Same same but different : credit risk provisioning under IFRS 9.
Date: 2023-08-21
Institutions: European Central Bank
Subjects: euro area, financial risk, financial stability, loan, monetary crisis

Working Paper Series 

Markus Behn, Cyril Couaillier 

Same same but different: credit risk 
provisioning under IFRS 9 

No 2841 

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

 
 
Abstract

We analyse the impact of the adoption of expected credit loss accounting (IFRS 9) on the

timeliness and potential procyclicality of banks’ loan loss provisioning. We use granular loan-

level data from the euro area’s credit register and investigate both firm-level credit events

and macroeconomic shocks (2020 COVID-19 pandemic, 2022 energy price shock). We find

that provisions under the new standard are higher before default and more responsive to

shocks. However, the majority of provisioning still occurs at the time of default and the

dynamics around default events are similar to pre-existing national standards. Additionally,

banks with a larger capital headroom provision significantly more, particularly for loans us-

ing IFRS 9. This suggests a higher risk of underprovisioning for less capitalized banks.

Keywords: bank regulation, financial stability, loan loss accounting, credit risk

JEL classification: G21, G28, G32

ECB Working Paper Series No 28411Non Technical Summary

In this paper, we examine how the adoption of expected credit loss accounting (IFRS 9) and bank

conditions have affected euro area banks’ provisioning behaviour around individual credit events,

over the course of the COVID-19 pandemic, and after the outbreak of war in Ukraine. Using

30 million quarterly loan observations from the European credit register (AnaCredit), our study

compares provisioning dynamics for IFRS 9 loans to those using national Generally Accepted

Accounting Principles (nGAAP). The comparison aims to assess whether post-crisis reforms

have fundamentally altered provisioning patterns. The study also examines whether well- and

less-capitalized banks exhibit divergent provisioning dynamics under IFRS 9 and nGAAP, to

gain insights on the role of discretion under different accounting approaches.

The paper finds that some features of IFRS 9 seem to work as intended, with ex ante

provisioning generally higher than with nGAAP and provisions increasing in the run-up to a

default. However, the pre-default increase is small, and the bulk of provisioning under IFRS 9

still occurs at the time of or shortly after a default. This results in provisioning dynamics around

credit risk shocks being similar between loans using IFRS 9 and those using nGAAP. One reason

for this is that banks appear to have problems with identifying significant increases in credit risk

(that trigger a move to “stage 2” and thus higher provisioning under IFRS 9) at a sufficiently

early stage, so that a large share of IFRS 9 loans continues to be classified as fully performing

(“stage 1”) shortly ahead of a default. Moreover, even loans in stage 2 exhibit sizable jumps in

provisioning at default, although smaller ones than stage 1 loans.

We also find evidence that IFRS 9 has amplified the impact of banks’ capital position on

their provisioning practices. Specifically, we find that banks with less capital headroom over

their capital requirements provision less than other banks, even for similar loans to the same

borrower in the same period. This is consistent with capital management motives and a strategy

of “provisioning as much as you can afford”, indicating that discretion seems to play a role under

both accounting approaches. However, when looking at provisioning dynamics around credit risk

shocks, we find that banks with more capital headroom increase provisions ahead of default more

than other banks for loans using IFRS 9 but not for loans using nGAAP. One interpretation

for this is that IFRS 9 affords banks with greater flexibility to avoid substantial increases in

provisions in cases where capital is scarce. Consistent with this interpretation, we also find that

ECB Working Paper Series No 28412less capitalised banks are less likely to move exposures to the higher credit risk stage 2 under

IFRS 9 and, if they do, to increase their provisions less after this move, so that they appear to

be using all levers available to them to reduce provisions. Thus, the new approach may have

fostered stronger divergence in accounting practices across banks, where less capitalised banks

in particular may be tempted to exploit available discretion for capital management purposes.

We then also analyse provisioning dynamics around two macroeconomic shocks (the COVID-

19 pandemic and the energy price shock in early 2022), which are particularly relevant from a

financial stability perspective. The findings show that provisioning for IFRS 9 loans and the

share of stage 2 loans increased over the course of the pandemic, even without a significant in-

crease in default rates. However, the implications for banks’ capital ratios are modest, and even

a doubling or tripling of the observed effects should have been manageable without procyclical

adjustment actions on the side of banks. Capital headroom had a strong impact on provisioning

behavior both during the pandemic and after the energy price shock, indicating capital man-

agement motives also around systemic events. Finally, provisions for IFRS 9 loans are increased

more risk-sensitively (i.e., more strongly for firms more exposed to the energy price shock), in

line with the new framework’s intended functioning.

Our findings have several important policy implications. First, the dynamics around default

events and the overall muted reaction by banks after two macroeconomic shocks suggest that

discussions on possible procyclicality of IFRS 9 may have been exaggerated, since the overall

patterns are not much different from those of previously existing nGAAP standards. Specifically,

our results suggest that IFRS 9 offers enough flexibility to spread provisioning increases around

macroeconomic shocks over several quarters, reducing the risk of sharp increases in provisioning.

Consistent with such flexibility, we also document a strong impact of capital headroom on

provisioning practices. While this requires close supervisory scrutiny, the overall implications

in terms financial stability are a priori ambiguous. On a positive side, flexibility can smooth

the potential procyclical impact of ECL accounting when the economy weakens: as mentioned

above, poorly capitalised banks can avoid a jump in provisions that would dent their capital and

force them to procyclically reduce lending. However, this reduces transparency and may imply

an underestimation of credit risk, potentially leading to stronger negative repercussions when

risks materialise eventually. Moreover, less capitalised banks may be at greater risk of being

underprovisioned, with potentially negative implications in terms of bank stability.

ECB Working Paper Series No 284131 Introduction

The adequate and timely provisioning of credit risk is key for banks.

It ensures that they

can withstand the materialisation of such risk without endangering their own solvency and the

stability of the financial system. It also makes the underlying risks transparent for investors

and supervisors, thus improving market discipline and facilitating supervision. Additionally,

proper provisioning creates incentives for banks to avoid engaging in overly risky behavior such

as zombie lending or evergreening of loans, which can destabilise the financial system over the

medium term. Given the implications for bank solvency and financial stability, determinants of

bank provisioning have long been studied by academics and policymakers alike, and provisioning

frameworks have substantially evolved in recent years to address identified shortcomings.

Following the crisis of 2007-09, existing provisioning methods were accused of inducing banks

to provision “too little, too late.” These methods mostly relied on backward-looking Incurred

Loss (IL) approaches, requiring the booking of provisions only after the occurrence of specific

credit events that made repayment unlikely.1 Consequently, aggregate provisioning ratios in-

creased substantially over the course of the crisis, quadrupling relative to previously observed lev-

els (see Figure 1). This sharp increase in provisioning triggered concerns about procyclicality and

a lack of transparency with further hidden risks in banks’ balance sheets. The Financial Stability

Forum (2009), predecessor of today’s Financial Stability Board, called on accounting standard

setters to reconsider the IL model and aim for more forward-looking approaches instead. This

resulted in the development and eventual adoption of Expected Credit Loss (ECL) approaches

such as the International Financial Reporting Standard 9 (IFRS 9). These approaches aim to

anticipate loss recognition by requiring provisions to be based on estimated future credit losses,

usually obtained from dedicated provisioning models operated by banks themselves. Using these

models, loan exposures are classified as performing (“stage 1”), underperforming (“stage 2”), or

non-performing (“stage 3”), with substantially higher provisioning expected already for loans

that saw a significant increase in credit risk and are thus placed in stage 2.

While ECL approaches were supposed to enhance transparency by inducing speedier and

more accurate loss recognition, their implementation soon triggered discussions on possible mal-

functioning and side effects (see Basel Committee on Banking Supervision 2021 for a review

1The prime example for an IL accounting approach is the International Accounting Standard 39 (IAS 39).

ECB Working Paper Series No 28414of the literature). First, there were concerns that a sudden and significant deterioration in

economic conditions could trigger significant loss provisioning in the early stages after a shock,

which might itself induce procyclical effects, only front-loading them when compared with the

IL approach (see, e.g., the simulation study by Abad and Suarez 2018). Specifically, increased

provisioning reduces bank capital, potentially leading banks to reduce credit supply in order to

protect their capital ratios and avoid breaching capital requirements. This can deprive firms

and households of necessary financing soon after a shock, which may in turn exacerbate the

crisis.2 Second, there were doubts that a move to an ECL approach alone would be sufficient

to change the incentives for banks to delay loss recognition (e.g., Bischof et al. 2021), given

an inherent reluctance to impair assets and recognise losses (e.g., Laux and Leuz 2009, 2010,

Huizinga and Laeven 2012). There were even concerns that the reliance on internal models and

the discretion afforded to banks in estimating expected losses could facilitate such delays and

induce more dispersion in provisioning practices, potentially conflicting with the objective of

enhanced transparency (e.g., European Systemic Risk Board 2017).3 The temptation to under-

estimate expected losses and thus reduce provisioning needs could be particularly strong for less

capitalised banks that are pressured to preserve capital.

In this paper, we pick up on these points and investigate how accounting rules and bank con-

ditions have affected euro area banks’ provisioning behaviour, around individual credit events

and over the course of the COVID-19 pandemic and after the outbreak of war in Ukraine. Using

a sample of around 30 million quarterly loan observations from the European credit register

(‘AnaCredit’), we compare provisioning dynamics for IFRS 9 loans to those of loans using na-

tional Generally Accepted Accounting Principles (nGAAP). The latter contain forward-looking

elements in some cases but are mostly of an IL nature (see Section 2). More importantly,

they have existed since long ago, so that the comparison with IFRS 9 allows to assess whether

post-crisis reforms have fundamentally altered provisioning patterns.4 Moreover, we examine

2See Berrospide et al. (2021) and Couaillier et al. (2022a) for recent evidence of such protective behaviour by
banks during the COVID-19 pandemic. Importantly, this procyclicality concern is similar to previous concerns
under the IL approach, where the main difference is in the timing of losses that may trigger procyclical adjustments.
Under the IL approach, the booking of provisions would induce sizable losses rather late in the crisis, potentially
triggering procyclical adjustments at that stage (or somewhat earlier if banks anticipate future losses), while the
concern under IFRS 9 is that substantial provisioning and losses may be observed soon after a shock.

3For the case of capital regulation, such side effects of relying on banks’ internal models for regulatory purposes

are well-documented (e.g., Begley et al. 2017, Colliard 2019, Plosser and Santos 2018, Behn et al. 2022).

4In the EU, application of IFRS 9 is mandatory for consolidated financial statements of public companies listed
in any Member State. In several member states, other companies (e.g., unconsolidated or non-listed entities) have
to or may choose to resort to nGAAP. For this reason, AnaCredit contains both IFRS 9 and nGAAP loans,

ECB Working Paper Series No 28415whether well- and less-capitalised banks exhibit divergent provisioning dynamics under IFRS 9

and nGAAP, respectively, to derive insights on the role of discretion under the different account-

ing approaches. Thanks to the granularity of our data set, we can compare provisioning levels

and dynamics for loans to the same borrower in the same period but issued by banks using

different accounting standards or differently capitalised banks. Thus, our identification strat-

egy systematically controls for the borrower’s credit quality and other borrower-specific factors

that may affect provisioning behaviour, akin to the the seminal paper by Khwaja and Mian

(2008). Moreover, the inclusion of loan-specific control variables ensures that we are comparing

provisioning for similar types of exposures.

Our findings indicate that some features of IFRS 9 seem to be working as intended: ex ante

provisioning is generally higher than with nGAAP and provisions increase in the run-up to a

default. However, this pre-default increase is rather small, and the bulk of provisioning under

IFRS 9 continues to occur at the time of or shortly after a default. As a result, provisioning

dynamics around credit risk shocks are rather similar between loans using IFRS 9 and those

using nGAAP. One reason for this is that banks appear to have problems with identifying

significant increases in credit risk (that trigger a move to “stage 2” and thus higher provisioning

under IFRS 9) at a sufficiently early stage, so that a large share of IFRS 9 loans continues

to be classified as fully performing (“stage 1”) shortly ahead of a default. Moreover, even

loans in stage 2 exhibit sizable jumps in provisioning at default, although smaller ones than

stage 1 loans. Somewhat in contrast with expectations, this suggests that the new approach

has not fundamentally altered provisioning patterns. Importantly, the results hold in a number

of robustness checks that control for support measures implemented during the pandemic and

differences between IFRS 9 and nGAAP banks and loans that could influence the regressions.

We also find evidence that IFRS 9 has amplified the impact of banks’ capital position on

their provisioning practices. Specifically, we find that banks with less capital headroom over

their capital requirements provision less than other banks, even for similar loans to the same

borrower in the same period. This is consistent with capital management motives and a strategy

where the accounting standard used for the reporting may vary also for loans from the same parent institution.
Of the 1,721 banks in our sample, 1,318 (including 47 Significant Institutions supervised by the ECB) report
under nGAAP for at least some of their loans. Such nGAAP exposures are particularly prevalent in Austria and
Germany but also observed in other countries. Banks mostly using IFRS 9 differ from banks mostly using nGAAP
on several dimensions, which we account for via econometric techniques such as propensity score matching (see
Appendix A), or by restricting the sample to loans from banks that rely on both types of approaches.

ECB Working Paper Series No 28416of “provisioning as much as you can afford”, indicating that discretion seems to play a role under

both accounting approaches. However, when looking at provisioning dynamics around credit risk

shocks, we find that banks with more capital headroom increase provisions ahead of default more

than other banks for loans using IFRS 9 but not for loans using nGAAP. One interpretation

for this is that IFRS 9 affords banks with greater flexibility to avoid substantial increases in

provisions in cases where capital is scarce. Consistent with this interpretation, we also find that

less capitalised banks are less likely to move exposures to the higher credit risk stage 2 under

IFRS 9 and, if they do, to increase their provisions less after this move, so that they appear to

be using all levers available to them to reduce provisions. Thus, the new approach may have

fostered stronger divergence in accounting practices across banks, where less capitalised banks

in particular may be tempted to exploit available discretion for capital management purposes.

Finally, we analyse provisioning dynamics around the macroeconomic shocks induced by

the COVID-19 pandemic and the energy price shock in early 2022. Doing so is particularly

relevant from a financial stability perspective, given the potential systemic repercussions that

can occur around correlated credit risk events, if many banks suffer losses and adjust their

behaviour simultaneously. Our findings show that provisioning for IFRS 9 loans and the share

of stage 2 loans increased around these shocks, even in the absence of a material increase in

default rates. However, implications for banks’ capital ratios have been very modest, and back-

of-the-envelope calculations suggest that even a doubling or tripling of the observed effects should

have been manageable for banks without triggering procyclical adjustments. This confirms the

rather muted relevance of stage 2 transitions for overall provisioning patterns. Moreover, in

line with the previous findings, capital headroom had a strong impact on provisioning behaviour

around these shocks, consistent with a strong impact of capital management motives also around

systemic events. Finally, we also find that provisions for IFRS 9 loans are increased in a more

risk sensitive manner (i.e., more strongly for firms that are more exposed to the energy price

shock), in line with the intended functioning of the new framework.

Our findings have several important policy implications. First, the dynamics around default

events and the overall muted reaction by banks after two macroeconomic shocks (that did not

trigger waves of default) suggest that discussions on possible procyclicality of IFRS 9 may have

been exaggerated, since the overall patterns are not much different from those of previously

existing nGAAP standards. While provisioning increases during the pandemic could have been

ECB Working Paper Series No 28417more forceful in the absence of support measures, our results suggest that IFRS 9 offers enough

flexibility to spread these increases over several quarters, reducing the risk of sharp increases

in provisioning. Consistent with such flexibility, we also document a strong impact of capital

headroom on provisioning practices. While this requires close supervisory scrutiny, the overall

implications in terms financial stability are a priori ambiguous. On a positive side, flexibility

can smooth the potential procyclical impact of ECL accounting when the economy weakens: as

mentioned above, poorly capitalised banks can avoid a jump in provisions that would dent their

capital and force them to procyclically reduce lending. However, this reduces transparency and

may imply an underestimation of credit risk, potentially leading to stronger negative repercus-

sions when risks materialise eventually. Moreover, less capitalised banks may be at greater risk

of being underprovisioned, with potentially negative implications in terms of bank stability.

Our paper contributes to a growing literature on the role of discretion and risk modelling in

banking and financial regulation. In a context of bank capital regulation, several papers point

out incentive problems and undesired effects that may be associated with the use of banks’

internal risk models for regulatory purposes (e.g., Mariathasan and Merrouche 2014, Begley

et al. 2017, Behn et al. 2016, 2022, or Plosser and Santos 2018). Similarly, evidence on the

impact of discretion in insurance regulation is provided by Koijen and Yogo (2015, 2016). But

modelling choices and discretion affect the behaviour and performance of financial institutions

also beyond the regulatory perimeter. For example, Rajan et al. (2015) demonstrate that failure

to account for changes in economic agents’ behaviour due to an increased level of securitisation

led to a failure of banks’ statistical default models, while Huizinga and Laeven (2012) and

Bischof et al. (2021) show that accounting discretion on the side of banks contributed to delayed

loss recognition during the global financial crisis.5 We add to this literature by examining the

performance of accounting risk models and the impact of banks’ capital on their provisioning

practices. To the best of our knowledge, our paper is the first to empirically assess whether the

recent shift to ECL accounting has altered the role of discretion in banks’ loss recognition.

We also add to literature that examines the interaction between accounting standards, bank

regulation, and financial stability. A focal point of this literature has been on the impact

of provisioning practices on bank lending and real economic outcomes, often in a context of

discussions on potential procyclicality of certain accounting features (e.g., Jim´enez et al. 2017,

5On the role of accounting discretion during the global financial crisis, see also Balasubramanyan et al. (2017).

ECB Working Paper Series No 28418Huizinga and Laeven 2019, Blattner et al. 2020).6 Following the shift to expected loss accounting

after the global financial crisis, several studies examined how the new approach would impact

banks’ lending behaviour, for example by making use of simulation methods or theoretical models

(Abad and Suarez 2018, Buesa et al. 2019, Mahieux et al. 2022, Kund and Rugilo 2023), by

analysing the effects of forward-looking elements of the previous incurred loss approach (Beatty

and Liao 2011, Bushman and Williams 2012), or by analysing earlier accounting reforms at the

national level (Morais et al. 2022). We expand on this literature by examining how expected

loss accounting performed and compared with incurred loss accounting during the period of

the pandemic and after the energy price shock in 2022. That is, we analyse the functioning of

the new approach under real life stressed economic conditions, which is highly relevant from a

financial stability perspective and provides important insights for academics and policymakers.

The remainder of this paper is organised as follows: In Section 2, we briefly describe institu-

tional arrangements and discussions surrounding the introduction of ECL accounting in recent

years. We introduce our data set in Section 3 and describe the empirical strategy in Section 4.

Section 5 presents our estimation results and Section 6 concludes.

2

Institutional background

2.1

Introduction of IFRS 9 in Europe

Expected credit loss (ECL) accounting was introduced after the global financial crisis of 2007-

09, as the previously prevalent incurred loss (IL) approach was widely criticised for delaying the

recognition of loan losses and inducing banks to provision “too little, too late.” Delayed loss

recognition under the “backward-looking” IL approach complicated the identification of weaker

institutions and may have amplified financial system procyclicality when losses eventually ma-

terialised and banks constrained credit supply (e.g., Financial Stability Forum 2009). The more

“forward-looking” ECL approach is supposed to enhance transparency and promote speedier

and more accurate recognition of losses by considering not only realised but also expected credit

risk losses (e.g., International Accounting Standards Board 2013, Cohen and Edwards 2017).

6The role of fair value accounting has also been examined in this context (e.g., Laux and Leuz 2010, Xie 2016).
Moreover, there is a rich empirical literature studying potential procyclical implications of certain features in
bank capital regulation (see, e.g., Behn et al. 2016, Gropp et al. 2019, or Fraisse et al. 2020, among many others).

ECB Working Paper Series No 28419Provisions under ECL accounting reflect predicted credit risk losses from the time of loan origi-

nation onward, making use of dedicated provisioning models and the available set of information

at each point in time. The move towards ECL accounting eventually resulted in the adoption

of the International Financial Reporting Standard 9 (IFRS 9) at a global level, which is in the

focus of our paper, and the similar U.S. Current Expected Credit Losses (CECL) standards.

The final version of IFRS 9 was published in July 2014 and replaced the previously applicable

International Accounting Standard 39 (IAS 39) in January 2018. Since the move to ECL ac-

counting was expected to lead to an initial increase in provisions, European legislators made use

of transitional arrangements that mitigated the initial impact on regulatory capital ratios.7 Ap-

plication of IFRS 9 is mandatory for consolidated financial statements of public companies that

are listed in any member state of the European Union. Other companies (e.g., unconsolidated

or non-listed entities) have to report under national Generally Accepted Accounting Principles

(nGAAP), although they have the option to voluntarily apply IFRS 9 in some but not all mem-

ber states.8 For this reason, our sample contains both IFRS 9 and nGAAP loans. While national

accounting standards contain forward-looking or discretionary elements in some cases (see de-

tailed discussion by Domikowsky et al. 2015, 2017), they are generally of an incurred loss nature

and expected to be less forward-looking than IFRS 9. Most importantly, they have existed since

long ago, so that the comparison with IFRS 9 allows to assess whether post-crisis reforms have

fundamentally altered provisioning patterns relative to pre-existing national regimes.

Under IL accounting, provisions are generally established only following a credit event, re-

sulting in low impairment ratios before a default and large increases at the time of default. In

contrast, under IFRS 9, loans are sorted into three distinct buckets – referred to as ’stages’

– which determine the amount of provisions that a bank needs to set aside for the respective

exposures (see Figure 2 for an overview). Performing loans are sorted into stage 1 from the

time at which they are originated or purchased. Provisions for stage 1 loans need to reflect the

7Specifically, over the transition period from 2018 to 2022, the Capital Requirements Regulation (CRR) allowed
banks to add back to their Common Equity Tier 1 (CET1) capital a declining share of the higher provisions arising
from the application of IFRS 9. That is, the provisions themselves were not altered and in line with IFRS 9 as of
2018, but their impact on regulatory capital ratios was partially neutralised via a prudential filter.

8More specifically, individual Member States have an option to require or permit IFRS 9 as adopted by the
EU (Member State Option) for the individual financial statements of listed companies, and the consolidated
or individual financial statements of non-listed companies (for further details, see the IFRS Jurisdictional Pro-
file for the European Union, published online under https://www.ifrs.org/content/dam/ifrs/publications/
jurisdictions/pdf-profiles/european-union-ifrs-profile.pdf). Our empirical analysis accounts for struc-
tural differences between institutions using IFRS 9 and those using nGAAP, as explained in Section 4.

ECB Working Paper Series No 28411012-month expected credit risk loss, defined as the product of the one-year ahead probability of

default and the expected lifetime loss given default. A loan is moved to stage 2 if it experiences

a significant increase in credit risk according to the bank’s criteria, and it is moved to stage 3

if in addition there are objective indications of credit impairment.9 In both cases, banks need

to provision an amount corresponding to the full lifetime expected credit risk loss, defined as

the product of the expected lifetime loss given default and the probability that the borrower

defaults at any point during the life of the loan. The switch in the provisioning horizon from 12

months under stage 1 to the loan’s full lifetime under stages 2 and 3 is supposed to account for

the deterioration in credit quality and the resulting higher likelihood of loss materialisation.10

Overall, IFRS 9 introduces three key modifications: (i) it induces higher ex ante provisioning

by resorting to expected credit losses; (ii) it makes provisioning more responsive to changes in

credit risk; and (iii) it enhances the role of internal models in estimating credit risk losses.

2.2 Discussions on possible procyclicality of IFRS 9

A first objective of our paper is to assess whether IFRS 9 has fundamentally altered provisioning

patterns around credit risk shock, which could have implications in terms of financial system

procyclicality. A certain degree of cyclicality is inherent in banking activity, inter alia because

the occurrence of losses usually depends on economic conditions and may induce banks to shrink

their balance sheets by putting pressure on bank capital (e.g., Bernanke and Lown 1991 and many

papers that followed). However, in recent years there has been a fierce debate on whether specific

elements of the accounting framework may act procyclically (for a comprehensive discussion, see

Basel Committee on Banking Supervision 2021). In fact, the introduction of ECL accounting was

supposed to address certain procyclical features of the IL approach, since it was widely believed

that earlier recognition of credit risk losses would have reduced uncertainty and dampened

cyclical fluctuations in the global financial crisis (e.g., Financial Stability Forum 2009). However,

9The precise sorting of loans into stages depends on the bank’s accounting practices. IFRS 9 requires that a
loan is sorted into stage 2 if the borrower is 30 days past due on payment obligations. Significant increases in
the estimated probability of default (PD), crossing of a specific PD threshold, or other credit events could also
indicate a significant increase in credit risk that would require a sorting into stage 2. A sorting into stage 3 is
required if credit losses are incurred, so that stage 3 provisions are often portrayed as being similar to provisions
under the IL approach. Stage 3 exposures usually correspond to defaulted exposures, since there is a strong
overlap between the prudential definition of default and the accounting definition of credit-impaired.

10The shorter provisioning horizon for stage 1 loans distinguishes IFRS 9 from CECL in the U.S., since the

latter requires banks to provision for expected lifetime credit risk losses from loan origination onward.

ECB Working Paper Series No 284111concerns that certain features of IFRS 9 might themselves be procyclical arose even before

the new standard was implemented. Specifically, some commentators feared that a sudden

and significant deterioration in economic conditions could trigger significant loss provisioning

in the early stages after a shock and before the defaults actually occur, which in turn could

exacerbate the downturn if banks constrained credit supply in reaction to the initial losses (see,

e.g., European Systemic Risk Board 2017, Abad and Suarez 2018), potentially amplifying the

coming wave of defaults. These concerns were aggravated by the possibility of so called “cliff

effects” that could occur if a large number of exposures are suddenly moved to stage 2 or stage 3

and subjected to full lifetime provisioning (recall Figure 2), resulting in a sudden increase in

overall provisioning. To be clear, the mechanism behind these concerns is similar to the one

that motivated the initial adoption of IFRS 9. The main difference is in the timing: under IL

approaches, concerns related to sizable provisions and losses trigger procyclical adjustments late

in a crisis, whereas under IFRS 9 such effects may occur soon after a shock.

The shock of the pandemic in early 2020 occurred while banks were still transitioning to

IFRS 9 and concerns about possible the procyclicality of the new approach were floating around

the regulatory and supervisory community. As a result, authorities around the globe adopted

several ad hoc support measures that aimed to prevent excessive procyclicality and facilitate

banks’ ability to support the economy throughout the economic downturn that followed the

initial pandemic shock. With respect to provisioning, support measures can be broadly grouped

into two categories (see Figure 3 for a brief chronology of the events): first, authorities encour-

aged banks to make use of the flexibility embedded in IFRS 9 and provided guidance to banks

on how to avoid excessive procyclicality in their provisioning models, e.g., in relation to the use

of forecasts or the role of public support measures. Second, authorities encouraged the use of

IFRS 9 transitional arrangements and later expanded the set of provisions that could be added

back to CET1 capital, to mitigate the impact of higher provisions on regulatory capital ratios.

Conceptually, the impact of these support measures on observed provisions may have gone in

different directions and is likely to have varied across measures, across banks, and over time.11

11 Guidance on avoiding excessive procyclicality is not a support measure per se, since it mainly points to
existing flexibility within IFRS 9. Nevertheless, such guidance may have reduced provisioning, depending on how
it was interpreted by individual banks. Importantly, the tone of the guidance started to change towards the end of
2020, as ECB Banking Supervision put increasingly more emphasis on sound credit risk management and ensuring
that loan exposures are allocated into the appropriate IFRS 9 stages (see Figure 3). Hence, it is likely that any
impact of the guidance on banks’ provisioning behaviour was strongest in 2020. In contrast, the possibility to
add back provisions to CET1 may have had a more neutral effect or may even have encouraged IFRS 9 banks to
provision more, since the impact of higher provisions on regulatory capital was neutralised.

ECB Working Paper Series No 284112Moreover, provisioning may have been affected also by other public support measures such as

loan moratoria or state guarantees, since the former may have reduced the need to classify loans

as under- or non-performing, whereas the latter reduced the amount of expected losses in the

case of a loan default. As we will explain in Section 4, our empirical analyses account for the

impact of public support measures and guidance during the pandemic in several ways.

2.3 Role of discretion in banks’ accounting practices

A second objective of our paper is to assess whether IFRS 9 has altered the role of discretion

in accounting standards. As pointed out in the literature reviews by Beatty and Liao (2014)

and the Basel Committee on Banking Supervision (2021), discretionary elements had a strong

influence on banks’ accounting practices even under the IL approach, where possible motives for

discretionary adjustments in provisions relate to earnings smoothing and capital management

(see, e.g., Laeven and Majnoni 2003, Bushman and Williams 2012, Bischof et al. 2021). Put

simply, accounting discretion could induce banks to provision “as much as they can afford” given

current headroom on top of capital requirements. While the use of discretion in this manner

could potentially help to mitigate procyclical effects during economic downturns (when earnings

are typically lower and capital headroom is tighter), it also undermines transparency and may

reduce trust in the accuracy of banks’ balance sheets.

IFRS 9 arguably affords banks with more discretion than IL accounting, since the calculation

of expected losses entails substantial judgment almost by definition. While the new approach

established broad operational principles, ultimate provisioning needs heavily depend on banks’

interpretation of the standards, their modelling approaches and the assumptions taken (Eu-

ropean Systemic Risk Board 2017). Thus, given an inherent reluctance to impair assets and

recognise losses (Laux and Leuz 2009, 2010, Huizinga and Laeven 2012), discretion offered by

the reliance on internal risk models may have tempted some banks to make modelling or data

choices that minimise provisioning needs or attenuate their responsiveness to macroeconomic

variables, where the latter was partly encouraged by authorities during the period of the pan-

demic (see previous subsection). Such behaviour would be similar to what has been documented

for bank capital regulation, where banks exploited discretion offered by the internal ratings-based

approach to reduce regulatory capital requirements (see, e.g., Begley et al. 2017, Plosser and

ECB Working Paper Series No 284113Santos 2018, Behn et al. 2022). While we are not aiming for an ultimate judgment on the desir-

ability of accounting discretion from a financial stability perspective, our empirical setup allows

exploring the role of capital management motives and comparing the relevance of discretionary

factors under IFRS 9 and IL approaches.

3 Data

3.1 Corporate loan data

We combine three different data sets to examine the evolution of euro area banks’ provisioning

practices in the period from 2018-Q3 to 2022-Q4. Our primary source of information is the

Eurosystem’s “Analytical Credit Database” (short “AnaCredit”), i.e., the euro area’s corporate

credit register. The data set includes granular information on euro area banks’ corporate loan

exposures, with data collection harmonised across the 20 member states. Specifically, banks have

to report all loans to euro area or non-euro area corporate borrowers for which their aggregate

exposure to the respective borrower is above EUR 25,000.12 Overall, AnaCredit covers more

than 30 million loans per quarter. We focus on loans to the non-financial private sector and

collect quarterly information on the following loan characteristics: the carrying amount, the

provisioning ratio, the maturity, the protection ratio (covering collateral and guarantees) and

whether the loan benefits from a COVID-related public credit guarantee and/or moratorium and

whether the accounting framework is IFRS 9 or nGAAP.13 We aggregate this information at the

bank-firm level, so that the unit of observation in our main data set is at the bank × firm ×

quarter level. Specifically, we sum up loan-level volume variables and compute weighted averages

for the maturity (using the loan-level sum of carrying amount and provisions as a weight). In

this aggregation, we consolidate banks at the level of the lender’s ultimate parent in the euro

area (making use of the ECB’s RIAD data set), which is necessary since a banking group can

separate its different credit relations with a single borrower across different subsidiaries. For

12For additional documentation on AnaCredit,

see https://www.ecb.europa.eu/stats/money_credit_

banking/anacredit/html/index.en.html as well as the descriptive paper by Israel et al. (2017).

13Specifically, our data set includes loans and revolving credit other than overdrafts, convenience credit,
extended credit, credit card credit, reverse repurchase agreements, and trade receivables and financial leases.
COVID-guaranteed loans have been identified by using registry information (e.g., LEI and RIAD codes) of the
promotional lenders charged with this task in each country since March 2020 (for example, ICO in Spain, KfW
in Germany, BPI in France, or SACE/Fondo di Garanzia in Italy).

ECB Working Paper Series No 284114simplicity, we refer to a bank-firm observation as a “loan” hereafter, although in practice each

bank-firm observation may comprise several individual loans, as just explained. Following the

supervisory definition, the provisioning ratio (or impairment ratio, as the terms “provision”

and “impairment” are used interchangeably throughout the paper) is defined as the ratio of

provisions over the sum of provisions and the carrying amount. From AnaCredit we also extract

information on borrowers’ economic sector. The latter is defined with the NACE (Nomenclature

of Economic Activities), the European statistical classification of economic activities. We use

the second level of granularity in NACE (hereafter NACE-2 level), which defines 86 sectors.

Panel A of Table 1 shows summary statistics for the 30,166,459 loans in our data set, while

Panel B shows the correlation matrix for the loan-level variables. All explanatory variables

are winsorised at the 1% and 99%.14 As expected, given the holistic nature of AnaCredit, the

loans differ massively in size and maturity. About 14 percent of them benefited from public

credit guarantees schemes and 2 percent from moratoria put in place during the COVID-19

pandemic. Moreover, about 6 percent of the loans are in default, while the provisioning ratio

stands at 4.3 percent on average but exhibits large heterogeneity (standard deviation of 15

percentage points). Panel C of Table 1 reports how our sample loans are split across accounting

classifications. Although our sample includes more banks using nGAAP (see Appendix A),

almost 85 percent of the loan observations are under IFRS 9, since larger banks that originate

the bulk of corporate loans in the euro area are primarily using the latter approach.15 Not

surprisingly, the bulk of observations is in the performing loan classes, i.e., stage 1 for IFRS 9

and the general allowance category under nGAAP (which corresponds to performing nGAAP

loans), which account for 79 percent and 87 percent of IFRS 9 and nGAAP loans, respectively.

3.2 Bank balance sheet and income statement information

We match the loan data with bank-level information from supervisory statistics (COREP/FIN-

REP reporting templates). The quarterly reporting covers accounting and prudential data,

providing us with information on banks’ balance sheets and income statements. Descriptive

statistics at the quarterly level for the 1,721 distinct banks in our sample are provided in Panel A

14The protection ratios are capped at 200% to avoid distorting the regressions with very high values, as many

loans have protection ratios well above 1000%.

15In very few cases (less than 1 percent of the data), a bank has both IFRS 9 and nGAAP loans with the same
firm in the same quarter. For these, aggregation is performed at the bank × firm × quarter × framework level.

ECB Working Paper Series No 284115of Table 2, while Panel B shows the correlation matrix. The table shows that banks typically

have capital headroom (CAP HEAD) on top of regulatory requirements, although there is sub-

stantial heterogeneity (standard deviation of around 6 pp). While correlation coefficients are not

excessively high, banks with more capital headroom tend to have lower risk weights and fund

themselves with fewer deposits. As we explain in Section 4, a potential identification issue for

our empirical analysis is that banks using IFRS 9 could be systematically different from those

using nGAAP.16 For this reason, we perform in a robustness exercise a Propensity Score Match-

ing (PSM) to arrive at a reduced sample of banks with comparable characteristics for banks

using the two approaches. As described in Appendix A, IFRS 9 banks are indeed different from

nGAAP banks in the full sample, while the PSM successfully mitigates significant differences.

3.3 Exposure to energy price shocks at the industry level

To explore the impact of the energy price shock in 2022 on banks’ provisioning behaviour, we

match the loan data with an energy intensity measure constructed by the European Central

Bank. The measure is defined at the economic sector (NACE-2 × country) level and computed

as the sum of (direct and indirect) inputs from the electricity, gas, steam and air conditioning

industries, expressed as a share of sectoral output. We exclude the energy production sectors,

as they are highly exposed to energy but benefit from higher energy prices. Data on industry

input and output are taken from the Trade in Value Added (TiVA) statistics of the Organisation

for Economic Cooperation and Development (OECD), using the OECD’s conversion table to

match TiVa and NACE-2 economic sectors.17 A higher value of the measure implies that energy

plays a larger role in the respective sector’s inputs, hence a stronger exposure of the sector to

an energy supply shock (see Gunnella et al. 2022 for further details). Descriptive statistics for

the measure are provided in Table 3, which shows that industrial and construction sectors are

particularly dependent on energy inputs. Importantly, there is ample intra-group heterogeneity

within the three sectoral categories displayed in Table 3, providing us with sufficient variation

that allows to identify the impact of the energy shock on provisions.

16We classify banks according to the most frequent accounting framework in their corporate credit portfolio.
17See https://www.oecd.org/industry/ind/TiVA-2021-industries.pdf.

ECB Working Paper Series No 2841164 Estimation strategy

We perform three types of tests to assess how accounting standards and bank characteristics have

affected provisioning behaviour during our sample period. First, we analyse determinants of pro-

visioning ratios in the entire sample of quarterly loan observations to obtain insights on general

drivers of provisioning behaviour. Second, we study provisioning dynamics around idiosyncratic

credit events, to assess potential cyclical implications of accounting patterns. Third, we look

at provisioning dynamics around macroeconomic shocks, to analyse cyclical patterns around

correlated credit events that are particularly relevant from a financial stability perspective.

In a first step, we use the entire quarterly loan data and investigate general determinants of

provisioning behaviour by estimating the following equation:

P rovb,f,t = αf,t + βXb,f,t−1 + γZb,t−1 + ϵb,f,t,

(1)

where b denotes the bank, f the firm, and t the quarter. The dependent variable is the loan-level

provisioning ratio in a given quarter. Explanatory variables include a set of loan-level variables,

Xb,f,t−1, comprising a dummy variable indicating the accounting standard (IFRS 9 vs. nGAAP),

the loan volume (in log), the residual maturity, the protection ratio, and variables indicating

whether the loan benefits from a public credit guarantee scheme or a credit moratorium estab-

lished during the COVID-19 pandemic; and a set of bank-level variables, Zb,t−1, capturing the

bank’s capitalisation (capital headroom), size (logarithm of total assets), profitability (return

on assets), asset risk (risk weight density), liability structure (deposit over total assets), liq-

uidity (cash over total assets), business model (total credit over total assets), and reliance on

central bank funding (central bank liquidity over total assets). The regression includes firm ×

quarter interactions, αf,t, which control for any observed and unobserved heterogeneity across

firm-quarters and ensure that the regression coefficients are identified from variation in provi-

sioning by different banks for loans to the same firm in the same period (see Khwaja and Mian

2008). Thus, our regressions control systematically for any differences in borrower risk that may

affect provisioning ratios. Finally, to account for potential correlation, standard errors in all our

regressions are double clustered at the firm × quarter and bank level, unless indicated otherwise.

As discussed in Sections 2 and 3, structural differences between banks using IFRS 9 and

ECB Working Paper Series No 284117those under national approaches as well as support measures implemented during the pandemic

complicate econometric identification of the effects we are interested in. We address these issues

in several ways. First, we use Propensity Score Matching (PSM) to construct a sample of IFRS 9

and nGAAP banks with comparable characteristics and repeat our regressions on this reduced

sample, thus controlling for bank heterogeneity and improving identification (see Appendix A

for details on the PSM). Alternatively, we restrict the sample to banks using both types of

approaches for at least 20 percent of their loan exposures and re-estimate our main regressions

on this subsample, thus ensuring that we are looking at similar types of banks. Second, we

exclude the imminent period of the pandemic in 2020, where the impact of supervisory guidance

on provisioning practices was strongest, and check whether our observed patterns persist also

outside this period. Moreover, we also analyse dynamics around the energy price shock induced

by the outbreak of war in Ukraine, which occurred at a time when the pandemic support

measures had already been phased out (see below). Third, we directly control for the impact

of COVID-related guarantees and moratoria by including corresponding control variables (see

above). Fourth, also the inclusion of firm × quarter interactions helps to control for additional

direct or indirect support measures that were implemented at the country- or firm-level. Finally,

while some of the support measures may have affected observed differences between IFRS 9 and

nGAAP loans (with potential biases going in different directions; see footnote 11), we note that

the differentiation between well- and less capitalised banks should be unaffected, since both

types of banks benefitted from the support measures in an equal manner.18

By estimating Equation 1, we can obtain important insights on how certain variables of inter-

est, such as the applicable accounting standard or the bank’s capitalisation, affect provisioning

behaviour during our sample period. However, as we want to derive insights on the cyclicality

of provisioning patterns under different standards or by different types of banks, we also need

to look at provisioning dynamics. Hence, in a second step, we restrict the sample to firms that

defaulted during our sample period, and estimate equations of the following type:19

P rovb,f,t = αf,t +

2
(cid:88)

h=−3

δhIhWb,f + ζWb,f + βXb,f,t−1 + γZb,t−1 + ϵb,f,t

(2)

18We also conduct sample splits and differentiate between well- and less-capitalised banks among IFRS 9 or
nGAAP loans only, to exclude that correlation between the accounting and capital variables is driving our results.
19More specifically, the estimation sample for this test comprises all loan-quarter observations of firms that
default at least once with at least one bank, provided the firm has both IFRS 9 and nGAAP loans in the quarter.

ECB Working Paper Series No 284118Most of the variables in this equation are defined as above. Additionally, h ∈ [−3, 2] is an index

variable that aligns observations around the default event of the respective loan. Thus, the

variables Ih are a set of dummies that take the value one if the respective default occurred h
quarters ago and zero otherwise.20 Depending on the specification, Wb,f is a dummy variable

that indicates the accounting framework (IFRS 9 vs. nGAAP), or the bank’s capital headroom.

Thus, by estimating Equation 2, we can assess whether provisioning dynamics around default

events differ between banks using IFRS 9 and those using nGAAP, or between well- and less-

capitalised banks. In an alternative specification, we align observations around the date where

an IFRS 9 loan is moved to stage 2 (rather than the default date), and re-estimate Equation 2

for this different type of credit event. In addition, we also examine the drivers of banks’ decisions

to place a loan in stage 2 in the run-up to default by estimating the following equation:

D(Loan in S2)b,f,t = αf,t +

−1
(cid:88)

h=−3

δhIhWb,f + ζWb,f + βXb,f,t−1 + γZb,t−1 + ϵb,f,t

(3)

The left-hand-side variable in this equation is a dummy taking the value one if the loan is in

stage 2 in quarter t and zero otherwise. The right-hand-side is the same as above, except for the

event window that ends in the quarter before default (since defaulting loans move to stage 3).

By looking at idiosyncratic credit events we can analyse whether provisioning dynamics

are generally different between loans using different accounting standards or loans issued by

different types of banks, which allows to derive insights on cyclical implications. However, from

a financial stability perspective it is particularly relevant to look at correlated credit events as

they are often induced by macroeconomic shocks, since such events can have broader systemic

implications if many banks simultaneously adjust their behaviour and constrain credit supply.

Therefore, in a third step, we look at provisioning dynamics around the macroeconomic shocks

induced by the COVID-19 pandemic and the outbreak of war in Ukraine.

The COVID-19 pandemic triggered a massive negative economic shock, due to heightened

uncertainty, health concerns and the economic disruptions due to the containment measures

adopted to tackle the pandemic. Many firms were forced to close altogether or to work under

strict conditions, massively reducing their income while they still had to pay their employees.

To examine how the resulting increase in corporate credit risk affected provisioning dynamics

20More specifically, a negative (positive) h indicates that the default is in the past (future).

ECB Working Paper Series No 284119during the pandemic, we estimate the following two local projections equations:

∆P rovb,f,h = αf,h + ζhWb,f + βhXb,f + γhZb + ϵb,f,h,

D(M ove S2)b,f,h = αf,h + ζhWb,f + βhXb,f + γhZb + ϵb,f,h,

(4)

(5)

with h the number of quarters since 2019-Q4. The first equation measures the change in the

provisioning ratio at the bank × firm level, while the second is a logit model with the dependent

variable taking the value one if the loan was placed in stage 2 in the respective quarter and zero

otherwise (this regression only includes loans that were in stage 1 in 2019-Q4).

As explained above, we already control for the support measures implemented during the

pandemic in several ways. However, to address remaining concerns about their possible impact,

we analyse provisioning dynamics around a second macroeconomic shock that occurred during

our sample period, namely the energy price shock induced by the outbreak of war in Ukraine.

While there was a clear upward trend in energy prices already in the second half of 2021, prices

spiked up in the first and second quarter of 2022, particularly after the start of the war. The

strong increase in gas and energy prices had a particularly pronounced effect on the European

economy, given its strong import dependence.21 Assuming that firms that are more reliant on

input from energy sectors were hit harder by the shock, we estimate the following equation to

assess the timeliness and risk sensitivity of the resulting provisioning adjustments:

∆P rovb,f = αf + θWb,f × Ef + ζWb,f + βXb,f + γZb + ϵb,f ,

(6)

with ∆P rovb,f the change in provisioning ratio at bank × firm level between 2022-Q1 and

2022-Q2 (or, alternatively, Q3 or Q4), Ef the sectoral energy dependence measure described in

Section 3.3, and all other variables defined as above. As before, Wb,f is a dummy that either

indicates the accounting framework or the magnitude of the bank’s capital headroom. Hence,

the regression allows assessing whether different types of banks adjusted provisions in response

to the energy shock in a generally stronger (coefficient ζ) and/or in a a more risk-sensitive

(coefficient θ) manner. Importantly, this test is unaffected by support measures adopted during

the pandemic, since the latter had already been phased out at the time when the shock occurred.

21For further discussion, see chapter 2.3 of the ECB’s Financial Stability Review (November 2022): https:

//www.ecb.europa.eu/pub/financial-stability/fsr/html/ecb.fsr202211~6383d08c21.en.html#toc13.

ECB Working Paper Series No 2841205 Empirical results

5.1 General determinants of provisioning ratios

We start the analysis by looking at general provisioning patterns during our sample period.

Figure 4 plots the evolution of aggregate provisioning ratios since 2018-Q3. In the upper panel,

we distinguish between loans under IFRS 9 and those using national accounting approaches.

Not surprisingly, and in line with the reform’s objectives, provisioning under IFRS 9 is generally

higher than under nGAAP. Specifically, towards the end of our sample period in 2022, aggregate

provisioning stood at around 2.0 percent for loans under IFRS 9, compared with around 1.5

percent for loans under nGAAP. The striking decline in provisioning ratios at the beginning of

our sample period reflects continuous improvement in euro area banks’ asset quality in the years

leading up to the pandemic, driven by a reduction in non-performing loans (NPL) in particular.22

NPL ratios continued to improve throughout the pandemic, but at the same time banks saw

an increase in stage 2 provisions (see Figure 5), so that aggregate provisioning ratios stabilised

around the levels mentioned above.23 In the lower panel of Figure 4, we distinguish between

banks with below and above median capital headroom (i.e., distance between capital ratio and

overall capital requirement). Less capitalised banks started off with higher provisioning ratios

because they were, on average, more affected by legacy issues relating to high NPL ratios after

the Great Financial Crisis and the European sovereign debt crisis.24 Strikingly, less capitalised

banks exhibit continuously declining provisioning ratios until 2020 and relatively stable ratios

thereafter, whereas better capitalised banks markedly increased aggregate provisions at the onset

of the pandemic in early 2020. Albeit only illustrative, these patterns are overall consistent with

capital management motives by which banks that can afford it increase provisions in a timely

manner following a shock, whereas more capital constrained banks may prefer to delay loss

recognition in order to avoid getting too close to the regulatory capital requirement.

To control for bank, firm and loan characteristics that may exert an impact on observed

22For further details, see chapter 3.1 of the ECB’s Financial Stability Review (November 2019): https://www.

ecb.europa.eu/pub/financial-stability/fsr/html/ecb.fsr201911~facad0251f.en.html#toc20.

23The offsetting effect between higher stage 2 and lower (non-performing) stage 3 loans applies to loans under
IFRS 9, since stage 2 provisioning exists only under this approach. As visible in Figure 5, aggregate provisioning
ratios remained relatively stable also under the (incurred loss) nGAAP approaches, since a sizable increase in
default rates was not observed during the pandemic.

24In 2018-Q3, banks with below median capital headroom had an average NPL ratio of 5.2 percent, compared

with 3.0 percent for banks with above median capital headroom.

ECB Working Paper Series No 284121provisioning patterns, we complement the charts on aggregate developments with a regression

analysis. Specifically, we study the determinants of provisioning ratios during our sample period

by estimating Equation 1 and present the results in Table 4. In line with aggregate patterns,

IFRS 9 loans generally exhibit higher provisioning ratios than loans subject to national account-

ing standards, also when controlling for a vast range of bank and loan characteristics as well

as borrower heterogeneity via the inclusion of firm × quarter fixed effects. The estimates in

column 1 indicate that provisioning ratios for IFRS 9 loans are, on average, about 0.50 per-

centage points (pp) higher than provisioning ratios for nGAAP loans to the same firm in the

same quarter. This is in the same ballpark as the magnitudes observed in Figure 4 (upper

panel), indicating that differences in provisioning practices (rather than differences in borrower

composition) are a strong driver of the observed provisioning gap between IFRS 9 and nGAAP

loans at the aggregate level. Moreover, the effect is economically meaningful, considering the

average provisioning ratio of 4.31 percent during our sample period. We also find that the

coefficient estimate for the bank’s capital headroom is positive and highly statistically signifi-

cant, meaning that banks with more headroom generally provision more, consistent with capital

management motives. Again, the effect is economically meaningful: an increase of one standard

deviation in capital headroom (5.76 pp) implies a corresponding increase in provisioning ratios

of around 0.48 pp. Other bank characteristics have a relatively muted impact on provisioning

ratios, whereas loan control variables affect ratios in the expected manner.

In columns 2 and 3 of Table 4, we replicate regressions on the subsamples of IFRS 9 and

nGAAP loans, respectively, with broadly similar results. Specifically, coefficients for capital

headroom are statistically significant and of similar magnitude in both subsamples, indicating

that general capital management motives may be prevalent under both accounting approaches.

Moreover, results persist in a number of robustness checks, housed in Table 5. First, we use a

Propensity Score Matching to build comparable samples of banks using IFRS 9 and nGAAP.

Specifically, we first classify banks as “IFRS 9” or “nGAAP” according to their dominant ac-

counting framework. The matching procedure then uses the “nearest neighbor” approach, a

caliper of 0.2, and matching on the bank-level explanatory variables of Equation 1 (see Ap-

pendix A for details). Results are reported in columns 1 to 3. Alternatively, to ensure that

we are looking at comparable banks, we restrict the sample to banks using both IFRS 9 and

nGAAP for at least 20% of their loans each, and re-estimate the regressions on this subsample

ECB Working Paper Series No 284122of banks (see columns 4 to 6 for the results). As a third alternative, in column 7, we saturate the

specification by including bank × quarter fixed effects. In this way, we control systematically for

any observed and unobserved time-varying bank heterogeneity that may affect provisioning, but

are no longer able to identify the impact of bank-level variables such as capital headroom (since

the same bank can have both IFRS 9 and nGAAP loans, we can still identify the coefficient on

the dummy indicating the loan’s accounting framework). Finally, we use an alternative data

set aggregating loans at the more granular bank-firm-quarter-type of loan level, to control for

the possibility that a firm contracts different types of loans from different banks, which could in

turn drive differences in provisioning.25 Results are reported in columns 8 to 10. As shown in

Table 5, our results persist in all these robustness checks.

For IFRS 9, we can further split the sample into loans in different stages. This additional

sample split is reported in Table 6 and reveals that differences in provisioning for banks with

different levels of capital headroom are particularly pronounced for under- and non-performing

loans in stages 2 and 3, as the coefficient estimate on capital headroom increases in magnitude

and statistical significance as the loan quality deteriorates. One explanation for this could be

that provisioning ratios are generally higher in the upper stages, so that there may also be

room for larger adjustments in both directions. In line with this argument, also the mitigating

effect of COVID-19 related guarantees is particularly pronounced in stage 3. Interestingly, some

bank characteristics have a differential impact on provisioning ratios in different stages:

for

example, larger banks tend to provision less in stage 1 but more in stage 3. This illustrates

that IFRS 9 affords banks with a lot of discretion, so that different types of banks may adopt

different accounting strategies, according to their respective preferences. That is, some banks

(in this case, smaller banks) may prefer to provision in a rather pre-cautious manner at an early

stage, resulting in higher stage 1 provision for the whole stock of loans, while other banks (in

this case, larger banks) may prefer to provision in a more targeted manner on loans identified

as credit impaired, possibly in line with higher market scrutiny on larger banks for such types

of exposures. Overall, the results indicate that accounting strategies differ with respect to the

timing and the magnitude of provisioning, leading to vast heterogeneity in accounting practices.

In the next sections, we examine what this implies in terms of provisioning dynamics.

25Specifically, for each bank-firm relationship, these tests additionally distinguish between overdrafts, credit
card debt, revolving credit other than overdrafts and credit card debt, credit lines other than revolving credit,
trade receivables, financial leases, and other loans, with the latter being by far the dominant category.

ECB Working Paper Series No 2841235.2 Provisioning dynamics around idiosyncratic credit risk shocks

5.2.1 IFRS 9 vs. nGAAP

To analyse the cyclical implications of accounting standards, it is particularly relevant to look

at provisioning dynamics, since large adjustments in provisioning following adverse events may

trigger the type of procyclical adjustment behaviour by banks that regulators and policy makers

usually seek to avoid (see discussion in Section 2.2). Therefore, we proceed by analysing the

evolution of provisioning ratios around credit events. Focusing on firms that defaulted during

our sample period, the left panel of Figure 6 plots weighted average provisioning ratios around

the respective default, separating loans according to their accounting approach. In line with

expectations and consistent with previous results, IFRS 9 loans exhibit higher provisioning

ratios ahead of default. However, provisioning dynamics are rather similar for IFRS 9 loans and

those using nGAAP. Specifically, a significant jump in provisioning at the time of default occurs

under both approaches, while increases in provisioning ahead of default are small and gradual.

This is markedly different from the conceptual illustration for IFRS 9 loans that is shown in

Figure 2, which exhibits substantial frontloading of provisions ahead of default.

To investigate the drivers of this striking difference between conceptual and observed pat-

terns, Figure 7 plots the share of IFRS 9 loans in different stages before default. While there

is a clear increase in the share of stage 2 loans in the run-up to a default, it is remarkable that

around 50 percent of the loans are still in stage 1 two quarters ahead of the default, and around

35 percent of the loans are still in stage 1 one quarter ahead of the default. Thus, banks appear

to be either unwilling or unable to identify significant increases in credit risk at a sufficiently

early stage, so that many loans remain in stage 1 and continue to exhibit very low provisioning

ratios shortly ahead of default. This is illustrated in the right panel of Figure 6, which continues

to show aggregate provisioning dynamics around default but additionally separates IFRS 9 loans

into those consistently in stage 1 in the four quarters before the default, those consistently in

stage 2, and those that switched from stage 1 to stage 2 in one of the four quarters ahead of

default. Indeed, provisioning ratios for stage 1 loans are markedly below those of stage 2 loans

ahead of default. Switcher loans are somewhere in between the two groups and gradually catch

up with the loans consistently in stage 2, with the most pronounced effect occurring only shortly

before default, however. Overall, the patterns suggest that IFRS 9 induces early provisioning

ECB Working Paper Series No 284124as intended, but only for the roughly 50 percent of loans that are moved to stage 2 sufficiently

early. Still, also these loans exhibit a sizable jump in provisioning at default, albeit a smaller

one than the other loans.26 This is mainly because the move to stage 2 induces a rather modest

increase in provisioning. First, the average provisioning ratio for a loan increases from 1.0% in

stage 1 to 3.8% in stage 2 in the quarter after the move, mitigating concerns about large “cliff

effects” at the time of the transition. Second, the average provisioning ratio in stage 2 is only

4.4%, rising to 6.0% for loans that eventually default and 8.5% just before default. As such,

this increase is far from enough to avoid a sizeable jump in provisions when the loan defaults,

so that the cliff effect at the time of default persists even for loans in stage 2.

We further substantiate the descriptive analysis by estimating Equation 2, thereby assessing

dynamics around default while controlling for bank, firm and loan heterogeneity. Regression

coefficients are plotted in Figure 8 and yield similar patterns as the descriptive charts. Specif-

ically, IFRS 9 loans exhibit significantly higher provisioning ratios than loans under national

standards to the same firm in the same period ahead of a default event, but the jump at the

time of default remains substantial under both approaches, suggesting that IFRS 9 has not

fundamentally altered provisioning patterns. Differences between the two approaches become

statistically insignificant at default and, somewhat surprisingly, even reverse sign after the de-

fault event. One possible explanation for the latter could be the support measures implemented

during the pandemic: as explained in Section 2.2, some of these measures may indeed have

induced IFRS 9 banks to avoid large increases in provisions, at least in the early stages of the

pandemic. However, we find very similar results when re-estimating Equation 2 while excluding

the imminent phase of the pandemic (2020-Q2 to 2020-Q4), suggesting that the supervisory

guidance at that time is unlikely to be a main driver of the observed inversion (Figure 9). In-

stead, we think that the pattern is likely due to an inherent reluctance of banks to recognise

losses, and greater flexibility to avoid such loss recognition for impaired assets under the IFRS 9

approach.27 We further investigate the validity of this assertion in the next subsection.

26In fact, the absolute magnitude of the jump in provisioning at default is not much different between loans
in stage 1 and those stage 2, since the former continue to exhibit markedly lower provisioning ratios also after
default. In the next subsection, we further examine the drivers of these patterns.

27Indeed, we find that the provisioning gap between IFRS 9 and nGAAP loans gradually closes when further

extending the sample after the default event (see Figure 10).

ECB Working Paper Series No 2841255.2.2 Capital headroom

To gauge the impact of accounting flexibility, we examine whether provisioning patterns around

default events depend on banks’ capital headroom, since less capitalised banks may have higher

incentives to reduce or delay provisions for impaired assets.28 As explained in Section 2.1, IFRS 9

offers discretion on both the classification of loans into stages and on the level of provisioning

for loans in a given stage. Therefore, in principle, less capitalised banks have two levers to limit

provisioning increases around credit events under IFRS 9: (i) they can delay the time at which

loans are moved to stage 2 and thus provisioned for their entire life rather than 12-months;29

and (ii) they can limit or delay the increase in provisions for loans in a given stage (either stage 2

or stage 3). In this section, we examine the practical relevance of these two levers.

The left panel of Figure 11 plots weighted average provisioning ratios for IFRS 9 loans around

default events, distinguishing between banks with above and below median capital headroom.

The chart shows that banks with more capital headroom book higher provisions than less cap-

italised banks, particularly after the default event. This is in line with a “provision as much

as you can afford” strategy and consistent with the findings in Section 5.1, which showed that

differences in capital headroom explain divergences in provisioning particularly in the higher

stages of IFRS 9. The right panel of Figure 11 continues to distinguish banks by capital head-

room but additionally separates loans according to the stages in which they are placed ahead

of default (similarly to the right panel of Figure 6). Several aspects are worth noting: first,

provisioning ratios for loans from less capitalised banks are always below those of loans from

better capitalised banks. Second, ahead of default, this difference is particularly pronounced for

loans consistently in stage 2, highlighting the impact of discretion in provisioning once substan-

tial credit risk is identified. What is more, we also find that better capitalised banks showcase

a higher proportion of loans that are placed in stage 2 in the quarter ahead of default (65.2%

versus 63.4% for less capitalised banks), indicating that banks are using both levers to reduce

provisioning needs. Finally, after the default, the difference in provisioning ratios between well-

28Of course, banks cannot permanently avoid loss recognition on impaired loans since the assets will eventually
have to be written off. The timing of the write-off varies across loans and depends on loan-, borrower- and
country-specific factors. Unfortunately, write-off dates and write-off rates are not consistently reported in the
credit register, which prevents us from analysing this issue in a systematic manner.

29In principle, they can also delay the time at which a loan is placed in stage 3. However, as explained in
Section 2.1, the criteria for placing a loan in stage 3 largely overlap with the criteria for default and are more
objective then the criteria for moving a loan to stage 2, thus offering less room for discretionary adjustments.

ECB Working Paper Series No 284126and less-capitalised banks is particularly pronounced for loans that are consistently in stage 1

ahead of the default. The muted reaction of less capitalised banks for this set of loans also

explains why their aggregate provisioning ratio does not catch up with that of stage 2 loans

after the default (recall Figure 6, right panel). Overall, the patterns suggest that less capi-

talised banks reduce provisioning needs under IFRS 9 by (i) moving less loans to stage 2, and

(ii) delaying the eventual recognition of losses by reducing provisioning ratios.

As in the previous subsection, we complement the descriptive statistics with a regression

analysis that controls for bank, firm and loan heterogeneity. Figure 12 presents regression coef-

ficients for a variant of Equation 2 in which the dummies capturing the distance to default are

interacted with the capital headroom. Banks with more capital headroom provision significantly

more than less capitalised banks, and this gap increases in the run-up to the default and even

more after the default. This corroborates the “provision as much as you can afford” strategy

and again suggests that less capitalised banks may under-provision to avoid further weakening of

their capital position. To assess whether IFRS 9 has altered the role of discretion in accounting

standards, we re-estimate Equation 2 while differentiating between IFRS 9 and nGAAP loans.

Indeed, Figure 13 shows a strong impact of bank capitalisation on provisioning for IFRS 9 loans

(left panel), for which an increase in the capital headroom by one standard deviation increases

banks’ provisioning ratios by about 2.8 pp in the quarter of default. In contrast, divergences are

smaller and mostly statistically insignificant for loans under national standards ahead of default

(right panel). Only post-default, more capitalised banks provision more, reflecting some discre-

tion already existing in the nGAAP standards. Overall, the findings corroborate the assertion

that discretionary factors relating to capital management motives affect provisioning behaviour.

The stronger effects for IFRS 9 loans could be related to more discretion and heterogeneity in

banks’ modelling approaches under the new accounting framework.

Next, we analyse the increase in provisioning when moving a loan to stage 2, and also

formally assess whether less capitalised banks move fewer loans to stage 2. First, we estimate a

logit regression with a dummy indicating whether a specific loan exposure is moved to stage 2

on the left-hand side, and a number of bank and loan characteristics as explanatory variables.30

30More specifically, for all firms for which at least one bank moves its respective loan to stage 2 in at least one

quarter, we estimate the following equation:

with Stage2b,f taking the value 1 if bank b moves its loan to firm f to stage 2 at any point in time. The other

Stage2b,f = αf + βXb,f + γZb + ϵb,f ,

ECB Working Paper Series No 284127Results are presented in Table 7 and show that banks with less capital headroom are generally

less inclined to move a specific loan to stage 2. Second, we estimate Equation 3 and thereby

assess whether a loan is more likely to be placed in stage 2 ahead of a default if it is provided by

a bank with more capital headroom. The results in Figure 14 confirm the descriptive statistics

reported above: in line with a “provision as much as you can afford” strategy, more capital is

associated with a higher probability of moving a loan to stage 2 ahead of default, where the

magnitude of this effect increases as default approaches. Third, we look at provisioning dynamics

around the time when a loan is moved to stage 2. Specifically, we re-estimate Equation 2, this

time aligning loans around the move to stage 2 rather than the default event.31 Figure 15 shows

that provisioning ratios for loans from better capitalised banks increase more after a move to

stage 2, compared with loans from less capitalised banks to the same firm in the same period.

Again, the results also hold when excluding the pandemic period, making it unlikely that the

findings are driven by supervisory guidance on provisioning at that time (Figure 16).

In sum, the findings reported in this section indicate that there is considerable heterogeneity

in accounting practices under IFRS 9, both with respect to the timing of transitioning across

stages and with respect to the amount of provisions attached to loans in different stages. Banks

with less capital headroom appear to be using two levers to manage aggregate provisioning

levels, i.e., (i) they are less likely to move a loan to a higher stage, and (ii) conditional on the

stage they attach lower provisions to a loan when compared with better capitalised banks.

5.3 Provisioning dynamics around macroeconomic shocks

5.3.1 The COVID-19 pandemic

Our analysis thus far has examined provisioning dynamics around credit risk shocks at the

individual loan level. Although the analysis has provided several important insights, supervisors

and policymakers are particularly interested in banks’ reactions to correlated credit risk events

(e.g., adverse economic shocks that weaken borrowers’ repayment capacity across the board),

variables are defined as above. We restrict the sample to loans from banks that were lending to firm f before (at
least one quarter), at and after (at least one quarter) the time when the first bank moved its exposure to firm
f to stage 2, to avoid including loans that terminated before or started after the period of high credit risk. The
explanatory variables are fixed at their value at the time when this first move occurred. That is, there is no time
dimension in this regression, which includes one observation for each bank × firm pair.

31Similarly to our approach for default events, we consider all firms whose loan is moved to stage 2 by at least

one bank for at least one quarter.

ECB Working Paper Series No 284128given potential systemic repercussions that can occur if many banks suffer large losses and adjust

their behaviour simultaneously. For this reason, we now examine provisioning dynamics in the

aftermath of two recent macroeconomic shocks, starting with the COVID-19 pandemic.

The COVID-19 pandemic hit Europe in early 2020 and soon triggered discussions about

possible procyclicality of provisioning under IFRS 9 (see Section 2.2). Results in the previous

section have illustrated that provisioning dynamics for IFRS 9 loans are not much different

from those of previously existing nGAAP loans. On the one hand, this suggests that concerns

about possible procyclical effects late in a crisis (i.e., when a lot of loans default) have not been

solved by the new approach, since the bulk of provisioning would occur at that time under both

approaches. On the other hand, it suggests that concerns about procyclical effects of IFRS 9

early on in a crisis may have been exaggerated, since significant adjustments in provisioning

ahead of default are not detectable. However, concerns could remain if a lot of loans are moved

to stage 2 at an early stage of a crisis for precautionary reasons, without necessarily defaulting

later on in the crisis. Indeed, while aggregate provisioning ratios for IFRS 9 loans remained

rather stable throughout the pandemic (recall Figure 4), also due to a continuous decline in

stage 3 loans, Figure 5 illustrates that the share of stage 2 loans increased from 9.2% in 2020-Q1

to 13.7% in 2021-Q2. Therefore, in this section, we study the evolution of provisioning during

the pandemic in more detail, also aiming to quantify the magnitude of effects that could have

occurred in the absence of confounding developments and support measures implemented.

For a start, we assess the implications of the 4.5 p.p. increase in the share of stage 2 loans for

bank capital. The average provisioning ratio for a stage 1 loan in the quarter before it is moved

to stage 2 is 1.0%, whereas the average ratio in the subsequent quarter is 3.8%. Thus, a move to

stage 2 implies an immediate increase in provisioning of around 2.8%. Assuming that corporate

loans account for 40% of a bank’s total assets, moving 4.5% of its corporate loans to stage 2

implies a decrease of 0.05 p.p. in the equity over assets ratio, all other things equal. With an

average risk weight density of 35%, this translates into a decline of the risk-weighted capital

ratio by 0.14 p.p.32 Thanks to the supervisory guidance during the pandemic, this increase

was spread out over six quarters (and additionally muted by the confounding decline in stage 3

loans, as mentioned). However, while a decline of 0.14 p.p. is non-negligible, even an immediate

32The decrease in the equity over assets ratio is calculated as 2.8 × 0.045 × 0.4 = 0.05 p.p. This is then divided

by 0.35 to translate it into the decline in risk-weighted capital ratios of 0.14 p.p.

ECB Working Paper Series No 284129decrease by such an amount should have been manageable for banks, not least because prudential

authorities released capital requirements amounting to 0.7 p.p. [2.1 p.p. when including Pillar

2 Guidance] with immediate effect, thus easing pressure on capital ratios (see Couaillier et al.

2022b). One could still argue that the increase in stage 2 loans was smaller than it could

have been, since the economy recovered quickly and the evolution of corporate credit risk was

more favourable than initially expected. However, the conclusions are not altered much when

considering a doubling [tripling] of the observed increase in the share of stage 2 loans, which

would have implied declines of 0.28 p.p. [0.42 p.p.] in capital ratios, respectively.

As an alternative to looking at absolute effects, we can also analyse the relative evolution

of provisioning ratios for IFRS 9 and nGAAP loans during the pandemic. Estimating the

local projection model specified in Equation 4, Figure 17 shows the impact of the accounting

approach on the change in provisioning since 2019-Q4. The impact was significantly positive

since 2020-Q2, indicating that provisioning for similar types of loans to the same borrower

increased relatively more for IFRS 9 exposures. The cumulative difference reached 0.4 p.p. in

2021-Q2, which compares with an average provisioning ratio of 4.3% in our sample. Applying

a similar back-of-the-envelope calculation and using the same assumptions as above, a 0.4 p.p.

increase in provisioning for the entire corporate loan portfolio translates into a decline of 0.46

p.p. in risk-weighted capital ratios, which again appears as non-negligible but manageable.

Finally, we also analyse the impact of capital headroom on provisioning during the pandemic.

Figure 18 shows that higher capital headroom was associated with larger increases in provisions

in all quarters but the first, where the cumulative impact increased over time.

In economic

terms, a one standard deviation (5.76 p.p.) increase in capital headroom resulted in an increase

of 0.3 p.p.

in provisions. Figure 19 provides similar estimates for IFRS 9 and nGAAP loans

separately. As expected, given their large dominance, results for IFRS 9 are close to those

for the whole sample. In contrast, the impact of capital headroom for nGAAP loans was less

consistent and more muted, with a one standard deviation increase in capital headroom raising

provisioning ratios by only 0.1 p.p.

In a last step, Figure 20 focuses on IFRS 9 loans and

shows the impact of capital headroom on banks’ decision to move a loan to stage 2 during the

pandemic (Equation 5). The impact was positive for the entire period and turned significant in

2020-Q4. In total, a one standard deviation increase in capital headroom increased the odds-

ECB Working Paper Series No 284130ratio of moving a loan to stage 2 between 2019-Q4 and 2021-Q2 by 27.5%33; this means that, for

a loan with an initial 50% probability of being moved to stage 2, this probability increased to

56%. Overall, the results fully confirm those in the previous subsection, namely that (i) higher

capital is associated with more provisioning, (ii) this effect is more pronounced for IFRS 9 loans,

and (iii) better capitalised IFRS 9 banks are also more likely to move a loan to stage 2.

5.3.2 The energy price shock in 2022

In a last step, we focus on a second macroeconomic shock to further improve our identification

and assess the robustness of our results. As explained in Section 4, the energy price shock in 2022

strongly affected the European economy largely dependent on imports for energy supply and

represented a considerable increase in credit risk for energy intensive companies in particular.

Moreover, when the shock occurred, the supervisory focus in the European Banking Union had

already shifted towards ensuring appropriate credit risk management and staging of loans, so

that provisioning patterns should be less affected by the supervisory guidance to avoid procyclical

assumptions in provisioning models that was issued during the pandemic.34 For these reasons,

we consider this second macroeconomic shock in addition to the one induced by the pandemic.

Estimation results for Equation 6 are presented in Table 8. To test for divergences in

the dynamics of provisioning after the outbreak of war in Ukraine, we regress the change in

provisioning at the bank × firm level between 2022-Q1 and Q2 (columns 1 and 2), Q1 and

Q3 (columns 3 and 4) and Q1 and Q4 (columns 5 and 6) on our usual variables of interest.

Columns 1, 3 and 5 show that provisioning dynamics over the course of 2022 are similar for

the average IFRS 9 and nGAAP loan, as the IFRS 9 dummy has no significant impact on the

change in provisions. However, provisions for IFRS 9 loans are increased in a more risk sensitive

manner after the credit risk shock, i.e., relatively more strongly for loans to firms in more energy

intensive sectors. This is indicated by the positive and significant coefficients for the interaction

terms between the IFRS 9 dummy and the energy dependence index described in Section 3.3

(see columns 2, 4 and 6). Thus, banks’ IFRS 9 models seem to be capturing the heterogeneous

33exp(0.0422 × 5.76) − 1
34See the discussion in Section 2.2 and recent speeches by Andrea Enria, the Chair of the ECB’s Su-
pervisory Board, here: https://www.bankingsupervision.europa.eu/press/speeches/date/2021/html/ssm.
sp210128~78f262dd04.en.html, and here:
https://www.bankingsupervision.europa.eu/press/speeches/
date/2022/html/ssm.sp221004~9c9e9504c2.en.html.

ECB Working Paper Series No 284131impact of the shock on European firms, in line with the intention to increase risk sensitivity. At

the same time, all columns show that banks’ with more capital headroom increase provisions

more than other banks after the shock (positive coefficient on CAP HEAD). This effect seems

to occur across the board, however, since the interaction term between capital headroom and

energy dependence is statistically insignificant and close to zero. Overall, findings for the energy

price shock in 2022 confirm that discretionary factors relating to capital management motives

substantially affected provisioning behaviour in the most recent period.

6 Conclusion

In this paper, we examine how the implementation of IFRS 9 has affected the timeliness and risk

sensitivity of banks’ loan loss provisioning in the euro area. The introduction of IFRS 9 in the

aftermath of the global financial crisis marked a major shift in policy making, aiming to reduce

financial system procyclicality by addressing ‘too little, too late’ problems in provisioning. To

the best of our knowledge, we are the first to analyse its functioning under real life economic

stress with granular (loan-level) data and sophisticated econometric techniques. Specifically, we

investigate the determinants of provisioning practices in general, around credit risk shocks at the

individual loan level (default, move to stage 2 under IFRS 9), and around the macroeconomic

shocks induced by the pandemic and the outbreak of war in Ukraine.

Our findings suggest that some aspects of IFRS 9 seem to be working as intended. The

new standard generally induces higher ex ante (precautionary) provisioning, and provisions

seem to react in a somewhat more timely and risk-sensitive manner to macroeconomic shocks.

However, the forward-looking nature of IFRS 9 also appears to fail in some aspects. First,

many loans are not moved to stage 2 ahead of default (or only shortly before default), as banks

seem to be either unwilling or unable to identify significant increases in credit risk for these

exposures at a sufficiently early stage. Second, provisioning appears particularly low for such

exposures, indicating potential underprovisioning for seemingly safe IFRS 9 loans that the banks’

provisioning models consider unlikely to default. Third, even loans that are moved to stage 2

experience only a modest (albeit statistically significant) increase in provisioning, so that the

bulk of the adjustment under IFRS 9 occurs at the time of default as under national approaches.

Relating to these observations, our results do not suggest that the new approach had substan-

ECB Working Paper Series No 284132tial implications in terms of financial system procyclicality (neither negative nor positive ones),

at least not during our sample period and when compared with national approaches. Specifi-

cally, provisioning dynamics around default events are rather similar between IFRS 9 loans and

those using nGAAP, with significant cliff effects at the time of default occurring under both

types of approaches. Moreover, the often cited cliff effect relating to the move of exposures to

stage 2 under IFRS 9 is not detectable when looking at dynamics around default events (e.g.,

Figures 6 and 8) or the evolution of aggregate provisioning patterns during our sample period

(Figures 4 and 5). Possible explanations for this are (i) substantial heterogeneity with respect

to the timing of moving exposures to stage 2 (implying that only a small fraction of loans is

moved in any given quarter; see Figure 7 for an illustration around default events), and (ii) the

rather modest increase in provisioning ratios when a loan is moved to stage 2.

Provisioning levels appear to be affected by capital management motives (‘provisioning as you

can afford’), meaning that banks with less excess capital tend to provision less. IFRS 9 may have

fostered this divergence in accounting strategies across banks, although discretionary elements

are also present under national standards. Specifically, banks with more excess capital above

requirements generally provision more than other banks, particularly for riskier exposures (i.e.,

those in stages 2 and 3 under IFRS 9). They also increase provisions more strongly around credit

risk shocks at the individual loan level, particularly for loans using the IFRS 9 standard that

affords banks with more discretion and allows for heterogeneity in banks’ modelling approaches,

and are more likely to move loans to stage 2. Finally, they react generally more strongly to the

macroeconomic shocks induced by the pandemic and the war in Ukraine.

Importantly, it is difficult to say ex ante whether divergences in provisioning across banks

are beneficial or harmful from a financial stability perspective. On the one hand, accounting

flexibility may help to mitigate potential procyclical effects, as it can ease pressure on profits

and capital ratios in the early stages after a shock. This, in turn, may make it easier for capital-

constrained banks to maintain the supply of key financial services to the real economy. On

the other hand, such behaviour reduces transparency and may imply underprovisioning by less

capitalised banks (‘too little’), possibly leading to broader systemic implications if and when

(potentially larger) losses eventually materialise (‘too late’). Thus, the ultimate outcome for

the financial system and the broader economy is likely to depend on the specific nature of

the shock (transitory vs. more persistent), including in particular the likelihood for and the

ECB Working Paper Series No 284133magnitude of potential further losses down the road. Assessing the net impact of all factors

on financial stability and bank lending behaviour remains a key area for future research.

In

any case, our results suggest that banks with less capital headroom are at greater risk of being

under-provisioned, possibly also due to the discretion offered by IFRS 9. This divergences point

to a need to further assess the adequacy of current provisioning levels at the individual bank

level, to address potential concerns in terms of credit risk management.

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ECB Working Paper Series No 284137Figures & Tables

Figure 1: Evolution of loan loss provisioning ratios during the global financial crisis

Note: This figure shows the evolution of weighted average provisioning ratios, defined as provisions for loan
losses over total gross loans, for a sample 84 European banks that later came under direct supervision by the
European Central Bank. The data is sourced from SNL Financial.

ECB Working Paper Series No 284138Figure 2: Overview of IFRS 9

Note: This figure provides an overview of the main features of the IFRS 9 accounting standard and compares
the evolution of provisions under IFRS 9 for a loan with deteriorating asset quality to the evolution of provisions
for the same loan under an IL approach. Adapted from International Accounting Standards Board (2013).

Figure 3: Support measures during the pandemic

Note: This figure describes support measures and guidance in relation to provisioning that were adopted during
the early stages of the pandemic in 2020.

ECB Working Paper Series No 284139Figure 4: Evolution of aggregate provisioning

Note: This figure plots the evolution of aggregate provisioning ratios for the banks in our sample, split either
by the accounting approach (upper panel) or the bank’s CET2 ratio (lower panel, top versus bottom half of banks).

02462019202020212022Impairment ratio [%]AllIFRSnGAAP02462019202020212022Impairment ratio [%]HighLowECB Working Paper Series No 284140Figure 5: Share of IFRS 9 loans in different stages

Note: This figure plots the evolution of the aggregate share of IFRS 9 loans in different stages over our sample
horizon. The figure shows the percentage of loans in stages 2 and 3, where remaining loans are in stage 1.

Figure 6: Evolution of provisioning ratios around default events

Note: This figure shows the evolution of weighted average provisioning ratios around default events, where the
left panel separates loans according to their accounting approach. The right panel additionally separates IFRS 9
loans into those that were consistently in stage 1 ahead of the respective default event, those that switched from
stage 1 to stage 2 in the four quarters ahead of default, and those that were consistently in stage 2 throughout
this period. The data set is an unbalanced panel.

051015202019202020212022Proportion of IFRS loans in stages 2 & 3 [%]stage 2stage 3010203040−3−2−1012Distance to defaultImpairment ratioIFRSnGAAP010203040−4−202Distance to defaultImpairment rationGAAPstage 1stage 2switchECB Working Paper Series No 284141Figure 7: Share of IFRS 9 loans in different stages ahead of default

Note: This figure plots the evolution of the aggregate share of IFRS 9 loans in different stages ahead of the
quarter in which the loans default. The data set is an unbalanced panel.

Figure 8: Provisioning ratio around default by accounting framework: regression

Note: This figure shows the regression coefficients for δh in Equation 2, distinguishing loans by the accounting
framework. The sample includes all firm-bank pairs reporting a default and without missing values in the interval
between [-3; +2] quarters around default. The x-axis reports the distance in quarters to the quarter in which the
bank starts reporting default. The vertical lines report the 90% confidence interval. Solid (dashed) confidence
interval if the Wald-test for difference of the coefficients is (non)-significant at the 10% level.

0%25%50%75%100%−8−6−4−2Distance to defaultProportion of IFRS stagesIFRS stagestage 3stage 2stage 1llllllllllll01020−3−2−1012Quarters to the date of defaultCoefficientllNot significant differenceSignificant differencellIFRSnGAAPECB Working Paper Series No 284142Figure 9: Provisioning ratio around default – excluding the pandemic

Note: This figure replicates the estimation shown in Figure 8, excluding loans that defaulted during the imminent
phase of the pandemic (2020-Q2 to 2020-Q4). For the remaining observations, the sample includes all firm-bank
pairs reporting a default and without missing values in the interval between [-3; +2] quarters around default.
The x-axis reports the distance in quarters to the quarter in which the bank starts reporting default. The vertical
lines report the 90% confidence interval. Solid (dashed) confidence interval if the Wald-test for difference of the
coefficients is (non)-significant at the 10% level.

Figure 10: Evolution of provisioning ratios around default events – long window

Note:This figure shows the evolution of weighted average provisioning ratios around default events, separating
loans according to their accounting approach and extending the time horizon after the default event when
compared with Figure 6. The data set is an unbalanced panel.

llllllllllll01020−3−2−1012Quarters to the date of defaultCoefficientllNot significant differenceSignificant differencellIFRSnGAAP02040−3036Distance to defaultImpairment ratioIFRSnGAAPECB Working Paper Series No 284143Figure 11: Aggregate provisioning ratios for loans in different stages

Note: This figure shows the evolution of weighted average provisioning ratios around default events for IFRS 9
loans, where the left panel differentiates between banks with above vs. below median capital headroom. The
right panel additionally separates IFRS 9 loans that were consistently in stage 1 ahead of the respective default
event, those that switched from stage 1 to stage 2 in the four quarters ahead of default, and those that were
consistently in stage 2 throughout this period. The data set is an unbalanced panel.

Figure 12: Provisioning ratio around default by capital headroom: regression

Note: This figure shows the regression coefficients for δh in Equation 2, distinguishing loans by the bank’s capital
headroom. The sample includes all firm-bank pairs reporting a default and without missing values in the interval
between [-3; +2] quarters around default. The x-axis reports the distance in quarters to the quarter in which the
bank starts reporting default. The vertical lines report the 90% confidence interval. Solid (dashed) confidence
interval if the coefficient is (non)-significant at the 10% level.

0102030−4−202Distance to defaultImpairment ratioHighLow0102030−4−202Distance to defaultImpairment ratiostage 1stage 2switchHighLowllllll0.20.40.60.8−3−2−1012Quarters to the date of defaultCoefficientlCAP HEADlSignificantECB Working Paper Series No 284144Figure 13: Provisioning ratio around default by capital headroom (IFRS 9 vs. nGAAP)

Note: This figure shows the regression coefficients for δh in Equation 2, distinguishing loans by the bank’s
capital headroom and additionally separating the sample into IFRS 9 (left) and nGAAP (right) loans. The
sample includes all firm-bank pairs reporting a default and without missing values in the interval between [-3;
+2] quarters around default. The x-axis reports the distance in quarters to the quarter in which the bank starts
reporting default. The vertical lines report the 90% confidence interval. Solid (dashed) confidence interval if the
coefficient is (non)-significant at the 10% level.

Figure 14: Impact of capital headroom on placing a loan in stage 2 ahead of default

Note: This figure shows the regression coefficients for δh in Equation 3. The sample includes all IFRS 9 firm-bank
pairs reporting a default and without missing values in the three quarters before the event. The x-axis reports
the distance in quarters to the quarter in which the bank moves the loan to default. The vertical lines report the
90% confidence interval. Solid (dashed) confidence interval if the coefficient is (non)-significant at the 10% level.

llllll0.00.20.40.6−3−2−1012Quarters to the date of defaultCoefficientlCAP HEADlSignificantllllll−1.0−0.50.00.51.0−3−2−1012Quarters to the date of defaultCoefficientllInsignificantSignificantlCAP HEADlll0.100.150.200.250.30−3−3−2−2−1−1Quarters to default dateCoefficientlCAP HEADlSignificantECB Working Paper Series No 284145Figure 15: Provisioning ratios around the transition to stage 2: regression

Note: This figure shows the regression coefficients for δh in Equation 2, distinguishing IFRS 9 loans by the
bank’s capital headroom and aligning them around the move to stage 2. The sample includes all firm-bank pairs
reporting a move to stage 2 and without missing values in the interval between [-3; +2] quarters around the
event. The x-axis reports the distance in quarters to the quarter in which the bank moves the loan to stage 2.
The vertical lines report the 90% confidence interval. Solid (dashed) confidence interval if the coefficient is
(non)-significant at the 10% level.

Figure 16: Provisioning ratios around the transition to stage 2: regression – excluding the
pandemic

Note: This figure replicates the estimation shown in Figure 15, excluding loans that defaulted during the
imminent phase of the pandemic (2020-Q2 to 2020-Q4). For the remaining observations, the sample includes all
firm-bank pairs reporting a move to stage 2 and without missing values in the interval between [-3; +2] quarters
around the event, excluding the move to stage 2 that occurred in 2020. The x-axis reports the distance in
quarters to the quarter in which the bank moves the loan to stage 2. The vertical lines report the 90% confidence
interval. Solid (dashed) confidence interval if the coefficient is (non)-significant at the 10% level.

llllll0.000.050.100.15−3−2−1012Quarters to the date of the move to stage 2CoefficientllInsignificantSignificantlCAP HEADllllll0.000.050.100.15−3−2−1012Quarters to the date of the move to stage 2CoefficientllInsignificantSignificantlCAP HEADECB Working Paper Series No 284146Figure 17: Impact of IFRS on provision ratios during the COVID pandemic

Note: This figure reports the coefficient of the IFRS 9 dummy in the estimation of Equation 4. The vertical lines
report the 90% confidence interval. Solid (dashed) confidence interval if the coefficient is (non)-significant at the
10% level.

Figure 18: Change in provisioning ratio during the COVID-19 pandemic by capital headroom

Note: This figure reports the coefficient for the capital headroom variable in the estimation of Equation 4.
The vertical lines report the 90% confidence interval. Solid (dashed) confidence interval if the coefficient is
(non)-significant at the 10% level.

llllll0.00.20.40.62020 Q12020 Q22020 Q32020 Q42021 Q12021 Q2CoefficientllInsignificantSignificantllllll0.000.020.040.062020 Q12020 Q22020 Q32020 Q42021 Q12021 Q2CoefficientllInsignificantSignificantECB Working Paper Series No 284147Figure 19: Change in provisioning ratio during the COVID-19 pandemic by capital headroom
(IFRS 9 vs. nGAAP)

Note: This figure reports the coefficient for the capital headroom variable in the estimation of Equation 4,
separating the sample into IFSR 9 (left) and nGAAP (right) loans. The vertical lines report the 90% confidence
interval. Solid (dashed) confidence interval if the coefficient is (non)-significant at the 10% level.

Figure 20: Impact of capital headroom on placing a loan in stage 2 during the COVID-19
pandemic

Note: This figure reports the coefficient for the capital headroom variable in the estimation of Equation 5.
The vertical lines report the 90% confidence interval. Solid (dashed) confidence interval if the coefficient is
(non)-significant at the 10% level.

llllll−0.020.000.020.040.062020 Q12020 Q22020 Q32020 Q42021 Q12021 Q2CoefficientllInsignificantSignificantllllll−0.020.000.020.040.062020 Q12020 Q22020 Q32020 Q42021 Q12021 Q2CoefficientllInsignificantSignificantllllll0.000.050.102020 Q12020 Q22020 Q32020 Q42021 Q12021 Q2CoefficientllInsignificantSignificantECB Working Paper Series No 284148Table 1: Descriptive statistics – Loan-level data

Panel A: Key characteristics of the distribution

Mean

S.D.

Credit volume
Provisioning ratio
Default
Guarantee
Moratoria
Maturity
Protection ratio

734310.33
4.31
6.19
14.40
1.70
3.53
158.04

Panel B: Correlation matrix

2116803.49
15.53
24.06
31.37
11.79
4.17
296.63

Min

429.48
0.00
0.00
0.00
0.00
0.00
0.00

Q1

Median

Q3

Max

50159.00
0.07
0.00
0.00
0.00
0.46
0.00

141591.03
0.27
0.00
0.00
0.00
2.50
89.74

448511.00
1.01
0.00
0.00
0.00
4.65
174.68

16148537.51
100.00
100.00
100.00
100.00
22.27
200.00

Credit
volume

Provisioning
ratio

Default

Guarantee Moratoria Maturity

Protection ratio

Credit volume
Provisioning ratio
Default
Guarantee
Moratoria
Maturity
Protection ratio

1
-0.03
-0.02
-0.06
-0.02
0.16
-0.07

1
0.76
-0.08
-0.03
-0.06
0.05

1
-0.06
-0.03
-0.05
0.04

Panel C: Distribution across accounting classifications

1
0.02
0.02
0.04

1
-0.07
0.04

Stage 1

IFRS 9
Stage 2

Stage 3

1
-0.02

1

nGAAP

General
allowance

Specific
allowance

# of observations

20,158,194

3,540,303

1,850,027

4,024,453

593,482

Notes: This table reports the descriptive statistics of variables defined at the bank-firm (loan) level for the 30,166,459
loans included in the dataset. Panel A reports key characteristics of the respective variable distributions, where data
for credit volumes and maturity are winsorised at the 1% and 99% level. Panel B reports the correlation matrix for the
loan-level variables, and Panel C reports the number of observations per accounting framework (IFRS 9 vs. nGAAP).
For IFRS 9, it separates observations per credit stage; for nGAAP, it separates observations according to their classifi-
cation as general or specific allowance, the latter indicating impaired credits.

ECB Working Paper Series No 284149Table 2: Descriptive statistics – Bank-level data

Panel A: Key characteristics of the distribution

CAP HEAD
TA.log
RWA/TA
DEP/TA
RoA
CASH/TA
LOAN/TA
CB DEPOSITS

Mean

6.70
21.45
53.59
84.75
0.26
9.15
82.47
1.34

S.D.

5.76
1.64
14.06
10.85
0.54
10.23
12.17
3.85

Panel B: Correlation matrix

Min

0.29
18.08
14.99
23.69
−2.12
0.42
29.79
0.00

Q1

Median

3.57
20.49
45.76
85.02
0.06
2.46
78.28
0.00

5.24
21.32
55.27
88.03
0.23
6.47
85.09
0.00

Q3

7.69
22.24
62.34
89.73
0.50
11.11
90.46
0.00

Max

38.68
27.04
88.34
93.63
2.07
57.45
97.63
19.20

CAP HEAD log(TA) RWA/TA DEP/TA RoA CASH/TA LOAN/TA CB DEPOSITS

CAP HEAD
log(TA)
RWA/TA
DEP/TA
RoA
CASH/TA
LOAN/TA
CB DEPOSITS

1
-0.09
-0.28
-0.34
0.06
0.25
-0.2
-0.05

1
-0.25
-0.37
-0.01
0.2
-0.26
0.34

1
0.11
0.02
-0.34
0.2
-0.24

1
-0.1
-0.14
0.24
-0.17

1
-0.04
0.09
0

1
-0.84
0.08

1
-0.05

1

Notes: This table reports the descriptive statistics of variables defined at the bank level for the 23,049 bank-quarter obser-
vations in our data set. Variables are winsorised at the 1% and 99% level. Panel A shows key distributional characteristics
and Panel B reports the correlation matrix.

Table 3: Descriptive statistics - Energy data

A Agriculture
B-F Industry and Construction
G-N Services (excl. financial & real estate)

136
1561
816

11.42
28.54
11.81

7.98
42.29
12.60

N

Mean

SD

Min

1.66
1.66
1.66

Q1 Median

Q3

Max

7.41
7.67
4.32

9.97
11.56
7.45

13.02
20.57
14.30

62.82
157.99
95.85

Notes: This table reports the descriptive statistics for the sectoral measure of exposure to energy price developments
(see Section 3.3 for a detailed description). Data are winsorised at the 1% and 99% level.

ECB Working Paper Series No 284150Table 4: Determinants of loan-level provisioning ratios

Model:

Variables
nGAAP

CAP HEAD

LOG(TA)

RWA/TA

DEP/TA

ROA

CASH/TA

LOAN/TA

CB DEPOSITS/TA

Maturity

Guarantee

Moratoria

Credit vol., log.

Protection ratio

Fixed effects
Firm-Quarter

Fit statistics
Observations
R2
Within R2

All
(1)

-0.5287∗
(0.2885)
0.0815∗∗∗
(0.0240)
0.1857∗∗
(0.0784)
-0.0074
(0.0101)
0.0180∗∗
(0.0090)
-0.1600
(0.1360)
-0.0679∗∗∗
(0.0247)
-0.0210
(0.0175)
-0.0781∗∗∗
(0.0219)
0.0553
(0.0414)
-0.0151∗∗∗
(0.0014)
-0.0038∗∗
(0.0018)
-0.6143∗∗∗
(0.2009)
-0.0011
(0.0010)

IFRS 9
(2)

nGAAP
(3)

0.0836∗∗∗
(0.0296)
0.2481∗∗∗
(0.0859)
-0.0093
(0.0136)
0.0360∗∗∗
(0.0126)
-0.0288
(0.1584)
-0.1317∗∗∗
(0.0350)
-0.0740∗∗
(0.0290)
-0.0751∗∗∗
(0.0282)
0.0686
(0.0540)
-0.0156∗∗∗
(0.0015)
-0.0035∗
(0.0020)
-0.7385∗∗∗
(0.2582)
-0.0007
(0.0010)

0.0703∗∗
(0.0319)
-0.0585
(0.0591)
-0.0084
(0.0062)
0.0271∗∗∗
(0.0066)
0.0385
(0.2529)
-0.0336∗∗∗
(0.0129)
0.0150
(0.0115)
0.0416
(0.0330)
0.0012
(0.0158)
-0.0185∗∗∗
(0.0021)
-0.0051∗∗∗
(0.0013)
-0.2671
(0.1822)
0.0003
(0.0051)

Yes

Yes

Yes

30,001,022
0.78063
0.01217

24,699,885
0.79438
0.01589

3,912,655
0.65817
0.00612

Clustered (Firm-Quarter & Bank) standard errors in parentheses
Signif. Codes: ***: 0.01, **: 0.05, *: 0.1

Notes: The table shows regression results for Equation 1, estimated on our loan sample covering the pe-
riod from 2018-Q3 to 2022-Q2. The dependent variable is the quarterly provisioning ratio at the bank ×
firm level. Explanatory variables are described in Section 4. Bank-level variables variables are lagged by
one quarter. All variables are winsorised at 1% and 99%.

ECB Working Paper Series No 284151s
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ECB Working Paper Series No 284152Table 6: Determinants of loan-level provisioning ratios – IFRS 9 stages

Model:

Variables
CAP HEAD

LOG(TA)

RWA/TA

DEP/TA

ROA

CASH/TA

LOAN/TA

CB DEPOSITS/TA

Maturity

Guarantee

Moratoria

Credit vol., log.

Protection ratio

Fixed effects
Firm-Quarter

Fit statistics
Observations
R2
Within R2

stage 1
(1)

0.0344
(0.0230)
-0.1018∗
(0.0551)
-0.0153∗
(0.0078)
0.0068
(0.0095)
0.0799
(0.0865)
-0.0485
(0.0340)
-0.0460
(0.0304)
-0.0202
(0.0196)
0.0994∗∗∗
(0.0275)
-0.0005
(0.0011)
-0.0017∗
(0.0010)
-0.7105∗∗∗
(0.1887)
0.0002
(0.0005)

stage 2
(2)

0.1832∗∗∗
(0.0418)
0.1672
(0.1105)
-0.0204
(0.0194)
0.0528∗∗
(0.0215)
0.1370
(0.2874)
-0.1960∗∗∗
(0.0737)
-0.1269∗∗
(0.0582)
-0.1064∗∗∗
(0.0402)
0.2391∗∗∗
(0.0305)
-0.0268∗∗∗
(0.0019)
0.0067
(0.0043)
-0.8171∗∗∗
(0.1573)
-0.0002
(0.0015)

stage 3
(3)

0.5327∗∗∗
(0.1841)
2.688∗∗∗
(0.5618)
-0.0011
(0.1141)
0.1939
(0.1561)
-1.220
(1.008)
-0.5035
(0.4196)
0.3185
(0.3801)
-0.9046∗∗∗
(0.1904)
-0.2705
(0.1971)
-0.1876∗∗∗
(0.0194)
0.0087
(0.0166)
-1.097∗∗∗
(0.3828)
-0.0491∗∗∗
(0.0055)

Yes

Yes

Yes

17,893,082
0.41469
0.04308

1,252,261
0.52903
0.03938

1,332,183
0.61709
0.07579

Clustered (Firm-Quarter & Bank) standard-errors in parentheses
Signif. Codes: ***: 0.01, **: 0.05, *: 0.1

Notes: The table shows regression results for Equation 1, estimated on the sample of IFRS 9 loans only
and split according to stages, covering the period from 2018-Q3 to 2022-Q4. The dependent variable is
the quarterly provisioning ratio at the bank × firm level. Explanatory variables are described in Section 4.
Bank-level variables variables are lagged by one quarter. All variables are winsorised at 1% and 99%.

ECB Working Paper Series No 284153Table 7: Determinants of moving a loan to stage 2

Dependent Variable:

Bank reports Stage 2

Variables
CAP HEAD

LOG(TA)

RWA/TA

DEP/TA

ROA

CASH/TA

LOAN/TA

CB DEPOSITS/TA

Maturity

Guarantee

Moratoria

Credit vol., log.

Protection ratio

Fixed effects
Firm

Fit statistics
Observations
Squared Correlation
Pseudo R2
BIC

0.0333∗
(0.0171)
0.5092∗∗∗
(0.0843)
0.0638∗∗∗
(0.0134)
0.0421∗∗
(0.0180)
-0.2667∗
(0.1554)
-0.0244
(0.0228)
-0.0478∗
(0.0253)
-0.0030
(0.0218)
-0.0263∗∗∗
(0.0101)
0.0049∗∗∗
(0.0013)
0.0017
(0.0023)
0.0887∗∗∗
(0.0202)
0.0017∗∗∗
(0.0005)

Yes

567,439
0.14705
0.11478
3,381,251.0

Clustered (Firm & Bank) standard-errors in parentheses
Signif. Codes: ***: 0.01, **: 0.05, *: 0.1

Notes: The table shows regression results for Equation 7, estimated on our loan sample cov-
ering the period from 2018-Q3 to 2022-Q4. The dependent variable is a dummy taking the
value 1 if bank b moves the loan to firm f to stage 2 at any point. Explanatory variables
are described in Section 4. For this regression, all variables are fixed at their value when the
first bank moves its loan to firm f to stage 2. All variables are winsorised at 1% and 99%.

ECB Working Paper Series No 284154Table 8: Impact of the energy price shock in 2022 on provisioning ratios

Dependent Variable:
Period:
Model:

Variables
IFRS

CAP HEAD

IFRS × Energy

Energy × CAP HEAD

LOG(TA)

RWA/TA

DEP/TA

ROA

CASH/TA

LOAN/TA

CB DEPOSITS/TA

Credit vol., log.

Protection ratio

Maturity

Guarantee

Moratoria

Fixed effects
Firm

Fit statistics
Observations
R2
Within R2

2022 Q1 - 2022 Q2
(2)
(1)

∆ provisioning ratio
2022 Q1 - 2022 Q3
(4)
(3)

2022 Q1 - 2022 Q4
(6)
(5)

-0.0224
(0.0537)
0.0155∗∗∗
(0.0051)

0.0152
(0.0189)
0.0002
(0.0024)
-0.0002
(0.0018)
0.1134∗
(0.0590)
0.0064∗
(0.0035)
0.0047
(0.0035)
-0.0011
(0.0034)
-0.0158∗∗∗
(0.0061)
-0.0004∗∗∗
(0.0001)
0.0026
(0.0017)
-0.0004
(0.0004)
0.0001
(0.0004)

-0.3009∗∗∗
(0.1095)
0.0168
(0.0135)
0.0341∗∗
(0.0152)
0.0003
(0.0012)
0.0351
(0.0349)
-0.0053
(0.0044)
-0.0039
(0.0038)
-0.0062
(0.1126)
-0.0097
(0.0087)
-0.0057
(0.0071)
0.0077
(0.0076)
-0.1390∗
(0.0808)
0.0010
(0.0010)
0.0096
(0.0075)
-0.0020∗∗
(0.0008)
0.0019∗∗
(0.0009)

-0.0058
(0.0877)
0.0139∗∗∗
(0.0054)

0.0121
(0.0258)
-0.0030
(0.0029)
-0.0036∗
(0.0020)
0.1011
(0.0641)
-0.0002
(0.0049)
0.0033
(0.0063)
0.0059
(0.0040)
-0.0839∗
(0.0485)
0.0002
(0.0005)
0.0071
(0.0054)
-0.0007∗
(0.0004)
0.0008∗
(0.0005)

-0.1027∗
(0.0608)
0.0163∗∗∗
(0.0054)
0.0152∗
(0.0087)
-0.0003
(0.0008)
0.0122
(0.0258)
-0.0030
(0.0030)
-0.0036∗
(0.0019)
0.1012
(0.0639)
-0.0002
(0.0049)
0.0033
(0.0063)
0.0058
(0.0040)
-0.0840∗
(0.0486)
0.0002
(0.0005)
0.0071
(0.0054)
-0.0007∗
(0.0004)
0.0008∗
(0.0005)

-0.0154
(0.1113)
0.0162∗
(0.0096)

0.0608∗
(0.0311)
-0.0015
(0.0037)
-0.0011
(0.0031)
0.0391
(0.1008)
-0.0045
(0.0072)
0.0014
(0.0063)
0.0039
(0.0064)
-0.1007
(0.0618)
0.0008
(0.0008)
0.0067
(0.0048)
-0.0019∗∗
(0.0007)
0.0020∗∗
(0.0009)

-0.2025∗∗∗
(0.0726)
0.0174∗∗
(0.0083)
0.0287∗∗
(0.0138)
-0.0002
(0.0012)
0.0610∗∗
(0.0310)
-0.0016
(0.0038)
-0.0012
(0.0030)
0.0394
(0.1006)
-0.0043
(0.0072)
0.0014
(0.0063)
0.0039
(0.0063)
-0.1009
(0.0619)
0.0008
(0.0008)
0.0067
(0.0048)
-0.0019∗∗
(0.0007)
0.0020∗∗
(0.0009)

Yes

Yes

Yes

Yes

Yes

Yes

2,612,281
0.80397
0.00216

1,404,886
0.79198
0.00373

2,548,375
0.81477
0.00266

2,548,375
0.81477
0.00268

2,415,775
0.82711
0.00250

2,415,775
0.82712
0.00254

Clustered (Firm & Bank) standard-errors in parentheses
Signif. Codes: ***: 0.01, **: 0.05, *: 0.1

Notes: The table shows regression results for Equation 6, estimated on our loan sample covering the period from
2022-Q1 to 2022-Q2 (columns 1 and 2), from 2022-Q1 to 2022-Q3 (columns 3 and 4) and from 2022-Q1 to 2022-Q4
(columns 5 and 6). The dependent variable is the change in provisioning ratio at the bank × firm level. Explanatory
variables are described in Section 4, fixed at their value in 2022-Q1, and winsorised at 1% and 99%.

ECB Working Paper Series No 284155Appendix

A Propensity Score Matching

Banks using IFRS 9 and those using nGAAP differ in several aspects. Panel A of Table A.1

presents descriptive statistics of key balance sheet and income statement variables for the two

groups of banks, along with a Welch test for equality of mean. The two groups differ significantly

in almost all dimensions under consideration. To account for these differences in a systematic

manner, we run a robustness exercise relying on a Propensity Score Matching (PSM) approach.

That is, we match pairs of IFRS 9 and nGAAP banks to obtain two groups of banks with similar

characteristics but different accounting frameworks, allowing for a clearer identification of the

impact of the latter.

The matching is performed on the vector of bank-level control variables that enters all our

regressions. We average these variables across all quarters at the bank-level, resulting in one

value per variable for each bank. Additionally, we include the logarithm of the number of

observations per bank in the data set as a matching variable. This is necessary to avoid that we

match banks with a very different presence in the data set, which would imply that one of the

banks would be practically absent and the other practically unmatched in the matched sample,

thus defeating the point of the PSM. To implement the PSM, we use the “nearest neighbour”

approach and a caliper of 0.2.

Results of the matching are presented in Panel B of Table A.1. The matched sample includes

207 banks in each group. Mean values for all of the variables under consideration come very

close to each other, and the Welch test indicates that differences between IFRS 9 and nGAAP

banks are no longer statistically significant, except for central banks deposits and marginally for

the return on assets. Thus, the PSM successfully controls for differences between treatment and

control group that could affect our estimation results presented in Section 5.1.

ECB Working Paper Series No 284156Table A.1: Comparison of IFRS 9 and nGAAP banks pre- and post PSM

Panel A: Pre PSM

CAP HEAD
LOG(TA)
RWA/TA
DEP/TA
ROA
CASH/TA
LOAN/TA
CB DEPOSITS/TA

Panel B: Post PSM

CAP HEAD
LOG(TA)
RWA/TA
DEP/TA
ROA
CASH/TA
LOAN/TA
CB DEPOSITS/TA

nGAAP
nb

IFRS
nb

nGAAP
mean

1130
1130
1130
1130
1130
1130
1130
1130

nGAAP
nb
207
207
207
207
207
207
207
207

454
454
454
454
454
454
454
454

IFRS
nb
207
207
207
207
207
207
207
207

7.121
21.018
55.776
86.216
0.25
6.085
85.282
0.146

nGAAP
mean
8.83
21.327
46.189
78.285
0.388
11.184
82.46
0.797

IFRS
mean

8.466
21.909
45.634
79.181
0.34
13.077
79.204
3.448

IFRS
mean
8.107
21.396
48.501
80.368
0.298
11.085
81.482
1.916

Welch
test

-4.04***
-9.09***
13.47***
10.84***
-3.54***
-12.31***
8.89***
-17.14***

Welch
test
1.15
-0.37
-1.54
-1.55
1.89*
0.09
0.76
-3.84***

Sig. Levels: ∗∗∗p < .01, ∗∗p < .05, ∗p < .1

Notes: This table reports the descriptive statistics of bank characteristics, averaged
over time at the bank level. It separates between IFRS 9 and nGAAP banks. Panel A
reports statistics for all banks in the sample. Panel B reports statistics for those banks
that remain after performing a Propensity Score Matching (PSM) between IFRS 9 and
nGAAP banks. The PSM matches banks on all the variables reported in the table,
using the “nearest neighbour” approach and a caliper of 0.2.

ECB Working Paper Series No 284157Acknowledgements 

We would like to thank Etienne de Bridiers, Maciej Grodzicki, Harry Huizinga (discussant), Gera Kiewiet, Melina Ludolph, Tamarah 
Shakir and conference and seminar participants at the European Central Bank, the Leibniz-Institut für Wirtschaftsforschung Halle (IWH) 
and the 1st Annual ECB Banking Supervision Research Conference for valuable comments and suggestions. A previous version of the 
paper has been presented under the title “Determinants of credit risk provisioning in uncertain times – the role of bank conditions and 
accounting standards”. 
This paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the 
authors and do not necessarily reflect those of the ECB. 

Markus Behn 
European Central Bank, Frankfurt am Main, Germany; email: markus.behn@ecb.europa.eu 

Cyril Couaillier 
European Central Bank, Frankfurt am Main, Germany; email: cyril.couaillier@ecb.europa.eu 

© European Central Bank, 2023 

Postal address  60640 Frankfurt am Main, Germany 
Telephone 
Website 

+49 69 1344 0 
www.ecb.europa.eu 

All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

PDF 

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ISSN 1725-2806 

doi:10.2866/846 

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