Using detailed data from the U.S. syndicated loan market, we find that women are persistently under-represented among senior commercial bankers. This gap is not closing over time due to unequal promotion rates between men and women working at the same institution in the same year and cannot be explained by a different individual or managerial performance. The gap is driven more by people than by institutions, with senior bankers both exhibiting assortative matching when switching employers and subsequently shifting the promotion gap in the direction of their previous workplace. We find evidence consistent with parts of the gap being driven by women shouldering more of the burden of family care. Hard credentials or female leadership at the top of banks do not attenuate the gender gap. In contrast, after being targeted by gender discrimination lawsuits, banks increasingly promote women.

The SEC punishes major financial crimes with both monetary fines and professional bars. We document that punishments differ starkly across gender. Female offenders receive longer bars from the finance industry and smaller money penalties than male offenders on average. While men tend to receive combinations of punishment, women receive either professional bars or monetary penalties, but not both. Females are 50% less likely than males to cooperate with the SEC. This evidence is consistent with a model of enforcement where for women admitting guilt through accepting a professional bar entails social stigma. The SEC’s two-dimensional punishment scheme thus entails economic disparities between men and women.

Using data from a lending platform that switched from a human-based to a machine learning-based system, we find that fintech may increase gender discrimination. The rationale is that machine learning algorithms allow the platform to better decipher differences in borrower preferences between female and male borrowers. Specifically, after the switch, the platform assigned higher interest rates and better credit ratings to less price-sensitive female borrowers. These results are not driven by changes in borrower credit risk or lender preferences. Instead, the behavior is consistent with the platform’s attempt to maximize its revenue by applying price discrimination to female borrowers.