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SUMMARY:The aggregate consequences of forbearance lending: Evidence from J
 apan - Dr Isabelle Roland\, Associate Lecturer\, St John’s College\, Uni
 versity of Cambridge
DTSTART:20211117T150000Z
DTEND:20211117T160000Z
UID:TALK166150@talks.cam.ac.uk
CONTACT:Emily Brown
DESCRIPTION:We study the impact of forbearance on aggregate economic perfo
 rmance in Japan over the period 2007-2017. Forbearance is a practice where
 by banks accommodate bad borrowers instead of terminating their loans\, wi
 th negative consequences for aggregate productivity. The Japanese policy r
 esponse to the global financial crisis of 2007-2008 (SME Financing Facilit
 ation Act) has revived this practice. Our novel theory driven empirical ap
 proach enables us to perform a quantitative assessment of the aggregate im
 pact of forbearance\, including its positive effects\, namely the avoidanc
 e of a large number of bankruptcies and increased unemployment. We develop
  a search theoretic model of credit markets with severance costs that capt
 ure forbearance frictions and estimate those frictions using the Tokyo Sho
 ko Research (TSR) dataset. Our estimates indicate a marked increase in for
 bearance frictions from 2010 onwards\, suggesting that the SME Financing F
 acilitation Act of 2009 has revived the practice of forbearance in Japan. 
 Our counterfactual exercises indicate that\, in the absence of forbearance
 \, the capital productivity of survivors would on average be 4.22% higher.
  On average\, there would be 6.89% fewer jobs and 3.93% fewer firms. Final
 ly\, we provide regression based evidence in support of our channel. First
 \, we relate our estimates of forbearance frictions to the zombieness meas
 ure of Caballero\, Hoshi and Kashyap (2008)\, and show that higher frictio
 ns are associated with a higher probability that a firm is classified as a
  zombie firm. Second\, we exploit geographical variation in search frictio
 ns across Japanese prefectures to show that forbearance frictions are more
  significant when search frictions are more stringent. This shows that our
  model captures a unique margin in the data\, which is not explained by mo
 dels that are not based on search and matching.
LOCATION:Seminar Room 1 (SSC)\, Cambridge Judge Business School
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