Sharing the pain? : credit supply and real effects of bank bail-ins.
Date: 2020-02-10
Institutions: European Banking Authority
Subjects: central bank, credit, economic policy, financial institution

EBA STAFF PAPER SERIES 

N. 1 – JUNE/2018 

SHARING THE PAIN? CREDIT 
SUPPLY AND REAL EFFECTS 
OF BANK BAIL-INS 

by Samuel Da-Rocha-Lopes, Thorsten Beck and André F. Silva 

The EBA Staff Papers Series describe research in progress by the author(s) and are published to elicit 
comments and to further debate. Any views expressed are solely those of the author(s) and so cannot be 
taken to represent those of the European Banking Authority (EBA) or to state EBA policy. Neither the EBA nor 
any person acting on its behalf may be held responsible for the use which may be made of the information 
contained in this publication, or for any errors which, despite careful preparation and checking, may appear. 

 
 
Sharing the Pain? Credit Supply and
Real Eﬀects of Bank Bail-ins∗

Thorsten Beck
Cass Business School,

Samuel Da-Rocha-Lopes
European Banking Authority

Andr´e F. Silva
Cass Business School

CEPR and CESifo

and Nova SBE

and IMF

June 2018

Abstract

We analyze the credit supply and real sector eﬀects of bank bail-ins by exploiting the
unexpected failure of a major Portuguese bank and subsequent resolution. Using a
matched ﬁrm-bank dataset on credit exposures and interest rates, we show that while
banks more exposed to the bail-in signiﬁcantly reduced credit supply at the intensive
margin, aﬀected ﬁrms compensated the tightening of overall credit with other sources
of funding. Nevertheless, SMEs were subject to a binding contraction of funds available
through credit lines and reduced investment and employment. These dampening eﬀects
are explained by the pre-shock internal liquidity position of smaller ﬁrms. (JEL E22,
E24, E58, G01, G21, G28, G32)

∗We would like to thank Tobias Berg, Pawel Bilinski, Diana Bonﬁm, Marco Bonomo (discussant),
Max Bruche, Charles Calomiris (discussant), Duckki Cho (discussant), Matteo Crosignani (discussant),
Jo˜ao Cocco, Ralph De Haas (discussant), Giovanni Dell’Ariccia, Mathias Dewatripont, Miguel Ferreira,
Mariassunta Giannetti, Piers Haben, Jos´e Liberti (discussant), Cath´erine Koch (discussant), Michael Koetter,
Fergal McCann (discussant), Ralf Meisenzahl, Steven Ongena, Sole Martinez Peria, Marco Pagano, Diane
Pierret (discussant), Alberto Pozzolo (discussant), Andrea Presbitero, Lev Ratnovski (discussant), Jo˜ao
Santos, Enrico Sette, Klaus Schaeck (discussant), Paolo Volpin, Spyridon Zarkos, an anonymous EBA
referee, conference participants at the 2nd CEPR Annual Spring Symposium in Financial Economics
(UK), De Nederlandsche Bank/EBC/CEPR Conference (Netherlands), 5th Emerging Scholars in Banking
and Finance Conference (UK), 32nd European Economic Association Conference (Portugal), 4th Bank of
Canada/Bank of Spain Workshop (Canada), 9th European Banking Center Network Conference (UK),
Research Task Force of the Basel Committee on Banking Supervision/CEPR Joint Workshop (Switzerland),
4th Workshop of the Empirical Financial Intermediation Research Network (Belgium), 2017 Sydney Banking
and Financial Stability Conference (Australia), Deutsche Bundesbank/IWH/CEPR Conference (Germany),
12th Luso-Brazilian Finance Meeting (Brazil), 2018 AFA PhD Poster Session (US), 2018 Sapienza/BAFFI
CAREFIN/RFS Conference (Italy), 11th Swiss Winter Conference on Financial Intermediation (Switzerland),
2018 Fixed Income and Financial Institutions Conference (US), 2018 Financial Intermediation Research
Society Conference (Spain), and seminar participants at the International Monetary Fund (US), Columbia
Business School (US), Bank of England (UK), BI Norwegian Business School (Norway), Single Resolution
Board (Belgium), Universidad Carlos III de Madrid (Spain) and Bank of Italy (Italy) for the valuable
comments and suggestions. We also thank Adelaide Cavaleiro, Gra¸ca Dami˜ao, Olga Monteiro, Paulo Jesus,
Paulo Taborda, Sandra Pinheiro and the Department of Statistics of the Bank of Portugal for providing
excellent research support. The views expressed in this paper are solely those of the authors and do
not necessarily represent the opinions of the European Banking Authority (EBA) or the Eurosystem,
or those of the International Monetary Fund (IMF), its Executive Board, or its management. E-mail:
tbeck@city.ac.uk, samuel.rocha.lopes@novasbe.pt, andre.silva.3@cass.city.ac.uk

1

1

Introduction

The recent ﬁnancial crisis highlighted the pressing need for a robust and consistent mechanism

to resolve banks in distress. Absent a viable alternative to bankruptcy that could lead to

contagion and a credit crunch, policymakers around the world opted to bail-out ﬁnancial

intermediaries using public funding. In Europe, for instance, taxpayers have covered more

than two-thirds of the cost of recapitalizing and resolving banks (Philippon and Salord, 2017).

These interventions were often accompanied by signiﬁcant government losses and austerity

programs associated with political frictions and distributional problems. To counter this

pervasive issue, most developed economies have recently introduced formal bail-in regimes

that involve the participation of bank creditors in bearing the costs of restoring a distressed

bank and include severe restrictions on taxpayer support.

An eﬀective bank resolution framework should minimize the trade-oﬀ between imposing

market discipline and reducing the external costs of a potential bank failure (Beck, 2011).

In fact, previous evidence has shown both the detrimental impact of public guarantees on

bank risk-taking (e.g., Gropp, Hakenes, and Schnabel, 2011; Dam and Koetter, 2012) and

the negative eﬀects of bank failures on real outcomes (e.g., Bernanke, 1983; Calomiris and

Mason, 2003; Ashcraft, 2005). The recently introduced bail-in regime reduces moral-hazard

due to creditors’ expectation of being bailed-in in case of distress (Sch¨afer, Schnabel, and

Weder, 2016), and should in principle also minimize the negative economic eﬀects since the

healthy part of the bank can continue functioning. However, there is little to no empirical

evidence on the eﬀects this new resolution mechanism may have on credit provision or the

real economy. Our study ﬁlls this gap in the literature by examining the credit supply and

real eﬀects of bank bail-ins using a unique dataset combining ﬁrm-bank matched data on

credit exposures and interest rates from the Portuguese credit register with balance-sheet

information for the ﬁrms and their lenders.

2

In detail, we exploit the unexpected collapse of a major bank in Portugal (Banco Esp´ırito

Santo - BES) in August 2014 that was coined “one of Europe’s biggest ﬁnancial failures”

(FT, 2014). The institution was resolved through a bail-in and split into a “good” and “bad”

bank, protecting taxpayers and depositors but leaving shareholders and junior bondholders

holding the toxic assets. The costs of this intervention fell not only on the bank’s creditors,

but also indirectly on other resident banks that had to provide additional funding to the

Bank Resolution Fund. Importantly, the bank failure was unrelated to fundamental risks in a

generalized group of borrowers or in the Portuguese banking sector. Instead, the collapse was

due to large risky exposures to a limited number of ﬁrms that were also owned by the Esp´ırito

Santo family (Economist, 2014). These reﬂected the “practice of management acts seriously

detrimental” to the bank and non-compliance with determinations issued by the Portuguese

central bank “prohibiting an increase in its exposure to other entities of the Group” (Banco

de Portugal, 2014a). From an identiﬁcation perspective, exploiting this (exogenous) shock is

therefore particularly attractive since the bank’s failure was purely idiosyncratic.

We start the analysis by examining over 115,000 bank-ﬁrm lending relationships and

running a within-ﬁrm diﬀerence-in-diﬀerences speciﬁcation comparing changes in credit supply

to the same borrower across banks exposed diﬀerently to the bail-in i.e., the bailed-in bank

itself, other banks that provided ad-hoc ﬁnancing to the Resolution Fund, and banks that

were exempt from making additional contributions. By exploiting the widespread presence

of Portuguese ﬁrms with multiple bank relationships, this approach allows us to control for

changes in observable and unobservable ﬁrm characteristics such as credit demand, quality,

and risk (Khwaja and Mian, 2008). In this regard, we show that the supply of credit from

banks more exposed to the bail-in declined signiﬁcantly to existing borrowers as a result of

the shock. In detail, comparing lending to the same ﬁrm by banks one standard deviation

apart in terms of exposure to the bail-in, we ﬁnd that more exposed banks reduced total

credit and granted credit lines by 3.0 and 5.7 percent more than banks exposed less. This

credit supply contraction was concentrated in ﬁrms that had the bailed-in bank as their

3

main lender prior to the shock and was less pronounced for SMEs as well as ﬁrms with lower

proﬁtability and non-performing loans in the pre-period.1

A fundamental follow-up question is whether more exposed ﬁrms could compensate the

credit supply tightening by accessing funds from other banks less aﬀected by the shock, and if

there were any real eﬀects associated with the intervention.2 Importantly, following Abowd,

Kramarz, and Margolis (1999) and Cingano, Manaresi, and Sette (2016), we are also able

to control for loan demand when looking at the cross-section of ﬁrms by including in the

regressions the vector of estimated ﬁrm-level ﬁxed eﬀects from the within-ﬁrm speciﬁcation.

We ﬁnd at the cross-sectional level that ﬁrms more exposed to the bail-in did not suﬀer

a reduction of overall credit after the intervention when compared to ﬁrms exposed less.

However, when isolating granted credit lines from total committed credit and focusing on

ﬁrms with credit lines at multiple banks, we show that SMEs more exposed to the resolution

were subject to a binding contraction in quantity of funds available through lines of credit,

an essential component for corporate liquidity management (Suﬁ, 2009; Jim´enez, Lopez, and

Saurina, 2009). Speciﬁcally, a one standard deviation increase in ﬁrm exposure to the bail-in

is associated with a 2.2 percent binding decrease in granted credit lines to SMEs.

Our results show that the resolution also came at the cost of moderately higher interest

rates for more exposed ﬁrms. In detail, a one standard deviation increase in ﬁrm exposure to

the shock is associated with a relative increase of 20 basis points in the interest rate charged

on credit lines for the average ﬁrm. We also observe a relative increase in interest rates

on new credit operations (though only for large ﬁrms more exposed to the shock), as well

as a moderate relative decrease in the maturity of new credit and increase in the share of

collateralized credit after the shock across both ﬁrm types.

1We conﬁrm our ﬁndings when using the complete sample of borrowing ﬁrms in Portugal (i.e., including
single-bank-relationship ﬁrms) in a model that replaces ﬁrm ﬁxed-eﬀects with location-size-sector ﬁxed-eﬀects
as in De Jonghe, Dewachter, Mulier, Ongena, and Schepens (2018).

2This issue is particularly important in the context of SMEs which usually ﬁnd it diﬃcult to substitute
credit from other sources because they are more opaque and thus mainly rely on existing banking relationships.
This is still a source of great concern among academics, regulators and policy-makers, particularly in Europe
(Giovannini, Mayer, Micossi, Di Noia, Onado, Pagano, and Polo, 2015)

4

Finally, regarding the eﬀect of the bank failure and subsequent bail-in on real sector

outcomes, we ﬁnd evidence of a negative adjustment of investment and employment policies at

SMEs borrowing from more exposed banks prior to the resolution. This eﬀect is economically

signiﬁcant: a one standard deviation increase in ﬁrm exposure to the shock leads to a relative

drop in investment and employment of up to 2.0 and 1.5 percent, respectively. These

dampening eﬀects of the bank resolution are driven by a response to increased liquidity

risk by ﬁrms with lower ex-ante internal liquidity. Consistent with the argument that the

option for ﬁrms to access liquidity from credit lines should be more valuable when internal

liquidity is scarce (e.g., Campello, Giambona, Graham, and Harvey, 2011), we ﬁnd that the

negative real eﬀects are concentrated on illiquid SMEs more exposed to the resolution that

responded to the funding shock by increasing cash holdings while decreasing investment and

employment.3 Instead, in line with precautionary cash savings being important in times of

dislocation in markets for external ﬁnance (e.g., Duchin, Ozbas, and Sensoy, 2010), more

exposed SMEs with high liquidity before the bail-in were able to use their available internal

cash holdings to compensate for the binding contraction in granted credit lines and thus

maintain employment and investment.4

This paper contributes to the literature examining how distressed banks should be resolved.

Kahn and Winton (2004) suggest that a “good-bank-bad-bank” split may be beneﬁcial as it

reduces risk-shifting incentives in the healthy bank and increases its incentive to screen and

monitor the “good” loans. More recent work, however, has mostly focused in describing the

potential beneﬁts and costs of the diﬀerent bank resolution mechanisms (e.g., Dewatripont,

2014; Conlon and Cotter, 2014; Avgouleas and Goodhart, 2015; Philippon and Salord, 2017)

and examining the interaction between bail-ins and bail-outs from a theoretical perspective

3This result is not explained by diﬀerences in anticipated growth opportunities across SMEs with low

and high levels of internal liquidity prior to the bank resolution.

4In a separate but related exercise, we gauge whether the bail-out of four Portuguese banks in 2012
resulted in similar negative eﬀects. We ﬁnd no signiﬁcant diﬀerences between borrowers of bailed-out and
non-bailed-out banks in terms of credit supply, investment or employment. This points to rather sharp
diﬀerences between diﬀerent bank resolution policies, although we caution that the macroeconomic situation
was considerably diﬀerent during these two episodes and that the public intervention in 2012 was more
systemic in nature.

5

(e.g., Klimek, Poledna, Farmer, and Thurner, 2015; Colliard and Gromb, 2017; Keister and

Mitkov, 2017; Walther and White, 2017; Bernard, Capponi, and Stiglitz, 2017). Our paper

contributes to this literature by assessing the eﬀects of a bank bail-in on credit supply and

real sector outcomes using detailed bank-, ﬁrm- and loan-level data. To the best of our

knowledge, this is the ﬁrst empirical study examining this issue.

We also contribute to the literature analyzing bank failures and the associated negative

real eﬀects. Bernanke (1983) and Calomiris and Mason (2003) highlight the economic

repercussions of bank failures in the 1920s and 1930s, while Ashcraft (2005) links the decrease

in lending following the closure of a large (solvent) aﬃliate in a regional bank holding company

in Texas in the 1990s to a decline in local GDP. Slovin, Sushka, and Polonchek (1993) show

that ﬁrms that were the main customers of Continental Illinois in the US saw their share

prices negatively aﬀected by its bankruptcy. Our paper shows that even when a bank partly

continues operating because of a more eﬃcient bank resolution mechanism, there are still

negative repercussions for certain borrower groups.

Finally, our paper also contributes to the empirical corporate ﬁnance literature on ﬁrm’s

liquidity management and its importance for the transmission of credit supply shocks to

the real economy. Under the precautionary demand for cash theory, ﬁrms hold cash as a

buﬀer as protection against adverse cash ﬂow shocks. This is particularly valuable for ﬁrms

that are ﬁnancially constrained (Almeida, Campello, and Weisbach, 2004), and following a

credit crunch (Duchin, Ozbas, and Sensoy, 2010). Directly relevant for our work, Berg (2018)

shows that while liquid SMEs are able to absorb credit supply shocks by using existing cash

buﬀers, their illiquid counterparts increase cash holdings when a loan application is rejected,

cutting non-cash assets by more than the requested loan amount, and thus investment and

employment. While Berg (2018) uses discontinuities in credit scores comparing accepted

and rejected loan applicants at a single German bank, we use an exogenous bank shock for

identiﬁcation and the entire set of banks operating in Portugal.5

5This paper is also part of an expanding literature using loan-level data to explore the eﬀect of regulatory,
liquidity and solvency shocks on credit supply and real outcomes. Khwaja and Mian (2008) and Schnabl

6

2 Background

After a rapid series of events including the disclosure of hefty losses of e3.6 billion in the

ﬁrst-half of 2014 arising from exposures to the parent family-controlled group of companies,

the Portuguese central bank decided to apply a resolution measure to Banco Esp´ırito Santo

(BES) on August 3, 2014. The bank was classiﬁed as a signiﬁcant credit institution by the

European Central Bank (World Bank, 2016) and was the third largest bank in Portugal

with a market share of 19 percent of credit granted to non-ﬁnancial corporations (Banco de

Portugal, 2014a). The scale of the losses came as a surprise to the Bank of Portugal, which

suggested that these “reﬂected the practice of management acts seriously detrimental” and

“noncompliance with the determinations issued prohibiting an increase in its exposure to

other entities of the Group” (Banco de Portugal, 2014a).

The resolution of the bank involved the transfer of sound activities and assets to a bridge

bank or “good bank” designated as Novo Banco (New Bank). In contrast, shareholders and

junior bondholders were left with the toxic assets that remained in a “bad bank” which is

in the process of liquidation.6 The e4.9 billion of capital of the newly-created bank was

fully provided by Portugal’s Bank Resolution Fund established in 2012 and ﬁnanced by

contributions of all the country’s lenders.7 Since the Fund did not yet have suﬃcient enough

(2012) gauge the eﬀect of exogenous liquidity shocks on banks’ lending behavior in Pakistan and Peru,
respectively. Jim´enez, Ongena, Peydr´o, and Saurina (2012, 2014b) use Spanish credit register data to explore
the eﬀect of monetary policy on credit supply and banks’ risk-taking. Cingano, Manaresi, and Sette (2016)
analyze the transmission of bank balance sheet shocks to credit and its eﬀects on investment and employment
in Italy. Ivashina and Scharfstein (2010) and Chodorow-Reich (2014) examine the eﬀects of the crisis in the
US. Iyer, Peydr´o, Da-Rocha-Lopes, and Schoar (2014), Alves, Bonﬁm, and Soares (2016) and Blattner,
Farinha, and Rebelo (2018) use the same credit register data from Portugal as we do to investigate the eﬀect
of the liquidity freeze in European interbank markets on credit supply, the role of the ECB as lender of last
resort in avoiding the collapse of the Portuguese ﬁnancial system during the European sovereign debt crisis,
and the impact of bank capital adequacy on productivity, respectively.

6The ﬁrms part of the Esp´ırito Santo Group that drove the collapse of the bank are not part of our
estimations since (i) these ﬁrms were mostly based abroad and our dataset only captures ﬁrms headquartered
in Portugal, and (ii) their credit claims were transferred to the “bad bank” and therefore do not appear in
the post-shock period even if a ﬁrm is based in Portugal – see the detailed list of assets transferred to “bad
bank” in subparagraph (a) of Annex 2 in Banco de Portugal (2014a).

7The CET1 ratio of the “good bank” immediately after the resolution was 10.3 percent, above the

regulatory minimum (Novo Banco, 2014).

7

resources to fully ﬁnance such a large operation, it had to take a loan from a group of eight

of its (largest) member banks (e0.7 billion) and another from the Portuguese government

(e3.9 billion). The government ensured the deal would have no direct or indirect costs for

taxpayers since the loan was made to the Bank Resolution Fund (i.e., not to the distressed

bank), and given that the country lenders who bear the risks will have several years to recoup

the shortfall with their ordinary contributions (FT, 2017).8

Figure 1 shows the unexpected and idiosyncratic nature of the bank failure. CDS spreads

of the bailed-in bank moved in line with the rest of the sector until late June 2014 when the

degree of exposures to the Group’s entities owned by the same family started to be revealed.

Within a month, the spreads moved from less than 2 percent to almost 7 percent. The event

came after a long period of increasing stability in the banking sector, with CDS spreads for

Portuguese banks having declined from its crisis peak of around 16 percent in late 2011.9

The ﬁgure also shows the limited contagion from the bailed-in bank to the remainder of

the banking system, with the average CDS spread for all other resident banks considered

signiﬁcant credit institutions by the ECB increasing only slightly in the weeks leading up to

the intervention and remaining below 3.5 percent until December 2015. This is consistent with

the simulation results of H¨user, Halaj, Kok, Perales, and van der Kraaij (2018) that, using

granular data on the securities cross-holdings among the largest euro area banking groups,

show that bail-ins lead to limited spillovers due to low levels of securities cross-holdings in the

interbank network and no direct contagion to creditor banks. Nevertheless, in our analysis

8The Portuguese central bank decided to move even further towards a bail-in type of intervention with a
re-resolution in the last days of 2015 - 16 months after the original intervention. In detail, a limited number
of bonds were transferred to the “bad bank”, imposing losses on almost e2 billion of senior bondholders
(Banco de Portugal, 2015; FT, 2016). In October 2017, Lone Star Funds (a US private-equity fund) acquired
75 percent of the “good bank” in return for a capital injection of e1 billion, with the remaining 25 percent
held by the Bank Resolution Fund (Banco de Portugal, 2017). Given that we only have loan and ﬁrm-level
data available until 2015, our analysis does not consider these two shocks and is instead solely focused on
the eﬀects of the original resolution in August 2014.

9Following demanding requirements imposed by the European Banking Authority and the Bank of
Portugal, the Core Tier 1 ratio in the Portuguese banking sector reached 12.3 percent at the end of 2013
(Banco de Portugal, 2014b). At the country-level, by the end of EC/ECB/IMF Economic Adjustment
Program in June 2014, Portugal was growing 0.3 percent faster than the EU, excluding Germany (Reis,
2015).

8

we still take into account the exposure of other banks to the bail-in, even if small, through the

institution-speciﬁc amount of ﬁnancing to the ad-hoc loan granted to the Resolution Fund.

[Figure 1 here]

In short, even when being conservative and considering this resolution a “hybrid of bail-in

and bail-out” (Economist, 2014), this intervention diﬀers markedly from the bail-outs of

most distressed banks during the ﬁnancial crisis as all losses were ultimately imposed on

shareholders and (junior and later senior) bondholders. This resolution was therefore also

distinctly diﬀerent from the bail-out and recapitalization of several large Portuguese banks

in 2012, a diﬀerence we also explore in our empirical analysis. Finally, while this resolution

occurred before transposition of the EU Bank Recovery and Resolution Directive (BRRD)

into national legislation, the Portuguese resolution regime introduced in 2012 and then in

force was already, in substance, very similar to the ﬁnal European directive (World Bank,

2016). Although this new framework hypothetically lets banks fail without resorting to

taxpayer funding (Avgouleas and Goodhart, 2015), it also allows for extraordinary public

support under speciﬁc conditions (Schoenmaker, 2017).10 As a result, this shock provides a

unique laboratory to study the potential eﬀects of future (similar) interventions.

3

Identiﬁcation Strategy

We investigate the credit supply and real eﬀects of a bank bail-in in two steps. First, we

assess whether the resolution induced signiﬁcant changes in the supply of credit to ﬁrms

that were diﬀerently exposed to the bail-in (within-ﬁrm analysis). Second, assuming that

the tightening of credit did occur, we investigate whether these ﬁrms were able to substitute

10The EU and the US strengthened their bank resolution regimes and introduced bail-in powers via the
Bank Recovery and Resolution Directive (BRRD) and the Dodd-Frank Act, respectively. Despite many
similarities between the EU and US resolution schemes, there are still some important diﬀerences such the
lack of a restructuring option in the US (Philippon and Salord, 2017).

9

funding from other (less exposed) banks, if they were able to maintain their average interest

rates on credit, as well as the consequences of this shock for ﬁrm real outcomes such as

investment and employment (cross-sectional analysis). The ﬁrst part of the analysis uses

ﬁrm-bank matched data to exploit variation within ﬁrms that have more than one lending

relationship, while the second makes use of variation across ﬁrms with diﬀerent pre-shock

exposures to the bail-in.

Within-Firm Analysis. The main challenge of our empirical analysis is to identify the

causal impact of bail-ins on loan supply, price conditions and real outcomes. In fact, this

shock may be correlated with underlying changes in the overall economic situation that may

aﬀect both credit provision and ﬁrms’ loan demand and risk. To address this identiﬁcation

problem, we exploit an exogenous shock in August 2014 corresponding to an unexpected

bank failure and subsequent resolution as discussed above, and use a diﬀerence-in-diﬀerences

approach to compare lending before and after the bank collapse across banks more and less

exposed to the resolution.

In detail, following the novel approach of Khwaja and Mian (2008), we exploit our panel

of matched bank-ﬁrm data and account for unobserved heterogeneity in ﬁrms’ loan demand,

quality and risk by saturating our model with ﬁrm ﬁxed eﬀects. As a result, our identiﬁcation

comes entirely from ﬁrms that were borrowing from at least two diﬀerent banks before and

after the resolution program. This strategy isolates the causal impact of the bail-in shock on

the change in credit supply by comparing the within-ﬁrm variation in the change in lending

from banks diﬀerently exposed by the intervention. The baseline speciﬁcation is deﬁned as:

∆log(Credit)bi = β(BankExposureb) + δ0Xb + αi + εbi

(1)

10

where the main dependent variable ∆log(Credit)bi is the log change in granted credit from

bank b to ﬁrm i from the pre to the post-period.11 We also consider the change in granted

credit credit lines from bank b to ﬁrm i from the pre to the post-period as an alternative

dependent variable.

In this case, our identiﬁcation comes from the sub-set of ﬁrms (35

percent) with credit lines from at least two diﬀerent banks before and after the resolution

program.12

As in Khwaja and Mian (2008), the quarterly data for each credit exposure is collapsed

(time-averaged) into a single pre (2013:Q4-2014:Q2) and post-shock (2014:Q3-2015:Q3) period.

This adjustment has the advantage that our standard errors are robust to auto-correlation

(Bertrand, Duﬂo, and Mullainathan, 2004). The main independent variable, BankExposureb

is the percentage of assets of each bank exposed to the bail-in: the percentage of assets that

was eﬀectively bailed-in for the resolved bank, the speciﬁc contribution to the ad-hoc e0.7

billion loan to the Resolution Fund granted as part of the resolution for the 8 participating

banks (as a percentage of assets), and 0 otherwise. We do not include in this measure the

ordinary contributions to the Fund that each bank made in 2013 as these were already priced

in before the resolution.13 αi are ﬁrm ﬁxed eﬀects that capture ﬁrm-speciﬁc determinants of

credit ﬂows and can be interpreted as a measure of credit demand (e.g., Cingano, Manaresi,

and Sette, 2016).

Xb refers to a set of bank-level controls measured in the pre-period, including bank

size (log of total assets), bank ROA (return-on-assets), bank capital ratio (equity to total

assets), bank liquidity ratio (liquid to total assets), and bank NPLs (non-performing loans to

11Since we want to ensure that changes in credit are not driven by sudden draw-downs of credit lines by
certain ﬁrms, we consider throughout the paper the total amount of committed credit i.e., the total amount
of credit that is available to a borrower, not only the portion that was taken up.

12While our identiﬁcation strategy relies on within-ﬁrm variation in credit supply, we test the robustness
of our ﬁndings by using the sample of all borrowers (i.e., including ﬁrms with only one bank relationship) and
replacing bank-ﬁxed eﬀects with location-size-sector ﬁxed-eﬀects to partially control for demand side factors
(De Jonghe, Dewachter, Mulier, Ongena, and Schepens, 2018).

13These bank-speciﬁc ﬁgures were manually collected from each of the banks publicly-available 2014 Annual
Reports. The percentage of assets that was eﬀectively bailed-in for the resolved bank amounts to 6.8 percent,
while the speciﬁc contribution to the ad-hoc loan to the Resolution Fund granted as part of the resolution
for the 8 participating banks ranges from 0.04 to 0.37 percent of assets.

11

total gross loans). These controls are particularly relevant in our setting since bank-speciﬁc

exposures to the bail-in are not randomly assigned but a function of bank characteristics (e.g.,

the contribution to the resolution fund is determined by each bank’s amount of liabilities),

which may be correlated with changes in their willingness to lend. Finally, since the shock

is bank-speciﬁc, changes in the credit granted from the same bank may be correlated. As a

result, we use robust standard errors clustered at the bank level in all within-ﬁrm regressions.

Cross-Sectional Analysis. Although the above speciﬁcation allows us to examine whether

there was indeed a credit contraction and which type of ﬁrms were more likely to be aﬀected

by the shock, it is not appropriate to assess aggregate eﬀects. This is because the within-ﬁrm

analysis is not able to capture credit ﬂows from new lending relationships and also ignores

all terminated lending relationships between the pre and post-shock period. Given the

importance of the extensive margin for credit adjustment, we also estimate the related

between-ﬁrm (cross-sectional) eﬀect of ﬁrm exposure to the shock as:

∆log(Y )i = β(F irmExposurei) + τ 0Fi + δ0 ¯Xi + ˆαi + εi

(2)

where ∆log(Y )i is the log change in total bank credit or in granted credit lines between

2013:Q4 to 2015:Q4 from all banks to ﬁrm i. We use the same model to examine the eﬀects

on other credit conditions and analyze potential real eﬀects.

F irmExposurei is the exposure of each ﬁrm to the bail-in computed as the weighted

average of Bank Exposure across all banks lending to a ﬁrm, using as weights the pre-period

share of total credit of each bank. Fi are ﬁrm characteristics including ﬁrm size (log of total

assets), ﬁrm age (ln(1+age)), ﬁrm ROA (net income to total assets), ﬁrm capital (equity to

total assets) and ﬁrm liquidity (current assets to current liabilities) - all measured in 2013:Q4.

We also include industry and district ﬁxed eﬀects in the model. Bank controls ¯Xi include

the same variables as in speciﬁcation (1) but are averaged at the ﬁrm-level according to the

share of total credit granted to the ﬁrm by each bank prior to the shock.

12

Finally, given that in the between-ﬁrm model (2) the ﬁrm-speciﬁc demand shock αi cannot

be absorbed, a OLS estimate of β would be biased if F irmExposurei is correlated with credit

demand (Jim´enez, Mian, Peydr´o, and Saurina, 2014a; Cingano, Manaresi, and Sette, 2016).

To control for loan demand when looking at the cross-section of ﬁrms, we follow the method

developed by Abowd, Kramarz, and Margolis (1999) and recently applied by Bonaccorsi di

Patti and Sette (2016) and Cingano, Manaresi, and Sette (2016), and include in (2) the vector

of ﬁrm-level ﬁxed eﬀects ˆαi estimated from the within-ﬁrm speciﬁcation (1).14 Standard errors

are clustered at the main bank and industry levels, where the main bank is the institution

that a certain ﬁrm has the highest percentage of borrowing with before the shock.

4 Data and Descriptive Statistics

The dataset we use throughout this study merges four unique databases held and managed by

the Bank of Portugal: (i) Central Credit Register (Central de Responsabilidades de Cr´edito);

(ii) Individual Information on Interest Rates (Informa¸c˜ao Individual de Taxas de Juro); (iii)

Central Balance Sheet Database (Central de Balan¸cos); and (iv) Bank Supervisory Database.

The Central Credit Register provides conﬁdential information on all credit exposures

above 50 euros in Portugal.15 It covers loans granted to non-ﬁnancial companies by all banks

operating in the country as reporting to the central bank is mandatory. Besides recording the

outstanding debt of every ﬁrm with each bank at the end of every quarter, each claim also

speciﬁes the amount that each borrower owes the bank in the short and long-term, and the

amount that is past due. The database also provides information on other loan characteristics

14Jim´enez, Mian, Peydr´o, and Saurina (2014a) propose an alternative method to correct for the bias that
arises if the ﬁrm exposure to the shock is correlated with credit demand in the ﬁrm-level regressions. They
use a numerical correction exploiting the diﬀerence between OLS and FE estimates of β in the Khwaja and
Mian (2008) within-ﬁrm regression. Cingano, Manaresi, and Sette (2016) show that the approach of Jim´enez,
Mian, Peydr´o, and Saurina (2014a) and the one we use in this paper are equivalent.

15This threshold alleviates any concerns on unobserved changes in bank credit to SMEs. In addition, it
has signiﬁcant advantages when studying credit supply restrictions of smaller ﬁrms when compared to other
widely-used datasets e.g., US Survey of Small Business Finances or the LPC Dealscan which have incomplete
coverage of entrepreneurial ﬁrms.

13

e.g., if the loan is an oﬀ-balance sheet item such as the undrawn amount of a credit line or

credit card.

The database on Individual Information on Interest Rates reports matched ﬁrm-bank

interest rate information on new loans. While only banks with an annual volume of new

corporate loans of more than e50 million were required to report between June 2012 and

December 2014, this requirement was extended to all resident banks in January 2015. For

consistency, we restrict the analysis to banks that reported interest rate information before

and after this reporting change. Besides interest rates, we have loan-level information on the

amount, maturity, date of origination, if the loan is collateralized or not, and loan type.

The Central Balance Sheet Database provides detailed ﬁnancial information with an

annual frequency for virtually all Portuguese ﬁrms e.g., total assets, year of incorporation,

equity, net income, number of employees, total debt, cash holdings. Finally, we also match the

above datasets with bank balance-sheet data from the Bank Supervisory Database e.g., bank

size, proﬁts, capital, liquidity and non-performing loans. Given the very low threshold to

capture credit exposures in the credit register, the zero minimum loan size of the interest rate

database and the compulsory reporting of balance sheet information by all ﬁrms and banks

operating in Portugal, the combined dataset we use is arguably one of the most comprehensive

loan-bank-ﬁrm matched databases available worldwide.

Table 1 presents ﬁrm-level descriptive statistics computed using the bank-ﬁrm matched

sample. Speciﬁcally, we present the mean, median and standard deviation of the dependent

variables, ﬁrm and bank characteristics across the 40,927 ﬁrms in our sample that have

more than one lending relationship. On average, ﬁrms’ total credit and granted credit lines

increased by 1.1 and 0.3 percent from the pre-shock (2013:Q4-2014:Q2) to the post-shock

period (2014:Q3-2015:Q3), respectively. Over the same period, ﬁrm investment shrank on

average by 2.6 percent, employment increased by 3.2 percent, while cash holdings increased

by 10.8 percent. Finally, there was an average decrease in interest rates from the pre- to the

post-resolution period of 88 and 69 basis points on total credit and credit lines, respectively,

14

an increase in loan maturity of 1.9 months, and a decrease in the share of collateralized credit

of 2.9 percentage points.

[Table 1 here]

Turning to ﬁrm characteristics, the average pre-failure ﬁrm exposure to the bail-in was

0.008, with a standard deviation of 0.013. Firms in our sample have on average 4 lending

relationships and 32 percent started a new lending relationship within a year after the

resolution. SMEs constitute 98 percent of all ﬁrms.

Before the shock, the average ﬁrm had e0.75 million in assets, was operating for 13.6

years, had a capital ratio of 26 percent, suﬀered losses of 0.6 percent of total assets and had

a current ratio of 2.2. Finally, we present bank characteristics, which are averaged at the

ﬁrm-level according to the pre-period share of total credit granted to the ﬁrm by each bank.

These are also measured in 2013:Q4 and include bank size (log of total assets), bank ROA

(return-on-assets), bank capital ratio (equity to total assets), bank liquidity ratio (liquid to

total assets), and bank NPLs (non-performing loans to total gross loans).

5 Results

In this section, we ﬁrst present results examining the eﬀect of the bank failure and subsequent

resolution through a bail-in on credit supply and ﬁrms’ borrowing conditions. We then trace

these eﬀects to real sector outcomes and examine the role of ﬁrms’ internal liquidity position

in explaining our ﬁndings.

15

5.1 Bank resolution and credit supply

5.1.1 Within-Firm Analysis

The results in Table 2 show a signiﬁcant reduction in credit supply, including granted credit

lines, from banks more exposed to the bail-in. Columns (1) and (2) present the results

without and with ﬁrm ﬁxed-eﬀects, while column (3) adds bank-level controls measured at

2013:Q4 – bank size, ROA, capital ratio, liquidity ratio and NPLs. Column (4) diﬀerentiates

the main eﬀect of interest across SMEs and large ﬁrms. The unit of observation is the

change in the log level of total committed credit between each of the 116,245 ﬁrm-bank

pairs, corresponding to 40,927 ﬁrms. As in Khwaja and Mian (2008), the quarterly data for

each credit exposure is collapsed (time-averaged) into a single pre (2013:Q4-2014:Q2) and

post-shock (2014:Q3-2015:Q3) period. Bank Exposure, the main explanatory variable, is the

percentage of assets of each bank exposed to the bail-in i.e., the percentage of assets that

was eﬀectively bailed-in for the resolved bank, the speciﬁc contribution to the ad-hoc loan

granted to the Bank Resolution Fund for the 8 participating banks (as a percentage of assets),

and 0 otherwise. All speciﬁcations focus on borrowers with more than one bank relationship.

This ensures that any observed changes in lending are due to the bank supply shock which is

orthogonal to idiosyncratic ﬁrm-level shocks such as changes in credit demand or borrowers’

risk proﬁle.

[Table 2 here]

The relative credit contraction from banks more exposed to the shock is both statistically

and economically signiﬁcant. The coeﬃcient of interest in column (3) indicates that a one

standard deviation increase in bank exposure to the bail-in (0.020) is associated with a

supply-driven decrease in credit for the average ﬁrm of 3.0 percent. Importantly, while the

eﬀect is signiﬁcant across diﬀerent ﬁrm size groups, the results in column (4) show that it

16

is more than twice as strong for large ﬁrms than small and mid-sized ﬁrms – 6.3 vs. 2.9

percent, respectively.

In columns (5) to (6) of Table 2 we focus on ﬁrms with multiple credit lines simultaneously

held at diﬀerent banks. This corresponds to 14,320 out of 40,927 ﬁrms, for a total of 39,573

ﬁrm-bank relationships. In line with Ippolito, Peydr´o, Polo, and Sette (2016) who ﬁnd that

following the 2007 freeze of the European interbank market Italian banks managed liquidity

risks by extending fewer and smaller credit lines, the coeﬃcient estimates show that granted

credit lines were the main channel through which banks more exposed to the bail-in reduced

credit supply – a decrease of 5.7 percent to the average ﬁrm for a one standard deviation

increase in bank exposure to the bail-in.

Identifying Assumptions. The validity of our identiﬁcation strategy relies on two main

assumptions. First, our quasi-experimental research design requires that in the absence of

treatment (i.e., the bank failure and subsequent resolution), banks more exposed to the shock

would have displayed a similar trend in terms of credit supply to that of other less exposed

banks. While the parallel trends assumption cannot be tested explicitly due to the absence

of a counterfactual, Figure 2 shows it is likely to be satisﬁed in our setting.

In speciﬁc, we use a modiﬁed version of the within-ﬁrm speciﬁcation (1), regressing for

each quarter the change in the log level of total committed credit between each ﬁrm-bank

pair in that quarter relative to 2014:Q2 (the last period before the shock) on BankExposureb

and ﬁrm ﬁxed-eﬀects. Bank Exposure is here standardized to have mean 0 and standard

deviation of 1, and the dashed lines indicate the 5%–95% conﬁdence interval using standard

errors clustered at the bank level. Before the shock, there is no signiﬁcant variation in

credit provision across banks more or less exposed to the resolution. Starting from 2014:Q3,

however, credit supply from banks more exposed to the bail-in decreased signiﬁcantly and

deteriorated over time.

[Figure 2 here]

17

Second, the implicit assumption behind using ﬁrm ﬁxed-eﬀects to control for idiosyncratic

demand shocks in the Khwaja and Mian (2008) within-ﬁrm speciﬁcation is that ﬁrm-speciﬁc

loan demand changes proportionally across all banks lending to the ﬁrm i.e., individual ﬁrms

take their multiple banks as providers of a perfectly substitutable good. In our setting, this

assumption could be violated if ﬁrms reduced credit demand from more exposed banks after

the shock while increasing it from other (healthier) banks.16 However, some factors suggest

the eﬀects we observe are indeed supply driven and unlikely to be explained by within-ﬁrm

changes in demand. First, as clearly stated in both its 2014 and 2015 Annual Reports,

after the resolution the bailed-in bank “conducted a very strict and selective lending policy,

without ceasing to support the small and medium-sized enterprises” (Novo Banco, 2014,

p. 100, 115; Novo Banco, 2015, p. 87, 97). The bank further reinforced that the contraction

in corporate loans was achieved “mainly through the reduction in large exposures” (Novo

Banco, 2015, p. 87) as well as through “the non-renewal of credit lines” (Novo Banco, 2014,

p. 71). Given that the bailed-in bank is by far the most exposed bank to the resolution (i.e.,

it has the highest Bank Exposure value), this deleveraging plan following the intervention

focused on reducing large exposures and credit lines is entirely consistent with the credit

supply contraction at the intensive margin we show in Table 2.

Finally, in contrast with a shift in ﬁrm demand from the bailed-in bank to other banks

explained by reputational damage or even liquidity and solvency concerns, the 13 percent

contraction in corporate loans from August 2014 to December 2015 was accompanied by a 7.4

percent increase in customer deposits (Novo Banco, 2015, p. 97). This suggests that despite

the challenges brought by the resolution measure, the bank was able to stabilize its funding

sources and at least partially recover its customers’ conﬁdence.17

16Although we argue here against this demand explanation, it is important to note that even such borrower
behavior would be a direct reaction to a supply-side shock and, therefore, would not constitute a demand-side
shift per se. In other words, even if part of a possible credit reduction was driven by customers rather than
the bank, we would argue that this is still a supply-side shock as caused by the bank failure rather than by
changes in ﬁrms’ credit demand.

17As highlighted by Paravisini, Rappoport, and Schnabl (2017), our identifying assumption may also be
violated if more exposed banks were specialized in certain industries or sectors such as export markets. In such
segments where some banks may have more expertise than others, credit is no longer a homogeneous good

18

Robustness Tests. The within-ﬁrm results presented above are robust to a number of tests.

First, we use an alternative bank exposure measure based on daily 5-year CDS spreads on

senior unsecured debt. In this case, we consider the four banks operating in Portugal that

are classiﬁed as signiﬁcant institutions by the ECB and for which we have CDS spread data.

In detail, we deﬁne bank exposure to the shock as the bank-speciﬁc increase in CDS spreads

from one month before to the day before the resolution. Given that, as we show in Figure

1, CDS spreads of the bailed-in bank moved in line with the rest of the banking sector until

late June 2014 when the exposures to the Group’s entities owned by the family started to

be revealed, this measure captures the market’s perception of the increase in the default

probability for the resolved bank as well as the magnitude of potential spillovers for the three

other major Portuguese banks. Consistent with the estimates in the baseline regressions,

the results in columns (1) to (3) of Table IA1 in the internet appendix show that a one

standard deviation increase in bank exposure to the shock captured through the reaction

of CDS spreads (0.014) leads to an decrease in the supply of credit of 2.93 percent for the

average ﬁrm – 2.58 for SMEs and 8.60 for large ﬁrms.18

Second, to ensure that our results are not conﬁned to ﬁrms with multiple bank relationships,

we use the complete sample of borrowing ﬁrms in Portugal (including single-bank-relationship

ﬁrms) in a model that replaces ﬁrm ﬁxed-eﬀects with location-size-sector ﬁxed-eﬀects as in

De Jonghe, Dewachter, Mulier, Ongena, and Schepens (2018). In detail, the group contains

only the ﬁrm itself in case the ﬁrm has multiple lending relationships, while ﬁrms with single

oﬀered across diﬀerent banks and, as a result, sector-level demand shocks may ultimately lead to ﬁrm-bank
speciﬁc loan demand. Nevertheless, untabulated results (for conﬁdentiality reasons) suggest that ﬁrm-bank
speciﬁc demand due to sector specialization is not a source of great concern in our setting.
In fact, the
bailed-in bank was active in all the main industries and did not control the majority of the lending activity
in any of them. Our results could also be biased if certain banks were targeting their lending to ﬁrms in
industries experiencing particularly severe (and correlated) demand-side shocks. However, when we compare
the relative importance of certain industries for the bailed-in bank vis-`a-vis all other banks, we observe no
discernible diﬀerences across industries between the two groups.

18Since there are only 4 banks with available CDS spread data, standard errors clustered at the bank-level
would be biased (Bertrand, Duﬂo, and Mullainathan, 2004; Cameron, Gelbach, and Miller, 2008). Thus,
in columns (1) to (3) of Table IA1 we use heteroskedasticity-consistent standard errors. The coeﬃcients of
interest are still signiﬁcant at conventional levels when using either the wild cluster bootstrap method of
Cameron, Gelbach, and Miller (2008) or clustering standard errors at the ﬁrm level.

19

bank relationships are grouped based on the district in which they are headquartered, their

industry, and deciles of loan size. The results are reported in columns (4) to (6) of Table

IA1. Despite the considerable increase in the number of ﬁrms (from 40,927 to 85,149), the

coeﬃcient estimates are remarkably similar to those in the smaller sample restricted to ﬁrms

with multiple bank relationships, both in terms of magnitude and statistical signiﬁcance.19

Third, we limit our sample to loan operations and thus disregard both used and unused

credit lines (Table IA2, columns 1-3). Only the coeﬃcient estimate for large ﬁrms enters

signiﬁcantly and is smaller in magnitude when compared to estimate for total credit. This

conﬁrms that banks more exposed to the shock reduced credit supply primarily by extending

fewer and smaller credit lines, particularly for SMEs. Finally, in columns (4) to (6) of

Table IA2 we follow Iyer, Peydr´o, Da-Rocha-Lopes, and Schoar (2014) and compare lending

immediately before (2014:Q2) and one year after the shock (2015:Q3) instead of time-averaging

the quarterly credit exposures into a pre (2013:Q4 - 2014:Q2) and post-resolution (2014:Q3

- 2015:Q3) period. Our results are the same, if not stronger, when compared to our baseline

regressions.

Firm Heterogeneity. While we observe a credit supply reduction on average and particularly

for larger ﬁrms, this contraction might vary across other ﬁrm characteristics, e.g., ﬁrm age,

capital, proﬁtability, liquidity or riskiness. In this respect, the results in Table 3 show further

variation in the eﬀect of the bank collapse and subsequent resolution across diﬀerent ﬁrms

by introducing an interaction term between Bank Exposure and a dummy variable splitting

ﬁrms into two sub-groups: (i) ﬁrms with low and high values of a certain pre-shock ﬁrm

characteristic; (ii) ﬁrms with and without NPLs with any bank before the resolution; and

(iii) ﬁrms whose main lender in the pre-period was or was not the bailed-in bank.

[Table 3 here]

19Comparing the results across multiple relationship ﬁrms (Table 2) and all ﬁrms (Table IA1), the
-1.441 for SMEs, and -2.915

coeﬃcients estimates are -1.339 vs. 1.520 for the average ﬁrm, -1.283 vs.
vs. -3.133 for large ﬁrms.

20

The results reported in columns (1) and (2) of Table 3 conﬁrm our earlier ﬁndings that the

credit reduction by banks more exposed to the bail-in was more pronounced for larger ﬁrms,

here measured by either total assets or number of employees instead of the deﬁnition in the

EU Recommendation 2003/361 that includes both a balance-sheet size and a staﬀ headcount

requirement. We also ﬁnd that ﬁrms with lower proﬁtability suﬀered a lower reduction in

lending by more aﬀected banks (column 5), while borrowers with a non-performing loan before

the shock with any bank in Portugal did not suﬀer from a reduction in lending by banks more

exposed to the bail-in (column 8). We do not ﬁnd any signiﬁcant interaction of Bank Exposure

with borrowers’ age, capital or liquidity situation. Riskier borrowers therefore suﬀered less

of a credit supply shock by more exposed banks, which is suggestive of evergreening of bank

loans by banks more exposed to the bail-in. This could point to a certain degree of moral

hazard that may be explained by the need for the “good bank” to show promising bank’s

ﬁnancials to potential buyers.20

Finally, the results in column (9) show a signiﬁcant and negative interaction term of

Bank Exposure with a dummy variable equal to one if the ﬁrm main lender before the shock

was the bailed-in bank, and zero otherwise. This suggests that those ﬁrms likely to have

stronger relationships with the resolved bank suﬀered relatively more from the failure. While

this result contrasts the evidence on the insulating eﬀect of relationship banking on the

quantity of credit following negative bank shocks (Sette and Gobbi, 2015; Bolton, Freixas,

Gambacorta, and Mistrulli, 2016; Liberti and Sturgess, 2018; Beck, Degryse, De Haas, and

Van Horen, 2018), it highlights the disruptive eﬀect that a bank failure can have on established

ﬁrm-bank relationships, particularly for bank-dependent borrowers. In fact, consistent with

the hypothesis that severely distressed banks may simply not have the resources to sustain

such mutually beneﬁcial relationships, Carvalho, Ferreira, and Matos (2015) ﬁnd that bank

20This ﬁnding is consistent with Blattner, Farinha, and Rebelo (2018) who show that an unexpected
increase in capital requirements for a subset of Portuguese banks in 2011 resulted in an increase in
underreporting of loan losses and a reallocation of credit to ﬁrms in ﬁnancial distress with prior underreported
losses, with negative repercussions for aggregate productivity.

21

distress is associated with equity valuation losses and investment cuts to ﬁrms with the

strongest lending relationships.

5.1.2 Cross-Sectional Analysis

So far we have gauged the eﬀect of bank resolution on the supply of credit to ﬁrms borrowing

from banks more and less exposed to the bail-in. However, the within-ﬁrm estimations ignore

credit ﬂows from new lending relationships as well as bank relationships that were terminated

from the pre- to the post-bail-in period. Therefore, we now turn to the cross-sectional

(between-ﬁrm) estimations that allow us to test for aggregate eﬀects. Since we cannot use

ﬁrm-ﬁxed eﬀects in the regressions analyzing the overall impact of bank shocks on credit

supply, we control for omitted ﬁrm-level factors such as credit demand with a two-step

estimation based on Abowd, Kramarz, and Margolis (1999). Speciﬁcally, we include in the

estimations the vector of ﬁrm-level dummies estimated in column (2) of Table 2.21 We also

include industry and district ﬁxed eﬀects as additional controls for unobservable demand and

risk-proﬁle diﬀerences.

The results in Table 4 show there was no decrease in overall credit after the shock for

ﬁrms more exposed to the bail-in when compared to ﬁrms exposed less, including when

diﬀerentiating between ﬁrms of diﬀerent size. However, we do observe a binding contraction

in credit lines for SMEs more exposed to the resolution. In detail, the explanatory variable

of interest, Firm Exposure, is computed as the weighted average of Bank Exposure across all

banks lending to a ﬁrm, using as weights the pre-period share of total credit from each bank.

Column (1) reports the results for the average ﬁrm, while column (2) diﬀerentiates between

SMEs and large enterprises. None of the coeﬃcients enters signiﬁcantly at conventional levels.

The results in columns (3) and (4), however, indicate that SMEs more exposed to the shock

21If biases due to endogenous matching between ﬁrms and banks were present in our data, we should
observe a substantial correlation between exposure and ˆαi (Jim´enez, Mian, Peydr´o, and Saurina, 2014a;
Cingano, Manaresi, and Sette, 2016). However, exploiting model (1), we ﬁnd that the estimated vector of
ﬁrm-level dummies is virtually uncorrelated with our main Bank Exposure measure (ρ=0.0014).

22

suﬀered a considerable decrease in the amount of credit lines available to them. Speciﬁcally,

for a one standard deviation in ﬁrm exposure to the bail-in, SMEs experienced a 2.2 percent

binding decrease in granted credit lines. As in columns (5) to (6) of Table 2, to ensure the

eﬀect is properly identiﬁed we restrict the sample to ﬁrms with credit lines from at least two

diﬀerent banks.

[Table 4 here]

Robustness Tests. The results above are robust to several tests. First, we consider an

alternative ﬁrm exposure measure that, as in Table IA1, is based on reaction of CDS spreads

on senior unsecured debt and considers the four banks operating in Portugal that are classiﬁed

as signiﬁcant institutions by the ECB. Speciﬁcally, in columns (1) to (4) of Table IA3 in the

internet appendix Firm Exposure is computed as the weighted average of Bank Exposure

across all banks lending to a ﬁrm (using as weights the pre-period share of total credit from

each bank), but where bank exposure to the shock is deﬁned as the bank-speciﬁc increase

in CDS spreads from one month before to the day of the resolution. Second, in columns (5)

to (8) of Table IA3 we follow the same procedure but deﬁne Bank Exposure as a dummy

variable equal to one for the bailed-in bank and 0 otherwise i.e., we implicitly assume that

only the bailed-in bank was aﬀected by the resolution and there were no spillover eﬀects to

other banks in the system. In either case, our conclusions remain the same. Finally, we also

conﬁrm our ﬁndings when considering the change in total committed credit excluding credit

lines (Table IA4, columns 1–3) or when comparing lending immediately before (2014:Q2) and

one year after the shock (2015:Q3) instead of time-averaging the quarterly credit exposures

into a pre and post-resolution period (Table IA4, columns 4–6).

The Role of New and Existing Lending Relationships. The results in columns (1) to

(3) of Table 5 show that ﬁrms more exposed to the bail-in were not more likely to start a

new lending relationship over our sample period. This result can be explained by the fact

that the average ﬁrm already has 4 bank relationships – see 1. The set-up of the table is

23

identical to Table 4, but the dependent variable is now a dummy that takes value one if a ﬁrm

takes out a loan from a bank with which it had no lending relationship before the shock, and

zero otherwise. The coeﬃcient estimated reported in columns (4) to (6) conﬁrm that lenders

other than the bailed-in bank (i.e., those banks that were less exposed to the resolution) were

crucial for ﬁrms to maintain credit. Speciﬁcally, the dependent variable is now the change

in the log level of total committed credit to each ﬁrm from all banks except the bailed-in

bank from the pre (2013:Q4-2014:Q2) to the post-resolution period (2014:Q3-2015:Q3). The

results show a signiﬁcant and positive relationship between Firm Exposure and credit growth

from banks other than the bailed-in bank.

In economic terms, a one standard deviation

increase in ﬁrm exposure to the bail-in is associated with a 5.9 percent increase in lending

from other banks. This eﬀect applies to both SMEs and large enterprises.

[Table 5 here]

Overall, our results show that both SMEs and large ﬁrms that were more exposed to

the bail-in did not suﬀer an overall reduction in credit when compared to ﬁrms exposed

less. In fact, these ﬁrms were able to compensate the reduction in credit with lending from

other (less exposed) ﬁnancial institutions they already had a relationship with. However,

when isolating credit lines from total committed credit by focusing on ﬁrms with multiple

credit lines, we show that SMEs more exposed to the resolution were subject to a binding

contraction in quantity of funds available through lines of credit, a crucial component for

corporate liquidity management (Suﬁ, 2009; Jim´enez, Lopez, and Saurina, 2009) and the

dominant source of liquidity for ﬁrms around the world (Duchin, Ozbas, and Sensoy, 2010).22

22According to Berger and Udell (1995), a credit line “is an attractive vehicle for studying the

bank-borrower relationship because the line of credit itself represents a formalization of this relationship”.

24

5.2 Bank resolution and credit conditions

We have mainly focused so far on the consequences of the supply shock for credit quantities.

Nevertheless, the resolution may have also impacted the interest rates charged on new loans

and credit lines, as well as other credit conditions such as loan maturities or the collateral

required.23 The disruption of established bank-ﬁrm relationships can ultimately have negative

eﬀects on real activity if borrowers are unable to replace these relationships with other lenders

on equal terms (Bernanke, 1983; Ashcraft, 2005).

The results in columns (1) to (4) of Table 6 show that ﬁrms across all size groups that

were more exposed to the bail-in saw a moderate increase in their interest rates on credit

lines, while only more exposed large ﬁrms suﬀered a moderate increase in interest rates on

all new credit operations. However, the economic eﬀect is modest: a one standard deviation

increase in ﬁrm exposure to the bail-in (0.013) is associated with a 20bp increase in the

interest rates on credit lines for the average ﬁrm – for comparison, the average interest

rate charged on credit lines is 11.05 percentage points. Since the interest rate dataset only

captures new operations rather than outstanding amounts, here we consider all new loans

and credit lines between a ﬁrm and a bank between 2013:M12 and 2014:M7 (pre-period) and

2014:M9 and 2015:M9 (post-period) when computing the loan-amount-weighted measures.

Compared to Table 4, we now also control for loan characteristics such as the pre-shock,

ﬁrm-speciﬁc, loan-amount-weighted maturity and share of collateralized credit for all new

loans and credit lines. These results are consistent with the evidence in Khwaja and Mian

(2008) and Cingano, Manaresi, and Sette (2016) who analyze a representative universe of

ﬁrms in Pakistan and Italy and ﬁnd that despite aﬀecting the quantity of credit, bank-level

shocks have no meaningful eﬀects on the interest rates charged.

[Table 6 here]

23Santos (2011), for instance, ﬁnds that ﬁrms that had relationships with less healthy lenders before the

subprime crisis paid relatively higher loan spreads afterwards.

25

In line with a moderate tightening of interest rates, the results in columns (5) and (6) of

Table 6 show a reduction in loan maturity across all ﬁrms, with a one standard deviation in

ﬁrm exposure resulting in a one month reduction in loan maturity. Finally, we also ﬁnd a

relative increase in the share of collateralized credit after the shock for ﬁrms more exposed

to the bail-in – a 2.75 percentage point increase for a one standard deviation increase in ﬁrm

exposure (columns 7 and 8), an eﬀect that holds for both SMEs and large enterprises.

5.3 Bank resolution and real sector eﬀects

Impact on investment and employment. What was the eﬀect of changes in ﬁnancing

conditions on investment and employment decisions taken by the aﬀected ﬁrms? On the one

hand, it is not clear that we should ﬁnd signiﬁcant real eﬀects given the continued access to

the same level of external funding, though at somewhat worse conditions and with smaller

granted credit lines. On the other hand, the results point towards higher uncertainty for

more exposed ﬁrms as they had to compensate the lost funding at the intensive margin with

credit from other banks and (re)-negotiate loan terms and conditions. We therefore turn to

investment and employment growth as real sector outcome variables, and then focus on the

role of ﬁrms’ internal liquidity in driving the results.

The results in Table 7 show a relative reduction in investment for SMEs that were more

exposed to the resolution. The dependent variable in columns (1) to (3) is the change in

the log level of tangible assets for each ﬁrm between 2013:Q4 and 2015:Q4, with column

(1) presenting a regression for the 14,320 ﬁrms with multiple credit lines at diﬀerent banks,

and columns (2) and (3) focusing on our main sample of 40,927 with more than one bank

relationship. As before, all speciﬁcations include ﬁrm and bank controls, a proxy for credit

demand, as well as industry and district ﬁxed eﬀects. In both cases, Firm Exposure enters

negatively and signiﬁcantly. This reduction in investment, however, is only signiﬁcant for

SMEs (column 3). We ﬁnd that a one standard deviation increase in ﬁrm exposure to the

bail-in is associated with a 2.0 percent relative reduction in investment for SMEs. Our

26

results remain the same when using as dependent variable the change in the log level of ﬁxed

assets (columns 4-6), our two alternative ﬁrm exposure measures as in Table IA3 (Table IA5,

columns 1-4), or when normalizing the change in tangible assets or ﬁxed assets the ﬁrms’

pre-period total assets (Table IA5, columns 5-8).

[Table 7 here]

In line with the evidence for investment, columns 1 to 3 of Table 8 show a signiﬁcant

and negative relationship between ﬁrm exposure to the bail-in and the growth of the number

of employees at ﬁrms. As before, this eﬀect is concentrated in SMEs and is not signiﬁcant

for large enterprises. Controlling for ﬁrm and bank characteristics, we ﬁnd a 1.3 percent

relative drop in the number of employees at SMEs for a one standard deviation increase

in exposure to the resolution. The economic eﬀect is smaller than for investment, in line

with stronger persistence in employment than in investment decisions. Our conclusion

is therefore consistent with Chodorow-Reich (2014) and Berton, Mocetti, Presbitero, and

Richiardi (2018) that ﬁnd that smaller ﬁrms are particularly vulnerable to the negative

impact of a credit crunch on employment. Bottero, Lenzu, and Mezzanotti (2017) also show

that while the credit supply contraction in Italy following the European sovereign crisis

was similar in magnitude for large and small ﬁrms, it led to a reduction in investment and

employment only in smaller ﬁrms.

[Table 8 here]

To capture diﬀerent margins of adjustment of employment, we also consider the log

change in the total number of hours worked by all ﬁrm employees as an alternative outcome

variable. The results are reported in columns 4 to 6. As before, the reduction in employment

is only present in more exposed SMEs – a 1.5 percent relative decrease for a one standard

deviation increase in ﬁrm exposure. Our ﬁndings also remain the same when considering our

27

two alternative ﬁrm exposure variables computed as the weighted average of Bank Exposure

across all banks lending to a ﬁrm (using as weights the pre-period share of total credit from

each bank), but where bank exposure to the shock is deﬁned as the bank-speciﬁc increase in

CDS spreads from one more before to the day of the resolution (Table IA6, columns 1 and

2), or as a dummy variable equal to one for the bailed-in bank and 0 otherwise (Table IA6,

columns 3 and 4).

The role of ﬁrms’ internal liquidity. The option for ﬁrms to access liquidity from credit

lines should be more valuable when internal liquidity is scarce (e.g., Campello, Giambona,

Graham, and Harvey, 2011). Thus, if the dampening eﬀects of the bank resolution on

real sector outcomes are indeed driven by a reduction in granted credit lines for SMEs,

we should observe heterogeneous eﬀects according to their pre-shock liquidity position i.e.,

if ﬁrms view cash and lines of credit as liquidity substitutes and given the tighter credit line

limits, illiquid SMEs might respond to the funding shock by increasing cash holdings while

decreasing investment and employment. Berg (2018), for instance, shows in a diﬀerent setting

that while liquid SMEs are able to absorb credit supply shocks by using the existing cash

buﬀers, their illiquid counterparts increase cash holdings when a loan application is rejected,

cutting non-cash assets by more than the requested loan amount, and thus investment and

employment.

We analyze this channel explicitly by splitting ﬁrms according to their ex-ante liquidity

position i.e., low liquidity (cash holdings-to-total assets ratio before the shock lower than the

median) vs. high liquidity ﬁrms (cash holdings-to-total assets ratio before the shock higher

than the median). Table 9 reports the results, with Panel A focusing on the sub-sample of

SMEs and Panel B on large ﬁrms. The coeﬃcient estimates in column (1) show a signiﬁcant

increase in cash holdings by low liquidity SMEs more exposed to the bail-in. This eﬀect is

not present across large ﬁrms. Conversely, in line with a precautionary savings motive where

ﬁrms hold cash as a buﬀer to protect themselves against adverse shocks (e.g., Duchin, Ozbas,

28

and Sensoy, 2010), high liquidity ﬁrms more exposed to the bail-in decrease cash holdings

considerably – a result that, in this case, holds for both SMEs and large ﬁrms. In economic

terms, a one standard deviation increase in ﬁrm exposure to the bail-in leads to an increase

in cash holdings for low liquidity SMEs of 17.5 percent, but a decrease for high liquidity

SMEs of 17.7 percent. Low liquidity large ﬁrms do not change cash holdings as a result of

the shock, but high liquidity large ﬁrms decrease cash holdings by 15.5 percent for a one

standard deviation in ﬁrm exposure to the resolution.

Column (2) reports the coeﬃcient estimates for investment, while column (3) focuses on

employment. The results show that the negative real eﬀects are concentrated in SMEs with

low pre-period levels of internal liquidity, corresponding to those ﬁrms that increased cash

holdings as a result of the shock. This suggests that more exposed SMEs and large ﬁrms with

high liquidity before the bail-in were able to use their available internal liquidity buﬀers to

compensate for the binding contraction in granted credit lines and thus maintain employment

and investment.

Instead, low liquidity SMEs more exposed to the bail-in responded by

increasing cash holdings while decreasing investment and employment.

[Table 9 here]

A potential concern regarding these ﬁndings is that low levels of ﬁrm liquidity prior to

the shock might reﬂect declining demand for investment given that cash holdings are chosen

at least partially based on anticipated growth opportunities (e.g., Opler, Pinkowitz, Stulz,

and Williamson, 1999). To help ruling out this possibility, in Table IA7 in the internet

appendix we split high and low liquidity SMEs according to the ﬁrm-speciﬁc pre-shock

asset growth before the shock i.e., below and above the median of the overall sample. Our

results hold across the two sub-samples. Speciﬁcally, SMEs with both lower and higher

growth opportunities before the resolution increase cash holdings and decrease investment

29

and employment if they had low levels of internal liquidity, and both decrease cash holdings

and maintain employment and investment if they were highly liquid before the shock.24

In summary, the results in Tables 7, 8 and 9 show that although there was on average

and across the diﬀerent ﬁrm size groups no reduction in aggregate borrowing after the

bank resolution, SMEs still decreased investment and employment. This is explained by

smaller ﬁrms with low levels of internal liquidity before the shock reacting to the tightening

of credit line limits by hoarding cash while at the same time cutting back on investment

and employment. The negative impact of the bank resolution shock on investment and

employment can thus be explained with heightened liquidity risk.

5.4 Bail-out vs. bail-in

Our evidence pointing towards negative real eﬀects after a bank bail-in is particularly relevant

given the growing evidence that, even if setting the stage for aggressive risk-taking and

future fragility, bank bail-outs can be eﬀective in supporting borrowers and the real economy

in the short-term.25 Giannetti and Simonov (2013), for instance, use loan-level data to

explore the real eﬀects of bank bail-outs during the Japanese crisis of the 1990s and ﬁnd

that listed ﬁrms had easier access to bank lending experienced positive abnormal returns

and were able to invest more when the recapitalizations were large enough. Using a similar

methodology, Augusto and F´elix (2014) show that bank bail-outs in Portugal during the

European sovereign debt crisis did not lead to a decrease in credit provision at the intensive

margin. Laeven and Valencia (2013) examine ﬁnancial sector interventions in 50 countries

after the 2007-2009 ﬁnancial crisis and show that these improved the value added growth of

24It is important to note that low prior liquidity may also reﬂect unobservably lower costs of external
ﬁnance. If that is the case, this would imply we are actually underestimating the eﬀect since we are treating
liquidity diﬀerences as random.

25The implicit or explicit commitment to bail-out distressed banks not only increases idiosyncratic bank
risk-taking (Dam and Koetter, 2012), but also give incentives for individual banks to engage in collective
risk-taking strategies (Farhi and Tirole, 2012). The resulting common exposures aimed at exploiting a
“too-many-to-fail” guarantee may ultimately increase systemic risk due to the higher correlation of defaults
and ampliﬁcation of the impact of liquidity shocks (Silva, 2017).

30

ﬁnancially dependent ﬁrms. Berger, Makaew, and Roman (2017) show that TARP-funded

bail-outs in the US resulted in an increase in credit supply for recipient banks’ borrowers as

well as more favorable loan conditions, while Berger and Roman (2017) ﬁnd that TARP led

to increased job creation and decreased business and personal bankruptcies.26

Therefore, a fundamental follow-up question is whether a bank bail-out would generate

the same negative eﬀects we show in the paper for a bail-in. While we cannot make this

comparison directly due to the lack of a counterfactual (e.g., a bank that was bailed-out during

the same period), we shed some light into this issue by exploiting the fact that the bail-in

of shareholders and junior bondholders we analyze so far in this paper diﬀered signiﬁcantly

from the approach taken by the Portuguese authorities during earlier bank failures during

the crisis. Notably, in June 2012 3 of the largest 5 banks (CGD, BCP and BPI) received

government-funded capital injections as well as the smaller Banif in December 2012. The

bail-outs allowed banks to comply with stricter minimum capital requirements deﬁned by the

European Banking Authority (EBA) in the case of the former, and by the Bank of Portugal

in the latter.27

To assess the eﬀects of the bail-outs on credit supply and real outcomes, we use both

within- and cross-ﬁrm regressions, with data averaged between the fourth quarter of 2011

and the second quarter of 2012 as pre-bail-out period and between the fourth quarter of 2012

and the fourth quarter of 2013 as post-bail-out period – see Figure 3. We have data on 45,062

ﬁrms who had relationships with at least two banks, including the four bailed-out banks, for

a total of 122,749 ﬁrm-bank relationships. Bank Exposure is a dummy variable that takes

on the value one for bailed-out banks and zero otherwise in the baseline case. Overall, 54

percent of all ﬁrms had a relationship with the bailed-out banks.

26By allowing the continuation of healthy lending relationships, either a bail-in or a bail-out should
nonetheless aﬀect borrowers less than a closure and liquidation of the bank. In fact, a decisive and eﬀective
intervention of either type may be able to reduce negative contagion eﬀects and help oﬀ-set any negative
credit supply eﬀects by allowing other banks to provide additional credit to aﬀected ﬁrms.

27The minimum Core Tier 1 ratio increased to 10 per cent in 2012 and banks had to comply until the end
of that year. At the same time, banks subject to the stress tests of the EBA were also subject to stricter
capital requirements. These additional capital requirements were one of the main factors contributing to the
Portuguese bank bail-outs in 2012 (Augusto and F´elix, 2014).

31

[Figure 3 here]

The results in column (1) of Table 10 show no signiﬁcant diﬀerence in credit growth at the

intensive margin between the bailed-out and other banks for the same borrower, suggesting

that the recapitalization and bail-out of failing banks during the the crisis had no relative

eﬀect on credit supply.28 The results in columns (2) to (4) present the cross-sectional results

where, as before, the outcomes variables of interest are the log change in credit, investment

and employment between the pre- and post-bail-out period. The coeﬃcient estimates show

no signiﬁcant variation in ﬁrm-level credit supply, investment or employment with exposure

to bailed-out banks, suggesting that the bail-outs fulﬁlled their objectives of protecting

borrowers of failing banks.29

[Table 10 here]

In summary, we ﬁnd no evidence of a negative impact of the bank bail-out in 2012 on

the relative credit supply by bailed-out vs. non-bailed-out banks. Consequently, there was

also no relative decline in investment or employment by ﬁrms more exposed to the bailed-in

banks. Overall, this points to rather sharp diﬀerences between bail-out and bail-in of banks,

with stronger negative eﬀects of the latter for credit supply and real sector activity. However,

we urge caution in interpreting this comparison directly since the macroeconomic situation

was considerably diﬀerent during these two episodes and that the public intervention in 2012

was more systemic in nature. Moreover, previous evidence has shown the detrimental impact

on bank risk-taking generated by public guarantees such as bailouts (Dam and Koetter, 2012)

28In robustness tests available in Table IA8 in the Internet Appendix, we ﬁnd no signiﬁcant eﬀect of the
bail-out for either large ﬁrms or SMEs and show the robustness of our ﬁndings to an alternative measure of
Bank Exposure, deﬁned as a continuous treatment variable equal to the injection amount as a share of assets
for each of the bailed-out banks and zero otherwise.

29Augusto and F´elix (2014) also analyze the impact of the 2012 bail-outs in Portugal on credit supply but
only examine intensive margin eﬀects. Instead, we also investigate the extensive margin eﬀects of the bank
recapitalizations, including the associated real eﬀects.

32

or even deposit insurance (Calomiris and Jaremski, 2018).30 Instead, bank bail-ins should

reduce moral-hazard due to creditors’ expectation of bearing the losses in case of distress

(Sch¨afer, Schnabel, and Weder, 2016).

6 Conclusion

Using loan-level data and exploiting within-ﬁrm and between-ﬁrm variation in exposure to

diﬀerent banks, including a failed and subsequently resolved bank, we show that banks more

exposed to a bail-in signiﬁcantly reduced credit supply after the shock but that aﬀected ﬁrms

were able to compensate this overall credit contraction with funding from other banks they

already had relationships with. However, SMEs more exposed to the resolution were subject

to a binding contraction in quantity of funds available through lines of credit. In addition,

we ﬁnd a moderate relative increase in lending costs for more exposed ﬁrms. In spite of the

limited eﬀects on credit supply, SMEs reduced both investment and employment, an eﬀect

that is concentrated among smaller ﬁrms with low pre-shock internal liquidity that increased

cash holdings at the expense of investment and employment.

Our ﬁndings show that a well-designed bank resolution framework that includes a bail-in

of shareholders and bondholders can mitigate the impact of bank failures on credit supply

and thus provide supporting evidence for the move from bail-out to bail-ins. However, the

negative real eﬀects we ﬁnd also suggest that such resolution mechanism is not a silver bullet.

Instead, only the combination of a robust supervisory and resolution frameworks can ensure a

sound banking system and minimize the adverse eﬀects of bank distress on the real economy.

30Government interventions also reinforce the negative feedback loop between banks and sovereigns
that characterized the euro area crisis (Brunnermeier, Langﬁeld, Pagano, Reis, Van Nieuwerburgh, and
for instance, show that the ECB’s
Vayanos, 2017). Crosignani, Faria-e Castro, and Fonseca (2018),
three-year Long-Term Reﬁnancing Operation incentivized Portuguese banks to purchase short-term domestic
government bonds that could be pledged to obtain central bank liquidity, thus exacerbating the bank-sovereign
negative feedback loop.

33

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38

Figure 1: Evolution of bank CDS spreads over time. This ﬁgure plots daily 5-year CDS
spreads on senior unsecured debt between January 1, 2010 and December 31, 2015. The resolution
occurred in August 2014 (dashed vertical line). CDS spreads for the group “Other Banks” are
computed as the equal-weighted average across banks headquartered in Portugal with available
information (Caixa Geral de Depositos, Banco BPI, Banco Millennium BCP). The banks considered
correspond to the four signiﬁcant institutions (SIs) operating in Portugal as deﬁned by the ECB.
Source: Thomson Reuters Datastream.

39

Figure 2: Impact of the bail-in on credit supply at the intensive margin. This ﬁgure
presents coeﬃcient estimates of a modiﬁed version of the within-ﬁrm speciﬁcation (1) where the
dependent variable (∆log(Credit)bi) is regressed on BankExposureb and ﬁrm ﬁxed-eﬀects. Each
coeﬃcient estimate in each quarter corresponds to a diﬀerent within-ﬁrm regression, where the
outcome variable is the change in the log level of total committed credit between each ﬁrm-bank
pair between the respective quarter and 2014:Q2. BankExposureb is standardized to have mean
0 and SD of 1. The dashed lines indicate the 5%–95% conﬁdence interval using standard errors
clustered at the bank level.

40

0-6-4-22Credit Growth Relative to 2014:Q2 (%)2013:Q22013:Q32013:Q42014:Q12014:Q22014:Q32014:Q42015:Q12015:Q22015:Q3Beta5-95% CIFigure 3: Timeline of events – bail-outs and bail-in. This ﬁgure shows the timeline of the
diﬀerent bank resolutions in Portugal: (i) June 2012 for Caixa Geral de Depositos, Banco BPI and
Banco Millennium BCP; (ii) December 2012 for BANIF; and (iii) August 2014 for BES.

41

Table 1: Summary statistics

N

Mean

Median

Dependent Variables:
∆ log Total Credit
∆ log Granted Credit Lines
∆ log Investment
∆ log No. Employees
∆ log Cash Holdings
∆ Interest Rates - Total Credit
∆ Interest Rates - Credit Lines
∆ Maturity
∆ Collateral
Firm Characteristics:
Firm Exposure
No. Bank Relationships
New Lending Relationship
SME
Firm Size
Firm Age
Firm ROA
Firm Capital Ratio
Firm Current Ratio
Bank Characteristics:
Bank Size
Bank ROA
Bank Capital Ratio
Bank Liquidity Ratio
Bank NPLs

40,927
14,320
40,927
40,927
40,927
31,472
12,429
31,472
31,472

40,927
40,927
40,927
40,927
40,927
40,927
40,927
40,927
40,927

40,927
40,927
40,927
40,927
40,927

0.011
0.003
-0.026
0.032
0.108
-0.875
-0.691
1.912
-0.029

0.008
4.106
0.323
0.983
13.53
2.679
-0.006
0.261
2.191

23.90
-0.010
0.054
0.012
0.064

-0.031
0.008
-0.054
0.000
0.117
-0.848
-0.578
0.000
0.000

0.002
3.000
0.000
1.000
13.40
2.773
0.008
0.286
1.414

24.36
-0.009
0.053
0.011
0.065

SD

0.485
0.570
0.978
0.433
1.526
4.265
3.321
27.35
0.320

0.013
2.280
0.467
0.129
1.516
0.752
0.143
0.424
3.555

1.349
0.008
0.021
0.005
0.020

The table presents the relevant ﬁrm-level summary statistics computed using the bank-ﬁrm matched sample.
The ﬁrm-speciﬁc change in the log level of total (committed) credit and the change in the log level of granted
credit lines are constructed by collapsing (time-averaging) the quarterly data for each credit exposure into
a single pre (2013:Q4-2014:Q2) and post-shock (2014:Q3-2015:Q3) period. Log change in investment (i.e.,
tangible assets), no. employees, and cash holdings are the ﬁrm-speciﬁc changes in the log level of the each
variable between 2013:Q4 and 2015:Q4. Change in interest rates on new credit operations and credit lines (in
percentage points), maturity (in months) and share of collateralized credit (in percentage points) refer to the
ﬁrm-level change in the loan-amount-weighted value of the respective variable. Since the interest rate dataset
only captures new credit operations (rather than outstanding amounts), we consider all new credit operations
for each ﬁrm between 2013:M12 and 2014:M7 (pre-period) and 2014:M9 and 2015:M9 (post period). Firm
Exposure captures the average exposure of each ﬁrm to the bail-in and is computed as the weighted average
of Bank Exposure across all banks lending to a ﬁrm, using as weights the pre-period share of total credit from
each bank. Bank Exposure is the percentage of assets of each bank exposed to the bail-in i.e., the percentage
of assets that was eﬀectively bailed-in for the resolved bank, the speciﬁc contribution to the ad-hoc loan
to the Resolution Fund granted as part of the resolution for the 8 participating banks (as a percentage of
assets), and 0 otherwise. New lending relationship is a dummy variable taking the value of 1 if the ﬁrm has
a new loan after the shock (2014:Q3-2015:Q3) with a bank that it had no loan before, and 0 otherwise. Firm
size categories are deﬁned according to the EU Recommendation 2003/361. Firm characteristics include size
(log of total assets), age (ln(1+age)), ROA (net income to total assets), capital ratio (equity to total assets)
and current ratio (current assets to current liabilities) - all measured as at 2013:Q4. Bank controls, averaged
at the ﬁrm-level according to the pre-period share of total credit granted to the ﬁrm by each bank, are also
measured as at 2013:Q4 and include bank size (log of total assets), bank ROA (return-on-assets), bank capital
ratio (equity to total assets), bank liquidity ratio (liquid to total assets), and bank NPLs (non-performing
loans to total gross loans).

42

Table 2: Credit supply and ﬁrm size – within-ﬁrm estimates

Bank Exposure

Bank Exposure × SMEs

Bank Exposure × Large Firms

∆logT otalCreditbi

∆logCreditLinesbi

(1)

(2)

(3)

(4)

(5)

(6)

-0.989*** -1.143*** -1.520*
(0.824)
(0.320)

(0.311)

-2.723***
(0.863)

-1.441*
(0.829)
-3.133***
(0.836)

-2.659***
(0.881)
-4.048***
(0.866)

No. Observations
No. Firms
Adj. R2

116,245
40,927
0.001

116,245 116,245 116,245
40,927
40,927
40,927
0.050
0.049
0.047

39,573
14,320
0.103

39,573
14,320
0.103

Bank Controls
Firm FE
No. Bank Relationships > 1
Credit Lines with 6= Banks

N
N
Y
N

N
Y
Y
N

Y
Y
Y
N

Y
Y
Y
N

Y
Y
Y
Y

Y
Y
Y
Y

The table presents estimation results of the within-ﬁrm speciﬁcation (1) where the dependent variables are
the change in the log level of total (committed) credit and the change in granted credit lines between each
ﬁrm-bank pair. The quarterly data for each credit exposure is collapsed (time-averaged) into a single pre
(2013:Q4-2014:Q2) and post-shock (2014:Q3-2015:Q3) period. Bank Exposure is the percentage of assets of
each bank exposed to the bail-in i.e., the percentage of assets that was eﬀectively bailed-in for the resolved
bank, the speciﬁc contribution to the ad-hoc loan to the Resolution Fund granted as part of the resolution
for the 8 participating banks (as a percentage of assets), and 0 otherwise. Bank Controls are measured
as at 2013:Q4 and include bank size (log of total assets), bank ROA (return-on-assets), bank capital ratio
(equity to total assets), bank liquidity ratio (liquid to total assets), and bank NPLs (non-performing loans
to total gross loans). Firm size categories are deﬁned according to the EU Recommendation 2003/361.
Heteroskedasticity-consistent standard errors clustered at the bank level are in parenthesis. Statistical
signiﬁcance at the 10%, 5% and 1% levels is denoted by *, **, and ***, respectively.

43

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Table 4: Credit supply and ﬁrm size – cross-sectional estimates

∆logT otalCrediti

(1)

(2)

∆logCreditLinesi

(3)

(4)

Firm Exposure

Firm Exposure × SMEs

-0.374
(0.352)

Firm Exposure × Large Firms

Firm Size

Firm Age

Firm ROA

Firm Capital Ratio

Firm Current Ratio

Credit Demand

No. Observations / Firms
Adj. R2

Bank Controls
Industry FE
District FE
No. Bank Relationships > 1
Credit Lines with 6= Banks

-0.008***
(0.001)
-0.058***
(0.004)
0.228***
(0.043)
0.039***
(0.009)
-0.002**
(0.001)
0.530***
(0.013)

40,927
0.378

Y
Y
Y
Y
N

-0.378
(0.355)
-0.267
(0.607)

-0.008***
(0.002)
-0.058***
(0.004)
0.228***
(0.046)
0.039***
(0.010)
-0.002**
(0.001)
0.530***
(0.018)

40,927
0.378

Y
Y
Y
Y
N

-1.785***
(0.485)

-0.006
(0.008)
-0.042***
(0.012)
0.575***
(0.133)
0.024
(0.029)
0.003
(0.004)
0.510***
(0.020)

14,320
0.175

Y
Y
Y
Y
Y

-1.839***
(0.572)
-0.526
(1.135)

-0.007
(0.009)
-0.042***
(0.011)
0.575***
(0.132)
0.024
(0.031)
0.003
(0.004)
0.510***
(0.017)

14,320
0.175

Y
Y
Y
Y
Y

The table presents estimation results of the between-ﬁrm speciﬁcation (2) where the dependent variables are
the change in the log level of total (committed) credit and the change in granted credit lines for each ﬁrm.
The quarterly data for each credit exposure is collapsed (time-averaged) into a single pre (2013:Q4-2014:Q2)
and post-shock (2014:Q3-2015:Q3) period. Firm Exposure captures the average exposure of each ﬁrm to
the bail-in and is computed as the weighted average of Bank Exposure across all banks lending to a ﬁrm,
using as weights the pre-period share of total credit from each bank. Bank Exposure is the percentage of
assets of each bank exposed to the bail-in i.e., the percentage of assets that was eﬀectively bailed-in for the
resolved bank, the speciﬁc contribution to the ad-hoc loan to the Resolution Fund granted as part of the
resolution for the 8 participating banks (as a percentage of assets), and 0 otherwise. Bank controls, averaged
at the ﬁrm-level according to the pre-period share of total credit granted to the ﬁrm by each bank, are
measured as at 2013:Q4 and include bank size (log of total assets), bank ROA (return-on-assets), bank capital
ratio (equity to total assets), bank liquidity ratio (liquid to total assets), and bank NPLs (non-performing
loans to total gross loans). Firm-level controls, deﬁned in Table 1, are also measured in 2013:Q4. Credit
demand is the vector of ﬁrm-level dummies estimated in the within-ﬁrm regression (Column 2 of Table 2).
Heteroskedasticity-consistent standard errors clustered at the main bank and industry levels are in parenthesis.
Statistical signiﬁcance at the 10%, 5% and 1% levels is denoted by *, **, and ***, respectively.

45

Table 5: New lending relationships and credit supply from less exposed banks

N ewLending
Relationshipi

∆logCrediti
(Except Bailed-in Bank)

(1)

(2)

(3)

(4)

(5)

(6)

Firm Exposure

Firm Exposure × SMEs

Firm Exposure × Large Firms

0.535
(0.352)

-0.659
(0.423)

4.020***
(0.518)

4.566***
(0.558)

-0.674
(0.433)
-0.220
(0.809)

4.540***
(0.585)
5.359***
(1.042)

No. Observations / Firms
Adj. R2

40,927
0.012

40,927
0.058

40,927
0.058

40,927
0.018

40,927
0.342

40,927
0.342

Firm Controls
Bank Controls
Credit Demand
Industry FE
District FE
No. Bank Relationships > 1

N
N
N
Y
Y
Y

Y
Y
Y
Y
Y
Y

Y
Y
Y
Y
Y
Y

N
N
N
Y
Y
Y

Y
Y
Y
Y
Y
Y

Y
Y
Y
Y
Y
Y

The table presents estimation results of the between-ﬁrm speciﬁcation (2) where the dependent variables are
either a dummy that takes value one if a ﬁrm takes out a loan from a bank with which it had no lending
relationship before the shock, and zero otherwise, or the the change in the log level of total (committed) credit
for each ﬁrm between 2013:Q4 and 2015:Q3 excluding the bailed-in bank. Firm Exposure captures the average
exposure of each ﬁrm to the bail-in and is computed as the weighted average of Bank Exposure across all banks
lending to a ﬁrm, using as weights the pre-period share of total credit from each bank. Bank Exposure is
the percentage of assets of each bank exposed to the bail-in i.e., the percentage of assets that was eﬀectively
bailed-in for the resolved bank, the speciﬁc contribution to the ad-hoc loan to the Resolution Fund granted as
part of the resolution for the 8 participating banks (as a percentage of assets), and 0 otherwise. Bank controls,
averaged at the ﬁrm-level according to the pre-period share of total credit granted to the ﬁrm by each bank, are
measured as at 2013:Q4 and include bank size (log of total assets), bank ROA (return-on-assets), bank capital
ratio (equity to total assets), bank liquidity ratio (liquid to total assets), and bank NPLs (non-performing loans
to total gross loans). Firm controls are also measured before the shock (2013:Q4) and include ﬁrm size (log of
total assets), ﬁrm age (ln(1+age)), ﬁrm ROA (net income to total assets), ﬁrm capital ratio (equity to total
assets) and ﬁrm current ratio (current assets to current liabilities). Credit demand is the vector of ﬁrm-level
dummies estimated in the within-ﬁrm regression (Column 2 of Table 2). Heteroskedasticity-consistent standard
errors clustered at the main bank and industry levels are in parenthesis. Statistical signiﬁcance at the 10%, 5%
and 1% levels is denoted by *, **, and ***, respectively.

46

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47

Table 7: Firm exposure to the bail-in and investment

Firm Exposure

Firm Exposure × SMEs

Firm Exposure × Large Firms

∆logT angibleAssetsi

∆logF ixedAssetsi

(1)

(2)

(3)

(4)

(5)

(6)

-1.680*** -1.497***

(0.312)

(0.327)

-1.349*** -1.000**
(0.396)

(0.249)

-1.531***
(0.337)
-0.489
(1.322)

-1.018**
(0.394)
-0.460
(1.242)

No. Observations / Firms
Adj. R2

14,320
0.045

40,927
0.041

40,927
0.041

14,320
0.043

40,927
0.039

40,927
0.039

Firm Controls
Credit Demand
Bank Controls
Industry FE
District FE
No. Bank Relationships > 1
Credit Lines with 6= Banks

Y
Y
Y
Y
Y
Y
Y

Y
Y
Y
Y
Y
Y
N

Y
Y
Y
Y
Y
Y
N

Y
Y
Y
Y
Y
Y
Y

Y
Y
Y
Y
Y
Y
N

Y
Y
Y
Y
Y
Y
N

The table presents estimation results of the between-ﬁrm speciﬁcation (2) where the dependent variables are
the change in the log level of tangible assets and in the log level of ﬁxed assets for each ﬁrm between 2013:Q4
and 2015:Q4 (the shock occurred in August 2014). Firm Exposure captures the average exposure of each ﬁrm
to the bail-in and is computed as the weighted average of Bank Exposure across all banks lending to a ﬁrm,
using as weights the pre-period share of total credit from each bank. Bank Exposure is the percentage of assets
of each bank exposed to the bail-in i.e., the percentage of assets that was eﬀectively bailed-in for the resolved
bank, the speciﬁc contribution to the ad-hoc loan to the Resolution Fund granted as part of the resolution
for the 8 participating banks (as a percentage of assets), and 0 otherwise. Bank controls, averaged at the
ﬁrm-level according to the pre-period share of total credit granted to the ﬁrm by each bank, are measured as
at 2013:Q4 and include bank size (log of total assets), bank ROA (return-on-assets), bank capital ratio (equity
to total assets), bank liquidity ratio (liquid to total assets), and bank NPLs (non-performing loans to total
gross loans). Firm controls are also measured before the shock (2013:Q4) and include ﬁrm size (log of total
assets), ﬁrm age (ln(1+age)), ﬁrm ROA (net income to total assets), ﬁrm capital ratio (equity to total assets)
and ﬁrm current ratio (current assets to current liabilities). Credit demand is the vector of ﬁrm-level dummies
estimated in the within-ﬁrm regression (Column 2 of Table 2). Heteroskedasticity-consistent standard errors
clustered at the main bank and industry levels are in parenthesis. Statistical signiﬁcance at the 10%, 5% and
1% levels is denoted by *, **, and ***, respectively.

48

Table 8: Firm exposure to the bail-in and employment

Firm Exposure

Firm Exposure × SMEs

Firm Exposure × Large Firms

∆logN o.Employeesi

∆logN o.W orkedHoursi

(1)

(2)

(3)

(4)

(5)

(6)

-1.183** -0.945***
(0.410)

(0.182)

-1.644*** -1.154***

(0.326)

(0.163)

-0.971***
(0.180)
-0.190
(0.501)

-1.182***
(0.169)
-0.325
(0.525)

No. Observations / Firms
Adj. R2

14,320
0.080

40,927
0.041

40,927
0.041

14,320
0.054

40,927
0.047

40,927
0.047

Firm Controls
Credit Demand
Bank Controls
Industry FE
District FE
No. Bank Relationships > 1
Credit Lines with 6= Banks

Y
Y
Y
Y
Y
Y
Y

Y
Y
Y
Y
Y
Y
N

Y
Y
Y
Y
Y
Y
N

Y
Y
Y
Y
Y
Y
Y

Y
Y
Y
Y
Y
Y
N

Y
Y
Y
Y
Y
Y
N

The table presents estimation results of the between-ﬁrm speciﬁcation (2) where the dependent variables are
the change in the log level of no. employees and in the log level of total no. worked hours for each ﬁrm between
2013:Q4 and 2015:Q4 (the shock occurred in August 2014). Firm Exposure captures the average exposure of
each ﬁrm to the bail-in and is computed as the weighted average of Bank Exposure across all banks lending to
a ﬁrm, using as weights the pre-period share of total credit from each bank. Bank Exposure is the percentage
of assets of each bank exposed to the bail-in i.e., the percentage of assets that was eﬀectively bailed-in for
the resolved bank, the speciﬁc contribution to the ad-hoc loan to the Resolution Fund granted as part of the
resolution for the 8 participating banks (as a percentage of assets), and 0 otherwise. Bank controls, averaged at
the ﬁrm-level according to the pre-period share of total credit granted to the ﬁrm by each bank, are measured as
at 2013:Q4 and include bank size (log of total assets), bank ROA (return-on-assets), bank capital ratio (equity
to total assets), bank liquidity ratio (liquid to total assets), and bank NPLs (non-performing loans to total
gross loans). Firm controls are also measured before the shock (2013:Q4) and include ﬁrm size (log of total
assets), ﬁrm age (ln(1+age)), ﬁrm ROA (net income to total assets), ﬁrm capital ratio (equity to total assets)
and ﬁrm current ratio (current assets to current liabilities). Credit demand is the vector of ﬁrm-level dummies
estimated in the within-ﬁrm regression (Column 2 of Table 2). Heteroskedasticity-consistent standard errors
clustered at the main bank and industry levels are in parenthesis. Statistical signiﬁcance at the 10%, 5% and
1% levels is denoted by *, **, and ***, respectively.

49

Table 9: Firm exposure to the bail-in and liquidity

∆log

∆log
CashHoldingsi T angibleAssetsi N o.Employeesi
(2)

∆log

(1)

(3)

Panel A: SMEs

Firm Exposure × High Liquidity Firms

Firm Exposure × Low Liquidity Firms

No. Observations / Firms
Adj. R2

Panel B: Large Firms.

Firm Exposure × High Liquidity Firms

Firm Exposure × Low Liquidity Firms

No. Observations / Firms
Adj. R2
Firm and Bank Controls
Credit Demand
Industry and District FE
No. Bank Relationships > 1

-13.579***
(3.899)
13.416***
(4.249)

40,234
0.022

-11.885**
(5.477)
1.735
(2.023)

689
0.075
Y
Y
Y
Y

-0.093
(0.861)
-1.680***
(0.420)

40,234
0.040

-1.485
(2.422)
-3.870
(2.342)

689
0.083
Y
Y
Y
Y

-0.113
(0.309)
-1.644***
(0.135)

40,234
0.067

2.106
(2.451)
-0.631
(1.705)

689
0.094
Y
Y
Y
Y

The table presents estimation results of the between-ﬁrm speciﬁcation (2) where ﬁrms are split according to
their ex-ante liquidity position i.e., low liquidity (cash holdings-to-total assets ratio before the shock lower
than the median) vs. high liquidity ﬁrms (cash holdings-to-total assets ratio before the shock higher than the
median). The dependent variables are the change in the log level of cash holdings, investment (tangible assets)
and employment (no. employees) for each ﬁrm between 2013:Q4 and 2015:Q4 (the shock occurred in August
2014). Panel A focuses on the sub-sample of SMEs and Panel B on large ﬁrms. Firm Exposure captures the
average exposure of each ﬁrm to the bail-in and is computed as the weighted average of Bank Exposure across
all banks lending to a ﬁrm, using as weights the pre-period share of total credit from each bank. Bank Exposure
is the percentage of assets of each bank exposed to the bail-in i.e., the percentage of assets that was eﬀectively
bailed-in for the resolved bank, the speciﬁc contribution to the ad-hoc loan to the Resolution Fund granted as
part of the resolution for the 8 participating banks (as a percentage of assets), and 0 otherwise. Bank controls,
averaged at the ﬁrm-level according to the pre-period share of total credit granted to the ﬁrm by each bank, are
measured as at 2013:Q4 and include bank size (log of total assets), bank ROA (return-on-assets), bank capital
ratio (equity to total assets), bank liquidity ratio (liquid to total assets), and bank NPLs (non-performing loans
to total gross loans). Firm controls are also measured before the shock (2013:Q4) and include ﬁrm size (log of
total assets), ﬁrm age (ln(1+age)), ﬁrm ROA (net income to total assets), ﬁrm capital ratio (equity to total
assets) and ﬁrm current ratio (current assets to current liabilities). Credit demand is the vector of ﬁrm-level
dummies estimated in the within-ﬁrm regression (Column 2 of Table 2). Heteroskedasticity-consistent standard
errors clustered at the main bank and industry levels are in parenthesis. Statistical signiﬁcance at the 10%,
5% and 1% levels is denoted by *, **, and ***, respectively.

50

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Internet Appendix

Sharing the Pain? Credit Supply and
Real Eﬀects of Bank Bail-ins

Thorsten Beck, Samuel Da-Rocha-Lopes and Andr´e F. Silva

June 2018

52

Table IA1: Credit supply and ﬁrm size – within-ﬁrm estimates

∆logT otalCreditbi

Alternative Bank
Exposure Measure
(CDS Spread Reaction)

Including Firms
With Only One
Bank Relationship

(1)

(2)

(3)

(4)

(5)

(6)

-1.917*** -2.031***

(0.297)

(0.345)

-0.714*** -1.339**
(0.649)

(0.261)

-1.787***
(0.350)
-5.956***
(1.703)

-1.283*
(0.652)
-2.915***
(0.667)

Bank Exposure

Bank Exposure × SMEs

Bank Exposure × Large Firms

No. Observations
No. Firms
Adj. R2

No. Banks

Bank Controls
Firm FE
Location-Size-Sector FE
No. Bank Relationships > 1

40,783
17,445
0.001

40,783
17,445
0.054

40,783
17,445
0.054

160,457
85,139
0.053

160,457
85,139
0.055

160,457
85,139
0.055

4

N
N
N
Y

4

Y
Y
N
Y

4

Y
Y
N
Y

98

N
N
N
N

98

Y
N
Y
N

98

Y
N
Y
N

The table presents estimation results of the within-ﬁrm speciﬁcation (1) where the dependent variables are
the change in the log level of total (committed) credit between each ﬁrm-bank pair. The quarterly data
for each credit exposure is collapsed (time-averaged) into a single pre (2013:Q4-2014:Q2) and post-shock
(2014:Q3-2015:Q3) period. Bank Exposure is deﬁned as the bank-speciﬁc increase in CDS spreads from one
more before to the day of the resolution in columns (1) to (3) and the percentage of assets of each bank exposed
to the bail-in in columns (4) to (6) i.e., the percentage of assets that was eﬀectively bailed-in for the resolved
bank, the speciﬁc contribution to the ad-hoc loan to the Resolution Fund granted as part of the resolution
for the 8 participating banks (as a percentage of assets), and 0 otherwise. Bank Controls are measured as at
2013:Q4 and include bank size (log of total assets), bank ROA (return-on-assets), bank capital ratio (equity
to total assets), bank liquidity ratio (liquid to total assets), and bank NPLs (non-performing loans to total
gross loans). Firm size categories are deﬁned according to the EU Recommendation 2003/361. In columns
(4) to (6) we control for credit demand by replacing the ﬁrm ﬁxed-eﬀect in the within-ﬁrm regressions by a
group (location-sector-size) ﬁxed-eﬀect. The group contains only the ﬁrm itself in case the ﬁrm has multiple
lending relationships, while ﬁrms with single bank relationships are grouped based on the district in which they
are headquartered, their industry, and deciles of loan size in the credit register. Heteroskedasticity-consistent
standard errors clustered at the bank level are in parenthesis. Statistical signiﬁcance at the 10%, 5% and 1%
levels is denoted by *, **, and ***, respectively.

53

Table IA2: Credit supply and ﬁrm size – within-ﬁrm estimates

∆logCreditbi
(Excluding Credit Lines)

∆logCreditbi
(2014:Q2-2015:Q3)

(1)

(2)

(3)

(4)

(5)

(6)

-0.963*** -1.108
(0.808)

(0.366)

-1.430*** -2.000**
(0.826)

(0.303)

-1.063
(0.806)
-1.925*
(0.986)

-1.812**
(0.832)
-5.460***
(0.927)

Bank Exposure

Bank Exposure × SMEs

Bank Exposure × Large Firms

No. Observations
No. Firms
Adj. R2

96,584
35,365
0.001

96,584 96,584
35,365 35,365
0.015
0.015

97,130
34,861
0.001

97,130
34,861
0.029

97,130
34,861
0.030

Bank Controls
Firm FE
No. Bank Relationships > 1

N
N
Y

Y
Y
Y

Y
Y
Y

N
N
Y

Y
Y
Y

Y
Y
Y

The table presents estimation results of the within-ﬁrm speciﬁcation (1) where the dependent variables
are the change in the log level of total credit between each ﬁrm-bank pair without considering used
and unused credit lines (columns 1-3), the change in the log level of total (committed) credit between
each ﬁrm-bank pair (columns 4-6). Bank Exposure is the percentage of assets of each bank exposed
to the bail-in i.e., the percentage of assets that was eﬀectively bailed-in for the resolved bank, the
speciﬁc contribution to the ad-hoc loan to the Resolution Fund granted as part of the resolution for
the 8 participating banks (as a percentage of assets), and 0 otherwise. Bank Controls are measured as
at 2013:Q4 and include bank size (log of total assets), bank ROA (return-on-assets), bank capital ratio
(equity to total assets), bank liquidity ratio (liquid to total assets), and bank NPLs (non-performing loans
to total gross loans). Firm size categories are deﬁned according to the EU Recommendation 2003/361.
Heteroskedasticity-consistent standard errors clustered at the bank level are in parenthesis. Statistical
signiﬁcance at the 10%, 5% and 1% levels is denoted by *, **, and ***, respectively.

54

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Table IA4: Credit supply and ﬁrm size – cross-sectional estimates

∆logCrediti
(Excluding Credit Lines)

∆logCrediti
(2014:Q2-2015:Q3)

(1)

(2)

(3)

(4)

Firm Exposure

Firm Exposure × SMEs

-0.279
(0.396)

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No. Observations / Firms
Adj. R2

35,365
0.280

Firm Controls
Bank Controls
Credit Demand
Industry FE
District FE
No. Bank Relationships > 1

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(0.425)
-0.206
(0.375)

35,365
0.279

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-0.478
(0.494)

-0.523
(0.464)
0.630
(1.372)

34,861
0.419

34,861
0.419

Y
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Y
Y
Y

Y
Y
Y
Y
Y
Y

The table presents estimation results of the between-ﬁrm speciﬁcation (2). Firm Exposure captures
the average exposure of each ﬁrm to the bail-in and is computed as the weighted average of Bank
Exposure across all banks lending to a ﬁrm, using as weights the pre-period share of total credit from
each bank. Bank Exposure is the percentage of assets of each bank exposed to the bail-in i.e., the
percentage of assets that was eﬀectively bailed-in for the resolved bank, the speciﬁc contribution to
the ad-hoc loan to the Resolution Fund granted as part of the resolution for the 8 participating banks
(as a percentage of assets), and 0 otherwise. Bank controls, averaged at the ﬁrm-level according to
the pre-period share of total credit granted to the ﬁrm by each bank, are measured as at 2013:Q4
and include bank size (log of total assets), bank ROA (return-on-assets), bank capital ratio (equity
to total assets), bank liquidity ratio (liquid to total assets), and bank NPLs (non-performing loans
to total gross loans). Firm controls are also measured before the shock (2013:Q4) and include ﬁrm
size (log of total assets), ﬁrm age (ln(1+age)), ﬁrm ROA (net income to total assets), ﬁrm capital
ratio (equity to total assets) and ﬁrm current ratio (current assets to current liabilities). Credit
demand is the vector of ﬁrm-level dummies estimated in the within-ﬁrm regression (Column 2 of
Table 2). Heteroskedasticity-consistent standard errors clustered at the main bank and industry
levels are in parenthesis. Statistical signiﬁcance at the 10%, 5% and 1% levels is denoted by *, **,
and ***, respectively.

56

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60

ACKNOWLEDGEMENTS 
ENTER HERE THE TITLE OF THE ARTICLE

We would like to thank Tobias Berg, Pawel Bilinski, Diana Bonfim, Marco Bonomo (discussant), Max Bruche, Charles 
Calomiris  (discussant),  Duckki  Cho  (discussant),  Matteo  Crosignani  (discussant),  João  Cocco,  Ralph  De  Haas 
(discussant), Giovanni Dell’Ariccia, Mathias Dewatripont, Miguel Ferreira, Mariassunta Giannetti, Piers Haben, José 
Liberti  (discussant),  Cathérine  Koch  (discussant),  Michael  Koetter,  Fergal  McCann  (discussant), Ralf  Meisenzahl, 
Steven  Ongena,  Sole  Martinez  Peria,  Marco  Pagano,  Diane  Pierret  (discussant),  Alberto  Pozzolo  (discussant), 
Andrea Presbitero, Lev Ratnovski (discussant), João Santos, Enrico Sette, Klaus Schaeck (discussant), Paolo Volpin, 
Spyridon Zarkos, an anonymous EBA referee, conference participants at the 2nd CEPR Annual Spring Symposium in 
Financial Economics (UK), De Nederlandsche Bank/EBC/CEPR Conference (Netherlands), 5th Emerging Scholars in 
Banking  and  Finance  Conference  (UK),  32nd  European  Economic  Association  Conference  (Portugal),  4th  Bank  of 
Canada/Bank of Spain Workshop (Canada), 9th European Banking Center Network Conference (UK), Research Task 
Force of the Basel Committee on Banking Supervision/CEPR Joint Workshop (Switzerland), 4th Workshop of the 
Empirical  Financial  Intermediation  Research  Network  (Belgium),  2017  Sydney  Banking  and  Financial  Stability 
Conference  (Australia),  Deutsche  Bundesbank/IWH/CEPR  Conference  (Germany),  12th  Luso-Brazilian  Finance 
Meeting  (Brazil),  2018  AFA  PhD  Poster  Session  (US),  2018  Sapienza/BAFFI  CAREFIN/RFS  Conference  (Italy),  11th 
Swiss Winter Conference on Financial Intermediation (Switzerland), 2018 Fixed Income and Financial Institutions 
Conference (US), 2018 Financial Intermediation Research Society Conference (Spain), and seminar participants at 
the International Monetary Fund (US), Columbia Business School (US), Bank of England (UK), BI Norwegian Business 
School (Norway), Single Resolution Board (Belgium), Universidad Carlos III de Madrid (Spain) and Bank of Italy (Italy) 
for  the  valuable  comments  and  suggestions.  We  also  thank  Adelaide  Cavaleiro,  Graça  Damião,  Olga  Monteiro, 
Paulo Jesus, Paulo Taborda, Sandra Pinheiro and the Department of Statistics of the Bank of Portugal for providing 
excellent research support.  
To the memory of Samuel’s beloved mother, Filomena Da-Rocha. 
The views expressed in this paper are solely those of the authors and do not necessarily represent the opinions of 
the European Banking Authority (EBA) or the Eurosystem, or those of the International Monetary Fund (IMF), its 
Executive Board, or its management. 

Samuel Da-Rocha-Lopes  
European Banking Authority and Nova SBE 

Thorsten Beck  
Cass Business School, CEPR and CESifo 

André F. Silva  
Cass Business School and IMF

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ISBN 978-92-9245-389-3 
ISSN 2599-7831 

DOI: 10.2853/91350 
DZ-AH-18-001-EN-N 

© European Banking Authority, 2018. 

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