Banking Crises and Crisis Dating: Theory and Evidence
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

Author’s E-Mail Address: gdenicolo@imf.org

Many empirical studies of banking crises have employed "banking crisis" (BC) indicators constructedusing primarily information on government actions undertaken in response to bank distress. Weformulate a simple theoretical model of a banking industry which we use to identify and constructtheory-based measures of systemic bank shocks (SBS). Using both country-level and firm-level samples, we show that SBS indicators consistently predict BC indicators based on four major BCseries that have appeared in the literature. Therefore, BC indicatorsactually measure lagged government responses to systemic bank shocks, rather than the occurrence of crises per se. We re-examine the separate impact of macroeconomic factors, bank market structure, deposit insurance, andexternal shocks on the probability of a systemic bank shocks and on the probability of governmentresponses to bank distress. The impact of these variables on the likelihood of a government responseto bank distress is totally different from that on the likelihood of a systemic bank shock.Disentangling the effects of systemic bank shocks and government responses turns out to be crucial inunderstanding the roots of bank fragility. Many findings of a large empirical literature need to be re-assessed and/or re-interpreted.

Abstract

Many empirical studies of banking crises have employed "banking crisis" (BC) indicators constructedusing primarily information on government actions undertaken in response to bank distress. Weformulate a simple theoretical model of a banking industry which we use to identify and constructtheory-based measures of systemic bank shocks (SBS). Using both country-level and firm-level samples, we show that SBS indicators consistently predict BC indicators based on four major BCseries that have appeared in the literature. Therefore, BC indicatorsactually measure lagged government responses to systemic bank shocks, rather than the occurrence of crises per se. We re-examine the separate impact of macroeconomic factors, bank market structure, deposit insurance, andexternal shocks on the probability of a systemic bank shocks and on the probability of governmentresponses to bank distress. The impact of these variables on the likelihood of a government responseto bank distress is totally different from that on the likelihood of a systemic bank shock.Disentangling the effects of systemic bank shocks and government responses turns out to be crucial inunderstanding the roots of bank fragility. Many findings of a large empirical literature need to be re-assessed and/or re-interpreted.

I. Introduction and Summary

The collapse of the subprime mortgage market in the U.S. in 2007 and ensuing financial instability have spurred renewed interest in banking crises. Some have stressed their similarities across countries and historical episodes (e.g. Reinhart and Rogoff, 2008a), while others have emphasized differences, both historical (e.g. Bordo, 2008) and as related to the specific mechanics of the shock triggering a crisis (e.g. Gorton, 2008). As pointed out by Allen and Gale (2007), however, the empirical literature on bank fragility has mainly focused on documenting empirical regularities. The definition and measurement of the object of study—what a banking crisis is, when it occurs, and how long it lasts—has been at best loosely derived from theory. As a result, this literature offers many—often contrasting—findings, which vary considerably in terms of samples used, banking crisis definitions and relevant dating.

A large portion of this literature has employed “banking crisis” (BC) indicators based on dating schemes that identify: crisis beginning dates, ending dates, and indicate whether the crisis was “systemic” or not. As we document, these schemes are based primarily on information about government actions undertaken in response to banking distress. A detailed review of the criteria used to identify banking crises shows that virtually all of them depend on information obtained from bank regulators and/or central banks. They do not rely on any theory to identify accounting or market measures that capture the realization of systemic bank shocks. In virtually all cases, what is measured is, effectively, a government response to a perceived crisis—not the onset or duration of an adverse shock to the banking industry.

One key implication is that these BC indicators are likely to date banking crisis onsets too late, at least on average. Government responses to banking distress may be lagging because of uncertainty about the actual extent of problems in the industry. In addition, political economy considerations dictate the speed and resolve of the government response.

More importantly, the problem is not limited to one of just systematically late dating. Equating the dating of a government response to banking distress to the dating of a systemic bank shock is like studying the evolution of a disease by dating the disease’s onset when the patient enters a hospital. As stressed by De Nicolò et al. (2004), the researcher will be unable to disentangle the effects of an adverse shock to the banking industry from the effects of the restorative policy response.1 Disentangling these effects is key to understanding the mechanics of bank fragility: this will be the main contribution of our paper.

Using a simple model of a banking industry, in which an adverse shock to the banking system and a government response are explicitly defined and modeled, we derive measures of systemic bank shocks (SBS). The main objective of the theoretical exercise is to obtain measures of an adverse shock to banking that are “empirically relevant”, by which we mean measures that can be obtained from available data for a large number of countries and years. The model is just a simple identification tool of theory-based measures of systemic bank shocks (SBS), and is not intended to be a contribution to the banking theory literature.

Our next task is to re-examine the empirical evidence presented in a large empirical literature on the causes and consequences of modern banking crises. We accomplish this using two samples: a country-level dataset and a firm-level dataset, both including a large number of countries and the latter including a large number of banks.

Our contribution is to separately identify binary indicators of SBS shocks and BC indicators. For the BC indicators, we employ four different data series that have appeared in the literature. It is important to note that the existing literature has interpreted an SBS event and a BC event as one and the same. There are two fundamental problems with that approach. First, the two events actually occur on different dates; and second, one event is bad for the industry (an SBS shock), while the other is good for it (government intervention to a perceived problem).

The causal variables that we study are some of those that the existing literature has identified as important determinants of the probability that a country will experience a banking crisis. These include the bank market structure, presence or lack of deposit insurance, and the occurrence of an external shock, (e.g. a currency crisis)2. We find that each of these explanatory variables has a different effect on the probability of an adverse shock to the banking industry (represented by SBS indicators) and on the probability of a government intervention (represented by BC indicators). As we hope to make clear, this has led to a great deal of confusion in the interpretation of many empirical results and, we shall argue, to a number of erroneous conclusions.

The rest of the paper proceeds as follows. Section II discusses the criteria used in the literature to date beginnings, severity, and endings of banking crises. We consider four well known crisis dating studies, and it becomes abundantly clear that the dating information is obtained from bank regulators and/or central banks and depends on the implementation of policy. Thus, the key contribution of this section is to show that these classifications record measures of government intervention, not necessarily the realization of adverse shocks to the banking industry.

In section III, we construct BC indicators based on the four major crisis classifications that are employed later in our own empirical work. We show that there are significant discrepancies among the four BC indicators in their dating the beginnings and endings of banking crises, indicating that there is disagreement among researchers in dating the same episodes of financial distress.

Section IV presents a theoretical model in which banking problems are produced by the arrival of exogenous shocks to the industry3. If a shock is large enough to translate into widespread bank insolvencies, the authorities will respond as soon as they recognize the shock. As noted, the main purpose of this exercise is to identify empirically useful measures of SBS arrivals.

In Section V we begin our empirical analysis employing a large country-level panel dataset similar to those employed by others in this literature. We estimate Logit regressions in which the dependent variable is a BC indicator, and the independent variables are contemporaneous macroeconomic variables identified in the literature as possible determinants of bank fragility. In essence, these are the standard tests searching for the “causes” of banking crises. First, we show that the results obtained are quite different across the BC indicators, either when only beginning crisis dates or all dates are used. Thus, these indicators are not all measuring the same thing and we argue for using BC indicators inclusive of all crises dates. Second, we construct two types of SBS indicators dictated by data availability for the country sample: they index extreme drops of bank real lending and deposits. We show that these indicators consistently and robustly predict all four BC indicators. This provides support for the notion that BC indicators represent lagged government responses to adverse banking shocks. Third, we estimate similar Logit regressions in which the depend variable is an SBS indicator. The results here are much stronger than those obtained with BC indicators, in the sense that many more explanatory variables have the expected sign and are statistically significant.

In Section VI we use the country dataset to assess the impact of bank concentration and deposit insurance on the probability of a systemic bank shock and, separately, on the probability of a government response to bank distress. These regressions are estimated controlling for contemporaneous values of a key set of macroeconomic variables. We obtain two key results. First, more concentrated banking systems significantly increase the probability of a systemic bank shock. However, these variables do not significantly affect the probability of a government response in this sample. In essence, more concentrated banking systems (exhibiting higher interest rate margins) are more likely to experience episodes of systemic bank fragility. As will be discussed, this finding is at odds with what has been reported elsewhere in the literature.

Second, the data suggest that the probability of a government response to bank distress identified by the BC indicators will be higher in banking systems with formal deposit insurance. This finding has been obtained previously in the literature and has been interpreted as evidence that deposit insurance results in greater moral hazard—and thus inherently riskier banking systems. In reality however, all that is occurring is that, in the presence of formal deposit insurance the government is more likely to respond to a negative shock of a given size. This is because, as we find, that the probability of a systemic bank shock does not depend on whether a deposit insurance system is in place.

In section VII we examine the impact of external shocks and currency crises on bank fragility. We continue to use the country-level dataset, but we specify Logit regressions with all independent variables lagged one period to minimize simultaneity and endogeneity problems. First, we find that exchange rate depreciations, worsening of terms of trade, and currency and twin crises have a positive and significant impact on the probability of a systemic bank shock and also find evidence of the reverse. By contrast, few of these “external” factors significantly affect the probability of a government response to bank distress. Currency crisis indicators only weakly predict such responses. Second, in this country-level dataset, both the probability of a systemic shock and that of a government response to bank distress are unaffected either by the degree of financial openness, or by the degree of flexibility of exchange rate arrangements.

We conclude our empirical analysis with Section VIII, where we use the firm-level dataset, one that employs individual bank data in a large number of emerging and developing countries. Importantly, with this dataset we can use SBS indicators which better capture the realization of systemic bank shocks. These are constructed on the basis of sharp declines in bank profitability, taking into account banks’ capitalization. As before, we examine whether SBS indicators predict BC indicators, and the main potential determinants of both systemic bank shocks and government responses to these shocks described previously. Tests on this sample are more powerful, as we use random effect Logit regressions that exploit more fully the information contained in banks’ heterogeneity. Remarkably, with this finer data set and richer statistical specification all earlier main results are confirmed.

Finally, Section IX concludes.

II. Major Classifications of Banking Crises

A variety of classifications of banking crises have been used since the mid 1990s by many researchers.4 Here we consider four systematic and generally comprehensive classifications. These classifications are well known in the literature, and some of them have been used in a large number of studies to analyze the determinants of banking crises.

These four classifications are all updates, modifications and/or expansions of the classification of banking crises first compiled by Caprio and Kinglebiel (CK) (1996, 1999). The CK classification is based on several narratives taken from supervisory and expert sources.5 Specifically, the CK classification “...relies upon the assessment of a variety of finance professionals in pulling together characterizations of factors that have caused crises” (1996, p. 1). It uses published sources or interviews with experts familiar with individual episodes. The dates attached to the crises in this classification “...are those generally accepted by finance experts familiar with the countries, but their accuracy is difficult to determine in the absence of the means to mark portfolios to market values” (1996, p. 2). CK noted that it is not easy to date episodes of bank insolvency, especially if an episode does not involve a run on banks and/or on a country’s currency. They further admit that an episode of banking distress can be detected a period of time after it has started. Similarly, “...it is not always clear when a crisis is over, and in the case of countries in which there are multiple episodes, it may well be that later events are merely a continuation of those occurring earlier”(1996, p. 2). The crisis is defined as systemic, if “...much or all of bank capital has been exhausted”(1996, p.2). Yet, a quantitative limit on the exhaustion of bank capital and its extent across a banking system is not spelled out. In sum, this classification relies mostly on supervisory sources and listings of government measures undertaken in response to a crisis. We turn now to the four classifications we use in the empirical analysis.

The first classification we examine is due to Demirgüç-Kunt and Detragiache (2002, 2005, hereafter DD). Based on the CK compilation, DD spelled out the criteria used to identify crises start-dates and duration for 94 countries in more details, covering crisis episodes during 1980-2002.6 DD define a systemic crisis as a “...situation in which significant segments of the banking sector become insolvent or illiquid, and cannot continue to operate without special assistance from the monetary or supervisory authorities”(2002, p. 1381). More precisely, episodes of banking distress were classified as systemic when at least one of the following occurred: (i) large scale nationalizations, (ii) emergency measures―such as bank holidays, deposit freezes, blanket guarantees to depositors or other bank creditors―were taken to assist the banking system, (iii) the cost of the rescue operations was at least 2 percent of GDP, or (iv) non-performing assets reached at least 10 percent of total assets at the peak of the crisis. However, the dates of the start and the end of a crisis are “...identified ....using primarily information from Lindgren et al. (1996) and Caprio and Klingebiel (1996).” (2002, p.1381).

The first three criteria in the DD classification characterize a banking crisis by dates of government responses to a systemic bank shock, rather than the systemic shock that has triggered a crisis. The criterion of a 10-percent non-performing asset ratio is the only one related to an accounting measure. However, it is recorded at the so-called peak of the crisis, but the peak of a crisis is not defined.7 Yet, it is well known that the recognition of non-performing assets occurs typically with a relatively long lag relative to the occurrence of a systemic bank shock (see, for example, the discussion in Bordo et al., 2001).

The second classification we examine is that compiled by Caprio et al. (2005) (CEA henceforth). CEA updated and extended the earlier CK classification covering 126 countries and bank insolvency episodes from the late 1970s to 2005. The authors emphasize that “...some judgment has gone into the compilation of the list, in particular in timing the episode of bank insolvency” (p. 307). CEA do not provide a definition of the start and end dates of a banking crisis episode and do not state whether the crisis was systemic or not. They just refer to the corresponding definitions in CK.

In their tables, CEA report an extensive narrative supporting their crisis dating in each country. A simple counting exercise reveals that in 94 percent of the classified cases the information used is one of government responses to address a crisis (in a few cases undated statistics on non-performing loans are reported), while in the remaining portion there is no explanation of the nature of a crisis indicator. In five out of 166 episodes, the beginning of a crisis is defined as a bank run, but neither quantification nor a precise dating is reported. Thus, the CEA classification, as the DD classification, identifies banking crises starting dates and duration essentially on the basis of an interpretation of reported government responses to banking distress.

The third classification of banking crises that we consider is the one recently compiled by Reinhart and Rogoff (2008b) (RR henceforth). The classification criteria used are essentially those used in Kaminsky and Reinhart (1999), whose classification was, in turn, based on CK’s classification. Kaminsky and Reinhart (1999) originally identified beginning and peak dates of crises for 20 countries for the period from 1970 to mid-1995 at a monthly frequency. In their classification, a banking crisis starts if either of the following occurs: “...(i) bank runs that lead to the closure, merging, or takeover by the public sector of one or more financial institutions, or (ii) if there are no runs, the closure, merging, takeover, or large-scale government assistance of an important financial institution (or group of institutions) that marks the start of a string of similar outcomes for other financial institutions” (p. 476). They clearly recognized the potential drawbacks of equating the date of the realization of a systemic shock leading to a crisis to the dating of a government response. They offered one possible fix to some of these drawbacks by introducing the notion of a crisis “peak,” defined as the date when the heaviest government intervention and/or bank closures occurred, based on CK and press chronicles (see sources in Table 2, p.478).

The updated RR classification is essentially based on the same criteria, using information from Caprio et. al (2005) and a variety of other sources of qualitative and narrative information (see Appendix, pp 79-81). Differing from the earlier Kaminsky and Reinhart work, however, RR do not identify the duration of a crisis on the grounds that it is difficult of even impossible to pinpoint its conclusion precisely (Table A2). In sum, all considerations already made with regard to CEA’s classification also apply to RR classification. It is based on qualitative information on government responses to banking distress.

Finally, the fourth classification that we consider is that recently constructed by Laeven and Valencia (2008) (LV henceforth), which extends previous classifications both in time and country coverage. LV modify the classification criteria of the earlier crisis database of Caprio et al. (2005) as follows. First, non-systemic crises are excluded on the basis of an identification of distress events that “were not systemic in nature” (Laeven and Valencia, 2008, p.5). Second, subject to data availability, crises years are identified with either a) deposit runs, defined as a monthly percentage decline in deposits in excess of 5 percent, or with b) the introduction of deposit freezes or blanket guarantees, or with c) liquidity support or bank interventions, defined as the ratio of monetary authorities’ claims on banks as a fraction of total deposits of “at least 5% and at least double the ratio compared to the previous year” (Laeven and Valencia, 2008, footnote 6, p.5). Using these more explicit quantitative measures, LV report that they are “able to confirm” only about two thirds of the crisis dating of the CEA classification. Yet, as already pointed out, their criteria b) and c) measure government responses to a systemic bank shock, while a) may be an imprecise and lagged gauge of such a realization. As in RR, but differing from DD and CEA, however, there is no estimate of the duration of a crisis.

A full description of the four classifications of banking crises is presented in the Appendix.

In sum, all four classifications are primarily constructed on the basis of information about government actions undertaken in response to banking distress obtained from bank regulators and/or central banks.

III. BC Indicators and Their Discrepancies

Here, we construct four series of BC indicators that will be used in our own empirical work. As we shall see next, these series are rather different since discrepancies in the dating of crisis onset and duration are pervasive.

The four binary BC indicators, where each indicator is set to 1 if a country-year is classified as a crisis year and 0 otherwise, are: DD, based on Demirgüç-Kunt and Detragiache(2005); CEA, based on Caprio et al. (2005); RR, based on Reinhart and Rogoff (2008b); and LV, based on Laeven and Valencia (2008).

We consider two versions of each indicator. The first excludes all country-years classified as crisis after the first crisis year. In practice, this kind of indicator identifies crises’ starting dates. These starting dates have been used extensively in event-type analyses since IMF (1998) and Kaminsky and Reinhart (1999). The second version includes all crisis country-years, beginning with and beyond the starting date. Since the RR and LV classifications do not report crisis durations, for these classifications we have used the duration and country years of the CEA classification, or the DD duration when the CEA duration was not available. In this way, we preserve the starting dates of the original classifications, but we augment them with the applicable duration of either the CEA or DD classifications.

Table 1 reports statistics of these classifications (Panel A), and pair-wise comparisons of crisis dating across classifications (Panel B). The most striking fact is that for many crisis episodes the dating classifications differ considerably both in terms of the starting date and the duration. For example, 15 country years are classified as first crisis years by RR but not by DD, while the reverse is true for 30 country years (Panel B, second line). Alternatively, it can be seen in the last column in Panel B, which shows the ratio of total crisis ranking discrepancies divided by total crisis rankings. This varies between 24.5% and 49.5%. In other words, in terms of dating crises (which is the heart of the matter), the different methods are in disagreement roughly between a quarter and a half of the time. All four classifications only agree on 41 dates of crisis onset.8

Table 1.

BC Indicators

DD: Demirgüç-Kunt and Detragiache (2005); CEA: Caprio et al. (2005); RR: Reinhart and Rogoff (2008b); LV: Laeven and Valencia (2008)

article image
Table 2.

Logit Regressions with Start Date BC indicators (crisis dates after the first crisis year excluded)

Dependent variables are the BC indicators with crisis dates after the first crisis year excluded: DDs, CEAs, RRs and LVEs. Full sample regressions (1)-(4) include all available observations. Common Sample regressions (5)-(8) include only observations common to all crisis classifications. Explanatory variables: rgdpgr is the GDP growth rate; rint is the real interest rate; infl is the percentage change in the GDP deflator; totch is the change in the terms of trade; depr is the exchange rate depreciation vs. the US$; m2res is the ratio of M2 to foreign exchange reserves; rgdpcp is real GDP per-capita; privcrd_gdp is bank credit to the private sector to GDP; L2.domcredgr is real domestic bank credit growth to the private sector lagged twice.

article image
Standard errors are clustered by country. Robust p-values are reported in brackets, with *** p<0.01, ** p<0.05, * p<0.1.

These widespread discrepancies across banking crisis classifications cast serious doubt about either the robustness or the comparability of many results obtained in a large empirical literature. Indeed, when we turn to our empirical analysis with the four BC indicators, it is not surprising that they often produce significantly different results.

IV. A Simple Banking Model

In this section, we present a simple model of a banking industry and a government deposit insurer, and use its comparative statics to identify measures of systemic bank shocks. The purpose of this theory exercise is very narrow, and we make no pretense of contributing to the theory of the banking firm. Rather, our objective is to identify from first principles, empirically useful measures of adverse shocks to a banking industry.

The banks in the model are Cournot-Nash competitors that raise insured deposits, make risky loans, and hold risk free government bonds. The deposit insurer bails out the banks when they fail. Thus, the economy is composed of a “government” and three classes of agents: entrepreneurs, depositors, and banks. All agents are risk-neutral, and the time is discrete.

Entrepreneurs

There is a continuum of entrepreneurs indexed by their reservation income levels a ∈ [0,1], which is distributed uniformly on the unit interval. Entrepreneurs have no initial resources but have access to identical risky projects that require a fixed amount of date t investment, standardized to 1, and yield a random output at date t + 1. Specifically, at date t the investment in a project yields Y with probability Pt +1 ∈ (0,1), and 0 otherwise. The probability of success Pt +1 is a random variable independent across entrepreneurs. Its realization is observed by them at date t + 1. Hence, entrepreneurs make their date t decisions on the basis of their conditional expectations of Pt +1, denoted by EtPt +1.

Entrepreneurs are financed by banks with simple debt contracts. The contract pays the bank a loan interest rate RL if the project is successful. Thus, an entrepreneur with reservation income level a will undertake the project if

EtPt+1(YRL)a.(1)

Let a* denote the value of a that satisfies (1) at equality. The total demand for loans is then given by XtF(a*)=0a*f(a)da where f(.) is the density of the uniform distribution function. This defines implicitly the inverse loan demand function:

RL(Xt,EtPt+1)=Y(EtPt+1)1Xt(2)

Bonds

One-period bonds are supplied by the government in amounts specified below. For simplicity, we assume that only banks can invest in bonds.9 A bond purchased at date t yields a gross interest rate rt at date t + 1.

Depositors

Depositors invest all their funds in a bank at date t to receive interest plus principal at date t + 1. Deposits are fully insured, so that the total supply of deposits does not depend on risk, and is represented by the upward sloping inverse supply curve RD (Zt) = αtZt, where Zt denotes total deposits. The slope of this function is a random variable, to be described below, whose realization is observed at date t.

Banks

Banks collect insured deposits, and pay a flat rate insurance premium standardized to zero. On the asset side, banks choose the total amount of lending and the amount of bonds. In both loan and deposit markets banks are symmetric Cournot-Nash competitors. Banks are perfectly diversified in the sense that for any positive measure of entrepreneurs financed, Pt +1 ∈ (0,1), is also the fraction of borrowers whose project turns out to be successful at date t + 1. Banks observe the realization of Pt +1 at date t + 1. Hence, as for the entrepreneurs, banks make their date t decisions on the basis of their conditional expectations EtPt +1.

Government

The government supplies a fixed amount of bonds to the market, denoted by B¯. The government also guarantees deposits. It will intervene whenever bank deposits payments cannot be honored in part or in full. When this occurs, the government will pay depositors all the claims unsatisfied by banks and all banks will be bailed out. These payments will be financed by issuing additional bonds, which will be purchased by banks who collect new deposits at date t + 1.10

The realization of a systemic banking shock occurs at date t +1 and, by definition, occurs when the banking system’s profits are negative. The government’s response to such a shock will be triggered when the government is able to ascertain that the banking system has become insolvent. By further assumption, the government observes date t + 1 bank profits at t + 2.

Sequence of events

In period t, suppose realized bank profits are non-negative. Banks collect deposits, entrepreneurs demand, and banks supply funds based on EtPt+1. Deposits, bank loans, and investment in bonds are determined for period t. In period t + 1, Pt +1 is realized and observed by entrepreneurs and banks. Borrowers pay loans and in turn, banks pay depositors, if possible. If bank profits are non-negative, depositors are paid in full. If profits are negative, depositors cannot be paid in full, and by definition, this is a systemic bank shock. Depositors are paid pro-rata by the banks. The government responds to the crisis at t + 2 by issuing bonds and paying depositors any claim unsatisfied by banks.

Equilibrium

We describe the equilibrium at date t by dropping time subscripts from all variables, and define pEtPt +1.

The bank problem

Let Di denote total deposits of bank i, Zi=1NDi denote total deposits, and Di ≡ ∑jiDj denote the sum of deposits chosen by all banks except bank i. Let Li ≡ ∑jiLj denote the sum of loans chosen by all banks except bank i. Each bank chooses deposits, loans, and bond holdings b so as to maximize expected profits, given the choices of other banks. Thus, a bank chooses (L,b,D)R+3 to maximize:

pRL(Li+L,p)L+rbRD(Di+D)D(3)

subject to

L+b=D.(4)

The government’s policy function

Let Πt(.) denote current realized aggregate profits. A government intervention is described by the indicator function: IG tt-1) = 1 if Πt-1 < 0, and 0 otherwise. The government supplies bonds in the amount BtS=B¯+Bt(Πt1) where Bt(Πt1)=ItG(Πt1)Πt1.

Given p, an equilibrium is a total amount of loans X, total bonds B, total deposits Z, bond interest rates, loan rates, deposit rates, and government responses such that: a) the banking industry is in a symmetric Nash equilibrium; b) the bond market is in equilibrium; and c) the government meets its commitment to deposit insurance.

Comparative Statics

We illustrate the comparative statics of the model using a simple linear specification: the loan supply is given by RL (X, p) = Y – p1X, and the demand for deposits is given by RD(Z) = αZ. The solutions for all endogenous variables are:

X=NN+1pY1+αα1+αBS; Z=NN+1pY1+α+11+αBS; B=BS;r=α1+α(N+1NBS+pY); RL=Y1+α(N+1)(N+1)(1+α)+p1α1+αBS;RD=α1+α(NN+1pY+BS)RLRD=YN+1(1+α(N(1p)+1)(1+α))+(p11)α1+αBS

The following table summarizes changes in the endogenous variables in response to an adverse shock.

article image

We can see from this table that a systemic bank shock can be triggered by any of the following shocks to the technology (p and Y) or to either preferences or wealth (α): a decline in firms’ probability of a good outcome, represented by a decline in p; a decline in firms’ demand for loans due to a decline in Y; or a decline in consumers’ demand for deposits, prompted by a decline in α.

Such adverse shocks are for the most part unobservable, but their occurrence results in predictable changes in certain variables that are observable. In particular, independently of the source of the shock, aggregate loans and deposits will decline, loan rates will increase, the difference between loan and deposit rates—the interest rate margin—will increase, and profits will decline. By contrast, the deposit rate and the bond rate will move in a different direction depending on the source of the shock.

Thus, the model allows us to identify a systemic bank shock with a severe decline in loans, deposits, bank profits, and significant increases in interest rate margins. Empirically, the adequacy of each of these measures in capturing systemic bank shocks will depend, inter alia, on the timing of the underlying shock. And of course, the use of any of these measures will also depend on data availability. Thus, in our empirical investigation we will use these properties of the model to create empirical measures of systematic banking shocks that can be constructed with the two different samples we use.

V. Evidence from Cross-Country Data: Benchmark Specifications

We begin our empirical investigation using a country-level dataset that merges and updates the large annual cross-country panel dataset used extensively in DD (2005) and Beck et al. (2006), with data for up to 91 countries for the 1980-2002 period.

We proceed in three steps. First, we estimate benchmark Logit regressions in which the dependent variable is one of the four BC indicators discussed above. The point of this exercise is to show how sensitive results are to each of the four indicators, when we use a set of explanatory variables that has been commonly employed in the literature.11

Second, we construct our theory-based indicators of systemic bank shocks (SBS indicators) for this sample and include lagged SBS indicators as an additional explanatory variable in the same regressions. This gives an assessment of the extent to which SBS indicators predict BC indicators. These tests are critical to our argument that BC indicators are measures of (lagged) government interventions in response to bank distress.

Third and finally, we estimate the same Logit regressions but now substitute the SBS indicators as the dependent variables. The goal here is to compare the overall explanatory power of the regressions with SBS and BC indicators, and assess their similarities and differences.

A. Logit Regressions with BC Indicators as Dependent Variables

In the benchmark Logit regressions with BC indicators as dependent variables, we use the following set of explanatory variables employed by Demirgüç-Kunt and Detragiache (2005) and Beck et al (2006): measures of the macroeconomic environment (real GDP growth, the real interest rate, inflation, changes in the terms of trade, and exchange rate depreciation); a measure of potential vulnerability of a country to a run on its currency (the ratio of M2 to international reserves); a measure of the economic size of a country (real GDP per capita); a measure of financial system development (bank credit to private sector GDP); and real bank credit growth lagged twice, which in this literature has been employed as a proxy measure for credit booms. In these and all other regressions presented later, standard errors are clustered by country, unless specified otherwise.

In Table 2, we first report results using the version of the four BC indicators that excludes all crisis years except the first. This is done for comparative purposes, since this exclusion has been made in many studies on the ground that “the behavior of some of the explanatory variables is likely to be affected by the crisis itself, and this could cause problems for the estimation” (Demirgüç-Kunt and Detragiache, 2002, p.1381). Also, we employ two different samples. The first sample (columns 1 – 4) employs all available data in each regression. The second sample (columns 5 – 8) employs only data points that are common to all four BC indicators. A comparison of the results obtained with these two samples can be useful to identify differences in results due to either country or crisis coverage.

It is apparent from Table 2 that real GDP growth and real interest rates are the only variables that enter significantly (negatively and positively respectively) in all eight regressions. For all other explanatory variables, there is at least one specification that yields results different from all the others. These differences in results occur not only between specifications within the same sample, but also comparing results of the same regressions between samples.12

We should stress that the use of BC indicators constructed by excluding crisis years after the first one seems unwarranted to us. As we have shown in section II, the BC classifications actually index a variety of government measures to address banking distress. Therefore, deleting observations of years during which a government implements measures in response to continued banking distress significantly reduces the informational content of these classifications. Moreover, excluding these observations requires taking a stand on the duration of a crisis. As documented in Table 1 of section III, excluded observations account for a sizeable portion of the sample, ranging from 10 to 15 percent of available country years, inducing sample biases difficult to control.13 For these reasons, in the sequel we focus on BC indicators including all crisis years observations.

Accordingly, in Table 3 we report regressions of exactly the same type as those in Table 2, but with BC indicators including all crisis dates. Now, real GDP growth appears to be the only variable that enters significantly in all (or even most) regressions. Prima facie, these results suggest that the lack of explanatory power of many standard macroeconomic variables in these regressions may be due to the considerable differences, documented earlier, in the BC classification schemes.

Table 3.

Logit Regressions with BC indicators (all observations with crisis dating)

Dependent variables are the BC indicators with all crisis dates: DD, CEA, RR and LVE. Full sample regressions (1)-(4) include all available observations. Common Sample regressions (5)-(8) include only observations common to all crisis classifications. Explanatory variables: rgdpgr is the GDP growth rate; rint is the real interest rate; infl is the percentage change in the GDP deflator; totch is the change in the terms of trade; depr is the exchange rate depreciation vs. the US$; m2res is the ratio of M2 to foreign exchange reserves; rgdpcp is real GDP per-capita; privcrd_gdp is bank credit to the private sector to GDP; L2.domcredgr is real domestic bank credit growth to the private sector lagged twice.

article image
Standard errors are clustered by country. Robust p-values are reported in brackets, with *** p<0.01, ** p<0.05, * p<0.1.

B. SBS indicators Predict BC indicators

For this sample, our choice of SBS indicators is dictated by data availability. Aggregate bank profits are unavailable in our dataset, while interest rates spreads are available only for a very limited number of country-years, and may not be measured in the same way across countries. That leaves changes in loan and deposit levels, which are available for almost all nations.

We construct two types of SBS indicators, one based on aggregate bank loans and the other based on aggregate bank deposits. For loans, we construct two indicator variables, SBSL25 and SBSL10, which represent sharp decreases in lending growth. They are equal to one if real domestic lending growth is lower than the 25% and 10%-percentile of the entire distribution of real domestic bank credit growth across countries. The second indicators represent sharp decreases in total bank deposits as a fraction of GDP. Analogously, we construct two indicator variables, SBSD25 and SBSD10, equal to one if the growth rate of the deposit-to-GDP ratio is lower than the 25% and 10% percentile of its distribution across countries respectively.14

Table 4 replicates the results in Table 3, the only change being that there are two additional explanatory variables, the SBS lending indicators. Now, if BC indicators are contemporaneous to systemic bank shock realizations, then SBS indicators should not predict BC indicators. As shown in Table 4, however, this is not the case. Lagged SBS lending indicators predict the BC indicators in all specifications. This is true both with the 25th percentile cut-off (columns 1 – 4), and the 10th percentile cut-off (columns 5 – 8).

Table 4.

Logit Regressions: Do SBS Lending Indicators Predict BC Indicators?

Dependent variables are the BC indicators with all crisis dates: DD, CEA, RR and LVE. All regressions are full sample regressions including all available observations for each classification. Explanatory variables: rgdpgr is the GDP growth rate; rint is the real interest rate; infl is the percentage change in the GDP deflator; totch is the change in the terms of trade; depr is the exchange rate depreciation vs. the US$; m2res is the ratio of M2 to foreign exchange reserves; rgdpcp is real GDP per-capita; privcrd_gdp is bank credit to the private sector to GDP; L2.domcredgr is real domestic bank credit growth to the private sector lagged twice. L.SBSL25 and L.SBSL10 are lagged SBS lending indicators.

article image
Standard errors are clustered by country. Robust p-values are reported in brackets, with *** p<0.01, ** p<0.05, * p<0.1.

Table 5 shows the same regressions as in Table 4, except that we include the SBS deposit indicators instead of the loan indicators. As shown in Table 5, SBS lagged deposit indicators are always positively associated with BC indicators, However, the relevant coefficients are (weakly) significant in only two of eight specifications. This is not surprising, as depositors may either react to a systemic bank shock with a lag due to information asymmetries, or not react at all if implicit or explicit guarantees on deposits are in place. Indeed, as illustrated below, SBS lending indicators predict SBS deposit indicators, suggesting complex dynamics not included the our simple model.

Table 5.

Logit Regressions: Do SBS Deposit indicators Predict BC Indicators?

Dependent variables are the BC indicators with all crisis dates: DD, CEA, RR and LVE. All regressions are full sample regressions including all available observations for each classification. Explanatory variables: rgdpgr is the GDP growth rate; rint is the real interest rate; infl is the percentage change in the GDP deflator; totch is the change in the terms of trade; depr is the exchange rate depreciation vs. the US$; m2res is the ratio of M2 to foreign exchange reserves; rgdpcp is real GDP per-capita; privcrd_gdp is bank credit to the private sector to GDP; L2.domcredgr is real domestic bank credit growth to the private sector lagged twice. L.SBD25 and L.SBSD10 are lagged SBS deposit indicators.

article image
Standard errors are clustered by country. Robust p-values are reported in brackets, with *** p<0.01, ** p<0.05, * p<0.1.

In sum, these findings indicate that BC indicators systematically record systemic bank shocks with a lag. This is because these indicators index the (lagged) start and duration of government responses to banking distress. As noted earlier, the lack of robust evidence on their macroeconomic determinants (apart from GDP growth and to some extent the real interest rate) is not surprising in light of the variety and differences across countries of the policies used to address systemic bank distress.

As we show next, this has important implications for the relevance and interpretation of results in a large literature. This literature has essentially focused on studying the determinants of government responses to banking distress – which is what the BC indicators are capturing - rather than on the realizations of systemic shocks to the banking sector.

C. Logit Regressions with SBS Indicators as Dependent Variables

What is the impact of the benchmark explanatory variables we have considered on the probability that a systemic bank shock occurs? Table 6 reports the results of the benchmark panel logit regression with our SBS indicators as dependent variables.

Table 6.

Logit Regressions with SBS indicators as Dependent Variables

Dependent variables are the SBS lending indicators,SBL25 and SBSL10, and the SBS deposit indicators, SBSD25 and SBSD10. Explanatory variables: rgdpgr is the GDP growth rate; rint is the real interest rate; infl is the percentage change in the GDP deflator; totch is the change in the terms of trade; depr is the exchange rate depreciation vs. the US$; m2res is the ratio of M2 to foreign exchange reserves; rgdpcp is real GDP per-capita; privcrd_gdp is bank credit to the private sector to GDP; L2.domcredgr is real domestic bank credit growth to the private sector lagged twice. L.SBL25 and L.SBSL10 are lagged SBS lending indicators.

article image
Standard errors are clustered by country. Robust p-values are reported in brackets, with *** p<0.01, ** p<0.05, * p<0.1

Two important facts emerge. First, the impact of many explanatory variables appears more in line with expectations when SBS indicators are dependent. The levels of significance is generally higher, and the overall explanatory power of the regressions is stronger, than in the regressions with the BC indicators as dependent variables. For example, the impact of the “external” variables now enters significantly in most regressions, consistent with the role of external shocks in triggering shocks to domestic banking systems. For example, the terms of trade change variable, totch, is positive and statistically significant in all eight specifications in Table 6. The exchange rate depreciation variable, depr, enters positively in all specifications in Table 6, and is statistically significant in six of eight cases.

Second, (and arguably more important), most explanatory variables have a significant impact on SBS indicators, but not on BC indicators. Recall that real GDP growth appears to be the only variable that enters significantly in all (or even most) regressions with BC indicators as dependent variables. By contrast, as shown in Table 6, the real interest rate and the inflation rates are negatively and contemporaneously associated with the probability of a systemic bank shock (regressions (1) (2) and (5)). Moreover, a systemic bank shock is less likely in more financially developed countries. That is, the coefficient of real GDP per capita, rgdppc, is negative in seven out of 8 cases, and statistically significant in six.

Finally, the last two regressions show the strong predictive power of SBS lending indicators for SBS deposit indicators. Indeed, SBS lending indicators predict SBS deposit indicators in Logit regressions with SBS deposit indicators as the dependent variable, suggesting complex dynamics that are not modeled in our simple static model.

Overall, this evidence indicates the importance of disentangling systemic bank shocks and government responses to such shocks. The SBS and BC indicators measure very different things: a systemic bank shock and the government response to bank distress, respectively. The importance and economic significance of these differences is illustrated next.

VI. Market Structure and Deposit Insurance

Here, we re-examine and re-interpret the evidence on the relationships between bank competition and banking fragility and between deposit insurance and banking fragility. In both these literatures, researchers have employed BC indicators as dependent variables and interpreted the results as if they were systemic banking shocks. We argue that this has produced a considerable mis-interpretation of results. To facilitate comparisons, we continue to use the country-sample used thus far.

A. Bank Market Structure and Competition

In an extensive set of logit regressions using the DD crisis classification dataset, Beck et al. (2006) conclude that banking crises are less likely in more concentrated banking systems. Table 7 reports the results of our baseline logit specification adding bank concentration measures identical to those used by Beck et.al (2006). The average C3 concentration ratio, concenmean represents the asset share of the largest three banks in the country. The variable avgherf is an inter-temporal average of the Hirschman-Herfindhal index for each country.15 Interestingly, our tests indicate that there is no evidence of any significant relationship between the bank concentration measures and the probability of a government response to banking distress. That conclusion is supported by all eight specifications in Table 7. Thus, the Beck et al (2006) results are seemingly not robust to either: the definition of a BC event, changes in sample composition, or the choice of other explanatory variables.

Table 7.

Logit Regressions: BC Indicators and Bank Concentration Measures

Dependent variables are the BC indicators with all crisis dates: DD, CEA, RR and LV. All regressions are full sample regressions including all available observations for each classification.Explanatory variables: rgdpgr is the GDP growth rate; rint is the real interest rate; infl is the percentage change in the GDP deflator; totch is the change in the terms of trade; depr is the exchange rate depreciation vs. the US$; m2res is the ratio of M2 to foreign exchange reserves; rgdpcp is real GDP per-capita; privcrd_gdp is bank credit to the private sector to GDP; L2.domcredgr is real domestic bank credit growth to the private sector lagged twice; concen_mean is the average C3 concentration ratio; avgherf is the average Herfindhal index.

article image
Standard errors are clustered by country. Robust p-values are reported in brackets, with *** p<0.01, ** p<0.05, * p<0.1.

In Table 8 we report the results of estimates of the same equations as in Table 7, but with our SBS indicators as dependent variables. In all but one specification using a C3 concentration ratio, and in all specifications using the (arguably more appropriate) Herfindhal index, systemic bank shocks are more likely to occur in more concentrated banking systems.

Table 8.

Logit Regressions: SBS Indicators and Bank Concentration Measures

Dependent variables are the SBS lending indicators,SBL25 and SBSL10, and the SBS deposit indicators, SBSD25 and SBSD10. Explanatory variables: rgdpgr is the GDP growth rate; rint is the real interest rate; infl is the percentage change in the GDP deflator; totch is the change in the terms of trade; depr is the exchange rate depreciation vs. the US$; m2res is the ratio of M2 to foreign exchange reserves; rgdpcp is real GDP per-capita; privcrd_gdp is bank credit to the private sector to GDP; L2.domcredgr is real domestic bank credit growth to the private sector lagged twice; concen_mean is the average C3 concentration ratio; avgherf is the average Herfindhal index.

article image
Standard errors are clustered by country. Robust p-values are reported in brackets, with *** p<0.01, ** p<0.05, * p<0.1.

Properly interpreted, these results are not necessarily inconsistent with those reported by Beck et al (2006) because the dependent variables are completely different. However, the results presented in Table 8 are perfectly consistent with the implications of the models by Boyd and De Nicolò (2005), Boyd, De Nicolò and Jalal (2006 and 2009) and De Nicolò and Loukoianova (2007), as well as the empirical evidence reported in these papers.16

B. Deposit Insurance

In logistic regressions of the kind employed thus far, Demirgüç-Kunt and Detragiache (2002) find—and Barth, Caprio and Levine (2004) and Beck et al.(2006) confirm—that banking “crises” are more likely in countries with a deposit insurance system is in place. This finding has been interpreted as consistent with the standard moral hazard incentives created by deposit insurance and other government guarantees. Yet, it is well known that this argument is valid only in a partial equilibrium context and absent sufficiently strong countervailing regulations limiting banks’ risk-taking, (such as capital requirements). In a general equilibrium context, and allowing contracts in nominal terms because of a non-trivial role for money, this simple moral hazard argument does not necessarily hold (e.g. Boyd, Chang and Smith 2002 and 2004).

Table 9, columns 1 – 4, reports the results of logistic regressions with the BC indicators as dependent variables, in which we retain the Herfindhal index as a control. In addition, we include the indicator variable di which takes on the value 1.0 if a government deposit insurance system is in place, zero otherwise. The indicator variable is obtained from Demirgüç-Kunt and Detragiache (2002). Indeed, there is evidence of a positive and significant relationship between the BC indicators and the deposit insurance variable, although it is not statistically significant for one BC indicator (Equations (3)). However, this result essentially suggests that government responses to systemic bank shocks are more likely if a deposit insurance system is in place. This seems an unsurprising finding in light of the stronger commitment of a government to intervene in the presence of explicit deposit guarantees.

Table 9.

Logit Regressions: BC Indicators, SBS Indicators and Deposit Insurance

Dependent variables are: the BC indicators with all crisis dates (DD, CEA, RR and LV);the SBS lending indicators,SBL25 and SBSL10, and the SBS deposit indicators, SBSD25 and SBSD10.Explanatory variables: rgdpgr is the GDP growth rate; rint is the real interest rate; infl is the percentage change in the GDP deflator; totch is the change in the terms of trade; depr is the exchange rate depreciation vs. the US$; m2res is the ratio of M2 to foreign exchange reserves; rgdpcp is real GDP per-capita; privcrd_gdp is bank credit to the private sector to GDP; L2.domcredgr is real domestic bank credit growth to the private sector lagged twice; avgherf is the average Herfindhal index; di is the binary indicator of deposit insurance.

article image
Standard errors are clustered by country. Robust p-values are reported in brackets, with *** p<0.01, ** p<0.05, * p<0.1.

But, again, results are different when we use our SBS indicators as dependent variables. As shown in regressions (5)-(8) of Table 9, in all specifications the probability of a systemic bank shock does not depend on whether there is a deposit insurance system in place. To explore this issue further, in Table 10 we report logit regressions where we have added an index of “moral hazard” associated with design features of deposit insurance systems, princom, and a variable indexing the quality of institutions, kk_compo, as used in Beck et al (2006). Since princom is never statistically significant in columns 1 – 4, there is no evidence that more generous deposit insurance systems induce a higher probability of a government response to banking distress. However, the variable kk_compo is negative and statistically significant in all four tests (columns 1 – 4), suggesting that the probability of a government response to banking distress is lower in countries with better institutions. Possibly, this is because better institutions include stronger supervisory and regulatory bodies likely to prevent banking distress.

Table 10.

Logit Regressions: BC Indicators, SBS Indicators Deposit Insurance Features and Quality of Institutions

Dependent variables are: the BC indicators with all crisis dates (DD, CEA, RR and LV);the SBS lending indicators,SBL25 and SBSL10, and the SBS deposit indicators, SBSD25 and SBSD10. Explanatory variables: rgdpgr is the GDP growth rate; rint is the real interest rate; infl is the percentage change in the GDP deflator; totch is the change in the terms of trade; depr is the exchange rate depreciation vs. the US$; m2res is the ratio of M2 to foreign exchange reserves; rgdpcp is real GDP per-capita; privcrd_gdp is bank credit to the private sector to GDP; L2.domcredgr is real domestic bank credit growth to the private sector lagged twice; avgherf is the average Herfindhal index.; di is the binary indicator of deposit insurance; princomp is the “moral hazard” index; kk_compo is the indicator of quality of institutions.

article image
Standard errors are clustered by country. Robust p-values are reported in brackets, with *** p<0.01, ** p<0.05, * p<0.1.