Some borrowers are more equal than others : bank funding shocks and credit reallocation.
Date: 2019-04-16
Institutions: European Central Bank
Subjects: banking policy, credit policy, financial institution, financial market, monetary policy

Olivier De Jonghe, Hans Dewachter, 
Klaas Mulier, Steven Ongena, Glenn Schepens 

Working Paper Series 

Some borrowers are  
more equal than others:  
bank funding shocks and 
credit reallocation 

No 2230 / January 2019 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

Abstract

This paper provides evidence on the strategic lending decisions made by banks

facing a negative funding shock. Using bank-ﬁrm level credit data, we show that banks

reallocate credit within their loan portfolio in at least three diﬀerent ways. First, banks

reallocate to sectors where they have a high market share. Second, they also reallocate

to sectors in which they are more specialized. Third, they reallocate credit towards

low-risk ﬁrms. These reallocation eﬀects are economically large. A standard deviation

increase in sector market share, sector specialization or ﬁrm soundness reduces the

transmission of the funding shock to credit supply by 22, 8 and 10%, respectively.

Keywords: Credit reallocation, bank funding shock, bank credit, sector market share, sector specialization, ﬁrm risk

JEL classiﬁcation: G01 G21

ECB Working Paper Series No 2230 / January 20191Non-technical summary

This paper studies the reallocation of credit within the domestic credit portfolio of banks after a

negative funding shock. The particular shock we focus on is freeze of the interbank funding market

around the Lehman collapse. To identify the reallocation in the supply of credit following these

funding problems, we rely on 160,223 fully documented bank-ﬁrm combinations for nearly all banks

and non-ﬁnancial ﬁrms active in Belgium.

We start by documenting the average impact of interbank funding shock on bank credit supply. A

ﬁrm borrowing from a bank hit by a funding shock of -10.3% (the average shock in the sample) has

a 6.04 percentage points lower probability of seeing an increase in granted credit (sample mean is

28.9%). At the aggregate level, our results imply a reduction in credit supply of EUR 4.2 billion

during the year after the Lehman shock.

However, not all borrowing ﬁrms are being hit equally hard. Banks’ sector presence and sector

specialization determine the reallocation of credit when a bank is hit by a negative funding shock.

Sector presence measures how important a bank is for a particular (non-ﬁnancial) sector while

sector specialization measures how important a (non-ﬁnancial) sector is for a bank.

We ﬁnd that a standard deviation increase in sector presence reduces the negative impact of the

funding shock on credit supply by 22% for the average ﬁrm. Similarly, a standard deviation increase

in sector specialization reduces the negative impact of the funding shock on credit supply by 8%

for the average ﬁrm. Hence, banks direct their attention to sectors where they can more easily

extract rents (higher sector presence) or where they have built up superior knowledge (higher sector

specialization). Additionally, we document the existence of a ﬂight to quality. Banks reallocate

credit towards ﬁrms with low debt levels, low default risk, high available collateral, and a high

interest coverage ratios. Importantly, this ﬂight-to-quality coexists with the two aforementioned

reallocation eﬀects.

The reallocation eﬀects are robust to a number of alternative explanations. We provide evidence

that our results are not driven by pre-shock solvability problems, government interventions during

our sample period, banks’ geographical specialization, or bank-ﬁrm relationship characteristics.

On the real side, we ﬁnd a moderate reduction in investments and asset growth for ﬁrms borrowing

from banks that were hit harder by the funding shock. The average ﬁrm borrowing from a bank that

ECB Working Paper Series No 2230 / January 20192experienced an average funding shock reduced its net investment rate by 0.85 percentage points.

Importantly, we show that ﬁrms that are borrowing from a bank with high sector presence can

partially oﬀset this negative impact on investment rates.

Our results provide useful information for policy makers that want to ensure access to ﬁnance for

non-ﬁnancial corporations during crisis times, as we show that riskier ﬁrms and ﬁrms borrowing

from banks that have low sector presence and specialization are more vulnerable to shocks in the

banking sector. Related to this, ﬁrms may prefer matching with banks with a larger sector presence

as the implied higher cost of borrowing during good times also acts as an insurance premium that

guarantees access to ﬁnance when the bank faces a funding shock.

Our ﬁndings also contain interesting information for bank regulators. Our results reveal a bright

sight of lending concentration during crisis times and are thus informative when making the trade-

oﬀ between portfolio concentration risk and having suﬃcient information about borrowers.

ECB Working Paper Series No 2230 / January 201931

Introduction

The collapse of Lehman Brothers in September 2008 was an unprecedented shock to banks’ funding

opportunities. Several papers show that banks transmitted this funding shock to their borrowers.1

However, recent evidence suggests that banks did not curtail credit equally across the board.

De Haas and Van Horen (2012), Giannetti and Laeven (2012) and Liberti and Sturgess (2016)

indicate that there is signiﬁcant heterogeneity in the geographical reallocation decisions of banks.

While many researchers have analyzed the role that geographical specialization plays for credit

reallocation after a funding shock, only few have focused on the impact of other types of lending

specialization (Paravisini et al., 2017; DeYoung et al., 2015; Liberti and Sturgess, 2016). This is

somewhat surprising, given the important role that loan portfolio allocation plays in many theo-

retical banking models (Diamond, 1984; Boyd and Prescott, 1986; Winton, 1999) and given the

severe consequences that credit reallocation after a funding shock might have for the real economy

(Ongena et al., 2015).

This paper aims to ﬁll this gap in the literature by providing a comprehensive and detailed analysis

of the reallocation that banks pursue across sectors and ﬁrms after a negative shock to their funding.

The particular shock we focus on is the collapse of the interbank funding market that was triggered

by the bankruptcy of Lehman Brothers. This exogenous event had a strong impact on the Belgian

ﬁnancial system. Figure 1 shows that the total volume of interbank funding of banks active in

Belgium dropped from more than EUR 500 billion in August 2008, to slightly above EUR 250

billion thirteen months after the Lehman Brothers collapse.2

FIGURE 1 around HERE

To identify the reallocation in the supply of credit following these funding problems, we rely on

160,223 fully documented bank-ﬁrm combinations. We combine monthly bank-ﬁrm level data

from a comprehensive credit register that contains all credit granted in Belgium by all ﬁnancial

institutions, monthly balance sheets of these ﬁnancial institutions, and annual balance sheets of

1 See Cetorelli and Goldberg (2011); Puri et al. (2011); Cetorelli and Goldberg (2012); Claessens and van
Horen (2013); Cull and Martinez Peria (2013); Albertazzi and Bottero (2014); Allen et al. (2014); Bertay
(2014); De Haas and van Lelyveld (2014); Iyer et al. (2014)

2 Interbank funding include overnight deposits, deposits redeemable at notice and term accounts of other

ﬁnancial institutions as well as repurchase agreements.

ECB Working Paper Series No 2230 / January 20194all registered ﬁrms. The richness of our data allows us to study various measures of credit growth

and makes it possible to disentangle credit supply from demand. The latter is done by saturating

the corresponding loan growth speciﬁcations with a comprehensive set of ﬁxed eﬀects in order to

control for credit demand (Khwaja and Mian, 2008; Jimenez et al., 2014).

We identify reallocation eﬀects along three diﬀerent lines based on a stylized expected return

equation (see Section 2.2).3 First, banks reallocate credit after a negative funding shock towards

sectors in which they have a high market share (deﬁned as the bank’s share in total credit granted

to a sector). We ﬁnd that a one standard deviation increase in sector market share reduces the

negative impact of the funding shock on credit supply by 22% for the average ﬁrm.4 The explanation

for this ﬁnding is that banks direct their attention to sectors where they can more easily extract

rents. As the funding shock increases the marginal cost of lending, banks start to prefer their

inframarginal borrowers over their marginal borrowers. Banks that face a funding shock are forced

to reduce lending, but they have an incentive to shield sectors where they can exploit their high

market share and charge relatively higher interest rates.

Second, we ﬁnd that banks reallocate credit towards sectors in which they are specialized (deﬁned

as the sector’s share in total credit granted by a bank). A one standard deviation increase in sector

specialization reduces the negative impact of the funding shock on credit supply by 8% for the

average ﬁrm.5 Banks will typically have gathered more sector-speciﬁc knowledge in sectors where

they are specialized, improving their screening abilities and reducing the need for costly monitoring

in these sectors. As such, banks that face a funding shock are forced to reduce lending, but they

have a strong incentive to shield sectors in which they are specialized and have superior screening

and monitoring skills.

Third, banks hit by a funding shock reallocate credit towards safe ﬁrms. We ﬁnd that banks

transmit a funding shock signiﬁcantly less to ﬁrms with low debt levels, high available collateral,

and high interest coverage ratios. A one standard deviation decrease in ﬁrm risk (for any of these

3 The focus of the paper is on the reallocation eﬀects, i.e., the heterogeneous transmission of a funding shock.

However, we also show the average (homogeneous) eﬀect of the funding shock on bank credit supply.

4 22% is the estimated average mitigation, due to a larger market share, of the average credit contraction
following an interbank funding shock. This number is computed from the 6 main regression models in
Table 5.

5 8% is the estimated average mitigation, due to sector specialization, of the average credit contraction
following an interbank funding shock. This number is computed from the 6 main regression models in
Table 5.

ECB Working Paper Series No 2230 / January 20195measures) reduces the negative impact of the funding shock on credit supply by on average 10%.

The impact of these three reallocation eﬀects is robust to a number of alternative explanations that

might be driving loan portfolio allocations. Speciﬁcally, the banks that are hit the hardest by the

funding shock are also the largest banks in our sample. Given that size correlates positively with

having global banking activities and with the probability of getting government support during our

sample period, one could be concerned that these factors are driving our results. We provide a

number of robustness checks to ensure that our results are not driven by international diversiﬁcation

choices, government interventions, or pre-crisis solvency or liquidity issues that banks might have

faced. Additionally, we show that it is unlikely that our results are driven by bank-ﬁrm speciﬁc soft

information which banks might have gathered through previous interactions with a speciﬁc ﬁrm.

As such, our ﬁndings indicate that sector market share, sector specialization and ﬁrm risk play a

more important role than ﬁrm-speciﬁc relationships when it comes to the reallocation choices of

banks during a funding shock.

After documenting these three reallocation channels, we investigate whether the magnitude of the

impact of funding shocks changes over time and whether the impact persists.6 We show that the

moderating impact of bank sector market share is almost instantaneous and stays signiﬁcant until

two years after the shock. Bank sector specialization on the other hand becomes important for the

reallocation of credit after about ten months and also stays signiﬁcant until two years after the

shock. The moderating eﬀect of ﬁrm risk becomes signiﬁcant three to four months after the shock.

Our results thus indicate that banks hit by a funding shock are at ﬁrst more concerned with staying

aﬂoat in the short run by focusing on loans that ensure larger cash inﬂows (in the form of relatively

high interest payments), while only being interested in long term proﬁtability (and hence focusing

on protecting their sector speciﬁc knowledge) and ﬁrm risk once these short term inﬂows are safe.

Finally, we analyze the impact of the funding shock on ﬁrm investments and asset growth. Firms

borrowing from banks that experience a larger funding outﬂow have a lower investment rate and

grow slower than other ﬁrms. Importantly, this negative impact is partially oﬀset for ﬁrms that

are borrowing from banks with a high sector market share and for large ﬁrms. In economic terms,

however, the real eﬀects of the shock are rather moderate.

6 The detailed timing of the reallocative impact of market-wide, bank- or borrower-speciﬁc events is rarely
investigated due to a lack of granular data, except in selected settings such as bank runs (Iyer et al., 2012),
bank mergers and acquisitions (See, e.g., Sapienza, 2002; Focarelli and Panetta, 2003; Degryse et al., 2011)
or borrower lock-in (Ioannidou and Ongena, 2010).

ECB Working Paper Series No 2230 / January 20196Related literature. Our paper adds to the literature in a number of ways. First, our analysis

contributes to the rapidly expanding literature on bank funding shock transmission. The existing

work on funding shocks and bank lending mainly focuses on cross-border eﬀects through global

banks (Peek and Rosengren, 1997, 2000; Cetorelli and Goldberg, 2011, 2012; Claessens and van

Horen, 2013; Albertazzi and Bottero, 2014; Bertay, 2014; De Haas and van Lelyveld, 2014; Liberti

and Sturgess, 2016). Others have focused on the average reduction in credit (Puri et al., 2011; Iyer

et al., 2014) or on the real eﬀects of funding shocks (Chodorow-Reich, 2014; Paravisini et al., 2014;

Ongena et al., 2015). A number of recent papers show that bank-ﬁrm relationships shield ﬁrms

from credit supply shocks (Puri et al., 2011; DeYoung et al., 2015; Liberti and Sturgess, 2016),

and that banks reallocate credit to less risky borrowers (Liberti and Sturgess, 2016) or less risky

sector-region combinations (Ongena et al., 2015) after a liquidity shock. DeYoung et al. (2015)

use a structural model to examine how U.S community banks’ past lending decisions aﬀect small

business credit granted in times of crisis. We add to this literature by providing a detailed overview

of the reallocation of credit across sectors and ﬁrms based on bank sector market share and bank

sector specialization.

Furthermore, by showing that banks reallocate credit according to their sector market share, this

paper relates to a vast empirical literature on bank market share, market power and credit (see,

e.g., Degryse et al. (2009) for a review). We contribute to this literature by documenting that

when banks face severe funding shocks, ﬁrms borrowing from banks with a higher sector market

share are better protected against credit supply shocks. This ﬁnding entails important information

for bank competition regulators, as it illustrates a potential beneﬁt of bank concentration within a

sector. Additionally, by showing that sector concentration matters, this result indirectly questions

the strong focus of bank competition regulators and researchers on indicators that solely focus on

the geographical dimension of bank competition.

The ﬁnding that banks reallocate credit according to sector specialization connects the bank funding

shock transmission literature with the literature on bank lending concentration, which mainly

analyzes the link between concentration, bank performance and risk taking (Acharya et al., 2006;

Degryse and Ongena, 2007; Tabak et al., 2011; Jahn et al., 2016; Beck et al., 2017). So far none

of these papers comprehensively assesses reallocation of credit according to sector specialization

or the potential beneﬁts and dangers of bank lending concentration for ﬁrms. Both our paper

and Paravisini et al. (2017) address these issues, albeit from a diﬀerent perspective. Paravisini

ECB Working Paper Series No 2230 / January 20197et al. (2017) document that credit supply shocks aﬀect a ﬁrm’s exports to markets where its lender

specializes in. Our paper shows that ﬁrms that are borrowing from specialized banks are less

impacted by a bank funding shock. This is consistent with previous literature showing that there

is a potential downside to bank diversiﬁcation (Laeven and Levine, 2007). Related to this, our

results emphasize that not only systemic risk and ﬁnancial stability issues should be taken into

account when studying the welfare implications of bank portfolio diversiﬁcation (e.g., Acharya,

2009; Wagner, 2010), but that it could also be relevant to consider the impact on ﬁrm credit

supply. This is useful information for bank regulators when deciding on lending concentration

limits.

Finally, our paper relates to the literature on ﬁrm characteristics and credit constraints. Researchers

have proposed various classiﬁcation schemes to identify ﬁnancing constraints based on ﬁrm size and

age (Beck et al., 2006; Hadlock and Pierce, 2010), asset tangibility (Almeida and Campello, 2007)

or leverage and cash ﬂows (Lamont et al., 2001; Whited and Wu, 2006). We add to this literature

by showing that collateral availability, leverage and interest coverage matter for credit supply when

bank funding is under stress. However, we do not ﬁnd any evidence that banks tighten credit supply

disproportionately more for smaller or younger borrowers when their funding is under stress. We

do ﬁnd that a given credit supply shock matters more for the investment and growth of small ﬁrms

than large ﬁrms, likely because the former have fewer alternative funding sources available.

Our paper is organized as follows. First, we discuss the data in Section 2.1, provide a detailed

description of the hypotheses we want to test in Section 2.2, and discuss the empirical methodology

in Section 2.3. Next, Section 3 provides the results on the average impact of the interbank funding

shock as well as the reallocation eﬀects. In Section 4, we focus on challenges to identiﬁcation and

causality as well as the robustness of the main ﬁndings. Section 5 provides some insights in the

timing of funding shock impact and the timing of the reallocation channels. Finally, Section 6

investigates whether the funding shock has real eﬀects on ﬁrm investment and growth. Section 7

concludes.

ECB Working Paper Series No 2230 / January 201982 Data, Hypotheses, and Methodology

2.1 Data

We combine information from three data sources available at the National Bank of Belgium (Bel-

gium’s central bank, henceforth NBB): The central corporate credit register, the regulatory bank

balance sheets and income statements, and ﬁrm balance sheets and income statements.

Bank-ﬁrm-month level credit data is extracted from the corporate credit register, which col-

lects information on credit granted by credit institutions and other types of ﬁnancial institutions

(leasing companies, factoring companies and insurance companies) to legal entities (i.e., enterprises)

and individuals with a business activity. We only include credit institutions established in Belgium

and licensed by the NBB. This includes both branches incorporated under foreign law that are

established in Belgium and institutions incorporated under Belgian law. A credit institution needs

to provide information to the credit register on a monthly basis on all debtors to which they have

an aggregate exposure exceeding EUR 25,000. We exclude ﬁrms operating in the ﬁnancial or insur-

ance sector, public administration, education, household activities or activities of extraterritorial

entities. The ﬁnal sample includes ﬁrms from sixteen sectors of which the ﬁve most important ones

are wholesale and retail trade, construction, professional activities, real estate, and manufacturing.

We construct three credit growth measures at the bank-ﬁrm level.7 First, ∆% Creditbf is deﬁned

as the logarithmic diﬀerence between the post-shock averaged (2008:9-2009:9) and the pre-shock

averaged (2007:8-2008:8) values of the granted amount by bank b to ﬁrm f. Secondly, we create a

dummy variable (Increase in creditbf ) which takes the value of one if credit growth was strictly

positive and zero otherwise. Doing so, this variable emphasizes the eﬀect on the propensity to grant

extra credit. Thirdly, we create a dummy (Large decrease in creditbf ) which equals one if the

ﬁrm’s credit growth is in the lowest quartile of credit growth of all the bank-ﬁrm observations in

the sample (corresponding with a reduction of 15.37% or more). This variable proxies for granted

amounts that have been reduced substantially, or matured without having been rolled over.

7 The Belgian corporate credit register reports two variables with monthly frequency related to the amount
of credit: the credit granted and the credit utilized. The credit granted (or authorized) is the total amount
in euro that a ﬁrm is allowed to borrow from a bank in that month. The credit utilized is the total amount
in euro that a ﬁrm is actually borrowing from the bank in that month (hence, credit outstanding). The
latter may be lower than the former when a ﬁrm has a credit line with that bank which it does not fully
draw upon. We work with the credit granted to estimate the eﬀect of bank funding on credit supply as
changes in utilized credit are more likely to be contaminated by credit demand.

ECB Working Paper Series No 2230 / January 20199We also use the credit data to construct our two main variables that are expected to be im-

portant for credit reallocation after a funding shock (we derive the hypotheses in Section 2.2).

Bank sector market sharebs is deﬁned as the ratio of total credit granted by bank b to sector s

relative to the total credit granted by all banks to sector s, in the pre-shock period (2007:8-2008:8):

Bank sector market sharebs =

(cid:80)F

f =1 Lbf s
(cid:80)F

f =1 Lbf s

(cid:80)B

b=1

(1)

where Lbf s is the credit granted by bank b to ﬁrm f in sector s and F (B) is the total number of

ﬁrms (banks).

Bank sector specializationbs is deﬁned as the ratio of total credit granted by bank b to sector s

relative to bank b’s total credit granted, in the pre-shock period (2007:8-2008:8):

Bank sector specializationbs =

(cid:80)F

f =1 Lbf s
(cid:80)F

f =1 Lbf s

(cid:80)S

s=1

(2)

Bank sector market share is thus the importance of a bank for a sector, while bank sector special-

ization is the importance of a sector for a bank. Note that both variables vary at the bank-sector

level.8 In our empirical setup we use the pre-shock time averaged values of these variables, in line

with the treatment of the credit and funding measures.

Bank balance sheets and income statements are gathered from the monthly regulatory ﬁlings

(“Schema A”) at the National Bank of Belgium. We use the unconsolidated statements as these

allow us to focus on the Belgian operations of the banks. The Belgian banking market is quite

concentrated; in 2007 the share of the largest ﬁve credit institutions in total banking assets was

83% (ECB, 2008). Out of the 38 banks in the sample, 16 are domestic. Ten of the foreign banks are

subsidiaries, the remaining 12 operate as branches incorporated under foreign law. The domestic

banks are substantially larger and cover 71% (82%) of the entire corporate credit market (banking

market) during our sample period.

8 Within and across sectors, we ﬁnd substantial variation in sector market share and sector specialization.
The average standard deviation (across the ﬁve most important sectors) of sector market share is 6.5%
and of sector specialization is 12% (relative to average means of 2.7% and 14%). In addition, we observe
for most sectors that both large and small banks appear in the outer quartiles (or terciles) of the sector
specialization measures.

ECB Working Paper Series No 2230 / January 201910The interbank funding shock (∆% Interbank f undingb) is deﬁned as the average value of interbank

funding post-shock minus the average value pre-shock, scaled by the average total assets pre-shock.

This focus on interbank funding is in line with the existing literature (see, e.g., Iyer et al. (2014),

and Ippolito et al. (2016)).9 To avoid serial correlation in the standard errors, we ﬁrst average the

monthly data for the credit growth and funding shock measures to obtain one pre-shock observation

and one-post shock observation at the bank-ﬁrm level (Bertrand et al., 2004). We limit the pre-

shock window to thirteen months to use the maximum amount of information available without

interference from other shocks (e.g., the turmoil in the ABCP market starting at the end of July

2007, as in Iyer et al. (2014)).10

At the bank-ﬁrm level, the average value of the interbank funding shock is -10.3%. This outﬂow

is predominantly due to a reduction in cross-border interbank funding, and hardly due to liquidity

hoarding of Belgian banks who stop lending to each other. The interbank funding shock is negative

for the median bank, but positive on average. This is due to the construction of the shock as the

diﬀerence in the average value of (monthly) interbank funding over the year post- versus the year

pre-Lehman’s failure (this construction is similar to the setup of Khwaja and Mian (2008)). The

actual outﬂow in interbank funding in the year following the Lehman bankruptcy (i.e., the diﬀerence

between August 2009 and August 2008) has both a negative mean (-4.3%) and median (-1.5%).

The between bank variation in both interbank funding shock measures is very similar (a standard

deviation of 11.9% and 11.3%) and both shocks are strongly and signiﬁcantly correlated. Hence,

the way one looks at the interbank funding shock does not signiﬁcantly inﬂuence the information

that is included in the shock measure.

We further compute a number of bank characteristics based on the average over the pre-shock

window (2007:8-2008:8). We consider banks’ reliance on interbank funding (interbank funding to

9 Ippolito et al. (2016) and Iyer et al. (2014) use the level of gross interbank funding, whereas Acharya
and Mora (2015) uses net interbank funding. Ippolito et al. (2016) argue that gross interbank funding is
preferred over net borrowing, as the former better captures the extent to which a bank is exposed to a run
on its funding during the crisis. Moreover, in contrast to the aforementioned authors, our set-up follows
Khwaja and Mian (2008) by using the realization of the shock (i.e., the change in interbank funding)
and not the exposure to the shock. In this case, excluding the change in interbank assets is even more
important as maintaining or cutting interbank lending during the global ﬁnancial crisis is by and large the
bank’s own choice and thus a decision variable rather than an exogenous shock.

10Initially, we use symmetric windows and hence also use a thirteen month post-shock period. However, in
some parts of the analysis, we use expanding post-shock windows varying in length between one month and
24 months to analyze the timing and time-varying magnitude of the reallocation behavior. Furthermore,
we will show that the chosen length of the pre-shock window is not really important for our ﬁndings as the
turmoil in the ABCP market had no eﬀect on the funding and credit reallocation of Belgian banks.

ECB Working Paper Series No 2230 / January 201911total assets), bank capitalization (common equity to total assets), bank proﬁtability (return on

average equity)11, credit risk (write-oﬀs to total loans), liquidity ratio (interbank assets plus cash

over total assets), stable deposit funding (demand and savings deposits to total assets) and bank

size (natural logarithm of total assets).

Firm balance sheets and income statements are collected by the NBB which also performs

a number of consistency checks on the data. Almost all Belgian ﬁrms incorporated under limited

liability (irrespective of their size) are obliged to report. The most notable exceptions are sole

traders or corporations whose legal situation implies an unlimited liability for the owner (typically

very small ﬁrms). We match the last available ﬁrm balance sheet and income statement data prior

to the Lehman collapse with the bank-ﬁrm credit exposures and bank balance sheets. From the

134, 368 ﬁrms that are present in the credit register, 117, 166 ﬁle balance sheets.

We compute a measure of ﬁrm size (pre-shock total assets), age (pre-shock number of years since

incorporation), leverage (pre-shock total debt to pre-shock total assets), collateral availability (pre-

shock pledged collateral to pre-shock tangible ﬁxed assets), ﬁnancial pressure (pre-shock interest

payments to pre-shock EBIT), and implicit interest rate (pre-shock interest payments to pre-shock

ﬁnancial debt).

Information on the construction of all variables is reported in Table 1, whereas summary statistics

are reported in Table 2.

TABLES 1 and 2 around HERE

2.2 Hypothesis development

In this section, we formulate our main hypotheses. To do so, we start from the following stylized

expected return equation for a loan:

E[RK] = p ∗ RL + (1 − p) ∗ γ − c

11One bank in our sample has more than 100 percent return on equity pre-shock, which is in large part

driven by a very low level of common equity.

ECB Working Paper Series No 2230 / January 201912where E[RK] is the expected return, the borrower is either successful (with probability, p) or fails,

RL is the contractually agreed interest rate, γ is the recovery rate upon default and c are the costs

associated with originating one unit of the loan.

A funding shock could either lead to an increase in c for a given level of funding, or a drop in

the availability of funds for a given level of c.

In both situations, a bank will start cutting on

its marginal borrowers, either because the increase in funding costs makes them negative NPV

projects or because the funding constraint leads the bank to prefer the inframarginal borrower

over the marginal borrower. This inframarginal borrower can diﬀer in three dimensions from the

marginal borrower. All else equal, borrowers with a higher repayment probability (p), borrowers

with a higher contractual interest rate (RL), or borrowers with a higher recovery value upon default

(γ) will yield a higher return.

If a bank has a dominant position in a market segment, it will be able to charge borrowers in that

segment a higher contractual interest rate. Therefore, when a bank faces a negative funding shock,

it will -all else equal- shield those borrowers in that market segment. In this paper, we look at

sectors as the relevant market segment and take banks’ sector market share as a proxy for pricing

power in that sector (implying higher RL). Panel A of Table 3 supports the assumption that a

dominant market position typically leads to higher interest rates. We ﬁnd that, during the year

before the Lehman collapse, ﬁrms borrowing from banks with a larger sector market share pay

higher interest rates, holding constant other ﬁrm and bank characteristics.12 On average over the

three speciﬁcations reported in panel A of Table 3, we ﬁnd that a one standard deviation increase

in sector market share leads to a 40 basis points increase in the implicit interest rate (which has

an unconditional mean of 11.6%). This leads us to formulate the following hypothesis:

H1 Bank sector market share: A bank that faces a negative funding shock will transmit this

shock relatively less to borrowers operating in sectors where the bank has a larger market share.

TABLE 3 around HERE

12Given that we do not have information on contractual interest rates, we compute ﬁrms’ implicit interest
rate from ﬁrm balance sheets - deﬁned as interest expenses over ﬁnancial debt - as a proxy for the actual
interest rate. We only include ﬁrms borrowing from one bank in this test, as for these ﬁrms the implicit
interest rate variable can more clearly be related to the speciﬁc bank and it’s sector market share.

ECB Working Paper Series No 2230 / January 201913Banks that have more and better information about their borrowers are better able to assess the

repayment probabilities of these borrowers as well as their recovery values in case of default. While

more and better information is beneﬁcial, collecting information about every borrower is costly.

However, there are economies of scale with respect to the information collection within a given

market segment. Banks that invest more resources in a given market segment gain an information

advantage in that segment that allows them to better screen prospective borrowers and to more

eﬃciently monitor existing borrowers (implying higher p and γ, respectively).13 Taking sectors as

the relevant market segment, we deﬁne banks’ sector specialization as the share of the sector in the

banks’ total lending. Bank sector specialization thus proxies for the amount of resources that a

bank has invested in a given sector and hence proxies for the information advantage that the bank

has gained in that sector.

We provide tentative evidence that banks in our sample have an information advantage in sectors

in which they are specialized. In Panel B of Table 3 we focus on borrowers that went bankrupt in

the year prior to the Lehman collapse. The results document that the number of bankrupt ﬁrms

in a bank-sector combination (Columns 1 and 2) as well as the bank’s share in a given sector’s

bankruptcies (Columns 3 and 4) is smaller for a bank with a higher sector specialization, holding

constant the bank’s market share and sector speciﬁcities.14 On average over the four speciﬁcations

reported in panel B of Table 3, we ﬁnd that a one standard deviation increase in sector specialization

leads to a 19.4% reduction of the dependent variable (relative to its unconditional mean). This

suggests that specialized banks are better able to select good projects and will have an incentive

to protect this information advantage. This ﬁnding is in line with Berger et al. (2017) and leads us

to formulate the following hypothesis:

H2 Bank sector specialization: A bank that faces a negative funding shock will transmit

this shock relatively less to borrowers operating in sectors where the bank has a larger sector

specialization.

13One could also think about p as the average repayment probability: p = φpH + (1 − φ)pL, as in e.g. Sharpe
(1990). Specialization then provides the bank with a better signal of whether borrower quality is pH or
pL.

14Sector ﬁxed eﬀects control for the sectors’ diﬀerent size and riskiness, whereas sector market share controls
for a bank’s relevance for the sector. The signiﬁcant and positive relationship between sector market share
and the dependent variables in panel B could be explained due to the possibility that higher loan rates
could imply (weakly) higher bankruptcy risk for bank borrowers (see, e.g. Boyd and De Nicolo (2005)).
The relation is also partly mechanical. All else equal, a bank with a larger market share in a given sector
will have a larger part of the borrowers that declare bankruptcy in that sector.

ECB Working Paper Series No 2230 / January 201914Finally, it is interesting to analyze how ﬁrm characteristics impact credit reallocation. Risky borrow-

ers might provide higher cash ﬂows. Safe borrowers will likely have higher repayment probabilities.

Both characteristics could thus lead to shielding. Which eﬀect dominates remains an empirical

question. Additionally, the degree of bank sector market share or specialization could be correlated

with borrower characteristics. Testing them jointly will allow us to rule out that the hypothesized

reallocation channels are not caused by the banks’ sector market share or specialization, but instead

by the speciﬁc type of the borrowers in those sectors where the banks have a higher market share

or are more specialized.

H3 Firm risk: Banks that face a negative funding shock will transmit this shock depending on

borrower characteristics.

2.3 Methodology

First, we analyze the average impact of a funding shock on bank credit supply. The average impact

will serve as a baseline when focussing on the reallocation eﬀect of the shock across sectors. The

shock we exploit corresponds in timing with the collapse of the investment bank Lehman Brothers

in September 2008 (‘the shock’). As can be seen in Figure 1, the volume of interbank funding

of banks active in Belgium severely dropped as of September 2008. Importantly, this world-wide

interbank funding dry-up after the collapse of Lehman Brothers was exogenous to the Belgian credit

market.

To analyze the average impact of the interbank funding shock on credit supply, we run a baseline

regression of our three diﬀerent credit growth measures (Creditbf ) on the bank funding shock, as

well as on a number of bank controls and ﬁrm credit demand controls. In particular, we estimate

the following equation:

Creditbf = β1 ∆% Interbank f undingb + γ Bank controlsb + αf + (cid:15)bf

(3)

Subsequently, to analyze the within-bank heterogeneity in shock transmission, due to sector market

share and specialization, we expand our baseline model with measures of bank sector market share

ECB Working Paper Series No 2230 / January 201915and bank sector specialization and their interaction terms with the bank funding shock:

Creditbf = β2 ∆% Interbank f undingb ∗ Sector market sharebs +

β3 ∆% Interbank f undingb ∗ Sector specializationbs +

θ1 Sector market sharebs + θ2 Sector specializationbs + αf + υb + (cid:15)bf

(4)

We control for bank-speciﬁc characteristics in both regression models, albeit diﬀerently. When

analyzing the average impact of the funding shock (Equation (3)) we add a set of bank-speciﬁc

control variables that capture banks’ pre-crisis characteristics. We consider banks’ reliance on

interbank funding, bank capitalization, bank proﬁtability, credit risk, liquidity ratio, stable deposit

funding, and bank size. An attractive feature of the market share and the specialization measure

in Equation (4) is that they vary at the bank-sector level. This allows us to control for observed

and unobserved bank-speciﬁc heterogeneity by including bank ﬁxed eﬀects (υb). We cannot do that

when studying the average eﬀect in Equation (3) as they would subsume the funding shock.

Next to including the funding shock and bank-speciﬁc controls, we control for observed and unob-

served ﬁrm heterogeneity (including changes in ﬁrm-speciﬁc credit demand). In our most conserva-

tive setup, we do this by means of a set of ﬁrm ﬁxed eﬀects, αf . As such, we isolate credit supply

by investigating how banks with diﬀerent degrees of funding outﬂow changed their lending towards

the same ﬁrm. The disadvantage of this setup is that one can only include ﬁrms that have at least

two bank relationships. This disadvantage is sizable given that 84% of the ﬁrms in Belgium borrow

from only one bank. As such, including ﬁrm ﬁxed eﬀects substantially reduces the sample size and

might create biased results if the multiple borrowers have vastly diﬀerent characteristics than other

borrowers. Therefore, we also report results for a sample that entails the full set of bank-ﬁrm pairs

and use a ﬁrm-cluster ﬁxed eﬀect to control for credit demand in this setup. The single bank ﬁrms

are grouped according to the deciles of loan size in the credit register, the two-digit NACE code

and the two-digit postal code (which broadly coincides with the district level). A similar approach

is used by Edgerton (2012), Degryse et al. (2018) and Morais et al. (2018). In case of a ﬁrm with

multiple bank relationships, the cluster is deﬁned as the ﬁrm itself. In Section 4.1 we explicitly

discuss the (dis)advantages of both setups and show that ﬁrm-cluster ﬁxed eﬀects are well suited

to control for ﬁrm demand.

ECB Working Paper Series No 2230 / January 201916We expect that banks facing severe interbank funding outﬂows will need to reduce their credit

supply relative to banks with no interbank funding outﬂow. Hence, in Equation (3), we expect

β1 > 0 in regressions with ∆% Creditbf or Increase in creditbf as dependent variable; and β1 < 0

in regressions with Large decrease in creditbf as dependent variable.

Based on hypotheses 1 and 2, we expect that banks facing severe interbank funding outﬂows will

try to shield borrowers that operate in sectors where the bank has a larger market share or where

the bank has a larger sector specialization from the reduction in credit supply. Hence, in Equation

(4), we expect β2 < 0 and β3 < 0 in regressions with ∆% Creditbf or Increase in creditbf as

dependent variable; and β2 > 0 and β3 > 0 in regressions with Large decrease in creditbf as

dependent variable.

In order to investigate reallocation eﬀects due to ﬁrm riskiness, we also expand model (4) with

interaction terms between the funding shock and ﬁve ﬁrm characteristics:

Creditbf = β2 ∆% Interbank f undingb ∗ Sector market sharebs +

β3 ∆% Interbank f undingb ∗ Sector specializationbs +

+

5
(cid:88)

x=1

βx+3 ∆% Interbank f undingb ∗ F irm V ariablex

f +

θ1 Sector market sharebs + θ2 Sector specializationbs + αf + υb + (cid:15)bf

(5)

More speciﬁcally, we proxy for ﬁrm size, ﬁrm age, leverage, pledged collateral, and ﬁnancial pressure.

We thus test if banks that faced a funding shock after the Lehman collapse, transmitted the

shock diﬀerently to their smaller, younger and/or riskier borrowers, while simultaneously shielding

borrowers in sectors where they are more present or more specialized. Including these interactions

also ensures that our sector reallocation results are not driven by (risk related) ﬁrm-level reallocation

eﬀects.

If banks facing severe interbank funding outﬂows would shield their larger, older, and safer bor-

rowers from the reduction in credit supply, we expect β4 < 0, β5 < 0, and β6 > 0, β7 > 0, β8 > 0

in regressions with ∆% Creditbf or Increase in creditbf as dependent variable; and opposite signs

in regressions with Large decrease in creditbf as dependent variable.

ECB Working Paper Series No 2230 / January 2019173 Empirical Results

3.1 Average impact of the funding shock

We ﬁrst analyze the average impact of the interbank funding shock on credit supply. Table 4

presents the results using the three diﬀerent credit growth indicators as dependent variable and

consists of two panels. In the upper panel, we present the results for the full sample of ﬁrms, while

controlling for ﬁrm demand by including ﬁrm-cluster ﬁxed eﬀects. The sample consists of 160, 223

observations from 117, 166 ﬁrms borrowing from 38 banks and grouped together in 34, 639 ﬁrm

clusters. We ﬁnd that the funding shock has a statistically signiﬁcant eﬀect on credit availability

in each of the three speciﬁcations. Firms borrowing from banks facing a larger funding outﬂow will

face a tighter credit supply, reﬂected by a lower credit growth (Column 1), a lower likelihood of

seeing an increase in their granted loan amount (Column 2) and a higher likelihood to experience a

large drop in the granted loan amount (Column 3). Overall, bank funding shocks play a signiﬁcant

role for the credit extension to ﬁrms operating in Belgium.

TABLE 4 around HERE

What does a point estimate of 0.414 in Column 1 of Table 4 imply in economic terms? The total

amount of granted credit prior to the shock to all ﬁrms in the sample is EUR 100 billion. The

average ﬁrm’s bank experiences a funding shock of 10.3%. A point estimate of 0.414 thus implies

that the average ﬁrm’s supply-induced drop in credit is -4.26%.15 Our results thus indicate that the

supply-shock induced ‘missing credit’ in the Belgian credit market is EUR 4.2 billion. The other

coeﬃcients can be interpreted as changes in probabilities. A ﬁrm borrowing from a bank hit by

a funding shock of -10.3% has a 6.04 percentage points lower probability of seeing an increase in

granted credit (sample mean is 28.9%) and a 9.03 percentage points higher probability of seeing a

large decrease in granted credit (sample mean is 25%, by construction).

One, potentially crucial, diﬀerence with the common practice in the literature on bank funding

shocks is the set of ﬁxed eﬀects we include. In the aforementioned results, we control for credit

15The 250 billion decrease in interbank funding was mainly absorbed by a large drop in interbank assets of
slightly more than 200 billion. Another major shock absorber that insulated the Belgian corporate market
was the reduction in cross-border lending (slightly less than 40 billion).

ECB Working Paper Series No 2230 / January 201918demand by including ﬁrm-cluster ﬁxed eﬀects. In Panel B of Table 4 we document the robustness

of our results when using the smaller sample of multiple bank-relationship ﬁrms using ﬁrm ﬁxed

eﬀects. This subsample of ﬁrms borrowing simultaneously from multiple banks consists of 47, 205

observations covering 21, 349 ﬁrms. When focusing on the smaller sample of multiple bank bor-

rowers, we ﬁnd results that are qualitatively and quantitatively similar to the results in Panel A of

Table 4. The funding shock has a statistically signiﬁcant eﬀect on all three credit growth indicators

and the coeﬃcients have a similar magnitude as in the full sample.

3.2 Bank funding shocks and credit reallocation

3.2.1 Bank sector market share and bank sector specialization

TABLE 5 around HERE

Table 5 documents the reallocation of credit across sectors after the funding shock. As in the

previous section, we report two sets of results. Panel A shows the results for the full sample of

ﬁrms. In this panel, we control for ﬁrm demand by means of ﬁrm-cluster ﬁxed eﬀects. Panel B

shows the results for the subsample of ﬁrms that borrow from more than one bank. In this sample,

we include ﬁrm ﬁxed eﬀects. In addition, in the speciﬁcations testing the reallocation channels, we

can also include bank ﬁxed eﬀects as the funding shock is interacted with measures computed at

the bank-sector level.

The results in Panel A of Table 5 ﬁrst of all show that the pass-through of the funding shock is

less severe in sectors where the bank has a large market share. Focusing on the actual growth of

granted credit (Column 1), the coeﬃcient of the interaction term between the funding shock and

sector market share is negative and signiﬁcant, indicating that banks shield ﬁrms in sectors in which

they have a larger market share. The impact is also economically important. For example, the

point estimates in the ﬁrst column imply that a one standard deviation increase in sector market

share (0.084, see Table 2) reduces the negative impact on credit growth of the average funding

shock from 4.26 to 3.37% (i.e., a reduction of 0.89 percentage points or 20%).16 Similarly, the

16The impact of a 10.3% reduction is based on the results in Table 4 : -10.3% * 0.414 = -4.26%. Based on
the results in Table 5, a one standard deviation increase in sector market share of 0.084 leads to an impact
of -10.3% * (0.414 - 1.029 * 0.084) = -3.37%.

ECB Working Paper Series No 2230 / January 201919second and third column illustrate that the impact of a negative funding shock on the probability

of increasing granted credit (Column 2) or having a large drop in granted credit (Column 3) is less

severe for ﬁrms operating in sectors where the bank has a larger market share. A one standard

deviation increase in sector market share reduces the negative impact of the average funding shock

on the probability of seeing an increase (large decrease) in granted credit by 29% (15%).

Apart from sector market share, sector specialization also plays an important role for the pass-

through of bank funding shocks. Our results indicate that, after a negative funding shock, credit

growth is less aﬀected in sectors that make up a relatively larger share of a bank’s portfolio. More

precisely, the results in the ﬁrst column of Table 5 imply that a one standard deviation increase in

sector specialization (0.083) reduces the negative impact of a 10.3% reduction in bank funding from

4.26 to 4.07% (i.e., a reduction in impact of 0.19 percentage points or 4%). The impact of sector

specialization on the probability of increasing granted credit (Column 2) and on the probability

of a large drop in granted credit (Column 3) conﬁrm that banks reallocate credit after a funding

shock towards ﬁrms operating in sectors in which the bank is specialized. A one standard deviation

increase in sector specialization reduces the negative impact of the average funding shock on the

probability of seeing an increase (large decrease) in granted credit by 5% (8%).

Panel B of Table 5 reports the results for the sample of ﬁrms that borrow from more than one

bank. In this sample, we can control for ﬁrm demand by means of ﬁrm ﬁxed eﬀects. In general,

the results in this sample go in the same direction as the ones in the full sample, have the same

economic magnitude, but become somewhat less precise.17 Further tests in Section 4.1 indicate

that this drop in precision is unlikely to be driven by the improvement in demand controls in this

sample, but are rather a consequence of the sample speciﬁcities of multiple-bank vs. single-bank

borrowers. Before digging deeper into this issue, we ﬁrst examine whether ﬁrm characteristics also

lead to reallocation eﬀects.

Overall, the results in Table 5 indicate that sector market share and specialization matter for the

pass-through of bank funding shocks to ﬁrms. Banks prefer to shield ﬁrms in sectors in which

they have a larger market share or in which they are more specialized. The results shown in

17A one standard deviation increase in sector market share in Columns 1, 2, and 3 in Panel B of Table 5
leads to an estimated average mitigation of the credit contraction caused by a 10% funding shock of 30%,
20%, and 16% respectively. Similarly, the estimated average mitigation due to a one standard deviation
increase in sector specialization is 4%, 11%, and 16% in Columns 1, 2, and 3, respectively of Panel B of
Table 5.

ECB Working Paper Series No 2230 / January 201920Table 3 provide empirical evidence for the economic channels at work (namely rent extraction and

information advantages, respectively).

3.2.2 Firm risk

Both theoretical and empirical work has indicated that smaller ﬁrms (size), ﬁrms without track

record (age) or ﬁrms with weaker balance sheets or less collateral (risk) are more likely to be

ﬁnancially constrained, due to asymmetric information between the bank and the ﬁrm. This holds

in general and is expected to be particularly relevant during periods characterized by adverse

economic shocks (e.g., tight monetary policy, economic recession, banking crisis). If the degree of

bank sector market share or specialization is correlated with the characteristics of ﬁrms that banks

lend to, it might be that the above-documented reallocation channel is not caused by the banks’

market share or specialization, but instead by the speciﬁc type of the borrowers in those sectors

where the banks are more present or specialized. In this section, we exploit the heterogeneity in

ﬁrm characteristics to explore whether banks diﬀerentially transmit a funding shock to ﬁrms in

excess of the reallocation due to bank sector market share and specialization.

We consider ﬁve ﬁrm characteristics. We proxy for ﬁrm size, age, leverage, collateral availability,

and ﬁnancial pressure. We thus test if banks that faced a funding shock after the Lehman collapse

transmitted the shock more to their smaller, younger and or riskier borrowers, while simultaneously

shielding borrowers in sectors where they are more present or more specialized.

TABLE 6 around HERE

The results in Panels A and B of Table 6 indicate that banks reallocate credit to safer ﬁrms. A low

leverage ratio, a low amount of already pledged collateral and a low ratio of interest payments over

earnings all shield ﬁrms from the transmission of the funding shock. These reallocation eﬀects are

not only statistically, but also economically signiﬁcant. Focussing on the full-sample results, a one

standard deviation decrease in the leverage ratio, in the ratio of pledged collateral to ﬁxed assets,

or in the ratio of interest payments over earnings reduces the negative impact on credit supply of

a 10.3% reduction in bank funding by 11, 6, or 12%, respectively. A change in risk characteristics

thus has a mitigating eﬀect that is roughly equal in magnitude compared to sector specialization.

ECB Working Paper Series No 2230 / January 201921The results in Table 6 also show that the sector reallocation documented in Section 3.2.1 is still

present after taking into account heterogeneity in the shock transmission according to ﬁrm risk.

Banks still reallocate credit towards sectors in which they are more present and towards sectors in

which they are more specialized, over and above reallocating credit towards safer ﬁrms. Hence, it

ensures that our sector reallocation results are not driven by (risk related) ﬁrm-level reallocation

eﬀects.

It also alleviates the concern that our sector reallocation ﬁndings could be driven by

characteristics that are typically rather sector-speciﬁc, such as having a high amount of collateral.

Finally, we do not ﬁnd evidence that banks transmit liquidity shocks more to small ﬁrms or to young

ﬁrms. The interaction of the funding shock with ﬁrm size and age is not signiﬁcant, independent

of the demand controls, which is in contrast with Khwaja and Mian (2008), Iyer et al. (2014) and

Liberti and Sturgess (2016). A potential explanation is that small ﬁrms in Belgium have to report

relatively detailed balance sheet information. As a consequence, asymmetric information problems

between (small or young) ﬁrms and banks are potentially much lower in Belgium than in the other

(emerging) countries investigated in the aforementioned papers.

4 Challenges to identiﬁcation

4.1 Demand control versus sample composition

Tables 4, 5 and 6 presented results for the full sample of borrowers in Panel A and the subset

of multiple bank borrowers in Panel B. In general, we observed that the results for the sample

of multiple ﬁrms go in the same direction as the ones for the full sample, but they become less

precise. This section shows that these less signiﬁcant results are not driven by the (theoretical)

improvement in credit demand controls, but are rather a consequence of the speciﬁcities of multiple

bank vis-`a-vis single bank borrowers.

Estimations on the sample of multiple bank borrowers allow for the inclusion of ﬁrm ﬁxed eﬀects,

which is the most conservative way to control for ﬁrm demand and is the current standard in the

literature. However, it also implies that one can only use data for ﬁrms that borrow from more

than one bank. The vast majority of ﬁrms in Belgium (84%) borrow from only one bank. Ignoring

these ﬁrms leads to a number of concerns when it comes to testing our two main hypotheses. First

ECB Working Paper Series No 2230 / January 201922of all, focussing on the small subgroup of multiple bank borrowers might bias our results if these

ﬁrms are not representative for the full sample of borrowers. Multiple bank borrowers are indeed

more likely to be larger and older ﬁrms than single bank borrowers (Degryse et al., 2018). More

importantly, they also have substitutes available, which reduces the potency of the sector market

share and sector specialization channels. Second, recent research by Degryse et al. (2016) shows

that an outside loan (i.e., a loan from another bank) decreases a bank’s willingness to lend to this

ﬁrm. This, together with the prospect of potential coordination problems between lenders when

a ﬁrm defaults, might make it less attractive to shield multiple bank borrowers. Overall, we thus

expect stronger results for the full sample than for the multiple bank borrower sample.

We run three additional tests to examine the importance of the trade-oﬀ between using the most

appropriate sample to test our hypotheses and using the most appropriate set of ﬁxed eﬀects to

control for credit demand. First, we run the baseline regression analyzing the average eﬀect of

the funding shock on the multiple bank borrower sample (as in Panel B of Table 4), but use ﬁrm

cluster ﬁxed eﬀects based on location-sector-size triples rather than ﬁrm ﬁxed eﬀects (the results

are reported in Panel A of Table 7). We do not ﬁnd any diﬀerence for the average impact of the

change in interbank funding on bank lending in the multiple bank borrower sample when using

ﬁrm cluster or ﬁrm ﬁxed eﬀects. This is a ﬁrst indication that ﬁrm cluster ﬁxed eﬀects reasonably

capture ﬁrm credit demand.

TABLE 7 around HERE

Second, we also run a regression examining the reallocation channels on the sample of multiple

bank borrowers, but use ﬁrm cluster ﬁxed eﬀects based on location-sector-size triples rather than

ﬁrm ﬁxed eﬀects. Comparing the results with ﬁrm cluster ﬁxed eﬀects (Panel B of Table 7) to

the results with ﬁrm ﬁxed eﬀects (Panel B of Table 6), we ﬁnd that the realloaction coeﬃcients

are very similar (8 out of 9 signiﬁcant coeﬃcients remain signiﬁcant), which again indicates that

ﬁrm cluster ﬁxed eﬀects reasonably capture ﬁrm credit demand. Third, we run regressions on the

subsample of single bank borrowers only (Panel C of Table 7) and ﬁnd similar eﬀects as in the full

sample that are, if anything, statistically more signiﬁcant.

All three tests thus indicate that the reduction in the precision of the coeﬃcients in the multiple bank

borrower sample is more likely driven by the speciﬁcities of the sample than by the appropriateness

ECB Working Paper Series No 2230 / January 201923of the demand control.

4.2 Bank solvency concerns

A potential concern is that exposure to the US subprime crisis might have led to solvency problems

at Belgian banks. If these exposures are correlated with our funding shock, this could bias our

results.

In this section, we therefore examine whether our results are biased due to pre-shock

bank solvency issues. Short of a measure of the direct exposure to the U.S. mortgage market, we

construct a proxy as follows. For each Belgian bank, we have the exposure to three sectors (public

sector, non-bank private sector, or banks) in a given country. We use the ultimate exposures to

the U.S. non-bank private sector and US banking sector to construct our solvency proxy.18

The exposure to the non-bank private sector can be seen as a proxy for exposure to U.S. households.

The exposure to the U.S. banking sector allows us to control for two related solvency concerns.

First, it allows us to proxy for the asset-side exposure to the ABCP market. Kacperczyk and

Schnabl (2010), for example, document that asset-backed commercial paper played a crucial role

during the 2007-2009 crisis, and that more than 90% of this paper was issued by the ﬁnancial

sector. While ABCP was often issued by ﬁnancial conduits owned by banks- and thus not directly

by banks - Acharya et al. (2013) show that a large part of the issuances was explicitly insured

by the banks. As such, the ultimate exposure to the U.S. banking sector should be a good proxy

for the exposure to the ABCP market. Second, another main part of the exposure to banks are

direct exposures to U.S. banks. Given that a large part of the U.S. banking sector was severely hit

by the crisis (Huizinga and Laeven (2012)), for example, document that 60% of US bank holding

companies had a market-to-book ratio of assets below one by the end of 2008) it is reasonable to

expect that a high exposure to these banks could also lead to solvency concerns.

We calculate the sum of the ultimate exposure to U.S. banks and to the U.S. private sector for each

month during our pre-period, and then take the average over this period. We scale this average

exposure by a bank’s average total assets over this period.

18The granular data is the source data used to construct the BIS consolidated international banking statistics
and is hence available on immediate and ultimate risk base. We use the ultimate exposures as they account
for net risk transfers. For example, suppose that a Belgian bank extends a loan to a company based in
Mexico and that the loan is guaranteed by a US bank. On an immediate risk basis, the exposure would
be to Mexico. However, at the ultimate risk basis, the loan is regarded as a claim of a Belgian bank on
the United States banking sector since that is where the ultimate risk resides.

ECB Working Paper Series No 2230 / January 201924Panel A of Table 8 shows that the average impact of the funding shock is still signiﬁcant when

controlling for the exposure to the U.S. Moreover, compared with the results in Table 4, the coeﬃ-

cients associated with the funding shock remain their sign and are of similar magnitude compared.

Columns 1, 3 and 5 of Table 8 show the results for the full sample, while Columns 2, 4 and 6 show

the results for the sample of multiple bank borrowers. The coeﬃcient on the U.S. exposure variable

is negative and signiﬁcant, implying that banks with a higher exposure did reduce credit more than

other banks, which is in line with the expectation that the U.S. exposure variable is a good proxy

for solvency concerns.

Panel B of Table 8 shows that also our reallocation results still hold when adding the interaction

terms between this solvency proxy and the banks’ sector market share and specialization. Compared

to the baseline results in Table 5, there is only a slight reduction in coeﬃcient size. The interaction

terms that are signiﬁcant in the baseline setup remain signiﬁcant when adding the solvency proxy,

with one exception being the interaction with sector market share in Column 4.

TABLE 8 around HERE

In unreported regressions, we also use an alternative measure for solvency concerns, which is the

outﬂow in corporate deposits. The outﬂow in corporate deposits proxies for the change in uninsured

deposits, which should respond to solvency concerns (if any). Our ﬁndings are unaﬀected when

using this alternative solvency concern measure.

4.3 Pre-shock liquidity events

Our next test addresses a potential bias due to pre-Lehman liquidity issues. Even when banks

had no direct asset-side exposure to the ABCP market, the 2007 crisis in this market might have

aﬀected their funding conditions. Existing studies such as Iyer et al. (2014) and Ippolito et al. (2016)

observe that the interbank funding of Portuguese, respectively Italian, banks dropped due to the

ABCP crisis. They also show that this interbank shock impacted bank credit supply (Portugal)

or drawdowns on existing credit lines (Italy). In line with these papers, we redo our analysis and

now take 2006:6-2007:7 as pre-period and 2007:7-2008:8 as post-period. The results are reported

in Table 9.

ECB Working Paper Series No 2230 / January 201925TABLE 9 around HERE

In contrast to Iyer et al. (2014) and Ippolito et al. (2016), we do not ﬁnd that the change in

interbank funding around July 2007 is statistically signiﬁcantly related to credit supply (none of

the three measures); neither for the full sample (Panel A of Table 9) nor for the multiple bank

borrower sample (Panel B of Table 9). In addition, we do not ﬁnd support for the reallocation

channels in this period. These results are not surprising as Figure 1 indicates that the interbank

funding of Belgian banks was not yet impaired prior to Lehman. The ﬁgure shows that funding was

still growing, though slightly more volatile, after July 2007 and (nearly) peaked around the Lehman

collapse. In addition, given the resilience of Belgian banks to the interbank funding turmoil in the

summer of 2007, the results in Table 9 could also be seen as a placebo test and are as such also

reassuring for the choice of our event window.

In sum, we can be conﬁdent that our results are not aﬀected by pre-Lehman interbank market

liquidity concerns.

4.4 Post-shock confounding events

The largest banks operating in Belgium experienced the largest interbank funding shock, but are

at the same time also the most internationally active. One concern could be that the choices

they make in their Belgian portfolio are not independent from their cross-border portfolio choices.

Furthermore, some of these large banks received government bail-outs, which might raise concerns

on whether or not these interventions could have aﬀected banks’ lending policies. Both concerns

imply that our results might be contaminated or driven by factors that correlate with bank size,

other than the interbank funding shock. In this section, we show that these potentially confounding

events are not driving our results.

We start by investigating the role of the adjustments in the foreign corporate lending market. An

ideal scenario would be that we have information on cross-border loans on a bank-ﬁrm basis or

bank-sectoral level. This would allow us to analyze whether banks adjust the Belgian portfolio

according to cross-border sector market share and specialization or vice versa. Unfortunately, such

data is not available. The second best is to obtain data on the total volume of cross-border lending

ECB Working Paper Series No 2230 / January 201926by bank on a monthly basis. We collect these data from the regulatory balance sheet ﬁlings of the

banks and use it to run additional tests.

We use the pre-shock share of cross-border lending in total lending, and analyze whether having

large cross-border lending operations aﬀects credit reallocation (similar to Table 5).19 Compared

with the baseline speciﬁcation (see Table 4), we ﬁnd a similar eﬀect of the interbank funding shock

on credit supply in Belgium when the pre-shock share of cross-border lending is included (as can be

seen in Panel A of Table 10). In terms of credit reallocation in response to the interbank funding

shock, there is hardly any eﬀect on the magnitudes of the point estimates of the reallocation channels

compared to a speciﬁcation without these additional cross-border lending interaction terms (panel

B of Table 10 versus Table 5).

TABLE 10 around HERE

Next, one might be concerned that the various government interventions may have come with some

(to us unobservable) strings attached for the banks. Anecdotal evidence on the bail-outs during our

sample period indicates that governments were almost exclusively occupied with avoiding a total

collapse of the banking sector and restoring conﬁdence of the public in the banking sector. We

also found no indications in either the media, government sources, banks’ annual reports, nor from

informal inquiries with bank supervisors and bank sector representatives that there would have been

political pressure on banks to particularly keep lending where the bank has more market power or

is more specialized. However, it is still important to clearly spell out how these interventions might

impact our results.20

One concern could be that (i) government interventions are strongly correlated with our interbank

funding shock and that (iia) these banks were forced to keep lending or (iib) saw a smaller funding

outﬂow than would have been the case without bailout.

It is indeed the case that there is a

19We use the pre-shock share of cross-border lending and not the actual change in cross-border lending given
that the latter is jointly determined with the change in domestic lending. In unreported results we observe
a positive and signiﬁcant relationship between the change in interbank funding and the change in cross-
border lending. Moreover, this relations is stronger for banks with a relatively higher share of cross-border
loans in their loan portfolio.

20We add a description of the guarantees and capital injections received by the large Belgian banks in the

online appendix, Section A1.

ECB Working Paper Series No 2230 / January 201927relatively strong correlation between government interventions and our funding shock measure: We

ﬁnd a correlation of -0.41 between a government intervention dummy and the percentage change

in interbank funding at the bank level. Now, if either scenario (iia) or (iib) is also true (which we

cannot observe), then our estimates reported in Table 4 are potentially underestimating the true

eﬀect and thus provide a lower bound for the impact of funding shocks.

A more complicated scenario would be that (i) government interventions are strongly correlated

with our interbank funding shock and (ii) the government pushes the bank to keep lending exactly

in the sectors where that bank is most specialized and/or has a larger market share. Again point

(ii) is less straightforward and much more diﬃcult to check. In order to show that our results are

driven by a funding shock, rather than by interventions, we set up a robustness test that focuses

on a funding shock that is far less correlated with government interventions: Deposit ﬂows. The

correlation between a bailout dummy and the change in deposit funding in our bank sample is

only -0.20 (versus -0.41 for the interbank funding shock). Even more important, while two of the

banks which received government support in our sample experienced a strong drop in deposits

after the Lehman collapse, the two other that received government support eﬀectively experienced

an inﬂow of deposits. Two large banks thus faced a bank-run on their deposits (on top of the

interbank funding dry-up),21 and a large portion of these deposits were deposited at the other

banks (including the two other large banks). This automatically makes it less plausible that any

results obtained using this deposit shock are driven by bailouts. Table 11 shows the results when

using a deposit shock instead of an interbank funding shock.

TABLE 11 around HERE

Table 11 documents that the reallocation eﬀects are similar when using the change in deposits

rather than the change in interbank funding as the shock. In fact, the statistical signiﬁcance as

well as the economic magnitude are even slightly stronger, indicating that, if anything, government

support might be working against ﬁnding support for our hypotheses. Our hypotheses are based

on the proﬁt-maximizing behavior of banks. At the same time, there exists convincing empirical

evidence that government-owned or -intervened banks behave in a non-proﬁt maximizing way,

shaping bank outcomes such as lending (see, e.g., Sapienza (2004), Dinc (2005), Bian et al. (2017)).

21The outﬂow in deposits at these two banks was substantial and amounted to -11% (-4.2%) and -13%

(-3.6%) of their total lending (assets).

ECB Working Paper Series No 2230 / January 201928If government support comes with strings attached (e.g., forced lending to certain sectors), this

would thus most likely not be with the idea of maximizing bank proﬁtability in mind, which works

against our hypotheses and would thus make it harder for us to ﬁnd signiﬁcant results.

4.5 Further robustness

We subsequently ensure that our main reallocation results are not driven by other potential reallo-

cation eﬀects or by underlying bank-ﬁrm speciﬁc relation characteristics.

A ﬁrst important concern might stem from the existing literature on relationship lending (see, e.g.,

Petersen and Rajan, 1994; Berger and Udell, 1995; Boot, 2000; Degryse and Ongena, 2005; Bolton

et al., 2016). Throughout the lending process, banks can gather ﬁrm-speciﬁc soft information

(e.g., management quality), through repeated contacts, which is diﬃcult to observe for outsiders.

Especially banks with market power might be more likely to engage in relationship lending (Petersen

and Rajan (1995)). This information advantage can allow a bank to extract monopoly rents (see,

e.g., Sharpe (1990) or Rajan (1992)). If banks want to shield these rents and if at the same time

banks are more likely to have better ﬁrm-speciﬁc information in sectors in which they have a strong

sector market share or where they are specialized, then our main results might be driven by this

ﬁrm-speciﬁc information advantage. Therefore, we add two widely used relationship lending proxies

to our setup. We include the length of a bank-ﬁrm relationship, measured by the number of months

that a ﬁrm has been borrowing from a bank before September 2008. We also add a dummy which

is equal to one if the bank is the main bank of the ﬁrm, and equal to zero otherwise, where the

main bank is deﬁned as the bank with the largest share in the total amount granted to a ﬁrm.

Another important concern could be that our main sector reallocation results are merely picking up

other types of bank portfolio choices that happen to be related with sector choices. Two examples

of such choices are geographical bank specialization or loan maturity specialization. Some banks

might be coincidentally specialized in a sector because this sector is over-represented in the area in

which the bank is doing business. Similarly, a bank might be coincidentally specialized in a sector

because ﬁrms in that sector mainly need short term credit, and the bank happens to be specialized

in providing that type of credit. Additionally, short-term credit might be easier to cut (or not rolled

over). If sector specialization is correlated with the share of short-term lending, we could be picking

up a spurious correlation. To ensure that this is not driving our main results, we add interaction

ECB Working Paper Series No 2230 / January 201929terms of the interbank funding shock with measures of geographical market share, geographical

specialization and the sector speciﬁc maturity structure of a bank’s portfolio. Geographical market

share and specialization are calculated in a similar way as sector market share and specialization,

but on the province level instead of sector level.22 The maturity structure indicator captures the

share of loans provided by a bank to a sector that matures within one year.

TABLE 12 around HERE

Results of these extended speciﬁcations are reported in Table 12. Most importantly, including these

additional measures does not alter our main ﬁndings. The results on the reallocation eﬀects based

on sector market share and specialization as well as ﬁrm risk are very similar compared to the

results reported in Table 6. Hence, our documented reallocation channels are robust relationships

rather than spurious correlations driven by other possible reallocation channels.

Regarding banks’ geographical orientation, we conduct two further tests, which are discussed in

detail in the online appendix (Section A2). First, rather than measuring bank market share and

bank specialization at the sector level considering Belgium as the relevant market, we now measure

it by province. This does not impact our main results. Second, a recent strand of papers on the

transmission of bank shocks in the US shows that branch presence matters for shock transmission

(Berrospide et al. (2016), Gilje et al. (2016)). This is unlikely to be of importance in our sample.

Almost all loans (98%) are granted in provinces where the bank has a branch. Moreover, we do

not ﬁnd that branch network density matters for credit reallocation or that the reallaction channels

documented in the body of this paper are aﬀected by branch network density. An interaction term

between the number of branches of a bank in a province and the interbank funding shock has the

expected negative sign, but is never signiﬁcant. The results of this additional test are discussed in

more detail in the online appendix.

Finally, in the online appendix (Section A3), we also shed some light on potential diﬀerences

between term credit and credit lines. On the one hand, it may seem easier for a bank to renegotiate

the committed amount on a credit line (in case it is not yet fully utilized) than to cancel term

credit. On the other hand, credit lines are also more likely to be given to better and well-known

22Belgium consists of ten provinces. On average, a province has one million inhabitants and spans 3,000

squared kilometers.

ECB Working Paper Series No 2230 / January 201930borrowers, hence they can be considered more like relationship-based, whereas term loans are more

often seen as transactional (Berger and Udell (1995)). It thus remains an empirical issue whether

and to what extent banks shield certain types of credit when facing a funding shock. We ﬁnd that

an outﬂow of interbank funding leads to a reduction in term credit supply. There is no statistically

signiﬁcant eﬀect on the growth in granted credit line amounts. In addition, we do ﬁnd that the

average utilization rate increases following a bank funding shock. These results are consistent

with Ivashina and Scharfstein (2010) and Ippolito et al. (2016). We also ﬁnd that term credit

borrowers in sectors where the bank has a larger market share are shielded more when the bank

is hit by a funding shock. We do not ﬁnd evidence of shielding in the credit line sample. It is

also reassuring that there are no reallocation eﬀects on the change in the utilization rate. Firms

do not seem to choose drawing signiﬁcantly more or less from banks with sector market share or

sector specialization, indicating that the results we ﬁnd in the overall sample (or in the term credit

sample) are banks’ choices and thus supply driven and not demand driven.

5 Expanding post-shock windows

In this section, we present results using expanding post-shock windows to analyze the timing of

the impact of the interbank funding shock. The purpose is twofold. First of all, such an analysis

can reveal time variation in the magnitude of the impact of an interbank funding shock on credit

supply as well as in the credit reallocation channels. Secondly, it also serves the purpose of simply

showing robustness of our main results for alternative post-event horizons. We do this expanding

window analysis both on the baseline regression (as reported in Panel A of Table 4) and on the

reallocation speciﬁcation with sector market share, sector specialization and ﬁrm characteristics

(Column 1 of Panel A of Table 6).

5.1 Dynamics: Baseline

The regression speciﬁcations in the expanding windows analysis remain similar to Equation (3).

However, by expanding the post-event window, we alter the construction of the interbank funding

shock as well as the dependent variable. We now estimate 24 diﬀerent speciﬁcations that diﬀer

from each other in the length of the post-shock period. We gradually expand the post-shock period

ECB Working Paper Series No 2230 / January 201931month by month, going from one to 24 months, and estimate model (3) each time to test which

factor is important at each horizon. The pre-shock period is always the same time average over the

thirteen months preceding the Lehman shock. Hence, when the post-crisis event window length

is for example four, we compute the growth in lending (or change in interbank funding) as the

diﬀerence in the four-month post-shock average and the thirteen month pre-shock average.

In

Figure 2, we graphically present our results on time variation in the point estimate of interest, i.e.,
ˆβ1 of model (3), for the three diﬀerent dependent variables. In each subplot, we depict the point
estimate on the interbank funding shock, ˆβ1 of model (3) as well as a 90% conﬁdence bound (dotted

lines).

FIGURE 2 around HERE

This expanding window analysis yields a number of interesting and complementary insights. First,

the four plots of Figure 2 indicate that the results presented in Table 4 are robust to varying the

length of the post-event period, except for very short window lengths. It takes three months before

the interbank funding shock starts to have a signiﬁcant impact on credit growth. The impact on

increase in credit and large decreases in credit is already signiﬁcant after two months. Statistically,

the point estimates reported in Table 4 for the full sample using a thirteen month post-event window

(indicated with a vertical line in the graph) are almost always within the conﬁdence bounds obtained

using alternative post-shock window lengths. The build-up of the eﬀects in the ﬁrst ﬁve months

following the shock can be explained by at least two factors. On the one hand it could indicate

that credit supply responds sluggishly to interbank funding shocks. (For instance because there is

a time gap between the signing of the credit contract and the actual provision and take-up of the

loan.) On the other hand it could also be attributed to the fact that the interbank funding outﬂow

hits banks gradually with the largest reductions in October 2008 and December 2008 (see Figure

1).

5.2 Dynamics: The impact of bank sector market share and spe-

cialization

In this subsection, we focus on the time variation in the impact of sector market share and special-

ization on the pass-through of the interbank funding shock, using a regression speciﬁcation similar

ECB Working Paper Series No 2230 / January 201932to Column 1 of Panel A of Table 6. Figure 3 illustrates the impact of the interaction term of the

shock in interbank funding with either sector market share (upper panel) or sector specialization

(lower panel) for every month starting from one month after the shock until 24 months after the

shock. For example, the impact after thirteen months in the upper panel corresponds with the

coeﬃcient on the interaction term between sector specialization and the funding shock in the ﬁrst

column of Table 6.

FIGURE 3 around HERE

The moderating impact of sector market share is nearly instantaneous and twice as large after three

months compared to the impact after six months. At three months, the coeﬃcient is close to two.

Recall that we ﬁnd that the average eﬀect, reported in Figure 2, is positive, but insigniﬁcant in the

ﬁrst three months. Tying these two results together, this implies that, during the ﬁrst three months,

the impact of an average interbank funding shock of -10.3% is non-existing for ﬁrms operating in

sectors in which a bank has a high market share. On the other hand, the impact of the funding

shock will be transmitted immediately, and signiﬁcantly so, if a ﬁrm borrows from a bank with a

substantially lower sector market share. After three months, the eﬀect gradually diminishes but

stays relative stable until fourteen months after the shock. Subsequently, the heterogeneous impact

of sector market share remains statistically signiﬁcant but becomes smaller in economic magnitude.

Sector specialization exhibits diﬀerent dynamics. The eﬀect is insigniﬁcant in the ﬁrst months

after the initial shock, but gradually becomes more important. As soon as the post-event window

exceeds eleven months, it becomes economically and statistically signiﬁcant, lasting at least until

24 months following the Lehman collapse.

Tying the results of both ﬁgures together, we ﬁnd that the magnitude of the pass-through of the

interbank funding shock to the average borrower is similar across diﬀerent event windows. However,

the source of heterogeneity in the pass-through varies across ﬁrms. During the ﬁrst months following

the shock, banks try to be more accommodating in the shock transmission to those ﬁrms where they

expect to have pricing power because of their dominant sector presence. Hence, they tend to screen

more on the price component of the expected loan pay-oﬀ. Subsequently, they start diﬀerentiating

between ﬁrms in sectors where they are specialized, indicating that their sector speciﬁc knowledge

becomes more important. Alternatively, one can interpret this diﬀerence in reaction as banks being

ECB Working Paper Series No 2230 / January 201933at ﬁrst more concerned with staying aﬂoat in the short run by focusing on loans that ensure larger

cash inﬂows (in the form of relatively high interest payments), while only being interested in long

term proﬁtability (and hence focusing on protecting their sector speciﬁc knowledge) once these

short term inﬂows are safe.

5.3 Dynamics: The role of ﬁrm risk

Next, we present evidence on the time-variation of the impact of ﬁrm characteristics on the in-

terbank funding shock transmission. Figure 4 shows 24 point estimates of the most extended

speciﬁcation, each corresponding to an estimation of model (4) (including all ﬁrm characteristics

jointly) where the post shock period is gradually expanded from one month to 24 months post

shock. It leads to two interesting observations. First, The insigniﬁcant ﬁndings on ﬁrm size and

age in our main analysis are not related to the choice of the post-shock time window. We do not

ﬁnd an immediate nor a long-term eﬀect of ﬁrm size or age on the pass-through of the interbank

funding shock. Second, Figure 4 shows that the amplifying eﬀect of ﬁrm risk is a very robust

result across risk measures and post-event window length. Firm leverage and ﬁnancial pressure

have a constant and signiﬁcant impact from three to four months after the shock, while the eﬀect

of pledged collateral to ﬁxed assets tends to decrease slightly over time, but not statistically so.

FIGURE 4 around HERE

6 Real eﬀects: Firm investment and growth

We have shown that banks operating in Belgium transmit interbank funding shocks to their borrow-

ers according to their sector market share and specialization as well as by diﬀerentiating between

ﬁrms with diﬀerent risk proﬁles. In this section, we investigate how this reduction in bank loan

supply aﬀects ﬁrm investment and growth. We analyze this in the following setup:

ECB Working Paper Series No 2230 / January 201934Real Ef f ectf = ψ1 ∆% IBFb + ψ2 ∆% IBFb ∗ Sector market sharebs + ψ3 ∆% IBFb ∗ Sector specializationbs+

5
(cid:88)

x=1

5
(cid:88)

x=1

ψx+3 ∆% IBFb ∗ F irm V ariablex

f + ψ9 Sector market sharebs + ψ10 Sector specializationbs+

ψx+10 F irm V ariablex

f + φ Control V ariablesf + υs + (cid:15)f

(6)

Real Ef f ectf , is computed as the diﬀerence between the last available value of the variable two

years post-shock (i.e., end of 2010) and the last available value of the variable pre-shock, relative

to the last available value of total assets pre-shock. We look at a two year post-shock horizon as

changes in ﬁrms’ strategies following credit constraints usually take time to materialize and show

impact. The two dependent variables we are interested in are growth in tangible ﬁxed assets (net

investment rate) and growth in total assets. β1 captures the extent to which the interbank funding

shock aﬀects ﬁrms’ real outcomes. If ﬁrms borrow from multiple banks, we compute a weighted

interbank funding shock, with weights resembling the pre-shock bank-ﬁrm credit exposure. β2

and β3 capture whether the ﬁrm-level impact of the interbank funding shock varies with bank

sector market share and specialization. Additionally, we interact the interbank funding shock with

ﬁrm-speciﬁc risk, size and age variables, similar to the setup in Table 6. We also control for

accommodating sources of credit and investment or growth opportunities. This includes a measure

of whether the ﬁrm received a new loan from a bank with which it had no prior relationship

(weighted by the importance of the new loan in the post-crisis period), a measure whether a bank

has terminated a loan with the ﬁrm, a dummy measuring whether the ﬁrm has loans with multiple

banks, a measure of the change in the ratio of utilized over authorized credit, the change in the

ﬁrm’s reliance on trade credit, the ﬁrms pre-shock cash holdings, the ﬁrm’s sales growth and a set

of sector ﬁxed eﬀects.

Table 13 shows the corresponding results. The ﬁrst two columns focus on the net investment

rate (proxied by the growth in tangible ﬁxed assets). The results ﬁrst of all indicate that ﬁrms

borrowing from banks with a larger interbank funding outﬂow experience a statistically signiﬁcant,

though economically small, reduction in the investment rate. The point estimate of 0.083 for the

funding shock variable in Column 1 of Table 13 indicates that the average ﬁrm in our sample which

borrowed from a bank that experienced a funding shock of -10.3%, reduced its net investment rate

by 0.85 percentage points. Interestingly, this small average result hides some important underlying

ECB Working Paper Series No 2230 / January 201935heterogeneity. Column 2 indicates that ﬁrms borrowing from banks that have a high sector market

share and large ﬁrms reduce their investment rate less than others. Based on the results in Column

2, the impact of an average interbank funding shock of -10.3% leads to a reduction of the investment

rate by -1.16 percentage points for a ﬁrm of average size that is borrowing from a bank with average

sector market share.23 A ﬁrm of average size but borrowing from a bank with an average interbank

funding shock of -10.3% for which the sector market share is one standard deviation lower, however,

reduces its net investment rate by about -1.80 percentage points. Similarly, a ﬁrm that is borrowing

from a bank with an average funding shock of -10.3% and an average sector market share but that

is a standard deviation smaller than the average ﬁrm will reduce its investment growth slightly

more than 2 percentage points. The last two columns show the impact of the interbank funding

shock on asset growth. As with investments, ﬁrms that borrow from a bank with a more negative

interbank funding shock grow slower than other ﬁrms. Reallocation eﬀects based on ﬁrm size are

similar to the eﬀects found for ﬁxed assets.

TABLE 13 around HERE

Overall, we ﬁnd a moderate reduction in investment and asset growth for ﬁrms that are borrowing

from banks that were hit harder by the interbank funding shock. Borrowing from a bank with

high sector market share helps to oﬀset this negative impact on investment. While the result on

asset growth with respect to bank sector market share is similar, it is not statistically signiﬁcant.

Additionally, large ﬁrms are better able to limit the reduction in investment after an interbank

funding shock. Given that Table 6 showed that there are no signiﬁcant diﬀerences between small

and large ﬁrms in terms of the credit supply shock they both receive, the smaller impact in terms

of real eﬀects for large ﬁrms might indicate that these ﬁrms have more alternative funding sources

available, over and above those controlled for.

23We use the summary statistics reported in Table 1 to calculate the impact. The average ﬁrm size is 13.38
and the average ﬁrm borrows from a bank which has a sector market share of 0.181 in its sector. As such,
the impact of an average interbank funding shock of -10.3% equals -0.103*[1.117+(0.181*-0.744)+(13.38*-
0.065)] = -1.16 percentage points. We do not take into account the remaining interaction terms given that
their impact is not statistically diﬀerent from 0.

ECB Working Paper Series No 2230 / January 2019367 Conclusion

We conduct a comprehensive analysis of the sector- and ﬁrm-speciﬁc strategies that banks follow

when their funding is aﬀected by a negative shock. While the current literature mainly focusses on

the average impact of funding shocks on the volume of bank lending, we investigate the reallocation

that occurs across sectors and ﬁrms and its persistence over time.

To identify the reallocation in the supply of credit that follows from the diﬃculties for banks to

obtain funding, we rely on a unique combination of data sets. We employ monthly bank-ﬁrm

level credit data from a comprehensive credit register that contains all credit granted in Belgium

by ﬁnancial institutions, monthly balance sheet data of these ﬁnancial institutions, and annual

balance sheets of all registered ﬁrms.

We start by benchmarking our study with related studies. The average ﬁrm in our sample borrows

from a bank that experiences a contraction in funding equivalent to 10.3% of its total assets. We

estimate that the average ﬁrm, as a direct consequence of this funding outﬂow, faces a decline in

the supply of credit by 4.26%. An investigation of the timing and duration of this eﬀect reveals that

the funding shock signiﬁcantly impacts credit supply already four months after the shock started,

reaches a maximum impact after nine months, and remains signiﬁcant and high up to 24 months

after the shock.

Our main results indicate that a bank’s business model, as reﬂected in its sector market share and

sector specialization, determines the reallocation of credit when a bank is hit by a negative funding

shock. Sector market share measures how important a bank is for a particular (non-ﬁnancial) sector

while sector specialization measures how important a (non-ﬁnancial) sector is for a bank. We ﬁnd

that a standard deviation increase in sector market share reduces the negative impact of the funding

shock on credit supply by 22% for the average ﬁrm. Similarly, a standard deviation increase in

sector specialization reduces the negative impact of the funding shock on credit supply by 8% for

the average ﬁrm. Hence, banks direct their attention to sectors where they can more easily extract

rents (higher sector market share) or where they have built up superior knowledge (higher sector

specialization). Additionally, we document the existence of a ﬂight to quality. Banks reallocate

credit towards ﬁrms with low debt levels, low default risk, high available collateral, and a high

interest coverage ratios. Importantly, this ﬂight-to-quality coexists with the two aforementioned

ECB Working Paper Series No 2230 / January 201937reallocation eﬀects.

The reallocation eﬀects are also robust to a number of alternative explanations. We provide evidence

that our results are not driven by pre-shock solvency problems, government interventions during

our sample period, banks’ geographical specialization, or bank-ﬁrm relationship characteristics.

On the real side, we ﬁnd a moderate reduction in investments and asset growth for ﬁrms borrowing

from banks that were hit harder by the funding shock. The average ﬁrm borrowing from a bank that

experienced an average funding shock reduced its net investment rate by 0.85 percentage points.

Importantly, we show that ﬁrms that are borrowing from a bank with high sector market share can

partially oﬀset this negative impact on investment rates.

Our results provide useful information for policy makers that want to ensure access to ﬁnance for

non-ﬁnancial corporations during crisis times, as we show that riskier ﬁrms and ﬁrms borrowing

from banks that have low sector market share and specialization are more vulnerable to shocks in

the banking sector. Related to this, ﬁrms may prefer matching with banks with a larger sector

market share as the implied higher cost of borrowing during good times also acts as an insurance

premium that guarantees access to ﬁnance when the bank faces a funding shock (as in Petersen

and Rajan (1995) and Berlin and Mester (1999)).

Our ﬁndings also contain interesting information for bank regulators. Our results reveal a bright

sight of lending concentration during crisis times and are thus informative when making the trade-

oﬀ between portfolio concentration risk and having suﬃcient information about borrowers. Finally,

our results suggest that not only systemic risk and ﬁnancial stability issues should be taken into

account when studying the welfare implications of portfolio diversiﬁcation, but that it could also

be relevant to consider the potentially beneﬁcial impact of lending concentration on ﬁrm credit

supply.

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ECB Working Paper Series No 2230 / January 201944Figure 1: Aggregate interbank funding

This ﬁgure depicts the evolution of the aggregate volume (all banks active in Belgium, in EUR billion) of
interbank funding over the period 2006:1 - 2011:12. Interbank funding include overnight deposits, deposits
redeemable at notice and term accounts of other ﬁnancial institutions as well as repurchase agreements. The
vertical lines correspond to the estimation window (pre-shock window and post-shock-window of thirteen
months) around the collapse of Lehman Brothers in September 2008. The ﬁrst line corresponds to July 2007,
the second line corresponds to August 2008, the third line corresponds to September 2009.

ECB Working Paper Series No 2230 / January 201945Figure 2: Timing of the interbank funding shock pass-through

This graph illustrates the impact of the interbank funding shock on three credit supply indicators: the
percentage growth in the granted loan amount (∆% Creditbf ), a dummy variable that is one if the granted
amount increases and zero otherwise (Increase in creditbf ), and a dummy variable that is one if the growth
in the granted loan amount belongs to the lowest quartile and zero otherwise (Large decrease in creditbf ).
We plot the coeﬃcients and 90% conﬁdence bounds (dashed lines) for the eﬀect of a shock to interbank
funding (∆% Interbank fundingb). The coeﬃcients plotted are obtained from 24 separate estimations. The
estimations diﬀer from each other in terms of the length of the post-shock horizon, which expands from one
to 24 months post Lehman, whereas the pre-shock horizon remains ﬁxed at thirteen months. The x-axis
indicates the sample length after the Lehman failure. The coeﬃcients at month thirteen coincide with the
results reported in Panel A of Table 4.

ECB Working Paper Series No 2230 / January 201946Figure 3: Timing reallocation eﬀects: Bank sector market share and specialization

This graph displays the timing and magnitude of the reallocation of credit across sectors following an interbank
funding shock. The panels contain information on the interaction eﬀect of the interbank funding shock and
either bank sector market share or bank sector specialization. We plot the coeﬃcients and 90% conﬁdence
bounds (dashed lines) for the interaction coeﬃcients obtained from 24 separate estimations. The estimations
diﬀer from each other in terms of the length of the post-shock horizon, which expands from one to 24 months
post Lehman, whereas the pre-shock horizon remains ﬁxed at thirteen months. The coeﬃcients at month
thirteen coincide with the results reported in Column 1 of Panel A of Table 6.

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ECB Working Paper Series No 2230 / January 201949CREDIT VARIABLES

bank-ﬁrm level

∆% Creditbf

Table 2: Summary statistics

Obs

Mean

StDev

Min

p25

p50

p75

Max

160,223

-0.024

0.278

-0.644

-0.155

-0.051

0.026

0.941

Increase in creditbf

Large decrease in creditbf

160,223

0.289

160,223

0.250

0.453

0.434

0

0

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0

0

1

1

1

1

160,223

-0.103

0.063

-0.163

-0.163

-0.110

-0.091

0.301

BANK VARIABLES

bank-ﬁrm level

∆% Interbank fundingb

Capital to total assetsb

Return on equityb

Credit riskb

Liquidity ratiob

160,223

0.045

0.021

0.001

0.041

0.041

160,223

0.080

0.116

-0.131

-0.072

0.106

160,223

0.026

0.025

-0.203

0.007

160,223

0.244

0.086

0.016

0.027

0.265

0.406

0.342

0.239

0.248

0.293

Deposits to total assetsb

160,223

0.404

0.146

0.000

Interbank funding to total assetsb

160,223

0.320

0.112

0.000

160,223

11.888

1.598

5.362

11.894

12.348

13.232

13.232

Bank sizeb

bank level

∆% Interbank fundingb

Capital to total assetsb

Return on equityb

Credit riskb

Liquidity ratiob

Deposits to total assetsb

Interbank funding to total assetsb

Bank sizeb

BANK-SECTOR VARIABLES

bank-ﬁrm level

Sector market sharebs

Sector specializationbs

bank-sector level

Sector market sharebs

Sector specializationbs

0.119

-0.163

-0.045

-0.002

0.051

38

38

38

38

38

38

38

38

0.020

0.070

0.152

0.007

0.270

0.473

0.330

8.203

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0.034

0.254

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0.084

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0.244

0.263

0.290

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0.000

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0.067

0.286

0.110

6.952

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0.109

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0.170

0.512

0.257

7.865

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0.181

0.084

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0.083

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0.158

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0.207

0.126

0.241

0.208

0.267

0.588

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0.124

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0.043

0.017

0.108

0.267

0.588

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0.172

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0.280

0.445

0.374

0.356

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0.301

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0.881

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0.077

0.161

0.025

0.416

0.668

0.442

9.060

13.232

ECB Working Paper Series No 2230 / January 201950Table 2: continued

Obs

Mean

StDev

Min

p25

p50

p75

Max

FIRM VARIABLES

bank-ﬁrm level

Total assetsf

Agef

Leveragef

141,762

13.38

1.288

10.79

141,762

13.97

10.19

0.615

141,762

0.724

0.263

0.126

Pledged collateral to ﬁxed assetsf

141,762

0.484

0.944

0.000

Financial pressuref

141,762

0.732

0.824

0.011

12.47

5.615

0.559

0.000

0.190

ﬁrm level

Total assetsf

Agef

Leveragef

117,166

13.18

1.209

10.79

12.35

117,166

13.03

9.773

0.615

5.15

117,166

0.726

0.272

0.126

0.552

0.000

0.184

Pledged collateral to ﬁxed assetsf

117,166

0.442

0.901

0.000

Financial pressuref

∆% Fixed assetsf

∆% Assetsf

117,166

0.744

0.839

0.011

114,436

0.078

0.410

-0.435

-0.082

-0.020

0.066

114,436

0.168

0.652

-0.681

-0.126

0.005

0.235

13.24

12.46

0.745

0.000

0.468

13.06

11.15

0.746

0.000

0.469

14.16

19.61

0.887

0.620

0.979

13.89

18.61

0.895

0.458

1.026

16.46

39.53

1.561

4.219

4.455

16.46

39.53

1.561

4.219

4.455

2.674

4.289

ECB Working Paper Series No 2230 / January 201951Table 3: Sector market share, sector specialization, implicit interest rates, and default probabili-

ties

Panel A of this table documents the relationship, during the pre-shock period, between a ﬁrm’s implicit
interest rate (derived from the ﬁrm’s balance sheet) and the sector market share and specialization of its
bank . We only include ﬁrms borrowing from one bank to improve the match between the implicit interest
rate and the actual interest rate charged by the bank. The dependent variable in panel A is the implicit
interest rate of the ﬁrm prior to the collapse of Lehman Brothers and is measured as interest expenses over
ﬁnancial debt. In Column 1 of Panel A, we regress the implicit interest rate on bank sector market share,
bank sector specialization as well as ﬁrm cluster ﬁxed eﬀects, where clusters are based on location-sector-size
triplets. In Column 2 and 3 we subsequently add ﬁrm controls and bank controls. Panel B of this table
documents the relationship, during the pre-shock period, between borrower bankruptcy at the bank-sectoral
level and the sector market share and sector specialization of the bank. The dependent variable in Columns
1 and 2 is the number of borrowers that a bank is lending to in a given sector in the two years prior to
Lehman, that go bankrupt prior to the funding shock. In Columns 3 and 4, the dependent variable is the
exposure of each bank to bankruptcies in a given sector, in the pre-Lehman period. It is computed as the
ratio the total number of ﬁrms going bankrupt in a sector that borrow from bank b to the total number of
ﬁrms going bankrupt in that sector. Columns 1 and 3 include all bank-sector observations, while Columns
2 and 4 include only bank-sector observations with strictly positive observations for the dependent variable.
Standard errors are clustered at the bank level. ***, ** and * denote p<0.01, p<0.05 and p<0.1 respectively.

Panel A

Sector market sharebs

Sector specializationbs

Observations
R-squared
Firm cluster FE
Firm controls
Bank Controls

(1)
Implicit
interest ratef

(2)
Implicit
interest ratef

(3)
Implicit
interest ratef

0.058***
(0.012)
0.016
(0.016)

89,926
0.006
Yes
No
No

(1)

0.039***
(0.010)
-0.002
(0.017)

89,926
0.214
Yes
Yes
No

(2)

0.050***
(0.012)
-0.013
(0.016)

89,926
0.279
Yes
Yes
Yes

(3)

(4)

Panel B

Number of bankrupt ﬁrmsbs

Share of the sector’s bankrupt ﬁrmsbs

Sector market sharebs

Sector specializationbs

Observations
R-squared
Sector FE

203.2***
(23.36)
-10.83**
(4.349)

513
0.684
Yes

227.9***
(23.65)
-26.22**
(9.921)

246
0.762
Yes

0.997***
(0.120)
-0.041***
(0.011)

513
0.824
Yes

0.970***
(0.108)
-0.073***
(0.022)

246
0.864
Yes

ECB Working Paper Series No 2230 / January 201952Table 4: Average eﬀect of an interbank funding shock on credit supply

This table shows the eﬀect of a shock to interbank funding (∆% Interbank fundingb) on credit
growth at the bank-ﬁrm level, while controlling for time-averaged (over the thirteen months pre-
ceding the shock) bank level covariates. The dependent variable is percentage growth in the granted
loan amount (Column 1), a dummy variable that is one if the granted amount increases and zero
otherwise (Column 2), and a dummy variable that is one if the growth in the granted loan amount
belongs to the lowest quartile and zero otherwise (Column 3). For each of the measures, we ﬁrst
time-average the bank-ﬁrm exposure in the thirteen months prior to the Lehman collapse as well as
the thirteen months following the Lehman collapse. We subsequently compute logarithmic growth
rates of the pre versus post Lehman time-averaged loan amounts. The independent variable of in-
terest is the change in interbank funding scaled by total assets. More speciﬁcally, it is the average
value of interbank funding in the thirteen months post Lehman minus the average value in the
thirteen months prior to the Lehman collapse, scaled by the average value of total assets over the
thirteen months preceding 2008:09. The table consists of two panels. Panel A reports the results
for speciﬁcations with the full sample, where we control for ﬁrm demand using ﬁrm cluster FE.
Firm clusters are based on location-sector-size triplets for single-bank ﬁrms and on the ﬁrm itself
for multiple-bank ﬁrms. Panel B reports the results for speciﬁcations with the sample of ﬁrms
borrowing from multiple banks only in which case we control for ﬁrm demand using ﬁrm FE. Bank
control variables are included in both panels, but not reported in Panel B. Standard errors are
clustered at the bank level. ***, ** and * denote p<0.01, p<0.05 and p<0.1 respectively.

(1)

∆% Creditbf

(2)
Increase in
creditbf

(3)
Large decrease
in creditbf

Panel A: Full sample

∆% Interbank fundingb

Capital to total assetsb

Return on equityb

Credit riskb

Liquidity ratiob

Deposits to total assetsb

Interbank funding to total assetsb

Bank sizeb

Observations
R-squared
Firm cluster FE

Panel B: Multiple-bank borrowers sample

∆% Creditbf

∆% Interbank fundingb

Observations
R-squared
Bank Controls
Firm FE

0.438***
(0.129)

47,205
0.455
Yes
Yes

0.414***
(0.121)

-0.081
(0.194)
-0.074
(0.056)
0.509***
(0.117)
0.078
(0.070)
-0.078
(0.087)
-0.057
(0.086)
-0.007
(0.006)

160,223
0.295
Yes

(1)

0.586*
(0.330)

-0.228
(0.443)
-0.072
(0.110)
0.646**
(0.301)
-0.045
(0.161)
-0.309
(0.212)
-0.219
(0.185)
-0.009
(0.013)

160,223
0.276
Yes

-0.877***
(0.174)

0.224
(0.233)
0.151
(0.092)
-0.853***
(0.166)
-0.243*
(0.122)
0.051
(0.132)
0.007
(0.112)
0.025**
(0.009)

160,223
0.290
Yes

(2)
Increase in
creditbf

(3)
Large decrease
in creditbf

0.597**
(0.260)

47,205
0.463
Yes
Yes

-0.630***
(0.204)

47,205
0.481
Yes
Yes

ECB Working Paper Series No 2230 / January 201953Table 5: Within bank credit reallocation according to sector market share and sector specialization

This table contains information on the estimated eﬀect of interbank funding shock (∆% Interbank
fundingb) on credit supply, conditional on banks’ sector market share and sector specialization. The
dependent variable is percentage growth in the granted loan amount (Column 1), a dummy variable that
is one if the granted amount increases and zero otherwise (Column 2), and a dummy variable that is
one if the growth in the granted loan amount belongs to the lowest quartile and zero otherwise (Column
3). The independent variables of interest are the interaction between bank sector market share and
sector specialization and the interbank funding shock. Panel A reports the results for speciﬁcations with
the full sample, where we control for ﬁrm demand using ﬁrm cluster FE. Firm clusters are based on
location-sector-size triplets for single-bank ﬁrms and on the ﬁrm itself for multiple-bank ﬁrms. Panel
B reports the results for speciﬁcations with the sample of ﬁrms borrowing from multiple banks only
where we control for ﬁrm demand using ﬁrm ﬁxed eﬀects. We control for all observed and unobserved
bank-speciﬁc covariates by including bank ﬁxed eﬀects. Standard errors are clustered at the bank level.
***, ** and * denote p<0.01, p<0.05 and p<0.1 respectively.

Panel A: Full sample

∆% Interbank fundingb * Sector market sharebs

∆% Interbank fundingb * Sector specializationbs

Sector market sharebs

Sector specializationbs

Observations
R-squared
Bank FE
Firm cluster FE

(1)

∆% Creditbf

(2)
Increase in
creditbf

(3)
Large decrease
in creditbf

-1.029***
(0.309)
-0.218*
(0.115)

-0.114**
(0.052)
0.007
(0.037)

160,223
0.298
Yes
Yes

(1)

-2.056***
(0.455)
-0.374
(0.349)

-0.299***
(0.099)
0.103
(0.081)

160,223
0.282
Yes
Yes

1.599***
(0.436)
0.874***
(0.178)

0.246***
(0.054)
-0.045
(0.048)

160,223
0.292
Yes
Yes

(2)
Increase in
creditbf

(3)
Large decrease
in creditbf

Panel B: Multiple-bank borrowers sample

∆% Creditbf

∆% Interbank fundingb * Sector market sharebs

∆% Interbank fundingb * Sector specializationbs

Sector market sharebs

Sector specializationbs

Observations
R-squared
Bank FE
Firm FE

-1.589***
(0.571)
-0.190
(0.268)

-0.164*
(0.082)
0.00446
(0.058)

47,205
0.460
Yes
Yes

-1.456**
(0.581)
-0.783*
(0.413)

-0.162
(0.113)
0.0434
(0.101)

47,205
0.469
Yes
Yes

1.235
(1.088)
1.239***
(0.439)

0.183
(0.164)
-0.126
(0.118)

47,205
0.486
Yes
Yes

ECB Working Paper Series No 2230 / January 201954Table 6: Heterogenous shock transmission: Sector market share, sector specialization and the role

of ﬁrm characteristics

This table shows the impact of an interbank funding shock (∆% Interbank fundingb) on credit supply, conditional
on banks’ sector market share, banks’ sector specialization and ﬁrm characteristics (ﬁrm size, ﬁrm age, leverage,
pledged collateral to ﬁxed assets and ﬁnancial pressure). We simultaneously include these variables and their
interaction with the funding shock. The dependent variable is percentage growth in the granted loan amount
(Column 1), a dummy variable that is one if the granted amount increases and zero otherwise (Column 2), and
a dummy variable that is one if the growth in the granted loan amount belongs to the lowest quartile and zero
otherwise (Column 3). Panel A reports the results for speciﬁcations with the full sample, where we control for ﬁrm
demand using ﬁrm cluster FE. Firm clusters are based on location-sector-size triplets for single-bank ﬁrms and
on the ﬁrm itself for multiple-bank ﬁrms. Panel B reports the results for speciﬁcations with the sample of ﬁrms
borrowing from multiple banks only where we control for ﬁrm demand using ﬁrm FE. We control for all observed
and unobserved bank-speciﬁc covariates by including bank ﬁxed eﬀects. Firm controls include all interacted ﬁrm
characteristics. Bank-sector controls include bank sector market share and specialization. Standard errors are
clustered at the bank level. ***, ** and * denote p<0.01, p<0.05 and p<0.1 respectively.

Panel A: Full sample

∆% Interbank fundingb * Sector market sharebs

∆% Interbank fundingb * Sector specializationbs

∆% Interbank fundingb * Total assetsf

∆% Interbank fundingb * Agef

∆% Interbank fundingb * Leveragef

∆% Interbank fundingb * Pledged collateral to ﬁxed assetsf

∆% Interbank fundingb * Financial pressuref

Observations
R-squared
Firm and bank-sector controls
Bank FE and Firm cluster FE

(1)

∆% Creditbf

(2)
Increase in
creditbf

(3)
Large decrease
in creditbf

-0.875***
(0.235)
-0.334**
(0.146)

0.016
(0.030)
0.001
(0.002)
0.178***
(0.048)
0.026**
(0.010)
0.061***
(0.016)

141,762
0.364
Yes
Yes

-1.499***
(0.372)
-0.426
(0.343)

0.008
(0.061)
-0.007***
(0.003)
0.405***
(0.136)
0.029
(0.021)
0.061**
(0.030)

141,762
0.320
Yes
Yes

1.137*
(0.571)
0.942***
(0.213)

-0.006
(0.026)
0.000
(0.003)
-0.224
(0.167)
-0.049**
(0.020)
-0.062***
(0.020)

141,762
0.337
Yes
Yes

Panel B: Multiple-bank borrowers sample

∆% Creditbf

Increase in
creditbf

Large decrease
in creditbf

∆% Interbank fundingb * Sector market sharebs

∆% Interbank fundingb * Sector specializationbs

∆% Interbank fundingb * Total assetsf

∆% Interbank fundingb * Agef

∆% Interbank fundingb * Leveragef

∆% Interbank fundingb * Pledged collateral to ﬁxed assetsf

∆% Interbank fundingb * Financial pressuref

Observations
R-squared
Bank-sector controls
Bank FE and Firm FE

-1.429**
(0.653)
-0.355
(0.275)

-0.007
(0.042)
0.004
(0.002)
0.335***
(0.090)
0.009
(0.020)
0.103***
(0.028)

44,904
0.459
Yes
Yes

-0.454
(0.818)
-0.931*
(0.475)

-0.008
(0.070)
0.000
(0.002)
0.833***
(0.300)
-0.014
(0.020)
0.097**
(0.046)

44,904
0.469
Yes
Yes

1.346
(1.548)
1.261**
(0.485)

0.031
(0.023)
-0.003
(0.003)
-0.408**
(0.181)
-0.009
(0.025)
-0.090***
(0.027)

44,904
0.485
Yes
Yes

ECB Working Paper Series No 2230 / January 201955Table 7: The importance of demand control (ﬁrm ﬁxed eﬀects vs. ﬁrm cluster ﬁxed eﬀects) and

sample composition (multiple-bank borrowers vs. single-bank borrowers)

This table shows the impact of an interbank funding shock (∆% Interbank fundingb) on credit supply (Panel A), and the
heterogenous impact conditional on banks’ sector market share, banks’ sector specialization and ﬁrm characteristics (Panels B
and C). Panels A and B reports the results for speciﬁcations with the sample of multiple-bank borrowers only, whereas Panel
C reports the results for speciﬁcations with the sample of single-bank borrowers only. Importantly, in all panels we control for
ﬁrms’ credit demand using ﬁrm cluster FE, where clusters are based on location-sector-size triplets. The dependent variable is
percentage growth in the granted loan amount (Column 1), a dummy variable that is one if the granted amount increases and
zero otherwise (Column 2), and a dummy variable that is one if the growth in the granted loan amount belongs to the lowest
quartile and zero otherwise (Column 3). We include bank ﬁxed eﬀects in Panels B and C. Firm controls include all interacted
ﬁrm characteristics. Bank-sector controls include bank sector market share and specialization. Standard errors are clustered at
the bank level. ***, ** and * denote p<0.01, p<0.05 and p<0.1 respectively.

Panel A: Multiple-bank borrowers sample

(1)

∆% Creditbf

(2)
Increase in
creditbf

(3)
Large decrease
in creditbf

∆% Interbank fundingb

Observations
R-squared
Bank controls
Firm cluster FE

0.417***
(0.142)

44,904
0.219
Yes
Yes

Panel B: Multiple-bank borrowers sample

∆% Creditbf

∆% Interbank fundingb * Sector market sharebs

∆% Interbank fundingb * Sector specializationbs

∆% Interbank fundingb * Total assetsf

∆% Interbank fundingb * Agef

∆% Interbank fundingb * Leveragef

∆% Interbank fundingb * Pledged collateral to ﬁxed assetsf

∆% Interbank fundingb * Financial pressuref

Observations
R-squared
Firm and bank-sector controls
Firm cluster FE and bank FE

-1.526***
(0.409)
-0.081
(0.298)
-0.002
(0.041)
0.002
(0.002)
0.270***
(0.098)
0.020
(0.023)
0.110***
(0.023)

44,904
0.244
Yes
Yes

Panel C: Single-bank borrowers sample

∆% Creditbf

0.616*
(0.321)

-0.642***
(0.201)

44,904
0.219
Yes
Yes
Increase in
creditbf

44,904
0.249
Yes
Yes
Large decrease
in creditbf

-1.162
(1.103)
-0.584
(0.501)
-0.006
(0.066)
-0.003
(0.004)
0.533**
(0.229)
0.010
(0.024)
0.142***
(0.039)

1.633
(1.185)
1.127**
(0.496)
0.030
(0.025)
0.001
(0.003)
-0.400*
(0.228)
-0.042
(0.036)
-0.098***
(0.031)

44,904
0.236
Yes
Yes
Increase in
creditbf

44,904
0.265
Yes
Yes
Large decrease
in creditbf

∆% Interbank fundingb * Sector market sharebs

∆% Interbank fundingb * Sector specializationbs

∆% Interbank fundingb * Total assetsf

∆% Interbank fundingb * Agef

∆% Interbank fundingb * Leveragef

∆% Interbank fundingb * Pledged collateral to ﬁxed assetsf

∆% Interbank fundingb * Financial pressuref

Observations
R-squared
Firm and bank-sector controls
Firm cluster FE and bank FE

-0.905***
(0.212)
-0.372**
(0.148)
0.006
(0.028)
-0.001
(0.002)
0.138***
(0.049)
0.034**
(0.015)
0.053***
(0.016)

96,855
0.314
Yes
Yes

-2.219***
(0.413)
-0.362
(0.440)
0.017
(0.059)
-0.010**
(0.004)
0.327**
(0.147)
0.052*
(0.028)
0.050
(0.036)

96,855
0.246
Yes
Yes

1.268**
(0.541)
0.808***
(0.249)
-0.007
(0.043)
0.002
(0.003)
-0.191
(0.171)
-0.069**
(0.033)
-0.057***
(0.020)

96,855
0.272
Yes
Yes

ECB Working Paper Series No 2230 / January 201956Table 8: Robustness on pre-shock confounding events: Potential solvency issues

This table shows the eﬀect of a shock to interbank funding (∆% IBFb) on credit growth at the bank-ﬁrm level,
while controlling for solvency concerns. Our proxy for solvency concerns is a bank’s exposure to the U.S. housing
and mortgage market. US exposureb is constructed using monthly bank-level data used to construct the BIS
consolidated international banking statistics. More speciﬁcally, we take the average ultimate risk exposures to
the U.S. non-bank private sector and US banking sector pre-shock, scaled by average total assets pre-shock.
The dependent variable is percentage growth in the granted loan amount (Columns 1 and 2), a dummy variable
that is one if the granted amount increases and zero otherwise (Columns 3 and 4), and a dummy variable that
is one if the growth in the granted loan amount belongs to the lowest quartile and zero otherwise (Columns 5
and 6). Panel A shows the results for the average impact of the funding shock on bank lending when controlling
for banks’ exposure to the U.S. housing and mortgage market. Panel B shows the reallocation results when
controlling for potential reallocation eﬀects due to banks’ exposure to the U.S. housing and mortgage market.
We report the results for speciﬁcations with the full sample where we control for ﬁrm demand using ﬁrm cluster
FE (Columns 1, 3 and 5) and for the sample of ﬁrms borrowing from multiple banks where we control for ﬁrm
demand using ﬁrm FE (Columns 2, 4 and 6). In the full sample, ﬁrm clusters are based on location-sector-
size triplets for single-bank ﬁrms and on the ﬁrm itself for multiple-bank ﬁrms. Bank controls include bank
capitalization, proﬁtability, credit risk, liquidity, stable deposit funding, and size. Standard errors are clustered
at the bank level. ***, ** and * denote p<0.01, p<0.05 and p<0.1 respectively.

Panel A

∆% IBFb

US exposureb

Observations
R-squared
Bank controls
Firm cluster FE
Firm FE

Panel B

(1)

(2)

(3)

(4)

(5)

(6)

∆% Creditbf

Increase in creditbf

Large decrease in creditbf

0.379***
(0.096)
-0.560***
(0.139)

0.484***
(0.113)
-0.573***
(0.135)

160,223
0.296
Yes
Yes
No
(1)

47,205
0.456
Yes
No
Yes
(2)

0.538*
(0.289)
-0.782**
(0.370)

160,223
0.277
Yes
Yes
No
(3)

0.641**
(0.253)
-0.545*
(0.298)

47,205
0.464
Yes
No
Yes
(4)

-0.816***
(0.120)
1.001***
(0.162)

-0.705***
(0.163)
0.921***
(0.196)

160,223
0.291
Yes
Yes
No
(5)

47,205
0.482
Yes
No
Yes
(6)

∆% Creditbf

Increase in creditbf

Large decrease in creditbf

∆% IBFb * Sector market sharebs

∆% IBFb * Sector specializationbs

US exposureb * Sector market sharebs

US exposureb * Sector specializationbs

Observations
R-squared
Bank-sector controls
Bank FE
Firm cluster FE
Firm FE

-0.872**
(0.335)
-0.253*
(0.133)

1.327
(2.787)
0.767
(0.782)

160,223
0.298
Yes
Yes
Yes
No

-1.143*
(0.667)
-0.404
(0.336)

2.835
(3.216)
1.822*
(0.928)

47,205
0.460
Yes
Yes
No
Yes

-1.656***
(0.537)
-0.479
(0.372)

-0.746
(0.540)
-1.171***
(0.419)

1.582***
(0.463)
0.880***
(0.173)

0.806
(1.210)
1.381***
(0.490)

2.317
(3.907)
2.469
(1.520)

160,223
0.282
Yes
Yes
Yes
No

2.897
(4.372)
3.422**
(1.427)

47,205
0.469
Yes
Yes
No
Yes

0.026
(3.359)
-0.172
(0.844)

160,223
0.292
Yes
Yes
Yes
No

-5.017
(4.041)
-1.028
(0.935)

47,205
0.486
Yes
Yes
No
Yes

ECB Working Paper Series No 2230 / January 201957Table 9: Robustness on pre-shock confounding events: Potential liquidity issues surrounding the

ABCP shock in July 2007

This table shows the eﬀect of a shock to interbank funding (∆% IBFb) on credit growth at the bank-ﬁrm
level, assuming that the shock took place in July 2007 (i.e., the start of the ABCP crisis). The pre-shock
period consists of the thirteen months prior to the ABCP shock (2006:7-2007:7) and the post-shock period
consists of the thirteen months after to the ABCP shock (2007:8-2008:8) and thus stops right before the
Lehman collapse. The table shows both the average eﬀect (Columns 1, 3, and 5) and heterogenous eﬀect
based on banks’ sector market share and sector specialization (Columns 2, 4, and 6). The dependent
variable is percentage growth in the granted loan amount (Columns 1 and 2), a dummy variable that is one
if the granted amount increases and zero otherwise (Columns 3 and 4), and a dummy variable that is one
if the growth in the granted loan amount belongs to the lowest quartile and zero otherwise (Columns 5 and
6). Panel A reports the results for speciﬁcations with the full sample, where we control for ﬁrm demand
using ﬁrm cluster FE. Firm clusters are based on location-sector-size triplets for single-bank ﬁrms and on
the ﬁrm itself for multiple-bank ﬁrms. Panel B reports the results for speciﬁcations with the sample of
ﬁrms borrowing from multiple banks only where we control for ﬁrm demand using ﬁrm FE. Bank controls
include bank capitalization, proﬁtability, credit risk, liquidity, stable deposit funding, and size. Bank-sector
controls include bank sector market share and specialization. Standard errors are clustered at the bank
level. ***, ** and * denote p<0.01, p<0.05 and p<0.1 respectively.

Panel A: Full sample

∆% Creditbf

Increase in
creditbf

(1)

(2)

(3)

(4)

(5)
(6)
Large decrease
in creditbf

∆% IBFb

∆% IBFb * Sector market sharebs

∆% IBFb * Sector specializationbs

Observations
R-squared
Bank controls
Bank-sector controls
Bank FE
Firm cluster FE
Shock timing

-0.163
(0.098)

-0.199
(0.215)

-0.009
(0.126)

-0.365
(0.421)
0.004
(0.182)

153,854
0.312
No
Yes
Yes
Yes
2007/07
(2)

-1.114
(0.710)
-0.050
(0.322)

153,854
0.298
No
Yes
Yes
Yes
2007/07
(4)

153,854
0.289
Yes
No
No
Yes
2007/07
(3)

153,854
0.309
Yes
No
No
Yes
2007/07
(1)

0.565
(1.042)
0.489**
(0.233)

153,854
153,854
0.293
0.290
No
Yes
Yes
No
Yes
No
Yes
Yes
2007/07
2007/07
(5)
(6)
Large decrease
in creditbf

Panel B: Multiple-bank borrowers sample

∆% Creditbf

Increase in
creditbf

∆% IBFb

∆% IBFb * Sector market sharebs

∆% IBFb * Sector specializationbs

Observations
R-squared
Bank controls
Bank-sector controls
Bank FE
Firm FE
Shock timing

-0.027
(0.114)

0.069
(0.189)

-0.010
(0.151)

-0.749
(0.866)
0.559
(0.351)

45,905
0.464
No
Yes
Yes
Yes
2007/07

-2.766
(1.926)
0.646
(0.777)

45,905
0.485
No
Yes
Yes
Yes
2007/07

45,905
0.475
Yes
No
No
Yes
2007/07

-0.185
(1.191)
-0.055
(0.443)

45,905
0.482
No
Yes
Yes
Yes
2007/07

45,905
0.477
Yes
No
No
Yes
2007/07

45,905
0.460
Yes
No
No
Yes
2007/07

ECB Working Paper Series No 2230 / January 201958Table 10: Robustness on post-shock confounding events: Potential impact of cross-border lending

This table shows the eﬀect of a shock to interbank funding (∆% Interbank fundingb) on credit growth at the
bank-ﬁrm level, while controlling for the bank’s pre-shock cross-border corporate lending share. The dependent
variable is percentage growth in the granted loan amount (Columns 1 and 2), a dummy variable that is one if
the granted amount increases and zero otherwise (Columns 3 and 4), and a dummy variable that is one if the
growth in the granted loan amount belongs to the lowest quartile and zero otherwise (Columns 5 and 6). Panel
A shows the results for the average impact of the funding shock on bank lending when controlling for a bank’s
pre-shock cross-border corporate lending share. Panel B shows the reallocation results when controlling for
potential reallocation eﬀects depending on bank’s pre-shock cross-border corporate lending share. We report
the results for speciﬁcations with the full sample where we control for ﬁrm demand using ﬁrm cluster ﬁxed
eﬀects (Columns 1, 3 and 5) and for the sample of ﬁrms borrowing from multiple banks where we control
for ﬁrm demand using ﬁrm ﬁxed eﬀects (Columns 2, 4 and 6). In the full sample, ﬁrm clusters are based on
location-sector-size triplets for single-bank ﬁrms and on the ﬁrm itself for multiple-bank ﬁrms. Bank controls
include bank capitalization, proﬁtability, credit risk, liquidity, stable deposit funding, and size. Standard errors
are clustered at the bank level. ***, ** and * denote p<0.01, p<0.05 and p<0.1 respectively.

Panel A

∆% Interbank fundingb

Cross-border lendingb

Observations
R-squared
Bank controls
Firm cluster FE
Firm FE

Panel B

∆% Interbank fundingb * Sector market sharebs

∆% Interbank fundingb * Sector specializationbs

Cross-border lendingb * Sector market sharebs

Cross-border lendingb * Sector specializationbs

Observations
R-squared
Bank-sector controls
Bank FE
Firm cluster FE
Firm FE

(1)

(2)

(3)

(4)

∆% Creditbf

Increase in
creditbf

(6)
(5)
Large decrease
in creditbf

0.423***
(0.116)
0.059
(0.048)

160,223
0.295
Yes
Yes
No

0.430***
(0.139)
0.029
(0.069)

47,205
0.455
Yes
No
Yes

0.614*
(0.321)
0.166
(0.110)

160,223
0.276
Yes
Yes
No

0.585**
(0.273)
0.049
(0.114)

47,205
0.463
Yes
No
Yes

(1)

(2)

(3)

(4)

∆% Creditbf

Increase in
creditbf

-0.920***
(0.314)
-0.217*
(0.124)

-1.132
(0.957)
0.426**
(0.163)

160,223
0.298
Yes
Yes
Yes
No

-1.357**
(0.549)
-0.227
(0.265)

-1.957
(1.429)
0.471**
(0.215)

47,205
0.460
Yes
Yes
No
Yes

-1.830***
(0.454)
-0.413
(0.364)

-0.680
(1.310)
0.763*
(0.426)

160,223
0.282
Yes
Yes
Yes
No

-1.032**
(0.506)
-0.902**
(0.389)

-2.296
(2.037)
0.905**
(0.442)

47,205
0.469
Yes
Yes
No
Yes

-0.861***
(0.170)
0.099
(0.063)

-0.648***
(0.215)
0.071
(0.088)

160,223
0.290
Yes
Yes
No

47,205
0.481
Yes
No
Yes

(5)
(6)
Large decrease
in creditbf

1.584***
(0.341)
0.806***
(0.197)

2.991**
(1.328)
-0.266
(0.306)

160,223
0.292
Yes
Yes
Yes
No

1.015
(0.734)
1.123**
(0.451)

5.580***
(2.016)
-0.318
(0.297)

47,205
0.486
Yes
Yes
No
Yes

ECB Working Paper Series No 2230 / January 201959Table 11: Robustness on post-shock confounding events: Potential impact of bank bail-outs

This table contains information on the estimated eﬀect of deposit shocks (∆% Depositsb) on credit supply,
conditional on banks’ sector market share and sector specialization. The dependent variable is percentage
growth in the granted loan amount (Column 1), a dummy variable that is one if the granted amount increases
and zero otherwise (Column 2), and a dummy variable that is one if the growth in the granted loan amount
belongs to the lowest quartile and zero otherwise (Column 3). The independent variables of interest are
the interaction between bank sector market share and sector specialization and the deposit shock. Panel A
reports the results for speciﬁcations with the full sample, where we control for ﬁrm demand using ﬁrm cluster
FE. Firm clusters are based on location-sector-size triplets for single-bank ﬁrms and on the ﬁrm itself for
multiple-bank ﬁrms. Panel B reports the results for speciﬁcations with the sample of ﬁrms borrowing from
multiple banks only where we control for ﬁrm demand using ﬁrm ﬁxed eﬀects. We control for all observed
and unobserved bank-speciﬁc covariates by including bank ﬁxed eﬀects. Standard errors are clustered at the
bank level. ***, ** and * denote p<0.01, p<0.05 and p<0.1 respectively.

Panel A

∆% Depositsb * Sector market sharebs

∆% Depositsb * Sector specializationbs

Sector market sharebs

Sector specializationbs

Observations
R-squared
Bank FE
Firm cluster FE

Panel B

∆% Depositsb * Sector market sharebs

∆% Depositsb * Sector specializationbs

Sector market sharebs

Sector specializationbs

Observations
R-squared
Bank FE
Firm FE

(1)

∆% Creditbf

(2)
Increase in
creditbf

(3)
Large decrease
in creditbf

-1.501***
(0.464)
-0.526**
(0.210)

-0.010
(0.038)
0.038
(0.040)

160,223
0.298
Yes
Yes

-2.564***
(0.613)
-1.600***
(0.486)

-0.121
(0.082)
0.204**
(0.093)

160,223
0.282
Yes
Yes

2.839***
(0.866)
0.937***
(0.270)

0.053
(0.057)
-0.101*
(0.056)

160,223
0.292
Yes
Yes

∆% Creditbf

Increase in
creditbf

Large decrease
in creditbf

-2.421***
(0.648)
-0.733
(0.494)

-0.009
(0.075)
0.051
(0.081)

47,205
0.460
Yes
Yes

-2.047***
(0.664)
-2.250***
(0.755)

-0.043
(0.094)
0.195
(0.118)

47,205
0.469
Yes
Yes

3.013*
(1.751)
2.317***
(0.525)

0.065
(0.118)
-0.282**
(0.120)

47,205
0.486
Yes
Yes

ECB Working Paper Series No 2230 / January 201960Table 12: Robustness: Geographic specialization, loan maturity and relationship strength

This table investigates alternative reallocation scenarios of the interbank funding shock (∆%IBFb) based on
geographical bank market share and specialization, loan maturity specialization and bank-ﬁrm relationship
lending. The dependent variable is percentage growth in the granted loan amount (Columns 1 and 2), a
dummy variable that is one if the granted amount increases and zero otherwise (Columns 3 and 4), and a
dummy variable that is one if the growth in the granted loan amount belongs to the lowest quartile and
zero otherwise (Columns 5 and 6). Both geographical market share and specialization are calculated in a
similar way as sector market share and specialization, but at the province level instead of the sector level.
The maturity structure indicator captures, at the bank-sector level, the share of loans that matures in more
than one year. The two relationship lending proxies are the length of a bank-ﬁrm relationship, proxied by the
number of months that a ﬁrm has an outstanding loan with a bank before 2008:9, and a dummy indicating
whether a bank is the main bank of a ﬁrm, calculated as the bank from which a bank borrows its largest
share of credit in the thirteen months before 2008:9. Odd columns contain the results for speciﬁcations with
the full sample, where we control for ﬁrm demand using ﬁrm cluster ﬁxed eﬀects. Firm clusters are based on
location-sector-size triplets for single-bank ﬁrms and on the ﬁrm itself for multiple-bank ﬁrms. Even columns
contain the results for speciﬁcations with the sample of ﬁrms borrowing from multiple banks where we control
for ﬁrm demand using ﬁrm ﬁxed eﬀects. We control for all observed and unobserved bank-speciﬁc covariates
by including bank ﬁxed eﬀects. Firm controls include all interacted ﬁrm characteristics. Bank-sector and
bank-ﬁrm controls include interacted bank-sector and bank-ﬁrm characteristics. Standard errors are clustered
at the bank level. ***, ** and * denote p<0.01, p<0.05 and p<0.1 respectively.
(4)

(1)

(3)

(2)

∆% Creditbf

Increase in
creditbf

(5)
(6)
Large decrease
in creditbf

∆% IBFb * Sector market sharebs

∆% IBFb * Sector specializationbs

∆% IBFb * Total assetsf

∆% IBFb * Agef

∆% IBFb * Leveragef

∆% IBFb * Pledged collateralf

∆% IBFb * Financial Pressuref

∆% IBFb * Geographical market sharebp

∆% IBFb * Geographical specializationbp

∆% IBFb * Maturity structurebs

∆% IBFb * Length of relationbf

∆% IBFb * Main bankbf

Observations
R-squared
Firm controls
Bank-sector and bank-ﬁrm controls
Bank FE
Firm cluster FE
Firm FE

-0.750**
(0.290)
-0.468**
(0.218)
0.021
(0.024)
-0.000
(0.002)
0.203***
(0.047)
0.025**
(0.010)
0.059***
(0.015)

1.748*
(0.926)
-0.101
(0.126)
-0.099
(0.190)
-0.001
(0.001)
0.0345
(0.045)

141,762
0.370
Yes
Yes
Yes
Yes
No

-0.877
(0.649)
-1.126***
(0.335)
0.006
(0.038)
0.001
(0.002)
0.400***
(0.090)
0.011
(0.019)
0.101***
(0.029)

1.524
(1.209)
-0.0291
(0.245)
-0.583**
(0.274)
-0.001
(0.001)
0.125**
(0.061)

44,904
0.470
No
Yes
Yes
No
Yes

-1.401***
(0.445)
-0.602
(0.468)
0.040
(0.057)
-0.007**
(0.003)
0.468***
(0.137)
0.029
(0.019)
0.058**
(0.028)

3.296**
(1.535)
-0.462**
(0.213)
-0.0245
(0.320)
-0.001
(0.002)
0.260***
(0.054)

141,762
0.322
Yes
Yes
Yes
Yes
No

0.0949
(0.537)
-1.804***
(0.432)
0.037
(0.065)
-0.002
(0.003)
0.988***
(0.302)
-0.017
(0.020)
0.101**
(0.048)

4.629**
(1.929)
-0.667**
(0.307)
-0.627*
(0.344)
-0.002
(0.003)
0.409***
(0.091)

44,904
0.477
No
Yes
Yes
No
Yes

1.093**
(0.427)
1.092**
(0.416)
-0.027
(0.031)
0.003
(0.002)
-0.278
(0.171)
-0.048**
(0.022)
-0.055***
(0.018)

-0.868
(1.147)
-0.110
(0.234)
0.081
(0.319)
-0.001
(0.001)
-0.202
(0.129)

141,762
0.344
Yes
Yes
Yes
Yes
No

0.801
(1.401)
2.045**
(0.793)
-0.014
(0.025)
-0.000
(0.003)
-0.544***
(0.187)
-0.012
(0.023)
-0.087***
(0.029)

-0.484
(1.998)
-0.368
(0.362)
0.592
(0.397)
0.002
(0.002)
-0.332**
(0.131)

44,904
0.496
No
Yes
Yes
No
Yes

ECB Working Paper Series No 2230 / January 201961Table 13: Real eﬀects: Firm investment and growth

This table investigates the impact of the interbank funding shock (∆% Interbank fundingb) on ﬁrm in-
vestment and ﬁrm growth. Firm investment is proxied by the growth in tangible ﬁxed assets (Columns
1 and 2), ﬁrm growth is measured as the growth in total assets (Columns 3 and 4). The growth rate
for both variables is computed as the diﬀerence between the last available value of the variable two
years post-shock (i.e., end of 2010) and the last available value of the variable pre-shock, scaled by the
last available value of total assets pre-shock. The independent variables of interest are the shock to
interbank funding (∆% Interbank fundingb) and its interactions with bank-sector-speciﬁc and ﬁrm-
speciﬁc characteristics. The set of characteristics we consider is identical to the ones in Table 6. We
include bank sector market share, bank sector specialization, ﬁrm size, ﬁrm age, leverage, pledged col-
lateral to ﬁxed assets, ﬁnancial pressure, and cash holdings. The regressions include further controls
for whether the ﬁrm received an additional loan from a new bank, for whether a bank terminated
a loan with a ﬁrm, for whether the ﬁrm is borrowing from more than one bank, for the change in
the ratio of utilized over authorized credit, for whether the ﬁrm increased its reliance on trade credit
and for growth opportunities by including the ﬁrm’s sales growth over the period and a set of sector
dummies. Standard errors are clustered at the bank level. ***, ** and * denote p<0.01, p<0.05 and
p<0.1 respectively.

VARIABLES

∆% Interbank fundingb

∆% Interbank fundingb * Sector market sharebs

∆% Interbank fundingb * Sector specializationbs

∆% Interbank fundingb * Total assetsf

∆% Interbank fundingb * Agef

∆% Interbank fundingb * Leveragef

∆% Interbank fundingb * Pledged collateral to ﬁxed assetsf

∆% Interbank fundingb * Financial pressuref

Observations
R-squared
Firm Controls
Sector FE

(2)
(1)
∆% Fixed assetsf

(3)

(4)

∆% Assetsf

0.083**
(0.040)

114,435
0.152
Yes
Yes

1.114***
(0.423)
-0.834***
(0.279)
0.002
(0.206)
-0.064**
(0.028)
-0.004
(0.002)
-0.184
(0.119)
-0.005
(0.019)
-0.008
(0.018)

114,435
0.152
Yes
Yes

0.041
(0.054)

114,435
0.337
Yes
Yes

1.073*
(0.647)
-0.787
(0.537)
0.291
(0.348)
-0.073
(0.045)
-0.001
(0.002)
-0.095
(0.167)
-0.008
(0.022)
-0.003
(0.036)

114,435
0.337
Yes
Yes

ECB Working Paper Series No 2230 / January 201962Online Appendix

A1 Information on bail-outs

List of main government interventions for banks active in Belgium:24

• Fortis Bank NV/SA: The Belgian government invested EUR 4.7 billion in Fortis Bank NV/SA

on September 29, 2008 (common equity investment). This deal was concluded on October 6,

2008. On the same day, the Belgian government agreed with BNP Paribas to transfer just

under 75 % of its stake in the capital of Fortis bank Belgium in exchange for shares to be

issued by BNP Paribas. On November 5, 2008, Fortis Bank received a guarantee of EUR

150 million for its interbank transactions from the Belgian government. On March 6, 2009,

the Belgian State also agreed to guarantee the losses on the toxic assets of Fortis Bank for

an amount of EUR 1.5 billion, on condition that Fortis Bank covers the ﬁrst losses for an

amount of EUR 3.5 billion. The Belgian State also committed to recapitalise Fortis Bank, if

necessary, for a period of 3 years for a maximum amount of EUR 2 billion.

• KBC Bank NV/SA: The Belgian State bought EUR 3.5 billion non-voting core capital se-

curities of KBC on October 27, 2008. This was approved by the European Commission on

December 18, 2008. On January 22, 2009, KBC received a capital injection of EUR 2 billion

from the Flemish regional Government. On May 13, 2009, the Belgian government granted

KBC a guarantee on a portfolio of structured credit products (initial portfolio value: EUR

22.5 billion). The guarantee covered 90 % of the default loss above a ﬁrst loss tranche of

EUR 5.7 billion, with a maximum commitment of EUR 2 billion.

• Dexia SA: Dexia SA received a total of EUR 2 billion of capital injections from Belgian

authorities On 30 September, 2008. The EUR 2 billion came from the Belgian federal gov-

ernment (EUR 1 billion), the Flemish region (EUR 500 million), the Walloon region (EUR

350 million) and the Brussels Capital region (EUR 150 million). Additionally, Dexia also

received EUR 1 billion from its current Belgian institutional shareholders (Arcoﬁn, Gemeen-

telijke Holding and Ethias Insurances- and a total of EUR 3.4 billion from the France and

24An even more detailed overview of all government interventions in the Belgian banking sector at the end
of 2008 and in early 2009 can be found in the 2009 Financial Stability Review of the National Bank of
Belgium (NBB, 2009). The summary provided below is based on Box 2 in this Financial Stability Review.

ECB Working Paper Series No 2230 / January 201963Luxembourg government. On December 9, 2008, The Belgian, French and Luxembourg State

agreed on an aggregate guarantee of EUR 150 billion (of which EUR 90.75 billion covered

by the Belgian State, EUR 54.75 billion by the French State and EUR 4.5 billion by the

Luxembourg State.) on a group of securities and ﬁnancial instruments issued by Dexia and

held by third party beneﬁciaries. The guarantee covered Dexia’s liabilities towards credit in-

stitutions and institutional counterparties, as well as bonds and other debt securities issued

for the same counterparties, all falling due before 31 October 2011.25

• ING: On October 19, 2008, the Dutch government increased the core capital of ING Group

(the Dutch holding company above ING Belgium NV/SA) with EUR 10 billion.26

25For more info on the guarantee, see https://www.nbb.be/doc/dq/warandia/pdf/20081209_gov_02_uk.

pdf
26See

the press

the Dutch National Bank,
nieuwsoverzicht-en-archief/persberichten-2008/dnb189474.jsp

announcement

of

https://www.dnb.nl/nieuws/

ECB Working Paper Series No 2230 / January 201964A2 Branches and location

Regarding banks’ geographical orientation, we conduct two further tests. First, rather than mea-

suring bank market share and bank specialization at the sector level considering Belgium as the

relevant market, we now measure it by province. In theory, local pricing power might matter for

banks, making it worthwhile to redo our analysis within province. However, note that there are

no restrictions nor regulations to supply or demand credit across province borders, making it more

likely that Belgium rather than the local market is the relevant market to compute market share

and market power. Furthermore, Belgium is a relatively small country and it is very likely that

sector-level knowledge in one province is also very useful for the same sector in another province.

As such a province-level specialization measure might not be too diﬀerent from country-wide bank

specialization. Nevertheless, as an additional test, we run the reallocation results when calculating

the sector specialization and market share measure at the bank-province level and report results in

Table A1. We report the results for our setup with ﬁrm cluster ﬁxed eﬀects (Columns 1, 3 and 5)

and for the sample where we can use ﬁrm ﬁxed eﬀects (Columns 2, 4 and 6). The results are very

similar to our main results, both for sector market share as for sector specialization.

Second, a recent strand of papers on the domestic transmission of bank shocks in the US shows that

branch presence matters for shock transmission. Gilje et al. (2016), for example, show that positive

shocks to bank deposits in one area lead to an increase in loan supply of that bank in other areas

where they have branches. Berrospide et al. (2016) show that high mortgage delinquencies in one

area lead to lower lending in other areas, and especially so in areas where the bank does not have

branches. Banks thus seem to have an incentive to protect areas where they have physical presence,

presumably because (i) this leads to better information about the local market and (ii) it is easier

to leave a market when you do not have any brick and mortar investments (i.e., actual branches)

in it; you can simply ‘cut and run’. Compared to the US, the ‘cut and run’ channel is unlikely to

be of any importance in our sample. The simple reason is that there are very few loans to ﬁrms in

provinces where its bank does not have a branch. For a subset of banks in our sample, we collect

branch data from SNL from 2011 (the earliest year available, but if anything, bank branches were

closed after the ﬁnancial crisis). We ﬁnd that almost all loans (98%) are granted in provinces where

the bank has a branch. Hence, interacting the shock with a dummy indicating whether lending is

done in a branchless province is empirically challenging. However, what might be relevant is the

ECB Working Paper Series No 2230 / January 201965Table A1: Bank-sector-province market share and specialization

This table contains information on the estimated eﬀect of interbank funding shocks (∆% Interbank fundingb) on
credit supply, conditional on banks’ sector market share and sector specialization, which are measured within
province. The independent variables of interest are the interaction between bank sector-province market share
and sector-province specialization and the interbank funding shock. The dependent variable is percentage
growth in the granted loan amount (Column 1), a dummy variable that is one if the granted amount increases
and zero otherwise (Column 2), and a dummy variable that is one if the growth in the granted loan amount
belongs to the lowest quartile and zero otherwise (Column 3). Panel A reports the results for speciﬁcations
with the full sample, where we control for ﬁrm demand using ﬁrm cluster FE. Firm clusters are based on
location-sector-size triplets for single-bank ﬁrms and on the ﬁrm itself for multiple-bank ﬁrms. Panel B reports
the results for speciﬁcations with the sample of ﬁrms borrowing from multiple banks only in which case we
control for ﬁrm demand using ﬁrm FE. We control for all observed and unobserved bank-speciﬁc covariates by
including bank-province ﬁxed eﬀects. Standard errors are clustered at the bank level. ***, ** and * denote
p<0.01, p<0.05 and p<0.1 respectively.

Panel A

∆% Interbank fundingb * Sector-province market sharebps

∆% Interbank fundingb * Sector-province specializationbps

Sector-province market sharebps

Sector-province specializationbps

Observations
R-squared
Bank-Province FE
Firm cluster FE

Panel B

∆% Interbank fundingb * Sector-province market sharebps

∆% Interbank fundingb * Sector-province specializationbps

Sector-province market sharebps

Sector-province specializationbps

Observations
R-squared
Bank-Province FE
Firm FE

(1)

∆% Creditbf

(2)
Increase in
creditbf

(3)
Large decrease
in creditbf

-0.423*
(0.236)
-0.228**
(0.111)

-0.063*
(0.035)
-0.018
(0.023)

160,183
0.299
Yes
Yes

-1.159**
(0.437)
-0.326
(0.275)

-0.198***
(0.067)
0.073
(0.060)

160,183
0.283
Yes
Yes

1.080**
(0.403)
0.504***
(0.183)

0.141**
(0.053)
0.020
(0.034)

160,183
0.294
Yes
Yes

∆% Creditbf

Increase in
creditbf

Large decrease
in creditbf

-0.921**
(0.396)
0.041
(0.202)

-0.127**
(0.060)
0.022
(0.041)

47,157
0.463
Yes
Yes

-0.593
(0.632)
-0.257
(0.420)

-0.082
(0.087)
0.052
(0.077)

47,157
0.474
Yes
Yes

1.084
(1.011)
0.712*
(0.420)

0.156
(0.128)
-0.079
(0.072)

47,157
0.489
Yes
Yes

ECB Working Paper Series No 2230 / January 201966branch density. We compute the number of branches that a bank has in each province and then

interact the log of this number with our funding shock and add this variable as a control variable

to our main setup. This allows us to check whether banks have an incentive to shield borrowers

in areas where they have a higher branch presence and, importantly, whether our main results are

impacted by this local information channel.

Table A2 shows that the interaction term between the number of branches and the interbank funding

shock has the expected negative sign in all columns (but Column 5), but is never signiﬁcant. Our

main results also do not change when adding this interaction term. Banks still protect ﬁrms in

sectors where they have a high market share and in which they are more specialized, both in the

full sample or multiple-bank borrower sample.

ECB Working Paper Series No 2230 / January 201967Table A2: Bank branches per province

This table contains information on the estimated eﬀect of the interbank funding shock (∆ Interbank fundingb)
on credit supply, conditional on banks’ sector market share and sector specialization as well as banks’ branch
presence. Compared to the baseline setup of the reallocation eﬀects, we now also add a variable, ln(Branches)bp,
measuring the number of branches a bank has in a given province, as well as its interaction with the interbank
funding shock. This branch location data is available for 21 out the 38 banks in our sample. Moreover, SNL
only started collecting branch location data in 2011. However, if anything, bank branches were closed after the
ﬁnancial crisis. The dependent variable is percentage growth in the granted loan amount (Column 1), a dummy
variable that is one if the granted amount increases and zero otherwise (Column 2), and a dummy variable that
is one if the growth in the granted loan amount belongs to the lowest quartile and zero otherwise (Column 3).
Panel A reports the results for speciﬁcations with the full sample, where we control for ﬁrm demand using ﬁrm
cluster FE. Firm clusters are based on location-sector-size triplets for single-bank ﬁrms and on the ﬁrm itself
for multiple-bank ﬁrms. Panel B reports the results for speciﬁcations with the sample of ﬁrms borrowing from
multiple banks only in which case we control for ﬁrm demand using ﬁrm FE. We control for all observed and
unobserved bank-speciﬁc covariates by including bank ﬁxed eﬀects. Standard errors are clustered at the bank
level. ***, ** and * denote p<0.01, p<0.05 and p<0.1 respectively.

Panel A

∆% Creditbf

Increase in
creditbf

Large decrease
in creditbf

∆ Interbank fundingb * Sector market sharebs

∆ Interbank fundingb * Sector specializationbs

∆ Interbank fundingb * ln(Branches)bp

Observations
R-squared
Bank FE
Firm cluster FE

Panel B

∆ Interbank fundingb * Sector market sharebs

∆ Interbank fundingb * Sector specializationbs

∆ Interbank fundingb * ln(Branches)bp

Observations
R-squared
Bank FE
Firm FE

-1.007***
(0.263)
-0.317*
(0.181)
-0.013
(0.053)

147,250
0.297
Yes
Yes

-1.830***
(0.460)
-0.744*
(0.399)
-0.126
(0.124)

147,250
0.280
Yes
Yes

1.438***
(0.334)
1.067***
(0.226)
-0.003
(0.087)

147,250
0.291
Yes
Yes

∆% Creditbf

Increase in
creditbf

Large decrease
in creditbf

-1.441*
(0.772)
-0.306
(0.423)
-0.078
(0.080)

41,667
0.462
Yes
Yes

-1.096*
(0.628)
-1.111**
(0.517)
-0.103
(0.092)

41,667
0.471
Yes
Yes

0.840
(1.273)
1.582**
(0.690)
0.046
(0.208)

41,667
0.489
Yes
Yes

ECB Working Paper Series No 2230 / January 201968A3 Term credit versus credit lines

The analysis in the paper pools credit at the bank-ﬁrm level. In this section, we shed some light

on potential diﬀerences between term credit and credit lines as both are pooled together but are

used to a diﬀerent extent by diﬀerent ﬁrms. While 87% of the ﬁrms (118,064 out of 134,367) have

at least one credit that can be categorized as a term credit, only half of that (44%) have a credit

line contract (58,542 out of 134,367). Both the number of ﬁrms using credit lines and the average

amount of a credit line is smaller. The authorized credit amount on the average credit line is

EUR 81,712, whereas the average term credit exposure of a ﬁrm to a bank is 7.5 times as large

(> EUR 600,000). The average borrower uses 32% of the authorized credit limit of a credit line.

Moreover, both types of contracts have speciﬁc properties that work both in favour and against

testing our hypotheses. While it may seem easier for a bank to renegotiate the committed amount

on a credit line (in case it is not yet fully utilized) than to cancel (part of) a term credit, this

may not necessarily be the case in practice. It depends on the contract terms of the credit line,

which are unknown to us. For example, we do not know whether banks used revocable lines of

credit or not. We also do not know the covenants which could stipulate under which conditions

(either on the customers’ side, banks’ side or general market conditions) a contract can be revoked.

Furthermore, such covenants can also be present for term credit. In addition, credit lines are also

more likely to be given to better and well-known borrowers, hence they can be considered more like

relationship-based Berger et al. (2017), whereas term loans are more often seen as transactional.

Hence, an opposite force could also be at work. While an unused credit line might be easier to

cancel, banks may be less willing to do so given the borrower’s status with the bank.

It thus remains an empirical issue whether and to what extent banks shield certain types of credit

when facing a funding shock. We analyze the impact of the funding shock as well as the reallocation

channels via sector market share and sector specialization on (i) term credit growth, (ii) credit line

growth and (iii) the change in the utilization rate of credit lines. The results are reported in Table

A3.

When looking at the average eﬀect of the funding shock (upper panel of Table A3), we ﬁnd that

an outﬂow of interbank funding leads to a reduction in term credit supply, both in the full sample

as well as the multiple borrower sample. There is no statistically signiﬁcant eﬀect on the growth in

ECB Working Paper Series No 2230 / January 201969Table A3: Term loans, credit lines and utilization rates

This table shows the impact of an interbank funding shock (∆IBFb) on credit supply, depending on the type
of credit. The table shows both the average impact (in Panel A) and the heterogenous impact conditional on
banks’ sector market share, banks’ sector specialization and ﬁrm characteristics (in Panel B). The dependent
variable is the percentage growth in the granted term credit (Columns 1 and 2), the percentage growth in the
granted credit lines amount (Columns 3 and 4), or the percentage change in the utilization rate of granted
credit lines (Columns 5 and 6). We report the results for speciﬁcations with the full sample where we control
for ﬁrm demand using ﬁrm cluster FE (Columns 1, 3 and 5) and for the sample of ﬁrms borrowing from
multiple banks where we control for ﬁrm demand using ﬁrm FE (Columns 2, 4 and 6). In the full sample, ﬁrm
clusters are based on location-sector-size triplets for single-bank ﬁrms and on the ﬁrm itself for multiple-bank
ﬁrms. Bank controls include bank capitalization, proﬁtability, credit risk, liquidity, stable deposit funding,
and size. Bank-sector controls include bank sector market share and specialization. Firm controls include all
interacted ﬁrm characteristics. Panel B includes bank ﬁxed eﬀects to control for all observed and unobserved
bank-speciﬁc covariates. Standard errors are clustered at the bank level. ***, ** and * denote p<0.01, p<0.05
and p<0.1 respectively.

Panel A

∆% IBFb

Observations
R-squared
Bank controls
Firm cluster FE
Firm FE

Panel B

(1)
(2)
∆% Term loansbf

(3)
(4)
∆% Credit linesbf

(5)

(6)

∆% Utilization ratebf

0.629***
(0.105)

0.623***
(0.150)

138,699
0.297
Yes
Yes
No

38,075
0.452
Yes
No
Yes

-0.279
(0.491)

65,089
0.240
Yes
Yes
No

-0.257
(0.335)

12,555
0.499
Yes
No
Yes

-0.407***
(0.107)

-0.332**
(0.162)

65,089
0.262
Yes
Yes
No

12,555
0.640
Yes
No
Yes

∆% Term Loansbf

∆% Credit linesbf

∆% Utilization ratebf

∆ IBFb * Sector market sharebs

∆ IBFb * Sector specializationbs

∆% IBFb * Total assetsf

∆% IBFb * Agef

∆% IBFb * Leveragef

∆% IBFb * Pledged collateralf

∆% IBFb * Financial Pressuref

Observations
R-squared
Bank-sector controls
Bank FE
Firm controls
Firm cluster FE
Firm FE

-1.320***
(0.416)
-0.265
(0.259)

0.030
(0.029)
0.003
(0.002)
0.125**
(0.056)
-0.013
(0.017)
0.077*
(0.038)

123,170
0.357
Yes
Yes
Yes
Yes
No

-2.442**
(0.958)
-0.172
(0.348)

-0.016
(0.062)
0.004
(0.003)
0.254**
(0.123)
-0.034
(0.021)
0.136***
(0.049)

36,253
0.454
Yes
Yes
No
No
Yes

0.204
(1.737)
1.095**
(0.496)

-0.030
(0.036)
0.015**
(0.007)
0.356**
(0.173)
0.041
(0.038)
0.046*
(0.024)

58,308
0.267
Yes
Yes
Yes
Yes
No

-1.466
(1.845)
-0.220
(0.865)

-0.080
(0.081)
0.002
(0.008)
0.153
(0.205)
0.158***
(0.034)
0.062
(0.056)

12,115
0.507
Yes
Yes
No
No
Yes

0.092
(0.360)
0.016
(0.150)

0.004
(0.015)
-0.000
(0.002)
-0.121**
(0.056)
-0.016
(0.018)
-0.021
(0.020)

58,308
0.286
Yes
Yes
Yes
Yes
No

-0.329
(0.662)
0.041
(0.492)

0.025
(0.058)
-0.001
(0.002)
-0.268
(0.179)
-0.056
(0.044)
0.066*
(0.038)

12,115
0.645
Yes
Yes
No
No
Yes

ECB Working Paper Series No 2230 / January 201970authorized credit line amounts. This is consistent with the view that relationship-based credit is

shielded more than transactional type of credit. In addition, we do ﬁnd that the average utilization

rate increases following a bank funding shock. It seems that banks are not able or willing to cut the

authorized credit line amount, and on the contrary that they are exposed to a double liquidity run.

Banks with a larger funding shock will also see an increase of the utilization rate (potentially to

oﬀ-set the lost term credit). These results are consistent with Ivashina and Scharfstein (2010) and

Ippolito et al. (2016). The latter, using similar data as ours, document that Italian banks facing a

larger interbank funding shock, in the quarter following the market turmoil in July 2007, (i) do not

signiﬁcantly reduce the granted amount on credit lines, (ii) are faced with an increased drawing

on credit lines, and hence (iii) observe a statistically signiﬁcant positive increase in the credit line

utilization rate. Given their detailed analysis of this “double bank run” phenomenon, we do not

dig deeper into this.

In the lower panel of Table A3, we show that term credit borrowers in sectors where the bank has

a larger sector market share are shielded more when the bank is hit by an interbank funding shock.

We also ﬁnd that sector specialization leads to a shielding eﬀect, but the point estimate is not

signiﬁcant. We do not ﬁnd evidence of shielding in the credit line sample. It is also reassuring that

there are no reallocation eﬀects on the change in the utilization rate (last two columns). The change

in the utilization rate is mainly the borrower’s decision to draw more heavily on pre-committed

credit, as banks do not seem to cut the authorized credit line amount. Hence, credit line usage

is more a reﬂection of credit demand than credit supply. Firms do not seem to choose drawing

signiﬁcantly more or less from banks with sector market share or sector specialization, indicating

that the results we ﬁnd in the overall sample (or in the term credit sample) are banks’ choices and

thus supply driven and not demand driven.

ECB Working Paper Series No 2230 / January 201971Acknowledgements 

Manuel Buchholz, Giovanni Dell’Ariccia, Hans Degryse, Ralph De Haas, Andrew Ellul, Florian Heider, Sanja Jakovljevic, Vlado Kysucky, 
Thomas Lejeune, Marco Machiavelli, Ralf Meisenzahl, Lars Norden, Marco Pagano, Andrea Presbitero, Rui Silva, Hirofumi Uchida, 
Gregg Udell as well as seminar and conference participants at Tilburg University, the National Bank of Belgium, the Finest Workshop 
(Rome), the European Central Bank, the Central Bank of Ireland, the European Banking Authority, the Ghent Corporate Finance Day, 
VU Amsterdam, Universite´ de Limoges, the Swiss Winter Conference on Financial Intermediation (Lenzerheide), the CEPR First 
Annual Spring symposium on Financial Economics (Imperial College London), the Belgian Financial Research Forum, the 5th MoFiR 
Workshop on Banking, the EFA meetings (Oslo), the 16th FDIC Bank Research Conference, the 2nd IWH Halle FIN FIRE workshop, the 
International Conference on Small Business Finance in Rio 2016, the ECB Task force on Banking analysis for monetary policy 
(Brussels) and ICMAIF Crete 2018. The views expressed are solely those of the authors and do not necessarily represent the views of 
the European Central Bank or the National Bank of Belgium. 

Olivier De Jonghe 
National Bank of Belgium, Brussels, Belgium; CentER, Tilburg University; e-mail: olivier.dejonghe@nbb.be 

Hans Dewachter 
National Bank of Belgium, Brussels, Belgium; email: hans.dewachter@nbb.be 

Klaas Mulier 
Ghent University, Ghent, Belgium; National Bank of Belgium; email: klaas.mulier@ugent.be 

Steven Ongena 
University of Zurich, Zurich, Switzerland; SFI and CEPR; email: steven.ongena@bf.uzh.ch 

Glenn Schepens 
European Central Bank, Frankfurt am Main, Germany; email: glenn.schepens@ecb.europa.eu 

© European Central Bank, 2019 

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This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

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