ArticlePDF Available

The Impact of Capital Requirements on Bank Lending

Authors:

Abstract

We estimate the effect of changes in microprudential regulatory capital requirements on bank capital ratios and bank lending. We do so by running panel regressions using a rich new data set, exploiting variation in individual bank capital requirements in the United Kingdom from 1990-2011. There are two key results. First, regulatory capital requirements affect the capital ratios held by banks – following an increase in capital requirements, banks gradually rebuild the buffers that they initially held over the regulatory minimum. Second, capital requirements affect lending with heterogeneous responses in different sectors of the economy – in the year following an increase in capital requirements, banks, on average, cut (in descending order based on point estimates) loan growth for commercial real estate, other corporates and household secured lending. The response of unsecured household lending is smaller and insignificant over the first year as a whole. Loan growth mostly recovers within three years. While estimated over a different policy regime and at the individual bank level, these results may contain some insights into how changing capital requirements might affect lending in a macroprudential regime. However, during the transition to higher global regulatory standards, the effects of changes in capital requirements may be different. For example, increasing capital requirements might augment rather than reduce lending for initially undercapitalised banks.
Working Paper No. 486
The impact of capital requirements on
bank lending
Jonathan Bridges, David Gregory, Mette Nielsen,
Silvia Pezzini, Amar Radia and Marco Spaltro
January 2014
Working papers describe research in progress by the author(s) and are published to elicit comments and to further debate.
Any views expressed are solely those of the author(s) and so cannot be taken to represent those of the Bank of England or to state
Bank of England policy. This paper should therefore not be reported as representing the views of the Bank of England or members
of the Monetary Policy Committee or Financial Policy Committee.
Working Paper No. 486
The impact of capital requirements on bank lending
Jonathan Bridges,(1) David Gregory,(2) Mette Nielsen,(3) Silvia Pezzini,(4)
Amar Radia(5) and Marco Spaltro(6)
Abstract
We estimate the effect of changes in microprudential regulatory capital requirements on bank capital ratios
and bank lending. We do so by running panel regressions using a rich new data set, exploiting variation in
individual bank capital requirements in the United Kingdom from 1990–2011. There are two key results.
First, regulatory capital requirements affect the capital ratios held by banks – following an increase in capital
requirements, banks gradually rebuild the buffers that they initially held over the regulatory minimum.
Second, capital requirements affect lending with heterogeneous responses in different sectors of the economy
— in the year following an increase in capital requirements, banks, on average, cut (in descending order
based on point estimates) loan growth for commercial real estate, other corporates and household secured
lending. The response of unsecured household lending is smaller and insignificant over the first year as a
whole. Loan growth mostly recovers within three years. While estimated over a different policy regime and
at the individual bank level, these results may contain some insights into how changing capital requirements
might affect lending in a macroprudential regime. However, during the transition to higher global regulatory
standards, the effects of changes in capital requirements may be different. For example, increasing capital
requirements might augment rather than reduce lending for initially undercapitalised banks.
Key words: Bank capital, bank lending, regulatory capital requirements, capital buffer, macroprudential
policy.
JEL classification: G21, G28.
(1) Bank of England. Email: jonathan.bridges@bankofengland.co.uk
(2) Bank of England. Email: david.gregory@bankofengland.co.uk
(3) Bank of England. Email: mette.nielsen@bankofengland.co.uk
(4) Bank of England. Email: silvia.pezzini@bankofengland.co.uk
(5) Bank of England. Email: amar.radia@bankofengland.co.uk
(6) Morgan Stanley. Email: marco.spaltro@morganstanley.com
The views expressed in this paper are those of the authors, and not necessarily those of the Bank of England. We would like
to thank Jas Ellis, Haroon Mumtaz, Vicky Saporta, Ron Smith, James Talbot, Garry Young, an anonymous referee, and
seminar participants at the Bank of England and at its Centre for Central Banking Studies, EEA 2013, the INFINITI
Conference on International Finance, the 45th Annual Money Macro and Finance Conference, and ECOBATE for helpful
comments, suggestions and advice, and various members of the Statistics and Regulatory Data Division for help in constructing
the data set. Marco Spaltro undertook his work on the paper while at the Bank of England. This paper was finalised on
13 December 2013.
The Bank of England’s working paper series is externally refereed.
Information on the Bank’s working paper series can be found at
www.bankofengland.co.uk/research/Pages/workingpapers/default.aspx
Publications Group, Bank of England, Threadneedle Street, London, EC2R 8AH
Telephone +44 (0)20 7601 4030 Fax +44 (0)20 7601 3298 email publications@bankofengland.co.uk
© Bank of England 2014
ISSN 1749-9135 (on-line)
Working Paper No. 486 January 2014 2
Contents
Summary 3
1Introduction 5
2Literature 6
2.1Impact of changes in capital resources on lending 7
2.2Impact of changes in capital requirements on lending 8
3Data 9
3.1Data set construction 9
3.2Descriptive statistics 13
4Methodology 14
5Results 16
5.1The impact of capital requirements on capital ratios 16
5.2The impact of capital requirements and capital ratios on sectoral loan growth 17
5.3Discussion 20
6Extensions and robustness checks 21
6.1Influence of the financial crisis 21
6.2Heterogeneity by size of bank 22
6.3Asymmetry between increases and decreases in capital requirements 22
6.4Business cycle variation in banks’ responses 22
6.5Heterogeneity by size of the capital buffer 23
7Conclusion 23
Appendices 24
Appendix 1 – Data cleaning 24
Appendix 2 – Key variables 24
Appendix 3 – Full table of results 25
Appendix 4 – Extensions and robustness checks 26
References 33
Working Paper No. 486 January 2014 3
Summary
This paper investigates the effect of changes in regulatory capital requirements on bank capital
and lending to UK households and firms. It is an empirical study drawing on a new bank-by-
bank data set, exploiting variation in individual bank capital requirements in the United
Kingdom between 1990 and 2011. There are two key results. First, regulatory requirements
impact bank capital ratios; banks typically rebuild the “buffer” in their capital ratios above the
regulatory minimum following an increase in that minimum requirement.1 Second, changes in
regulatory capital requirements affect bank lending. Results vary across sectors, but in response
to an increase in capital requirements, loan growth typically falls in the year following the
regulatory change and recovers within three years.
Empirical evidence on the link between regulatory capital requirements and bank lending is also
of interest to policymakers. The financial crisis has led to support for the use of capital
requirements as a tool to mitigate risks in the financial system. In the United Kingdom, the
Financial Policy Committee (FPC) is responsible for setting time-varying capital requirements
on sectoral lending.
The effect of such capital requirements might differ from the effect of microprudential policy.
As a result, the results from our study cannot be directly mapped across to how changing capital
requirements are likely to affect bank capital and lending in a macroprudential framework; but
they provide a useful guide to how banks have adjusted their capital ratios and lending structure
on average in response to past microprudential supervisory actions. For example, banks might
take a different approach to restoring capital buffers when other banks are subject to the same
policy change and measures are public; expectations of forthcoming policy changes might lead
to earlier reactions by banks; and there might be a different degree of ‘leakages’ where entities
not domestically regulated step in with new lending. Also, during the transition to higher global
regulatory standards, increasing capital requirements might augment rather than reduce lending
for initially undercapitalised banks if confidence effects boost their resilience and capacity to
lend. Furthermore, macroprudential regulators are often required to consider the wider
implications of changing capital requirements, which could include any adverse impact on
lending – for example, while the FPC’s primary objective is to protect and enhance the
resilience of the UK financial system, it also has a secondary objective to support the economic
policy of the Government.
This paper uses a rich new data set constructed at the bank group level. It matches high-quality
lending data with supervisory data on bank capital and capital requirements. Supervisory data
include confidential bank-specific and time-varying capital requirements set by the Bank of
England and the Financial Services Authority (FSA) in the United Kingdom between 1990 and
2011, which allow us to estimate directly the relationship between changes in capital
requirements and individual bank lending behaviour. Lending data are adjusted to give a unique
measure of true lending flows, rather than relying on changes in stock positions as a proxy; and
we analyse lending responses at the sectoral level, such that both credit supply and demand
conditions are allowed to vary across different sectors of the economy.
1 A bank’s capital ratio is given by total regulatory capital as a proportion of total risk-weighted assets. A bank’s capital “buffer” is
given by the actual capital ratio minus that bank’s minimum required capital ratio, as determined by the regulator.
Working Paper No. 486 January 2014 4
The bank-by-bank data set is exploited using two sets of panel regressions. First, we regress the
actual capital ratio held by each bank on that bank’s regulatory minimum capital ratio. That
allows an assessment of whether regulatory requirements affect the capital banks hold. Second,
the loan growth of each bank to different parts of the economy is regressed on that bank’s
individual regulatory requirement and on its actual capital ratio. By estimating these two
equations, both the direct impact of a change in capital requirements on lending and any indirect
impact via the response of bank capital can be taken into account when plotting the response of
bank lending over time.
These regressions suggest that changes in regulatory capital requirements did impact bank
behaviour over the sample period. First, we find that changes in regulatory requirements
typically lead to a change in actual capital ratios – in response to an increase in the minimum
ratio, banks tend to gradually rebuild the buffers that they initially held above the regulatory
minimum. Second, capital requirements affect lending with different responses in different
sectors of the economy – in the year following an increase, banks tend to cut (in descending
order) lending to commercial real estate, to other corporates and household secured lending.
The response of unsecured household lending is close to zero over the first year as a whole.
Loan growth mostly recovers within three years. Finally, preliminary analysis suggests that
banks’ responses vary depending on bank size, capital buffers held, the business cycle, and the
direction of the change in capital requirements.
These findings contribute to the debate on whether the Modigliani-Miller propositions hold (i.e.
whether changes in the composition of a bank’s liabilities affect the bank’s overall cost of funds
and credit supply), in which case changing banks’ capital requirements would not affect lending.
In practice, the empirical literature has identified a range of frictions (with taxation of debt
versus equity being frequently mentioned) such that the debt/capital structure of banks may not
be neutral for credit supply. Our paper confirms that regulatory requirements tend to affect
capital ratios permanently and credit supply temporarily.
Working Paper No. 486 January 2014 5
1 Introduction
In this paper, we attempt to identify the effect of changing regulatory capital requirements on
bank capital and bank lending. Having built a rich new data set, we run panel regressions of,
first, lending to different parts of the economy on regulatory capital requirements and observed
capital ratios, and second, of capital ratios themselves on capital requirements. We use the
estimates to build impulse responses that trace banks’ capital and sectoral lending responses to a
permanent one percentage point increase in capital requirements. The shape of the impulse
responses is allowed to vary freely both in the short and the long run and takes account of both
the direct impact of a change in capital requirements on lending, and the indirect impact via the
response of bank capital. We also do preliminary analysis to examine differences in responses
across time periods, types of banks, and whether capital requirements increase or decrease.
We are able to exploit several unique features of our data set. By using data on confidential
bank-specific and time-varying capital requirements set by the Bank of England and Financial
Services Authority (FSA) in the UK between 1990 and 2011, we are able to directly estimate the
relationship between changes in capital requirements and individual bank lending behaviour.
By examining the response of lending at the sectoral level (as advocated by Den Haan, Sumner
and Yamashiro, 2007), we allow both credit supply and credit demand to vary across different
sectors of the economy – to our knowledge a novel extension to the existing literature. We also
estimate responses at the bank group level (rather than for individual entities) and use a unique
measure of ‘true’ lending flows (rather than changes in stocks) – a key innovation as
demonstrated below.
We find two key results. First, regulatory capital requirements affect the capital ratios held by
banks – following an increase in capital requirements, banks gradually rebuild the buffers that
they initially held over the regulatory minimum. Second, capital requirements affect lending
with heterogeneous responses in different sectors of the economy – in the year following an
increase in capital requirements, banks cut (in descending order based on point estimates) loan
growth for commercial real estate (CRE), other corporates and household secured lending. The
response of unsecured household lending is shallower and not significantly different from zero
over the first year as a whole. We find that loan growth mostly recovers within 3 years. The
exception is CRE lending for which there is evidence of a long-run effect. But, given this result
may be driven by episodes in which capital requirements were falling, and is not significant
before the crisis, we refrain from placing too much weight on it. Finally, preliminary analysis
suggests that banks’ responses differ depending on their size, capital buffers held, the business
cycle, and the direction of the change in capital requirements.
These findings help shed light on a widely debated theoretical question. The existence of an
effect of capital requirements on lending hinges on the failure of the Modigliani-Miller (1958)
propositions. In the context of the banking sector, Modigliani-Miller implies that, for a given
portfolio of assets, changes in the composition of a bank’s liabilities should not affect the overall
cost of funds for the bank, and therefore the supply of credit. But a range of possible frictions
might mean that capital ratios – and capital requirements, to the extent that they influence
capital ratios – are not neutral for credit supply. Frictions that are often cited include the tax
deductibility of debt and asymmetric information. But in the short term, there could also be
Working Paper No. 486 January 2014 6
frictions associated with the raising of equity capital (eg inelastic investor demand). Ultimately,
this question can only be settled empirically.
Empirical evidence on the link between capital requirements and bank lending, especially at the
sectoral level, is also of great interest to policymakers, given that the financial crisis has led to
widespread support for the use of capital requirements as a policy tool (for example Yellen
(2010) and Hanson, Kashyap and Stein (2011)). The FPC has Direction powers over sectoral
capital requirements and Her Majesty’s Government has proposed making the FPC responsible
for setting the countercyclical capital buffer.2 Although our findings are derived from a
microprudential supervisory regime, they may contain insights about how changes in capital
requirements will affect lending in the forthcoming macroprudential regime.
The remainder of this paper is structured as follows. In Section 2 we briefly review the existing
literature on the effects of capital and capital requirements on bank lending. Section 3 describes
our data set and presents summary statistics. We explain our econometric methodology in
Section 4. Section 5 presents our results and discusses their implications. Section 6 presents
some extensions and robustness checks and Section 7 concludes.
2 Literature
Friedman (1991) noted that: “traditionally, most economists have regarded the fact that banks
hold capital as at best a macroeconomic irrelevance and at worst a pedagogical inconvenience.”
Since then, a large literature has developed seeking to identify the effect of bank balance sheet
conditions – including bank capital – on lending and the wider economy. In this section, we
review the theoretical and empirical literature on the effects of capital, and capital requirements,
on the supply of credit, and place our study in the context of that literature.
The theoretical benchmark for understanding the impact of such a shock remains the
Modigliani-Miller theorem (Modigliani and Miller (1958)). In the context of the banking sector,
the key prediction is that changes in the composition of a bank’s liabilities should not affect the
overall funding cost, assuming an unchanged level of risk on the asset side of the balance sheet.
And without a change in funding costs, there is no reason why a change in the capital ratio of a
bank, ceteris paribus, should impact on the price or quantity of credit.
There may be various frictions in the market for bank equity, however, which cause changes in
capital requirements to have real effects, either in the short or long term. The most often cited
long-term friction is the tax deductibility of debt interest payments, which implies an increase to
bank’s funding costs when capital requirements are raised. Other long-term frictions include
asymmetric information – Myers and Majluf (1984) – and debt overhang – Myers (1977). The
existence of short-run frictions might depend on how a bank chooses to meet a change in its
capital requirements. For example, the costs associated with different ways of adjustment (eg
cutting dividends versus raising equity) may have implications for funding costs, and
consequently, lending decisions.
In this paper, by investigating the effects of a change in bank capital requirements on lending
behaviour, we implicitly test the existence of such failures of Modigliani-Miller. Identifying
specific frictions is, however, beyond the scope of the paper.
2 Bank of England (2014) provides additional information on these tools.
Working Paper No. 486 January 2014 7
The growing number of studies on the relationship between bank capital and lending behaviour
can be divided into two broad buckets: those investigating the impact of shocks to capital
resources on bank lending – that is shocks to observed bank capital levels or ratios; and those
investigating the impact of shocks to regulatory capital requirements on bank lending. Given
the poverty of data on actual capital requirements, shocks to capital resources are often used as
a proxy for capital requirements by making an assumption about how banks alter capital ratios
in response to a regulatory change. Alfon, Argimón and Bascuñana-Ambrós (2005) provide
evidence for that assumption; they find that UK banks pass through around 50% of an increase
in capital requirements to their capital ratios, though the rate of pass-through is only 20% for
reductions in capital requirements.
2.1 Impact of changes in capital resources on lending
Much of the literature on the impact of capital shocks on bank lending emerged after the US
recession in the early 1990s, prompted by questions as to whether the economic situation was
exacerbated by capital-constrained banks cutting back on lending – the so-called ‘capital
crunch’ hypothesis. Bernanke and Lown (1991) found that in some regions, a shortage of equity
capital – caused in some cases by bank losses on real estate lending – limited banks’ ability to
make loans, although the authors are sceptical that the credit crunch played a major role in
worsening the recession.3 Furfine (2000), in a theoretical model calibrated to the US data, does
find a role for capital regulation in explaining the decline in loan growth and rise in bank capital.
But Sharpe (1995) argues that the evidence in favour of a capital crunch is not particularly
conclusive.
Peek and Rosengren (1997) use a natural experiment to overcome difficulties in identifying
whether changes to bank lending reflect shocks to credit supply or credit demand. They analyse
the effects of capital shocks on the lending of the branches and subsidiaries of Japanese banks
located in the United States. The parent Japanese banks, which were allowed to treat unrealised
gains on equity investments as capital, suffered a large capital shock after the collapse of equity
prices in the late 1980s. By focusing on the US lending operations of these banks, the authors
were able to isolate the credit supply effects of a fall in bank capital. They find that for Japanese
banks’ US branches, a one percentage point fall in the risk-based capital ratio led to an annual
fall in loan growth relative to assets of 4 percentage points, roughly translating into a 6
percentage point fall in the stock of lending.
In the absence of natural experiments, an alternative identification strategy exploits individual
loan-level data (where availability allows), including matched bank and borrower information.
Jimenez et al (2010) exploit a matched panel for Spain and find that lending varies with the
capital and liquidity positions of both banks and borrowers as well as with macroeconomic
conditions. Albertazzi and Marchetti (2010) find similar results when using loan-level data on
Italian banks for the period following the collapse of Lehman Brothers.
Heid, Porath and Stolz (2004), using dynamic panel data techniques on data from German
savings banks over the period 1993-2000, find evidence that capital buffers influence decisions
over both capital and risk-weighted assets. They find that banks with lower buffers attempt to
3 This was on account of the low coefficients on the capital ratio, suggesting, for example, that the 1988-90 fall in capital in New
England banks explained only 2 to 3 percentage points of that region’s decline in lending.
Working Paper No. 486 January 2014 8
rebuild them by simultaneously raising capital and lowering risk-weighted assets and that banks
with larger buffers maintain them by increasing risk-weighted assets when capital increases.
Stolz and Wedow (2005), however, using data for German cooperative banks as well as savings
banks, find that poorly capitalised banks do not decrease risk-weighted assets by more in a
downturn than their better capitalised rivals. Similarly, Rime (2001), in a study of Swiss banks
during the period 1989-95, finds that banks with a lower capital buffer tend to try to increase
their capital ratio, but that they adjust through the level of capital rather than through risk-
weighted assets.
In a more top-down approach, Noss and Toffano (2014) study the dynamics of capital and
lending at the aggregate level in the United Kingdom, using sign restrictions to identify shocks
that fit an assumed pattern of responses for macro-financial variables and assuming that capital
and capital requirements move in lockstep. They find that the level of bank lending might be
reduced by as much as 4.5% in response to a 1 percentage point increase in macroprudential
capital requirements during an economic boom.
Finally, in a study of banks in over 92 countries, Fonseca, Gonzalez and Pereira da Silva (2010)
find that banks with larger capital buffers charge lower interest rates on their lending and pay
lower interest rates on their borrowing. They find that this effect is larger in developing
countries and during downturns.
2.2 Impact of changes in capital requirements on lending
Our approach falls into the second branch of literature, which makes the direct connection
between changes in capital requirements and bank lending behaviour. Recent micro-
econometric studies tend to focus on the UK because of the relatively unique nature of the
regulatory capital regime, where capital requirements have been set differently across firms for
the past two decades.
Ediz, Michael and Perraudin (1998), in a study using confidential supervisory data for UK banks
over the period 1989-95, find that capital requirements significantly affect bank’s capital ratios,
but that firms appear to adjust by directly boosting their level of capital rather than reducing
lending.
One approach in this area is the partial adjustment model, in which banks adjust over time to
their target level of capital. Following the partial adjustment process of Hancock and Wilcox
(1994) and using 1996-2007 data, Francis and Osborne (2009) estimate a target capital ratio for
each bank in the UK, which is found to depend principally on the individual bank capital
requirement (positively) and bank size (negatively). The authors then regress bank lending
behaviour on the deviation of the actual capital ratio from target and estimate that a one
percentage point increase in capital requirements is found to lead on average to a fall in total
lending of 0.8% and a fall in risk-weighted assets of 1.6% after one year. The Macroeconomic
Assessment Group (2010), which was established by the Financial Stability Board and the Basel
Committee on Banking Supervision to assess the impact of higher regulatory capital and
liquidity requirements under Basel III, used the methodology in Francis and Osborne (2009)
amongst others to arrive at a series of estimates across different jurisdictions for the impact of a
one percentage point increase in the target capital on lending volumes. For an increase in the
Working Paper No. 486 January 2014 9
capital requirement taking place over two years, these estimates ranged from a 0.7% to a 3.6%
fall in lending.
The paper most closely related to ours is Aiyar, Calomiris and Wieladek (2014). Their focus is
on the question of whether increases in capital requirements ‘leak’ in the sense that foreign
branches can offset reductions in lending by regulated banks. As part of their study, they also
make use of UK data on individual capital requirements, but use a simpler panel data fixed
effects framework, regressing loan growth directly on changes in capital requirements and
considering only lending to PNFCs (which comprises about a quarter of the stock of loans to the
UK real economy; Table A). They find that the average effect of a 1pp increase in capital
requirements is a cumulative reduction in PNFC loan growth of 5.7-8.0 percentage points.
There are significant differences between our approach and Aiyar, Calomiris and Wieladek
(2014) in terms of model specification and data. Our model takes account of the effect of
capital requirements on capital resources, and in turn on lending; and does not restrict the effect
on loan growth to be zero in the long run. We consider a longer sample period – 1990-2011
rather than 1998-2007; use consolidated rather than unconsolidated data; and establish a new
banking unit after mergers rather than synthetically consolidating pre-merger entities. We also
offer – to our knowledge – a novel extension by estimating panel data lending models at the
sectoral rather than aggregate level. This allows us to test whether banks’ behavioural response
to a change in capital requirements is uniform across household secured, household unsecured,
CRE and other corporate lending. By estimating regressions for loan growth to each sector
separately, we are also better able to control for sectoral variations in credit demand, allowing us
to more accurately identify the response of lending supply to changes in regulatory capital
requirements. Finally we use data on ‘true’ lending flows, an important innovation as
demonstrated by Chart 1 below. These data have not been used in any previous UK studies on
this topic. A fuller discussion of each of these aspects of the data can be found in the next
section.
3 Data
3.1 Data set construction
A strength of our study is the rich panel data set of UK-supervised banks that we have
constructed. This data set marries high-quality sectoral lending data with unique supervisory
data on capital and capital requirements, and possesses several valuable features. First, it
contains data on bank-specific, time-varying, capital requirements. Second, the lending flows in
our data reflect ‘true’ bank lending behaviour, a novel improvement in the empirical panel
literature on bank lending. Third, we construct those true lending flows at the sectoral level.
Fourth, the data are constructed at the bank group level. And, fifth, it covers a longer time
period (1990-2011) than previously used data sets. In this section we describe the data set,
highlight its strengths and present descriptive statistics.
An important contribution is that our paper makes use of novel data on ‘true lending flows’ as
opposed to ‘changes in loan stocks’, a distinction that is far from trivial as we explain below.
We retrieve true lending flows such that they reflect only ‘transactions’, as defined by
international standards for economic statistics (in particular the European System of Accounts,
ESA 95). Data used in other UK studies on bank lending typically come from the monetary
Working Paper No. 486 January 2014 10
returns collected by the Bank of England (or their equivalent in other countries). These contain
detailed information on bank balance sheets, including the stock of loans. However, changes in
loan stocks over time also reflect a range of other factors that may potentially contaminate the
data. These include write-offs, exchange rate effects, reporting changes, changes to group
structures, reclassifications and changes in the values of securities and repos (Equation (1)).
The ‘true flows’ used in this paper correct for those effects at the bank level by using additional
information from individual banks’ monetary returns collected by the Bank of England.

 


∆∆
1
Comparing the true flows to differences in stocks reveals substantial differences, as shown in
Chart 1 for a representative bank (where x and y values have been rescaled to preserve
anonymity). Failing to take account of these issues, and simply using differences in stocks to
proxy flows, would lead to biased estimates. This contamination is potentially especially severe
when examining the role of bank capital. For example, a write-off would lead to a
contemporaneous fall in both capital and the loan stock, thereby generating a spurious
correlation between the two.
Chart 1: Data quality of true lending flows
Source: Bank of England.
Note: these data are based on a real bank, but have been rescaled by a constant
factor.
Throughout this paper, we use the ‘true’ lending flows described above calculated at the
(National Accounts) sectoral level. Neither of these elements has been used in previous UK
studies into the effects of capital requirements on bank behaviour.4 As argued by Den Haan,
Sumner and Yamashiro (2007), empirical studies that consider only total lending can be
misleading. The intuition is that if different constituent parts of total lending have different laws
of motion, then parameter estimates derived from the sum of the parts will be inaccurate. In our
4 To our knowledge, existing literature which exploits non-UK data does not use ‘true’ lending flows either. Though it would be
possible to construct a true flows series from loan-level credit register data used in some of the literature – eg Jimenez et al (2010).
-400
-200
0
200
400
600
800
1000
1200
1 4 7 10131619222528313437404346495255
Bank 1 - True lending flow
Bank 1 - Difference in stocks Amounts
Time periods
Working Paper No. 486 January 2014 11
context, for example, we would expect shifts in demand for loans from CRE companies to differ
from shifts in demand for unsecured credit from households. We therefore estimate separate
equations for loan growth to each sector. This allows us to better control for time variation in
macroeconomic factors that impact the demand for different types of lending differently,
improving our ability to identify the effect of regulatory capital requirements on lending supply
conditions.
We therefore calculate loan growth for each of four sectors: i) secured lending to households; ii)
unsecured lending to households; iii) lending to CRE corporations; and iv) lending to non-real
estate non-financial corporations. The level of granularity to distinguish between (iii) and (iv)
is, however, only available since 1997. Table A shows each sector’s share in the stock of loans
to the real economy at the end of 20115 and the Basel I and II regulatory risk weights applied to
each.
Table A: Size and regulatory risk weights of each lending sector
Share of outstanding
stock of loans Basel I
risk weights Basel II (standardised)
risk weights6
Secured lending to households 65% 50%
35% for LTV 80%
Up to 45% for LTVs in
excess of 100%
Unsecured lending to
households
8% 100% 100%
Lending to CRE corporations 11% 100% 100%
Lending to non-real estate
corporations 16% 100%
20%-100%, dependent
on credit rating
Source: Bank of England
Another key feature of our data set is that it is constructed at the banking group level, on what is
termed a ‘consolidated’ basis, as opposed to an unconsolidated (individual entity) basis. The
reason is that both lending and capital decisions are, in our view, likely to be determined at the
group level. Banking groups typically report their lending strategy and results at the group
level. And the capital resources and constraints of a subsidiary should influence decisions at a
group level because shocks to these resources and constraints permeate through the whole
group. This importance of group cash flow and capital resources is highlighted in Houston,
James and Marcus (1997). And Ashcraft (2008) shows that parent groups act as a source of
strength in times of distress by providing liquidity and capital.
For this reason we ‘quasi-consolidated’ the monetary returns data (which are submitted only on
an unconsolidated basis to reflect the UK operations of an individual entity) by summing across
constituent parts of a banking group.7 As an example, Figure 1 shows a simplified version of
5 ‘Real economy’ lending is defined as the stock of loans to households and PNFCs.
6 Note, however, that larger UK banks implemented the Internal Ratings Based (IRB) approach rather than Standardised approach under
Basel II.
7 ‘Quasi-consolidated’ data do not strip out intra-group activity that is not included in truly consolidated data.
Working Paper No. 486 January 2014 12
Lloyds Banking Group. Our consolidated data reflect lending and capital for the group as a
whole, whereas an unconsolidated data set would contain each of the six sub-entities (Lloyds,
TSB, Cheltenham & Gloucester, Halifax, Bank of Scotland, Birmingham Midshires) separately.
Figure 1: Example group structure for Lloyds Banking Group
The data on bank capital resources and capital requirements, which come from the regulatory
returns collected initially by the Bank of England and later by the FSA, are available on both a
consolidated and unconsolidated basis. We use the former, which reflects the global balance
sheets of banking groups regulated by the FSA, and previously, the Bank of England.
8
The
regulatory returns contain detailed information on capital adequacy, such as the total amount of
risk-weighted assets and the bank-specific capital requirement (sometimes called the ‘trigger’
capital requirement – see Box 1).
Our data set is adjusted to account for the considerable number of mergers and acquisitions that
occurred in our sample period. We split the series at the time of any M&A activity and
excluded at least a quarter of data as balance sheets can demonstrate peculiar behaviour around
the time of mergers and acquisitions.
9
This treatment makes our sample less balanced. But we
see this cost as preferable to backwards engineering a synthetic aggregate of merged banks, as
done elsewhere in the literature. We are not convinced that separate competitor banks, with
different business models and balance sheets, can be treated as if they were one unit before the
merger. In addition, several other manipulations have been made in order to clean the data, as
detailed in Appendix 1.
Box 1: The UK prudential regime
Under both the Basel Accord and European Directives on capital requirements, a bank’s total
capital ratio (total capital / risk-weighted assets) had to be at least 8% of risk-weighted assets
(RWA). On top of the hard floor of 8%, the UK regulators set bank-specific minimum capital
requirements.
8
It is worth noting that the two data sources differ in scope, even after the monetary returns are quasi-consolidated. The monetary
returns capture the UK operations of a wide set of UK and foreign banks, whereas the regulatory returns capture the UK and foreign
operations of UK-regulated banking groups.
9
For example, following Lloyds acquisition of HBOS (Halifax Bank of Scotland) in January 2009, both the Lloyds Banking Group and
HBOS series terminate in 2008Q4 and a new series for Lloyds Banking Group commences in 2009Q3, excluding 2009Q1 and Q2.
Lloyds
Banking
Group
Lloyds
Banking
Group
Lloyds TSB Cheltenham&
Gloucester
HBOS
Halifax Bankof
Scotland
Birmingham
Midshires
Working Paper No. 486 January 2014 13
Before the establishment of the FSA, the Bank of England set bank-specific minimum total
capital requirements (known as ‘trigger ratios’) as well as target ratios, typically set 50-100bps
above the trigger to avoid an accidental breach.10 After this power was handed over to the FSA
in 2001, trigger ratios were renamed Individual Capital Guidance (ICG) and subsequently
became set as part of the Pillar 2 process under Basel II.
Trigger ratios are set to compensate for the uniformity of other aspects of the capital adequacy
framework (e.g. risk weights). A bank’s trigger ratio is based on bank-specific factors such as
the quality of risk management, the quality of internal control and accounting systems, plans for
future developments of the business, its size and position in chosen markets, and the future
outlook in those markets.
3.2 Descriptive statistics
Our panel data set includes data from 1990 Q1 until 2011 Q3 and thus captures a full business
cycle. We have included all banks, both active and inactive, who have reported total UK assets
greater than £5 billion at any time since 1990 Q1. As a result, our panel contains 53 banking
groups, each with an average of 30 quarters of data.
Table B presents the summary statistics of the most important capital adequacy and lending
variables: the minimum capital requirement, its changes, the observed capital ratio, household
secured loan growth, household unsecured loan growth, CRE loan growth and the growth in
loans to non-CRE PNFCs.11
Table B: Summary statistics12
# Obs Mean Std Dev
10%
percentile
90%
percentile
Minimum capital requirement (% of RWA) 1,590 9.93 1.79 8.00 11.99
Changes in minimum capital requirement
(Percentage point) 1,590 0.03 0.32 -0.03 0.05
Changes in minimum capital requirement
(Percentage point) – excluding [-0.1; 0.1] range 253 0.17 0.79 -0.53 0.95
Capital ratio (% of RWA) 1,549 15.82 8.27 10.65 22.57
Secured loan growth (%, q on q) 1,298 0.50 5.78 -6.43 5.54
Unsecured loan growth (%, q on q) 1,459 1.62 5.27 -2.78 7.38
Non-CRE PNFC loan growth (%, q on q) 857 1.55 8.78 -9.44 11.96
CRE loan growth (%, q on q)
897 2.49 10.73 -7.26 12.51
Sources: Bank of England and FSA.
Chart 2 shows the variation in the minimum capital requirements over the sample period.
Excluding negligible changes (smaller than 0.1 in absolute value), there were 253 changes in the
10 The Bank of England policy of setting trigger and target ratios dates back to before the implementation of Basel I.
11 Throughout this paper, minimum capital requirements and actual capital ratios are defined in ‘total capital’ terms. In other words, the
numerator of these ratios includes all types of regulatory capital.
12 For further detail on some of the variables see Appendix 2.
Working Paper No. 486 January 2014 14
sample, with a slight prevalence of increases (143 occurrences) over decreases (110
occurrences). Chart 3 shows that the bulk of changes in minimum capital requirements over the
sample period were between 0 and 1 in absolute value.
Chart 4 illustrates how capital buffers (i.e. the difference between the overall capital ratio and
the minimum requirement) broadly fell across banks in the decade leading up to the crisis,
before being rebuilt in the last two years of the sample.
Chart 2: Variation in minimum capital
requirements
Chart 3: Magnitude of changes in
minimum capital requirements
Sources: Bank of England and FSA Sources: Bank of England and FSA
Note: Excludes when minimum capital requirements did not
change. In total there are 253 episodes of changes larger than
|0.1|
Chart 4: Capital buffer
Sources: Bank of England and FSA
4 Methodology
To determine how banks typically react to a change in capital requirements, we estimate
dynamic panel equations for bank capital and loan growth for each sector as follows:
0
5
10
15
20
25
30
35
1990 1993 1996 1999 2002 2005 2008 2011
Increases (n=143)
Decreases (n=110)
Frequency of absolute changes>0.1
0
20
40
60
80
100
120
140
< -1 -1 to -
0.1
< | 0. 1| 0 .1 to 1 1 t o 2 2 to 3 M or e
than 3
Percentage points
Episodes
923
episodes
(437
positive,
486
negative)
0
2
4
6
8
10
12
14
90 92 94 96 98 00 02 04 06 08 10
interquartile range
median
Per cent of RWA
Working Paper No. 486 January 2014 15
 

,
 
, 
 

 2

 


,


,
,  
 
 

 3

Where  is actual capital as a fraction of risk-weighted assets for bank i in quarter t; ,
is the capital requirement (trigger ratio) set by the regulator; 
 is quarterly loan growth
based on the true flows as described in Section 3.1; and , is a vector of bank-specific micro
controls that might affect lending, namely proportion of Tier 1 capital and the leverage ratio.
These variables describe the quality of capital resources, in addition to the quantity captured by
trig. s denote bank and time fixed effects, and and are error terms.
The number of lags in each equation was determined in a general to specific procedure, testing
down from four lags and restricting the number of lags for capital and the capital requirement to
be the same both within and across equations. The lagged dependent variables have the effect of
mopping up residual autocorrelation. Equation (2) is estimated only once, restricting the sample
to those observations where both secured and PNFC loan growth was non-missing while
equation (3) is estimated for each different type of lending (secured, unsecured, CRE, and PNFC
non-CRE). Each equation is estimated separately – the correlation between error terms in the
lending and capital equation is small and insignificantly different from zero for each lending
equation, which allows us to treat the responses similarly to as if estimated as a system.
We use fixed effects for banks and for quarterly time periods. Banks’ fixed effects control for
unobserved heterogeneity at the bank level; for example, systematic differences in business
models, domicile or size. Quarterly time fixed effects control for macroeconomic and demand-
side effects that are common to all banks at a given point in time; for example, if all banks’
lending flows were lower in a certain period because of weak demand, the time dummies would
capture this by taking a lower value in that particular period. Estimating each sectoral lending
equation separately, with separate time fixed effects, allows for different patterns of demand in
each sector, improving our ability to identify the impact of regulatory capital requirements on
bank lending supply conditions. Nonetheless, we do not claim watertight identification: even
with fixed effects at the sectoral level, demand effects might confound our estimates if, for
example, capital requirements were increased for banks mainly operating in a particular area of
the UK at the same time as demand fell in that particular area.13 The results are broadly robust
to explicitly including macro controls (GDP and inflation) instead of time fixed effects, but the
13 In addition, it is possible that supervisors tended to increase capital requirements when they were concerned about asset quality. In
that case, the estimated effect might be too large as it would capture the bank’s response to both higher capital requirements and
concerns about asset quality. However, Aiyar, Calomiris and Wieladek (2014) provide some evidence that this is unlikely to be the
case. They cite Francis and Osborne (2009) noting that the UK discretionary regime was meant to ‘fill gaps in the early Basel I system,
which did not consider risks related to variation in interest rates, or legal, reputational and operational risks’ and then continue to find
that changes in write-offs (lagged, present and future) cannot predict changes in capital requirements.
Working Paper No. 486 January 2014 16
latter are better at soaking up all factors common to banks at any point in time without the need
to model them.
The presence of both lagged dependent variables and fixed effects causes a well-known bias
(Nickell, 1981). But as our sample contains a relatively large number of time periods and only a
moderate number of banks, using panel data techniques with fixed effects remains preferable to
Generalised Method of Moments (GMM). That is because the lagged dependent variable bias
declines as the number of time period increases, and our estimates will be consistent as long as
there is no autocorrelation of the error terms. Judson and Owen (1999) suggest using standard
fixed effects estimation rather than GMM in unbalanced panels when T is large (T=30).
Standard errors are robust and clustered at the bank level.
An additional issue arises because methods that involve pooling data (such as the fixed effects
estimator and other panel methods) assume homogeneity of coefficients across banks. Pesaran
and Smith (1995) suggest using the Mean Group estimator to tackle this issue. However, the
highly unbalanced nature of our panel (which is partly a result of the treatment of mergers and
acquisitions, see Section 3.1) means that this estimator is not appropriate. That is because the
Mean Group estimator would give a very large weight to coefficients estimated for banks with
only few observations, leading to very high standard errors. We instead relax the homogeneity
assumption in Section 6 by investigating the impact of capital requirements for different types of
banks.
Central estimates for impulse responses are calculated using the point estimates from equations
(2) and (3), while the calculation of confidence intervals follows the methodology used in Beyer
and Farmer (2006). Specifically, we take 2,500 draws from the joint normal distribution with
mean and variance-covariance matrices given by the vectors of point estimates and variance-
covariance matrices estimated from equations (2) and (3). The impulse responses are ranked
within each quarter and the upper and lower bounds of the confidence interval are given by the
16th and 84th percentiles, as is typical in the macro literature following Sims (1999).
5 Results
This section presents the main results on how banks adjust their capital and lending following a
change in capital requirements, based on the estimates from the two dynamic panel equations (2)
and (3). Tables C and D present the capital ratio and sectoral lending responses to a one
percentage point permanent increase in capital requirements, while Charts 5 to 9 illustrate the
dynamics of the adjustment process. A full set of coefficient estimates for all of our preferred
specifications can be found in Appendix 3.
5.1 The impact of capital requirements on capital ratios
Equation (2) examines how a bank’s capital ratio behaves in relation to its own capital
requirement and past capital ratios. Changes in capital requirements are found to significantly
affect the observed capital ratio – in other words, regulatory change significantly impacts bank
behaviour, rather than being passively absorbed in an equal and offsetting change in the capital
buffer held above the regulatory minimum. This result is in line with other studies that use UK
data, such as Alfon, Argimón and Bascuñana-Ambrós (2005) and Francis and Osborne (2009).
Working Paper No. 486 January 2014 17
Our central estimate suggests that, following a one percentage point permanent increase in
capital requirements, banks start to increase their capital ratio (potentially via a combination of
raising new capital, retaining profits or reducing assets) (Chart 5). After one year, banks have
increased their capital ratio by 0.4pp and after 3 years by 0.9pp (Table C); the initial buffer is
fully restored in less than four years. Our central estimate suggests that the adjustment settles
just above 1pp, indicating that banks increase their capital ratio broadly one for one in response
to an increase in capital requirements. The confidence interval in Chart 5 highlights the
considerable uncertainty around this central estimate, but suggests that the positive response of
actual bank capital ratios to an increase in regulatory requirements is statistically significant.
Chart 5: Capital ratio impulse response Table C: Capital ratio response to 1pp
increase in capital requirements
Response to
a 1pp
increase in
capital
requirements
Change in observed capital ratio
after 1 year 0.41*
(68% CI) [0.16 : 0.68]
Change in observed capital ratio
after 3 years 0.95*
(68% CI) [0.38 : 1.54]
Dependent variable (no. of lags) 4
R2 0.95
Observations 1,095
Note: * denotes significantly different from zero using
the 68% confidence interval. The regression includes
bank and quarterly time fixed effects.
Note: Capital ratio impulse response following a permanent one
percentage point increase in the capital requirement at time 0.
5.2 The impact of capital requirements and capital ratios on sectoral loan growth
Equation 3 examines how banks’ sectoral loan growth is related to their individual capital
requirement, observed capital ratio and past loan growth.
a) Household secured lending
An increase in capital requirements is associated with a temporary reduction in secured loan
growth, which on our central estimate lasts less than a year (Chart 6). In the first quarter
following the regulatory change, household secured loan growth falls sharply, with a peak
impact of reducing the quarterly growth rate by 0.8pp. The cumulative effect over the first year
is -0.9pp (Table D). After the first year, as the bank accumulates capital towards restoring its
buffer, loan growth returns to its previous rate.
b) Household unsecured lending
Household unsecured loan growth exhibits a much shallower response to an increase in capital
requirements (Chart 7). Our central estimate is for a trough fall in quarterly loan growth of
0.5 11.5
Percentage points
0 1 2 3 4 5
Year
Central estimate 68% CI
Working Paper No. 486 January 2014 18
0.2pp after five quarters and a reduction in the loan growth rate of 0.7pp cumulatively after a
year and 0.2pp in the long run. Of these effects, only the trough fall in the 5th quarter is
significantly below zero.
Chart 6: Secured loan growth impulse response Chart 7: Unsecured loan growth impulse
response
Note: Secured loan growth impulse response following a
permanent one percentage point increase in the capital
requirement at time 0.
Note: Unsecured loan growth impulse response following a
permanent one percentage point increase in the capital
requirement at time 0.
c) CRE lending
Turning to corporate lending, we analyse lending to CRE and other industries separately.14
Following an increase in capital requirements, CRE lending falls sharply (Chart 8). The results
suggest that, faced with a 1pp increase in capital requirements, banks reduce CRE loan growth
by around 4pp after a quarter; this effect is statistically significant. The cumulative fall in loan
growth over the first year is 8pp. Further, our main specification suggests a permanent effect,
with quarterly loan growth remaining 1.3pp (around 5pp annualised) lower. However, as noted
in Sections 6.1 and 6.3 respectively, this result is not significant before the crisis and may be
driven by decreases in banks’ capital requirements.
14 We have also estimated the model for lending to all PNFCs over the longer time series, but there appears to be little effect from changes in
capital requirements to lending. This may not be surprising as the equations for all PNFCs rely on lending data from a range of diverse
industries with potentially different responses.
-1 -.75 -.5 -.25 0.25 .5
Percentage points
0 1 2 3 4 5
Year
Central estimate 68% CI
-1 -.75 -.5 -.25 0.25 .5
Percentage points
0 1 2 3 4 5
Year
Central estimate 68% CI
Working Paper No. 486 January 2014 19
Chart 8: CRE loan growth impulse response Chart 9: Non-CRE PNFC loan growth impulse
response
Note: CRE loan growth impulse response following a permanent
one percentage point increase in the capital requirement at time 0.
Note: Non-CRE PNFC loan growth impulse response following a
permanent one percentage point increase in the capital
requirement at time 0.
d) Non-CRE corporate lending
Following an increase in capital requirements, loan growth to PNFCs in other industries15 also
falls significantly; the magnitude of this effect is more muted than for real estate but stronger
than for secured lending (Chart 9). The central estimate suggests a trough fall of 2.1pp in
quarterly loan growth in the first quarter and a 3.9pp fall in annual growth by the end of the first
year. There is no significant long-run impact.
Table D: Loan growth response to 1pp increase in capital requirements
(1) (2) (3) (4)
Household
secured loan
growth
Household
unsecured
loan growth
CRE loan
growth
Non-CRE
PNFC loan
growth
Peak impact on loan growth
quarterly rate (pp) -0.77* -0.19* -4.04* -2.05*
(68% CI) [-1.07 : -0.49] [-0.37 : -0.01] [-5.56 : -2.63] [-3.51 : -0.73]
(quarter) 1 5 1 1
Impact on loan growth rate over
year 1 (annual, pp) -0.94* -0.68 -8.07* -3.86*
(68% CI) [-1.69 : -0.20] [-1.43 : 0.03] [-10.46 : -5.70] [-6.05 : -1.54]
Long-run impact on quarterly
loan growth (at end-year 3, pp) 0.18 -0.16 -1.33* -0.67
(68% CI) [-0.01 : 0.39] [-0.36 : 0.04] [-1.85 : -0.77] [-1.34 : 0.05]
Dependent variable (no. of lags) 2 2 2 2
R2 0.21 0.12 0.10 0.09
Observations 1,143 1,358 809 760
Note: * denotes significantly different from zero using the 68% confidence interval. The effect over year 1 is calculated
as the sum of the four quarterly effects. All regressions include bank and quarterly time fixed effects.
15 PNFC non real estate lending is calculated as PNFC lending less CRE lending. Due to differences in definitions, especially related to a
reclassification of housing associations, this measure tends to be less precise than that of CRE lending.
-6 -5 -4 -3 -2 -1 0 1
Percentage points
0 1 2 3 4 5
Year
Central estimate 68% CI
-6 -5 -4 -3 -2 -1 0 1
Percentage points
0 1 2 3 4 5
Year
Central estimate 68% CI
Working Paper No. 486 January 2014 20
5.3 Discussion
Taking the above together, there are two key findings. First, regulatory capital requirements
affect the capital ratios held by banks – following an increase in capital requirements, banks
gradually rebuild the buffers that they initially held above the regulatory minimum. Second,
capital requirements affect lending with heterogeneous responses in different sectors of the
economy – in the year following an increase in capital requirements, banks cut (in descending
order based on point estimates) loan growth for CRE, other corporates and household secured
lending. The response of unsecured household lending is shallower and insignificant over the
first year as a whole. Loan growth mostly recovers within 3 years. The exception is CRE
lending for which there is evidence of a long-run effect. But, given this result may be driven by
episodes in which capital requirements were falling, and is not significant before the crisis, we
refrain from placing too much weight on it.16
These results are not directly comparable to those of other studies. But a very rough comparison
to Macroeconomic Assessment Group (2010), Aiyar, Calomiris and Wieladek (2014) and Noss
and Toffano (2014) can be made by calculating the cumulative effect over three years17 for each
sector in our study, and then calculating the total effect using the sector shares from Table A as
weights. On that measure, we find that the impact of a 1pp increase in the capital requirement
on loan volumes is about -3.5%, compared to between -0.7 and -3.6% in Macroeconomic
Assessment Group (2010), -5.7 to -8.0% in Aiyar, Calomiris and Wieladek (2014) and -4.5% in
Noss and Toffano (2014). The finding of Noss and Toffano (2014) that the effect is larger for
lending to corporates (and in particular CRE) than to households is consistent with our point
estimates in Table D, and could be one explanation why Aiyar, Calomiris and Wieladek (2014)
– who look at PNFC lending only – find a relatively large effect.
Our results reflect how, on average, individual banks respond to a change in their own
confidential and microprudential capital requirements. Whilst our findings may contain some
insights into how macroprudential policy will impact bank behaviour, there are likely to be a
number of differences in the macroeconomic implications. First, the extent of ‘leakages’ –
where entities not subject to a change in capital requirements step in to pick up any slack in
lending left by banks subject to the change – may be different when capital requirements are
changed for a large set of banks simultaneously.
Second, a macroprudential policy regime may have different implications for the way in which
banks adjust their capital ratios to a regulatory chance. Following a system-wide increase in
capital requirements, banks might not restore their capital buffers in the same way as in the past
because they may not be able to all simultaneously acquire capital. On the other hand, a
synchronised regulatory change may diminish any signalling problems associated with raising
additional capital. Also, during the transition to higher global regulatory standards, increasing
capital requirements might augment rather than reduce lending for initially undercapitalised
16 During the crisis, lenders suffered large losses on their CRE and other corporate loan books (in absolute terms and relative to
household lending). The CRE lending market is a particularly cyclical industry; this may be one explanation for our result, discussed in
Section 6.4, that CRE lending is more sensitive to capital requirements when there is a negative output gap.
17 The effect in Macroeconomic Assessment Group (2010) is for the 18th quarter of simulation; Aiyar, Calomiris and Wieladek (2014)
assumes that changes in capital requirements have no effect on loan growth after four quarters; while Noss and Toffano (2014) estimate
the effect over 4 years. The studies also differ along other dimensions.
Working Paper No. 486 January 2014 21
banks if confidence effects boost their resilience and capacity to lend. Furthermore,
macroprudential regulators are often required to consider the wider implications of changing
capital requirements, which could include any adverse impact on lending – for example, while
the FPC’s primary objective is to protect and enhance the resilience of the UK financial system,
it also has a secondary objective to support the economic policy of the Government.
Third, macroprudential capital requirements are intended to operate within a more systematic
and transparent framework than their microprudential counterpart. For example, we might
expect banks to react in a different way to anticipated and unanticipated changes in their capital
requirements, such that a transparent and well-communicated macroprudential regime may
induce different bank behaviour, to the extent that future policy decisions are more easily
anticipated.
As a result, our study cannot be read as a like-for-like map of how changing capital
requirements likely affects bank capital and lending in a macroprudential framework; but it is a
useful guide to how banks have adjusted their capital ratios and lending structure on average in
response to past microprudential supervisory actions.
6 Extensions and robustness checks
We investigate the robustness of our results along five dimensions: a) influence of the financial
crisis; b) heterogeneity by size of bank; c) asymmetry between increases and decreases in capital
requirements; d) business cycle variation in banks’ responses; and e) heterogeneity by size of the
capital buffer. We note that the results in this section are based on preliminary analysis intended
to provide some idea of where our main results come from and interrogate their robustness,
rather than on fully developed econometric exercises. More details on the methodology and
charts of impulse responses are available in Appendix 4.
6.1 Influence of the financial crisis
To examine the influence of the financial crisis on our results, we re-estimate equations (2) and
(3) excluding data from 2008 onwards. The results on capital and on secured lending are
generally robust to this exclusion. In the case of unsecured lending, lending responds more
negatively when estimated on data until 2007 only. One possible explanation is that, while
unsecured loan growth responded to changes in individual banks’ capital requirements before
2007, after 2007 it became less responsive because of pricing and demand effects. First, when
setting loan quantities and prices, it is plausible that the increase in riskiness of unsecured
borrowers after 2007 and the cost to cover potential credit losses from defaulting borrowers
dwarfed any reaction to changes in capital requirements. Button, Pezzini and Rossiter (2010)
show that the cost of capital is only a very small fraction of the overall price of an unsecured
loan.18 Second, demand for unsecured credit may have increased since the start of the crisis as
households used relatively more unsecured credit to smooth banks’ restrictions in secured credit.
Effects on corporate lending, on the contrary, become more muted if the crisis years are
excluded. For CRE lending, the long-run growth rate effect is not present when estimating only
18 Specifically, in decomposing the pricing of unsecured loans, Button, Pezzini and Rossiter (2010) estimated that a 10% unsecured
loan rate (for a £10,000 personal loan) would comprise around 450bps to cover credit losses and only 80bps to cover the cost of setting
aside regulatory capital.
Working Paper No. 486 January 2014 22
until 2007. And for lending to other corporations, there are no significant effects before the
crisis.
6.2 Heterogeneity by size of bank
The results do appear to differ between large and small banks, although the degree is dependent
on how ‘large’ is defined – whether by the size of total assets, real economy lending or sectoral
lending; whether by size of loan stocks or by growth rates; whether the definition is dynamic,
such that a bank can change from large to small or vice versa over time, or static. This
dependence limits inference on how large and small banks behave differently. But to give an
example, when defining large banks in a given quarter as the top 50% in terms of total assets in
that quarter, small banks generally appear to be the main driver of the results on lending. Large
banks tend to exhibit less negative effects initially, with the exception of lending to non-CRE
PNFCs. That may be because large, most likely international, banking groups have more
flexibility as to how they raise and allocate capital than small banks. As such, they may be able
to better insulate themselves from, or respond to, regulatory actions
6.3 Asymmetry between increases and decreases in capital requirements
The results presented in Section 5 assume that banks react symmetrically, i.e. banks’ responses
to an increase in capital requirements are the mirror image of their response to a decrease.
Initial analysis suggests this is not the case for all sectors empirically. The strong initial reaction
for CRE and household secured lending in particular appear to be driven by increases rather
than decreases in capital requirements: CRE lending is lowered by 4.7pp in response to a 1pp
increase in capital requirements, but increased only 2.1pp in response to a similar decrease in
capital requirements, and secured lending to households is not significantly affected by
reductions in capital requirements. This result chimes with Elliott, Feldberg and Lehnert (2013),
who find, in a study of macroprudential policy actions – taking place throughout the twentieth
century and spanning a wide range of instruments, including interest rate controls, reserve
requirements and capital requirements – in the United States, a policy tightening has a larger
effect on lending than an easing. On the other hand, the long-run growth effect for the CRE
sector appears to be driven by reductions in banks’ capital requirements. The effects for the
non-CRE PNFC sector are more symmetrical.
6.4 Business cycle variation in banks’ responses
Banks’ response to changes in capital requirements might vary over time with the business
cycle. Here we examine the extent to which responses vary between times when the output gap
is positive or zero (‘good’ times), and when the output gap is negative (‘bad’ times).19
We find that banks tend to cut corporate lending more when the output gap is below zero; CRE
lending is initially reduced by 4.6pp and non-CRE PNFC lending by 3.9pp in that case. In
contrast, CRE lending is reduced by only 1.8pp and non-CRE PNFC lending by 0.5pp when the
output gap is positive, and these effects are insignificant. For unsecured lending to households,
the immediate reaction is also stronger when the output gap is negative, but loan growth returns
more quickly to normal in that case. Finally, the initial response is similar for household
19 An alternative would be to split the sample based on whether GDP growth was above or below its long-run trend, but a dummy based
on this split is too volatile for the exercise to be meaningful.
Working Paper No. 486 January 2014 23
secured lending, but the response for times when the output gap is positive exhibits a somewhat
puzzling positive response in the long run.
6.5 Heterogeneity by size of the capital buffer
It is possible that banks with capital buffers close to zero are particularly sensitive to changes in
the regulatory capital requirement. We examine this hypothesis by estimating impulse
responses for banks with lagged capital buffers above and below 1.5 per cent. Based on our
central estimates, we do indeed find that the initial lending reaction is stronger for banks with
smaller capital buffers, with the exception of CRE lending where the initial reaction is similar.
But further out, CRE and non-CRE PNFC loan growth remains subdued for banks with large
buffers while it recovers completely for banks with small buffers. Lending to households
recovers for both sets of banks.
7 Conclusion
Our results suggest that changes in capital requirements affect both capital and lending. In
response to an increase in capital requirements, banks gradually increase their capital ratios to
restore their original buffers held above the regulatory minimum. Banks also reduce loan
growth – in the year following an increase in capital requirements, banks cut (in descending
order based on point estimates) loan growth for CRE, other corporates and household secured
lending. The response of unsecured household lending is shallower and not significant over the
first year as a whole. Loan growth mostly returns to normal within 3 years. Finally, initial
analysis suggests that banks’ responses differ depending on bank size, capital buffers held, the
business cycle, and the direction of the change in capital requirements.
These results reflect how, on average, individual banks responded in the past to a change in their
own confidential and microprudential capital requirements. As such, they cannot be used to
directly infer the macroeconomic effects of macroprudential policy.20 But – for obvious reasons
– we lack empirical evidence on as yet untried macroprudential capital requirements. And to the
extent that there will be similarities in the way in which banks respond to changes in capital
requirements across regimes, our results will contain some quantitative insights into how
changing capital requirements in a macroprudential regime might affect lending.
20 See Section 5.3 for a fuller discussion.
Working Paper No. 486 January 2014 24
Appendices
Appendix 1 – Data cleaning
Mergers and acquisitions: As discussed in Section 3, we have split bank groups in order to
take account of mergers and acquisitions. As reported balance sheet characteristics often
display volatility around the time of a merger or acquisition, we excluded the quarter associated
with the merger, following Kashyap and Stein (2000). However, even then jumps in the data
remained common around M&A activity, so in some cases we excluded additional quarters
based on judgement.
Start-ups and wind-downs: Similarly, data jumps are often present when a bank is starting up
or winding down. Therefore we eliminated the first four quarters in a start-up and the last four
quarters in a wind-down.
Outliers: We removed banks with less than five time-series observations. We also removed
outliers by excluding some observations at the top and bottom of the range of each variable,
cutting the top and bottom 1-5% depending on the noisiness of the original data.
Appendix 2 – Key variables
Table A1: Key variables
Variable Definition Source Notes

,, Quarterly growth of secured loans
to households
Monetary returns Uses true flow of
M4Lx

,, Quarterly growth of unsecured
loans to households
Monetary returns Uses true flow of
M4Lx

,, Quarterly growth of loans to CRE
private non-financial corporations
Monetary returns Uses true flow of
M4Lx

,, Quarterly growth of loans to non
real estate private non-financial
corporations
Monetary returns Uses true flow of
M4Lx
, Published total capital ratio
(includes all types of qualifying
regulatory capital)
Regulatory returns % of risk-weighted
assets
, Trigger requirement:
Required totalcapital resources
Ris
weightedassets
Regulatory returns % of risk-weighted
assets
1, Tier 1 capital ratio:
Tier 1 capital
Totalregulatorycapital
Regulatory returns
, Leverage:
Total assets
Tier1capital
Regulatory returns
Working Paper No. 486 January 2014 25
Appendix 3 – Full table of results
Table A2 shows the full set of coefficient estimates for our main specifications described in
equations (2) and (3). These estimates are used to generate the impulse responses shown in
Section 5 using the method explained in Section 4.
Table A2: Results for main loan growth and capital equations
(1) (2) (3) (4)
(5)
Secured loan
growth
Unsecured loan
growth
CRE loan growth Non-CRE PNFC loan
growth
Capital
Trigger ratio (-1) -0.771** -0.131 -4.044** -2.055
0.118
(0.304) (0.509) (1.517) (1.434) (0.082)
Trigger ratio (-2) 0.838** -0.015 2.685* 1.391
-0.088
(0.310) (0.546) (1.351) (1.658) (0.094)
Trigger ratio (-3)
0.058
(0.307)
Trigger ratio (-4)
-0.009
(0.180)
Capital (-1) 0.132 -0.149 0.203 0.348
1.673***
(0.181) (0.133) (0.516) (0.298) (0.056)
Capital (-2) -0.096 0.180 -0.168 -0.385
-1.294***
(0.184) (0.121) (0.479) (0.340) (0.142)
Capital (-3)
0.878***
(0.186)
Capital (-4)
-0.333***
(0.096)
Tier 1 ratio (-1) 0.026 0.019 -0.006 0.003
(0.021) (0.016) (0.055) (0.037)
Leverage ratio (-1) 0.040 0.027 0.175 -0.166
(0.046) (0.040) (0.140) (0.107)
Dependent variable (-1) 0.269*** 0.051 0.006 -0.066
(0.053) (0.059) (0.041) (0.065)
Dependent variable (-2) 0.180*** 0.172*** -0.002 0.051
(0.046) (0.040) (0.065) (0.054)
Time and bank fixed effects yes yes yes yes yes
Constant -3.900 -1.171 5.871 12.496 0.754*
(3.164) (2.639) (10.941) (10.617) (0.415)
Observations 1,143 1,358 809 760 1,095
R-squared 0.213 0.120 0.103 0.090 0.945
Number of banks 41 50 37 39 41
Note: Fixed effects regressions of loan growth and capital. Capital is actual capital as a fraction of risk-weighted assets. Trigger ratio is the
capital requirement set by the regulator. Tier 1 ratio is the ratio of tier 1 capital to total regulatory capital. Fisher-type panel unit root tests
suggest no unit roots for any of the variables used. Robust standard errors in parentheses. ***p<0.01, ** p<0.05, * p<0.10.
Working Paper No. 486 January 2014 26
Appendix 4 – Extensions and robustness checks
a) Influence of the financial crisis
The robustness check for the effect of the financial crisis explained in Section 6.1 was
conducted by estimating equations (2) and (3) for a subsample containing data from before the
crisis (up to end 2007). Chart A1 shows the impulse responses for lending to each sector for the
pre-crisis period.
Chart A1: Loan growth impulse responses pre-crisis
Secured
Unsecured
CRE
PNFC non-CRE
Loan growth impulse responses following a permanent one percentage point increase in the capital requirement at
time 0.
To test formally for the existence of a structural break during the crisis, we created interaction
variables between a crisis dummy (taking value 1 from 2008 Q1 onwards) and the regressors in
the lending equations. We then estimated the regressions with the additional interaction
variables and tested for their joint significance. According to this test, there is a structural break
if the interaction variables are jointly significant. The results are presented in Table A3.
Table A3: F-tests for structural break during the crisis
F Prob>F Structural break?
Secured lending 1.21 0.3181 No
Unsecured lending 2.89 0.0102 Yes
CRE lending 4.54 0.0007 Yes
PNFC non-CRE lending 1.20 0.3236 No
F-tests for structural break during the crisis. The null hypothesis is that there was no structural break.
-1 -.75 -.5 -.25 0.25 .5
Percentage p oints
0 1 2 3 4 5
Year
Central esti mate 68% CI
-1 -.75 -.5 -.25 0.25 .5
Percentage p oints
0 1 2 3 4 5
Year
Central esti mate 68% CI
-6 -5 -4 -3 -2 -1 0 1
Percentage points
0 1 2 3 4 5
Year
Central estimate 68% CI
-6 -5 -4 -3 -2 -1 0 1
Percentage points
0 1 2 3 4 5
Year
Central estimate 68% CI
Working Paper No. 486 January 2014 27
Our tests suggest that the null hypothesis is not rejected for secured and non-CRE PNFC
lending, so that there was no structural break in those series. On the other hand, we find
structural breaks for household unsecured and CRE lending.
b) Heterogeneity by size of bank
To examine whether there is heterogeneity in the lending and capital results depending on bank
size (as discussed in Section 6.2), we estimated the following dynamic panel equations:
 

,
 
,
 
,
, ,


, ,
 
 
 1

 


,
 
,


,  
,

,
 ,


,
 , 
,
 , 
,
 
 2
where , is a dummy variable taking the value 1 if a bank is ‘large’ and 0 if a bank is
‘small’. We use lagged size to avoid any potential endogeneity problems. Our preferred
definition of ‘large’ is a bank that is in the top 50% of the distribution at that point in time in
terms of total assets. We also tried an array of alternative definitions of bank size, based on the
stock of lending to the real economy (households and PNFCs) and the stock of lending to each
sector. We also considered the growth rate of these variables rather than the stock to test for
differential effects for fast-growing banks. We also estimated our equations separately for
subsamples of small and large banks. As discussed in Section 6.2, the results are sensitive to the
choice of definition. Chart A2 shows the impulse responses of lending in each sector for small
and large banks using our preferred definition of ‘large’.
c) Asymmetry between increases and decreases in capital requirements
As a preliminary attempt to see whether banks respond symmetrically to increases and decreases
in capital requirements, we estimate equations (2) and (3) for subsamples containing episodes of
increases and decreases in capital requirements separately. We define an increase in capital
requirements episode as one in which the capital requirement has ‘net’ increased over the
previous year (ie , 
,0, so offsetting changes do not count). Column 1 in
Chart A3 shows the impulse responses of lending in each sector following an increase in capital
requirements, and column 2 shows the impulse responses following a decrease in capital
requirements.
Working Paper No. 486 January 2014 28
Chart A2: Loan growth impulse responses for small and large banks
Small banks Large banks
Secured
Unsecured
CRE
PNFC non-CRE
Loan growth impulse responses following a permanent one percentage point increase in the capital requirement at
time 0.
-1.5 -1 -.5 0.5
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-1 -.5 0 .5 1
Percentage poi nts
0 1 2 3 4 5
Year
Central estimate 68% CI
-1 -.5 0 .5
Percentage poi nts
0 1 2 3 4 5
Year
Central estimate 68% CI
-.5 0 .5 1 1.5
Percentage poi nts
0 1 2 3 4 5
Year
Central estimate 68% CI
-6 -4 -2 0
Percentage poi nts
0 1 2 3 4 5
Year
Central estimate 68% CI
-5 -4 -3 -2 -1 0
Percentage poi nts
0 1 2 3 4 5
Year
Central estimate 68% CI
-4 -3 -2 -1 0
Percentage poi nts
0 1 2 3 4 5
Year
Central estimate 68% CI
-10 -5 0 5
Percentage poi nts
0 1 2 3 4 5
Year
Central estimate 68% CI
Working Paper No. 486 January 2014 29
Chart A3: Loan growth impulse responses for increases and decreases in capital
requirements
1pp increase in capital requirement 1pp decrease in capital requirement
Secured
Unsecured
CRE
PNFC non-CRE
The first (second) column shows loan growth impulse responses following a permanent one percentage point
increase (decrease) in capital requirements, estimated on banks that experienced an increase (decrease) or no
change in their capital requirement over the previous four quarters.
-1.5 -1 -.5 0.5
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-1 -.5 0.5 1
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-1 -.5 0.5
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-3 -2 -1 0 1 2
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-8 -6 -4 -2 0 2
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
01234
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-4 -3 -2 -1 0 1
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-1 0 1 2 3 4
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
Working Paper No. 486 January 2014 30
d) Business cycle variation in banks’ responses
We examine variation over the business cycle by estimating responses when the output gap is
positive and when it is negative. The specification is similar to that in equations (A1) and (A2),
but with the lagged size dummy replaced by a lagged output gap dummy.21 We use output gap
figures from the Office for Budget Responsibility (see Pybus (2011) and OBR (2013)). The
results are presented in Chart A4.
e) Heterogeneity by size of capital buffer
Finally, we look at the extent to which banks with large capital buffers tend to respond
differently to a change in capital requirements than those with small buffers. We do so using a
specification similar to that in equations (A1) and (A2), but with the lagged size dummy based
on whether the bank’s lagged capital buffer is above or below a threshold. The choice of
threshold reflects a trade-off between having sufficient observations in both groups for
estimation and being close enough to zero that one would expect banks in the low capital buffer
group to be particularly affected by a change in capital requirements. The results presented in
Chart A5 are based on a threshold of 1.5 per cent.
21 Contrary to the lagged size dummy, the lagged output gap dummy is not included on its own because it does not vary across firms
within quarters.
Working Paper No. 486 January 2014 31
Chart A4: Loan growth impulse responses by output gap
Output gap below zero Output gap above or equal zero
Secured
Unsecured
CRE
PNFC non-CRE
Loan growth impulse responses following a permanent one percentage point increase in the capital requirement at
time 0. Output gap figures used to split the sample are from the Office for Budget Responsibility (see Pybus
(2011) and OBR (2013)).
-1 -.5 0.5
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-1 -.5 0.5 11.5
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-1.5 -1 -.5 0.5
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-.5 0.5 1
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-8 -6 -4 -2 0 2
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-4 -3 -2 -1 0 1
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-8 -6 -4 -2 0
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-2 -1 0 1
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
Working Paper No. 486 January 2014 32
Chart A5: Loan growth impulse responses by capital buffer size
Capital buffer below 1.5 Capital buffer above or equal to 1.5
Secured
Unsecured
CRE
PNFC non-CRE
Loan growth impulse responses following a permanent one percentage point increase in the capital requirement at
time 0.
-2 -1.5 -1 -.5 0.5
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-1 -.5 0.5
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-3 -2 -1 0 1
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-1 -.5 0.5
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-8 -6 -4 -2 0
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-8 -6 -4 -2 0
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-8 -6 -4 -2 0 2
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
-4 -3 -2 -1 0
Percentage p oints
0 1 2 3 4 5
Year
Central estimate 68% CI
Working Paper No. 486 January 2014 33
References
Aiyar, S, Calomiris, C W and Wieladek, T (2014), ‘Does Macropru Leak? Evidence from a
UK Policy Experiment’, Journal of Money, Credit and Banking, forthcoming.
Albertazzi, U and Marchetti, D J (2010), ‘Credit supply, flight to quality and evergreening: an
analysis of bank-firm relationships after Lehman’, Banca d’Italia Working Paper 756.
Alfon, I, Argimón and Bascuñana-Ambrós, P (2005), ‘How individual capital requirements
affect capital ratios in UK banks and building societies’, Banco de Espana Working Paper 515.
Ashcraft, A B (2008), ‘Are Bank Holding Companies a Source of Strength to Their Banking
Subsidiaries?’, Journal of Money, Credit and Banking, 40:2-3, pp. 273-294.
Bank of England (2014), ‘The Financial Policy Committee’s powers to supplement capital
requirements. A policy statement’.
Bernanke, B S and Lown, C S (1991), ‘The Credit Crunch’, Brookings Papers on Economic
Activity, no. 2, pp. 205-39.
Beyer, A and Farmer, R E A (2006), ‘A method to generate structural impulse-responses for
measuring the effects of shocks in structural macro models’, ECB Working Paper Series 586.
Button, R, Pezzini, S and Rossiter, N (2010), ‘Understanding the price of new lending to
households’, Bank of England Quarterly Bulletin, Vol. 50, No. 3, pp. 172-182.
Den Haan, W J, Sumner, S W and Yamashiro, G M (2007), ‘Bank loan portfolios and the
monetary transmission mechanism’, Journal of Monetary Economics, 54:3, pp. 904-924.
Ediz, T, Michael, I and Perraudin, W (1998), ‘The Impact of Capital Requirements on UK
Bank Behaviour’, FRBNY Economic Policy Review, October.
Elliott, D J, Feldberg, G and Lehnert, A (2013), ‘The History of Cyclical Macroprudential
Policy in the United States’, Finance and Economics Discussion Series, 2013-29.
Fonseca, A R, Gonzalez, F and Pereira da Silva, L (2010), ‘Cyclical Effects of Bank Capital
Buffers with Imperfect Credit Markets: International Evidence’, Banco Central Do Brasil
Working Paper Series, 216.
Francis, W and Osborne, M (2009), ‘Bank regulation, capital and credit supply: Measuring the
impact of Prudential Standards’, FSA Occasional Paper Series, 36.
Friedman, B (1991), ‘Comments and discussion’, Brookings Panel on Economic Activity.
Furfine, C (2000), ‘Evidence on the Response of US Banks to Changes in Capital
Requirements’, Bank of International Settlements Working Paper, No. 88.
Working Paper No. 486 January 2014 34
Hancock, D and Wilcox, J (1994), ‘Bank Capital and the Credit Crunch: The Roles of Risk-
Weighted and Unweighted Capital Regulation’, Journal of the American Real Estate and Urban
Economics Association, 22:1, pp.s 59-94.
Hanson S G, Kashyap A K and Stein J C (2011), ‘A Macroprudential Approach to Financial
Regulation’, Journal of Economic Perspectives, 25:1, pp. 3-28.
Heid, F, Porath D and Stolz, S (2004), ‘Does capital regulation matter for bank behaviour?
Evidence for German savings banks’ Discussion Paper Series 2: Banking and Financial Studies
2004, 03, Deutsche Bundesbank, Research Centre, (3), pp. 493513.
Houston J, James C and Marcus D (1997), ‘Capital Market Frictions and the Role of Internal
Capital Markets in Banking’, Journal of Financial Economics, 46:2, pp. 135-164(30).
Jimenez, G, Ongena, S, Peydro, J and Saurina, J (2010), ‘Credit Supply: Identifying Balance
Sheet Channels with Loan Applications and Granted Loans’, ECB Working Paper 1179.
Judson, R A and Owen, A L (1999), ‘Estimating Dynamic Panel Data Models: A Guide for
Macroeconomists’, Economics Letters, 65:1, pp. 9-15.
Kashyap, A K and Stein, J C (2000), ‘What do a million observations on banks say about the
transmission of monetary policy?’, American Economic Review, 90:3, pp. 407-428.
Macroeconomic Assessment Group (MAG), established by the Financial Stability Board
and the Basel Committee on Banking Supervision (2010), ‘Assessing the macroeconomic
impact of the transition to stronger capital and liquidity requirements: Interim Report’, August,
available at www.bis.org/publ/othp10.pdf.
Modigliani, F and Miller, M (1958), ‘The cost of capital, corporation finance and the theory of
investment’, American Economic Review, 48:3, pp. 261–297.
Myers, S C (1977), ‘Determinants of Corporate Borrowing’, Journal of Financial Economics,
5:2, pp. 147-175.
Myers, S C and Majluf, N S (1984), ‘Corporate financing and investment decisions when firms
have information that investors do not have’, Journal of Financial Economics, 13:2, pp. 187-
221.
Nickell, S (1981), ‘Biases in Dynamic Models with Fixed Effects’, Econometrica, 49:6, pp.
1417-1426.
Noss, J and Toffano, P (2014), ‘Estimating the impact of changes in bank capital requirements
during a credit boom’, Bank of England Working Paper, forthcoming.
OBR (2013), ‘Economic and Fiscal Outlook’, Office for Budget Responsibility, March.
Peek, J and Rosengren, E S (1997), ‘The international transmission of financial shocks: the
case of Japan’, American Economic Review, 87:4, pp. 495-505.
Working Paper No. 486 January 2014 35
Pesaran, M H and Smith, R P (1995), ‘Estimating Long-Run Relationships from Dynamic
Heterogeneous Panels’, Journal of Econometrics, 68:1, pp. 79-113.
Pybus, T (2011), ‘Estimating the UK’s historical output gap’, Office for Budget Responsibility
Working Paper No. 1.
Rime, B (2001), ‘Capital Requirements and Bank Behaviour: Empirical Evidence for
Switzerland’, Journal of Banking and Finance, 25(4), pp. 789-805.
Sharpe, S (1995), ‘Bank Capitalization, Regulation, and the Credit Crunch: A Critical Review
of the Research Findings’, DP 95-20, Financial and Economics Discussion Series, Board of
Governors of the Federal Reserve System, Washington, DC.
Sims, C A (1999), ‘Error bands for impulse responses’, Econometrica, 67:5, pp. 1113-1156.
Stoltz, S and Wedow, M (2005) ‘Banks' Regulatory Capital Buffer and the Business Cycle:
Evidence for German Savings and Cooperative Banks’, Deutsche Bundesbank Discussion
Paper, No. 07/2005.
Yellen, J L (2010), ‘Macroprudential Supervision and Monetary Policy in the Post-crisis
World’, Speech.
... 38 A bank's optimal capital ratio is estimated as a function of the regulatory ratio requirement. regulatory ratios, there is evidence that banks reduced loan volumes ; Fang et al (2020)), but also that banks may tend to adjust the composition of their assets (substituting into lower risk-weighted assets) rather than reducing volumes (Francis and Osbourne (2012)), and that there might be heterogenous responses in lending types (Bridges et al (2014)) or by bank characteristics (Fang et al (2019)). These papers have typically used micro-prudential regulation variation for identification because of the lack of policy variation both within and across countries in capital ratio rules 39 . ...
... In many cases, this is a practical constraint: the stock of loans is an easily available measure from published financial statements. As argued in Bridges et al (2014), however, the stock of loans can be an imperfect measure of bank lending and decision-making, because it may reflect changes in write-offs and reclassifications, exchange rate effects (to both the outstanding loan portfolio and the magnitude of recorded non-performing loans), interest-rate revaluations, and more 79 . In an economic sense, we want to capture how exogenous changes in capital influence the banks' decisions to lend (or take on deposits), and not other short-term factors outside of banks' immediate control. ...
... We follow Bridges et al (2014), where possible, and construct adjusted lending flows, reflecting, as close as possible, loan portfolio 'transactions'. We aim calculate: ...
... This means that increases in capital requirements reduce bank lending and can promote involuntary financial exclusion (Musau et al. 2018a). Under this approach, Bridges et al. (2014) use a sample of UK banks, and later Anarfo and Abor (2020) employ the study in Sub-Saharan African countries. Both evidenced that regulation, specifically the capital requirement, reduces the ability to provide bank credit and thus makes financial inclusion programs less effective. ...
... Our results suggest that financial regulation, specifically the regulatory capital requirement for risk-weighted assets, is statistically significant and negatively related to competitiveness in the banking sector (iboone and ilerner). This result is consistent with the literature on financial regulation, which emphasizes that rigidities in regulatory capital requirements reduce banks' ability to offer financial products and services, as well as forcing some banks that are not financially sound to close or merge with other banks, and this can cause barriers to entry into the banking sector, thus impeding competitiveness (Alemu 2016;Bridges et al. 2014;Igan and Mirzaei 2020;Oduor et al. 2017). This result also coincides with those presented by Batuo et al. (2018) and Gudmundsson et al. (2013), when they found that financial liberalization increases competitiveness in the banking sector. ...
Article
Full-text available
Financial inclusion is a widely used measure to improve the living standards of households and foster inclusive economic growth. Thus, financial inclusion is one of the main policy objectives in developing countries. Besides, financial regulation (capital adequacy requirement) is a policy measure used to ensure financial stability. The objective of this study is to examine the effect of financial regulation on competitiveness and financial inclusion in 15 countries in the SADC (Southern Africa Development Community) region and 8 countries in the SAARC (South Asian Association for Regional Cooperation) region over the period 2005–2018. The result of Feasible Generalized Least Squares (FGLS) estimation suggests that financial regulation reduces competitiveness and hampers financial inclusion in the banking sector in the two regions. Furthermore, we find that financial stability moderates the negative effect of financial regulation on competitiveness and financial inclusion, meaning that financially stable banks remain competitive and normally offer financial products and services even if strong capital adequacy requirements are implemented. Additionally, we find that competitiveness increases financial inclusion in countries in the SADC region. The policy implication of this study focuses on regulatory flexibility to preserve the need for greater financial inclusion in the two regions. As for the practical implication, the study calls for strategic measures to preserve stability such as complementing financial inclusion with financial literacy, fostering corporate governance.
... Moreover, if the bank forms an intentional capital surplus in order to match a planned increase in credit supply, then higher capital requirements may slow down or even decrease lending growth via its effect on the intentional capital surplus. The bank may tend to re-build the intentional capital surplus in the long run and to restore the lending growth, as shown, for example, by Bridges et al. (2015); Berrospide and Edge (2010b); Adrian and Shin (2010). ...
... pp in the short run and by about 5-7 pp in the long run. 21 These numbers are very much in line with those estimated by other studies which rely on similar bank-level data samples (Aiyar et al. 2014;Bridges et al. 2015). Similarly to us, the authors of both papers explore the effect of higher capital requirements rather than higher capital adequacy ratios; they find the effect to be between − 1 and − 8 pp in the short run (depending on the type of loan) and between − 6 and − 8 pp in the long run. ...
Article
Full-text available
The existing literature has displayed mixed results in terms of the relationship between tighter bank capital regulation and lending, which may be due to poor approximation of capital requirements. We emphasise the crucial role of the excess of bank capital over the minimum capital requirement, the capital surplus, in the transmission of more stringent capital regulation. Specifically, we explore the effect of higher capital requirements on bank credit growth in the Czech Republic, drawing on a unique confidential bank-level dataset. Our results indicate that higher additional capital requirements have a negative effect on the credit supply of banks maintaining lower capital surplus. We estimate the effect on annual credit growth to be between 1.2 and 1.8 pp, using a wide range of model specifications and estimation techniques. Furthermore, the relationship between the capital surplus and credit growth proves to be significant also at times of stable capital requirements, i.e., the capital surplus does not serve only as an intermediate channel of higher capital requirements.
... Additionally, Oyetade, Obalade & Muzindutsi (2020) found that Basel IV capital requirements will positively impact on securitization activities of commercial banks in South Africa. Contrariwise, Bridges et al. (2014) for UK banks, Peek and Rosengren (1995) for US banks, Roulet (2018) for EU banks establish a negative impact of higher CAR on bank lending. Bridges et al. find that an increase in CAR reduces loan growth, but the loan growth recovers on average within three years. ...
Article
Full-text available
This research examines the potential impact of Basel IV capital requirements (CAR) on bank lending ability in Africa. To achieve the objective, the study simulated Basel IV capital ratio using historical data to create sample representative banks as if the selected banks had implemented Basel IV CAR for the period 2000 and 2018 and used actual data for existing Basel II and III CAR. Dynamic panel regression analyses, namely the System GMM and P-ARDL, were utilised. First, our results suggest that higher Basel CAR, particularly the new Basel IV, portends short-term negative impacts on bank lending while the long-term impact on bank lending is favorable. Second, the weight of non-performing loans tends to decline as banks transitioned from lower to higher Basel CAR. Lastly, this study shows that complying with Basel IV CAR will help African banks to achieve financial deepening and increase bank lending ability.
... Berger et al. (2008) also conclude that banks have individual target capital ratios above the minimum regulatory requirements, actively manage their capital structure, and promptly adjust it towards their targets. Empirical evidence indicates that banks target the managerial buffer above the regulatory minima rather than the total level of the capital ratio: Bridges et al. (2014) find that, in response to an increase in capital requirements, UK banks gradually rebuild the buffers that they initially held. Even well-capitalised banks are responsive to changes in capital requirements (De Jonghe et al., 2020). ...
Article
Full-text available
We analyse the recent policy decisions made by the European Central Bank and the national authorities related to capital and shareholders’ remuneration aimed at promoting banking credit supply in COVID-19-afflicted economies. We forecast the impact of the regulatory decisions based on the empirical literature and discuss the factors that reduce the banks’ incentives to expand their loan portfolios. We argue that the introduction of the dividend ban caused a surge in regulatory uncertainty and undermined banks’ market valuation raising the expected funding costs and contributing to the banks’ reluctance to make use of the capital buffers. We develop policy suggestions intended to mitigate this effect.
... On the effect of changes in capital requirements, there have been extensive studies about the tightening effect of capital requirements. For instance, Bridges et al (2014), based on a sample of UK banks, find that an increase in bank-specific capital requirements reduce loan growth of banks. Aiyar et al (2014) further find the extent of the contractionary effect of capital requirement on bank lending is dependent on the exiting level of the capital ratio of banks, based on a similar sample of banks. ...
Article
Full-text available
Based on a panel of banks in Hong Kong, we found that banks with a relatively thin capital buffer and liquidity before the pandemic may constrain their post-pandemic loan growth. We further found strong evidence that the release of Counter-cyclical Capital Buffer (CCyB) requirements amid the pandemic mitigated the capital constraint to support continued provision of bank credit to the real economy, but mainly to non-hard hit economic sectors. Nevertheless, the credit flow to hard-hit economic sectors is found to be well supported by the SME Financing Guarantee Scheme (SFGS). The findings together have three policy implications. First, the release of the CCyB is found to be effective in supporting bank lending in times of stress, thus achieving its policy objective as a countercyclical tool. Secondly, Hong Kong’s experience highlights the benefit of maintaining an adequate level of releasable capital buffer to withstand unexpected system-wide shocks. This supports the view that there may be a need to set a positive neutral rate of CCyB even in periods without excessive credit growth. Finally, the findings show the complementary roles between broad-based (e.g. CCyB) and targeted measures (e.g. SFGS) in enhancing the overall effectiveness of policy measures, echoing the growing view that a combination of different policy measures should be considered to maintain stable flows of credit in times of stress.
... This deregulatory trend ended after the financial crisis with the Dodd-Frank Wall Street Reform and Consumer Protection Act in 2010, which aimed to put back in place some of the regulations previously stripped from the industry. Prior work has shown that this period of deregulation significantly affected banks' behavior (Dell'Ariccia et al., 2010) and that the re-instatement of regulations to follow also affected bank lending (Bridges et al., 2014). Therefore, an indicator variable was created adopting the value of '1' for all observations between the years 1980 and 2010 (Deregulation). ...
Article
Full-text available
Proponents of the Super Bowl claim beneficial 'development' effects of hosting the game on local businesses. Thus, one might expect to see corresponding adjustments in the local market for debt capital-the predominant source of business creation and reinvestment-around the time of the Super Bowl. Leveraging a panel dataset of 165 local lenders headquartered in 13 Super Bowl host cities between 1971 and 2011, the analysis finds no consistent trends in the volume of debt capital lent or lenders' external risk exposure. Somewhat consistent trends were documented for lenders' internal risk structure and financial performance. Results also suggest that in cities that host the Super Bowl regularly, there are lending contractions around the time of the game, which are followed by reactionary expansions after the game.
... Examining minimum capital requirements of British financial institutions Bridges et al. (2014) find that it serves as buffer against losses. Its management can be used as a control factor of loan growth. ...
Article
This research endeavors to examine credit risk management capabilities for Basel accord implementation in Pakistani banks. The results obtained depict that the banks in Pakistan do not have adequate human resource capabilities for Credit Risk Management (CRM) that is affecting their risk performance and Basel accord implementation. This research introduces a new measure of credit risk measurement, which is credit risk weighted assets to credit risk employees ratio. This ratio has been used for measuring capabilities of banks for implementation of Basel Accord. Keeping in view the results it is suggested that further work is required by the banks and the regulators to improve the availability and training of human resources for CRM and ultimately to reap the benefits of Basel Accord implementation.
Article
Full-text available
Purpose - This paper aims to investigate the nonlinear effects of bank regulation stringency on bank lending in 23 sub-Saharan African (SSA) countries over the period 1997-2017. Design/methodology/approach - This study employs the dynamic panel threshold regression (PTR) model, which addresses endogeneity and heterogeneity problems within a nonlinear framework. It also uses indices of entry barriers, mixing of banking and commerce restrictions, activity restrictions and capital regulatory requirements from the updated databases of the World Bank's Bank Regulation and Supervision Surveys as measures of bank regulation. Findings - The linearity test results support the existence of nonlinear effects in the relationship between bank lending and entry barriers or capital regulations in the selected SSA economies. The dynamic PTR estimation results reveal that bank lending responds positively when the stringency of entry barriers is below the threshold of 62.8%. However, once the stringency of entry barriers exceeds that threshold level, bank credit reacts negatively and significantly. By contrast, changes in capital regulation stringency do not affect bank lending, either below or above the obtained threshold value of 76.5%. Practical implications - These results can help policymakers design bank regulatory measures that will promote the resilience and safety of the banking system but at the same time not bring unintended effects to bank lending. Originality/value - To the best of the authors' knowledge, this is the first study to examine the nonlinear effects of bank regulatory measures on bank lending using the dynamic PTR model and SSA context.
Article
South Africa has one of the lowest savings rates of the emerging economies. However, the minimum capital and liquidity coverage prudential requirements are consistently exceeded by local banks. This paper investigates whether low savings have any impact on bank’s cost of capital, leverage and valuation through the lens of the resource dependence theory. The Autoregressive Distributed Lag Bounds Test estimates of panel data from 2008 to 2018 suggest that low savings have a long-run relationship with cost of equity, leverage and bank’s value. Furthermore, bank’s value and cost of equity are negatively affected in the short run but positively in the long run, implying that South African banks benefit from their interactions with contractual savings institutions in the long run and transfer cost increases to customers. As for leverage, the impact is positive in the short and not significant in the long run. The policy implications of these findings are, at the banks’ level, to act on the financial determinants of savings and household over-indebtedness. Banks should promote financial literacy and trust, avoid predatory lending and cut back bank charges in order to attract households informal savings (called stokvels in South Africa) by offering, for example, fixed floor rate on deposits when repo rate decreases and no ceiling when repo rate increases. At a national level, competitive exchange rate as well as the protection of property rights as per the literature would drive individual and national savings through investment. https://www.tandfonline.com/eprint/TKRF8KDV6MUPHNKV67YW/full?target=10.1080/17520843.2022.2033932 for download
Article
Full-text available
This paper analyzes the cyclical effects of bank capital buffers using an international sample of 2,361 banks from 92 countries over the 1990-2007 period. We find that capital buffers reduce the bank credit supply but – through what could be “monitoring or signaling effects” – have also an expansionary effect on economic activity by reducing lending and deposit rate spreads. This influence on lending and deposit rate spreads is more pronunced in developing countries and during downturns. The results suggest that capital buffers have a counter-cyclical effect in these countries. Our data do not suggest differences in the cyclical effects of capital buffers between Basel I and Basel II.
Article
We study the monetary-transmission mechanism with a data set that includes quarterly observations of every insured U.S. commercial bank from 1976 to 1993. We find that the impact of monetary policy on lending is stronger for banks with less liquid balance sheets - i.e., banks with lower ratios of securities to assets. Moreover, this pattern is largely attributable to the smaller banks, those in the bottom 95 percent of the size distribution. Our results support the existence of a "bank lending channel" of monetary transmission, though they do not allow us to make precise statements about its quantitative importance.
Article
This paper estimates the effect of changes in capital requirements applied to all UK-resident banks on lending by studying the joint dynamics of the aggregate capital ratio of the UK banking system and a set of macro-financial variables. This is achieved by means of sign restrictions that attempt to identify shocks in past data that match a set of assumed directional responses of other variables to future changes in capital requirements aimed at increasing the resilience of the banking system to losses during an upswing. This may provide policymakers with a plausible ‘upper bound’ on the short-term effects of future increases in macroprudential capital requirements in certain states of the economic cycle. An increase in the aggregate bank capital requirement during an economic upswing is associated with a reduction of lending, with the effect larger for lending to corporates than for that to households. The impact on GDP growth is statistically insignificant.
Article
Since the financial crisis of 2007-2009, policymakers have debated the need for a new toolkit of cyclical "macroprudential" policies to constrain the build-up of risks in financial markets, for example, by dampening credit-fueled asset bubbles. These discussions tend to ignore America's long and varied history with many of the instruments under consideration to smooth the credit cycle, presumably because of their sparse usage in the last three decades. We provide the first comprehensive survey and historic narrative of these efforts. The tools whose background and use we describe include underwriting standards, reserve requirements, deposit rate ceilings, credit growth limits, supervisory pressure, and other financial regulatory policy actions. The contemporary debates over these tools highlighted a variety of concerns, including "speculation," undesirable rates of inflation, and high levels of consumer spending, among others. Ongoing statistical work suggests that macrop rudential tightening lowers consumer debt but macroprudential easing does not increase it.
Article
We analyze the impact of monetary policy on the supply of bank credit. Monetary policy affects both loan supply and demand, thus making identification a steep challenge. We therefore analyze a novel, supervisory dataset with loan applications from Spain. Accounting for time-varying firm heterogeneity in loan demand, we find that tighter monetary and worse economic conditions substantially reduce loan granting, especially from banks with lower capital or liquidity ratios; responding to applications for the same loan, weak banks are less likely to grant the loan. Finally, firms cannot offset the resultant credit restriction by applying to other banks. The published version [‘Credit Supply and Monetary Policy: Identifying the Bank Balance-Sheet Channel with Loan Applications’, Gabriel Jiménez, Steven Ongena, José-Luis Peydró, Jesús Saurina, American Economic Review, August 2012, 102(5): 2301-26] is available online at: https://www.aeaweb.org/articles?id=10.1257/aer.102.5.2301 DOI: 10.1257/aer.102.5.2301 Also European Central Bank Working Paper no. 1179 (https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1179.pdf) and UPF e-repository (http://hdl.handle.net/10230/44791)