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Predictably Unequal? The Effects of Machine Learning on Credit Markets
1Imperial College London; 2Yale School of Management; 3Swiss National Bank
Innovations in statistical technology, including in predicting creditworthiness, have sparked concerns about differential impacts across categories such as race. Theoretically, distributional consequences from better statistical technology can come from greater flexibility to uncover structural relationships, or from triangulation of otherwise excluded characteristics. Using data on US mortgages,
we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups; these changes are primarily attributable to greater flexibility.
Discrimination in the Auto Loan Market
1Rice University, United States of America; 2Southern Methodist University, United States or America
We provide evidence of discrimination in the auto loan market. Combining credit bureau records with borrower characteristics, we find that Black and Hispanic applicants’ loan approval rates are 1.5 percentage points lower than White applicants’, even controlling for creditworthiness. In aggregate, this discrimination leads to over 80,000 minorities failing to secure loans each year. Results are stronger in more racially biased states and where banking competition is lower. Minorities who receive loans pay interest rates 70 basis points higher than comparable White borrowers. Ceteris paribus, minority borrowers have lower ex post default rates, consistent with preference-based racial discrimination. An anti-discrimination enforcement policy initiated in 2013, but halted in 2018, was effective in reducing unexplained racial disparities in interest rates by nearly 60%.
Performance Isn't Everything: Personal Characteristics and Career Outcomes of Mutual Fund Managers
1Brandeis University, United States of America; 2University Of California Davis; 3Board of Governors of the Federal Reserve System
We investigate the determinants of mutual fund manager career outcomes. We find that, although career outcomes are largely determined by past performance, measured by returns and fund flows, personal attributes also factor in. All else equal, female managers are less likely to be promoted and have shorter tenures than male fund managers. This finding applies to a greater extent to women who co-manage funds with other managers, which suggests that working in teams negatively affects women's careers when compared to men's. Moreover, we show that, all else equal, younger managers, U.S.-educated managers, and managers who attended elite schools experience better career outcomes than otherwise similar managers.
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