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IMF Working Paper
© 1997 International Monetary Fund
This is a
Working
Paper and the author(s) would welcome
any comments on the present text. Citations should refer
to a
Working
Paper of the International Monetary
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The views expressed are those of the author(s) and do not
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WP/97/106 INTERNATIONAL MONETARY FUND
Research Department
The Determinants of Banking Crises: Evidence from Developing
and Developed Countries
Prepared by Ash Demirguc-Kunt and Enrica Detragiache
1
Authorized for distribution by Peter Wickham
September 1997
Abstract
The paper studies the factors associated with the emergence of systemic banking crises in a
large sample of developed and developing countries in 1980-94, using a multivariate logit
econometric model. The results suggest that crises tend to erupt when the macroeconomic
environment is weak, particularly when growth is low and inflation is high. Also, high real
interest rates are clearly associated with systemic banking sector problems, and there is some
evidence that vulnerability to balance of payments crises has played a role. Countries with an
explicit deposit insurance scheme were particularly at risk, as were countries with weak law
enforcement.
JEL Classification Numbers: E44, G21
Keywords: Banking Crises, Financial Fragility, Deposit Insurance
Author's E-Mail Address: ademirguckunt@worldbank.org; edetragiache@imf.org
1
Asli
Demirguc-Kunt is a Senior Economist in the Development Research Group, The World
Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the World Bank, the
International Monetary Fund, their Executive Directors, or the countries they represent. The
authors are indebted to Jerry Caprio, Stijn Claessens, George Clarke, Steve Cosslett, Harry
Huizinga, Ed Kane, Ross Levine, Miguel Savastano, Mary Shirley, Dimitri Vittas, and Peter
Wickham for helpful comments, and to Anqing Shi for excellent research assistance.
- 2 -
Contents Page
I. Introduction 4
II.
The Theory 6
III.
The Empirical Specification and the Choice of Explanatory Variables 10
A. The Sample 10
B.
The Econometric Model 10
C. The Banking Crisis Variable 12
D.
The Explanatory Variables 13
IV.The Results 16
A. Overall Model Performance and Prediction Accuracy 16
B.
Significance of the Explanatory Variables 20
V. The Cost of Banking Crises 23
VI.
Conclusions 26
Text Tables
1.
Banking Crises by Country 14
2.
Determinants of Banking Crisis—Panel Excluding Years After the First Crisis .... 17
3.
Financial Crisis Determinants—Panel Excluding Years
While the Crisis is On-Going 18
4.
The Model As An Early Warning System 19
5.
Interpreting Regression Coefficients—The 1994 Mexican Crisis 22
6. Determinants of the Cost of a Crisis 25
Appendix I
Sample Composition and Data Sources 28
Appendix Tables
A1. Composition of the Samples 28
A2.
Description of the Explanatory Variables and Sources 29
References 30
- 3 -
SUMMARY
In the 1980s and early 1990s several countries experienced severe banking crises. This
study attempts to identify which features of the economic environment tend to breed banking
sector problems by econometrically estimating the probability of
a
systemic crisis using a
multivariate logit model. The data come from a large panel of countries including both
developed and developing economies, and cover the period 1980-94. Countries that never
experienced banking problems are included in the panel, and serve as controls.
We find that crises tend to occur in a weak macroeconomic environment characterized
by slow GDP growth and high inflation; also high real interest rates are typically associated
with the emergence of banking sector problems. When these effects are controlled for, neither
the rate of currency depreciation nor the fiscal deficit are significant. The tests also indicate
that vulnerability to sudden capital outflows, a high share of credit to the private sector, and
high past credit growth may be associated with a higher probability of a crisis.
Another factor that leads to increased banking sector vulnerability in our sample is the
presence of explicit deposit insurance, suggesting that moral hazard has played a major role.
Finally, countries with weak institutions (as measured by a
"law
and order" index) are more at
risk.
- 4 -
I. INTRODUCTION
In the 1980s and early 1990s a number of developed economies, developing countries,
and economies in transition experienced severe banking crises. Such proliferation of large
scale banking sector problems has raised widespread concern, as banking crises disrupt the
flow of credit to households and enterprises, reducing investment and consumption and
possibly forcing viable firms into bankruptcy. Banking crises may also jeopardize the
functioning of the payments system and, by undermining confidence in domestic financial
institutions, they may cause a decline in domestic savings and/or a large scale capital outflow.
Finally, a systemic crisis may force sound banks to close their doors.
In most countries, policymakers have responded to banking crises with various
interventions, ranging from loose monetary policy to the bail out of insolvent financial
institutions with public funds. Even when they are carefully designed, however, rescue
operations have several drawbacks: they are often very costly for the budget; they may allow
inefficient banks to remain in business; they are likely to create the expectation of future
bailouts thereby reducing incentives for adequate risk management by banks. Rescue
operations may also weaken managerial incentives when, as it is often the case, they force
healthy banks to bear the losses of
ailing
institutions. Finally, loose monetary policy to
prevent banking sector losses can be inflationary and, in countries with an exchange rate
commitment, it may trigger a speculative attack against the currency.
Preventing the occurrence of systemic banking problems is undoubtedly a major
concern of policymakers, and understanding the mechanisms that are behind the surge in
banking crises in the last fifteen years is a first step in this direction. Recently, a number of
studies have analyzed various episodes of banking sector distress in an effort to draw useful
policy lessons (see Section III below).
2
Most of this work consists of
case
studies, and
econometric analyses are few. Gonzalez-Hermosillo et al. (1997) use an econometric model
to predict bank failures using Mexican data for 1991-95. In a paper focused primarily on
the connection between banking crises and balance of payments crises, Kaminsky and
Reinhart (1996) examine the behavior of a number of macroeconomic variables in the months
before and after a crisis in a sample of 20 countries; using a methodology developed for
predicting the turning points of business cycles, they attempt to identify variables that act as
"early warning signals" for crises.
3
The best signals appear to be a loss of foreign exchange
reserves, high real interest rates, low output growth, and a decline in stock prices.
2
Some of these studies also review the strategies adopted to rescue the banking system, a
topic that we do not address in this paper.
3
While this approach provides numerous interesting insights, it is open to the criticism that the
criteria used to establish which variables are useful signals are somewhat arbitrary.
- 5 -
The goal of this study is to further investigate the features of the economic
environment that tend to breed banking sector fragility and, ultimately, lead to systemic
banking crises. Rather than focusing on the behavior of high frequency time series around
the time of the crisis, we study the determinants of the probability of
a
banking crisis in a
multivariate logit specification with annual data.
4
Our panel includes all market economies for
which data were available over the period 1980-94.
5
Many countries in our sample
did
not
experience systemic banking crises in the period under consideration, and therefore serve as
controls. The explanatory variables capture many of the factors suggested by the theory and
highlighted by case studies, including not only macroeconomic variables but also structural
characteristics of the economy in general and of the financial sector in particular. This
approach allows us to identify a number of interesting correlations; however, because we
estimate a reduced form relationship without deriving it from a specific structural model of
the economy, such correlations should be interpreted with caution as they may not necessarily
reflect direct causal links.
The first issue that we explore is which (if any) elements of the macroeconomic
environment are associated with the emergence of banking crises. We find that low GDP
growth, excessively high real interest rates, and high inflation significantly increase the
likelihood of systemic problems in our sample; thus, crises do not appear to be solely driven
by self-fulfilling expectations as in Diamond and Dybvig (1983). This confirms the evidence
presented by Gorton (1988) on the determinants of bank runs in the United States during the
nineteenth century.
6
Adverse terms of trade shocks also tend to increase the likelihood of
banking sector problems, but here the evidence is weaker. The size of the fiscal deficit and
the rate of depreciation of the exchange rate, on the other hand, do not seem to have an
independent effect in our sample.
A weak macroeconomic environment, however, is not the sole factor behind systemic
banking sector problems. Structural characteristics of the banking sector and of the economic
environment in general also play a role. Our tests show that—as hypothesized by Calvo
4
Our methodology is similar to that recently used by Eichengreen et
al.
(1996) to study
currency crises, and by Knight and Santaella (1994) to study the factors leading to Fund
financial arrangements.
5
Economies in transition are excluded from our study even though they have experienced
some of the worst banking crises. We believe that some of the banking problems in these
economies are due to the process of transforming a centrally planned economy into a market
economy, and are therefore of a distinctive nature.
6
It should be pointed out, however, that without a theory of how beliefs are formed in rational
expectations models with multiple equilibria, this evidence cannot rule out that crises have a
self-fulfilling component, since pessimistic, self-fulfilling beliefs may tend to emerge when
macroeconomic fundamentals are weak.
- 6 -
et al. (1994)—vulnerability of the system to sudden capital outflows increases the probability
of
a
banking crisis. This result, however, is not robust to the specification of the regression.
We also find some evidence that problems are more likely where a larger share of credit goes
to the private sector, possibly indicating a connection between the emergence from a state of
financial repression and banking sector fragility.
Another interesting result, which is quite robust to the specification of the regression,
is that the presence of an explicit deposit insurance scheme makes bank unsoundness more
likely. While explicit deposit insurance should reduce bank fragility by eliminating the
possibility of self-fulfilling panics, it is well-known that it creates incentives for excessive
risk-taking by bank managers (moral
hazard).
Our evidence suggests that, in the period under
consideration, moral hazard played a significant role in bringing about systemic banking
problems, perhaps because countries with deposit insurance schemes were not generally
successful at implementing appropriate prudential regulation and supervision, or because the
deposit insurance schemes were not properly designed. Also, a variable capturing the
effectiveness of the legal system is found to be significantly negatively correlated with the
emergence of banking sector problems, possibly suggesting that banking crises are more likely
where outright fraud or more minor violations of contractual covenants, corporate charters,
and prudential regulation tend to go unpunished.
Using estimates of the cost of banking crises from Caprio and Klingebiel (1996), we
also study the factors that account for the severity of each episode. We find that most of the
variables that tend to make crises more likely also tend to make them more costly. Since the
size of the sample is small due to data limitations, however, these results should be interpreted
with caution.
The paper is structured as follows: the next section reviews the theory of the banking
firm to identify potential sources of systemic banking crises. Section III explains the design of
the econometric tests, while Section IV contains the main results. In Section V, we study the
cost of the crises; finally, Section VI summarizes the results, and discusses policy implications
and directions for future research.
II. THE THEORY
Banks are financial intermediaries whose liabilities are mainly short-term deposits and
whose assets are usually short and long-term loans to businesses and consumers. When the
value of their assets falls short of the value of their liabilities, banks are insolvent. The value
of a bank's assets may drop because borrowers become unable or unwilling to service their
debt (credit risk). Credit risk can be reduced in various ways, such as screening loan
applicants, diversifying the loan portfolio by lending to borrowers who are subject to different
risk factors, or asking for collateral. Appropriate screening can ensure that projects that are
unprofitable ex ante are not financed; but risky projects that are profitable in an ex ante sense
may still fail ex
post.
Also, portfolio diversification is unlikely to eliminate default risk
- 7 -
completely, especially for banks that operate in small countries or regions, or that specialize
in lending to a particular sector. Finally, collateral is costly to establish and monitor, and its
value is typically subject to fluctuations. Thus, default risk cannot be entirely eliminated
without severely curtailing the role of banks as
financial
intermediaries.
7
If loan losses exceed
a bank's compulsory and voluntary reserves as well as its equity cushion, then the bank is
insolvent. When a significant portion of the banking system experiences loan losses in excess
of its capital, a systemic crisis occurs.
Thus,
the theory predicts that shocks that adversely affect the economic performance
of bank borrowers and whose impact cannot be reduced through risk diversification should
be positively correlated with systemic banking crises. Furthermore, for given shocks banking
systems that are less capitalized should be more vulnerable. The shocks associated with
episodes of banking sector problems highlighted by the literature include cyclical output
downturns, terms of trade deteriorations, declines in asset prices such as equity and real estate
(Gorton, 1988, Caprio and Klingebiel, 1996, Lindgren et al., 1996, Kaminsky and
Reinhart, 1996).
Even in the absence of an increase in nonperforming loans, bank balance sheets can
deteriorate if the rate of return on bank assets falls short of the rate that must be paid on
liabilities. Perhaps the most common example of this type of problem is an increase in
short-term interest rates that forces banks to increase the interest rate paid to depositors.
8
Because the asset side of bank balance sheets usually consists of long-term loans at fixed
interest rates, the rate of return on assets cannot be adjusted quickly enough, and banks must
suffer reduced profits or bear losses. All banks within a country are likely to be exposed to
some degree of interest rate risk because maturity transformation is one of the typical
functions of the banking system; furthermore, high real interest rates are likely to hurt bank
balance sheets even if they can be passed on to borrowers, as high lending rates result in a
larger fraction of nonperforming loans. Thus, a large increase in short-term interest rates is
likely to be a major source of systemic banking sector
problems.
In turn, the increase in
short-term interest rates may be due to various factors, such as an increase in the rate of
inflation, a shift towards more restrictive monetary policy that raises real rates, an increase in
international interest rates, the removal of interest rate controls due to financial liberalization
7
The amount of risk that bank managers choose to take on, however, is likely to exceed what
is socially optimal because of limited liability (Stiglitz, 1972). Hence the need for bank
regulators to impose minimum capital requirements and other restrictions. When bank
deposits are insured, incentives to take on excessive risk are even stronger (see below). On the
theory of bank prudential regulation, see Dewatripont and Tirole (1994).
8
According to Mishkin (1996), most banking panics in the United States were preceded by an
increase in short-term interest rates.
- 8 -
(Galbis, 1993), or the need to defend the exchange rate against a speculative attack
(Velasco, 1987, Kaminsky and Reinhart, 1996).
9
Another case of rate of return mismatch occurs when banks borrow in foreign
currency and lend in domestic currency. In this case, an unexpected depreciation of the
domestic currency threatens bank profitability. Many countries have regulations limiting
banks'
open foreign currency positions, but sometimes such regulations can be circumvented
(Garber, 1996). Also, banks that raise funds abroad may choose to issue domestic loans
denominated in foreign currency, thus eliminating the open position. In this case, foreign
exchange risk is shifted onto the borrowers, and an unexpected devaluation would still affect
bank profitability negatively through an increase in nonperforming loans. Foreign currency
loans were a source of banking problems in Chile in 1981 (Akerlof and Romer, 1993), in
Mexico in 1995 (Mishkin, 1996), in the Nordic countries in the early 1990s (Drees and
Pazarbasioglu, 1995, Mishkin, 1996), and in Turkey in 1994.
When bank deposits are not insured, a deterioration in the quality of a bank's asset
portfolio may trigger a run, as depositors rush to withdraw their funds before the bank
declares bankruptcy. Because bank assets are typically illiquid, runs on deposits accelerate the
onset of insolvency. In fact, as Diamond and Dybvig (1983) have shown, bank runs may be
self-fulfilling, i.e. they may take place simply because depositors believe that other depositors
are withdrawing their funds even in the absence of an initial deterioration of the bank's
balance sheet. The possibility of self-fulfilling runs makes banks especially vulnerable
financial institutions. A run on an individual bank should not threaten the banking system as a
whole unless partially informed depositors take it as a signal that other banks are also at risk
(contagion).
10
In these circumstances, bank runs turn into a banking panic.
Bank runs should not occur when deposits are insured against the risk of bank
insolvency; deposit insurance may be explicit, i.e. banks may purchase full or partial insurance
on behalf of depositors from a government agency or from a private insurer, or it may be
implicit, if depositors (correctly) believe that the government will either prevent the bank from
failing or that, in case of failure, it would step in and compensate depositors for their losses.
If the premia do not fully reflect the riskiness of bank portfolios, then the presence of deposit
insurance creates incentives for taking on excessive risk (moral hazard) (Kane, 1989). The
effects of moral hazard are likely to be negligible when the banking system is tightly
controlled by the government or by the Central Bank. On the other hand, when financial
liberalization takes place—as it has been in many countries in the last
15
years—the
opportunities for risk-taking increase substantially. Thus, if financial liberalization takes place
in countries with deposit insurance, and it is not accompanied by a well-designed and
9
On the determinants of high interest rates in developing and transition economies see Brock
(1995).
10
For an in-depth discussion of the theory of bank runs, see Bhattacharya and Thakor (1994).
- 9 -
effective system of prudential regulation and supervision, then excessive risk-taking on the
part of bank managers is possible, and banking crises due to moral hazard may occur. To
summarize, the theory is ambiguous as to the sign of the correlation between deposit
insurance and banking crises: on the one hand, when deposits are insured self-fulfilling crises
should not occur; on the other hand, banking crises due to adverse macroeconomic shocks
could be more likely because bank managers choose riskier loan portfolios.
In countries in which the banking sector is liberalized but bank supervision is weak
and legal remedies against fraud are easy to circumvent, banking crises may also be caused by
widespread "looting": bank managers not only may invest in projects that are too risky, but
they may invest in projects that are sure failures but from which they can divert money for
personal
use.
Akerlof and Romer (1993) claim that looting behavior was at the core of the
savings and loan crisis in the United States and of the Chilean banking crisis of the late 1970s.
Thus,
a weak legal system that allows fraud to go unpunished should increase the probability
of
a
banking crisis.
A sudden withdrawal of bank deposits with effects similar to those of a bank run may
also take place after a period of large inflows of foreign short-term capital, as indicated by the
experience of
a
number of Latin American, Asian, and Eastern European countries in the early
1990s. Such inflows, often driven by the combined effect of capital account liberalization and
high domestic interest rates due to inflation stabilization policies, result in an expansion of
domestic credit (Khamis, 1996). When foreign interest rates rise, domestic interest rates fall,
or when confidence in the economy wavers, foreign investors quickly withdraw their funds,
and the domestic banking system may become illiquid (Calvo et al., 1994). As discussed by
Obstfeld and Rogoff (1995) among others, in countries with a fixed exchange rate banking
problems may also be triggered by a speculative attack against the currency: if a devaluation
is expected to occur soon, depositors (both domestic and foreign) rush to withdraw their bank
deposits and convert them into foreign currency deposits abroad, thus leaving domestic banks
illiquid.
11
Banking sector problems may also follow successful stabilization in countries with a
history of high inflation; as shown by English (1996), chronic high inflation tends to be
associated with an overblown financial sector, as
financial
intermediaries profit from the float
on payments. When inflation is drastically reduced, banks see one of their main sources of
revenue disappear, and generalized banking problems may follow.
12
11
This mechanism seems to have been at work in Argentina in 1995: following the Mexican
devaluation in December 1994, confidence in the Argentinean peso plunged, and the banking
system lost 16 percent of its deposits in the first quarter of 1995 (IMF, 1996).
12
Recently, banking sector difficulties in Brazil and Russia have been explained in this way
(Lindgren et al., 1996).
- 10 -
III. THE EMPIRICAL SPECIFICATION AND THE CHOICE OF EXPLANATORY VARIABLES
A. The Sample
Because of data availability, our study is limited to the 1980-94 period. To determine
which countries to include, we began with all the countries in the
IFS;
we then eliminated
centrally planned economies and economies in transition because the interrelation between
the banking system and the rest of the economy is likely to be of
a
distinctive nature in these
countries. Other countries had to be eliminated because the main macroeconomic and
financial data series were missing or mostly incomplete. A few countries, such as Bangladesh
and Ghana, were left out because their banking systems were in a state of distress for much of
the period under consideration. Finally, three countries (Argentina, Brazil, and Bolivia) were
excluded because they are outliers with respect to two of the regressors that we use (inflation
and the real interest rate).
13
This process of elimination left us with a number of countries
ranging from a maximum of 65 to a minimum of 45 depending on the specification of the
regression.
14
A list of the countries included in the sample can be found in the data appendix.
B.
The Econometric Model
We estimate the probability of
a
banking crisis using a multivariate logit model. In
each period the country is either experiencing a crisis, or it is not. Accordingly, our dependent
variable, the crisis dummy, takes the value zero if there is no crisis, and takes the value one if
there is a crisis. The probability that a crisis will occur at a particular time in a particular
country is hypothesized to be a function of
a
vector of n explanatory variables X(i, t). The
choice of explanatory variables is discussed below. Let P(i, t) denote a dummy variable that
takes the value of one when a banking crisis occurs in country i and time t and
a
value of zero
otherwise, p is
a
vector of n unknown coefficients and F(p'X(i, t)) is the cumulative
probability distribution function evaluated at p'X(i, t). Then, the log-likelihood function of
the model is:
Ln
L
=
£
MT
E^
13
Not surprisingly, when the three outlier countries are left in the sample inflation and the real
interest rate lose significance.
14
Due to lack of
data,
for some countries the observations included in the panel do not cover
the entire 1980-94 period.
- 11 -
In modeling the probability distribution we use the logistic functional form.
15
Thus, when
interpreting the regression results it is important to remember that the estimated coefficients
do not indicate the increase in the probability of
a
crisis given a one-unit increase in the
corresponding explanatory variables. Instead, in the above specification, the coefficients
reflect the effect of
a
change in an explanatory variable on ln(P(i,t)/(l-P(i,t)). Therefore, the
increase in the probability depends upon the original probability and thus upon the initial
values of all the independent variables and their coefficients. While the sign of the coefficient
does indicate the direction of the change, the magnitude depends on the slope of the
cumulative distribution function at p'X(i,t). In other
words,
a change in the explanatory
variable will have different effects on the probability of
a
crisis depending on the country's
initial crisis probability. Under the logistic specification, if a country has an extremely high (or
low) initial probability of
crisis,
a marginal change in the independent variables has little effect
on its prospects, while the same marginal change has a greater effect if the country's
probability of
crisis
is in an intermediate range.
After the onset of
a
banking crisis, the behavior of some of the explanatory variables
is likely to be affected by the crisis
itself.
For instance, as described below one of the
explanatory variables used in the regressions is the credit-to-GDP ratio; this ratio is likely to
fall as a result of the banking crisis, and the reduction in credit may, in turn, affect another
explanatory variable, GDP growth. Another regressor that may be affected by the banking
crisis is the real interest rate, which is likely to fall due to the loosening of monetary policy
that often accompanies banking sector rescue operations. Clearly, these feed-back effects
would muddle the relationships that we try to identify, so in a first set of regressions we
eliminate from the panel all observations following a banking crisis. The drawback of this
approach is that we lose episodes of multiple crises, and that many observations for the late
1980s and early 1990s are excluded from the sample.
As an alternative approach, we identify the year in which each banking crisis ended
based on information available in existing case studies, and in a second set of regressions we
include in the panel all observations following the end date. This panel, of course, is
considerably larger than the first, and it includes repeated banking crises. The drawback of
this approach is that determining when the effects of
a
banking crisis come to an end is quite
difficult, so the choice of which observations to include in the panel is somewhat arbitrary.
Furthermore, in this set of regressions the probability that a crisis occurs in a country that had
problems in the past is likely to differ from that of a country where no crisis ever occurred.
To take this dependence into account, we include different additional regressors in the
estimated equations such as the number of past crises, the duration of the last spell, and the
time since the last crisis.
15
The logistic distribution is commonly used in studying banking difficulties. See for example,
Cole and Gunther (1993) and Gonzalez-Hermosillo, et al. (1997).
- 12 -
When using panel data, country fixed effects are often included in the empirical model
to allow for the possibility that the dependent variable may change cross-country
independently of the explanatory variables included in the regression. In logit estimation,
including country fixed effects would require omitting from the panel all countries that did
not experience a banking crisis during the period under consideration (Greene, 1997, p. 899).
In our case, this would imply disregarding a large amount of available information, since—as
discussed below—countries that did not experience crisis are more than half of the
total.
Furthermore, limiting the panel to countries with crises would produce a biased sample. Given
these drawbacks, we believe that estimating the model using the full sample but without fixed
effects is the preferable approach.
16
C.
The Banking Crisis Variable
A key element in our study is the construction of the banking crisis dummy variable.
To do it, we have identified and dated episodes of banking sector distress during the period
1980-94 using primarily five recent studies: Caprio and Klingebiel (1996), Drees and
Pazarbasioglu (1995), Kaminsky and Reinhart (1996), Lindgren et al. (1996), and
Sheng (1995). Taken together, these studies form a comprehensive survey of banking sector
fragility around the world; from our perspective, it was important to distinguish between
fragility in general and crises in particular, and between localized crises and systemic crises.
To this end, we established—somewhat arbitrarily—that for an episode of distress to be
classified as a full-fledged crisis in our panel at least one of the following four conditions
had to hold:
1.
The ratio of nonperforming assets to total assets in the banking system exceeded
10 percent;
2.
The cost of the rescue operation was at least 2 percent of GDP;
3.
Banking sector problems resulted in a large scale nationalization of
banks;
4.
Extensive bank runs took place or emergency measures such as deposit freezes,
prolonged bank holidays, or generalized deposit guarantees were enacted by the
government in response to the crisis.
Therefore, the premise behind our work is that when one or more of the above
conditions obtains the problem is of a systemic nature and should be considered a banking
16
An alternative strategy would be to estimate a probit model with random effects, since such
a methodology would be compatible with using the entire data set. However, this model
produces unbiased estimates only if the random effects are uncorrelated with the regressors,
which is unlikely to be true in practice (Judge et al., 1985, p. 527).
- 13 -
crisis,
while when none of the above occurs the problem is localized and/or relatively minor.
17
The criteria above were sufficient to classify as a crisis or not a crisis almost all of the fragility
episodes identified by the literature. In a few cases, however, we had insufficient information
and made a decision based on our best judgement. According to these classification criteria,
in the largest of our samples there were 31 episodes of systemic banking crises (out of 546
observations, Table 1). Of these, 23 crises took place in developing countries and 8 in
developed countries. Of the crises in developing countries, 6 were in Latin America, 7 in Asia,
7 in Africa, and 3 in the Middle East. Thus, our sample of banking crises includes a relative
diverse set of economies.
D.
The Explanatory Variables
Our choice of explanatory variables reflects both the theory of the determinants of
banking crises summarized in Section II above and data availability. A list of the variables and
their sources is in the data appendix. To capture adverse macroeconomic shocks that hurt
banks by increasing the share of nonperforming loans, we use as regressors the rate of growth
of real GDP, the external terms of trade, and the real short-term interest rate. High
short-term real interest rates also affect bank balance sheets adversely if banks cannot
increase their lending rates quickly enough, as explained in Section II. Finally, the real interest
rate may also be considered a proxy for financial liberalization, as Galbis (1993) found that
the liberalization process tends to lead to high real rates. Financial liberalization, in turn, may
increase banking sector fragility because of increased opportunities for excessive risk-taking
and fraud.
18
Pill and Pradhan (1995) find that the variable that best captures the extent to
which financial liberalization has progressed is the ratio of credit to the private sector to GDP.
Accordingly, we introduce
this
variable as a regressor in our equations. Another variable that
can proxy the progress with financial liberalization is the change in the credit-to-GDP ratio.
Since case studies point to a number of episodes in which banking sector problems were
preceded by strong credit growth, we experiment with various lags of this variable.
17
We also estimated the model using a more restrictive and a less restrictive definition of
a
crisis (ratio of nonperforming loans to bank assets above 15 percent and/or cost of crises
above 3 percent of
GDP,
and ratio of nonperforming loans to bank assets above 5 percent
and/or cost of
crises
above
1
percent of
GDP).
The results remain essentially unchanged.
18
We explored the possibility of constructing a
financial
liberalization dummy using country
by country information on the timing of liberalization; however, we abandoned the idea
because for most countries in our panel the transition to a more liberalized regime was
a
very
gradual process, sometimes taking a decade or
more.
Kaminsky and Reinhart (1996) find that
a financial liberalization dummy variable tends to predict the occurrence of banking crises in
their sample of 20 countries.
Table 1. Banking Crises by Country
Country Banking Crisis Date
Colombia 1982-85
Finland 1991-94
Guyana 1993-95
Indonesia 1992-94
India 1991-94
Israel 1983-84
Italy 1990-94
Jordan 1989-90
Japan 1992-94
Kenya 1993
Sri Lanka 1989-93
Mexico 1982,1994
Mali 1987-89
Malaysia 1985-88
Nigeria 1991-94
Norway 1987-93
Nepal 1988-94
Philippines 1981-87
Papua New Guinea 1989-94
Portugal 1986-89
Senegal 1983-88
Sweden 1990-93
Turkey 1991,1994
Tanzania 1988-94
US 1981-92
Uganda 1990-94
Uruguay 1981-85
Venezuela 1993-94
South Africa 1985
- 15 -
Inflation is introduced as an explanatory variable because it is likely to be associated
with high nominal interest rates, and because it may proxy macroeconomic mismanagement
which adversely affects the economy and the banking system through various channels. In
addition, the rate of depreciation of the exchange rate is used to test the hypothesis that
banking crises may be driven by excessive foreign exchange risk exposure either in the
banking system itself or among bank borrowers. To test whether systemic banking sector
problems are related to sudden capital outflows in countries with an exchange rate peg, we
introduce as a regressor the ratio of M2 to foreign exchange reserves. According to
Calvo (1996), this ratio is a good predictor of a country's vulnerability to
balance-of-payments crises.
The government surplus as a percentage of
GDP
captures the financing needs of the
central government. This variable may matter for two reasons: first, governments strapped for
funds often postpone measures to strengthen bank balance sheets, with the result that
relatively small problems grow to systemic proportions. According to Lindgren et al. (1996):
"Supervisors often are prevented from intervening in banks because this would bring
problems out in the open and 'cause' expenditure. Typical justifications for inaction are that
'there is no room in the
budget'
or that the fiscal situation is 'too
weak'
to allow for any
consideration of banking problems. "
(p.
166)
Even when government officials are prepared to intervene despite budgetary difficulties, the
public may believe that they are not, and bank runs may compound the initial problems
turning them into a full-fledged crisis. A second reason for including the government fiscal
position in the regressions is that failure to control the budget deficit may be a serious
obstacle to successful financial liberalization (McKinnon, 1991). Foiled attempts at financial
liberalization may, in turn, create problems for the banking system.
Adverse macroeconomic circumstances should be less likely to lead to crises in
countries where the banking system is liquid. To capture liquidity we use the ratio of bank
cash and reserves to bank assets. We also construct a dummy variable that takes a value of
one in countries/years in which an explicit deposit insurance scheme is in
place.
As discussed
in Section II, the expected sign of this variable is ambiguous, because explicit deposit
insurance should reduce the incidence of bank runs but it is likely to increase risk due to
moral hazard. Finally, banking sector problems may be due to widespread fraud, or to weak
enforcement of loans contracts and/or of prudential regulation in countries where the legal
system is not very efficient; to test this hypothesis, we introduce as regressors indexes of the
quality of the legal system, of contract enforcement, and of the bureaucracy, as well as GDP
per capita. These proxies may also capture the government's administrative capability which,
in turn, is likely to be positively correlated with the effectiveness of prudential supervision of
the banking system. Thus, low values of the proxies may mean more opportunities for moral
hazard.
- 16 -
IV. THE RESULTS
Tables 2 and 3 contain the main results of our econometric investigation. Table 2
reports four regressions using the panel that excludes observations following the first banking
crisis,
while Table 3 reports the same regressions for the panel in which observations
following the end of a crisis episode are included. The first specification includes only the
macroeconomic variables and GDP per capita, and it encompasses the largest set of countries.
In the second specification we add variables capturing banking sector characteristics; in the
third regression the deposit insurance dummy variable is included. The fourth regression relies
on the smallest sample, and it includes the
"law
and order" index.
A. Overall Model Performance and Prediction Accuracy
The quality of the model specification is assessed based on three criteria
recommended by Amemiya (1981): model chi-square, Akaike's information criterion (AIC),
and in—sample classification accuracy. The model chi-square tests the joint significance of the
regressors by comparing the likelihood of the model with that of
a
model with only the
intercept; as shown in Tables 2 and 3, in all the specifications the hypothesis that the
coefficients of the independent variables
are
jointly equal to zero is rejected at the one percent
significance level. The AIC criterion is computed as minus the log-likelihood of the model
plus the number of parameters being estimated, and it is therefore smaller for better models.
This criterion is useful in comparing models with different degrees of freedom. The
regressions including only observations before the first crisis seem to perform better, and
model four appears to be the best based on AIC.
To assess the prediction accuracy of the various specifications, we report the
percentage of crises that are correctly classified, the percentage of noncrises that are correctly
classified, and the total percentage of observations that are correctly classified. The model
appears to perform fairly
well:
the overall classification accuracy varies between 67 percent
and 84 percent, while up to 70 percent of the banking crises are accurately classified. It should
be pointed out that the percentage of noncrisis observations that are correctly classified tends
to downplay the performance of the model, because in a number of episodes the estimated
probability of
a
crisis increases significantly a few years before the episode begins and those
observations are considered as incorrectly classified by the accuracy criterion. To illustrate
this point, Table 4 reports more details about the classification accuracy of the best of the
specifications, namely specification (3) in the second
panel.
While 26 percent of the crisis
episodes were not correctly classified by the model, in 35 percent of the cases the estimated
probability jumps up exactly in the year of the crisis; in 26 percent additional cases the model
classifies as a crisis also the year before the crisis began, and, finally, in another 13 percent of
the episodes the estimated probability of crisis jumps as early as three years prior to the
starting date. These results suggest that the elements that contribute to systemic banking
sector fragility may be in place one or more years before problems become manifest.
- 17 -
Table 2. Determinants of Banking Crises—Panel Excluding Years After the First Crisis
1
(1) (2) (3) (4)
Macro Variables:
GROWTH -.067*** -.136*** -.252***
-.228***
(.025) (.039) (.063) (.059)
TOT CHANGE -.030* -.025
-.043*
-.045
(.019) (.020) (.027) (.032)
DEPRECIATION .002 -.001 -.002 -.012
(.006) (.007) (.008) (.012)
RL.
INTEREST
.088***
.086***
.131***
.113***
(.024) (.025) (.039) (.035)
INFLATION .040*** .044***
.053**
.079**
(.016) (.018) (.023) (.035)
SURPLUS/GDP .012 .024 .016 .013
(.034) (.036) (.053) (.048)
Financial Variables:
M2/RESERVES .012** .014**
.018**
(.005) (.007) (.009)
PRIVATE/GDP .019*
.033**
.009
(.012) (.015) (.010)
CASH/BANK .009 .018 -.049
(.016) (.023) (.039)
CREDIT GRO
t
_
2
.007 .022** -.003
(.012) (.010) (.020)
Institutional Variables:
GDP/CAP
-.034
-.090*
-.
158**
(.033) (.055) (.079)
DEPOSIT
INS.
1.415**
(.738)
LAW
&
ORDER
-.516**
(.238)
No.
of
Crisis
28 26 20 18
No.of
Obs.
546 493 395 268
% total correct 74 77 79 67
% crisis correct 61 58 55 61
% no-crisis correct 75 78 81 67
model x
2
31.88*** 40.86*** 53.79*** 30.37***
AIC 204 187 131 126
1
The dependent variable takes the value one if there is a crisis and the value zero otherwise. Standard errors are
given in parenthesi.. *, **and *** indicate significance levels of
10,
5 and
1
percent respectively.
- 18 -
Table 3. Financial Crisis Determinants - Panel Excluding Years While the Crisis is On-Going
1
(1) (2) (3) (4)
Macro Variables:
GROWTH -.076*** -.149*** -.254*** -.226***
(.024) (.040) (.059) (.056)
TOT CHANGE -.027 -.025 -.034 -.035
(.019) (.020) (.027) (.028)
DEPRECIATION .008 .006 .006 .001
(.006) (.006) (.007) (.007)
RL.
INTEREST .067*** .072*** .106***
.083***
(.020) (.022) (.034) (.028)
INFLATION
.023**
.035*** .037**
.043**
(.012) (.013) (.018) (.020)
SURPLUS/GDP -.016 -.009 -.032 -.008
(.030) (.032) (.049) (.043)
Financial Variables:
M2/RESERVES .016*** .016***
.021***
(.006) (.007) (.009)
PRIVATE/GDP .013 .024* -.001
(.013) (.015) (.011)
CASH/BANK -.013 -.004 -.046*
(.019) (.025) (.031)
CREDIT GRO
t-2
.011 .024*** .007
(.010) (.009) (.014)
Institutional Variables:
GDP/CAP -.032 -.089* -.126*
(.033) (.056) (.071)
DEPOSIT INS.
1.130**
(.630)
LAW & ORDER -.389*
(.218)
Past Crisis:
DURATION of .157*** .180*** .119* 219**
last period (.053) (.059) (.075) (.089)
No.
of Crisis 31 29 23 20
No.of
Obs.
645 581 483 350
percent correct 75 77 84 74
percent crisis correct 55 66 70 65
percent no-crisis 76 77 84 75
correct
model x
2
42.63*** 55.54*** 64.15*** 37.86***
AIC 224 201 149 141
1
The dependent variable takes
the
value one if there is a crisis and value of zero otherwise. Standard errors are in
parenthesis. *, **and *** indicate significance levels of
10,
5 and
1
percent respectively.
- 19 -
Table
4.
The Model
As An Early
Warning System
The model used is specification (3) from Table 3. The cut-off probability is equal to the in
sample crisis
frequency,
which is .05.
Country Crisis Date Not Predicted Predicted Predicted
predicted as as a crisis in as a crisis as a crisis
a crisis the year of starting 1 starting 3
the crisis year prior or more
years prior
Colombia 1982 X
Finland 1991 X
Indonesia 1992 X
India 1991 X
Israel 1983 X
Italy 1990 X
Jordan 1989 X
Japan 1992 X
Kenya 1993 X
Sri Lanka 1989 X
Mexico 1982 X
1994 X
Malaysia 1985 X
Nigeria 1991 X
Norway 1987 X
Philippines 1981 X
Portugal 1986 X
Turkey 1991 X
1994 X
United 1981 X
States
Uruguay 1981 X
Venezuela 1993 X
S. Africa 1985 X
Percent in 23 crisis 26 35 26 13
each episodes
category
- 20 -
B.
Significance of the Explanatory Variables
In both panels, low GDP growth is clearly associated with a higher probability of
a
banking crisis, confirming that developments in the real side of the economy have been a
major source of systemic banking sector problems in the 1980s and 1990s.
19
Also a decline
in the terms of trade appears to worsen banking sector unsoundness, but this variable is
significant only in two of the specifications and only at the 10 percent confidence level. GDP
growth loses significance if it is lagged by one period, indicating that negative shocks work
their way to bank balance sheets relatively quickly. Another possible interpretation is that the
banking crisis itself causes a decline in the contemporaneous rate of GDP growth as credit to
the economy withers. This interpretation would imply that causality runs in the opposite
direction than that suggested. However, since credit goes to finance future production and not
current production, it seems likely that a decline in credit would affect GDP only with a lag.
This interpretation is also supported by the findings of Kaminsky and Reinhart (1996), who
examine monthly data around the time of
a
banking crisis and find that the decline in GDP
growth tends to precede the onset of the banking crisis by about 8 months.
20
Both the real interest rate and inflation are highly significant in all the specifications
and have the expected sign, confirming the well-known vulnerability of the banking system to
nominal and real interest rate shocks; on the other hand, the behavior of the exchange rate
does not have an independent effect on the likelihood of
a
banking sector crisis once inflation
and terms of trade changes are controlled for.
21
The fiscal surplus is also not significant.
External vulnerability as measured by the ratio of M2 to reserves significantly increases the
probability of
a
crisis in most of the specifications, as predicted by the theory. This variable,
however, tends to loose significance when the surplus-to-GDP variable is omitted.
22
In the previous sections we conjectured that countries where the banking sector has a
larger exposure to private sector borrowers should be more vulnerable to banking crises. This
19
The GDP growth variable remains strongly significant even if the deviation of the growth
rate from its country mean is used.
20
Recall that our panels exclude years in which banking crises are under way, so periods in
which growth is likely to be negatively affected by the decline in credit due to the crisis are
not in the sample.
21
When inflation is excluded from the regression, the coefficient of the rate of depreciation
becomes significant and negative in most of the specifications.
22
Other measures of external vulnerability such as the ratio of foreign exchange liabilities
(gross and net) of the banking sector to reserves and the capital account surplus are less
significant than the M2-to-reserves ratio.
- 21 -
conjecture finds some support in our regression results, but the level of significance is low
except in one of the specifications. Also the other
financial
variables (credit growth and
the liquidity variable) do not develop a consistently significant coefficient in all of the
specifications, although the liquidity variable is significant in the fourth regression using the
second panel, and credit growth is significant (and positive) if lagged by two periods in the
third specification of the both
panels.
Thus, there is some evidence that a boom in credit
precedes banking crises, but the evidence is not very strong.
As predicted by the theory, low values of the
"law
and order" index, which should
proxy more opportunities to loot and/or a lower ability to carry out effective prudential
supervision, are associated with a higher likelihood of a crisis. It should be noted, however,
that it is difficult to disentangle the effect of this index from that of GDP per capita, given the
high degree of correlation between the two variables in our sample. Indexes of corruption,
quality of contract enforcement, quality of the bureaucracy, and delays in the justice system
are less significant than the "law and order" index.
Finally, the deposit insurance dummy variable has a significant positive sign in both
panels. Thus, the presence of an explicit insurance scheme, although it may have reduced
the incidence of self-fulfilling bank runs, appears to have worsened banking sector fragility
through moral hazard. This result may be taken as evidence that no deposit insurance or
perhaps implicit deposit insurance is preferable from the point of view of minimizing
banking sector fragility; however, it may more simply reflect weaknesses in the design and
implementation of deposit insurance schemes in our sample of
countries.
23
Clearly, more
work is needed to sort out this issue.
As explained in Section III above, because the empirical model is nonlinear the
estimated coefficients do not measure the percentage change in the estimated probability of a
crisis associated with a given percentage change in the explanatory variable, as in the standard
linear regression model. Rather, the impact of a change in each explanatory variable depends
upon the initial values of
all
the independent variables and their coefficients. To gain some
insight on the relative impact of each explanatory variable, using estimated coefficients from
equation (3) in Table 3, we have computed elasticities for a much-studied episode, the
Mexican banking crisis of
1994.
As shown in Table 5, the largest elasticities are those of the
rate of output growth and of the share of private credit to GDP (the latter
variable,
though,
is significant only at the 10 percent confidence level). The real interest rate and lagged credit
growth have elasticities of around 0.5, while the external vulnerability variable (the ratio of
M2 to reserves) and the rate of inflation have elasticities of 0.27 and 0.22 respectively. A
switch from explicit to no deposit insurance would have decreased the probability of
a
crisis
23
On the design and implementation of deposit insurance schemes, see Garcia (1995)
- 22 -
Table 5. Interpreting Regression Coefficients — the 1994 Mexican Crisis
1
The model used is specification (3) from Table 3. Given a change in an explanatory variable
the change in the probability of a crisis depends on the country's initial crisis probability, thus
on the initial values of all the independent variables and their estimated coefficients. Below,
we calculate the impact of
a
given change in the variables with significant coefficients on the
predicted probability of the 1994 Mexican crisis.
Initial Value Percent Change in Percent Change in
Initial Value the Probability of
Crisis
GROWTH 3.7 +10
-7.0***
RL.
INTEREST 6.7 +10 +5.6***
INFLATION 7.3 +10 +2.2**
M2/RESERVES 20.5 +10 +2.7***
PRIVATE/GDP 39.7 +10 +7.8*
CREDIT GRO
t
_
2
28.9 +10 +5.4***
GDP/CAP 1830 +10 -1.7*
DEPOSIT INS.
1
(=explicit) -100 (0-implicit) -61.6**
1*, **and *** indicate significance levels of
10,
5 and
1
percent respectively.
- 23 -
by over 60 percent. This large impact, of
course,
is due to the fact that a change in the dummy
variable from one to zero represents a 100 percent decline. As pointed out in the introduction,
these numbers have to be interpreted with caution, since the coefficients come from a reduced
form equation and we do not provide a structural model that makes explicit the connections
among the various explanatory variables.
V. THE COST OF BANKING CRISES
The approach taken so far treats all banking crises as uniform events. In practice,
however, the crises in our panel were of different severity. In this section, we test whether
the set of macroeconomic, structural, and institutional variables that are associated with the
occurrence of banking crises can also explain observed differences in the severity of the crisis.
We measure the severity of the crises by their cost (as a share of
GDP)
using the estimates in
Caprio and Klingebiel (1996), which are available for 24 of the 31 crisis episodes in our
sample. These estimates reflect the fiscal cost of each episode. The explanatory variables are
measured in the year in which the crisis begins. In interpreting the results it is important to
take into account that the cost of
a
crisis is an imperfect measure of the severity of the
problems because it is influenced also by how well monetary authorities and bank supervisors
deal with the crisis. Thus, some of the explanatory variables may be correlated with factors
affecting the quality of the policy response rather than with the severity of the crisis.
24
Table 6 reports the regression results. The coefficients are estimated using OLS, and
the standard errors are White's heteroskedasticity-consistent measures. Because the degrees
of freedom are few, these results should be taken with caution. Overall, the variables that are
significantly correlated with the probability of
a
crisis are also significantly correlated with the
cost of
a
crisis: among the macro variables, low GDP growth, adverse terms of trade changes,
high real interest rates, and high inflation tend to increase the cost of
a
crisis. Vulnerability to
a balance-of-payments crisis, a larger share of credit to the private sector, and lagged credit
growth are also significant and of the expected sign (although credit growth is significant only
in one of the two specifications in which it is included); the liquidity variable is significant only
if the other financial variables are excluded. The deposit insurance dummy and the "law and
order" index are also significant, indicating that the presence of explicit deposit insurance may
not only make banking crises more likely, but it may also make the such crises more expensive
to clean up. Conversely, an effective legal system that sanctions fraudulent behavior is likely
to reduce both the occurrence of systemic banking problems and their cost.
Finally, a variable capturing the length of the crisis episodes is negatively correlated
with the cost. Thus, crises that are cleaned up more quickly appear to be also the most
expensive. One possible explanation of this result is that more severe crises force
24
For a review of recent episodes of bank restructuring, see Dziobek and Pazarbasioglu
(1997).
-
24 -
Table
6.
Determinants of the Cost
of
a
Crisis
1
(1)
(2) (3) (4)
GROWTH
.580 .313
-1.119*** -1.233***
(.407) (.279) (.393) (.389)
TOT CHANGE
-.215 -.025
-1.226*** -1.470***
(.223) (.200) (.285) (.347)
DEPRECIATION
.016
.083*
.157***
.037
(.077) (.049) (.054) (.069)
RL.
INTEREST .466*** .564*** .456***
.28*
(.143) (.131) (.093) (.150)
INFLATION .454***
.533***
.417***
.273**
(.142) (.129) (.087) (.138)
CASH/BANK .338***
.197 .266
(.112) (.151) (.170)
M2/RESERVES
.151***
.232***
(.057) (.050)
PRIVATE/GDP .362*** .215**
(.127) (.122)
CREDIT GRO
t-2
.174
.289***
(.112) (.095)
GDP/CAP
.531 .281
(.311) (.337)
DEPOSIT INS 8.242** 11.699***
(3.460) (3.340)
LAW & ORDER -5.796** -5.026***
(2.207) (1.690)
DURATION -2.252**
(.795)
Adj.
R
2
.32 .40 .43 .54
No.
of
Obs.
24 24 19 19
1
The dependent variable is the cost of
a
banking crisis as a share of
GDP.
White's
heteroskedasticity consistent standard errors are in parenthesis. *, **and
***
indicate
significance levels of
10,
5 and
1
percent respectively.
- 25
policymakers to take quick and drastic action and, therefore, result in a speedier resolution
of the problems. Another interpretation could be that rescue operations that put the banking
system back on its feet relatively quickly require more budgetary resources, perhaps because
they involve an across-the-board bail out instead of more selective intervention aimed at
separating out efficient banks from inefficient institutions.
VI. CONCLUSIONS
Since the early 1980s systemic banking sector problems have emerged repeatedly all
over the world, and the need to understand the connections between banking sector fragility
and the economy is all the more urgent. The now numerous case studies indicate that, while
experiences vary quite substantially across countries and over time, there may be factors
common to all banking crises. This paper attempts to identify some of these factors by
estimating a multivariate logit model for a large panel of countries.
We find that banking crises tend to emerge when the macroeconomic environment is
weak; in particular, low GDP growth is significantly correlated with increased risk to the
banking sector. Vulnerability to aggregate output shocks is not necessarily a sign of
an
inefficient banking system, as the role of banks as
financial
intermediaries by
its
very nature
involves some risk-taking. However, banks could hedge some of the credit risk due to
fluctuations of the domestic economy by lending abroad. From this perspective, the expansion
of cross-border banking activities should improve the strength of banks all over the world.
Small developing countries, whose output is typically more volatile, should especially benefit
from increased internationalization. Entry by foreign banks could also be beneficial by
increasing competition and putting pressure on local authorities to upgrade the institutional
framework for banking activities, although lack of knowledge of local firms and of domestic
market conditions may constitute a significant barrier. In future work, we plan to explore in
more depth the connection between volatility, country size, and banking sector fragility.
Our results also indicate that an increased risk of banking sector problems may be one
of the consequences of
a
high rate of inflation, possibly because the high and volatile nominal
interest rates associated with high inflation make it difficult for banks to perform maturity
transformation. Thus, restrictive monetary policies that keep inflation in check are desirable
from the point of view of banking sector stability. However, when such policies are
implemented in the context of an inflation stabilization program they may lead to a sharp
increase in real interest rates; as our empirical evidence shows, high real rates tend to increase
the likelihood of
a
banking crisis. Thus, the design and implementation of effective inflation
stabilization programs should be accompanied by a careful evaluation of the impact on the
domestic banking system, and, in countries where the banking system appears weak, the
benefits of inflation stabilization should be carefully weighted against the costs of
a
possible
banking crisis.
- 26 -
High real interest rates may be the result of a host of factors other than inflation
stabilization policies (Brock, 1995). Among these factors is financial liberalization which, in
turn, is often named as one of the culprits for banking sector fragility in the policy debate. We
have found some (not very strong) evidence that a proxy for the degree of financial
liberalization significantly increases the likelihood of banking crises even when real interest
rates are controlled for; we plan to explore this issue further in future extensions by
developing more accurate indicators of financial liberalization.
Our regressions indicate rather unambiguously that the presence of an explicit deposit
insurance scheme tends to increase the probability of systemic banking problems. This
suggests that, while deposit insurance may reduce the incidence of self-fulfilling banking
panics, it introduces a significant degree of moral hazard which often has not been
successfully curbed through appropriate design of the insurance scheme or through effective
prudential supervision and regulation. Thus, reducing the moral hazard induced by deposit
insurance should be a priority for policy-makers interested in strengthening the banking
system; also, opting for an implicit rather than explicit deposit insurance scheme may be
preferable while the administrative capability needed to enforce a system of prudential
regulation is being created. To explore this issue further, we plan to test whether banking
sector fragility is affected by specific features of the deposit insurance system such as the
extent of the coverage, the type of premia charged to banks, the public or private nature of the
scheme, the presence of coinsurance and deductibles, and others.
Our study has several limitations: first, it leaves open the question of how sensitive the
correlations are to different aspects of the methodology, such as the estimation technique, the
treatment of
crisis
years, and the set of other explanatory variables included in the regression.
We plan to address this issue more satisfactorily in future work. Also, this study has focused
on macroeconomic and institutional variables at the expense of variables that capture the
structure of the banking system and, more generally, of financial markets. Aspects such as the
degree of capitalization of
banks,
the degree of concentration and the structure of competition
of the market for credit, the liquidity of the interbank market and of the bond market, the
ownership structure of the banks (public versus private), the quality of regulatory supervision,
and so on are likely to play an important role in breeding banking crises, but they are
neglected here because of lack of
data.
Perhaps a study limited to a smaller set of countries
that includes more structural variables could yield interesting results.
- 27 - APPENDIX I
SAMPLE
COMPOSITION AND DATA SOURCES
The countries included in the largest sample (regression
No.
1
in Table 3) are the
following: Austria, Australia, Burundi, Belgium, Bahrain, Canada, Switzerland, Chile, Congo,
Colombia, Cyprus, Denmark, Ecuador, Egypt, Finland, France, United Kingdom, Germany,
Greece, Guatemala, Guyana, Honduras, Indonesia, India, Ireland, Israel, Italy, Jamaica,
Jordan, Japan, Kenya, Korea, Sri Lanka, Mexico, Mali, Malaysia, Niger, Nigeria,
Netherlands, Norway, Nepal, New Zealand, Peru, Philippines, Papua New Guinea, Portugal,
Paraguay, Senegal, Singapore, El Salvador, Sweden, Swaziland, Seychelles, Syria, Togo,
Thailand, Turkey, Tanzania, Uganda, Uruguay, United States, Venezuela, South Africa,
Zaire, Zambia.
For most countries the years included are 1980-94; for some countries, however, a
shorter subperiod was included because of lack of data. Thus, some countries in the sample
had a banking crisis during 1980-94, but because of missing data in the years of the crisis that
crisis does not appear in Table
1
(Chile, Thailand, and Peru are such examples). The
following table provides details on the composition of each of the samples used.
Table A1. Composition of the Samples
Countries excluded from sample No. 1
Regression 2, Table 3 United Kingdom, Sweden, Zaire
Regression 3, Table 3 Burundi, Bahrain, Congo, Cyprus, United
Kingdom, Guyana, Mali, Niger, Nepal, Papua New
Guinea, Senegal, Singapore, Sweden, Swaziland,
Seychelles, Tanzania, Zaire
Regression 4, Table 3 Burundi, Congo, United Kingdom, Niger, Nepal,
Senegal, Singapore, Swaziland, Seychelles, Zaire
Regression 1, Table 2 Chile, Peru, Turkey
Regression 2, Table 2 Chile, United Kingdom, Peru, Singapore, Sweden,
Turkey, Zaire
Regression 3, Table 2 Burundi, Bahrain, Chile, Congo, Cyprus, United
Kingdom, Guyana, Mali, Niger, Nepal, Peru, Papua
New Guinea, Senegal, Singapore, Sweden,
Swaziland, Seychelles, Turkey, Tanzania, Zaire
Regression 4, Table 2 Burundi, Bahrain, Chile, Congo, Cyprus, United
Kingdom, Guyana, Israel, Mali, Niger, Nepal, Peru,
Papua New Guinea, Senegal, Singapore, Sweden,
Swaziland, Seychelles, Turkey, Tanzania, Zaire
- 28 - APPENDIX I
Table A2. Description of the Explanatory Variables and Sources
Variable Name Definition Source
Growth Rate of growth of
GDP
IFS data base where available. Otherwise,
WEO data base.
Tot change Change in the terms of WEO
trade
Depreciation Rate of change of the IFS
exchange rate
Real interest rate Nominal interest rate minus IFS. Where available, nominal rate on
the contemporaneous rate short-term government securities.
of inflation Otherwise,
a
rate charged by the Central
Bank to domestic banks such as the
discount
rate;
otherwise, the commercial
bank deposit interest rate
Inflation Rate of change of the GDP IFS
deflator
Surplus/GDP Ratio of Central IFS
Government budget surplus
to GDP
M2/reserves Ratio of M2 to foreign M2 is money plus quasi-money (lines 34 +
exchange reserves of the 35 from the IFS). Reserves are from the
Central Bank IFS.
Private/GDP Ratio of domestic credit to Domestic credit to the private sector is line
the private sector to GDP 32d from the IFS.
Cash/bank Ratio of bank liquid Bank reserves are line 20 of the
IFS.
Bank
reserves to bank assets assets are lines 21 + lines 22a to 22f of the
IFS.
Credit growth Rate of growth of real IFS
domestic credit
Deposit insurance Dummy variable for the Kyei (1995) and Tally and Mas (1990)
presence of
an
explicit
deposit insurance scheme
Law and order An index of the quality of International Country Risk Guide
law enforcement
-
29 -
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