer s choce of effort depends on the ncentve scheme mplemented by the bank, the nformaton asymmetry between the loan offcer and the bank, and the loan offcer s career concerns. Our results add a new facet to ths lterature. They 4

6 suggest that not only the nsttutonal desgn of fnancal nsttutons matter (Berger et al., 2005; Man, 2006), but also the gender of the people operatng n t. Our study s also related to the lterature studyng gender dspartes n rsk takng and performance. Several papers have shown that female decson makers are more rsk averse than male decson makers (Barsky et al., 1997; Agnew et al., 2003) and that ths hgher rsk averson affects fnancal decson (Charness and Gneezy, 2007; Chrstansen et al., 2006; Barber and Odean, 2001). Other authors have explored the behavor of women n dfferent compettve envronments and ther treatment wthn fnancal nsttutons (Gneezy et al., 2003, Forthcomng; Nederle and Vesterlund, Forthcomng; Black and Strahan, 2001, Goldn and Rouse, 2000). Green et al. (2008) analyze the performance of male versus female Wall Street equty analysts and document that the male analysts seem to have better forecastng abltes,.e. women seem to perform worse at hard, quantfable tasks. On the other hand, they also report that female analysts seem to perform better at non-quantfable aspects of the job such as clent servce. Our work contrbutes to ths lterature by documentng that women may perform better than men at quantfable job aspects such as the management of default rsk. The results further suggest that ths s not drven by a hgher degree of rsk averson as we do not fnd sgnfcant dfferences between female and male loan offcers n the loan approval decson. The remander of the paper s organzed as follows. Secton II dscusses the data, and secton III the methodology. Secton IV presents our man results and secton V contans robustness checks and further analyses. Secton VI concludes. II. Data We use a unque data set of both rejected and accepted loan applcants from a mcrocredt lender n Albana. Specfcally, we have nformaton on over 43,000 loan applcatons and 31,000 5

7 loans gven by the lender over the perod January 1996 to December 2006,.e. the frst eleven years of operaton of ths mcrocredt nsttuton. Whle the mcrocredt lender s part of an nternatonal network, t works wth local management and loan offcers. Specfcally, our data set contans nformaton on 203 loan offcers and covers fve branches of the lender n the Albanan captal, Trana. Unlke other mcrocredt nsttutons, the lender grants only ndvdual (not jont lablty or group) loans, for busness, real estate, and consumpton purposes. Whle the lender clearly focuses on the low-ncome and small-enterprse segment, and has thus a doublebottom lne approach of both proftablty and ncreasng access to credt, fnancal sustanablty and therefore proftablty s the prmary goal. Table 1 provdes some basc data about the lender. Specfcally, t shows for the 11 years of our sample perod the number of loan applcatons, number of approved loans, loan characterstcs, basc borrower characterstcs, loan usage and the share of female loan offcers. The lender grew substantally over the past 11 years, from orgnally 350 loans and 300 borrowers n 1996 to over 7,000 loans and over 6,000 borrowers n Over ths perod, the approval rato, defned as the number of approved applcatons dvded by the number of all applcatons, ncreased substantally. It rose from 44 percent n 1996 over 60 percent n 2000 to 71 percent n 2006, whch can be partly explaned by the ncreasng share of repeat borrowers. The average loan sze was 4,372 US dollars, llustratng that the loan portfolo of the lender conssts manly of mcroloans and loans to small and medum szed enterprses (SME). Whle the lender ntally gave only loans for busness purposes, n 2006 almost 30 percent were for consumpton purposes. 2 Loan defaults n the table and our emprcal analyses are defned as the occurrence of payments beng n arrears for more than 30 days, that s, f at least one of a borrower s payments 2 Gven the fungblty of resources, the share of consumpton loans mght actually be underreported. 6

8 was n arrears for more than 30 days at any pont over the whole lfetme of the loan, we count ths as a loan default. As robustness tests, we also use tme perods of 15, 60, and 90 days n the emprcal analyses. The default rate vared sgnfcantly over the sample perod, from a hgh of 24.5 percent n the frst year to a low of 1.3 percent n 2006, most lkely reflectng an ncrease n experence of the lender. 3 The share of female borrowers s surprsngly low for a lender operatng n a developng economy, wth, on average, only 20 percent, though ncreasng over the last years of the sample perod, to 25 percent n The share of female loan offcers, on the other hand, s very hgh wth an average of 66 percent of loan offcers beng female. Ths share, however, has been decreasng over tme, droppng to below 50 percent n 2005 and For our followng emprcal analyss, we restrct and cut the data n several ways. For the man analyss, we restrct our attenton to actual borrowers and ther default behavor and thus drop unsuccessful loan applcants. Second, we focus on a set of borrowers that have had only one loan wth the lender, for several reasons. The frst reason s that the database we use s constructed n a way that all soco-demographc borrower data are overwrtten whenever a new loan applcaton s forwarded by a customer that had already appled for a loan before. Hence, some of the soco-demographc data we use as control varables mght not be up to date f we use also further loan applcatons by the same borrower. 4 The second reason s that the comparson of frst (and at the same tme last) loan applcatons allows for a consstent comparson as all loan offcers have the same lmted nformaton about the respectve borrower at the tme of the 3 Note that the default frequency s not the yearly default frequency, but rather the default frequency of all loans beng granted n 1996, 1997, and so on. Therefore, the low default frequency n the last year s partly due to the effect of loans stll outstandng at the end of our sample perod. 4 For nstance, a certan customer mght have appled for a loan n 1996 when she was not marred and agan n 2000 when she was marred. As the data we use were provded by the lender n January 2007, the database would classfy that partcular customer as beng marred also n 1996, although n 1996 ths was not the case. 7

9 applcaton. 5 In the case of repeat borrowers, loan offcers already have hstorc nformaton, whch they can take nto consderaton when grantng and montorng the loan. Focusng on the frst loan by each successful loan applcant thus allows us to study n a clean way gender-specfc loan offcer performance effects. Thrd, we drop loans wth mssng gender nformaton on the borrower or the loan offcer level. For that purpose, we exclude loans by borrowers classfed as corporate clents n the database because n these cases we cannot observe the borrowers gender nformaton. Fourth, we drop certan outlers from the sample. Specfcally, we drop loans wth amounts of less than 100 US dollars and more than US 100,000 dollars. Whle very low values mght result from false entres n the database we want to exclude very large loans that do not ft the defnton of mcro and SME loans. Addtonally, we exclude loans wth an unreasonable borrower age (smaller than 18 or larger than 75 years). Fnally, we exclude loans approved n December 2006 as we cannot observe these loans performance. 6 Ths reduces our sample from 31,000 to 6,775 loans granted by 141 loan offcers for the man regresson analyss. In robustness tests we use a dfferent cut of the data and obtan samples contanng more than 14,000 loans. We nclude a vast array of borrower, loan offcer and loan characterstcs n the regresson of loan defaults. Table 2 presents descrptve statstcs and correlatons for these varables. Specfcally, n addton to controllng for the borrower s gender, we control for her martal status, employment status (self-employed or salared employee) and age. We expect female, marred and employed borrowers to be less lkely to default, because of hgher opportunty costs of defaultng and more stable ncomes. We also nclude the number of persons n the borrower s household and whether there s a phone avalable. Whle the avalablty of a phone mght 5 Ths rests on the reasonable assumpton that loan applcants and loan offcers dd not know each other before the loan applcaton was forwarded. 6 The overwhelmng majorty of the loans have an nstallment frequency of one month. As the data covers the perod untl December 31, 2006, t s mpossble for a borrower who was gven a loan n December 2006 to default on her loan. 8

10 ncrease the ease of montorng by the loan offcers, there s no clear a-pror relatonshp between household sze and default probablty. The descrptve statstcs n Table 2A ndcate that on average 23.1 percent of the loans are gven to female borrowers, whle 57.7 percent are approved by female loan offcers. These numbers are smlar to the ones presented n Table 1 and ndcate that the data selecton process dd not nduce a strong sample bas. On average, borrowers are 39 years old, whle loan offcers are 25 years old percent of borrowers are marred, whle 12.4 percent are self-employed. On average, there are almost fve persons n a borrower s household and there s a phone avalable n 93.2 percent of borrowers households. The correlatons n Panel B of Table 2 show that female, older, and marred borrowers and borrowers wth a phone face a lower default probablty, whle household sze and employment status are not correlated wth default probablty. There are also many sgnfcant correlatons among borrower characterstcs. For example, female borrowers are less lkely to be marred or self-employed and lve n smaller households. We also control for several loan characterstcs that mght affect a loan s default probablty. Specfcally, we control for the annualzed nterest rate, the log of the approved amount and the log of the adjusted maturty of the loan. 7 Further, we nclude the rato of approved to appled loan amount and the type of collateral (personal, mortgage, or chattel guarantee) provded. 8 Hgher nterest rates can result n adverse selecton of borrowers wth rsker projects and n rsker behavor of borrowers (Stgltz and Wess, 1981). Smlarly, a lower approved share mght sgnal hgher default rsk, whle longer-term loans tend to be rsker. On the other hand, there s a-pror no clear relatonshp between collateral or loan purpose and default 7 Some loans n the database mature after These loans maturty was adjusted to December 31, 2006 n order to be able to compare the outstandng loans wth already matured loans. 8 The use of chattel guarantees s qute common n developng economes as objects from the household of a borrower (such as a frdge or a televson) often have very hgh (not necessarly monetary) values for the borrowers. 9

11 rsk. The descrptve statstcs n Table 2A show that annualzed nterest rates vared between 4.3 and 24 percent, wth an average of 13.8 percent. The average loan sze s 3,700 US dollars, whle the loan maturty vares between 1 month and 6 years, wth an average of 16 months. On average, borrowers receved 88.8 percent of the amount they appled for percent of all loans were secured wth chattel collateral, whle 12.4 percent provded mortgages and 15.0 percent personal guarantees. The correlatons n Panel B of Table 2 show that longer-term loans, loans wth hgher nterest rates and loans that are smaller relatve to the amount orgnally appled for are more lkely to default, whle loan sze s not sgnfcantly correlated wth default probablty. Loans wth a personal guarantee are more lkely to default, whle other guarantees are not sgnfcantly correlated wth default probablty. Larger and longer-term loans, loans wth personal and mortgage guarantees carry lower nterest rates. Some of the loan characterstcs are also correlated wth borrower characterstcs. Female borrowers, for example, pay lower nterest rates and are less lkely to default. Fnally, we control for several loan offcer characterstcs. Specfcally, n addton to the gender of loan offcers, we nclude ther age and the number of loan applcatons they have processed, counted from the frst loan they ever processed snce they started workng for the lender. The correlaton of age and experence wth default probablty s ex-ante not clear. Whle age and experence mght mprove loan offcers performance (Anderson, 2004), the career concern vew dscussed n Agarwal and Wang (2008) would predct the opposte relatonshp. The age of loan offcers n our sample ranges from 19 to 32 years, wth an average of 25 years. On average, loan offcers have processed already 223 loan applcatons. Addtonally, we fnd huge dfferences n ther experence because the number of already processed loans ranges from 1 9 We wnsorze the approved share at the frst and 99 th percentle to account for outlers. 10

12 to over 1,000 loans. The correlatons n Table 2B ndcate that female loan offcers are, on average, younger, whle they do not have more experence n terms of loan applcatons processed. Older analysts have processed more loan applcatons. Female loan offcers are more lkely to process loan applcatons of female, younger, non-marred, and not self-employed borrowers. Female loan offcers provde larger loans, for longer maturtes and at lower nterest rates. They are more lkely to process loans wth personal or mortgage guarantees, but less lkely to process loans collateralzed wth chattel guarantees. III. Methodology We use several regresson specfcatons to dsentangle the relatonshp between loan default probablty and the gender of borrowers and loan offcers. We pay partcular attenton to loan offcer experence to nvestgate whether dfferent experence levels can explan our results. Specfcally, t may be that loan offcer gender related loan performance dfferences are drven by hgher experence levels of a specfc loan offcer group. We explctly control for ths n our regressons. The sgnfcant correlatons between the dfferent borrower, loan offcer and loan characterstcs n the prevous secton stress the mportance of multvarate regressons. Specfcally, for the frst set of results we utlze a bnary probt model of the followng form: Default = α + β * + 1 * Female + β 2 * Female loan offcer j + γ * D + δ X j ε (1) where Default s a bnary varable takng the value 1 f customer defaulted on her loan (.e. had arrears for at least 30 days once durng the lfetme of the loan), Female s a dummy varable takng the value 1 for female borrowers, Female loan offcer j s a dummy varable takng the value 1 f the loan offcer j servng borrower s female, D s a vector of control varables 11

13 referrng to borrower and loan, X j s a vector of control varables referrng to loan offcer j and s an error term. In addton, we nclude dummes for the fve branches of the lender to control for potental clusterng of loan offcers of a certan gender or ablty n a specfc branch, year dummes to control for macroeconomc factors that mght affect default rsk of borrowers, and fve busness sector dummes (constructon, producton, other servces, trade, transport) to control for rsk dfferences assocated wth the busness sector the borrower operates n. Results for these addtonal controls wll be omtted from the tables. Standard errors are clustered at the loan offcer level, thus allowng for unobserved correlaton between loans processed and montored by the same loan offcer (Froot, 1989). 10 Gven that loan offcers may be more lkely to deal wth borrowers of the same sex, for our second set of results we wll utlze several nteracton terms to dsentangle the relatonshp between default probablty and gender of borrower and loan offcer Default = α + β * Female * Female loan offcer + β Male * Female loan offcer + β Male * Male loan offcer + γ * D + δ * X + ε 3 1 j j j 2 j (2) where the combnaton female borrower-male loan offcer s the omtted category. The coeffcent β 1 thus ndcates whether female borrowers are more or less lkely to default wth a female than wth a male loan offcer, whle the dfference between β 2 and β 3 ndcates whether male borrowers are more or less lkely to default wth a female than wth a male loan offcer. Ths specfcaton therefore allows us to not only control for the correlaton between borrower and loan offcer gender, but also to dstngush between the performance dfference of female and male loan offcers among borrowers of dfferent genders. Smlarly, we can assess the 10 As suggested by Petersen (Forthcomng) we also reproduced all our results usng heteroskedastcty robust standard errors wthout accountng for cluster correlaton (Whte, 1980), the results are sgnfcant at smlar or even hgher statstcal levels. They are avalable upon request. 12

14 performance of female vs. male borrowers by consderng the dfference between β 1 and β 2 (for female loan offcers) and the coeffcent on β 3 (for male loan offcers). Fnally, wth our thrd set of results we assess whether the relatonshp between gender and default probablty vares wth the experence of loan offcers. For that purpose we add to specfcaton (2) nteracton terms between the borrower-loan offcer dummy of nterest and a varable proxyng for the loan offcer s experence. Specfcally, we control for loan offcer experence by nteracton the borrower-loan offcer gender wth four experence quartles Default β 2, k = α + β 1, k Male loan offcer * Experence quartle j * Female * Female loan offcer * Experence quartle Male * Female loan offcer * Experence quartle j j j k, j + γ * D + δ * X + β j 3, k + ε Male * k, j + (3) where k denotes the experence quartle (1: 0-25 percent, 2: percent, 3: percent, 4: percent). Ths regresson specfcaton yelds twelve borrower-gender-experence nteracton terms, the omtted category beng the combnaton female borrower-male loan offcer. The experence proxes we use are the number of loan applcatons already handled by the loan offcer, the number of years the loan offcer has worked for the mcrolender, and the loan offcer s age. The sgn and sgnfcance of the coeffcent β 1,1 (β 1,4 ) ndcate whether female loan offcers wth very low (very hgh) experence have lower default rates for female borrowers than male loan offcers, ndependent of the experence of the male loan offcers. If experence dfferences drve the superor performance of female loan offcers, then we would expect to fnd a sgnfcant effect only for hgh experence levels (.e. β 1,3 and β 1,4, that s, the thrd and fourth experence quartle). Whle specfcaton (3) tests the nfluence of experence on loan offcer performance for the case of female borrowers, we also run a regresson where the combnaton male borrowerfemale loan offcer s the omtted category. Ths specfcaton allows us to test f and how 13

15 potental performance dfferences between female and male loan offcers for male borrowers depend on loan offcer experence. Whle the sgns of the estmated coeffcents of the explanatory varables ndcate whether an ncrease of that explanatory varable ncreases or decreases the probablty of loan default, the estmated coeffcents of probt models do not allow us to assess the economc sze of a change n the explanatory varable. In the results secton, we therefore only present margnal coeffcent estmates that are computed at the sample mean n order to be also able to derve the economc sgnfcance of our results. IV. Man results The results n Column 1 of Table 3 suggest that female borrowers and borrowers served by female loan offcers are less rsky. The default probablty of female borrowers s 4.2 percent lower than that of male borrowers across our sample of frst (and last) loans. We also fnd that the default probablty of borrowers served by female loan offcers s 4.7 percent lower than the default probablty of borrowers served by male loan offcers. Both effects are economcally sgnfcant, as the average default rate n our sample s 13.5 percent. On the other hand, the default probablty does not vary wth the experence of the loan offcer. The number of loan applcatons the loan offcer has already processed, one of our proxes for a loan offcer s experence, does not enter sgnfcantly. Several other loan offcer, borrower and loan characterstcs enter sgnfcantly n the column 1 regresson of Table 3. Frst, older borrowers and borrowers served by older loan offcers are less lkely to default. The latter result contradcts the career concern hypothess by Aggarwal and Wang (2008). Second, consstent wth Stgltz and Wess (1981), the nterest rate 14

16 s postvely, sgnfcantly, and economcally very substantally assocated wth a hgher default probablty. Thrd, marred borrowers and borrowers from households where a phone s avalable are less lkely to default, suggestng hgher opportunty costs for these borrowers. Fourth, larger loans and loans wth longer maturtes are more lkely to turn non-performng. Ffth, the hgher the rato of approved to appled loan amount, the lower s the default probablty. Fnally, loans wth personal guarantees are more lkely to turn bad, whle loans guaranteed wth mortgages are less lkely to default. An explanaton for ths fndng may be that personal guarantees, whch are thrd-party guarantees, nduce a moral hazard, whle the potental loss of the own house sets strong repayment ncentves. Overall, the ft of our model s satsfactory, wth 75% of the defaulted loans predcted correctly and 61% of the non-defaulted loans and a Pseudo R-square of 13%. 11 Snce the fndng that female loan offcers experence lower default rates mght be drven by the fact that female borrowers are less rsky than male borrowers and mght be more often served by female loan offcers, we next construct borrower gender-loan offcer gender combnatons as dummy varables and run a regresson usng regresson specfcaton (2). Specfcally, we nteract borrower and loan offcer gender, wth the combnaton female borrower-male loan offcer beng the omtted category. In our baselne sample, 68% (55%) of female (male) borrowers are screened and montored by female loan offcers. Column 2 of Table 3 shows the robustness of our prevous fndngs to controllng for the correlaton between borrowers and loan offcers genders. Compared to female borrowers montored by male loan offcers, female borrowers montored by female loan offcers have a default probablty that s 4.3 percent lower. Smlarly, we fnd that the default probablty of 11 In classfyng observatons, predcted probabltes sgnfcantly hgher than 13.5% (average default probablty) are classfed as default observatons and those below 13.5% are classfed as no default. We adjust ths benchmark dependng on the sample and default defnton. 15

17 male borrowers montored by female loan offcers s 4.8 percent lower than the default probablty of male borrowers montored by male loan offcers. Ths suggests that, ndependent of the gender of the borrower, female loan offcers are better n managng default rsk. Comparng the margnal effects of the dfferent borrower-loan offcer dummes, we also confrm that male borrowers are more lkely to default than female borrowers. In the case of female loan offcers, male borrowers default 4.4 percent more often and n the case of male loan offcers they default 3.8 percent more often. Our prevous fndngs on the dfferent loan offcer, loan and borrower characterstcs are confrmed by ths regresson. Columns 3 to 5 of Table 3 show the robustness of our results to usng alternatve defntons of default. Specfcally, we redefne default as havng a payment n arrears for more than 15 days (column 3), 60 days (column 4) and 90 days (column 5). Our fndngs are all confrmed for the strcter default defnton of 15 days. Here we also fnd that the advantage of female loan offcers vs-à-vs ther male peers appears to be stronger for female borrowers (5.8 percent) than for male borrowers (4.9 percent). In the case of less strct defntons (columns 4 and 5), the sze of the margnal effect of loan offcer s gender for female borrower declnes but stays sgnfcant, whle the effect of loan offcer s gender turns nsgnfcant for male borrowers. Fnally, n column 6 of Table 3 we confrm our fndngs for a larger sample of frst loans for whch we have also subsequent loan nformaton. Here, we do not restrct our attenton to the frst loans that were at the same tme the last (and thus only) loans by the borrowers, but we use all frst loans avalable n the database. As n ths case we cannot be sure that the socodemographc nformaton has not changed after the frst loan, we exclude all soco-demographc varables from the regresson. Ths less strct cut of the data leaves us wth a sample contanng 14,020 frst loans. The column 6 results of Table 3 show that even n ths larger sample, we confrm our fndng that female loan offcers are more effcent n preventng a loan default than 16

18 ther male peers. 12 Whle the margnal effects are somewhat smaller n sze, we stll fnd that female loan offcers are better n preventng loan defaults than ther male peers, both for female and for male borrowers. The results for the other controls are very smlar to our prevous regressons. The overall ft of the model decreases, as can be seen from the Pseudo R-squares and the percentages of correctly predcted observatons, underlnng the mportance of the socodemographc borrower characterstcs n predctng default. Our results so far suggest that female loan offcers are more effcent n preventng loan defaults than male loan offcers. However, these results mght be drven by dfferent levels of job experence. For nstance, f female loan offcers were more experenced n montorng borrowers than ther male peers, we mght expect them to perform better, that s, have lower default rates. To control for ths possble drver of the results, we nteract the borrower gender-loan offcer gender dummy varable wth dfferent levels of loan offcer experence. Specfcally, we utlze four experence quartles and buld an nteracton term for each quartle. Ths yelds four nteracton terms for each borrower-loan offcer-experence quartle combnaton, and twelve nteracton terms overall. As before, the combnaton female borrower-male loan offcer s the reference category. Loan offcer experence s proxed by the number of prevous loan applcatons the loan offcer has processed 13, the number of years the loan offcer has worked for the mcrolender, and the age of the loan offcer. The Table 4 regressons are thus based on regresson specfcaton (3). The Table 4, Panel A, column 1 regresson shows that the advantage of female loan offcers n managng the default rsk of female borrowers holds for all, but the lowest quartle of experence. Specfcally, for the second, thrd and fourth quartles of job experence, we can 12 For ths regresson we use the 30 days n arrears default defnton. In unreported regressons we confrm our earler fndngs usng ths bgger sample wthout soco-demographc data for the alternatve default defntons. 13 We dvde the number of loan applcatons per loan offcer by 1,000 for scalng reasons. 17

19 confrm that female loan offcers perform better than male loan offcers. The szes of the margnal effects are smlar to our prevous fndng from Table 3, column 2, and frst ncrease up to the thrd quartle of experence before decreasng agan. In Panel B, we use the combnaton male borrower-female loan offcer as reference category. We fnd that female loan offcers are better than male loan offcers n managng default rsk of male borrowers for the thrd and fourth quartles of experence. The economc sgnfcance of the effect s consderably hgher than before, n partcular for very hgh levels of experence. Column 2 presents the results when usng the tme snce the loan offcer works for the mcrolender as experence proxy. The use of ths alternatve experence proxy shows that the performance dfference for female borrowers exsts at all but the hghest levels of experence. Interestngly, though, the effect vanshes at the fourth quartle of experence. The magntude of the effect s agan smlar to before. We further fnd that the performance gap wth regard to male borrowers exsts already for medum experence and remans sgnfcant up to very hgh experence, beng statstcally sgnfcant for the second, thrd and fourth quartles. Fnally, column 3 presents the results when usng loan offcer age as experence proxy. Whle beng only a crude experence proxy, the use of ths thrd alternatve does not alter our fndngs. As before, the performance of female loan offcers wth regard to female borrowers s only ndstngushable from male loan offcers for low levels of experence, but sgnfcantly better for the second, thrd and fourth quartles of ther age. For male borrowers, we agan fnd performance dfferences for hgh and very hgh experence levels. All n all, we conclude from these tests that the superor performance of female loan offcers for female borrowers s not drven by ther hgher experence. Only for male borrowers we fnd slght evdence that the performance advantage of female loan offcers relatve to ther male counterparts s sgnfcant only at experence levels above the medan. 18

20 V. Robustness and Addtonal Tests We subject our fndngs to several senstvty analyses n order to test ther robustness but also to explore the channels through whch the relatonshp between loan offcer gender and default probablty works. We fst loosen the strct sample selecton that we had chosen for our baselne regresson. Specfcally, we expand the sample from frst loans to borrowers subsequent, that s, repeat, loans. Ths allows us a robustness test n two aspects: frst, we have a dfferent sample, but, second, we expect a less sgnfcant relatonshp between the gender of the loan offcer and default probablty as the nformaton asymmetres and thus agency problems between bank and borrower should be lower. We thus would nterpret a somewhat weaker fndng of a female performance advantage on the sample of subsequent borrowers as confrmaton of women s advantage n montorng borrowers. We also test for ths drectly by ncludng an nteracton term wth a varable ndcatng the duraton of the borrower s relatonshp wth the mcrobank. For ths robustness check we nclude several control varables that capture a borrower s loan hstory wth the bank. Whle for frst loans there s no loan hstory avalable, here we can make use of hstorc nformaton. Specfcally, we control for the duraton of the lendng relatonshp n years, whether any prevous loan applcaton of the borrower has been rejected and whether the borrower has ever defaulted on any loan granted by the lender before applyng for a new loan. We thus use specfcaton (2) and add the three control varables for the borrowers loan hstory wth the bank. As n the baselne regresson, we frst focus on a sample of last loans to be able to control for soco-demographc borrower characterstcs. Cuttng the data n ths way leaves us wth 6,448 repeat loans. We then drop the soco-demographc varables and focus on a broader sample of repeat loans. Ths yelds a sample sze of 12,940 loans. Focusng on further 19

21 loans and ncludng loan hstory varables ncreases the ft of the model sgnfcantly, as can be seen from the hgher Pseudo R-square and percentages of correctly predcted defaults. The results n column 1 of Table 5 confrm the fndngs and ther nterpretaton wth a regresson usng repeat nstead of frst loans. We contnue to fnd that female borrowers screened and montored by female loan offcers have a lower default probablty than f screened and montored by male loan offcers, whle there s no sgnfcant dfference for male borrowers. However, even n the case of female borrowers, the economc sze s substantally smaller than before, wth only 1.8 percent, compared to the 4.3 percent we found n Table 3, column 2. Large proportons of the explanatory power seem to shft to the loan hstory data. Ths observaton s consstent wth Mester et al. (2007) who show that prevous customer nformaton help fnancal nsttutons to montor ther borrowers. Specfcally, we fnd that defaults are on average 37.1 (3.7) percent more lkely f the same borrower defaulted on a prevous loan (had a rejected loan applcaton before). In spte of ths, however, we contnue to fnd a loan offcer effect. Ths s a very nterestng fndng because t llustrates that even f hstorc, loan default relevant borrower nformaton s used, there are stll dfferences between female and male loan offcers. 14 The column 2 regresson of Table 5 shows that ths performance gap s not a functon of how long the borrower has been borrowng from the nsttuton because the nteracton terms between the borrower-loan offcer gender pars and the duraton of the lendng relatonshp do not enter sgnfcantly. The results n columns 3 and 4 of Table 5 largely confrm these fndngs for a larger sample of 12,940 subsequent loans that s not lmted to last loans. As before, we do not use the soco-demographc borrower characterstcs for these regressons, whch agan reduces the ft of 14 Note, however, that ths s only the case for female borrowers because results of unreported regressons show that male borrowers served by female loan offcers do not have dfferent default probabltes compared wth male loan offcers. 20

22 the model. The column 3 results wthout the nteracton term show that the performance advantage of female vs-à-vs male loan offcers s now only 1.5 percent for female borrowers. For male borrowers the advantage s 1.0 percent, but only weakly sgnfcant. The sze of the performance gap for female borrowers n column 4 remans, but looses sgnfcance, and the unreported margnal effect for male borrowers does also not enter sgnfcantly. Agan, we do not fnd that the performance gap s a functon of the duraton of borrowers lendng relatonshp wth the bank. Taken together, the results n Table 5 suggest that the performance advantage of female vs-à-vs male loan offcers contnues to hold for repeat loans. However, ths s true only n the case of female borrowers. We also fnd that ths effect s smaller for repeat loans compared wth frst loans, whle t s not a functon of the duraton of borrowers relatonshp wth the bank. It thus seems that the learnng effect that reduces the performance advantage of female loan offcers vs-à-vs ther male counterparts kcks n wth the second loan. Fnally, we test whether the advantage of female loan offcers vs-à-vs ther male counterparts arses from ther better screenng capactes of loan applcants. For ths test, we use a sample contanng both successful and unsuccessful loan applcatons and run the followng regresson Approval = α + β * Female * Female loan offcer + β Male * Female loan offcer + β Male * Male loan offcer + γ * D + δ * X + ε 3 1 j j j 2 j (4) Specfcaton (4) dffers from (2) snce the dependent varable s now a dummy varable ndcatng whether a loan applcaton was approved (Approval = 1) or not. Ths enables us to test f female loan offcers are less lkely than ther male counterparts to accept loan applcants of a specfc gender. Performng ths test allows us to exclude ex ante borrower selecton as the drver of the performance dfferences between female and male loan offcers. Specfcally, f we do not fnd any sgnfcant dfference between female and male loan offcers, then ex ante selecton does 21

23 not drve our prevous fndngs. In contrast to specfcaton (2) we are not able to use some loanrelated control varables, such as the nterest rate, because these are not avalable at the tme of the loan applcaton. Note also that rather than usng the approved loan amount as a loan sze proxy, we use the appled loan amount, and rather than usng the approved maturty we use the appled maturty. We test for ex ante sample selecton usng four dfferent samples. At frst, we use a sample of frst loan applcatons, whch at the same tme were the last applcatons, thus correspondng to the specfcaton of Table 3 (columns I to V), wth 8,297 loan applcants, around 92% of whch were accepted. 15 Second, we drop the soco-demographc varables and nclude all frst loan applcatons, yeldng a sample of 15,986 loan applcatons. Thrd, we use a sample of repeat borrowers. Agan, we run a specfcaton wth loan applcatons that were at the same tme last loan applcatons (sample sze of 7,240 loan applcatons) and a specfcaton wthout ths restrcton and thus wthout soco-demographc borrower characterstcs (14,502 loan applcatons). The results n Table 6 llustrate that our fndng of a superor performance of female vs-àvs male loan offcers s not drven by selecton bas of the borrowers or better screenng capacty of female loan offcers. We do not fnd any sgnfcant dfference n the lkelhood of borrowers to be accepted by female or male loan offcers, ndependent of whether the borrower s male or female. Further, we do fnd that male loan offcers are more lkely to accept loan applcatons of male clents. Overall, ths test suggests that screenng dfferences between female and male loan offcers do not drve the performance gap between them. It rather ndcates that the results are drven by better montorng of the female loan offcers. 15 Here we also nclude loans approved n December 2006, unlke for the arrears regressons. 22

24 VI. Conclusons Ths study s, to the best of our knowledge, the frst to consder gender dfferences n loan offcer performance. Whle some papers have reported gender dfferences wth regard to nvestment decsons (e.g. Barber and Odean, 2001; Charness and Geezy, 2007) or the general behavor of women n compettve envronments (e.g. Gneezy et al., Forthcomng; Nederle and Vesterlund, Forthcomng), we provde novel results about the role of gender n fnancal nsttutons. Contrary to Green et al. (2008) who document that women seem to perform worse than men n quantfable aspects of the job, we fnd convncng evdence that women may also perform better than men n quantfable job aspects such as the management of loan default rsk. Although the job envronment n fnancal nsttutons s usually hghly compettve, we further fnd counter-evdence to several papers (e.g., Gneezy et al., 2003) whch show that females underperform ther male peers n hghly compettve envronments. Addtonally, we fnd that borrowers served by older loan offcers are less lkely to default whch s n lne wth results from Anderson (2004) but on the other hand contradcts the career concern hypothess by Aggarwal and Wang (2008). Our estmatons also shed lght on the mechansms. We fnd that female loan offcers are not more or less lkely to accept borrowers wth the same characterstcs. Further, ex-ante rsk dfferences captured by nterest rates do not nfluence our fndngs as we explctly control for the nterest rate n our regressons. It thus seems to be the better montorng of borrowers that explans the lower default rsk n the case of female loan offcers. Fnally, there s no convncng evdence that better experence explans the advantage of female loan offcers vs-à-vs ther male colleagues. 23

25 Our results do not only contrbute to the lterature on gender dfferences n fnance and economcs, but also to the growng body of lterature on the role of loan offcers n fnancal nsttutons. They suggest that the performance of loan offcers n fnancal nsttutons s not only drven by settng the rght ncentves, for nstance by mplementng a routne job rotaton mechansm as n Hertzberg et al. (2008), or by the degree of asymmetrc nformaton n the nsttuton as n Agarwal and Fang (2008), but also by gender-specfc dfferences between female and male loan offcers. 24

26 References Agarwal, S., Wang, F.H., 2008, Motvatng Loan Offcers: An Analyss of Salares and Pece Rates Compensaton, Workng Paper, Federal Reserve Bank of Chcago. Agnew, J., Balduzz, P., Sundén, A., 2003, Portfolo Choce and Tradng n a Large 401(k) Plan, Amercan Economc Revew 93, Andersson, P., 2004, Does Experence Matter n Lendng? A Process-tracng Study on Experenced Loan Offcers'and Novces'Decson Behavor, Journal of Economc Psychology 25, Armendarz de Aghon, B., Morduch, J., 2005, The Economcs of Mcrofnance, MIT Press. Barber, B.M, Odean, T., 2001, Boys wll be Boys: Gender, Overconfdence, and Common Stock Investment, Quarterly Journal of Economcs 116, Barsky, R.B., Juster, F.T., Kmball, M.S., Shapro, M.D., 1997, Preference Parameters and Behavoral Heterogenety: An Expermental Approach n the Health and Retrement Study, Quarterly Journal of Economcs 112, Berger, A.N., Mller, M., Petersen, M., Rajan, R., Sten, J., 2005, Does Functon Follow Organzatonal Form? Evdence from the Lendng Practces of Large and Small Banks, Journal of Fnancal Economcs 76, Berger, A.N., Udell, G.F., 2004, The Insttutonal Memory Hypothess and the Procyclcalty of Bank Lendng Behavor, Journal of Fnancal Intermedaton 13, Black, S., Strahan, P. E., 2001, The Dvson of Spols: Rent-Sharng and Dscrmnaton n a Regulated Industry, Amercan Economc Revew 91,

27 Charness, G., Gneezy, U., 2007, Strong Evdence for Gender Dfferences n Investment, Workng Paper. Chrstansen, C., Schröter Joensen, J., Rangvd, J., 2006, Gender, Marrage, and the Decson to Invest n Stocks and Bonds: Do Sngle Women Invest More n Less Rsky Assets?, Workng Paper. Darty Jr., W.A., Mason, P.L., 1998, Evdence on Dscrmnaton n Employment: Codes of Color, Codes of Gender, Journal of Economc Perspectves 12, Froot, K.A., 1989, Consstent Covarance Matrx Estmaton wth Cross-Sectonal Dependence and Heteroskedastcty n Fnancal Data, Journal of Fnancal and Quanttatve Analyss 24, Gneezy, U., Leonard, K.L., Lst, J.A., Forthcomng, Gender Dfferences n Competton: Evdence from a Matrlneal and a Patrarchal Socety, Econometrca. Gneezy, U., Nederle, M., Rustchn, A., 2003, Performance n Compettve Envronments: Gender Dfferences, Quarterly Journal of Economcs 118, Goldn, C., Rouse, C., 2000, Orchestratng Impartalty: The Impact of Blnd Audtons on Female Muscans, Amercan Economc Revew 90, Green, C. T., Jegadeesh, N., Tang, Y., 2008, Gender and Job Performance: Evdence from Wall Street, NBER Workng Paper No Hertzberg, A., Lbert, J.M., Paravsn, D., 2008, Informaton and Incentves Insde the Frm: Evdence from Loan Offcer Rotaton, Workng Paper. Lbert, J.M., Man, A., Forthcomng, Estmatng the Impact of Herarches on Informaton Use, Revew of Fnancal Studes. Mester, L.J., Nakamura, L.I., Renault, M., 2007, Transactons Accounts and Loan Montorng, Revew of Fnancal Studes 20,

28 Man, A., 2006, Dstance Constrants: The Lmts of Foregn Lendng n Poor Economes, Journal of Fnance 61, Nederle, M., Vesterlund, L., Forthcomng, Do Women Shy Away From Competton? Do Men Compete Too Much?, Quarterly Journal of Economcs. Petersen, M., Forthcomng, Estmatng Standard Errors n Fnance Panel Data Sets: Comparng Approaches, Revew of Fnancal Studes. Stgltz, J.E., Wess, A.M., 1981, Credt Ratonng n Markets wth Imperfect Informaton, Amercan Economc Revew 71, Whte, H., 1980, A Heteroskedastcty-Consstent Covarance Matrx and a Drect Test for Heteroskedastcty, Econometrca 48,

29 Table 1: Some statstcs on the lendng nsttuton Ths table contans a broad overvew for the 5 Trana branches of the Albanan mcrolender. The loan sze s gven n US dollars and the nterest rate s per annum. The default frequency s measured as the occurrence of a borrower beng n arrears for more than 30 days durng the lfetme of her loan. It s not the yearly default frequency, but rather the default frequency of all loans beng granted n 1996, 1997, and so on. Busness loans ncorporate nvestments nto fxed assets and workng captal. Real estate loan usages nclude the purchase, constructon, mprovement and extenson of houses. New loan Loan usage Share Share Year of Approved Loan volume Default Busness Real of female of female applcaton Applcatons loans sze (1,000) Borrowers frequency Loans estate Consumng borrowers loan offcers ,646 2, ,348 1, ,616 4, , ,287 5, ,390 1,438 4,062 9,709 1, ,230 1,456 3,674 8,193 1, ,495 1,907 5,762 14,400 1, ,737 2,941 6,455 24,100 2, ,656 7,836 4,068 39,300 7, ,437 7,339 3,996 37,700 6, ,944 7,024 3,176 31,600 6, Sum 43,126 31, ,307 Average 4,

30 Table 2A: Descrptve statstcs Ths table contans borrower, loan, and loan offcer characterstcs for the 5 Trana branches of the Albanan mcrolender. All varables are provded for a sub sample of 6,775 approved loans to ndvdual, prvate borrowers. The table concentrates on the frst and last loans for each borrower. We further drop loans wth unreasonable entres for the borrower s age (smaller than 18 or larger than 75 years), mssng gender nformaton for borrower and loan offcer, and appled loan sze (smaller than 100 or larger than 100,000 US dollars). Female s a dummy varable ndcatng the gender of the borrower (female = 1), Female loan offcer s a dummy varable ndcatng the gender of the loan offcer (female = 1), Age of borrower s the age of the borrower at the tme of the loan applcaton, Cvl status s a dummy varable ndcatng whether the borrower s marred (marred = 1), Self employed s a dummy varable ndcatng whether the borrower s self-employed or a wage earner, Number persons household ndcates how many persons other than the borrower are n the household of the borrower, Phone avalablty s a dummy varable ndcatng whether the borrower has a phone or not (phone avalable = 1), Approved amount s the loan sze granted n US dollars, Adjusted maturty s the of the loan maturty adjusted such that no loan has a maturty greater than December 31, 2006, Interest rate s the annual nterest rate charged on the loan, Approved share s the rato of appled amount to approved amount n percent, Personal guarantee, Mortgage guarantee, and Chattel guarantee are all dummy varables ndcatng whether any of the three respectve types of collateral are pledged by the borrower, Applcatons per loan offcer s a loan offcer experence proxy, whch ndcates the number of loan applcatons handled by the loan offcer untl the respectve loan was granted, Age of loan offcer s the age of the loan offcer at the tme the loan was granted measured n years. Varable Mean Mnmum 25%-Quartle Medan 75%-Quartle Maxmum Default Female Female loan offcer Age of borrower Cvl status Self employed Number persons household 4, Phone avalablty Approved amount 3, ,433 2,322 3, ,000 Adjusted maturty Interest rate Approved share Personal guarantee Mortgage guarantee Chattel guarantee Applcatons per loan offcer Age of loan offcer

31 Table 2B Correlaton matrx Ths table contans the par-wse correlatons for borrower, loan, and loan offcer characterstcs for the 5 Trana branches of the Albanan mcrolender. All varables are provded for a sub sample of for 6,775 approved loans to ndvdual, prvate borrowers. Refer to Table 2A for a descrpton of the varables and the sample selecton. * ndcates a sgnfcance level of at least Female loan Age of Cvl Self Number persons Phone Approved Adjusted Interest Approved Personal Mortgage Chattel Applcatons Default Female offcer borrower status employed household avalablty amount maturty rate share guarantee guarantee guarantee per loan offcer Default Female * Female loan offcer * * Age of borrower * * Cvl status * * * * Self employed * * * * Number persons household * * * * * Phone avalablty * * * * Approved amount * * * * Adjusted maturty * * * * * * * Interest rate * * * * * * * * * Approved share * * * * * * * * * * Personal guarantee * * * * * * * * * * Mortgage guarantee * * * * * * * * * * Chattel guarantee * * * * * * * * * * * Applcatons per loan offcer * * * * * * * * * * * * Age of loan offcer * * * * * * * * * * *

32 Table 3: Default probablty and loan offcers gender frst loans Ths table contans the margnal effects of the outcome test wth the gender of borrowers and loan offcers. The frst fve regresson models are based on sub samples of approved loans to ndvdual, prvate borrowers. They are at the same tme frst and last loans per borrower. Regresson model VI comprses all 14,020 frst loans and does not contan soco-demographc varables. For regresson models I, II, and VI, the dependent varable s the occurrence of a borrower beng n arrears for more than 30 days durng the lfetme of her loan. Regresson models III, IV, and V use arrear defntons of 15, 60, and 90 days. The ndependent varables are as descrbed n Table 2A except the number of loan applcatons per borrower whch s dvded by 1,000. Instead of the raw numbers we employ the natural logarthm for the approved amount (ln(approved amount)) and the adjusted maturty (ln(adjusted maturty)). In regresson models II to VI, we nteract the borrower and loan offcer gender: Female & Female loan offcer s a dummy varable ndcatng the combnaton of a female borrower and a female loan offcer, Male & Female loan offcer ndcates the combnaton of a male borrower and a female loan offcer, Male & Male loan offcer ndcates the combnaton of a male borrower and a male loan offcer. The combnaton Female & Male loan offcer serves as the reference group. We also control for fve loan destnatons (workng captal, fxed assets, mxed purpose, real estate, and consumng), fve busness sectors (constructon, producton, trade, transport, other servces), fve branches, and the years from Results for these control varables are omtted. Standard errors are clustered at the loan offcer level. *,**,*** ndcate sgnfcance at the 10%, 5% and 1% level, respectvely. Margnal effects for regresson model Independent varable I II III IV V VI Female *** Female loan offcer *** Female & Female loan offcer *** *** *** *** *** Male & Female loan offcer Male & Male loan offcer 0.044*** 0.042** *** Loan applcatons per loan offcer Age of loan offcer *** *** *** ** * *** Interest rate 0.882*** 0.883*** 1.078*** 0.384*** 0.296*** 0.559*** Age of borrower *** *** *** *** *** *** Cvl status *** *** *** Self employed Number persons household Phone avalablty *** *** *** ** ** ln(approved amount) 0.015* 0.015* *** 0.017*** 0.015** ln(adjusted maturty) 0.029** 0.029** 0.057*** *** Approved share *** *** *** *** *** *** Personal guarantee 0.026** 0.026** 0.036** * Mortgage guarantee ** ** ** *** *** Chattel guarantee Observatons 6,775 6,775 7,107 6,770 6,571 14,020 Pseudo R square Share of default correctly predcted , Share of non-default correctly predcted

33 Table 4: Default probablty and loan offcers gender nteracton wth experence Ths table contans the margnal effects of the outcome test wth the gender of borrowers and loan offcers together wth nteractons wth loan offcer experence. All four regresson models are based on the sub sample of 6,775 approved loans to ndvdual, prvate borrowers, correspondng to regresson model II n Table 3. They are at the same tme frst and last loans per borrower. The dependent varable s the occurrence of a borrower beng n arrears for more than 30 days durng the lfetme of her loan. In addton to the already used ndependent varables descrbed n Table 3 we nteract Female & Female loan offcer wth the loan offcer s experence that s proxed by the number of loan applcatons handled by the respectve loan offcer untl a certan loan was granted (I); the tme snce the loan offcer works for the mcrolender (II), and the age of the loan offcer at the tme of the loan approval. To test whether the loan offcer effects depends on loan offcer experence we use nteractons wth the four experence quartles for each experence proxy. Control varables are the same as n Table 3, results for most of these controls are omtted. In Panel A, the combnaton Female & Male loan offcer serves as the reference group, n Panel B, the combnaton Male & Female loan offcer serves as the reference group. Standard errors are clustered at the loan offcer level. *,**,*** ndcate sgnfcance at the 10%, 5% and 1% level, respectvely. Margnal effects for regresson model Independent varable I II III Panel A: Performance dfferences for female borrowers Female & Female loan offcer & 0-25% Experence * Female & Female loan offcer & 25-50% Experence * ** *** Female & Female loan offcer & 50-75% Experence *** *** * Female & Female loan offcer & % Experence *** *** Male & Female loan offcer & 0-25% Experence Male & Female loan offcer & 25-50% Experence Male & Female loan offcer & 50-75% Experence Male & Female loan offcer & % Experence ** Male & Male loan offcer & 0-25% Experence 0.036* * Male & Male loan offcer & 25-50% Experence ** Male & Male loan offcer & 50-75% Experence 0.061*** 0.035* 0.045** Male & Male loan offcer & % Experence 0.063** 0.098*** 0.063*** Loan applcatons per loan offcer Age of loan offcer *** *** ** Tme snce frst loan applcaton * Panel B: Performance dfferences for male borrowers Male & Male loan offcer & 0-25% Experence Male & Male loan offcer & 25-50% Experence ** Male & Male loan offcer & 50-75% Experence 0.073*** 0.032* 0.057** Male & Male loan offcer & % Experence 0.111*** 0.083*** 0.093*** Observatons 6,775 6,775 6,775 Pseudo R square Share of default correctly predcted Share of non-default correctly predcted /5

34 Table 5: Default probablty and loan offcers gender further loans Ths table contans the margnal effects of the outcome test wth the gender of borrowers and loan offcers together wth nteractons wth the duraton of the lendng relatonshp. Regresson models I and II (III and IV) are based on the sub sample of 6,448 frst and last (12,940 frst) loans to ndvdual, prvate borrowers. The dependent varable s the occurrence of a borrower beng n arrears for more than 30 days durng the lfetme of her loan. The ndependent varables are as n prevous tables except for three varables for the loan hstory of each borrower wth the mcrolender: Duraton relatonshp provdes the number of years snce the frst loan applcaton of the borrower, Any prevous applcaton rejected s a dummy varable ndcatng any prevous rejecton of a loan applcaton (1 = rejecton), Any prevous loan defaulted s a dummy varable ndcatng any prevous default (1 = default). We further use three nteracton terms between the borrower gender-loan offcer gender pars and Duraton relatonshp n regresson models II and IV. Results for our addtonal control varables are omtted. Standard errors are clustered at the loan offcer level. *,**,*** ndcate sgnfcance at the 10%, 5% and 1% level, respectvely. Margnal effects for regresson model Independent varable I II III IV Female & Female loan offcer *** ** ** Male & Female loan offcer Male & Male loan offcer Female & Female loan offcer & Duraton relatonshp Male & Female loan offcer & Duraton relatonshp Male & Male loan offcer & Duraton relatonshp Duraton relatonshp *** *** *** * Any prevous applcaton rejected 0.037*** 0.036*** 0.027*** 0.027*** Any prevous loan defaulted 0.371*** 0.374*** 0.298*** 0.297*** Loan applcatons per loan offcer Age of loan offcer Interest rate 0.228** 0.226** 0.270*** 0.268*** Age of borrower *** *** *** *** Cvl status ** ** Self employed Number persons household Phone avalablty ** ** ln(approved amount) *** 0.009*** ln(adjusted maturty) 0.032*** 0.032*** 0.044*** 0.044*** Approved share *** *** *** *** Personal guarantee Mortgage guarantee *** *** * * Chattel guarantee 0.015* 0.015* Observatons 6,448 6,448 12,940 12,940 Pseudo R square Share of default correctly predcted Share of non-default correctly predcted

35 Table 6: Loan approval and loan offcers gender Ths table contans the margnal effects for a sample selecton test wth the gender of borrowers and loan offcers. The regresson models are based on dfferent sub samples of requested loans by ndvdual, prvate borrowers: model I s based on 8,297 loan applcatons that are at the same tme frst and last applcatons per borrower; model II uses 15,986 frst loan applcatons; model III employs 7,240 loan applcatons that are at the same tme further and last applcatons per borrower; model IV s based on 14,502 further loan applcatons. The dependent varable s the approval decson (1 for an approved loan, 0 otherwse). We use a dfferent set of control varables because we cannot use varables that are not avalable at the tme of the loan applcaton, such as the nterest rate. Specfcally, we employ the natural logarthm of the appled nstead of the approved loan sze, and the natural logarthm of the appled nstead of the approved maturty. We use the same further control varables descrbed n Table 3. Results for these control varables are omtted. Standard errors are clustered at the loan offcer level. *,**,*** ndcate sgnfcance at the 10%, 5% and 1% level, respectvely. Margnal effects for regresson model Independent varable I II III IV Female & Female loan offcer Male & Female loan offcer Male & Male loan offcer Duraton relatonshp Any prevous applcaton rejected *** *** Any prevous loan defaulted *** *** Loan applcatons per loan offcer Age of loan offcer *** *** ** *** Age of borrower *** 0.001*** Cvl status Self employed 0.022** Number persons household 0.009*** 0.003* Phone avalablty 0.033** 0.037*** ln(appled amount) * ** ** *** ln(appled maturty) 0.034*** 0.022*** 0.030*** 0.040*** Personal guarantee 0.025*** 0.018*** Mortgage guarantee Chattel guarantee 0.151*** 0.096*** 0.074*** 0.036** Observatons 8,297 15,986 7,240 14,502 Pseudo R square Share of approvals correctly predcted Share of non-approvals correctly predcted