Description
The recurrence of banking crises throughout the 1980s and 1990s, and in the more recent
2008-09 global financial crisis, has led to an expanding empirical literature on crisis explanation and
prediction. The purpose of this paper is to provide an analytical review of proxies for and important
determinants of banking crises-credit growth, financial liberalization, bank regulation and supervision.
Journal of Financial Economic Policy
International comparisons of bank regulation, liberalization, and banking crises
Puspa Amri Apanard P. Angkinand Clas Wihlborg
Article information:
To cite this document:
Puspa Amri Apanard P. Angkinand Clas Wihlborg, (2011),"International comparisons of bank regulation,
liberalization, and banking crises", J ournal of Financial Economic Policy, Vol. 3 Iss 4 pp. 322 - 339
Permanent link to this document:http://dx.doi.org/10.1108/17576381111182909
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Users who downloaded this article also downloaded:
Mikael Petitjean, (2013),"Bank failures and regulation: a critical review", J ournal of Financial Regulation and
Compliance, Vol. 21 Iss 1 pp. 16-38http://dx.doi.org/10.1108/13581981311297803
Wenling Lu, David A. Whidbee, (2013),"Bank structure and failure during the financial crisis", J ournal of
Financial Economic Policy, Vol. 5 Iss 3 pp. 281-299http://dx.doi.org/10.1108/J FEP-02-2013-0006
J ames R. Barth, Gerard Caprio, Ross Levine, (2013),"Bank regulation and supervision in 180
countries from 1999 to 2011", J ournal of Financial Economic Policy, Vol. 5 Iss 2 pp. 111-219 http://
dx.doi.org/10.1108/17576381311329661
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International comparisons of bank
regulation, liberalization,
and banking crises
Puspa Amri
School of Politics and Economics, Claremont Graduate University,
Claremont, California, USA
Apanard P. Angkinand
Milken Institute, Santa Monica, California, USA, and
Clas Wihlborg
Argyros School of Business and Economics, Chapman University,
Orange, California, USA
Abstract
Purpose – The recurrence of banking crises throughout the 1980s and 1990s, and in the more recent
2008-09 global ?nancial crisis, has led to an expanding empirical literature on crisis explanation and
prediction. The purpose of this paper is to provide an analytical review of proxies for and important
determinants of banking crises-credit growth, ?nancial liberalization, bank regulation and supervision.
Design/methodology/approach – The study surveys the banking crisis literature by comparing
proxies for and measures of banking crises and policy-related variables in the literature. Advantages
and disadvantages of different proxies are discussed.
Findings – Disagreements about determinants of banking crises are in part explained by the
difference in the chosen proxies used in empirical models. The usefulness of different proxies depends
partly on constraints in terms of time and country coverage but also on what particular policy question
is asked.
Originality/value – The study offers a comprehensive analysis of measurements of banking crises,
credit growth, ?nancial liberalization and banking regulations and concludes with an assessment of
existing proxies and databases. Since, the review points to the choice of proxies that best ?t speci?c
researchobjectives, it shouldserve as a reference point for empirical researchers inthe bankingcrisis area.
Keywords Banking, Financial institutions, Financial economics, Government policy, Regulation,
Financial markets, Financial crisis, Financial services, International ?nancial markets
Paper type Research paper
1. Introduction
The recurrence of ?nancial crises in both advanced and emerging markets throughout
the 1980s and 1990s, the recent severe global ?nancial crisis and the Eurozone debt crisis
have led to an expanding literature on the determinants of ?nancial crises. The literature
has grown to encompass a diversity of crisis types such as: balance of payments,
currency, banking, debt, in?ation, stock market and real estate crises. More often than
not, they represent different aspects of the same crisis episodes. Financial crises are of
particular concern because they often have real repercussions on economic growth and
employment.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cations – G01, G0, G, G28, G2, G, G15, G1
JFEP
3,4
322
Journal of Financial Economic Policy
Vol. 3 No. 4, 2011
pp. 322-339
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381111182909
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In this paper, we focus entirely on banking crises, which are particularly interesting
for two reasons. First, in most countries banks play a dominant role in the ?nancial
system, compared to equity and debt markets. Second, the special characteristics of
banks as providers of liquidity with longer term assets make them vulnerable to bank
runs and contagion effects from interbank positions. In times of ?nancial distress, even
a solvent bank may fail to meet its obligations given the illiquid and opaque nature of
its assets. Depositors and other creditors are often unable to distinguish between
solvent and insolvent banks (Diamond and Dybvig, 1983).
While it has been argued that “blind” bank runs can be mitigated by developing
deposit insurance systems, explicit and implicit deposit guarantees can increase the
likelihood of crisis because they tend to increase banks’ incentives to shift risk to
deposit insurance authorities and taxpayers while reducing the incentives of holders of
bank liabilities to monitor the riskiness of banks’ lending activities. This moral hazard
problem can lead to excessive risk taking on the part of bankers. Excess risk taking as
a result of explicit and implicit guarantees of depositors and other creditors seems to
have been a central feature in most ?nancial crises in modern times according to many
observers (Reinhart and Rogoff (RR, 2009)).
Empirical work on banking crises generally focus on one of two aspects: early
warning signals or factors explaining banking crisis. The signals are typically
indicators of macroeconomic activity such as credit expansion, which often interact with
indicators of “?nancial fragility”. High fragility implies that the banking systemis crisis
prone in response to relatively mild economic downturns or external shocks (Kaminsky
and Reinhart, 1999). Meanwhile, studies on the determinants of banking crises have
identi?ed a number of policy-related contributing factors such as government-created
safety net features for the banking system (e.g. deposit insurance) and institutional
arrangements (e.g. ?nancial liberalization, ?nancial regulatory structures, quality of
supervision, legal systems, and exchange rate regimes)[1].
The literature has so far been unable to produce a general consensus onthe key causal
factors leading to banking crises. For example, Barth et al. (2006) ?nd that of?cial
supervisory power and stricter capital requirements have no signi?cant effect on
banking crisis probabilities, while Noy (2004) and Amri and Kocher (2010) argue the
opposite. Angkinand et al. (2010) and Shehzad and de Haan (2009) ?nd evidence that
the direction of the effect of liberalization on banking crises depends on the strength of
capital regulation and supervision (CRS). The relationship among credit growth,
?nancial liberalization and banking crises are similarly subject to disagreements. We
return to these issues in Sections 3 and 4.
Given that these existing studies use different ways of operationalizing both the
dependent and independent variables, one question that naturally arises is to what
extent are the contradictory results driven by differences in chosen proxies? Differences
in country samples and time-period coverage explain some differences in results but the
choices of proxies for crisis, liberalization, strength of supervision and credit growth
seem to help explain contradictory results as well[2]. This contention was con?rmed
empirically in a longer working paper version of this study[3]. There, we report on tests
of robustness for determinants of banking crises by changing proxies one by one in
estimations for a speci?c group of countries over a certain period and a ?xed set of
macroeconomic controls. For example, strength of CRS as de?ned by Abiad et al. (2008)
has a signi?cant negative effect while an index constructed by Barth et al. (2006)
International
comparisons
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is not a signi?cant explanatory factor. Similarly, the choices of proxies for ?nancial
liberalization and credit growth affect results. We return to these ?ndings below.
Motivated by disagreements in the empirical literature, this study surveys
existing proxies and measurements of banking crises and the key crisis determinants.
Section 2 focuses on the de?nitions and proxies of banking crises. In Sections 3-5, we
survey proxies for each of three important explanatory variables-credit expansion,
?nancial liberalization, and bank CRS, respectively. Each section also provides a brief
literature review as well as discussion of existing proxies. Section 6 concludes by
assessinghowthe usefulness of different proxies depends onthe objective of the analysis.
2. De?nitions and proxies for banking crises
Banking crises can be studied on the country level as well as the bank level. A banking
crisis on the country level refers to a situation when there are bank failures on a
large-scale in the ?nancial system. A crisis of an individual bank can be de?ned more
unambiguously but for policy purposes the country level is obviously more interesting
from the point of view of repercussions on the real economy. On the country level,
likelihood of a banking crisis, banking system instability, lack of banking system
soundness and fragility are often used more or less interchangeably. Banking
instability generally has a broader de?nition than banking crisis. Instability may refer
to disruption in the payment system or volatility of asset prices that potentially could
lead to crises (Mishkin, 1996).
2.1 Banking crisis on the country level
To identify episodes of banking crises caused by bank runs, data on bank deposits
could in principle be used. Crises originating on the asset side of banks’ balance sheets
through the deterioration of asset values could be identi?ed by studying, for example,
non-performing loans (NPLs). The data for these variables are not available for a long
time span and they do not necessarily re?ect or capture widespread bank failures in
the banking system. Another reason why so few studies use NPL data is because the
reliability and comparability of the NPL data can be questioned in a cross-country
analysis. Most countries have been reluctant to reveal the existence of severe banking
problems in of?cial statistics, and the de?nition of NPL varies from country to country
although some convergence is ongoing.
Most studies of banking crises on the country level proceed by identifying dates
of crises from explicit events. Generally, crisis episodes are identi?ed based on a
combination of objective data and interviews with experts. In some cases, quantitative
data such as decline in deposit, NPLs and liquidity support are used with subjective
judgment to identify the timing of a crisis event. As summarized in Table I, the banking
crisis datasets using this event-based approach have been provided in a small number of
studies and cover a large number countries as well as decades. The data usually provide
the beginning and ending dates of each crisis episode in each country.
The very ?rst, comprehensive dataset using this event-based approach was compiled
by Lindgren et al. (LGS, 1996) and Caprio and Klingebiel (CK, 1996, 2002, and updated by
Caprio et al., 2005). Databases are also compiled byDemirgu¨c¸-Kunt andDetragiache (DD,
1998 and updated in DD (2005)) and RR (2009). The most recent studies that
provide banking crisis dates including the recent global ?nancial crisis are Laeven and
Valencia’s (LV, 2008, 2010) studies. Dates of banking crisis episodes from these studies
JFEP
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Table I.
Main data sources
for banking crises
at the country level
International
comparisons
325
D
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Table I.
JFEP
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326
D
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a
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d
b
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D
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t
2
1
:
4
3
2
4
J
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2
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1
6
(
P
T
)
are fairly highly correlated as they are compiled in a similar way and partly represent
re?nements of earlier studies. However, there are clearly great scope for judgmental
differences with respect to the beginning and end of crises (Barrell et al., 2010). For
example, the datingof the USsavings andloancrisis inLGSandDDis from1980 through
1992, while RR date the same crisis from 1986 through 1993 and CK from 1988 through
1991. LV identify the crisis as a one-year crisis which started and ended in 1988[4].
Dziobek and Pazarbasioglu (1997) limit the dataset to “systemic crises,” wherein
problem banks together hold at least 20 percent of total deposits in a country. Only
24 crises worldwide are covered in the study. Kaminsky and Reinhart (1999) identify
crises based on existing studies and on the ?nancial press. They include 20 countries
from1970 to mid-1995 in the study. To avoid dating too early or too late, they identify the
peak period when there is the heaviest government intervention and/or bank closures.
The objective of their paper is to examine the value of a number of macroeconomics
variables as signals or leading indicators of banking crises[5].
Existing empirical studies on banking crises employ the banking crisis indicator
fromthe sources mentioned in Table I by assigning a 0/1 dummy for non-crisis and crisis
periods. There are studies pointing out limitations and disadvantages of such datasets.
Boyd et al. (2009), for example, argue that this crisis-dating scheme in fact re?ects
government responses to perceived crises rather than the onset or duration of adverse
shocks to the banking industry. Serwa (2010) points out that these datasets fail to
measure the extent of a crisis. Governments also have great scope to employ, for
example, forbearance to prevent a crisis from erupting.
2.2 Indicators of banking sector fragility and distress of individual banks
A second group of studies in the banking crisis literature uses a continuous scale
for banking crisis based on variables from banks’ balance sheet and market data.
The variables commonly used are NPLs, provision for loan losses, and equity capital in
the banking systems. These variables are more suitable as proxies for risk taking or
fragility in a banking systemor an individual bank since there are no clear trigger points
for these variables to indicate a crisis that is associated with a sudden increase in ?scal
and more general economic costs[6].
There are a number of proxies for “?nancial stability” indices for “?nancial
soundness” and “?nancial stress” based on various components of balance sheet and
market data for the banking sector. For example, Corsetti et al. (2001) use NPL as an
indicator of ?nancial instability, but only if there is a presence of a “lending boom.”
Das et al. (2004) construct anindex of ?nancial systemsoundness fromthe average of the
capital ratio and the (inverted) ratio of NPL to assets. This index is weighted by the
credit-to-GDPratio in order to capture the extent of ?nancial intermediation in a country.
Kibritcioglu (2002) constructs a “?nancial fragility index” using proxies for liquidity
risk (bank deposits), credit risk (bank credits to the domestic private sector) and
exchange rate risk (foreign liabilities to banks). Illing and Liu (2006) and Hakkio
and Keeton (2009) create an “index of ?nancial stress” using the market data such as the
bond spreads for various bond types. An extreme value of the index is used to identify
periods of ?nancial crises. The International Monetary Fund (IMF) performs country
studies on the health of the banking system under the Financial Sector Assessment
Program instituted by the IMF (2003) and The World Bank. Indicators of the health
of the banking system in each country are presented in these occasional studies.
International
comparisons
327
D
o
w
n
l
o
a
d
e
d
b
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P
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Indicators included in the ?nancial stress index are falling asset prices, exchange rate
depreciation and/or losses of of?cial foreign reserves, insolvency of market participants,
defaults of debtors, rising interest rates, and increasing volatility of ?nancial market
returns. These indicators of ?nancial stress are used as leading indicators of weaknesses
and disruptions of the ?nancial system.
Some of the indicators of ?nancial fragility and stress discussed above build on
balance sheet and market data for individual banks. Thus, proxies for and indicators of
bank-speci?c crises or distress can be constructed from variables like NPL and capital
to asset ratios. The z-score as a measure of “distance to default” (see footnote [6])
belongs to the bank-speci?c category. Market prices on securities issued by individual
banks can also be used to extract implicit probabilities of default. Angkinand et al.
(2011) review the timeliness of equity prices, subordinated debt yields and credit
default spreads as indicators of distress of individual banks.
Interest in measures of the contribution of individual bank risk to the likelihood of a
systemic crisis has increased as a result of the recent ?nancial crisis. Fear of contagion
from banks that are “too big to fail” has led to attempts to identify “systematically
important ?nancial institutions” and their contribution to the likelihood of a crisis.
There is no clear consensus on howa bank’s contribution to the systemic risk should
be measured. Drehmann and Tarashev (2011) use variables such as bank size and
interbank lending and borrowing as measures of a bank’s systemic importance. Agroup
of researchers at NewYork University have developed anearly warning measure geared
towards providing a signal for the contribution of individual banks to systemic risk.
This measure, the marginal expected shortfall (MES), is an equity market-based signal
and it depends on the volatility of a bank’s equity price, the correlation with the market
return, and the co-movement of the tails of the distributions. Thus, it is designed to
capture special characteristics of the tails of distributions associated with systemic
shocks. The MES is described in Brownlee and Engle (2010)[7].
3. Measures of credit growth
The role of private credit growth has been a source of disagreement within
the banking crisis literature. There are theoretical as well as empirical grounds
for the diverging views on credit booms. Proponents of the predominant view point to
the boom-bust credit cycle explanation, along with distorted incentives to allocate credit
away from market-determined criteria during periods of credit expansion. The story is
straightforward: over-optimism about future earnings (i.e. the boom) boosts asset
valuations and the net worth of the ?rms, “arti?cially” in?ating their ability to
borrow[8], but when pro?t expectations are unmet (i.e. the bust), the process is reversed,
and banks face serious balance sheet problems. Others (Gourinchas et al., 2001),
however, viewexpansion of credit as a normal phase of ?nancial development. Far from
being a transitory development, Gourinchas et al. argue that credit booms can be
symptomatic of improvements in investing opportunities.
The relationship between rapid credit growth and banking crises remains
controversial although most of the studies listed in Table II reveal a link between
credit growth and subsequent crises. One reason is that results vary in multivariate
regressions when other, possibly correlated variables, are included. For example, the
signi?cance of credit growth in the empirical model of Joyce (2010) did not hold when
a proxy for ?nancial liberalization was included. This observation is consistent with
JFEP
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D
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Table II.
Measures of credit
growth in the banking
crisis literature
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Mendoza and Terrones (2008). They show that credit booms were preceded by ?nancial
liberalization in 20 percent of cases. Amri et al. (2011) ?nd that the interaction between
credit growth and ?nancial liberalization is signi?cant in predicting banking crisis
probability but credit growth alone is not.
The main variable used in these studies to construct a credit boom indicator is the
ratio of bank credit to the private sector relative to GDP[9] but there are variations in
how to operationalize the “credit boom” variable. One way is to employ a continuous
measure of private credit growth, another to de?ne a dichotomous measure of credit
boom episodes. Within the latter group, credit boom is coded as 1 when there occurs
“usually large” credit growth. However, there has been much debate about what is
“unusually large” vis-a` -vis “normal” credit growth – whether it can be captured by the
deviation in the growth of credit from its trend, above a certain “normal” threshold,
or the pace of credit growth, as compared with the growth of GDP itself[10] – as well as
the appropriate statistical ?lters to employ.
The continuous measure of credit growth has been used in most multivariate
logit/probit banking crisis models with the purpose of estimating the marginal effect of
private credit growth on the probability of a banking crisis. The dichotomous measure
has been used in frequency analysis and event studies relating episodes of credit
booms and banking crises (Mendoza and Terrones, 2008). Gourinchas et al. (2001)
compare probabilities of banking crises before and after credit boom episodes.
4. Datasets for ?nancial liberalization
Many countries relaxed internal and external restrictions on their ?nancial sectors
during the 1980s and the 1990s. Many argue that ?nancial liberalization lowers the cost
of capital and encourages banks to engage in more risky projects. It has also been
argued that increased competition can make banks become more vulnerable.
Most early studies of the impact of ?nancial liberalization on banking crises focused
on the elimination of interest rate controls (DD, 2001; Weller, 2001). A 0/1 dummy was
used to distinguish between periods before and after liberalization. The literature has
later expanded along with databases including liberalization of controls on credit
allocation, external capital ?ows, equity markets and entry. Eichengreen and Arteta
(2002), Noy (2004) and Ranciere et al. (2006) emphasized the difference between the
effects of domestic and external liberalization including relaxation of current and
capital account restrictions[11].
The disadvantage of a dummy variable for liberalization is that it does not capture
the extent or speed of liberalization. Continuous measures of degrees of ?nancial
liberalization require assumptions about the impact of liberalizationona variable affected
by liberalization. For example, Eichengreen and Arteta (2002) use the ratio of capital
?ows to GDP as a proxy for the degree of external liberalization. Bekaert et al. (2005) use
market capitalization to capture the intensity of equity market liberalization. These
continuous measures are obviously affected by a number of factors besides liberalization.
With an increased interest on the study of ?nancial liberalization, scholars have
developed the dichotomous measures of ?nancial liberalization fromsimple dummies to
incorporate intensity of liberalization. Two recent available databases are constructed
by Kaminsky and Schmukler (2008) and Abiad et al. (2008). The former includes
three-level ?nancial liberalization indices for capital account controls, interest rate
controls, and equity market restrictions in 28 countries from 1973 through 1999.
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The Financial Reforms Database in Abiad et al. (2008) categorizes ?nancial reforms
into seven dimensions each year from1973 to 2005[12]. Six of themrefer to liberalization
in the form of elimination of credit allocation controls, interest rate controls, capital
account controls, equity market controls, entry barriers and privatization while the
seventhdimension captures strength of bank CRS. The intensity of each reformcategory
is captured on a four-point scale from fully repressed to fully liberalized for the six
dimensions of liberalization. The CRS-dimension will be discussed in the next section.
The data are available for 98 countries.
The more comprehensive databases have allowedanalyses of effects of different types
of liberalization. However, as pointedout byAbiadandMody(2005) andAngkinandet al.
(2010), all dimensions of ?nancial liberalization are highly correlated since one type of
liberalizationis often accompaniedor followedbyother types of liberalization. Therefore,
identi?cation of effects of speci?c types of liberalization can be uncertain. Abiad and
Mody (2005) use only an aggregate index based on all available categories of ?nancial
reforms intheir empirical study. Angkinandet al. (2010) use anaggregate indexas well as
three types of liberalization which are grouped based on six ?nancial liberalization
variables. They?nd that the most important type of liberalizations in associatingwithan
increased likelihood of a banking crisis is the relaxation of restrictions on banks’ actions
and behavior (i.e. relaxation of interest and credit controls), but this relationship can be
clearly distinguished from the effects of other types of liberalization only when it is
conditioned on the strength of CRS in the domestic banking system.
Finally, we note that recent studies by Angkinand et al. (2010) and Shehzad and de
Haan (2009) do not con?rmthe conventional wisdomthat ?nancial liberalization always
increases the likelihood of banking crises. The former study ?nds that the likelihood of
crises is at a maximum with partial liberalization while the latter ?nds that after some
reform additional reforms lead to more stable ?nancial systems. In the next section we
will see how these results from these two studies are modi?ed by interaction between
liberalization and CRS.
5. Datasets for bank regulation and supervision
Several studies investigate the effects of bank regulation and supervision on
banking crises. These effects can be direct or captured by interaction between regulation
or supervision and other variables like deposit insurance coverage or ?nancial
liberalization.
The variables directly measuring bank regulation and supervision are available only
fromfewdata sources. There are relatedproxies for the qualityof countries legal systems
and bureaucracy. These proxies have greater coverage of countries and periods and they
are likely to be highly correlated with effectiveness of regulation and supervision of
banking systems. The databases commonly used in the literature are the following:
.
The World Bank’s Regulation and Supervision of Banks around the World:
a New Database, compiled by Barth et al. (2006) (The World Bank Survey).
.
Financial Reforms Database from Abiad et al. (2008), IMF (the variable called
enhancement of capital regulations and prudential supervision of the banking
sector).
.
International Country Risk Guide (ICRG) (the variables include law and order,
corruption, bureaucratic quality)[13].
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The ?rst database is the most commonly used as it is available for more than
140 countries, but the data availability is currently limited to three survey years; 1999,
2003 and 2005. One advantage of the World Bank Survey database is that it comprises
of a large number of survey questions on how bank are regulated and supervised.
Different aspects of regulation and supervision can be studied. The database includes
questions on restrictions on bank activities, formal supervisory powers, ownership,
organization, accounting and disclosure. The different dimensions can be objectively
de?ned. Studies using this database generally create a composite index from a certain
number of related survey questions to capture the extent of banking regulation to serve
the purpose of their studies.
The Financial Reforms Database provides data for the six types of ?nancial
liberalization discussed above as well as for “enhancement of capital regulations and
prudential supervision of the banking sector” (CRS). The scale of this variable goes from
0 to3 where 0 – unregulatedandunsupervisedand3 – stronglyregulatedandsupervised.
This dataset is available annually from 1973 to 2005 for 91 countries. However, this
database provides more limited aspects of banking regulation and supervision than the
?rst database. It has only one dimension and is based on assessment of available
information from different countries. Thus, it has a judgmental component[14].
We compare the data for CRS variables from the two mentioned sources by
country group in Table III. The data refer to 2005, the latest World Bank Survey year.
We construct an index from the three World Bank Survey questions that are
comparable to the components of CRS in the Financial Reforms Database.
Table III shows that on average the Financial Reforms Database assigns a higher
value of CRS to developed countries, a lower value for emerging market economies, and
the lowest value for other less-developed countries. The World Bank Survey, on the
other hand, ranks the group of other less-developed countries above emerging markets
in terms of having better bank regulation and supervision. The correlation between the
two variables is as low as 0.4.
Turning to the proxies for legal system and institutional quality, the most widely
used variables in the banking crisis literature include law and order, corruption,
bureaucratic quality from the ICRG. The data are available for more than 150 countries
and from 1984, and have been continuously updated. Other variables and sources,
which have been less frequently used, in part due to the limited number of country
and/or period coverage, are the Fraser Institute’s Economic FreedomIndex, the Heritage
Foundation’s Index of Economic Freedom, the Worldwide Governance Indicators
The World Bank Survey
(Barth, Caprio and Levine)
Financial Reforms Database
(Abiad, Detragiache and Tressel)
Developed economies 0.82 0.89
Emerging market economies 0.74 0.65
Other less-developed countries 0.78 0.55
All countries 0.78 0.68
Notes: The data in the table are average values of the CRS variable in 2005 by country group; the CRS
variable in the third column is from the Financial Reforms Database by Abiad et al. (2008); the CRS
variable in the second column is constructed based on the comparable survey questions from the
World Bank Survey by Barth et al. (2006); see the working version of this paper for detail explanations
Table III.
Comparison of bank
regulation data
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by Kaufmann et al. (2009). The real GDP per capita is also used in some studies as proxy
the general quality of domestic institutions. All these proxies re?ect only general aspects
of quality of institutions, and they may not directly measure bank regulation and
supervision in the speci?c way researchers desire. However, the advantage of using the
proxies from ICRG and GDP per capita is that they allow time-series analysis.
The general trend of institutional quality over time is positive although there are periods
of reversals in some countries. The trend for CRS in the Financial Reform Database is
positive as well.
Given the relatively low correlation in cross-section between the two datasets for
CRS it is not surprising that researchers come to different conclusions regarding the
relationship between banking crisis and strength of regulation and supervision. For
example, Barth et al. (2006) ?nd that the probability of banking crises is reduced in
countries with a high quality of law and order but not in countries with relatively
strong CRS as measured by the World Bank Survey. However, Angkinand et al. (2010)
?nd that CRS from the Financial Reforms Database reduces the likelihood of banking
crises in a cross-section time-series analysis. Barrell et al. (2010) also conclude that
bank regulation and supervision reduce banking crisis probabilities for the sample of
14 OECD countries from 1980 to 2007. Their conclusion is based on continuous
variables for the capital adequacy and liquidity ratios in banking systems. They do not
observe signi?cant effects for OECD countries of other proxies from Kaufmann et al.
(governance variable), the heritage foundation (banking and economic freedom
index), and the World Bank Survey database (selected banking regulatory variables).
Klomp (2010) does not ?nd any evidence that a credit market regulations index
from The Fraser Institute has a signi?cant impact on the stability of the banking
sector.
Turning ?nally to the interaction between strength of regulation and supervision,
and ?nancial liberalization Shehzad and De Haan (2009) ?nd that a positive impact on
?nancial stability is conditional on adequate supervision. Similarly, Angkinand et al.
(2010) ?nd that the decline in likelihood of banking crisis associated with increased
liberalization occurs only in countries with strong CRS. In countries with weak
regulation and supervision the effect of liberalization is the opposite.
6. Assessment of usefulness of existing proxies and conclusions
The analytical review and comparisons of various proxies for banking crises, as well
as important banking crisis determinants – credit expansion, ?nancial liberalization,
and bank regulation and supervision – show that differences between proxies explain
some contradictory results in the literature, as well as differences in sensitivity of the
likelihood of banking crises to changes in explanatory factors. Understanding of the
construction of different proxies can help the researcher choose the relevant proxy for
the research objective in cross-country and time-series analyses. Thus, the usefulness
of a particular proxy depends on the research objective. We emphasize the following
observations with respect to different variables:
.
Banking crises. Data for crisis dates, which are available in few studies, are
compiled in a similar way and sometimes draw upon one another. Banking crisis
episodes from the available data sources are highly correlated but there are
differences as a result of differences in judgment about what de?nes a crisis.
The researcher concerned primarily with very costly systemic crisis should
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choose the most restrictive crisis proxy. Differences in beginning and end dates
of crises matter less as long as one crisis is considered one observation whether it
lasts one or three years. It is obviously necessary to consider country and time
coverage; statistical signi?cance may have to be traded off against
appropriateness of proxy.
A fruitful area of research is the analysis of the relation between market-based
measures of probability of distress and other proxies for distress of individual banks as
well as for systemic crises:
.
Credit expansion. A continuous measure captures simply the growth of private
bank credit while dichotomous measures place emphasis on identi?cation of
lending boom episodes. The former measure seems most appropriate for studies
using continuous proxies for probability of crisis rather than 0/1 crisis dummies,
and for tests of theoretical hypotheses with respect to determinants and effects of
credit growth. The dichotomous measures are associated with much debate about
the identi?cation of “excess” credit growth relative to the growth endogenously
associated with the economy’s performance. There is scope for theoretical as well
as empirical research on this issue.
.
Financial liberalization. Attempts to measure ?nancial liberalization have
progressed from simple dichotomous measures to indices capturing a number
of dimensions and degree of liberalization over time. Although the recently
constructed comprehensive datasets, e.g. the IMF Financial Reforms Database in
Abiad et al. (2008) allow researchers to study the effects of different types of
liberalization, the different types are highly correlated since most countries have
moved towards greater liberalization. Moving away from simple dichotomous
measures has led to results contradicting the conventional wisdom that
liberalization contributes to the likelihood of banking crises. One remaining
challenge is to be able to identify effects of different types of liberalization in order
to produce meaningful policy implications with respect to effects of ?nancial
liberalization policies.
.
Bank regulation and supervision. There are tradeoffs in the choice between
available proxies for strength of regulation and supervision. The data from the
World Bank Survey of bank regulation and supervision by Barth et al. (2006)
consist of objective measures of many dimensions of regulation and supervision.
Thus, they seem to be appropriate for studies of effects of speci?c reforms. The
Financial Reform Database covers fewer dimensions but include informal
judgment about effectiveness of regulation and supervision. Another tradeoff
exists because the World Bank Survey data exist only for three years so far. The
literature commonly assumes that bank regulations rarely change over time, but
the proxies for bank regulation and institutional quality from other data sources
show otherwise.
Finally, we note that results with respect to the effects of ?nancial liberalization and
strength of regulation and supervision on banking crises are strongly affected by
interaction between these variables. We suspect that banking crises are in?uenced
by interaction between these variables and credit growth as well.
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Notes
1. See, for example, Angkinand et al. (2010) on the effect of ?nancial liberalization and bank
regulation on banking crises and Angkinand and Willett (2011) on the impact of exchange
rate regimes.
2. In cross-country analyses, the difference in empirical ?ndings on the early warnings and
determinants of banking crises could also be driven by the difference in methodologies used.
See, for example, DD (2005) and Davis and Karim (2008) for the review of different
methodologies used in the banking crisis literature.
3. Available at: www.cgu.edu/pages/1380.asp
4. Additional statistical comparisons of the number of banking crisis episodes identi?ed by
these ?ve studies can be found in the working paper version of this paper (see footnote [3]).
5. There are few other studies using the event-based approach to identify banking crisis
episodes. Their data have been less frequently used in the literature. For the additional
review (Kibritcioglu, 2002).
6. There are also more theory based proxies for risk taking on the bank level intended to
measure “distance to default.” The z-score based on accounting data used in Boyd and
Graham (1986) or market data used in Hovakimian et al. (2003) incorporates the capital asset
ratio, the return on assets and the SD of this return.
7. Available at:http://vlab.stern.nyu.edu/analysis/RISK.USFIN-MR.MES
8. This is formally demonstrated by Bernanke et al. (1998) through the ?nancial accelerator
model.
9. Studies generally obtain the data for domestic bank credit to the private sector from two
sources: (1) WDI; and (2) IFS (line 22d and42d). In some studies such as Mendoza and Terrones
(2008), this variable is transformed into “real credit per capita,” by adjusting to consumer price
in?ation and the total population, while some others (Table II) employ “net domestic credit”
which includes bank credit to both the government and the public sector.
10. This measurement issue is similarly experienced in de?ning “sudden stops” as discussed in
Sula et al., also in this special issue.
11. Measures of external liberalization, i.e. the degree of capital controls, are discussed in the
paper in this special issue on “international aspects of currency policies” and
Potchamanawong et al. (2008).
12. The data are available to the public at: www.imf.org/external/pubs/cat/longres.aspx?
sk¼22485.0 (accessed July 31, 2011).
13. The World Bank Survey data are available to the public at:http://go.worldbank.org/
SNUSW978P0. The data from ICRG, available at: www.prsgroup.com/ICRG.aspx, require
subscriptions. See also footnote [12].
14. For other sources of bank regulation datasets, Neyapti and Dincer (2005) develop an index of
legal quality of bank regulation and supervision. They identify a total of 98 criteria related to
the quality of banking regulation and code them using information retrieved from actual
banking laws. However, we do not ?nd that their datasets have been used in existing studies.
References
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Further reading
von Hagen, J. and Ho, T. (2007), “Money market pressure and the determinants of banking
crises”, Journal of Money, Credit and Banking, Vol. 39 No. 5, pp. 1037-66.
About the authors
Puspa Amri is a PhD candidate in Economics and Political Science at Claremont Graduate
University. She has a BA degree in Economics and Development Studies from the University of
Indonesia, Jakarta, and holds an MA degree in International Relations and Economics from the
School of Advanced International Studies, Johns Hopkins University. She has also served as an
Adjunct Faculty at the University of Indonesia, and an Economist at the Jakarta-based Center
for Strategic and International Studies. Puspa Amri is the corresponding author and can be
contacted at: [email protected]
Apanard P. Angkinand (PhD, Claremont Graduate University) is an Economist in the
Financial Research Group at the Milken Institute. Her research focuses on ?nancial institutions,
international ?nance, emerging market economies and ?nancial crises. Her work has been
published in various academic journals, including the Journal of International Money
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and Finance, International Review of Finance, Open Economies Review, Journal of International
Financial Markets, Institutions & Money, and International Journal of Economics and Finance,
as well as in several books and edited volumes. Prior to joining the Institute, Dr Angkinand was
an Assistant Professor of Economics at the University of Illinois at Spring?eld, 2005-2008. While
completing her PhD, she also held Visiting Scholar positions at the Claremont Institute for
Economic Policy Studies and the Freeman Program in Asian Political Economy at the Claremont
Colleges, as well as a Lecturer of Economics at Pitzer College, California State Polytechnic
University, Pomona and the University of Redlands. She received a PhD in Economics from
Claremont Graduate University in 2005.
Clas Wihlborg has held the Fletcher Jones Chair of International Business at the Argyros
School of Business and Economics, Chapman University since January 2008. He was Professor of
Finance at the Copenhagen Business School (CBS) 2000-2007 and Director of the Center for Law,
Economics and Financial Institutions at CBS (LEFIC). He was the Felix Neubergh Professor of
Banking and Finance at Gothenburg University 1990-2000 after having held positions at New
York University and the University of Southern California in the USA. He obtained his PhD in
Economics in 1977 at Princeton University and an Honorary Doctorate at Lund University in
2009. Research and teaching activities have focused on ?nancial institutions, international
?nance, and corporate ?nance. Publications include numerous articles and books; most recently
Corporate Decision-making with Macroeconomic Uncertainty: Performance and Risk
Management (with Lars Oxelheim) (Oxford University Press, 2008). He is a member of the
European Shadow Financial Regulatory Committee and the Royal Swedish Academy of
Engineering Sciences (IVA).
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doc_856221101.pdf
The recurrence of banking crises throughout the 1980s and 1990s, and in the more recent
2008-09 global financial crisis, has led to an expanding empirical literature on crisis explanation and
prediction. The purpose of this paper is to provide an analytical review of proxies for and important
determinants of banking crises-credit growth, financial liberalization, bank regulation and supervision.
Journal of Financial Economic Policy
International comparisons of bank regulation, liberalization, and banking crises
Puspa Amri Apanard P. Angkinand Clas Wihlborg
Article information:
To cite this document:
Puspa Amri Apanard P. Angkinand Clas Wihlborg, (2011),"International comparisons of bank regulation,
liberalization, and banking crises", J ournal of Financial Economic Policy, Vol. 3 Iss 4 pp. 322 - 339
Permanent link to this document:http://dx.doi.org/10.1108/17576381111182909
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Wenling Lu, David A. Whidbee, (2013),"Bank structure and failure during the financial crisis", J ournal of
Financial Economic Policy, Vol. 5 Iss 3 pp. 281-299http://dx.doi.org/10.1108/J FEP-02-2013-0006
J ames R. Barth, Gerard Caprio, Ross Levine, (2013),"Bank regulation and supervision in 180
countries from 1999 to 2011", J ournal of Financial Economic Policy, Vol. 5 Iss 2 pp. 111-219 http://
dx.doi.org/10.1108/17576381311329661
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International comparisons of bank
regulation, liberalization,
and banking crises
Puspa Amri
School of Politics and Economics, Claremont Graduate University,
Claremont, California, USA
Apanard P. Angkinand
Milken Institute, Santa Monica, California, USA, and
Clas Wihlborg
Argyros School of Business and Economics, Chapman University,
Orange, California, USA
Abstract
Purpose – The recurrence of banking crises throughout the 1980s and 1990s, and in the more recent
2008-09 global ?nancial crisis, has led to an expanding empirical literature on crisis explanation and
prediction. The purpose of this paper is to provide an analytical review of proxies for and important
determinants of banking crises-credit growth, ?nancial liberalization, bank regulation and supervision.
Design/methodology/approach – The study surveys the banking crisis literature by comparing
proxies for and measures of banking crises and policy-related variables in the literature. Advantages
and disadvantages of different proxies are discussed.
Findings – Disagreements about determinants of banking crises are in part explained by the
difference in the chosen proxies used in empirical models. The usefulness of different proxies depends
partly on constraints in terms of time and country coverage but also on what particular policy question
is asked.
Originality/value – The study offers a comprehensive analysis of measurements of banking crises,
credit growth, ?nancial liberalization and banking regulations and concludes with an assessment of
existing proxies and databases. Since, the review points to the choice of proxies that best ?t speci?c
researchobjectives, it shouldserve as a reference point for empirical researchers inthe bankingcrisis area.
Keywords Banking, Financial institutions, Financial economics, Government policy, Regulation,
Financial markets, Financial crisis, Financial services, International ?nancial markets
Paper type Research paper
1. Introduction
The recurrence of ?nancial crises in both advanced and emerging markets throughout
the 1980s and 1990s, the recent severe global ?nancial crisis and the Eurozone debt crisis
have led to an expanding literature on the determinants of ?nancial crises. The literature
has grown to encompass a diversity of crisis types such as: balance of payments,
currency, banking, debt, in?ation, stock market and real estate crises. More often than
not, they represent different aspects of the same crisis episodes. Financial crises are of
particular concern because they often have real repercussions on economic growth and
employment.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cations – G01, G0, G, G28, G2, G, G15, G1
JFEP
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Journal of Financial Economic Policy
Vol. 3 No. 4, 2011
pp. 322-339
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381111182909
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In this paper, we focus entirely on banking crises, which are particularly interesting
for two reasons. First, in most countries banks play a dominant role in the ?nancial
system, compared to equity and debt markets. Second, the special characteristics of
banks as providers of liquidity with longer term assets make them vulnerable to bank
runs and contagion effects from interbank positions. In times of ?nancial distress, even
a solvent bank may fail to meet its obligations given the illiquid and opaque nature of
its assets. Depositors and other creditors are often unable to distinguish between
solvent and insolvent banks (Diamond and Dybvig, 1983).
While it has been argued that “blind” bank runs can be mitigated by developing
deposit insurance systems, explicit and implicit deposit guarantees can increase the
likelihood of crisis because they tend to increase banks’ incentives to shift risk to
deposit insurance authorities and taxpayers while reducing the incentives of holders of
bank liabilities to monitor the riskiness of banks’ lending activities. This moral hazard
problem can lead to excessive risk taking on the part of bankers. Excess risk taking as
a result of explicit and implicit guarantees of depositors and other creditors seems to
have been a central feature in most ?nancial crises in modern times according to many
observers (Reinhart and Rogoff (RR, 2009)).
Empirical work on banking crises generally focus on one of two aspects: early
warning signals or factors explaining banking crisis. The signals are typically
indicators of macroeconomic activity such as credit expansion, which often interact with
indicators of “?nancial fragility”. High fragility implies that the banking systemis crisis
prone in response to relatively mild economic downturns or external shocks (Kaminsky
and Reinhart, 1999). Meanwhile, studies on the determinants of banking crises have
identi?ed a number of policy-related contributing factors such as government-created
safety net features for the banking system (e.g. deposit insurance) and institutional
arrangements (e.g. ?nancial liberalization, ?nancial regulatory structures, quality of
supervision, legal systems, and exchange rate regimes)[1].
The literature has so far been unable to produce a general consensus onthe key causal
factors leading to banking crises. For example, Barth et al. (2006) ?nd that of?cial
supervisory power and stricter capital requirements have no signi?cant effect on
banking crisis probabilities, while Noy (2004) and Amri and Kocher (2010) argue the
opposite. Angkinand et al. (2010) and Shehzad and de Haan (2009) ?nd evidence that
the direction of the effect of liberalization on banking crises depends on the strength of
capital regulation and supervision (CRS). The relationship among credit growth,
?nancial liberalization and banking crises are similarly subject to disagreements. We
return to these issues in Sections 3 and 4.
Given that these existing studies use different ways of operationalizing both the
dependent and independent variables, one question that naturally arises is to what
extent are the contradictory results driven by differences in chosen proxies? Differences
in country samples and time-period coverage explain some differences in results but the
choices of proxies for crisis, liberalization, strength of supervision and credit growth
seem to help explain contradictory results as well[2]. This contention was con?rmed
empirically in a longer working paper version of this study[3]. There, we report on tests
of robustness for determinants of banking crises by changing proxies one by one in
estimations for a speci?c group of countries over a certain period and a ?xed set of
macroeconomic controls. For example, strength of CRS as de?ned by Abiad et al. (2008)
has a signi?cant negative effect while an index constructed by Barth et al. (2006)
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is not a signi?cant explanatory factor. Similarly, the choices of proxies for ?nancial
liberalization and credit growth affect results. We return to these ?ndings below.
Motivated by disagreements in the empirical literature, this study surveys
existing proxies and measurements of banking crises and the key crisis determinants.
Section 2 focuses on the de?nitions and proxies of banking crises. In Sections 3-5, we
survey proxies for each of three important explanatory variables-credit expansion,
?nancial liberalization, and bank CRS, respectively. Each section also provides a brief
literature review as well as discussion of existing proxies. Section 6 concludes by
assessinghowthe usefulness of different proxies depends onthe objective of the analysis.
2. De?nitions and proxies for banking crises
Banking crises can be studied on the country level as well as the bank level. A banking
crisis on the country level refers to a situation when there are bank failures on a
large-scale in the ?nancial system. A crisis of an individual bank can be de?ned more
unambiguously but for policy purposes the country level is obviously more interesting
from the point of view of repercussions on the real economy. On the country level,
likelihood of a banking crisis, banking system instability, lack of banking system
soundness and fragility are often used more or less interchangeably. Banking
instability generally has a broader de?nition than banking crisis. Instability may refer
to disruption in the payment system or volatility of asset prices that potentially could
lead to crises (Mishkin, 1996).
2.1 Banking crisis on the country level
To identify episodes of banking crises caused by bank runs, data on bank deposits
could in principle be used. Crises originating on the asset side of banks’ balance sheets
through the deterioration of asset values could be identi?ed by studying, for example,
non-performing loans (NPLs). The data for these variables are not available for a long
time span and they do not necessarily re?ect or capture widespread bank failures in
the banking system. Another reason why so few studies use NPL data is because the
reliability and comparability of the NPL data can be questioned in a cross-country
analysis. Most countries have been reluctant to reveal the existence of severe banking
problems in of?cial statistics, and the de?nition of NPL varies from country to country
although some convergence is ongoing.
Most studies of banking crises on the country level proceed by identifying dates
of crises from explicit events. Generally, crisis episodes are identi?ed based on a
combination of objective data and interviews with experts. In some cases, quantitative
data such as decline in deposit, NPLs and liquidity support are used with subjective
judgment to identify the timing of a crisis event. As summarized in Table I, the banking
crisis datasets using this event-based approach have been provided in a small number of
studies and cover a large number countries as well as decades. The data usually provide
the beginning and ending dates of each crisis episode in each country.
The very ?rst, comprehensive dataset using this event-based approach was compiled
by Lindgren et al. (LGS, 1996) and Caprio and Klingebiel (CK, 1996, 2002, and updated by
Caprio et al., 2005). Databases are also compiled byDemirgu¨c¸-Kunt andDetragiache (DD,
1998 and updated in DD (2005)) and RR (2009). The most recent studies that
provide banking crisis dates including the recent global ?nancial crisis are Laeven and
Valencia’s (LV, 2008, 2010) studies. Dates of banking crisis episodes from these studies
JFEP
3,4
324
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Table I.
Main data sources
for banking crises
at the country level
International
comparisons
325
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Table I.
JFEP
3,4
326
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are fairly highly correlated as they are compiled in a similar way and partly represent
re?nements of earlier studies. However, there are clearly great scope for judgmental
differences with respect to the beginning and end of crises (Barrell et al., 2010). For
example, the datingof the USsavings andloancrisis inLGSandDDis from1980 through
1992, while RR date the same crisis from 1986 through 1993 and CK from 1988 through
1991. LV identify the crisis as a one-year crisis which started and ended in 1988[4].
Dziobek and Pazarbasioglu (1997) limit the dataset to “systemic crises,” wherein
problem banks together hold at least 20 percent of total deposits in a country. Only
24 crises worldwide are covered in the study. Kaminsky and Reinhart (1999) identify
crises based on existing studies and on the ?nancial press. They include 20 countries
from1970 to mid-1995 in the study. To avoid dating too early or too late, they identify the
peak period when there is the heaviest government intervention and/or bank closures.
The objective of their paper is to examine the value of a number of macroeconomics
variables as signals or leading indicators of banking crises[5].
Existing empirical studies on banking crises employ the banking crisis indicator
fromthe sources mentioned in Table I by assigning a 0/1 dummy for non-crisis and crisis
periods. There are studies pointing out limitations and disadvantages of such datasets.
Boyd et al. (2009), for example, argue that this crisis-dating scheme in fact re?ects
government responses to perceived crises rather than the onset or duration of adverse
shocks to the banking industry. Serwa (2010) points out that these datasets fail to
measure the extent of a crisis. Governments also have great scope to employ, for
example, forbearance to prevent a crisis from erupting.
2.2 Indicators of banking sector fragility and distress of individual banks
A second group of studies in the banking crisis literature uses a continuous scale
for banking crisis based on variables from banks’ balance sheet and market data.
The variables commonly used are NPLs, provision for loan losses, and equity capital in
the banking systems. These variables are more suitable as proxies for risk taking or
fragility in a banking systemor an individual bank since there are no clear trigger points
for these variables to indicate a crisis that is associated with a sudden increase in ?scal
and more general economic costs[6].
There are a number of proxies for “?nancial stability” indices for “?nancial
soundness” and “?nancial stress” based on various components of balance sheet and
market data for the banking sector. For example, Corsetti et al. (2001) use NPL as an
indicator of ?nancial instability, but only if there is a presence of a “lending boom.”
Das et al. (2004) construct anindex of ?nancial systemsoundness fromthe average of the
capital ratio and the (inverted) ratio of NPL to assets. This index is weighted by the
credit-to-GDPratio in order to capture the extent of ?nancial intermediation in a country.
Kibritcioglu (2002) constructs a “?nancial fragility index” using proxies for liquidity
risk (bank deposits), credit risk (bank credits to the domestic private sector) and
exchange rate risk (foreign liabilities to banks). Illing and Liu (2006) and Hakkio
and Keeton (2009) create an “index of ?nancial stress” using the market data such as the
bond spreads for various bond types. An extreme value of the index is used to identify
periods of ?nancial crises. The International Monetary Fund (IMF) performs country
studies on the health of the banking system under the Financial Sector Assessment
Program instituted by the IMF (2003) and The World Bank. Indicators of the health
of the banking system in each country are presented in these occasional studies.
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Indicators included in the ?nancial stress index are falling asset prices, exchange rate
depreciation and/or losses of of?cial foreign reserves, insolvency of market participants,
defaults of debtors, rising interest rates, and increasing volatility of ?nancial market
returns. These indicators of ?nancial stress are used as leading indicators of weaknesses
and disruptions of the ?nancial system.
Some of the indicators of ?nancial fragility and stress discussed above build on
balance sheet and market data for individual banks. Thus, proxies for and indicators of
bank-speci?c crises or distress can be constructed from variables like NPL and capital
to asset ratios. The z-score as a measure of “distance to default” (see footnote [6])
belongs to the bank-speci?c category. Market prices on securities issued by individual
banks can also be used to extract implicit probabilities of default. Angkinand et al.
(2011) review the timeliness of equity prices, subordinated debt yields and credit
default spreads as indicators of distress of individual banks.
Interest in measures of the contribution of individual bank risk to the likelihood of a
systemic crisis has increased as a result of the recent ?nancial crisis. Fear of contagion
from banks that are “too big to fail” has led to attempts to identify “systematically
important ?nancial institutions” and their contribution to the likelihood of a crisis.
There is no clear consensus on howa bank’s contribution to the systemic risk should
be measured. Drehmann and Tarashev (2011) use variables such as bank size and
interbank lending and borrowing as measures of a bank’s systemic importance. Agroup
of researchers at NewYork University have developed anearly warning measure geared
towards providing a signal for the contribution of individual banks to systemic risk.
This measure, the marginal expected shortfall (MES), is an equity market-based signal
and it depends on the volatility of a bank’s equity price, the correlation with the market
return, and the co-movement of the tails of the distributions. Thus, it is designed to
capture special characteristics of the tails of distributions associated with systemic
shocks. The MES is described in Brownlee and Engle (2010)[7].
3. Measures of credit growth
The role of private credit growth has been a source of disagreement within
the banking crisis literature. There are theoretical as well as empirical grounds
for the diverging views on credit booms. Proponents of the predominant view point to
the boom-bust credit cycle explanation, along with distorted incentives to allocate credit
away from market-determined criteria during periods of credit expansion. The story is
straightforward: over-optimism about future earnings (i.e. the boom) boosts asset
valuations and the net worth of the ?rms, “arti?cially” in?ating their ability to
borrow[8], but when pro?t expectations are unmet (i.e. the bust), the process is reversed,
and banks face serious balance sheet problems. Others (Gourinchas et al., 2001),
however, viewexpansion of credit as a normal phase of ?nancial development. Far from
being a transitory development, Gourinchas et al. argue that credit booms can be
symptomatic of improvements in investing opportunities.
The relationship between rapid credit growth and banking crises remains
controversial although most of the studies listed in Table II reveal a link between
credit growth and subsequent crises. One reason is that results vary in multivariate
regressions when other, possibly correlated variables, are included. For example, the
signi?cance of credit growth in the empirical model of Joyce (2010) did not hold when
a proxy for ?nancial liberalization was included. This observation is consistent with
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Table II.
Measures of credit
growth in the banking
crisis literature
International
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Mendoza and Terrones (2008). They show that credit booms were preceded by ?nancial
liberalization in 20 percent of cases. Amri et al. (2011) ?nd that the interaction between
credit growth and ?nancial liberalization is signi?cant in predicting banking crisis
probability but credit growth alone is not.
The main variable used in these studies to construct a credit boom indicator is the
ratio of bank credit to the private sector relative to GDP[9] but there are variations in
how to operationalize the “credit boom” variable. One way is to employ a continuous
measure of private credit growth, another to de?ne a dichotomous measure of credit
boom episodes. Within the latter group, credit boom is coded as 1 when there occurs
“usually large” credit growth. However, there has been much debate about what is
“unusually large” vis-a` -vis “normal” credit growth – whether it can be captured by the
deviation in the growth of credit from its trend, above a certain “normal” threshold,
or the pace of credit growth, as compared with the growth of GDP itself[10] – as well as
the appropriate statistical ?lters to employ.
The continuous measure of credit growth has been used in most multivariate
logit/probit banking crisis models with the purpose of estimating the marginal effect of
private credit growth on the probability of a banking crisis. The dichotomous measure
has been used in frequency analysis and event studies relating episodes of credit
booms and banking crises (Mendoza and Terrones, 2008). Gourinchas et al. (2001)
compare probabilities of banking crises before and after credit boom episodes.
4. Datasets for ?nancial liberalization
Many countries relaxed internal and external restrictions on their ?nancial sectors
during the 1980s and the 1990s. Many argue that ?nancial liberalization lowers the cost
of capital and encourages banks to engage in more risky projects. It has also been
argued that increased competition can make banks become more vulnerable.
Most early studies of the impact of ?nancial liberalization on banking crises focused
on the elimination of interest rate controls (DD, 2001; Weller, 2001). A 0/1 dummy was
used to distinguish between periods before and after liberalization. The literature has
later expanded along with databases including liberalization of controls on credit
allocation, external capital ?ows, equity markets and entry. Eichengreen and Arteta
(2002), Noy (2004) and Ranciere et al. (2006) emphasized the difference between the
effects of domestic and external liberalization including relaxation of current and
capital account restrictions[11].
The disadvantage of a dummy variable for liberalization is that it does not capture
the extent or speed of liberalization. Continuous measures of degrees of ?nancial
liberalization require assumptions about the impact of liberalizationona variable affected
by liberalization. For example, Eichengreen and Arteta (2002) use the ratio of capital
?ows to GDP as a proxy for the degree of external liberalization. Bekaert et al. (2005) use
market capitalization to capture the intensity of equity market liberalization. These
continuous measures are obviously affected by a number of factors besides liberalization.
With an increased interest on the study of ?nancial liberalization, scholars have
developed the dichotomous measures of ?nancial liberalization fromsimple dummies to
incorporate intensity of liberalization. Two recent available databases are constructed
by Kaminsky and Schmukler (2008) and Abiad et al. (2008). The former includes
three-level ?nancial liberalization indices for capital account controls, interest rate
controls, and equity market restrictions in 28 countries from 1973 through 1999.
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The Financial Reforms Database in Abiad et al. (2008) categorizes ?nancial reforms
into seven dimensions each year from1973 to 2005[12]. Six of themrefer to liberalization
in the form of elimination of credit allocation controls, interest rate controls, capital
account controls, equity market controls, entry barriers and privatization while the
seventhdimension captures strength of bank CRS. The intensity of each reformcategory
is captured on a four-point scale from fully repressed to fully liberalized for the six
dimensions of liberalization. The CRS-dimension will be discussed in the next section.
The data are available for 98 countries.
The more comprehensive databases have allowedanalyses of effects of different types
of liberalization. However, as pointedout byAbiadandMody(2005) andAngkinandet al.
(2010), all dimensions of ?nancial liberalization are highly correlated since one type of
liberalizationis often accompaniedor followedbyother types of liberalization. Therefore,
identi?cation of effects of speci?c types of liberalization can be uncertain. Abiad and
Mody (2005) use only an aggregate index based on all available categories of ?nancial
reforms intheir empirical study. Angkinandet al. (2010) use anaggregate indexas well as
three types of liberalization which are grouped based on six ?nancial liberalization
variables. They?nd that the most important type of liberalizations in associatingwithan
increased likelihood of a banking crisis is the relaxation of restrictions on banks’ actions
and behavior (i.e. relaxation of interest and credit controls), but this relationship can be
clearly distinguished from the effects of other types of liberalization only when it is
conditioned on the strength of CRS in the domestic banking system.
Finally, we note that recent studies by Angkinand et al. (2010) and Shehzad and de
Haan (2009) do not con?rmthe conventional wisdomthat ?nancial liberalization always
increases the likelihood of banking crises. The former study ?nds that the likelihood of
crises is at a maximum with partial liberalization while the latter ?nds that after some
reform additional reforms lead to more stable ?nancial systems. In the next section we
will see how these results from these two studies are modi?ed by interaction between
liberalization and CRS.
5. Datasets for bank regulation and supervision
Several studies investigate the effects of bank regulation and supervision on
banking crises. These effects can be direct or captured by interaction between regulation
or supervision and other variables like deposit insurance coverage or ?nancial
liberalization.
The variables directly measuring bank regulation and supervision are available only
fromfewdata sources. There are relatedproxies for the qualityof countries legal systems
and bureaucracy. These proxies have greater coverage of countries and periods and they
are likely to be highly correlated with effectiveness of regulation and supervision of
banking systems. The databases commonly used in the literature are the following:
.
The World Bank’s Regulation and Supervision of Banks around the World:
a New Database, compiled by Barth et al. (2006) (The World Bank Survey).
.
Financial Reforms Database from Abiad et al. (2008), IMF (the variable called
enhancement of capital regulations and prudential supervision of the banking
sector).
.
International Country Risk Guide (ICRG) (the variables include law and order,
corruption, bureaucratic quality)[13].
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The ?rst database is the most commonly used as it is available for more than
140 countries, but the data availability is currently limited to three survey years; 1999,
2003 and 2005. One advantage of the World Bank Survey database is that it comprises
of a large number of survey questions on how bank are regulated and supervised.
Different aspects of regulation and supervision can be studied. The database includes
questions on restrictions on bank activities, formal supervisory powers, ownership,
organization, accounting and disclosure. The different dimensions can be objectively
de?ned. Studies using this database generally create a composite index from a certain
number of related survey questions to capture the extent of banking regulation to serve
the purpose of their studies.
The Financial Reforms Database provides data for the six types of ?nancial
liberalization discussed above as well as for “enhancement of capital regulations and
prudential supervision of the banking sector” (CRS). The scale of this variable goes from
0 to3 where 0 – unregulatedandunsupervisedand3 – stronglyregulatedandsupervised.
This dataset is available annually from 1973 to 2005 for 91 countries. However, this
database provides more limited aspects of banking regulation and supervision than the
?rst database. It has only one dimension and is based on assessment of available
information from different countries. Thus, it has a judgmental component[14].
We compare the data for CRS variables from the two mentioned sources by
country group in Table III. The data refer to 2005, the latest World Bank Survey year.
We construct an index from the three World Bank Survey questions that are
comparable to the components of CRS in the Financial Reforms Database.
Table III shows that on average the Financial Reforms Database assigns a higher
value of CRS to developed countries, a lower value for emerging market economies, and
the lowest value for other less-developed countries. The World Bank Survey, on the
other hand, ranks the group of other less-developed countries above emerging markets
in terms of having better bank regulation and supervision. The correlation between the
two variables is as low as 0.4.
Turning to the proxies for legal system and institutional quality, the most widely
used variables in the banking crisis literature include law and order, corruption,
bureaucratic quality from the ICRG. The data are available for more than 150 countries
and from 1984, and have been continuously updated. Other variables and sources,
which have been less frequently used, in part due to the limited number of country
and/or period coverage, are the Fraser Institute’s Economic FreedomIndex, the Heritage
Foundation’s Index of Economic Freedom, the Worldwide Governance Indicators
The World Bank Survey
(Barth, Caprio and Levine)
Financial Reforms Database
(Abiad, Detragiache and Tressel)
Developed economies 0.82 0.89
Emerging market economies 0.74 0.65
Other less-developed countries 0.78 0.55
All countries 0.78 0.68
Notes: The data in the table are average values of the CRS variable in 2005 by country group; the CRS
variable in the third column is from the Financial Reforms Database by Abiad et al. (2008); the CRS
variable in the second column is constructed based on the comparable survey questions from the
World Bank Survey by Barth et al. (2006); see the working version of this paper for detail explanations
Table III.
Comparison of bank
regulation data
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by Kaufmann et al. (2009). The real GDP per capita is also used in some studies as proxy
the general quality of domestic institutions. All these proxies re?ect only general aspects
of quality of institutions, and they may not directly measure bank regulation and
supervision in the speci?c way researchers desire. However, the advantage of using the
proxies from ICRG and GDP per capita is that they allow time-series analysis.
The general trend of institutional quality over time is positive although there are periods
of reversals in some countries. The trend for CRS in the Financial Reform Database is
positive as well.
Given the relatively low correlation in cross-section between the two datasets for
CRS it is not surprising that researchers come to different conclusions regarding the
relationship between banking crisis and strength of regulation and supervision. For
example, Barth et al. (2006) ?nd that the probability of banking crises is reduced in
countries with a high quality of law and order but not in countries with relatively
strong CRS as measured by the World Bank Survey. However, Angkinand et al. (2010)
?nd that CRS from the Financial Reforms Database reduces the likelihood of banking
crises in a cross-section time-series analysis. Barrell et al. (2010) also conclude that
bank regulation and supervision reduce banking crisis probabilities for the sample of
14 OECD countries from 1980 to 2007. Their conclusion is based on continuous
variables for the capital adequacy and liquidity ratios in banking systems. They do not
observe signi?cant effects for OECD countries of other proxies from Kaufmann et al.
(governance variable), the heritage foundation (banking and economic freedom
index), and the World Bank Survey database (selected banking regulatory variables).
Klomp (2010) does not ?nd any evidence that a credit market regulations index
from The Fraser Institute has a signi?cant impact on the stability of the banking
sector.
Turning ?nally to the interaction between strength of regulation and supervision,
and ?nancial liberalization Shehzad and De Haan (2009) ?nd that a positive impact on
?nancial stability is conditional on adequate supervision. Similarly, Angkinand et al.
(2010) ?nd that the decline in likelihood of banking crisis associated with increased
liberalization occurs only in countries with strong CRS. In countries with weak
regulation and supervision the effect of liberalization is the opposite.
6. Assessment of usefulness of existing proxies and conclusions
The analytical review and comparisons of various proxies for banking crises, as well
as important banking crisis determinants – credit expansion, ?nancial liberalization,
and bank regulation and supervision – show that differences between proxies explain
some contradictory results in the literature, as well as differences in sensitivity of the
likelihood of banking crises to changes in explanatory factors. Understanding of the
construction of different proxies can help the researcher choose the relevant proxy for
the research objective in cross-country and time-series analyses. Thus, the usefulness
of a particular proxy depends on the research objective. We emphasize the following
observations with respect to different variables:
.
Banking crises. Data for crisis dates, which are available in few studies, are
compiled in a similar way and sometimes draw upon one another. Banking crisis
episodes from the available data sources are highly correlated but there are
differences as a result of differences in judgment about what de?nes a crisis.
The researcher concerned primarily with very costly systemic crisis should
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choose the most restrictive crisis proxy. Differences in beginning and end dates
of crises matter less as long as one crisis is considered one observation whether it
lasts one or three years. It is obviously necessary to consider country and time
coverage; statistical signi?cance may have to be traded off against
appropriateness of proxy.
A fruitful area of research is the analysis of the relation between market-based
measures of probability of distress and other proxies for distress of individual banks as
well as for systemic crises:
.
Credit expansion. A continuous measure captures simply the growth of private
bank credit while dichotomous measures place emphasis on identi?cation of
lending boom episodes. The former measure seems most appropriate for studies
using continuous proxies for probability of crisis rather than 0/1 crisis dummies,
and for tests of theoretical hypotheses with respect to determinants and effects of
credit growth. The dichotomous measures are associated with much debate about
the identi?cation of “excess” credit growth relative to the growth endogenously
associated with the economy’s performance. There is scope for theoretical as well
as empirical research on this issue.
.
Financial liberalization. Attempts to measure ?nancial liberalization have
progressed from simple dichotomous measures to indices capturing a number
of dimensions and degree of liberalization over time. Although the recently
constructed comprehensive datasets, e.g. the IMF Financial Reforms Database in
Abiad et al. (2008) allow researchers to study the effects of different types of
liberalization, the different types are highly correlated since most countries have
moved towards greater liberalization. Moving away from simple dichotomous
measures has led to results contradicting the conventional wisdom that
liberalization contributes to the likelihood of banking crises. One remaining
challenge is to be able to identify effects of different types of liberalization in order
to produce meaningful policy implications with respect to effects of ?nancial
liberalization policies.
.
Bank regulation and supervision. There are tradeoffs in the choice between
available proxies for strength of regulation and supervision. The data from the
World Bank Survey of bank regulation and supervision by Barth et al. (2006)
consist of objective measures of many dimensions of regulation and supervision.
Thus, they seem to be appropriate for studies of effects of speci?c reforms. The
Financial Reform Database covers fewer dimensions but include informal
judgment about effectiveness of regulation and supervision. Another tradeoff
exists because the World Bank Survey data exist only for three years so far. The
literature commonly assumes that bank regulations rarely change over time, but
the proxies for bank regulation and institutional quality from other data sources
show otherwise.
Finally, we note that results with respect to the effects of ?nancial liberalization and
strength of regulation and supervision on banking crises are strongly affected by
interaction between these variables. We suspect that banking crises are in?uenced
by interaction between these variables and credit growth as well.
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Notes
1. See, for example, Angkinand et al. (2010) on the effect of ?nancial liberalization and bank
regulation on banking crises and Angkinand and Willett (2011) on the impact of exchange
rate regimes.
2. In cross-country analyses, the difference in empirical ?ndings on the early warnings and
determinants of banking crises could also be driven by the difference in methodologies used.
See, for example, DD (2005) and Davis and Karim (2008) for the review of different
methodologies used in the banking crisis literature.
3. Available at: www.cgu.edu/pages/1380.asp
4. Additional statistical comparisons of the number of banking crisis episodes identi?ed by
these ?ve studies can be found in the working paper version of this paper (see footnote [3]).
5. There are few other studies using the event-based approach to identify banking crisis
episodes. Their data have been less frequently used in the literature. For the additional
review (Kibritcioglu, 2002).
6. There are also more theory based proxies for risk taking on the bank level intended to
measure “distance to default.” The z-score based on accounting data used in Boyd and
Graham (1986) or market data used in Hovakimian et al. (2003) incorporates the capital asset
ratio, the return on assets and the SD of this return.
7. Available at:http://vlab.stern.nyu.edu/analysis/RISK.USFIN-MR.MES
8. This is formally demonstrated by Bernanke et al. (1998) through the ?nancial accelerator
model.
9. Studies generally obtain the data for domestic bank credit to the private sector from two
sources: (1) WDI; and (2) IFS (line 22d and42d). In some studies such as Mendoza and Terrones
(2008), this variable is transformed into “real credit per capita,” by adjusting to consumer price
in?ation and the total population, while some others (Table II) employ “net domestic credit”
which includes bank credit to both the government and the public sector.
10. This measurement issue is similarly experienced in de?ning “sudden stops” as discussed in
Sula et al., also in this special issue.
11. Measures of external liberalization, i.e. the degree of capital controls, are discussed in the
paper in this special issue on “international aspects of currency policies” and
Potchamanawong et al. (2008).
12. The data are available to the public at: www.imf.org/external/pubs/cat/longres.aspx?
sk¼22485.0 (accessed July 31, 2011).
13. The World Bank Survey data are available to the public at:http://go.worldbank.org/
SNUSW978P0. The data from ICRG, available at: www.prsgroup.com/ICRG.aspx, require
subscriptions. See also footnote [12].
14. For other sources of bank regulation datasets, Neyapti and Dincer (2005) develop an index of
legal quality of bank regulation and supervision. They identify a total of 98 criteria related to
the quality of banking regulation and code them using information retrieved from actual
banking laws. However, we do not ?nd that their datasets have been used in existing studies.
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crises”, Journal of Money, Credit and Banking, Vol. 39 No. 5, pp. 1037-66.
About the authors
Puspa Amri is a PhD candidate in Economics and Political Science at Claremont Graduate
University. She has a BA degree in Economics and Development Studies from the University of
Indonesia, Jakarta, and holds an MA degree in International Relations and Economics from the
School of Advanced International Studies, Johns Hopkins University. She has also served as an
Adjunct Faculty at the University of Indonesia, and an Economist at the Jakarta-based Center
for Strategic and International Studies. Puspa Amri is the corresponding author and can be
contacted at: [email protected]
Apanard P. Angkinand (PhD, Claremont Graduate University) is an Economist in the
Financial Research Group at the Milken Institute. Her research focuses on ?nancial institutions,
international ?nance, emerging market economies and ?nancial crises. Her work has been
published in various academic journals, including the Journal of International Money
JFEP
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and Finance, International Review of Finance, Open Economies Review, Journal of International
Financial Markets, Institutions & Money, and International Journal of Economics and Finance,
as well as in several books and edited volumes. Prior to joining the Institute, Dr Angkinand was
an Assistant Professor of Economics at the University of Illinois at Spring?eld, 2005-2008. While
completing her PhD, she also held Visiting Scholar positions at the Claremont Institute for
Economic Policy Studies and the Freeman Program in Asian Political Economy at the Claremont
Colleges, as well as a Lecturer of Economics at Pitzer College, California State Polytechnic
University, Pomona and the University of Redlands. She received a PhD in Economics from
Claremont Graduate University in 2005.
Clas Wihlborg has held the Fletcher Jones Chair of International Business at the Argyros
School of Business and Economics, Chapman University since January 2008. He was Professor of
Finance at the Copenhagen Business School (CBS) 2000-2007 and Director of the Center for Law,
Economics and Financial Institutions at CBS (LEFIC). He was the Felix Neubergh Professor of
Banking and Finance at Gothenburg University 1990-2000 after having held positions at New
York University and the University of Southern California in the USA. He obtained his PhD in
Economics in 1977 at Princeton University and an Honorary Doctorate at Lund University in
2009. Research and teaching activities have focused on ?nancial institutions, international
?nance, and corporate ?nance. Publications include numerous articles and books; most recently
Corporate Decision-making with Macroeconomic Uncertainty: Performance and Risk
Management (with Lars Oxelheim) (Oxford University Press, 2008). He is a member of the
European Shadow Financial Regulatory Committee and the Royal Swedish Academy of
Engineering Sciences (IVA).
International
comparisons
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This article has been cited by:
1. Chien-Chiang Lee, Chun-Wei Lin, Jhih-Hong Zeng. 2016. Financial liberalization, insurance market,
and the likelihood of financial crises. Journal of International Money and Finance 62, 25-51. [CrossRef]
2. Rudiger Ahrend, Antoine Goujard. 2014. Drivers of Systemic Banking Crises: The Role of Financial
Account Structure and Financial Integration. International Finance 17:10.1111/infi.v17.2, 135-160.
[CrossRef]
3. Steven B. Caudill, Daniel M. Gropper, Valentina Hartarska. 2012. Microfinance institution costs: effects
of gender, subsidies and technology. Journal of Financial Economic Policy 4:4, 292-304. [Abstract] [Full
Text] [PDF]
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