Andrew G Haldane: The dog and the frisbee
Speech by Mr Andrew G Haldane, Executive Director, Financial Stability, Bank of England,
and Mr Vasileios Madouros, Economist, Bank of England, at the Federal Reserve Bank of
Kansas City's 366th economic policy symposium, "The changing policy landscape",
Jackson Hole, Wyoming, 31 August 2012.
* * *
The views are not necessarily those of the Bank of England or the Financial Policy Committee. We would like to
thank Dele Adeleye, Rita Babihuga, Tamiko Bayliss, James Benford, Charles Bean, Johanna Cowan, Jas Ellis,
Sujit Kapadia, Kirsty Knott, Priya Kothari, David Learmonth, Colin Miles, Emma Murphy, Paul Nahai-Williamson,
Tobias Neumann, Victoria Saporta, Rhiannon Sowerbutts, Gowsikan Shugumaran and Iain de Weymarn for their
comments and contributions. We are particularly grateful to Gerd Gigerenzer for stimulating conversations on
these issues.
(1) Introduction
Catching a frisbee is difficult. Doing so successfully requires the catcher to weigh a complex
array of physical and atmospheric factors, among them wind speed and frisbee rotation.
Were a physicist to write down frisbee-catching as an optimal control problem, they would
need to understand and apply Newton's Law of Gravity.
Yet despite this complexity, catching a frisbee is remarkably common. Casual empiricism
reveals that it is not an activity only undertaken by those with a Doctorate in physics. It is a
task that an average dog can master. Indeed some, such as border collies, are better at
frisbee-catching than humans.
So what is the secret of the dog's success? The answer, as in many other areas of complex
decision-making, is simple. Or rather, it is to keep it simple. For studies have shown that the
frisbee-catching dog follows the simplest of rules of thumb: run at a speed so that the angle
of gaze to the frisbee remains roughly constant. Humans follow an identical rule of thumb.
Catching a crisis, like catching a frisbee, is difficult. Doing so requires the regulator to weigh
a complex array of financial and psychological factors, among them innovation and risk
appetite. Were an economist to write down crisis-catching as an optimal control problem,
they would probably have to ask a physicist for help.
Yet despite this complexity, efforts to catch the crisis frisbee have continued to escalate.
Casual empiricism reveals an ever-growing number of regulators, some with a Doctorate in
physics. Ever-larger litters have not, however, obviously improved watchdogs'
Frisbee-catching abilities. No regulator had the foresight to predict the financial crisis,
although some have since exhibited supernatural powers of hindsight.
So what is the secret of the watchdogs' failure? The answer is simple. Or rather, it is
complexity. For what this paper explores is why the type of complex regulation developed
over recent decades might not just be costly and cumbersome but sub-optimal for crisis
control. In financial regulation, less may be more.
Section 2 sets out the reasons why that might be, drawing on examples from outside
economics and finance. Section 3 contrasts that thinking with the rapidly rising-tide of
financial regulation. Sections 4 to 6 consider some experiments to assess whether less could
be more in financial systems. Section 7 draws out some public policy implications.
(2) When less is more
Mainstream economics and finance is dominated by models of decision-making under risk.
Modern macroeconomics has its analytical roots in the general equilibrium framework of
Kenneth Arrow and Gerard Debreu (Arrow and Debreu (1954)). In the Arrow-Debreu
framework, the probability distribution of future states of the world is known by agents. Risk
can be securitised and thereby priced and hedged.
Modern finance has its origins in the portfolio allocation framework of Harry Markowitz and
Robert Merton (Markowitz (1952), Merton (1969)). This Merton-Markowitz framework
assumes a known probability distribution for future market risk. This enables portfolio risk to
be calculated and thereby priced and hedged.
Together, the Arrow-Debreu and Merton-Markowitz frameworks form the bedrock of modern
macroeconomics and finance. They help explain patterns of behaviour from consumption
and investment to asset pricing and portfolio allocation. This has been a well-trodden path for
the past 50 years.
The path less followed has been to study optimal choice under uncertainty - the inability to
form priors on the distribution of future outcomes - rather than risk (Knight (1921)). Neither
the Arrow-Debreu nor Merton-Markowitz frameworks admit such uncertainty. Instead,
modern macro and finance has been built on often stringent assumptions about humans'
state of knowledge and cognitive capacity.
For the past 40 years, the most popular of those informational assumptions has been rational
expectations (Muth (1961)). That, too, has dominated modern macro and finance for a
generation. In its strongest form, rational expectations assumes that information collection is
close to costless and that agents have cognitive faculties sufficient to weight probabilistically
all future outturns.
Those strong assumptions about states of knowledge and cognition have not always been at
the centre of the economics profession. Many of the dominant figures in 20th century
economics - from Keynes to Hayek, from Simon to Friedman - placed imperfections in
information and knowledge centre-stage. Uncertainty was for them the normal state of
decision-making affairs.
Hayek's Nobel address, "The Pretence of Knowledge", laid bare the perils of over-active
policy if we assumed omniscience (Hayek (1974)). For Friedman, lack of knowledge justified
a k% monetary policy rule (Friedman (1960)). For physicist Richard Feynman: "It is not what
we know, but what we do not know which we must always address, to avoid major failures,
catastrophes and panics." Through Arrow-Debreu and Merton-Markowitz, economists may
have failed to heed Feynman's catastrophe warning.
Despite occupying a small corner of the profession, decision-making under uncertainty has
begun to attract recent interest (Hansen and Sargent (2010)). That is in part recognition of
the limitations of the rational-expectations-cum-general-equilibrium framework for capturing
key elements of the current crisis (Kirman (2010)). More positively, it may also be because it
yields powerful, and in some cases surprising, insights.
Take decision-making in a complex environment. With risk and rational expectations, the
optimal response to complexity is typically a fully state-contingent rule (Morris and Shin
(2008)). Under risk, policy should respond to every raindrop; it is fine-tuned. Under
uncertainty, that logic is reversed. Complex environments often instead call for simple
decision rules. That is because these rules are more robust to ignorance. Under uncertainty,
policy may only respond to every thunderstorm; it is coarse-tuned.
Herbert Simon, the father of decision-making under uncertainty, believed human behaviour
followed simple rules. More than that, it was precisely because humans operated in a
complex environment that they sought such simple behavioural rules. "Human beings,
viewed as behaving systems, are quite simple. The apparent complexity of our behavior over
time is largely a reflection of the complexity of the environment in which we find ourselves."1
Simon used the word "heuristics" to describe these simple rules. These heuristics were not
plucked from the ether. Rather they were evolutionary responses, honed in the light of
experience passed down generationally from the past (nature) or accumulated locally in the
present (nurture). Neurological and experimental research has since lent support to Simon's
thinking (for example, Selten (2001)).
In the 1970s and 1980s, Daniel Kahneman and Amos Tversky used heuristics as a means of
explaining violations of the rational expectations axioms, often drawn from psychological
experiments (Kahneman and Tversky (1974)). Like Simon, these simple rules of thumb had
neurological roots. Kahneman and Tversky's work laid the foundations for behavioural
economics (for example, Camerer (2003)).
Part of the attraction of these approaches is that they fit the decision-making facts. In many
real-world settings heuristics are the rule, not the exception. This is not confined to
Frisbee-catching dogs (Gigerenzer (2007)) - though heuristic decision-making is found
throughout the animal world, in complex decisions such as the choice of mate. It has also
been found to be true in many aspects of human decision-making, from finding a soulmate to
finding a checkmate (Gigerenzer (2007)).
So why are such simple rules of thumb ubiquitous? And what are their implications?
(a) The costs of cognition
The simplest explanation is that collecting and processing the information necessary for
complex decision-making is costly, perhaps punitively so. Fully defining future states of the
world, and probability-weighting them, is beyond anyone's cognitive limits. Even in relatively
simple games, such as chess, cognitive limits are quickly breached. Chess grandmasters are
unable to evaluate fully more than 5 chess moves ahead. The largest super-computers
cannot fully compute much beyond 10 moves ahead (Gigerenzer (2007)).
Most real-world decision-making is far more complex than chess - more moving pieces with
larger numbers of opponents evaluated many more moves ahead. Simon coined the terms
"bounded rationality" and "satisficing" to explain cost-induced deviations from rational
decision-making (Simon (1956)). A generation on, these are the self-same justifications
being used by behavioural economists today. For both, less may be more because more
information comes at too high a price.
(b) Ignorance can be bliss
There is a second, quite different, rationale for simple decision rules. This does not rely on
the sub-optimality of satisficing behaviour. Instead it states that heuristics may be the
optimising response to a complex environment. Disregarding information can make not only
for cheaper but also for better decisions. Ignorance can be bliss (Gigerenzer (2010)).
Oscar Wilde said of cynics that they knew the price of everything and the value of nothing.2
Autistic savants have an acute case of this problem. Often, they combine prodigious memory
skills (knowing "the price of everything") with serious deficiencies in value judgements
(understanding "the value of nothing"). They are penny-wise and pound-foolish.
These neurological traits are linked. Too great a focus on information gathered from the past
may retard effective decision-making about the future. Knowing too much can clog up the
cognitive inbox, overload the neurological hard disk. One of the main purposes of
sleep - doing less - is to unclog the cognitive inbox (Wang et al (2011)). That is why, when
making a big decision, we often "sleep on it".
"Sleeping on it" has a direct parallel in statistical theory. In econometrics, a model seeking to
infer behaviour from the past, based on too short a sample, may lead to "over-fitting". Noise
is then mistaken as signal, blips parameterised as trends. A model which is "over-fitted"
sways with the smallest statistical breeze. For that reason, it may yield rather fragile
predictions about the future.
Experimental evidence bears this out. Take sports prediction. In principle, this should draw
on a complex array of historical data, optimally weighted. That is why complex, data-hungry
algorithms are used to generate rankings for sports events, such as the FIFA world rankings
for football teams or the ATP world rankings for tennis players. These complex algorithms
are designed to fit performance data from the past.
Yet, when it comes to out-of-sample prediction, these complex rules perform miserably. In
fact, they are often inferior to simple alternatives. One such alternative would be the
"recognition heuristic" - picking a winning team or player purely on the basis of
name-recognition. This simple rule out-performs the ATP or FIFA rankings (Serwe and
Frings (2006), Scheibehenne and Broder (2007)). One good reason beats many.
It is not just sports prediction. Experimental evidence has found the same to be true across a
range of other activities. Among physicians diagnosing heart attacks, simple decision trees
beat a complex model.3 Among detectives locating serial criminals, simple locational rules
trump complex psychological profiling.4 Among investors picking stocks, simple passive
strategies outperform complex active ones.5 And among shopkeepers understanding
spending patterns, repeat purchase data out-predict complex models.6
Applying complex decision rules in a complex environment may be a recipe not just for a
cock-up but catastrophe. In "Natural Accidents", sociologist Charles Perrow demonstrated
how catastrophe was more likely to strike in complex, interconnected - "tightly
coupled" - systems (Perrow (1984)). Drawing on experience from a variety of real world
systems - nuclear power plants, oil rigs, aircraft navigation systems - Tim Harford (2011)
illustrates how complex control of a complex environment has often been calamitous.
The general message here is that the more complex the environment, the greater the perils
of complex control. The optimal response to a complex environment is often not a fully
state-contingent rule. Rather, it is to simplify and streamline (Gigerenzer (2010)). In complex
environments, decision rules based on one, or a few, good reasons can trump sophisticated
alternatives. Less may be more.
In Isaiah Berlin's famous essay "The Hedgehog and the Fox" (Berlin (1953)), the fox knew
many things, the hedgehog one big thing. Philosophers continue to debate their relative
merits. But sports fans, doctors and detectives have made their choice. They think the
hedgehog has the upper paw. Selective unclogging of the cognitive inbox can make for
better decisions.
(c) Weighting may be in vain
John von Neumann and Oskar Morgenstern established that optimal decision-making
involved probabilistically-weighting all possible future outcomes (von Neumann and
Morgenstern (1944)). Multiple regression techniques are the statistical analogue of von
Neumann-Morgenstern optimisation, with behaviour inferred by probabilistically-weighting
explanatory factors.
In an uncertain environment, where statistical probabilities are unknown, however, these
approaches to decision-making may no longer be suitable. Probabilistic weights from the
past may be a fragile guide to the future. Weighting may be in vain. Strategies that simplify,
or perhaps even ignore, statistical weights may be preferable. The simplest imaginable such
scheme would be equal-weighting or "tallying". Gigerenzer and Brighton (2009).7
In complex environments, tallying strategies have been found to be superior to risk-weighted
alternatives. Take avalanche prediction. Avalanches are difficult to predict, as they are drawn
from a fat-tailed (Power Law) distribution. Yet simple tallying of a small number of avalanche
indicators has been found capable of predicting over 90% of historical accidents. It has also
been found to be superior to more complex decision methods (McCammon and Hägeli
(2007)).
A similar finding has emerged from studies of asset prices. They too are drawn from a
fat-tailed distribution. When investing across N assets, the Merton-Markowitz portfolio
strategy would weight by risk and return. A far simpler strategy would equally-weight all
assets - a 1/N rule. In out-of-sample trials, the 1/N rule outperforms complex optimising
alternatives, including Merton-Markowitz (DeMiguel et al (2007)). Indeed, Markowitz himself
pursued a 1/N, rather than Markowitz, strategy when investing for retirement.
(d) Small samples and simple rules
The choice of optimal decision-making strategy depends importantly on the degree of
uncertainty about the environment - in statistical terms, model uncertainty. A key factor
determining that uncertainty is the length of the sample over which the model is estimated.
Other things equal, the smaller the sample, the greater the model uncertainty and the better
the performance of simple, heuristic strategies.
Small samples increase the sensitivity of parameter estimates. They increase the chances of
inaccurately over-fitting historical data. This risk becomes more acute, the larger the
parameter space being estimated. Complex models are more likely to be over-fitted. And the
parametric sensitivity induced by over-fitting makes for unreliable predictions about the
future. Simple models suffer fewer of these parametric excess-sensitivity problems,
especially when samples are short.
Experimental evidence again bears out that conclusion. For example, simple rules have been
shown to outperform complex ones when tracking serial criminals, provided the number of
committed crimes in a sequence is in single figures (Snook et al (2005)).
Investment strategies tell a similar story. Simple tallying rules, like 1/N, outperform complex
strategies, like mean-variance, unless the sample size is very large. In the study by DeMiguel
et al (2007), the sample threshold at which complex rules out-perform simple ones is in
excess of 3000 months (around 250 years) of data. Less is more, at least without much
(much) more data.
(d) Complex rules and defensive behaviour
There is a final, related but distinct, rationale for simple over complex rules. Complex rules
may cause people to manage to the rules, for fear of falling foul of them. They may induce
people to act defensively, focussing on the small print at the expense of the bigger picture.
Studies of the behaviour of doctors illustrate this pattern (Gigerenzer and Kurzenhäuser
(2005)). Fearing misdiagnosis, perhaps litigation, doctors are prone to tick the boxes. That
may mean over-diagnosing drugs or over-submitting patients to hospital. Both are defensive
actions, reducing risks to the doctor. But both are a potential health hazard to the patient. For
example, submitting patients to hospital increases significantly their risk of secondary
infection. Hospitals are, after all, full of sick people.
Doctors unencumbered by a complex rulebook will have fewer incentives to act defensively.
They may also be better able to form their own independent judgements when diagnosing
medical problems, using their accumulated experience. That ought to more closely align a
doctor's risk incentives with their patient's. The same is likely to be true of other professions,
from lawyers to policemen to bank supervisors.
Of course, simple rules are not costless. They place a heavy reliance on the judgement of
the decision-maker, on picking appropriate heuristics. Here, a key ingredient is the
decision-maker's level of experience, since heuristics are learned behaviours honed by
experience. A dog will outperform a puppy at frisbee-catching because it has had time to
fine-tune its "gaze heuristic". An expert baseball player or cricketer will outperform a novice
sportsman for the same reason. So too will an experienced doctor or detective or fund
manager or shopkeeper.
These are "Five Commandments" of decision-making under uncertainty. That description is
apt. Like disease detection, frisbee catching, sports prediction and stock-picking, living a
moral life is a complex task. The Ten Commandments are heuristics to help guide people
through that moral maze, the ultimate simple rules. They have proven remarkably robust
through the millennia. Less has been more.
(3) Finance - more is more?
The response to the financial crisis by banks and regulators has been swift and sizeable.
Gaps in risk management have been filled, deficiencies in regulation plugged, errors by
regulators corrected. This is a self-healing and familiar response. Past crises have also been
met by a combination of more risk management, more regulation and more regulators. More
has been more.
(a) The Tower of Basel
The foundations for today's financial regulatory framework were laid in the 1980s. The Basel
Accord of 1988 was a landmark. It was the first-ever genuinely international prudential
regulatory agreement. For the first time in financial history, a minimum standard had been
established for all internationally-active banks. Yet despite its breadth, the Basel I agreement
was only 30 pages long.8
This brevity came courtesy of focussing on a limited set of credit risks measured at a broad
asset class, rather than individual exposure, level. Only five different risk weights were
defined under Basel I, varying from zero to 100%. Calculating regulatory capital ratios was
possible using pad and pen.
In taking this simplified approach, the Basel Committee fully recognised its limitations. It was
recognised that the role of regulatory rules was not to capture every raindrop. Rather, they
served as a backstop to banks' own risk assessments. Basel rules were there to support, not
supplant, commercial risk decisions.
During the 1990s, the bluntness of the risk judgements embodied in Basel I came
increasingly to be questioned - and arbitraged. Basel I was perceived as lacking
risk-sensitivity, at least by comparison with the new wave of credit and market risk models
emerging at the time. Change came in 1996 with the Market Risk Amendment.9 This
introduced the concept of the regulatory trading book and, for the first time, allowed banks to
use internal models to calculate regulatory capital against market risk.
With hindsight, a regulatory rubicon had been crossed. This was not so much the use of risk
models as the blurring of the distinction between commercial and regulatory risk judgements.
The acceptance of banks' own models meant the baton had been passed. The regulatory
backstop had been lifted, replaced by a complex, commercial judgement. The Basel regime
became, if not self-regulating, then self-calibrating.
A revised Basel Accord, Basel II, was agreed in 2004. It followed closely in the footsteps of
the trading book amendment. Internal risk models were allowed as a means of calibrating
credit risk. Indeed, not so much permitted as actively encouraged, with internal models
designed to deliver lower capital charges. By design, Basel II served as an incentive device
for banks to upgrade their risk management technology.
As a by-product, there was a step change in the granularity of the Basel framework. Risk
exposures were no longer captured at a broad asset class level. And risk weights on these
exposures were no longer confined to five buckets. That meant greater detail and complexity.
Reflecting these changes, Basel II came in at 347 pages - an order of magnitude longer than
its predecessor.10
The ink was barely dry on Basel II when the financial crisis struck. This exposed gaping
holes in the agreement. In the period since, the response has been to fill the largest of these
gaps, with large upwards revisions to the calibration of the Basel framework. Agreement on
this revised framework, Basel III, was reached in 2010. In line with historical trends the
documents making up Basel III added up to 616 pages, almost double Basel II.11
The length of the Basel rulebook, if anything, understates its complexity. The move to
internal models, and from broad asset classes to individual loan exposures, has resulted in a
ballooning in the number of estimated risk weights. For a large, complex bank, this has
meant a rise in the number of calculations required from single figures a generation ago to
several million today (Haldane (2011)).
That increases opacity. It also raises questions about regulatory robustness since it places
reliance on a large number of estimated parameters. Across the banking book, a large bank
might need to estimate several thousand default probability and loss-given-default
parameters (Table 1). To turn these into regulatory capital requirements, the number of
parameters increases by another order of magnitude.
It is close to impossible to determine with complete precision the size of the parameter space
for a large international bank's banking book. That, by itself, is revealing. But a rough guess
would put it at thousands, perhaps tens of thousands, of estimated and calibrated
parameters. That is three, perhaps four, orders of magnitude greater than Basel I.
If that sounds large, the parameter set for the trading book is almost certainly larger still. To
give some sense of scale, consider model-based estimates of portfolio Value at Risk (VaR),
a commonly-used technique for measuring risk and regulatory capital in the trading book. A
large firm would typically have several thousand risk factors in its VaR model. Estimating the
covariance matrix for all of the risk factors means estimating several million individual risk
parameters. Multiple pricing models are then typically used to map from these risk factors to
the valuation of individual instruments, each with several estimated pricing parameters.
Taking all of this together, the parameter space of a large bank's banking and trading books
could easily run to several millions. These parameters are typically estimated from limited
past samples. For example, a typical credit risk model might comprise 20-30 years of sample
data - barely a crisis cycle. A market risk model might comprise less than five years of
data - far less than a crisis cycle.
Regulatory complexity has also found its way into the numerator of the capital ratio - the
definition of capital. Under Basel I, the focus was on common equity capital, with restrictions
on non-equity instruments. Progressively, a complex undergrowth of new non-equity capital
instruments began to emerge. Additional "tiers" of regulatory capital, and tiers within tiers,
were added.
In the end, it did all end in tears. During the crisis, investors lost confidence in non-equity
capital instruments. Basel III simplified the definition of core regulatory capital, basing it
around a common equity Tier 1 definition. Yet measuring capital remains a complex task.
The numerator of the capital ratio adds dozens, perhaps hundreds, of parameters to the
complexity quotient.
This degree of complexity complicates greatly the task for investors pricing banks' financial
instruments. For example, serious concerns have been expressed about the opacity of the
Basel risk weights and their consistency across firms (Haldane (2011), Le Leslé and
Avramova (2012)). Their granularity makes it close to impossible to account for differences
across banks. It also provides near-limitless scope for arbitrage.
This degree of complexity also raises serious questions about the robustness of the
regulatory framework given its degree of over-parameterisation. This million-dimension
parameter set is based on the in-sample statistical fit of models drawn from short historical
samples. If previous studies tell us it may take 250 years of data for a complex asset pricing
model to beat a simple one, it is difficult to imagine how long a sample would be needed to
justify a million-digit parameter set.
(b) The legislative blanket
Basel Accords are non-statutory. But since the 1980s, the trend has been towards
underpinning these non-statutory agreements with national legislation or additional
rule-making. At first, this was simple. The 30 pages of Basel I were translated into 18 pages
in the US and 13 pages in the UK. By the time of Basel III, the domestic documentation
topped 1,000 pages in both countries.
Viewed over an historical sweep, this pattern is even more striking. Contrast the legislative
responses in the US to the two largest financial crises of the past century - the Great
Depression and the Great Recession. The single most important legislative response to the
Great Depression was the Glass-Steagall Act of 1933. Indeed, this may have been the single
most influential piece of financial legislation of the 20th
century. Yet it ran to a mere 37 pages.
The legislative response to this time's crisis, culminating in the Dodd-Frank Act of 2010,
could not have been more different. On its own, the Act runs to 848 pages - more than
20 Glass-Steagalls. That is just the starting point. For implementation, Dodd-Frank requires
an additional almost 400 pieces of detailed rule-making by a variety of US regulatory
agencies.
As of July this year, two years after the enactment of Dodd-Frank, a third of the required
rules had been finalised. Those completed have added a further 8,843 pages to the
rulebook. At this rate, once completed Dodd-Frank could comprise 30,000 pages of
rulemaking. That is roughly a thousand times larger than its closest legislative cousin,
Glass-Steagall. Dodd-Frank makes Glass-Steagall look like throat-clearing.
The situation in Europe, while different in detail, is similar in substance. Since the crisis, more
than a dozen European regulatory directives or regulations have been initiated, or reviewed,
covering capital requirements, crisis management, deposit guarantees, short-selling, market
abuse, investment funds, alternative investments, venture capital, OTC derivatives, markets
in financial instruments, insurance, auditing and credit ratings.
These are at various stages of completion. So far, they cover over 2000 pages. That total is
set to increase dramatically as primary legislation is translated into detailed rule-writing. For
example, were that rule-making to occur on a US scale, Europe's regulatory blanket would
cover over 60,000 pages. It would make Dodd-Frank look like a warm-up Act.
(c) The regulatory response
The density and complexity of financial regulation has had predictable consequences for the
scale and scope of regulatory resources. One metric for that would be the number of human
resources devoted to financial regulation. In the UK up until the late-1970s, bank supervision
was performed by the Bank of England on an informal basis, with a team of around
30 employees. Even when the Bank was given statutory responsibility in 1979, fewer than
80 people were engaged in the supervision of financial firms.
In the period since, the number of UK financial supervisors has increased dramatically, rising
almost forty-fold (Chart 1). In response to the current crisis, regulatory numbers are set to
rise further. Over the same period, the number of people employed in the UK financial
services sector has risen fractionally. In 1980, there was one UK regulator for roughly every
11,000 people employed in the UK financial sector. By 2011, there was one regulator for
every 300 people employed in finance.
The pattern in the US, looked at over a longer historical sweep, is broadly similar. The FDIC
and SEC were set up as part of the regulatory response to the Great Depression. In 1935,
together with the OCC and supervisors housed in the Federal Reserve banks, they had
combined regulatory resources of around 4,500 people. There was one regulator for every
three banks in the US (Chart 2). Today, the combined regulatory resources of the FDIC,
OCC, Federal Reserve banks and SEC is closer to 18,500 people. That is three regulators
for every US bank.
As numbers of regulators have risen, so too have regulatory reporting requirements. In the
UK, regulatory reporting was introduced in 1974. Returns could have around 150 entries. In
the Bank of England archives is a memo to George Blunden, who was to become Deputy
Governor, on these proposed regulatory returns. Blunden's handwritten comment reads: "I
confess that I fear we are in danger of becoming excessively complicated and that if so we
may miss the wood from the trees".12
Today, UK banks are required to fill in more than 7,500 separate cells of data - a fifty-fold
rise. Forthcoming European legislation will cause a further multiplication. Banks across
Europe could in future be required to fill in 30-50,000 data cells spread across 60 different
regulatory forms. There will be less risk of regulators missing the wood from the trees, but
only because most will have needed to be chopped down.
In the US, regulatory reporting has a history going back to the early 19th century.
Nationally-chartered banks began to submit quarterly returns after the formation of the OCC
in 1863. In 1869, following a legislative amendment, these became "call reports", so named
because banks were asked to report on surprise dates to prevent window-dressing. The
Federal Reserve Act of 1913 required all state-chartered member banks to file reports with
the OCC and in 1917 responsibility for collecting these passed to the Federal Reserve. By
1930, these reports might contain around 80 entries.
Today, regulatory reporting is on an altogether different scale. Since 1978, the Federal
Reserve has required quarterly reporting by bank holding companies. In 1986, this covered
547 c