POLICY RESEARCH WORKING PAPER   2682
Deposit Insurance and                                               Do deposit msurance
programs contribute to
Financial Development                                               financial development? Yes,
but only if the regulatory
environment is sound.
Robert Cull
Lemma W. Senbet
Marco Sorge
The World Bank
Development Research Group
Finance
September 2001



POLICY RESEARCH WORKING PAPER 2682
Summary findings
Cull, Senbet, and Sorge examine the effect of different         insurance leads to financial instability in lax regulatory
design features of deposit insurance on long-run financial    environments. But in sound regulatory environments,
development, defined to include the level of financial          deposit insurance does have the desired impact on
activity, the stability of the banking sector, and the          financial development and growth.
quality of resource allocation. Their empirical analysis is       Thus countries introducing a deposit insurance scheme
guided by recent theories of banking regulation that            need to ensure that it is accompanied by a sound
employ an agency framework.                                     regulatory framework. Otherwise, the scheme will likely
The authors examine the effect of deposit insurance on        lead to instability and deter financial development. In
the size and volatility of the financial sector in a sample     weak regulatory environments, policymakers should at
of 58 countries. They find that generous deposit                least limit deposit insurance coverage.
This paper-a product of Finance, Development Research Group-is part of a larger effort in the group to design financial
safety nets for developing countries. The study was funded by the Bank's Research Support Budget under the research
project "Deposit Insurance" (RPO 682-90). Copies of this paper are available free from the World Bank, 1818 H Street
NW, Washington, DC 20433. Please contact Kari Labrie, mail stop MC3-300, telephone 202-473-1001, fax 202-522-
1155, email address klabrie@worldbank.org. Policy Research Working Papers are also posted on the Web at http://
econ.worldbank.org. Robert Cull may be contacted at rcull Cworldbank.org. September 2001. (61 pages)
7'he Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the
countries they represent.
Produced by the Policy Research Dissemination Center



Deposit Insurance and Financial Development
By
Robert Cull, Lemma W. Senbet, and Marco Sorge*
* The authors are from the World Bank, University of Maryland, and Stanford
University. We thank the World Bank for funding and Asli Demirguc-Kunt and
participants at the World Bank conference on deposit insurance for their comments and
suggestions.






Deposit Insurance and Financial Development
I. Introduction
Do deposit insurance programs contribute to financial development? Why have
so many governments adopted such programs, and what did they expect to achieve? We
conduct an empirical analysis to examine the effect of deposit insurance on the size and
volatility of the financial sector, using a sample of fifty-eight countries. Governments in
advanced economies and many developing economies grant formal deposit insurance in
the hope of reducing the risk of systemic failure of banks and hence stabilizing the
payments and financial system. On occasions in the past, bank runs have destroyed the
payments system, with the resultant depression. Credible deposit insurance is presumed
to forestall such runs. Moreover, by bolstering depositors' faith in the stability of the
system, deposit insurance may lead to a deeper financial system, which could contribute
to higher economic growth rates.'
However, deposit insurance can be socially counterproductive if the system is
not appropriately structured. Under many deposit insurance schemes, if a depository
institution, such as a savings and loan firm, goes bankrupt, the government absorbs all
(or nearly all) of the depositors' losses. This weakens market discipline (i.e. monitoring
of bank activities by depositors and other bank stakeholders) and creates a moral hazard
problem, since there is now an incentive for depository institutions to engage in
excessively high risk activities, relative to socially optimal outcomes. Especially in lax
'See Levine (1997) on the links between financial depth and economic growth.
2



regulatory environments, these incentives are likely to lead to greater systemic
instability.
The central question that we address in this paper deals with the impact of deposit
insurance programs on financial stability and financial development. The stability
question is complementary to existing papers, particularly a recent study by Demirguc-
Kunt and Detragiache (DD,2000). Based on evidence for 61 countries between 1980 and
1997, DD find that variations in coverage, funding or management of deposit insurance
schemes are significant determinants of the likelihood of banking crisis, especially across
countries where interest rates have been deregulated and the overall institutional
framework is weak. We focus on the impact of deposit insurance on financial stability
and development in a longer horizon before the financial or banking system collapses
into a crisis. Accordingly, our empirics are not based on crisis data.
Thus, our aim is to understand the impact of alternative deposit insurance design
features over a longer horizon. We focus on financial development, broadly defined to
include the level of financial activity, the stability of the banking sector, and the quality
of resource allocation as reflected in real sector performance (i.e., growth). The
empirical construct is guided by recent theories of banking regulation that employ an
agency framework.
In short, we focus on the steady-state, forward looking effects of deposit
insurance. Recent events have shown that in times of crises, no matter whether deposit
insurance is explicit or explicit, depositors tend to be bailed out anyway when systemic
problems arise. Ex ante bail-out expectations, however, do influence bank risk-taking
3



behavior even in stable circumstances, by truncating the negative tail of the distribution
of expected returns, and our empirical tests are designed to measure these effects.
Moreover, our paper extends the empirical analysis of deposit insurance schemes
in a couple of ways. First, we directly address the sample selection problems inherent in
analyzing the effects of deposit insurance programs. The sample of countries that adopt
explicit deposit insurance is clearly not random, and thus we employ statistical
techniques that account for this selection process. Another reason to adopt this approach
is that it may not be easy to categorize variations in coverage or funding within the
heterogeneous spectrum of countries lacking an official insurance arrangement.
Further, the same explicit deposit insurance program will likely have different
effects depending on the general institutional environment. For example, a recent article
by Reint Gropp and Jukka Vesala (2000) argues that in Europe implicit insurance has
meant an even higher potential for moral hazard than explicit systems. This is because,
though it introduces some uncertainty of being bailed out, the coverage of implicit
insurance may extend to a larger set of bank stakeholders compared to the case of explicit
laws protecting depositors alone. In less developed countries, this might not hold -
lacking the institutional development to make limits binding, explicit deposit insurance
might offer no benefits over implicit. We test, therefore, whether the effects of explicit
deposit insurance are dependent on proxies for the quality of banking regulation and
supervision.
Second, in our empirical tests, we attempt to summarize the features of explicit
deposit insurance as completely as possible, rather than test the effects of individual
program features or a subset of features. When considering the safety of their deposits, it
4



is unlikely that depositors consider only one feature of a deposit insurance scheme, but
rather all the features together (coverage limits, types of deposits covered, and the
credibility of the insurer). Similarly, when choosing whether to participate in a deposit
insurance program, banks likely consider not only their premium payments, but also
whether and how future payments will be adjusted to reflect portfolio risk and when those
payments will be collected. To better account for the complementarities between
features of deposit insurance schemes, and to better reflect the totality of those features,
we categorize deposit insurance program features as reflecting either the generosity of
coverage to depositors or the requirements imposed upon member banks (which we call
entry hurdles). We then test whether indices based on these two sets of features lead to
higher levels of financial activity, and whether any such financial sector growth comes at
a price in terms of instability.
Section II provides a motivation using some stylized facts about deposit insurance
programs. In addition, it provides descriptive statistics, and highlights some puzzling
relationships between the individual program features, and between program features and
financial sector outcomes. The main purpose of this section is to introduce the reader to
the complexity of the problem under investigation and to show how simple bivariate
econometrics is not enough to draw robust conclusions from our sample data.
We look therefore (in section III) for a conceptual framework of how deposit
insurance affects the level and volatility of financial development. A simple model of
optimal portfolio selection is presented to illustrate how the introduction of insured
deposits tilts the optimal risk-taking behavior of banks.
5



Section IV spells out the main hypotheses we draw from our theory and describes
the estimation techniques we employ to test them empirically. The nature of the data
available and the objectives of our analysis require us to summarize design features by
means of principal component indices and to adopt a generalized Tobit estimation
approach in order to avoid any sample selection bias.
Section V presents our results and section VI concludes. This combination of
theory and evidence should, we hope, generate more reliable predictions about the effects
of deposit insurance on financial development for policy makers in developed and
developing countries alike.
II. Motivation: Stylized Facts
In this section we provide the observed design features of deposit insurance
programs and some stylized facts and descriptive statistics as a motivation for a more
detailed analysis conducted later.
A. Design Features of Deposit Insurance
Table 1 provides design features of deposit insurance categorized as reflecting
either generosity or entry hurdles. By generosity, we mean those features that determine
how much compensation a depositor receives in the event that her bank fails. This group
also contains features that determine the credibility of that compensation. Entry hurdles
are the requirements imposed on banks in order to become a member of a deposit
insurance program.
The six variables grouped under the heading generosity are (1) coverage per
depositor, (2) a dummy indicating whether foreign currency deposits are covered, (3) a
dummy indicating whether interbank deposits are covered, (4) a categorical variable
6



indicating whether the source of the program's funding is banks, government, or joint, (5)
a categorical variable indicating whether the program is managed officially (by
government), privately, or jointly, and (6) a dummy indicating whether the program
requires depositor co-insurance (a deductible). The entry hurdle variables (shaded in
Table 1) include (1) a dummy to indicate whether membership is compulsory, (2) a
dummy to indicate whether funding is on an ex ante or an as needed basis, (3) the annual
premium payment by member banks (expressed as a percentage of insured deposits), and
(4) a dummy indicating whether the premium payments are risk-adjusted (for the member
bank's asset portfolio).
The two variables on funding source and on program management are, perhaps,
less clearly identified as aspects of generosity towards insured depositors. Our idea is
that both variables may affect depositor perceptions of the credibility of the scheme. A
scheme may advertise generous coverage, but that may make little difference for
financial development if potential depositors do not find it credible. In that sense, these
variables measure the credibility of generosity. In the empirical analysis that follows,
however, the qualitative results remain largely unchanged when we drop these variables
as measures of generosity, or even when we treat them as entry hurdles.
Policy makers could conceivably achieve the same objectives regarding
generosity or selectivity of member banks through different deposit insurance provisions.
The design features, therefore, may be either substitutes or complements for one another.
For example, a generous scheme may be one that grants high coverage per depositor, or
one that covers a wider variety of deposits (including, perhaps, foreign currency or
interbank deposits). A less generous scheme might impose co-insurance on depositors,
7



which obligates them to pay a 'deductible' before their coverage is activated. High entry
hurdles could be achieved through high member premium, or through risk-adjusted
premium payments. Programs that require ex ante funding might also impose higher
costs on members than those that do not.
Table 1 presents simple pairwise correlation coefficients for deposit insurance
features for the twenty-nine countries in our sample that adopted explicit deposit
insurance schemes.2 If two design features are substitutes for one another, we expect a
negative correlation - when one of those features appears, the other is less likely to be
found. If features are complements, we expect a positive correlation. That is, if a feature
is better able to achieve its intended objective when another feature is also adopted, we
expect the presence of one of them to make it more likely that the other is present.
The correlations among the generosity variables in Table 1 do not show any clear
pattern. Only one is significantly different from zero, a negative relationship between
private management and coverage of interbank deposits, which suggests that private
schemes are less generous than publicly managed schemes in at least one way. Others
approach significance. For example, private management is positively associated with
co-insurance, while a government funding source is negatively associated with co-
insurance. Both of those relationships also suggest that government-based programs are
more generous than privately managed programs. However, taken as a whole, the low
significance levels and the relationships between the generosity variables defy a simple
summary explanation.
2Variable means are also included in parentheses in the far left column of Table I. Throughout the
analysis, implicit deposit insurance is simply defined as lack of an explicit scheme.
8



The only significant relationship among the entry hurdle variables is the positive
one between premium payment level and ex ante funding, which may indicate that, in
creating higher entry hurdles, countries have tended to both increase premium payments
and require that they be made up front. Other than that, however, there are no other
obvious relationships between the hurdles variables. Because there are no strong patterns
of substitutability or complementarity between the deposit insurance variables, it may be
instructive to treat each feature individually and examine its association with the
dependent variables of primary interest, namely the growth rate and the volatility of
financial development.
B. Deposit Insurance Features and Financial Development
For the growth rate of financial development we use one variable from the asset
side of bank balance sheets, the growth of the ratio of credit to the private sector to GDP,
and another from the liabilities side, the growth of the ratio of liquid liabilities to GDP.3
Similarly, to measure volatility of financial development, we use the coefficient of
variation (standard deviation divided by mean) in the private credit ratio and the liquid
liabilities ratio.4 All four variables are measured over all years for which data are
available between 1960-1995, but only after the country adopted explicit deposit
insurance. We focus on bank-based variables because banks are the primary financial
3 Because this is a study of the effects of insuring bank deposits, we focus on bank-based indicators of
financial development. We recognize that bank-based financial development could have implications for
development of the rest of the financial sector, although Levine and Zervos (1998) show that greater stock
market liquidity imnplies faster economic growth no matter what the level of banking development. The
converse also holds - greater banking development implies faster growth, regardless of the level of stock
market liquidity. These results suggest that bank-based development is unlikely to dampen the benefits of
market-based development. In addition, Deniirguc-Kunt and Maksimovic (1998) show that increases in
stock market development actually tend to increase the use of bank finance in developing countries. If
anything, the evidence suggests that these two aspects of the financial system may act as complements in
fostering growth.
9



actors in most, if not all, of the countries in our analysis.5 These indicators are standard
measures of financial development that have been used by other authors. King and
Levine (1993, 1994) and Levine, Loayza, and Beck (2000) find strong, often causal,
links between these bank-based indicators of financial development and economic
growth. If we find that these indicators are affected by deposit insurance, therefore, it
should have implications for growth.
Late in the analysis, we also examine whether deposit insurance affects a more
explicitly structural variable, a concentration ratio measuring the assets of the top three
banks relative to total banking sector assets. The Data Appendix provides additional
information on the sources and the construction of our variables. Simple correlations
between our five dependent variables and individual deposit insurance features also do
not yield clear insights as to the effect of deposit insurance on financial development
(Table 2). For example, coverage of foreign currency deposits is negatively associated
with the volatility of liquid liabilities, while coverage of interbank deposits is positively
associated with the same volatility measure. Neither of those coverage measures is,
however, significantly linked to the volatility of credit to the private sector. Nor are any
of the other deposit insurance features significantly linked to either volatility measure.
Based on these results, it would be difficult to conclude that the moral hazard introduced
by a generous deposit insurance scheme causes greater financial sector volatility.
Results are just as puzzling for the growth rate variables. The only significant
correlations are the negative ones between premium payments and the growth rates for
4 We refer to these measures as the 'volatility of liquid liabilities' and the 'volatility of credit to the private
sector' from this point forward.
10



both liquid liabilities and private credit. This is somewhat surprising in that higher
premium payments, which could be an entry hurdle that might lead to a better roster of
banks, were actually associated with less financial development. On the other hand, the
premium payments may have been higher expressly because of the low level of financial
development associated with an unstable financial sector prone to systemic failure.
Indeed, the correlations between premium payments and our two volatility measures are
positive and approach significance.
These premium results may reflect a deeper underlying incentive problem
associated with explicit deposit insurance. Due to limited liability, bank equityholders
enjoy the upside benefits associated with a risky asset portfolio, but are largely protected
against the downside losses associated with non-performing assets. By increasing savers'
faith in the formal financial system, credible explicit insurance could increase the level of
deposits. Additional deposits imply more investable resources for the bank, and should
increase the upside benefits associated with holding a (larger) portfolio of risky assets.
However, these results are still puzzling. We will explain the puzzle later in the context
of the theoretical analysis and the multivariate empirical analysis.
Finally, the results for the asset concentration variable are intriguing, but not very
robust. For example, ex ante funding, which we thought of as an entry hurdle, is, as we
would expect, associated with greater concentration. Similarly, compulsory membership
is negatively (though not quite significantly) associated with concentration. However,
the most pronounced relationship is the negative one between co-insurance and
5 To cite just one example, the United States, a country whose financial sector is dominated by stock
markets rather than banks, is excluded from the analysis because it adopted deposit insurance in 1934, well
before our sample period.
11



concentration. Perhaps co-insurance, by limiting generosity, means fewer deposits in
total, which implies fewer sector entrants?
Aside from presenting the descriptive statistics and stylized facts, this section
illustrates the difficulty in isolating simple relationships between deposit insurance
features and financial development in a univariate analysis. These preliminary results
indicate that few, if any, of the variables that we thought of as affecting incentives either
through generosity or through bank entry hurdles is associated with either financial
volatility or financial development in a simple, predictable way. Motivated by these
initially puzzling results, we will begin setting out a conceptual foundation as a guide to
the more detailed empirics, a foundation that appeals to agency paradigms in finance and
specifies how deposit insurance could conceivably affect financial development. We will
find that some of the puzzles are consistent with the incentive effects of deposit
insurance, which will be examined in greater detail in the multivariate analysis of Section
IV.
III. The Role of Deposit Insurance in Financial Stability and Resource
Mobilization: Agency Theoretic Framework
We will characterize the potential investment distortion and resultant financial
instability that could arise from an ill-designed deposit insurance scheme by appealing to
agency paradigms in corporate finance. The view that is widely held in finance is that
the firm (say a depostitory institution) is a nexus or network of contracts among various
parties or stakeholders, such as shareholders (bank owners), creditors (depositors),
employees, and other stakeholders (regulators and tax payers). The rights of each class
of stakeholders in the firm are defined in contracts. While most stakeholders contract for
12



fixed payoffs, the firm's owners hold residual claims on cash flow earnings. This gives
rise to potential conflicts among the stakeholders. Left alone, each class of stakeholders
pursues its own interest which may be at the expense of other stakeholders. 6
Consider now just two classes of stakeholders: equityholders (bank owners) and
debtholders (bank depositors). The debt contract may enable managers, working on
behalf of owners (equityholders), to make investment and financing decisions sub-
optimally by departing from the principle of value maximization. The reason is that
equityholders and creditors hold disjointed interests and that equity value maximization
would not be equivalent to overall firm value maximization (optimal resource
allocation). The primary concern for equity value maximization is over the cash flows
in the non-bankrupt states, rather than the entire region of firm cash flows. Now
management, working in the best interests of existing shareholders, has an incentive to
alter the riskiness of the firm's investment activities (asset risk-shifting). Riskier
investments, if successful, will benefit equityholders, but they will reduce the value of
collateralization to debtholders, if they fail.
Thus, projects that are otherwise profitable may be foregone in exchange for
high risk but inferior counterparts, leading to economic inefficiency.7 Departures from
efficient investment strategies are detrimental to economic growth and development.
Therefore, the economic and financial envirom-nent that fosters efficient contracting
among parties with diverse interests, promotes efficient allocation of resources and
economic development.
6 See Bamea, Haugen, and Senbet (1985) for further discussion of agency issues.
7In addition to the asset substitution problern, the existence of outstanding debt inhibits profitable
investments, since the benefits would enhance the safety to creditors at the expense of equityholders. This
problem manifests itself in the form that has come to be known as "underinvestment."
13



Now we wish to pursue the agency analogy to bank deposits and deposit
insurance. Consider a bank that issues equity and makes investments in loans (e.g.,
commercial real estate). The bank faces a menu of investment opportunities
characterized by rewards (loan quality) and risks or volatilities. In this case, the all-
equity bank makes a risk choice that maximizes bank value (V*). However, from the
standpoint of the society at large, all-equity banks may be sub-optimal, if by issuing debt
(deposits), they enhance liquidity services. This presumes that bank deposits and equity
are not perfect substitutes in terms of liquidity provision.8 In this setting, the objective
function of the social planner can be thought of as being guided by these goals: (1)
minimizing the loss of value resulting from distortionary investment policy (agency
costs); (2) maximizing the value of banking activity in the liquidity services that banks
provide (bank liability side) and in their role as informed agents in an environment of
imperfect information - screening and monitoring of borrowers, for instance (bank asset
side). Thus, the social value considers the entire picture of the role of banks as informed
agents (asset side) and liquidity providers (liability side).9
8 Diamond and Rajan (1998, 1999) provide a theory of how such liquidity services arise. They start from
the proposition that entrepreneurs have projects in which the cash flows that they can generate exceed those
that anyone else could generate in the same circumstances. They assume that an outside financier who
invests in these projects at an early stage develops specific knowledge about how best to re-deploy the
project's assets. Such financiers can more credibly liquidate a project. The specific abilities of the
entrepreneur and the financier make the project and its financing an illiquid bundle of assets - by
construction, no other financier/entrepreneur pair value the project as highly.
Banks act as a commitment device that solves this liquidity problem. Because of its fragile deposit-based
capital structure, a bank can commrit to pass through to depositors the entire amount that it expects to
collect using its specific abilities. Any attempt to extort rents by threatening to withdraw these specific
abilities will be met by a run, which disintermediates the bank and drives its rents to zero. When some
depositors want their money back in the 'ordinary course of business,' the bank does not have to pressure
the entrepreneur, it simply borrows from new depositors that also understand the bank's underlying
incentive to behave well. In this way banks, "enter into a Faustian bargain, accepting a rigid and fragile
capital structure in return for the ability to create liquidity."
9 See John, John, and Senbet (1991) for a complete discussion of the social planner's objective function in
the context of depository institutions.
14



In this section, we focus on the economic consequences of deposit insurance by
taking it as given that deposit insurance is already in place and that banks issue deposit
liabilities, along with equity (bank capital). In the advanced economies and many
developing countries, deposits issued by banks and thrift institutions are insured by the
governments. Given the risk incentive problems that arise, abolishing deposit insurance
may seem reasonable. However, in countries that lack formal deposit insurance
schemes, including most African countries, deposits are implicitly insured even when
they are not explicitly insured.'0
Consider partitioning of the payoffs to bank shareholders in Figure 1 when
deposit financing with a promised payment of F is outstanding. Given that bank asset
cash flows are X, bank owners face a residual payoff of MAX (0, X-F), and uninsured
depositors face a payoff of MIN (X, F). If deposits were fully insured, the insuring
agency picks up the shortfall, MAX (0, F-X), so that depositors end up with the full
promised payment, F.
Now the incentive effects of deposit insurance are analogous to private agency
conflicts. The payoff to the bank owners is isomorphic to that of a call option and the
government obligation is equivalent to a put option. When deposits are guaranteed,
depositors themselves face no risk. However, risk due to the risk increasing incentives
of the banker is transferred to an insuring agency. For bank equityholders, the value of
their option increases with both the value of future cash flows and the volatility of those
flows:
10 In the empirical tests that follow, we can cornpare financial sector performance before and after the
adoption of explicit insurance. This provides some information about the relative merits of explicit versus
implicit insurance, but the findings are based on the subset of countries that eventually adopted an explicit
15



Value of equityholder's option = f(expected cash flows, a)
(+)         (+)
where a is the volatility of bank asset cash flows. Because the option becomes more
valuable as the volatility of cash flows increases, the portfolio of bank assets that
generates those flows is riskier than it otherwise might be.
In short, bank owners gain by choosing riskier asset portfolios. Due to the
convexity of payoffs to bank equity capital, they take full advantage of the up-side
benefits but face limited down-side risk due to limited liability. Thus, owners of banks
financed by deposits have incentives to take risk beyond that which is optimal for an "all
equity" bank. In fact, this risk-shifting behavior by banks has been widely viewed as a
major culprit in the savings and loan crisis in the United States. The financial deregulation
of the 1 980s led to increased incentives for limited liability thrifts and banks to engage
in excessively risky lending, such as LDC loans and real estate loans, hoping for big
payoffs under favorable conditions and transferring losses to the insurance agencies
under adverse conditions.
In this paper we will draw some testable implications from the agency perspective
of risk-shifting on the relationship between deposit insurance and financial stability, as well
as development. The analysis needs to be formalized so as to draw such implications. We
use the framework of John, Saunders, and Senbet (2000) to provide a reduced form
characterization of bank investment incentives. 1 A representative depository institution
(bank) has a representative portfolio of risky assets (loans). These investment
opportunities can be characterized by their rewards (a schedule of means) and their risks
scheme. Potential effects may be different for countries in Africa, or elsewhere, that have yet to adopt
explicit insurance.
16



(volatilities) of the terminal cash flows from the loan. A typical investment opportunity
set {(V()s),) g Q} is shown in Figure 2 as a concave production fumction. For
generality, we also include a function that has a flat region as volatility changes so as to
admit banks that have investments with zero net present value.
Central to the bank asset or lending risk incentive problem is imperfect
observability by outsiders (depositors and regulators) of the asset or lending quality
choices made by corporate insiders (bank managers). If asset risk choices were to be
observed completely, forcing contracts (or regulatory devices) can be structured to
achieve the first-best, efficient solution. Thus, in the context of our analysis, the
investment and the associated risk choices made by the bank (as embodied in the loans
extended or assets selected) are viewed as "private action." That is, there is imperfect
external monitoring of the risk choices by outsiders (including regulators).
Given incomplete contracting regarding the risk choices, bank insiders
(management) make investment and risk choices to maximize the value of the structure
of their own claims, rather than maximizing the total value of the bank, V(C). The value
cs denotes the bank's value-maximizing risk choice [ i.e., a* maximizes V(C), see Figure
2]. Consider that deposit financing, with promised payment F, is currently outstanding. Let
a(F) be the risk level at which the value of bank equity, E(a), is maximized, presuming
management is totally aligned with bank owners. As in Figure 1, bank equity can be
viewed as a call option which increases in value as the volatility of bank assets
increases. That means bank equity value is maximized along the value frontier on the
right hand side of V* in Figure 2. However, the risk incentive effect is limited by the
The more detailed formalization is given in the Appendix.
17



concavity of the bank investment schedule, since the decline in V adversely affects the
value of equity. At the margin, the pure volatility effect is offset by the value effect (see
Appendix for a fortnal representation).
With a sufficiently high level of debt (deposit financing), insiders or bank
management (deciding on behalf of bank owners) depart from the first best risk
outcome. Thus, the investment implemented will be affected by the amount of bank
capital in place and its complement, the level of debt financing. Bank management will
invest up to an asset risk choice level of cai(F), which is higher than cai, to maximize the
value of bank equity. The distortion in risk choice, as represented by the risk deviation,
aAF) - Yj depends on the level of bank capital as well as the investment schedule i faced
by the bank. Moreover, since ai(F) - C is decreasing in the fraction of bank capital in
place, it provides a motivation for capital regulation, and for linking the level of bank
capitalization to the pricing of deposit insurance. Thus, deposit insurance premium should
be based on measures of risk, along with measures of bank capital.
Now consider multiple banks with their own unique investment schedules, such
as in Figure 3.  The value-maximizing level of risk for the investment opportunity i is ai*,
i = 1,2,3. For unimodal structures, such as VI and V2, there is a unique value-maximizing
level of risk, represented by a,* and a2*, respectively. In other words, the value-
maximizing levels of risk differ for different portfolios of bank activities, and this has
regulatory implications. First, since banks may have different opportunities to exploit
risk within any capital zone, categorizing all within the same zone into an identical risk
classification is misleading. In this sense, capital ratios may be poor proxies for
18



measuring bank safety. Second, the regulatory corrective actions should not be designed
to homogenize all banks to some common pool of risk in the guise of restrictions on
asset risk choices. For instance, these measures may push a bank with activity set 2 to
go below a2*. Third, capital regulation can be beneficial for critically undercapitalized
banks. As ai(F) - a, is larger with greater leverage (lower capital ratios), regulatory
measures designed to move a bank which is critically undercapitalized to a higher level
of capitalization will reduce the risk distortion, a1(F) - ari. In this sense, capital
regulation increases bank value or moves the bank closer to an efficiency boundary.
Finally, it should be mentioned that the deposit insurance premium will now be set
corresponding to a level of risk ca(F), which is a function of the degree of capitalization,
which is inversely related to F (see Appendix), along with measures of the risk class of
bank activities.
The foregoing discussion underscores that the effectiveness of capital regulation
depends crucially on banks' asset characteristics. To dramatize this point further, we
can resort to an investment technology or bank asset characteristic for which capital
regulation is entirely ineffective. Consider the investment schedule 2, graphed in Figure
2, where U2 is not unique. In fact, all levels of a2 yield value V2(a2) along the flat stretch
of the opportunity curve. This investment schedule is entirely feasible when there is a
large supply of risky investments with zero net present value (e.g., risky assets in
financial markets). In this limiting case, large premiums may be required to take
account of high levels of risk-shifting, even with a high degree of capital regulation.
12 Consider, for example, the differences between the investment schedules for money center banks and
rural credit institutions.
19



The foregoing simple theoretical framework (and the Appendix) are rich enough
to draw some testable implications that guide and motivate the empirics in the next
section:
1. Financial Instability and Moral Hazard (Volatility Effect): Deposit
insurance may prevent panic and bank runs, but it transfers risk to the insuring agency.
In a poorly regulated environment, banks have an incentive to engage in investment
(loan) activities which are excessively risky, relative to the socially desirable level of
risk. The excessive risk taking behavior of a bank means that the bank assets have
become more volatile beyond the socially optimal level of risk. With many such banks,
the financial system becomes more unstable; a+(F) - cyi > 0. Thus, with unresolved
moral hazard, deposit insurance is counterproductive, and it induces more, not less,
stability in the financial system.
2. Economic Inefficiency (Value Effect): Not only do the incentive effects of
deposits and deposit insurance lead to excessive risk-taking, they also distort bank
investment activities (loans) away from the socially optimal level of investments. In
essence, the existence of deposit insurance leads to a decline in the overall economic
performance as the overall bank values diminish in association with increased risk
taking and financial instability (see figure 2); V(F) - V* < 0. The regulator may also
impose mandatory restrictions on bank asset risk choices so as to limit risk-taking, as
done in certain regulatory regimes, such as the US. However, this would lead to socially
counterproductive outcomes. This is because, direct monitoring of bank asset portfolio
(loans) through mandating its risk levels may push the bank to levels below the socially
optimal level of bank risk, a (q*). Suppose the regulator wishes to induce banks to a
20



common pool of risk (or more generally to minimal risk). This would be distortionary,
given that banks are characterized by differential investment opportunity sets as in
Figure 3, with different risk choices and hence differential maximal (q*j), that are
optimal from the standpoint of overall value maximization or efficiency.
3. Capital Regulation Effect (Entry Hurdle and Limited Effectiveness):
Various reforms have been proposed to correct the distorted incentives facing
bankers. Some proposals, such as risk-based deposit insurance premium and risk-based
capital, attempt to replicate the incentives that would be provided by the market. Like
risk-based premium, risk-based capital has some intuitive appeal as an entry hurdle.
Theory predicts, however, that neither will be a completely satisfactory solution, but for
different reasons. Risk-based premium payments suffer from a time inconsistency
problem described in more detail below. Similarly, risk-based capital regulation is of
limited effectiveness for the reasons listed next.
a. Capital Regulation: The motivation for capital-based regulation is clear
from the Appendix. As shown there [see (b) and (c) of A4], the extent of risk
undertaken beyond the optimal level, a[q(F)] - a(q*), as well as the value lost due to
risk-shifting, NPV(q*) - NPV[q(F)], are both functions of bank equity capital. With
higher bank capital, (i.e., lower F), the incentive for risk-shifting is mitigated, so that a
is lower. Looking at Figure 2, additional capital infusion moves the bank back toward
the efficient level of risk and investment value.
Unfortunately, there are limitations with capital regulation. We begin dramatizing
them by considering the limiting case of an all-equity bank. By definition, such a bank
receives no deposit funding and thus poses no risk to anyone but its owners. In that
21



sense, one incentive problem is resolved regarding the riskiness of bank assets. But, as
described above, a key component of the social value of financial services comes from
the liquidity that banks provide, and an all-equity bank does not adequately serve that
purpose.
Capital regulation also has limitations under more realistic conditions because:
(a) although the incentives are improved with more infusion of bank capital, the
distortions and excessive risks are never eliminated for deposit insurance which even
minimally imposes risk on the insuring agency. In the parlance of the Appendix, there
will be risk-shifting for all values of F > L, although the magnitudes of the costs are
reduced;13 and (b) given the variation in bank asset risk characteristics (portfolios),
capital regulation has to be bank-specific, and hence hard to implement. This also brings
home that standardized capital requirements are sub-optimal, and casts serious doubt on
the usefulness of rules-based approaches, such as in the Basle Accord.
b. Deposit Insurance Premium Effect (Time-Inconsistency and
Ineffectiveness as Entry Hurdle): Our framework is rich enough to allow for the
specification of a fair deposit insurance premium which can be structured as a function of
the bank capital ratio (F) and the observable parameters of the bank investment schedule
as specified in the Appendix {I, H, L}. The pricing of deposit insurance is possible
despite the existence of the moral hazard problem arising from imperfect observability of
private investment incentives controlled by bank insiders or decision-makers. This is
because the regulator can calculate the incentive-based risk choices induced by the level
13 Ideally, we would perform cross-country tests on whether higher capital standards imnply lower sector
volatility. However, at this point, cross-country data on capital standards are not nearly as comprehensive
as for deposit insurance programs. As a result, the empirical section of the paper focuses only on deposit
insurance features.
22



of bank capitalization employed by the institution and the parameters of the bank asset
investment schedule. From the Appendix, a deposit level F > L induces an investment
policy [q(F)] so that a fairly priced, revenue neutral insurance premium can be specified
as follows:
7r(F) = q(F) max(O, F - I) + (1/2)(I - q(F))2 max(O, F - L)
where q(F) is specified in A4 of the Appendix. Under the preceding
specification, a fair deposit insurance should only cover the states of nature yielding low
investment returns (L) and possibly internediate returns (I) for high levels of deposits. If
the bank can obtain an insurance with a premium 7n < n (F), the equityholders gain a
transfer of wealth of 7t (F) - rt from the regulator. However, it should be noted that,
although a fair insurance premium covers the regulator's loss, it does not induce the
Pareto optimal investment policy, since NPV(q(F)) is still lower than NPV(q*). Due to a
time inconsistency problem, higher premium payment does not generally imply that
banks will hold less risky assets. There is a need for incentive based regulation in view
of the inadequacy of deposit insurance premia.14
There is another way to see the time inconsistency problem of deposit insurance
premium. The govemment agency or private entity that offers deposit insurance might
set risk-adjusted premia that account for these incentives. However, once such premia
are paid, bank equityholders have no incentive to reduce the risk profile of their assets.
To maximize the value of equity under limited liability, they will continue to hold the
14 An appropriately designed incentive compatible compensation may help alleviate managerial agency
problems. In particular, if the incentive features of compensation are tied to performance through stock
appreciation rights, stock option contracts, and deferred compensation, managers may have incentives to
increase the value of the firm (see John, Saunders, Senbet, 2000).
23



portfolio of risky assets that maximizes their expected upside benefits. The choice of
bank assets may be completely divorced from the premia level. If the benefits associated
with additional deposits and a larger risky portfolio outweigh premium costs, deposit
insurance may be ultimately destabilizing. By broadening the pool of savings in the
formal banking sector, explicit deposit insurance may therefore contribute to increased
volatility, and risk-adjusted premia may be unable to prevent it. This may be what
underlies the insignificant univariate relationships between risk-adjusted premium
payments and financial volatility. We will investigate this more thoroughly in the
multivariate tests that follow.
4. Optimal Regulation and the Rule of Law: The preceding discussion
underscores the distortionary effects of deposit insurance in a poorly regulated
environment. Capital regulation is one way to mitigate the problem, but its effectiveness
is limited (see 3 above). However, as John, Saunders, and Senbet (2000) show, it is
possible to come up with a more efficient banking regulatory scheme by exploiting the
incentive features of bank management compensation. The existence of explicit deposit
insurance facilitates this, since the insurance premium can be determined on the basis of
not only capital rules but also incentive features that include base salary, equity
participation, and bonus. The basic idea is these features can be designed to make bank
management sensitive to the interests of both depositors (and regulators) and bank
owners. Of course, the environment that fosters the rule of law and enforceability of
contracts will facilitate the effectiveness of such an incentivized regulation. For instance,
in an environment where regulators themselves have distorted incentives, the optimally
designed banking regulation may not be implemented (see Hauswald and Senbet, 1999).
24



In the following empirics we control for optimal regulation through rule of law
indicators. 15
IV. Empirics
A. Motivation for Indices and Estimation Technique.
Our central predictions are centered around the effects of deposit insurance features
on financial volatility and growth. The specific hypotheses are spelled out in Section B
below. To construct empirical tests of our predictions on the effects of deposit insurance,
we re-packaged the data described in Tables 1 and 2. We first synthesized the
information contained in our database regarding the generosity and selectivity (entry
hurdles) of deposit insurance systems into two main principal component indices.'6 As
noted above, using the original features instead of a few conglomerate indices makes it
more difficult to produce a simple, coherent analysis of the extremely diverse regulatory
schemes adopted around the world.
On a practical level, the effects of deposit insurance programs on the structure and
performance of the banking sector may not be evident for some time. As a result, we
computed our indicators of financial sector growth and stability at the country level, and
aggregated the data over long periods of time. The study is, therefore, more akin to a
cross-sectional rather than a time series analysis. This limits our degrees of freedom, thus
making it unwise to include each design feature as a separate explanatory variable.
However, our approach yields correlations that are quite robust.
In addition, the use of indices makes for a more direct correspondence between our
regression variables and the theoretical concepts outlined above; this greatly facilitates
15 We recognize that adequate rule of law is necessary but, in some cases, not sufficient for an optimal
regulatory scheme.
25



the task of studying simple linear relationships between deposit insurance features and
financial performance. It also makes it possible to formulate more flexible policy
recommendations, allowing for tailor-made country specific solutions. In other words, if
high scores on a conglomerate index are found to be positively (or negatively) correlated
with financial performance indicators, there is still a number of possible configurations of
the underlying design features that a country may be advised to pursue. As the same
purpose is conceivably achieved in different settings by different means, a one-size-fits-
all, best practice approach (in terms of single design features) may not be optimal in the
face of country-specific socioeconomic constraints.
From an econometric perspective, since some design features are closely related
(as they express different aspects of coverage, etc.), including principal component
indices in the regression analysis represents a way to avoid, at the same time, potential
problems both of multicollinearity (if we were to include in the regression highly
correlated design features) and of omitted variable bias (in case we decided to omit some
arbitrarily due to our limited degrees of freedom). As a technical note, we employ
principal component analysis rather than alternative techniques, such as factor analysis,
because the resulting indices are simple linear combinations of the original design
variables using "optimal" weights.17 Therefore, regression results involving such indices
can easily be translated in terms of a package of single design features, with the
additional flexibility noted above.
16 See pp. 5-7 for detailed description of both generosity and entry hurdle variables.
a By contrast, in factor analysis, weights are obtained by mninimizing the information lost in replacing a
whole matrix of design features with one or more vectors that account for most of the variation in the
original component variables. Those vectors do not correspond directly to any particular design feature.
This makes it more difficult to provide specific policy advice regarding program design.
26



A few remarks should be made to motivate our generalized Tobit estimation
approach. An important goal of this line of research is to advise countries contemplating
the adoption of explicit deposit insurance whether they should do so, and what types of
programs have worked best in fostering financial development. It would be ideal,
therefore, if the estimated relationships between deposit insurance features and financial
performance consistently extended to the whole population of countries. Such hopes are
dashed, however, because data on design features are available only for the restricted
(clearly non-random) sub-sample of countries that have adopted an explicit deposit
insurance system in the first place.
We address the risk of estimating a relationship which only suits a subset of
selected countries by adopting a two-step sample selection model. We first estimate for
all countries, the probability of adopting explicit deposit insurance and then use these
"sample weights" in the second stage (the actual regression), to minimize the
distortionary impact of the observations which are most likely to be selected in the first
stage. The second stage regressions, which describe the effects of different types of
explicit deposit insurance (as summarized by our indices) on barnking sector growth and
stability, can also offer comparisons of average sector performance before and after the
adoption of a program. In that sense, they do provide information about the relative
merits of implicit versus explicit deposit insurance, but based on the subset of countries
selected in the first-stage regression.
As described more fully below, selected countries, those that adopt explicit
insurance, tend to be more institutionally developed and display lower financial sector
volatility than countries that retain implicit schemes. In that sense, our results may not be
27



an adequate guide as to the effects of explicit insurance on less institutionally developed
countries. But to foreshadow our results a bit, we do find that, even within the subset of
countries that adopt explicit insurance, subsequent sector volatility is relatively high and
long-run sector growth is relatively low in those countries that have relatively weak
institutions. We suspect that, had they adopted explicit insurance, the results might have
been even more devastating for the subset of countries that retained implicit schemes
throughout. Finally, as for concerns of simultaneity bias, if it is well possible that the
decision to adopt explicit deposit insurance (or none at all) may depend on financial
performance, it is perhaps conceptually less likely that specific deposit insurance features
are designed conditional on these macro-indicators.18
B. Hypotheses
Armed with variables (indices) that better summarize the generosity and entry
hurdle concepts, we conduct empirical tests which are motivated by an incentive-based
theory of depository institutions as discussed in the last section. The indices enable us to
state our hypotheses succinctly. Before we do, however, we need to introduce one
additional variable. As noted above, any adverse effects of deposit insurance may be
mitigated by effective bank regulation and supervision. Even if depositors have little
incentive to monitor their banks, a sound regulatory environment may limit some abuses
and lead to healthier bank asset portfolios.
Lacking a perfect summary statistic for the quality of the regulatory environment,
we rely on a six-point measure of the quality of the rule of law created by the
Is Moreover, in a very similar setting, Dernirguc-Kunt and Detragiache (1999) provide a series of
instrumental variable tests refuting the hypothesis that the specific program features are endogenous.
28



International Country Risk Guide (ICRG). 19 Higher scores indicate "sound political
institutions, a strong court system, and provisions for an orderly succession of power."
While not a perfect indicator -- the U.S., for example, which scores well on the index, did
suffer the S&L crisis -- the index has been used in the empirical financial literature for
purposes similar to ours. Authors have used the index in growth regressions as a general
measure of institutional development (Knack and Keefer, 1995) and as a proxy for
institutional development in the financial sector (Demirguc-Kunt and Detragiache,
1997).2o
Using simple interaction terms, the ICRG legal index enables us to measure the
effects of generosity and entry hurdles in weak versus strong regulatory environments.
As laid out in the theory section, more generous deposit insurance schemes should
increase F, the level of deposits outstanding, and a, the riskiness of banking sector asset
portfolios. We expect these effects to be reflected in increases in our two volatility
measures. Again, however, the incentive to increase riskiness, and thus volatility, should
be more effectively curtailed in sound regulatory environments. These thoughts lead to
our first two hypotheses:
19 Legal tradition data are averaged over 1985-1991 for each of our countries.
20 More specifically, because the index is a good predictor of the use of long-term debt by large finms in
their cross-country sample, Demnirguc-Kunt and Maksimovic (1998) use it as an indicator of the ease with
which firms can enter into long-term contracts. They point out that it is a better indicator of the quality of
the contracting environment than specific differences in legal codes because firns may be able to
compensate for the absence of specific legal protections by altering the provisions of contracts. La Porta et
al. (1999) demonstrate that countries with legal codes that afford a relatively high level of protection for
investors tend also to have stronger enforcement of laws as reflected in higher ICRG index scores. Our
index, therefore, rnight be thought of as a summarizing both the quality of the regulations on the books and
the quality of enforcement of those regulations. Finally, Barth, Caprio, and Levine (2000) create a general
measure of good government by computing the average value of three variables: our rule of law index and
the ICRG indices for "risk of expropriation by the government" and for "the degree of corruption." They
find that those countries that score well on their good government index tend to impose fewer regulatory
restrictions on their banks and have less state ownership of the banking sector. While each of these studies
illustrates a slightly different aspect of financial sector development that is captured by the rule of law, it is
29



HI: Generous deposit insurance schemes lead to greater financial sector
volatility. If so, the coefficient for 'generosity' should be positive and
significant in the volatility regressions.
H2: Any increased volatility due to relatively generous deposit insurance
should be mitigated by a sound regulatory environment. If so, the
coefficient for 'generosity x law' should be negative and significant.
Hypotheses regarding entry hurdles are qualitatively similar to those for
generosity, but they tie into the theory section slightly differently. In particular, one
could view entry hurdles as a selection device that creates a roster of insured banks less
prone to take on risky assets. In that sense, much like some forms of bank regulation or
supervision, the entry hurdles could contribute to a less volatile financial sector. Indeed,
risk-adjusted premium payments, a component of the entry hurdle index, may provide an
incentive for banks with riskier asset portfolios to eschew insurance. On the other hand,
because of the time inconsistency problem discussed in the theoretical section, even well-
priced deposit insurance may have no impact on risk incentives. We leave it to our
empirical tests to sort between these hypotheses.
For purposes of exposition, it will be easier to invert our entry hurdle index (a
"good" thing) into an index of weak selection criteria (a "bad" thing). In that way,
predictions about the coefficients for generosity (also a "bad" thing) and its interaction
with the legal index are now identical to those for poor selection:
H3: Lower entry hurdles lead to greater financial sector volatility. If so,
the coefficient for 'poor selection' should be positive and significant.
H4: Increased volatility due to low entry hurdles should be mitigated by a
sound regulatory environment. If so, the coefficient for 'poor selection x
law' should be negative and significant.
a proxy for the quality of regulation and supervision that has been widely used in the empirical financial
literature.
30



Our theory does not unambiguously resolve whether increased riskiness (6)
coincides with improvements or declines in V, the value of financial intermediation.
Some volatility (riskiness) is probably necessary to create a vibrant financial sector
capable of identifying new, relatively productive ventures. Too much volatility can be a
sign of too much risk taking. Moreover, the dislocation associated with frequent
systemic collapses is likely to lead to slower long-run financial development and
economic growth. As proxies for V we use the growth rates in our financial indicators
described above. We view them as measures of long-run financial development. The
mechanism by which generosity affects, first, the level of deposits, and, second, the
riskiness of sector assets and the value of financial intermediation, makes it the more
natural channel through which deposit insurance could affect financial development, and
thus our growth rates. The mechanism by which entry hurdles affect long-run financial
development is less clear, at least in the context of the theory section, but we also test
whether they, too, have an effect on growth rates. We expect that the potential positive
effects of deposit insurance on financial sector growth are likely to be most pronounced
in a sound regulatory environment, one less prone to systemic collapse. Our final two
hypotheses are, therefore:
H5: Overly generous deposit insurance and poor selection of member
banks may lead to sector instability and slower long-run financial
development. If so, the coefficients for 'generosity' and 'poor selection'
should be negative in the financial sector growth rate regressions.
H6: The adverse growth effects of generosity and poor selection should be
reduced by a sound regulatory environment. If so, the coefficients for
'generosity x law' and 'poor selection x law' should be positive in the
financial sector growth rate regressions.
31



We can state our central testable predictions with the following simple regression
model:
Yi = ao + 8GlGi + fiG2GiLaw! + PHIHi + fH2HjLawI + 8xXj + frRi + Ģi
where Yi is the growth rate of an indicator of financial development, or a measure of the
volatility of that indicator in country i; G is the generosity of deposit insurance; H
represents the weakness of the entry hurdles, or requirements, imposed by the deposit
insurance program on member banks; Law is an index of the quality of the rule of law; X
is a set of macroeconomic control variables that could also affect financial sector
development and volatility; and R represents other potentially relevant aspects of the
deposit insurance program or the banking sector. For the regressions in which financial
volatility is the dependent variable, Hypothesis 1 (HI) implies that fGI>O; H2 implies
3G2<0; H3 implies flHI>O; and H4 implies flH2<0. For the regressions in which the growth
rate of a indicator of financial development is the dependent variable, H5 implies that
13G1<O and flHI<0, while H6 implies that flG2>0 and #H2>0.
In addition, as described above, we include a first-stage regression to control for
the selection bias associated with the adoption of explicit deposit insurance. In that stage
we rely on two measures - prior financial sector volatility and the legal index - to
separate countries that adopted deposit insurance from those that did not. As in Cull
(1998) we expect that strong legal tradition is positively associated with adoption of
explicit deposit insurance, while prior volatility is negatively associated with adoption, as
countries have been less apt to enact deposit insurance in turbulent periods.2'
21 Like our other volatility measures, prior volatility is measured as the coefficient of variation in our two
financial indicators. For countries that never adopted explicit deposit insurance, the prior volatility
measure was calculated over the entire period 1960-1997 (although data availability implied that, in
practice, most observations were calculated over a smaller subset of that period). For countries that
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The control variables in the second-stage regressions (which use either volatility
or growth rates of our financial indicators as the dependent variable) include inflation,
real growth, the years since deposit insurance has been in place, and the banking sector
asset concentration ratio (described above). High inflation, which may make some types
of financial contracting more problematic, should have an adverse effect on financial
growth rates. The effect on sector volatility is more difficult to predict. Real growth
should have a positive effect on growth rates, but a less clear effect on volatility. Years
since adoption may be positively associated with both volatility and financial growth as it
may take time before the effects of deposit insurance are evident.
Predictions regarding concentration are ambiguous. It may be associated with
reduced volatility, but have a negative effect on growth rates, because a small number of
dominant banks may be less inclined to hunt down new investment opportunities than
would a higher number of less-established banks. Risk taking on the part of less-
established banks may, however, be so destabilizing as to retard long-term financial
sector growth. While not our central focus, these control variables should help us to
estimate more accurately the effects of generosity and entry hurdles on financial
development. Like the dependent variables in the second-stage regressions, these control
variables are averaged over all years since the adoption of deposit insurance.
V. Results
Regardless of the dependent variable (the volatility of either private credit or
liquid liabilities), all specifications in Table 3 provide support for hypotheses one and
two. The significant positive coefficient for generosity indicates that relatively generous
adopted explicit deposit insurance, the prior volatility measure was calculated over the period just prior to
adoption. Qualitative results are similar regardless of whether we use the three-year or the five-year
33



deposit insurance schemes do coincide with greater subsequent financial sector volatility.
The significant negative coefficient on the generosity/law interaction term indicates that a
sound regulatory environment can mitigate the volatility associated with generous deposit
insurance. Recall that the legal index is measured on a six-point scale. At the risk of
reading too much into our model, the estimated coefficients imply that only countries
with the highest scores on the legal index (five or six) can expect to experience little or
no increase in financial volatility due to the generosity of their deposit insurance scheme.
None of the developing countries in our sample satisfy that criterion.
The specifications in Table 3 provide no support for hypotheses three and four.
The insignificant coefficients for the index of weak selection criteria and for the
selection/law interaction term indicate that the entry hurdles imposed on member banks
have little effect on subsequent volatility, regardless of the quality of regulation and
supervision. Taken together, the generosity and the selection results imply that, if a
country adopts a generous deposit insurance program, it should not expect to curtail
subsequent increases in financial volatility through careful selection of member banks.
The overall quality of regulation and supervision may help limit volatility associated with
deposit insurance, but the specific rules governing which banks have access to insured
deposits appear to play no role. Again, the ineffectiveness of entry hurdles, especially
risk-adjusted premium payments, may stem from time inconsistency problems.
The specifications in Table 4 help us assess whether the increased volatility
associated with deposit insurance accelerates or retards long-run financial development.
The negative significant coefficient for generosity indicates that depositors that feel
relatively secure may inadvertently contribute to slower long-term financial development.
interval just prior to adoption. Results for the three-year period appear below.
34



However, the positive significant coefficient on the generosity/law interaction term
indicates that a sound regulatory environment may act as an adequate substitute for
monitoring of banks by depositors. Indeed, the estimated coefficients imply that
countries with better legal traditions experience greater long-term financial development
after introducing generous deposit insurance. By drawing more savers into the formal
financial system generous deposit insurance can, therefore, have a positive impact on
financial development, but only if the appropriate regulatory and supervisory safeguards
are in place. Again, most of the World Bank's client countries appear to lack those
safeguards.
A few words should be said about differences between the results for private
credit growth (specifications 1-4, Table 4) and for liquid liabilities growth (specifications
5-8, Table 4). The estimated coefficients imply that liquid liabilities grow more rapidly
after the introduction of generous deposit insurance for all but the countries with the
weakest legal traditions (i.e., a zero score on the legal index). By contrast, the models
indicate that generosity coincides with a higher subsequent private credit growth rate, but
only in countries that score five or six on the legal index. The two results suggest that the
effects of generous deposit insurance are reflected differently on the asset versus the
liability side of bank balance sheets. Generosity will prompt more savers to deposit in
banks in all but the most chaotic situations. But the increase in deposits coincides with
increased credit to the private sector in only those countries with sound institutional
structures. This suggests that, for countries with weak, but not atrocious, institutional
35



development (i.e., 2-4 on the legal index), additional deposits attracted through insurance
are not intermediated effectively.22
A. Implicit Versus Explicit Deposit Insurance
In order to obtain unbiased coefficients in our financial development regressions,
we have corrected for potential sample selection bias associated with the adoption of
explicit deposit insurance using a two-stage estimation procedure. Although we are
convinced that this is the correct methodological approach, it does not pernit us to
compare directly the financial development of countries with explicit schemes with that
of countries that retained implicit deposit insurance. In this sub-section, we use simple
estimation techniques to make some comparisons between these two groups of countries.
Controlling for income and the average level of inflation from 1990-92, the
partial correlation between explicit deposit insurance and financial depth in 1992 is
actually negative (specifications 1 and 2, Table 5). The estimated coefficients for the
explicit deposit insurance variable imply large reductions M2/GDP (roughly ten
percentage points). However, the t-statistic for that coefficient is not significant. The
models in Table 5 are taken from Cull (1998). Here we report results only for one
indicator of financial development M2/GDP, but similar results hold for additional
indicators.
Regression analysis of short-term financial deepening provides a somewhat
stronger indication that explicit deposit insurance is negatively associated with financial
development. Controlling again for inflation, and for income (both changes and levels),
22 In many African countries, a majority of banks' assets are in government bonds, which is partially
responsible for our results. That is, in those countries, there is a much greater propensity to channel
36



the dumrny variable for explicit deposit insurance is negatively and significantly
associated with changes in M2/GDP (Table 5, specifications 3-4). The estimated
coefficients indicate that, holding other factors constant, countries with explicit deposit
insurance experienced 2-3% less growth in M2/GDP after the program's inception than
did the typical country that retained implicit insurance. The overall fit of the change in
financial depth regressions is good - income level enters positively and significantly;
income changes also enter positively, though insignificantly; and, inflation enters
negatively and significantly. On the basis of models 1-4, one could conclude that explicit
deposit insurance has typically been negatively correlated with financial development.
However, models 5 and 6 provide some support for the idea that explicit deposit
insurance can contribute to a deeper financial system, but only under the right
circumstances. In model 5, the interaction of past volatility with explicit deposit
insurance has a negative and significant relationship with short-term changes in
M2/GDP; by contrast, the dummy variable for explicit insurance enters positively and
significantly. In model 6, the interaction of the rule of law and explicit insurance is
positively linked to changes in M2/GDP; the dummy variable for explicit insurance
enters negatively and significantly.
We have less faith in these results because of the sample selection bias described
above, and because the explicit insurance dummy variable contains no information about
the design of the program. However, the results in Table 5 suggest that explicit deposit
insurance has been associated with greater financial depth, but only if past sector
volatility was low or the rule of law (a proxy for the quality of regulation) was well
deposits to the government rather than private endeavors.
37



established. The conditional nature of these results squares well with the others in the
paper.
B. Deposit Insurance and Banking Sector Concentration
As with the volatility specifications in Table 3, the growth rate specifications in
Table 4 indicate that entry hurdles have no significant effect on subsequent financial
development. We found it somewhat surprising that the requirements imposed on
member banks had no effect on the structure (and, therefore, the subsequent perforinance)
of the sector. As a final empirical exercise, we decided to look a bit more closely at the
relationships between deposit insurance features, sector concentration, and financial
development. In Table 3, concentration is negatively and significantly linked to volatility
in all four specifications in which it appears. In Table 6, we present a simple reduced
form to better explain concentration. Neither index of deposit insurance features,
selection nor generosity, is significantly associated with concentration. Nor are control
variables like per capita GNP or the legal index. The only variable with significant
explanatory power is the years since deposit insurance has been in place. The longer a
deposit insurance scheme has been in place, the less concentrated the sector, regardless of
the specific features of the program.
Although we are not sure about the channel through which deposit insurance
affects the competitive structure of the banking sector, the results in Table 6 might
indicate that, regardless of features such as high entry hurdles, the lure of insured deposits
eventually leads to less concentration. Those in Table 3 indicate that concentration is
associated with lower financial sector volatility. The two results may point to another
38



channel through which deposit insurance affects financial development. Its mere
presence may contribute to an increasingly fragmented sector over time, which brings
with it higher volatility, and the specific selection criteria applied to screen member
banks appear to do little to stop it. We admit that the results on concentration are a bit
more speculative than the others, but they do provide additional support for the notion
that entry hurdles have not been an effective way to curtail the increased volatility that
often accompanies deposit insurance.
VI. Conclusions
Although many countries in advanced and developing economies grant formal
deposit insurance so as to reduce the risk of systemic failure of depository institutions,
our knowledge of the impact of deposit insurance programs on financial development and
stability is quite limited. This paper has provided empirical evidence on the impact of
deposit insurance on financial development and stability, broadly defined to include the
level of financial activity, the stability of the banking sector, and the quality of resource
allocation as reflected in real sector performance (i.e., growth). We use a unique dataset
capturing a variety of deposit insurance features, such as coverage, entry hurdles,
premium structure, etc.
The empirical construct is guided by recent theories of banking regulation that
employ an agency framework. The basic moral hazard problem studied is the incentive
for depository institutions to engage in excessively high risk activities, relative to socially
optimal outcomes, and it is used to state several testable hypotheses on the impact of
deposit insurance. The overall empirical evidence is consistent with the likelihood that
39



deposit insurance leads to financial instability in lax regulatory environments. However,
the deposit insurance schemes have a desired impact on financial development and
growth in sound regulatory environments as proxied by quality indices of the rule of law.
Thus, the introduction of deposit insurance scheme needs to be accompanied by a sound
regulatory scheme; otherwise, the scheme would lead to instability and deter financial
development.
On the other hand, the results on entry hurdles are rather surprising. The entry
hurdles, such as the insurance premium requirements on member banks, have no
significant effect on subsequent financial development. These results can be explained in
the context of our theory which suggests that ex ante premia are time-inconsistent and
have no incentive effects once they are in place. Thus, one cannot expect to impact bank
risk-taking behavior by putting into place entry hurdles alone, unless they are somehow
linked to incentives. Finally, we have a subsidiary empirical result showing that bank
sector concentration is negatively and significantly linked to volatility, but not to deposit
insurance features. On the other hand, the longer a deposit insurance scheme has been in
place, the less concentrated the sector, regardless of the specific features of the program.
The paper lends itself to a number of natural extensions. For example, deposit
insurance is but one aspect of the incentives produced by the regulatory environment.
Another important regulatory feature that might affect incentives is capital regulation.
Our theory suggests that such regulation has limited effectiveness in reducing financial
volatility, and that, because banks have different investment opportunity sets, a one-size-
fits-all approach to capital regulation is likely inappropriate. Given adequate cross-
country data, we could incorporate capital regulation into our empirical tests. The results
40



would have implications for the debate on standardization of regulation (e.g., through the
Basle Accord).
More generally, if cross country data were available on multiple aspects of the
regulatory and supervisory environment, we could incorporate better indicators of
institutional development than the rule of law in our empirical work. Finally, we focus
on bank-based indicators as a proxy for overall financial sector development and
performance. Although banks play a key role in the financial system of most of the
countries in our sample, the increasing importance of capital markets as well as of non-
bank financial institutions (and the growing attention they receive in the literature)
suggests that it may be worthwhile to extend the analysis of this paper to a broader range
of agents and mechanisms which constitute the financial system.
41



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44



Appendix
A Model of Deiosit Insurance and Risk Shiftig
The main issues in the risk incentive effects of bank deposits and deposit
insurance can be captured in a more formal, but simple framework following John, John
and Senbet (1991) and John, Saunders, and Senbet (2000).
Bank Investment and Financing Environment
Consider a three-date time line. At the initial date, t=0, the bank collects
deposits while promising depositors an amount F and finances residually through equity
(bank capital). Bank capital infusion is subject to existing regulatory constraints. For
sharper focus, we assume that bank deposits are insured in their entirety (i.e., complete
coverage) by an insuring government agency (e.g., US FDIC). The agency charges the
relevant insurance premium, it, to be paid by the bank. Thus, we restrict the financing
claims to debt and equity. Contracts are written and priced at the initial date (i.e., t = 0),
given the information available and the admissible contracting opportunities.
At the intermediate date, t=1, bank insiders or managers make investment
decisions so as to achieve a particular level of investment risk. For simplicity and for a
focus on optimality of risk choices, bank investment opportunities take two forms: (1)
safe investments with zero risk and non-negative net present value (NPV), and (2) one
from a menu of possible risky investments (loans) which are indexed by a risk parameter
q. Investments of I in riskless projects yield I at at the terminal date, t=2.23 On the other
hand, the gross cash flow outcomes from the risky projects are high (H) or low (L), with
H>I>L>O, where q is the probability of the high outcome H, and (1-q) the probability of
the low outcome, L.
There is a moral hazard problem in this framework, since the investment
decisions are taken as "private action" to be undertaken by bank insiders (bank
management). This notion is captured as follows. Bank management observes the
parameter q at t=1 before a choice is made between the riskless invesments and risky
invesments. The outside stakeholders include bank depositors, bank equityholders, and
regulators, and they do not not observe q. However, bank management is presumed to
work in the best interests of bank owners or those with equity stake.24 Although q is
observed by insiders only, both insiders and outsiders are assumed to know that q is
distributed uniforrnly over the interval [0,1]. This approach captures the idea that the
bank as an insider faces a discretionary risk choice, since it has additional information
about the characteristics of the investments (say risky loans as captured in q).
23 To focus on risk incentives, we ignore issues of discounting by assuming that the base risk free interest is
zero, so that investments of I in riskless projects yield I at at the terninal date, t=2.
24 See John, Saunders, and Senbet (1999) for the analysis that incorporates conflicts between management
and bank owners.
45



At the final date, t=2, the investments are liquidated and the proceeds collected
for gross cash flows of X (or the loan proceeds are collected at maturity). If the riskless
investment was chosen at t=l, X will be equal to I. Otherwise, X will be equal to H or L
if the risky investment was chosen. If there is a shortfall relative to the deposit
obligations, the government insurance agency honors the guarantee, since deposits are
all insured. Thus, the government guarantee payoff is isomporhic to that of a put option,
max(0, F-X).
Bank Risk-Shifting Incentives
Now let us go back to the initial date, t=O, when all contracts are priced. Let us
assume that deposits are in the form of pure discount debt of promised payment F due at t
= 2. Since the deposits are fully insured, they will be priced as riskless instruments. In
addition, this framework allows us to detennine the deposit insurance on a rational
expectations basis as the fair value of the deposit insurance provided. The risk-shfiting
incentives and the resulting investment distortions can be characterized by using as a
benchmark the optimal decisions that would be undertaken when the bank faces no
depository financing (say an all-equity bank funding its loans through bank capital
alone). The optimal investment policy would be one of choosing between risky and
riskless investmnents (say risky loans and government bonds). Thus, at the intermediate
date, t=l, bank management, working in the best interests of bank owners, observes q
and decides to invest in the risky project if:
qH+(l-q)L> I             (Al)
That is, bank management only invests in the risky project if it has positive NPV;
otherwise they invest in the riskless project, since it has a zero NPV. Given (Al), there
is a cut-off value, q*, of q, such that the risky investment dominates the riskless one. In
other words, q* is the lowest value of q that satisfies (Al). This signifies an investment
policy characterized by [q* ], a policy of investing in the risky asset (loan) for q > q*,
and in the riskless asset (loan) for q < q* The optimal investment policy is now given
below:
q* = (I - H)/(H - L)     (A2)
The risky projects are chosen only for values q > q*. Given that q is uniformly
distributed over the interval [0,1], an investment policy [q*] produces the distribution of
terminal cashflows as follows:
H with a probability l/2[1-q*2]
I with a probability q* and
L with a probability l/2[l 1q*]2
Thus, the net present value of an investment policy [q*] is given by
V(q*)=q*I + L/2[I-q*]2 + H/2[1-q*2] - I  (A3)
46



An all-equity bank chooses investment policy [q*]. Then, NPV(q*) is the value
achieved if q is perfectly observed by all the parties. Alternatively, NPV(q*) is the
maximum value that can be achieved under full information and complete contracting.
Then, [q*] is the Pareto efficient investment strategy that we can use as a benchmark for
evaluating the distortions arising from risk shifting due to the introduction of deposits.
The cut-off probability that departs from q* is distortionary relative to the optimal risk
choice.
Now introduce bank deposits, with deposit claims of promised payment F.
Deposits are complements to bank equity capital so that lower F is reflected in larger
bank equity (capital). Thus, for analytical purposes, it is convenient to parameterize the
level of bank equity capital in terms of F, where F and bank capital are inversely related.
For sufficiently high deposits (or low bank capital), the bank asset cashflows would be
insufficient to repay the depositors in some state of the world (i.e., L < F), inducing the
management to implement a riskier investment policy than [q*]. Formally, the distorted
investment incentives can be characterized as follows. For bank deposits of promised
payment F > 0, the manager will implement an investment policy [q(F)], where q(F) is
given as:
(a) Riskless Deposits (F < L):   q(F)  q*
(b) Risky Case (L<F<I):         q(F) = (I - F)/(H - F)    (A4)
(c) Risky: Extreme (F > I):     q(F) = 0
The proofs are straightforward and they are given in John, John, and Senbet
(1991) and John, Saunders, and Senbet (2000). Here we wish to provide the intuition.
For any value of deposits with F > L , where in some states the bank fails to honor the
deposit claims, the manager implements an investment policy [q(F)] which is sub-
optimal (i.e., NPV(q(F) < NPV(q*)) and riskier than the efficient one, [q*] s, i.e., q(F)
< (q*) . To see this, consider an arbitrary cut-off probability level. When the cut-off
probability (qc) is varied from 1 to 0, the value of the terminal cash flows, expressed as
(A3), increases from I to V (qj) and then decreases to (H +L)/2. The maximum value is
achieved when qc =q*. The volatility of the terminal cash flows, or(qc) can also be
specified in the like manner. As the cut-off (q,) is varied from 1 to 0, co(qc) varies from
0 to [(H - L)/2]2 . Thus, the cut-off probability can be thought of an index of volatility
(risk), and risk increases with decreasing qc.
We can actually characterize the pair of value-risk [V (q), G(q)] so that, as risk
as (qc) increases from 0 to ca (q*), the value increases first I to V (q*). With any further
increases in risk beyond a(q*), the value declines. The investment opportunity shape
that would be generated would be similar to Figure 2 in the text. Now in the presence
of depository financing, the bank insiders will invest for values of q > q(F), where the
residual pay-offs of the equity-holders are higher than those for safe investment
[(q max(0, H - F) + (1 - q) max(0, L - F) > max (0, I - F)], and hence
47



(I - F)/(H - F) = q(F) < q* = (I - L)/(H - L).
Thus, in the range of (b) above, the terminal cashflow distribution resulting from
the investment policy [q(F)], implemented by the manager is strictly increasing in risk for
increasing F, such that a c(q(F)) / 8 F > 0, and decreasing in value, i.e.,8 NPV(q(F)) / a
F3< 0. This establishes the basis for the predictions on volatility effect of deposits
(financial instability effect) and value effect (economic inefficiency effect) in the text.
48



Table 1: Deposit Insurance Program Features, Pairwise Correlations
Variable                                  Depositor Coverage Variables                                            Bank Membership Variables
'Genersity"                                                          "Entry Hurdles"
Coverage      Foreign       Interbank     Source        Manage-       Co-           Cornpul-       Funded Ex    Premium        Risk-
Currency                                  ment          insurance     sory          ante?                        Adjusted
Coverage         1.00
(2.13)           (n=29)
Foreign          -0.13         1.00
(0.55)           (n 29)        (n=29)
Interbank        0.15          -0.05          1.00
(0.21)           (n=29)        (n=29)        (n=29)
Source           0.07          0.40          0.08           1.00
(0.76)           (n-=29)       (n=29)        (n=29)        (n=29)
Management    -0.12            -0.03         -0.37**       -0.25          1.00
(1.59)           (n=29)        (n=29)        (n=29)        (n=29)        (n=29)
Co-insurance    -0.12          0.02          -0.09         -0.21          0.19          1.00
(0.24)           (n-=29)       (n=29)        (n=29)        (n=29)        (n=29)        (n=29)
Compulsory       0.14          0.03          0.14           0.14          0.20          0.15          1.00
(0.93)           (n=29)        (n=29)        (n=29)        (n=29)        (n=29)        (n=29)        (n=29)
Funded ex ante   0.14          0.06          0.32*         0.16          -0.51***      -0.37**        0.14          1.00
(0.72)           (n=-29)       (n=29)        (n=29)        (n=29)        (n=29)        (n=29)        (n=29)         (n=29)
Premium          -0.10         -0.22         0.06          -0.04         -0.22         -0.19          0.15          0.38*          1.00
(0.31)           (n=27)        (n=27)        (n=27)        (n=27)        (n=27)        (n=27)        (n=27)         (n=27)        (n=27)
Risk-adjusted    0.01          0.41 **       -0.23          0.22          0.23         -0.04          0.12          0.08          -0.07          1.00
(0.17)           (n=29)        (n=29)        (n=29)        (n=29)        (n=29)        (n=29)        (n=729)        (n=29)        (n=27)        (n=29)
Table I provides pairwise correlations between deposit insurance program features. Coverage is a continuous variable equal to the coverage limnit divided by GDP per capita. Foreign
Currency is a dummy variable = I if foreign currency deposits are covered. Interbank is a dummy variable = I if interbank deposits are covered. Source is a discrete variable = 0 if the
source of the program's funding is banks, = I if banks and government, and = 2 if government only. Management is a discrete variable = I if the program is officially managed (i.e., by
govemment), = 2 ifjointly managed by official and private entities, and = 3 if solely privately managed. Co-insurance is a dunmmy variable = I if depositors must pay a deductible to
receive compensation after a bank closure. Compulsory is a dummy variable = I if bank membership in the program is compulsory. Funded ex ante is dummy variable = I if member
banks pay contributions prior to bank failures (as opposed to in response to them). Premium is the premium payments required of member banks, expressed as a percentage of insured
deposits. Risk-adjusted is a dummy variable = I if member banks' premium payments are adjusted to reflect the risk of their assets.
Variable Means in () in first column. * indicates significantly different from zero at the 10% level; ** indicates significance at the 5% level; *** indicates significance at the 1% level.
Deposit insurance system features are taken from Demirguc-Kunt, Asli, and Enrica Detragiache, "Does Deposit Insurance Increase System Stability? An Empirical Investigation," July,
1999. Demirguc-Kunt and Detragiache relied on information from numerous sources including Kyei, Alexander, 1995, "Deposit Protection Arrangements: A Survey," IMF working paper
WP/90/134 and Garcia, Gillian, 1999, "Deposit Insurance: A Survey of Actual and Best Practices," IMF working paper WP/99/54.
49



Table 2: Financial Development and Deposit Insurance Features, Pairwise Correlations
Deposit          Concentration   Coefficient of   Coefficient of   Growth Rate    Growth Rate
Insurance            Ratio          Variation,      Variation,      (Liquid Liab      (Credit to
Feature         (assets of top 3   (Liquid Liab      (Credit to        /GDP)        Private Sector
banks relative      /GDP)        Private Sector                        /GDP)
to sector)                        /GDP)_
Mean  .63       Mean  .12        Mean  .18        Mean   0.7%    Mean   0.5%
St Dev .16      StDev.10         St Dev .16       St Dev 4.0%      StDev 6.1%
Min  .36        Min  .02         Min  .03         Min -10.9%       Min  -15.4%
Max  .88        Max  .34         Max  .68         Max   6.9%       Max   9.9%
Coverage        -0.03             0.02            -0.29             0.10             0.12
(n=29)          (n=27)           (n=28)           (n=28)           (n=28)
Foreign Curr.   -0.07            -0.46**          -0.07            -0.04            -0.18
(n=29)          (n=27)           (n=28)           (n=28)           (n=28)
Interbank       -0.00             0.36*            0.06            -0.11             0.00
(n=29)          (n=27)           (n=28)           (n-=28)          (n=28)
Source           0.11            -0.10            -0.10            -0.24            -0.18
(n=29)          (n=27)           (n=28)           (n=28)           (n=28)
Management    -0.22              -0.01             0.28            -0.02            -0.05
(n=29)          (n=27)           (n=28)           (n=28)           (n=28)
Co-insurance    -0.52***          0.18             0.16             0.29             0.23
(n=29)          (n-=27)          (n=28)           (n=28)           (n=28)
Compulsory      -0.27             0.20             0.17            -0.00            -0.07
(n=29)          (n=27)           (n=28)           (n=28)           (n=28)
Funded           0.33*            0.17            -0.09            -0.04            -0.07
(n=29)          (n=27)           (n=28)           (n=28)           (n=28)
Prernium         0.12             0.28             0.30            -0.59***         -0.44***
(n-=27)         (n=725)          (n=26)           (n=26)           (n=26)
Risk-adjusted    0.07            -0.19            -0.16            -0.06            -0.19
(n=29)          (n=-27)          (n=28)           (n=28)           (n=28)
Table 2 provides pairwise correlations between deposit insurance program features and indicators of financial
development. The indicators include a measure of sector concentration equal to the assets of the three largest banks
relative to the total for the sector, and two measures of banking sector volatility, one measured as the coefficient of
variation (standard deviation divided by mean) for the ratio of liquid liabilities to GDP, and another measured as the
coefficient of variation for the ratio of credit to the private sector to GDP. Finally, there are two indicators measuring
the growth rate of indicators of financial development, one for the ratio of liquid liabilities to GDP, another for the ratio
of credit to the private sector to GDP.
Deposit insurance features are explained in the text and in the notes to Table 1.
* indicates significantly different from zero at the 10% level; ** indicates significance at the 5% level; *** indicates
significance at the I% level.
50



Table 3: Effects of Deposit Insurance on the Volatility of Financial Development
Indicators
Explanatory                                          Depedent Variable
Variable              Ln(Coeff    Ln(Coeff    Ln(Coeff    Ln(Coeff    Ln(Coeff    Ln(Coeff
Var BNK)'  Var BNK)   Var BNK)   Var LL)b               Var LL)       Var LL)
(1)          (2)           (3)           (4)            (5)           (6)
First Stage
Rule of Law            .415*        .364**         .344*         .325**        .366**        .299**
(1.84)        (2.13)        (1.71)        (2.16)        (2.36)        (2.04)
Past Volatility of     -9.80         -7.05         -7.83         -7.45**       -7.63**       -7.13*
BNK                    (1.22)        (1.57)        (1.31)        (2.12)        (2.48)        (1.88)
Second Stagel
Conditional on
Adoption of
Deposit Insurance
Generosity             1.84*         0.97*         0.98*         1.78**        1.40***       1.34***
(1.84)        (1.89)        (1.79)        (2.06)        (5.08)        (2.91)
Generosity x Law       -0.37         -0.21*        -0.23*        -0.36*        -0.30***      -0.32**
(1.60)        (1.74)        (1.81)        (1.79)        (4.25)        (2.28)
Poor Selection                                     .085                                      -.379
(0.09)                                    (1.09)
Poor Selection xLaw                                -0.96                                     .038
(0.39)                                    (0.27)
Inflation              -.014        .005           -.014         -.066         .011          .012
(0.39)        (0.02)        (0.50)        (0.24)        (0.43)        (0.05)
Real Growi             -.174         -.065         -.027         -.273         -.011         .029
(0.64)        (0.38)        (0.14)        (1.41)        (0.10)        (0.22)
Years in Place         -.039        .014          .095           -.047         .085          .017
(0.98)        (0.51)        (0.29)        (1.29)        (0.67)        (0.61)
Concentration                        -2.80***      -2.57*                      -3.82***      -3.87***
(2.97)        (1.84)                      (4.04)        (3.57)
Observations           56            56            53            56            56            53
# with Explicit DI    27             27            24            27            27            24
Log Likelihood         -53.08        -43.78        -37.98        -60.68        -47.75        -43.80
(a) BNK is the ratio of bank credit to the private sector to GDP. (b) LL is the ratio of liquid liabilities to GDP. The
dependent variables are the log of the coefficient of variation (standard deviation divided by mean) for BNK and for
LL. They are intended to measure the volatility of financial development.
We employ a two-stage estimation technique to correct for sample selection bias associated with the adoption of
explicit deposit insurance. In the first stage, explanatory variables include 'Law,' a six-point measure of the quality of
the rule of law averaged over 1985-1992 (see data appendix for further description). Higher values indicate greater
adherence to the rule of law. The other first stage regressors are measures of past financial sector volatility, calculated
over all years prior to the adoption of explicit deposit insurance (from 1960-1997). 'Volatility BNK' is the coefficient
of variation (standard deviation divided by mean) for BNK. 'Volatility LL' is the coefficient of variation for LL.
The second stage estimation is conditional on the adoption of deposit insurance. Regressors include 'generosity' a
principal components index derived from six deposit insurance program features - coverage limits, coverage of foreign
currency deposits, coverage of interbank deposits, the program's funding source, its management type, and co-
insurance requirements (deductibles). 'Poor Selection' is a principle components index of four program features that
describe the requirements that are imposed upon member banks: compulsory membership requirements, ex-ante
funding requirements, premium levels, and requirements that premium be risk-adjusted for the assets held by the
member bank. Program features are more fully described in the text and in the notes to Table 1. 'Law,' which is used
as an interaction term in the second stage, is computed as described above.
51



Inflation and real growth are averaged over all years for which data are available from 1960-97 (again, all years after
the adoption of explicit deposit insurance). Concentration is computed in the same way. It measures the share of total
banking sector assets held by the three largest banks in the country. Years in place is simply the number of years that
explicit deposit insurance has been in place.
* indicates significantly different from zero at the 10% level; ** indicates significance at the 5% level; *** indicates
significance at the 1% level.
52



Table 4: Effects of Deposit Insurance on the Growth Rate of Financial Development
Explanatory                                             Dependent Variable
Variable              BNKa   BNK    BNK    BNK    LO - LL                                LL          LL
Growth    Growth    Growth    Growth    Growth    Growth    Growth    Growth
Rate       Rate       Rate       Rate        Rate       Rate       Rate       Rate
_  _        (1)        ~~~~~~(2)    - 3)      (4) - (5)              (6)        (7)  -     (8)
First Stage
Law                   .442***   .590***   .418***   .419***   .349***   .496***   .320***   .350***
(4.01)     (4.85)     (4.06)     (4.26)      (3.79)     (3.14)     (3.23)     (3.79)
Volatility                       -10.6***                                     -10.18**
BNK                              (3.66)                                       (2.18)
Volatility            -13.01**               -13.55**   -11.5***   -10.2***               -9.79***   -10.2***
LL                    (2.01)                 (2.45)     (2.72)     (3.26)                 (2.89)     (3.22)
Second Stage/
Conditional on
Adoption of
Deposit Insurance
Generosity            -.071***   -.059**    -.064**    -.067***   -.034**    -.038        -.036**    -.033**
(3.24)     (2.24)     (2.35)     (2.72)      (2.15)     (1.57)     (2.20)      (2.05)
Generosity x Law      .018***   .015**    .014**    .017***   .075**    .086              .074**    .074**
(3.72)     (2.39)     (2.00)     (3.22)      (2.06)     (1.56)     (1.97)      (1.99)
Poor Selection                               -.020                                        .018
(0.42)                                       (0.36)
Poor Selection xLaw                         .067                                          -.052
(0.50)                                       (0.47)
Inflation             -.044***   -.037**    -.044***   -.037***   -.024***   -.023***   -.025***   -.024***
(4.82)     (2.55)     (2.94)     (2.82)      (4.00)     (4.27)     (2.87)      (3.77)
Real Growth           .014***   .014**    .013          .014**    .098***   .096***   .098           .096***
(2.81)     (2.03)     (1.33)     (2.41)      (3.41)     (3.29)     (1.62)      (3.13)
Years in Place        .011       .006        .011       .016       .004        .004       .004       .004
(1.04)     (0.50)     (0.66)     (1I.10)     (0.78)     (0.78)     (0.55)      (0.53)
Concentration                                           -.023                                        .212
(0.51)                                       (0.08)
Observations          57         57          54         57         57          56         54         57
# with Explicit DI    28         28          25         28         28          27         25         28
Log Likelihood        33.20      33.31       29.86      32.41      46.17      47.17       40.83      46.18
(a) BNK is the ratio of bank credit to the private sector to GDP. (b) LL is the ratio of liquid liabilities to GDP. The
dependent variables are growth rates for BNK and for LL. They are intended to measure the rate of financial sector
development. There is one observation per country, and growth rates are calculated as an average over all years after
the adoption of deposit insurance for which data are available from 1960-1997.
We employ a two-stage estimation technique to correct for sample selection bias associated with the adoption of
explicit deposit insurance. In the first stage, explanatory variables include 'Law,' a six-point measure of the quality of
the rule of law averaged over 1985-1992 (see data appendix for further description). Higher values indicate greater
adherence to the rule of law. The other first stage regressors are measures of past financial sector volatility, calculated
over all years prior to the adoption of explicit deposit insurance (from 1960-1997). 'Volatility BNK' is the coefficient
of variation (standard deviation divided by mean) for BNK. 'Volatility LL' is the coefficient of variation for LL.
The second stage estimation is conditional on the adoption of deposit insurance. Regressors include 'generosity' a
principal components index derived from six deposit insurance program features -- coverage limits, coverage of
foreign currency deposits, coverage of interbank deposits, the program's funding source, its management type, and co-
insurance requirements (deductibles). 'Poor Selection' is a principle components index of four program features that
describe the requirements that are imposed upon member banks: compulsory membership requirements, ex-ante
53



funding requirements, premium levels, and requirements that premium be risk-adjusted for the assets held by the
member bank. Program features are more fully described in the text and in the notes to Table 1. 'Law,' which is used
as an interaction term in the second stage, is computed as described above.
Inflation and real growth are averaged over all years for which data are available from 1960-97 (again, all years after
the adoption of explicit deposit insurance). Concentration is computed in the same way. It measures the share of total
banking sector assets held by the three largest banks in the country. Years in place is simply the number of years that
explicit deposit insurance has been in place.
* indicates significantly different from zero at the 10% level; ** indicates significance at the 5% level; *** indicates
significance at the 1% level.
54



Table 5: Effects of Implicit Versus Explicit Deposit Insurance on Levels and Growth Rates of
Financial Development
Explanatory          Dep Variable: Level of   Dep Variable: Average Change in M2/GDP, 1980-95
Variable                  M2/GDP, 1992
(1)          (21           (3)           (4)           (5)           (6
Constant            0.275         0.283         2.268         1.800"        -1.48         .333
(7.06)       (7.02)        (3.15)        (2.01)        (1.11)        (0.25)
Per Capita Inc       0.043        0.043         0.247"        0.276
(6.51)       (6.48)        (2.29)        (2.21)
Explicit            -0.097        -0.104        -2.980        -2.867        3.79          -3.02
(1.53)       (1.62)        (3.58)        (2.85)        (2.36)        (1.85)
Explicit x Past                                                             -.576
Volatility                                                                  (3.67)
Past Volatility                                                             .357"         .211"
(3.54)       (2.16)
Explicit x Law                                                                            .688*)
(2.08)
Avg Inflation                     -.004         -0.084        -0.080"       -.093         -.093.
(0.83)       (5.71)        (4.70)        (5.42)        (4.85)
% change, Real                                                0.276        .108          .174
Per Capita GDP                                                (1.39)        (0.79)        (1.16)
Adj. R-Squared       .49          .47           .52           .46           .58           .47
Observations        51            51            43            40            40            40
Table 5 provides partial correlations between explicit deposit insurance and the level of financial depth in 1992 (models
I and 2). It also provides partial correlations between explicit deposit insurance and average changes in financial depth
from 1980-1995 (models 3-6). The table is designed to provide information on the effects of explicit versus implicit
insurance on financial development. See Cull (1998) for models that use additional indicators of financial development
as dependent variables.
Models (1) and (2): The dependent variable is M2 divided by GDP in 1992. 'Per capita income' in 1992 is taken from
the Summers and Heston database, and is measured in $US 1985. 'Explicit' is a dummy variable = I if a country had
an explicit deposit insurance program in 1992. 'Avg. Inflation' is measured as an average percentage change in the
consumer price index from 1990-92.
Models (3)-(6): The dependent variable is the three-year change in M2/GDP (i.e., M2/GDP3 - M2IGDPO). For
countries that adopted explicit insurance, the dependent variable is measured over the three years just after the
program's adoption. For those countries that maintained implicit deposit insurance (the control category), changes in
financial depth were first calculated over all possible three year periods from 1980-95; those three-year figures where
then averaged. The same procedure was used to compute average inflation rates, % changes in real per capita GDP,
and 'past volatility.' Past volatility is defined as the coefficient of variation in M2/GDP (standard deviation divided by
mean).
As in models I and 2, 'Per capita income' in 1992 is taken from the Summers and Heston database, and is measured in
$US 1985. 'Law' is a six-point measure of the quality of the rule of law averaged over 1985-1992 (see data appendix
for further description).
* indicates significantly different from zero at the 10% level; ** indicates significance at the 5% level; *** indicates
significance at the I% level.
55



Table 6: Effects of Deposit Insurance on Banking Sector Concentration
Explanatory Variable        Dep: Concentration Ratio (Top 3 banks)
Poor Selection                                       -.005
(0.17)
Generosity                                           .009
(0.37)
GNP Per Capita                                       -.012
(1.14)
Law                                                  .041
(1.49)
Years in Place                                      -.005**
(2.15)
Observations                                          39
R-square                                              .27
Adj R-square                                          .17
The dependent variable, 'Concentration Ratio,' is intended to measure the degree of concentration in the banking
sector. It is equal to the share of total sector assets attributable to the three largest banks. There is one observation per
country, and the concentration ratio is calculated as an average over all years for which data are available from 1960-
1997.
Regressors include 'generosity' a principal components index derived from six deposit insurance program features --
coverage limits, coverage of foreign currency deposits, coverage of interbank deposits, the program's funding source,
its management type, and co-insurance requirements (deductibles). 'Poor Selection' is a principle components index of
four program features that describe the requirements that are imposed upon member banks: compulsory membership
requirements, ex-ante funding requirements, premium levels, and requirements that premium be risk-adjusted for the
assets held by the member bank. Program features are more fully described in the text and in the notes to Table l.
'Law,' a six-point measure of the quality of the rule of law is averaged over 1985-1992 (see data appendix for further
description). Higher values indicate greater adherence to the rule of law. GNP per capita is taken from Summers and
Heston. 'Years in Place' is simply the number of years that explicit deposit insurance has been in place.
* indicates significantly different from zero at the 10% level; ** indicates significance at the 5% level; *** indicates
significance at the 1% level.
56



Data Appendix
Countries included in active observation set (57)
Explicit Deposit Insurance: Austria, Belgium, Bangladesh, Brazil, Canada, Chile,
Colombia, Denmark, Finland, France, Great Britain, Greece, Hungary, Ireland, Italy,
Kenya, Luxembourg, Nigeria, Netherlands, Philippines, Poland, Portugal, Spain, Sri
Lanka, Sweden, Switzerland, Trinidad and Tobago, Venezuela.
Implicit Deposit Insurance: Australia, China, Cote d'Ivoire, Costa Rica, Egypt, Ghana,
Guatemala, Guyana, Hong Kong, Honduras, Indonesia, Iran, Iraq, Israel, Jordan, Libya,
Malaysia, New Zealand, Pakistan, Paraguay, Saudi Arabia, Singapore, South Africa,
Syria, Togo, Thailand, Uruguay, Zambia, Zimbabwe.
NOTE: Our empirical analysis of the impact of different deposit insurance features on financial
development employs a post hoc, ergo propter hoc approach. In other words, our indicators of
financial development and volatility are averaged for each country over all the years in the sample
following the adoption of an explicit deposit insurance system. The years of adoption of explicit
deposit insurance for all countries in our sample are shown in the following table.
country  Year adopted                     country  Year adopted
AUT        1979                           HRV         1997
BEL        1974                           HUN        1993
BGD        1984                           IRL         1989
BRA        1995                           ITA         1987
CAN        1967                           KEN         1985
CHE        1984                           LUX         1989
CHL        1986                           NGA        1988
COL        1985                           NLD         1979
DNK        1988                           PHL         1963
ESP        1977                           POL        1995
FIN       1969                           PRT         1992
FRA        1980                          SWE          1996
GBR        1982                           TTO         1986
GRC        1993                           VEN         1985
57



Variable names, definitions and sources.
Variable Name                           Definition                      Source
Growth Rate of Bank Credit    Average annual growth rate in the  Ratio equals IFS (line 551)
ratio of bank credit to the private  divided by IFS (line 99b)
sector to GDP
Growth Rate of Liquid Liabilities  Average annual growth rate in the  Ratio equals IFS (lines 22a-d)
ratio of liquid liabilities to GDP    divided by IFS (line 99b)
Law and Order                 Index of the quality of the rule of  ICRG
law ranging from 0 to 6; scores
for each country average over
data for 1985-1991.
Generosity                    Principal components index of six  Features are from Demirguc-Kunt
deposit insurance features (Table  and Detragiache (1999)
4)
Entry Hurdles                 Principal components index of    Features are from Demirguc-Kunt
five deposit insurance features  and Detragiache (1999)
(Table 4)
Volatility in Bank Credit     Coefficient of variation (standard  IFS (see above)
deviation/mean) in the ratio of
bank credit to nominal GDP.
Volatility in Liquid Liabilities  Coefficient of variation in the  IFS (see above)
ratio of liquid liabilities to GDP
Inflation                     Average inflation              SIMA (wdi, ifs)
Real Growth                   Rate of growth of real GDP     SIMA (wdi, ifs)
Years in Place                Years since the adoption of    Demirguc-Kunt and Detragiache
explicit deposit insurance    (1999)
Concentration                 Assets of largest three banks  Beck, Dermirguc-Kunt, Levine
divided by total banking assets    (1999) [from Bankscope]
ICRG is the International Country Risk Guide, published by Political Risk Service, Syracuse, NY. IFS is
International Financial Statistics, published by the IMF.
58



Payoffs to bank
stakeholders
Max (O, X-F)
Payoff to       to
owners 
/ ~~~~~~~~Min (X, F)
4 /-Qi&\       /        ~~~~~Payoff to
depositors
(uninsured)
F              Bank cash flow (X)
Figure 1: Partitioning of Income from Bank Asset Portfolio
59



Bank value
I                                           Value under bank
asset risk-shifting
1
;                    \      ~~~~~~~~Bank 2
Bank I
orI*        or1   Risk (a)
Figure 2: Bank Investment Opportunities
60



Bank value        V1 (a)
a 1*          a 2*          or2*        Risk(or)
Figure 3: Capital Requirements and Multiple Banks
61



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