Signaling in Online Credit Markets
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).
We thank Steve Berry, Judy Chevalier, Phil Haile, Igal Hendel, Hide Ichimura, Alessandro Lizzeri, Harikesh Nair, Aviv Nevo, Isabelle Perrigne, Rob Porter, Jeffery Prince, Paulo Somaini, Quang Vuong, Yasutora Watanabe, Michael Whinston and seminar participants at various places for their valuable comments and suggestions. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the staff, by the Board of Governors of the Federal Reserve System, or by the Federal Reserve Banks. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Kei Kawai & Ken Onishi & Kosuke Uetake, 2022. "Signaling in Online Credit Markets," Journal of Political Economy, vol 130(6), pages 1585-1629.