Identifying credit supply shocks with bank-firm data Hans Degryse (KU Leuven, IWH, and CEPR) Olivier De Jonghe (NBB and Tilburg University) Sanja Jakovljević (Lancaster University) Klaas Mulier (Ghent University and NBB) Glenn Schepens (ECB) Disclaimer: These views are our own and do not necessarily represent the views of the ECB or the NBB.

Motivation Banks are important providers of external finance to firms in general and SMEs in particular Key question: How much do bank-loan supply shocks impact credit availability, bank behavior, and ultimately the real economy? This question is on top of agenda of policymakers, supervisors and academics since the global financial and sovereign crises (e.g., Campello et al, JFE2010; Ivashina and Scharfstein, JFE2010; Chodorow-Reich, QJE2014; Iyer et al., RFS2014; Ongena et al., IMFEc2015; Amiti and Weinstein, JPE2017, De Jonghe et al.,2017, Beck et al., JFE2018,…). Main identification challenge to study the impact of bank shocks on credit availability and the real economy is to separate the “firm-borrowing” and the “bank-lending” channels

Identification of firm-borrowing and bank-lending channels Two methodological choices may limit the generality of conclusions on the impacts of bank shocks: 1. Identification based on one-off exogenous shocks Shocks to Japanese banks spill over to US firms (Peek and Rosengren, AER1996) drops in asset prices and real estate exposures of banks (Gan, RFS2007) nuclear tests and the collapse of the dollar deposit market (Khwaja and Mian, AER2008) ∆ 𝐿 𝑓𝑏 = 𝛼 𝑓 +𝛿∙ 𝐵 𝑏 + 𝜀 𝑓𝑏 does not say much on the behaviour of credit supply in other less turbulent periods

Identification of firm-borrowing and bank-lending channels Two methodological choices may limit the generality of conclusions on the impacts of bank shocks: 2. Following Khwaja and Mian (AER2008), direct identification of loan supply shocks is typically performed on a sample of firms borrowing simultaneously from multiple banks 𝐿 𝑓𝑏𝑡 − 𝐿 𝑓𝑏𝑡−1 𝐿 𝑓𝑏𝑡−1 ≡∆ 𝐿 𝑓𝑏𝑡 = 𝛼 𝑓𝑡 + 𝛽 𝑏𝑡 + 𝜀 𝑓𝑏𝑡 of limited use in samples with plentiful single-bank firms (Ongena and Smith, JFI2000; Degryse, Kim and Ongena, OUP2009; Kysucki and Norden MS2016)) matching of firms to banks: single-bank and multiple-bank firms might not be similar firms with only ‘bank finance’ may match with different banks than firms having ‘bonds and banks’ (Schwert JFforth)

This paper Methodology that allows to identify cross-sectional differences in bank-loan supply shocks using (almost) all firms – single-relationship firms as well as multiple-relationship firms that are time-varying In this way, we address two important criticisms single relationship firms are important in many countries bank-loan supply shocks not only happen when exogenous events occur We study the real effects of bank-loan supply shocks using data provided by the NBB, and compare the results across methods

Our methodology We address the methodological challenges by: developing an indicator of bank-loan supply shocks which captures cross-sectional variation and this over an extended time period also including (most) firms borrowing from just one bank We consider alternative demand controls which allow to encompass the vast majority of firms. Our analysis suggests to replace firm-time fixed effects with industry-location-size-time fixed effects (2-digit NACE codes; 2-digit postal codes, deciles by total assets)  ∆ 𝐿 𝑓𝑏𝑡 = 𝛼 𝑓𝑡 + 𝛽 𝑏𝑡 + 𝜀 𝑓𝑏𝑡 ∆ 𝐿 𝑓𝑏𝑡 = 𝛼 𝑰𝑳𝑺𝑡 + 𝛽 𝑏𝑡 + 𝜀 𝑓𝑏𝑡 𝛼 1𝑡 = 𝛼 2𝑡 =…= 𝛼 𝐹𝑡 , (1..𝐹)∈𝐼𝐿𝑆

Our data We use the following datasets: Time coverage: 2002m1 – 2012m3 monthly bank-firm information on authorized credit to firms incorporated in Belgium (Corporate Credit Register of the NBB) Reporting threshold: 25,000 EUR annual financial accounts of Belgian firms (Central Balance Sheet Office of the NBB) Monthly bank balance sheet information (Schema A of NBB) Time coverage: 2002m1 – 2012m3 Estimation sample: around 17 mil. bank-firm time observations We exclude banks with less than 30 loans outstanding in a period In every period around 36 banks, with the 4 largest banks having a joint market share of about 80%

Our data How relevant are firms borrowing from just one bank? The number of borrowing relationships and their share in total loan volume

Our data Characteristics of single-bank and multiple-bank firms Multiple-bank firms are on average older, larger, have lower investment ratios, borrow larger credit amounts.

Our data Number of observations and firms in the sample Our extension from a multiple-bank firm to a multiple-bank 𝐼𝐿𝑆 setting allows us to keep 94% of observations on 97% of firms from the eligible sample. In this way we also prevent that we include industry*location*size bins where banks are extremely specialized

I. Comparing bank-lending channel estimates (1) 1) We first test our methodology on the multiple-bank firms sample: 𝑆𝑡𝑒𝑝 1:∆ 𝐿 𝑓𝑏𝑡 = 𝛼 𝑖𝑡 + 𝛽 𝑏𝑡 𝑖 + 𝜀 𝑓𝑏𝑡 , 𝑖=∙,𝐿,𝐼𝐿,𝐼𝐿𝐶, 𝐼𝐿𝑅𝑖𝑠𝑘 1−3 , 𝐼𝐿𝑆,𝐹 𝑆𝑡𝑒𝑝 2: 𝛽 𝑏𝑡 𝐹 =𝛿∙ 𝛽 𝑏𝑡 𝑖 + 𝜇 𝑡 + 𝜀 𝑏𝑡 , 𝑖=∙,𝐿,𝐼𝐿,𝐼𝐿𝑆 Table 2a. Comparison of bank-loan supply estimates; Multiple-bank firm sample

Estimation and verification of bank-loan supply shocks Summarizing: Within the multiple-bank firm sample, our bank shock estimates get closer to the “standard” bank shocks (i.e. obtained using firm-time effects as demand controls) as we make the demand control more sophisticated ILS provides the best fit Model performs equally well during crisis periods Variables that are readily available important for empirical work 2) As we extend the sample towards multiple-bank ILS, our bank shock estimates are departing from the “standard” bank shocks 3) We can meaningfully relate our bank shock estimates from the ILS sample to: Tightening of lending standards (Bank Lending Survey) Growth in interbank liabilities Note on the estimation: we can only study cross-sectional variation in bank-supply shocks within each period

II. Effects of bank shocks on firm outcomes and bank risk-taking We analyse how bank-loan shocks relate to: Firm-level outcomes asset growth sales growth investment (growth in fixed assets) Bank risk-taking Study how credit supply shocks impact the riskiness of a bank’s portfolio at the extensive margin

Application of credit supply shocks – firms Comparison with firm level outcomes – growth: Using ILS shocks on all firms: a one standard deviation decrease in credit supply reduces (i) asset growth with 0.12 percentage points, (ii) sales growth with 0.29 percentage points, (iii) investment growth with 0.3 percentage points Using FT shocks on all firms: mostly not significant ∆ 𝑌 𝑓𝑡 𝑇𝐴 =𝛿∙ 𝑏 𝜃 𝑓𝑏𝑡−1 𝛽 𝑏𝑡−1 𝐼𝐿𝑆 𝑜𝑟 𝐹𝑇 + 𝜇 𝑡 + 𝛾 𝑓 +𝜀 𝑓𝑡 Table. Bank credit supply estimates and firm asset growth, sales and investment

Application of credit supply shocks – banks We consider risk taking at the extensive margin: Riskiness of new and dropped bank-firm relationships: altman Z score A negative supply shock leads to bigger difference in Z score between entry and exiting firms; so less risky firms are relatively more added

Application of credit supply shocks – banks We consider risk taking at the extensive margin: Share in credit volume to new and dropped relations One standard deviation negative supply shock leads to a 0.366 percentage point increase in loan volume at the extensive margin, but not in crisis) Taken together it suggests that negative supply shocks lead to less bank risk- taking

Conclusions Identification of credit supply shocks mostly relies on exogenous events and/or using firms borrowing from multiple-banks only In many countries however firms borrower from only one bank We develop a methodology to identify credit supply shocks in the presence of a multitude of single-bank firms The overall credit growth rate is thus better captured. Industry-location-size seems a reasonable alternative demand control that can be implemented in most datasets. Our application to Belgium reveals that Using the “broader supply shocks” are more informative Firms borrowing from lenders with a more negative bank-loan supply shock have lower asset growth, sales growth and investment growth Banks with more positive supply shocks take on more risk.

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