Signaling in Online Credit Markets

57 Pages Posted: 20 Sep 2021 Last revised: 3 Jun 2022

See all articles by Kei Kawai

Kei Kawai

University of California at Berkeley

Ken Onishi

Board of Governors of the Federal Reserve System

Kosuke Uetake

Yale School of Management

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Date Written: September 2021

Abstract

We study how signaling affects equilibrium outcomes and welfare in an online credit market using detailed data on loan characteristics and borrower repayment. We build and estimate an equilibrium model in which a borrower may signal her default risk through the reserve interest rate. Comparing a market with and without signaling relative to the benchmark with no asymmetric information, we find that adverse selection destroys as much as 34% of total surplus, up to 78% of which can be restored with signaling. We also estimate backward-bending supply curves for some markets, consistent with the prediction of Stiglitz & Weiss (1981).

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Suggested Citation

Kawai, Kei and Onishi, Ken and Uetake, Kosuke, Signaling in Online Credit Markets (September 2021). NBER Working Paper No. w29268, Available at SSRN: https://ssrn.com/abstract=3926949

Kei Kawai (Contact Author)

University of California at Berkeley ( email )

Berkeley, CA 94720
United States

Ken Onishi

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

Kosuke Uetake

Yale School of Management ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

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