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UID:f232c14c459c058d46c6b94c6d204453
CATEGORIES:Seminars
CREATED:20250416T080555
SUMMARY:Andrea Caggese - UPF, CREI, and Barcelona GSE
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:<p style="text-align: justify;"><strong>Zombies? Growth Options, Heterogene
 ous Firms, and the Allocative Effects of Zombie Lending"</strong></p><p sty
 le="text-align: justify;">Abstract:&nbsp;</p><p style="text-align: justify;
 ">We develop a model with firm dynamics, imperfect competition, and growth 
 options. Firms borrow using credit lines offered by competitive lenders tha
 t can optimally provide zombie lending (that is, reducing the interest rate
  on borrowing below the risk free rate). We solve and calibrate the model a
 nd show that zombie lending is used in equilibrium to subsidise two very di
 fferent types of firms: ``stagnant zombies'' previously borrowed to fund pr
 ojects that are no longer feasible. They are offered zombie lending so that
  they can gradually reduce their debt and avoid default. ``high-potential z
 ombies'' have growth options and borrow to pursue them. Zombie lending ``bu
 ys them more time'', in the hope they will become more profitable in the fu
 ture and will thus be able to repay the debt. The main insight of the model
  is that while both types of zombie firms are consistent with the way such 
 firms are usually identified in the empirical literature, they have opposit
 e implications for misallocation and aggregate productivity and welfare. We
  use the model to propose novel criteria to identify the different zombie t
 ypes and new testable predictions of their allocative effects: i) An increa
 se in markup dispersion within industries should increase the share of zomb
 ie firms and the share of zombies that in the future recover and reach top 
 quartile performance. ii) Sectors where zombies behave like high-potential 
 ones should exhibit positive TFP responses to zombie prevalence, while thos
 e dominated by stagnant zombies should show declining productivity. Using f
 irm-level data from Italian firms between 2007-2016, we provide robust empi
 rical evidence confirming these predictions.</p>
DTSTAMP:20260429T054617Z
DTSTART:20251204T163000Z
DTEND:20251204T180000Z
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