Closing the gender gap in financial access is important. Most research tends to empirically uncover the direct effect of gender on decisions. Yet, this view overlooks other indirect channels of gender discrimination, leading to systemic bias in identifying the overall discrimination effect. In this work, by collaborating with one of the largest online P2P lending platforms in China, we estimate a broadened discrimination notion called unwarranted gender disparity (UGD). UGD recognizes any disparate lending decisions that do not commensurate with the loan's return rate, encompassing direct, indirect, and proxy discrimination. We develop a two-stage predictor substitution (2SPS) approach to estimate UGD. Somewhat surprisingly, we find significant female favoritism at almost all return rate levels. On average, female borrowers are 3.97% more likely to be funded than male borrowers with identical return rates. We further decompose and find at least 37.1% of UGD is indeed indirect or proxy discrimination. However, we also identify the observed UGD favoring female can be completely attributed to \emph{accurate statistical distribution}, which is rationalized by women being less likely to default on their P2P loans. Our results suggest that online P2P lending can complement traditional bank lending in closing the gender gap, by providing an alternative credit market where the affirmative action to support women can arise naturally from the rational crowd.
翻译:在这项工作中,我们与中国最大的在线P2P贷款平台之一合作,估计了扩大的歧视概念,称为无正当理由的性别差距(UGD)。UGD承认任何与贷款回报率不相称的不同贷款决定,包括直接、间接和代理歧视。我们制定了两个阶段的预测替代(2SPS)办法来估计UGD。有些令人惊讶的是,我们发现几乎所有回报率都存在明显的女性偏好。平均而言,女性借款人比男性借款人更有可能获得3.97%的资金,回报率相同。我们进一步拆分和发现至少37.1%的UGD确实是间接或代理歧视。然而,我们还发现,观察到的有利于女性的贷款决定可完全归因于\emph{accurent 统计分布},这是由女性较少可能拖欠P2P贷款的两阶段替代方法。我们的结果是,女性借款比男性借款者更可能获得3.97%的资金。我们进一步认为,至少37.1%的UGDGD确实属于间接或代理歧视。但我们还确认,观察到的有利于女性的贷款的决定可以完全归因于女性贷款(emph{acurn)统计分布。我们发现,因为女性在银行贷款方面没有多少可能拖欠P2贷款,因此可以通过在线贷款而形成一种稳定市场。