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UID:4b4f7ac74ce90725e910930e3df74142
CATEGORIES:Seminars
CREATED:20180608T102534
SUMMARY:Lunch Seminar: Andrés Liberman - NYU
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:\n\nMeasuring Bias in Consumer Credit (joint with Will Dobbie, Daniel Parav
 isini, and Vikram Pathania)\n\n\nAbstract:\nThis paper tests for bias in co
 nsumer lending decisions using administrative data from a high-cost lender 
 in the United Kingdom. We motivate our analysis using a simple model of len
 ding, which predicts that profits should be identical for different groups 
 at the margin if loan examiners are unbiased. We identify the profitability
  of marginal applicants exploiting variation from the quasi-random assignme
 nt of loan examiners. We find significant bias against non-native and older
  loan applicants when using the firm's preferred measure of long-run profit
 s. In contrast, there is no evidence of bias when using a short-run measure
  used to evaluate examiner performance, suggesting that our results are due
  to the misalignment of firm and examiner incentives. We conclude by showin
 g that a decision rule based on machine learning predictions of long-run pr
 ofitability can simultaneously increase profits and eliminate bias.\n
DTSTAMP:20260406T222831Z
DTSTART:20180718T130000Z
DTEND:20180718T140000Z
SEQUENCE:0
TRANSP:OPAQUE
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