The Impact of Interest Rates on Bank Profitability: A Retrospective Assessment Using New Cross-country Bank-level Data

The Impact of Interest Rates on

Bank Protability: A Retrospective Assessment

Using New Cross-country Bank-level Data

Callan Windsor, Terhi Jokipii and Matthieu Bussiere

International Banking Research Network contributors:

Callan Windsor and Marcus Miller (Australia); Yaz Terajima (Canada); Alejandro Jara (Chile);

SimonaMalovana (Czech Republic); Stefano Ungaro (France); Henrike Michaelis (Germany);

CaoJin (Norway); Gajewski Krzysztof (Poland); Li Jieying (Sweden);

Terhi Jokipii and Javier Rodriguez-Martin (Switzerland)

Research Discussion Paper

RDP 202305

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The Impact of Interest Rates on Bank Profitability: A Retrospective

Assessment Using New Cross-country Bank-level Data

Callan Windsor*, Terhi Jokipii** and Matthieu Bussiere***

International Banking Research Network contributors:

Callan Windsor and Marcus Miller (Australia); Yaz Terajima (Canada); Alejandro Jara (Chile);

Simona Malovana (Czech Republic); Stefano Ungaro (France); Henrike Michaelis (Germany); Cao Jin (Norway);

Gajewski Krzysztof (Poland); Li Jieying (Sweden); Terhi Jokipii and Javier Rodriguez-Martin (Switzerland)

Research Discussion Paper

2023-05

June 2023

*Reserve Bank of Australia

**Swiss National Bank

***Banque de France

We would like to thank Anthony Brassil, Meredith Österholm, Linda Goldberg, Mattia Girotti,

Viktors Stebunovs, Guillaume Horny, Laurent Clerc, Philippe Billard and participants at the

Summer 2021 International Banking Research Network Meeting for their helpful comments and

suggestions. The views expressed in this paper are those of the authors and do not necessarily

reflect the views of contributing central banks. The authors are solely responsible for any errors.

Authors: windsorc at domain rba.gov.au, terhi.jokipii at domain snb.ch and Matthieu.BUSSIERE at

domain banque-france.fr

External Communications: rbainfo@rba.gov.au

https://doi.org/10.47688/rdp2023-05

Abstract

This paper provides a retrospective assessment of the relationship between bank profitability and

interest rates, focusing on the period when rates were very low or negative. To do this we use new

confidential bank-level data covering about 1,500 banks operating in 10 banking systems, with most

samples spanning the two decades up to the end of 2019. Our analysis confirms the empirical

regularity that declining interest rates reduce banks’ net interest margins. However, we find a smaller

effect than in previous studies: on average across countries, a 100 basis point fall in short-term

interest rates results in a 5 basis point decline in net interest margins in the short run. Notably, there

are substantial cross-country differences, and, in some cases, the estimated effect is greater.

Importantly, the effect of lower interest rates on net interest margins is larger than the effect on

asset returns, suggesting that banks can shield overall profitability in the face of lower interest rates.

For example, lower interest rates alleviate debt-servicing burdens and are associated with a fall in

provisions set aside to cover losses on loans. There is therefore no one-size-fits-all result for the

impact of low interest rates on overall profitability: in some jurisdictions banks maintained their level

of profitability as the beneficial impact of lower rates on loan-loss provisions and other factors,

including an increased focus on cost efficiencies and streamlining business models, materially offset

the drag from lower interest margins.

JEL Classification Numbers: E52, F34, F36, G21

Keywords: interest rates, bank profitability, net interest margin, monetary policy

  
Table of Contents 
 Introduction  1 
 Overview of the Data and Methodology  2 
 Literature on Bank Profitability and Interest Rates  3 
 Channels of Monetary Policy to Bank Profitability  4 
1  Other factors  5 
 Data Description  7 
 Analytical Framework  12 
 Empirical Findings across Countries  13 
1  Net interest margins  13 
2  Overall bank profitability  21 
2.1  Overall bank profitability: quantitative results in more detail  23 
 Conclusion  24 
Appendix A: Summary of Selected Bank-level Papers  25 
References  26 
 
 

1. Introduction

For much of the past decade, interest rates in many countries were at or near historically low levels.

This raised questions about the consequences of low interest rates for bank profitability and

implications for the transmission of monetary policy. While interest rates have risen more recently

due to high inflation, this paper provides a retrospective assessment of the effect of low interest

rates on bank profitability. The challenges and consequences of low rates may arise again at some

point in the future, particularly given the neutral rate is estimated to have fallen significantly in

advanced economies over the past few decades (Holston, Laubach and Williams 2017). This paper

is unique in using proprietary bank-level data for 10 countries, with the effect of lower interest rates

on bank profitability estimated by banking sector experts from each country.

This paper presents new insights on the

direct

impact of lower rates on bank profitability, after

controlling for other factors that operate indirectly through monetary policy's independent impact on

aggregate demand.

All else equal, lower interest rates are likely to directly impact bank profitability

by eroding banks’ net interest margins (NIMs). This is because most bank assets earn a rate of

interest that varies to some extent with the policy rate. However, some bank liabilities, including

equity and transaction deposits, pay no or little interest and banks may choose not to reprice

transaction deposits in line with the policy rate. As a result, lower interest rates are likely to lower

NIMs. Moreover, the impact on margins could be larger in low-rate environments, when rates are at

or near their effective lower bounds. This is because of the higher share of deposits at low rates

that banks choose not to reprice lower in line with lending rates. The impact on margins could also

be larger if rates have been ‘lower for longer’ as interest rate hedges become less effective over

time.

While NIMs will tend to decline with interest rates, the overall direct impact on bank profitability is

not obvious (CGFS 2018). Lower interest rates can decrease loan-loss provisions by reducing the

cost of servicing debt and lowering default probabilities. Banks can also respond endogenously by

increasing their non-interest income (for example, fee income) and reducing their costs of operating.

Ultimately, teasing out the balance of these effects is an empirical question.

Quantifying the impact of lower rates on bank profitability and its components is relevant for

policymakers. On the one hand, lower profits erode banks’ ability to build capital buffers to absorb

future losses. Lower capital can also weigh on lending (Gambacorta and Shin 2018), with reduced

credit availability potentially weighing on economic activity. In a theoretical model, Brunnermeier

and Koby (2018) propose a ‘reversal rate’ below which further reductions in the policy rate become

contractionary under specific conditions.

This is because of the negative effects of lower profitability

on bank capital and the associated contractionary effects on bank lending. On the other hand, if

banks choose to protect their profitability by not lowering interest rates on their lending after a fall

in the policy rate then this could impair the transmission of monetary policy. Central banks have

While we are only concerned with estimating the direct impact of lower rates, it is important to recognise that lower

interest rates

indirectly

contribute to higher bank profits by stimulating economic activity.

Banks in some jurisdictions engage in interest rate swaps to hedge interest rate risk stemming from holding a greater

amount of fixed-rate liabilities relative to fixed-rate assets. However, these hedges become less effective when rates

have been lower for longer because they gradually roll onto lower interest rates.

These include banks being subject to an occasionally binding equity constraint (and so being unable to raise external

equity) as well as being net investors in debt securities.

acknowledged these potential side effects and have in some cases adapted their operations in low-

rate environments to lower the burden of low or negative interest rates on bank profitability.

2. Overview of the Data and Methodology

This paper summarises the association between interest rates and bank profitability using unique

confidential bank-level data across 10 countries. These data were made accessible as part of a

collaboration between 10 central banks organised by the International Banking Research Network

(IBRN).

Each participating central bank in the IBRN examined the association between interest rates

and bank profitability using a common methodology that takes into account underlying economic

conditions as well as differences in banks’ business models. This paper also draws on qualitative

information obtained from a survey of each contributing central bank. The survey asked respondents

to describe, among other things, the impact of low rates on banks’ profits; actions taken by banks

to mitigate any negative impact; and various features of the operating environment, such as the

interest rate structure of banks’ assets.

The use of confidential bank-level data – complemented by the survey information – sharpens

existing cross-country empirical evidence on the association between rates and profitability. Previous

studies have tended to rely on commercially available databases such as BankScope or S&P Global

Market Intelligence’s SNL Financial, which use strict criteria to ensure all variables are consistently

reported across countries.

While this consistency is invaluable to researchers, it typically results in

more missing observations and smaller sample sizes. Conversely, the use of confidential data gives

our banking sector experts more flexibility to adjust sample sizes and adjust the construction of

particular variables to best represent the underlying concept of interest. For example, the use of

confidential bank-level data for Australia – made available to central bank researchers by the

prudential regulator – significantly increases the available sample size and reduces the incidence of

missing observations. Our research goal, and the main contribution of this paper, is to use the best

available data to answer our research question and add a degree of nuance to existing cross-country

work in this area by drawing on the insights from our qualitative survey.

Relative to previous approaches, our estimation strategy is akin to a completely flexible cross-country

panel regression, in which every independent variable is allowed to vary by country. By allowing our

estimated effects to vary by country under a common methodology, we are better able to compare

results between countries relative to other large cross-country studies using estimates obtained from

a pooled cross-country sample. We focus on four different dependent variables: the return on assets

(ROA), the NIM, non-interest income (Non-II) and loan-loss provisions (LLPs). This way we can

better identify the channels through which low and negative interest rates affect profitability. In

contrast to previous research, we also permit country-specific thresholds for what are considered

See, for example, Mario Draghi’s quote from 27 March 2019: ‘We will continue monitoring how banks can maintain

healthy earning conditions while net interest margins are compressed. And, if necessary, we need to reflect on possible

measures that can preserve the favourable implications of negative rates for the economy, while mitigating the side

effects, if any. That said, low bank profitability is not an inevitable consequence of negative rates.’ The proportionality

assessment of non-conventional measures and the need to ‘counteract undesirable side effects’ is explicitly mentioned

in the Strategy Review of the ECB, which was concluded in July 2021 – see the overview note (ECB 2021).

Further information on the IBRN can be found on its official website (https://www.newyorkfed.org/ibrn).

See, for example, Borio, Gambacorta and Hofmann (2017), CGFS (2018) and Claessens, Coleman and Donnelly (2018).

Altavilla, Boucinha and Peydró (2018) use a mix of proprietary data in conjunction with data from several commercial

providers, but only focus on the euro area.

low interest rate episodes and identify these from the history of short-term interest rates within each

country. A common definition of ‘low’ across all countries – such as the 1.25 per cent threshold used

in Claessens

et al

(2018) – would mean that several countries in our sample – such as Australia,

Canada and Chile – only spend limited periods of time below the low-rate threshold. Country-specific

thresholds ensure there is sufficient within-country variation in interest rates within the low-rate

environment to identify any ‘nonlinear’ effects.

While the magnitude of any nonlinear effects (should

they exist) could differ between countries – depending on the proximity of their low-rate thresholds

to zero – our approach allows these effects to be identified for all countries in the sample.

Furthermore, we separately examine whether prolonged low or negative interest rates

disproportionately impair bank profitability. Finally, the richness of our data allows us to disaggregate

banks by size: large, global banks – defined here as the 80 or so banks that are included in the

Bank for International Settlements global systemically important banks (G-SIB) assessment sample

– could have the capacity to better shield their profit margins in a low interest rate environment,

and we can test this empirically.

The general result that – all else equal – lower policy rates decrease margins is much clearer for the

NIM than for ROA, suggesting that many banks can partially offset the effect of low interest rates

on overall profitability. Our results for banks’ LLPs suggest that lower rates reduce debt-servicing

burdens. There also appear to be subtle nonlinearities in low interest rate environments and results

tend to indicate that the reaction of bank profitability to interest rate changes can differ between

larger, often more sophisticated banks, relative to their smaller peers. More broadly, a key finding

from this work is that there is not a one-size-fits-all answer to the impact of monetary policy on

overall bank profitability. The qualitative information obtained with the abovementioned survey also

informs some of these cross-country differences.

The rest of the paper is organised as follows. Section 3 reviews the relevant literature. Section 4

discusses the main channels that link interest rates to bank profitability. Section 5 presents the data

and key stylised facts. Section 6 turns to the analytical framework and empirical strategy followed

in the paper. The results are presented in Section 7. Section 8 concludes the paper.

3. Literature on Bank Profitability and Interest Rates

There is widespread empirical support that lower interest rates are associated with a decline in

banks’ NIMs. However, there is less agreement on the impact of monetary policy on overall bank

profitability as well as the impact of negative rates.

Table A1 summarises some of the existing bank-

level studies. One goal of this paper is to bring the best available bank-level data to bear on these

questions and tie together the seemingly disparate evidence about the impact on overall profitability

and nonlinear effects when short-term interest rates are below zero.

Starting with the impact on banks’ interest margins, several studies identify a nonlinear relationship

between interest rates and NIMs, with the marginal impact of a cut to the cash rate larger in low

interest rate environments – see, for example, Borio

et al

(2017). A prolonged period of low rates is

Throughout this paper, we capture ‘nonlinear’ effects by allowing the linear impact of rates on profitability to change

if the bank is operating in a low-rate regime.

As defined by the Basel Committee on Banking Supervision, see <https://www.bis.org/bcbs/gsib/gsib_assessment_samples.htm>

for details of the sample.

See, for example, Borio

et al

(2017), Claessens

et al

(2018), Altavilla

et al

(2018), Bikker and Vervliet (2018),

CGFS (2018) and Beauregard and Spiegel (2020).

also found by several studies to have a larger negative effect on margins than a relatively short

period – see, for example, Claessens

et al

(2018).

However, there is less consensus in the literature on the impact of monetary policy on overall

profitability. Several country-specific papers find modest effects of lower interest rates on bank

profitability – for example, Alessandri and Nelson (2015) for UK banks and Busch and

Memmel (2015) for German banks – while larger impacts are reported in cross-country studies – for

example, Borio

et al

(2017). In contrast, other papers find a negligible effect of interest rates on

bank profitability. For example, Genay and Podjasek (2014) and Bikker and Vervliet (2018) both find

that interest rates have a negligible effect on US banks’ profitability, mainly because higher fees and

lower LLPs offset downward pressure on NIMs.

In recent years studies have focused specifically on the effect of negative rates on bank profitability,

with no common ground established. For Denmark and Sweden, Turk (2016) finds that the

profitability of banks was resilient following the introduction of negative interest rates, at least in the

short and medium term, as does Basten and Mariathasan (2018) for Swiss banks. Focusing on a

large cross-country sample of European and Japanese banks, Lopez, Rose and Spiegel (2020) report

that the benign implications of negative rates for bank profitability were because banks were able

to offset interest income losses under negative rates with gains in non-interest income, including

fees and capital gains. By contrast, Rostagno

et al

(2019) estimate that euro area bank profitability

would have been lower in counterfactual scenarios in which the policy interest rate remained at zero

or above. Urbschat (2018), Molyneux, Reghezza and Xie (2019) and Beauregard and Spiegel (2020)

find that negative interest rates reduce bank profitability in the longer run, partly because of banks’

limited ability to pass on negative rates to depositors or otherwise adjust their business models.

4. Channels of Monetary Policy to Bank Profitability

A bank’s overall profitability, as measured by its ROA, can be decomposed simplistically according

to the identity below – see Brassil (2022) for a more complete decomposition:

( ) ( )

- , where

t t t t t A L L

i A i L E

ROA Non II NIM LLP NIM i i i

−



 + −  = − +





In this identity,

ROA

-Non II

N IM

and

LLP

are defined as a share of assets. The average

interest rates on banks’ interest-bearing assets and liabilities are

and

respectively, and

, and

are the values of banks’ assets, liabilities and equity. This decomposition motivates us to

examine not only the association between interest rates and ROA, but also the association with

NIMs, Non-II and LLPs. This allows us to unpack the channels through which changes in interest

rates and the slope of the yield curve affect overall profitability. These channels are considered in

detail below.

NIMs: As interest rates fall a larger share of bank deposits pay very low interest rates. This can

squeeze NIMs because as rates fall, deposits that already receive zero or very low interest rates

have not been repriced lower in line with lending rates or the return on liquid assets. This is especially

true if market rates become negative, as banks may be unable to adjust deposit rates. There is also

a mechanical association between interest rates and banks’ NIMs. This occurs because a share of

banks’ funding is from equity, which does not bear interest. This limits the extent to which a

reduction in interest rates flows through to lower funding costs and mechanically reduces NIMs. To

see this, note that in the identity above, even with constant spreads, the

N IM

falls as the level of

interest rates declines. The slope of the yield curve also matters, as banks’ loans and other assets

typically have longer durations than their liabilities.

The impact of policy rates on profits is also likely to vary by bank size. Large banks have more

complex business models and more diverse sources of income which may make them more nimble

in shoring up profitability as NIMs decline. Larger banks also tend to rely less on deposit funding

and more on market-based sources of funding, and so their NIMs could be expected to compress

less when interest rates decline because of the effective lower bound on deposit rates. This

hypothesis is consistent with the idea that larger banks with global operations are more insulated

from changes in monetary policy (Cetorelli and Goldberg 2012).

Non-II: The reduction in NIMs could be offset by changes in Non-II. When interest rates fall, banks

gain from the revaluation of longer-term assets given their role in maturity transformation and the

associated positive duration gap between assets and liabilities. Banks can also offset lower NIMs

through other endogenous adjustments to the way they operate. For instance, banks can pivot to

Non-II-generating activities as well as increase their fees.

LLPs: Low interest rates may also affect LLPs. Lower rates make the existing stock of debt easier

to service, thereby reducing overall debt burdens and estimated probabilities of default (PDs). These

PDs are an important input into banks’ forward-looking provisioning models. On the other hand, low

interest rates may also lower the quality of new loans through the risk-taking channel of monetary

policy. The literature on a risk-taking channel of monetary policy suggests falling interest rates can

increase risk-taking by banks in three ways. First, lower profits and sticky nominal return targets can

increase banks’ willingness to extend loans to riskier borrowers (Rajan 2006; Haldane 2011). Second,

higher income and collateral values may lead to falling risk perceptions (Jiménez

et al

2014). And

finally, forward guidance by central banks might reduce risk premia in low-rate environments (Borio

and Zhu 2012). However, a risk-taking channel of monetary policy (to the extent that it matters) will

only affect the flow of new loans. Given the stock of variable-rate loans is larger than the flow, lower

interest rates are expected to lower provisions.

4.1 Other factors

Several other factors mean the channels outlined above are unlikely to have a uniform impact across

countries.

Funding behaviour: Differences in the composition of banks’ liabilities will affect the impact of

very low rates on profits. Funding from wholesale markets (such as bonds) is not constrained by the

zero lower bound in contrast to what has been observed for deposit funding. As a result, banks that

rely more heavily on wholesale funding markets are less likely to be affected by a reduction in the

policy rate from already low levels. Macroeconomic and institutional factors mean that some banking

systems make greater use of wholesale funding relative to others. For instance, the Australian,

Norwegian and Swedish banking systems are less reliant on deposit funding relative to their

international peers, which could be expected to attenuate the impact of lower rates on profitability,

all else equal.

Prevalence of fixed rate loans: Countries with a higher share of fixed-rate lending are likely to

be more affected by changes in the slope of the yield curve relative to countries with a higher share

of variable-rate loans. For example, in France, fixed-rate loans comprise around 90–95 per cent of

the stock of lending. This contrasts with other countries such as Canada, where the stock of fixed

rates is around one-half, and Sweden and Australia where the share is lower still.

Hedging: Likewise, in countries where hedging is less common – for example, countries in the euro

area; see Hoffmann

et al

(2019) – movements in the yield curve are likely to have a larger impact

on bank profits. Ordinarily, NIMs will narrow when yield curves flatten because banks are exposed

to interest rate risk from maturity mismatches because of borrowing short and lending long. The

extent to which banks reduce their exposure to this risk by hedging will impact their sensitivity to

changes in the yield curve.

Banks are also exposed to interest rate risk stemming from holding a greater amount of fixed-rate

liabilities (such as non-interest bearing deposits) relative to fixed-rate assets, such that when interest

rates decline net income from these positions falls. Banks can choose to hedge this risk using swaps

whereby the bank receives cash flows linked to fixed rates and pays cash flows linked to variable

rates. As a result, when variable rates decline the income from these hedges increases, thereby

providing the necessary hedge. The extent to which some banking systems use interest rate swaps

to hedge this risk will also cause differences in the pass through of lower rates to profits in the short

run.

Competition: Banks operating in countries with less competitive banking systems will have more

pricing power. As a result, following a cut to cash rates, these banks can ensure the fall in their

lending rates is closer to the decrease in their funding costs, leaving NIMs less affected than

otherwise similar banks operating in more competitive environments.

Central bank term funding facilities: The introduction of various funding schemes since the

2008 financial crisis is another factor that is likely to cause cross-country heterogeneity in the

responsiveness of bank profits to lower rates. Term funding schemes involve providing low-cost,

longer-term funding to banks, often with incentives for banks to increase their lending to the private

sector. These schemes have been used as an alternative tool to provide stimulus when policy rates

are near their effective lower bounds. Differences in the availability of these schemes, their design

features and take-up across countries are likely to drive differences in the impact of low rates on

bank profitability.

Tiered rates in the implementation of monetary policy: Finally, another measure that can be

deployed by central banks to address the side effects of low/negative interest rates is a so-called

tiering system. For example, the ECB introduced tiering in September 2019 to support the bank-

based transmission of monetary policy. Under this scheme, banks’ holdings of excess liquidity were

exempted from the negative deposit rate facility. As explained later, this system is particularly

relevant for French and German banks (both included in our sample) who hold a large share of total

system liquidity – see, for example, Baldo

et al

(2017) and Grossmann-Wirth and Hallinger (2018).

Other countries in our sample have also used similar schemes, including Norway and Switzerland.

In the long run these hedges become less effective and so are unlikely to drive cross-country differences in the long-

run responsiveness of bank profits to changes in the cash rate.

5. Data Description

The current project is conducted under the auspices of the IBRN, a network of central bank

researchers who focus on global banking. To examine the impact of interest rates on banks’

profitability, we rely on confidential bank-level data. Each country’s data are only available to subject

matter experts from that country’s central bank and we rely on the expert judgement of each

contributor to construct the most appropriate sample for their jurisdiction.

Details of the sample for each country are provided in Table 1. Around 1,500 banks across the

10 jurisdictions are examined, with a bank broadly defined as an institution whose business is to

receive deposits and/or close substitutes and grant credits or invest in securities on their own

account. Most jurisdictions use data at a quarterly frequency, although some use semiannual and

annual data. Seven countries use unconsolidated banking data, with the decision to rely on

consolidated versus unconsolidated data left to subject matter experts from each central bank. While

most data series start in the early 2000s or earlier, for Sweden they start only in 2010.

Table 1: Key Statistics for the Country Samples

AUS

(a)

CAN

CHL

CZE

FRA

DEU

NOR

POL

SWE

CHE

Start date

2003

1994

2004

2002

2000

2010

2000

Frequency

QoQ

HoH

YoY

QoQ

YoY

No of banks

181

562

203

221

104

No of time periods

104

Consolidation

Low-rate threshold

2.0

1.2

2.8

0.5

1.2

1.6

1.9

0.9

0.2

Negative rates

Yes

Windsorisation (%)

0.5

Notes: International Monetary Fund country abbreviations: AUS: Australia; CAN: Canada; CHL: Chile; CZE: Czech Republic;

FRA: France; DEU: Germany; NOR: Norway; POL: Poland; SWE: Sweden; CHE: Switzerland.

(a) Sample includes banks, credit unions and building societies, foreign branches and foreign subsidiaries.

Source: Contributing central banks

Table 2 provides a full list of variables used in our analysis. Unlike previous studies, our definition of

a ‘low-rate environment’ is country-specific, defined as the 25th percentile of each country’s historical

rate distribution. In choosing the most appropriate time period over which to define ‘low’ rates we

again rely on our participating subject matter experts, with the sample period not necessarily the

same as that shown in Table 1. The low-rate threshold for each central bank is plotted in Figure 1.

Table 2: Definitions

Term

Definition

Panel A: Dependent variables

Return on assets (

ROA

)

The ratio of net income expressed as a percentage of average total assets

(ATA).

Net interest margin (

NIM

)

The ratio of net interest income (NII) expressed as a percentage of average

interest earning assets (AEA). NII includes gross interest and dividend income

minus total interest expense. AEA is the sum of total loans, total securities,

investments in property and earning assets not otherwise categorised,

including non-current assets held for sale which are not loans.

Non-interest income (

-Non II

)

The ratio of Non-II expressed as a percentage of ATA. Non-II is the value of

operating income from continuing operations for the reporting period,

excluding the value of interest income and interest expense.

Loan-loss provisions (

LLP

)

(a)

The ratio of LLPs (or impairment expenses) to cover non-performing loans

expressed as a percentage of ATA. This is a flow item from the income

statement.

Panel B: Variables of interest

Short-term interest rate

Three-month interbank rate.

Spread

Difference between the 10-year sovereign bond yield and the 3-month interest

rate.

Low-rate dummy variable

Equal to 1 when the 3-month interbank rate is in the first quartile of the

country-specific historical rate distribution.

Lower-for-longer variable

The number of consecutive years that a country’s 3-month interest rate is in

the ‘low’ period.

Large bank dummy

(b)

Equal to 1 if the bank is in the group of global banks that made the main G-SIB

assessment sample.

Panel C: Baseline bank controls

Deposits over liabilities

The ratio of deposits and short-term funding (STF) expressed as a percentage

of total liabilities. Deposits and STF include total customer deposits, deposits

from banks, money market instruments, certificates of deposit and other

deposits.

Liquid assets over total assets

The ratio of cash, liquid assets and securities expressed as a percentage of

ATA. Securities include reverse repos and cash collateral, trading securities, all

in-the-money trading derivatives and derivatives recognised for hedging (less

the value of netting arrangements), available for sale securities, held to

maturity securities, at-equity investments, and other securities.

Equity ratio

The ratio of total equity expressed as a percentage of ATA. Total equity

includes common equity, non-controlling interest, securities revaluation

reserves, foreign exchange revaluation reserves, and other revaluation

reserves.

Panel D: Baseline macro controls

Real GDP growth

Year-on-year growth in real GDP.

CPI growth

Year-on-year inflation.

Housing price growth

Year-on-year growth in housing prices (apartments and houses).

Notes: (a) Canada uses the stock of provisions.

(b) For Poland the large bank dummy variable is equal to 1 for Polish subsidiaries of G-SIB banks. For Sweden a bank is

classified as a large bank if it is included in main assessment sample at least once during the time period 2014–18. For

Germany and the Czech Republic the dummy is equal to 1 for all domestic systemically important banks.

Source: Contributing central banks

Figure 1: Policy Rates

Notes: AUS: Australia; CAN: Canada; CHL: Chile; CZE: Czech Republic; FRA: France; DEU: Germany; NOR: Norway; POL: Poland;

SWE: Sweden; CHE: Switzerland.

(a) Does not show the full history over which the low-rate threshold was calculated for some countries.

Source: Contributing central banks

Trends in profitability for the mean and median banking systems in the sample are plotted in

Figure 2. Despite the broad-based decline in short-term interest rates over the sample, on average

across the countries examined in this paper, banks’ ROAs were largely unchanged over the sample,

although volatile around the financial crisis in 2008. By contrast, the median NIM has been declining

over the past decade, consistent with the decline in policy rates. For both the NIM and ROA the

median is more volatile than the mean, suggesting there is substantial heterogeneity in country

experiences.

Figure 2: Trends in Bank Profitability

Average of contributing central banks

Note: Australia, Canada, Chile, Czech Republic, France, Germany, Norway, Poland, Sweden (from 2010) and Switzerland.

Source: Contributing central banks

For a cursory and preliminary look at the association between changes in interest rates and

profitability, Figures 3 and 4 plot the period-to-period change in NIMs and ROAs against the change

in interest rates for the mean bank in all countries. There appears to be a positive association

between changes in interest rates and banks’ NIMs; however, there is no equivalent relationship for

banks’ ROAs. This preliminary evidence, along with the trends presented in Figure 2, is consistent

with the idea that banks’ may have shielded the impact of lower rates on overall profitability by

increasing fee-based business and reducing costs. This notwithstanding, these bivariate associations

fail to control for a host of relevant factors, including country and bank fixed effects as well as other

relevant balance sheet controls, motivating our much more careful assessment of these relationships

in the next section.

Net interest margins

2013

2007

1.8

2.0

2.2

2.4

2.6

2.8

Mean

Median

Return on assets

2013

2007

2019

0.5

0.7

0.9

1.1

1.3

1.5

Mean

Median

Figure 3: Policy Rates and NIMs

Notes: Australia, Canada, Chile, Czech Republic, France, Germany, Norway, Poland, Sweden (from 2010) and Switzerland. First and

last percentile of the distribution removed.

Source: Contributing central banks

Figure 4: Policy Rates and ROAs

Notes: Australia, Canada, Chile, Czech Republic, France, Germany, Norway, Poland, Sweden (from 2010) and Switzerland. First and

last percentile of the distribution removed.

Source: Contributing central banks

-4

-2

-0.50

-0.25

0.00

0.25

0.50

0.75

Change in interest rates – ppt

Change

inNIMs

– ppt

-4

-2

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Change in interest rates – ppt

Change

inROAs

– ppt

6. Analytical Framework

In this project, 10 central banks estimate the same regressions using bank-level data for their

country – that is, Equation (1) below is estimated separately for each country. Our baseline model

is a variant of the specification used in Borio

et al

(2017):

, 0 1 , 1 2 3 4 5 6 7

8 9 10 11

1 2 , 1 ,

i t i t t t t i t t t i

t t i t t t i t t i

t i t i i t

profit profit r spread low large r low r large

r low large spread low spread large spread low large

       

   

   

−

= + + + + + +  + 

+   +  +  +  

+ + + +

(1)

Here

,it

profit

is the profit of bank

in period

. In our baseline specification,

profit

is measured

as the ROA. We also explore specifications in which profit is replaced by one of its underlying

components – the

N IM

-Non II

and

LLP

The variables of interest are the 3-month interest rate,

, and the spread between the yield on

10-year government bonds and the short-term rate,

spread

, as well as their interactions. The

coefficient



on the interaction term

r low

indicates the differential impact a change in the short-

term rate has on the profitability of smaller banks when interest rates are low.

low

is a dummy

equal to 1 if the home country in a specific year

is in a ‘low-rate environment’. In our baseline

specification, we consider a country to be in a low-rate environment when its 3-month interest rate

is in the first quartile of the country’s sample distribution (Figure 1). The coefficient



on the

interaction term between

and the

large

dummy indicates the differential impact a change in the

policy rate has on profitability for large banks compared to their smaller counterparts in a normal

rate environment. The

large

dummy is equal to 1 if the bank is in the group of around 80 global

banks that made the BIS’ main G-SIB assessment sample for the end-2019 G-SIB exercise. Finally,

the coefficient



on the triple interaction term

t t i

r low large

indicates the differential impact of

a change in short-term rates in a low-rate environment for larger banks compared to their smaller

counterparts. All of these interactions are repeated for the spread between the 10-year rate and the

3-month rate,

spread

are macroeconomic controls and consist of real GDP growth, housing price growth and CPI

inflation.

,1it

−

are bank-level controls that include deposits over total liabilities, the liquidity ratio

and total equity capital over total assets, all lagged one period (definitions are provided in Table 2).

These controls remove any correlation interest rates might indirectly have with profitability via their

impact on the state of the economy and funding conditions. Bank-level controls are lagged one

period as bank profitability could have a contemporaneous impact on these controls.



are bank

fixed effects and

,it



is an error term. We use robust standard errors, clustered by bank to

accommodate within-bank serial correlation.

In addition to the baseline regression given by Equation (1), contributing central banks estimated

two additional regressions. The first of these replaces the

low

dummy with a variable that captures

for how long interest rates have been low. A longer period of low interest rates could be expected

to increase the negative effect of lowering rates on profitability because interest rate hedges become

less effective in a protracted low-rate environment. The second of these replaces the

low

dummy

with a dummy variable for whether rates are negative. Negative interest rates could have a

detrimental effect on banks’ profitability because of banks’ limited willingness to pass along negative

rates to depositors.

Each contributing country estimated this model over their confidential data using a fixed effects (FE)

estimator. Because of the lagged dependent variable we have relaxed the strict exogeneity

assumption (

( )

, , , 0

it i iT i

E controls controls





) and our regressors are instead weakly

exogenous (

 

( )

it is i

E controls





) , assuming

is serially uncorrelated. The implication of

non-strictly exogenous regressors is that the FE estimator is downward biased. However, when the

time period is reasonably large, as it is here for most jurisdictions, this bias is negligible. This

notwithstanding, estimates obtained for Germany, Norway, Sweden and Switzerland should be

considered lower bounds.

Finally, the dynamic specification used in Equation (1) allows us to examine the short- and long-run

impact of changes in policy rates on profitability. The longer-term impact of a permanent change in

interest rates on profitability for small banks is given by the expression

( )



−

which is obtained

by recursively substituting for

,1it

profit

−

in Equation (1).

The benefit of the simple baseline specification used here is it could be easily communicated to each

central banking expert and commonly estimated over their own confidential bank-level data.

However, this simplicity means we necessarily omit a number of other interactions that might have

been interesting to explore. First, additional evidence might be obtained by interacting the interest

rate level and the slope of the yield curve with bank-specific variables, such as excess reserves and

deposit dependence, which both could increase the sensitivity to rates. We also do not control for

expected macroeconomic conditions. Others have argued that policy and profitability might share a

common association with expected economic conditions, and failing to control for these could result

in biased estimates (Altavilla

et al

2018). For example, improvement to the economic outlook could

give rise to higher rates and profitability by stimulating investment and increasing current loan

demand. On the supply side, banks might also increase their profitability by increasing their business

lending as the improved economic outlook translates into lower credit risk. While these channels are

plausible, finding good controls for expected demand that are consistently available across countries

is challenging (beyond controlling for current economic conditions). Finally, our specification

assumes that bank profitability does not affect monetary policy decisions. As noted in Borio

et al

(2017), while aggregate banking conditions might affect the stance of monetary policy, the

profitability of any given bank is unlikely to affect central bank decisions. This is a key feature of

running our baseline regression at the bank level, rather than aggregating across all banks for a

given country and estimating a time series regression.

7. Empirical Findings across Countries

7.1 Net interest margins

In line with expectations, our estimates point to a clear positive relationship between the short-term

interest rate and banks’ NIMs during normal times: a fall in the interest rate is associated with a fall

in NIMs. From our sample of 10 countries spanning around 1,500 banks, the estimates suggest that

during normal times, a 100 basis point reduction in short-term interest rates reduces smaller banks’

NIMs by around 5 basis points in the short run (Table 3 top panel; Figure 5, top panel).

The mean

long-run impact, calculated using the expression towards the end of Section 6, which assumes the

change in interest rates is permanent, is much higher, at around 15 basis points.

Similarly, there

is broad-based evidence that a flattening of the yield curve (as measured by the spread between

10-year and 3-month interest rates) is associated with lower NIMs. Our findings are broadly

consistent with those reported by Claessens

et al

(2018) for their sample spanning 47 countries, but

is noticeably smaller than the impact reported in Borio

et al

(2017) for their sample of large,

advanced economy banks.

We find limited evidence that the effect of interest-rate changes differs for larger banks compared

to smaller banks (Table 3, top panel; Figure 5, bottom panel), though Germany, Canada and

Australia are exceptions. In Germany, the expected positive association between short-term interest

rates and margins is only evident for larger banks. By contrast, larger Canadian banks’ margins

appear completely insulated from changes in the short-term interest rate, reflecting the higher

degree of diversification between interest and non-interest income. Likewise, in Australia, the impact

of a reduction in interest rates on the margins of larger banks is significantly lower relative to smaller

banks, possibly reflecting their greater use of wholesale funding markets.

Figure 5: Monetary Policy and Banks’ NIMs

Impact of a 100 basis point cut to the policy rate

Notes: AUS: Australia; CAN: Canada; CHL: Chile; CZE: Czech Republic; FRA: France; DEU: Germany; NOR: Norway; POL: Poland;

SWE: Sweden; CHE: Switzerland.

(a) Large banks defined as those in the BIS main G-SIB assessment sample.

Source: Contributing central banks

The full regression outputs are available upon request.

This figure is obtained using the average coefficient on the lagged dependent variable,



, in Equation (1), which is

0.7.

Smaller banks, normal rate environment

-10

bps

-10

bps

Marginal impact of being a larger bank

(a)

CAN

CHE

NOR

AUS

CZE

FRA

POL

CHL

DEU

SWE

-20

-10

-20

-10

Significant (p-value 0.1) Insignificant

Table 3: Results for Low Rate and Large Bank Interactions

(

continued next page

)

AUS

CAN

CHL

CZE

DEU

FRA

NOR

POL

SWE

CHE

Net interest margin (

NIM

)

0.070***

0.160***

–0.017

0.068***

–0.038

0.024**

0.101***

0.010

–0.040

0.14***

spread

0.054***

–0.044

0.014

0.049*

0.075

0.136***

0.088***

0.029*

–0.281

0.08***

large r

–0.056***

–0.186***

0.046

0.041

0.082*

0.005

0.004

0.019

0.059

0.04

large spread

–0.026**

–0.334*

0.015

0.036

–0.028

–0.026

–0.013

0.069

0.352

0.03

low r

–0.024

0.069

0.184***

–0.182

0.111

–0.012***

–0.219**

–0.531*

0.319

–0.04

low spread

–0.040***

0.191**

0.076***

0.009

0.025

0.002

–0.273***

–0.039

0.382*

–0.01

large low r

0.021

0.270*

0.052

0.254

–0.215***

–0.026***

0.589

0.336

–0.294

–0.78**

large low spread

–0.012

0.318*

0.052

–0.023

–0.012

–0.033

0.206*

–0.053

–0.467

–0.50*

Observations

6,940

4,830

696

1,274

2,909

12,380

3,092

1,249

408

1,857

Within R

0.790

0.705

0.842

0.571

0.471

0.690

0.565

0.895

0.492

0.73

Return on assets (

ROA

)

0.063***

–0.047

0.009

0.064**

–0.064

0.042***

–0.014

–0.017*

–0.237

–0.01

spread

0.046***

–0.159*

0.043**

0.037

0.043

–0.012

0.167***

–0.030**

0.190

–0.04*

large r

–0.067***

0.069

0.013

0.047

–0.003

–0.012

–0.122***

–0.013

0.077

–0.38***

large spread

–0.110***

0.150*

–0.003

0.108**

–0.010

0.000

–0.516***

–0.025

–0.414

–0.10

low r

0.008

0.280

0.026

–0.045

0.453**

–0.051

–0.014

–0.116

0.556**

0.04

low spread

–0.021

0.207

–0.011

–0.050

–0.197

0.048*

–0.272**

–0.029

–0.217

0.07

large low r

0.002

0.029

–0.105**

0.396

–0.356

–0.003

0.288

0.071

–0.183

–0.05

large low spread

0.265**

–0.126

–0.005

–0.051

0.232

–0.010

0.944**

0.039

0.463

0.52

Observations

6,940

4,830

696

1,274

2,909

12,380

3,092

1,249

408

1,857

Within R

0.164

0.173

0.812

0.105

0.092

0.250

0.184

0.279

0.178

0.19

Table 3: Results for Low Rate and Large Bank Interactions

(

continued

)

AUS

CAN

CHL

CZE

DEU

FRA

NOR

POL

SWE

CHE

Non-interest income (

-Non II

)

0.102***

0.091

0.028*

0.055**

–0.111

0.175***

–0.021***

0.045***

–0.128

–0.05***

spread

0.059***

0.113

0.031*

0.038

–0.158**

0.105**

–0.014*

0.044**

–0.046

–0.02

large r

–0.037**

0.012

–0.001

0.053**

–0.029

0.115*

0.000

0.012

–0.062

–0.22**

large spread

–0.048**

–0.045

0.021

0.045

0.029

0.112

–0.001

-0.009

–0.223

–0.20

low r

–0.050**

1.190*

–0.016

–0.077

0.101

–0.234

0.077

0.126

0.312

0.09**

low spread

–0.061***

0.367

–0.010

–0.007

0.105*

0.016

0.035

–0.097***

0.058

–0.02

large low r

0.074***

–0.864

–0.154*

0.183

–0.082

–0.120

0.123

–0.240

0.068

–0.08

large low spread

0.160**

–0.283

–0.063

–0.008

0.102

–0.276*

0.222

0.004

0.121

0.55

Observations

6,940

4,830

696

1,274

2,909

12,380

3,092

1,249

408

1,857

Within R

0.439

0.719

0.815

0.392

0.181

0.450

0.456

0.873

0.604

0.54

Loan-loss provisions (

LLP

)

0.015***

0.031***

0.017

0.009

0.128

0.009***

0.062***

0.062**

0.017

0.00

spread

0.007*

0.024***

0.001

0.056***

0.138

0.009***

0.045***

0.102***

0.162

0.00

large r

0.023***

–0.027***

0.012

–0.002

–0.075

–0.017***

–0.024

0.018

–0.028

0.07***

large spread

0.078***

–0.026**

0.021

–0.030

0.042

–0.021***

–0.029

0.075

–0.081

–0.25**

low r

–0.039***

–0.109**

0.055

0.269*

0.236*

–0.010

–0.235**

0.671**

–0.032

0.01

low spread

0.008

0.010

0.033

0.006

–0.343**

–0.008

–0.157**

0.044

–0.119

–0.01

large low r

–0.050*

0.120**

–0.041

0.247

–0.064

–0.011

–0.354

0.018

–0.064

0.18**

large low spread

–0.155***

–0.001

–0.024

–0.029

0.101

0.030***

–0.379

–0.127

0.023

–0.13

Observations

6,940

4,830

696

1,274

2,909

12,380

3,092

1,249

408

1,857

Within R

0.083

0.894

0.845

0.944

0.189

0.160

0.157

0.886

0.356

0.24

Notes: ***, ** and * denote a positive coefficient and significance at the 1, 5 and 10 per cent levels, respectively. Explanatory variables are presented for regressions run with return on assets

(

ROA

), net interest income (

NIM

), non-interest income (

-Non II

) and loan-loss provisions (

LLP

) separately. Coefficients on the lagged dependent variable, bank-level controls,

macroeconomic controls as well as dummy variables for

low

large

low large

are omitted for brevity. Full regression results are available upon request.

Source: Contributing central banks

During low interest rate periods, and for most countries, the relationship between the short-term

interest rate and NIMs is not statistically distinguishable from that during normal times. In fact, for

France, Norway and Poland a further reduction in the cash rate from already low levels appears to

have a relatively lower impact on NIMs. For larger banks operating in a low-rate environment, the

available evidence tends to suggest that a further reduction in short-term interest rates has a

relatively lower impact on NIMs for banks in Germany, France and Switzerland.

This is despite the

distribution of liquidity in the euro area, which sees large banks from ‘core’ countries tending to hold

large amounts of liquidity in the deposit facility, which should make them more sensitive to

reductions in interest rates.

One explanation for this is the ECB’s tiering scheme, which may have

shielded larger euro area banks’ NIMs, although this is unlikely to be a major driver of our results

given it was introduced only towards the end of our sample period. Another factor that could have

contributed to this result is the increase in high margin lending that was facilitated by the introduction

of the Targeted Longer-Term Refinancing Operations II program (Jobst and Lin 2016). Between

June 2016 and the end of the period studied, French and German banks could borrow up to 30 per

cent of the volume of eligible private non-mortgage loans for a four-year period at the prevailing

main refinancing operations (MRO) rate, which was 0 per cent. With lower short-term interest rates

compressing NIMs, banks reacted by increasing loan volumes, which were funded at the MRO rate.

The increase in volumes led to an increase in NIMs given the favourable spread between lending

rates (positive) and the MRO rate (0 per cent).

Next, we examine the effect of policy rates that are not just briefly low but ‘low-for-long’. In these

regressions, we replace the ‘low’ dummy variable (a binary variable equal to 1 when interest rates

are low and zero otherwise) with a low-for-long variable, equal to the number of time periods during

which interest rates have been low. The motivation for doing this is that the nonlinear effects one

may expect in a low interest rate environment may materialise with a delay, perhaps because interest

rate hedges become less effective over time.

The results are reported in Table 4. To facilitate

comparison with the low interest rate findings, Table 4 compares the coefficients for the variables

of interest from both specifications: the low-for-long results are in bold, directly below the low

interest rate coefficients as reported in Table 3. For brevity, we do not report the coefficients of the

non-interacted variables (the interest rate, the yield curve, the dummy for large banks interacted

with the interest rate and the yield curve) as they are almost identical to those of Table 3. These

additional results are available upon request.

By contrast, for large Canadian banks, there is an interesting nonlinearity: while a rate reduction in normal times does

not appear to affect their profitability, a reduction from already low levels is detrimental. This suggests there could be

limits to the benefits of diversification for larger Canadian banks.

The ECB deposit facility rate reached zero in July 2012; it became negative in 2014; and was set to –0.40 per cent in

2016 and –0.5 per cent in 2019.

Here we are assuming the marginal impact of being in a low-rate environment changes linearly with the amount of

time spent in the low-rate environment. If the impact is nonlinear then it is unclear whether the dummy or our linear

variable is a better approximation of the true nonlinear effect.

Table 4: Results Comparing the Low and Low-for-long (LFL) Rates

AUS

CAN

CHL

CZE

DEU

FRA

NOR

POL

SWE

CHE

Net interest margin (

NIM

)

low r

–0.024

0.069

0.184***

–0.182

0.111

–0.012***

–0.219**

–0.531*

0.319

–0.04

LFL

0.006***

0.008

0.060**

0.478

0.024***

0.125***

–0.105*

–0.090

–1.510*

–0.07***

low spread

–0.040***

0.191**

0.076***

0.009

0.025

–0.002

–0.273***

–0.039

0.382*

–0.01

LFL

–0.005***

0.040**

0.030***

–0.029

0.003

–0.043***

–0.099***

–0.002

omitted

–0.01**

large low r

–0.002

0.270*

0.052

0.254

–0.215***

–0.026***

0.589

0.336

–0.294

–0.78***

LFL

0.003

–0.000

0.050*

1.050*

–0.249*

0.031

–0.031

0.031

–0.140

–0.12

Return on assets (

ROA

)

low r

0.008

0.280

0.026

–0.045

0.453**

–0.051

–0.014

–0.116

0.556**

0.04

LFL

0.009***

0.021

0.002

–0.770

0.967**

–0.010

–0.057

–0.146

–0.348

0.00

low spread

–0.021

0.207

–0.011

–0.050

–0.197

0.048*

–0.272**

–0.029

–0.271

0.07

LFL

–0.005***

0.026

–0.002

–0.009

–0.135

0.017

–0.095**

–0.001

omitted

0.00

large low r

0.010

0.029

–0.105**

0.396

–0.356

–0.003

0.288

0.071

–0.183

–0.05

LFL

0.002

0.008

–0.042**

0.991

–0.660

0.004

–0.054

–0.084

0.035

0.37***

Non-interest income (

-Non II

)

low r

–0.050**

1.190*

–0.016

–0.077

0.102

–0.234

0.077

0.126

0.312

0.09**

LFL

–0.001

0.123

–0.012

0.381

–0.195

0.084

0.017

–0.053

–0.713

0.01

low spread

–0.061***

0.367

–0.010

–0.007

0.105*

0.016

0.035

–0.097***

0.058

–0.02

LFL

–0.006***

0.051

–0.003

–0.024

–0.111***

–0.010

0.006

–0.007***

omitted

–0.01

large low r

0.024

–0.864

–0.154*

0.183

–0.082

–0.120

0.123

–0.240

0.068

–0.08

LFL

0.001

–0.111

–0.057

1.086**

–0.331

0.036

0.058

–0.234

0.069

0.06

Loan-loss provisions (

LLP

)

low r

–0.039***

–0.109**

0.055

0.269*

0.236*

–0.010

–0.235**

0.671**

–0.032

0.01

LFL

–0.007***

–0.019***

0.019

0.555***

0.931***

0.001

–0.058*

0.427**

0.354

–0.01

low spread

0.008

0.010

0.033

0.006

–0.343**

–0.008

–0.157**

0.044

–0.119

–0.01

LFL

0.003***

0.007*

0.013

–0.024

–0.114

–0.004*

–0.038*

0.003

omitted

–0.00

large low r

–0.088***

0.120**

–0.041

0.247

–0.064

–0.011

–0.354

0.018

–0.064

0.19**

LFL

–0.002

0.020**

–0.003

0.495**

–0.007

–0.012**

–0.165

–0.247

–0.101

–0.14***

Notes: ***, ** and * denote a positive coefficient and significance at the 1, 5 and 10 per cent levels, respectively. Explanatory variables are presented for regressions run with return on assets

(

ROA

), net interest income (

NIM

), non-interest income (

-Non II

) and loan-loss provisions (

LLP

) separately. Coefficients are only presented for the interaction term of interest. Full

regression results are available upon request.

Source: Contributing central banks

The main takeaway from Table 4 is that the results using the low-for-long variable are qualitatively

similar to those using the low dummy. Starting with the effect on the NIM in the top panel, the

coefficient of the interacted variable is significant for more countries using the low-for-long variable,

suggesting that the effect of low interest rates on NIMs is nonlinear and larger when rates have

been kept low for a while. In the case for Australia, Chile, Germany and France, NIMs decline as

rates are kept lower-for-longer; however, for Norway, Sweden and Switzerland the reverse is true.

For the other dependent variables (discussed in more detail in the next section) there is some weak

evidence that the impact of a rate reduction on ROA starts to exert itself slightly more after a period

of time (e.g. for smaller Australian and German banks). But overall, the results using the low-for-

long variable are similar to those using the low dummy for both ROA and LLPs. For Non-II, there is

no evidence of lower-for-longer impacts.

Finally, there is mixed evidence that negative rates exacerbate the detrimental impact of low interest

rates on banks’ NIMs. In two of the four jurisdictions that have implemented negative short-term

interest rates, the marginal impact of a cut to the interest rate is significantly larger in negative-rate

environments (Table 5). For German banks, by contrast, a further cut to the short-term rate when

it is already negative has a beneficial impact on larger banks’ NIMs. For smaller banks, it is the

opposite.

One important caveat to this analysis is the limited within-country variation in policy rates once they

reach the zero lower bound, which makes it harder to identify the effect on the dependent variable.

As a consequence, results need to be interpreted with caution. Still, the results indicate that there

is no clear-cut nonlinearity below zero, which is consistent with the mixed findings from the existing

literature.

Table 5: Results with Negative Rate and Large Bank Interventions

DEU

FRA

SWE

CHE

Net interest margin (

NIM

)

0.018

0.036***

–0.331***

0.12***

spread

0.142***

0.143***

–0.623***

0.10***

large r

–0.010

0.018**

0.164

0.04

large spread

–0.096***

–0.009

0.347

0.30**

neg r

0.476***

0.181**

–0.311

0.06

neg spread

0.013

–0.142***

0.163

–0.15

large neg r

–0.484***

0.147

–0.331

–0.32

large neg spread

0.019

–0.124*

–0.425

–0.15

Within R

0.473

0.680

0.491

0.74

Return on assets (

ROA

)

–0.002

0.041***

0.190

–0.02

spread

0.105

–0.006

0.694***

–0.04*

large r

0.090**

–0.004

–0.050

–0.28

large spread

0.027

–0.002

–0.405

0.26*

neg r

0.409

0.125

1.473**

0.01

neg spread

–0.231*

0.076

0.101

–0.02

large neg r

0.416

0.009

0.372

0.18

large neg spread

0.113

–0.017

0.691

–0.25

Within R

0.085

0.250

0.177

0.19

Non-interest income (

-Non II

)

–0.009

0.102***

0.130

–0.06

spread

–0.036

0.035

0.264

–0.01

large r

–0.013

0.042

–0.105

–0.02

large spread

0.018

–0.052

–0.211

–0.09

neg r

–0.529***

0.225

0.916*

–0.07

neg spread

–0.121**

–0.246

0.274

–0.01

large neg r

0.480**

0.168

0.385

0.10

large neg spread

0.241

0.303

0.261

0.05

Within R

0.185

0.450

0.604

0.55

Loan-loss provisions (

LLP

)

0.033

0.004**

0.022

–0.01

spread

0.022

0.003

0.167

–0.00

large r

0.103***

–0.003

–0.044

0.07

large spread

0.118**

–0.005

–0.082

–0.02

neg r

0.370

–0.044

–0.006

–0.02

neg spread

–0.214*

–0.021

–0.106

0.01

large neg r

0.531

–0.061

–0.258

–0.30

large neg spread

–0.103

0.163***

–0.092

0.18

Within R

0.182

0.170

0.356

0.24

Notes: ***, ** and * denote a positive coefficient and significance at the 1, 5 and 10 per cent levels, respectively. Explanatory

variables are presented for regressions run with return on assets (

ROA

), net interest income (

NIM

), non-interest income

(

-Non II

) and loan-loss provisions (

LLP

) separately. Coefficients on the lagged dependent variable, bank-level controls,

macroeconomic controls as well as dummy variables for

neg

large

low large

are omitted for brevity. Full regression

results are available upon request.

Source: Contributing central banks

7.2 Overall bank profitability

In Section 7.1, we observed the relationship between short-term interest rates and banks’ NIMs

during normal times to be positive and significant, as expected. The effect of lower interest rates on

overall bank profitability (as measured by banks’ ROAs) is less clear owing to other mitigating factors.

Both Table 3 (second panel) and Figure 6 (second panel) show the results for our ROA regression.

Our results confirm that there is not a clear-cut association between lower interest rates and overall

bank profitability; for about half the sample a reduction in short-term interest rates lowers bank

profitability, while for the other half it is associated with an increase in profits. The magnitude of the

impact of monetary policy on overall profitability is modest. The largest impact among the

10 countries examined is that a 100 basis point fall in the short-term interest rate is associated with

a 6 basis point reduction in smaller banks’ ROAs in the short run. These estimates would be smaller

still if one were to add back any effects that operate indirectly through monetary policy's effect on

aggregate demand.

Figure 6: Effect of Monetary Policy

Impact of a 100 basis point cut to the policy rate for smaller banks in a normal rate environment

Note: AUS: Australia; CAN: Canada; CHL: Chile; CZE: Czech Republic; FRA: France; DEU: Germany; NOR: Norway; POL: Poland;

SWE: Sweden; CHE: Switzerland.

Source: Contributing central banks

NIM

-15

bps

-15

bps

ROA

-15

bps

-15

bps

Non-II

-15

bps

-15

bps

LLPs

CAN

CHE

NOR

AUS

CZE

FRA

POL

CHL

DEU

SWE

-30

-15

bps

-30

-15

bps

Significant (p-value 0.1) Insignificant

There is only limited evidence that Non-II has played a counterbalancing role (Table 3, third panel;

Figure 6, third panel). That is, there is no consistent result across countries indicating that non-

interest income increases as interest rates fall, either owing to one-off revaluations of long-term

assets or banks shifting their business towards non-interest sources of income. Nor does the non-

interest income effect appear to be significantly different for larger banks or during low-rate periods.

Our results do, however, show that lower rates uniformly lower LLPs by reducing debt-servicing

burdens on the stock of existing debt (Table 3, fourth panel; Figure 6, fourth panel). There is no

evidence that this effect is meaningfully offset by an increase in risk-taking by banks during low-rate

periods (i.e. a risk-taking channel of monetary policy), which would instead result in a positive and

significant coefficient on LLPs.

There is considerable country-specific heterogeneity underlying these key results. Some of these

quantitative

differences are discussed in the next sub-section, including the impact on overall

profitability when rates are negative.

qualitatively

inform some of the cross-country differences and unpack some of the attenuating

factors that have enabled banks to maintain their level of overall profitability as short-term rates

have fallen, we can turn to the insights received from our

qualitative survey.

Overall, the responses

received point to a range of mitigating actions, including banks striving to become more cost efficient

and streamlining their business models.

For example, starting first with Canada, responses from our survey indicate that banks whose NIM

was affected by the low interest rate environment took considerable measures to safeguard profits

by becoming more cost efficient, selling non-core underperforming businesses and pivoting to focus

on higher growth areas in the short term. Moreover, in some cases, banks adjusted the pricing of

assets/liabilities to preserve spread (e.g. lower deposit rates) to mitigate the impact on margins.

For larger Swiss banks, the survey noted that fee and commission income accounts for the largest

share of their operating income, with revenues coming mostly from wealth and asset management

businesses. As such, their overall profitability was less affected by the impact of the low-rate

environment on interest rate margins and benefited from fee and commission revenue streams. For

individual Swiss banks, the survey pointed to evidence of search-for-yield behaviour among smaller

banks when interest rates were low and negative as they increased mortgage volumes and took on

more risk. Some banks also grew their fee and commission business, particularly in the low interest

rate environment.

The

qualitative survey

responses highlight that in Poland there was little discernible impact of lower

rates on overall profitability due to banks increasing the share of more profitable credit products

(e.g. consumer loans); a reduction in less profitable credit products; and a decrease of the interest

on liabilities. The NIM in Poland remained relatively high compared to other EU countries, which was

also related to the level of interest rates in Poland remaining significantly higher than the zero lower

bound. In addition, while fee and commission margins have declined – in part due to statutory and

regulatory activities aimed at increasing consumer protection by reducing the costs incurred by them

– banks were able to reduce their operating costs partly through digitalising processes.

In Australia, lower rates do appear to lower bank profitability. However, the size of this effect is not

economically significant. Several factors, highlighted in the

qualitative survey

, help to explain this

result. Around one-third of Australian banks’ funding comes from wholesale markets, which is a

larger share than for many international banks. The cost of wholesale funding is not constrained by

an effective zero lower bound in contrast to what has been observed for deposit interest rates

internationally. Moreover, Australian banks tend to have interest rate hedges, which smooth profits

and so delays the reduction in profits from falls in interest rates. Moreover, around three-quarters

of Australian banks' assets are variable-rate loans that are funded with variable-rate deposits and

other debt. The implication of this is that changes in the slope of the yield curve do not have a large

impact on Australian banks' profits.

7.2.1

Overall bank profitability: quantitative results in more detail

At the individual country level, several channels are at play. In Canada, Switzerland and Norway

there is suggestive evidence that banks have taken measures to offset the low-rate impact on NIMs

to safeguard ROAs. That is, in these jurisdictions, the effect of lower interest rates on NIMs has not

transferred through to ROAs (Table 3; Figure 6). For Switzerland and Norway, the effect on bank

profitability of larger banks differs significantly from smaller banks, with larger banks benefiting from

lower rates.

In the case of France, the rate impact on ROAs is almost double in size relative to the impact on

NIMs. In the case of the Czech Republic, the magnitude of the rate impact on ROAs is similar in

magnitude to that for NIMs but is not statistically significant. However, for the Czech Republic this

result does not hold in the low-rate environment. Additional tests indicate that the positive

association between NIMs and interest rates only holds when rates are increasing, suggesting banks

in the Czech Republic shielded themselves against rate decreases, including through lower loan-loss

provisions.

In negative interest rate regimes, we find no clear evidence that the impact of policy on overall

profitability is any different, with exceptions for Germany and to some extent Sweden. In Germany,

a decline in short-term interest rates reduces ROAs for smaller banks during the low interest rate

regime, whereas it seems insignificant during the negative interest rate regime. The insignificant

impact might arise from two sub-components of ROAs – the NIM and Non-II – that seem to

counterbalance each other: on the one hand, smaller banks seemed to have safeguarded profits by

increasing their Non-II (for example, fee and commission income), while on the other hand their

NIM declined (Michaelis 2022). This suggests that small banks in Germany found means to shield

overall profitability in the face of negative interest rates. The story is different for Sweden. Here, a

rate cut during the negative interest rate regime reduces ROAs for smaller banks by much more

than during the low interest rate regime.

8. Conclusion

This paper has provided an empirical analysis of the effect of lower interest rates on bank profitability

– including differential responses when rates are very low – looking back at the period when central

banks lowered interest rates to mitigate the risks of deflation. At the time, particular concerns were

raised about possible nonlinear effects of low interest rates in the neighbourhood of the effective

and zero lower bounds. Banks tend not to reduce deposit rates below a certain threshold, a threshold

which may vary across jurisdictions and over time. Because of this, lower interest rates may

significantly affect bank profitability near these bounds and hamper monetary policy transmission.

It is therefore of utmost importance for policymakers and researchers to carefully assess the

magnitude of this effect.

Based on bank-level data for 10 different countries, we have investigated these effects empirically

with a consistent methodology between countries, focusing on different measures and components

of profitability (NIM, ROA, Non-II and LLPs). A key takeaway from this investigation is that the effect

of a decline in interest rates on bank profitability is small, economically speaking. The immediate

effect of a 1 percentage point decrease in the short-term interest rate on NIMs is only 5 basis points.

While this hides sizeable cross-country differences (and the long-run effect is closer to 15 basis

points), the measured effect is lower than previous estimates.

We find some evidence of nonlinearities in low interest rate environments, but only for a handful of

countries in the sample. More importantly perhaps, we find that the effect of low interest rates on

ROAs is smaller than on NIMs, suggesting that banks have found ways to offset the effect of lower

interest rate margins. In addition, holding interest rates ‘lower for longer’ does not appear to change

the results noticeably. Similarly, focusing on the countries that moved interest rates into negative

territory yields coefficients that are not economically large. Overall therefore, the evidence presented

here points to smaller effects of falling and low policy rates on bank profitability than previously

estimated.

Appendix A: Summary of Selected Bank-level Papers

Table A1: Summary of Selected Bank-level Papers

Paper

Sample

Data

Method

Main findings

Borio,

Gambacorta and

Hofmann (2017)

• Sample: 14 advanced

economies

• Banks: 109; large

international

• Time period: 1995–2012

BankScope

Dynamic

panel

regressions

• Lower rates are associated with lower

profits

• Lower rates lower net interest

income, which more than offsets

positive impact on non-interest

income and loan-loss provisions

Claessens,

Coleman and

Donnelly (2018)

• Sample: 47 countries

• Banks: 3,385

• Time period: 2005–13

BankScope

Panel with FE

regressions

• Lower interest rates lower bank

profitability

• Impact is larger for net interest

margins compared to overall

profitability

Altavilla,

Boucinha and

Peydró (2018)

• Sample: euro area

• Banks: 288

• Time period: 2000–16

iBSI;

BankScope;

SNL Financial;

Bloomberg;

Capital IQ

Panel with FE

regressions

• Lower rates have a negative impact

on net interest margins, which is

offset by a positive impact on loan-

loss provisions

• Lower rates are not associated with

lower profits if current and expected

economic and financial conditions are

controlled for

Bikker and

Vervliet (2018)

• Sample: US

• Banks: 3,582

• Time period: 2001–15

Federal Deposit

Insurance

Corporation

Panel GMM

estimation

• Lower interest rates compress net

interest margins, but lower loan-loss

provisions

• Lower rates are not associated with

lower profits

Molyneux,

Reghezza and

Xie (2019)

• Sample: 33 OECD

countries

• Banks: 7,352

• Time period: 2012–16

Orbis

BankFocus;

SNL Financial

Panel DiD

regressions

• Bank margins and overall profitability

fared worse in countries with

negative interest rate policies

• Large banks were able to mitigate

negative effects; stronger adverse

effects were found in countries with

more competitive banking systems

Lopez, Rose and

Spiegel (2020)

• Sample: 27 European

countries and Japan

• Banks: 5,200

• Time period: 2010–17

Fitch Global

Banking

Panel with FE

regressions

• Negative rates lower net interest

income

• This impact largely offset by

increases in non-interest income

stemming from ‘other income’

sources, such as capital gains on

securities

Beauregard and

Spiegel (2020)

• Sample: 27 European

countries and Japan

• Banks: 5,300

• Time period: 2010–18

Fitch Global

Banking

Panel with FE

regressions

• A protracted period of negative rates

reduces bank profitability, primarily

due to banks’ reluctance to pass

negative rates along to retail

depositors

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