Abstract
We exploit a leading FinTech peer-to-peer lending platform paired with an automated robo-advising lending tool to test for and quantify the effects of cultural biases in large-stake risky choices for which the scope for statistical discrimination is minimal. Comparing the choices lenders make with those made by the robo-advising tool on their behalf, we find that both in-group vs. out-group discrimination and stereotypical discrimination are pervasive and economically sizable. Discrimination makes lenders worse off in terms of consumption utility—discriminating lenders face 32% higher default rates and about 11% lower returns on the loans they issue to borrowers belonging to favored demographics, relative to borrowers in the discriminated groups. All the results are stronger for lenders who reside in regions where the salience of cultural biases is higher.
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