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I
Depositor Preference Legislation
and Failed Banks' Resolution Costs
by William I? Osterberg
and James B. Thornson
I
Working P a ~ e97
r 15
DEPOSITOR PREFERENCE LEGISLATION AND FAILED BANKS'
RESOLUTION COSTS
by William P. Osterberg and James B.Thomson
William P. Osterberg is an economist at the Federal
Reserve Bank of Cleveland. James B. Thomson is a vice
president and economist with the Financial Services
Research Group of the Federal Reserve System, located at
the Cleveland Fed. The authors thank James Barth,
William Greene, Joseph Haubrich, and Stanley Longhofer
for helpfhl comments and suggestions. Sandy Sterk
provided outstanding research assistance.
Working papers of the Federal Reserve Bank of Cleveland
are preliminary materials circulated to stimulate discussion
and critical comment. The views stated herein are those of
the authors and are not necessarily those of the Federal
Reserve Bank of Cleveland or of the Board of Governors of
the.Federa1 Reserve System.
Federal Reserve Bank of Cleveland working papers are
distributed for the purpose of promoting discussion of
research in progress. These papers may not have been
subject to the formal editorial review accorded official
Federal Reserve Bank of Cleveland publications.
Working papers are now available electronically through
the Cleveland Fed's home page on the World Wide Web:
http://www.clev.frb.org.
December 1997
Abstract
Included in the Omnibus Budget Reconciliation Act of 1993was a provision that
improved the priority of depositors and thus of the FDIC in the event of a depository
institution's failure. While intended to reduce the FDIC's cost of resolving commercial
bank failures, this provision might have induced general creditors to react so as to offset
the intended benefit. Depositor preference legislation (DPL) might also have affected
the FDIC's choice of resolution type.
Here we examine the empirical impact of DPL on resolution -typeand on
resolution costs for commercial banks. Given the short time period since the passage of
national DPL in 1993, we focus on the impact of state DPL statutes, utilizing call-report
data and FDIC data on resolution costs and resolution types for all operating FDIC-BIF
insured commercial banks that were closed or required FDIC financial assistance from
January 1986 through December 1992. We improve on previous studies by controlling
for the endogeneity of 'book capital and by adjusting for the sample selection bias
induced by regulatory closure rules.
We find that DPL has 1) tended to increase, rather than reduce, FDIC resolution
costs and 2) induced the FDIC to choose assisted mergers over liquidations. However,
the source of the higher resolution costs is unclear and there is no evidence that general
creditors reacted by increasing collateralization.
On August 10,1993 Congress passed the Omnibus Budget Reconciliation Act of
1993. Contained in this legislation was a provision that revised the priority of claims on
failed depository institutions by making other senior claimants junior to depositors.
Congress apparently hoped to reduce Federal Deposit Insurance Corporation (FDIC)
losses by thus changing the capital structure of banks to enhance the priority of
depositors and thus of the FDIC.
Unlike subordinated debenture holders, however, general creditors of
depository institutions can restructure their claims to effectively make them senior to
depositors. For example, in response to the implementation of depositor preference
laws (DPLs) a general creditor might collateralize her claim. Alternatively, she could
shorten the maturity of her claim to increase the probability she could exit before the
bank is closed. While there have been theoretical analyses of how DPL should affect the
values of various bank claimants, there have been no empirical analyses of whether or
not the FDIC's losses have been reduced, or whether general creditors have responded
so as to offset the intended benefits to the FDIC.'
Although little time has passed since the passage of the 1993legislation some
individual states already had DPL in effect. In this paper, we examine the impact on
FDIC resolution costs of such state legislation from 1984-1992, extending the empirical
analyses of closed-bank resolution cost models by James (1991), Osterberg and
Thomson (1995) (henceforth, OT), and Osterberg (1996). The theoretical framework
follows Hirschhorn and Zervos (1990) and Osterberg and Thomson (1994), where DPL
reduces the value of the FDIC claim unless general creditors undertake some offsetting
a ~ t i o n We
. ~ also test the hypothesis advanced by some analysts that DPL might
influence the FDIC's choice of resolution type, control for the endogeneity of book
measures of bank capital, and correct for sample selection bias introduced by
regulatory closure rules.
See Thomson (1994) for an example of how FDIC losses may increase as a result of depositor preference
laws.
The remainder of the paper is as follows. Section I outlines the depositor
preference legislation and the FDIC's implementation of it. Section 11reviews the
existing literature. The data and the empirical method are presented in III. Our results
and conclusions are appear in sections IV and V respectively.
I. The Legislative Provisions
Title III of the Omnibus Budget Reconciliation Act of 1993 instituted depositor
preference for all insured depository institutions by amending Section ll(d)(ll)of the
Federal Deposit Insurance Corporation Act [12 U.S.C. 1821(d)(11)].3The amendment
establishes the following priority of payment in the resolution of a failed depository
institution:
(1) Administrative expenses of the receiver.
(2) Deposit liabilities.
(3) General or senior liabilities.
(4) Subordinated obligations.
(5) Shareholder claims.
Prior to DPL, general or other senior liabilities had the same priority of payment as
deposits. However, regardless of the presence of depositor preference, secured
creditors of the failed depository will have their claims satisfied first, up to the amount
of the collateral. This implies that general or senior creditors could protect their claim
by responding to the passage of DPL by increasing collateral.
Clearly, the value of deposit liabilities and claims lower in the pecking order
depends on the interpretation of "administrative expenses of the receiver." On August
13,1993 the FDIC issued an interim rule which clarified its interpretation of these
expenses, indicating that such expenses include "post appointment obligations incurred
Birchler (1997) provides a contract-theoretic explanation of the priority structure of bank deposits.
At the time national depositor was enacted 29 states had similar laws covering state-chartered banks
and 18 had depositor preference statutes covering state-charteredthrift institutions.
3
2
3
by the receiver as part of the liquidation of an institution." and that "thispriority also
covers certain expenses incurred prior to the appointment of the receiver." 4 In other
words, the receiver (which for most banks and thrifts is the FDIC) may pay expenses it
deems to be consistent with the orderly closure of the institution, even if those expenses
were incurred prior to the depository's closure. These pre-receivership expenses include.
the payment of the institution's last payroll, guard services, data processing &ices,
utilities aitd lease payments. Examples of expenses that would be excluded are items
such as golden parachute claims, severance pay claims, and liabilities arising from the
repudiation of contracts.
11. Related Literature
The purported impact of DPL is to decrease the FDIC's costs of resolving bank
failures. Such costs derive from three sources. First are the losses that reflect the
underlying insolvency of the bank. These are the realization of the downside risk
associated with a bank's investment and financing decisions. On an economist's
extended balance sheet, these losses equal the negative market net worth of the firm
(excluding the value of government guarantees). Second are the losses related to
forbearance, which are incurred after the depository is no longer economically viable
but before it is closed. 5,6 Third are the costs associated with receivership, including
administrative and legal expense^.^
Several adverse impacts of DPL have been hypothesized. The most frequent has
been increased collateralization by nondepositors or general creditors which include
See Federal Register (1993). At the time of this writing the FDIC had not issued a final rule on depositor
preference.
Although the costs of forbearance have not been explicitly calculated for banks, DeGennaro and Thornson
(1996) find that these costs were considerable for thrifts.
6 Kane (1986)argues that information, funding, administrative and legal, and political constraints cause
bank regulators to adopt suboptimal closure rules. Allen and Saunders (1993) model deposit insurance as a
callable perpetual put option. The value of forbearance is the difference between the value of the call option
under unconstrained regulatory closure rules and its value under constrained closure rules.
For example, expenses for the FDIC's division of liquidation averaged 8.3 percent of collections in 1991
(see the FDIC's 1991Annual Report). Moreover, at the end of 1992, the FDIC's estimated contingent
4
.
include trade creditors, beneficiariesof guarantees, foreign depositors (to the extent that
their treatment is different from that of domestic depositors), holders of bankers
acceptances, unsecured lenders, landlords, fed funds sellers, and counterparties to
various contingent liabilities. Collateralization would move such secured lenders ahead
of all depositors in terms of the priority of claims in the event of failure. While
collateralization would have this impact even without depositor preference Hirschhorn
and &NOS
(1990)claim that the new legislation increases the incentive to collateralize.
They further conclude that the damage done by DPL to the insurer and the uninsured
depositor increases with the degree of collateralization of nondeposit claims and the
extent of insolvency.
It has also been claimed that depositor preference would harm smaller
community banks and thrifts. Banks with lower levels of capital supposedly would
have a harder time floating debt, borrowing federal funds, leasing computers, and
renting space. Some banks might be shut out of the derivatives markets or see their
credit rating on bankers' acceptance or bank notes downgraded? Large banks and, in
particular, those seen as too-big-to-let fail supposedly would have an enhanced
advantage in attracting deposits over $100,000 since such deposits may not be seen as
being at risk, though the risk would increase for smaller banks with depositor
preference. Claims have also been made of a negative impact on market discipline
though others claim a positive impact due to the increased risk of loss among
n~nde~ositors.~
There has been little empirical research on the impact of depositor preference
legislation (DPL), despite repeated claims of benefits. Hirschhorn and &NOS
(1990)
found that, following the passage of state DPL, general creditors of affected savings and
loans increased collateralization and interest rates on uninsured certificates of deposits
Liability for unresolved legal cases was $404 million. Costs of receivership also include losses that arise
from the inefficient asset salvage operation of the receiver (see Kane [1990]).
See Rehm (1993)
9 See Kaufman (1997).
5
fell. While Osterberg (1996) finds evidence that resolution costs for commercial banks
closed in states with DPL were lower than in other states, the exact role played by DPL
is unclear. In studies which provide no role for DPL, Bovenzi and Murton (1988),James
(1991), and OT model resolution costs as determined by problem assets,-riskyassets,
and core deposits. OT also include proxies for fraud and off-balance sheet risk. Below
we will attempt to tike into account empirical findings on the determinants of
resolution costs and to discern the mechanism through which DPL might play an
additional role.
111. Data and Empirical Methods
Since the preponderance of bank failures occurred prior to 1993, we choose to
analyze the impact of state-level DPLs already in effect. It is notable that state-level
DPLs apply to state-chartered institutions which differ along several dimensions from
national banks (see Osterberg [1996]).A list of the state depositor preference laws for
banks and thrifts can be found the appendix.
The sample includes all operating FDIC-BIF insured commercial banks on
December 31,1992 and those FDIC-BIF insured commercial banks that were closed or
required FDIC financial assistance to remain open from January 1,1986 through
December 31,1992. Quarterly balance sheet and income data for these banks are from
the Federal Financial Institution Examination Council's Quarterly Reports of Condition
and Income (call reports) from March 31,1984 through December 31,1992. Closure
data, estimated resolution cost (to the FDIC) and resolution type are from FDIC (1993).
We address three econometric problems with previous studies of closed-bank
resolution costs. The first is that these studies usually fail to control for the endogeneity
of book capital [see (Maddala (1986) and Thomson (1992)l. The second is that estimates
of a single equation model of closed-bank resolution costs suffer from sample selection
bias induced by regulatory closure rules [see Barth et al. (1990)l. Finally, these studies
fail to control for the endogeneity of the choice of resolution type. In the estimation of
-
our empirical model we will econometrically correct for these effects.
Our empirical model focuses on two equations that are estimated sequentially.
In equation (1) the dependent variable equals 0 when failure is resolved by
liquidation, which provides no de facto deposit guarantees to uninsured depositors and
general creditors and equals 1otherwise. Prior to the Federal Deposit Insurance
Corporation Improvement Act (FDICIA) of 1991 the FDIC could resolve bank
insolvencies in one of three ways. First, the FDIC could choose to liquidate the
institution, in what is commonly referred to a payout. While there are several different
ways to implement a payout the implications for the FDIC, uninsured depositors, and
unsecured general creditors are the same for each; they receive no de facto guarantees
of their claims and thus are fully exposed to loss. The second way in which the FDIC
could resolve an insolvency is through a purchase and assumption transaction (P&A).
Prior to FDICIA, a P&A typically involved the transfer of all deposits and general
creditor obligations to another bank, thereby providing de facto guarantees to senior
creditors. The k r d method has the FDIC infusing capital into an open institution. The
net effect of such open-bank assistance (OBA) is the extension of de facto deposit
insurance coverage to depositors and general creditors.10
The key to understanding how and why DPL might influence the FDIC's choice
of resolution type is an outline of the way in which DPL would affect the outcomes to
The Competitive Equality Banking Act of 1987 gave the FDIC an intermediate option for handling a
failed bank, the bridge bank. Under bridge bank authority (which was expanded by FIRREA 1989) the
FDIC can pass the assets and liabilities of the failing bank into a specially chartered National bank which
the FDIC can operate for up to three years. The bridge bank option gives the FDIC more flexibility in
resolving closed banks by extending the time it has to weigh its alternative resolution options.
7
10
various creditor claims under different types of resolutions. As was noted by
Hirschhorn and Zervos (1990), under liquidation and without depositor preference, the
FDIC will share with both the uninsured depositors and nondepositors. In a n assisted
merger, on the other hand, all deposits are covered even without depositor preference
and the nondeposit claims are passed on to the acquiring institution. However, with
depositor preference the nondeposit claims may not be passed on.
From this analysis, Hirschhorn and Zervos conclude that the only case where
depositor preference will unambiguously benefit the FDIC is in an assisted merger (e-g.
P&A), suggesting a positive relationship between DPL and the use of P&As. Other
authors reach different conclusions. Ely (1993), for example, argues that depositor
preference would reduce the usage of the 'purchase and assumption' resolution method
in which all assets and liabilities are transferred to the new owner. He thus predicts the
increased usage of deposit transfers in which only deposits were transferred.
For the estimation of the resolution type equation, we group the OBA banks and
the P&A banks into a single category and estimate equation (1)using probit.11 12
Variable definitions are given in Table 2 and the top panel of Table 6 lists the variables
included in the resolution type equation. These variables were chosen by stepwise
regression with the order shown in the top panel of Table 3 being the order in which the
variables were chosen.l 3
The coefficient on our dummy variable for depositor preference status will be
negative (positive) if state banks in states with DPL are more likely to be resolved via
liquidation (P&A or OBA). The discussion above also implies that higher levels of
COREDEP (and thus lower levels of nondeposit claims) would encourage the use of
l1 Application of multinomial probit to (1) is infeasible due to the small number of OBA transactions in
our sample. In addition, since the OBA and P&A have the same implications for senior creditors the
choice between them should not be affected by the presence of depositor preference laws.
lZ The failure equation probit provides the selectivity condition for the resolution type probit.
l3 A theory of how resolution type is chosen would have at least narrowed the list of variables. However,
another important consideration is that the right-hand side variables for the two equations permit
identification.
8
P&As and OBA. Keeley (1990) claims that COREDEP controls for the franchise (charter)
value and is a source of unbooked gains. Buser, Chen, and Kane (1981) argue that the
FDIC will try to mimirnize its losses by closing banks in a manner that preserves the
value of the charter. We include as an explanatory variable the predicted value of net
worth/total assets generated as described below.
Equation12) is the resolution cost equation. Since little case-specific data on
receivership costs is available, let alone the marginal receivership cost for each closed
institution, we measure the dependent variable as the total resolution cost. The list of
independent variables extends that in OT. We estimate (2) by weighted-least squares
and, as is the case for all the equations, regressors are dated 4 to 6 months before the
closure date.
RESCOST is the FDIC's estimated resolution cost as published by the FDIC (1993,
Appendix A). OREO, PD30, and PDNA are proxy variables for asset quality. Given that
the primary sources of unbooked losses are losses on the asset portfolio, on-book
problem assets should be a good proxy for these unbooked losses. As discussed above,
COREDEP controls for the franchise value and is a source of unbooked gains. UNCOL
is a proxy for problem assets not reported by the bank. As Bovenzi and Murton (1988)
note, distressed banks have incentives to cover up the amount of problem assets in their
portfolio. One method for doing this is to book income on a nonperforming loan to
prevent it from being classified as past due or nonaccrual. This implies that UNCOL
would be positively correlated with unbooked losses. Book equity plus reserves,
CAPPRED, represent the cushion between the value of assets and the promised
payments to debt holders. NCRASST is included as a proxy for portfolio risk.
OT included dummy variables for filer types (filer type is related to size) and size
categories defined by the dummies DSZ1-DSZ6. We replace these categories by
LNASST, a decision supported by a standard specification test, and add variables
capturing regional variation in banking
condition^.'^
Predicted resolution type is also included: if the FDIC minimizes resolution cost
(subject to various legal and regulatory constraints) through its choice of resolution
type, DPL'S impact on resolution cost may be partly absorbed through the inclusion of
predicted resolution type in the resolution cost equation. We also include a predicted
level of net worth. Predicted values for both resolution type and the level of net worth
are included to control for their endogeneity. The standard errors are adjusted as
described below.
Although (1) and (2) are the main equations of interest, we first estimate two
other equations and assume that the model has a recursive structure. The first is for net
worth with book capital being predicted by bank balance sheet variables. The second
equation is a probit for failure (bank closure). As in Barth, Bartholomew, and Bradley
(1990)we construct an instrument to control for sampleselection bias in the resolutiontype and resolution-cost equations. Following Thomson [I9921 the predicted value of
book capital from the net worth equation is included as a proxy for net worth on the
right-hand side of the closure, resolution-type, and resolution-cost equations.
The inclusion of the predicted value of net worth on the right-hand side of the
other three equations and the inclusion of a predicted value of resolution type on the
right-hand side of the resolution cost equation requires that the standard errors be
adjusted, following Murphy and Tope1 (1985). The implementation of this procedure is
discussed in the appendix. The four equations are estimated using LIMDEP 6.0. First
the net worth equation is estimated in both ratio and levels form. Then the failure and
resolution type equations are estimated as an ordered probit (the failure equation
provides the selection rule for resolution type) including predicted values of the net
14 In addition, the sigrhcance of individual filer type dummies is eliminated when federal funds
variables are included.
10
worth ratio on the right-hand sides.'' Finally, the resolution cost equation is estimated
with weighted-least squares (dividing by the square root of total assets), including the
predicted level of net worth and the predicted resolution type on the right-hand side,
with the failure equation providing a selection rule.
V. Results
-
Tables 3,4, and 5 present the results for the net worth equations and the closure
equation, respectively. The interested reader is referred to Thornson (1992) for an
interpretation of the coefficients of these equations.
Table 6 contains the estimated coefficients for equation (1) (resolution type) from
the ordered probit. The negative and sigruficant coefficient on LPRBSAD indicates that
the FDIC is more likely to liquidate a closed bank when the value of banking franchises
are low. The negative and sigruficant coefficient on the northeast dummy, DUMNES, is
consistent with the this explanation.
The relationship between the southwest dummy variable and resolution type is
more complex. Prohibition of branching in Texas lead to the creation of large multibank holding companies (BHCs). The collapse of the depository institutions sector in
the Southwest included FDIC resolution of five of the eight largest Texas BHCs and the
death-bed acquisition of two of the other three large Texas BHCs by out-of-state
banking organizations. Finally, despite the economic problems in Southwest in the
mid-1980s this region was expected to be a high growth region in terms of both
population and income. Hence, the positive and significant sign on DUMSW appears to
be controlling for constraints faced by the FDIC in resolving the insolvency of large
multi-bank BHCs and the value of Texas banking franchises to out-of-state BHCs.
DSBRNCH, the dummy variable for branching regulations, is negative and
significant. Given that the number of potential acquirers for a closed bank is higher in
Although the failure and resolution type equations together constitute an ordered probit (with neither
dependent d u w y variable appearing on the right-hand side of the other equation), L1MDEF"sbivariate
probit routine could be utilized.
15
11
states without intrastate branching restrictions we would expect DSBRNCH be
positively related to the use of the purchase and assumption resolution option (and
other types of assisted mergers).
Thomson (1992) finds that the probability a bank is closed is inversely related to
its capitalization. This suggests that closed banks with high book capital ratios are
,
,
,
likely to have high levels of unbooked losses.16 Another important source of unbooked
losses on a bank's balance sheet is other real estate owned, which is essentially
repossessed properties. Hence, the negative and significant coefficients on CAPTAPS
and OREOAS are consistent with the FDIC's choice of a liquidation when faced with
large contingent liabilities.
The positive and sigruficant coefficients on LNASST and CORDEPA are
consistent with expectations. A positive and sigruficant relationship between size and
de facto insurance of all senior claimants (uninsured depositors and general creditors) is
consistent with the too-big-to-let fail doctrine practiced prior to FDICIA (see Camel1
[I9931 and Todd and Thomson [1991]). The positive and sigruficant coefficient on
CORDEPA is consistent with the model of Buser et al. (1981).
The results from equation (1)suggest that DPL induces the FDIC to choose
P&As. There are two possible explanations for the positive coefficient on DPL. First,
general creditors in banks subject to depositor preference might successfully exit the
bank or effectively collateralize their exposure before the bank is closed, thereby raising
the cost of a liquidation vis-i-vis purchase and assumption and open-bank assistance
transactions. Hirschhorn and Zervos (1990) provide evidence of this type of general
creditor response in their study of thrifts. Second, as in Kane (1986), constraints faced
by the FDIC may cause it to choose the resolution option that jointly minimizes its
fiduciary, political, and other costs associated with resolving the closed bank. In a
liquidation the FDIC would have to strictly observe depositor preference, whereas, in
16 This effect may be partially absorbed by taking account of the selection rule provided by the closure
equation.
12
P&A and OBA it could choose to ignore it. Hence, DPL could increase the nonfiduciary costs to the FDIC associated with liquidations, increasing the relative
attractiveness the its alternative failed-bank resolution options.
Estimated coefficients for equation (2) from the selection model appear in table 7
and 8.. Table 9 compares these results with those found in OT and Osterberg (1996) and
thus indicates the importance of correcting for sample selection bias induced by
regulatory closure rules. Estimated coefficients on the proxy variables for unbooked
losses and gains in closed bank portfolios are larger (in absolute value) than those in
previous studies, and in many cases the differences are statistically sigxuficant.
The coefficient on our instrument for book capital, CAPPRED, is negative and
sigruficantly different from both zero and (-1). This corroborates the findings of James
(1991) and OT of sigruficant unbooked losses on the balance sheets of failed banks. On
the other hand, unlike James (1991) and OT, income earned but not received, UNCOL,
was not signhcant at the 10 percent level although the sign of its impact was positive.
However, the coefficient on UNCOL is positive and sigruficant in when equation (2)
omits measures of fed funds sold, fed funds purchased and other borrowed money as
regressors.
PDNA and ORE0 are included in equation (2) as proxies for asset quality. Both
of these control for unbooked losses and have positive and significant coefficients. As
in OT we find loans to insiders and portfolio risk to be positively and significantly
related to resolution costs. Moreover, as in Osterberg (1996) we find that a positive and
significant coefficient on FFSOLD. This is consistent with banks in depressed regional
economies using fed funds as the residual asset item in managing the asset side of their
balance sheet. Hence, FFSOLD may be proxying for the quality of the loan portfolio.
Off-balance sheet activities are negatively and significantly related to resolution
costs. Similar results are found by OT when OBS is split into off-balance sheet loan
items and other off-balance sheet activities. A negative and significant coefficient on
OBS is consistent with the market discipline hypothesisof Boot and Thakor (1991) and
the hypothesis that banks use derivative contracts to hedge against on-balance-sheet
risk."
In Table 8, as in OT, bank size remains an important determinant of resolution
costs. The positive and sigruficant coefficient on WASST is consistent with higher
FDIC administratiw and legal expenses for resolving large banks. The results are
qualitativily the same found when dummy variables are used to proxy for size.
Resolution type does not have a significant impact on resolution cost. The
coefficients on predicted resolution type from equation (1) (PRESTYPE), and its product
with COREDEP, (ICORE23), are statistically insigruficant. This is consistent with the
hypothesis that the FDIC chooses resolution type to minimize resolution cost.
The positive and sigruficant coefficient on the depositor preference dummy in the
resolution cost equation is inconsistent with the hypothesis that depositor preference
laws lower the FDIC's costs. However, we cannot distinguish the source of the higher
resolution costs. The positive coefficient on DPL is consistent with either the FDIC
adopting resolution options that provided de facto guarantees of all senior creditors
found in equation (1) or with offsetting responses by general creditors. Which of these
explanations holds has important implications for the efficacy of the national depositor
preference laws. If higher resolution costs associated with DPL are driven by FDIC
behavior then reforms in FDICU (1991), such as prompt corrective action and the
constraints on too-big-to-let fail, could eliminate or reverse this effect of DPL. If, on the
other hand, general creditor behavior is driving the positive coefficient on DPL then the
net effect of the national depositor preference may be to increase FDIC closed-bank
resolution costs.
FFPURCH and OBM are included in equation (2) as proxies for general creditor
claims. Ceteris paribus, higher levels of FFPURCH, as unfunded liabilities, would be
l7
See Avery and Berger (1991)and Koppenhaver and Stover (1991).
14
expected to increase costs. However, the negative and significant coefficient on
FFPURCH and the negative coefficient on OBM are not consistent with general
creditors increasing their collateralization. FFPURCH includes repurchase agreements
whicli are collateralized so that we might have expected the average collateralization of
this category to rise. One alternative explanation is similar to that suggested by the
coefficient on OBS, namely that F~PURCHand OBM provide market discipline and that
banks able to utilize these funding channels have lower unbooked losses than we have
captured with our call report proxies for balance sheet quality.
VI. Conclusion and Policy Implications
An examination of the period preceding the passage of the national depositor
preference law provides no evidence to support claims that depositor preference will
result in lower FDIC resolution costs. On the contrary, we find a positive relationship
between depositor preference and the cost of resolving a closed bank. We also find a
positive relationship between the presence of depositor preference laws and the use of
P&A and OBA transactions, both of which minimize the benefit to the FDIC from
depositor preference. These results are largely consistent with Kane's (1986) analysis of
FDIC behavior.
The sample period we study precedes the implementation of FDICIA (1991)
which placed limits on the FDIC's ability to extend de facto guarantees to uninsured
depositors and general creditors.18 As a result, the FDIC's choice of resolution type
may no longer be affected by depositor preference laws. On the one hand, this
provision of FDICIA may induce general creditors to increase collateralization or
shorten maturities. On the other hand, if FDICIA's prompt corrective action provisions
result in the closure of capital deficient but book solvent banks, then depositor
preference should have no impact on FDIC resolution costs.
18
See Camell (1993)for a discussion of FDICIA.
15
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DeGennaro, R. P., and J. B. Thornson, 1996, "Capital Forbearance and Thrifts:
Examining the Costs of Regulatory Gambling," Journal of Financial Services Research
10 (September),199-211.
Ely, Bert. "Surprise Congress Just Enacted the Core Banking System, "American Banker,
vol. 158, September 21,1993, p.24.
Federal Deposit Insurance Corporation, 1993, "Failed Bank Cost Analysis: 1986-1992,"
Washington, D.C. (FDIC).
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Greene, W.H., 1993, Econometric Analysis, New York, MacMillan.
Hirschhorn, E., and D.Zervos, 1990,"Policies to Change the Priority of Claimants: The
Case of Depositor Preference Laws," Journal of Financial Semices Research 4,1990,11125.
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(September), 1223-1242.
Kane, E. J., 1986, "Appearance and Reality in Deposit Insurance Reform," Journal of
Banking and Finance 10,175-188.
Kane, E. J., 1990, "Principal-Agent Problems in S&L Salvage, Journal of Finance 45
(July),755-764.
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Economic Review 80 (December), 1183-1200.
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**.
Maddala, G.S., 1986, "Econometric Issues in the Empirical Analysis of Thrift
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State
Table 1:State Depositor Preference Legislation for Banks
Date Effective
Alaska
Arizona
California
Colorado
Connecticut
Florida
Georgia
Hawaii
Idaho
Iowa
Kansas
Louisiana
Maine
Minnesota
Missouri
Montana
Nebraska
New Hampshire
New Mexico
North Dakota
Oklahoma
Oregon
Rhode Island
South Dakota
Tennessee
Utah
Virginia
West Virginia
: Legislation became effective on either January 1or July 1.
Passed by both houses on July 1,enactment date unclear.
In other cases when only the year is indicated, neither the month nor the day of
enactment is available.
2:
Table 2: Variable Definitions
BFLIAB
Yearly total bank failure liabilities by state @un and Bradstreet)
BRKDEP
Brokered deposits
CAPPED
Predicted level of net-worth from net worth equation
CAPTAPS
Scaled version of predicted net worth/total asset
COREDEP
Core deposits - domestic deposits under $10,000
COREDEPA
Core deposits/Total Assets
DBHC
= 1if bank is in a bank-holding company (=0 otherwise)
DPL
= 1if state bank in a state with depositor preference legislation
DSBRANCH
= 1if bank's home state has statewide branching (=0 otherwise)
DUMNES=l
if bank is in Boston, New York, or Philadelphia Fed districts
DUMSW
=I if bank is in the Dallas Fed district
EFFICNYS
.Annualizednon-interest expense/Total Assets
FFSOLD
Federal Fund sold (lent)
FFPURCH
Federal Funds purchased (borrowed)
HOTFUNDS
Foreign deposits + FFPURCH + OBM + BRKDEP
ICOE23
Predicted ESTYPE* Core Deposits
UNCOL
Interest income earned on loans that is uncollected
INSLNS
Loans to insiders
LCAP
Net worth lagged one call report
LCAPTA
LCAP/Total Assets
LAMBDA
Mills' ratio from failure equation (selectivity correction)
LIQL
Hotfunds/Total Assets
LIQLS
Scaled version of LIQL
LNASST
Natural logarithm of total assets
LNBFLIAB
Natural logarithm of BFLLQB
LNBFLIBD
Scaled version of LNBFLIAB
LOANHERR
Loan Herfindahl index (see Thornson [1992])
LPRBASD
Logarithm of commercial problem bank assets (quarterly, scaled)
NCRASSTA
Risky assets not in OREO, PDNA, or INSLNS/Total Assets
NETCHARG
Charge-offs minus recoveries (annualized)/Total Assets
NETCHARGL
Level of Charge-offs minus recoveries (annualized)
NETINCA
Annualized net income
OBM
Other borrowed money
OBS
Off-balance sheet items
OBSLN
Off-balance sheet loans
OBSOTHER
Off-balance sheet items other than loans
OBSTA
Off-balance sheet items/Total Assets
ORE0
Other real estate owned
OREOA
OREO/Total Assets
OREOAS
OREOAS scaled
PC1
Per capital income (by state, yearly)
PCrD
Scaled PC1
PDNA
Loans 90 days past-due or non-accruing
PDNAA
PDNA/Total assets
RESTYPE =
1 for purchase and assumptions or open bank assistance
= 0 for Payouts, (Deposit Transfers; Liquidations)
Table 4: OLS Estimation of Net Worth Equation
Ordinary least squares regression.
12554
Observations =
= 0.2574261E+05
Mean of LHS
StdDev of residuals= 0.9529209E+04
R-squared
0.9983717E+OO
F[ 14,125391 = : 0.5491533E+06
Log-likelihood = -0.1328271E+06
Amemiya Pr. Criter.= 0.9091432E+08
ANOVA Source
Variation
0.6981284E+15
Regression
Residual
0.1138614E+13
Total
0.6992671E+15
Durbin-Watson stat.=
1.9899685
Variable
Constant
LCAP
UNCOL
PDNA
ORE0
INSLNS
NCRASST
OBS
COREDEP
NETCHRGL
LOANHERR
HOTFUNDS
NETINCA
BFLLAB
PC1
Coefficient
Std. Error
.
Dep. Variable
=
CAP
Weights .
ONE
Std.Dev of LHS. = 0.2360194E+06
Sum of squares = 0.1138614E+13
Adjusted R-sq. = 0.9983699E+00
Prob value
= 0.3217295E-13
Restr.(b=O) Log-1 = . -0.1731268E+06
Akaike 1nfo.Crit. = 0.2116331E+02
Degrees of Freedom Mean Square
0.4986632E+14
14.
12539.
0.9080583E+08
12553.
0.5570517E+11
Autocorrelation = 0.0050158
t-ratio
Prob.
Mean & S.D. of Var.
.
Table 5: Probit Estimation of Failure/Closure Equation for Selection
in Resolution Cost Equation
Maximum Likelihood Estimates
Log-Likelihood.............. -897.79
Restricted (Slopes=O)Log-L. '4047.1
Chi-Squared(12)............ 6298.7
Sigruhcance Level.......... 0.32173E-13
N(0,l) used for sigruficance levels.
Variable
Coefficient
Std. Error
t-ratio
-
Constant
CAPTAPS
UNERNINC
LIQL
OBSTA
INSIDELN
LNASSTD
NCRASSTA
PCID
LNBFLIBD
DBHC
EFFICNCY
COREDEPA
Frequencies of actual & predicted outcomes
Predicted outcome has maximum probability.
Predicted
Actual
Total
0
1 TOTAL
11450 1104
12554
Prob.
Mean & S.D. of Var.
Table 8: Estimation of Resolution Cost Equation with Selection
Equation Based on Failure/Closure
Two stage least squares regression.
Dep. Variable = RESCOST
Observations =
1240
Weights
= ISQRTASS
Mean of LHS
= 0.7785666E+04
Std.Dev of LHS = 0.3580882E+05
StdDev of residuals= 0.2826802E+05
Sum of squares = 0.9764769E+12
R-squared
=
0.3763211E+00
Adjusted-R-squared= 0.3676447E+00
F[17, 12221 = - 0.4337303E+02
Prob value
= 0.3217295E-13
Log-likelihood = -0.1445978E+05
Restr.(b=O) Log-1 = -0.1476156E+05
Amemiya Pr. Criter.= 0.8106805E+09
Akaike 1nfo.Crit. = 0.2335126E+02
Standard error corrected for selection..... 28274.
Correlation of disturbance in regression
and Selection Criterion (Rho).............. 0.41150E-01
N(0,l) used for significance levels.
Variable
Constant
CAPPRED
PRESTYPE
UNCOL
PDNA
ORE0
INSLNS
NCRASST
OBS
COREDEP
LNASST
DPL
FFSOLD
FFPURCH
OBM
BRKDEP
ICORE23
LAMBDA
Coefficient
Std. Error
t-ratio
Prob.
Mean & S.D. of Var.
Table 9: The Impact of Depositor Preference Legislation on Resolution Costs
Osterberg (1996)
Selection Model
O/T(1995)
'Basic' plus DPL
with Predicted
Res.Type
constant
CAP'
UNCOL
PDNA
ORE0
INSLNS
NCWST
OBSLN
OBSOTHER
OBS
FFSOLD
FFPURCH
COREDEP
ICORE'
LNASST
BRKDEP
DUMNE
DUMSW
DPL
OBM
PRESTYPE
LAMBDA
# Observations
Adj. R2
Notes:,
Standard errors are in parentheses.
Observations are weighted by one divided by the square root of total assets.
The first column is from the Table 3, Osterberg and Thomson (1995). The second column
is unreported, referenced in from Osterberg (1996).
* : significant at the 10% level.
**: significant at the 5% level.
Results in third column use predicted capital and resolution type.
Appendix A
Adjustment of Standard Errors for hclusion of Predicted Values as Regressors
We follow Murphy and Tope1 (1985) in deriving the correct standard errors
when one or more variables on the right-hand side of either the failure/closure,
resolution type, or resolution cost equation has been generated by a prior estimation. In
the case of the failure/closure equation, and the resolution type equation, which are
estimated by probit (maximum likelihood [MLE]), the right-hand side includes the
predicted value of net worth/total assets, which was generated by ordinary least
squares (OLS). In the case of resolution cost, the right-hand side includes the predicted
level of net worth, estimated by OLS, and the predicted resolution type, predicted by
probit (MLE).
Murphy and Tope1 present the correct adjustment when the first stage estimation
is maximum likelihood and the second MLE or when both are MLE. Here we detail the
derivation for the slightly different case when the first stage is OLS and the second
MLE.
The OLS estimation yields:
&(S, -8;) = Xi X,)-I t X;U,,where X,U, and 8 denote the right-hand side variables,
*
(+
estimated residuals, and parameters, respectively.
The MLE estimation yields:
where l2denotes the log-likelihood for the second equation, 8, denotes the parameters
in the second equation (including those associated with the predicted values from prior
equations).
Substitution yields:
where R, is Fisher's information matrix which can be written as
-
]
ae2ae; and will be easily retrieved from the estimation of the second
equation.'
4 must be derived and is equal to -E
}el;{:
Then we need the form of the variance-covariance matrix
a where
-
Then, for the estimated parameters for the second equation (8, ) we have
~ ( 6-,8;) N(0, Z)where
Z = R;' + R;' [R;c2(x;X, )-I nR3 + Q;($4 + R; Q-I
above, Qr is the lower-left hand quadrant of
Q,]R;'where Rz and h are defined as
a, and & = -1 (x~x,
1'.which is easily
n
retrievable from the results of the first-stage OLS estimation.
For the standard error adjustment for both the failure and resolution type
equations, the log-likelihood is based on the bivariate probit as discussed by Greene
(1993). As mentioned in the text, since neither the failure dummy nor the resolution
type dummy appears in the other's equation, while the failure equation supplies the
selection rule for the resolution type equation, the two are better thought of as an
ordered probit. However, although there is no simultaneity the presence of selectivity
implies that p # 0.
Given that the failure/dosure and resolution type equations are estimated as an ordered probit, R is
retrieved as the appropriate quadrant of the variance covariance matrix from the two equations
1
31
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