G2TT
来源类型Discussion paper
规范类型论文
来源IDDP14327
DP14327 The Aggregate Consequences of Default Risk: Evidence from Firm-level Data
Tim Besley; John Van Reenen; Isabelle Roland
发表日期2020-01-20
出版年2020
语种英语
摘要This paper studies the implications of perceived default risk for aggregate output and productivity. Using a model of credit contracts with moral hazard, we show that a firm's probability of default is a sufficient statistic for capital allocation. The theoretical framework suggests an aggregate measure of the impact of credit market frictions based on firm-level probabilities of default which can be applied using data on firm-level employment and default risk. We obtain direct estimates of firm-level default probabilities using Standard and Poor's PD Model to capture the expectations that lenders were forming based on their historical information sets. We implement the method on the UK, an economy that was strongly exposed to the global financial crisis and where we can match default probabilities to administrative data on the population of 1.5 million firms per year. As expected, we find a strong correlation between default risk and a firm's future performance. We estimate that credit frictions (i) cause an output loss of around 28% per year on average; (ii) are much larger for firms with under 250 employees and (iii) that losses are overwhelmingly due to a lower overall capital stock rather than a misallocation of credit across firms with heterogeneous productivity. Further, we find that these losses accounted for over half of the productivity fall between 2008 and 2009, and persisted for smaller (although not larger) firms.
主题Financial Economics ; Macroeconomics and Growth
关键词Productivity Default risk Credit frictions Misallocation
URLhttps://cepr.org/publications/dp14327
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/543219
推荐引用方式
GB/T 7714
Tim Besley,John Van Reenen,Isabelle Roland. DP14327 The Aggregate Consequences of Default Risk: Evidence from Firm-level Data. 2020.
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