Andrew G Haldane: Curbing the credit cycle
Speech presented by Andrew G Haldane, Executive Director, Financial Stability, Bank of
England, at the Columbia University Center on Capitalism and Society Annual Conference,
New York, 20 November 2010.
* * *
The speech was prepared by Messrs David Aikman, Andrew G Haldane and Benjamin Nelson. The views are not
necessarily those of the Bank of England. We are grateful to Piergiorgio Alessandri, Richard Barwell, Salina
Ladha, Roland Meeks, Victoria Saporta and Alan Taylor for comments. Thanks to Vijay Balle and Clare Rogowski
for excellent research assistance and to Moritz Schularick and Alan Taylor for making their data available to us.
1. Introduction
Credit lies at the heart of crises. Credit booms sow the seeds of subsequent credit crunches.
This is a key lesson of past financial crashes, manias and panics (Minsky (1986),
Kindleberger (1978), Rogoff and Reinhart (2009)). It was a lesson painfully re-taught to
policymakers during the most recent financial crisis.
This time's credit cycle has been particularly severe and synchronous. In 2006, private sector
credit across the UK, US and euro area rose by around 10%. During 2009, private credit in
these countries fell by around 2%.1 The knock-on consequences for real growth were equally
severe and synchronous. Peak to trough, G7 real output fell by 3.6% during the Great
Recession.
In response, there have been widespread calls for remedial policy action. These proposals
come in various stripes. Some have proposed a more active role for monetary policy in
addressing financial imbalances (Taylor (2010), White (2009)). Others have suggested using
new macro-prudential tools to rein in credit excesses (Borio and Lowe (2002), Bank of
England (2009), Kashyap et al (2010)). Others still have proposed a radical root-and-branch
reform of the structure of banking (Kay (2009), Kotlikoff (2010)).
Evaluating the merits of these proposals requires a conceptual understanding of the causes
of the credit cycle and an empirical quantification of its dynamic behaviour. What is the
underlying friction generating credit booms and busts? Are credit cycles distinct from cycles
in the real economy? And how have they evolved, both over time and across countries?
Answers to these questions should help frame public policy choices for curbing the credit
cycle.
To fix ideas, Section 2 sketches a model of the credit cycle. In this model, a lack of
information provides incentives for banks to expand their balance sheets to boost profits and
signal their ability to investors. This gives rise to a coordination failure, as banks collectively
risk-up. That, in turn, generates a systematic credit boom and subsequent bust when risk is
realised.
Sections 3 and 4 present some empirical evidence on the credit cycle. Across countries and
across a sweep of history, credit cycles are both clearly identifiable and regular. Typically,
they presage banking crises. In their frequency and amplitude, credit cycles are distinct from
the business cycle.
There is also evidence of them arising from coordination failures, which generate spillover
effects across banks and countries.
Drawing on this evidence, Section 5 identifies some implications for the design of public
policy. It suggests that neither monetary nor micro-prudential policy may be well-equipped to
tackle the credit cycle. Instead, some new policy apparatus may be needed which (unlike
monetary policy) targets bank balance sheets directly but which (unlike micro-prudential
policy) does so systematically. This is one key dimension of so-called macro-prudential
policy.2
Various international macro-prudential policy committees are, or are about to be, put in
place - in the US the Financial Stability Oversight Committee, in the euro-area the European
Systemic Risk Board and in the UK the Financial Policy Committee. These provide one
element of a macroprudential policy framework. Other elements remain to be put in place.
Knowledge of the sources and dynamics of the credit cycle will be important in assembling
those missing pieces. This paper is intended to be a contribution towards that goal.
2. A model of the credit cycle
We begin by sketching a model which captures some key features of past and in particular
the present, credit cycle. There are a number of existing models of the credit, or leverage,
cycle. In all of these models, cyclicality in financial variables is aggravated by various
microeconomic frictions. These frictions typically then amplify fluctuations in the real economy.
Broadly, these models can be classified according to the underlying micro-economic friction.
For example, a well-established body of literature has looked at the effects of asymmetric
information between borrowers and lenders in placing limits on credit (Bernanke, Gertler and
Gilchrist (1996), Holstrom and Tirole (1997)). These constraints can be loosened by the
borrower pledging collateral to the lender, in effect as a substitute for information (Kiyotaki
and Moore (1997)). This solves one problem, but at the potential expense of another:
movements in the prices of collateral then have the potential to aggravate cycles in leverage
and credit (Geanakoplos (2010)). These cycles can in turn act as a "financial accelerator" for
the business cycle. These are typically models of a representative bank and
creditconstrained investor.
A second potential source of credit market friction arises from coordination failures among
lenders (Gorton and He (2008)). In these models, banks are heterogeneous and their
behaviour strategic. The individually rational actions of heterogeneous lenders can generate
collectively sub-optimal credit provision in both the upswing (a credit boom) and the
downswing (a credit crunch), perhaps through herding (Acharya (2009), Acharya and
Yorulmazer (2008)). This is the result of a collective action, or co-ordination, problem among
banks.
In credit markets, these co-ordination failures are far from new. Keynes memorably noted:
"A sound banker, alas, is not one who foresees danger and avoids it, but one
who, when he is ruined, is ruined in a conventional and orthodox way with his
fellows, so that no one can really blame him" (Keynes (1931)).
Eighty years later, Chuck Prince, then-CEO of Citibank, captured the collective action
problem thus:
"As long as the music is playing, you've got to get up and dance. We're still
dancing" (Prince (2006)).
As Prince's quote attests, these incentives were a key driver of risk-taking behaviour in the
run-up to the crisis. In the face of stiffening competition, banks were increasingly required to
keep pace with the returns on equity offered by their rivals - a case not so much of "keeping
up with the Joneses" as "keeping up with the Goldmans". To achieve these higher returns, it
was individually rational for banks to increase their risk profiles. They did so in various ways
including through higher leverage, marked to market gains on trading books and writing
contracts with deep out-of-the-money option payoffs (Alessandri and Haldane (2009)).
These strategies had the desired effect. They generated high and synchronous reported
returns (Chart 1). But they did so at the expense of higher risk in aggregate - a case of a
competitive coordination failure. Reported returns were, in this sense, risk illusory. As those
risk illusions were shattered, all of the pre-crisis gains in banks' reported returns were lost.
Our model captures the spirit of these dynamics, with short-run risk-taking to preserve
reputation and boost returns (a credit boom) in time giving way to longer-term collective costs
when the music ceases (a credit crunch).
Chart 1
Price to book ratios for UK, US and European institutions(a)
Sources: Bloomberg, Thomson Datastream and Bank calculations
(a) Chart shows the ratio of share price to book value per share.
Simple averages of the ratio in each peer group are used.
The chart plots the three month rolling average.
(b) Excludes Nationwide and Britannia from Major UK Banks
peer group.
2.1 The set up
We develop a simple framework, in the spirit of Rajan (1994), to capture these collective
action failures. Rajan's (op.cit.) model generates multiple equilibria. We use a version of the
Morris and Shin (2003) "global games" framework to pin down a unique equilibrium for the
coordination game among banks, which then allows an evaluation of policy options.3
The set up is as follows. Each period there is a continuum of agents, indexed by ,
all of whom work as financial intermediaries for a single period. In other words, bankers
have, by assumption, short horizons. They aim to maximise their reputation in the market at
the end of this period.4 At the beginning of each period, each bank originates a risky asset,
the return on which depends on (i) the banker's ability, which can be high or low, and (ii) the
macro state, which can be good or bad. Both are unobservable to the market.
The macro state is good with probability . High bank ability and a good macro state
increase the probability of positive asset returns. That is, when the macro state is bad, assets
turn out to be bad, irrespective of bankers' ability. But when the macro state is good, both
high and low ability bankers achieve high returns with some probability, with high ability types
always achieving high returns and low ability types achieving high returns with probability
.
Asset returns are realised in the middle of each period and are observed only by banks. If the
asset is good, banks make a profit (normalised to be a negligible amount). If the asset is bad,
banks face a loss of -1. Because of their short horizons, bankers care about both the present
value of their asset returns and their reputation in the market, where is the probability
assigned by the market to a banker being high ability.
The market is unable to observe banks' actions directly, so instead infers bankers' ability
from observed bank earnings. Banks' earnings are affected by their choices when returns are
low. If an asset turns out to be bad, banks can hide negative earnings in the short run by
engaging in risky policies to boost returns - in effect, engaging in risk illusion. When a bank
engages in a risky policy, an immediate loss is avoided with probability . But risky policies
always involve longer term expected losses of .
If instead of setting risky policies banks choose immediately to liquidate assets, they set a
tight policy, realising a loss of -1 for sure. So banks' choices when faced with low asset
returns determine their risk profiles. If returns are low, under a risky policy banks can
increase short term earnings with some positive probability, but must incur a long term loss;
while under a tight policy, banks can accept low short term earnings for sure now, avoiding
losses in the future.
Suppose banks attach weight to their reputation in the market and to the
net present value of future profits. When the asset is good, banks earn a return normalised to
zero. When the asset is bad, two strategies are possible. If a bank chooses a risky
(superscript ) policy, it obtains
where a proportion l of all banks also set a risky policy. The bank makes a loss of 1 with
5, 6
probability , incurring long term cost while enjoying reputation
When the loan is bad, setting a tight (superscript ) policy yields
as the probability of a loss is unity under a tight policy and the bank obtains reputation
7
.
The evolution of reputation is key for determining behavioural dynamics in the model. The
marginal effect on reputation of banks adopting risky rather than tight policies is given by
We posit improvements in the macro state increase the incentive banks face to
liberalise their risk management policies. When the macro state is good, any negative
earnings are more likely to be attributed to low ability. This is the "Prince constraint". A
concern for reputation incentivises banks to seek risky ventures (to keep dancing), the more
so the better is the market's prior on the macro state (the louder the music).
We also posit as a larger proportion of banks set risky credit policies, the larger the
incentive to pursue similarly risky policies. When others are posting positive earnings, the
reputational loss from foreclosing and taking losses is that much greater. This is the "Keynes
constraint". It is better for your reputation to fail conventionally than to succeed
unconventionally.
These conditions generate the potential for credit booms and busts. The better is the macro
state, the greater banks' incentive to pursue risky projects to preserve reputation, as the
market attributes low earnings to low ability. But one bank's announcement of positive
earnings encourages others to announce positive earnings by setting risky policies too. In
Rajan's model this strategic complementarity between banks generates multiple equilibria: in
sufficiently good states, banks coordinate on risky policies ("credit booms"), while in bad
states they coordinate on tight policies ("credit crunches"). In between, either equilibrium is
possible.
Define the payoff function . Substituting gives:
This captures the marginal return to adopting a risky policy. It allows us to define the regions
of fundamentals over which risky policies and tight policies dominate respectively. A risky
policy dominates a tight policy, even when no other banks set risky policies ( ), when
where
A tight policy dominates a risky policy, even when other banks set risky policies ( ),
when where
In the intermediate range , there are multiple equilibria, with risky (tight) credit
policies optimal when others adopt risky (tight) policies too.
2.2 The model in a "global game"
Next, we apply the technology of Morris and Shin (2003) to define a unique equilibrium from
this game. Assume that banks observe fundamentals with some small amount of noise, with
bank receiving signal
where the noise terms are distributed in the population with continuous density with
support on the real line. Our model satisfies the conditions set out in Morris and Shin (2003)
for there to exist a unique equilibrium in this game. In particular, each bank's strategy
conditional on its signal satisfies when in receipt of a low signal falling short
of some critical level , and adopting strategy when in receipt of a high signal
exceeding some critical level . The condition determining this threshold is (see
Appendix)
(1)
Condition (1) allows us to perform comparative statics on the unique coordination
equilibrium. Using (1) it follows that the threshold level of fundamentals above which banks
set risky policies is (a) increasing in the long-term cost of extending bad credit , and
(b) decreasing in relative reputational concerns , or
Both results accord with intuition. First, increasing the cost to banks of pursuing risky
strategies causes banks to coordinate on risky policies only at very high levels of
fundamentals. Or, put differently, safety prevails over a larger range of fundamentals when
risky policies are costly. Prudential policies (discussed further in Section 5) are one means of
raising the cost to banks of pursuing such risky strategies.
These prudential policies would have both direct effects and strategic effects in the model.
A rise in long term costs raises the direct cost to bank of adopting a risky strategy. But it
also leads bank to expect fewer other banks to adopt risky policies. So a rise in has both
and
a direct effect on banks' actions an effect through banks' expectations of others'
actions.8 This expectational channel is crucial from a policy perspective.
Second, reputational concerns act in the opposite direction. As greater weight is placed on
shortterm reputation, incentives are sharpened to signal high ability by pursuing risky
policies, even when their signalling effect is relatively small ( is low). As in Rajan (1994), a
tight credit policy becomes optimal for all levels of fundamentals as   , or as reputational
concerns vanish. Conversely, if increased competition and deregulation increase reputational
concerns, this increases the propensity of the system to periodic credit booms and
subsequent busts.
2.3 Dynamics
This model can generate credit cycles which amplify cycles in the real economy relative to a
world without credit cycle frictions.9 Without frictions, bad loans are liquidated immediately,
allowing new assets to be originated next period to unencumbered borrowers. By contrast,
extending credit to bad risks eventually results in balance sheet impairment for both
borrowers and lenders.
We perform a simple simulation to illustrate these dynamics. Suppose the macro state
moves stochastically around its trend as a result of a sequence of iid productivity shocks.
The macro state is mean-reverting as long as banks set tight policies when they realise low
asset returns. This occurs when reputational effects are switched off .
Next suppose reputational concerns are introduced, leading banks to risk up to signal high
ability. Because of strategic complementarities, banks do this whenever . This leads
to subsequent impairment of the borrowing sector. These dynamics are captured in the
simple specification
where indexes time, are iid normal shocks and is the indicator
function taking value 1 when and value otherwise. As reputational concerns
disappear, the term goes to zero, leaving simple autoregressive dynamics. But
with reputational externalities, productivity shocks become amplified by the credit cycle: a
high realisation of in period could push the macro state above the risky policy
threshold, at which point the dynamics of begin also to shape the real economy.
The distribution of is assumed to have a higher mean and variance than to simulate the
effects of the credit boom. In particular, we constrain the distribution of relative to such
that expected risk-adjusted fundamentals are constant over time.10 So we have, in effect, a
regime-switching model. When banks play safe strategies, fundamentals are relatively
stable. When banks risk up, they create a temporary improvement in expected fundamentals
at the cost of greater volatility.
This dynamic specification has some interesting properties. First, even small shocks can
have disproportionately large effects on fundamentals depending on the initial conditions. In
particular, if fundamentals happen to be close to the switching threshold, a further positive
shock will lead all firms to coordinate on the high risk-taking strategy. That drives up both the
expected value of fundamentals and its volatility. This generates a form of path dependence.
Second, credit cycles are endogenous in this framework. As firms coordinate on high risk
strategies, this drives up the expected value of fundamentals. But it also drives up volatility
due to risk illusion. Because riskadjusted returns are constant, this must entail an increase in
the probability of there being a severe negative shock, so the probability of a crash rises.
Chart 2 shows a simulation of the model when for 100 periods
with normally distributed shocks. The frictionless benchmark model generates the path
shown by the maroon line. As specified, the path is mean reverting, with movements in the
macro state around trend. Allowing for reputational effects generates the blue path, giving a
threshold level of fundamentals shown by the green dashed line. As the macro state hits
the threshold, banks adopt risky policies to signal high ability. In this simulation this happens
four times, in periods 3, 50, 68 and 72.
Consider the threshold breach in period 72. As during the Great Moderation, the good macro
state at first persists. The credit boom fuels high fundamentals. But while headline
fundamentals have improved, risk-adjusted fundamentals have not. Eventually, this results in
a crunch in credit and a sharp deterioration in the macro state, as risk is realised.
Now suppose that financial liberalisation generates heightened competition between banks,
increasing the market premium on reputation. This raises the value of in the model.
Chart 3 shows the effects of the reputational weight being raised . The threshold for
risky policies falls. Relative to the baseline, there is an additional large and persistent boom
in the middle of the simulation, followed by a period of below counterfactual fundamentals.
The path of the economy is more volatile, with deviations from trend larger and more
persistent. In other words, the credit cycle has real and adverse consequences.
Chart 2
Model simulation with low reputational weight
Source: Bank calculations.
Chart 3
Model simulation with high reputational weight
2.4 Empirical implications
Several empirical implications follow from the model. First, strategic complementarities
incentivise banks to adopt risky strategies in a coordinated fashion during the boom. So the
dynamics of the model predict that we should observe cycles in financial activity at a
macroeconomic level. Initial productivity improvements are amplified into lending booms,
which are followed by credit busts and, potentially, crises. Second, at a micro-economic
level, the coordination of risky strategies during the boom should compress the dispersion of
bank earnings, as low ability banks masquerade as high ability banks during good times. But
during the bust, when the macro state turns bad, the dispersion of banks' earnings should
increase as low ability types crystallise losses while high ability types do not. We turn next to
the macro and micro evidence.
3. Credit cycle dynamics
To what extent are the macroeconomic credit cycle dynamics predicted by the model present
in the real world? To assess that, we draw on a dataset recently developed by Schularick
and Taylor (2009). This covers a lengthy time-series (often more than a century of data)
across 12 developed countries.11 It enables us to identify lower (than business cycle)
frequency movements in credit, just as Comin and Gertler (2006) do for post World War II
US GDP.12 We concentrate on results for the UK and US, though broadly similar patterns are
evident for the other ten countries.
Table 1 presents some summary statistics (mean and standard deviation) of real GDP
growth, real loan growth and real bank asset growth in the UK and the US since 1880.13 The
sample is split into four periods: 1880-1913; 1914-1945; 1945-79; and 1980-2008. Charts 4
and 5, meanwhile, plot loan or asset to GDP ratios for the UK and US over the sample.
Several features are clear:

Average real GDP growth is little changed either side of the wars (Table 1). But real
credit has grown around twice as quickly since 1945. In consequence, loan/GDP
ratios trend upwards from around 1945, consistent with financial liberalisation and
deepening (Charts 4 and 5).

The same general pattern is evident in the volatilities of output and credit. The
variability of real GDP growth has, if anything, fallen in the period since 1945. The
standard deviation of real credit growth rose in the years immediately following 1945
and has remained above those levels, especially since 1980.

Since 1945, the standard deviation of real credit growth has been around five times
that of real activity.
On the face of it, these summary statistics are consistent with credit dynamics being distinct
from GDP dynamics. It is possible to formalise this intuition by using filtering techniques to
extract the cycle in credit. To do so, we apply a band-pass filter to the data.14 This isolates
the cyclical component of a series operating in a frequency range specified by the user.15
Charts 6-7 show cyclical fluctuations in real loan growth in the UK and US. The specified
frequency domain is 8-20 years. Shorter-term, business cycle fluctuations, say between
2-8 years, were typically not found to account for much of the overall cyclical variation in
credit. In other words, credit growth exhibits a clear cyclical pattern with a medium-term
orientation. The credit cycle appears to be a well-defined empirical regularity. It also appears
to have been operating for well over a century. And its frequency suggests factors other than
the business cycle may be responsible for driving it - including, for example, financial
liberalisation and competition.
Table 1
Summary statistics of real GDP growth, real loan growth
and real bank asset growth in the UK and the US
Sources: Shularick and Taylor (2009) and Bank calculations.
14
We also experimented with the Hodrick-Prescott filter. Although the choice of an appropriate smoothing
parameter is not straightforward a priori, this approach produced broadly similar results to the band-pass filter.
See e.g. Harvey and Jaeger (1993) and Canova (1998) for discussions.
15
In what follows, we use Christiano and Fitzgerald's (2003) optimal finite sample approximation to the band
pass filter. An alternative to Christiano and Fitzgerald's procedure is provided by Baxter and King (1999).
Christiano and Fitzgerald provide evidence to suggest that their optimal finite sample approximation is
preferable to Baxter and King's when extracting cycles at lower frequencies.) Suppose the time series in
question is a stationary stochastic process with expectation . Its auto-covariance is
. The properties of the time series can be captured in the frequency domain by taking
a Fourier transform of the auto-covariances yielding the power spectrum
where is the frequency (in radians) in the range . Since the power spectrum is a
function of the auto-covariances (including the variance , it can be viewed as a decomposition of the
variance of the time-series in terms of frequency. When the power spectrum is normalised by the variance
, the resulting standardised function is known as the spectral density. The filter then isolates 'bands' in the
frequency domain of the spectral density and returns the resulting series.
Chart 4
Ratio of loans to GDP and assets to GDP (UK)
Sources: Shularick and Taylor (2009) and Bank calculations.
Chart 5
Ratio of loans to GDP and assets to GDP (US)
Sources: Shularick and Taylor (2009) and Bank calculations.
The credit cycle is distinct from the business cycle in amplitude as well as frequency. To
show this, Charts 6-7 plot the medium-term cycles in real GDP for the UK and US alongside
the credit cycle. The amplitude of the credit cycle is twice that of fluctuations in GDP over the
medium term. It is roughly five times that of fluctuations in GDP at conventional business
cycle frequencies. The peak-to-trough variation in the typical credit cycle has been around
20 percentage points in the UK. For real GDP, it is around 10 percentage points. As a result,
ratios of credit to GDP themselves exhibit a distinct cyclical pattern in the UK and US
(Charts 4-5).
Chart 6
Medium-term cycle in real GDP and credit (UK)
Source: Bank calculations.
Chart 7
Medium-term cycle in real GDP and credit (US)
Source: Bank calculations.
The same broad patterns are evident in the data when moving from credit to asset (equity
and house) prices in the UK and US (Charts 8-9). A clear financial cycle is evident in both
these series, distinct from the typical business cycle in its amplitude as well as frequency.
The peak to trough variation in asset prices is, if anything, greater than for credit: around
40 percentage points for equities and around 15 percentage points for house prices over
medium-term horizons in the UK.
Charts 10 and 11 demonstrate those differences, plotting medium-term frequency cycles in
GDP and asset prices in the UK and US since 1945.
Chart 8
Medium-term cyclical fluctuation in UK real equity and house prices
Source: Bank calculations.
Chart 9
Medium-term cyclical fluctuation in US real equity and house prices
Source: Bank calculations.
Chart 10
Medium-term frequency cycles in GDP and asset prices in the UK
Source: Bank calculations.
Chart 11
Medium-term frequency cycles in GDP and asset prices in the US
In Annex 2 we show results for the other 10 countries, applying the same techniques to real
GDP and real credit. Although the cycles are sometimes not as regular, the same general
cyclical patterns in output and credit are present using the wider panel of countries. As Irving
Fisher noted almost eighty years ago:
"The old and apparently still persistent notion of "the" business cycle, as a single,
simple, self-generating cycle,...is a myth. Instead of one cycle, there are many
co-existing cycles, constantly aggravating or neutralising each other, as well as
co-existing with many non-cyclical forces" (Fisher (1933)).
Historically, the credit cycle appears to have been just such a phenomenon.
But why should we care? One reason might be that credit booms and busts are
systematically related to the incidence of crises, with their associated social costs. Using the
filtered credit series for the 12 countries, and the dating of banking and currency crises from
Bordo et al (2001), it is possible to test this hypothesis.16 Over the sample period, these
countries were in a state of banking and/or currency crisis anywhere between 10% and 25%
of the time. This broadly matches the frequency of the credit cycle.
Table 3
The credit cycle and subsequent crises
% peaks
Crisis % peaks
Banking with
years** with crisis
crisis within Banking
Total peaks within years within
5 years crisis within
1880-2008* 5 years the
following a the
following a following
peak following
peak 5 years
5 years
AUS 9 6 66.7% 2 22.2%
CAN 11 6 54.5% 2 18.2%
DEU 9 2 22.2% 1 11.1%
DNK 10 4 40.0% 3 30.0%
ESP 8 5 62.5% 2 25.0%
FRA 5 3 60.0% 1 20.0%
GBR 9 7 77.8% 3 33.3%
ITA 11 8 72.7% 6 54.5%
NLD 8 1 12.5% 1 12.5%
NOR 13 5 38.5% 2 15.4%
SWE 10 4 40.0% 2 20.0%
USA 9 6 66.7% 5 55.6%
112 57 50.9% 30 26.8%
* Interwar data missing for most countries. Data coverage incomplete for other countries e.g. only post-1945
data available for France. ** Defined as years in which either a banking crisis or a currency crisis or both
("twin crisis") occur.
Source: Bordo et. al. (2001) and Bank calculations.
Table 3 asks what proportion of crisis years occurred within 5 years of the peak in the credit
cycle in those countries. On average, more than half of all financial crisis years across the
12 countries appear to have been preceded by a credit boom. Among Anglo-Saxon
countries, such as the US, UK and Australia, closer to 75% of crisis years occurred following
a credit boom. This is relatively concrete evidence of the credit cycle having real and
18
damaging effects on output.17,
4. Credit cycle spillovers
This empirical evidence to date, operating at a macro-economic level, is consistent with the
model in Section 2: a credit cycle is clearly discernible; its frequency is different and its
amplitude larger than the business cycle; and its fluctuations may exacerbate the business
cycle. But the model also has implications at a micro-economic level, arising from strategic
complementarities, or spillover effects, across firms. In this section we consider empirical
evidence on such credit spillover effects.
The model implies that the cross-sectional distribution of returns to banking should be
compressed during a credit boom, as banks seek to keep up with competitors by collectively
boosting returns. This is a time-series phenomenon. A second implication is that the
dispersion in returns may be smaller for financial than for non-financial companies to the
extent that former are more susceptible to risk illusion. This is a cross-sectional phenomenon.
To assess these hypotheses, Charts 12 and 13 look at the dispersion of returns for publicly
listed US banks calculated in two ways: implied market returns on bank equity and reported
returns on banks' equity (ROE). For comparison, the dispersion of returns among the largest
US non-financial companies is also shown, together with identified periods of credit boom.
Two features are striking.
First, measures of return dispersion are consistently lower for banks than for non-banks:
simple t-tests reject the null of equal mean dispersion between the two types of institution at
the 1% level. Given the much higher levels of leverage among banks than non-banks, that is
surprising: high leverage should bias against finding lower dispersion in banking. It is consistent
with stronger herding incentives in banking. Second, measures of return dispersion tend to
fall during periods of credit boom, statistically significantly so at the 1% level. For example,
measures of equity return dispersion hit all-time lows at the height of the recent credit boom
in 2006-2007.19
Counter-cyclical variation in equity return dispersion is well documented in the finance
literature. For example, Schwert (1989) finds no single macroeconomic variable that can
explain low frequenc