Download Guillermo Calvo LOOKING AT FINANCIAL CRISES IN THE EYE Some Basic Observations

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LOOKING AT FINANCIAL CRISES IN THE EYE
Some Basic Observations
Guillermo Calvo*
Columbia University
Abstract. The paper discusses frameworks in which the financial sector is at the eye of the
storm. In that context, policies that do not repair the damage in the financial sector are, by
necessity, second‐best. Actually, aggregate demand policies – emphasized in textbook
Keynesian models – may be inferior to heterodox policies that rely on credit targeting.
Moreover, based on salient characteristics of modern financial systems, the paper offers a
rationale for the fact that many major financial crises appear to come from nowhere. An
example is discussed in which the creation of asset‐backed securities may generate an asset
price boom. However, asset‐backed securities are subject to runs that, as in Morris and Shin
(1998), depend on unobservables. The run, in turn, provokes a crash in the attendant asset
prices. The liquidity crisis could spill over to the real sector through a Sudden Stop in domestic
credit and/or a sharp change in credit composition in favor of governments and large firms, and
against small and medium‐size enterprises. Domestic credit Sudden Stop and sharp changes in
credit composition are shown to be prominent features in financial crises, including the
subprime.
*This is a thoroughly revised version of a paper with the same main title circulated in November 2009. I am
thankful to Ricardo Hausmann, Alejandro Izquierdo and Carmen Reinhart for their very useful comments; and to
Ivan Khotulev and Rudy Loo‐Kung for research assistance.
I.
Introduction
Most macroeconomists would agree that money is not just a “veil” and that monetary
policy can help to attenuate output fluctuations. Interestingly, though, mainstream
macroeconomics, starting with the IS-LM model, has kept the financial sector in the shadows
most of the time. While the microeconomics of the financial sector has attracted an enormous
amount of attention, for much of macro theory the financial sector boils down to a few interest
rates and straightforward arbitrage conditions. Most of the attention is devoted to price/wage
stickiness and imperfections in the labor and product markets. There are exceptions, of course,
like the introduction of the Financial Accelerator in monetary theory (see Bernanke et al (1999))
but even in that literature financial factors are conceived as magnifiers of shocks originating
somewhere else, and not as a major source of macroeconomic fluctuations. Possibly this can be
explained by the fact that since the Great Depression advanced economies have been relatively
free from financial turmoil that causes severe shock in the real sector. Moreover, even though
since the 1980s emerging market economies, EMs, exhibit a long series of financial crises
associated with major output collapse (for example, Argentina’s GDP fell almost 20% from peak
to trough in the 2001/2 “corralito” crisis), those crises were easy to dismiss by the mainstream
macroeconomist as stemming from institutional/political immaturity that will likely disappear
once these economies embraced pro-market financial reform.1
The present crisis is forcing us to sharpen our focus on the financial sector. However,
intellectual inertia prevails and the models and empirical evidence employed by the profession
are heavily tinted by the conventional wisdom that largely ignores financial sector
1
This view is forcefully espoused in Mishkin (2006). Outside the mainstream, there are a large number of papers
that address key macro problems in EMs. Interestingly, however, most EM central banks employ models that are
not substantially different from those in advanced economies and that ignore key EM financial vulnerabilities, like
the existence of sizable foreign-currency denominated debt (Liability Dollarization or Original Sin). See Calvo
(2006) for a discussion of monetary policy in EMs.
1
malfunctioning. I think it is fair to say that if you pick a macroeconomist at random and ask
him/her about the role of fiscal policy in the present circumstances, he/she will instinctively grab
a textbook model that ignores the financial sector, and output is assumed to be demanddetermined. This disconnect between standard macro theory and current conditions is the main
motivation behind these notes. The central message is that if we know how to look and step out
of the conventional macro box, there are a good number of things that become much clearer and,
the ones that don’t, look far from mysterious, especially if financial market incompleteness is
explicitly taken into account.
Section II examines the straightforward implications of credit market rupture in a nonmonetary context, and shows simple examples in which fiscal stimuli could be effective if
focused on individual sectors, while it might be counterproductive if it aims at stimulating
aggregate demand. Section III is the core of the paper. It models the implications of liquidity
creation and destruction. The model shows, for example, that enhancing the liquidity of assetbacked securities could contribute to increasing the relative price of the assets involved (e.g., real
estate). However, liquidity has feet of clay as argued in the seminal paper by Diamond and
Dybvig (1983), and may be destroyed by the equivalent of a bank run if there is no effective
lender of last resort. Therefore, the initial relative price boom may be followed by a crash,
displaying the distinguishing characteristics of an unsustainable bubble. Section III combines
previous sections to argue that liquidity destruction could cause credit market rupture, even
though a lender of last resort succeeds in bailing out the financial sector. This helps to
rationalize the case in which the real sector suffers a heavy blow while monetary policy keeps
the financial sector unscathed. The paper is closed with Section IV in which key results are
listed.
2
II.
The Non-Monetary Economy: Credit Market Malfunctioning
Consider a non-monetary endowment economy. To simplify, I will assume that
population is equally divided between two types of agents. Each type-1 agent’s endowment
equals one unit of homogeneous output at even periods, and zero otherwise. On the other hand,
the pattern of type-2 agents’ endowment is the same, except that their endowment is positive
only at odd periods. Therefore, there is room for Pareto-efficient intertemporal trade. Thus, for
example, if individuals are identical in terms of tastes, the utility index is time separable, and
period utility is strictly concave and subject to a constant subjective rate of discount, then it is
straightforward to show that under perfect capital markets, consumption of each type will be
constant over time. This implies that borrowing and lending will take place at every point in
time. This is Exhibit A. Consider now the same economy with no capital market in which,
therefore, agents are forced to consume their endowment at each point in time. This is Exhibit B.
Clearly, Exhibit A Pareto-dominates Exhibit B because individuals in exhibit A can replicate
their consumption path in Exhibit B. Suddenly switching from exhibit A to B is much like a
surprise financial crisis for individuals living under Exhibit A. After the switch, savings cannot
reach Exhibit A dissavers. Hence, in equilibrium consumption of potential dissavers will
necessarily fall. Thus, if savers attempt to keep their saving at the same level prevailing prior to
crisis – which, contrary to dissavers, they can do by just consuming less than their endowment –
a savings glut will arise, which would have all the characteristics of a Keynesian impasse in
aggregate demand. This is not the result of people waking up in the morning with a burning
desire for extra liquidity. It is the result of a breakdown of the financial system which, by the
way, can take place in a non-monetary economy. What can the government do in these
circumstances? Clearly, the first best would be to repair the credit channel and the sooner the
3
better in order to avoid major institutional disarray. If that is not possible, all the other policies
are second or third-best – and rank far behind. One possibility is “doing nothing” and let the
autarky equilibrium be reached. This is certainly inferior to Exhibit A, and could be improved
upon by microeconomic policies that allow some intertemporal trade outside the regular credit
channels. This illustrates an important general feature of second-best policies in times of
financial crisis, namely, that these policies may call for a detailed knowledge of the situation that
exceeds the information typically collected by central banks, for example. Moreover, aggregate
policy could actually be counterproductive. For example, if the government announces that it
will try to offset demand shortfalls by increasing government expenditure, it may make it
rational for savers to keep saving as much as prior to crisis. Macro equilibrium will prevail and
the policy may even be called a success. But deeper analysis may reveal a radically different
situation. Take the case in which, not unrealistically, emergency government expenditure is
largely wasteful; then both potential savers and dissavers are likely to be worse off than in
Exhibit B. Potential dissavers would suffer a double whammy because not only will they have to
cut back consumption, relative to Exhibit A, but their future taxes will increase – unless the
government can discriminate between prior-to-crisis savers and dissavers, which is highly
unlikely.
The Kahn-Keynes multiplier. A Detour
The main point that I would like to highlight is that the relevance of Keynesian multiplier
also relies on the existence of financial imperfections. In a perfect world, the Permanent Income
Hypothesis is likely to prevail and the multiplier (more precisely, the multiplier – 1) is small or
negative (given attendant inefficiencies). The situation is quite different if financial
intermediation comes to a grinding stop. Under those conditions, individuals are much more
4
constrained by current income, like the unlucky citizens of our Exhibit B. Gross savers would
like to lend to gross dissavers but there are no credible institutions to make that effective. Thus,
effective aggregate demand, as Keynes chose to label ex ante aggregate demand under market
disequilibrium conditions (relative to normal market conditions), is a function of current income,
and the Kahn-Keynes multiplier may apply with great force.2 Thus, the multiplier’s relevance
hinges more on financial disruption than the questionable consumption’s psychological norms
emphasized in the General Theory.
This conclusion may sound a bit hasty given the abundant evidence that income shocks
display high persistence and, hence, under well-functioning financial markets optimal
consumption is also likely to be closely linked to current income. Let us grant that persistence is
also a feature in deep crisis situations, despite evidence to the contrary from EMs and the present
episode (see Calvo et al (2006) and Calvo and Loo-Kung (2010)). However, shock persistence
in deep crisis is of a different nature than that of productivity shocks. While productivity shocks
are largely independent of short-term macroeconomic policy, under deep crisis shock persistence
is likely to be very sensitive to monetary and fiscal policy and, more to the point, the rate at
which financial problems are resolved. Therefore, once again, the relevance of the multiplier
relies on the existence of financial disruption. For example, the higher is the public’s confidence
that macroeconomic policy will succeed in achieving full employment, the lower will be income
persistence in the short run, and the smaller will likely be the multiplier, and viceversa.
2
Keynes makes no reference to financial sector dysfunction in the General Theory’s chapters in which aggregate
consumption is discussed. This is interesting because Keynes was aware of financial problems as is evident in many
of his other writings. I suspect that a reason may be that he did not want to open too many battle fronts in an already
controversial book. This strategy is suggested by his clear effort at the beginning of the book to keep as many
“classical” or, rather, “establishment” assumptions as possible.
5
Debt and Relative-Price Smoothing
I will extend the model to the case in which there is an outstanding stock of debt and two
types of goods. This allows addressing the debt-overhang issue that arises during financial
crises. I will assume that prior to crisis there are static (atemporal) and intertemporal capital
market transactions; after crisis, I will assume that only static trade transactions are left standing.
Clearly, without government intervention, shifting from one regime to the other will likely cause
a change in relative prices. The change could be large because it is not dictated by slow-moving
tastes or technology but by a shock in the capital market that severely damages (actually,
eliminates in our example) intertemporal trade. This shock may not cause serious financial
distress if, prior to crisis, individuals include the capital-market implosion as a contingency,
making repayment conditional on financial crisis. Otherwise, the most likely scenario,
bankruptcies might mushroom overnight, led by those individuals whose debt obligations
increase relative to their income as a result of the relative-price change. This problem was
identified by Irving Fischer (1933) upon noticing that price deflation during the Great
Depression significantly increased the real value of debts and contributed to massive
bankruptcies. Fischer (1933) coined the phrase “Debt Deflation” to characterize the
phenomenon. Debt Deflation is a major threat in countries like the US in which debts are largely
denominated in US dollars, and the increase in the demand for liquid dollar assets (coupled with
the evaporation of part of the stock as a result of the subprime crisis) threatens to generate price
deflation. However, there is, in principle, nothing inherently monetary about this problem. For
example, in EMs, where it is not unusual for debts to be denominated in terms of tradable goods
(or foreign exchange), a real depreciation (i.e., an increase in the relative price of tradables with
respect to nontradables) produces effects similar to Debt Deflation. Actually, EM financial
6
crises are typically accompanied by large nominal devaluation and sharp increase in inflation
(not deflation); see Calvo et al (2006). Thus, even if monetary phenomena are behind the
relative-price change, Debt Deflation is not necessarily a result of inflation or deflation. The
basic fact is a change – typically large – in some key relative price, a non-monetary
phenomenon.3
Bankruptcies are socially costly, especially if they are caused by “black swan” events (as
the expression is used in Taleb (2007)) that take most agents by surprise, and are unlikely to be
part of state-contingent financial contracts. Under these conditions, economic activity is
seriously impaired even if Chapter 11-type arrangements are possible. Two important amplifiers
are network effects and sheer congestion. Network effects arise because bankruptcy of firm i
could be due to its own problems or to problems in which its partners are involved. This makes
it difficult to assess the extent of a given bankruptcy situation, and the probability that a firm
taken at random will eventually undergo serious financial problem. On the other hand,
congestion is likely to occur in the system of justice given the spike in the number of bankruptcy
cases. A bankruptcy epidemic will generate a long queue of unresolved cases. With luck,
unresolved Chapter-11 lawsuits may keep production at full capacity. However, capital
expansion and R&D are likely to be seriously impaired because they involve access to new
credit, which will be hard to generate until bankruptcy procedures are concluded.
Therefore, there exists a role for government in stabilizing, albeit temporarily, relative
prices. One option is to increase public expenditure in sectors that would otherwise see a sharp
decline in their relative price. In this respect, it would make perfect sense to stimulate
3
Debt Deflation has been systematically ignored in macro textbooks, a casualty of leaving financial sector
difficulties out of the picture. The topic, however, recently resurfaced in the discussion of the Great Depression and
EM financial crises; see Bernanke (2000) and Calvo (2005).
7
expenditure in the construction sector if the crisis results in a major collapse of private sector
demand in real estate, for example. A problem with this kind of fiscal stimulus is that by picking
and choosing the sectors to which help will be channeled, policymakers could easily become
hostage of vested interests.
A major issue about fiscal stimulus packages is their duration. In the simple A and B
Exhibits discussed at the outset, “full employment” could be permanently guaranteed if the
government dumps excess supply into the river day in and day out. This is an undesirable
outcome but one that might be difficult to dismantle because the latter may cause a rise in
unemployment. Thus, emphasis should be put on policies that can be phased out without
regenerating the same conditions that prompted them to be put into place. If firms are given to
understand that this is a temporary respite, they will have incentives to find alternative sources of
finance, e.g., cutting down dividend distributions and investment projects. This helps output
recovery but may hurt long-term prospects if liquidity accumulation is done at the expense of
investment or technical change.4
EM crises and the subprime crisis. Another Detour.
As shown in Calvo and Loo-Kung (2010), output recovery from financial crisis in EMs
was accompanied by large real currency devaluation and export growth. In terms of timing,
output recovery in the US during the subprime crisis is very similar to that in the EMs. If
anything, output fell by less (peak-to-trough) in the US, and appears to be recovering faster than
in the EMs. However, the dollar has not weakened and US exports are still below their pre-crisis
level. Recovery is instead being accompanied by an expansion in government expenditure,
which is much larger than in EMs. Thus, the US case appears to share the characteristics of the
4
For further discussion of recovery without credit, see Calvo et al (2006) and Calvo and Loo-Kung (2010).
8
example discussed above in which the government fills the vacuum left by private demand. This
raises serious questions about efficiency and sustainability of US recovery.
III.
The Monetary Economy: Liquidity Creation and Destruction
The previous section took financial market disruption as an entirely exogenous shock.
This is a good first approximation because the deep causes of financial market disruption are
often hard to identify. Economists are still debating the deep causes of the Great Depression, for
example. In this section, I will show an example that may help to rationalize a possible cause for
financial crisis that may escape the attention of observers looking for conventional fundamentals.
The key mechanism will amount to some form of liquidity destruction which, in addition, could
have severe effects on some key relative prices. The mechanism does not explicitly involve the
credit market.5 Possible linkages will be discussed in the next section.
A central characteristic of a monetary economy is that, in contrast to the standard general
equilibrium non-monetary model, not all goods and assets can serve as a means of payment. In
the conventional macro model one asset is singled out as means of payment and called “money.”
However, in the real world there are many assets that play that role; cash, bank checks, US
Treasury Bills, for example.6 The financial sector spends a great deal of resources creating
goods or assets that serve as means of payment, an activity that goes back to banks’ inception.
As shown in the seminal paper by Diamond and Dybvig (1983), bank liquidity that
dominates cash is created at the expense of setting up a system that is subject to runs.7 This
situation is more general and not inherent only to banks. Any arrangement by which an
5
The model here discussed bears some resemblance to Kiyotaki and Moore (1997) and related literature. However,
the present model highlights “liquidity” effects that are central to monetary policy, and ignores the credit channel
that is central to the other literature. These approaches, therefore, are highly complementary to each other.
6
This is the main feature of a string of papers that I co-authored with Carlos Vegh in which a key assumption is that
the central bank set the interest rate on assets that provide liquidity services. See, for instance, Calvo and Vegh
(1995). For a more recent application, see Calvo (2009).
7
See Allen and Gale (2009) for a recent exposition of the Diamond-Dybvig model and a number of useful
alternative models.
9
economic unit issues obligations that can be used by the holder as a means of payment is subject
to runs, unless the issuer invests the proceeds in a similar asset, which would not be profitable
unless the issuer has information that is clearly superior to the rest of the market or competition
is seriously curtailed. Thus, the creation and destruction of liquidity is a potential source of
financial disruption. I will illustrate this by an example that, for simplicity, abstracts from the
credit market.
Suppose homogeneous output is produced by land, which is in fixed supply, and real
liquid balances.8 The latter, in turn, is generated by (conventional) money and the market value
of land, both in terms of output. The liquidity of land could stem from mortgage-backed
securities or any asset of wide market circulation that employs land as collateral. More
concretely, I will assume that, for an individual competitive firm, liquidity is given by m + θpk,
where m is real monetary balances (in terms of output), p is the output price of land, k is the
stock of land employed by the representative firm, and θ is a parameter (from the point of view
of the individual firm), such that 0 ≤ θ < 1. Output, y, is given by
y = αk + φ( m + θpk ),
(1)
where α is a positive parameter and represents a standard (linear) production function; the
contribution of liquidity to output is captured by function φ, such that φ′ > 0, and φ′′ < 0. Setting
θ < 1 implies that land is assumed to be dominated by cash as a means of payment, given the
same opportunity costs. Let r, π and im denote the real interest rate (= the land rental if the
relative price of land, p, is expected to be constant), the expected rate of (output) inflation, and
the interest rate on money, respectively. Variable im is a shortcut that helps to capture the effect
of a policy interest rate set by the central bank. In this fashion, im can be thought of as the
8
For a discussion of the “money in the production function” literature, see Fischer (1974). For an application in the
context of uniqueness of equilibrium in monetary economies, see Calvo (1979). The example in the text is just a
straightforward application.
10
interest rate on a composite of official liquid instruments including, for instance, non-interestbearing money and short-term highly liquid Treasury bills. Thus, a change in the policy interest
rate would push im in the same direction (see, Calvo and Vegh (1995) for greater detail).
Therefore, assuming that p is expected to be constant through time,9 the firm’s quasi-rent is
given by
y − (r + π − im )m − rpk .
(2)
Consider, without loss of generality, the benchmark case in which r = α. Hence, by (1) and (2),
profit maximization calls for maximizing
αk + φ(m + θpk ) − αpk − (α + π − im )m
(3)
with respect to m and k. The first-order conditions with respect to m and k are, respectively
φ′ = α + π − im ,
α + φ′θp = αp.
(4)
Therefore, by (4), at interior solutions (the only ones analyzed here) one gets the following
central result:
p=
1
.
 im − π 
− 1
1 + θ
 α

(5)
Suppose π = im = 0. Then, p = 1/(1 – θ) and, hence, the relative price of land in terms of output
is an increasing function of θ; if θ = 0 (the conventional case in which land yields no liquidity
services), p = 1, as expected, given the assumption that the conventional marginal productivity
9
This holds at steady state, which can be shown to be the only equilibrium path that converges to the steady state.
For a more complete analysis with liquidity in the utility function, showing similar results, see Calvo (2009).
Putting money in the production function simplifies the proof of central results.
11
of land equals the rate of interest. Moreover, if θ > 0, the relative price of land increases with
inflation, π, and declines with the interest rate on money, im.10
The main point that I would like to stress here is that financial innovation can have a
positive effect on the price of land, which would not be explained by conventional
“fundamentals” (in this case, the conventional marginal productivity of land α). Moreover, the
effect could be large. Again, take the case in which π = im = 0, then as θ converges to 1, p rises
without bound. Standard analysis will mistakenly identify the liquidity-linked rise in the price of
land as a potential “bubble.” Moreover, if the associated assets are subject to a Diamond-Dybvig
type of run, the liquidity coefficient θ could suffer a sudden fall, and the price of land would
crash. The crash would seem to support the view that we are looking at an unsustainable bubble,
but in the present setup the crash would be a result of failure, not in the market for land, but in
financial markets. The effects of this type of failure, incidentally, could be cushioned by a
Lender of Last Resort that is prepared to buy land at the pre-crash relative price.
The example, thus, illustrates the possibility that a crisis stems from obscure sources
linked to the existence of liquid financial assets subject to runs. It bears some resemblance to
asset-backed securities created during the go-go years of the 2000s and their demise during the
subprime crisis. It also suggests that the purchase of “toxic” assets by the Fed may have helped
to stop the price deflation. However, a lesson from the example is that preventing price deflation
may not be enough to restore the relative price of land (p in the model) since, by equation (5), p
is not a function of total liquidity, unless monetary policy can help to restore the liquidity
coefficient θ to its value prior to crisis. However, higher inflation or lower policy interest rates
would help to increase p, as long as land can still provide liquidity services (i.e., if θ > 0). The
10
Notice that if α + π - im = 0, the Optimum Quantity of Money holds, and the price of land would be independent of
the liquidity coefficient θ.
12
latter, incidentally, provides some rationale for the statement that the US real estate bubble was
fueled by low policy interest rates. Notice, however, that, by equations (1) and (4), while higher
inflation and lower policy interest rate increases the price of land, these policies bring about a
decline in output. 11
This section leaves a key question unanswered, namely, what causes the economy to
switch from a “good” to a “bad” equilibrium? Economics has no fully convincing answer for
that despite some outstanding contributions in that respect (see, e.g., Morris and Shin (1998)).
What emerges from that literature is that explanations may rely on factors that are largely hidden
from the econometrician and policymaker. Therefore, the scant theory that we have is not
inconsistent with the view that financial crises stemming from liquidity crash are likely to appear
mysterious and coming from nowhere. Is this progress? I think so, because the alternative
hypotheses that have been put forward in recent debate are, for example, that these crises stem
from (1) psychological mirage that induce otherwise clever and ambitious investors to behave
irrationally, or (2) sheer corruption, the victims of which are typically unsuspecting naive
investors. The model in this section adds a new hypothesis based on a well-established
Diamond-Dybvig insight, namely, that financial institutions that are not subject to 100-percent
reserve or liquidity requirements are able to print “liquidity,” which opens up the door to
liquidity crash. Fixing this problem is much closer at hand than curing society from irrationality
or moral turpitude. One such solution towards which the world seems to be presently groping is
the creation of facilities that increase the ability of the IMF to operate as a global lender of last
11
Moreover, by first-order condition with respect to m in equation (4), under price flexibility total real liquidity is
independent of the liquidity coefficient θ. The liquidity effect of a fall in θ is fully offset by a rise in real monetary
balances m. Thus, since land supply is given, output remains the same. Under sticky prices, however,
underutilization of capacity would follow.
13
resort (an example being the creation of the Flexible Credit Line). Another, which is even
simpler to enact (although not so easy to enforce) is to increase reserve or liquidity requirements.
IV.
Explaining and Measuring Large Credit Shocks
The last sections highlight two “pure colors.” Section II analyzes the implications of a
rupture in the credit channel, while Section III studies the effect of liquidity destruction with no
reference to credit. I will now start connecting the two but rather than developing a model
encompassing the previous ones, I will simply rely on the intuitions that each of them provide,
and refer to some interesting recent facts in the credit market.
Financial intermediaries are the primary producers of “inside” liquidity. Endowing land
with liquidity is an alchemy that large financial intermediaries can achieve, in part because they
enjoy (or at least, enjoyed) a high degree of credibility. The ability to produce liquid assets
allows financial intermediaries to compete as loan suppliers by offering lower loan interest rates,
which, at equilibrium, may enhance the flow of credit and possibly stimulate growth. However,
this liquidity has feet of clay and can evaporate as a result of a Diamond-Dybvig run. Thus, in a
context in which actual financial intermediation takes place (as in Section II), a liquidity crash is
likely to provoke a rupture in the credit market. On the spur of the moment, projects that looked
attractive to the financial intermediary, stop being so – not because the project got worse, but
because the financial intermediary itself suffers from a run on its liquid liabilities. This type of
shock, by the way, is very different from those emphasized in DSGE models.
Moreover, financial intermediaries subject to runs cannot be easily replaced by other
intermediaries. In the first place, if a potential borrower loses access from its customary credit
supplier (e.g., its bank), it will have to go to another supplier who is likely to know less about the
borrower, possibly creating a situation akin to a Lemon’s problem (see, e.g., Calvo (2005,
14
Chapter 12)). Secondly, the fall in the relative price of a large set of goods (land in the model of
Section III), lowers the borrower’s collateral and puts further constrain on credit. Thus, to the
extent that production and employment rely on credit lines, the liquidity shock may force large
adjustment in the real economy.
An issue that has led to some debate in the literature is how to identify a credit crunch
episode with potentially severe consequences on the real sector. A central question is: what
should one focus on, stocks or flows? If the objective is to assess the severity of the impact on
the real economy, I would argue that damage can occur simply as a result of a contraction in
credit flows. Therefore, an indicator would be the size of credit flow contraction. Credit stock
may not fall and even rise and, yet, the reduction in credit flows could have a severe impact on
the real sector. The intuition is based on the following identity:
expenditure = income (including transfers) + asset sales + net new borrowing.
Therefore, a large credit flow contraction will force some economic units to implement a large
asset sale and/or cut in expenditure. If the flow contraction is large and highly unexpected, asset
sales are likely to produce large capital losses (e.g., fire sales). This phenomenon has been
studied in the EM Sudden Stop literature that focuses on flows of international capital (see, e.g.,
Calvo (2008)). Faced with a Sudden Stop, countries first rely on reserves’ depletion in order to
cushion the blow; however, when reserves run out there is no option but to incur in heavy capital
losses and resort to expenditure contraction. This is the point at which the real sector gets
involved. The fall in expenditure gives rise to nominal and real currency devaluation, and a
change in a variety of other relative prices. Since, by definition, a Sudden Stop is large and
largely unanticipated, the attendant relative price changes are likely to bring about serious
15
financial disruption, possibly causing severe consequences in the real sector, as argued above. A
similar sequence of events follow if the fall in domestic credit flows is large and largely
unanticipated. Under the present global-depression circumstances, the resulting liquidity crunch
could be more severe than in previous EM crises because the latter took place in the context of
favorable conditions in advanced economies.
Another fact that is also becoming evident in the current crisis is that the real sector can
be badly hurt even though the financial sector is left standing. Typically, financial sector
bailouts are tailored to prevent the spread of bankruptcies in that sector. The reason for this is
that central banks are charged with ensuring the health of the financial sector, and are only
tangentially responsible for the real sector. Central banks usually engage in monetary easing in
response to real sector troubles only after those troubles are reflected in some macro variable like
the output gap (as in Taylor’s rule). Therefore, help may take a long time to come, and when it
comes the house may have already burnt to the ground! This observation, incidentally, suggests
that Taylor rules should take into account more timely activity indicators, and central banks may
want to follow heterodox credit policies that attack credit problems at the source and not only
through the manipulation of a policy interest rate. These observations become even more
relevant when credit composition is taken into account.
Recent Evidence on Credit Contraction and Composition.
The collapse of Lehman Brothers offers an interesting perspective about the sensitivity of
credit flows. In line with the Sudden Stop literature, I will define a Domestic Credit Sudden
Stop, DC_SS, as a fall in domestic bank credit flow to the private sector exceeding two standard
deviations below its mean (both moments computed on the basis of the historical data on the
16
domestic credit to the private sector). The rationale for this empirical definition is that the
objective is to isolate episodes in which the credit flow contraction is “large and highly
unexpected.”12 “Large and unexpected” are important considerations because, as pointed out
above, those shocks are less likely to be explicitly contemplated in financial contracts and, thus,
wholesale bankruptcy with its attendant social costs is more likely to happen. The focus on the
private sector is justified because the government can usually find alternative forms of finance,
including credit lines from multilateral institutions.
The subprime crisis started on an unexpected note, namely, some evidence indicating that
the crisis in advanced economies might not spillover and could actually bring about a period of
high growth in EMs. This was labeled “decoupling” and fueled sizable capital flows towards
EMs in good standing, i.e., economies that had heeded the lessons of the 1990s, ran current
account surpluses and accumulated sizable stocks of international reserves. But the Lehman
episode changed all that overnight. Chile, for example, a country that is widely deemed a
paradigm of wise and prudent macroeconomic policy, suffered a DC_SS of unprecedented size,
widely exceeding the contraction registered during the Russian 1998 crisis (see Figure 1). There
are many other examples. This is illustrated in Figure 2, showing the share of Sudden Stop (of
capital inflows) and DC_SS in a large sample of EMs. Both series exhibit unprecedented spikes
around the Lehman episode. These facts are in line with the approach suggested in these notes
because Lehman was not a large financial institution or big player in EM markets. Why would
that episode change the way the market thought about the prospects of EM in good standing? It
is not easy to find conventional fundamentals that can account for that and, yet, the shock had
12
This is a rough and ready methodology that, however, looks more satisfactory than the more popular alternative of
defining Sudden Stop as a fall in credit flows that exceeds some arbitrary percentage of GDP. To identify Sudden
Stops that are supply-driven, Calvo et al (2008) focuses on systemic episodes. However, this issue will not be
pursued here.
17
severe implication on output for both advanced economies and EMs (see Figure 3 showing that
growth fell more than 4 percentage points in both types of economies). But, equally important,
the recovery appears to be fast (see Calvo and Loo-Kung (2010)). This can also be interpreted
within the present approach, because soon after the Lehman episode advance-economy
governments were eager to reassure the markets that they would not let another “too big to fail”
financial institution fail – and AIG soon gave them the chance to prove it. Therefore, this
brought the system back to the “good” or at least “not so bad” equilibrium. And, once again, no
conventional fundamental can account for that.
I will now turn to the issue of composition of credit flows. As shown in Figure 2, there is
a clear positive correlation between DC_SS and Sudden Stop of capital inflows in EMs,
implying that external and internal finance move in sync. This does not hold in advanced
economies. In the US, for example, there are several episodes of DC_SS in the period from
January1990 to April 2009. The most recent one starts in August 2008, one month prior to the
Lehman episode.13 On the other hand, as shown in Figure 5, external Sudden Stop occurs on
January 2009 for the first time. This suggests that, contrary to several previous studies, the
subprime crisis is different from EM Sudden Stops. The explanation is straightforward: contrary
to its EM counterparts, the US government had ample credit to finance a bulging fiscal deficit
from the first signs of trouble, but this was not so in the private sector where the subprime crisis
started. This change in credit composition is reflected in the US private sector current account
(see Figure 6). By definition, the private sector current account is the difference between the
total current account and fiscal deficit. Figure 6 shows that the US private sector exhibits an
adjustment equivalent to around 9% of GDP from 2006-IV to the last available data. This
13
The DC_SS starts earlier if one uses the broader credit concept employed in Adrian and Shin (2009), for example.
18
suggests that the US private sector suffered a severe credit crunch – likely the major proximate
cause for the sizable loss of output and employment – even though there is no Sudden Stop in
international credit flows. In sum, this is an example in which credit composition plays a critical
role in explaining the macroeconomic impact of financial crisis. Country aggregate credit data
largely conceal this effect.
As pointed out above, in EMs both the private and the public sectors suffer the
consequences of Sudden Stop, at least during the first stages of the crisis. However, again in
these economies credit composition tends to move in favor of the public sector and large firms,
and against small and medium enterprises, SMEs. That point is illustrated in Figure 7 for the
case of Brazil during the Lehman episode. The collapse of Lehman Brothers brought about a
freeze in global credit, which induced large firms with access to international funding to turn
around and tap the local market. Their collateral and credibility make them attractive costumers
to local banks relative to SMEs and households. Moreover, if the global credit freeze is a well
known fact, as in the subprime crisis, tapping the local market does not raise serious suspicions
about Lemon’s problems. Thus, banks have incentives to redirect credit to large firms in
detriment of its other customers. Crowding out of SMEs from the credit market can have serious
output and employment consequences.
V.
Final Words
Financial crises are very complex phenomena. One research strategy is to sprinkle
conventional models with a myriad of “financial frictions.” Computers can undoubtedly give us
a hand here, but my inclination is to look for intuitive, self-evident and straightforward clues that
have some chance of being present in a wide variety of crises. This strategy will not take us to
19
destination, but may help us choose the best road in a more expeditious way. Moreover, in the
meantime it may avoid us making gross mistakes as a result of ignoring financial sector
difficulties. The paper does not claim a great deal of originality. The basic pieces can be found
scattered in the macro/micro literature. However, by presenting them in a somewhat unified
form, I hope to raise awareness about their relevance.
The central findings are as follows:
1. A rupture of the credit channel could generate conditions that are observationally
equivalent to a Keynesian savings glut or aggregate demand impasse.
2. The first best policy is to restore the credit channel, although this is unlikely to be
feasible in the short run. All the other alternatives discussed in the literature are second
best or worse.
3. Increasing government expenditure under financial crisis may be counterproductive,
possibly driving the economy into a rut, and postponing painful but necessary adjustment.
4. However, government expenditure directed to sectors hardest hit by a fall in demand may
help to prevent unnecessary bankruptcy and unclog bankruptcy courts. This illustrates
the fact that in the midst of financial crisis policy should be prepared to remove its
“neutrality” white gloves and go wrench in hand to fix some loose screws in the engine.
5. A way to rationalize the fact that financial crises seem to come from “nowhere” or be
much bigger than anticipated is to trace them back to the process of creating and
destroying liquidity. Diamond-Dybvig (1983) offers a solid basis to develop a model that
exhibits those properties. The model does not help to forecast which equilibrium will
prevail, but it provides an explanation grounded on some indisputable characteristics of
the financial sector.
20
6. The creation of liquid asset-backed securities could help to explain the rise in the relative
price of attendant assets, e.g., real estate. Likewise, the destruction of those securities’
liquidity provokes a fall in their relative price. The resulting upswing and downswing
would look like a bubble, and maybe called a bubble because it would not be geared to
conventional fundamentals.
7. Liquidity destruction is likely to spill over to the credit channel provoking a contraction
of credit flows. This is enough to damage the credit channel, especially if the shock
brings about large changes in relative prices that are not taken into account in financial
contracts.
8. The paper identifies episodes that may have the above characteristics and calls them
Domestic Credit Sudden Stops, DC_SS. The Lehman episode is shown to have given
rise to a large number of DC_SS, even in unsuspected cases like Chile. This is claimed
as further evidence of the existence of bank-run type liquidity crises.
9. Bailing out financial institutions is a necessary but a far from sufficient condition to
prevent heavy loss of output and employment. It is fair to say we have not yet developed
an effective lender of last resort for the non-financial private sector. Although some nonfinancial behemoths have been bailed out, a large share of output and employment is
generated in SMEs, which are poorly protected from DC_SS.
21
References
Adrian, T. and Hyun Song Shin (2009), “Money, Liquidity, and Monetary Policy,” American
Economic Review, 99, 2, pp. 600–605.
Allen, Franklin, and Douglas Gale, 2007, Understanding Financial Crises; New York,
NY:
Oxford University Press.
Bernanke, Ben, 2000, Essays on the Great Depression; Princeton, NJ: Princeton University
Press.
Bernanke, Ben S., Mark Gertler and Simon Gilchrist, 1999, “The Financial Accelerator in
a Quantitative Business Cycle Framework,” in John B. Taylor and Michael
Woodford (eds.) Handbook of Macroeconomics; Amsterdam, The Netherlands:
Elsevier Science, Chapter 21.
Calvo, Guillermo, 1979, “On Models of Money and Perfect Foresight,” International
Economic Review, 20, February pp. 83-103.
Calvo, Guillermo, 2005, Emerging Markets in Turmoil: Bad Luck or Bad Policy?
Cambridge, MA: MIT Press.
Calvo, Guillermo, 2006, "Monetary Policy Challenges in Emerging Markets: Sudden Stop,
Liability Dollarization, and Lender of Last Resort,” NBER Working Paper No.12788.
22
Calvo, Guillermo, 2008, "Crises in Emerging Market Economies: A Global Perspective," in
Cowan, K., S. Edwards and R. Valdés, eds., Current Account and External Financing,
Central Bank of Chile, Santiago, Chile.
Calvo, Guillermo, 2009, “Financial Crises and Liquidity Shocks: A Bank-Run Perspective,”
Columbia University, August 24, www.columbia.edu/~gc2286.
Calvo, Guillermo, Alejandro Izquierdo and Luis-Fernando Mejia, 2008, “Systemic Sudden Stop:
The Relevance of Balance-Sheet Effects and Financial Integration,” NBER Working
Paper No. 14026.
Calvo, Guillermo, Alejandro Izquierdo and Ernesto Talvi, 2006, “Sudden Stops and
Phoenix Miracles in Emerging Markets,” American Economic Review, (96/2) May, pp.
405-410.
Calvo, Guillermo, and Rudy Loo-Kung, 2010, “US Recovery: A New Phoenix Miracle?” Voxeu,
April 10, www.voxeu.org/index.php?q=node/4858.
Calvo, Guillermo, and Carlos Vegh, 1995, “Fighting Inflation with High Interest Rates: The
Small-Open-Economy under Flexible Prices,” Journal of Money, Credit, and Banking,
27, pp 49-66.
Diamond, Douglas W., and Philip H. Dybvig, 1983, “Bank Runs, Deposit Insurance and
Liquidity,” Journal of Political Economy, 91(3), pp 401-419.
Fischer, Stanley, 1974, “Money and the Production Function,” Economic Inquiry, December, pp.
517-533.
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Fisher, Irving, 1933, “The Debt-Deflation Theory of Great Depressions,” Econometrica, pp.
337-357.
Keynes, John M., 1936, The General Theory of Employment, Interest and Money; London:
MacMillan.
Kiyotaki, Nobuhiro, and John Moore, 1997, “Credit Cycles,” Journal of Political Economy, 105,
2, pp. 211-247.
Mishkin, Frederic S., 2006, The Next Great Globalization: How disadvantaged nations can
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Morris, Stephen and Hyung Shon Shin (1998), “Unique Equilibrium in a Model of
Currency Attacks,” American Economic Review, 88, 3, pp. 587-597.
Taleb, Nassim Nicholas, 2007, The Black Swan: The Impact of the Highly Improbable; New
York, NY: The Random House Publishing Group.
24
FIGURES
Figure 1. Chile: Domestic Credit Sudden Stop
6.E+06
4.E+06
2.E+06
0.E+00
-2.E+06
-4.E+06
-6.E+06
Jan-90
Oct-90
Jul-91
Apr-92
Jan-93
Oct-93
Jul-94
Apr-95
Jan-96
Oct-96
Jul-97
Apr-98
Jan-99
Oct-99
Jul-00
Apr-01
Jan-02
Oct-02
Jul-03
Apr-04
Jan-05
Oct-05
Jul-06
Apr-07
Jan-08
Oct-08
-8.E+06
Domestic SS (DC_SS)
y-o-y change in credit flows
1 std. deviation Band
2 std. deviation Band
Source: Own calculations based on IFS data. The solid line depict the y-o-y change in the (real) flow of
credit to the private sector. Dashed lines represent the mean minus 1 and 2 std. deviation bands,
respectively.
25
Figure 2. Incidence of Sudden Stops. Percentage of EMs exhibiting
Sudden Stop and DC_SS
80%
Lehman files
for bankruptcy
70%
60%
50%
40%
30%
20%
10%
DC_SS
Dec-08
Oct-07
May-08
Mar-07
Jan-06
Aug-06
Jun-05
Nov-04
Sep-03
Apr-04
Jul-02
Feb-03
Dec-01
Oct-00
May-01
Mar-00
Jan-99
Aug-99
Jun-98
Nov-97
Sep-96
Apr-97
Jul-95
Feb-96
Dec-94
Oct-93
May-94
Mar-93
Jan-92
Aug-92
0%
SS
Countries included: Argentina, Bolivia, Brazil, Chile, Colombia, Czech Republic, Dominican Republic,
Ecuador, Egypt, El Salvador, India, Indonesia, Korea, Malaysia, Mexico, Peru, Philippines, Russia, South
Africa, Thailand, Turkey, Uruguay and Venezuela.
26
Figure 3. Growth in Advanced and EMs
10
8
6
4
2
0
-2
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
-4
-6
Advanced Economies
Emerging Market Economies
Source: IMF WEO, October 2009
Figure 4. US: Domestic Credit Sudden Stop
6.E+05
4.E+05
2.E+05
0.E+00
-2.E+05
-4.E+05
-6.E+05
-8.E+05
Jan-90
Oct-90
Jul-91
Apr-92
Jan-93
Oct-93
Jul-94
Apr-95
Jan-96
Oct-96
Jul-97
Apr-98
Jan-99
Oct-99
Jul-00
Apr-01
Jan-02
Oct-02
Jul-03
Apr-04
Jan-05
Oct-05
Jul-06
Apr-07
Jan-08
Oct-08
-1.E+06
Domestic SS (DC_SS)
y-o-y change in credit flows
1 std. deviation Band
2 std. deviation Band
Source: Own calculations based on IFS data. The solid line depict the y-o-y change in the (real) flow of
credit to the private sector. Dashed lines represent the mean minus 1 and 2 std. deviation bands,
respectively.
27
Figure 5. US: Sudden Stop of Capital Inflows
2.E+03
2.E+03
1.E+03
5.E+02
0.E+00
-5.E+02
-1.E+03
-2.E+03
-2.E+03
Jan-90
Oct-90
Jul-91
Apr-92
Jan-93
Oct-93
Jul-94
Apr-95
Jan-96
Oct-96
Jul-97
Apr-98
Jan-99
Oct-99
Jul-00
Apr-01
Jan-02
Oct-02
Jul-03
Apr-04
Jan-05
Oct-05
Jul-06
Apr-07
Jan-08
Oct-08
-3.E+03
External SS
y-o-y change in capital flows
1 std. deviation Band
2 std. deviation Band
Source: Own calculations based on IFS data. The solid line depict the y-o-y change in (real) capital flows.
Dashed lines represent the mean minus 1 and 2 std. deviation bands, respectively.
Figure 6. US Total and Private Current Accounts
(% of GDP, last four quarters)
10%
8%
6%
4%
2%
0%
-2%
-4%
-6%
Current Account Balance
2009:III
2008:I
2008:IV
2007:II
2006:III
2005:I
2005:IV
2004:II
2003:III
2002:I
2002:IV
2001:II
2000:III
1999:I
1999:IV
1998:II
1997:III
1996:I
1996:IV
1995:II
1994:III
1993:I
1993:IV
1992:II
1991:III
1990:I
1990:IV
-8%
Private Current Account Balance
Source: Own calculatons based on data from the Bureau of Economic Analysis (BEA). Private current
account balance is equal to the total Current Account Balance minus the Fiscal Balance.
28
Figure 7. Brazil: Composition of Domestic Credit to Corporations
(share of total corporate credit)
50%
Lehman files for
bankruptcy
45%
40%
35%
30%
25%
20%
up to R$100 mil
Oct-09
Jul-09
Apr-09
Jan-09
Jul-08
Oct-08
Apr-08
Jan-08
Oct-07
Jul-07
Apr-07
Jan-07
Oct-06
Jul-06
Apr-06
Jan-06
Oct-05
Jul-05
Apr-05
Jan-05
15%
from R$100 mil to R$10 million
more than R$10 million
Source: Own calculations based on data from the BCB.
29