Financial Crises
Chapter

Chapter 6. External Imbalances and Financial Crises

Author(s):
Stijn Claessens, Ayhan Kose, Luc Laeven, and Fabian Valencia
Published Date:
February 2014
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Author(s)
Alan M. Taylor

External Imbalances Versus Credit Booms

Is it true that “global imbalances helped to fuel the financial crisis” (King, 2011, p. 1)? In the years since the 2007–09 global crisis, current account imbalances have narrowed, but endogenously, because trade collapsed, and because emerging market economies outgrew the United States and other advanced economies. But before 2008 these global capital flows were much larger. Many prominent policymakers, commentators, and economists had focused on large current account imbalances in the United States, but also in other countries with pronounced booms, and had warned about the potential for a jarring shock should those flows be subject to adjustments caused by incipient changes in portfolio allocation, and concomitant shifts in interest rates, growth rates, and perceived country or currency risks. Harsh adjustments, sudden stops, or reversals, it was thought, could wreak serious havoc. Much attention was given to the role of the large lenders and creditors in emerging Asia (especially China) causing a “saving glut” while others focused on saving shortfalls in large borrowers and debtors like the United States.1

In these arguments, the public or official sectors tended to attract the most scrutiny, be it the official reserves accumulation trends in developing countries, or the path of government deficits and debt in the United States. However, those focusing on the public sector dimensions of the flows ended up missing the main story. Without minimizing fiscal challenges (many of them a result of the crisis), the kind of crises the world ended up having were in almost all cases not fiscal crises at all. In the United States, where large-scale financial pressure was first seen, the dollar has rallied on the flight to safety, as have treasuries, notwithstanding what credit rating agencies have said. In Europe, intraregional imbalances are now seen to have been a source of instability, but before the crisis these cross-border flows (with the exception of Greece) were largely private sector debt flows, much of them flowing through bank channels from savers in the “North” to finance real estate or consumption booms in the “South”; public debts and deficits in places like Ireland and Spain only exploded later, as harsh recessions and banking rescues ate resources.

Thanks to scholars taking a more granular view of the data, more revealing trends are now visible. It is clear, for example, that China and other emerging market purchasers of U.S. assets focused on truly safe AAA assets like treasuries, or government-sponsored-enterprise issues; in contrast, it was advanced country investors, notably in Europe, that crowded into the securitized products channeling funds to the real estate bubble. Within Europe, the growing debt exposure between countries is now seen to have been a two-way street, with gross flows much larger than net flows. The cross-sectional experience of the euro area economies, as well as U.S. states and counties, also strongly suggests that even in a currency union, where balance of payments problems and currency risk are, in theory, absent, or defined away, the threat of a macrofinancial crisis via private debt dynamics is still very much present. In light of these experiences, the answer to “does the current account matter?” is probably still yes; but in a world in which bank-driven expansions in private sector leverage have reached historically unprecedented levels in advanced economies, it is no longer the only, or even necessarily the most important, question one might ask when evaluating macroeconomic and financial risks.2

Historical Perspective: Ebb and Flow of Finance Across and Within Borders

Historically, there has been a broad correlation between the prevalence of external imbalances and the frequency of financial crises. This correlation is all too apparent in the data, but needs to be subject to careful causal interpretation.

It is a stylized fact that international capital mobility has followed a U-shape over the course of recent history (Obstfeld and Taylor, 2004). Under the classical gold standard, until 1913 there were virtually no policy barriers to cross-border financial flows, and the last serious technological impediments were broken down by the arrival of the cable. The interwar period, especially the 1930s, saw policies veer toward autarky; this configuration persisted until the 1970s, as governments reconfigured their responses to the trilemma when confronted by shocks like wars and depressions. Only since the 1980s has consensus moved back toward freer capital movements as tolerance for floating exchange rates accommodated ongoing monetary policy autonomy. This trend has gone furthest in the advanced economies. Documenting these trends, Figure 6.1 shows both policy-based and outcome-based indicators of capital mobility over the past century and a half.

Figure 6.1Capital Mobility Since 1870

Sources: Foreign-assets-to-GDP-ratio from Obstfeld and Taylor (2004) up to 1970 and from Lane and Milesi-Ferretti (2007) thereafter. Capital account openness from index used by Quinn and Voth (2008).

Note: OECD = Organization for Economic Co-operation and Development.

Coincidentally or not, a similar historical pattern characterizes financial crisis events (Reinhart and Rogoff, 2009). Financial instability was a normal feature of all advanced economies in the late nineteenth century, a feature that continued into the 1930s when the intensity of crises reached an all-time high during the Great Depression. But from the 1940s until the early 1970s, the world was virtually free of financial crises, with a few crises witnessed in emerging markets, but none seen at all in advanced economies. This unusually prolonged period of financial calm stands out from what went before and what has happened since (Bordo and others, 2001). In the 1980s and 1990s, emerging markets experienced many financial crises; a few also occurred in advanced economies, followed by one of history’s worst globally synchronized financial crises, in 2008, across a large swaths of so-called advanced economies. To illustrate these patterns, Figure 6.2 shows financial crisis indicators for the past two centuries.

Figure 6.2Banking Crises in the Past Two Centuries

Source: Data from Qian, Reinhart, and Rogoff (2010).

Note: The figure shows the cumulative percent of economies in a banking crisis in each year from 1800 to 2008, 10-year moving average.

Looking at the only available economic laboratory—that is, history—these two summary figures would appear on the surface to support the notion that, at least empirically, international financial integration (the scope for external imbalances) go hand in hand with financial instability (the prevalence of banking crises). But correlation is not causation, and such inferences may not be justified for various reasons. For example, no statistical controls have been performed here, nor have concerns been addressed about possible simultaneity and the role of omitted common factors potentially driving both patterns.

One obvious area for concern with respect to proper statistical control would be changes in other aspects of the macroeconomic and financial policy regime over time and across countries. For example, the period of unusual calm in the 1940–70 period is known to have coincided with what was historically the most stringent era of capital controls (imposed under IMF auspices as the very basis of the Bretton Woods fixed exchange rate regime), and thus the era in which global imbalances were at their all-time nadir. However, it also coincided with a very stringent era of domestic financial regulation in most countries around the world. Policymakers reacted strongly to the bank panics and financial distress of the 1930s with a combination of rules and supervision, plus backstops and insurance (in the U.S. case, for example, Glass-Steagall ring-fencing, supervisory agencies, reserve requirements, the Federal Deposit Insurance Corporation, and Federal Reserve lender-of-last-resort actions).

Even absent the move toward financial autarky in this period, changes in the domestic financial landscape also pushed economies toward a less risky, less leveraged macroeconomic and financial regime. It would be a mistake, without further careful analysis, to claim that one or the other set of policies played a primary role in creating that stable environment. If we are to learn from the past, such work is needed as we sit at another historical turning point when the policy architecture is again under heated discussion and under pressure to be redesigned.

Event Studies: Correlates of Crises

One clear and simple way to begin to explore at least the proximate causes of financial crises is to use event study techniques. This approach looks systematically at the behavior of key variables in the run-up to, and in the aftermath of, financial crisis events, with the goal of identifying systematic differences between tranquil periods or “normal” times” outside the crisis window, and what happens in periods close to a financial crisis. Such analysis serves several purposes: overall patterns impose theoretical discipline on economic models designed to account for crises; precrisis patterns may lead to early warning signals of use to policymakers and others wishing to avert or anticipate problems; and postcrisis patterns should set appropriate historical benchmarks for the evaluation of conditional economic performance (e.g., disputes over whether a recovery is sluggish).

A number of works in the economic literature have followed this approach successfully, such as Cerra and Saxena (2008), Reinhart and Rogoff (2009), Claessens and others (2010), Gourinchas and Obstfeld (2011), Reinhart and Reinhart (2011), Schularick and Taylor (2012), and Reinhart, Reinhart, and Rogoff (2012), among many others. The technique is also widely used in the policy world, for example, in IMF analyses for the World Economic Outlook and other publications since the 2008 crisis. Other related works in this vein include Chamon and Crowe (2012) focusing on a range of indicators; Goldstein (2012) on the link between fundamentals and panics; Dell’Ariccia and others (2012) looking at credit boom warning signals; and Claessens, Kose, and Terrones (2012) who look at the coherence of business and financial cycles.

Most of this literature is in broad agreement. For representative evidence from a recent sample that includes both advanced and emerging market economies using annual data from 1973 to 2010, Figure 6.3 shows empirical regularities for nine key macroeconomic and financial variables in five-year windows on each side of banking crisis events drawn from Gourinchas and Obstfeld (2011).

Figure 6.3Empirical Regularities during Banking Crises, 1973–2010

Source: Gourinchas and Obstfeld, 2011.

Note: ADV = advanced economies; EM = emerging market economies. Units are percent per year (inflation and real interest rate); percent deviation from log trend (output gap and real exchange rate); and percent of GDP (all other variables). The estimates of conditional means of each variable, relative to “tranquil times” are reported on the vertical axes. The horizontal axes represent the number of years before (negative sign) and after a crisis (in the different columns). Estimates in the top row are for emerging market economies; in the bottom row for advanced economies. The dots denote a 95 percent confidence interval for each conditional mean.

The results can be summed up as follows, with some tentative hypotheses that can be carried forward:

  • Output is slightly above normal just before a crisis, but collapses dramatically afterward. The boom may be slightly larger in emerging economies. Advanced economies fare no better than emerging in the aftermath. A long recession is typical.

  • Inflation is close to normal just before a crisis, but collapses afterward. Real interest rates are not atypical before a crisis, but can rise afterward, with the effect seemingly stronger in emerging economies. Deflationary pressures are strong.

  • Public debt levels are normal just before a crisis, but increase dramatically afterward, with a wide range. Crises have adverse fiscal consequences.

  • Domestic credit expansion is typically much higher than normal before a banking crisis event. The shift is very strong in advanced economies and highly statistically significant. Credit booms tend to precede banking crises.

Looking at external indicators, external leverage (gross positions) and the current account do not seem out of line in the window, although in advanced economies these variables get close to borderline significance. Real exchange rates tend to be strong before a crisis, and weaken a lot afterward, compared with normal times. Foreign reserves show no unusual precrisis trend but tend to accumulate afterward as the currency weakens and the external accounts move more to surplus. Thus, external imbalances and currency appreciation may also be indicative of added crisis risk.

Credit and the Current Account: Two Sides of the Same Coin?

The main argument in this chapter is that unusually high rates of credit growth tend to be the primary warning signal of incipient financial crises. However, as the preceding discussion indicates, other indicators could also be relevant, and one goal of the chapter is to relate these perspectives to external imbalances, which have been a focus of debate since 2000.

From a simple accounting perspective, and thinking in conventional theoretical terms, simultaneous correlation between higher credit growth and external deficits in open economies might be expected. Countries experiencing booms tend to have higher investment, and may also have lower savings, if consumption-smoothing motives are at work. The investment may, to some degree, be financed through bank lending channels, suggesting that loan growth and current accounts might be negatively correlated.

However, in the data, this correlation is far from perfect. Consider the long-term advanced economy data set of Schularick and Taylor (2012). If the change in credit-to-GDP ratios were to be regressed on the change in current-account-to-GDP ratios in every year, then this bivariate relationship has significance (an F-statistic greater than 5) in about one out of every six years over the course of history since 1870. Some panel tests over multiyear samples are shown in Table 6.1.

Table 6.1Credit Booms and External Imbalances: Only Weakly Correlated since 1870
Dependent variable: Change in credit-to-GDP ratio
(1)(2)(3)(4)
All yearsPost-1980Pre-19141914–80
Change in current-account-to-GDP ratio−0.122**−0.311*−0.184**−0.0731
(−2.83)(−2.31)(−2.80)(−1.38)
Number of observations1,531392412727
Source: Data from Jordà, Schularick, and Taylor (2011a).Note: t-statistics are in parentheses. *, **, and *** indicate that the results are significant at the 10 percent, 5 percent, and 1 percent levels, respectively.
Source: Data from Jordà, Schularick, and Taylor (2011a).Note: t-statistics are in parentheses. *, **, and *** indicate that the results are significant at the 10 percent, 5 percent, and 1 percent levels, respectively.

Over the entire sample, in column (1), the coefficient on the external imbalance is only –0.12, reflecting the fact that capital inflows can come in a variety of forms, including foreign direct investment or private portfolio securities, or sovereign loans, which have nothing to do with the destination-country banking sector. Indeed, this “pass-through” coefficient suggests that about 90 percent of the time, such flows have bypassed banks. This coefficient rises to –0.31 in the post-1980 era of financial globalization, suggesting that the conduits of external imbalances in recent decades have shifted more toward banking channels; but even then 70 percent of flows appear to be moving outside bank channels.

These results caution that the nexus of financial crises—the domestic banking sector—is only partially coupled to the external balance of payments imbalances of any country, an obvious point. Countries can experience capital inflows that take nonbank forms, so the causation from external to internal is not a given; and they can have credit booms driven by expansion of leverage in domestic banks that need not be related to any new financing flows from abroad, so the causation from internal to external is not a given either.

The historical data back up this idea that the two measures are for the most part distinct, and should therefore not necessarily be expected to play the same role with respect to crisis risk, a point the chapter now examines in greater detail.

Let the Data Speak 1: Predictive Ability Tests

Up to now the chapter has documented some basic empirical regularities, but in that framework only so much can be achieved. The comparisons are just one variable at a time and ultimately a more formal analysis is needed to evaluate which variables really do seem to have distinct dynamics in crisis times as compared with their normal behavior. Given the focus of this chapter, and the results of the last section, the analysis now concentrates on the competing hypotheses relating to whether it is external imbalances or credit booms that are the main feature of crisis events.

Research has turned to the question of predictive modeling, that is, attempting to establish whether certain past variables may contain ex ante early warning information about the likelihood of a financial crisis today. In the wake of the 2007–09 crisis, which caught most economists and policymakers by surprise, the need for careful, robust, and replicable work in this area is urgent, but this is not to say important previous work did not exist. Work on the determinants of emerging market financial crises certainly existed (among others, Kaminsky and Reinhart, 1999). Work on financial crises in samples including advanced economies had also been undertaken (e.g., Bank for International Settlements studies, including, famously, Borio and White, 2004; and Eichengreen and Mitchener, 2004), although it was not heeded by many. This literature tended to find that credit booms, meaning faster growth in bank lending relative to “normal times” were indicative of elevated crisis risk. There was also evidence that higher levels of foreign reserves in emerging markets could perhaps mitigate risks, all else equal.

These findings have been echoed in more recent work, for example, in the logit predictive models presented by Gourinchas and Obstfeld (2011) and Schularick and Taylor (2012). The former employs a short-wide annual panel of both advanced and emerging economies since the 1970s; the latter constructs a long-narrow annual panel from a historical data set for the advanced economies going back to 1870.

In the context of this chapter, however, it is important to ask whether in these and other works one can find any role, much less an independent role, for external imbalances as crisis determinants. The answer, so far at least, seems to be no. In the Gourinchas and Obstfeld (2011) study, the current account is unrelated to banking crisis risk in both the advanced country sample and the emerging market sample, once other controls are included, the most important of which is the credit variable.

Similar results were found by Jordà, Schularick, and Taylor (2011a), using the long-wide panel of advanced economies; a concise exposition of their tests is shown in Figure 6.4 using a tool referred to as the Correct Classification Frontier, or CCF.3 Using any one of a family of competing logit models, such as those described above, the CCF curve plots the frontier of true positives (TP) and true negatives (TN) that each model delivers depending on how its trigger threshold is set. In a given set of data, with any model, a low enough threshold gets 100 percent TP but 0 percent TN; a high enough threshold scores the opposite. An uninformative model (a random signal) will achieve a CCF curve of TP and TN scores on the diagonal simplex between these points. Statistical tests are needed to evaluate whether a model can be judged to be informative, which amounts to having a CCF curve that lies above the diagonal.

Figure 6.4Using Lagged Credit Growth Plus Current Accounts or Public Debts as a Classifier to Forecast Financial Crises: The Correct Classification Frontier

Sources: Jordà, Schularick, and Taylor (2011a); and Taylor (2012).

Note: AUC = area under curve; CA = current account; FE = fixed effects. “CA” uses a five-year lagged moving average of change in the current-account-to-GDP ratio. In this figure, for all models, the predictions of separate prewar and postwar country-fixed-effects logit models are combined. Relative to either the “Null” or the “Credit” model, the addition of “CA” does not significantly improve the classifier.

A straightforward test, which requires no modeling of preferences, would be to look at the “area under the curve” or AUC as a test statistic. Under the uninformative null, AUC equals 0.5 and hypothesis tests are simplified by the asymptotically normal distribution of this statistic. Among other results, Jordà, Schularick, and Taylor (2011a) present tests based on the AUC for four models using the long panel:4

  • A model with country fixed effects only (CFE, a better-than-random null);

  • A model with the lagged credit variable (five-year moving-average change) plus CFE;

  • A model with the lagged current account variable (five-year moving-average change) plus CFE;

  • A model with both the lagged credit and current account variables plus CFE.

As Figure 6.4 shows, adding the current account variable to the model slightly improves predictive ability relative to the country-fixed-effects null (AUC rises from 0.641 to 0.685; p = 0.0165), but adding the credit variable improves predictive ability much more (AUC rises to 0.745; p = 0.0010). Once credit is in the model, adding the current account on top achieves little. Why? As history has shown, over the long term economies can have credit booms fueled by external imbalances, but they can also have homegrown credit booms that are unrelated to shifts in the current account. Either type can potentially increase banking crisis risk, so changes in the balance of payments may not be all that informative.

Let the Data Speak 2: Beyond Binary Classification

Finally, it is worth noting the relevance of the credit cycle, not just for the rare events called financial crises but for all recessions (Jordà, Schularick, and Taylor, 2011b). To underscore this point, all recession events in all countries can be classified as normal recessions or financial recessions based on coincidence (±2 years) with a crisis event. In about 140 years for 14 countries from 1870 to 2008, 50 financial recessions, 173 normal recessions, and 223 recessions in total are observed. The corresponding event frequencies are 3.3 percent for financial recessions and 11.4 percent for normal recessions (approximately 1 in 30 years versus 1 in 9).5

Figure 6.5 shows that there is a more generalized echo of a credit boom in all recessions. A larger run-up in credit each year during the previous expansion years (in percentage of GDP per year) can be traced to weaker performance (lower levels of real GDP per capita) in the subsequent recession and recovery phase out to a horizon of five years beyond the cyclical peak. Thus, unusually rapid credit growth poses extra dangers. Not only does it raise the likelihood of a once-in-a-generation financial crisis event, as the binary prediction analysis shows, it is also systematically related to weaker recession paths in all peak-trough episodes, whether the country falls prey to a financial recession or a normal recession.

Figure 6.5Credit Bites Back: “Excess” Credit Growth in the Expansion Phase and the Deviation of Real GDP per Capita in the Five-Year Next Recession and Recovery Phase

Source: Based on the data in Jordà, Schularick, and Taylor (2011b).

Note: coef. = coefficient; se = standard error; t = t-statistic. The figures show simple added-variable plots (partial scatters) between the deviation of the level of log real GDP per capita in recession or recovery years one through five after a normal or financial peak, and the annual rate of change of credit to GDP in the previous expansion. Panel a shows financial crisis recessions only, panel b normal recessions only. In the underlying regression, additional control variables include five-year time fixed effects interacted with normal and financial recession dummies. Both partial correlations are statistically significant at the 1 percent level.

To put the “marginal effects” of the excess credit treatment in Figure 6.5 into perspective, the slope is 0.5 for normal recessions and 0.75 for financial recessions. Excess credit, measured in percent of GDP per year, has a historical mean of about 1¼ in expansions prior to financial recessions (standard deviation = 2½) and a mean of ¼ in expansions prior to normal recessions (standard deviation = 2). This implies that a 1 standard deviation increase in the credit variable during a “high leverage” expansion is associated with a five-year drag of –1 percent of the level of real GDP per capita after the peak in a normal recession, or –2 percent in the event of financial recession.

These are nontrivial differences and deserve further scrutiny and causal investigation: credit booms sow the seeds of future deleveraging pain in all cycles. Monitoring credit is, therefore, a legitimate issue for policymakers concerned with overall macroeconomic stability at business-cycle frequencies, that is, even in more typical cycles when crises are averted and the economy suffers only a “normal” recession (see, e.g., Drehmann, Borio, and Tsatsaronis, 2011; and Turner, 2011).

Conclusions

The history of advanced economies shows that credit booms and busts can be driven just as easily by domestic saving as foreign saving. Gross stocks and flows can often be delinked from net flows across borders, so balance sheets can expand even if no cross-border flows are recorded. At a disaggregated level, current account flows can be composed of a widely varying mix of bank instruments, debt, equity, foreign direct investment, and other claims, and each type has very different risk characteristics, with bank and debt flows being the ones with rollover risk (stops, flight).

Thus, there is absolutely no preordained reason why any dollar of gross or net capital flows should make a difference to the risk of a financial crisis in the home country. It is highly likely instead that the nature of the flow, and its route into the local economy, will matter far more. It is when financial flows of local or foreign origin build up into large credit exposures in the domestic financial system that the risks of a financial crisis are elevated and the likelihood of future deleveraging costs is increased.

Analysts need to move beyond monocausal stories in which the current account is relied upon as a unique, special indicator. Evidence shows that domestic credit conditions are a more salient feature of crisis dynamics, and even of the dynamics of normal business cycles.

A natural dichotomy is emerging. An “external variable” like current accounts may make sense as a key indicator in the analysis of proximate causes of “external crisis”—meaning capital market access, bad spreads, default, or recourse to IMF programs (Catão and Milesi-Ferretti, 2012). But an “internal variable” like credit might make much more sense as a key indicator in the analysis of proximate causes of “internal crisis,” meaning distress in the domestic financial system, bank panics, failures, and so forth.

Future economic and policy analysis may benefit greatly if we can move beyond the narrow and simplistic “global imbalance” framework that all too often dominated discussions in the past decade.6

References

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    ReinhartCarmen M. and Vincent R. Reinhart and Kenneth S. Rogoff2012Debt Overhangs: Past and PresentNBER Working Paper No. 18015 (Cambridge, Massachusetts: National Bureau of Economic Research).

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    SchularickMoritz and Alan M. Taylor2012Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises, 1870–2008American Economic Review Vol. 102 No. 2 pp. 102961.

    SchularickMoritz and PaulWachtel2012The Making of America’s ImbalancesDiscussion Paper Economics 2012/16 (Berlin: Free University of Berlin, School of Business and Economics).

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The term “saving glut” is credited to the chairman of the Federal Reserve Board (Bernanke, 2005).

See, among others, Schularick and Wachtel (2012) on private versus public financing of the U.S. lending boom; Shin (2012) on the “global banking glut”; and Lane (2012) and Obstfeld (2012) on the importance of gross versus net positions.

The CCF is a variant of the receiver operating characteristic curve.

The discussion draws on Taylor (2012).

To cleanse the effects of the two world wars from the analysis, the war windows 1914–18 and 1939–45 are excluded, as are data corresponding to peaks that are within five years of the wars looking forward, or two years looking backward (because these leads and lags are used in the analysis below).

See, for example, Lane (2012) and Obstfeld (2012) for suggestions as to the way ahead. See also the IEO (2011) post mortem of the global financial crisis, for example: “For much of the period [2004–07] the IMF was drawing the membership’s attention to the risk that a disorderly unwinding of global imbalances could trigger a rapid and sharp depreciation of the dollar, and later on the risks of inflation from rising commodity prices. The IMF gave too little consideration to deteriorating financial sector balance sheets, financial regulatory issues, to the possible links between monetary policy and the global imbalances, and to the credit boom and emerging asset bubbles. It did not discuss macro-prudential approaches that might have helped address the evolving risks” (IEO, 2011, p. 7).

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