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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.
