Sharing Information on Lending Decisions: An Empirical Assessment

47 Pages Posted: 18 Feb 2015

Date Written: October 23, 2014

Abstract

We present the first empirical study of information spillover and signalling on loan search and its outcomes in a setting where a bank observes whether a loan applicant has already been rejected by other lenders. We do so by taking advantage of the fact that Italy’s Central Credit Register discloses such information. The results show that disclosing information on past rejections negatively affects the probability of continuing a loan search. At the same time, the information on former rejections is associated with a higher probability of being funded for borrowers who are not discouraged and continue the search, provided they are not opaque. With the aid of a theoretical model, we show that banks interpret the information on previous rejections as a signal of unobservable quality for the average borrower but not for more opaque borrowers, whose past rejections negatively affect the outcome of later applications. We also show that banks differ in the extent to which they rely on this information, in a way that at least partly reflects the different informational content that this signal carries for them.

Keywords: sequential lending decisions, credit supply, winner’s curse, informational spillover

JEL Classification: E51, G21, G28

Suggested Citation

Albertazzi, Ugo and Bottero, Margherita and Sene, Gabriele, Sharing Information on Lending Decisions: An Empirical Assessment (October 23, 2014). Bank of Italy Temi di Discussione (Working Paper) No. 980. Available at SSRN: https://ssrn.com/abstract=2566214 or http://dx.doi.org/10.2139/ssrn.2566214

Ugo Albertazzi

ECB -DG Monetary Policy ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

Margherita Bottero (Contact Author)

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

Gabriele Sene

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

Register to save articles to
your library

Register

Paper statistics

Downloads
36
Abstract Views
399
PlumX Metrics