Advanced search
1 file | 913.30 KB Add to list

Identifying credit supply shocks with bank-firm data: Methods and applications

Author
Organization
Abstract
Current empirical methods to identify and assess the impact of bank credit supply shocks rely strictly on multi-bank firms and ignore firms borrowing from only one bank. Yet, these single-bank firms are often the majority of firms in an economy and most prone to credit supply shocks. We propose and underpin an alternative demand control (using industry–location–size–time fixed effects) that allows identifying time-varying cross-sectional bank credit supply shocks using both single- and multi-bank firms. Using matched bank-firm credit data from Belgium, we show that firms borrowing from banks with negative credit supply shocks exhibit lower financial debt growth, asset growth, investments, and operating margin growth. Positive credit supply shocks are associated with bank risk-taking behavior at the extensive margin. Importantly, to capture these effects it is crucial to include the single-bank firms when identifying the bank credit supply shocks.
Keywords
Economics and Econometrics, Finance

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 913.30 KB

Citation

Please use this url to cite or link to this publication:

MLA
Degryse, Hans, et al. “Identifying Credit Supply Shocks with Bank-Firm Data: Methods and Applications.” Journal of Financial Intermediation, Elsevier, 2019.
APA
Degryse, H., De Jonghe, O., Jakovljević, S., Mulier, K., & Schepens, G. (2019). Identifying credit supply shocks with bank-firm data: Methods and applications. Journal of Financial Intermediation.
Chicago author-date
Degryse, Hans, Olivier De Jonghe, Sanja Jakovljević, Klaas Mulier, and Glenn Schepens. 2019. “Identifying Credit Supply Shocks with Bank-Firm Data: Methods and Applications.” Journal of Financial Intermediation.
Chicago author-date (all authors)
Degryse, Hans, Olivier De Jonghe, Sanja Jakovljević, Klaas Mulier, and Glenn Schepens. 2019. “Identifying Credit Supply Shocks with Bank-Firm Data: Methods and Applications.” Journal of Financial Intermediation.
Vancouver
1.
Degryse H, De Jonghe O, Jakovljević S, Mulier K, Schepens G. Identifying credit supply shocks with bank-firm data: Methods and applications. Journal of Financial Intermediation. 2019;
IEEE
[1]
H. Degryse, O. De Jonghe, S. Jakovljević, K. Mulier, and G. Schepens, “Identifying credit supply shocks with bank-firm data: Methods and applications,” Journal of Financial Intermediation, 2019.
@article{8631391,
  abstract     = {Current empirical methods to identify and assess the impact of bank credit supply shocks rely strictly on multi-bank firms and ignore firms borrowing from only one bank. Yet, these single-bank firms are often the majority of firms in an economy and most prone to credit supply shocks. We propose and underpin an alternative demand control (using industry–location–size–time fixed effects) that allows identifying time-varying cross-sectional bank credit supply shocks using both single- and multi-bank firms. Using matched bank-firm credit data from Belgium, we show that firms borrowing from banks with negative credit supply shocks exhibit lower financial debt growth, asset growth, investments, and operating margin growth. Positive credit supply shocks are associated with bank risk-taking behavior at the extensive margin. Importantly, to capture these effects it is crucial to include the single-bank firms when identifying the bank credit supply shocks.},
  articleno    = {100813},
  author       = {Degryse, Hans and De Jonghe, Olivier and Jakovljević, Sanja and Mulier, Klaas and Schepens, Glenn},
  issn         = {1042-9573},
  journal      = {Journal of Financial Intermediation},
  keywords     = {Economics and Econometrics,Finance},
  language     = {eng},
  pages        = {15},
  publisher    = {Elsevier},
  title        = {Identifying credit supply shocks with bank-firm data: Methods and applications},
  url          = {http://dx.doi.org/10.1016/j.jfi.2019.01.004},
  year         = {2019},
}

Altmetric
View in Altmetric