Abstract
We provide fresh evidence on demographics-based discrimination in credit markets by analyzing transaction level data from a large peer-to-peer (P2P) lending platform in the Chinese market. Despite the wisdom of the lending crowds, the online market displays both statistical and taste-based discriminations. Women are more likely than men to obtain funding and pay less for loans. Applicants with less education, falling into the low-income group, or employed in low-paying industries are less likely to get loans and generally pay higher rates. Married borrowers fare better than their unmarried counterparts. The evidence on the relationship between competition and discrimination is mixed.
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