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Small Business Economics
Erschienen in: Small Business Economics 1/2021

04.01.2020

Spillovers to small business credit risk

verfasst von: Dennis Bams, Magdalena Pisa, Christian C. P. Wolff

Erschienen in: Small Business Economics | Ausgabe 1/2021

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Abstract

Do large credit risk shocks spill over to small businesses and affect their real economic activity? Using information on small business credit risk, we find that small businesses show increased default and bankruptcy rates following a shock to a customer industry. On an industry level, the shock to a customer industry is followed by a decrease in industry markups, disproportionate closure of firms, and cutbacks in inventories. Our analysis quantifies the elevated credit risk among small businesses and suggests a non-negligible 0.83% increase in expected losses on a diversified loan portfolio following a credit risk shock. This study provides banks and supervisors with greater clarity on timing and on the extent of elevated small business credit risk. It also allows them to assess the exposure of a bank portfolio to fluctuations in small business default rate. Such improved default prediction reduces credit rationing to the small business economy.
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Fußnoten
1
The Small Business Credit Survey defines microbusinesses as those with annual revenues less than $100K and large firms as those with annual revenues more than $10 million.
 
2
Throughout this paper, we choose credit events reported by S&P as signals of shocks that potentially are followed by spillovers to small businesses.
 
3
In the literature, the spillovers to (1) customers and suppliers through production linkages and to (2) peers through collateral and product market linkages is well established. See, for example, Ferris et al. (1997) Kiyotaki and Moore (1997, 2002), Hertzel et al. (2008), Jorion and Zhang (2007, 2009), Helwege and Zhang (2016), and Kolay et al. (2016).
 
4
Our work is limited to explaining fluctuations in small business default rates. The motivation behind this choice is to deliver tangible and applied implications for supervisors and banks’ risk management departments to improve predictions regarding fluctuations in small business default rate. To the extent that we do not claim causality, the assumption of exogenous shocks can be relaxed in our setting.
 
5
For a discussion on the matching estimator, we refer to Malmendier and Tate (2009).
 
6
For a detailed description of this classification, please refer to Appendix 3.
 
7
Our main analysis is conducted on a quarterly basis, as opposed to an annual basis, for two reasons. First, our aim is to propose a signal with a potential to serve as an early warning signal for supervisors and banks. Thus, the timing of the signal (shock in an economically linked industry) is crucial. If we use the annual frequency data, the timing of the signal and the changes in small business default rate is unspecified. Due to the fact that we obtain the annual version of Dose variable by aggregating all shocks occurring in 1 year, the signal can occur at any point during the year, but the default rate is measured for a cohort of financially sound small businesses at the beginning of the year. A more accurate timing and length of the spillover is delivered by analysis on a quarterly basis. Second, the annual analysis includes data on 77 industries in 5 years, which means we have 385 annual observations. The small number of the annual observations would have precluded us from conducting a detailed analysis that follows in this section.
 
8
The aim of introducing the matching estimation methodology is to find robust evidence of spillover following a shock to a customer industry (as spillovers following a shock to the supplier or the same industry are not present or robust to a lesser degree). The covariate balancing undertaken for the matching estimators differs only on Sales and Inventories in this case. The variables Sales and Inventories enter only model 3. Stuart (2010) and Rubin (2001) argue that rebalancing of the covariates can be done until the balance is achieved without introducing any bias into the matching estimator, meaning our estimates from the baseline case and from models 1 to 2 remain unbiased since they do not match on Sales and Inventories. Moreover, Stuart (2010) and Rubin (2001) discuss a strategy in matching estimators that considers a small set of covariates related to the dependent variable (in our case, small business default rate). Neither Sales nor Inventories show up significant in any of our analyses and seem not to be related to the dependent variable.
 
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Metadaten
Titel
Spillovers to small business credit risk
verfasst von
Dennis Bams
Magdalena Pisa
Christian C. P. Wolff
Publikationsdatum
04.01.2020
Verlag
Springer US
Erschienen in
Small Business Economics / Ausgabe 1/2021
Print ISSN: 0921-898X
Elektronische ISSN: 1573-0913
DOI
https://doi.org/10.1007/s11187-019-00308-9

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