How Costly are Cultural Biases? Evidence from FinTech

Abstract

We study the nature and effects of cultural biases in choice under risk and uncertainty by comparing peer-to-peer loans the same individuals (lenders) make alone and after observing robo-advised suggestions. When unassisted, lenders are more likely to choose co-ethnic borrowers, facing 8% higher defaults and 7.3pp lower returns. Robo-advising does not affect diversification but reduces lending to high-risk co-ethnic borrowers. Lenders in locations with high inter-ethnic animus drive the results, even when borrowers reside elsewhere. Biased beliefs explain these results better than a conscious taste for discrimination: lenders barely override robo-advised matches to ethnicities they discriminated against when unassisted.

Keywords: Taste-based Discrimination, Statistical Discrimination, Cultural Finance, Robo-Advising, Lending, Disintermediation, Cultural Economics

JEL Classification: D90, G41, G51, J71, Z10