Chapter

Chapter 5. The Real Effect of Banking Crises

Author(s):
Christopher Crowe, Simon Johnson, Jonathan Ostry, and Jeronimo Zettelmeyer
Published Date:
August 2010
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Author(s)
Giovanni Dell’Ariccia, Enrica Detragiache and Raghuram Rajan1 

5.1. Introduction

Banks are thought to be central to business activity. Therefore, when they experience financial distress, governments usually come to the rescue, offering emergency liquidity and various forms of bailout programs. The case for generous bank support, however, is murky for a number of reasons. First, we have the standard identification problem: if bank distress and economic distress occur at the same time, how can we tell the direction of causality? Second, if bank distress does in fact impair economic activity, under what circumstances is this likely to be most harmful? Third, whereas interventions may save banks, they may not necessarily prevent the distressed banks from affecting economic activity. So do any interventions prevent banks from impairing economic activity, and if so, which ones are they? Fourth, how do the costs of intervention weigh up against the benefits? This chapter focuses on the first two questions, shedding limited light on the last two issues.

Empirical studies show that credit to the private sector and aggregate output do in fact decelerate during banking crises (see, for example, Kaminsky and Reinhart, 1999; Eichengreen and Rose, 1998; and Demirgüç-Kunt and others, 2006). However, this is not necessarily evidence that banking problems contribute to the decline in output: first, the same exogenous adverse shocks that trigger banking problems may also cause a decline in aggregate demand, leading firms to cut investment and working capital and, ultimately, demand for bank credit. These same shocks may also cause a temporary increase in uncertainty, leading firms to delay investment and borrowing decisions. In addition, adverse shocks might hurt borrower balance sheets and exacerbate the effects of asymmetric information and limited contractibility, prompting banks–even healthy ones–to curtail lending to riskier borrowers (“flight to quality”) or raise lending spreads. To summarize, output and bank credit are likely to decelerate around banking crises even in the absence of a feedback effect from bank illiquidity and insolvency to credit availability.2 To identify the real effects of banking crises it is necessary to sort out this joint endogeneity problem.

Problems of joint endogeneity are familiar in studies of whether finance matters to the real economy. They are central to the literature on financial development and growth (Levine, 2005) and to the work on whether financial market imperfections worsen economic downturns (the so-called credit channel literature). To test whether banking crises have real effects, we adopt the “difference-in- difference” approach used by Rajan and Zingales (1998) to study the effects of finance on growth.3 Our premise is that, if industries more dependent on external finance are hurt more severely after a banking crisis, then it is likely that banking crises have an independent negative effect on real economic activity. Using panel data from 41 countries from 1980 to 2000, we test whether more financially dependent industries experienced slower growth in banking crisis periods, after controlling for industry-year, country-year, and industry-country fixed effects. This profusion of dummy variables controls for all possible time-specific, country-specific, and industry-specific shocks that may affect firm performance, thereby avoiding the usual difficulties of choosing an appropriate set of control variables.

In Rajan and Zingales (1998) industry dependence on external finance is measured by the fraction of investment not financed through retained earnings. We use the same index in our main specification.4 As an alternative measure of bank dependence, we use average establishment size in a sector, under the assumption that sectors dominated by small firms are more dependent on domestic bank financing.5 In the credit channel literature, identification based on firm size has been used, for instance, by Gilchrist and Himmelberg (1995).

The results are supportive of the joint hypothesis that banking crises have real effects, and at least part of this effect is through the lending channel. More financially dependent sectors perform significantly worse during banking crises, and the magnitude of the effect is nontrivial: more financially dependent sectors (in the fourth quartile of the dependence distribution) lose about 1 percentage point of growth in each crisis year compared to less financially dependent sectors (in the first quartile of the dependence distribution). Of course, not all doubts about causality are laid to rest by this methodology, and we conduct a number of additional tests.

In particular, one criticism of our testing strategy is that because of balance sheet effects or other financial market imperfections, externally dependent sectors may grow more slowly during any economic downturn, whether a banking crisis exists or not (Braun and Larraín, 2005). A related concern is that the differential effect might be driven by balance sheet effects following currency crises (which often accompany banking crises). This may happen if more externally dependent sectors tend to have more foreign currency debt. When we allow for separate differential effects during recessions or currency crises, however, the differential effect during banking crises remains significant, suggesting that we are not simply picking up balance sheet effects.

We also address the issue of the residual endogeneity of the banking crisis variable. If bank dependent sectors are relatively more represented in bank portfolios, asymmetric sectoral shocks affecting these sectors might cause both the banking crisis and the relative underperformance of these sectors. However, we find that more external dependent industrial sectors perform poorly during banking crises even in countries/crises where they are likely to represent a smaller share of bank portfolios. This suggests that our correlations are not driven only by asymmetric sectoral shocks.

Another criticism may be our reliance on the Rajan–Zingales measure of external dependence. When instead we differentiate across industries based on average establishment size, our tests show that small-scale sectors suffer more during crises, consistent with the hypothesis that the lending channel is operative.

Tornell and Westermann (2002, 2003) have argued that asymmetries in the response to financial crises in emerging markets are not just between large and small firms, but also between firms in traded and nontraded sectors, because the firms in traded sectors have better access to alternative sources of financing (especially foreign finance) when domestic credit is depressed. We also examine if such asymmetric effects are present in our data. We do not, however, find significant differences across manufacturing sectors during banking crises based on their propensity to export, though we do find such differences during currency crises.

The second question we posed at the outset is to examine where the differential effect is stronger. On the one hand, this gives us a sense of where intervention may be more critical; on the other, if the differential effect is stronger where the theory plausibly suggests the costs of banking crises are likely to be larger, the differential effect itself gains credibility as a measure of the impact of the crisis. We find the differential effects to be stronger in developing countries, in countries where the private sector has less access to foreign finance, and where the crises are more severe (in a way we will make more precise). These results make intuitive sense: externally dependent sectors should suffer less from a banking crisis if they can tap domestic bond or stock markets (as in developed countries) or foreign capital markets. Also, the more severe the disruption in the banking sector, the stronger should be the differential effect.

We turn next to the question of how different government intervention policies might affect the bank lending channel. Using data on intervention policies for 22 crisis episodes from Honohan and Klingebiel (2003), we find some evidence that regulatory forbearance is associated with a lower cost of crisis. Because the sample is small, however, the evidence is only suggestive. Nonetheless, the finding is consistent with our hypothesis: if banks are special, keeping them alive is essential for credit to flow to financially dependent industries. Moreover, banks that are kept alive might focus on squeezing borrowers in order to regain liquidity. That they do not seem to do so when given maneuvering room is interesting.

Of course, policymakers are particularly interested in whether the benefits of an intervention outweigh the cost. Because our methodology allows us only to identify the differential effect of an intervention and not the aggregate effect (for instance, if spillovers from the increased growth of financially dependent industries prevents the whole economy from falling into recession) we have little to say here other than interventions that do not affect the differential are unlikely to affect activity through the lending channel, and therefore have to be justified for other reasons.

The chapter is structured as follows. In Section 5.2 we review the related literature; in Section 5.3, we explain the empirical methodology and the data; in Section 5.4, we present the results;Section 5.5 concludes.

5.2. Related Literature

There is a long literature focusing on the effects of banking crises. For example, Lindgren, Garcia, and Saal (1996) summarizes many early experiences, and concludes that “episodes of fragility in the banking sector have been detrimental to economic growthin the countries concerned” (p. 58). Cross-country studies of banking crises have also shown that output growth and private credit growth drop significantly below normal levels in the years around banking crises, but do not attempt to sort out the direction of causality (Kaminsky and Reinhart, 1999 ; Eichengreen and Rose, 1998; Demirgüç-Kunt and others, 2006;).

In their study of the so-called capital crunch in the United States in 1990, Bernanke and Lown (1992) in fact express skepticism that the credit crunch played a major role in the recession of 1990. Instead, they stress demand effects, pointing to the fact that there was little relation between bank capital ratios and employment growth across states, and all types of credit, not just bank credit, fell.

The question of whether banking crises cause a credit crunch was resurrected once more following the Asian crises of 1997–98. Some studies attempted to provide answers, reaching different conclusions. For instance, Domaç Ferri (1999) interpreted evidence that small- and medium-sized enterprises were hurt disproportionately in Malaysia and Korea as indicative of a credit crunch, whereas most Thai firms surveyed after the crisis attributed low production levels not to lack of credit, but to poor demand (Dollar and Hallward-Driemeier, 2000).

A number of studies have tried to tackle the identification problem in clever ways. Some have examined the issue from the side of banks. Peek and Rosengren (2000) use geographical separation as their means of identifying supply shocks: Japanese banks lost capital as a result of bad loans made in Japan. The authors then show that the withdrawal of these banks from lending to real estate in the United States had a strong dampening effect on U.S. commercial real estate markets. Clearly, it is hard to attribute the fall in real activity to demand-side effects. Kashyap and Stein (2000) suggest a lending channel for monetary policy by pointing out that small, less liquid banks seem to curtail credit more in response to tight monetary conditions than large, liquid banks.

Our study differs from these in that it attempts to identify supply effects by looking to see if borrowing sectors that are more likely to be sensitive to a supply shock are indeed disproportionately affected by it. In this, our study is closely related to two recent papers.

Braun and Larraín(2005) test whether industries more dependent on external finance experience a sharper output contraction than other industries during economic downturns, and find a large positive differential effect. They also find this effect to be larger in countries with poor accounting standards and for industries whose assets are less tangible, supporting the interpretation that financial frictions are at work, and thus may amplify economic fluctuations especially for industries more dependent on external finance. In contrast, in the present chapter, we focus more narrowly on the effects of banking sector distress on the real economy. This allows us to identify the presence of a bank lending channel to the extent that the effects of the disruption in loan supply associated with the crises are greater than those stemming from the deterioration of firm balancesheet quality (possibly also associated with the crisis).

In a contemporaneous and closely related paper, Krozner and others (2007) study whether banking crises impact sectors dependent on external finance more severely in countries with a less developed financial system. Although both studies investigate how banking crises affect the real economy, they examine two different aspects of this relationship. Here, we first present evidence in support of the assumption that banking crises have real effects by showing that it is the sectors more dependent on external finance that suffer the most during these crises. Then, we consider how several country characteristics influence these effects.

In other words, we are interested in the differences within a country over time of the relative growth of financially dependent industries. We find they do particularly badly during a banking crisis, suggesting that those are periods of low availability of finance. By contrast, Krozner and others (2007) examine the effects of the financial development of a country on the relative growth of financially dependent industries in noncrisis and crisis periods. They find that the relative growth in value added of financially dependent industries is faster in financially developed countries in precrisis periods but slower in crisis periods. This has implications for the effects of financial development in different states of the economy, but has little light to shed on the effects of the different states of the economy themselves. Econometrically speaking, we look for a within-country across-industry effect over time (including country-industry indicators along with the usual panoply of country and industry indicators), while they examine the differential effect between industries across countries for two different states of these countries (not including country-industry indicators). Their finding is that the differential effect found by Rajan and Zingales is present in precrisis periods, but becomes insignificant (and even changes sign) during crises. The interpretation is that operating in an environment where financial markets are well developed is an advantage for more financially dependent industries in good times, but a disadvantage in times of banking crises.

The problem of separating out the effect of bank distress from other contemporaneous shocks hinders efforts to measure the economic cost of banking crises and to understand the determinants of these costs. Most existing studies have looked at the decline in output as a yardstick to differentiate across crises. For instance, Bordo and others (2001) argue that financial crises (currency crises, banking crises, or both) have entailed similar-sized output losses in recent years as compared to previous historical periods, although they are more frequent now than during the gold standard and Bretton Woods periods and as frequent as in the interwar years. Hoggarth and others (2002) claim that, contrary to popular belief, output losses associated with banking crises are not more severe in developing countries than in developed countries.

More recently, Claessens, Klingebiel, and Laeven (2003) study how output losses following banking crises are affected by institutions and policy interventions. As in our study, the latter are identified through the Honohan- Klingebiel dataset. The main finding is that generous support to the banking system does not reduce the output cost of banking crises. This conclusion, however, does not take into account that omitted exogenous shocks may cause both a stronger output decline and more generous intervention measures. Using a measure of the cost of crises less marred by this problem, we find that depositor protection and forbearance may indeed be effective in reducing the real cost of crises.

5.3. The Basic Test

5.3.1. Methodology

To study whether banking crises have real effects, we ask whether industries more dependent on external finance experience a more severe output loss following a banking crisis. In the benchmark specification, value-added growth in industry j at time t in country i is regressed on three sets of fixed effects (industry-year, country-year, and industry-country) and the variable of interest, an interaction term equal to the product of the financial dependence measure for industry j and the banking crisis dummy for year t and country i. Following Rajan and Zingales (1998), we also include the lagged share of industry j in country i to account for “convergence” effects (i.e., the tendency of larger industries to experience slower growth). The benchmark regression is:

where d denotes dummy variables. A negative and significant δ indicates that banking crises have a relatively worse impact on industries that depend more heavily on external finance. The three sets of fixed effects should control for most shocks affecting firm performance, including– for instance– the severity of the banking crisis, the level of financial development, global shocks to the industry, and aggregate country-specific shocks. This gets around the usual difficulties with omitted variable bias. Indeed, the only shocks not controlled for are those varying simultaneously across countries, industrial sectors, and time. Standard errors are clustered by industry and country. As robustness tests, we also use gross capital formation, employment, and number of establishments as the dependent variable instead of value added.

5.3.2. Data

Data on manufacturing value added, investment, and number of establishments are disaggregated at the three-digit ISIC level and come from the UNIDO, Industrial Statistics, 2003(summary statistics for these variables are in Table 5.1). There are 28 industries at this level of disaggregation. Value added is deflated using consumer price indices from the International Financial Statistics.6

External dependence is defined as the share of capital expenditure not financed with cash-flow from operations. The data come from Rajan and Zingales (1998), who take them from Compustat. Following Krozner and others (2007), and in contrast with Rajan and Zingales, to preserve sample size we include only the three-digit ISIC level sector rather than a mixture of three- and four-digit level sectors.7 The figures are for U.S. manufacturing firms and reflect industry medians during the 1980s (seeTable 5.11 in the Appendix). An important assumption underlying our approach is that external dependence reflects technological characteristics of the industry that are relatively stable across space and time (see Rajan and Zingales, 2008, for a discussion of this assumption). In Section 5.5 we explore alternative proxies for a sector’s reliance on bank finance: average establishment or plant size and export orientation (Table 5.12 in the Appendix reports the correlations between these measures).8

Table 5.1Summary Statistics
MeanMedianStandard Dev.MaxMinNo. of obs.
NormalCrisisNormalCrisisNormalCrisisNormalCrisisNormalCrisisNormalCrisis
Value-added growth4.201.702.22−0.4222.2624.63107.44107.40−54.12−54.09131683059
(in percent)
Growth in capital formation12.7510.602.76−1.0955.9157.49240.70239.99−80.51−79.7078581894
(in percent)
Employment growth1.23−0.830.49−1.228.589.1929.0028.88−20.43−20.36130532887
(in percent)
Growth in number of2.130.680.000.009.9610.3845.7745.95−22.12−22.1475982086
establishments (in percent)
Crisis refers to observations that correspond to the year of inception of a banking crisis or the two subsequent years. Normal refers to all other observations.
Crisis refers to observations that correspond to the year of inception of a banking crisis or the two subsequent years. Normal refers to all other observations.

To identify banking crisis inception dates, we rely on information from case studies, including Lindgren and others (2006,) and Caprio and Klingebiel (2003,). Following Demirgüç-Kunt and Detragiache (1998,), we consider episodes of bank distress to be systemic crises when at least one of the following conditions holds: there were extensive depositor runs; the government took emergency measures to protect the banking system, such as bank holidays or nationalization; the fiscal cost of the bank rescue was at least 2 percent of GDP; or nonperforming loans reached at least 10 percent of bank assets. A list of banking crises is in Table 5.13, in the Appendix.

The crisis dummy variable takes the value 1 for the crisis inception year and the two following years, under the hypothesis that the real effect of the crisis dissipate after three years or so. Table 5.14 in the Appendix shows that if crises are set to last four years there is not much difference in aggregate value-added growth rates between crisis and noncrisis periods, whereas for shorter durations crisis years have lower growth. Also, in a sample of 36 crises, Demirgüç-Kunt and others (2006) find that GDP growth returns to its precrisis level in the fourth year of a crisis. For robustness, we also consider narrower and wider crisis windows.

To maximize sample size we use an unbalanced panel in which some country/ year/sector observations are missing. We exclude, however, country/years for which less than 10 industrial sectors are available to ensure that there is enough information to estimate the differential effect. Constraints on the availability of banking crisis and sectoral value-added information leave us with data from 41 countries from 1980 to 2000 for a total of over 16,000 observations, after excluding 2 percent of outliers on either tail of the distribution.9 Summary statistics for the alternative dependent variables (manufacturing value added, investment, employment, and number of establishments) for crisis and noncrisis observations are in Table 5.1.

5.4. Results

5.4.1. The Benchmark Test

Estimates from the benchmark regression support the hypothesis that banking crises have an exogenous effect on the real economy. The coefficient of the interaction term is negative and significant at the 5 percent level, indicating that the growth rate of sectors that rely more heavily on external finance is relatively more affected in crisis years compared to sectors that rely less on external finance (Table 5.2). The economic magnitude of this effect is substantial. On average, in a country experiencing a banking crisis, the difference in value-added growth between a sector at the 25th percentile and one at the 75th percentile of the external dependence distribution is 1.1 percentage point per year of crisis. This compares with an average rate of growth of 3.7 percent in the sample as a whole and 1.7 percent during crisis years.

Table 5.2Differential Effect of Banking Crises on Value-Added Growth
BenchmarkSectoral CorrelationsRecessionsCurrency Crises
Crisis3*Dep−2.74−2.55−2.87
[2.27]**[1.98]**[2.32]**
High-Corr*Crisis3*Dep−2.07
[1.36]
Low-Corr*Crisis3*Dep−3.39
[1.93]*
Recession*Dependence−0.77
[0.64]
Currency Crisis*Dep1.38
[0.98]
Lagged Share−2.44−2.44−2.44−2.44
[7.51]***[7.51]***[7.50]***[7.52]***
Constant8.468.498.53−29.61
[1.45][1.45][1.46][3.23]***
Observations16227162271622716227
R-squared0.350.350.350.35
t-statistics in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Dep is a parameter measuring an industry’s dependence on external finance (Rajan and Zingales, 1998). High corr are crisis episodes in which sectoral dependence for the country is highly correlated with sectoral share. Lagged share is the share of the sector’s value added in total value added lagged by one period. Regressions are estimated with OLS. Standard errors are clustered by industry-country, and regressions also include time-country, time-industry, and industry-country dummy variables.
t-statistics in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Dep is a parameter measuring an industry’s dependence on external finance (Rajan and Zingales, 1998). High corr are crisis episodes in which sectoral dependence for the country is highly correlated with sectoral share. Lagged share is the share of the sector’s value added in total value added lagged by one period. Regressions are estimated with OLS. Standard errors are clustered by industry-country, and regressions also include time-country, time-industry, and industry-country dummy variables.

As sensitivity analysis, we drop from the sample the 5 percent tails of the dependent variable distribution. When this is done, the coefficient of the interaction term remains negative and significant.10 The results are also robust to correcting standard errors for first-order autocorrelation in the residuals and to clustering standard errors by country.

5.4.2. Are the Result Driven by Asymmetric Sector-Specific Shocks?

The methodology employed in this chapter greatly reduces the concern for simultaneity biases in the relationship between growth and banking crises. However, the endogeneity of the banking crisis variable is still an issue as bank-dependent sectors are likely to be more heavily represented in bank portfolios than less bank-dependent sectors. Asymmetric sectoral shocks concentrated in bank-dependent sectors could cause both the banking crisis and relatively poor growth in those sectors.

To address these concerns, we proceed as follows. We do not have data about the sectoral composition of bank portfolios, but we conjecture that, in each country, sectors are relatively more represented in bank portfolios if they are relatively largeand they are relatively more dependent on external finance. For each country and year, we compute the correlation between the sectoral share and the external dependence variable. In countries where this correlation is high, bank-dependent sectors are likely to account for a significant share of bank balance sheets, whereas in countries with low correlation, they are not. Then, under the null hypothesis of asymmetric sectoral shocks, crisis episodes in which this correlation is high should exhibit larger differential costs of crises than crisis episodes in which this correlation is low. In other words, because countries with a high correlation are ones where external-finance-dependent industries account for a large share of the economy, it is more plausible that the banking crises in these countries were caused by problems originating in externally dependent industries. A finding that our interaction coefficient is significant in these countries but not in countries with a low correlation would lend support to the reverse causality explanation, that is, it is the slow growth of dependent industries that caused the banking crisis rather than vice versa.

To test this, we split the sample around the cross-country median of the distribution of the correlation between external dependence and relative size, and rerun the baseline specification allowing the coefficient of the interaction term to differ between the two groups (Table 5.2 Column 2). We find that the coefficient for the crises where bank-dependent sectors represent a relatively smaller portion of bank portfolios islarger than that in our baseline regression and remains significant at the 10 percent level. The coefficient for the other crises, on the other hand, is not significant. This evidence suggests that the hypothesis of asymmetric sectoral shocks should be rejected.

5.4.3. Bank Distress or Balance Sheet Effects?

A concern with our interpretation of the basic regression is that the differential effects we document may reflect balance sheet problems among borrowers rather than their banks. In other words, banking crises often coincide with economic downturns which worsen firm balance sheets. This, in turn, aggravates agency problems and other financial frictions, causing all banks (even healthy ones) to cut back on lending, presumably hurting bank-dependent sectors disproportionately more.11 As discussed in Section 5.2 previously, Braun and Larraín (2005) find that during recessions output declines disproportionately more in sectors more reliant on external finance.

To separate out the effect of financial frictions during recessions from the specific effect of banking crises, we construct a recession dummy variable using GDP data from the World Bank World Development Indicators. Following the peak-to-trough criterion (Braun and Larraín, 2005), we date recessions as follows: first, a trough is identified when GDP falls more than one countryspecific standard deviation below its trend level (where trend is computed with a standard Hodrick-Prescott filter). Then, a peak is identified as the last year with positive GDP growth before the trough. The recession dummy variable takes the value of one from the year after the peak to the year of the trough. Using this dummy variable, we estimate the following equation:

If the coefficient δ captures the differential effect of recessions rather than the banking crises, it would lose significance in this specification, whereas ξ would be negative and significant.

As it turns out, there is an overlap between recessions and banking crises, but the overlap is far from perfect: not all recessions coincide with banking crises and not all banking crises occur during economic downturns. When we estimate the regression with both interaction terms, the coefficient of the crisis/dependence interaction term becomes a bit smaller, as one might expect, but remains significant at 5 percent in both specifications (Table 5.2 Column 3). On the other hand, the coefficient of the recession/dependence interaction term has the expected sign (negative), but it is not significant. This finding supports the interpretation that we are picking up not only balance sheet effects, but also disruptions in credit supply because of the banking crisis.12

This result may be in part driven by the fact that we consider only countries that experienced at least one crisis, whereas Braun and Larraín consider a broader sample. This may also reflect different mechanisms in advanced economies and developing countries because these represent the majority in our sample (more on this in the next section).

Similar arguments apply to currency crises. These events, especially in countries where the corporate sector has large unhedged foreign currency exposures, may cause large balance sheet effects. If more leveraged firms are also more dependent on external finance, and if large currency depreciations occur in association with banking crises (the “twin crises”), then the differential effect found in the baseline regression may reflect the balance sheet channel rather than distress in the banking sector. To sort out this issue, we rerun the benchmark regressions by adding an interaction term between external dependence and a currency crisis dummy. Following Milesi-Ferretti and Razin (1998), a currency crisis is defined as a year in which the exchange rate satisfies the following three conditions: it depreciates (vis-à-vis the U.S. dollar) at least 25 percent; it depreciates at least twice as fast as in the previous year; and the previous year it depreciated by less than 40 percent.13

When currency crises are controlled for, the coefficient of the bank-crisis/ dependence interaction term remains negative and significant and of similar magnitude as in the baseline regression (Table 5.2 Column 4). The coefficient of the currency-crisis/dependence interaction term has a positive sign, perhaps because more externally dependent sectors tend to be exporting sectors which benefit from a devaluation, but is not significant. It could also be that twin crises are banking crises in which the government provides banks with more extensive liquidity support. Whereas the exchange rate depreciates as a result of the liquidity injections, the real effects of the crisis may be mitigated.

5.4.4. Where Do Crises Matter Most?

In our baseline specification all banking crises are treated as having the same differential effect on industries. In practice, this is unlikely to be the case, as different characteristics of the economy may affect the impact of the banking crises, and the crisis itself may be of different nature and magnitude. So the question we now turn to is if bank distress does in fact impair economic activity, under what circumstances is this likely to be most harmful?

Banking crises are likely to have relatively larger real effects in developing countries where bond and equity markets are less developed and where governments may find it more difficult to provide support for troubled banks. For this reason we consider an alternative specification where the coefficient of the interaction term is allowed to differ across advanced and developing countries as defined by the IMF’sWorld Economic Outlook). The results confirm this conjecture (Table 5.3 Column 1). Whereas the coefficient for advanced countries is not significant, that for developing countries is larger than in the benchmark specification and significant at the 5 percent level. The difference in value-added growth between a sector at the 25th percentile and one at the 75th percentile of the external dependence distribution becomes 1.5 percentage points per year of crisis. For robustness, we ran alternative specifications with different crisis windows and with and without outliers (Table 5.3, Column 2 and 3).

Interestingly, the Braun and Larraín coefficient of the recession/dependence interaction term is larger (and almost significant) for advanced economies where banking crises tend to be less common and for which the crisis/dependence interaction term is not significant (Table 5.3, Column 4.) This suggests that in advanced economies, possibly because of the existence of sources of external finance other than the banking system, overall macroeconomic conditions are more important than the health of the banking system in determining how funds are allocated to the real sector. In emerging markets and developing countries, the absence of alternative sources of finance may make growth differentials among sectors with different reliance of external finance more sensitive to banking crises than to the business cycle.

In a related vein, the effects of banking crises should differ across countries with different access to foreign finance, under the hypothesis that industries dependent on external finance should be more severely affected by banking crises in countries with more limited access to foreign sources of capital.

Table 5.3Differential Effects of Banking Crises on Value-Added Growth: Differences between Developed and Developing Countries
Three-YearFour-YearExcluding
WindowWindow5 Percent OutliersRecessionsCurrency Crises
Crisis3*Dep*DC−0.07−1.430.72−0.02
[0.04][1.00][0.37][0.01]
Crisis3*Dep*LDC−3.73−2.24−3.66−4.01
[2.46]**[1.74]*[2.30]**[2.56]**
Crisis4*Dep*DC0.52
[0.36]
Crisis4*Dep*LDC−2.58
[1.91]*
Recession*Dep*DC−2.07
[1.38]
Recession*Dep*LDC−0.34
[0.23]
Currency Crisis*Dep*DC−1.66
[0.98]
Currency Crisis*Dep*LDC2.41
[1.35]
Share (t-1)−2.44−2.44−1.69−2.44−2.45
[7.52]***[7.51]***[7.18]***[7.52]***[7.53]***
Constant8.418.3710.823.908.04
[1.44][1.43][1.43][0.76][1.25]
Observations1622716227152131622716227
R-squared0.350.350.360.350.35
t-statistics are in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Crisis4 is a dummy variable for the year of a banking crisis and the following three years. Dep is a parameter measuring an industry’s dependence on external finance (Rajan and Zingales, 1998). DC is a dummy for developed countries. LDC is a dummy for developing countries. Recession is a dummy for recession years. Currency crisis is a dummy for currency crisis years. Lagged share is the share of the sector’s value added in total value added lagged by one period. Lagged share is the share of the sector’s value added in total value added lagged by one period. Regressions are estimated with OLS. Standard errors are clustered by industrycountry, and regressions also include time-country, time-industry, and industry-country dummy variables.
t-statistics are in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Crisis4 is a dummy variable for the year of a banking crisis and the following three years. Dep is a parameter measuring an industry’s dependence on external finance (Rajan and Zingales, 1998). DC is a dummy for developed countries. LDC is a dummy for developing countries. Recession is a dummy for recession years. Currency crisis is a dummy for currency crisis years. Lagged share is the share of the sector’s value added in total value added lagged by one period. Lagged share is the share of the sector’s value added in total value added lagged by one period. Regressions are estimated with OLS. Standard errors are clustered by industrycountry, and regressions also include time-country, time-industry, and industry-country dummy variables.

To proxy for access to alternative sources of finance we use data on disbursement of foreign loans and bonds to the private sector (scaled by the sum of imports and exports). The data come from the Global Development Finance database of the World Bank. Because developed countries are not covered by this database, we arbitrarily set the value for these countries at the largest sample observation, under the assumption that developed country firms have broad access to alternative finance. We then allow for separate interaction coefficients between crisis and external dependence for countries with access above the sample median and countries with access below the sample median. The estimation results suggest that the real effects of banking crises are more pronounced when access to foreign finance is more limited (Table 5.4 Column 1.) This suggests that access to foreign finance can help mitigate the real effects of banking crises.14

Table 5.4Differential Effect of Banking Crises on Value Added: Difference Among Countries and Crises
Foreign AccessCrisis SeverityLarge CrisesTwin Crises
Crisis3*Dep*High Access−1.83
[1.27]
Crisis3*Dep*Low Access−4.28
[2.18]**
More Severe Crisis3*Dep−4.18
[2.14]**
Less Severe Crisis3*Dep−2.51
[1.26]
Dep*Crisis3*Large Output Loss−4.48
[3.04]***
Dep*Crisis3*Small Output Loss−0.58
[0.30]
Twin Crisis*Dep−1.25
[0.75]
Non-Twin Banking Crisis*Dep−3.74
[2.24]**
Share (t–1)−2.43−2.39−2.47−2.44
[7.49]***[6.70]***[7.20]***[7.51]***
Constant9.371.11−10.148.45
[0.81][0.14][1.42][1.45]
Observations15640134641590916227
R-squared0.350.360.350.35
t-statistics are in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Dep is a parameter measuring an industry’s dependence on external finance (Rajan and Zingales, 1998). High (low) access is a dummy for countries with access to foreign capital markets above (below) the sample median. More (less) severe denotes crises where the banking sector was more (less) severely disrupted than the median. Large (small) output loss denotes crises where the decline in output relative to trend was above (below) the sample median. Twin crises are banking crises accompanied by currency crises. Lagged share is the share of the sector’s value added in total value added lagged by one period. Regressions are estimated with OLS. Standard errors are clustered by industry-country, and regressions also include time-country, time-industry, and industry-country dummy variables.
t-statistics are in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Dep is a parameter measuring an industry’s dependence on external finance (Rajan and Zingales, 1998). High (low) access is a dummy for countries with access to foreign capital markets above (below) the sample median. More (less) severe denotes crises where the banking sector was more (less) severely disrupted than the median. Large (small) output loss denotes crises where the decline in output relative to trend was above (below) the sample median. Twin crises are banking crises accompanied by currency crises. Lagged share is the share of the sector’s value added in total value added lagged by one period. Regressions are estimated with OLS. Standard errors are clustered by industry-country, and regressions also include time-country, time-industry, and industry-country dummy variables.

If our hypothesis is correct, banking crises should have more significant real effects in those cases where they are more pervasive and involve the disruption of the orderly functioning of the banking system. We consider three indicators of crisis severity: the fiscal cost of the crisis, the share of nonperforming loans in total loans, and the fraction of insolvent bank assets in total bank assets. The sample is then split according to whether the severity ranking (an average of these three measures) is above or below its median, and the usual regression is estimated with two separate interaction terms, one for more severe and one for milder crises. As expected, we find that externally dependent sectors suffer more in more severe crises (Table 5.4, Column 2). Similar results are obtained if we split the sample according to the aggregate output loss experienced during the crisis, where the loss is computed as the difference in average GDP growth between the three years preceding a crisis and the three years of the crisis (Table 5.4, Column 1).

Another interesting question is whether the differential effects of crises are more pronounced when bank distress is accompanied by a currency crisis, as has been the case in a number of well-known episodes. When we split the sample between “twin crises” and stand-alone crises, differential effects are significant only for the latter episodes. This might be explained by the fact that during twin crises, the adverse effects on the bank lending channel might be offset by the (favorable) effects of exchange rate devaluation on exports and profitability (Table 5.4, Column 4).

Finally, thus far we have looked at overall value-added growth. One might expect the effects of lending to be more direct and pronounced on capital formation. Using investment growth as the dependent variable (dropping 5 percent of outliers, because this variable is noisier) in the baseline regression, the coefficient on the interaction term remains negative and statistically significant at the 5 percent level (Table 5.5). The differential effect is economically more significant than in the case of value added: an industry at the 25th percentile of the external dependence distribution has investment growth 4 percentage points higher than one at the 75th percentile during crisis years.

Another measure that is likely to be sensitive to bank lending is employment. This variable has the advantage of not being affected by changes in relative prices across sectors, which we cannot control for because of lack of data. Consistent with the importance of the bank lending channel, we find that employment growth is slower in more financially dependent sectors during banking crises. When we differentiate between developed and developing countries, the effect on employment seems to be more pronounced in the latter, consistent with the result for value added.

A third alternative dependent variable is growth in number of establishments. To the extent that this variable reflects the birth of new firms, it has the advantage of being less sensitive to balance sheet effects than value added (see earlier): a new firm is unencumbered by past liabilities, and therefore growth in the number of firms will not be influenced by how the roots of the crisis affect firm balance sheets. In addition, like employment growth this variable is not muddled by relative price changes. The differential effect is again negative and significant in developing countries, though it is not significant in advanced economies. An industry at the 25th percentile of the external dependence distribution has growth in establishments 0.6 percentage points higher than one at the 75th percentile during crisis years. This result is consistent with the hypothesis in Aguiar and Gopinath (2005) that firm liquidity may play a role in determining the crossindustry pattern of mergers and acquisitions. To the extent that illiquid firms make easier targets, and conditionally on sufficient variability of liquidity within sectors, one banking crisis may lead to industry consolidation in more bankdependent sectors.

Table 5.5Differential Effects of Banking Crises on Growth in Capital Formation and the Number of Establishments
Capital FormationEmploymentNumber of Establishments
BenchmarkDC-LDC SplitBenchmarkDC-LDC SplitBenchmarkDC-LDC Split
Crisis3*Dependence−9.85−1.47−1.11
[2.31]**[2.01]**[2.27]**
Crisis3*Dependence*−9.32−0.93−1.25
Developed[1.83]*[0.89][1.57]
Crisis3*Dependence*−10.12−1.71−1.06
Developing[1.77]*[1.79]*[1.72]*
Share (t–1)−2.21−2.21−0.47−0.47−0.83−0.83
[3.53]***[3.53]***[2.54]**[2.54]**[7.46]***[7.46]***
Constant28.5228.51−7.8017.57−0.6618.73
[1.20][1.20][1.14][1.92]*[0.24][5.09]***
Observations97529752968496841594015940
R-squared0.320.320.440.440.380.38
Robust t-statistics are in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Dep is a parameter measuring an industry’s dependence on external finance (Rajan and Zingales, 1998). DC is a dummy for developed countries. LDC is a dummy for developing countries. Lagged share is the share of the sector’s value added in total value added lagged by one period. Regressions are estimated with OLS. Standard errors are clustered by industry-country, and regressions also include time-country, time-industry, and industry-country dummy variables.
Robust t-statistics are in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Dep is a parameter measuring an industry’s dependence on external finance (Rajan and Zingales, 1998). DC is a dummy for developed countries. LDC is a dummy for developing countries. Lagged share is the share of the sector’s value added in total value added lagged by one period. Regressions are estimated with OLS. Standard errors are clustered by industry-country, and regressions also include time-country, time-industry, and industry-country dummy variables.

In sum, our methodology suggests that banking crises have the most effect where we would expect from the theory that the lending channel to be most operative. Next we turn to alternative ways of identifying differences in reliance on domestic banking across industries.

5.4.5. Differences Among Sectors Based on Firm Size

In corporate finance it is well known that small firms tend to rely more on domestic bank finance than large firms, as the latter can raise capital through domestic securities markets or international capital markets. Thus, other things being equal, sectors dominated by small firms should be more severely affected by disruptions in the domestic banking sector. The distinction between small and large firms, therefore, can provide an identification strategy alternative to the Rajan- Zingales index.

Although we do not have cross-country panel data on value added by firm size, we construct a proxy for this variable using industry level data on employment and number of establishments. We conjecture that industries with a larger average number of employees per establishment are dominated by large, less bankdependent firms. As such, they should experience a less pronounced contraction during banking crises than industries with a smaller average plant size. To avoid endogeneity issues, we measure plant size as the logarithm of the average over the sample period.15 In contrast to the Rajan-Zingales index, which is common to all countries, this measure of bank dependence is country specific, and can thus capture differences in technology and product mix across countries.

Table 5.6 presents the results of regressing value-added growth on countrytime, industry-time, and country-industry dummies and an interaction term between average industry plant size and the banking crisis dummy. The positive and significant coefficient for the interaction term indicates that industries with larger plant size tend to grow faster during banking crises, which we interpret as evidence of the bank lending channel. This result is robust to controlling for differential effects during currency crises, but loses significance when controlling for recessions (more on this below), during which large scale sectors do relatively better, consistent with the credit channel literature (Gertler and Gilchrist, 1994).

Table 5.6Differential Effects of Banking Crises on Value-Added Growth: Industries Differentiated Based on Establishment Size
BaselineCurrency CrisesRecessions
BenchmarkDC-LDC SplitBenchmarkDC-LDC SplitBenchmarkDC-LDC Split
Size*Crisis31.521.361.18
[2.09]**[1.83]*[1.54]
Size*Crisis3*DC1.041.03−0.07
[0.88][0.86][0.06]
Size*Crisis3*LDC1.671.451.53
[1.86]*[1.56][1.65]
Currency Crisis*Size0.99
[1.13]
Currency Crisis*Size*DC0.67
[0.72]
Currency Crisis*Size*LDC1.06
[0.95]
Recession*Size1.29
[1.87]*
Recession*Size*DC2.84
[2.99]***
Recession*Size*LDC0.65
[0.76]
Lagged Share−2.46−2.46−2.46−2.46−2.45−2.46
[7.41]***[7.42]***[7.39]***[7.40]***[7.35]***[7.42]***
Constant7.7245.456.6772.6145.01−13.81
[1.30][5.54]***[1.11][7.66]***[5.54]***[1.57]
Observations159851598515985159851598515985
R-squared0.350.350.350.350.350.35
t-statistics are in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Size is average employees per establishment in sector j in country i averaged over the sample period. DC is a dummy for developed countries. LDC is a dummy for developing countries. Recession is a dummy for recession years. Currency crisis is a dummy for currency crisis years. Lagged share is the share of the sector’s value added in total value added lagged by one period. Regressions are estimated with OLS. Standard errors are clustered by industry-country, and regressions also include time-country, time-industry, and industry-country dummy variables.
t-statistics are in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Size is average employees per establishment in sector j in country i averaged over the sample period. DC is a dummy for developed countries. LDC is a dummy for developing countries. Recession is a dummy for recession years. Currency crisis is a dummy for currency crisis years. Lagged share is the share of the sector’s value added in total value added lagged by one period. Regressions are estimated with OLS. Standard errors are clustered by industry-country, and regressions also include time-country, time-industry, and industry-country dummy variables.

When we introduce separate interaction terms for developed and developing countries, once again we find the differential effects to be larger and more statistically significant in developing countries. This may indicate that asymmetries in access to finance between large and small firms are stronger in developing countries, or that shocks leading to crises, on average, are more severe in developing countries, which magnifies the effect of asymmetries.

Notably, these estimates also confirm the prevalence of the effect of recessions (as identified by Braun and Larraín, 2005) in advanced economies and of the effect of banking crises in developing countries and emerging markets. When we control for recessions, the differential effect during banking crises is borderline significant in developing countries, but essentially zero for developed economies.

The recession coefficient is strongly significant for developed countries, but, as before, is not significant for developing countries. Again, a possible explanation is that in developed countries banks are not special because firms have alternative sources of finance. As a result, asymmetries between large and small firms are only driven by differential access to finance, which gets accentuated by weakened small borrower balance sheets and consequent borrower agency problems in recessions. In developing countries, by contrast, small firms may be restricted to borrowing only from banks so bank financial distress accentuates large-firm/small-firm growth differentials.

5.4.6. Differences Among Sectors Based on Export Orientation

As argued in Tornell and Westermann (2002 and 2003), firms in the traded sector may have better access to alternatives to domestic bank finance, especially foreign finance, and thus suffer less than firms in nontraded sectors during financial crises. If this conjecture is true, trade orientation can provide an identification strategy to test for the presence of a bank lending channel.

In the next set of regressions (Table 5.7), we interact the banking crisis dummy with the ratio of exports to value added for each industry and country (averaged over the sample period).16 The coefficient of the interaction term has the correct sign, but is far from being statistically significant. This remains the case when we control for currency crises, when export sectors can be expected to perform better on account of the real exchange rate depreciation. Interestingly, the interaction term of export orientation with currency crises is positive and significant, so our regressions do pick up this effect. During banking crises, however, we find no evidence that more export-oriented sectors perform better, casting doubt on a credit channel interpretation of asymmetries across industry based on export orientation. We should note that one reason we may not find strong support for the hypothesis is that our data are confined to the manufacturing sector, leaving out important segments of nontraded productive activities, such as construction and services.

Table 5.7Differential Effects of Banking Crises on Value-Added Growth: Industries Differentiated Based on Export Orientation
BaselineCurrency Crises
BenchmarkDC-LDC SplitBenchmarkDC-LDC Split
Crisis3*Export/VA0.780.71
[0.97][0.87]
Crisis3*Export/VA*DC0.980.92
[0.94][0.89]
Crisis3*Export/VA*LDC0.710.65
[0.69][0.63]
Currency Crisis*Export/VA2.11
[2.34]**
Currency Crisis*Export/VA*DC3.08
[2.90]***
Currency Crisis*Export/VA*LDC1.78
[1.57]
Share (t–1)−2.44−2.44−2.44−2.44
[6.56]***[6.56]***[6.60]***[6.60]***
Constant30.1113.16−21.3912.99
[2.39]**[1.23][2.98]***[1.21]
Observations14499144991449914499
R-squared0.350.350.350.35
t-statistics are in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Export/VA is the ratio of export to value added in industry j and country i averaged over the sample period. DC is a dummy for developed countries. LDC is a dummy for developing countries. Currency crisis is a dummy for currency crisis years. Lagged share is the share of the sector’s value added in total value added lagged by one period. Regressions are estimated with OLS. Standard errors are clustered by industrycountry, and regressions also include time-country, time-industry, and industry-country dummy variables.
t-statistics are in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Export/VA is the ratio of export to value added in industry j and country i averaged over the sample period. DC is a dummy for developed countries. LDC is a dummy for developing countries. Currency crisis is a dummy for currency crisis years. Lagged share is the share of the sector’s value added in total value added lagged by one period. Regressions are estimated with OLS. Standard errors are clustered by industrycountry, and regressions also include time-country, time-industry, and industry-country dummy variables.
Table 5.8Policy Interventions during Crises as Classified by Honohan and Klingebiel (2003)
BlanketLiquidityForbearanceForbearanceRepeatedRelief to
EpisodeGuaranteeSupportABRecapsDebtorsTotal
Ghana 19821111015
Turkey 19941001002
Malaysia 19971001103
Brazil 19940011013
Finland 19911101003
Korea 19971111105
Colombia 19821100002
United States 19800011002
Turkey 19820000000
Philippines 19810111014
Ecuador 19950011013
Mexico 19941101115
Argentina 19950000000
Malaysia 19850101002
Sweden 19901000001
Japan 19921101104
Norway 19871101003
Uruguay 19811101115
Sri Lanka 19891001103
Indonesia 19920001001
Chile 19810101013
Venezuela 19930101002
Total episodes121261867
Blanket guarantee is a dummy for extensive depositor protection. Forbearance A is a dummy for letting insolvent banks operate unrestricted. Liquidity support is a dummy for providing extensive liquidity to troubled banks. Forbearance B is a dummy for letting insolvent banks operate unrestricted or not enforce some regulations. Repeated recaps is a dummy for repeated government recapitalizations of banks. Debtor relief is a dummy for government programs to subsidize bank debtors.
Blanket guarantee is a dummy for extensive depositor protection. Forbearance A is a dummy for letting insolvent banks operate unrestricted. Liquidity support is a dummy for providing extensive liquidity to troubled banks. Forbearance B is a dummy for letting insolvent banks operate unrestricted or not enforce some regulations. Repeated recaps is a dummy for repeated government recapitalizations of banks. Debtor relief is a dummy for government programs to subsidize bank debtors.

5.4.7. Interventions and the Lending Channel

We now turn to estimating the effect of different forms of intervention on the lending channel. We obtain a list of policy interventions undertaken in each of 22 crises in our sample from Honohan and Klingebiel (2003) (Table 5.8). These authors classify interventions into six categories: blanket depositor protection (including both explicit blanket guarantees to depositors and cases in which depositors are implicitly protected because most of the banking sector is publicly owned); prolonged and extensive liquidity provision to banks; forbearance of type A (when insolvent/illiquid banks are allowed to continue operating without restriction for at least 12 months); forbearance of type B (either there is forbearance of type A or some regulations, such as loan classification and provisioning, are not enforced); repeated recapitalizations; and, finally, government-sponsored debt relief initiative for corporate or private borrowers. All these variables are captured by simple zero-one dummies.

To test whether the differential effect of banking crises depends on policy intervention, we interact the intervention dummies of Honohan and Klingebiel with the interaction term between crisis and external dependence (Table 5.9). First, we establish that financially dependent sectors grow less during crises also in this drastically restricted sample of 22 crises (Column 1). Next, we test whether differential effects were smaller in countries with a larger number of interventions (Column 2). This does not appear to be the case. When we examine the effects of each type of intervention in isolation, the policy with the largest positive coefficient is forbearance A. Other policies have much smaller or even negative coefficients. Although none of the coefficients is statistically significant at the usual confidence levels, we still think that this evidence is suggestive that allowing insolvent banks to continue operating during the initial phase of a crisis may help alleviate the real cost of the crisis. Obviously, more research is necessary to understand what are successful crisis mitigation strategies.

Table 5.9Differential Effects of Banking Crises and Intervention Policies
(1)(2)(3)(4)(5)(6)(7)(8)
Crisis3*Dep−3.45−4.30−5.02−1.23−5.19−1.67−3.42−3.33
[1.89]*[1.34][1.66]*[0.42][2.56]**[0.68][1.67]*[1.73]*
Crisis*Dep*Relief−0.43
to Debtors[0.09]
Crisis*Dep*−0.10
Repeated Recap[0.03]
Crisis*Dep*−2.14
Forbearance B[0.67]
Crisis*Dep*6.95
Forbearance A[1.45]
Crisis*Dep*−3.61
Liquidity Provision[1.02]
Crisis*Dep*2.73
Blanket Guarantee[0.79]
Crisis3*Dep*Number0.30
of Interventions[0.29]
Lagged share−2.55−2.55−2.55−2.54−2.55−2.55−2.55−2.55
[4.39]***[4.39]***[4.40]***[4.38]***[4.36]***[4.39]***[4.39]***[4.39]***
Constant−2.3611.475.7111.64−2.25−2.56−0.48−2.37
[0.41][1.83]*[0.60][1.86]*[0.39][0.49][0.08][0.41]
Observations90409040904090409040904090409040
R-squared0.390.390.390.390.390.390.390.39
t-statistics are in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Dep is a parameter measuring an industry’s dependence on external finance (Rajan and Zingales, 1998). Blanket guarantee is a dummy for extensive depositor protection. Forbearance A is a dummy for letting insolvent banks operate unrestricted. Liquidity support is a dummy for providing extensive liquidity to troubled banks. Forbearance B is a dummy for letting insolvent banks operate unrestricted or not enforce some regulations. Repeated Recap is a dummy for repeated government recapitalizations of banks. Debtor relief is a dummy for government programs to subsidize bank debtors. Regressions are estimated with OLS. Standard errors are clustered by industry-country, and regressions also include time-country, time-industry, and industry-country dummy variables.
t-statistics are in parentheses. ***, **, and * denote significance levels of 1 percent, 5 percent, and 10 percent, respectively. Crisis3 is a dummy variable for the year of banking crisis inception and two following years. Dep is a parameter measuring an industry’s dependence on external finance (Rajan and Zingales, 1998). Blanket guarantee is a dummy for extensive depositor protection. Forbearance A is a dummy for letting insolvent banks operate unrestricted. Liquidity support is a dummy for providing extensive liquidity to troubled banks. Forbearance B is a dummy for letting insolvent banks operate unrestricted or not enforce some regulations. Repeated Recap is a dummy for repeated government recapitalizations of banks. Debtor relief is a dummy for government programs to subsidize bank debtors. Regressions are estimated with OLS. Standard errors are clustered by industry-country, and regressions also include time-country, time-industry, and industry-country dummy variables.

5.5. Conclusion

We have studied the effects of banking crises on growth in industrial sectors and find that in sectors that are more dependent on external finance value added, capital formation, and the number of establishments grew relatively less than in sectors less dependent on external finance. We interpret this finding as evidence that there is a real cost to banking crises. Specifically, although adverse shocks cause both poor economic performance and bank distress, bank distress has an additional, adverse effect on growth, as banks must cut back their lending. As might be expected, the differential effect is stronger in developing countries (where alternatives to bank financing are more limited), in countries with less access to foreign finance, and where bank distress is more severe. In addition, we find that the effect we have measured is not just the reflection of balance sheet effects during recessions or currency crises, but appears to be special to periods in which banks experienced liquidity and solvency problems.

These results lend support to the view, often expressed by policymakers, that banks need more support than other commercial enterprises in times of financial distress. If bank credit cannot be easily replaced by other sources of finance, at least for some businesses, then profitable production activities may have to be cut back and viable investment projects abandoned, leading to a misallocation of resources. In addition, the bank lending channel can ratchet up the macroeconomic effects of an adverse shock, leading to a downward spiral in which a contraction in economic activity and bank distress reinforce each other.

How to design and implement appropriate policies to support banks during crises, however, remains difficult in practice. With our results it is possible to study how the differential effect of crises changes with different intervention policies. Unfortunately, data on interventions are hard to come by and quantify and, perhaps more importantly, unobservable shocks affect both the lending channel impact and the propensity and modalities of intervention. Future research to tackle these difficulties would undoubtedly be very valuable.

Data Appendix
Table 5.10External Dependence Index
Industrial SectorExternal DependenceIndustrial SectorExternal Dependence
Tobacco−0.45Rubber products0.23
Pottery−0.15Furniture0.24
Leather−0.14Metal products0.24
Footwear−0.08Industrial chemicals0.25
Nonferrous metal0.01Wood products0.28
Apparel0.03Petroleum and coal products0.33
Petroleum refineries0.04Transportation equipment0.36
Nonmetal products0.06Other industries0.47
Beverages0.08Glass0.53
Iron and steel0.09Machinery0.6
Food products0.14Other chemicals0.75
Paper and products0.17Electric machinery0.95
Textile0.19Professional goods0.96
Printing and publishing0.2Plastic products1.14
Sources: Rajan and Zingales (1998) and Krozner and others (2007).
Sources: Rajan and Zingales (1998) and Krozner and others (2007).
Table 5.11Summary Statistics
MeanMedianStandard Dev.Max.Min.No. of Obs.
VA growth (in percent)3.71.822.7107.4−54.116227
Growth in capital formation12.32.056.2240.7−80.59752
(in percent)
Employment growth0.90.18.729.0−20.415940
(in percent)
Growth in number of1.80.010.145.9−22.19684
establishments (in percent)
Access to foreign financing1.80.63.025.50.0482
(in percent of trade volume)
Output loss during crisis1.82.03.912.0−7.446
(in percent; by episode)
Rajan-Zingales index0.30.20.41.1−0.528
(by industry)
Average plant size125.365.6232.34197.71.51012
(by country/industry)
Export/value-added71.241.373.1297.80.0872
(by country/industry)
(in percent)
Table 5.12Correlations Between Measures of External Dependence
Rajan-ZingalesAverage Plant SizeExports/VA
Rajan-Zingales1
Average plant size−0.161.00
Exports/VA0.02−0.031
Table 5.13Banking Crises Inception Dates
CountriesBanking Crisis InceptionCountriesBanking Crisis Inception
Argentina1989Malaysia1997
Argentina1995Mexico1994
Bolivia1986Nepal1988
Bolivia1994Nigeria1991
Brazil1994Norway1987
Cameroon1995Panama1988
Central African Republic1988Papua New Guinea1989
Chile1981Peru1983
Colombia1982Philippines1981
Colombia1999Portugal1986
Costa Rica1994Senegal1983
Ecuador1995South Africa1985
Finland1991Sri Lanka1989
Ghana1982Swaziland1995
India1991Sweden1990
Indonesia1992Tanzania1988
Israel1983Tunisia1991
Italy1990Turkey1982
Japan1992Turkey1991
Jordan1989Turkey1994
Kenya1993Turkey2000
Korea1997United States1980
Madagascar1988Uruguay1981
Malaysia1985Venezuela1993
Total number of crises = 48.
Total number of crises = 48.
Table 5.14Average Growth of Real Value Added in Crisis and Noncrisis Years
Crisis DurationCrisisNo. of Obs.NoncrisisNo. of Obs.
One-year dummy0.1011304.0015097
Two-year dummy−0.9221674.4514060
Three-year dummy1.7030594.2013168
Four-year dummy3.3340123.8612215
Five-year dummy3.8448513.6911376
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Note:This chapter is a slightly revised version of an article that appeared in the Journal of Financial Intermediation, Vol. 17 (2008), pp. 89–112.

We wish to thank Eisuke Okada for outstanding research assistance and Philip Strahan, Frank Westermann, Elu Von Thadden, and participants at the 2004 Annual Research Conference at the IMF and to the joint IMF-ECB workshop on Global Financial Integration, Stability, and the Business Cycle in Frankfurt for useful comments and suggestions.

There are also measurement issues. Specifically, changes in the aggregate stock of real credit to the private sector are not a good measure of the flow of credit available to the economy, especially around banking crises. The stock may fall because a jump in inflation erodes the value of nominal contracts, or because restructuring operations transfer nonperforming loans to agencies outside the banking system. On the other hand, a devaluation increases the domestic currency value of foreigncurrency denominated debt (Demirgüç-Kunt and others, 2006).

The “difference-in-difference” methodology has also been used in a variety of related problems (see, for example, Cetorelli and Gambera, 2001) Beck, 2003); and Bonaccorsi di Patti and Dell’Ariccia, 2004).

For several countries in our sample banks are overwhelmingly the main (and often the sole) source of external capital for firms. On average, in our sample the stock of bank credit is about seven times larger than equity market capitalization.

An establishment is better thought of as a plant rather than a firm. In general, the majority of firms in any sector consist of single-plant firms, so there will be a strong correlation between establishment size and firm size.

The producer price index would be a more appropriate measure of prices in manufacturing, but it was not available for a number of countries in our sample. In any case, the price index does not affect differences in growth rates across sectors, which is what matters to our tests.

Table 5.10 in the Appendix reports the Rajan and Zingales index.

It should be emphasized that, if the Rajan-Zingales index does not capture meaningful differences across sectors in our sample, then our coefficient estimates should be insignificant and not biased toward overrejection.

Countries that did not experience banking crises during the 1980s or 1990s are excluded from the sample. Including these observations would only serve to estimate more accurately the time-industry dummies, but would sharply increase the already large number of parameters to be estimated.

We also change the sample by considering only observations for which data for all the 28 sectors are available. The sample size drops by almost one half. For the baseline specification the coefficient of the interacted term remains negative but is no longer significant. However, when we allow the effect of a crisis to vary between advanced economies and developing countries, the coefficient for the latter is significant. Similar results arise if the crisis window is changed from three to four years. These results are not reported.

On a related point, we find a very low correlation between sectoral cyclicality (measured as the correlation between the cyclical components of real GDP and sector-specific value added) and external dependence (about 0.1 on average). This addresses the potential concern that our interacted term picks up the effects of sectoral cyclicality rather than the effect of banking crises.

This result is also consistent with what is reported by Krozner and others (2007)) in their Table 10.

The latter condition serves to eliminate cases of chronically high inflation countries, in which large rates of depreciation are recorded on a regular basis. This definition corresponds to the second of the four definitions of crisis considered by Milesi-Ferretti and Razin (1998)).

An intriguing question is whether the presence of foreign banks can mitigate crisis costs. Unfortunately, measures of foreign bank presence for a cross-section of countries are available only beginning in the mid- 1990s. In a study of the Malaysian crisis of 1997–98, Detragiache and Gupta (2006)) find that foreign banks from outside the region performed better than domestic banks or foreign banks with a regional focus.

The results are robust to using plant size at the beginning of the sample to identify bank dependence.

Export data by sector are from the World Bank’s World Integrated Trade Solution (WITS) database.

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