Abstract

Facial information is essential in daily life, but relatively little is known about whether seeing a face improves people’s decision quality. This experimental paper studies the loan-approval decisions based on the historical cash-loan data with real repayment outcomes and exogenously varies whether and how a borrower’s facial information is provided. We find that facial information does not improve subjects’ decisions, despite the fact that it can predict repayment behavior in a machine-learning algorithm. This is because subjects have various biases in evaluating facial photos, and they rely excessively on facial information in making the loan-approval decisions. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: J. Meng received financial support from the National Natural Science Foundation of China [Grants 71103003, 71471004, and 71822301]. Supplemental Material: Data files and the online appendix are available at https://doi.org/10.1287/mnsc.2022.4436 .

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