Andrew G Haldane: The $100 billion question
Comments by Mr Andrew G Haldane, Executive Director, Financial Stability, Bank of
England, at the Institute of Regulation & Risk, Hong Kong, 30 March 2010.
* * *
I am grateful to Dele Adeleye, David Aikman, Marnoch Aston, Richard Davies, Colm Friel, Vaiva Katinaite,
Sam Knott, Priya Kothari, Salina Ladha, Colin Miles, Rhiannon Sowerbutts and Aron Toth for comments and
contributions.
The car industry is a pollutant. Exhaust fumes are a noxious by-product. Motoring benefits
those producing and consuming car travel services - the private benefits of motoring. But it
also endangers innocent bystanders within the wider community - the social costs of exhaust
pollution.
Public policy has increasingly recognised the risks from car pollution. Historically, they have
been tackled through a combination of taxation and, at times, prohibition. During this century,
restrictions have been placed on poisonous emissions from cars - in others words,
prohibition. This is recognition of the social costs of exhaust pollution. Initially, car producers
were in uproar.
The banking industry is also a pollutant. Systemic risk is a noxious by-product. Banking
benefits those producing and consuming financial services - the private benefits for bank
employees, depositors, borrowers and investors. But it also risks endangering innocent
bystanders within the wider economy - the social costs to the general public from banking
crises.
Public policy has long-recognised the costs of systemic risk. They have been tackled through
a combination of regulation and, at times, prohibition. Recently, a debate has begun on direct
restrictions on some banking activities - in other words, prohibition. This is recognition of the
social costs of systemic risk. Bankers are in uproar.
This paper examines the costs of banking pollution and the role of regulation and restrictions
in tackling it. In light of the crisis, this is the $100 billion question. The last time such a debate
was had in earnest followed the Great Depression. Evidence from then, from past crises and
from other industries helps define the contours of today's debate. This debate is still in its
infancy. While it would be premature to be reaching policy conclusions, it is not too early to
begin sifting the evidence. What does it suggest?
Counting the systemic cost
One important dimension of the debate concerns the social costs of systemic risk.
Determining the scale of these social costs provides a measure of the task ahead. It helps
calibrate the intervention necessary to tackle systemic risk, whether through regulation or
restrictions. So how big a pollutant is banking?
There is a large literature measuring the costs of past financial crises.1 This is typically done
by evaluating either the fiscal or the foregone output costs of crisis. On either measure, the
costs of past financial crises appear to be large and long-lived, often in excess of 10% of
pre-crisis GDP. What about the present crisis?
The narrowest fiscal interpretation of the cost of crisis would be given by the wealth transfer
from the government to the banks as a result of the bailout. Plainly, there is a large degree of
uncertainty about the eventual loss governments may face. But in the US, this is currently
estimated to be around $100 billion, or less than 1% of US GDP. For US taxpayers, these
losses are (almost exactly) a $100 billion question. In the UK, the direct cost may be less
than £20 billion, or little more than 1% of GDP.
Assuming a systemic crisis occurs every 20 years, recouping these costs from banks would
not place an unbearable strain on their finances. The tax charge on US banks would be less
than $5 billion per year, on UK banks less than £1 billion per year.2 Total pre-tax profits
earned by US and UK banks in 2009 alone were around $60 billion and £23 billion
respectively.
But these direct fiscal costs are almost certainly an underestimate of the damage to the wider
economy which has resulted from the crisis - the true social costs of crisis. World output in
2009 is expected to have been around 6.5% lower than its counterfactual path in the
absence of crisis. In the UK, the equivalent output loss is around 10%. In money terms, that
translates into output losses of $4 trillion and £140 billion respectively.
Moreover, some of these GDP losses are expected to persist. Evidence from past crises
suggests that crisis-induced output losses are permanent, or at least persistent, in their
impact on the level of output if not its growth rate.3 If GDP losses are permanent, the present
value cost of crisis will exceed significantly today's cost.
By way of illustration, Table 1 looks at the present value of output losses for the world and
the UK assuming different fractions of the 2009 loss are permanent - 100%, 50% and 25%.
It also assumes, somewhat arbitrarily, that future GDP is discounted at a rate of 5% per year
and that trend GDP growth is 3%.4 Present value losses are shown as a fraction of output in
2009.
As Table 1 shows, these losses are multiples of the static costs, lying anywhere between one
and five times annual GDP. Put in money terms, that is an output loss equivalent to between
$60 trillion and $200 trillion for the world economy and between £1.8 trillion and £7.4 trillion
for the UK. As Nobel-prize winning physicist Richard Feynman observed, to call these
numbers "astronomical" would be to do astronomy a disservice: there are only hundreds of
billions of stars in the galaxy. "Economical" might be a better description.
It is clear that banks would not have deep enough pockets to foot this bill. Assuming that a
crisis occurs every 20 years, the systemic levy needed to recoup these crisis costs would be
in excess of $1.5 trillion per year. The total market capitalisation of the largest global banks is
currently only around $1.2 trillion. Fully internalising the output costs of financial crises would
risk putting banks on the same trajectory as the dinosaurs, with the levy playing the role of
the meteorite.
It could plausibly be argued that these output costs are a significant over-statement of the
damage inflicted on the wider economy by the banks. Others are certainly not blameless for
the crisis. For every reckless lender there is likely to be a feckless borrower. If a systemic tax
is to be levied, a more precise measure may be needed of banks' distinctive contribution to
systemic risk.
One such measure is provided by the (often implicit) fiscal subsidy provided to banks by the
state to safeguard stability. Those implicit subsidies are easier to describe than measure. But
one particularly simple proxy is provided by the rating agencies, a number of whom provide
both "support" and "standalone" credit ratings for the banks. The difference in these ratings
encompasses the agencies' judgement of the expected government support to banks.
Table 2 looks at this average ratings difference for a sample of banks and building societies
in the UK, and among a sample of global banks, between 2007 and 2009. Two features are
striking. First, standalone ratings are materially below support ratings, by between 1.5 and
4 notches over the sample for UK and global banks. In other words, rating agencies explicitly
factor in material government support to banks.
Second, this ratings difference has increased over the sample, averaging over one notch in
2007 but over three notches by 2009. In other words, actions by government during the crisis
have increased the value of government support to the banks. This should come as no
surprise, given the scale of intervention. Indeed, there is evidence of an up-only escalator of
state support to banks dating back over the past century.5
Table 3 takes the same data and divides the sample of UK banks and building societies into
"large" and "small" institutions. Unsurprisingly, the average rating difference is consistently
higher for large than for small banks. The average ratings difference for large banks is up to
5 notches, for small banks up to 3 notches. This is pretty tangible evidence of a second
recurring phenomenon in the financial system - the "too big to fail" problem.
It is possible to go one step further and translate these average ratings differences into a
monetary measure of the implied fiscal subsidy to banks. This is done by mapping from
ratings to the yields paid on banks' bonds;6 and by then scaling the yield difference by the
value of each banks' ratings-sensitive liabilities.7 The resulting money amount is an estimate
of the reduction in banks' funding costs which arises from the perceived government subsidy.
Table 4 shows the estimated value of that subsidy for the same sample of UK and global
banks, again between 2007 and 2009. For UK banks, the average annual subsidy for the top
five banks over these years was over £50 billion - roughly equal to UK banks' annual profits
prior to the crisis. At the height of the crisis, the subsidy was larger still. For the sample of
global banks, the average annual subsidy for the top five banks was just less than $60 billion
per year. These are not small sums.
Table 4 also splits UK banks and building societies into "Big 5", "medium" and "small"
buckets. As might be expected, the large banks account for over 90% of the total implied
subsidy. On these metrics, the too-big-to-fail problem results in a real and on-going cost to
the taxpayer and a real and on-going windfall for the banks. If it were ever possible to mint a
coin big enough, these would be the two sides of it.
These results are no more than illustrative - for example, they make no allowance for
subsidies arising on retail deposits. Nonetheless, studies using different methods have found
similarly-sized subsidies. For example, Baker and McArthur ask whether there is a difference
in funding costs for US banks either side of the $100 billion asset threshold - another
$100 billion question.8 They find a significant wedge in costs, which has widened during the
crisis. They calculate an annual subsidy for the 18 largest US banks of over $34 billion per
year. Applying the same method in the UK would give an annual subsidy for the five largest
banks of around £30 billion.
This evidence can provide only a rough guide to systemic scale and cost. But the qualitative
picture it paints is clear and consistent. First, measures of the costs of crisis, or the implicit
subsidy from the state, suggest banking pollution is a real and large social problem. Second,
those entities perceived to be "too big to fail" appear to account for the lion's share of this risk
pollution. The public policy question, then, is how best to tackle these twin evils.
Taxation and prohibition
To date, the public policy response has largely focussed on the role of prudential regulation
in tackling these problems. Higher buffers of capital and liquid assets are being discussed to
address the first problem. And add-ons to these capital and liquidity buffers for institutions
posing the greatest systemic risk are being discussed to address the second.9 In essence,
taxation
this is a solution to the systemic risk pollution problem.10
There is a second approach. On 21 January 2010, US President Barack Obama proposed
placing formal restrictions on the business activities and scale of US banks. Others have
made complementary proposals for structural reform of banking.11 Typically, these involve
separation of bank activities, either across business lines or geographies. In essence, this is
the prohibition solution to the systemic pollution problem.
This sets the scene for a great debate. It is not a new one. The taxation versus prohibition
question crops up repeatedly in public choice economics. For centuries it has been central to
the international trade debate on the use of quotas versus subsidies. During this century, it
has become central to the debate on appropriate policies to curtail carbon emissions.12
In making these choices, economists have often drawn on Martin Weitzman's classic public
goods framework from the early 1970s.13 Under this framework, the optimal amount of
pollution control is found by equating the marginal social benefits of pollution-control and the
marginal private costs of this control. With no uncertainty about either costs or benefits, a
policymaker would be indifferent between taxation and restrictions when striking this
cost/benefit balance.
In the real world, there is considerable uncertainty about both costs and benefits. Weitzman's
framework tells us how to choose between pollution-control instruments in this setting. If the
marginal social benefits foregone of the wrong choice are large, relative to the private costs
incurred, then quantitative restrictions are optimal. Why? Because fixing quantities to achieve
pollution control, while letting prices vary, does not have large private costs. When the
marginal social benefit curve is steeper than the marginal private cost curve, restrictions
dominate.
The results flip when the marginal cost/benefit trade-offs are reversed. If the private costs of
the wrong choice are high, relative to the social benefits foregone, fixing these costs through
taxation is likely to deliver the better welfare outcome. When the marginal social benefit
curve is flatter than the marginal private cost curve, taxation dominates. So the choice of
taxation versus prohibition in controlling pollution is ultimately an empirical issue.
To illustrate the framework, consider the path of financial regulation in the US over the past
century. The US announcements in January are in many respects redolent of US financial
reforms enacted during the late 1920s and early 1930s. Then, restrictions were imposed on
both bank size and scope, in the form of the McFadden (1927) and Glass-Steagall (1933)
Acts. The history of both, viewed through Weitzman's lens, is illuminating for today's debate.
The McFadden Act (1927) in the US gave nationally-chartered banks broadly the same
branching rights as state banks within the state. But it also confirmed the effective prohibition
on national banks opening new branches across state lines that had previously been implicit
in the US National Banking Act (1864). It covered a wide range of banking functions,
including deposit-taking and brokerage.
The motivation behind the Act appears to have been in part political, reflecting lobbying by
small unit banks under threat from larger competitors. But it also had an economic
dimension, as a check on the dangers of "excessive concentration of financial power".14 The
same too-big-to-fail arguments are of course heard today, though the concerns then were
competition rather than crisis-related ones. Weitzman's marginal social benefit curve was
perceived to be steep, made so by state-level competition concerns.
McFadden appeared to be fairly effective in limiting the size of US banks from the 1930s right
through to the mid-1970s. Over this period, the average asset size of US banks in relation to
nominal GDP was roughly flat (Chart 1). As recently as the early 1980s, it was still at around
its level at the time of the Great Depression.
The 1980s marked a watershed, with interstate branching restrictions progressively lifted.
States began to open their borders to out-of-state Bank Holding Companies (BHCs). The
1982 Garn-St Germain Act allowed any BHC to acquire failed banks and thrifts, regardless of
the state law. Finally, the Riegle-Neal Act of 1994, which took effect in 1997, largely lifted
restrictions on interstate branching for both domestic BHCs and foreign banks.
The rationale for this change of heart was a mirror-image of the 1920s. Large banks
convinced politicians of the high private costs of restrictions, which inhibited the efficiency of
their offering to the public. In Weitzman's framework, private costs trumped social benefits.
The effects of the removal of interstate restrictions were dramatic. The average size of US
banks, relative to GDP, has risen roughly threefold over the past 20 years (Chart 1).
Too-bigto-fail was reborn in a new guise.
The US Banking Act (1933) was co-sponsored by Senator Carter Glass and Representative
Henry Steagall - hence "Glass-Steagall". It prevented commercial banks from conducting
most types of securities business, including principal trading, underwriting and securities
lending. It also banned investment banks from taking deposits. The key functions of
commercial and investment banking were effectively prised apart.
The Act was motivated by stability concerns in the light of the Great Depression. The stock
market boom of the 1920s had been fuelled by cheap credit from the banks. The stock
market crash of 1929 brought that, and a great many US banks, to a shuddering halt. Among
many banks, net losses on securities were as great as losses on loans. These losses
transmitted to the real economy through a collapse in lending, whose stock halved between
1929 and 1933.
Against this economic backdrop, and amid heated banker-bashing, it is easy to see how the
social benefits of segregation were perceived as far outweighing the private costs at the time.
Kennedy (1973) describes how "Stock dealings which had made bankers rich and respected
in the era of affluence now glared as scarlet sins in the age of depression. Disillusionment
with speculators and securities merchants carried over from investment bankers to
commercial bankers; the two were often the same, and an embittered public did not care to
make fine distinctions". Glass and Steagall made just such a distinction. They underpinned it
with legislation, signed by President Roosevelt in June 1933.
As with McFadden, Glass-Steagall appears to have been effective from the 1930s right up
until the latter part of the 1980s. Measures of concentration in the US banking system
remained broadly flat between the 1930s and the late 1980s (Chart 2). But competitive
pressures were building from the late 1970s onwards. Strains on US commercial banks
intensified from alternative lending vehicles (such as mutual funds and commercial paper
markets) and from overseas banks. The private costs of restrictions were rising.
Legislators responded. After 1988, securities affiliates within BHCs were permitted, though
were still subject to strict limits. In 1999, the Gramm-Leach-Bliley Act revoked the restrictions
of Glass-Steagall, allowing co-mingling of investment and commercial banking. This came as
a specific response to the perceived high private costs of restrictions relative to the perceived
social benefits - again, in a reversal of the Weitzman calculus from the early 1930s.
As with size, the effects of liberalisation on banking concentration were immediate and
dramatic. The share of the top three largest US banks in total assets rose fourfold, from 10%
to 40% between 1990 and 2007 (Chart 2). A similar trend is discernible internationally: the
share of the top five largest global banks in the assets of the largest 1000 banks has risen
from around 8% in 1998 to double that in 2009.
This degree of concentration, combined with the large size of the banking industry relative to
GDP, has produced a pattern which is not mirrored in other industries. The largest banking
firms are far larger, and have grown far faster, than the largest firms in other industries
(Chart 3). With the repeal of the McFadden and Glass-Steagall Acts, the too-big-to-fail
problem has not just returned but flourished.
In the light of the Great Recession, and the large apparent costs of too-big-to-fail, does
Weitzman's cost-benefit calculus suggest there is a case for winding back the clock to the
reforms of the Great Depression? Determining that requires an assessment of the benefits
and costs of restrictions.
The benefits of prohibition
The potential benefits of restricting activity in any complex adaptive system, whether financial
or non-financial, can roughly be grouped under three headings: modularity, robustness and
incentives. Each has a potentially important bearing on systemic resilience and hence on the
social benefits of restrictions.
(a) Modularity
In 1973, Nobel-prizing winning economist Robert Merton showed that the value of a portfolio
of options is at least as great as the value of an option on the portfolio.15 On the face of it,
this seems to fly in the face of modern portfolio theory, of which Merton himself was of
course one of the key architects. Whatever happened to the benefits of portfolio
diversification?
The answer can be found in an unlikely source - Al'Qaeda. Although the precise
organisational form of Al'Qaeda is not known with certainty, two structural characteristics are
clear. First, it operates not as a centralised, integrated organisation but rather as a highly
decentralised and loose network of small terrorist cells. Second, as events have shown,
Al'Qaeda has exhibited considerable systemic resilience in the face of repeated and
on-going attempts to bring about its collapse.
These two characteristics are closely connected. A series of decentralised cells, loosely
bonded, make infiltration of the entire Al'Qaeda network extremely unlikely. If any one cell is
incapacitated, the likelihood of this undermining the operations of other cells is severely
reduced. That, of course, is precisely why Al'Qaeda has chosen this organisational form.
Al'Qaeda is a prime example of modularity and its effects in strengthening systemic
resilience.
There are many examples from other industries where modularity in organisational structure
has been deployed to enhance systemic resilience. Computer manufacture is one. During
the late 1960s, computers were highly integrated systems. Gradually, they evolved into the
quintessential modular system of today, with distinct modules (CPU, hard disk, keyboard)
which were replaceable if they failed without endangering the functioning of the system as a
whole. This improved resilience and reliability.
In the computing industry, modularity appears to have had an influence on industry structure.
Since the 1970s, the computer hardware industry has moved from a highly concentrated
structure to a much more fragmented one. In 1969, IBM had a market share of over 70%. By
this century, the market share of the largest hardware firm was around a third of that.
Modularity has meant the computer industry has become less prone to "too-big-to-fail"
problems.
Other examples of modularity in organisational structures include:

The management of forest fires, which typically involves the introduction of
firebreaks to control the spread of fire;16

The management of utility services, such as water, gas and electricity, where the
network often has built-in latencies and restrictions to avoid overload and contagion;

The management of infectious diseases which these days often involves placing
restrictions on travel, either within a country (as in the case of foot-and-mouth
disease in the UK) or outside of it (as in the case of H5N1);17

The control of computer viruses across the world wide web, which is typically
achieved by constructing firewalls which restrict access to local domains;

Attempts on the world domino toppling record, which involve arranging the dominos
in discrete blocks to minimise the risk of premature cascades.
These are all examples where modular structures have been introduced to strengthen
system resilience. In all of these cases, policy intervention was required to affect this change
in structure. The case for doing so was particularly strong when the risk of viral spread was
acute. In some cases, intervention followed specific instances of systemic collapse.
The North American electricity outage in August 2003 affected 55 million people in the US
and Canada. It had numerous adverse knock-on effects, including to the sewage system,
telephone and transport network and fuel supplies. A number of people are believed to have
died as a consequence. This event led to a rethinking of the configuration of the North
American electricity grid, with built-in latencies and stricter controls on power circulation.
In the mid-1980s, an attempt on the world domino-toppling record - at that time,
8000 dominos - had to be abandoned when the pen from one of the TV film crew caused the
majority of the dominos to cascade prematurely. Twenty years later a sparrow disturbed an
attempt on the world domino-toppling record. Although the sparrow toppled 23,000 dominos,
750 built-in gaps averted systemic disaster and a new world record of over 4 million dominos
was still set. No-one died, except the poor sparrow which (poetically if controversially) was
shot by bow and arrow.
So to banking. It has many of the same basic ingredients as other network industries, in
particular the potential for viral spread and periodic systemic collapse. For financial firms
holding asset portfolios, however, there is an additional dimension. This can be seen in the
relationship between diversification on the one hand and diversity on the other.18 The two
have quite different implications for resilience.
In principle, size and scope increase the diversification benefits. Larger portfolios ought to
make banks less prone to idiosyncratic risk to their asset portfolio. In the limit, banks can
completely eradicate idiosyncratic risk by holding the market portfolio. The "only" risk they
would face is aggregate or systematic risk.
But if all banks are fully diversified and hold the market portfolio, that means they are all, in
effect, holding the same portfolio. All are subject to the same systematic risk factors. In other
words, the system as a whole lacks diversity. Other things equal, it is then prone to
generalised, systemic collapse. Homogeneity breeds fragility. In Merton's framework, the
option to default selectively through modular holdings, rather than comprehensively through
the market portfolio, has value to investors.
The precise balance between diversification and diversity depends on banks' balance sheet
configuration. What does this suggest? Charts 4 and 5 plot the income variability of a set of
24 global banks against their asset size and a measure of the diversity of their business
model.19 There is no strong relationship between either size or diversity and income volatility.
If anything the relationship is positively sloped, with size and diversity increasing income
variability, not smoothing it.
Charts 6 and 7 look at banks' experience during the crisis. Size and diversity are plotted
against banks' write-downs (per unit of assets). Again, if anything, these relationships are
positively sloped, with larger, more diversified banks suffering proportionally greater losses.
This is consistent with evidence from econometric studies of banking conglomerates which
has found that larger banks, if anything, exhibit greater risk due to higher volatility assets and
activities.20
This evidence is no more than illustrative. But it suggests that, in the arm wrestle between
diversification and diversity, the latter appears to have held the upper hand. Bigger and
broader banking does not obviously appear to have been better, at least in a risk sense. In
banking, as on many things, Merton may have had it right.
(b) Robustness
The Merton result holds in a world in which investors form judgements based on knowledge
of the distribution of risk. But in complex dynamic systems, the distribution of risk may be
lumpy and non-linear, subject to tipping points and discontinuities.21 Faced with this, the
distribution of outcomes for the financial system as a whole may well be incalculable. The
financial system may operate in an environment of uncertainty, in the Knightian sense, as
distinct from risk.
There is a literature on how best to regulate systems in the face of such Knightian
uncertainty.22 It suggests some guideposts for regulation of financial systems. First, keep it
simple. Complex control of a complex system is a recipe for confusion at best, catastrophe at
worst. Complex control adds, not subtracts, from the Knightian uncertainty problem. The US
constitution is four pages long. The recently-tabled Dodd Bill on US financial sector reform is
1,336 pages long. Which do you imagine will have the more lasting impact on behaviour.
Second, faced with uncertainty, the best approach is often to choose a strategy which avoids
the extreme tails of the distribution. Technically, economists call this a "minimax" strategy -
minimising the likelihood of the worst outcome. Paranoia can sometimes be an optimal
strategy. This is a principle which engineers took to heart a generation ago. It is especially
evident in the aeronautical industry where air and space disasters acted as beacons for
minimax redesign of aircraft and spaceships.
Third, simple, loss-minimising strategies are often best achieved through what economists
call "mechanism design" and what non-economists call "structural reform". In essence, this
means acting on the underlying organisational form of the system, rather than through the
participants operating within it. In the words of economist John Kay, it is about regulating
structure not behaviour.23
Taken together, these three features define a "robust" regulatory regime - robust to
uncertainties from within and outside the system. Using these robustness criteria, it is
possible to assess whether restrictions might be preferable to taxation in tackling banking
pollution. To illustrate this, contrast the regulatory experience of Glass-Steagall (a restrictions
approach) and Basel II (a taxation approach).
Glass-Steagall was simple in its objectives and execution. The Act itself was only 17 pages
long. Its aims were shaped by an extreme tail event (the Great Depression) and were
explicitly minimax (to avoid a repetition). It sought to achieve this by acting directly on the
structure of the financial system, quarantining commercial bank and brokering activities
through red-line regulation. In other words, Glass-Steagall satisfied all three robustness
criteria. And so it proved, lasting well over half a century without a significant systemic event
in the US.
The contrast with Basel II is striking. This was anything but simple, comprising many
thousands of pages and taking 15 years to deliver. It was calibrated largely to data drawn
from the Great Moderation, a period characterised by an absence of tail events - more
minimin than minimax. Basel II was underpinned by a complex menu of capital risk weights.
This was fine-line, not red-line, regulation. In short, Basel II satisfied few of the robustness
criteria. And so it proved, overwhelmed by the recent crisis scarcely after it had been
introduced.
(c) Incentives
Tail risk within some systems is determined by God - in economist-speak, it is exogenous.
Natural disasters, like earthquakes and floods, are examples of such tail risk. Although
exogenous, even these events have been shown to occur more frequently than a normal
distribution would imply.24 God's distribution has fat tails.
Tail risk within financial systems is not determined by God but by man; it is not exogenous
but endogenous. This has important implications for regulatory control. Finance theory tells
us that risk brings return. So there are natural incentives within the financial system to
generate tail risk and to avoid regulatory control. In the run-up to this crisis, examples of such
risk-hunting and regulatory arbitrage were legion. They included escalating leverage,
increased trading portfolios and the design of tail-heavy financial instruments.25
The endogeneity of tail risk in banking poses a dilemma for regulation. Putting uncertainties
to one side, assume the policymaker could calibrate perfectly tail risk in the system today
and the capital necessary to insure against it. In an echo of the 1979 Madness song, banks
would then have incentives to position themselves "One Step Beyond" the regulatory buffer
to harvest the higher returns that come from assuming tail risk. They do so safe in the
knowledge that the state will assume some of this risk if it materialises. Tail risk would
expand to exhaust available resources. Countless crises have testified to this dynamic.
This dynamic means it is hazardous to believe there is a magic number for regulatory ratios
sufficient to insure against tail risk in all states of the world. Because tail risk is created not
endowed, calibrating a capital ratio for all seasons is likely to be, quite literally, pointless -
whatever today's optimal regulatory point, risk incentives mean that tomorrow's is sure to be
different.
In response, some economists have proposed corner solutions to the systemic risk problem -
in effect, radical structural redesign. Starting with Irving Fis