Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Most of the customers of microfinance institutions are female. But do men and women benefit from the same credit conditions? We investigate this issue by presenting an original model and testing its predictions on an exceptional database comprising 34,000 loan applications from a Brazilian microfinance institution. The model determines the optimal loan size fixed by a gender-biased lender, depending on the borrower’s creditworthiness and the intensity of the lender’s bias. The empirical analysis detects no gender bias in loan denial, but uncovers disparate treatment with regard to credit conditions. In particular, we find a “glass ceiling” effect. The gender gap in loan size increases disproportionately with respect to the scale of the borrower’s project. The results are insensitive to the loan officer’s gender.