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Does Gender Affect Funding Success at the Peer-to-Peer Credit Markets?: Evidence from the Largest German Lending Platform

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  • Nataliya Barasinska
  • Dorothea Schäfer

Abstract

Studies of peer-to-peer lending in the USA find that female borrowers have better chances of getting funds than males. Is differential treatment of borrowers of different sexes a common feature of peer-to-peer lendingmarkets or is it subject to specific businessmodels, ways of fixing loan contracts and even national financial systems? We aim at answering this question by providing evidence on loan procurement at the largest German peer-to-peer lending platform Smava.de. Our results show that gender does not affect individual borrower's chances of funding success on this platform, ceteris paribus. Hence, gender discrimination seems to be a platform-specific phenomenon rather than a common attribute of this innovative form of credit markets.

Suggested Citation

  • Nataliya Barasinska & Dorothea Schäfer, 2010. "Does Gender Affect Funding Success at the Peer-to-Peer Credit Markets?: Evidence from the Largest German Lending Platform," Discussion Papers of DIW Berlin 1094, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1094
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    1. Douglas Holtz-Eakin & David Joulfaian & Harvey S. Rosen, 1994. "Entrepreneurial Decisions and Liquidity Constraints," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 334-347, Summer.
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    Cited by:

    1. Jackson J. Mi & Tianxiao Hu & Luke Deer, 2018. "User Data Can Tell Defaulters in P2P Lending," Annals of Data Science, Springer, vol. 5(1), pages 59-67, March.
    2. Juanjuan Chen & Yabin Zhang & Zhujia Yin, 2018. "Education Premium In The Online Peer-To-Peer Lending Marketplace: Evidence From The Big Data In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 63(01), pages 45-64, March.
    3. Jiang, Jiajun & Liu, Yu-Jane & Lu, Ruichang, 2020. "Social heterogeneity and local bias in peer-to-peer lending – evidence from China," Journal of Comparative Economics, Elsevier, vol. 48(2), pages 302-324.
    4. Wang, Yao & Drabek, Zdenek & Wang, Zhengwei, 2022. "The role of social and psychological related soft information in credit analysis: Evidence from a Fintech Company," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 96(C).
    5. Doris Neuberger & Solvig Räthke-Döppner, 2013. "Leasing by small enterprises," Applied Financial Economics, Taylor & Francis Journals, vol. 23(7), pages 535-549, April.
    6. Kraus, Daniel, 2013. "Does borrowers' impatience disclose their hidden information about default risk?," Thuenen-Series of Applied Economic Theory 132, University of Rostock, Institute of Economics.
    7. Huang, Wenli & Qian, Yanhong & Xu, Nanyan, 2020. "The signaling effects of education in the online lending market: Evidence from China," Economic Modelling, Elsevier, vol. 92(C), pages 268-276.

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    Keywords

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    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination

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