How Costly are Cultural Biases? Evidence from FinTech

69 Pages Posted: 8 Jan 2021 Last revised: 28 Feb 2023

See all articles by Francesco D'Acunto

Francesco D'Acunto

Georgetown University

Pulak Ghosh

Indian Institute of Management (IIMB), Bangalore

Alberto G. Rossi

Georgetown University

Date Written: October 25, 2021

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

Suggested Citation

D'Acunto, Francesco and Ghosh, Pulak and Rossi, Alberto G., How Costly are Cultural Biases? Evidence from FinTech (October 25, 2021). Available at SSRN: https://ssrn.com/abstract=3736117 or http://dx.doi.org/10.2139/ssrn.3736117

Francesco D'Acunto (Contact Author)

Georgetown University ( email )

Washington, DC 20057
United States

Pulak Ghosh

Indian Institute of Management (IIMB), Bangalore ( email )

Bannerghatta Road
Bangalore, Karnataka 560076
India

Alberto G. Rossi

Georgetown University ( email )

McDonough School of Business
Georgetown University
Washington, DC 20057
United States

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