Measuring Gender Bias in Chinese Peer-to-Peer Lending
Summary
The paper examines gender disparities in loan funding on a major Chinese peer-to-peer lending platform. It argues that conventional disparate-treatment measures capture direct discrimination but can miss indirect or proxy effects. To assess broader disparate impact, the authors compare funding rates with borrowers’ actual return rates and use a two-stage predictor-substitution method on observational data.
The analysis finds that female borrowers are more likely to receive funding when actual returns are held equal, and estimates that a meaningful portion of this advantage is indirect or proxy discrimination. Yet the aggregate advantage does not mean every source of bias favors women: investors’ return predictions can create rational statistical favoritism, while evidence of a higher expected-return threshold for female borrowers points to taste-based bias against them. The authors caution that these forces can coexist and obscure one another. The findings describe one platform and rely on observational estimates, so they should not be treated as a universal account of lending markets or investor behavior.
Key ideas
- The study measures funding disparities relative to actual loan returns, extending beyond direct disparate treatment.
- A two-stage predictor-substitution approach is used to estimate disparate impact from observational data.
- Overall funding outcomes favor female borrowers, but the estimated disparity includes indirect or proxy effects.
- Rational statistical discrimination and taste-based discrimination can operate at the same time and in opposing directions.
- The evidence comes from a prominent Chinese P2P lending platform and may not generalize to other markets.
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Full text
# Gender Animus Can Still Exist Under Favorable Disparate Impact: a Cautionary Tale from Online P2P Lending # Gender Animus Can Still Exist Under Favorable Disparate Impact: a Cautionary Tale from Online P2P Lending This paper investigates gender discrimination and its underlying drivers on a prominent Chinese online peer-to-peer (P2P) lending platform. While existing studies on P2P lending focus on disparate treatment (DT), DT narrowly recognizes direct discrimination and overlooks indirect and proxy discrimination, providing an incomplete picture. In this work, we measure a broadened discrimination notion called disparate impact (DI), which encompasses any disparity in the loan's funding rate that does not commensurate with the actual return rate. We develop a two-stage predictor substitution approach to estimate DI from observational data. Our findings reveal (i) female borrowers, given identical actual return rates, are 3.97% more likely to receive funding, (ii) at least 37.1% of this DI favoring female is indirect or proxy discrimination, and (iii) DT indeed underestimates the overall female favoritism by 44.6%. However, we also identify the overall female favoritism can be explained by one specific discrimination driver, rational statistical discrimination, wherein investors accurately predict the expected return rate from imperfect observations. Furthermore, female borrowers still require 2% higher expected return rate to secure funding, indicating another driver taste-based discrimination co-exists and is against female. These results altogether tell a cautionary tale: on one hand, P2P lending provides a valuable alternative credit market where the affirmative action to support female naturally emerges from the rational crowd; on the other hand, while the overall discrimination effect (both in terms of DI or DT) favors female, concerning taste-based discrimination can persist and can be obscured by other co-existing discrimination drivers, such as statistical discrimination.
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