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1996 Quarter 3
The Impact of Depositor
Preference Laws
by William P. Osterberg

Cultural Affinity and
Mortgage Discrimination
by Stanley D. Longhofer

FEDERAL RESERVE BANK
OF CLEVELAND

Vol. 32, No. 3
http://clevelandfed.org/research/review
Economic Review 1996 Q3

1996 Quarter 3
The Impact of Depositor
Preference Laws

2

by William P. Osterberg

Cultural Affinity and
Mortgage Discrimination
by Stanley D. Longhofer

12

2

The Impact of Depositor
Preference Laws
by William P. Osterberg

Introduction
On August 10, 1993, Congress passed the
Omnibus Budget Reconciliation Act. This legislation contained an amendment to section
11(d)(11) of the Federal Deposit Insurance Corporation Act that changed the priority of claims
on failed depository institutions. It gave depositors, and by implication the FDIC, claims on a
failed bank’s assets that are superior to those of
general creditors. The stated objective of this
shift was to reduce the FDIC’s expected losses
from bank failures. Several states had previously passed similar legislation.
There has been little empirical research concerning the impact of depositor preference legislation (DPL), despite repeated claims of its
benefits. Arguments that this legislation could
reduce the FDIC’s exposure are based on the
assumption that creditors will make no offsetting responses. The only relevant study, by
Hirschhorn and Zervos (1990), found that following the passage of state-level DPL, general
creditors of affected savings and loans increased collateralization, and interest rates on
uninsured certificates of deposit fell. No
analogous study has been conducted for commercial banks.

William P. Osterberg is an economist at the Federal Reserve Bank of
Cleveland. The author is grateful to
Charles Carlstrom, Joseph
Haubrich, Kevin Lansing, Jacky
So, and James Thomson for helpful suggestions and comments.

In this article, I analyze the impact of DPL
on commercial banks. I first present a partial
equilibrium analysis of its effects on the value
and rates of return of various types of bank liabilities when failed banks are assumed to be
resolved through liquidation. Next, I discuss
creditors’ possible responses. (The appendix
shows how the FDIC’s position would be affected by increased collateralization from general creditors.) In the third section, I give some
descriptive statistics from Call Report data on
portfolio shares, distinguishing between banks
that were subject to state DPL in existence prior
to the 1993 legislation and those that were not.
In the fourth section, I present a regression
analysis of DPL’s impact on the costs of resolving bank failures. The fifth section concludes.
The finding presented here—that average resolution costs were lower under DPL—is consistent with the view that the legislation has
increased the value of the FDIC’s claims. However, there is some evidence that creditors’
actions may have partially offset the benefit to
the FDIC.

3

B O X

1

Depositor Preference Legislation
and Resolution Type
When bank failures are resolved through liquidation and without
DPL, the FDIC shares the assets with uninsured depositors and
nondepositors. With DPL, all depositors stand ahead of nondepositors. In an assisted merger, all deposits are covered and,
without depositor preference, the nondeposit claims are passed
on to the acquiring institution. Under depositor preference, nondeposit claims are de jure subordinate to those of the depositors
and the FDIC. However, assisted mergers may continue to provide
de facto insurance. Hence, while losses to the FDIC might be
lower under depositor preference for either resolution type, costs
under liquidation are likely to be reduced more.
As a result, DPL might influence the type of resolution procedure adopted. Bank regulatory agencies are required to utilize the
least costly resolution method. Ely (1993) speculated that depositor preference would increase the use of liquidations (or deposit
transfers) and reduce the use of assisted mergers (or purchase
and assumptions).

I. DPL and the
Values of Bank
Claimants: A Basic
Framework
This section uses the cash-flow capital-asset
pricing model developed by Chen (1978) to
examine the impact of DPL on the values and
rates of return for uninsured depositors, general creditors, and the FDIC.1 I assume that the
value of the FDIC’s position is always negative.
If correct pricing is defined as that which maintains the value of the FDIC’s position at zero, I
assume underpriced deposit insurance. However, correct pricing would imply that DPL
could have no impact on the FDIC’s position.
For simplicity, I assume that the premium is
fixed and unrelated to the bank’s risk.
A related concern might be how the priority
of claims is determined and whether the effects
of priority are negated so as to maintain the
claims’ previous value, but that issue is beyond
the scope of this article. The assumption made
here is that the priority of claims is exogenous
to the determination of values and rates of return. More generally, the framework cannot
anticipate general creditors’ responses, but it
assumes that they correctly foresee regulators’
choice of a failure resolution method. Because
regulators have a mandate to choose the least
costly method (liquidation, assisted merger, or
open bank assistance), their choice of resolution type may vary endogenously (see box 1).

This article focuses on liquidation, by far the
most commonly chosen method.
Total liabilities against the bank (initially D,
then K with depositor preference) equal the
sum of the end-of-period claims of uninsured
depositors (Bu ), insured depositors (Bi ), general creditors (G), and the FDIC (z). Defining
the fixed insurance premium on each dollar of
insured deposits as ρ implies that z = ρBi . Under depositor preference, the claims of general
creditors are subordinated to those of uninsured depositors and the FDIC. The effective
bankruptcy threshold is lowered from D to
B = K – G.

The Impact
on Uninsured
Depositors
In the absence of depositor preference, uninsured depositors are paid in full if cash flow to
the bank (X ) exceeds total liability claims (D).
Otherwise, under liquidation, a positive cash
flow will be split proportionately with the other
net claimants. The cash flow to uninsured
depositors is Yb u .
Ybu = Bu

if X > D = Bi + Bu + G + z,

= Bu X/D

if D > X > 0, and

= 0

if 0 > X.

With depositor preference, the pecking order
of lower claimants is irrelevant to valuing the
claims of uninsured depositors.
Ybu = Bu

if X > B = Bi + Bu + z,

= Bu X/B if B > X > 0, and
= 0

if 0 > X.

To calculate the impact of DPL, I control for
possible changes in the level of total promised
payments. The expected cash flow to an uninsured deposit with one-dollar par value is separated into one part that equals the cash flow
in the no-DPL case and one that has the following value:

■ 1 Osterberg and Thomson (1991) use the same framework to
analyze the impact of subordinated debt and surety bonds.

4

∆Ybu = 0

if

X > D,

= 1–X/D

if

D > X > B,

= X/B – X/D

if

B > X > 0, and

=0

if

0 > X.

The change in the value of uninsured
deposits due to depositor preference is thus
(1)

The impact of depositor preference on VG
depends on whether K > D or D > K. I assert
that K is at least as large as D, or else stockholders would choose to issue debt subordinate to deposits. Following the procedure utilized for uninsured depositors to calculate the
change that depositor preference makes in
cash flows to general creditors and in the
value of their claims, we have:
∆YG = 0

∆Vbu = R –1[F (D) – F (B)
– B)
B (X )
+ (D
B(D) CEQ 0
1 CEQ D (X )] > 0.
– D
B

In this case, F (.) is the cumulative distribution
function defined over the uncertain cash flow
X. The certainty equivalent of X when it lies
between 0 and D, CEQ D0 (X ), is equal to
E D0 (X ) – λCOV (X, RM ), where λ is the market
price of risk and RM is the return on the market. As long as D > B, Vbu increases with DPL.
For a given distribution of X and level of Bu,
uninsured depositors are paid over a greater
range of possible outcomes for X.

if X > K,

= (X – B)/G – 1

if K > X > D,

= (X – B)/G – X/D

if D > X > B,

= – X/D

if B > X > 0, and

=0

if 0 > X.

Then
(2)

∆VG = R –1 {–[F (K ) – F (D)]
– (B/G) [F (K ) – F (B)]
+ (1/G ) CEQBK (X)
– (1/D)CEQ D0 (X ) } < 0.

The Impact on
General Creditors
Without depositor preference, general creditors
have the same priority of claims as uninsured
depositors:

Since total liability claims do not decrease
(K > D), DPL cannot increase the values of
general creditors’ claims.

if

X > D = Bi + Bu + G + z,

The Impact
on the FDIC

= GX/D

if

D > X > 0, and

The value of the FDIC’s claim is the net value

=0

if

0 > X.

YG = G

With depositor preference, general creditors’
claims are senior only to equityholders’, and
their cash flows become
if

X > K = Bi + Bu+ G + z,

= X–B

if

K > X > B, and

= 0

if

B > X.

YG = G

The value of general credit behaves like
that of subordinated debt, except for the protection afforded by the latter. However, when
B < X < K, general credit behaves like equity.

of deposit insurance. Without depositor preference, the net cash flow to the FDIC is
YFDIC = z

if X > D,

= (Bi + z)X/D – Bi

if D > X > 0,

= –Bi

if 0 > X.

Depositor preference affects the net value of
the FDIC’s claim by changing senior claimants’
probability of loss and by altering the FDIC’s
weight in the pool of senior claims.
YFDIC = z

if X > B,

= (Bi + z)X/B – Bi

if B > X > 0, and

= –Bi

if 0 > X.

5

B O X

Then

2

State Depositor Preference
Legislation for Banks
State

(3) ∆YFDIC =
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

October 15, 1978
September 21, 1991
June 27, 1986
May 1, 1987
May 22, 1991
July 3, 1992
1974a
June 24, 1987
1979b
January 1, 1970
July 1, 1985
January 1, 1985
April 16, 1991
April 24, 1990
September 1, 1993
1927c
1909c
June 10, 1991
June 30, 1963
July 1, 1987
May 26, 1965
January 1, 1974
February 8, 1991
July 1, 1969
1969c
1983c
July 1, 1983
May 11, 1981

a. Legislation became effective on either January 1 or July 1.
b. Passed by both houses of the state legislature on July 1; enactment
date is unclear.
c. Neither the month nor the day of enactment is available.
SOURCE: Compiled from state statistics.

It follows that the change in the value of the
FDIC guarantee on a one-dollar par-value
deposit is the value of a security that has the
following cash flows:
∆YFDIC = 0

if

X > D,

= ρ – (1 + ρ) [X/D] + 1
if
D > X > B,

~
(1 + ρ)
1
[F (D) – D CEQBD (X )
R
~
1
– D CEQ B0 (X )
~
1
+ B CEQ B0 (X ) – F (B)].

The FDIC’s subsidy must be reduced by DPL
because 1) if D > X > B, then X /D < 1, and the
FDIC’s cash flow increases; and 2) if D > B > 0,
then 1/B > 1/D, and the FDIC’s cash flow increases. Thus, ∆VFDIC > 0.

II. Possible
Impacts of DPL
on Bank Portfolios
Many of the possible effects of depositor preference could have the unintended result of
decreasing the benefit to the FDIC, thus potentially invalidating the result on VFDIC in the partial equilibrium analysis above. The appendix
presents an analytical exposition of how
increased collateralization by general creditors
would affect the FDIC’s claims.2 General creditors include trade creditors, beneficiaries of
guarantees, foreign depositors (to the extent
that their treatment differs from that accorded
domestic depositors), holders of bankers’
acceptances, unsecured lenders, landlords, suppliers of fed funds, and counterparties to swaps
and other contingent liabilities. In the event of
failure, collateralization would give such secured lenders priority over all depositors. Other
possible responses include increases in interest
rates on general credit, adjustment of maturities, or the introduction of accelerator clauses.
It has been asserted that depositor preference would harm smaller community banks
and thrifts. Banks with less capital would supposedly 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’ acceptances or letters of credit
downgraded (see Rehm [1993]). Mutual funds
and large banks, particularly those seen as “toobig-to-let-fail,” would have an enhanced advantage in attracting deposits over $100,000, which
might not be seen as being at risk.

= (1+ ρ)X/B – (1 + ρ)X/D
if
B > X > 0, and
=0

if

0 > X.

■ 2 Hirschhorn and Zervos (1990) claim that DPL increases the
incentive to collateralize and that the damage to the insurer and to the
uninsured depositor increases with the degree of collateralization of nondeposit claims and the extent of insolvency.

6

B O X

3

Variable Definitions
Federal funds lent/total assets
Federal funds borrowed/total assets
Foreign deposits/total assets
Off-balance-sheet loans and letters
of credit/total assets
OBSOTH
Other off-balance-sheet items/
total assets
OBS
Total of OBSLNS and OBSOTH items/
total assets
UNCOL
Loan interest earned but not collected/
total assets
EQCAP
Equity capital
CAP
(Equity capital + loan-loss reserves
+ allocated risk transfer reserves)/
total assets
PDNA
Loans 90 days past due or nonaccruing/
total assets
OREO
Other real estate owned/total assets
INSLNS
Loans to insiders/total assets
COREDEP
Domestic deposits under $100,000/
total assets
ICORE
Equals COREDEP if bank resolved
via payout, otherwise equals 0
BRKDEP
Brokered deposits/total assets
NCRASST
(Risky assets not included in PDNA,
OREO, or INSLNS)/total assets
DUMNE
Equals 1 if bank is in Boston, New York,
or Philadelphia Federal Reserve Districts
DUMSW
Equals 1 if bank is in Dallas Federal
Reserve District
DPL
Equals 1 if bank is a state bank in a state
with depositor preference legislation
CAPDPL
CAP * DPL
UNCOLDPL
UNCOL * DPL
PDNADPL
PDNA * DPL
OREODPL
OREO * DPL
INSLNSDPL
INSLNS * DPL
NCRASSTDPL NCRASST * DPL
OBSDPL
OBS * DPL
FFSOLDDPL
FFSOLD * DPL
FFPURCHDPL FFPURCH * DPL
COREDEPDPL COREDEP * DPL
ICOREDPL
ICORE *DPL
LNASSTDPL
Logarithm of total assets * DPL
BRKDEPDPL BRKDEP * DPL
DUMNEDPL
DUMNE * DPL
DUMSWDPL DUMSW * DPL
FFSOLD
FFPURCH
FORDEP
OBSLNS

III. Descriptive
Measures of
Portfolio Impacts
The partial equilibrium framework described
above implies that DPL affects the values and
rates of return for certain categories of bank
creditors. However, given the short time since
national DPL was passed and the lack of data
on values and rates, I choose instead to study
the impact of state DPL that was in effect prior
to 1993, using bank balance sheet data (quarterly reports of the Federal Financial Institutions Examination Council, or Call Reports)
and FDIC resolution cost estimates for failed
banks. The states that passed DPL and the
years the legislation became effective are listed
in box 2, while the variable definitions are
shown in box 3.
Table 1 presents portfolio measures from
pooled Call Reports for 1984–92. DPL might
affect bank behavior either in a cross-section or
through time. Totals for general credit (federal
funds, foreign deposits, and off-balance-sheet
items) might decline as a share of total assets.
As a link to our subsequent examination of
DPL and closed-bank resolution costs, we also
examine variation in portfolio measures that
have been shown to affect resolution costs.
One immediately apparent difference
between banks subject to state DPL and others
is that only state-chartered banks—which are
generally smaller than national banks—are
affected by DPL. I compare state-chartered
banks in states where they are subject to DPL
with national banks in the same states, and also
contrast state-chartered banks located in DPL
states versus non-DPL states. New York banks
are excluded because of their size and unique
regulatory status.
DPL has no statistically significant impact on
borrowing or lending of federal funds, foreign
deposits, or off-balance-sheet sources of funding.3 State banks that are subject to this legislation utilize federal funds somewhat less than
do national banks in the same states or state
banks not subject to it. Foreign deposits are
utilized somewhat more by state banks subject
to DPL than by national banks in the same
states. However, foreign deposits are utilized
more by state banks than national ones. Offbalance-sheet borrowing is somewhat lower at
state banks under DPL than national banks in
the same states.

■ 3 The t-test results are available from the author upon request.

7

T A B L E

1

Sample Statistics on
the Impact of State DPL
DPL

Non-DPL
State
National
Banks
Banks

State
Banks

National
Banks

LNASST

10.377
(1.050)

10.917
(1.281)

10.709
(1.117)

11.182
(1.358)

FFSOLD

0.056
(0.064)

0.067
(0.090)

0.060
(0.074)

0.070
(0.093)

FFPURCH

0.006
(0.026)

0.018
(0.053)

0.012
(0.042)

0.019
(0.044)

FORDEP

0.090
(0.122)

0.031
(0.054)

0.078
(0.105)

0.050
(0.073)

OBSLNS

0.053
(0.210)

0.120
(3.450)

0.047
(0.161)

0.116
(4.473)

OBSOTH

0.002
(0.053)

0.008
(0.140)

0.004
(0.039)

0.016
(0.200)

UNCOL

0.010
(0.006)

0.008
(0.005)

0.008
(0.005)

0.007
(0.017)

EQCAP

0.094
(0.050)

0.091
(0.065)

0.093
(0.052)

0.086
(0.059)

PDNA

0.009
(0.015)

0.010
(0.016)

0.007
(0.012)

0.009
(0.014)

OREO

0.007
(0.013)

0.007
(0.013)

0.005
(0.011)

0.006
(0.017)

INSLNS

0.005
(0.010)

0.005
(0.011)

0.006
(0.011)

0.006
(0.017)

COREDEP

0.809
(0.119)

0.790
(0.141)

0.782
(0.136)

0.752
(0.155)

BRKDEP

0.003
(0.026)

0.002
(0.016)

0.002
(0.020)

0.003
(0.022)

NOTE: Banks in New York are excluded from the last two columns.
DPL/non-DPL refer to whether or not banks operated in states where
depositor preference legislation was in effect.
SOURCE: Author’s calculations.

The bottom half of table 1 compares asset
shares of some items with predictive power for
resolution costs. Higher levels of income
earned but not collected (UNCOL), loans past
due or nonaccruing (PDNA), other real estate
owned (OREO), and insider loans (INSLNS) are
expected to increase costs.4 Core deposits
(COREDEP), equity capital (EQCAP), and brokered deposits (BRKDEP) tend to be associated with lower costs. None of the items differs
significantly according to DPL status. However,
the lower levels of EQCAP, COREDEP, and
BRKDEP would imply higher costs when DPL
is in effect.

Table 2 focuses on failed banks and compares movements in portfolio shares during the
five quarters prior to failure for banks that are
subject to DPL and those that are not.5 The
portfolio measures for the five quarters before
failure are able to predict resolution costs.6
DPL has no significant effect on these shares.

IV. Does DPL Affect
Resolution Costs?
Other things being equal, DPL’s impact on the
value of the FDIC’s claim should be reflected in
FDIC losses resulting from resolution of bank
failures. An increase in VFDIC should be reflected
in less costly resolutions.7 I focus here on failed
banks and analyze resolution-cost data from the
FDIC (1993) and balance-sheet data from Call
Reports (table 2). The sample includes all commercial banks insured by the FDIC and the
Bank Insurance Fund that were closed or
required FDIC assistance between January 1,
1986 and December 31, 1992. The quarterly
balance-sheet data for these banks cover the period from March 31, 1984 to December 31, 1992.
I estimate the resolution-cost equation using
weighted least squares, with all variables being
divided by the square root of total assets. Several categories of variables appear on the righthand side. First, I list balance-sheet measures
elsewhere shown to have predictive power for
resolution costs (see Osterberg and Thomson
[1995]). CAP proxies for unbooked gains or
losses and is expected to have a coefficient
equal to (–1) in the absence of gains or losses.
Income earned but not collected (UNCOL) may
represent hidden problem assets expected to
increase resolution costs. PDNA, OREO, and
NCRASST each proxy for categories of problem
assets and raise costs. Insider loans (INSLNS)
may be associated with relaxed credit standards
and thus with higher costs. Core deposits
(COREDEP) represent the unbooked gains associated with franchise value and should reduce

■ 4 These findings are detailed in Osterberg and Thomson (1995).
■ 5 A preferable way to gauge the impact of introducing DPL would
be to examine portfolios before and after such legislation was passed, but
passage dates were too close to either the beginning or the end of the sample period to permit such a comparison.
■ 6 This can be interpreted as evidence of regulatory forbearance.
See the discussion and references in Osterberg and Thomson (1995).
■ 7 An important caveat is that failure, as a regulatory decision, might
be influenced by the same factors that determine costs. Resolution type
might also be affected.

8

T A B L E

2

Sample Statistics for Failed Banks
Prior to Failure
Panel A: Banks in States without DPL
Number of Call Reports Prior to Failure (Mean)

LNASST
FFSOLD
FFPURCH
OBSLNS
OBSOTH
UNCOL
EQCAP
PDNA
OREO
INSLNS
COREDEP
BRKDEP

1

2

3

4

5

10.704
(1.310)
0.103
(0.144)
0.010
(0.035)
0.056
(0.076)
0.008
(0.053)
0.009
(0.006)
–0.001
(0.064)
0.055
(0.042)
0.050
(0.050)
0.010
(0.017)
0.828
(0.151)
0.022
(0.065)

10.761
(1.310)
0.093
(0.133)
0.010
(0.033)
0.057
(0.073)
0.009
(0.066)
0.009
(0.005)
0.019
(0.049)
0.049
(0.038)
0.044
(0.046)
0.011
(0.017)
0.794
(0.148)
0.019
(0.060)

10.823
(1.298)
0.087
(0.116)
0.012
(0.038)
0.065
(0.082)
0.011
(0.092)
0.010
(0.006)
0.047
(0.046)
0.035
(0.031)
0.031
(0.037)
0.013
(0.023)
0.730
(0.146)
0.013
(0.047)

10.812
(1.285)
0.072
(0.089)
0.013
(0.036)
0.074
(0.106)
0.014
(0.076)
0.011
(0.006)
0.088
(0.061)
0.012
(0.015)
0.009
(0.016)
0.014
(0.024)
0.628
(0.155)
0.007
(0.024)

10.726
(1.333)
0.074
(0.087)
0.014
(0.033)
0.081
(0.113)
0.004
(0.036)
0.011
(0.006)
0.088
(0.061)
0.012
(0.015)
0.009
(0.016)
0.014
(0.024)
0.628
(0.155)
0.007
(0.024)

10.307
(1.172)
0.051
(0.064)
0.006
(0.014)
0.053
(0.084)
0.005
(0.024)
0.015
(0.011)
0.067
(0.026)
0.028
(0.024)
0.023
(0.028)
0.011
(0.013)
0.738
(0.144)
0.012
(0.052)

10.277
(1.182)
0.043
(0.043)
0.009
(0.022)
0.052
(0.069)
0.000
(0.000)
0.014
(0.009)
0.077
(0.028)
0.021
(0.020)
0.017
(0.023)
0.012
(0.017)
0.680
(0.157)
0.012
(0.049)

Panel B: Banks in States with DPL
LNASST
FFSOLD
FFPURCH
OBSLNS
OBSOTH
UNCOL
EQCAP
PDNA
OREO
INSLNS
COREDEP
BRKDEP

10.154
10.199
(1.140) (1.147)
0.051
0.050
(0.052) (0.048)
0.004
0.004
(0.012) (0.011)
0.042
0.048
(0.050) (0.056)
4.2E-6
0.000
(3.1E-5) (0.000)
0.012
0.012
(0.008) (0.008)
0.013
0.025
(0.049) (0.038)
0.052
0.046
(0.049) (0.041)
0.044
0.040
(0.037) (0.033)
0.010
0.010
(0.015) (0.013)
0.887
0.861
(0.137) (0.144)
0.026
0.025
(0.119) (0.113)

10.259
(1.157)
0.045
(0.045)
0.006
(0.016)
0.047
(0.057)
0.000
(0.000)
0.013
(0.009)
0.046
(0.029)
0.039
(0.032)
0.033
(0.029)
0.010
(0.013)
0.804
(0.144)
0.019
(0.082)

NOTE: Standard errors are in parentheses.
SOURCE: Author’s calculations.

resolution costs. However, ICORE, the product
of core deposits and a dummy variable for resolution type, accounts for the loss of franchise
value to the acquirer under liquidation. The logarithm of total assets captures the impact of size.
Higher levels of brokered deposits (BRKDEP),
by which troubled banks often staged last-ditch
efforts to stave off failure, may lower costs.
Second, I include measures of general
credit.8 The partial equilibrium analysis suggests that higher levels of general credit imply
a greater increase in the value of the FDIC’s
claim.9 On the other hand, off-balance-sheet
liabilities (OBS ), one item included in general
credit, might allow a reduction in resolution
costs by hedging on-balance-sheet risk. Thus,
if DPL discourages the use of such items, resolution costs could be higher. I include two
other measures of general credit—federal
funds borrowed (FFPURCH ) and federal funds
lent (FFSOLD). Since federal funds are highly
liquid, one would expect that failing banks
which can borrow would have lower costs and
lenders would have higher ones.
Third, intercept and interactive slope dummies allow DPL to affect both the average resolution cost and the impact of each balance-sheet
item on cost. The DPL dummy is equal to one
only for state banks operating under state DPL.
A finding that the intercept is significantly less
than zero would be consistent with the views
of DPL’s proponents. On the other hand, finding that the interactive terms differed with DPL
but not with the average costs would be consistent with general creditors’ offsetting the impact
of DPL. The interactive terms COREDPL and
ICOREDPL give some indication of the role
played by resolution type. ICORE is intended
to capture the loss of franchise value, proxied
by CORE, under liquidation. If DPL encouraged
deposit transfers, then COREDPL, the differential
impact under DPL, would be negative. However, one would expect ICOREDPL to equal
zero, since it is conditioned on resolution type.
If general creditors did not adjust to DPL, then
general credit that tended to increase resolution
costs would have less effect under DPL, since
it would be less likely that such claims would
be paid off.

■ 8 Hirschhorn and Zervos (1990) analyze data on collateralization
for savings and loan associations. Such data are not readily available for
commercial banks.
■ 9 The relevant comparison is between total liabilities before DPL
(D ) and K –G, where K is the new level of total liabilities and G is the level
of general credit.

9

T A B L E

3

The Impact of Depositor Preference
Legislation on Resolution Costs
Variable

constant
CAP
UNCOL
PDNA
OREO
INSLNS
NCRASST
OBSLNS
OBSOTH

Osterberg and
Thomson (1995)

Basic
Model

With DPL
Dummies

69,842
(13,394)a
–1.165
(0.0720)a
4.376
(0.893)a
0.786
(0.049)a
0.453
(0.0560)a
1.775
(0.276)a
0.202
(0.020)a
–0.158
(0.016)a
–0.038
(0.007)a

–8,030.7
(10,310)
–1.1820
(0.2222)a
5.0136
(2.520)a
1.0893
(0.1713)a
0.5222
(0.1593)a
2.4643
(0.7712)a
0.3128
(0.0572)a

–1,267.4
(12,670.0)
–1.2516
(0.2306)a
4.1047
(2.807)
1.1824
(0.1822)a
0.5135
(0.1655)a
2.3718
(0.8193)a
0.3467
(0.0600)a

OBS
FFSOLD
FFPURCH
COREDEP
ICORE

–0.088
(0.010)a
0.062
(0.010)a

LNASST
BRKDEP
DUMNE
DUMSW

–0.095
(0.034)a
5,856.9
(1,692.8)a
1,345.0
(593.1)a

Number of observations
Adjusted R2

Variable

DPL
CAPDPL
UNCOLDPL
PDNADPL
OREODPL
INSLNSDPL
NCRASSTDPL
OBSDPL
FFSOLDDPL

–0.0167
(0.0219)
0.3045
(0.0609)a
–0.3486
(0.0708)a
–0.2011
(0.0370)a
0.0369
(0.0241)
1,048.3
(1,097.0)
–0.0793
(0.0952)
5,111.8
(4,996.0)
–1,133.8
(1,802.0)

–0.0573
(0.0266)a
0.3256
(0.625)a
–0.3358
(0.0744)a
–0.2128
(0.0390)a
0.0311
(0.0251)
170.01
(1,333.0)
0.2777
(0.1675)b
6,856.6
(5,740.0)
577.21
(2,092.0)

1,240
0.3692

1,240
0.3727

FFPURCHDPL
COREDPL
ICOREDPL
LNASSTDPL
BRKDEPDPL
DUMNEDPL
DUMSWDPL

With DPL
Dummies

–48,034
(26,740)b
–0.7300
(1.3260)
6.1371
(7.700)
–0.2377
(0.6672)
–1.5992
(1.120)
–3.1048
(2.871)
–0.5785
(0.2970)b
1.7027
(0.9482)
–1.1600
(1.018)
1.2378
(1.3460)
0.1554
(0.1609)
0.2209
(0.1216)b
5,769.5
(2,943.0)a
–0.3151
(0.2495)
–9,546.4
(12,100)
–158.91
(6,715.0)

a. Significant at the 5 percent level.
b. Significant at the 10 percent level.
NOTE: Standard errors are in parentheses. Observations are weighted by one divided by the square root of total assets. Results in the first
column are from Osterberg and Thomson (1995), table 2, column 1.
SOURCE: Author’s calculations.

The second column of table 3 adds federal
funds categories to the specification of Osterberg and Thomson (1995).10 ICORE (the loss
of franchise value associated with liquidations)
no longer increases resolution costs, and neither off-balance-sheet items nor brokered deposits seem to reduce them. The dummy variable for the Southwest region likewise has no
substantial effects. The significantly positive

coefficient on FFSOLD and the significantly
negative sign on FFPURCH are consistent with
the view that liquidity assessments influence
closure decisions. Banks liquid enough to lend
(sell) federal funds are not closed as quickly as
■ 10 We also substituted LNASST for separate size categories, and
imposed the restriction that the coefficients on OBSLN S and OBSOTH are
equal. The latter restriction was not rejected by a standard F-test.

10

net borrowers, and the delay in closure may be
associated with increased resolution costs.11
The findings for the Southwest dummy and
ICORE are consistent with anecdotal evidence
about regulators’ practice of lending to major
subsidiaries who borrowed federal funds from
minor subsidiaries who borrowed from outside
the holding company.
The third column of table 3 shows the results of adding intercept and slope dummies to
capture any differences in average costs or in
the impacts of the cost determinants. An F-test
implies that we cannot reject the hypothesis
that the DPL intercept and slope differences
sum to zero (F[16,1209] = 1.42). The DPL intercept in the second column indicates that depositor preference is associated with significantly lower resolution costs. However, the
F-test for the addition of that term is only 2.064
(F[1,1024]).12 Few of the interactive terms are
significantly different from zero. This implies
that any decrease in resolution costs from DPL
results from lower totals of balance-sheet items
that increase costs or from higher levels of
items that reduce costs. The impacts of other
risky assets, off-balance-sheet financing, size,
and ICORE are all affected by DPL. Since OBS
activity decreases resolution costs, the finding
here is that one dollar of OBS activity decreases
FDIC costs somewhat less for banks operating
under DPL. The result for ICOREDPL is also
paradoxical, since the loss of franchise value
associated with liquidation should not be affected by any shift toward assisted mergers induced by DPL.

costs during the 1986–92 period were significantly lower for banks subject to such legislation, although the impacts of only a few portfolio share items differ with depositor preference
status. It is notable that the role played by nondepositor claims, such as federal funds and offbalance-sheet items, is not consistent with the
purported mechanism for reducing the FDIC’s
costs. One possible extension of this result, to
be explored in future work, is that DPL affects
the FDIC’s choice of resolution type. However,
the evidence given here does not provide
strong proof that DPL is achieving its intended
benefits.

V. Summary
This paper presents the basic theory of how the
1993 national depositor preference legislation
might reduce the FDIC’s exposure to commercial bank failure by improving the priority of
uninsured depositors. The appendix analyzes
the impact of increased collateralization by general creditors in response to deterioration in
their status. The results are similar to those of
Hirschhorn and Zervos (1990), who analyzed
data on collateralization by savings and loan
associations.
This paper’s empirical section utilizes FDIC
resolution costs and commercial bank balancesheet data from Call Reports to examine the
impact of state DPL in effect prior to 1993. Portfolio shares did not seem affected by whether
banks were operating under depositor preference. On the other hand, failed-bank resolution

■ 11 See Thomson (1992) for more detail regarding this point.
■ 12 The other regression necessary for the comparison (omitting
only the DPL dummy from the list of variables in column 2) is not shown
but is available from the author.

11

Appendix

References

The Impact
of Increased
Collateralization
on the FDIC’s Claim

Chen, A.H. “Recent Developments in the Cost
of Debt Capital,” Journal of Finance, vol. 33,
no. 3 (June 1978), pp. 863–77.

To illustrate how an arbitrary increase in collateralization would affect the value of the FDIC’s
claim, I recalculate the impact of DPL, making
the assumption that collateralized claims increase from zero to C. In the event of failure,
such claims (which belong to the category of
nondeposit claims) are first in line and can take
their collateral from the overall pool of assets.
The cash flows to the FDIC become
if X > B + C,

YFDIC = z
= –Bi + (Bi + z)(X – C )/B

if B + C > X > C,

= –Bi

if C > X.

Comparison with the case prior to DPL and
increased collateralization implies that the
change in the cash flows to a one-dollar parvalue claim are
∆YFDIC = 0

if X > D > B + C,

= ρ – (1+ ρ)(X/D) + 1

if D > X > B + C,

= (1+ ρ)[(X–C )/B –X/D]

if B + C > X > C,

= – (1 + ρ)[X/D]

if C > X > 0.

Here, we have assumed that D > B +C > C.
The decrease in the value of the FDIC’s position can be expressed as
1

(1A) ∆VFDIC = R –1(1 + ρ){F (D) – D CEQ D0 (X~ )
1
– [F (B + C ) – B CEQ BB +C ]
C
– B [F (B + C ) – F (C )]} .

Ely, B. “Surprise Congress Just Enacted the Core
Banking System,”American Banker, vol. 158,
no. 181 (September 21, 1993), p. 24.
Federal Deposit Insurance Corporation.
Failed Bank Cost Analysis: 1986 –1992.
Washington, D.C.: FDIC, 1993.
Hirschhorn, E., and D. Zervos. “Policies to
Change the Priority of Claimants: The Case
of Depositor Preference Laws,” Journal of
Financial Services Research, vol. 4, no. 2
(July 1990), pp. 111–25.
Osterberg, W.P., and J.B. Thomson. “The
Effect of Subordinated Debt and Surety
Bonds on the Cost of Capital for Banks and
the Value of Federal Deposit Insurance,”
Journal of Banking and Finance, vol. 15,
nos. 4–5 (September 1991), pp. 939–53.
, and
. “Underlying
Determinants of Closed-Bank Resolution
Costs,” in F. Cottrell, M.S. Lawlor, and J.H.
Wood, eds., The Causes and Costs of Depository Institution Failures. Boston: Kluwer
Academic Publishers, 1995, pp. 75–92.
Rehm, B.A. “Budget Provision Threatens Credit
of Weak Banks,” American Banker, vol. 158,
no. 148 (August 4, 1993), p. 1.
Thomson, J.B. “Modeling the Bank Regulator’s
Closure Option: A Two-Step Logit Regression
Approach,” Journal of Financial Services
Research, vol. 6, no. 1 (May 1992), pp. 5–23.

12

Cultural Affinity and
Mortgage Discrimination
by Stanley D. Longhofer

Introduction
In October 1992, researchers at the Federal Reserve Bank of Boston released their groundbreaking study on mortgage lending patterns in
that area.1 They found that black and Hispanic
applicants were over 50 percent more likely to
be denied mortgage loans than comparable
whites, even after accounting for such factors as
loan-to-value ratios, obligation ratios, and certain credit-history variables. In the end, they
concluded that this disparity resulted from widespread, systematic discrimination in the Bostonarea mortgage market. Although this study’s
validity has been hotly debated, since its publication a variety of theories have been developed to explain how such discrimination might
persist in a market so many view as being
highly competitive.2
This article reviews and expands one prominent theoretical source of discrimination in the
residential mortgage market: the cultural affinity
hypothesis proposed by Calomiris, Kahn, and
Longhofer (1994; hereafter CKL). This theory
argues that lenders find it easier (or less costly)
to evaluate the creditworthiness of applicants
with whom they have a common experiential
background, or “cultural affinity.” As a result,

Stanley D. Longhofer is an economist at the Federal Reserve Bank
of Cleveland. The author thanks
Paul Bauer, Paul Calem, Charles
Carlstrom, Ben Craig, Stephen
Peters, João Santos, and Mark
Schweitzer for helpful comments
and discussions.

CKL contend that lenders make more mistakes
when evaluating minority applicants, which
gives them an incentive to discriminate against
such applicants.
The CKL depiction of the mortgage market is
based on the idea that lenders find it easier to
assess the true creditworthiness of applicants
with whom they have an affinity. This affinity
may arise because the applicant and the loan
officer share a common cultural background or
because the lender has developed specialized
expertise in evaluating the creditworthiness of
members of a particular group. Thus, a lender’s
affinity may be considered inherent, learned,
or both.3 Regardless of its source, having an
■ 1 Munnell, Browne, McEneaney, and Tootell (1992); hereafter
Munnell et al. This paper was revised and published in the American Economic Review (Munnell, Tootell, Browne, and McEneaney [1996]).
■ 2 For a sampling of criticisms of Munnell et al., see Day and Liebowitz (1993), Horne (1994), and Yezer, Phillips, and Trost (1994). Browne
and Tootell (1995) provide a rebuttal.
■ 3 Ferguson and Peters (forthcoming) emphasize the idea that cultural affinities may arise endogenously because of a lender’s underwriting
activity. Furthermore, lenders may choose to actively develop such affinities; an example of this specialized expertise would be a community development bank targeting low-income and minority neighborhoods. Conversely, some lenders may develop an affinity for white suburbanites
simply because most of their applications come from such individuals.

13

affinity with a group enables a lender to gather
additional information about the true creditworthiness of that group’s members.
The traditional theory of taste-based discrimination was developed by Nobel laureate Gary
Becker (1971). He argues that individuals discriminate against minorities for the same reasons they discriminate between products in the
marketplace—personal preferences. Translated
to the mortgage market, this means that rather
than being “profit maximizers,” bigoted lenders
are “utility maximizers” who are willing to sacrifice profits in order to satisfy their “tastes for discrimination.” They accomplish this by forgoing
some marginally profitable loans to members
of groups that they dislike.4 In other words,
bigoted lenders would require that members of
disfavored groups meet a higher cutoff standard
in order to be approved for loans.
Although the results of Munnell et al. may
suggest that taste-based discrimination is a
problem in the mortgage market, evidence on
default rates contradicts this conclusion. In particular, if taste-based discrimination were prevalent in the home mortgage market, marginally
qualified minority borrowers would default less
frequently than their white counterparts.5
Berkovec et al. (1994), however, analyze the
performance of FHA mortgage loans and show
that the opposite is true: Even after controlling
for other factors associated with credit risk,
black borrowers default significantly more often
than their white counterparts.6
CKL’s cultural affinity hypothesis is important
because it helps reconcile the “Becker Paradox”
posed by the seemingly inconsistent results of
Munnell et al. and Berkovec et al. In particular,
if lenders have an affinity with white applicants,
minority applicants will be held to a higher cutoff standard and be denied loans more frequently.7 In addition, minority applicants who
are actually approved will tend to default more
frequently on average than whites (although the
default rate of the marginal applicants will be
the same for both groups). If regulators insist
that lenders treat all applicants the same, the
average default rates for the two groups will
diverge even further, with marginal minority
applicants (that is, the least creditworthy applicants who are approved) defaulting more frequently than their white counterparts.
CKL’s cultural affinity hypothesis is able to
reconcile the seemingly inconsistent results of
Munnell et al. and Berkovec et al., but their
analysis has at least two limitations. First, they
assume that lenders reject a majority of all
applications they receive. Given that denial
rates over the last several years have ranged

between 15 and 40 percent, it is reasonable to
question this assumption. One might interpret
their analysis as beginning after some initial
screen through which clearly qualified applicants are approved and obviously uncreditworthy applicants are weeded out.8 Alternately,
one might interpret CKL’s model as focusing on
the percentage of the entire population that
receives loans, not just those who actually apply. Under either interpretation, CKL’s assumption that most applicants are rejected might be
more reasonable. Unfortunately, the deficiencies of existing data on denials make it nearly
impossible to test any of the model’s empirical
predictions under either of these stories.
My analysis allows for either possibility, but
concentrates on the assumption that lenders
do, in fact, approve a majority of applications
they receive. The primary by-product of this
assumption is that lenders now have an incentive to discriminate against groups with whom
they have an affinity, typically white applicants. This happens because the added information lenders receive about the quality of
these applicants allows them not only to
approve some who were previously deemed
uncreditworthy, but also to detect and weed
out uncreditworthy applicants who would otherwise have been accepted.
While this result may initially seem counterintuitive, I argue that it actually reflects lender
behavior in the mortgage market more accurately than does CKL’s original analysis. First,
lenders may not act symmetrically on new information they receive about white applicants,
either because they can sell loans to the secondary market, or because such information is

■ 4 It is sometimes argued that bigoted lenders might deny random
applications, rather than selecting the least profitable members of the
group they dislike. Although randomization is certainly possible, utility
maximization would still require that they deny less profitable applicants
more frequently than more profitable ones.
■ 5 See Becker (1993) for a discussion of this idea.
■ 6 Berkovec et al. (1994, p. 282) note, “For example, in the 1987 cohort, black borrowers are predicted to have cumulative default rates that are
about two percentage points higher than white borrowers, all else equal.”
The competitiveness of the mortgage market also makes a conclusion of
taste-based discrimination problematic. Longhofer (1995) provides a brief
discussion.
■ 7 CKL allow for the possibility that lenders can develop a screening
technology for minorities that is equal to the one they use for white applicants. The results discussed here relate to the case where lenders choose
not to invest in that technology because it is too costly.
■ 8 Calem and Stutzer (1995) make this assumption in their model of
statistical discrimination.

14

costly to obtain. Second, even if lenders do
wish to discriminate against white applicants,
these effects may be outweighed by the fact that
the minority applicant pool is less creditworthy
on average than the white one.
A second limitation of CKL’s analysis is its
assumption that the signals lenders receive
from both applicant groups are directly comparable and that outside observers can objectively
measure them. Much of the premise behind the
cultural affinity hypothesis, however, depends
on the fact that many indicators of creditworthiness are subjective and fully observable only by
lenders. I therefore extend CKL’s analysis to
consider the consequences of this feature of
mortgage underwriting, showing how it complicates efforts to detect discrimination.
In addition to correcting these two shortcomings of the model, the present analysis provides
at least three important extensions to the cultural affinity hypothesis. First, it clarifies the
process by which lenders update their prior
beliefs about an applicant’s creditworthiness,
thus making the mortgage underwriting process
more transparent.9 Second, it allows for differences in average creditworthiness across races
and analyzes the impact this might have on the
theory’s empirical predictions.10 Finally, it
examines how other features of the mortgage
market, such as the secondary market and
minority-owned lenders, might interact with cultural affinity to affect outcomes in the model.
In the next section, I develop a simple
model of the mortgage underwriting process in
which lenders have an affinity with members of
one applicant group. I use this model to show
how cultural affinity problems can affect relative denial rates across groups and create incentives to discriminate. I then extend the
model, allowing groups to differ in their average creditworthiness and allowing lenders to
sell their loans on the secondary market. In
section II, I tie all of these results together, discussing some of the model’s empirical and policy implications.

In practice, we can think of these groups as
identifying members of different races, genders, or other protected classes. Policymakers
may wish to know how the distribution of
inferred creditworthiness, the denial rate, or
the likelihood of default differs between these
two applicant groups. Most important, however, they would like to know if either group is
discriminated against. To analyze these issues,
I assume that lenders have an affinity with
members of group W, the effects of which I
will describe in a moment.
Suppose that each applicant has a true creditworthiness θ, which can be thought of as his
or her probability of repaying a loan. In particular, this parameter is assumed to capture all
factors that lead to default, including possible
income disruptions, the value of the house
being purchased, and the borrower’s personal
compunction about defaulting on an obligation.
Based on its cost of funds and the competitive
market interest rate it charges, each lender has
a cutoff creditworthiness θ * that defines which
applications it will approve or reject.11 That is,
applicants with creditworthiness below θ * will

I. A Model
of Mortgage
Underwriting

Although individual lenders seem to announce a single rate to prospective
applicants, the mortgage market as a whole does appear to apply risk-based
pricing. By requiring deposit insurance for lenders with high loan-to-value
ratios, lenders implicitly demand higher interest rates from riskier borrowers. Furthermore, although individual lenders do not appear to price for
other factors associated with risk, Avery, Beeson, and Sniderman (1996)
show that individual lenders sort themselves by selecting the rate–risk
combination with which they are most comfortable. There is even a secondary market for so-called “B” and “C” loans, which are loans that fail to
meet the underwriting guidelines established by Fannie Mae and Freddie
Mac. Thus, although borrowers may not be able to obtain a full menu of
prices from any one lender, they can nevertheless obtain a rate commensurate with their personal risk.

Consider a world in which individuals want to
buy a house but, lacking sufficient cash to do
so, must obtain a mortgage loan. I assume that
individuals are divided into two groups, W and
M; when the variables or density functions introduced below differ between the two groups,
I will use subscripts to denote this difference.

■ 9 For a similar approach, see Cornell and Welch (1996).
■ 10 In this respect, the present analysis incorporates Ferguson and
Peters’ (forthcoming) extensions of the cultural affinity hypothesis. They
show that cultural affinity problems, when combined with differences in
average creditworthiness among races, lead to ambiguous implications
about denial rates across racial groups.
■ 11 I accept as a stylized fact that lenders offer a single interest rate to
all applicants, rejecting those who are not profitable at that interest rate. Yet,
it is worth asking why lenders do not accept all applicants and “price for
risk.” One explanation appeals to antidiscrimination laws: Since creditworthiness is correlated with race, it would rapidly become apparent that
lenders charge minorities higher interest rates. Alternatively, CKL argue that
if lenders have better information about an applicant’s true creditworthiness
than does the applicant, risk-averse borrowers prefer a single offered interest rate with a commitment to lend to all who qualify at that rate. Finally,
Ferguson and Peters (1996) show how “portfolio effects” can make riskbased pricing suboptimal for lenders; in their model, the gains from making
more loans offset the losses from loans to the least creditworthy borrowers
who are approved. Under any of these justifications for offering a single rate
to all applicants, lenders would make loans only to applicants who are sufficiently creditworthy.

15

be rejected, while those who are more creditworthy will be accepted.12
Unfortunately, lenders cannot perfectly
observe an individual’s true creditworthiness.
Instead, they observe a signal, s1, that is correlated with θ. For example, lenders typically collect information about the applicant’s property,
loan-to-value ratio, obligation ratios, credit history, income, employment, and so forth. While
this information can never predict default perfectly, it does allow a lender to infer the likelihood of this event. This signal is observed for
members of both groups and can be verified by
outsiders (such as regulators).
I model cultural affinity by assuming that
lenders receive a second, private, signal (s2 ) for
group W applicants that they do not receive for
group M applicants. As a result, lenders have
more information with which to assess the
creditworthiness of group W applicants than
they have for group M applicants. Intuitively,
we can think of the signal s 2 as encompassing
any subjective information beyond the standard
underwriting variables that lenders gather during the application process. Such information is
often referred to as “compensating factors.” For
example, an applicant may provide information
that explains a past default or job instability. Or
a lender may be willing to make a “character
loan” because he “knows” the borrower is a
good credit risk. Alternatively, the loan interview may give the lender new information suggesting that an applicant really is a bad credit
risk.13 I am assuming that s2 incorporates only
these subjective compensating factors.14
Since the information that lenders receive
about an applicant’s creditworthiness depends
on his group, this difference may give lenders
an incentive to discriminate against members of
one group or the other. I now analyze the
lender’s underwriting decision, starting with the
more simple case of group M applicants.

Group M
Underwriting
For simplicity, assume that applicant creditworthiness is distributed normally with mean θ and
variance σ θ2; denote the respective probability
density function as f (θ). For now, I assume that
this distribution is the same for both groups; this
assumption will be relaxed later. Let p (s 1|θ) be
the probability that the bank observes signal s 1
from an individual of type θ; assume that p is a
normal density with mean θ and variance σs2.15

Using this information, we see that
(1)

E

ωM (s 1) = p (s 1|θ)f (θ)d θ

is the density of signals received by a lender
observing the entire population of group M
applicants; DeGroot (1989, p. 304) shows that
this distribution is normal with mean θ and
variance σ θ2 + σs2. Using this, we define
(2)

πM (θ|s 1)

p (s 1|θ)f (θ)
ωM (s 1)

as the likelihood that a group M applicant with
signal s 1 has a true creditworthiness of θ.
Ultimately, lenders are interested not in the
signal the applicant sends, but rather in the
applicant’s inferred “quality” given this signal.
Under this setup, the inferred quality of any
group M applicant is simply the expectation of
πM (θ|s), which Hogg and Craig (1978, p. 232)
show to be16
(3)

qM (s1) =

s 1σ θ2 + θσS2 .
σ θ2 + σS2

Thus, an applicant’s inferred quality is simply
the weighted average of his signal and the average creditworthiness of the applicant pool; the
weights are based on the precision of each of

■ 12 Clearly, lenders consider more than just the likelihood of default
when evaluating a mortgage application. A more complete analysis would
also incorporate such factors as the likelihood of delinquency (and the
associated costs and fees) and any cross-selling profits (from credit card
or consumer loans) the lender might earn. Such additions should not have
any qualitative effects on my conclusions.
■ 13 Campbell and Dietrich (1983) provide evidence of adverse
selection problems in the market for mortgage insurance around the point
of an 80 percent loan-to-value ratio. Because lenders typically do not require applicants to purchase mortgage insurance when the loan-to-value
ratio is below 80 percent, the fact that they require some such borrowers
to obtain this insurance and that these borrowers default more often suggests that lenders do observe—and act on—negative information about
applicants.
■ 14 It is possible to extend the model to allow these signals to be
observed sequentially, with the lender deciding whether to invest in the
second signal only after observing the value of the first. If the marginal
cost of using the second signal is zero, the results are formally identical to
what I derive below. If it is costly to obtain additional information about an
applicant, then it is feasible that lenders will not wish to acquire this information for some applicants whose initial signal is particularly good or particularly bad. Nonetheless, I believe that my qualitative results would continue to hold in such an environment. If the cost of obtaining the second
signal depends on the value of s 1, however, the results will differ.
■ 15 More generally, one might think of s 1 as the “sample mean” of n
draws from this distribution, with each draw representing one piece of
information collected by the loan officer.
■ 16 This q (s) is analogous to the p (s) used by CKL.

16

these factors. If mortgage applicants vary greatly
in their underlying creditworthiness (a high σ θ2 ),
lenders tend to discount the characteristics of
the applicant population, instead relying more
heavily on the individual’s signal. On the other
hand, if the signals are quite imprecise (a high
σS2 ), lenders are more likely to ignore this signal
and treat all applicants alike by placing more
weight on the population’s average quality, θ.
Based on this signal, a group M applicant will
be accepted if his qM (s1) > q * = θ*.
It will later be useful to know the distribution of inferred quality of the applicant pool.
Since qM (s1) is simply a linear function of s1,
“quality” in the group M applicant population is
normally distributed with mean θ and variance
σ θ4/(σ θ2 + σS2).

Group W
Underwriting
The decision to grant credit to group W applicants is essentially the same as for group M
applicants, except that the lender receives the
additional information, s 2 , with which to make
the decision. Like the first signal, s 2 is a random draw from a distribution with mean θ (the
applicant’s true creditworthiness) and variance
σS2 .17 I will denote the combined signal as
s = (s 1 + s 2 )/2. For an individual applicant with
underlying creditworthiness θ, s is distributed
normally with mean θ and variance σS2 /2.
Thus, distribution of s in the group W applicant population is normal, with mean θ and
variance σ θ2 + σS2 /2.
The fact that this combined signal is more
precisely centered around the applicant’s true
creditworthiness allows lenders to make a better estimate of a groupW applicant’s quality:
(4)

qW (s ) =

s σ θ2 + θσS2 /2 .
σ θ2 + σS2/2

Here we see that a group W applicant’s inferred
creditworthiness is again simply the weighted
average of his signal and the average creditworthiness of the applicant pool. With the information added by the second signal, however,
relatively less weight is placed on the applicant’s group membership and relatively more
on his individual signal.
As before, qW (s ) is normally distributed with
mean θ. The added information, however, increases the variance of this distribution to
(5)

σ4θ
2
σ θ + σS2/2

,

meaning that the distribution of inferred quality
for groupW applicants more closely resembles
the true distribution of creditworthiness in the
applicant pool than does the corresponding
distribution for group M. Recall that the variance of qM (s1) is σ θ4 /(σ θ2 + σS2 ).
The final decision about whether to approve
the loan is the same as it was before. Given the
final value of the signal s, the lender evaluates
a group W applicant’s inferred creditworthiness
and approves the loan only if qW (s ) > q *.

Cultural Affinity
and Discrimination
We are interested in analyzing how a lender’s
affinity with members of group W might affect
its incentive to discriminate against members
of one group or the other and, regardless of
whether the lender discriminates, what impact
affinity has on the relative denial rates of the
two groups.
To do this, it is necessary to define what is
meant by “discrimination.” Simply stated, lenders discriminate if the cutoff signal, s *, differs
among groups, even if there are valid, profitmotivated reasons for their doing so. This definition corresponds to the legal definition of discrimination and is the one that is regularly used
implicitly in policy debates. Of course, actual
discrimination will often be difficult to detect,
since I assume that outsiders (that is, regulators
and econometricians) cannot observe the second signal. As a consequence, lenders will typically be accused of discrimination if the “apparent” cutoff using the initial signal, s 1*, differs
across groups.
I start by focusing on the difference in the
denial rate—the proportion of the applicant
pool that is rejected—across the two groups. In
this simple framework, the denial rate is easy to
calculate as the probability that a randomly
selected applicant’s inferred quality is less than
q *. The denial rate of group M applicants is
then simply
(6)

DM = Φ (

q* – θ
2
2
σθ2/Ïσ
ww+
σw
θ ww
S

),

where Φ is the cumulative standard normal distribution function. Similarly, the groupW denial
rate is

■ 17 As before, s 2 could be modeled as a sample mean of draws
from the density p. By varying the number of observations used to make
up s 1 and s 2, these two signals could be given different relative weights.
All of my basic results would continue to hold in this more general model.

17

(7)

DW = Φ (

q* – θ
2
2
σθ2/Ïσ
ww+
σw/2
θ ww
S w

).

Expressions (6) and (7) make it clear that
which group’s denial rate is higher will depend
on whether the minimum acceptable creditworthiness, q *, is above or below θ, the average
creditworthiness of the population.
If q * > θ, it is straightforward to see that
increases in the dispersion of creditworthiness
in the applicant pool (increases in σθ2) have the
effect of decreasing the denial rate for both
groups. In contrast, when applicant signals are
imprecise (a high σS2), lenders get relatively little useful information about an applicant’s true
quality, and therefore rely more heavily on the
characteristics of the applicant pool. Thus, if
the “average” applicant is uncreditworthy, a
high σS2 will tend to raise the denial rate for
both groups.
Under this assumption about the creditworthiness of the “average” applicant, we get
RESULT 1: If lenders have an affinity with members of group W and if q * > θ, the group M
denial rate is higher than that for group W
applicants.
This result mirrors that obtained by CKL
(theorem 4). Essentially, the added information provided by s2 helps to make the inferred
quality of a group W applicant more accurate,
making “good” signals from group W applicants more credible than “good” signals from
group M applicants. Put another way, because
their signals are not very precise, group M
applicants tend to look more like the “average” applicant, and hence less creditworthy,
than do group W applicants.
It is worth questioning, however, the reasonableness of the assumption that q* is
greater than θ. If this were true, the properties
of the normal cumulative distribution function
would imply that a majority of all mortgage
applicants are rejected. Yet, raw denial rates
for conventional home mortgage loans in the
United States have ranged from 15 to 20 percent for whites and from 30 to 40 percent for
blacks and Hispanics in the last several years,
a fact seemingly at odds with this prediction.
One way to rationalize this assumption is to
argue that the relevant population includes
both mortgage applicants and those who do
not apply because they believe they are not
creditworthy. This is CKL’s implicit assumption.
In this case, it may be reasonable to assume

that only a minority of all (potential) applicants
obtain loans; some are denied loans, while the
rest never even bother to apply, believing that
they will be rejected. An alternate assumption
would be that lenders use some initial screen
to distinguish clearly qualified from unqualified
applicants, obtaining the signals s1 and s2 only
for “marginal” applicants.
The problem with both of these stories is
that they make it impossible to test the model’s
empirical predictions. Home Mortgage Disclosure Act (HMDA) data are inadequate, since
they include only households that actually
apply for loans (not potential applicants) and
do not identify marginal applicants. Similarly,
general home-ownership rates cannot be used
to test the model. Although it is likely that
many households do not apply for a mortgage
because they fear being rejected, there are
other reasons for not becoming a homeowner.
Indeed, many creditworthy households prefer
the flexibility and smaller capital commitment
of renting.
Because of these limitations, I extend CKL’s
analysis to consider what happens when the
cutoff for creditworthiness is below the average
in the applicant pool. I maintain this assumption throughout the rest of this article. Yet, readers should remember that it is also possible to
assume the opposite, and some of the results
that follow are reversed when this is done.
On the surface, it might seem strange that
lenders accept below-average applications.
Indeed, if evaluating applications is costly, the
implication is that lenders would be better off
accepting all applicants without screening.
This, of course, ignores the fact that the makeup of the applicant pool depends on the use of
a screen; lenders that accept all comers will
soon find that everyone will apply, and the distribution of their applicant pool will be much
worse than average. Although I do not model
or discuss this feature of the mortgage market
further, it is an important one to keep in mind.
Once we assume that q * is less than θ,
changes in the model’s parameters have the
opposite effects from those they had before:
The higher σθ2 and the lower σS2 are, the higher
the denial rate will be for both groups. In addition, the presence of the signal s2 now has the
opposite effect on relative denial rates.

18

RESULT 2: If lenders have an affinity with members of group W and if q * < θ, the denial rate is
higher for group W than it is for group M.
Note that, although lenders in this world
require members of both groups to be equally
creditworthy to be approved for a loan, they
will require that group W applicants meet a
higher cutoff signal. That is, the cutoff value of
the final signal s * is, in fact, higher than s 1*.
Why? Because their signal does less to distinguish creditworthiness, members of group M
tend to look like the average applicant regardless of their signal; in contrast, the inferred
quality of group W applicants is more disperse.
Since by assumption the cutoff creditworthiness level is below the average in the applicant
pool (that is, q * < θ ), lenders infer that “marginal” group W applicants (that is, those with
below-average signals) are worse credit risks
than essentially similar group M applicants:
qW (s) < qM (s) for all s < θ. Thus, rational
lenders would require group W applicants to
have a better signal than group M applicants.
Of course, the “better” signal required of
group W applicants does not necessarily mean
that this discrimination will be detected, since
outsiders cannot observe its value. Nonetheless,
lenders’ use of it increases the average s 1 of all
group W applicants, both approved and denied,
giving the appearance that lenders require
members of this group to clear a higher hurdle.
RESULT 3: If lenders have an affinity with
group W applicants, they will want to discriminate against members of group W. This discrimination will be apparent to outsiders.
Although lenders will seem to require members of group W to meet a higher cutoff signal,
this standard will appear more flexible than it is
for members of group M.
RESULT 4: If lenders have an affinity with members of group W, it will appear that members of
group M are held to a rigid cutoff standard,
while regular exceptions are made for members
of group W.
Thus, the initial underwriting decision for group
W applicants will often be overridden. As it
turns out, positive overrides will be outnumbered by negative ones.
Why do negative overrides dominate? With
any signal, lenders make mistakes—they
approve some loans that should be rejected

and reject some that should be approved. Since
the cutoff quality q* is below the average for
the applicant pool, lenders know that truly
“bad” applicants are uncommon. Hence, they
are willing to accept applicants with relatively
low signals, knowing that these signals are
quite noisy. As they gather more information
about applicants, however, they place relatively
less weight on the population’s average creditworthiness and more on the individual applicant’s signal. Consequently, many group W
applicants who really are uncreditworthy will
send a second, marginal signal, and lenders will
correctly ascertain that this information is more
reliable and deny the loan. Of course, there will
be positive overrides as well, but these will be
relatively less numerous precisely because
lenders were giving applicants “the benefit of
the doubt.”
Hence, cultural affinity, combined with the
assumption that q * < θ, leads to three strong
empirical predictions: Groups that share an
affinity with lenders will 1) be denied loans
more frequently, 2) appear to be required to
meet a higher cutoff signal, and 3) be held less
rigidly to that cutoff signal.
One might argue that, on the surface, these
results suggest that the cultural affinity hypothesis is incorrect, since the prediction that group
W applicants are denied loans more frequently
is counterfactual. After all, we are inclined to
think of group W as representing white applicants, who are rejected much less frequently
than blacks and Hispanics. Likewise, it seems
odd to conclude that lenders want to discriminate against white applicants. It becomes clear,
then, that when q * < θ, the cultural affinity
hypothesis in isolation cannot fully explain any
apparent discrimination that may exist in the
residential mortgage market.
There are, however, at least two important
characteristics of the mortgage market that I
have thus far left out of the model. First, it is
well known that black and Hispanic mortgage
applicants are less creditworthy on average than
their white counterparts, which can affect lenders’ inferences about an individual’s likelihood
of repaying a loan.18 Second, the presence of a
large, active secondary market for mortgage
loans gives lenders an incentive to “game” any
subjective information they obtain about an
applicant. Once these facets of the market are
incorporated into the model, we see that the
cultural affinity hypothesis can explain many
features of the residential mortgage market.

■ 18 For evidence of this fact, see Munnell et al. (1996), table 1.

19

Differential Group
Creditworthiness
The analysis above assumes that the two applicant groups are identical; lenders treat them differently only because they receive more precise
signals from members of group W. Yet, black
and Hispanic mortgage applicants are less creditworthy on average than their white counterparts. If the factors that lenders consider in
deciding whether to accept an application cannot fully account for this, lenders will have a
profit-based incentive to discriminate against
such applicants.19 As a result, the effects of cultural affinity may simply be partially offsetting
the stronger effects of average group creditworthiness, whether or not any actual discrimination occurs.
To understand this idea more fully, assume
that lenders have no affinity with either group
(they receive only signal s 1 from members of
both groups). Suppose that group W applicants
are, on average, better credit risks than group
M applicants (that is, θW > θM ) and that lenders
know this. Now recall that an applicant’s inferred quality, q (s 1), is the weighted average
of his signal and the average creditworthiness
of his group. It follows that an applicant from
group W is more creditworthy than a group
M applicant who sends the same signal:
(8) qW (s 1) =
>

s1σ θ2 + θW σs2
σ θ2 + σs2
s1σ θ2 + θM σs2
σ θ2 + σs2

= qM (s1).

Since lenders are willing to make loans only to
applicants whose inferred creditworthiness is at
least q*, they will have an incentive to use a different cutoff signal, s 1* , for group M than they
do for group W. This idea is summarized in
RESULT 5: If the average creditworthiness of
group M is below that of group W, lenders will
rationally want to discriminate against members of group M by requiring them to meet a
higher cutoff signal than is required for members of group W.
Notice that lenders here do not ask members
of group M to be more creditworthy; the minimum acceptable quality is the same for both
groups. However, because lenders cannot perfectly observe an applicant’s true creditworthiness, and since the applicant’s group is correlated with creditworthiness, lenders will want

to account for this when deciding whether to
make a loan. Since q is increasing in s1, lenders
in this world will want to set a higher cutoff
signal, s 1*, for group M applicants than for
group W applicants. This behavior would constitute classic statistical discrimination.
The denial rate of each group in such a
world is easily calculated as

(9)

s * – θW
DW = Φ ( W
) , and
2
2
Ïσ
ww+
σw
θ ww
s
DM = Φ (

sM* – θM

).
2
2
Ïσ
ww+
σw
θ ww
s

From this expression, it is clear that even
if lenders did not discriminate and used the
same cutoff signal for both groups, the denial
rate would be higher for group M; the fact
that lenders would like to use a more stringent
cutoff signal for members of group M just exacerbates this effect, making the difference in
denial rates between the two groups even
more dramatic.20
RESULT 6: When members of group M are less
creditworthy on average than members of group
W, the denial rate for group M will be higher
than that for group W, even in the absence of
discrimination; when lenders discriminate, this
disparity becomes still greater.

Cultural Affinity with
Differential Group
Creditworthiness
When we return to the assumption that lenders
have an affinity with members of group W, we
see two countervailing effects. On the one
hand, the denial rate for group M is higher because of its members’ lower average creditworthiness and any actual statistical discrimination
that occurs. On the other hand, the added
information provided by the second signal
causes lenders to deny loans to group W applicants more frequently and to discriminate

■ 19 Calem and Stutzer (1995) develop an alternative model of statistical discrimination based on adverse selection in the mortgage market.
■ 20 It is worth noting that this result does not depend on the
assumption that q * < θ.

20

against them. The final outcome in the mortgage market depends on the balance between
these two effects.21
By comparing the denial rates of the two
groups, it is easy to see that group M applicants will be denied loans more frequently if
their average creditworthiness is sufficiently
low:
(10)

θM < q * + ( θW – q*)

2
2
Ïσ
ww+
σw/2
θ ww
s w.
2
2
Ïσ
ww+
σw
θ ww
s

RESULT 7: Assume lenders have an affinity with
members of group W. If the average creditworthiness of group M is sufficiently low, the
denial rate of group M will exceed that of group
W. When lenders discriminate against members
of group M, such discrimination may or may
not be detected by outsiders.
This last conclusion follows because cultural
affinity creates the appearance that lenders are
discriminating against members of group W,
offsetting the effects of their discrimination
against members of group M.

Secondary Market
Distortions
Another factor that can alter the effects of cultural affinity in the home mortgage market is the
presence of a secondary market for mortgage
loans. Recall that I originally assumed that
lenders used the signal s2 to update their evaluation of group W applicants’ creditworthiness,
approving some applications that were initially
rejected and rejecting some that were initially
approved. Ultimately, I argued, negative overrides outweighed positive ones, leading to
higher denial rates for groupW applicants.
Yet, anecdotal evidence suggests that many
lenders seem only rarely to reject applicants
who have passed the initial screen, raising the
question of whether negative overrides really
do outnumber positive ones. Once a group W
applicant has been approved using the first
(objective) signal, lenders may choose to ignore
any additional “bad” information they receive
about that applicant or, perhaps more likely,
may never bother to observe the second signal
at all. If we treat the first signal as a proxy for
the information that secondary market institutions—Fannie Mae and Freddie Mac—require
to purchase or guarantee a loan, lenders that
sell their loans to the secondary market may
have no incentive to consider negative informa-

tion about applicants whom secondary market
agencies are willing to approve.22
Positive information, however, will always be
used by a lender. First of all, Freddie Mac and
Fannie Mae guidelines allow originators to consider compensating factors when evaluating an
application. If the originator can document an
applicant’s creditworthiness, his or her loan will
still be salable on the secondary market if it fails
to pass muster based on the initial signal. Furthermore, even if the lender cannot document a
loan’s quality to the secondary market’s satisfaction, lenders can choose to hold obviously creditworthy applications in their own portfolios.
Thus, there are at least two strong reasons why
an originator might use the second signal to
make positive overrides, while never rejecting
applicants who pass the initial screen.
In such a world, we get
RESULT 8: Assume that lenders have an affinity
with members of group W. When lenders ignore
“bad” information contained in the signal s2 ,
group M applicants will be denied loans more
frequently than group W applicants. Furthermore, even in the absence of discriminatory
behavior, lenders will appear to discriminate
against group M applicants (that is, appear to
require them to pass a more stringent signal).
Finally, it will be apparent to outsiders that
group M applicants are held more rigidly to
traditional underwriting standards than are
group W applicants.
Effectively, lenders collect only the second
signal from group W applicants with s 1 < s 1*;
the rest are immediately approved and sold to
the secondary market. Now, it is groupW
applicants who are given the “benefit of the
doubt,” since they have the opportunity to
overcome a poor initial signal with new information. In contrast to before, cultural affinity
problems can exacerbate differences in denial
rates that arise if group M applicants are less
creditworthy on average.23

■ 21 Note that if q * > θ, the effects of cultural affinity and differential group creditworthiness compound one another to the detriment
of group M.
■ 22 Alternatively, one could imagine that an individual loan officer
might have little incentive to relay negative information about an applicant
to the loan committee, since doing so would reduce the chance of earning
a commission.
■ 23 If q * > θ, both the disparity in denial rates and the apparent discrimination are even more pronounced.

21

Finally, note that I would reach the same
conclusions even without a secondary market,
as long as lenders found it more costly to verify
“good” initial signals than “bad” ones. This
might occur, for instance, if regulations make it
difficult to justify denials based on the information contained in the second (subjective) signal.
If this were the case, lenders would make only
positive overrides, and result 8 would hold even
in the absence of a secondary market.

discriminate against group M applicants. Nonetheless, the possibility of detection and punishment will cause them to discriminate less than
they would in the absence of fair lending laws.
As a result, they will select a lower cutoff signal for group M applicants and a higher one
for group W applicants, making the implied
creditworthiness of marginal group W applicants higher than that of group M applicants.
In other words, enforcement of fair lending
laws can lead to default rate disparities like
those found by Berkovec et al. (1994).

A Note on
Default Rates
In the introduction, I argued that Munnell et al.’s
conclusion of taste-based discrimination was
unsatisfying partly because of evidence that
marginal black and Hispanic borrowers default
more frequently than their white counterparts.
Up to this point, we have not discussed the
impact of cultural affinity on default rates.
In its purest form, discrimination that arises
either from cultural affinity or from differential
group creditworthiness will have the effect of
equalizing the probability that a marginal applicant in each group defaults. Lenders choose
their cutoff signals to ensure that the inferred
quality of the last applicant approved will earn
them a non-negative expected return. But this
just means that their expected probability of
default is the minimum acceptable to the firm:
qM (s1* ) = qW (s * ) = θ* .
As a result, discrimination of this sort will not
lead to cross-racial differences in the default
rates of marginal applicants. Indeed, it will tend
to equalize these default rates, even if other factors tend to pull them apart. In the real world,
however, setting different cutoff signals for different groups is patently illegal. As a result, fair
lending laws can create disparities in the default
rates of marginal applicants across groups, even
where none would have existed otherwise.
To see this, consider the case of a portfolio
lender—that is, a lender that does not sell its
loans on the secondary market—with a cultural affinity for group W applicants in a market where group M applicants are less creditworthy on average. Suppose also that θM is
low enough to make lenders want to discriminate against group M applicants by using a
higher cutoff signal. Finally, assume that the
more a lender’s cutoff signal varies across
groups, the more likely regulators are to detect
and punish that lender for discrimination. In
such a world, lenders may well choose to

II. Concluding
Thoughts
Of course, in the real world there are many different lenders. Most high-volume lenders sell a
large proportion of their loans to the secondary
market, while other, typically smaller, institutions hold most of their loans in their own portfolios. As a consequence, we would expect to
see different market behaviors by different
types of lenders. Furthermore, there is strong
evidence to suggest that minority applicants
are, in fact, less creditworthy on average than
their white counterparts. Putting all of these
ideas together allows us to make some strong
empirical predictions that seem consistent with
what we see in the mortgage market.
Assuming that most lenders have an affinity
with white applicants and that the cutoff “quality” is below the average creditworthiness of
both groups, we would expect to see large
lenders that sell most of their loans to the secondary market rejecting minority applicants
more frequently than white ones, whether or
not they discriminate. Furthermore, because
they regularly make exceptions for marginally
qualified white applicants (positive overrides),
these lenders will appear to discriminate against
minority applicants by holding them to a more
stringent credit standard. If such lenders actually
do discriminate against minority applicants,
these effects will merely be exacerbated. Finally,
the few loans that such lenders hold in portfolio
will tend to be from white applicants and will
perform better than ostensibly similar loans sold
to the secondary market.
In contrast, small portfolio lenders will show
fewer outward signs of discrimination. Although
they too will likely reject relatively more minority applications, the difference in denial rates
will be less stark than at larger institutions. Indeed, if the distribution of minority creditworthiness in the applicant pool is sufficiently good,

22

there may be no difference in denial rates at
these institutions. This moderated denial rate
differential, however, will not arise because of
more frequent lending to minorities. Rather, it
will result from the fact that such lenders do not
pass off their “bad” white loans to the secondary market, but reject them instead. Finally,
if these small lenders do discriminate against
minority applicants, it may be difficult to detect
this behavior. Because they refuse more white
applicants than larger lenders do, the disparity
in their denial rates may still appear less severe,
even when they hold minority applicants to a
more stringent signal.
Existing empirical evidence seems consistent
with these implications of the cultural affinity
hypothesis. Black and Hispanic mortgage applicants are rejected more frequently than whites,
as evidenced by HMDA data over the last six
years. Furthermore, Hunter and Walker (1996)
show that higher denial rates for blacks and Hispanics seem to result from lenders giving more
“breaks” to white applicants, meaning that
blacks and Hispanics seem to be held much
more rigidly to standard underwriting criteria.24
Finally, it is worth asking what happens
when lenders have a cultural affinity with black
and Hispanic applicants rather than with whites.
This might be true, for instance, for minorityowned institutions and other lenders that make
a particular point of marketing to minority communities. Typically, such institutions are smaller
portfolio lenders. In contrast to white-owned
portfolio lenders, however, these institutions
tend to deny minority loans at an even higher
rate than other lenders. This happens because
their affinity makes them better at weeding out
unprofitable minority applications and keeping
only those that are sufficiently creditworthy. Just
as white-owned institutions appear to discriminate against white applicants, black-owned
institutions seem to hold minority applicants to
harsher, if more flexible, underwriting criteria.
However, in contrast to its effect on the white
applicants discussed earlier, this discrimination
exacerbates the higher denial rate that would
arise from the deficiencies of minority applicants’ average credit prospects.

It may seem strange to suggest that blackowned banks are more likely to discriminate
against black applicants, but recent evidence
suggests that this may well be the case. Black,
Collins, and Cyree (forthcoming) use logistic
regression techniques (similar to those of
Munnell et al.) to show that black-owned banks
also appear to require that black applicants
meet a higher cutoff credit standard. The theory
of cultural affinity can, therefore, explain this
seemingly counterintuitive finding.
The above empirical predictions make it
clear that detecting and eradicating discrimination in a market with cultural affinities can be
quite difficult. In particular, the observable behavior of a discriminating lender will vary according to its position in the market (that is,
whether it is a portfolio lender or one that sells
its loans to the secondary market, and whether
it specializes in making minority loans). Focusing solely on a lender’s denial rates provides little, if any, information about its true actions.
Instead, examiners must concentrate their efforts on understanding the makeup of the lender’s applicant pool and the screen it is using to
evaluate those applicants. By comparing a lender’s denial rate with that implied by its credit
standards and applicant population, regulators
can more accurately determine what is causing
the denial rate disparities: the effects of cultural
affinity, differential group creditworthiness, or
illegal discrimination.

■ 24 See also Bostic (1995). Interestingly, he finds that minorities
receive favorable treatment regarding loan-to-value ratios, but face negative biases with respect to obligation ratios.

23

References
Avery, R.B., P.E. Beeson, and M.S.
Sniderman. “Posted Rates and Mortgage
Lending Activity,” Journal of Real Estate
Finance and Economics, vol. 13, no. 1 (July
1996), pp. 11–26.
Becker, G.S. The Economics of Discrimination,
2d ed. Chicago: University of Chicago Press,
1971.
———— . “Nobel Lecture: The Economic Way
of Looking at Behavior,” Journal of Political
Economy, vol. 101, no. 3 (June 1993),
pp. 385–409.

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