Research on investors’ preference for the borrowers’ occupational identity: Evidence from P2P lending platform in China

Abstract

This paper explores the intricate issue of credit discrimination based on occupational identity within China's rapidly growing P2P lending sector. Leveraging the rich transactional data from the RENRENDAI lending platform, the study carefully examines how a borrower's occupational identity (OI) subtly influences investor preferences and thoroughly investigates the underlying currents of credit discrimination that may exist on such platforms. A logit model serves as the core analytical tool to empirically uncover and quantify investors' nuanced inclinations toward various occupational identities (OIs). The findings reveal a distinct pattern: investors show a clear tendency to favor borrowers whose occupational identity (OI) aligns with that of a wage-earner. This form of occupational-based credit discrimination, when viewed through the lens of investor rationality, can be understood as a calculated risk assessment, whereas the discriminatory treatment of network merchants often leans toward irrationality. The origins of such credit discrimination, it is found, lie not solely in social status prejudice but rather in investors' informed understanding and interpretation of the inherent economic characteristics and stability profiles associated with different borrowers' occupational identities. The irrational discriminatory behavior directed at network merchants is primarily attributed to cognitive biases that cloud investors' judgment. Furthermore, the study observes that occupational identity (OI) emerges as a strong predictor of credit risk, suggesting that the methodologies proposed here hold significant potential for practical application in real-world P2P lending platforms, potentially enhancing both efficiency and fairness. The primary contribution of this study lies in bridging a critical gap in existing literature by offering novel insights into the complex relationship between occupational identity and credit allocation within digital lending ecosystems.