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Gender discrimination in online peer-to-peer credit lending: evidence from a lending platform in China

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  • Dongyu Chen

    (Soochow University)

  • Xiaolin Li

    (Towson University)

  • Fujun Lai

    (Soochow University
    University of Southern Mississippi)

Abstract

Gender discrimination in accessing financial resources is a mounting concern in developing countries, but empirical evidence of such discrimination is limited. Using data collected from one of the largest peer-to-peer (P2P) lending platforms in China, we investigate potential gender discrimination in online P2P credit lending market in China. The results illustrate that female borrowers are more likely to be funded than male borrowers; but in exchange for higher funding success, female borrowers are found to pay higher interest rates. Moreover, the default rates of loans from female borrowers are significantly lower than those from male borrowers. Finally, we found that a borrower’s gender moderates the relationships between the borrower’ attributes and lending outcomes. The findings imply that profit-based statistical discrimination and costly taste-based discrimination co-exist in this online peer-to-peer lending market, but the underlying reasons for the discriminations are different. The implications for research and practice along with the limitations of this study are discussed accordingly.

Suggested Citation

  • Dongyu Chen & Xiaolin Li & Fujun Lai, 2017. "Gender discrimination in online peer-to-peer credit lending: evidence from a lending platform in China," Electronic Commerce Research, Springer, vol. 17(4), pages 553-583, December.
    • Handle: RePEc:spr:elcore:v:17:y:2017:i:4:d:10.1007_s10660-016-9247-2
    DOI: 10.1007/s10660-016-9247-2
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    19. Xiyang Hu & Yan Huang & Beibei Li & Tian Lu, 2022. "Uncovering the Source of Machine Bias," Papers 2201.03092, arXiv.org.
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