Financial Crises
Chapter

Chapter 10. What Have We Learned about Creditless Recoveries?

Author(s):
Stijn Claessens, Ayhan Kose, Luc Laeven, and Fabian Valencia
Published Date:
February 2014
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Author(s)
Abdul Abiad, Giovanni Dell’Ariccia and Bin Li The authors thank Olivier Blanchard, Stijn Claessens, Gianni De Nicolò, Prakash Kannan, Angela Maddaloni, David Romer, and participants in seminars at the IMF, the Bank for International Settlements, the European Central Bank, the 2012 American Economic Association meeting, the 2012 Midwest Macro Meeting, the 2012 International Conference on Economic and Financial Challenges in Asia-Pacific, the 2010 Econometric Society World Congress, and the 2010 Financial Intermediation Research Society Conference (Fiesole) for helpful comments. Zeynep Elif Aksoy provided excellent research assistance.

Bank credit is considered to be a critical factor in facilitating economic activity. However, creditless recoveries, that is, economic growth without credit growth, can be observed after some recessions. This phenomenon was first documented by Calvo, Izquierdo, and Talvi (2006), who study what happens to output and credit after global or systemic sudden stop episodes. They find that, on average, output returns quickly to precrisis levels, but with weak investment and virtually no recovery in domestic or external credit (so-called Phoenix miracles).

Abiad, Dell’Ariccia, and Li (2011) address a broad set of questions regarding creditless recoveries. How common are they, and under what conditions do they tend to occur? How do they differ from “normal” recoveries? Do they reflect impaired financial intermediation? And finally, can and should policymakers respond to them? This chapter provides a nontechnical summary of the findings in Abiad, Dell’Ariccia, and Li (2011) and discusses some of the policy-related issues.

The study proceeds in two steps. First, macroeconomic data are used to identify and examine creditless recoveries in a broad set of countries. This analysis focuses on correlations and studies the frequency, duration, shape, and composition of the recoveries. It investigates which types of downturns are more prone to being followed by creditless recoveries. And it asks whether creditless recoveries are associated with worse growth performance and, if so, which components of growth are most affected. Second, the study turns to sectoral data to investigate the mechanism behind creditless recoveries. In particular, it uses a difference-in-difference approach to identify causal links between credit growth and output performance. If disruptions of financial intermediation are at the root of creditless recoveries, their effect should be felt disproportionately more by those sectors that rely more heavily on external finance.

The findings indicate that creditless recoveries—defined as episodes in which real credit growth is negative in the first three years following a recession—are not rare. They follow about one in five recessions in a wide set of countries. And although they seem to be more common in developing economies and emerging markets, they also occur in advanced economies.

Creditless recoveries are incomplete “miracles.” On average, activity recovers substantially less than in recoveries with credit: output growth is on average a third lower. Put differently, creditless recoveries tend to be weaker and more protracted (i.e., it takes longer for output to return to trend). This result remains when controlling for the characteristics of the preceding recession. However, these averages mask wide variations—many creditless “recoveries” are followed by stagnant growth.

Considering the preconditions that tend to precede creditless recoveries, the frequency of creditless recoveries doubles when the downturns follow credit booms, and more than doubles when the downturns follow or coincide with a banking crisis. If the downturn is preceded by both a banking crisis and a credit boom, the subsequent recovery is almost certain to be creditless. Currency and sovereign debt crises have smaller effects, and in the presence of a banking crisis they do not significantly increase the likelihood of a creditless recovery. These findings suggest that the relatively weak macroeconomic performance during creditless recoveries is the result of constrained growth caused by impaired financial intermediation. This is consistent with Calvo, Izquierdo, and Talvi (2006), who argue that the lack of credit growth during these recoveries can be rationalized by financial frictions preventing firms from obtaining funding for new investment.

Output decompositions buttress this perspective. Investment, which is likely to depend more on credit than does consumption, has a disproportionately smaller contribution to growth in creditless recoveries relative to other recoveries, although consumption takes a hit as well. An interesting finding is that creditless recoveries are not jobless recoveries—employment dynamics are no different on average from those in normal recoveries. Instead, productivity and capital deepening are adversely affected.

Using sectoral data, the analysis more formally tests the hypothesis that the weaker macroeconomic performance during creditless recoveries stems from disruptions of financial intermediation. Industry-level data are used covering 28 manufacturing industries in 48 countries, from 1964 through 2004. The analysis follows Braun and Larrain (2005), who focus on recessions rather than recoveries and analyze an industry’s performance using the growth rate of industrial production. This measure is then regressed on an array of controls, including multiple sets of fixed effects (to take care of industry-year- and industry-country-specific omitted factors), and the variable of interest, which is the interaction of a measure of the industry’s financial dependence and the creditless recovery dummy.

Braun and Larrain (2005) find that more financially dependent industries perform relatively worse during recessions. Consistent with their result, Abiad, Dell’Ariccia, and Li (2011) finds that these industries perform relatively better than less financially dependent industries during all typical recoveries (although, similar to Braun and Larrain’s analysis of “booms,” the result is generally weak and not always significant). During creditless recoveries, however, industries that are more dependent on external finance tend to grow disproportionately less than those that are more self-financed. This result appears economically meaningful. During creditless recoveries, the growth rate of industries that are highly dependent on external finance (at the 85th percentile of the index distribution) is more than 1.5 percentage points lower than in normal recoveries. The same difference drops to 0.4 percentage point for low-dependence industries (those at the 15th percentile). This differential effect appears robust. It is present in both advanced economies and emerging markets. It survives when controlling for capital inflows. And it does not seem to depend on measurement issues that may stem from large fluctuations in credit aggregates caused by exchange rate movements (in the presence of foreign-currency-denominated loans).

The finding that creditless recoveries are suboptimal outcomes associated with impaired financial intermediation is relevant from a policy standpoint. Had causality gone the other way—that is, had creditless recoveries resulted instead from an exogenous decline in the demand for credit caused, for example, by weak growth prospects—there would be little room for policy action beyond countercyclical macro measures typically adopted in normal recoveries. Given the evidence, however, policies aimed at restoring credit supply should lead to fewer credit constraints and higher growth. The findings are also relevant for the 2007–09 global financial crisis. Given the widespread financial sector distress, the retrenchment in cross-border capital flows, and the occurrence of credit and property booms in several countries, the recovery from the crisis is likely to be creditless in a number of economies, and thus slower than average. To contain this effect, continued policy action is required to restore the supply of credit, cushion the effects of deleveraging, and address the undercapitalization of several financial institutions.

The rest of the chapter is organized as follows: the first section examines creditless recoveries from a macro perspective. The second section presents the sectoral analysis. The final section concludes.

Macro Perspective

This section studies creditless recoveries from a macro perspective. It examines how creditless recoveries differ from normal recoveries, and analyzes and compares the duration, shape, and frequency of these recoveries. It also examines whether creditless recoveries are peculiar to certain sets of countries or follow particular events such as banking crises, currency crises, debt crises, sudden stops, or credit booms. For now, the analysis focuses on associations and does not attempt to establish causal links between the variables, leaving that for the sectoral analysis in the next section.

What has been learned about creditless recoveries?

  • How are they defined?

  • How common are they?

  • How are they different from other recoveries?

  • How can they be decomposed?

Before creditless recoveries can be defined a definition of what countries are recovering from is needed. Economic downturns are identified following the methodology in Braun and Larrain (2005). Recessions are identified based on fluctuations of real annual GDP.1 Specifically, a Hodrick-Prescott filter is used to extract the trend in the logarithm of real GDP. The smoothing parameter is set at 6.25 as recommended for annual data by Ravn and Uhlig (2002). Recessions are identified whenever the cyclical component of GDP (detrended real output) exceeds one country-specific standard deviation below zero. The recession is then dated as starting the year following the previous peak in detrended real output, and continuing until the year of the trough (when the cyclical component is at its lowest point). The recovery period is then defined as the first three years following the trough of a recession. This simplifies the distinction between creditless and normal recoveries and limits problems associated with “double-dip” recessions. This methodology identifies 388 recoveries, roughly equally divided between advanced Organization for Economic Cooperation and Development countries, emerging markets, and low-income countries.2

The focus is bank credit to the private sector, as measured in line 22d of the IMF’s International Financial Statistics. This is a choice of necessity. This series is the only one available with broad cross-country and time series coverage. One shortcoming is that it does not include credit extended by nonbank financial intermediaries. For most countries this is not a major issue. But for a couple of cases, such as the United States, a critical portion of the financial sector is not covered by the data. A creditless recovery is then defined as one in which the growth rate of real bank credit (deflated by the GDP deflator) is zero or negative in the first three years of recovery.

Creditless recoveries are not rare. They represent about one-fifth of all recoveries. However, the differences in their distribution across country groups are not trivial. In particular, creditless recoveries are more common in low-income countries and emerging markets than in advanced economies, where they represent only about 10 percent of all recoveries. A Pearson chi-square test rejects at the 10 percent level the null hypothesis that the relative frequency of creditless recoveries is the same across country groups. This outcome suggests that these events tend to be more common in countries with less-developed financial markets. The cross-country correlation between financial development (measured by the average credit-to-GDP ratio during the sample period) and the frequency of creditless recoveries is about −0.2.

There is also substantial time series variation in the relative frequency of creditless recoveries. In particular, creditless recoveries tend to be clustered geographically and around three peak periods (Figure 10.1). These clusters follow the Latin American debt crisis of the early 1980s, the European Exchange Rate Mechanism crisis and Scandinavian banking crisis of the early 1990s, and the Asian crises of the late 1990s.

Figure 10.1Creditless Recoveries over Time

Source: Authors’ calculations.

Note: ERM = Exchange Rate Mechanism.

The questions then arise: To what extent are creditless recoveries associated with the nature of the preceding recession? In particular, what is the predictive power of specific events such as credit booms, banking and currency crises, and real estate booms and busts? If creditless recoveries are the result of impaired financial intermediation, they should be more likely to occur in the aftermath of events associated with disruptions in the credit supply.

The analysis first focuses on downturns associated with systemic banking crises, as defined by Laeven and Valencia (2008). The frequency of creditless recoveries is three times as high if a systemic banking crisis occurs in the two years before or the year coinciding with a downturn than when there is no banking crisis. Nevertheless, only about half of banking crises are followed by creditless recoveries.

Both currency and sovereign debt crises seem to have some influence independent of the effect of banking crises. In the absence of a banking crisis, a currency crisis preceding a recession doubles the frequency of creditless recoveries, and a sovereign debt crisis more than doubles it. But if there is a banking crisis, the occurrence of either a currency crisis or a sovereign debt crisis does not seem to be associated with a significantly higher frequency of creditless recoveries.

Finally, the analysis looks at downturns preceded by credit booms, using the methodology developed in Mendoza and Terrones (2008). The occurrence of credit booms before downturns doubles the relative frequency of creditless recoveries. However, the effects of a credit boom are weak if there is no banking crisis; instead, creditless recoveries become most likely when downturns are preceded by both credit booms and banking crises.

If creditless recoveries tend to follow a credit boom-bust cycle, do they also tend to follow boom-bust cycles in the property market? In the absence of reliable cross-country house price data, construction investment data are used as a proxy, and the findings indicate that creditless recoveries are associated with construction boom-bust cycles. In particular, on average, creditless recoveries are preceded by collapses in construction investment (with an average decline of about 17 percent). In contrast, construction investment growth is essentially zero before recoveries with credit. To the extent that a collapse in construction investment signals a house price bust, this result is interpreted as evidence that creditless recoveries are associated with the destruction of collateral value (and the consequent increase in agency problems) stemming from sharp declines in real estate prices.

Creditless recoveries are less desirable than normal ones from a growth performance standpoint. For the broader sample of recessions, average output growth in creditless recoveries is 4.5 percent per year, compared with about 6.3 percent in recoveries with credit. As a consequence, output is also slower to return to trend. Output returns to trend within three years of the end of the recession in fewer than half of creditless recoveries, compared with more than two-thirds of recoveries with credit. In part, this difference reflects the fact that creditless recoveries tend to be preceded by deeper recessions, but it is also the result of the differential in growth rates. This is consistent with financial accelerator models. Greater destruction of collateral value associated with a deeper recession will translate into more sluggish credit and weaker growth in the recovery, as shown in Figure 10.2.

Figure 10.2Comparison of Creditless with Normal Recoveries

Source: Authors’ calculations.

Calvo, Izquierdo, and Talvi (2006) document the characteristics of recoveries after systemic sudden stop (3S) episodes. They find that after these episodes, economies on average experience a quick, but creditless, recovery; they dubbed the phenomenon a “Phoenix miracle.” Abiad, Dell’Ariccia, and Li (2011) find that more than half of 3S episodes are creditless, and average growth during 3S creditless recoveries is quite high—3.9 percent, compared with 4.3 percent during 3S recoveries with credit—which is consistent with Calvo, Izquierdo, and Talvi’s (2006) findings.

A closer inspection, however, reveals a bimodal distribution, similar to that described by Huntley (2008). But going beyond Huntley (2008), this analysis identifies the cause of the bimodality: what matters is whether the 3S episode is associated with a banking crisis. For 3S episodes that do not result in banking crises, the recovery has always exhibited positive real credit growth, and output returns to trend within three years in most (five out of six) cases. In contrast, 80 percent of the recoveries following 3S episodes are creditless, and in two-thirds of these episodes, output does not return to trend within three years.

However, a few “true miracles”—exceptional cases in which output recovers sharply in the absence of credit growth—are found. In the sample, Chile and Uruguay in 1984–86, Mexico in 1995–98, and Argentina in 2003–05 fit this description. Figure 10.3 shows an example of a true Phoenix miracle, observed in Mexico in the 1995 episode. Phoenix miracles follow exceptionally deep recessions. Mexico, the possible exception, experienced a drop in output in excess of 6 percent in 1996, and the other three countries all witnessed double-digit falls during their recessions. Thus, it is possible that these “miracles” are in part due to rebound effects.

Figure 10.3A True Phoenix Miracle: Mexico, 1995

Sources: IMF, International Financial Statistics and World Economic Outlook databases; World Bank, World Development Indicators database; and authors’ calculations.

To shed light on the difference in macroeconomic performance between creditless and normal recoveries, aggregate growth is decomposed into its demand components. During creditless recoveries, the contributions of consumption and investment to output growth are roughly 1 percentage point lower than during normal recoveries, fully accounting for the 2 percentage point difference in output growth between creditless and normal recoveries. In relative terms, however, the contribution of investment falls by roughly half as compared with a fall of a third in consumption’s contribution. This suggests that the components of aggregate demand that are more dependent on credit contribute the most to the difference in growth rates relative to normal recoveries. Net exports do not, on average, contribute to output growth during recoveries, regardless of credit dynamics. To be clear, the external sector does contribute positively to growth during the recession as the current account improves (often swinging from negative to positive); but during the recovery, both exports and imports increase, resulting on average in a roughly null contribution to growth.

Growth accounting points in the same direction. Lower growth during credit-less recoveries can be ascribed to lower capital accumulation and lower total factor productivity growth. These results are consistent with what Calvo, Izquierdo, and Talvi (2006) find for 3S episodes. Lower capital accumulation is consistent with the results for demand decomposition. Lower total factor productivity growth may indicate that younger and start-up firms, which typically have higher productivity growth, find it more difficult than others to obtain credit during these episodes. It is also consistent with the notion that an impaired financial system is less efficient in reallocating capital across sectors as needed to absorb asymmetric shocks.

In contrast, employment growth (or alternatively, the decline in the unemployment rate) seems to be independent of the evolution of credit during the recovery. These results are interpreted as suggesting that it is, again, the more credit-dependent components that suffer during creditless recoveries. As pointed out by Calvo, Izquierdo, and Talvi (2006), these results are consistent with a situation in which, because of financial frictions, firms can obtain short-term credit for working capital but cannot obtain long-term financing for physical capital.

Sectoral Analysis

This section empirically tests the hypothesis that creditless recoveries (and the associated lower output performance) result from impaired financial intermediation. The identification strategy relies on the notion that, in the presence of market imperfections, different sources of funds (bank credit, the issuance of tradable bonds, and equity) are not perfect substitutes. Then, if creditless recoveries stem from disruptions in the supply of bank credit, firms and industries that are more reliant on credit should perform relatively worse. By contrast, if the creditless nature of the recovery were demand driven, sectors’ performances should not differ in a systematic way.

The analysis follows the difference-in-difference approach used by several studies focusing on the real effects of banking crises and financial development. The exercise uses industry-level data from manufacturing sectors in both advanced and emerging market economies for 1970–2004. Industries are ranked according to the Rajan and Zingales (1998) index of external financial dependence, defined as capital expenditures minus cash flow from operations divided by capital expenditures. The differential performance of growth in real value added and industrial production during recoveries across these industries within a particular country is the main channel through which the real impact of credit is identified.

The study adopts the same working assumptions as in Rajan and Zingales (1998), later used by, among others, Braun and Larrain (2005); Krozner, Laeven, and Klingebiel (2007); and Dell’Ariccia, Detragiache, and Rajan (2008): external dependence is determined by technological factors, such as production time, capital intensity, and the importance of investment in research and development. Although the absolute value of the index may vary across countries and time, for the methodology to work it is sufficient that the industry ranking remains broadly the same. Rajan and Zingales (1998) support this assumption with data from Canada.

The starting point is the relative performance of credit-dependent sectors during all recoveries (irrespective of credit conditions). Braun and Larrain (2005) find that more-credit-dependent sectors suffer disproportionately during recessions (when agency problems become more severe). Hence, one would expect them to perform relatively better during recoveries as agency problems diminish.

The following regression is run on recoveries as the baseline specification:

The dependent variable is the growth rate of industrial production in industry i at time t in country c. Regressors include two sets of fixed effects (industry-year and industry-country) and the variable of interest, an interaction term equal to the product of the financial dependence measure for industry i and the recovery dummy for year t and country c. Following Rajan and Zingales (1998), the regression also includes the lagged share of industry i in country c to account for convergence effects, that is, the tendency of larger industries to experience slower growth.

The variable di,t denotes the industry-year dummy, and di,c is the industry-country dummy. Sharei,c,t–1 is the size of the industry in the country at the time t − 1. Dependencei is the industry-level financial dependence, which follows the Rajan and Zingales (1998) methodology, and is assumed to be constant across years. Recoveryc,t is a dummy taking value 1 in the three years following the trough of a recession in country c at year t. CreditlessRecoveryc,t is a dummy equal to 1 when real credit growth is negative during a recovery. The sum of α2 and α3, reflecting the level effect of creditless recoveries, is expected to be positive. However, based on the results from the macro section, α3 is expected to be negative; macroeconomic performance during creditless recoveries is weaker than during normal ones. Furthermore, the coefficient α5 allows a comparison to be made between sectoral growth and the type of recovery. In particular, a negative α5 would indicate that sectors more reliant on external finance perform relatively worse during creditless recoveries. This would, in turn, lend support to the claim that creditless recoveries are the result of disruptions in the credit supply.

The evidence from sectoral data suggests that creditless recoveries are indeed the result of impaired financial intermediation. During these episodes, sectors more dependent on external finance perform relatively worse. These results are statistically and economically significant and survive several robustness tests.

The findings of the regression are shown in Table 10.1. The level coefficient for creditless recoveries is negative as expected, but is not significant, suggesting that the gap in performance between creditless and normal recoveries identified in the macro analysis depends in large part on sectoral effects. The coefficient of the interaction term of creditless recoveries and credit dependence is consistently negative across all specifications, indicating that industries more dependent on external finance perform relatively worse when the recovery is not accompanied by credit growth. The result loses some significance but remains stable when the sample is split between advanced economies and emerging markets. The difference in performance is economically meaningful. During creditless recoveries, the growth rate of industries that are highly dependent on external finance (at the 85th percentile of the index distribution) is more than 1.5 percentage points lower than in normal recoveries. The same difference drops to 0.4 percentage point for low-dependence industries (those at the 15th percentile). This across-industry difference in performance is even more pronounced in emerging markets (the cross-sector differential is 1.5 percentage points versus 1.2 percentage points for advanced economies), likely reflecting the scarcity of alternative sources of funding or more pervasive agency problems.

Table 10.1The Effect of Creditless Recoveries on Sectoral Growth
VariablesOECD + EMOECDEM
Size (lagged)–0.00640.0703*–0.0654
[–0.187][1.873][–1.249]
Recovery0.0273***0.0230***0.0328***
[17.645][14.366][11.473]
Creditless recovery–0.004–0.0048–0.004
[–1.147][–1.291][–0.639]
Recovery x dependence0.0091**0.00490.0147**
[2.380][1.193][2.105]
Creditless recovery x dependence–0.0190**–0.0200**–0.0265*
[–2.169][–2.033][–1.730]
Observations35,79620,00615,790
R-squared0.2070.3470.186
Creditless recovery
Change in growth rate for high dependence industry (percent)–1.5–1.6–2.0
Change in growth rate for low dependence industry (percent)–0.4–0.4–0.4
Implied differential effect (percent)–1.1–1.2–1.5
Source: Authors’ calculations.Note: EM = emerging market; OECD = Organization for Economic Cooperation and Development. Robust t-statistic in brackets. The symbols *, **, and *** indicate significance at the 10, 5, and 1 percent levels, respectively. The dependent variable is the yearly growth rate in the production index of each ISIC-3 industry in each country computed from the UNIDO Indstat-3 (2006) data set. Lagged size is the share of a country’s total manufacturing value added that corresponds to the industry in the previous year.
Source: Authors’ calculations.Note: EM = emerging market; OECD = Organization for Economic Cooperation and Development. Robust t-statistic in brackets. The symbols *, **, and *** indicate significance at the 10, 5, and 1 percent levels, respectively. The dependent variable is the yearly growth rate in the production index of each ISIC-3 industry in each country computed from the UNIDO Indstat-3 (2006) data set. Lagged size is the share of a country’s total manufacturing value added that corresponds to the industry in the previous year.

In addition to the baseline specification, a variety of robustness tests are performed. Details are provided in Abiad, Dell’Ariccia, and Li (2011). The results of the robustness tests support the baseline findings. First, all episodes with exchange rate depreciations in excess of 20 percent are excluded. The concern is that sharp exchange rate falls may lead to a misclassification of creditless recoveries as normal recoveries, through their effect on the stock of foreign credit measured in domestic currency. The main coefficient of interest maintains sign and significance. Furthermore, consistent with the concern that exchange rate depreciation might blur the line between creditless and normal recoveries, the coefficient is larger than in the baseline specification. Second, the effect of capital inflows is controlled for. Again, the coefficient of interest maintains sign and significance, and remains broadly stable in size. The coefficient of the capital-flows-to-GDP variable is positive and significant as expected. In addition, capital flows seem to favor sectors that are more heavily dependent on external finance.

In addition, to control for omitted country-time-specific variables, a third set of fixed effects is included in the regression. As discussed above, these will take care of any omitted variable that does not vary simultaneously across all three dimensions of the data. All coefficients maintain the same sign and significance as in the previous regressions. The differential effect between sectors at the 85th percentile and the 15th percentile of the distribution of the external dependence index continues to range between about 1 percentage point and 1.5 percentage points, which is roughly the same magnitude as in the other regressions.

Conclusion

This chapter summarizes the findings in Abiad, Dell’Ariccia, and Li (2011) regarding the puzzling phenomenon of creditless recoveries. In contrast to previous studies, the analysis finds the following: First, creditless recoveries, while not the norm, are far from rare. They follow about one in five recessions. Second, creditless recoveries are somewhat less desirable than normal recoveries. Output growth is on average a third lower. Third, they are preceded by events likely to disrupt the supply of credit, such as banking crises, credit booms, and real estate boom-bust cycles. Fourth, investment has a disproportionately lower contribution to growth than in normal recoveries, and productivity and capital deepening are adversely affected. Finally, industries more reliant on external finance seem to grow disproportionately less during creditless recoveries.

Overall, both the macro-level and sectoral evidence support the hypothesis that creditless recoveries are the result of impaired financial intermediation: the lower growth performance in creditless recoveries is likely the outcome of a constrained allocation of resources. The results are consistent with agents delaying or downsizing their more credit-dependent investment and expenditure decisions and firms more dependent on external finance being forced to curtail their activities.

This finding is relevant from a policy standpoint. During creditless recoveries, policy measures aimed at restoring financial intermediation are likely to lead to higher growth. Of course, the obstacles to efficient financial intermediation will vary from case to case, and policies should be adapted accordingly. For instance, the lack of credit growth may be caused by stress on banks’ balance sheets that could be addressed by recapitalizing banks (possibly with public intervention). Alternatively, the lack of credit growth could result from an overindebted private nonfinancial sector. Even in the presence of relatively healthy banks, debt overhang would exacerbate agency problems and prevent the efficient allocation of capital. In this case, the response would be much more complex and would have to include policies to facilitate deleveraging or possibly debt restructuring. Finally, given the association of creditless recoveries with banking crises, credit booms, and real estate boom-bust cycles and their lower growth performance, supportive measures (including a more expansionary macroeconomic stance) could be taken in anticipation of a less buoyant recovery phase when a recession is associated with these events.

References

    AbiadA. G. Dell’Ariccia and B. Li2011Creditless RecoveriesIMF Working Paper 11/58 (Washington: International Monetary Fund).

    BraunM. and B. Larrain2005Finance and the Business Cycle: International, Inter-Industry EvidenceJournal of Finance Vol. 15 No. 3 pp. 1097128.

    CalvoG. A. Izquierdo and E. Talvi2006Sudden Stops and Phoenix Miracles in Emerging MarketsAmerican Economic Review Vol. 96 No. 2 pp. 40510.

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    HuntleyJ.2008Phoenix Falling: Recovering from Sudden Stops in Emerging Markets” (unpublished; Evanston, Illinois: Northwestern University).

    KroznerR. L. Laeven and D. Klingebiel2007Banking Crises, Financial Dependence and GrowthJournal of Financial Economics Vol. 84 No. 1 pp. 187228.

    LaevenL.A. and F.V. Valencia2008Systematic Banking Crises: A New DatabaseIMF Working Paper 08/224 (Washington: International Monetary Fund).

    MendozaE. and M. Terrones2008An Anatomy of Credit Booms: Evidence from Macro Aggregates and Micro DataNBER Working Paper No. 14049 (Cambridge, Massachusetts: National Bureau of Economic Research).

    RajanR.G. and L. Zingales1998Financial Dependence and GrowthAmerican Economic Review Vol. 88 No. 3 pp. 55986.

    RavnM.O. and H. Uhlig2002On Adjusting the Hodrick-Prescott Filter for the Frequency of ObservationsReview of Economics and Statistics Vol. 84 No. 2 pp. 37176.

Real GDP data from the World Bank’s World Development Indicators are used, extended to 2007–09 using the IMF’s World Economic Outlook data where available. These data cover 172 countries, 1960–2009 (unbalanced).

The country groups are defined in the Data Appendix of Abiad, Dell’Ariccia, and Li (2011). Emerging markets are the 26 countries covered in the MSCI Emerging Markets Index, advanced OECD refers to the 23 members of the Organization for Economic Cooperation and Development not in the emerging markets group, and LIC refers to low-income countries according to the World Bank’s income classification.

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