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Bio: Adair Morse

Adair Morse is Associate Professor at the Haas School of Business at the University of California at Berkeley, where she teaches New Venture Finance. She holds a Ph.D. in finance from the University of Michigan. Adair’s research spans multiple areas of finance: household finance, governance, FinTech, Impact Investing, and asset management, with the unifying theme that she tries to choose topics useful for leveling economic playing fields. Her publications appear in the top economics and finance journals, and she has won a number of top finance research prizes, including the Brattle Prize, the Jensen Prize, the European Finance Association Asset Management Prize, the Sonoran Finance Conference Prize, the China International Finance Conference Prize, the WFA Prize, and the Moskowitz Impact prize, and her various works have been directly implemented into policy. Within household finance, Adair has a particular interest in household debt and welfare, studying low and middle income credit products and their use via both observational studies and field experiments with companies. Her recent work studies many aspects of marketplace lending/crowdfunding. The latest piece asks whether platform lenders ameliorate discrimination because they do not involve facial contact or induces more discrimination in lending with statistical discrimination via big dat. Examples of Adair’s other noteworthy publications in household finance include work on the effect of income inequality on consumption and disclosure in financial services.

Abstract

Racial discrimination in lending can materialize in loan officer decisions or in algorithmic scoring, especially with big-data use by FinTech lenders. To investigate these discrimination channels, we estimate a treatment-based Oaxaca-Blinder decomposition based on the unique mortgage-default-risk setting of the GSEs. Overall, we estimate that lenders reject African-American and Hispanic applicants 5% more often, leaving money on the table. Discrimination in rejection rates is especially pronounced among low-credit-score applicants, but less pronounced for FinTech lenders. Among approved loans, ethnic-minority borrowers pay higher rates of interest, with both traditional and FinTech lenders charging non-white borrowers 0.08% higher interest for purchase mortgages and 0.03% higher for refinance mortgages. Our results point to lenders extracting rents in weaker competitive environments such as financial deserts. In aggregate, ethnic-minority borrowers pay almost $0.5B per year in extra interest. Finally, we document that the GSEs play a crucial role in minimizing algorithmic discrimination.