The procyclicality of banking : evidence from the euro area.
Date: 2019-05-26
Institutions: European Central Bank
Subjects: accounting, bank, capital market, financial regulation, financial stability, loan, market capitalisation, monetary crisis

Harry Huizinga, Luc Laeven 

Working Paper Series 

The procyclicality of banking: 
evidence from the euro area 

No 2288 / June 2019 

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

Abstract: Loan loss provisions in the euro area are negatively related to GDP growth, i.e., 

they are procyclical. Loan loss provisions tend to be more procyclical at larger and better 

capitalized banks. The procyclicality of loan loss provisions can explain about two-thirds of 

the variation of bank capitalization over the business cycle. We estimate that provisioning 

procyclicality in the euro area is about twice as large as in other advanced economies. This 

difference reflects a larger procyclicality of provisioning in euro area countries already prior 

to euro adoption, and the divergent growth experiences of euro area countries following the 

global financial crisis. 

JEL classification: G20 

Keywords: Banks, financial regulation, accounting, bank capital, loan loss provisioning, 

financial stability 

ECB Working Paper Series No 2288 / June 20191Executive Summary 

Banks set aside loan loss provisions in anticipation of future loan losses. Loan loss provisions 

reduce bank profits and capital. As banks are subject to minimum capital requirements, 

additional loan loss provisions can constrain a bank’s capacity to make new loans. Several 

studies have shown that loan loss provisions are lower at times of high economic growth, and 

vice versa. This makes loan loss provisions procyclical. 

A high procyclicality of banks’ loan loss provisioning is undesirable from a financial stability 

perspective, as it implies that bank capitalizations are more negatively affected at the trough 

of the business cycle, which is exactly when capital market conditions for banks are at their 

weakest. In addition, the procyclicality of loan loss provisions can be a driver of cyclical loan 

supply, as lower bank equity during economic downturns can lead to a credit crunch.  

In this paper, we examine the procyclicality of loan loss provision for banks internationally. 

A main finding is that provisioning procyclicality— the impact of GDP growth on 

provisioning— in the euro area is about twice as big as in other countries. The higher 

provisioning procyclicality in the euro area is partly caused by an already higher loan loss 

procyclicality of euro area banks before their respective countries of location adopted the 

euro. In addition, the higher provisioning procyclicality in the euro area reflects the more 

negative growth experience of euro area countries during the global financial crisis, as the 

relation between GDP growth and provisioning is stronger during economic downturns. 

In addition, there is considerable heterogeneity in provisioning procyclicality among euro 

area banks. In particular, provisioning is more procyclical at bigger banks, which could 

reflect that bigger banks are willing to take on more business-cycle related risks to their 

capitalization due to their too-big-to-fail status. Similarly, banks that are directly supervised 

ECB Working Paper Series No 2288 / June 20192by the European Central Bank (ECB), which tend to be larger, display more procyclical 

provisioning patterns. Furthermore, provisioning is more procyclical at better capitalized 

banks, which could reflect that better capitalized banks can better sustain the risks to capital 

inherent in provisioning procyclicality. Consistent with this, provisioning is more procyclical 

in countries with more stringent capital regulations.  

Loan loss provisioning is the main driver of the cyclicality of bank capitalization in the euro 

area, as the sensitivity of provisioning to GDP growth in the euro area can explain about two-

thirds of the variation of bank capitalization over the business cycle. In addition, we show 

that banks with more procyclical provisioning also display more procyclical lending over the 

business cycle. 

In early 2018, the European Union moved from the incurred loss model of provisioning to an 

expected loss model, with the implementation of International Financial Reporting Standard 

(IFRS) 9 on Financial Instruments. The expected credit loss model of IFRS 9 requires banks 

to set credit impairment allowances for all loans rather than just for loans where loss is 

probable or has already occurred. The broader application of the expected loss model to all 

loans should lead to an increase in the average level of allowances. The introduction of IFRS 

9 thus could mitigate the negative relationship between provisioning and economic growth, 

with a potentially positive effect on financial stability.  

However, some have argued that the impact of IFRS 9 on the procyclicality of loan loss 

provisioning is ambiguous. Provisioning for a next economic downturn under the expected 

credit loss model may be rather abrupt, if an initial turning point in the business cycle is taken 

to forebode a serious business cycle downturn, triggering large loan loss provisioning in 

anticipation of future loan impairment. The introduction of the expected credit loss model 

ECB Working Paper Series No 2288 / June 20193thus could lead to a concentration of loan loss allowances at the time of an initial economic 

downturn, with possible negative ramifications for financial stability. 

Our evidence on loan loss provisioning has important implications for the supervision of euro 

area banks going forward. First, the relatively large provisioning cyclicality in the euro area 

stresses the need to make efforts to reduce this procyclicality, given that this is likely to 

remain a problem after the introduction of IFRS 9. Second, the considerable heterogeneity in 

provisioning among euro area banks could reflect that banks apply the accounting rules 

regarding provisioning unevenly, which should be a concern for bank supervisors. This 

heterogeneity is also likely to persist under IFRS 9, and hence supervisors will need to apply 

efforts to make the application of loan loss provisioning rules across euro area banks more 

uniform. More generally, our findings call for increased attention to potential undesirable 

consequences of provisioning rules for the procyclicality of lending. 

ECB Working Paper Series No 2288 / June 201941.

Introduction

Banks set aside loan loss provisions for future loan losses. Such provisions make up a

large part of bank expenses, reducing bank profitability and potentially regulatory capital. By 

implication, loan loss provisions can influence credit cycles, especially when bank capital is a 

constraint on the bank’s lending capacity. When studying the procyclicality of banking, loan 

loss provisions are therefore a natural starting point. 

A high procyclicality of banks’ loan loss provisioning is undesirable from a financial 

stability perspective, as it implies that bank capitalizations are more negatively affected at the 

trough of the business cycle which is exactly when capital market conditions for banks are at 

their weakest. Furthermore, the procyclicality of loan loss provisions can be a driver of a 

cyclical bank loan supply arising from changes in bank capital, which is also unwarranted. 

Provisioning procyclicality is particularly problematic in a monetary union where a single 

monetary policy is ill equipped to absorb shocks transmitted through financial linkages and 

divergent domestic economic and financial cycles, and the burden of adjustment falls 

primarily on fiscal and (macro)prudential policies. 

This paper provides new empirical evidence on the cyclicality of banks’ provisioning 

for loan losses in the euro area – the largest monetary union in the world – in comparison to 

non-euro area countries. In the euro area, loan loss provisions are a very sizeable component 

of bank earnings, amounting to 41% of earnings before provisions on average. 

There is a long-standing debate between bank regulators and accounting standard 

setters over the calculation of loan loss provisions by banks. Accounting standard setters have 

long employed a relatively straightforward incurred loss model which requires banks to 

record loss provisions for expected loan losses based solely on information that a loss is 

probable at the balance sheet date. Bank regulators, however, are focused on the safety and 

soundness of banks.  

ECB Working Paper Series No 2288 / June 20195Some in the regulatory and academic communities have argued that banks should use 

countercyclical provisions so that banks record larger provisions during upswings of the 

economy and lower provisions during downturns to cushion the negative impact of recessions 

on bank capital. Laeven and Majnoni (2003), for instance, argue that loan loss reserves 

should meet a minimum requirement on average over the business cycle. To achieve this, 

banks would smooth loan loss provisions so that provisions compensate for the difference 

between realized credit losses and average credit losses by taking positive values during 

cyclical expansions and possibly negative values during downturns. As a result loan loss 

reserves would increase in good times and decrease in bad times. Along these lines, Spain has 

introduced dynamic provisioning rules in 2000 that require banks to build up a dynamic 

provision fund through retained earnings in good times to cover loan losses in bad times. 

Jimenez et al. (2017) find that the Spanish dynamic provisioning rules smooth credit cycles, 

and support firm performance in bad times. 

On January 1, 2018, the European Union (EU) moved from the incurred loss model of 

provisioning to a more comprehensive expected loss model, with the implementation of 

International Financial Reporting Standard (IFRS) 9 on Financial Instruments. The expected 

credit loss model of IFRS 9 requires banks to set credit impairment allowances for all loans 

rather than just for loans where loss is probable or has occurred. This accounting rule change 

should increase the average level of allowances, and make them also timelier and potentially 

less procyclical.1 

Our empirical analysis shows that loan loss provisions in the euro area are negatively 

related to Gross Domestic Product (GDP) growth, i.e., they are procyclical. The sensitivity of 

provisioning to GDP growth in the euro area can explain about two-thirds of the variation of 

bank capitalization over the business cycle. The fluctuation of loan loss provisioning over the 

1 In an initial assessment, the European Banking Authority (2018) reports that the immediate impact of IFRS 9 
was a reduction in the Common Equity Tier 1 ratio by 47 basis points on average for a sample of 38 European 
banks.  

ECB Working Paper Series No 2288 / June 20196business cycle thus is the main driver of changes in bank capitalization and lending capacity 

over the cycle. Consistent with this, we find that loan growth is positively related to bank 

capitalization and negatively to loan loss reserves. In addition, we show that banks with more 

procyclical provisioning also display more procyclical credit provision. 

Among euro area banks, we find considerable heterogeneity in provisioning 

procyclicality. In particular, provisioning is more procyclical at bigger banks, which could 

reflect that bigger banks are willing to take on more business-cycle related risk to their 

capitalization due to their too-big-to-fail status. Similarly, banks that are directly supervised 

by the European Central Bank (ECB), which tend to be larger, display more procyclical 

provisioning patterns. Furthermore, provisioning is more procyclical at better capitalized 

banks, which could reflect that better capitalized banks can better sustain the risks to capital 

inherent in provisioning procyclicality or alternatively that they have less of a need to delay 

provisioning during business cycle downturns. Consistent with this, provisioning is more 

procyclical in countries with more stringent capital regulations. In addition, provisioning is 

more procyclical in countries with less competitive banking markets, as a lack of competition 

may enhance opportunities for bank managers to engage in accounting discretion regarding 

loan loss provisioning. 

 We estimate that provisioning procyclicality in the euro area is about twice as big as 

in other countries. Higher provisioning procyclicality in the euro area is found to reflect an 

already higher procyclicality of euro area banks before their respective countries adopted the 

euro. In addition, the higher euro area provisioning procyclicality reflects the divergent GDP 

growth experiences of euro area countries during the global financial crisis and its aftermath.  

We find that the procyclicality of loan loss provisioning is negatively related to a 

country’s rate of real exchange rate appreciation, which could reflect that real exchange rate 

appreciations tend to be expansionary in the euro area. This suggest that the procyclicality of 

ECB Working Paper Series No 2288 / June 20197 
loan loss provisioning in the euro area could be higher, because the introduction of the euro 

limits the potential for real exchange rate adjustments following a negative GDP shock. 

Our results have important implications for the supervision of euro area banks going 

forward. First, our finding of a relatively large provisioning cyclicality in the euro area 

stresses the need to make efforts to reduce this procyclicality, given that this is likely to 

remain a problem after the introduction of IFRS 9. Second, our findings of considerable 

provisioning heterogeneity among euro area banks suggest that banks have used material 

discretion in applying the incurred loss model of loan loss provisioning. This heterogeneity is 

unlikely to vanish on account of IFRS 9, and hence supervisors will need to apply 

considerable efforts to effect a more uniform application of loan loss provisioning rules 

across euro area banks. 

Several studies have previously examined the determinants of loan loss provisioning 

for international samples of banks. Using data for 45 countries during the period 1988–1999, 

Laeven and Majnoni (2003) find that loan loss provisions are positively related to bank 

profitability consistent with bank income smoothing, but negatively to GDP growth. Similar 

results are obtained by Bikker and Metzemakers (2005) for a set of 29 OECD countries 

during the period 1991–2001. Olszak, Pipien, Kowalska, and Roszkowska (2017) examine 

provisioning procyclicality of banks in the European Union during 1996–2011. These authors 

find that large, publicly traded, and commercial banks display more procyclical provisioning, 

while more restrictive bank capital standards are associated with weaker procyclicality. 

However, none of these studies considers the impact of the euro and the implications of loan 

provisioning for the cyclicality of bank capital and lending. 

Bouvatier and Lepetit (2012) provide a theoretical model where procyclical 

provisioning, which depends only on current loan performance, amplifies loan procyclicality. 

Loan procycliality arises as banks underestimate the costs of new loans during economic 

ECB Working Paper Series No 2288 / June 20198 
upswings due to currently low provisioning levels, leading to loan interest rates that are too 

low and credit that is too high, and vice versa. Loan mispricing over the business cycle arises, 

as banks base their lending decisions on accounting profits, as affected by the procyclical 

provisioning, rather than on true economic profits. In this setting, the introduction of forward-

looking provisioning based on expected credit losses brings accounting profits in line with 

true economic profits, and hence eliminates the mispricing of loans over the business cycle 

and also loan procyclicality. This theoretical analysis suggests that the introduction of 

provisioning rules based on expected credit losses, as in the case of IFRS 9, is welfare 

improving. 

In the remainder of the paper, section 2 offers some institutional and regulatory 

background on loan loss provisions, including the change from the incurred loss model to the 

expected loss model, and how this affects provisioning procyclicality and discretion. Section 

3 outlines the empirical approach. Section 4 discusses the data. Section 5 presents the 

empirical results. Section 6 concludes. 

2.

Institutional and regulatory background of loan loss provisioning

A loan loss provision is an expense set aside as an allowance for bad loans. Loan loss

provisions enter the bank’s income statement as a non-interest expense item thereby reducing 

profits, and they cumulate over time into loan loss reserves on the bank’s balance sheet after 

subtracting loan write offs. The loan loss reserves account is a “contra-asset” account, 

accounting for the difference between the gross loans and the net loans recorded on the asset 

side of the bank’s balance sheet (see Figure 1).  

Loan loss reserves are in principle supposed to cover expected loan losses, while bank 

capital should cover unexpected loan losses (Figure 2). Expected losses are losses that occur 

on average and can be measured by the mean value of the frequency distribution of loan 

ECB Working Paper Series No 2288 / June 20199losses, while unexpected losses are losses that are large but infrequent and that are located far 

in the tail of the frequency distribution of loan losses. Basel capital regulations allow 

(general) loss provisions to be included in Tier 2 capital up to a limit of 1.25% of credit risk-

weighted assets. Loan loss provisions therefore subtract from regulatory capital (i.e., the sum 

of Tier 1 and Tier 2 capital) to the extent that loan loss reserves exceed this limit. 

2.1 

Accounting rules for loan loss provisioning 

Loan loss provisioning has to follow a set of accounting rules. For internationally 

active banks, these accounting rules are typically based on International Financial Reporting 

Standard (IFRS) guidelines promulgated by the International Accounting Standards Board 

(IASB). Of relevance for provisioning is the new set of accounting principles under IFRS 9, 

which specifies how an entity should classify and measure financial assets, financial 

liabilities, and some contracts to buy or sell non-financial items.  

Under IFRS 9, banks have to set credit impairment allowances for all loans and other 

fixed income assets using an expected loss model.2 The credit impairment provisions of IFRS 

9 replace International Accounting Standard (IAS) 39 which required banks to set loan loss 

provisions for their loan portfolio using an incurred loss model. Under the incurred loss 

model, banks only had to take loan loss provisions after a loan became impaired.3  

Loans are more likely to become impaired during economic downturns, and hence the 

application of IAS 39 implied that loan loss provisions were concentrated during economic 

downturns. Loan loss provisions that are not based on expected losses reduce not only bank 

income but also reduce bank capital when actual losses exceed expected losses. Therefore, 

2  This  section  draws  on  the  discussion  of  the  introduction  of  IFRS  9  and  its  implications  for  provisioning 
proyclicality and discretion in Huizinga (2017, pp. 7-9). 
3 Often a distinction is made between “general” and “specific” loan loss provisions, with “general” provisions 
referring  to  “ex-ante”  provisions  related  to  future  uncertain  events  and  “specific”  provisions  referring  to  “ex-
post” provisions related to certain events (such as past due payments or other default-like events) for which a 
specific documentation can be produced. The introduction of IFRS 9 strengthens the role of ‘ex ante’ provisions. 

ECB Working Paper Series No 2288 / June 201910 
 
                                                           
the incurred loss model of IAS 39 caused banks’ capitalization rates to decline especially 

during economic downturns. In other words, accounting standards rendered loan loss 

provisioning procyclical. This can pose a threat to financial stability, as banks find it difficult 

to raise additional capital at times of negative economic growth and low profitability.4 In the 

analysis, we will capture this procyclical behavior of loan loss provisioning through the 

inclusion of an economic growth variable. 

The new accounting rules under IFRS 9 require banks to take material provisions also 

during economic expansions, in the absence of significant credit impairment, to reflect the 

probability of a business cycle turn, which could induce greater credit losses later. More 

provisioning in anticipation of the next economic downturn implies that banks have higher 

loan loss reserves once a downturn occurs, and that they need to take fewer provisions during 

economic declines. In the extreme, and absent any other frictions, if loan loss provisions 

accurately reflect expected losses and bank capital absorbs unexpected losses, then under 

IFRS 9 loan loss provisioning should no longer affect bank capital. The introduction of IFRS 

9 thus could mitigate the negative relationship between provisioning and economic growth, 

with a potentially positive effect on financial stability (see European Systemic Risk Board, 

2017). 

2.2 

A simple conceptual framework of loan loss provisioning 

To illustrate the rationale for loan loss provisioning rules and their implications for 

the variability of lending, we next develop a simple conceptual framework. We consider a 

bank with assets only in the form of loans, L, financed in part by capital, K, and for the 

remainder by debt. The bank incurs an actual economic credit loss, E, each period, which is 

4 Using US data, Berger and Bouwman (2013) find that capital helps small banks to increase their probability of 
survival  at  all  times,  while  capital  enhances  the  performance  of  medium  and  large  banks  primarily  during 
banking  crises.  Also  using  US  data  Bushman  and  Williams  (2015)  find  that  delayed  expected  loan  loss 
recognition is associated with greater downside tail risk of banks and codependence of downside tail risk among 
banks. 

ECB Working Paper Series No 2288 / June 201911 
  
 
                                                           
taken to be independently and identically distributed. Let P be the bank’s loan loss 

provisioning. Loan loss provisions add to loan loss reserves, R, and they subtract from 

current income and, other things equal, from capital, K.  

As a benchmark, we can counterfactually think about a world in which a bank failure 

entails no externalities to bank customers and the overall economy. In this setting, there are 

no external costs of failure that set banks apart from other firms. Hence, there is no reason to 

introduce banking sector specific regulation, and banks should be free to choose their 

capitalization rate given by K/L, trading off, among other things, the advantage of debt 

finance offered by the deductibility of interest from corporate taxation against the 

disadvantage of higher prospective bankruptcy costs. In this case, there also is no reason to 

regulate the loan loss provisioning of banks, as they will choose the appropriate level of 

economic capitalization regardless of any accounting provisioning for future loan losses.  

Now consider the more realistic case where a bank failure entails substantial negative 

externalities to the economy, say through a reduction in credit provision. This could make a 

bank too big (or generally too important) to fail, and cause public authorities to offer explicit 

or implicit bank debt liability guarantees, for instance in the form of deposit insurance. This 

introduces the incentive for the bank’s owners to enhance the valuation of public guarantees 

by taking on more risk and by lowering their rate of capitalization. To counter the latter, the 

authorities can introduce a minimum capital requirement given by K ≥ γL, where γ is the 

minimum capital requirement rate. As loan loss provisions subtract from capital, banks have 

an incentive to reduce their loan loss provisioning, P, below the level of economic credit loss, 

E, or even to zero. This introduces the need to regulate loan loss provisioning as well.  

If the incurred loss model of loan loss provisioning is prescribed, the bank will set 

provisions, P, equal to the incurred economic credit loss, E, each period. Setting P equal to E 

implies that loan loss provisions are procyclical with respect to GDP, as credit losses tend to 

ECB Working Paper Series No 2288 / June 201912 
 
 
occur when GDP declines. In this setting, a large value of E, and thus P, implies a large 

reduction in capital, K, and also in loans, L, if the capital requirement is binding, unless the 

bank is able to immediately raise enough new equity to stabilize its capital. In practice, 

however, a bank can experience increasing marginal costs of funding in a given period, which 

prevents the bank from raising enough capital immediately to make up for a large credit loss. 

Rather, the bank could take some time to replenish its capital following a large realization of 

E, which implies that loans, L, and hence new lending, will be relatively low for one or 

several periods. This implies that lending can decline with GDP.  

Alternatively, we can consider that the bank is required to apply an expected loss 

model of loan loss provisioning along the lines of IFRS 9 so that provisions, P, each period 

are set equal to the expected value of the credit loss, E, which eliminates provisioning 

procyclicality. The expected loss model of provisioning generally results in a build-up of loan 

loss reserves, R, over time. In this scenario, a larger than average credit loss, E, can be 

accommodated by a reduction of loan loss reserves, R, rather than by a reduction of capital, 

K. With more stable capital, K, also lending can be more stable over time, eliminating the 

dependence of lending on (prior) GDP shocks. In this simple framework, an accounting 

reform that replaces the incurred loss model by the expected loss model thus can smooth 

lending over time. 

2.3 

The expected credit loss model of loan loss provisioning in practice 

While our simple conceptual framework suggests that the introduction of expected 

loss accounting under IFRS 9 will make loan loss provisioning less procyclical, some have 

argued that the impact of IFRS 9 on the procyclicality of loan loss provisioning is ambiguous. 

Provisioning for a next economic downturn under the expected credit loss model may be 

rather abrupt if an initial turning point in the business cycle is taken to forebode a serious 

ECB Working Paper Series No 2288 / June 201913 
 
 
 
business cycle downturn, triggering large loan loss provisioning in anticipation of future loan 

impairment. The introduction of the expected credit loss model thus eliminates the 

concentration of loan loss provisioning at the trough of the economic cycle at the expense of 

concentrating them at the time of an initial downturn, with possible negative ramifications for 

financial stability. Indeed, simulations by Abad and Suarez (2017) show that IFRS 9 will 

concentrate the impact of credit losses on profit and losses and on the Common Equity Tier 1 

(CET1) ratio at the beginning phases of the economic cycle, yielding that banks will face a 

higher yearly probability of having to be recapitalized. Moreover, the application of IFRS 9 

will depend on banks’ internal risk models. To the extent that these models depend on 

historical data, the application of IFRS 9 could lead banks to provision less than would have 

been the case under IAS 39 if losses occur following a long period of exceptionally low 

defaults, as was the case during the 2007-08 global financial crisis.5 More generally, even 

when provisions are set aside based on expected losses at a future date, as in the case of IFRS 

9, provisioning will remain procyclical because recessions are by definition outcomes that are 

worse than anticipated.6 The empirical analysis in this paper pre-dates IFRS 9, which entered 

into force on 1 January 2018. We expect to find that provisioning in the euro area is 

procyclical.  We leave it a question for future research whether IFRS 9 will increase or 

decrease the procyclicality of provisioning. 

2.4 

Discretion over loan loss provisioning 

Beyond the accounting rules that render provisioning procyclical, provisioning for 

credit impairment may also reflect bank discretion on the implementation of formal rules. In 

terms of the earlier conceptual framework, banks can have discretion over loan loss 

5 Consistent with this, Gaffney and McCann (2018) find that the application of IFRS 9 accounting reforms to the 
calculation of credit risk of mortgage loans in Ireland, which went through a major boom-bust cycle in housing, 
if anything, is making provisioning for such loans more procyclical. 

6 A similar argument has been made by Kashyap and Stein (2004) for capital requirements. 

ECB Working Paper Series No 2288 / June 201914 
 
 
                                                           
 
provisioning, if the economic credit loss, E, is not perfectly verifiable by the bank’s outside 

auditors. Accounting discretion over loan loss provisioning then can introduce a wedge 

between a bank’s reported loan loss provisioning, profitability and capitalization and true 

economic values. In practice, banks can have varying motives in setting discretionary loan 

loss provisions, which could have repercussions for provisioning procyclicality and hence 

financial stability. The literature identifies the following three main motives:7 

•  An  income  smoothing  motive:  Bank  managers  may  aim  to  take  provisions  when 

profits  are  relatively  high  in  order  to  smooth  reported  income  after  provisions. 

Conversely,  banks  can  smooth  their  earnings  by  drawing  from  loan  loss  reserves  if 

actual  losses  exceed  expected  losses.  This  type  of  bias  could  reduce  provisioning 

procyclicality,  as  bank  profitability  and  GDP  growth  tend  to  be  positively  related 

(Laeven and Majnoni, 2003).8 In the analysis, we will capture this income-smoothing 

motive through the inclusion of a bank earnings variable. 

•  A risk-taking motive: Banks with higher loan growth often find it difficult to maintain 

the same credit standards, in part because loan officers may have perverse incentives 

to expand credit as their compensation often depends on the volume of loans granted 

rather than the quality of the average loan. Indeed, there is ample evidence that credit 

standards  deteriorate  during  economic  expansions  (Asea  and  Blomberg,  1998;  and 

Dell’Ariccia  et  al.  2002).  In  the  analysis,  we  will  capture  this  risk-taking  motive 

through the inclusion of a loan growth variable. 

•  A capital management motive: Banks can time loan loss provisions to ensure that they 

meet  minimum  regulatory  capital  requirements.  This  implies  that  banks  reduce 

provisioning  levels  when  regulatory  capital  levels  are  relatively  low.  Capital 

7 See Beatty and Liao (2014) and Ozili and Outa (2017) for surveys. 
8  Laeven  and  Majnoni  (2003)  find  evidence  that  loan  loss  provisioning  is  positively  related  to  the  ratio  of  a 
bank’s earnings before-tax and before loan loss provisioning for an international set of banks during 1988–1999. 

ECB Working Paper Series No 2288 / June 201915 
                                                           
management  of  this  kind  reduces  provisioning  procyclicality,  as  bank  capitalization 

rates are more likely to be stressed during economic downturns (Ahmed et al. 1999).9 

However,  risk-based  capital  requirements  per  se  are  procyclical  because  the 

deterioration  of  the  quality  of  bank  loan  portfolios  during  economic  downturns 

inevitably  increases  banks’  risk  exposure—and  therefore  the  level  of  risk-weighted 

capital  requirements—exactly  when  capital  becomes  more  expensive  (Laeven  and 

Majnoni  2003;  Repullo and  Suarez  2013;  Aiyar,  Calomiris,  and Wieladek  2015).  In 

the analysis, we will capture this capital-management motive through the inclusion of 

an interaction between bank capital and economic growth. 

The implementation of IFRS 9 affects the incentives as well as the opportunities for 

banks to apply discretion in setting credit impairment allowances.10 By potentially reducing 

provisioning procyclicality, the new accounting rules should also mitigate the desire of banks 

to manipulate provisioning levels as motivated by the income smoothing and capital 

management motives. However, in practice banks need to use complex statistical models that 

take into account various probability-weighted scenarios as based on forward-looking 

macroeconomic information to calculate expected credit losses under IFRS 9. This introduces 

considerable additional accounting discretion over credit impairment provisions.11 On net, it 

is thus not clear how IFRS will affect the role of accounting discretion in setting loan loss 

provisions and their implied procyclicality.  

9 Ahmed et al. (1999) provide evidence in favor of this hypothesis for the case of US banks during 1986–1995. 
Consistent with a capital management motive, Huizinga and Laeven (2012) find that during the crisis in 2008 
US banks with large exposures to mortgage-backed securities that had declined in value displayed relatively low 
loan loss provisions.  
10 Following the Basel Committee on Banking Supervision (2015), the European Banking Authority (2017) and 
the ECB (2017) provide guidance on how banks should account for expected credit losses and nonperforming 
loans, no doubt with the aim of limiting the scope for bank discretion.  
11 The Global Public Policy Committee (2017, p. 12) representing the major accounting firms indicates that ‘it is 
expected that for most banks, the estimates of expected credit losses (ECL) will present a risk of material 
misstatement that is not low’.  

ECB Working Paper Series No 2288 / June 201916 
 
                                                           
 
3. 

Empirical approach 

In this paper we examine the sensitivity of loan loss provisions to the business cycle 

as potentially affected by the introduction of the euro. Our main hypothesis is that 

provisioning is procyclical, i.e., comoves with economic growth. The basic estimating 

equation is as follows: 

Provisions/assetsict = α + β1EBP/assetsict + β2Loan growthict + β3GDP growthict + ui + vt + εit    

(1) 

In equation 1, the dependent variable is Provisions/assetsict, which is loan loss 

provisions over lagged total assets for bank i located in country c at time t, while ui and vt are 

bank and year fixed effects.12  As a first explanatory variable, EBP/assetsict is a bank’s profits 

before tax and loan loss provisions over lagged total assets. A positive relation between loan 

loss provisioning and current profitability, as evidenced by a higher β1, suggests that bank 

managers use their discretion with respect to loan loss provisioning to smooth bank income 

over time, either because they wish to portray a picture of stable bank income or they think it 

is prudent to provision more when bank profits are higher. A positive β1 is consistent with an 

income smoothing motive. 

As a second explanatory variable, Loan growthict  is the growth rate of net loans, i.e. 

gross loans net of loan loss reserves. This variable captures the risk-taking motive. In 

practice, many banks are able to expand their loan portfolios only by extending riskier loans. 

A positive relation between Loan growth and the Provisioning/assets variable, i.e. β2  > 0, 

would be consistent with this. Alternatively, a bank that imprudently expands its loan 

12  Equation  (1)  does  not  include  country  fixed  effects  because  these  are  already  subsumed  by  the  bank  fixed 
effects. 

ECB Working Paper Series No 2288 / June 201917 
 
 
                                                           
portfolio could similarly take imprudently low loan loss provisions, which could explain that 

we find β2  < 0. 

The explanatory variable of main interest is GDP growthict, which is the growth rate 

of real per capita GDP of country c where bank i is located. A lower rate of GDP growth is 

expected to lead to higher loan loss provisioning consistent with β3  < 0, as loans are more 

likely to become impaired during economic downturns. We will consider loan loss 

provisioning to be more procyclical if Provisioning/assets is more negatively related to GDP 

growth, i.e., if β3 is more negative. 

We consider a variety of additional causes of heterogeneity in loan loss provisioning 

and its procyclicality. To this end, we estimate regressions of the following form: 

Provisions/assetsict = α + β1EBP/assetsict + β2Loan growthict + β3GDP growthict + β4Xict  

+ β5Xict × GDP growthict + ui + vt + εit    

(2) 

in which Xict is a potential determinant of provisioning procyclicality for bank i located in 

country c at time t. The factor Xict enhances provisioning procyclicality if β5 < 0, and vice 

versa. Among potential drivers of provisioning procyclicality, we consider differences in 

bank characteristics and country traits. In terms of bank characteristics, we consider 

differences in bank size, bank equity, and nonperforming loan ratios.  

The interaction with bank equity captures a capital management motive. Banks could 

reduce provisioning levels when regulatory capital levels are relatively low, especially during 

economic downturns when it is more difficult to raise additional capital. We include bank 

size because larger banks may be more diversified and better able to withstand shocks. Larger 

banks may also benefit from too-big-to-fail subsidies, reducing their default risk. We also 

account for differences in (changes in) nonperforming loan ratios because some banks may 

ECB Working Paper Series No 2288 / June 201918 
 
 
 
 
 
 
 
 
systematically have higher shares of impaired loans due to their location or business model, 

and this may impact future loan losses and provisioning behavior.  

In terms of country characteristics, we consider GDP growth experiences during and 

following the crisis, real exchange rate adjustment, the stringency of capital regulation, and 

bank competition. The inclusion of the GDP growth rates during and following the crisis 

accounts for the possibility that the procyclicality of provisioning is disproportionately 

affected by the crisis shock (i.e., procyclicality is asymmetric over the cycle). Real exchange 

rate adjustment could attenuate the relation between loan loss provisioning and GDP growth. 

If so, more limited real exchange rate adjustment in the euro in response to GDP shocks 

could explain a higher estimated procyclicality of loan loss provisioning.  Country level 

variation in the stringency of capital regulation captures variation in the enforcement of bank 

capital rules. A stringent application of such rules could limit bank’s discretion to use 

provisioning to smooth earnings. Similarly, recent research suggests that bank competition 

reduces bank opacity (see, for instance, Jiang et al., 2016), suggesting that bank competition 

could reduce provisioning procyclicality to the extent that it reflects accounting discretion. 

4. 

The data 

The accounting data in this study are obtained from Bankscope. We use the most 

recent version of Bankscope, which contains data for 8 years, as well as earlier annual 

editions to put together a comprehensive panel data set for the period 1996–2015. We 

consider banks located in the 32 countries that are listed in Table 1. These countries are the 

28 EU member states plus Canada, Norway, Switzerland and the United States. For the 19 

current euro area countries, the table also provides the date of euro adoption. As reflected in 

the table, a group of 11 countries initially acquired the euro on January 1, 1999. The other 8 

current euro countries adopted the euro at various later dates, with the most recent euro 

ECB Working Paper Series No 2288 / June 201919 
 
 
adoption by Lithuania on January 1, 2015. Using these euro adoption dates, we construct a 

euro sample of banks located in euro countries after euro introduction. Similarly, we define 

an EU sample of banks located in EU countries. 

Table 2 provides summary statistics for the main variables in the analysis for the full 

and euro samples in Panels A and B, respectively. All bank level variables based on 

accounting data are winsorized at the 1st and 99th percentiles to mitigate the effect of 

outliers. The mean Provisions/assets variable is 0.33% in the full sample compared to 0.44% 

in the euro sample. Banks in the full sample are more profitable as indicated by an average 

EBP/assets of 1.29% compared to 1.08% for euro area banks. In our sample, loan loss 

provisioning amount to about 26.2% of EBP compared to 41.3% for euro area banks. Indeed, 

loan loss provisions are the largest accounting item driving net income in our sample. Banks 

in the full sample also achieve a higher average loan growth of 10.0% in comparison to 

8.16% for the euro sample. Finally, average GDP growth of 1.27% in the overall sample of 

banks is higher than the average of 1.04% for euro area banks. 

Figure 3 shows aggregate provisioning levels over time, together with real per capita 

GDP growth aggregated over countries in our sample. The chart clearly shows that bank 

provisioning is procyclical, being higher during economic downturns. Downturns are 

highlighted in grey using NBER recession dates for the United States. 

5.  

Empirical results 

Subsection 5.1 presents estimates of equation (1) for the overall, the euro and the EU 

samples and of equation (2) while taking into account country characteristics that affect 

provisioning procyclicality in the euro area. Subsection 5.2 considers how bank 

characteristics influence provisioning procyclicality. Finally, subsection 5.3 broadens the 

ECB Working Paper Series No 2288 / June 201920 
 
analysis by jointly considering the procyclicality of provisioning, bank capitalization, and 

loan growth. 

5.1 

Evidence on provisioning procyclicality and country characteristics 

Table 3 shows the results of estimating equation (1) for the full sample, the EU 

sample, and the euro area sample to be able to compare loan loss procyclicality across the 

three sets of countries. The regressions includes bank and year fixed effects, and errors are 

clustered at the bank level. In regression 1 based on the full sample, the EBP/assets variable 

obtains a positive and significant estimated coefficient of 0.073. This result is evidence in 

support of the hypothesis that bank managers set loan loss provisions to smooth bank income 

over time. Loan growth is estimated with a negative and significant coefficient of -0.0009 

which could reflect that bank managers experiencing high loan growth rates underestimate 

the need to take commensurate loan loss provisions. The estimated coefficient for the GDP 

growth rate variable is negative and significant at -0.061 pointing at provisioning 

procyclicality. This effect is economically strong. The regression estimates imply that a one 

standard deviation increase in GDP growth is associated with a decrease in Provisions/assets 

of 0.0012 or 18% of its standard deviation in the full sample. 

Regression 2 includes EU and Euro dummies that obtain a negative and significant 

coefficient of -0.0015, and a positive and significant coefficient of 0.0007, respectively. Thus, 

euro area membership is associated with higher loan loss provisioning rates compared to non-

euro EU countries. 

In regression 3, we consider the relation between euro membership and provisioning 

procyclicality.  In particular, this regression includes the Euro dummy and interactions of this 

dummy with EBP/assets, Loan growth, and GDP growth starting from regression 1. The Euro 

dummy is estimated with an insignificant coefficient, but the three interaction terms obtain 

ECB Working Paper Series No 2288 / June 201921 
 
 
 
coefficients that have the same signs as the uninteracted variables and are significant. Hence, 

loan loss provisioning appears to be more positively related to bank profitability in the euro 

area consistent with stronger income smoothing, and at the same time it is more procyclical 

with GDP. In particular, the estimated coefficients of -0.055 and -0.050 for the GDP growth 

and Euro × GDP growth variables suggest that provisioning procyclicality is about twice as 

high in the euro area compared to non-euro countries. 

Regression 4 estimates equation (1) for the EU sample of banks. The GDP growth 

variable is estimated with a negative and significant coefficient of -0.096, which is more 

negative than the corresponding estimate of -0.061 in regression 1 for the full sample. 

Provisioning procyclicality thus is estimated to be higher in the EU than elsewhere. 

Regression 5 includes the Euro dummy in regression 4, which is estimated to be 

insignificant. Regression 6 in addition includes interactions of the Euro dummy with 

EBP/assets, Loan growth, and GDP growth to see whether provisioning procyclicality is 

different in euro countries compared to EU member states that have not adopted the euro. The 

three interaction terms receive estimated coefficients with the same signs as the uninteracted 

variables that are significant in the cases of Euro × EBP/assets and Euro × GDP growth. 

Relative to non-euro EU countries, there thus appears to be stronger income smoothing 

through loan loss provisions in the euro area and a higher provisioning procyclicality with 

respect to GDP. 

In regressions 7 and 8, we split the overall sample into euro and non-euro samples. 

This yields significant estimated coefficients for the GDP growth variable of -0.107 and -

0.061 in the two regressions, as additional evidence of a higher provisioning procyclicality in 

the euro area compared to other countries. The economic effect in regression 7 for the euro 

sample is indeed much larger than in the full sample. The regression estimates in this 

ECB Working Paper Series No 2288 / June 201922 
 
 
 
regression imply that a one standard deviation increase in GDP growth is associated with a 

decrease in provisioning of 0.0026 or 40% of its standard deviation in the euro area. 

The higher provisioning procyclicality in the euro area could simply reflect that banks 

in countries that adopted the euro already experienced higher procyclicality before euro 

adoption. To examine this, regressions 1 and 2 in Table 4 are based on pre-1999 samples of 

banks located in countries that subsequently did and did not adopt the euro, respectively. The 

significantly estimated coefficients for the GDP growth variable of -0.082 and -0.063 in these 

two regressions suggest that prior to euro introduction provisioning already was more 

procyclical in countries that subsequently adopted the euro. The coefficient of -0.082 in 

regression 1 is less negative than the corresponding coefficient of -0.107 in regression 7 in 

Table 3 for the euro sample, which could mean that euro adoption has increased provisioning 

procyclicality further. 

Next, we consider whether a greater provisioning procyclicality in euro countries 

compared to non-euro countries can be attributed to differences in growth experiences since 

2007 when the recent global economic and financial crisis started. To this end, we include a 

Crisis dummy (signaling the years 2007–2015) and its interactions with EBP/assets, Loan 

growth, and GDP growth in regression 3 of Table 3 for the full sample, with the results 

presented in column 3 of Table 4. In this regression, the Euro × GDP growth and Crisis × 

GDP growth variables receive negative and significant coefficients of -0.030 and -0.060, 

respectively. This is evidence that there already was a greater provisioning procyclicality in 

the euro area prior to 2007, which became more pronounced from 2007 onwards. The results 

also indicate that the provisioning procyclicality in all countries can in large part be 

contributed to the post-2007 period. After controlling for the differential effects of the euro 

and the post-2007 period, the coefficient on GDP growth obtains a coefficient of -0.029 

which is much reduced from the coefficient of -0.061 in the unconditional regression 1 in 

ECB Working Paper Series No 2288 / June 201923 
 
 
Table 3. Regression 4 shows that this result on the post-2007 period holds when contrasting 

euro area countries with non-euro EU countries.  

To further distinguish between the effects from the euro and the post-2007 

experience, we include an interaction term between the Euro and Crisis dummies and its 

interactions with EBP/assets, Loan growth, and GDP growth in regressions 5 and 6 of Table 

3 for the full and EU samples. In regression 5, the Euro × Crisis × GDP growth variable 

obtains a negative and significant coefficient of -0.107. This is evidence that the greater 

provisioning procyclicality in the euro area is mainly driven by the post-2007 period, perhaps 

reflecting differences in growth experiences of the euro area countries during the crisis 

compared to other countries. This result is maintained when limiting the sample to EU 

countries in regression 6. These findings may reflect a greater growth dispersion of euro area 

countries in the post-2007 period, with a standard deviation of GDP growth rates of 0.031 as 

compared to 0.018 for non-euro area countries. 

Overall, Table 4 provides evidence that the greater provisioning procyclicality in the 

euro area reflects a selection of banks or countries with greater pre-euro adoption 

procyclicality into the euro area as well as different post-adoption growth experiences 

following the global economic and financial crisis. 

Table 5 provides additional evidence on how macroeconomic developments related to 

GDP growth and real exchange rate adjustment affect provisioning procyclicality in the euro 

area. Regression 1 adds an interaction of GDP growth with a Negative growth dummy to the 

baseline regression specification in column 7 of Table 3 for the euro sample. In this 

regression, GDP growth and its interaction with the Negative growth dummy receive 

negative and significant coefficients of -0.047 and -0.204, respectively. This is evidence that 

provisioning in the euro area is far more procyclical at times of negative economic growth. 

Since these negative growth experiences are concentrated in the post-2007 period this result 

ECB Working Paper Series No 2288 / June 201924 
 
 
 
reinforces the findings in Table 4 that greater provisioning procyclicality in the euro area 

partly reflects different growth experiences following the start of the global economic and 

financial crisis. More generally these results indicate that provisioning is highest during 

economic downturns. 

Regression 2 alternatively includes an interaction of GDP growth with a GIIPS 

dummy variable that signals a bank located in Greece, Ireland, Italy, Portugal or Spain. These 

five countries were greatly affected by the crisis, as reflected by relatively large negative 

growth rates. Hence, the GIIPS dummy represents a crisis shock. The negative and significant 

interaction GIIPS × GDP growth shows that provisioning is more procyclical in the crisis-hit 

GIIPS countries. 

Provisioning procyclicality may also be affected by real exchange rate adjustments. 

Such effects are of particular interest for the euro area which as a currency union provides for 

limited exchange rate adjustment. The effect of real exchange rate adjustments on 

provisioning depends on the expansionary effect of exchange rate movements. If exchange 

rate movements are expansionary, then they may stabilize output and enhance financial 

stability, reducing the procyclicality of loan loss provisioning. The expansionary effect of real 

appreciations is theoretically ambiguous. On the one hand, they make exports less 

competitive, lowering export demand (Johnson, 1976). On the other hand, they lower import 

prices, improving the terms of trade, and increasing consumption (Krugman and Taylor, 

1978). For the euro area, recent evidence by Lane and Stracca (2018) suggests that the terms 

of trade gains outweigh the losses in competitiveness of the export sector, such that on 

average real appreciations are expansionary. The impact on the banking sector depends in 

part on whether banks cater more to the tradable or nontradable sectors. If the main clients of 

banks are exporters, then the loss in competitiveness could be destabilizing for banks.  

ECB Working Paper Series No 2288 / June 201925 
 
 
To examine how provisioning procyclicality is affected by real exchange rate 

adjustment, regression 3 includes an interaction of GDP growth with ΔREER, which is the 

log annual change in the real effective exchange rate of the country from the Bank for 

International Settlements, using the full sample. An increase in ΔREER represents an 

appreciation of the real exchange rate. This interaction obtains a positive coefficient that is 

significant, which suggests that real exchange rate appreciations reduce the procyclicality of 

loan loss provisioning: a lower GDP growth rate increases provisioning less if at the same 

time the real exchange rate appreciates. This result is consistent with appreciations being 

expansionary, stabilizing output and enhancing financial stability, thereby reducing the 

procyclicality of loan loss provisioning. In addition we find that the ΔREER variable obtains 

a negative and significant coefficient, suggesting that a real exchange rate appreciation 

reduces loan loss provisioning, possibly because of its stabilizing effects on the economy. 

The euro area can be interpreted as a fixed exchange rate system that prevents 

nominal and, to some extent, real exchange rate adjustment in response to GDP shocks. A 

diminished role for real exchange rate adjustment to steady nonfinancial and financial firms 

in the face of GDP shocks is consistent with a greater estimated provisioning procyclicality in 

the euro area. Lower real exchange rate flexibility in the euro area can be a reason why euro 

adoption per se has occasioned greater provisioning procyclicality at euro area banks. 

Following monetary union, another significant institutional change in the euro area 

has been the introduction of a banking union, resulting in common supervision and a 

convergence of supervisory practices. A strict enforcement of regulatory capital standards 

may influence provisioning procyclicality by constraining banks’ discretion on loan loss 

provisioning. Regression 4 includes interactions with the Capital stringency variable, which 

is an index of the stringency of bank capital regulations that ranges from 0 to 10 (from Barth, 

Caprio, and Levine, 2013). The interaction Capital stringency × GDP growth receives a 

ECB Working Paper Series No 2288 / June 201926 
 
 
 
 
negative and significant coefficient, indicating that provisioning is more procyclical in 

countries with more stringent capital regulation. This suggests that stringent capital regulation 

reduces incentives to use discretionary provisioning for capital management purposes.  

Similarly, bank competition may shape provisioning procyclicality by reducing 

banks’ incentives to manipulate their financial accounts and to engage in earnings 

management. We expect that enhanced competition, by reducing bank opacity, reduces 

provisioning procyclicality. Regression 5 includes interactions with a proxy of bank 

competition: the Lerner index. The Lerner index is a measure of market power in the banking 

market, defined as the difference between output prices and marginal costs, relative to output 

prices. Prices are calculated as total bank revenue over assets, whereas marginal costs are 

obtained from an estimated translog cost function with respect to output. Higher values of the 

Lerner index indicate less bank competition. We obtain data on the Lerner index at the 

country level from the World Bank’s Global Development Finance database. Data for the 

year 2015 is missing for the Lerner index. As expected, we find that the interaction Lerner 

index × GDP growth obtains a negative and significant coefficient. This suggests that a more 

contestable banking market, as measured by a lower Lerner index, reduces provisioning 

procyclicality. 

5.2 

Evidence on provisioning procyclicality and bank characteristics 

Heterogeneity in provisioning procyclicality potentially reflects variation in bank 

characteristics such as size and bank capitalization. To examine the role of bank size, we 

construct the Size dummy signaling that a bank’s total assets are above the sample median. 

Regression 1 of Table 6 includes the Size variable and its interactions with EBP/assets, Loan 

growth, and GDP growth in regression 7 of Table 3 for the euro sample. The GDP growth 

variable and its interaction with Size receive negative and significant coefficient of -0.095 

ECB Working Paper Series No 2288 / June 201927 
 
 
 
and -0.006, respectively, indicating that bigger banks display more provisioning 

procyclicality. This could reflect that bigger banks can afford to implement more procyclical 

and riskier provisioning strategies on account of their too-big-to-fail status or alternatively 

that bigger banks have loan portfolios for which expected losses objectively are more 

negatively correlated with GDP growth. 

The greater provisioning procyclicality of bigger banks could be driven by differences 

in bank business models rather than bank size. We therefore include the bank’s loans-to-

assets ratio to account for the extent to which banks are engaged in lending rather than other 

banking activities, such as investment banking or asset management. The results are 

presented in column 2 of Table 6. As expected, we find that banks that specialize in lending 

(i.e., have higher loans-to-assets ratios) tend to have more procyclical provisioning. 

Moreover, controlling for this difference in bank business models does not materially alter 

the effect of bank size on provisioning procyclicality. 

Regression 3 includes the SSM variable that signals that a bank is directly supervised 

by the ECB as part of the Single Supervisory Mechanism (SSM), and interactions of this 

variable with EBP/assets, Loan growth, and GDP growth. GDP growth and SSM × GDP 

growth are estimated with negative and significant coefficients of -0.100 and -0.026, 

respectively, providing evidence that procyclicality at banks that are directly supervised by 

the ECB is greater than at other euro area banks. 

Next, we consider the role of bank capitalization in explaining differences in 

provisioning procyclicality among euro area banks. Regression 4 includes the Equity 

variable, constructed as the lagged ratio of equity to total assets, and its interactions with 

EBP/assets, Loan growth, and GDP growth. The estimated coefficient for the Equity × GDP 

growth variable is negative and significant at -0.110.  Thus, provisioning procyclicality at less 

well capitalized banks is lower, consistent with a capital smoothing hypothesis: less well 

ECB Working Paper Series No 2288 / June 201928 
 
 
 
capitalized banks avoid taking higher loan loss provisions when GDP growth declines to limit 

the negative implications for capitalization.  

By taking loan loss provisions, a bank sets aside funds in preparation for expected 

future loan losses. Expected loan losses may well be higher at banks that are already 

experiencing high loan losses, as indicated by currently high nonperforming loans. To 

account for the role of nonperforming loans, regression 1 of Table 7 includes the NPL ratio, 

which is the ratio of nonperforming loans to lagged gross loans. This variable receives a 

positive and significant coefficient of 0.065, indicating that provisioning is higher at banks 

that already have high nonperforming loan ratios. In this regression, the estimated coefficient 

for the GDP growth variable is negative and significant at -0.049, which is less negative than 

the corresponding estimated coefficient of -0.107 in regression 7 of Table 3, possibly 

reflecting that NPL ratios tend to higher at times of negative GDP growth. 

Regression 2 instead includes the Change in NPL ratio variable, constructed as the 

change in nonperforming loans divided by lagged total assets, as an additional control 

variable, while regression 3 includes the NPL ratio as well as the Change in NPL ratio as 

additional variables. In these two regressions, the additional control variables receive positive 

and significant coefficients, while the GDP growth variable receives significant coefficients 

of -0.058 and -0.060, which in both instances are less negative than the estimate of -0.107 in 

regression 7 of Table 3. Overall, Table 7 provides evidence of a lower estimated provisioning 

procyclicality in the euro area once we control for the nonperforming loans ratio and the 

change in the nonperforming loans ratio. 

5.3 

Implications for the cyclicality of bank capitalization and loan growth 

Higher loan loss provisions translate into lower earnings and ceteris paribus lower 

equity. This suggests that the cyclicalities of loan loss provisions and bank equity with 

ECB Working Paper Series No 2288 / June 201929 
 
 
respect to GDP are directly related. In particular, we would expect that greater increases in 

loan loss provisions as GDP declines translate into greater reductions in bank equity. To 

compare these two cyclicalities, regressions 1 and 2 of Table 8 provide estimates of the 

cyclicalities of Provisions/assets variable and the Change in equity/assets variable, 

constructed as the change in bank equity divided by lagged total assets. These two 

regressions include the Equity ratio as a control variable. In the Provisions/assets regression 

1, GDP growth receives a negative and significant negative coefficient of -0.100, while in the 

Change in equity/assets regression 2 the GDP growth rate receives a positive and significant 

coefficient of 0.154. If we assume a one-to-one relationship between provisions and changes 

in equity, these coefficient estimates suggest that about two-thirds of the cyclicality of the 

change in equity can be attributed to the cyclicality of provisioning.  

Another way of comparing the magnitudes of the effects is to compute the economic 

effects of a change in GDP growth in the two regressions. Based on the estimates of 

regression 1, a one standard deviation increase in GDP growth translates into a decrease in 

the provisioning ratio of 0.15 times its standard deviation; similarly, based on regression 2, a 

one standard deviation increase in GDP growth corresponds to an increase in the change in 

bank equity of 0.19 times its standard deviation. These are significant effects and produce 

similar results as a direct comparison of regression estimates. 

The link between the cyclicality of loan loss provisions and bank equity could in part 

be driven by changes in dividend policy as reductions in earnings reduce the banks’ capacity 

to pay out dividends. To assess the cyclicality of dividends, in regression 3 the dependent 

variable is the Dividends/net income variable, which is constructed as the ratio of dividends 

paid to net income. This regression has fewer observations than regressions 1 and 2 as not all 

banks pay dividends. In this regression, GDP growth is estimated with a negative coefficient 

of -0.192 that is insignificant. This result suggests that dividend payouts remain broadly 

ECB Working Paper Series No 2288 / June 201930 
stable over the business cycle, consistent with dividend signaling theories, contributing to the 

procyclicality of equity. 

Next we consider the procyclicality of loan growth. Loan loss provisions can 

influence loan growth both directly and indirectly. The direct effect arises when current 

provisioning levels are an indicator of future credit quality. The indirect effect arises when 

provisioning affects the procyclicality of bank capital, and bank lending channels are present. 

In regression 4, the dependent variable is the Loan growth variable. The estimated coefficient 

for GDP growth in this regression is positive and significant at 1.408, showing that loan 

growth is procyclical with GDP growth. The coefficient on Equity is also positive and 

significant, consistent with existing evidence that a shortage of bank capital can lead to a 

credit crunch (Bernanke and Lown, 1991). The economic effect is substantial. Based on the 

coefficient estimates in regression 4, a one standard deviation increase in GDP growth 

translates into an increase in loan growth of 0.15 times its standard deviation.  

Next we consider the link between the build-up of loan loss provisions and loan 

growth. Loan loss provisions cumulate over time into loan loss reserves on the bank’s 

balance sheet as a “contra-asset” account. Loan loss reserves increase with loan loss 

provisions and decrease with loan charge-offs. Regression 5 includes the LLR/assets variable, 

which is lagged loan loss reserves over lagged assets, as an additional control variable. The 

negative and significant coefficient for this variable shows that banks with high loan loss 

reserves experience lower loan growth. This could reflect that past provisions are a harbinger 

of additional provisioning in the future, which can already constrain current loan growth. The 

economic effect is substantial. Based on the coefficient estimates in regression 5, a one 

standard deviation increase in the ratio of loan loss reserves to assets translates into a 

decrease in loan growth of 0.23 times its standard deviation. The effect of GDP growth is not 

materially affected when controlling for loan loss reserves. 

ECB Working Paper Series No 2288 / June 201931 
Thus far the results in Table 8 indicate that provisioning procyclicality implies 

reductions in equity and additions to loan loss reserves at times of negative growth. These 

relations and the positive and negative coefficients for the Equity and LLR/assets variables in 

the Loan growth regression in column 5 together suggest that provisioning procyclicality can 

constrain loan growth during recessions.  

Finally, we gauge the implications of the procyclicality of loan loss provisioning for 

the procyclicality of lending in a more direct way. To do so, we proceed in two steps. In the 

first step, we estimate regression 1 in Table 8 for each bank, without fixed effects, over the 

pre-crisis period 1996-2006. We then compute loan loss provisioning procyclicality for each 

bank as the coefficient on GDP growth in these regressions. We exclude from this 

computation banks with less than 5 observations over this period. As a second step, we 

extend regression model 4 in Table 8 by including an interaction between this provisioning 

procyclicality coefficient and the GDP growth variable, and estimate this model over the 

post-crisis period 2007-2015. The reason for estimating the two stages over different sample 

periods is to mitigate concerns of reverse causality given that provisioning and lending 

decisions are jointly determined. However, even in the two-step procedure, there remains 

possible endogeneity, as macroeconomic variables that drive both provisioning and lending 

decisions tend to be persistent over time. All the same, the two-step procedure potentially 

informs on the correlation between provisioning and lending procyclicalities. 

The results of the second-step regression are presented in column 7 of Table 8. To 

ease comparison, column 6 reports results when estimating regression model 4 over the post-

crisis period. We lose observations because we estimate over a shorter sample and exclude 

banks with less than 5 (annual) observations. We find that more provisioning procyclicality 

(i.e., the negative relation between provisioning and GDP growth) is associated with more 

lending procyclicality, as indicated by a negative coefficient of -0.765 on the interaction 

ECB Working Paper Series No 2288 / June 201932 
between the estimated provisioning procyclicality coefficient, called LLP procyclicality, and 

GDP growth. The economic relevance of this association is substantial given the sample 

standard deviation of LLP procyclicality of 0.142. In particular, a one standard deviation 

increase in provisioning procyclicality, i.e., a reduction in LLP procyclicality of -0.142,  is 

associated with an increase in the procyclicality of loan growth of 0.109. This is a large 

increase compared to the unconditionally estimated average procyclicality of lending of 

0.921, as reported in regression 6. Thus, our results suggest that provisioning procyclicality 

correlates strongly with lower credit growth during recessions, with possibly detrimental 

implications for the economy.  

6. 

Conclusions 

We have analyzed the cyclical behavior of banks’ provisioning for loan losses in the 

euro area in comparison to that of non-euro countries. We find that loan loss provisions in the 

euro area are procyclical in the sense that they are negatively related to GDP growth. The 

sensitivity of provisioning to GDP growth in the euro area can explain about two-thirds of the 

variation of bank capitalization over the business cycle. This has significant implications for 

bank lending, as we find that loan growth is positively related to bank capitalization and 

negatively to loan loss reserves. 

Among euro area banks, we find considerable heterogeneity in provisioning 

procyclicality. We find that loan loss provisioning is more procyclical at banks that are larger, 

better capitalized and located in countries with more stringent capital regulations and less 

competitive banking markets. 

We estimate that provisioning procyclicality in the euro area is about twice as large as 

in other advanced economies. This difference can in part be explained by a larger 

procyclicality of provisioning in euro area countries already prior to adopting the euro, and 

ECB Working Paper Series No 2288 / June 201933 
 
the divergent growth experiences of euro area countries following the global financial crisis. 

The greater provisioning procyclicality in the euro area could also reflects an effect of the 

euro per se, as the euro limits potential real exchange rate adjustment that could stabilize 

commercial borrowers in the face of GDP shocks. 

Our results have important implications for the supervision of euro area banks going 

forward. First, our finding of a relatively large provisioning cyclicality in the euro area 

stresses the need to make efforts to reduce this procyclicality, given that this is likely to 

remain a problem after the introduction of IFRS 9. Second, our finding of considerable 

heterogeneity in provisioning among euro area banks suggests that banks have used material 

discretion in applying the incurred loss model of loan loss provisioning. This heterogeneity is 

unlikely to vanish on account of IFRS 9, and hence supervisors will need to apply 

considerable efforts to effect a more uniform application of loan loss provisioning rules 

across euro area banks. More generally, our results call for increased attention to potential 

undesirable consequences of provisioning rules for the procyclicality of lending.  

ECB Working Paper Series No 2288 / June 201934 
 
 
 
 
 
 
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ECB Working Paper Series No 2288 / June 201935 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ECB Working Paper Series No 2288 / June 201936 
 
 
 
 
 
 
 
 
 
  
 
 
 
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ECB Working Paper Series No 2288 / June 201937 
 
 
 Figure 1. Provisioning and loan loss reserves in banks’ income statement and balance sheet 

Income statement 

Net interest income 

Plus: Noninterest income 

Less: Salaries and benefits 

Less: Provision for loan losses 

Less: Other noninterest expense 

Equals: Income before taxes 

Less: Income taxes 

Equals: Net income 

Balance sheet 

Cash 

Securities 

Total loans 

Assets 

Liabilities and Equity 

Deposits 

Other liabilities 

Sum: Total liabilities 

Less: Reserves for loan losses 

Equity 

Equals: Net loans 

Other assets 

Sum: Total assets 

Source: Balla, Rose, and Romero (2012). 

Sum: Total liabilities and equity 

ECB Working Paper Series No 2288 / June 201938 
 
 
 
 
 
Figure 2. Loan losses, provisions and regulatory capital 

General
Provisions

Credit risk capital

y
t
i
l
i

b
a
b
o
r
P

99th percentile

Expected 
losses

Unexpected 
losses

O                           A                         Loss   

B

Source: Laeven and Majnoni (2003).

ECB Working Paper Series No 2288 / June 201939 
 
Figure 3. Loan loss provisions and economic growth 

NBER recession

llp

growth

0.8%

0.6%

0.4%

0.2%

0.0%

4.0%

2.0%

0.0%

-2.0%

-4.0%

6
9
9
1

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9
9
1

0
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2

Notes: Llp denotes the ratio of loan loss provisioning to lagged total assets; growth denotes real per capita GDP 
growth; NBER recession denotes recession years according to the NBER. Llp and growth are aggregated values 
across banks in the full sample of countries used for the analysis in this paper.

ECB Working Paper Series No 2288 / June 201940 
 
Table 1. Countries included in the analysis and their EU membership and euro adoption dates 

Canada, Norway, Switzerland, and the United States are included non-EU countries 

Country 
Austria 
Belgium 
Bulgaria 
Canada 
Croatia 
Cyprus 
Czech Republic 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Norway 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
Switzerland 
United Kingdom 
United States 

EU member since 
1 January 1995 
1 January 1958 
1 January 2007 
- 
1 July 2013 
1 May 2004 
1 May 2004 
1 January 1973 
1 May 2004 
1 January 1995 
1 January 1958 
1 January 1958 
1 January 1981 
1 May 2004 
1 January 1973 
1 January 1958 
1 May 2004 
1 May 2004 
1 January 1958 
1 May 2004 
1 January 1958 
- 
1 May 2004 
1 January 1986 
1 January 2007 
1 May 2004 
1 May 2004 
1 January 1986 
1 January 1995 
- 
1 January 1973 
- 

Euro since 
1 January 1999 
1 January 1999 
- 
- 
- 
1 January 2008 
- 
- 
1 January 2011 
1 January 1999 
1 January 1999 
1 January 1999 
1 January 2001 
- 
1 January 1999 
1 January 1999 
1 January 2014 
1 January 2015 
1 January 1999 
1 January 2008 
1 January 1999 
- 
- 
1 January 1999 
- 
1 January 2009 
1 January 2007 
1 January 1999 
- 
- 
- 
- 

ECB Working Paper Series No 2288 / June 201941 
 
Table 2. Summary statistics 

Provisions/assets is loan loss provisions over lagged total assets. EBP/assets is profits before tax and loan loss 
provisions over lagged total assets. Loan growth is the growth rate of net loans. GDP growth is the growth rate 
of real per capita GDP. Panel A provides summary statistics for the full sample of banks in countries listed in 
Table 1 during 1996-2015. Panel B provides summary statistics for the euro area sample of banks in euro 
countries after their euro adoption dates. 

Panel A. Full sample  

Variable 
Provisions/assets 
EBP/assets 
Loan growth 
GDP growth 

Obs 
173,814 
173,814 
171,710 
186,891 

Mean 

Std. Dev. 
0.003386  0.006512 
0.012908  0.012914 
0.100456  0.247748 
0.0193 
0.012737 

Min 
-0.00474 
-0.02597 
-0.32985 
-0.1456 

Max 
0.039998 
0.075488 
1.571429 
0.130815 

Panel B. Euro area sample  

Variable 
Provisions/assets 
EBP/assets 
Loan growth 
GDP growth 

Obs 
43,022 
43,022 
42,488 
44,517 

Mean 
0.004482 
0.010849 
0.081602 
0.010405 

Max 

Std. Dev.  Min 
0.006438 
0.010971 
0.231439 
0.024321 

-0.00474  0.039998 
-0.02597  0.075488 
-0.32985  1.571429 
-0.08998  0.096202 

ECB Working Paper Series No 2288 / June 201942 
 
 
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ECB Working Paper Series No 2288 / June 201951 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Acknowledgements 

We would like to thank Sebnem Kalemli-Ozcan and Philippe Martin, Richard Portes, Ed Sibley, Dimitri Vayanos, and participants at the 
IMF/IMFER/CBI Conference on “The Euro at 20” in Dublin for useful comments, and Francesca Barbiera for research assistance. The 
views expressed are our own and do not reflect those of the ECB or Eurosystem. 

Harry Huizinga 
Tilburg University, Tilburg, The Netherlands; CEPR; email: h.p.huizinga@uvt.nl 

Luc Laeven 
European Central Bank, Frankfurt am Main, Germany; CEPR; email: luc.laeven@ecb.europa.eu 

© European Central Bank, 2019 

Postal address  60640 Frankfurt am Main, Germany 
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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 

ISBN 978-92-899-3550-0 

ISSN 1725-2806 

doi:10.2866/37202 

QB-AR-19-069-EN-N