Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/261464 
Erscheinungsjahr: 
2022
Schriftenreihe/Nr.: 
LawFin Working Paper No. 34
Verlag: 
Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin), Frankfurt a. M.
Zusammenfassung: 
We estimate the cost of cultural biases in high-stake economic decisions by comparing agents' peer-to-peer lending choices with those the same agents make under the assistance of an automated robo-advisor. We first confirm substantial in-group vs. out-group and stereotypical discrimination, which are stronger for lenders who reside where historical cultural biases are higher. We then exploit our unique setting to document that cultural biases are costly: agents face 8% higher default rates on favored-group borrowers when unassisted. The returns they earn on favored groups increase by 7.3 percentage points when assisted. The high riskiness of the marginal borrowers from favorite groups largely explains the bad performance of culturally-biased choices. Because varying economic incentives do not reduce agents' biases, inaccurate statistical discrimination-unconscious biased beliefs about borrowers' quality-can explain our results better than taste-based discrimination.
Schlagwörter: 
Trust
Social Capital
Discrimination
Cultural Norms
Robo-Advising
Biased Beliefs
Inter-ethnic Conflict
Social Conditioning
Religion
Caste
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