does deposit insurance increase banking - demigüc-kunt

7/31/2019 Does Deposit Insurance Increase Banking - Demigc-Kunt

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Journal of Monetary Economics 49 (2002) 13731406

Does deposit insurance increase banking system

stability? An empirical investigation$

Asli Demirg .u-c-Kunta,*, Enrica Detragiacheb

a

Development Research Group, World Bank, 1818 H Street NW, Washington, DC 20433, USAb International Monetary Fund, European I Department, 700 19th Street NW, Washington, DC, 20431, USA

Received 2 March 2001; received in revised form 9 October 2001; accepted 24 October 2001

Abstract

Based on evidence for 61 countries in 19801997, this study finds that explicit deposit

insurance tends to increase the likelihood of banking crises, the more so where bank interest

rates are deregulated and the institutional environment is weak. Also, the adverse impact of

deposit insurance on bank stability tends to be stronger the more extensive is the coverageoffered to depositors, where the scheme is funded, and where it is run by the government

rather than the private sector.

r 2002 Elsevier Science B.V. All rights reserved.

JEL classification: G28; G21; E44

Keywords: Deposit insurance; Banking crises

1. Introduction

The oldest system of national bank deposit insurance is the U.S. system, which

was established in 1934 to prevent the extensive bank runs that contributed to the

Great Depression. It was not until the Post-War period, however, that deposit

insurance began to spread around the world (Table 1). The 1980s saw an acceleration

$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, IMF, their Executive Directors, or the

countries they represent. We received very helpful comments from George Clark, Roberta Gatti, Alex

Hoffmeister, Ed Kane, Francesca Recanatini, Marco Sorge, Colin Xu and an anonymous referee. We aregreatly indebted to Anqing Shi, Tolga Sobaci and Paramjit Gill for excellent research assistance.

*Corresponding author. Tel.: +1-202-473-7479; fax: +1-202-522-1155.

E-mail address: [email protected] (A. Demirg.u-c-Kunt).

0304-3932/02/$ - see front matter r 2002 Elsevier Science B.V. All rights reserved.

P I I : S 0 3 0 4 - 3 9 3 2 ( 0 2 ) 0 0 1 7 1 - X


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Table1

Depositinsurancesystemfeatures

Countries

Banking

crisis

date

Type

explicit

1implicit

0

Date

esta-

blishe

dCoinsu-

rance

Coverage

limit

Foreign

currency

deposits

covered

Interbank

deposits

covered

Funding

Funded

=1

Unfunded

=0

Sourceof

funding

Banks

only=0

Banksand

Gov.=1

Government

only=2

Banks

premiumof

depositsor

liabilities

Manage-

ment

official

=1

joint

=2

private

=3

Membership

compulsory

=1

voluntary

=0

Australia

0

Austria

1

1979

0

$24,075but

0

0

0

1

Callable

3

1

coinsurancefor

businesses

Bahrain

1

1993

0

1

0

0

0

Callable

2

1

Belgium

1

1974

0

ECU15,0

00,

20,0

00

0

0

1

1

0.0002ofdeposits

2

1

inyear2000

fromclients

Belize

0

Burundi

0

Canada

1

1967

0

$40,770

0

1

1

1

0.0033ofinsured

1

1

deposits(max)

Chile

19811987

1

1986

1

Demanddepositsin

1

0

0

2

Callable

1

1

fulland90%

coinsurancetoUF

120or$3600for

savingdeposits

Colombia

19821985

1

1985

1

Fulluntil2001,then

0

1

1

0

0.003insureddeposits

1

1

coins.75%to$5500

Congo

1

1999

0

$3557

0

1

1

1

0.0015ofdeposits

2

0

+0.005ofnpls

Cyprus

0

Denmark

1

1988

0

ECU20,0

00

1

0

1

1

0.002oftotaldeposits

2

1

A. Demirg .u-c-Kunt, E. Detragiache / Journal of Monetary Economics 49 (2002) 137314061374


3/34

Ecuador

19951997

1

1999

0

Infulltoyear2001

1

1

1

N/A.

0.0065ofdeposits

1

1

Egypt

0

ElSalvado

r

1989

1

1999

0

$4,7

20

1

0

1

1

0.001to0.003of

1

1

insureddeposits

Finland

19911994

1

1969

0

$29,435

1

0

1

1

0.0005to0.0030of

3

1

insureddeposits

France

1

1980

0

$65,387

0

0

0

0

Callable

3

1

Germany

1

1966

1

Private:30%of

1

0

1

0

0.0003ofdeposits

3

1

capital;official

coinsurance90%to

ECU20,0

00

Greece

1

1993

0

ECU20,0

00

0

0

1

0

0.000250.0125of

2

1

eligibledeposits

Guatemala

0

Guyana

19931995

0

Honduras

0

India

19911997

1

1961

0

$2,3

55

1

0

1

1

0.0005ofdeposits

1

1

Indonesia

19921997

0

Ireland

1

1989

1

Coinsurance90%

0

0

1

0

0.002ofdeposits

1

1

toECU15,0

00

Israel

19831984

0

Italy

19901995

1

1987

1

$125,0

00

1

0

0

1

Callable

3

1

Jamaica

19961997

1

1998

0

$5,5

12

1

0

1

1

0.001ofinsureddeposits

1

1

Japan

19921997

1

1971

0

$71000butinfull

0

0

1

1

0.00084ofinsureddeposits2

1

untilyear2000

Jordan

19891990

0

Kenya

1993

1

1985

0

$1,7

50

1

1

1

1

0.0015ofdeposits

1

1

Korea

1997

1

1996

0

$14600butinfulluntil

year2000

0

0

1

1

0.0005ofinsureddeposits

1

1

Malaysia

19851988,1997

0

Mali

19871989

0

Mexico

1982,

199419971

1986

0

Infull,except

1

1

1

1

0.003ofcoveredliab.

1

1

subordinateddebt,until2005

Nepal

19881997

0

Netherland

s

1

1979

0

ECU20,0

00

1

0

0

1

1

1

NewZeala

nd

0

Nigeria

19911995

1

1988

0

$588/$2435n

0

1

1

1

0.009375ofdeposits

1

1

Norway

19871993

1

1961

0

$260,8

00

1

0

1

1

0.0001ofdeposits

1

1

Panama

19881989

0

PapuaNew

Guinea19891997

0

A. Demirg .u-c-Kunt, E. Detragiache / Journal of Monetary Economics 49 (2002) 13731406 1375


4/34

Peru

19831990

1

1992

0

$21,160

0

0

1

1

0.0065to0.0145of

2

1

1

insureddeposits

Philippines

19811987

1

1963

0

$2,3

75

1

1

1

1

0.002oftotaldeposits

1

1

Portugal

19861989

1

1992

0

ECU15,0

00,

1

0

1

1

0.0008to0.0012

1

1

coinsuranceto

ECU45,0

00

Seychelles

0

Singapore

0

SouthAfrica

1985

0

SriLanka

19891993

1

1987

0

$1,4

70

1

0

1

1

0.0015ofdeposits

1

0

Sweden

19901993

1

1996

0

ECU28663,$31,412

1

0

1

1

0.005

1

1

Swaziland

1995

0

Switzerland

1

1984

0

$19,700

0

0

0

0

Callable

1

0

Tanzania

19881997

1

1993

0

$376

0

0

1

1

0.001ofdeposits

3

1

Thailand

19831987,19970

Togo

0

Turkey

1982,

1991,

19941

1983

0

Infull

1

0

1

1

0.01to0.012

1

1

U.K.

1

1982

1

Largerof90%

0

0

0

0

Callable

3

1

coinsuranceto

$33,333or

ECU22,2

22

Table1(continued)

Countries

Banking

crisis

date

Type

explicit

1implicit

0

Date

esta-

blished

Coinsu-

rance

Coverage

limit

Foreign

currency

deposits

covered

Interbank

deposits

covered

Funding

Funded

=1

Unfunded

=0

Source

of

funding

Banks

only=

0

Banks

and

Gov.=

1

Government

only=

2

Banks

premiumof

depositsor

liabilities

Manage-

ment

official

=1

joint

=2

private

=3

Membership

compulsory

=1

voluntary

=0



5/34

U.S.

19801992

1

1934

0

$100,0

00

1

1

1

1

0.00to0.0027

1

1

Uruguay

19811985

0

Venezuela

19931997

1

1985

0

$7,3

09

0

0

1

1

0.02oftotaldeposits

1

1

Zambia

0

Implicitisdefinedaslackofanexplicitscheme.Dateestablishedreferstothe

dateofthestatutebywhichtheschemeisestablished.

Coinsurance

isadummy

variablethattakesonthevalueoneifdepositorsfaceadeductibleintheirinsu

redfunds.Coveragelimitrefersto

theexplicitamounttheauthoritiespromiseto

insure.Foreigncurrencydepositsandinterb

ankdepositstakevalueoneifinsurancecoverageextendstothoseare

as,respectively.Fundingtakesthe

valueoneif

theschem

eisfundedexanteandzerooth

erwise.Sourceoffundingcanbe

fromgovernmentonly(2),banks

andgovernment(1),orbanksonly(0).The

premium

bankspayisgivenaspercentageofdepositsorliabilities.Manag

ementofthefundcanbeofficial(1),official/privatejoint(2),or

private(3).

Membershiptothefundcanbecompulsory

orvoluntary.Sources:Kyei(1995);Garcia(1999);InstituteofIntern

ationalBankersGlobalSurveys(1998,1997,

1996,19

95,

1994).Koreaintroducesbank

depositinsurancescheme,InternationalFinancialLawReview;L

ondon;April1997;DongWonKo.

Lawon

depositinsurancefund,CentralBankofT

urkeyUnofficialTranslation.

Bankingfailuresindevelopingcou

ntries:anauditorsperspective,International

JournalofGovernmentAuditing:Washing

ton,

January1998;JavedNizam.

Belgiumimplementsdepositguarantee-scheme,InternationalFin

ancialLaw

Review,London,

June1995;Bruyneel,Andre,

Miller,Axel.Venezuela:Ministryrepresentativeviewsbankin

gsystem,FEDWORLD,

08/05/96athttp://

wnc.fedw

orld.gov/cgi-bin/retrieve.Bankof

FinlandBulletin,

March1998,Vol.72,

No:3.Japan:stimulation

package,OxfordAnalyticaBrief

,December

1997.E

Cdeposit-guaranteedirective,InternationalFinancialLawReview;London;December1995,

Fredborg,

Lars.

http://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrievehttp://wnc.fedworld.gov/cgi-bin/retrieve


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in the diffusion of deposit insurance with most OECD countries and an increasing

number of developing countries adopting some form of explicit depositor protection.

In 1994, deposit insurance became the standard for the newly created single banking

market of the European Union.1 More recently, the IMF has endorsed a limitedform of deposit insurance in its code of best practices (Folkerts-Landau and

Lindgren, 1998).

Despite its increased favor among policymakers, the desirability of deposit

insurance remains a matter of some controversy among economists. In the classic

work of Diamond and Dybvig (1983), deposit insurance (financed through money

creation) is an optimal policy in a model where bank stability is threatened by self-

fulfilling depositor runs. If runs result from imperfect information on the part of

some depositors, suspensions can prevent runs, but at the cost of leaving some

depositors in need of liquidity in some states of the world (Chari and Jagannathan,

1988). As pointed out by Bhattacharya et al. (1998), in this class of models deposit

insurance (financed through taxation) is better than suspensions provided the

distortionary effects of taxation are small. In Allen and Gale (1998) runs result from

a deterioration in bank asset quality, and the optimal policy is for the Central Bank

to extend liquidity support to the banking sector through a loan.2 Whether or not

deposit insurance is the best policy to prevent depositor runs, all authors

acknowledge that it is a source of moral hazard: as their ability to attract deposits

no longer reflects the risk of their asset portfolio, banks are encouraged to finance

high-risk, high-return projects. As a result, deposit insurance may lead to more bank

failures and, if banks take on risks that are correlated, systemic banking crises maybecome more frequent.3 The U.S. Savings & Loan crisis of the 1980s has been widely

attributed to the moral hazard created by a combination of generous deposit

insurance, financial liberalization, and regulatory failure (see, for instance, Kane,

1989). Thus, according to economic theory, while deposit insurance may increase

bank stability by reducing self-fulfilling or information-driven depositor runs, it may

decrease bank stability by encouraging risk taking on the part of banks.

When the theory has ambiguous implications it is particularly interesting to look

at the empirical evidence, yet no comprehensive empirical study to date has

investigated the effects of deposit insurance on bank stability. This paper is an

attempt to fill this gap. To this end, we rely on a newly constructed data baseassembled at the World Bank which records the characteristics of deposit insurance

systems around the world. A quick look at the data reveals that there is considerable

cross-country variation in the presence and design features of depositor protection

schemes (Table 1): some countries have no explicit deposit insurance at all (although

1For an overview of deposit insurance around the world, see Kyei (1995) and Garcia (1999).2Matutes and Vives (1996) find deposit insurance to have ambiguous welfare effects in a framework

where the market structure of the banking industry is endogenous.3Even in the absence of deposit insurance, banks are prone to excessive risk taking due to limited

liability for their equityholders and to their high leverage (Stiglitz, 1972).



7/34


8/34

The paper is organized as follows: Section 2 contains an overview of the data and

of the methodology. The main results are in Section 3. Section 4 addresses the role of

institutions. Section 5 contains the sensitivity analysis, Section 6 explores the role of

banking system characteristics for which we lack time-series data, and Section 7concludes.

2. The data set

2.1. An overview of deposit insurance protection in the sample countries

Information about depositor protection arrangements in the countries included in

our study comes from a new data set assembled at the World Bank. This data set,

which expands on an earlier study conducted at the IMF (Kyei, 1995), contains

cross-country information about the date in which a formal deposit insurance system

was established and about a number of characteristics of the system, including the

extent of coverage (the presence of a ceiling and/or of coinsurance, whether or not

foreign exchange deposits or interbank deposits are covered), how the system is

funded and managed, and others. Table 1 reports the design features of deposit

insurance for the 61 countries in the sample.

The first noticeable feature of the data is that explicit deposit insurance was not

common at the beginning of the sample period, as less than 20 percent of the samplecountries had a depositor protection scheme in place. Deposit insurance became

much more popular after 1980, however, and the fraction of sample countries with

an explicit scheme reached 40 percent in 1990, and stood slightly above 50 percent in

1997. In total, 33 countries had deposit insurance in 1997, compared to only 12 in

1980.4 Turning now to the design features of the schemes, it is apparent from Table 1

that there is substantial heterogeneity across countries, and no worldwide accepted

blueprint exists for deposit insurance. As far as the extent of coverage, coinsurance

seems to be relatively rare (only 6 out of 33 countries have it). Coverage limits are

common, but their extent varies considerably: for instance, Norway covers deposits

as large as $260,800, while in Switzerland deposits are protected only up to $19,700.In a majority of countries coverage includes foreign currency deposits, while

interbank deposits are insured in only 9 countries. Most deposit insurance schemes

are funded, and the most common source of funds is a combination of government

and bank resources. In 22 countries the system is managed by the government, in six

it is run privately, while in the remaining seven countries some form of joint public

and private management exists. Finally, in almost all countries membership in the

insurance scheme is compulsory.

4The diffusion of deposit insurance would look much more pervasive if countries were weighted by

GDP per capita or by population; although there are exceptions, it is mostly the richer and larger countries

that have adopted explicit depositor protection.



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2.2. Sample selection, the banking crisis variable, and the control variables

To test the effect of explicit deposit insurance on bank stability, we estimate the

probability of a systemic banking crisis using a multivariate logit model in whichalternative variables capturing the nature of the deposit protection arrangement

enter as explanatory variables along with a set of other control variables. The model

is estimated using a panel of 61 countries over the period 19801997. To select the

sample, we started with all the countries covered in the International Financial

Statistics and then excluded economies in transition, non-market economies, and

countries for which data series were mostly incomplete. Years in which banking

crises were under way were excluded from the panel because during a crisis the

behavior of some of the explanatory variables is likely to be affected by the crisis

itself, and this feed-back effect would cause problems for the estimation.5 The

benchmark sample includes 61 countries and 898 observations; for about half of the

observations a deposit insurance system is present, so the panel is balanced with

respect to this variable.

To build the banking crisis dummy variable, we identified and dated episodes of

banking sector distress using primarily information from Lindgren et al. (1996) and

Caprio and Klingebiel (1996). A systemic crisis is a situation in which significant

segments of the banking sector become insolvent or illiquid, and cannot continue to

operate without special assistance from the monetary or supervisory authorities. To

make this definition operational, we classified as systemic episodes of distress in

which emergency measures were taken to assist the banking system (such as bankholidays, deposit freezes, blanket guarantees to depositors or other bank creditors),

or large-scale nationalizations took place. Also, episodes were classified as systemic if

non-performing assets reached at least 10 percent of total assets at the peak of the

crisis, or if the cost of the rescue operations was at least 2 percent of GDP.6 These

criteria identify 40 systemic banking crises in our panel (Table 1), corresponding to

4.4 percent of the observations in the baseline sample. This method of constructing

the dependent variable does not distinguish among crises of different magnitude or

of different nature. However, trying to differentiate among episodes based on the

often sparse information available would be too arbitrary.7

Turning now to the control variables, the rate of growth of real GDP, the changein the external terms of trade, and the rate of inflation capture macroeconomic

developments that are likely to affect the quality of bank assets. The short-term real

interest rate reflects the banks cost of funds and affects bank profitability directly,

since bank assets are often long-term at fixed interest rates. Also, even if lending rates

5This rule also resulted in the exclusion of a few countries that were in a crisis before the beginning of

the sample period and never emerged.6Based on this definition, countries with a large banking system relative to GDP are more likely to have

a systemic crisis based on our definition, since bailout costs are measured relative to GDP. However,

controlling for banking sector size in the regression does not change the results.7Both Lindgren et al. (1996) and Barth et al. (1999) distinguish between systemic and non-systemic

crises, but arrive at different conclusions. Of the 30 episodes that are included in both studies, 63 percent

are classified as non-systemic in the first study, versus only 10 percent in the second study.



10/34

can be adjusted upwards when short-term rates rise, as would be the case with

adjustable-rate loans, default rates may increase as well, hurting bank profitability

through that avenue. Bank vulnerability to sudden capital outflows triggered by a

run on the currency and bank exposure to foreign exchange risk are measured by therate of exchange rate depreciation and by the ratio of M2 to foreign exchange

reserves.8 Since high rates of credit expansion may finance an asset price bubble that,

when it bursts, causes a banking crisis, lagged credit growth is used as an additional

control. Finally, GDP per capita is used to control for the level of development of

the country, which can proxy for the quality of regulation and of the legal

environment. Detailed variable definitions and sources are given in the Appendix A.

3. The results

Table 2 reports estimation results for the first model specification, which uses the

simple explicit/implicit dummy as the deposit insurance variable. When the dummy

is entered directly in the regression, it has a positive coefficient significant at the 8

percent confidence level, suggesting that explicit deposit insurance increases banking

system vulnerability.9 Among the control variables, GDP growth and per capita

GDP enter negatively, while the real interest rate and depreciation enter positively,

as suggested by economic theory. Inflation and the change in the terms of trade have

insignificant coefficients. In the second and third regression of Table 2, the binary

deposit insurance dummy is interacted with the control variables to test whether the

presence of explicit deposit insurance tends to make countries more sensitive to

systemic risk factors. This hypothesis finds some support, as economies with deposit

insurance seem to be more vulnerable to increases in real interest rates, exchange rate

depreciation, and to runs triggered by currency crises.10

In these regressions we ignore elements of the banking system safety net other than

deposit insurance, but such elements could be as important as deposit insurance in

determining bank fragility. Nonetheless, this omission is unlikely to drive the

8Note that deposit insurance guarantees the domestic currency value of deposits, not their foreign

currency value. Thus, the expectation of a devaluation would trigger withdrawals of domestic currency

deposits to purchase foreign assets even in the presence of deposit insurance. Instead of using the exchange

rate depreciation, we have also explored using the spread between domestic interest rates and 3 month US

T-bill rates as a proxy for the anticipated depreciation. The coefficient was positive but not significant.

Introducing a variable that captures the foreign liabilities of the banking sector relative to its total

liabilities and interacting it with the depreciation variable also led to insignificant coefficients.9 In Demirg.u-c-Kunt and Detragiache (1998) we found a similar result for a sample including only 24

banking crisis episodes.10An interesting conjecture is whether deposit insurance ceases to matter when macroeconomic shocks

are very severe. To gain some insight on this issue, we have introduced additional interaction terms

between the deposit insurance dummy and extreme values of the macroeconomic controls, where

extreme is defined as beyond two standard deviations from the sample mean. Because of the small number

of extreme observations with deposit insurance, however, these regressions were difficult to estimate.

When estimation was possible, we did not find evidence that deposit insurance matters only when shocks

are moderate.



11/34

Table 2

Deposit insurance and banking crises

(1) (2) (3) (4)

Risk factorsGrowth 0.148nnn 0.124nnn 0.125nnn 0.158nnn

(0.033) (0.036) (0.036) (0.039)Tot change 0.015 0.011 0.013 0.027

(0.016) (0.019) (0.016) (0.018)Real interest 0.024nnn 0.021nnn 0.021nnn 0.025nnn

(0.002) (0.008) (0.008) (0.008)Inflation 0.000 0.004 0.001 0.001

(0.009) (0.010) (0.010) (0.010)M2/reserves 0.000 0.001 0.001 0.005

(0.000) (0.006) (0.006) (0.004)Depreciation 0.012nnn 0.008 0.010nn 0.013nnn

(0.005) (0.006) (0.005) (0.005)Credit Grot2 0.017

n 0.024nn 0.020nn 0.030nnn

(0.010) (0.013) (0.010) (0.012)GDP/CAP 0.065nn 0.093 0.071nn 0.081nnn

(0.033) (0.068) (0.034) (0.032)

Deposit insurance and risk factorsDeposit ins. 0.696n8%

(0.397)Growth 0.158 0.166n

dep. ins. (0.107) (0.102)Tot change 0.003dep. ins. (0.037)

Rl. interest 0.070nn

0.069nn

dep. ins. (0.035) (0.032)Inflation 0.019dep. ins. (0.025)M2/reserves 0.024nn 0.024nn

dep. ins. (0.011) (0.010)Depreciation 0.022n 0.013n

dep. ins. (0.013) (0.007)Credit Grot2 0.013dep. ins. (0.026)GDP/CAP 0.029dep. ins. (0.072)Deposit ins. and 0.997nnn

liberalization (0.292)

No. of crisis 40 40 40 36No. of obs. 898 898 898 714% correct 74 76 76 75% crisis correct 68 65 65 69Model w2 50.53nn 63.56nnn 62.42nnn 55.44nnn

AIC 297 298 291 250

The dependent variable is a crisis dummy which takes the value one if there is a crisis and the value zero

otherwise. We estimate a logit probability model. Deposit insurance variable takes the value 1 if there is

explicit deposit insurance and 0 otherwise. Deposit Insurance and Liberalization is a dummy that takes the

value 2 if the country has liberalized its interest rates and has explicit deposit insurance; value 1 if the

country has either liberalized or has explicit deposit insurance; and value 0 if it has neither liberalized norhas explicit deposit insurance. Standard errors are given in parentheses.

n , nn and nnn indicate significance levels of 10, 5 and 1 percent, respectively.



12/34

positive correlation between the deposit insurance variable and the banking crisis

probability, unless countries without deposit insurance have alternative safety net

institutions that are even more effective at preventing depositor runs than deposit

insurance itself. This seems to us rather unlikely.11

In the last regression presented in Table 2, the binary deposit insurance dummy is

replaced by a dummy variable taking the value of zero for observations with no

deposit insurance, the value of one for observations with deposit insurance but

interest rate controls, and the value of two for observations with deposit insurance

and liberalized interest rates.12 This modified dummy variable, therefore, allows for a

different impact of deposit insurance on bank fragility in systems in which interest

rates are deregulated relative to systems in which controls remain. The conjecture is

that controls on bank interest rates limit the ability of banks to benefit from

investment in high-risk, high-return projects, thereby curbing the moral hazard

created by deposit insurance (Bhattacharya, 1982; Hellman et al., 2000). The new

dummy variable has a positive coefficient that is significant at the 1 percent

confidence level. Thus, this dummy fits the data better than the simple zero-one

dummy, suggesting that the moral hazard due to deposit insurance may be more

severe in liberalized banking systems.13

Table 3 presents the results of estimating banking crisis probabilities using

variations in the deposit insurance dummy that allow us to distinguish among

systems with different degrees of coverage. According to the theory, more

comprehensive coverage should be a better guarantee against depositor runs, but

it would also create more incentives for excessive risk taking. All coverage-relatedvariables assign the value of zero to observations with no explicit deposit insurance

and assign larger values to deposit insurance systems with broader coverage. The

no coinsurance dummy assigns the value of one to observations without

coinsurance and the value of two if there is no coinsurance. In the second

coverage-related variable all systems with a coverage limit are treated as ones, and

systems in which coverage is unlimited are treated as twos. A third variable is

constructed assigning to observations with a deposit insurance scheme the actual

share of deposits covered, computed as the individual coverage limit divided by bank

11 In a recent study, Rossi (1999) examines the impact on banking crisis probabilities of a bank safety

net index in a sample of 15 countries for 19901997. The index captures the presence of deposit insurance,

of lender of last resort facilities, and whether or not there is a history of bank bailouts. The extent of the

safety net appears to increase bank fragility. These results, however, need to be taken with caution given

the small number of banking crises in the sample.12The data on interest rate liberalization are from Demirg .u-c-Kunt and Detragiache (1999). This dummy

variable takes the value of zero in economies where bank interest rates are regulated and the value of one

in economies where the process on interest rate liberalization has begun. The correlation between this

dummy and the deposit insurance dummy is about 32 percent; thus, although there is a tendency for

deposit insurance to be introduced along with financial liberalization, the tendency is far from being

universal.13This result is not due to the different sample size: when the baseline model is estimated using the same

sample used in the regression with interest rate liberalization, the deposit insurance dummy remains

significant only at the 10 percent confidence level.



13/34

Table 3

Deposit insurance design features and banking crises: variations in coverage

(1) (2) (3) (4) (5)

Risk factors

Growth 0.149nnn 0.153nnn 0.150nnn 0.150nnn 0.147nnn

(0.033) (0.033) (0.034) (0.033) (0.033)

Tot change 0.015 0.015 0.016 0.014 0.015

(0.016) (0.016) (0.016) (0.016) (0.016)

Real interest 0.024nnn 0.024nnn 0.024nnn 0.024nnn 0.024nnn

(0.008) (0.008) (0.008) (0.008) (0.008)

Inflation 0.001 0.002 0.006 0.001 0.000

(0.009) (0.009) (0.009) (0.009) (0.009)

M2/reserves 0.000 0.000 0.000 0.000 0.000

(0.000) (0.000) (0.000) (0.000) (0.000)

Depreciation 0.012nnn 0.012nnn 0.008n 0.012nn 0.012nn

(0.005) (0.005) (0.005) (0.005) (0.005)

Credit Grot2 0.017n 0.015 0.019n 0.017n 0.018n

(0.010) (0.010) (0.012) (0.010) (0.010)

GDP/CAP 0.067nn 0.069nn 0.055 0.063nn 0.054n

(0.032) (0.032) (0.037) (0.031) (0.031)

Deposit insurance design features

No coinsurance 0.397nn

(0.204)

Unlimited explicit 0.699nnn

coverage (0.272)

Explicit coverage 0.019nnn

limit (0.006)

Foreign currency 0.471nn

deposits covered (0.216)

Interbank deposits 0.414n

covered (0.248)

No. of crises 40 40 34 40 40

No. of obs. 898 898 827 898 898

% correct 74 74 78 74 74

% crisis correct 68 68 71 68 68

Model w2

51.17nnn

53.69nnn

47.03nnn

52.02nnn

50.13nnn

AIC 296 293 257 295 297


otherwise. We estimate a logit probability model. Coverage variables are defined as follows: No

coinsurance dummy takes the value 0 if implicit insurance, 1 if explicit insurance with coinsurance, and 2 if

explicit insurance with no coinsurance. Unlimited explicit coverage dummy takes the value 0 for implicit

insurance, 1 if explicit insurance has limited coverage, and 2 if explicit insurance has unlimited coverage.

Explicit coverage limit takes the value 0 if implicit insurance but equals coverage limit divided by deposits

per capita lagged one period. Foreign currency deposit dummy takes the value 0 if implicit insurance, 1 if

explicit insurance does not cover foreign currency deposits and 2 if explicit insurance covers foreign

currency deposits. Interbank dummy is constructed similarly, based on coverage of interbank deposits.

Standard errors are given in parentheses.n , nn and nnn indicate significance levels of 10, 5 and 1 percent, respectively.



14/34

deposit per capita.14 This variable, of course, is not a dummy variable. Countries/

periods with unlimited coverage are excluded from this regression. Finally, systems

that extend coverage to foreign currency deposits or to interbank loans should be

more vulnerable than systems with more narrow coverage. To test this hypothesis,we introduce two additional three-way dummy variables, assuming the value of zero

where there is no deposit insurance, of one if there is deposit insurance but foreign

currency (interbank) deposits are not covered, and the value of two otherwise.

As evident from Table 3, estimation results uniformly suggest that explicit deposit

insurance tends to increase bank fragility, and the more so the more extensive is the

coverage. All five coverage-related variables have positive signs and are strongly

significant (except for the interbank deposit variable, which is significant only at the

10 percent confidence level15). It is noteworthy that the coefficient of the deposit

insurance variable is estimated more precisely when differences in coverage are taken

into account. This is consistent with an interpretation of the baseline results in terms

of moral hazard. Also, these findings lend support to the view that the pitfalls of

deposit insurance can be reduced by limiting the extent of coverage (Garcia, 1999).16

To get a sense for the magnitude of the effect, we have computed estimated banking

crisis probabilities for four episodes under the hypothesis that the coverage of the

deposit insurance system in the four countries is reduced to the level of Switzerland,

where coverage is limited to 45 percent of deposit per capita (about 50 percent of per-

capita GDP). For the 1993 crisis in Kenya, the estimated crisis probability would

decline from 26.8 percent to 16.6 percent; for the 1981 crisis in the Philippines it

would go from 21.0 percent to 3.8 percent; and for the 1980 crisis in the U.S. it wouldbecome 2.5 percent from 4.3 percent. Finally, the crisis probability in Venezuela in

1993 would have fallen from 17.0 percent to 12.5 percent. So the estimated effect of a

change in coverage on fragility is not trivial.

A second element that differentiates deposit insurance schemes is the type of

funding. Here we experiment with three different dummy variables. The first is a

zero-one-two variable based on whether there is no scheme, an unfunded scheme, or

a funded scheme. The second dummy further distinguishes between schemes that are

funded with callable funds and schemes that are funded with paid-up resources (the

latter providing a more credible guarantee). The conjecture is, of course, that

unfunded schemes are more similar to implicit schemes than funded schemes.Another aspect of funding is whether the resources are provided by the banks

themselves, by the government, or by both. In this case, we hypothesize that moral

hazard is stronger if the scheme is funded by the government, and it is milder if the

14 If a banking crisis is accompanied by a decline in deposits, this ratio may increase in banking crisis

years even though the deposit insurance system has not become more generous. To avoid this problem, we

have used deposits lagged by 1 year to compute the coverage ratio.15This may be because among the countries with explicit deposit insurance, coverage of interbank

deposits is negatively correlated with per-capita GDP at about 30 percent. However, dropping GDP per

capita from the specification does not make this coefficient more significant.16We have also tested for threshold effects concerning coverage, namely whether deposit insurance

tends to increase fragility only if coverage extends beyond a certain threshold, but we have not been able to

identify any such effects.



15/34

scheme is completely privately funded, so we set the dummy variable at zero for

implicit schemes, at one for privately funded programs, at two for programs that are

funded by both the public and the private sector, and at three for government-

financed schemes. As in the case of coverage, also in the case of funding estimationresults show that differentiating among systems based on the type of funding yields

better coefficient estimates for the deposit insurance variable relative to the baseline

(Table 4). Also, the hypothesis that funded systems give rise to more moral hazard

finds empirical support, suggesting that the credibility of the safety net plays a

significant role. Thus, ensuring that the deposit insurance system is well funded, as

recommended for instance by Garcia (1999), while it may have other advantages,

appears to have costs in terms of bank fragility. In the last regression reported in

Table 4 we have tested whether distinguishing among systems with different

insurance premiums improves the estimation results. This does not appear to be the

case, perhaps because what matters is whether premiums are adjusted to reflect the

risk of bank portfolios.17

Differences in management and membership rules may also be relevant in shaping

the impact of deposit insurance on bank stability. In a system managed by the banks

themselves there may be less room for abuse than in a system managed by the

government if banks have better information to monitor each other. This hypothesis

finds support in the estimation results reported in Table 5, where we introduce a

dummy variable that takes the value of zero for implicit systems, of one for explicit

systems that are privately managed, two for explicit systems that are managed jointly

by the private sector and the government, and three for systems managed by thegovernment alone. As a further test, we also introduce three dummies for each of the

three alternative forms of management. The four-way dummy has a coefficient that

is positive and significant (at the 5 percent confidence level). When separate dummies

are introduced, the dummy for government management is the only one to be

significant. Thus, it appears that the relevant distinction is between systems that are

entirely run by the government and systems in which the banking sector plays at least

some role. Finally, in the last banking crisis regression we introduce a membership

dummy that is zero for implicit schemes, one for schemes with compulsory

membership, and two for schemes with voluntary membership. Here the conjecture

is that compulsory membership, by reducing adverse selection among banks, shouldmake the banking systems less unstable than deposit insurance with voluntary

membership. This hypothesis is supported by the data.

At this point the reader may wonder whether the alternative deposit insurance

dummies constructed using different design features really convey additional

information: if all the dummies are strongly positively correlated because countries

with high coverage are also countries in which deposit insurance is funded and the

17Six countries in the sample reported that their insurance premiums were risk-adjusted. Assuming that

premiums were risk-adjusted from the inception of the deposit insurance scheme, we constructed a dummy

that takes the value of zero when there is no deposit insurance, a value of one if there is deposit insurance

and premiums are risk-adjusted, and a value of two otherwise. This variable has positive and significant (at

the 5 percent level) coefficient in the banking crisis regression suggesting that risk-adjusted premiums are

better at mitigating excessive risk taking.



16/34

Table 4

Deposit insurance design features and banking crises: variations in funding

(1) (2) (3) (4)

Risk factors

Growth 0.152nnn 0.152nnn 0.150nnn 0.137nnn

(0.033) (0.033) (0.033) (0.033)

Tot change 0.015 0.015 0.015 0.012

(0.016) (0.016) (0.016) (0.015)

Real interest 0.024nnn 0.024nnn 0.024nnn 0.023nnn

(0.008) (0.008) (0.008) (0.008)

Inflation 0.001 0.001 0.001 0.002

(0.009) (0.009) (0.009) (0.009)

M2/reserves 0.000 0.000 0.000 0.002

(0.000) (0.000) (0.000) (0.006)

Depreciation 0.012nnn 0.012nnn 0.012nn 0.012nn

(0.005) (0.005) (0.005) (0.005)

Credit Grot2 0.017n 0.017n 0.021nn 0.018n

(0.010) (0.010) (0.010) (0.010)

GDP/CAP 0.064nn 0.066nn 0.062nn 0.042

(0.031) (0.032) (0.031) (0.035)


Implicit/unfunded/funded 0.454nn

(0.203)

Implicit/unfunded/callable/funded 0.304nn

(0.136)

Source of funding 0.397nn

(0.187)

Bank premiums 0.034

(0.049)

No. of crises 40 40 40 38

No. of obs. 898 898 898 785

% correct 75 75 74 74

% crisis correct 68 68 68 68

Model w2 52.30nnn 52.36nnn 51.75nnn 45.28nnn

AIC 295 295 295 279


otherwise. We estimate a logit probability model. Funding variables are defined as follows: The first one

takes the value 0 if implicit insurance, 1 if explicit insurance with no fund, and 2 if explicit insurance with

deposit insurance fund. The second one takes the value 0 for implicit insurance, 1 if explicit insurance has

no fund, 2 if explicit insurance is funded ex-post (callable payments), and 3 if it is funded ex-ante. The

source of funding variable takes the value 0 if implicit insurance, 1 if the funding comes from banks only, 2

if it comes from banks and government, and 3 if it comes from government only. Bank premiums are zero

if implicit insurance and are given as percentage of deposits in the case of explicit insurance. Standard

errors are given in parentheses.n , nn and nnn indicate significance levels of 10, 5 and 1 percent, respectively.



17/34

Table 5

Deposit insurance design features and banking crises: variations in management and membership

(1) (2) (3)

Risk factors

Growth 0.149nnn 0.150nnn 0.147nnn

(0.033) (0.033) (0.033)

Tot change 0.014 0.014 0.014

(0.016) (0.016) (0.016)

Real interest 0.024nnn 0.024nnn 0.024nnn

(0.008) (0.008) (0.008)

Inflation 0.001 0.001 0.003

(0.009) (0.009) (0.009)

M2/reserves 0.000 0.000 0.000

(0.000) (0.000) (0.000)

Depreciation 0.012nnn 0.012nnn 0.012nn

(0.005) (0.005) (0.005)

Credit Grot2 0.017n 0.018n 0.017n

(0.010) (0.010) (0.010)

GDP/CAP 0.057nn 0.054 0.067nn

(0.031) (0.037) (0.032)


Management 0.269nn

(0.134)

Official 0.800nn

(0.419)

Joint 0.617

(1.163)

Private 0.297

(0.881)

Membership 0.663nn

(0.347)

No. of crises 40 39 40

No. of obs. 891 869 891

% correct 74 75 75

% crisis correct 68 64 68

Model w2

51.10nnn

50.32nnn

50.71nnn

AIC 295 292 296


otherwise. We estimate a logit probability model. Deposit insurance design variables are defined as

follows: Management variable takes the value 0 if implicit insurance, 1 if explicit insurance with private

management, 2 if explicit insurance with joint privateofficial management, and 3 if explicit insurance with

official management. Individual dummy variables take the value 1 if private, joint, or official management

and zero otherwise, respectively. The membership dummy takes the value 0 for implicit insurance, 1 if

explicit insurance with compulsory membership and 2 if explicit insurance with voluntary membership.

Standard errors are given in parentheses.n , nn and nnn indicate significance levels of 10, 5 and 1 percent, respectively.



18/34

government manages the system, for instance, then it would be difficult to claim that

we can disentangle the effect of each design feature on bank stability. As it turns out,

however, the dummies are highly positively correlated only because they all have

zeroes for countries with no deposit insurance. If we compute correlations among thedummies only for countries with deposit insurance, then such correlations are only

around 30 percent, suggesting that there is considerable variation in design features

in the sample. A perusal of the information in Table 1 suggests as much.

4. Deposit insurance, bank fragility, and the institutional environment

To investigate further the relationship between bank stability and deposit

insurance, in this section we examine to what extent the institutional environmentaffects this relationship. More specifically, advocates of deposit insurance often

claim that the risk of moral hazard can be contained through effective prudential

regulation and supervision of the banking system. If this is true, then we should find

the impact of deposit insurance on banking crisis probabilities to be small or even

negligible in economies where bank regulation is strong, and vice versa.

Unfortunately, no comprehensive measure of the quality of bank regulation exists

to date, so to test this hypothesis we rely on proxies consisting of indexes capturing

different aspects of the institutional environment: the degree to which the rule of law

prevails (law and order), the quality of contract enforcement, the quality of the

bureaucracy, the extent of bureaucratic delay, and, finally, the degree of

corruption.18 All indexes are increasing in the quality of the institutions, and range

from zero to six (except for the indexes of contract enforcement and of bureaucratic

delay, which range from zero to four). We hypothesize that where institutions are of

high quality so is bank prudential regulation and supervision. Accordingly, if the

institutional index is interacted with the deposit insurance variable and entered in the

banking crisis probability regression, we expect this interaction term to have a

negative coefficient.

The institutional indexes capturing the quality of the legal system may also affect

the relationship between deposit insurance and bank stability even if they are notaccurate proxies for prudential regulation and supervision: in economies where the

legal protection for creditors and investors is weak, so that the potential for strategic

default, fraud, and abuse is higher, there may be more opportunities for gambling

with insured deposits.19

Table 6 summarizes the results. Each regression includes the control variables used

in the baseline regression (except for GDP per capita, which is itself a proxy for

institutional quality), one of the deposit insurance variables used in Section 3 above,

18The sources for these series are described in Appendix A.19 Indeed, when we enter the indexes without interacting them with the deposit insurance variable they

develop negative signssignificant in the case of quality of contract enforcement, bureaucratic quality,

and the extent of bureaucratic delayconsistent with the expectation that better protection against fraud

and abuse would decrease likelihood of banking crises.



19/34

Table6

Depositinsurancedesignandinstitutionalquality

GDP/CAP

Lawand

Contract

Bureaucratic

Bureaucratic

Corrup

tion

order

enforce

ment

quality

delay

Dep.

ins.

0.859nn

2.007nnn

2.624nn

2.566nnn

2.848nn

2.234nn

(0.4

70)

(0.8

32)

(1.2

68)

(0.9

04)

(1.2

28)

(1.0

46)

Dep.

ins.

0.059

0.410nn

1.068nn

0.483nnn

1.311nn

0.516nn

Institutio

nalvar.

(0.0

37)

(0.1

80)

(0.5

17)

(0.1

93)

(0.6

25)

(0.2

49)

Crises,N

;

AIC

40,8

98,2

98

24,4

95,1

78

26,523,1

75

32,6

48,2

15

23,4

64,1

55

27,5

19,1

96

Nocoins

.

0.500nn

1.130nnn

1.329nn

1.304nnn

1.414nn

1.212nn

(0.2

40)

(0.4

25)

(0.6

56)

(0.4

56)

(0.6

19)

(0.5

31)

Nocoins

.

0.033n

0.226nn

0.522nn

0.238nnn

0.634nn

0.269nn

Institutio

nalvar.

(0.0

19)

(0.0

93)

(0.2

69)

(0.0

98)

(0.3

21)

(0.1

26)

Crises,N

;

AIC

40,8

98,2

98

24,4

95,1

77

26,523,1

76

32,6

48,2

15

23,4

64,1

55

27,5

19,1

96

Unlimitedcov.

0.740nn

1.150n

1.743nn

1.976nnn

2.134nn

1.430n

(0.3

30)

(0.6

41)

(0.9

26)

(0.7

63)

(1.0

13)

(0.8

25)

Unlim.cov.

0.033

0.184

0.618n

0.321nn

0.871n

0.262

Institutio

nalvar.

(0.0

28)

(0.1

41)

(0.4

04)

(0.1

66)

(0.5

44)

(0.1

91)

Crises,N

;

AIC

40,8

98,2

97

24,4

95,1

80

26,523,1

76

32,6

48,2

15

23,4

64,1

55

27,5

19,1

98

Coverage

0.019nnn

0.014

0.019

0.024

0.018

0.021

(0.0

07)

(0.2

33)

(0.0

47)

(0.0

20)

(0.0

14)

(0.0

24)

Coverage

0.001

0.003

0.004

0.000

0.008

0.001

Institutio

nalvar.

(0.0

01)

(0.0

04)

(0.0

26)

(0.0

07)

(0.0

10)

(0.0

10)

Crises,N

;

AIC

34,8

27,2

58

19,4

52,1

51

22,491,1

51

26,5

97,1

80

20,4

41,1

36

21,4

76,1

65

For.cur.

cov.

0.528nn

1.124nnn

1.508nn

1.255nnn

1.408nn

1.032n

(0.2

58)

(0.4

52)

(0.7

68)

(0.5

02)

(0.6

89)

(0.5

88)

For.cur.

cov.

0.027

0.209nn

0.573n

0.202n

0.545

0.216

Institutio

nalvar.

(0.0

22)

(0.1

05)

(0.3

37)

(0.1

15)

(0.3

76)

(0.1

44)

Crises,N

;

AIC

40,8

98,2

98

24,4

95,1

78

26,523,1

76

32,6

48,2

16

23,4

64,1

56

27,5

19,1

98

Interbnk

cov.

0.497n

1.040nn

1.695nn

1.330nnn

1.774nn

1.338nn

(0.2

85)

(0.5

16)

(0.8

59)

(0.5

20)

(0.7

67)

(0.6

86)

Interbnk

cov.

0.033

0.247nn

0.749n

0.260nn

0.889nn

0.363nn

Institutio

nalvar.

(0.0

28)

(0.1

36)

(0.4

01)

(0.1

27)

(0.4

50)

(0.1

95)

Crises,N

;

AIC

40,8

98,2

99

24,4

95,1

80

26,523,1

76

32,6

48,2

17

23,4

64,1

55

27,5

19,1

97

Funding

0.509nn

1.050nnn

1.218nn

1.249nnn

1.365nn

1.170nn

(0.2

42)

(0.4

23)

(0.6

58)

(0.4

67)

(0.6

33)

(0.5

45)



20/34

Table6(

continued)

GDP/CAP

Lawand

Contract

Bureaucratic

Bureaucratic

Corrup

tion

order

enforce

ment

quality

delay

Funding

0.026

0.191nn

0.461n

0.209nn

0.586n

0.243nn

Institutio

nalvar.

(0.0

20)

(0.0

96)

(0.2

77)

(0.1

04)

(0.3

37)

(0.1

33)

Crises,N

;

AIC

40,8

98,2

98

24,4

95,1

78

26,5

23,

177

32,6

48,2

16

23,4

64,1

56

27,5

19,1

97

Funding

(callable)

0.344nn

0.708nnn

0.834nn

0.855nnn

0.921nn

0.806nn

(0.1

61)

(0.2

81)

(0.4

33)

(0.3

09)

(0.4

18)

(0.3

62)

Funding

(callbl)

0.018

0.130nn

0.317n

0.146nn

0.395n

0.170nn

Institutio

nalvar.

(0.0

13)

(0.0

63)

(0.1

81)

(0.0

68)

(0.2

21)

(0.0

88)

Crises,N

;

AIC

40,8

98,2

98

24,4

95,1

78

26,523,1

76

32,6

48,2

15

23,4

64,1

56

27,5

19,1

96

Source

0.427nn

1.020nnn

1.412nn

1.215nnn

1.433nn

0.993nn

(0.2

24)

(0.4

11)

(0.6

52)

(0.4

54)

(0.6

19)

(0.5

08)

Source

0.021

0.189nn

0.530nn

0.212nn

0.610nn

0.214n

Institutio

nalvar.

(0.0

19)

(0.0

94)

(0.2

70)

(0.1

01)

(0.3

21)

(0.1

27)

Crises,N

;

AIC

40,8

98,2

98

24,4

95,1

78

26,523,1

76

32,6

48,

216

23,4

64,1

55

27,5

19,1

97

Premium

0.115

0.371nn

0.630

0.380

0.736nn

0.670nn

(0.0

82)

(0.1

93)

(0.4

42)

(0.2

82)

(0.3

32)

(0.3

31)

Premium

0.031

0.086n

0.408

0.123

0.532nn

0.217nn

Institutio

nalvar.

(0.0

28)

(0.0

51)

(0.2

86)

(0.0

99)

(0.2

53)

(0.1

13)

Crises,N

;

AIC

38,7

85,2

79

23,4

20,1

67

24,434,1

58

30,5

47,2

01

21,3

89,1

40

26,4

38,1

82

Managem

ent

0.335nn

0.721nnn

0.846nn

0.843nnn

0.950nn

82

9nn

(0.1

57)

(0.2

79)

(0.4

49)

(0.3

16)

(0.4

24)

(0.3

80)

Managmt

0.020

0.154nn

0.332n

0.155nn

0.426nn

0.198nn

Institutio

nalvar.

(0.0

15)

(0.0

70)

(0.1

93)

(0.0

75)

(0.2

32)

(0.1

02)

Crises,N

;

AIC

40,8

91,2

97

24,4

90,1

77

26,523,1

76

32,6

48,2

16

23,4

64,1

55

27,5

15,1

96

Membership

0.847nn

1.555nnn

2.582nn

2.435nnn

2.813nn

2.246nnn

(0.3

96)

(0.5

87)

(1.2

60)

(0.8

04)

(1.2

27)

(0.9

43)

Membership

0.058n

0.335nnn

1.050nn

0.468nnn

1.291nn

0.517nn

Institutio

nalvar.

(0.0

33)

(0.1

40)

(0.5

09)

(0.1

81)

(0.6

20)

(0.2

34)

Crises,N

;

AIC

40,8

91,2

97

24,4

90,1

77

26,523,1

75

32,6

48,2

15

23,4

64,1

55

27,5

15,1

95

VariablesareasgiveninTables35.SpecificationsareasgivenintheprevioustablesbuttheyexcludeGDP/

CAPandincludeaninteractiontermofthe

depositin

surancevariablewiththerelevant

institutionalvariable.Onlythedep

ositinsuranceanditsinteractiontermarereported.Standarderrorsa

regivenin

parentheses.

n

,nnandnnn

indicatesignificancelevelsof10,

5and1percent,respectively

.



21/34

and an interaction term between the variable and an index of institutional quality. In

the first column, the exercise is conducted using GDP per capita as the institutional

variable. For brevity, the table only reports the coefficient and standard errors of the

deposit insurance variables and of the interaction terms, as well as the number ofcrises, the number of observations, and the value of the Akaike Information

Criterion (AIC) for each regression.20

The first observation about the results in Table 6 is that the coefficients of all the

interaction terms have the expected negative sign, with the exception of those using

the extent of coverage as the deposit insurance variable. The latter are positive but

not significant. Furthermore, the great majority of the interaction variables are

significant. We interpret this as evidence that indeed good institutions (and,

therefore, presumably better bank regulation and supervision) perform an important

role in curbing the negative effect of deposit insurance on bank stability. In fact, in a

number of cases the point estimate of the coefficient of the interaction variable is

large enough that for the higher values of the institutional indexes the impact of

deposit insurance on banking system fragility is no longer significant.

Interestingly, if GDP per capita is used as the institutional variable, the interaction

terms are mostly insignificant. This is not due to the different sample size, as running

the regression including GDP for the samples used for the other institutional

variables yields equally insignificant results. Therefore, it appears that the

institutional indexes capture aspects of the environment that are relevant to bank

stability over and beyond the general level of development of the country. Finally,

among the different indexes, law and order and the index of the quality of thebureaucracy seem to yield marginally better results.

5. Robustness

5.1. Testing for simultaneity bias

A potential criticism to the regression results derived in the previous sections is

that the decision to adopt deposit insurance may be influenced by the fragility of the

banking sector, so that the two variables are really jointly determined. If this is thecase, then treating deposit insurance as exogenous would lead to simultaneity bias in

the estimates. In our sample the raw correlation between the crisis dummy and the

deposit insurance dummy is 0.002 and insignificant which makes this unlikely. Also,

while a number of countries introduced deposit insurance after having experienced a

banking crisis, this is not driving the correlation between the two variables in our

sample, because only four countries in the panel had a second crisis after an earlier

one ended, and, of these four, two never introduced deposit insurance.

Nonetheless, to try and assess whether simultaneity bias is what drives the results,

in this section we perform a two-stage estimation procedure: in the first-stage, a logit

20Due to the limited availability of the institutional indexes, the size of the panel is considerably smaller

than the baseline.



22/34

model of the determinants of the deposit insurance regime is estimated. In the

second-stage, we estimate the benchmark specification of Section 3 using the

probability of adopting deposit insurance estimated in the first stage as the deposit

insurance variable. Essentially, this is an instrumental variable estimation, where wetry to purge the endogenous component of the deposit insurance variable in the first-

stage. For the two-stage logit model to be properly identified, there has to be at least

one variable that is correlated with the probability of adopting an explicit deposit

insurance scheme but is uncorrelated with the countrys probability of experiencing a

crisis. To find an instrument, we hypothesize that, when deciding whether to

implement deposit insurance, policymakers are influenced by the choices of

policymakers in other countries. As explicit depositor protection becomes more

widespread, it becomes enshrined as a sort of universal best practice, and countries

become more prone to adopt it. Also, policymakers may learn from neighboring

countries about the workings of deposit insurance. To capture this fad element in

the deposit insurance adoption decision, we use the proportion of countries in the

sample that has already adopted explicit deposit insurance. A problem with this

variable is that there may be unobserved common elements that make deposit

insurance more desirable in all countries as time goes by; to control for these

unobservables, we also introduce time dummies in the first-stage regression.

The results of the two-stage logit are presented in Table 7. Of all the control

variables which also appear in the crisis probability regression, only per-capita GDP

is significant in the first-stage regression. Furthermore, a higher GDP per capita,

while it tends to reduce the probability of a banking crisis, makes the adoption ofdeposit insurance more likely. This evidence strongly suggests that the risk elements

that make banking systems more fragile have little to do with the decision to adopt

deposit insurance.21 Given this lack of correlation, the question of whether the

instrument is a good onewhich is usually key in sorting out simultaneity issues

becomes rather secondary. Nonetheless, the contagion variable has a positive and

significant effect, suggesting some sort of fad among policymakers concerning the

adoption of deposit insurance, while the time dummies, which are not reported, are

not significant.22

In the second-stage regression, the deposit insurance variable is now slightly more

significant, while the signs of the coefficients and the significance levels of the controlvariables remain virtually unchanged. While the second-stage estimation results are

consistent, the use of standard errors from the second stage to judge whether or not

the coefficients are significant is incorrect since this procedure ignores the fact that

deposit insurance variable is now an estimated variable. The computation of the

correct covariance matrix for double limited dependent variable models can be quite

cumbersome (Maddala 1983, Chapter 8). However, Angrist (1991) has shown

21As an additional test, we have rerun this regression introducing as additional controls the banking

sector characteristics described in Section 6 below. Because the latter variables are not available in time

series, this is a cross-sectional regression for 1995. The explanatory variables, including the banking sector

characteristics, explain almost none of the cross variation in the adoption of deposit insurance.22Using a time trend instead of the time dummies yields similar results.



23/34

through Monte Carlo techniques that standard instrumental variable estimation is a

viable alternative to the double logit model. In other words, if we ignore the fact that

deposit insurance and banking crisis are binary variables and estimate the system

with a standard two-stage least squares (2SLS) the estimates would have all thedesirable properties. This is equivalent to assuming that the crisis and deposit

insurance models can be estimated using a linear probability model. The last two

Table 7

Deposit insurance and banking crisestwo-stage estimation

I. Two-stage logit II. 2SLS

(1) (2) (1) (2)

Deposit insurance Banking crisis Deposit insurance Banking crisis

Growth 0.002 0.148nnn 0.001 0.007nnn

(0.019) (0.033) (0.003) (0.002)

Tot change 0.003 0.018 0.001 0.001

(0.010) (0.016) (0.002) (0.001)

Real interest 0.002 0.020nnn 0.001 0.002nnn

(0.003) (0.008) (0.001) (0.001)

Inflation 0.001 0.000 0.000 0.001

(0.002) (0.009) (0.001) (0.001)

M2/reserves 0.000 0.000 0.000 0.000(0.000) (0.000) (0.000) (0.000)

Depreciation 0.000 0.012nnn 0.001 0.001nnn

(0.002) (0.005) (0.001) (0.000)

Credit Grot2 0.003 0.018n 0.001 0.001

(0.005) (0.010) (0.001) (0.001)

GDP/CAP 0.157nnn 0.141nn 0.032nnn 0.006nn

(0.012) (0.061) (0.002) (0.002)

Contagion 4.756nnn 0.690nnn

(1.448) (0.253)

Predicted deposit 2.964nn 0.126nn

insurance (1.585) (0.065)

No. of obs. 1032 898 898 898R2 0.28 0.08

% correct 73 76

% expl. dep. 65 63

ins. cor. or

% crisis cor.

Model w2 325.95nnn 51.10nnn

AIC 1102 296

The first two columns present results of two-stage Logit estimation. The first column estimates a logit

probability model of having an explicit deposit insurance system. Contagion is the proportion of countries

that have adopted explicit deposit insurance at each point in time. The second column estimates the crisis

probability using the predicted deposit insurance variable from the first-stage. The next two columnsreport 2SLS results, assuming a linear probability model for the deposit insurance and crisis equations. In

both cases column 1 includes time dummies that are not reported. Standard errors are given in

parentheses.n , nn and nnn indicate significance levels of 10, 5 and 1 percent, respectively.



24/34

columns in Table 7 report the results of the 2SLS. These results are very similar to

the ones obtained using the two-stage logit.23 Indeed, correcting for the endogeneity

of the deposit insurance variable does not lead to significant differences compared to

the baseline. Thus, the results of the two-stage estimation exercise also suggest thatdeposit insurance tends to increase bank fragility, as in the one-equation models of

Section 3.

5.2. Further sensitivity tests

In Section 3, we examined the impact of the design features of deposit insurance

on banking crisis probabilities by looking at each feature in isolation. In practice, of

course, each deposit insurance system is a combination of different design features

and, if our interpretation of the evidence is correct, systems incorporating more of

the features associated with moral hazard should be more vulnerable to banking

crises. To test this hypothesis, we construct an aggregate index of the moral hazard

associated with each deposit insurance scheme in the sample, and then use this index

as the deposit insurance variable in the banking crisis regression.

To build an aggregate index of moral hazard we use principal components

analysis.24 The principal components are linear combinations of the original design

features, computed using weights that minimize the loss of information due to

replacing the matrix of design features with a single vector. Using as design features

the dummies for no coinsurance, foreign currency deposits covered, interbankdeposits covered, type of funding, source of funding, management, membership and

the level of explicit coverage, we find that the first principal component explains over

83 percent of the total variation in these variables. The next principal component

explains less than 10 percent of the variation, while each additional component

explaining about 1 percent. When we use the first principal component as the

aggregate index of moral hazard in the benchmark banking crisis regression, we find

that the index has a positive coefficient that is significant at the 5 percent confidence

level (Table 8). This confirms the results obtained with the individual dummy

variables.

Using the aggregate index of moral hazard as the deposit insurance variable, wehave also performed other sensitivity tests. First, we have tested for the presence of

fixed effects by introducing country dummies and (separately) year dummies. None

of the dummies was significant, suggesting that fixed effects models are not

23Note that while significance levels will be similar, the coefficients from logistic and linear probability

models are not directly comparable. Amemiya (1981) shows that coefficients of the logistic model are

larger than those of the linear probability model. While it is possible to multiply the coefficients of the

linear probability model by a certain factor to obtain the coefficients of the logistic model, these are rough

approximations and the factors change for different probability ranges. So, Amemiya suggests that it is

better to compare probabilities directly rather than comparing the estimates of the coefficients even after

an appropriate conversion.24See Greene (1997) pp. 424427 for a detailed discussion of principal component analysis.



25/34

appropriate.25 A second test involves using the definition of systemic banking crises

in a recent paper by Caprio and Klingebiel (1999). This definition is based on a

subjective assessment of the severity of bank distress, and classifies as crises a smaller

number of episodes relative to our definition. The results, however, are robust to

adopting this alternative definition (Table 8). Also, dropping from the regression

Table 8

Sensitivity analysis

(1) (2) (3) (4)

Risk factors

Growth 0.151nnn 0.114nnn 0.151nnn 0.150nnn

(0.033) (0.036) (0.033) (0.033)

Tot change 0.015 0.014 0.015

(0.016) (0.016) (0.015)

Real interest 0.024nnn 0.001 0.025nnn 0.024nnn

(0.008) (0.006) (0.008) (0.008)

Inflation 0.001 0.008 0.000

(0.009) (0.008) (0.009)

M2/reserves 0.000 0.000 0.000

(0.000) (0.005) (0.000)

Depreciation 0.012nnn 0.011nn 0.012nnn 0.012nnn

(0.005) (0.005) (0.004) (0.016)

Credit Grot2 0.017n 0.013 0.017n 0.018n

(0.010) (0.011) (0.010) (0.010)

GDP/CAP 0.065nn 0.077nn 0.065nn 0.067nn

(0.031) (0.035) (0.031) (0.032)

Past crisis 0.845

(1.219)

Deposit insurance

Moral 0.161nn 0.152nn 0.156nn 0.170nn

hazard index (0.074) (0.078) (0.073) (0.075)

No. of crises 40 32 40 40

No. of obs. 898 947 898 898

% correct 78 72 70 79

% crisis correct 65 60 68 68

Model w2 52.06nn 29.43nnn 50.92nnn 52.62nnn

AIC 295 270 290 297


otherwise. We estimate a logit probability model. The moral hazard index is the first principal component

of deposit insurance design features. The second column uses the definition of systemic banking crisis in

Caprio and Klingebiel (1999).. Past crisis is a dummy variable that takes the value 1 if a country has had

a crisis in the last 3 years and zero otherwise. Standard errors are given in parentheses.n , nn and nnn indicate significance levels of 10, 5 and 1 percent, respectively.

25These results are not reported. It should also be noted that in the fixed effects model, countries (years)

with no banking crises drop out of the sample, thus resulting in a substantial loss of information (Greene,

1997, p. 899).



26/34

control variables that have insignificant coefficients, the index of moral hazard

remains significant at 5 percent confidence level and the coefficient does not change

much. Finally, it could be argued that banking crises are not independent events,

namely that the probability of a crisis differs for countries that experienced crises inthe past. To allow for this type of dependence in the crisis probabilities, in the last

regression of Table 8 we introduce a dummy variable that takes the value of 1 if the

country was experiencing a crisis in the 3 years before the observation and the value

of zero otherwise.26 This dummy has a negative but insignificant coefficient, and the

rest of the regression shows little change.

6. Controlling for additional banking sector characteristics

The control variables used in the regressions presented above fail to capture some

aspects of the banking system that may affect bank fragility, such as the

concentration of the credit market, the level of capitalization, the ability to diversify

debtor-specific shocks, and others. If these characteristics are relevant determinants

of crisis probabilities and are positively correlated with deposit insurance, omitting

them may bias upwards the coefficients of the deposit insurance variables, clouding

our conclusions. Unfortunately, for most of these characteristics, while it is possible

to find cross-sectional data, no time series of observations is available. As a tentative

exploration, we use the cross-sectional variables in the panel regressions under the

assumption that the characteristics of interest do not vary much over the sampleperiod.

The first banking sector characteristic that we introduce in the banking crisis

regression is the extent of regulatory restrictions on bank activities. In some

countries banks are free to engage in non-banking activities, such as security

underwriting or insurance, and they can also own non-financial firms. In other

countries, regulators impose strict limits on these activities. If restrictions keep banks

from entering lines of business that are too risky, or whose risk they may not be able

to adequately evaluate or manage, banking systems with fewer restrictions may be

less stable. On the other hand, the ability to diversify outside their traditional lines of

business may actually make banks more stable (Mishkin, 1999). A recent study by

Barth et al. (1999) builds an index of restrictions on three types of non-standard

bank activities (securities, insurance, and real estate), and on banks ability to own

shares of non-financial firms. The data is only cross sectional and refer to the late

1990s but, according to Barth et al. (1999), these aspects of bank regulation have not

seen much change in the last 20 years. As the deposit insurance variable we use the

moral hazard index derived in the previous section, since this variable summarizes

the features of the depositor protection system. The new variables are introduced one

at a time in order not to overload the regressions. Table 9 summarizes the estimation

26For some countries in the sample we lacked information about the occurrence of a banking crisis in

the 3 years before the beginning of the sample period. We assumed that such countries had not experienced

a crisis in those years.



27/34

Table 9

Controlling for bank characteristicsrestrictions on banking activities

(1) (2) (3) (4) (5)

Risk factors

Growth 0.197nnn 0.192nnn 0.188nnn 0.191nnn 0.196nnn

(0.045) (0.044) (0.044) (0.044) (0.045)

Tot change 0.020 0.020 0.020 0.021 0.024

(0.019) (0.020) (0.020) (0.019) (0.019)

Real interest 0.024nnn 0.023nnn 0.024nnn 0.024nnn 0.020nnn

(0.008) (0.008) (0.008) (0.008) (0.008)

Inflation 0.001 0.003 0.000 0.001 0.000

(0.010) (0.011) (0.010) (0.011) (0.010)

M2/reserves 0.001 0.002 0.002 0.001 0.002

(0.004) (0.004) (0.004) (0.004) (0.004)

Depreciation 0.011nn 0.011nn 0.011nn 0.011nn 0.011nn

(0.006) (0.005) (0.005) (0.005) (0.005)

Credit Grot2 0.019n 0.019n 0.020n 0.019n 0.016

(0.011) (0.011) (0.011) (0.011) (0.011)

GDP/CAP 0.045 0.058n 0.062n 0.051 0.045

(0.037) (0.035) (0.035) (0.038) (0.035)

Deposit insurance

Moral hazard 0.171nn 0.177nn 0.183nn 0.181nn 0.178nn

index (0.086) (0.086) (0.086) (0.086) (0.087)

Restrictions on banking activities

Average 0.534n

restrictions (0.331)

Securities 0.322

(0.253)

Insurance 0.139

(0.243)

Real estate 0.266

(0.251)

Banks owning 0.409n

non-financial (0.255)

firms

No. of crises 31 31 31 31 30

No. of obs. 689 689 689 689 673

% correct 82 81 80 80 83

% crisis correct 61 65 61 61 67

Model w2 51.37nnn 50.31nnn 49.10nnn 49.94nnn 49.71nnn

AIC 224 225 226 225 218


otherwise. We estimate a logit probability model. The moral hazard index is the first principal component

of deposit insurance design features. Standard errors are given in parentheses.n , nn and nnn indicate significance levels of 10, 5 and 1 percent, respectively.



28/34

results. There is some evidence that more restrictions on ownership of non-financial

firms tend to increase crisis probabilities, and similarly for an overall average of

restrictions. This is consistent with the result of the cross-sectional regressions of

Barth et al. (1999). Nonetheless, introducing the restriction variables changes little inthe rest of the regression, and the deposit insurance variable remains significant.

Another potentially important characteristic is to what extent banks are publicly

owned: it may be conjectured that countries where most of the banks are public do

not need explicit deposit insurance, since depositors know that the government will

back deposits in case of insolvency. If in those countries the banking system is also

less fragile, then failing to control for the public ownership of banks would bias

upwards the coefficient of the deposit insurance variable. Based on data about public

ownership of banks in La Porta et al. (1999), we find little empirical support for this

argument.27 In our sample, public ownership is more widespread in countries with

deposit insurance than in countries without it (the shares are 40 percent versus 30

percent). Also, the public

does deposit insurance increase banking - demigüc-kunt

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