Should the FDIC Worry about the FHLB? 

The Impact of Federal Home Loan Bank Advances on the Bank Insurance Fund

Rosalind L. Bennett 

Senior Economist 

Division of Insurance and Research 

Federal Deposit Insurance Corporation 

Washington, D. C. 

Mark D. Vaughan* 

Assistant Vice President and Economist 

Banking Supervision and Regulation 

Federal Reserve Bank of Richmond 

Timothy J. Yeager 

Assistant Vice President and Economist 

Banking Supervision and Regulation 

Federal Reserve Bank of Saint Louis 

*corresponding author 

P.O. Box 85029 

Richmond, Virginia 23261 

Voice: 804-697-2797 

Fax: 804-397-4021 

mark.vaughan@rich.frb.org

First Draft: December 2003 

This Draft: July 2005 

We thank Adam Ashcraft, Eliana Balla, Linda Barriga, Ray Brastow, Alton Gilbert, Jim Garven, Richard

Grossman, Jeff Gunther, Ben Jones, Tom King, Paul Kupiec, Kathleen McDill, Bob Moore, Mike Riddle,

Ken Robinson, Sherrill Shaffer, Dusan Stojanovic, Luis Somoza, Rick Sullivan, James Thomson, Jim 

Tipton, and Larry Wall for helpful comments. In addition, we are grateful to seminar participants at

Baylor University, the Federal Deposit Insurance Corporation (FDIC), the Federal Reserve Bank of

Richmond, and the Southern Finance Association meetings for useful exchanges. All remaining errors 

and omissions are ours alone. Andy Meyer provided valuable help with estimation of the CAMELSdowngrade model. We also thank the Federal Housing Finance Board for bank-level data on Federal 

Home Loan Bank advances. Much of this paper was written while Mark Vaughan worked as a visiting 

scholar in the Center for Financial Research at the FDIC; he gratefully acknowledges research support.

The views expressed do not represent official positions of the FDIC, the Federal Reserve Bank of

Richmond, or the Federal Reserve Bank of St. Louis. 

1
Should the FDIC Worry about the FHLB? 

The Impact of Federal Home Loan Bank Advances on the Bank Insurance Fund

Abstract 

Does growing commercial-bank reliance on Federal Home Loan Bank (FHLBank) advances increase

expected losses to the Bank Insurance Fund (BIF)? Our approach to this question begins by modeling the 

link between advances and expected losses. We then quantify the effect of advances on default

probability with a CAMELS-downgrade model. Finally, we assess the impact on loss-given-default by

estimating resolution costs in two scenarios: the liquidation of all banks with failure probabilities above 

two percent and the liquidation of all banks with advance-to-asset ratios above 15 percent. The evidence 

points to non-trivial increases in expected losses. The policy implication is that the FDIC should price 

FHLBank-related exposures. 

Keywords: Deposit-insurance pricing, Federal Deposit Insurance Corporation (FDIC), Federal 

Home Loan Bank (FHLB), moral hazard, secured funding 

JEL codes: G21, G28, K23

CFR Research Programs: deposit insurance; banking performance & the economy

2
1. Introduction 

Does growing commercial-bank reliance on Federal Home Loan Bank (FHLBank) advances 

increase expected losses to the Bank Insurance Fund (BIF)?1

 The answer is important to academic studies 

of deposit-insurance pricing and policy debates over deposit-insurance reform. Since the early 1990s, 

banks of all shapes and sizes have come to view the FHLBanks as a critical source of funding. At yearend 2003, the FHLBank System boasted nearly 6,000 commercial-bank members with advances topping 

$240 billion. By way of comparison, jumbo CDs at all 7,770 U.S. commercial banks totaled $597.3

billion. Broadly speaking, BIF faces potential exposure through two channels. Advances could increase 

default probability by subsidizing risk-taking or losses-given-default by subordinating the Federal Deposit

Insurance Corporation’s (FDIC) position at resolution.2

 

Recent discussion of the housing GSEs (government-sponsored enterprises) has ignored BIF 

exposure to FHLBank funding. The policy debate has instead centered on Freddie Mac and Fannie Mae. 

Poole (2003), for example, has warned of the systemic risk arising from their rapid growth while 

Passmore (2003) has argued that implicit government subsidies benefit shareholders of the two GSEs 

more than homeowners. Emmons and Sierra (2004) have also noted that performance incentives for 

Freddie and Fannie executives exacerbate moral-hazard problems. To date, scientific study of the

FHLBanks has focused on the wisdom of the mortgage-partnership program (Bair, 2003; Frame, 2003) 

and implicit subsidy of community-bank lending (Craig and Thomson, 2003). Some attempt has also 

been made to model the decision of community banks to join the System (Collender and Frizzell, 2002), 

quantify the influence of advances on the behavior of troubled thrifts (Ashley, Brewer, and Vincent, 

1998), assess the impact of the Gramm-Leach-Bliley Act of 1999 on System solvency (Nickerson and 

Phillips, 2002), and measure the effect of FHLBank membership on community-bank risk (Stojanovic,

 

1. The Federal Deposit Insurance Corporation (FDIC) administers two funds—one for banks (the Bank Insurance Fund, or 

BIF) and one for thrifts (the Savings Association Insurance Fund, or SAIF). 

2. Throughout the paper, we define “default” as liquidation of a bank by the FDIC. 

3
Vaughan, and Yeager, 2001). We are aware of no GSE studies that explore the link between advances 

and BIF solvency. 

Theory and practice of deposit-insurance pricing have also neglected the impact of FHLBank 

funding on BIF. Under the current pricing structure, banks with different advance levels but similar 

supervisory ratings and capital protection pay the same premiums (Murton, 2004). Prior to 1989, when

the Financial Institutions Reform, Recover, and Enforcement Act of 1989 (FIRREA) opened FHLBank

membership to commercial banks, this assumption was reasonable.3

 It may no longer be. Recent 

scientific work has also sidestepped the issue. Fair-pricing models have been calibrated with average 

long-term FDIC losses or auditing expenses—an approach that extrapolates from an era when FDICinsured institutions could not borrow from FHLBanks (Duffie et al., 2003; Falkenheim and Pennacchi, 

2003). Moreover, ex post (James, 1991) as well as ex ante (Cooperstein, Pennacchi, and Redburn, 1995) 

estimates of the aggregate cost of deposit insurance have abstracted from FHLBank borrowings. We are 

the first to ask whether such abstraction is appropriate. Recent evidence that the Federal Deposit

Insurance Corporation Improvement Act of 1991 (FDICIA) has not deterred banks from shifting risk to

BIF makes our analysis all the more timely (Hovakimian and Kane, 2000). 

We model and quantify BIF exposure to FHLBank funding. The overarching goal is to determine 

whether past disregard of this exposure was justified. We begin by conducting comparative-static 

exercises to “sign” the relationship between advances and expected losses. We then turn to empirical 

tests, looking at the effect of advances on default probability and loss-given-default. To get at default 

probability, we examine the contribution of the advances-to-assets ratio to a CAMELS-downgrade 

model.4

 To get at loss-given-default, we estimate incremental resolution costs in two scenarios: the

 

3. Before FIRREA, membership was open only to thrift institutions and insurance companies. 

4. “CAMELS” is an acronym representing six dimensions of bank safety and soundness: capital protection (C), asset quality

(A), management competence (M), earnings strength (E), liquidity-risk exposure (L), and market-risk sensitivity (S). Once 

per year, examiners review these dimensions on site and assign a composite rating, which takes an integer value ranging 

from 1 (best) to 5 (worst). Composite ratings of 1 or 2 denote satisfactory condition while ratings of 3, 4, or 5 denote 

unsatisfactory condition. We define a CAMELS downgrade as migration from a satisfactory to an unsatisfactory rating. 

Note: “S” ratings were added to the framework in 1997; tests on pre-1997 data use composite CAMEL ratings. 

4
liquidation of all banks with failure probabilities above two percent and the liquidation of all banks with 

advance-to-asset ratios above 15 percent. The evidence indicates that access to advances has increased 

default probabilities and losses-given-default. The magnitudes imply the FDIC should price expected 

losses arising from FHLBank funding. 

The remainder of the paper is organized as follows. Section two provides additional institutional 

details to frame the research question. Section three provides comparative-static analysis of the impact of 

FHLBank funding on expected BIF losses. Section four estimates the impact of advances on CAMELS 

downgrade probability, and section five quantifies the impact of advances on resolution costs. Section six 

concludes with policy implications. 

2. FHLBank Funding and Expected BIF Losses: A Closer Look at the Issues

In recent years, numerous proposals have been put forward to reform the U.S. deposit-insurance 

system. (For example, see Bloecher, Seale, and Vilim, 2003; Blinder and Wescott, 2001; FDIC, 2000; 

and Hanc, 1999.) A key assumption behind these proposals is that design of the system has not kept pace 

with financial innovation. The FDIC has itself recognized the need for extensive reform and has 

recommended merging the Bank-Insurance Fund (BIF) and the Savings Association Insurance Fund 

(SAIF), loosening the link between assessment rates and Fund balances, and indexing the depositinsurance ceiling to inflation (FDIC, 2001).5

 

An important issue overlooked by these reforms is the impact of secured funding—like FHLBank 

advances—on expected BIF losses (Shibut, 2002). These claims have grown rapidly as Table 1

illustrates; between 1992 and 2003 secured funding at commercial banks grew at an average annual clip 

of 13.7 percent. Meanwhile, total assets rose at a 6.9 percent annual rate and domestic deposits—the

 

5. FDICIA requires the FDIC to set premiums so that BIF and SAIF reserves do not fall below 1.25 percent of insured

deposits. 

5
assessment base for insurance premiums—rose at a 5.5 percent annual rate. Private insurers price 

expected losses: the probability of a claim times the magnitude of the claim if filed. In theory, secured

funding could affect both failure probability and resolution costs. But the FDIC sets bank premiums

based on supervisory ratings and capital ratios—subject to limits based on BIF reserves. Research 

indicates that supervisory ratings focus more on a bank’s current condition than failure probability

multiple quarters ahead (Berger, Davies, and Flannery, 2000; Cole and Gunther, 1998). Research also

indicates that Prompt Corrective Action capital thresholds—on which premiums depend—are too low to

matter (Peek and Rosengren, 1997; Jones and King, 1995).6

 Table 2 contains the pricing matrix at yearend 2004. Nearly 93 percent of U.S. banks will pay no premiums during the first assessment period for

2005. The spread between assessment rates for the riskiest and the safest banks is only 27 basis points, 

well under recent estimates of actuarially fair spreads. More to the point, these estimates reflect historical

FDIC losses and operational expenses. History may not offer a good guide because commercial-bank

reliance on FHLBank funding is a post-1989 phenomenon. 

FHLBank advances are not the only secured claims on bank balance sheets, to be sure. Others

include discount loans, public deposits, and repurchase agreements (repos).7

 But these claims do not pose

large risks to BIF. Discount lending has actually declined since the early 1990s—reflecting the effect of 

bank consolidation (Hakkio and Seldon, 2000), the stigma attached to the Window (Peristiani, 1998), and 

the sophistication of the overnight-funding market (Furfine, 2001, 1999).8 Repos do not fund long-term 

bank growth; such agreements consist largely of overnight inter-bank borrowings to meet reserve 

requirements and avoid daylight overdrafts. The increase between 1992 and 2003 reflects improvement

in bank liquidity management and development of a sophisticated overnight market. Finally, the supply

 

6. For example, only 78 U.S. banks were less than “well-capitalized” at year-end 2004. 

7. “Public deposits” are held by state and local governments; typically, state law requires that the uninsured portion of these

deposits be secured by high-quality marketable securities, such as Treasuries or Agencies. 

8. In addition, there are two key differences between discount loans and FHLBank advances. First, discount loans are 

designed to help banks cover payments imbalances that occur late in the business day; FHLBank lending is predominately 

long-term. Second, FDICIA strictly limited the ability of Federal Reserve Banks to lend to troubled depository institutions. 

6
of uninsured public deposits is limited by the fiscal activity of states or municipalities; growth in the 

1990s reflects the impact of robust economic expansion on tax revenues. 

FHLBank advances, on the other hand, could materially raise expected BIF losses. Advances 

accounted for 47 percent of the dollar increase between 1992 and 2003, and they now fund over three 

percent of bank assets. By way of comparison, at year-end 2003 jumbo CDs funded 7.9 percent of bank 

assets, and repos—the largest category of secured liabilities—funded 4.3 percent. More important, the 

supply of advances—unlike other secured funding—is highly elastic. Because of its extraordinary creditrisk record and implicit government guarantee, the FHLBank System can easily place debt in world 

capital markets. And the Financial Modernization Act of 1999 significantly relaxed membership and 

collateral requirements, thereby expanding the ability of banks to borrow. Between 1992 and 2003, the 

number of banks in the System climbed from 1,284 to 5,946. Over the same period, advances outstanding 

to banks jumped from $6 to $240.8 billion. The FHLBanks impose only two constraints on member 

borrowing: the borrower must have eligible collateral and an acceptable supervisory rating. Because

FHLBanks will advance funds to purchase eligible assets—including assets in abundant supply such as

mortgage-backed securities—the collateral constraint is not binding. Moreover, most FHLBanks define 

an “unacceptable” supervisory rating as a CAMELS 4 or 5 composite. At year-end 2004, only 0.92 

percent of U.S. banks posted such a rating, and just 47 of those banks were FHLBank members.9

 

Congress considered two deposit-insurance reform bills in 2004: H.R. 522 and S.229. Although

differing in a few details, both bills would have merged BIF and SAIF, increased the coverage ceiling,

indexed the new ceiling to inflation, granted the FDIC additional flexibility to set premiums, and

mandated rebates when Fund balances exceed a certain level. Neither bill linked premiums to bank

funding strategies. So, even if proposed reforms were to pass in the future, FHLBank advances would

still constitute a potential risk to BIF. 

 

9. Typically, FHLBanks do not revoke membership or call advances when members encounter financial trouble—a rational 

policy given safeguards against credit losses. These safeguards are discussed in more detail in section 3.4. 

7
3. FHLBank Funding on Expected BIF Losses: A Simple Model

We now present a simple model of the impact of advances on expected net losses to BIF. The 

net present value of expected losses at time (NLt) equals expected gross payouts to insured depositors 

minus total paid-in deposit-insurance premiums.10 Expected gross payouts, in turn, equal default

probability (p) for bank i times the FDIC’s gross losses from liquidating bank i (LS), summed across all

banks. Total premiums equal the product of the assessment rate on bank i (ri) and the assessable deposits

in bank i (Di), again summed over all banks. Assessable deposits at bank i approximately equal the sum 

of domestic insured and uninsured deposits. Specifically, 

∑∑ [ ] = =

⎟ − ⎠

⎞ ⎜

⎝

⎛

+ =

T

t

N

i

it it it it

t

t

t

NL p LS r D

0 1 1

1

δ

(1)

where: 

NLt = present value of expected net losses

pit = probability bank i will default, estimated at time t

LSit = gross loss to BIF from liquidation of bank i at time t

rit = deposit-insurance assessment rate on bank i at time t

Dit = assessable deposits of bank i at time t 11

δ = discount rate 

T = number of periods 

Nt = number of banks at time t

Each bracketed term in equation (1) is potentially affected by advances. Default probability (p) 

could depend on advances because FHLBanks do not price failure risk, thereby creating a potential moral

hazard. Gross loss-given-default (LS) could depend on advances because FHLBank funding could 

purchase new assets or replace uninsured deposits—in either case, potentially increasing BIF exposure.

Finally, the assessment rate (r) could depend on advances because FHLBank funding might lead banks

 

10. Throughout the paper, “gross” losses are losses from liquidating a failed bank, without accounting for any premiums paid 

into BIF. “Net” losses, in contrast, refer to gross losses minus paid-in premiums. 

11. Technically, (rit)(Dit) should be multiplied by (1-pit). As written, equation (1) implies banks continue paying premiums after 

default. We assume away the probabilistic nature of premiums to simplify the comparative statics; no results are affected. 

8
into riskier pricing cells, and the assessment base (D) could depend on advances because FHLBank

funding allows banks to operate with fewer assessable deposits. 

Differentiating equation (1) with respect to A highlights the impact of advances on BIF losses:

∑∑ [ ] ( ) ( = =

⎟ + − + ⎠

⎞ ⎜

⎝

⎛

+ =

T

t

N

i

it it it it it it it it

t t

p LS p LS r D r D

dA

dNL

0 0

' ' ' '

1

1

δ ) (2)

where (pit LS’it) represents the change in net losses traceable to the impact of advances on gross lossgiven-default, (p’it LSit) represents the change in net losses traceable to the impact of advances on default 

probability, (r’it Dit) represents the change in net losses traceable to the impact of advances on the

assessment rate, and (rit D’it) represents the change in net losses traceable to the impact of advances on

assessable deposits. The change in gross losses arising from advances, LS’, depends on the quantity of

loans (L), quantity of insured deposits (ID), quantity of uninsured deposits (UD), level of equity (EQ), 

and ratio of loan losses (LR). The Appendix explores the link between and these variables, assuming 

equity is endogenous. Substituting equation (A5) from the Appendix into equation (2) yields a 

framework general enough to analyze the impact of advances on expected losses under a wide set of 

assumptions. Bank- and time-specific variables are dropped to simplify the exposition. 

LS'

( ) ( ' ' ' 1

1 ' ' 1 EQ EQ p LS r D rD p

dA

dNL − − +

⎟ + + ⎠

⎞ ⎜

⎝

⎛

+ = − δ γ γ δ ) (3)

where: 

= + = ID UD

ID

γ insured-depositor share of total deposits 

EQ = value of equity (presumed negative for insolvent banks) 

Broadly speaking, advances can fund assets or replace deposits. And new assets can be high or low risk. 

In sections 3.1 through 3.4 below, we use equation (3) to explore the various cases. 

9
3.1 Asset Growth 

Banks can use FHLBank funding to book new loans. To focus on the link between asset growth 

and BIF losses, we assume advances increase loans dollar-for-dollar (L’=1 and UD’=ID’=D’=0) but do

not affect default probabilities, assessment rates, or loan-loss rates (p’=r’=LR’=0)12 Applying these

assumptions—and equations (A6) and (A9) from the Appendix—to equation (3) yields: 

0

1 ⎟ > ⎠

⎞ ⎜

⎝

⎛

+ = LR pdA

dNL

γ δ (4) 

where γ represents the share of insured deposits in the assessment base. Because default probability (p), 

the discount rate (δ), insured-deposit share (γ), and the loan-loss ratio (LR) are positive, the derivative is 

positive. In words, advances increase expected net losses to BIF because dollar loan losses rise with the 

size of the portfolio, but deposit-insurance premiums do not. 

Table 3 illustrates the impact of advance-funded asset growth on BIF losses. Initially, Bank A 

funds $800 in loans with $700 of insured deposits and $100 of capital [panel (a)]. Now, an economic 

shock reduces loan value 15 percent and produces insolvency. Upon closure, the FDIC pays insured 

depositors $700, sells assets for $680, and suffers a loss of $20. Suppose Bank A had used $200 in 

FHLBank advances to book new loans before the shock occurred [panel (b)]. Once again, let the value of

Bank A’s loan portfolio decline 15 percent.13 The FDIC pays depositors $700 at liquidation as before but

now must also pay $200 to an FHLBank because advances are fully secured. The $850 recovery from 

asset sales falls short of the $900 payout. The resulting gross loss ($50) is $30 higher than in the noadvances case. Net BIF losses rise by $30 as well because paid-in premiums do not change; the increase 

in advances does not affect the assessment base (by construction) or assessment rate (by assumption). 

 12. These assumptions allow no room for moral hazard. Section 3.4 explores the case when FHLBank advances induce a

change in the bank’s risk profile. 

13. Assuming the economic shock forces Bank A to charge off 15 percent of loans, irrespective of the size of the portfolio, is 

tantamount to assuming that loans funded with advances are equal in quality to loans already on the books—that is, 

p’=LR’=0. 

10
3.2 Insured-Deposit Substitution 

Banks can also substitute advances for insured deposits. Dollar-for-dollar substitution implies 

ID’= D’= -1, and L’= UD’=0. If, as before, p’=LR’=r’=0, equation (3) reduces to: 

0

1 (1 ) 1 <

>

+

+⎥

⎦

⎤ ⎢

⎣

⎡

+ − + = δ γ δ

r

ID UD

p EQ

dA

dNL (5) 

The bracketed term captures the impact of a change in the insured deposit-assessment base ratio on 

expected BIF losses —the “insured-share” effect. Again, default probability (p), the discount rate (δ) and 

insured-deposit share (γ) are positive. To simplify further, assume the insured-deposit share is less than

one, and the value of equity (EQ) is less than zero.14 In this case, the bracketed term is negative—net 

losses fall because the decline in insured deposits increases the share of losses absorbed by uninsured 

depositors. The unbracketed term represents the change in net losses due to the change in premiums—the 

“assessment-base” effect. Provided the assessment rate (r) is positive, this term is positive: net losses rise 

because advances reduce the assessment base (and, therewith, premiums paid into BIF).15 The overall

sign of equation (5) is ambiguous, depending on the size of the bracketed term (insured-deposit effect) 

and unbracketed term (assessment-base effect). 

Table 4 illustrates the impact of an advances-for-insured-deposits swap on net BIF losses. Panel 

(a) shows Bank B’s balance sheet before and after an economic shock reduces loan value 15 percent—

assuming no advances in the funding mix. Initially, Bank B finances $800 in loans with $600 in insured 

deposits, $100 in uninsured deposits, and $100 in capital. The assessment base for premiums is $700—

the sum of insured and uninsured deposits. The economic shock reduces loan value to $680 and produces

insolvency. Upon closure, the FDIC pays $600 to insured depositors. As representative of these 

depositors, the FDIC receives 85.7 percent ($600 divided by the $700 assessment base) of the $680 

 14. Again, we assume banks are closed only when liabilities exceeds assets. If the bank holds no uninsured deposits, the 

bracketed term is zero, and the unbracketed term (the effect of advances on premiums) determines the sign of equation (5). 

15. For a bank not paying premiums, the bracketed term determines the sign of equation (5). As noted, this term is negative as

long as the funding mix includes uninsured deposits. If the bank does not pay premiums or hold uninsured deposits, 

advances have no impact on expected net losses. 

11
recovery from asset sales, or $583. The FDIC suffers a gross loss of $17—the $600 paid out minus the 

$583 received. Panel (b) illustrates the impact of the same shock when FHLBank advances replace 

insured deposits. The FDIC now pays $450 to insured depositors, sells assets for $680 and disburses

$150 to an FHLBank. The FDIC receives 81.8 percent ($450 divided by the $550 assessment base) of the 

$530 that remains after selling assets and paying the FHLBank, or $434. The gross loss is now $16. The 

gross loss declines $1 in panel (b) because the insured-deposit share is smaller after advances replace

insured deposits. The smaller assessment base, however, implies premiums decline—provided Bank B is

paying any. The impact on net BIF losses depends on whether the decline in premiums is greater or less

than the $1 decline in gross losses. 

3.3 Uninsured-Deposit Substitution 

Banks can replace uninsured deposits with advances. To analyze the impact on BIF, we again let 

p’=LR’=r’=0. Dollar for dollar substitution, implies UD’= D’= -1, and L’= UD’=0. Under these 

assumptions, equation (3) becomes: 

0

1 ( ) 1

>

+

+⎥

⎦

⎤ ⎢

⎣

⎡

+ − + = δ γ δ

r

ID UD

p EQ

dA

dNL

(6)

Now, the bracketed term is positive: the default probability (p), discount rate (δ) and insured-deposit

share (γ) are positive while the equity level (EQ) is negative. In words, replacing uninsured deposits with 

advances increases gross resolution costs because the FDIC can shift losses to uninsured depositors but 

not to FHLBanks. As with insured-deposit substitution, a positive assessment rate makes the unbracketed 

term positive; premiums fall because the assessment base includes uninsured deposits. 

Table 5 provides a clarifying example. Panel (a) shows Bank C’s balance sheet before and after 

an economic shock reduces loan value 15 percent. Initially, the bank finances $800 of loans with $550 of

insured deposits, $150 of uninsured deposits, and $100 of capital. The assessment base is again $700. 

12
The shock reduces loan value to $680 and produces insolvency. Upon closure, the FDIC pays $550 to 

insured depositors. As representative of these depositors, the FDIC receives 78.6 percent ($550/$700) of 

the proceeds from asset sales, or $534. The gross loss is $16. Panel (b) illustrates the impact of the same 

shock after advances replace uninsured deposits. The FDIC now must pay $150 to an FHLBank, leaving

$530 in assets to cover insured-depositor claims of $550. The gross loss ($20) rises $4 because losses 

cannot be shared with uninsured depositors as in panel (a). Because the assessment base declines from

$700 to $550, the net loss to BIF will rise by more than $4 if Bank C is paying premiums. 

3.4 Moral Hazard 

Access to advances creates a moral-hazard problem. When a bank assumes more risk, it must pay a 

higher default premium to uninsured, unsecured creditors. Insured depositors, in contrast, do not demand

extra compensation because the FDIC stands ready to make them whole. The resulting moral hazard is 

well known (Merton, 1977). What is not well known is that FHLBanks, like insured depositors, face no 

credit risk and, consequently, have no incentive to price failure risk. FHLBanks face no credit risk

because of privileges conferred by their GSE status and monopoly position. For example, FHLBanks 

insist on collateralization far in advance of that demanded by other secured creditors (GAO, 2003). 

FHLBanks are also privy to confidential commercial-bank examination reports, so they learn about 

deterioration in a member’s loan portfolio before other creditors. Finally, should a member fail and

collateral prove insufficient, the exposed FHLBank can assert statutory lien priority on other assets—

thereby moving ahead of all unsecured creditors. Because of this protection, no FHLBank has ever lost a 

penny on an advance. It is rational, therefore, for FHLBanks to ignore failure risk when pricing advances. 

13
A moral hazard arises because the FDIC cannot charge actuarially fair premiums to offset the unpriced

failure risk. So banks can take risks with advances, keep the upside, and shift the downside to the FDIC.16

Discipline from other funding markets will not reduce the moral hazard. Uninsured, unsecured 

creditors will still demand higher default premiums as risk increases—even if the bank uses insured 

deposits and FHLBank advances. (Empirical evidence confirms a link between failure probability and 

default premiums on uninsured, unsecured bank debt. For example, see Morgan and Stiroh, 2001;

Flannery, 1998; Flannery and Sorescu, 1996; and Gilbert, 1990). But, as Billett, Garfinkel and O’Neal

(1998) have noted, any discipline is undermined by the availability of funds with no default premium. 

They noted that risky bank holding companies substituted insured deposits for market-priced debt in the

early 1990s to escape market discipline. FHLBank funding provides an easier escape than insured 

deposits. Insured deposits are expensive to raise. Indeed, the relatively inelastic supply of such deposits 

in part explains the popularity of FHLBank funding. In contrast, an FHLBank member can borrow as 

long as eligible collateral is available or can be purchased.17 

To isolate the impact of moral hazard on BIF, we assume deposit levels and assessment rates

remain constant as advances increase, that is ID’=UD’=D’=r’=0. We further assume advances fund loan

growth exclusively (L’=1). Finally, we assume new loans are high risk, that is p’> 0 and LR’> 0. Under

these assumptions, equation (3) reduces to: 

[ ] ( ) δ γ δ +

+ +

+ = 1

' ' 1p LS LR L LR p

dA

dNL

> 0 (7) 

 16. Option-pricing theory provides another way to frame the moral-hazard issue: the value of an FHLBank loan commitment

increases in asset risk and leverage, but the option premium does not change. See Thakor (1982) and Thakor, Hong, and

Greenbaum (1981) for analysis of loan commitments as options. 

17. Some FHLBanks impose total borrowing constraints on members, ranging from 35 to 55 percent of assets (GAO, 2003). 

Only a handful of commercial banks, however, operate near these constraints. Advances do carry an implicit cost—

members hold more pledgeable assets than they otherwise would—but theory and evidence suggest this cost is small. First, 

pledgeable assets decrease liquidity risk. Second, FHLBank members cite loan growth as the principal justification for

borrowing. Funding growth with advances implies the marginal cost—including the opportunity cost of holding more

pledgeable assets—is lower the marginal cost of core deposits. Finally, members report that another important motivation

for borrowing is purchasing long-term mortgage-backed securities. This strategy has been attractive in recent years because 

of the large spread between short- and long-term rates. For more discussion of an FDIC survey of motives for FHLBank

borrowings, see Stark and Spears-Read (2004). 

14
Every term is positive, so the derivative is positive. In words, expected net losses to BIF rise because 

default probability and loss given default increase with advances. 

Table 6 illustrates the impact of moral hazard on net BIF losses. In panel (a), Bank D funds 

$1,000 in loans of normal risk with $700 of insured deposits, $200 of advances, and $100 of capital. 

Suppose, as in the other examples, an economic shock reduces loan value 15 percent and produces 

insolvency. The FDIC uses $850 in assets to settle FHLBank and insured-depositor claims of $900, 

resulting in a gross loss of $50. In panel (b) advances fund high-risk loans, thereby increasing the 

probability of loan loss and decreasing the likely recovery in asset liquidation. So now the same shock 

produces a 20 percent decline in loan value. The FDIC recovers $800 from asset sales, pays $900 to the 

FHLBank and insured depositors, and suffers a gross loss of $100. Moral hazard increases gross losses 

by $50. Net BIF losses also rise by $50 because, by assumption, the assessment rate and base do not 

change. If the bank migrates into a riskier pricing cell before failure, net losses will be less than $50.18 

4. FHLBank Funding and Risk-Taking: Evidence from a CAMELS Downgrade Model 

The comparative-static exercises in the previous section indicate that advances are likely to 

increase expected net losses to BIF. But these exercises say nothing about the economic importance of 

the effect. Gauging this importance involves estimating the impact of advances on default probability and 

loss-given-default. We now turn to the first of these tasks. 

 18. Prior research found a positive correlation between FHLBank funding and risk at troubled thrifts in the late 1980s and early 

1990s (Ashley, Brewer, and Vincent, 1998) and community banks in the 1990s (Stojanovic, Vaughan, and Yeager 2001). 

But conceivably advances could reduce bank risk, despite the moral hazard implied by theory and evidence. FHLBanks

make advances against a broad range of collateral, thereby reducing liquidity risk for their members. In addition, the 

flexible terms make advances a potentially effective tool for hedging interest-rate-risk exposures arising elsewhere on the 

balance sheet. Finally, the FHLBanks provide asset/liability-management (ALM) consulting at minimal cost to help

members make the best use of advances. Community financial institutions, which often lack the expertise necessary to

employ interest-rate caps or floors, particularly value FHLBank hedging products and ALM consulting. So, ultimately, the 

net effect of FHLBank advances on bank risk is an empirical issue. Empirically “signing” the net effect, which we do in 

section 4, is crucial because advances could, in theory, induce a fall in default probability large enough to offset the rise in 

loss-given-default, thereby leaving expected losses unchanged. 

15
To test for moral hazard, we add the advances-to-assets ratio to a probit model currently used by 

one Federal Reserve Bank in off-site surveillance (Gilbert, Meyer, Vaughan, 2002). This model estimates 

the likelihood a healthy bank (CAMELS 1 or 2 composite rating) will encounter financial distress

(CAMELS 3, 4, or 5 composite rating) in the coming year. We rely on a downgrade-prediction model

because failures have been too infrequent since 1991 to permit re-specification and re-estimation of a

failure-prediction model. This approach does not produce an exact estimate of “p” (and the relationship 

between advances and default probability) as defined in the previous section, but it still offers clues about 

the importance of moral hazard—in large part because the correlation between downgrade and failure

probability since 1988 exceeds 90 percent (Hall, King, Meyer, and Vaughan, 2005). Table 7 describes 

the explanatory variables and the expected relationship between each variable and downgrade risk. Most 

variables are financial ratios related to leverage, credit, and liquidity risk—three risks consistently found

to produce financial distress in commercial banks (Cole, Cornyn, and Gunther, 1995; Putnam, 1983). 

We estimate the downgrade-prediction model yearly, beginning in 1992—just after banks started 

joining the FHLBank System—and ending in 2003. In each equation, downgrade status (“1” = bank 

examined and downgraded, “0” = bank examined but not downgraded) in year t+1 is regressed on 

accounting and supervisory data for non-de-novo, 1- or 2-rated banks in the fourth quarter of year t. In 

the first regression, for example, downgrade status in 1993 is regressed on year-end 1992 data. We

proceed with this convention, estimating equations year by year, through a regression of downgrade status 

in 2003 on 2002:Q4 data. We run the model year-by-year because pooling constrains coefficients to be 

equal across years, and previous research has demonstrated considerable time variance. For

completeness, however, we also offer evidence from a pooled approach. Table 8 contains the results.19

 19. The regression analysis was conducted with financial data from the Call Reports and CAMELS data from the National 

Information Center (a confidential supervisory database). The Federal Housing Finance Board—the safety-and-soundness 

supervisor of the FHLBanks—supplied bank-level data on advances for 1992-2000. In 2001, commercial banks began

reporting advances to their principal supervisors, so the resolution-cost analysis in section 5 draws on Call-Report data. The

advances-to-assets ratio was not a candidate variable in the specification search for the CAMELS-downgrade model because 

the Call Report did not include advances at that time. 

16
The evidence suggests FHLBank advances have a modest impact on bank risk. The advances-toassets ratio is positive and significant in nine of the eleven year-by-year regressions. And the statistically

insignificant regressions, 1993 downgrades on 1992:Q4 financials and 1996 downgrades on 1995:Q4 

financials, correspond to early years in the sample when few banks used advances on a large scale. In the 

pooled regression, the advances-to-assets ratio is positive and significant at the one-percent level.

Analysis of standardized coefficients indicates that the impact of advances on downgrade risk is small

compared with the credit-risk variables but comparable to the impact of the liquidity-risk variables. 

But there are reasons to believe FHLBank funding has increased BIF exposure by more than the 

coefficients suggest. Banks enjoyed record earnings in the 1990s. And the brief recession of 2001 did

not produce a significant uptick in charge offs. As a consequence, capital protection has risen to robust 

levels unseen since the 1940s—and higher levels of capital put bank owners at greater risk of loss, which

in turn impels them to constrain risk-taking. When capital ratios mean revert and bank owners have less 

money at stake, advances may well have a larger impact on downgrade probability.20

 20. Adding FHLBank funding to the downgrade model raises a causality issue: risk could be driving advances instead of

advances driving risk (as we argue). Put another way, it is possible that banks already dedicated to ramping up risk simply

opted to fund with advances. Conceptually, the distinction does not matter for this analysis. Absent the availability of

advances, risk-loving banks would have been deterred by the high default premium on market funding or the high marketing 

costs on insured deposits. Because of the potential for coefficient bias, however, we tested for adverse selection, looking for 

evidence that risky banks were more likely to join the FHLBank System. Specifically, we regressed membership status in

1992 on risk variables in 1989. We also estimated time-to-FHLB-membership between 1992 and 2000 using a Cox 

proportional-hazards model. These exercises produced no evidence of adverse selection. 

17
History affords an interesting parallel. Grossman (1992) noted that fixed-rate deposit insurance 

did not induce significant moral hazard in the thrift industry in the early years of the program. The 

financial crisis of the 1930s eliminated all but the most well-capitalized and well-managed thrifts. In 

addition, the Federal Savings and Loan Insurance Corporation (FSLIC) conducted rigorous safety-andsoundness examinations of all applicants for deposit insurance. Finally, the experience of the 1930s 

convinced owners of all depository institutions to hold more securities (relative to loans) to safeguard 

against another wave of contagious runs (Friedman and Schwartz, 1963). These factors combined to keep 

failure risk low. Only after interest-rate shocks (White, 1991) and financial deregulation in the 1970s and 

1980s (Keeley, 1990) did capital levels fall low enough to tempt thrift owners to take large risks. Turning 

to the banking parallels, FIRREA opened FHLBank membership to commercial banks in 1989—but only 

safe-and-sound institutions were eligible to join. Meanwhile, the banking crisis of the late 1980s and 

early 1990s weeded out institutions with low capital and poor management. And bad experiences with 

commercial-real-estate loans during that crisis gave bank owners a more conservative attitude toward

credit risk. Finally, unexpectedly robust economic expansion in the 1990s enabled banks to accumulate

record levels of capital. These factors have combined to hold risk-taking in check. But a shock to capital 

could induce bankers to exploit the risk subsidy associated with FHLBank funding just as capital shocks

induced excessive risk-taking in the thrift industry. 

5. FHLBank Funding and Loss-Given-Default: Evidence from FDIC Resolution Costs 

To obtain a range of possible BIF losses, we examine two scenarios: 

High-Risk Scenario: The FDIC liquidates all banks with failure probabilities above 

two percent. 

Heavy-User Scenario: The FDIC liquidates all banks with advances-to-asset ratios

above 15 percent. 

18
Failure probabilities for the high-risk scenario come from the SEER Risk-Rank model—one of two early

warning models used by the Board of Governors of the Federal Reserve for off-site monitoring.21 The

two-percent cutoff reflects the threshold used to compile watch lists of institutions most at risk of failure. 

At year-end 2003, 334 banks (or 4.33 percent of U.S. banks) qualified as high risk. These banks are 

likely to be small and unlikely to be FHLBank members, so we also analyze a heavy-user scenario.

(Table 9 provides size distributions for all U.S. banks, high-risk banks, and heavy-user banks.) The 15-

percent cutoff reflects the results of an informal poll of bank supervisors at the Federal Reserve and FDIC

about unsafe-and-unsound dependence on advances. At year-end 2003, 367 institutions (or 4.76 percent

of U.S. banks) qualified as heavy users. Examining scenarios with over 300 liquidations might, at first 

glance, seem extreme. But annual failures reached this level three times during the bank and thrift crisis 

of the late 1980s and early 1990s. Also, the analysis could be viewed as suggestive of cumulative 

resolution costs over a longer time span—as implied in equation (1)—given a small discount rate.22 

We estimate incremental resolution costs using year-end 2003 balance sheets. We start by

computing baseline resolution costs for each scenario. The high-risk baseline, for example, is the

estimated cost of liquidating all banks with failure probabilities above two percent, given their 2003:Q4 

financials. Next, for each scenario, we calculate the cost of liquidating all sample banks at year-end 2003, 

assuming counterfactually that their balance sheets contained no FHLBank advances. Counterfactual

costs for the high-risk scenario, for example, were obtained by removing advances from all 334 banks, 

making offsetting balance-sheet adjustments, and recalculating resolution costs. The difference between

baseline and counterfactual resolution costs is the “increase” in BIF losses from advances. We compute 

 21. The SEER Risk-Rank model was estimated on 1985-91 data. As noted, the dearth of post-1991 failures precludes respecification and re-estimation and explains the use of a downgrade probability model in section 4. Every quarter, the 

Surveillance Section at the Board of Governors feeds the latest Call Report data into the Risk-Rank model to generate

failure probabilities for all U.S. banks. All Fed-supervised institutions with failure probabilities above two percent are 

tagged for on-site or off-site follow up. 

22. This approach may seem odd because we assessed the impact of advances on default probability with an econometric model. 

Indeed, the logical way to proceed would be to estimate a model with loss-given-failure on the left-hand side and advance 

dependence on the right-hand side. Unforturnately, failures since 1990 have been too rare to permit estimation of such a

model. For this reason, we were forced to estimate default probability with a CAMELS-downgrade model. 

19
counterfactual costs by assuming advances could have been used to (1) replace insured deposits, (2) 

replace uninsured deposits, (3) book real-estate loans, (4) book consumer loans, (5) book commercial

loans, or (6) purchase investment securities. Losses differ across the six counterfactuals because 

liquidation-loss rates vary across asset categories. Also, the FDIC collects premiums from all domestic

depositors but protects only insured depositors.23 

To estimate resolution costs, we use average FDIC loss rates on consumer loans, commercial

loans, investment securities, real-estate loans, “other real estate owned” and “other assets” between 1990 

and 2002.24 Table 10 contains these loss rates. For all liquidations, we subtract estimated losses from the

book value of assets to obtain the net value available for distribution to the creditors of the receivership. 

Next, we deduct total claims on the receivership from assets available for distribution to arrive at losses

on the receivership. These losses are then distributed across claimants according to priority.25 We

assume secured and preferred claims are paid in full; that is, the value of collateral equals the value of the

claim. For advances, the FDIC prepays face value to the FHLBank to get control of pledged assets. 

Table 11 presents resolution-cost estimates from the high-risk scenario. In the baseline case—

liquidation of all banks with failure probabilities above two percent—gross BIF losses equal $3.66 

billion, or 6.11 percent of assets in all 334 institutions. Incremental costs from advances range from 

$388.9 million in the commercial-loan counterfactual (that is, BIF losses would have been smaller if highrisk banks had not funded commercial loans with advances) to -$34.5 million in the insured-deposit

counterfactual (that is, BIF losses would have been larger if high-risk banks had not replaced insured

deposits with advances). On average over the six counterfactuals, advances “increase” BIF losses by

 23. In theory, banks could use advances to purchase any asset or reduce any liability. We focus on the six most likely 

strategies—as revealed through a recent FDIC survey (Stark and Spears-Reed, 2004) and an analysis of balance-sheet trends

among new FHLBank members (Stojanovic, Vaughan, and Yeager, 2001). 

24. Previous studies assumed the historical ratio of fund losses to fund balances will obtain in the future. Our approach is more

granular. We apply 1990-2002 loss rates on specific assets to the year-end 2003 asset balances of high-risk and heavy-user

banks. See Bennett (2003) for more details on the calculation of loss rates. 

25. Since the Omnibus Budget Reconciliation Act in 1993, the FDIC has paid claimants in the following order: administrative 

costs of the receivership, secured creditors (including FHLBank advances), domestic depositors, general creditors (including 

foreign depositors), subordinated creditors and stockholders. 

20
$145.9 million—a figure equal to 3.99 percent of the baseline. Insured deposits tend to stay with a bank 

through different economic and interest-rate environments, so another, perhaps more realistic, way to 

gauge incremental losses is to focus on the other five counterfactuals.26 In this case, advances increase

average losses by $182.0 million—a figure equal to 4.97 percent of the baseline. Comparing these 

estimates to BIF offers insight into economic significance. By this metric the impact of FHLBank

funding is modest; incremental costs represent well under one percent of year-end 2003 Fund value. 

But, as noted, looking only at high-risk banks potentially underestimates BIF exposure. High-risk

banks depend less on FHLBank funding than the banking sector as a whole—because these banks tend to 

be small in operating scale, flush with insured deposits, and short of pledgeable assets. To obtain another 

perspective, we calculate resolution costs from liquidation of all banks with advance-to-asset ratios above

15 percent. Table 12 presents the results of these calculations. In the baseline case, resolution costs come

to $15.36 billion, or 5.92 percent of assets in the 367 heaviest users of FHLBank funding. Incremental 

costs from advances vary from $8.72 billion under the real-estate-loan counterfactual (that is, BIF losses 

would have been lower had heavy-user banks not funded real-estate loans with advances) to -$2.07

million under the insured-deposit counterfactual (that is, BIF losses would have been higher had heavyuser banks not replaced insured-deposits with advances). On average across all six counterfactuals, 

advances increase BIF losses by $3.07 billion—a figure equal to nearly 20 percent of the baseline. If the 

insured-deposit counterfactual is ignored, advances raise average BIF losses by $4.1 billion—almost 27

percent of the baseline. These estimates imply significant BIF exposure to FHLBank funding;

incremental resolution costs from advances represent 9 to 12 percent of year-end 2003 Fund value. 

To check robustness, we created “random” samples of banks matched in size to the high-risk and 

heavy-user banks and then re-calculated incremental resolution costs. As expected, the new estimates fell 

in the range defined by the two scenarios. The sample of 334 banks comparable in size to the high-risk 

 26. A recent FDIC survey indicates that only four percent of the sampled banks used advances to replace core deposits. For 

more details, see Stark and Spears-Reed (2004). 

21
banks, for example, yielded advance-induced increases in average resolution costs equal to 6.99 percent 

of the baseline, or 0.61 percent of year-end 2003 BIF. Ignoring the insured-deposit counterfactual 

produced incremental resolution costs equal to 8.82 percent of the baseline, or 0.77 percent of BIF. For

367 randomly selected banks of comparable size to the heavy users, advances raised average resolution

costs by 7.11 percent of the baseline, or 2.5 percent of BIF. Averaging over every counterfactual except

the insured-deposit counterfactual produced incremental resolutions costs equal to 9 percent of the 

baseline, or 3.17 percent of year-end 2003 BIF. 

We also used a more complicated method of excising advances from sample-bank balance sheets.

In prior calculations, we removed advances up to the amount available in an asset category. In some 

cases, consumer loans, for example, advances remained on the balance sheet because volume was low. In

the more complicated method, we continued excising advances after a specific asset category was

exhausted by moving to other assets, from highest to lowest loss rate. So, for example, once consumer

loans were exhausted, we removed “other real estate owned” (OREO), then commercial loans, and so on 

until advances equaled zero. We also computed incremental resolution costs when assets were removed

in reverse order (that is, lowest-to-highest loss rate). The new approach did not materially change cost 

estimates for the high-risk sample. For example, under lowest-to-highest-loss removal, advances 

increased average resolution costs by 5.72 percent of the baseline (0.62 percent of BIF). The highest-tolowest-loss-rate method raised average costs 6.31 percent from the baseline estimate (0.68 percent of

BIF). The new approach did, however, inflate incremental resolution costs for the heavy-user sample.

Under lowest-to-highest loss removal, advances increased average resolution costs by 39.57 percent of 

the baseline (17.99 percent of BIF); removing assets in reverse order produced a jump of 42.08 percent

(19.13 percent of BIF). Removing assets from highest-to-lowest loss and ignoring the insured-deposit

counterfactual produced staggering estimates of incremental resolution costs—advances increased BIF

losses by an average of 55.97 percent, or 25.4 percent of BIF. 

22
Our estimates of resolution costs suggest that FDIC exposure from advances ranges from modest

to large, depending on the assumptions. But these figures may still understate potential losses. As noted, 

U.S. banks currently hold record levels of capital, and capital constitutes the “deductible” for deposit 

insurance. High levels reduce expected BIF losses by deterring risk-taking and absorbing losses from any

given level of risk-taking. Reversion of capital ratios to historical norms would increase both default 

probability and loss-given default. 

A second reason to view the estimates as conservative is our assumption that banks migrate to

failure without altering balance-sheet composition. In actuality, banks shrink, often shedding those assets

and liabilities most likely to reduce liquidation costs. In the late 1980s and early 1990s, for example, 

much of the contraction in thrift liabilities prior to failure came from uninsured deposits (Goldberg and 

Hudgins, 2002); holders of these deposits would be forced to bear a large portion of any resolutions costs. 

On the asset side, much of the contraction came from loans and securities with deep secondary markets 

(such as residential mortgages and Treasury or Agency debt), thereby leaving assets likely to lose 

significant value in liquidation. 

A third reason the analysis may understate the problem is our neglect of FHLBank pre-payment

penalties. Closing an institution requires pre-payment of outstanding advances, and all FHLBanks assess

a penalty based on interest foregone from making new advances at lower rates and the cost of unwinding 

associated interest-rate hedges. This sum can be large for long-term advances in a falling rate

environment. (And nearly 50 percent of advances outstanding to banks at year-end 2003 had maturities 

over three years.) Moreover, some FHLBanks apply additional charges. The FDIC as receiver must pay

these penalties to get control of pledged assets for liquidation. The recent resolution of Bank of Alamo 

(Alamo, Tennessee) illustrates the potential size of the expense. To assume control of the $69 millionasset bank, which failed in November 2002, the FDIC had to pre-pay $6.4 million of advances from the 

Federal Home Loan Bank of Cincinnati. The prepayment penalties came to $906,000—14 percent of the 

advances outstanding (Bair, 2003; Blackwell, 2003). 

23
A final reason the cost estimates may be low is that historical asset-recovery rates could prove

weak guides to future resolution costs. The overwhelming majority of banks that fail are small, with 

simple on- and off-balance-sheet positions. Our estimation of aggregate resolution costs in the high-risk 

and heavy-user scenarios reflects 1990-2002 loss rates on specific assets. The FDIC has not resolved a 

large-bank failure since Continental Illinois in the 1980s, and in the interim large banks have grown

considerably more complex. Resolution costs could prove to be orders of magnitude higher than

application of historical loss rates suggest, particularly if concerns about systemic risk outweigh concerns 

about FDIC exposure. By volume of advances outstanding, large banks are the FHLBank System’s best 

customers. At year-end 2003, the 424 U.S. banks with more than $1 billion in assets held 78.5 percent of 

outstanding advances. The position of the FHLBanks as senior secured creditors means the FDIC will

absorb the extra costs arising from resolution of a large, complex bank. 

In any event, it is important to remember that prior to FIRREA expected BIF losses due to 

FHLBank advances were zero. Whether incremental losses over time turn out to be nearer the modest

numbers suggested by the high-risk scenario or the large numbers suggested by the heavy-user scenario, 

the increase in exposure is not trivial and has not been priced. 

6. Conclusion 

Does growing commercial-bank reliance on Federal Home Loan Bank (FHLBank) funding

increase risk to BIF? We argued this research question is important because of its implications for

academic studies of deposit-insurance premiums and policy debates over deposit-insurance reform. We

presented a model showing advances increase expected BIF losses by subsidizing risk-taking and 

subordinating the FDIC’s position in failure resolutions. To quantify the exposure, we examined the 

effect of advances on default probability using a CAMELS-downgrade model. We then assessed the 

impact on loss-given-default by calculating incremental resolution costs under two scenarios: liquidation

24
of all banks with failure probabilities above two percent and liquidation all banks with advance-to-asset 

ratios above 15 percent. The evidence suggests advances have increased expected losses by a non-trivial

magnitude. We noted our research strategy may understate the problem because we estimated downgrade

probabilities and resolution costs in a high-capital environment, ignored penalties arising from prepayment of advances, and overlooked complications associated with the resolution of large-bank failures. 

The policy implication is that the FDIC should charge for increases in expected losses arising 

from advances. Of course, the ideal solution would involve explicitly linking premiums with FHLBank

borrowings. A step toward this ideal would be vitiation of pre-payment penalties in failure resolutions. 

At the margin, this reform would strengthen incentives for FHLBanks to monitor the condition of their 

members, thereby reducing moral hazard. But these reforms require legislative action. In contrast, 

supervisors have discretion to levy capital charges to preserve safety and soundness without legislative 

sanction. A capital charge on heavy use of advances would protect BIF by increasing the deductible for

deposit insurance. A higher deductible would curb appetites for risk and trim resolution costs by shifting 

losses to bank owners. In any event, the first step toward a solution is for scholars and policymakers to

recognize that a problem exists. Theory and evidence presented here indicate that failure to price 

expected losses from FHLBank funding is a problem. 

25
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Putnam, Barron H. “Early Warning Systems and Financial Analysis in Bank Monitoring: Concepts of 

Financial Monitoring.” Federal Reserve Bank of Atlanta Economic Review (November 1983): 6-

13. 

Shibut, Lynn. “Should Bank Liability Structure Influence Deposit Insurance Pricing?” Working Paper

2002-01, FDIC, January 2002. 

Stark, William A. and Darlene Spears-Reed. “Federal Home Loan Bank Advances: A Supervisory

Perspective.” Supervisory Insights, Federal Deposit Insurance Corporation, June 25, 2004. 

Stojanovic, Dusan; Mark D. Vaughan; and Timothy J. Yeager. “Do Federal Home Loan Bank 

Membership and Advances Lead to More Community-Bank Risk-Taking?” In the Proceedings 

of 37th Annual Conference on Bank Structure and Competition, Federal Reserve Bank of 

Chicago, May 2001. 

28
Thakor, Anjan. “Toward a Theory of Bank Loan Commitments.” Journal of Banking and Finance 6 

(1982): 55-83.

Thakor, Anjan; Hai Hong; and Stuart I. Greenbaum. “Bank Loan Commitments and Interest Rate

Volatility.” Journal of Banking and Finance 5 (1981): 497-510. 

United States General Accounting Office. Federal Home Loan Bank System: Key Loan Pricing Terms 

Can Differ Significantly. GAO-03-973, September 2003. 

White, Lawrence J. The S&L Debacle: Public Policy Lessons for Bank and Thrift Regulation. New

York: Oxford, 1991. 

29
Table 1 

How fast has secured funding grown at U.S. commercial banks? 

FHLBank advances accounted for 47 percent of the dollar increase in secured funding at U.S. banks between 1992

and 2003. Unlike other secured liabilities, the supply of advances is highly elastic. Because of its extraordinary

credit-risk record and implicit Treasury guarantee, the FHLBank System can easily sell debt in world capital 

markets. And the Financial Modernization Act of 1999 significantly relaxed membership and collateral

requirements, thereby expanding commercial-bank access to advances. Between 1992 and 2003, the number of 

banks in the FHLBank System climbed from 1,284 to 5,946, and advances outstanding to banks jumped from $6 to

$240.8 billion. 

1992 2003

Secured Liabilities Billions 

($) 

Percent of 

Total Assets 

Billions 

($) 

Percent of 

Total Assets 

Average 

Annual 

Growth Rate 

(percent) 

Public deposits 47.1 1.3 73.5 1.0 4.1 

FHLBank advances 6.0 0.2 240.8 3.2 33.6 

Repurchase agreements 88.2 2.5 322.5 4.3 11.8 

Discount window loans 0.2 0.0 0.1 0.0 -4.1 

All secured liabilities 141.5 4.0 636.9 8.5 13.7

Total assets 3,496.2 7,469.5 6.9 

Assessable deposits 2,691.4 4,942.2 5.5 

Source: Reports of Condition and Income, all U.S. commercial banks, 1992 and 2003 

Consolidated Financial Statements of the FHLBanks, Federal Housing Finance Board, 1992 and 2003 

30
Table 2 

How does the FDIC price deposit insurance? 

This table contains the matrix used to assess Bank Insurance Fund (BIF) premiums for the first semiannual

assessment period of 2005. It also shows the number and percentage of banks in each risk cell as of July 1, 2004.

Assessment rates are expressed in basis points (i.e., cents per $100 of assessable deposits) per year. Most estimates 

of actuarially fair premiums imply risk spreads far larger than 27 basis points. Moreover, the pricing matrix does 

not consider differences in resolution costs arising from differences in bank funding. 

BIF Supervisory Subgroups

 A B C 

Well Capitalized 

Assessment Rate (basis points) 0 3 17

Number of Institutions 7,343 (92.6%) 421 (5.3%) 84 (1.1%)

Adequately Capitalized 

Assessment Rate (basis points) 3 10 24

Number of Institutions 60 (0.8%) 4 (0.1%) 10 (0.1%)

Undercapitalized 

Assessment Rate (basis points) 10 24 27

Number of Institutions 2 (0.0%) 0 (0.0%) 2 (0.0%)

Estimated Annual Assessment Revenue $84 million 

Assessment Base $4,334 billion

Average Annual Assessment Rate (basis points) 0.19

Source: Murton (2004)

NOTES: 

Capital Group Descriptions:

Group 1 – “Well Capitalized” 

• Total Risk-Based Capital Ratio equal to or greater than 10 percent, 

• and Tier 1 Risk-Based Capital Ratio equal to or greater than 6 percent,

• and Tier 1 Leverage Capital Ratio equal to or greater than 5 percent. 

Group 2 – “Adequately Capitalized” 

• Not Well Capitalized and Total Risk-Based Capital Ratio equal to or greater than 8 percent, 

• and Tier 1 Risk-Based Capital Ratio equal to or greater than 4 percent,

• and Tier 1 Leverage Capital Ratio equal to or greater than 4 percent. 

Group 3 – “Undercapitalized” 

• Neither Well Capitalized nor Adequately Capitalized. 

Supervisory Group Descriptions: 

Subgroup A: This subgroup consists of financially sound institutions with only a few minor weaknesses and

generally corresponds to the primary federal regulator's CAMELS composite rating of “1” or “2.” 

Subgroup B: This subgroup consists of institutions that demonstrate weaknesses that, if not corrected, could

result in significant deterioration of the institution and increased risk of loss to the BIF. This subgroup

assignment generally corresponds to the primary federal regulator's CAMELS composite rating of “3.”

Subgroup C: This subgroup consists of institutions that pose a substantial probability of loss to the BIF unless

effective corrective action is taken. This subgroup assignment generally corresponds to the primary federal

regulator's CAMELS composite rating of “4” or “5.”

31
Table 3 

How does FHLBank funding affect net BIF losses? 

Case One: The Bank Uses Advances to Grow 

Comparative-static analysis indicates that advance-funded asset growth increases expected net losses to the Bank 

Insurance Fund (BIF). This table illustrates with a simple numerical example. Initially, Bank A funds $800 in loans 

with $700 in insured deposits and $100 in capital [panel (a)]. Now, an economic shock reduces loan value by 15

percent and renders the bank insolvent. The FDIC pays insured depositors $700, sells bank assets for $680, and 

suffers a loss of $20. Suppose Bank A had used $200 in FHLBank advances to book new loans before the shock

occurred [panel (b)]. Once again, let loan value decline 15 percent. Supervisors close the insolvent bank, and the 

FDIC pays depositors $700. The FDIC must now also pay $200 to an FHLBank because advances are fully secured. 

The $850 recovery from asset sales is insufficient to cover the $900 payout, leaving a gross loss of $50 ($30 higher

than in the no-advance case). Net BIF losses also increase by $30 because premiums do not increase with advances. 

Panel (a) Panel (b) 

Bank A’s Balance Sheet Bank A’s Balance Sheet

Before and After Shock, with No Advances Before and After Shock, with Advances 

Assets Liabilities Assets Liabilities

Loans $800 $680 Insured

Deposits $700 Loans $1000 $850 InsuredDeposits $700

FHLBank 

Advances 

0 FHLBank 

Advances 

$200

Capital $100 $-20 Capital $100 $-50

Gross BIF Loss: $20 Gross BIF Loss: $50

Assessment Base: $700 Assessment Base: $700 

32
Table 4 

How does FHLBank funding affect net BIF losses? 

Case Two: The Bank Replaces Insured Deposits with Advances 

Comparative-static analysis indicates that expected net losses to the Bank Insurance Fund (BIF) could rise or fall 

when advances replace insured deposits. Panel (a) shows Bank B’s balance sheet before and after an economic 

shock reduces loan value 15 percent—assuming no advances in the funding mix. Initially, the bank finances $800 in

loans with $600 in insured deposits, $100 in uninsured deposits, and $100 in capital. The assessment base is $700—

the sum of insured and uninsured deposits. The economic shock reduces loan value to $680 and produces

insolvency. Upon closure, the FDIC pays $600 to insured depositors. As representative of these depositors, the

FDIC receives 85.7 percent ($600 divided by the $700 assessment base) of the $680 recovery from asset sales, or 

$583 (rounded). The gross BIF loss is $17. Panel (b) illustrates the impact of the same shock when advances

replace insured deposits. At liquidation, the FDIC pays $450 to insured depositors, sells assets for $680, and 

disburses $150 to an FHLBank. The FDIC receives 81.8 percent ($450 divided by the $550 assessment base) of the

$530 that remains after selling assets and paying the FHLBank, or $434 (rounded). The gross loss is $16—the $450

paid to insured depositors minus the $434 net proceeds from asset sales. The gross loss falls by $1 because the

insured-deposit share is smaller after advances replace insured deposits (81.8 percent versus 85.7 percent). The

smaller assessment base, however, implies premiums decline. The impact on net BIF losses will depend on whether 

the decline in premiums is greater or less than the $1 decline in gross losses. 

Panel (a) Panel (b) 

Bank B’s Balance Sheet Bank B’s Balance Sheet

Before and After Shock, with No Advances Before and After Shock, with Advances 

Assets Liabilities Assets Liabilities

Loans $800 $680 Insured

Deposits $600 Loans $800 $680 InsuredDeposits $450

Uninsured 

Deposits $100 Uninsured 

Deposits $100

FHLBank 

Advances 

$0 FHLBank 

Advances 

$150

Capital $100 $-20 Capital $100 $-20 

Gross BIF Loss: $17 Gross BIF Loss: $16 

Assessment Base: $700 Assessment Base: $550 

33
Table 5 

How does FHLBank funding affect net BIF losses? 

Case Three: The Bank Replaces Uninsured Deposits with Advances 

Comparative-static analysis indicates that expected net losses to the Bank Insurance Fund (BIF) rise when advances 

replace uninsured deposits. Panel (a) shows Bank C’s balance sheet before and after a shock forces liquidation by

reducing loan value 15 percent. Upon closure, the FDIC pays $550 to insured depositors. As representative of these 

depositors, the FDIC receives 78.6 percent ($550/$700) of the proceeds from asset sales, or $534 (rounded). The 

gross BIF loss is $16. Panel (b) shows the impact of the same shock when Bank C substitutes FHLBank advances 

for uninsured deposits. The FDIC must now pay $150 to an FHLBank, leaving $530 in assets to cover depositor 

claims of $550. The gross loss is now $20—$4 larger because losses cannot be shared with uninsured depositors as 

in panel (a). Because the assessment base declines, net BIF losses will exceed $4 if Bank C is paying premiums. 

Panel (a) Panel (b) 

Bank C’s Balance Sheet Bank C’s Balance Sheet

Before and After Shock, with No Advances Before and After Shock, with Advances 

Assets Liabilities Assets Liabilities

Loans $800 $680 Insured

Deposits $550 Loans $800 $680 InsuredDeposits $550

Uninsured 

Deposits $150 Uninsured Deposits $0 

FHLBank 

Advances 

$0 FHLBank 

Advances 

$150

Capital $100 $-20 Capital $100 $-20

Gross BIF Loss: $16 Gross BIF Loss: $20

Assessment Base: $700 Assessment Base: $550 

 

34
Table 6 

How does FHLBank funding affect net BIF losses? 

Case Four: The Bank Uses Advances to Make High-Risk Loans (Moral Hazard) 

Comparative-static analysis indicates that the moral hazard associated with FHLBank funding increases expected net 

losses to the Bank Insurance Fund (BIF). Panel (a) shows the initial balance sheet for Bank D, assuming $200 in

advances funds loans of normal risk. Suppose, as before, an economic shock reduces loan value 15 percent and

produces insolvency. The FDIC uses $850 in assets to settle combined FHLBank and insured-depositor claims of

$900, resulting in a gross loss of $50. In panel (b) advances fund high-risk loans, thereby increasing the probability 

of loan loss and decreasing the likely recovery in asset liquidation. So the same shock now produces a 20 percent

decline in loan value. The FDIC recovers $800 from asset sales, pays $900 to the FHLBank and insured depositors, 

and suffers a gross loss of $100. Moral hazard increases the gross BIF loss by $50. Net losses will rise by 

somewhat less than $50 if Bank D migrates into a riskier pricing cell before failing. 

Panel (a) Panel (b) 

Bank D’s Balance Sheet Bank D’s Balance Sheet

Before and after Shock, Advances Induce No Moral Hazard Before and after Shock, Advances Induce No Moral Hazard 

Assets Liabilities Assets Liabilities

Loans $1000 $850 Insured

Deposits $700 Loans $1000 $800 InsuredDeposits $700

FHLBank 

Advances 

$200 FHLBank 

Advances 

$200

Capital $100 $-50 Capital $100 $-100

Gross BIF Loss: $50 Gross BIF Loss: $100

Assessment Base: $700 Assessment Base: $700 

35
Table 7 

Which factors predict financial distress in commercial banks? 

This table lists the independent variables in the CAMELS downgrade-prediction model. Gilbert, Meyer, and

Vaughan (2002) developed this model before the Call Report required data on advances, so FHLBank dependence 

was not a candidate in the specification search. We test for moral hazard by adding the advances-to-assets ratio. 

Signs note the expected relationship between each variable and the likelihood of a downgrade from satisfactory 

status (a CAMELS 1 or 2 composite rating) to unsatisfactory status (a CAMELS 3, 4, or 5 rating) in the next year.

For example, the negative sign for the net-worth ratio indicates that, other things equal, higher capital reduces 

downgrade probability. Note: the relationship between asset size and downgrade probability is theoretically

ambiguous. Opportunities to reduce risk by diversifying across product lines and geographic regions increase with

operating scale. But, as Demsetz and Strahan (1997) have noted, product and geographic diversification relax a

constraint, permitting assumption of more risk. 

Independent Variables Symbol 

Expected

Impact on 

Downgrade 

Probability 

Loans past due 30-89 days as a 

percentage of total assets PAST-DUE-30 + 

Loans past due 90+ days as a 

percentage of total assets PAST-DUE-90 + 

Non-accrual loans as a percentage of total assets NONACCRUING + 

Other real estate owned as a 

percentage of total assets OREO + 

Commercial and industrial loans as a 

percentage of total assets 

Credit Risk Variables 

COMMERCIAL + 

Residential real estate loans as a 

percentage of total assets RESIDENTIAL – 

Total net worth (equity capital minus goodwill) as a 

percentage of total assets NET-WORTH – 

Leverage

Risk 

Variables 

Net income as a percentage of average assets ROA – 

Book value of investment securities as a 

percentage of total assets SECURITIES – 

Liquidity 

Risk 

Variables 

Deposits > $100M (jumbo CDs) as a 

percentage of total assets JUMBO CDs + 

Natural logarithm of total assets, 

in thousands of dollars SIZE ? 

Dummy variable if the bank’s 

CAMELS rating is 2 CAMELS-2 + 

Gilbert, Meyer, and Vaughan Variables 

Control Variables 

Dummy variable if the bank’s management rating is

weaker than its composite CAMELS rating BAD-MANAGE + 

FHLBank 

Variable Advances as a percentage of total assets ADVANCES + 

36
Table 8 

Does dependence on FHLB funding make financial distress more likely? 

This table presents the results of probit regressions of downgrade status on financial ratios and control variables. In

each equation, downgrade status (“1” = bank examined and downgraded from a CAMELS one or two composite

rating to a three, four or five; “0” = bank examined, but not downgraded) in year t+1 is regressed on accounting and

supervisory data for non-de-novo, 1- and 2-rated banks at year-end t. Standard errors appear in parentheses below

the coefficients. One asterisk denotes statistical significance at the 10-percent level, two asterisks at the five-percent

level, and three at the one-percent level. FHLBank advances increase downgrade probability—though coefficient 

magnitudes suggest the impact is modest. 

Independent Variable Pooled 

1992-2003 

1993

Downgrades 

(on 1992:Q4

Financials) 

1994

Downgrades 

(on 1993:Q4

Financials) 

1995

Downgrades 

(on 1994:Q4

Financials) 

1996

Downgrades 

(on 1995:Q4

Financials) 

1997

Downgrades 

(on 1996:Q4

Financials) 

Intercept -1.166***

(0.134) 

0.270 

(0.458) 

-1.308***

 (0.454) 

-0.062

(0.583) 

0.306 

(0.541) 

-0.846

(0.560) 

PAST-DUE-30 

0.165***

(0.011) 

0.204***

(0.038) 

0.129***

 (0.039) 

0.180***

(0.046) 

0.195***

(0.042) 

0.084** 

(0.038) 

PAST-DUE-90 

0.233***

(0.020) 

0.258***

(0.068) 

0.220***

 (0.071) 

0.259***

(0.081) 

0.259***

(0.080) 

0.386***

(0.067) 

NONACCRUING 

0.235***

(0.015) 

0.202***

(0.052) 

0.209***

 (0.051) 

0.214***

(0.061) 

0.192***

(0.056) 

0.299***

(0.055) 

OREO 

0.052** 

(0.023) 

0.129***

(0.047) 

 0.169*** 

 (0.050) 

0.227***

(0.060) 

0.069 

(0.072) 

0.221***

(0.083) 

COMMERCIAL 

0.007***

(0.002) 

0.008 

(0.005) 

 0.008 

 (0.006) 

0.013*

(0.007) 

0.012** 

(0.006) 

-0.003

(0.006) 

Credit Risk 

RESIDENTIAL -0.004***

(0.001) -0.005

(0.004) 

 0.002 

 (0.004) 

-0.017***

(0.005) 

-0.011**

(0.005) 

-0.004

(0.004) 

NET-WORTH -0.050***

(0.004) -0.133***

(0.018) 

 -0.021 

 (0.015) 

-0.058***

(0.017) 

-0.033**

(0.017) 

-0.078***

(0.022) 

Leverage 

Risk 

ROA -0.143***

(0.013) -0.140***

(0.046) 

-0.341***

 (0.056) 

-0.213***

(0.045) 

-0.518***

(0.077) 

-0.106* 

(0.064) 

SECURITIES -0.006***

(0.001) 

-0.006* 

(0.003) 

 -0.001 

 (0.003) 

-0.005

(0.004) 

-0.014***

(0.004) 

-0.013***

(0.004) 

Liquidity 

Risk 

JUMBO CDs 

0.020***

0.001 

0.009 

(0.006) 

 0.026***

 (0.005) 

0.016** 

(0.007) 

0.025***

(0.006) 

0.021***

(0.006) 

SIZE -0.117***

(0.010) -0.181***

(0.034) 

 -0.131***

 (0.036) 

-0.213***

(0.047) 

-0.203***

(0.042) 

-0.132***

(0.040) 

CAMELS-2 

0.698***

(0.035) 

0.604***

(0.146) 

0.612***

 (0.130) 

0.515***

(0.145) 

0.458***

(0.137) 

0.795***

(0.155) 

Contr

ol 

Variables 

BAD-MANAGE 

0.460***

(0.028) 

0.541***

(0.080) 

0.394***

 (0.085) 

0.554***

(0.101) 

0.296***

(0.107) 

0.604***

(0.109) 

ADVANCES 0.024***

(0.002) 

0.033 

(0.025) 

0.033*

(0.017) 

0.063***

(0.012) 

0.007 

(0.021) 

0.038***

(0.012) 

Pseudo R2 0.192 0.184 0.164 0.223 0.240 0.250

Observations 81166 8267 8661 8011 7813 6994

37
Table 8 (Continued) 

Independent Variable 

1998

Downgrades 

(on 1997:Q4

Financials) 

1999

Downgrades 

(on 1998:Q4

Financials) 

2000

Downgrades 

(on 1999:Q4

Financials) 

2001

Downgrades 

(on 2000:Q4

Financials) 

2002

Downgrades 

(on 2001:Q4

Financials) 

2003

Downgrades 

(on 2002:Q4

Financials) 

Intercept -1.617***

(0.452) 

-1.186***

(0.435) 

-1.243***

(0.474) 

-1.475***

(0.452) 

-2.235***

(0.414) 

-1.152**

(0.459) 

PAST-DUE-30 

0.177***

(0.038) 

0.140***

(0.036) 

0.233***

(0.042) 

0.136***

(0.033) 

0.174***

(0.033) 

0.178***

(0.038) 

PAST-DUE-90 

0.248***

(0.071) 

0.118*

(0.065) 

0.207***

(0.068) 

0.380***

(0.072) 

0.091 

(0.075) 

0.234***

(0.068) 

NONACCRUING 

0.178***

(0.054) 

0.326***

(0.051) 

0.177***

(0.054) 

0.283***

(0.050) 

0.302***

(0.048) 

0.268***

(0.046) 

OREO 

0.164 

(0.112) 

0.151*

(0.089) 

0.037 

(0.127) 

0.205** 

(0.104) 

0.043 

(0.094) 

0.227** 

(0.093) 

COMMERCIAL 

0.014***

(0.005) 

0.013***

(0.005) 

0.007 

(0.005) 

0.015***

(0.005) 

0.007 

(0.005) 

-0.005

(0.006) 

Credit Risk 

RESIDENTIAL -0.002

(0.004) -0.004

(0.004) 

-0.005

(0.004) 

0.002 

(0.004) 

-0.003

(0.004) 

-0.006

(0.004) 

NET-WORTH

-0.023* 

(0.013) -0.043***

(0.012) 

-0.041***

(0.013) 

-0.037**

(0.015) 

0.003 

(0.009) 

-0.038**

(0.016) 

Leverage 

Risk 

ROA -0.067***

(0.022) -0.157***

(0.036) 

-0.290***

(0.053) 

-0.204***

(0.049) 

-0.233***

(0.048) 

-0.405***

(0.061) 

SECURITIES -0.005

(0.004) -0.004

(0.003) 

-0.003

(0.003) 

-0.006

(0.004) 

-0.008**

(0.003) 

-0.004

(0.003) 

Liquidity 

Risk 

JUMBO CDs 

0.007 

(0.006) 

0.013** 

(0.005) 

0.016***

(0.005) 

0.024***

(0.005) 

0.017***

(0.005) 

0.006 

(0.005) 

SIZE -0.097***

(0.034) -0.107***

(0.033) 

-0.118***

(0.036) 

-0.118***

(0.033) 

-0.061**

(0.031) 

-0.063* 

(0.033) 

CAMELS-2 

0.630***

(0.108) 

0.732***

(0.104) 

0.923***

(0.123) 

0.884***

(0.124) 

0.952***

(0.128) 

0.573***

(0.111) 

Contr

ol 

Variables 

BAD-MANAGE 

0.514***

(0.099) 

0.456***

(0.098) 

0.648***

(0.105) 

0.439***

(0.113) 

0.616***

(0.104) 

0.351***

(0.115) 

ADVANCES 0.027***

(0.009) 

0.009*

(0.005) 

0.035***

(0.007) 

0.017** 

(0.007) 

0.016** 

(0.007) 

0.022***

(0.007) 

Pseudo R2 0.181 0.198 0.235 0.241 0.230 0.207

Observations 6128 6105 5927 5814 5665 5409

PAST-DUE-30 Loans > 30 days past due / total loans ROA Net income / total assets 

PAST-DUE-90 Loans > 90 days past due / total loans SECURITIES Book value of securities / total assets

NONACCRUING Loans in non-accrual status / total loans JUMBO-CDs Jumbo-CDs / total assets 

OREO Other real estate owned / total assets SIZE Natural logarithm of total assets, in thousands of dollars

COMMERCIAL Commercial and industrial loans / total assets CAMELS-2 Dummy variable if the bank has a CAMELS rating of 2 

RESIDENTIAL Residential real-estate loans / total assets BAD-MANAGE Dummy variable if the management rating > CAMELS rating 

NET-WORTH Equity less goodwill / total assets ADVANCES FHLB advances as a percentage of total assets 

38
Table 9 

How does FHLBank dependence at high-risk banks and heavy-user banks compare with all U.S. b

This table presents mean and median ratios of FHLBank advances to total assets for all U.S. commercial banks, high failure-risk bank

as of year-end 2003. On average, dependence on advances at high-risk banks is about 74 percent of dependence for the banking sectoaverage dependence on advances at heavy user banks is nearly six times the average for U.S. banks.

All U.S. Commercial Banks High-Risk Banks H

FHLBank Advances 

as a Percent of Assets 

FHLBank Advances 

as a Percent of Assets 

Total Assets Number Mean Median Number Mean Median Number <$50 million 1,937 1.76 0.00 118 1.66 0.00 34 

$50-100 million 1,955 3.17 0.38 106 2.91 0.00 65 

$100-500 million 3,035 4.41 2.60 99 3.50 1.59 170 

$500-$1 billion 374 6.36 4.79 6 4.46 3.87 50 

$1-5 billion 281 6.93 4.77 4 3.82 1.38 39 

>$5 billion 133 5.86 3.25 1 0.00 0.00 9 

Total 7,715 3.64 0.84 334 2.68 0.00 367 

Source: Reports of Condition and Income for all U.S. Commercial Banks, 2003 

39
Table 10 

What are the FDIC’s historical loss rates on various asset classes? 

Previous studies estimated actuarially fair deposit-insurance premiums or aggregate costs of providing deposit

insurance by assuming historical ratios of fund losses to fund balances will obtain in the future.. Our approach is 

more granular. We apply average 1990-2002 loss rates on specific assets—noted below—to year-end 2003 balances

of high-risk and heavy-user banks. OREO refers to “other real estate owned,” typically collateral seized in

foreclosure. 

Weighted-Average Losses by Asset Category 

1990-2002 

Asset Categories

Consumer Loans 25.1

Commercial Loans 40.0

Securities 1.1

Real Estate Loans 22.0

OREO 62.1

Other Assets 26.4

Source: Bennett (2003) 

40
Table 11 

How much do FHLBank advances increase losses-given-default? 

High-Risk Scenario: Liquidation of All Banks with Failure Probabilities above Two Percent 

If all high-risk banks were liquidated, losses to the Bank Insurance Fund (BIF) would equal $3.66 billion, or just over six percent of institutions. To gauge the impact of FHLBank funding, we remove advances from these banks, make offsetting balance-sheet adjustm

costs, and compare the new estimates with the baseline estimates. The difference is the amount advances “increase” BIF losses. On aincreases losses by $146 million—a figure equal to four percent of the baseline. If the insured-deposit adjustment is ignored, advanceby $182 million—a figure equal to five percent of the baseline. Comparing these estimates to BIF offers insight into economic signif

impact of FHLBank funding is modest; incremental losses-given-default represent less than one percent of year-end 2003 Fund value.

Estimated 

Losses 

($000)

Total Assets 

($000)

Loss 

Perce

Ass

Baseline resolution costs 3,660,268 59,934,900 6.

Strategies for Using FHLBank Advances (Balance-Sheet Adjustments) 

Replace insured deposits with advances 3,694,776 59,934,900 6.Replace uninsured deposits with advances 3,503,993 59,934,900 5.Use FHLBank advances to expand real-estate loan portfolio 3,414,360 58,584,010 5.Use FHLBank advances to expand consumer-loan portfolio 3,551,897 59,421,448 5.9Use FHLBank advances to expand commercial-loan portfolio 3,271,366 58,746,112 5.5Use FHLBank advances to purchase investment securities 3,649,651 58,765,363 6.2Average resolution costs over all strategies 3,514,341 59,231,122 5.9Average increase in resolution costs (in $000 from the baseline) 145,928 Average increase in resolution costs (as a percentage of the baseline) 3.99% Average increase in resolution costs (as a percentage of BIF) 0.43% Average resolution costs over strategies excluding insured-deposit strategy 3,478,253 59,090,367 5.8Average increase in resolution costs (in $000 from the baseline) 182,015 Average increase in resolution costs (as a percentage of the baseline) 4.97% Average increase in resolution costs (as a percentage of BIF) 0.54% 

41
Table 12 

How much would FHLBank advances increase losses-given-default? 

Heavy-User Scenario: Liquidation of All Banks with Advance-to-Asset Ratios above 15 percen

If all heavy-user banks were liquidated, losses to the Bank Insurance Fund (BIF) would equal $15.4 billion, or nearly six percent of institutions. To gauge the impact of FHLBank funding, we remove advances from these banks, make offsetting balance-sheet adjustm

costs, and compare the new estimates with the baseline estimates. The difference is the amount advances “increase” BIF losses. On aincreases losses by $3.1 billion—a figure equal to 20 percent of the baseline. If the insured-deposit adjustment is ignored, advances bobillion—a figure equaling 27 percent of the baseline. When viewed as a percentage of BIF, incremental resolution costs traceable to ad

from 9 to 12 percent of Fund value at year-end 2003.

Estimated 

Losses 

($000)

Total Assets 

($000)

Los

Perc

As

Baseline resolution costs 15,360,264 259,400,567 5

Strategies for Using FHLBank Advances (Balance-Sheet Adjustments) 

Replace insured deposits with advances 17,431,584 259,400,567 6Replace uninsured deposits with advances 11,207,852 259,400,567 4Use FHLBank advances to expand real-estate loan portfolio 6,642,516 199,429,888 3Use FHLBank advances to expand consumer-loan portfolio 12,414,840 245,020,693 5Use FHLBank advances to expand commercial-loan portfolio 11,066,518 244,870,836 4Use FHLBank advances to purchase marketable securities 14,982,044 214,447,716 6Average resolution costs over all strategies 12,290,892 237,095,045 5.Average increase in resolution costs (in $000 from the baseline) 3,069,372Average increase in resolution costs (as a percentage of the baseline) 19.98% Average increase in resolution costs (as a percentage of BIF) 9.09% Average resolution costs excluding insured-deposit strategy 11,262,754 232,633,940 4.Average increase in resolution costs (in $000 from the baseline) 4,097,510Average increase in resolution costs (as a percentage of the baseline) 26.68% Average increase in resolution costs (as a percentage of BIF) 12.13%

42
Appendix 

A Closer Look at the Impact of FHLBank Advances on Loss-Given-Failure 

In a payoff resolution, the FDIC as receiver assumes the assets and liabilities of the failed bank. The 

FDIC pays insured depositors in full and then takes their place in line to receive proceeds from asset

liquidation. Since passage of National Depositor Preference in 1993, claimants have been paid in the

following order (Marino and Bennett, 1999): 

• Administrative expenses of the receiver 

• Secured claims (including FHLBank advances) 

• Domestic deposits (insured and uninsured) 

• Foreign deposits and other general creditor claims

• Subordinated debt 

• Shareholders 

Upon resolution, the FDIC pays secured claimants, including an FHLBank, and divides proceeds from 

the sale of remaining assets with uninsured domestic depositors. Gross BIF losses (LS) are insured 

deposits less the FDIC’s share of asset sales after paying secured claimants as shown in equation (A1). 

We assume administrative expenses are zero, and the only secured claim is FHLBank advances. Each

right-hand side variable depends on advances. Bank subscripts are also omitted to simplify the 

exposition. 

LS = ID − γπ (A1)

where: 

LS = gross BIF losses (i.e., losses before deduction of paid-in premiums)

ID = insured deposits 

= + = ID UD

ID

γ insured deposit share of total domestic deposits 

and the proceeds from liquidation, after paying the advances, are 

π = (1− LR)L − A (A2) 

where: 

LR = loan-loss ratio, or the percentage mark down of loans upon resolution 

L = book value of loans before resolution 

A = amount of FHLBank advances. 

By definition, the value of assets equals the value of liabilities and equity: 

(1− LR)L = A + ID +UD + EQ (A3)

Using equations (A2) and (A3), we can express gross BIF losses as

43
LS = −γEQ (A4)

Note that gross losses are positive if and only if the value of equity is negative. We will assume 

throughout this analysis. This assumption is another way of saying that banks are closed and 

liquidated only when the value of assets falls below the value of liabilities. 

(EQ)

EQ < 0

To investigate incremental gross losses traceable to FHLBank advances, we differentiate (A4) with 

respect to A, yielding: 

' 'EQ EQ' dA

dLS LS = = −γ − γ (A5)

The marginal change in the insured-deposit share is: 

2 ( )

' ' ' ID UDID UD UD ID

+

− γ = (A6) 

Using equation (A6) we can rewrite the marginal change in gross BIF losses as:

ID UD

ID EQ

ID UD

ID UD UD ID EQ LS

+ − +

− = ( )( ')

( )

( ' ' ) ' 2 (A7) 

Following equation (A3), the value of equity can be written as: 

EQ = (1− LR)L − A − ID −UD (A8) 

and the marginal change in equity can be written as: 

EQ'= L'−(LR')L − (LR)L'−1− ID'−UD' (A9) 

To make equation (A7) more intuitive, we illustrate the influence of different assumptions (Case 1, Case 

2, and Case 3) on the link between advances and BIF losses. 

Case 1: Advances Fund Loan Growth Exclusively

In this case, each additional dollar of advances is channeled into new loans. This assumption implies 

L’=1 and UD’=ID’=0. The marginal change in gross BIF losses is given by: 

LS'= γ (LR'L + LR) > 0 (A10)

In words, the incremental loss is the insured depositor’s share of any increase in loan losses (plus the preadvance loan-loss ratio). The insured-deposit share and loan-loss ratio are positive, so net losses are 

increasing in advances—even if advances does not produce a rise in the loan-loss ratio. 

44
Case 2: Advances Substitute for Insured Deposits

Alternatively, we may assume advances substitute for insured deposits, all else equal. This assumption 

implies L’=UD’=LR’=0 and ID’=-1. The loan-loss rate does not change because, by assumption, the

change in funding mix has no impact on the left side of the balance sheet. In this case, incremental gross

losses become: 

⎥

⎦

⎤ ⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛

+ = − ID UD

EQ LS' (1 γ ) < 0 (A11)

By assumption, insured-deposit share is positive (or advances could not substitute for insured deposits). 

The assumption of negative equity implies equation A11 is negative—an increase in advances reduces

gross losses. Gross losses fall because the increase in advances reduces the share of FDIC-insured claims

in the bank’s funding mix. 

Case 3: Advances Substitute for Uninsured Deposits

Finally, we may assume advances replace uninsured deposits, all else equal. This assumption implies

L’=ID’=LR’=0 and UD’=-1. In this case, incremental losses become: 

' > 0

+ = − ID UD

EQ LS γ

(A12)

Assuming the insured-deposit share is initially positive, equation A12 is positive—an increase in

advances increases gross losses. Gross losses rise because the increase in advances reduces the FDIC’s

ability to shift losses to uninsured depositors. 

45