Are Women Better Loan Officers? Thorsten Beck Patrick Behr André Güttler
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1 Are Women Better Loan Offcers? Thorsten Beck Patrck Behr André Güttler
2 Motvaton Women often seen as better mcrocredt borrowers, but what about gender dfferences n loan offcers? Incentve structure for loan offcers does gender matter? Whle studes on gender dfferences n nvestment behavor, fnancal lteracy etc., no study yet on gender dfferences n loan offcer performance
3 Hypotheses Women are better loan offcers Hgher opportunty costs, thus hgher effort More conservatve, more afrad of sanctons Skeptcs Male loan offcers mght have easer task n tradtonal, patrarchc, herarchcal socetes Gender match mght be mportant Female loan offcers mght be better snce t s harder for them to get a ob,.e. more qualfed to start wth The role of experence More experence, lower default Younger loan offcers have hgher opportunty costs, exert more effor
4 Our contrbuton Unque dataset for an Albanan mcrobank for the perod 1996 to 2006 Borrowers characterstcs Loan characterstcs Loan offcers gender, age, and experence Includes reected loan applcant 43,000 loan applcatons and 31,000 loans 5 branches n Trana Indvdual loans
5 Some nformaton about the mcrobank (1) ,372 Average 179,307 31,614 43,126 Sum ,152 31,600 3,176 7,024 9, ,582 37,700 3,996 7,339 9, ,010 39,300 4,068 7,836 9, ,697 24,100 6,455 2,941 3, ,746 14,400 5,762 1,907 2, ,308 8,193 3,674 1,456 2, ,277 9,709 4,062 1,438 2, ,588 5, , ,302 4, ,520 3, ,895 3, Applcatons Default frequency Borrowers New loan volume (1,000) Loan sze Approved loans Year of applcaton
6 Some nformaton about the mcrobank (2) Year of applcaton Loan usage Busness Loans Real estate Consumpton Share of female borrowers Share of female loan offcers Sum Average
7 The model Default = α + β * Female loan offcer + γ * D + δ * X + ε 1 * Female + β 2 Default = α + β * Female 1 * Female loan offcer + β Male 2 * Female loan offcer + β Male 3 * Male loan offcer + γ * D + δ * X + ε Default β 2, k Male loan Male = α + β offcer 1, k * Female loan * Female offcer * Female loan * Experence quartle * Experence quartle offcer + γ * D * Experence quartle k, + δ * X + β 3, k + ε Male * k, + Default: at least one payment more than 30 days n arrears Probt model Clustered by loan offcers
8 Control varables Borrowers characterstcs Age Cvl status Employment status Phone avalable Household sze Loan characterstcs Interest rate Approved amount Approved maturty Approved share (relatve to appled share) Guarantee (personal, mortgage, chattel, other) Loan use (consumpton, producton, real estate) Loan offcer characterstcs Age Experence (Applcatons processed, tme wth bank) Branch and year dummes
9 Splttng the sample Man analyss: actual loans Restrct to frst loans: gender dfference should be stronger Restrct to frst and last loan (so only one loan): soco-demographc borrower data overwrtten Drop outlers (loan sze, borrower age) Baselne analyss: 6,775 loans granted by 141 loan offcers Robustness tests: Relax last loan restrcton Look at subsequent loans
10 Female Female loan offcer Female & Female loan offcer Male & Female loan offcer Male & Male loan offcer Loan applcatons per loan offcer Age of loan offcer Interest rate Age of borrower Cvl status Self employed Number persons household Phone avalablty ln(approved amount) ln(adusted maturty) Approved share Personal guarantee Mortgage guarantee Chattel guarantee Observatons Share of default correctly predcted Share of non-default correctly predcted Baselne Results *** *** *** 0.882*** *** *** *** 0.015* 0.029** *** 0.026** ** , *** *** *** 0.883*** *** *** *** 0.015* 0.029** *** 0.026** ** ,
11 Alternatve default defntons and samples 15 days 30 days 60 days All frst loans, 30 days Female & Female loan offcer *** *** *** *** Male & Female loan offcer Male & Male loan offcer 0.042** *** Observatons 7,107 6,770 6,571 14,020 Pseudo R square Share of default correctly predcted , Share of non-default correctly predcted
12 Controllng and nteractng wth experence 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***
13 Robustness test: subsequent loans 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 reected 0.037*** 0.036*** 0.027*** 0.027*** Any prevous loan defaulted 0.371*** 0.374*** 0.298*** 0.297*** Observatons 6,448 6,448 12,940 12,940 Pseudo R square Share of default correctly predcted Share of non-default correctly predcted
14 Loan approval decsons Female & Female loan offcer Male & Female loan offcer Male & Male loan offcer Duraton relatonshp Any prevous applcaton reected *** *** Any prevous loan defaulted *** *** Loan applcatons per loan offcer Age of loan offcer *** *** ** *** Age of borrower 0.001*** 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
15 Conclusons Women are better loan offcers, experence lower default on ther borrowers Not drven by hgher experence Montorng not screenng!
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