Gender Animus Can Still Exist Under Favorable Disparate Impact: a Cautionary Tale from Online P2P Lending
| Autor: | Shen, Xudong, Tan, Tianhui, Phan, Tuan Q., Keppo, Jussi |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1145/3593013.3594042 |
| Popis: | 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. Comment: published at FAccT'23 |
| Databáze: | arXiv |
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