University of Cambridge > > Cambridge Finance Workshop Series > The Aggregate Consequences of Forbearance Lending: Evidence from Japan

The Aggregate Consequences of Forbearance Lending: Evidence from Japan

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  • UserIsabelle Roland (University of Cambridge, Economics) World_link
  • ClockThursday 14 October 2021, 13:00-14:00
  • HouseOnline.

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We study the impact of forbearance on aggregate economic performance in Japan over the period 2007-2017. Forbearance is a practice whereby banks accommodate bad borrowers instead of terminating their loans, with negative consequences for aggregate productivity. The Japanese policy response to the global financial crisis of 2007-2008 (SME Financing Facilitation Act) has revived this practice. Our novel theory-driven empirical approach enables us to perform a quantitative assessment of the aggregate impact of forbearance, including its positive effects, namely the avoidance of a large number of bankruptcies and increased unemployment. We develop a search-theoretic model of credit markets with severance costs that capture forbearance frictions and estimate those frictions using the Tokyo Shoko Research (TSR) dataset. Our estimates indicate a marked increase in forbearance frictions from 2010 onwards, suggesting that the SME Financing Facilitation 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. Finally, we provide regression-based evidence in support of our channel. First, we relate our estimates of forbearance frictions to the zombieness measure of Caballero, Hoshi and Kashyap (2008), and show that higher frictions are associated with a higher probability that a firm is classified as a zombie firm. Second, we exploit geographical variation in search frictions 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 models that are not based on search and matching.

This talk is part of the Cambridge Finance Workshop Series series.

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