This papers 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.

翻译：本文研究了中国一家知名在线点对点（P2P）借贷平台上的性别歧视及其潜在驱动因素。现有P2P借贷研究主要关注差别对待（DT），但DT仅狭隘地识别直接歧视，忽视了间接歧视和代理歧视，未能提供完整图景。本研究衡量了一种更广泛的歧视概念——差异化影响（DI），即贷款融资率中与实际回报率不匹配的任何差异。我们开发了一种两阶段预测替代方法，从观测数据中估计DI。研究发现：（i）在实际回报率相同的情况下，女性借款人获得融资的概率高出3.97%；（ii）这种对女性有利的DI中至少37.1%属于间接或代理歧视；（iii）DT确实将整体女性偏袒程度低估了44.6%。然而，我们也发现整体女性偏袒可由一个特定歧视驱动因素——理性统计歧视——解释，即投资者从不完美的观测中准确预测预期回报率。此外，女性借款人仍需高出2%的预期回报率才能获得融资，表明另一种驱动因素——偏好型歧视——同时存在且针对女性。这些结果共同讲述了一个警示故事：一方面，P2P借贷提供了有价值的替代信贷市场，其中支持女性的平权行动自然源于理性群体；另一方面，尽管整体歧视效应（无论是DI还是DT）对女性有利，但令人担忧的偏好型歧视可能持续存在，并被其他共存的歧视驱动因素（如统计歧视）所掩盖。