Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Financial Intermediation. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Financial Intermediation, 40, 2019 DOI: 10.1016/j.jfi.2019.01.004
Accepted author manuscript, 1.21 MB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Identifying credit supply shocks with bank-firm data
T2 - Methods and applications
AU - Degryse, Hans
AU - De Jonghe, Olivier
AU - Jakovljević, Sanja
AU - Mulier, Klaas
AU - Schepens, Glenn
N1 - This is the author’s version of a work that was accepted for publication in Journal of Financial Intermediation. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Financial Intermediation, 40, 2019 DOI: 10.1016/j.jfi.2019.01.004
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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 behaviour 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.
AB - 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 behaviour 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.
KW - Credit supply identification
KW - Bank lending
KW - Corporate investments
KW - Bank risk-taking
U2 - 10.1016/j.jfi.2019.01.004
DO - 10.1016/j.jfi.2019.01.004
M3 - Journal article
VL - 40
JO - Journal of Financial Intermediation
JF - Journal of Financial Intermediation
SN - 1042-9573
M1 - 100813
ER -