Does Competition Reduce Racial
Discrimination in Lending?
Greg Buchak
Adam Jørring
University of Chicago
University of Chicago
June 27, 2016
Abstract
This paper examines whether increases in bank competition reduce discriminatory practices in mortgage lending. It demonstrates that lenders
are significantly less likely to approve black applicants’ loan applications
despite facing similar credit risk. However, following the relaxation of
interstate bank branching laws in the 1990s, increases in local lending
competition reduced the approval differential between potential white
and black borrowers by roughly one quarter. The reduction was driven
partially by incumbent banks altering their lending policies, and partly
by the entry of new banks. Incumbent banks that were less likely to
lend to black applicants lost more market share than other incumbents.
While both entry and competitive pressures significantly reduced racial
discrimination in lending, the results suggest that neither force completely eliminates it, suggesting that there is room for both market-based
and direct legal approaches targeting discrimination.
1
Introduction
A policymaker seeking to reduce racial discrimination in a market faces two
choices: Use legal sanctions to force discriminators to change their behavior,
or reduce barriers for non-discriminating competing businesses to enter the
market and thus pressure the discriminators to leave or reform their policies.
The former approach directly raises the cost of discriminating through the
threat of direct litigation; the latter indirectly raises the cost of discrimination
through the threat of market share losses to competitive entrants. Skeptics
of the direct approach may fear behavioral distortions, costly compliance, and
difficulty in enforcement. Skeptics of the market-based approach may question
its strength and empirical relevance.
This paper draws on a natural experiment to examine the potential strength
of market forces in reducing discrimination and explore the channel by which
they do so. It begins by examining racial bias in acceptances and rejections of
home mortgage applications during a time period in which changes to interstate bank branching laws led to exogenously increased competition in local
lending markets. In particular, it compares the impact of a potential borrower’s race in whether economically similar loan applications are accepted
or rejected before and after the relaxation of interstate bank branching restrictions. When lenders face similar ex-ante expected losses, black borrowers
in the early 1990s were roughly eight percent less likely to have their loan
applications accepted than white borrowers. This lending differential closed
by roughly one quarter as lending markets became more competitive when
states relaxed interstate bank branching laws during the mid-1990s and early
2000s. Two factors led to the gap narrowing: First, previously discriminating incumbent banks lost more market share than non-discriminating banks
and subsequently changed their behavior to lend to black applicants. Second, the banks that entered new markets tended to discriminate less than
the incumbents. These findings confirm and quantify economic predictions
that increased competition will reduce but not necessarily eliminate racial discrimination, and that a taste for discrimination is costly to the discriminator.
2
Additionally, they suggest that policy makers seeking to reduce discrimination
should take seriously the role that policies designed to increase competition
can play in achieving their goals.
Discrimination based on an mortgage applicant’s race has been illegal
since the Fair Housing Act of 1968. Additionally, the Community Reinvestment Act of 1977 makes discrimination based on the racial makeup of the
applicant’s neighborhood illegal. Despite its illegality, numerous case studies
conducted after the passage of these laws, documented by Benston (1979), indicate that the practice continued to be widespread. To facilitate monitoring,
the Home Mortgage Disclosure Act of 1975 (HMDA) requires lenders to report
acceptances and rejections of all mortgage applications, along with the applicants race, location, and income. Empirical evidence following HMDA has
been mixed on whether widespread discrimination actually occurs. Holmes
and Horvitz (1994) study lending in Houston between 1998 and 1991 and find
little evidence for discrimination against areas with high minority populations. Tootell (1996) find a similar result in Boston in 1990. The conclusions
are that after controlling for economic conditions in the area, the area’s racial
makeup has little explanatory power. Instead, what appears to be discrimination against minority areas is actually due to poor economic conditions that
are correlated with an area being a high-minority area.
There is more evidence for discrimination against individuals. Charles and
Hurst (2002) find that black mortgage applicants are 73 percent more likely to
be rejected than whites after controlling for economic observables. This paper
also looks for evidence of discrimination against individuals, and finds that it
is widespread, particularly when lending markets were less competitive before
interstate branching was allowed. Moreover, while Holmes and Horvitz (1994)
and Tootell (1996) studied discrimination at particular places and times, this
paper looks for whether incidences of discrimination vary across times and
locations.
The paper’s primary focus is on how competition impacts discrimination.
After measuring discrimination, it tests whether discrimination changes with
changes in lending competition within an area. The theory that increased
3
competition should decrease racial discrimination dates to Becker (1971). In
short, those with a preference for prejudice must pay for it. Becker focuses on
racial discrimination in the labor market. In a market with discriminatory and
non-discriminatory agents, the non-discriminatory agents face lower costs because they are willing to hire minority, whom the discriminatory agents are less
willing to hire. Because there is less demand for minority workers, their wages
are lower, so the non-discriminator faces lower labor costs on average and is at
a competitive advantage relative to the discriminator. Wage differences among
races can persist in equilibrium, but entry of new non-discriminatory agents
will reduce the differences. Moreover, in markets with large wage differences,
the reward to entry for a non-discriminator is high. These effects suggest that
markets that previously saw large wage differences should see those differences
diminish as competition increases.
Similar arguments apply in the context of lending. Discriminatory lenders
forgo profitable lending opportunities by not lending to African Americans or
by charging higher rates. Non-discriminatory lenders who are willing to make
these loans therefore have access to better lending opportunities than discriminatory lenders. To the extent that non-discriminatory lenders are allowed to
enter the market, it will be profitable to do so, and their entry should reduce the
wedge between minority and non-minority borrowers. Moreover, they should
put downward pressure on lending rates to local whites and upward pressure on
deposit rates for local depositors, leading to worsening business conditions for
incumbents who continue to discriminate. Therefore, lenders demonstrating
a larger preference for discrimination should disproportionately lose lending
market share as competition increases, even relative to incumbents who discriminate less. Peterson (1981), and more recently, Zenou and Boccard (2000)
consider a theoretical formalization of this argument. Under particular assumptions, a corollary of Becker (1971) is that if there is racial discrimination,
loans made to minorities should perform better on average than those made
to whites because conditional on receiving a loan, the minority borrower must
have been a better borrower ex-ante.1 Berkovec et al. (1994) find that on
1
Yinger (1996) spells out and criticizes these assumptions. In particular, one must
4
the contrary, for a sample of loans originated between 1987 and 1989, default
rates were higher among minorities than whites after conditioning on other
observable risk factors.2
This paper’s test for discrimination is more direct. Using applicationlevel data collected after the passage of the Home Mortgage Disclosure Act
(HMDA), it tests whether conditional on observables, the race of an applicant
impacts her probability of loan approval. The paper then asks whether an
increase in competition reduces the impact of race on the lending decision,
and find that it does. Using the passage of Interstate Banking and Branching
Efficiency Act as a source of exogenous variation in banking competition, the
paper shows that following its passage, treated states saw approximately a
one-quarter reduction in disparities in loan acceptances between similar black
and white borrowers. Moreover, a one standard deviation increase in the level
of a state’s lending competition causes nearly a sixty percent reduction in the
amount of discrimination taking place.
Having provided evidence on racial discrimination and how increased competition leads to reduced discrimination, the paper conducts an analysis of
what happens to banks’ racial lending practices and what happens to discriminating banks’ market shares following the implementation IBBEA. In particular, the paper finds that discriminating banks lose greater market share
than banks that do not discriminate. This is consistent with the predictions
of Becker (1971). Next, the paper shows that as local banking markets become more competitive, banks in those markets become change their behavior
assume (1) unobserved credit characteristics are not correlated with race, (2) black and white
applicants receive equal treatment in foreclosure proceedings, and (3) the losses incurred
when a black borrowers defaults are at least equal to those incurred when a white borrower
defaults.
2
Berkovec et. al. examine FHA-insured loans between 1980 and 1990. They find that
loans made to black applications perform worse than loans made to white applicants with
similar observable characteristics. If lenders were discriminating, they argue, loans made to
black applicants should perform better because the marginal black applicant needs to be a
better ex-ante credit risk than the marginal white applicant. They examine performance of
FHA loans for data-availability purposes. However, because the loans are insured by the
FHA, the lender has little exposure to loan performance and hence it is not clear why the
ex-post default probability is a major concern for the lender. See Yinger (1996) for more
discussion of the Berkovec/default-rate methodology.
5
and discriminate less. Finally, the paper quantifies the extent to which overall reductions in discrimination have been driven by incumbent banks changing behavior and the extent to which reductions in discrimination have been
driven by new entrants that discriminate less. The data show that both that
incumbents discriminate less, and that the new entrants discriminate slightly
less than the reformed incumbents. The paper’s findings suggest that policy
makers looking to reduce non-economic discrimination should consider marketbased approaches and direct-sanctions as complementary in eliminating discrimination.
The paper proceeds as follows: Section 2 details the empirical design and
data sources. Section 3 presents the main results regarding discrimination and
competition. Section 4 investigates what happens at discriminating banks.
Section 5 discusses the implications of the paper’s findings, and Section 6
concludes.
2
2.1
Empirical Design and Data
Empirical Design
Defining and measuring racial discrimination is central to this paper. Here,
it defines racial discrimination as occurring when a borrower’s race impacts
the lender’s decision of whether or not to lend even though the loan offers the
lender the same ex-ante expected return. Quantification of racial discrimination presents a challenge for two reasons: First, there is a data problem in
that loan officers deciding whether to issue a loan observe more about applicants’ ability to repay than is recorded in HMDA or is otherwise available.
For instance, while HMDA records an applicant’s income, it does not record
an applicant’s credit score, which would be available to a loan officer. Second, an applicant’s race may serve as a proxy for economically relevant factors
impacting the applicant’s ability to repay the loan that are unobservable to
both loan officer and researcher. That is, a loan officer may refuse a loan to a
black applicant who is otherwise observationally equivalent to a white appli-
6
cant that he would grant the loan to. The refusal may not be due to the loan
officer’s disliking lending to minorities, but rather because he believes (rightly
or wrongly) that race is correlated with the applicant’s ability to repay. For
instance, race may be correlated with differences in future economic prospects
not accounted for by other observables. Under this paper’s definition, this
would not be racial discrimination, and therefore this paper seeks to avoid
including lending differences between races resulting from these differences in
its measure of discrimination.3
This paper overcomes this challenge in two ways: First, it looks for racial
disparity among loans whose expected losses to lenders are as similar as possible. To do this, it first restricts the sample to loans applied for under the
Federal Home Administration (FHA) insurance program. To qualify for FHA
insurance, loans must conform to certain underwriting standards including
credit score and income requirements.4 Once approved by the FHA, the lender
has the final say in whether to make the loan. An FHA-approved borrower
pays an insurance premium to the FHA, and in exchange, the FHA insures
the lender in case of default. As per Berkovec et al. (1994), FHA loans are
not primarily rationed based on price, but rather through qualification standards imposed by the FHA. Hence, FHA-loans, conditional on approval by the
FHA, offer the same expected return to lenders because lenders do not bear
credit risk upon the borrower’s default.5 HMDA does not record whether a
3
The lender’s using a race as a proxy for economically important unobservables to make
a lending decision is often termed “statistical discrimination.” It is illegal in lending markets.
however, because it may genuinely reflect diffferent economic risks in making the loan, it is
not the type of discrimination this paper attempts to identify. Instead, this paper attempts
to isolate “pure” non-economic discrimination.
4
See Appendix 7.1 for details.
5
Note that expected losses to the insurer —the FHA—may differ significantly if race is
a predictor of economically relevant outcomes. While denying loans on this basis may be
socially desirable, from the economic calculus of the lender, this is still discrimination in
the sense that the lender is using race as a factor in rejecting loans that offer him the same
expected return. The existance of this potential moral hazard problem is a broader policy
question regarding the implementation of the FHA-insurance program and while interesting,
is beyond the scope of this paper. This paper paper avoids making broad welfare statements
of whether the relaxation of branching laws exascerbated this moral hazard problem, and
simply measures discrimination occuring at the level of the economic decision-maker.
7
loan applying for FHA insurance indeed qualifies for FHA insurance but only
whether the borrower applied for the loan under the FHA program. The FHA
may deny the loan if the borrower fails to meet its minimum lending standards.6 Thus FHA applications in the sample may be denied either due to
failure to meet the minimum requirements of the FHA or due to the lender’s
decision to not extend credit even though the FHA has approved the loan.
FHA requirements, however, are mostly objective and quantitative, and
most of the requirements are observable in the data set.7 Hence, by controlling
for a rich set of observables, including most observables relevant to the FHA’s
objective standards, the paper is able to compare loans that are more-or-less
equally likely to be approved by the FHA but only differ on race, which is
not a factor in the FHA’s decision. Thus, when comparing loan application
A with a white applicant and loan application B with a black applicant that
are otherwise identical on observables, if A was accepted and B was not, the
inferences is that both loans met FHA standards and hence the lender for
B would have been insured.8 The paper therefore identifies B’s rejection as
racially motivated discrimination. Table 1 illustrates the intuition:
(1)
(2)
(3)
A Outcome
Accepted
Rejected
Accepted
B Outcome
Accepted
Rejected
Rejected
Interpretation
Both approved at FHA and bank
Both denied at either FHA or bank
A approved at both; B Denied at Bank
Table 1: Illustration of empirical design. Identification comes from differenes
in outcomes for observationally similar loans that differ on race. If loan A with
a white borrower and loan B with a black borrower are observationally identical
and A is accepted, the inference is that A met FHA standards and therefore
so did B. Because the lender would be insured on B, the paper identifies B’s
rejection as being racially motivated.
Still, measures of FHA compliance may be noisy, particularly because
6
The minimums are fairly leinient. See Appendix Section 7.1 for details.
The notable exception is the applicant’s credit score, which the paper proxies for using
census-tract level economic information and zip-level credit information.
8
As an even more conservative robustness check, Section 7.2.2 constructs a sample of
applications indexed by borrower, where the data is conditioned on the borrower applying
for multiple FHA loans and getting approved for at least one loan. This insures that this
subsample of borrowers are indeed approved for FHA insurance. Among this subset of
borrowers, the results are similar.
7
8
HMDA does not record the individual’s credit score. The paper’s proxy for individual credit score—the credit conditions inside the applicant’s ZIP Code—
are imperfect. Moreover, even if FHA compliance is well-measured, lenders
still bear the risk of losses from default due to reputational concerns, due to
fear of an FHA audit if too many insured loans default, or due to defaults
before insurance coverage begins. Therefore, this strategy may still overstate
discrimination to the extent that applicant’s race is predictive of these residual losses. The primary concern with this paper, however, is not the level
of discrimination per se, but rather how discrimination changes in response
to increased competition.9 This motivates the second prong of the paper’s
empirical strategy.
It is typically difficult to identify the causal link between increases in competition and endogenous economic outcomes because changes in competition
in an area are often themselves endogenous responses to economic conditions.
In the context of lending, suppose that a positive economic shock is anticipated
in an area that will disproportionately benefit minorities. In response, banks
are more likely than before to lend to minorities. Simultaneously, anticipating
better lending conditions in the future, outside banks enter the market and increase local competition. Regressing changes in proportional minority lending
on changes in competition will yield a positive coefficient even though both
effects were caused by the (unobservable) changes in local economic outlook.
Of particular concern in this paper is whether competition is correlated
9
Suppose that the true degree of discrimination in state s at time t, dst , is mismeasured
as
dˆst = dst + ηst
Where ηst is (non-necessarily mean-zero) mismeasurement coming from the concerns raised
above. If γ is the true causal effect of how discrimination dst varies with competition cst ,
regressing dˆst on a measure of competition will yield the following estimator in the limit:
p
γ̂ −
→γ+
cov(ηst , cst )
var(cst )
γ̂ will be a consistent estimator for γ so long as the mismeasurement ηst is uncorrelated with
the measure of competition. A standard instrumental-variables approach detailed below will
provide exogenous variation in cst so that the the measurement error in discrimination is
uncorrelated with the variation in competition.
9
with mis-measurement in discrimination. For instance, consider the case of
an urban zip code with many affluent white borrowers and many poor black
borrowers. Credit score is only observed at the zip level, but the poorer black
applicants will tend to have lower credit scores than the affluent white borrowers. A white borrower here will be more likely to comply with FHA standards
than a black borrower because of the credit score difference, but this difference is not observable in the HMDA data. The paper will misidentify this as
discrimination even though it is due to measurement error. Simultaneously,
this measurement error occurs in an urban area with significant lending competition. To the extent that these within-zip credit-score disparities occur
systematically in areas with high banking competition, the measured impact
of competition in reducing discrimination would be biased towards zero because the the paper mismeasures discrimination as being high precisely in
areas where competition is high.
To address these concerns, this paper uses the staggered implementation
of the Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994
(IBBEA). Before this act, banks were not allowed to branch across state lines.
Following its passage, states were able to relax branching laws into their states.
When states allowed branching, local banking markets were opened to nationwide competition. Many papers have used the IBBEA as an instrument for
increases in bank competition to study subsequent lending outcomes. Favara
and Imbs (2015) show that branching deregulation significantly increased the
supply of mortgage credit and consequently housing prices. Closer to this
paper, Tewari (2014) finds that the relaxation of these laws increased consumer
credit access, particularly to low-income households and minorities. This paper
differs from Tewari in that it focuses on changes in discriminatory lending
to minorities rather than credit access per se. That is, while Tewari finds
increased credit availability to minorities following the branching deregulation,
this paper provide a mechanism for this increased access—a provides in racial
discrimination.
The use of the IBBEA as an instrument for banking competition completes the primary identification strategy. Recall the concern that omitted
10
variables may bias upwards the estimate of discrimination because FHA decisions and residual losses to lenders even conditional on FHA approval may be
correlated with factors prediction default—particularly credit score. Because
the paper is chiefly concerned with changes in discrimination, however, the law
change will be a valid instrument for how competition impacts discrimination
so long as the law change is uncorrelated with measurement error in lending
discrimination. This identifying assumption sidesteps the issue of consistently
estimating the level of discrimination by allowing a consistent estimate the
impact of competition on discrimination.
2.2
Data
Before proceeding to the analysis, this section summarizes the data used in
the paper. The unit of observation for all following regressions is a loan application from HMDA. HMDA records, among other things, the outcome of
the application, the borrower’s race, income, loan amount, year, census tract,
and bank at which the lender applies.10 It also has an indicator for whether
the application was done as an FHA loan. The following table summarizes the
individual-level used in the regressions.11 Importantly, roughly 80% of loans
are accepted; roughly 16% of applicants are black.
The study merges the individual HMDA data with data at the censustract and zip-code level. The census-tract level data is from the decennial
census conducted in 1990 and 2000.12 Data for intermediate years uses the
most recent census. The zip-level data are updated yearly and are consumercredit data from equifax. Importantly, this data contains measurements of
10
Regarding loan acceptance: HMDA codes outcomes as (1) Loan Originated, (2) Application approved but not accepted by borrower, (3) Application denied by the financial
institution, (4) Application withdrawn by applicant before a credit decision, (5) File closed
for incompleteness, (6) Loan purchased by the institution, (7) Preapproval denied, and (8)
Preapproval granted but not accepted by the applicant. This study counts (1), (2), and
(8) as acceptances; it counts (3), (4), (5), and (7) as denials. Regarding race: The study
compares only white and black borrowers.
11
Table 12 in the appendix has the full set of covariates.
12
At the census-tract level: % Black Residents, median household income, % adults with
less than a high-school education, % adults with a college education, % of the census living
in poverty.
11
Table 2: Summary Statistics. See Table 12 for full covariates.
Variable
Accepted
Black
Income
Loan Amount
Post
Herf
N
Mean
St. Dev.
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
0.787
0.161
46.305
81.175
0.529
0.052
0.409
0.367
145.944
300.825
0.499
0.038
credit scores and defaults within the zip code.13 Finally, at the state-year level,
the paper uses information on the relaxation of IBBEA from Rice and Strahan
(2010), which includes both the first time an IBBEA reform was implemented
in the state and an index measuring the degree of deregulation. Competition
is measured as a Herfindahl index of all (including non-FHA) accepted loans in
a county. Because the exogenous variation is at the state level, the Herfindahl
index is aggregated from a county to a zip level, weighted by county share of
loans.14
The main specifications are a linear probability model of acceptance as
follows.
13
At the zip-year level: % with credit scores below 620, % with credit scores below 640,
% with credit scores below 660 (subprime), % of total credit accounts in default, % of
mortgages in default, % consumers with mortgages, % consumers with home equity loans,
% consumers with credit cards, and % consumers with auto loans.
14
In particular, within state-county-year:
ACCEP T EDbsct
b∈s,c,t ACCEP T EDbsct
X
2
=
SHAREbsct
∈ [0, 1]
SHAREbsct = P
HERFsct
b∈s,c,t
Exogenous variation is at state level; The paper calculates a weighted Herfindahl in state:
State Herfst =
X
ACCEP T EDsct
HERFsct
c∈s ACCEP T EDsct
P
c∈s
12
Ordinary Least Squares
acceptit = β1 Racei
+ ΓA Xit + ΓC Xct + ΓZ Xzt + γs + γt + γb + it
(1)
(1) measures the extent to which an applicant’s race impacts her lending outcome. Xit , Xct and Xzt are individual, census-tract, and zip-level controls,
respectively. γs is a state-county fixed effect; γt is a year fixed effect; γb is a
bank fixed effect.
OLS Difference in Difference:
acceptit = β1 Racei + β2 HERFst + βCOLS Racei × HERFst
+ ΓA Xit + ΓC Xct + ΓZ Xzt + γs + γt + γb + it
(2)
(2) estimates a (likely correlative) association between state-level competition
and the degree to which race impacts the lending decision. HERFst is the
state-level Herfindahl index of size-weighted accepted loans by bank. A larger
Herfindahl index is associated with less competition, so if βCOLS is negative it
indicates that in areas with greater competition, the race of the applicant is
relatively less important. The interpretation of βCOLS is not causal because the
degree of competition in an area may be correlated with unobserved reasons
related to race for denying the loan and measurement error in discrimination.
Reduced Form Difference in Difference:
acceptit = β1 Racei + β2 POSTst + βCRF Racei × POSTst
+ ΓA Xit + ΓC Xct + ΓZ Xzt + γs + γt + γb + it
(3)
(3) estimates the causal effect of the IBBEA relaxation on how race matters in
lending under the assumption that the timing of the relaxation is unrelated to
unobserved reasons for denying loans to black applicants. A positive coefficient
on βCRED implies that post-IBBEA, black applications became more likely to
be approved.
13
Instrumental Variables Difference in Difference:
\ st + βCIV Race\
acceptit = β1 Racei + β2 HERF
i × HERFst
+ ΓA Xit + ΓC Xct + ΓZ Xzt + γs + γt + γb + it
(4)
Where the first stage is as follows:
Racei\
× HERF st = η1 POST_IBBEAst + η2 POST_IBBEAst × Racei + ζst
\ st = η1 POST_IBBEAst + η2 POST_IBBEAst × Racei + ζst
HERF
(4) measures the causal link between state-level lending competition and the
degree to which race impacts the lending decision. A negative coefficient βCIV
implies that increased competition reduces racial discrimination. As the experiment occurs at the state-year level, standard errors are clustered by State
× Year.
With the empirical framework and data described, the paper’s main results follow.
3
Discrimination and Competition
This section presents the main results regarding lending discrimination and
the impact of competition and the passage of the IBBEA on discrimination.
Section 3.1 gives the main results; section 3.2 provides a placebo test to rule
out the change reflects a decrease in lending standards broadly. Appendix
Section 7.2 contains a number of robustness checks on these results.
3.1
Main Results
Table 3 presents the main results of the paper.15 Column (OLS) shows that a
black applicant is 7% less likely to have his loan application accepted than an
observationally equivalent white applicant, which given the empirical setup,
15
For readability, the in-text tables exclude the controls that are not of interest. Full
tables of coefficients are given in the appendices.
14
provides evidence of non-economic discrimination. Given that the base rate of
rejection is a relatively low 21%, this corresponds to a rejection rate roughly
35% for a black applicant relative to an observationally equivalent white applicant.
Table 3: Results for specifications (1), (2), (3), (4). See Table 13 for full
covariates.
Dependent variable:
Application Accepted
(OLS)
(OLS DiD)
(RED DiD)
(IV DiD)
State-County, Year, Bank FE
State × Year Clusters
−0.070∗∗∗
(0.006)
Y
Y
−0.047∗∗∗
(0.007)
−0.488∗∗∗
(0.085)
Y
Y
−0.081∗∗∗
(0.009)
0.022∗∗
(0.010)
Y
Y
−0.019
(0.017)
−1.076∗∗∗
(0.394)
Y
Y
Observations
R2
Adjusted R2
4,367,944
0.102
0.100
4,367,944
0.102
0.100
4,367,944
0.102
0.100
4,367,944
0.102
0.100
Black
Herf × Black
Post × Black
∗
Note:
p<0.1;
∗∗
p<0.05;
∗∗∗
p<0.01
More central to the objective of this paper, columns (OLS DiD), (RED
DiD), and (IV DiD) show that the degree to which race matters in the lending
decision declines in the face of more competition. In particular, from the
OLS regression, a one standard deviation increase in lending competition as
measured by a state-level Herfindahl index decreases the average disparity a
black applicant faces from a mean value of -7.2% to -5.4%—a difference of
roughly 25%. Similarly, after the implementation of the IBBEA, the reducedform regression points to roughly a 25% change. The IV regression suggests
that the causal effect takes the mean racial disparity from -7.5% to -3.4%—a
54% difference.
The upshots from Table 3 are as follows: (1) The paper finds clear evidence for racial discrimination. (2) Increased competition, coming through
the implementation of the IBBEA, reduces but does not fully eliminate racial
15
discrimination. The paper now proceeds to run tests to rule out an alternative
explanation of these findings—namely, that the applicant’s race is a proxy for
unobserved but economically relevant variables, and that increased competition depresses lending standards across the spectrum of economically relevant
variables.
3.2
Competition and Applicant Income
This section attempts to rule out an alternative explanation of Table 3. In
particular, suppose that an applicant’s race proxies for economically relevant
but unobserved variables, like credit score, and that increased competition
forces banks to lowers standards on all economically-relevant variables. If the
impact of competition on race merely measures declining lending standards
broadly, running a similar test with an interaction of IBBEA and income
should yield significant results indicating that applicant income becomes less
important over time.
To rule out this explanation end, the paper again runs the regression
described in equation (3), this time including an interaction term of Post ×
log(Income). If income has become less important due to broadly declining
lending standards, the coefficient on the interaction term should be negative;
on the other hand, a well-estimated zero coefficient on this term will indicate
that lending standards for FHA loans—at least those relating to applicant
income—did not decline across the board, and that observed change in Table
3 was indeed specific to the applicant’s race and non-economic discrimination.
Table 4 shows the results. Columns (Placebo 1) and (Placebo 2) show that
the interaction coefficient on Post × log(Income) is a well-estimated zero, as
desired.
Because the coefficients on Post × log(Income) are close to zero, this provides evidence that the changes in the race coefficient do not simply reflect
lower lending standards broadly. Instead, the applicant’s race is indeed important in whether the loan is accepted, and the fact that similar effects do
not show up with clearly economically relevant variables like the applicant’s
16
Table 4: Results for specification (3) with different or additional interractions. Column (Baseline) is (3) as before. Column (Placebo 1) adds a Post
× log(Income) term. Column (Placebo 2) replaces the race interraction term
with the income interraction term. See Table 15 for full covariates.
Dependent variable:
Application Accepted
(Baseline)
(Placebo 1)
(Placebo 2)
State-County, Year, Bank FE
State × Year Clusters
−0.081
(0.009)
0.054∗∗∗
(0.006)
0.022∗∗
(0.010)
Y
Y
∗∗∗
−0.082
(0.009)
0.052∗∗∗
(0.008)
0.022∗∗
(0.010)
0.004
(0.008)
Y
Y
−0.070∗∗∗
(0.006)
0.052∗∗∗
(0.008)
0.003
(0.008)
Y
Y
Observations
R2
4,367,944
0.102
4,367,944
0.102
4,367,944
0.102
Black
log(Income)
Post × Black
Post × log(Income)
∗∗∗
∗
Note:
p<0.1;
∗∗
p<0.05;
∗∗∗
p<0.01
income support the contention that there was racial discrimination and that
it decreased after the passage of IBBEA.
Appendix 7.2 provides further robustness tests by (1) investigating the
plausible importance of omitted variable bias, and (2) concentrating on a
sample of loans known to be FHA-compliant. The results are consistent with
the main results of this section. Namely, (1) discrimination occurs and (2)
competition reduces it. Having established that discrimination occurs and
that increased competition reduces it, the paper now investigates the channel
through which this change happens.
4
Bank-Level Mechanism
The paper has so far demonstrated that lending discrimination occurs and that
the amount of discrimination decreases in more competitive lending markets
and following the implementation of IBBEA. It now focuses on the mechanisms
17
by which the changes in discrimination occur by examining discrimination at
the bank level. In particular, the objective is to study whether the overall
level of discrimination decreases because the relaxation of bank branching laws
allows incumbents to enter who discriminate less, or because discrimination is
indeed costly, and the threat of entry induces discriminatory banks to change
behavior. To answer this question, the paper first identifies, on a bank-by-bank
basis, how much the applicant’s race impacts the bank’s lending decision. The
strategy for doing so is similar in spirit to the main empirical design and is
discussed in detail in Section 4.1. It next tests whether discriminating banks
pay for their discrimination by disproportionately losing market share relative
to less discriminatory competitors when competition increases. As the paper
shows in Section 4.2, they do. Finally, the paper tests whether competition
pressures discriminating banks to discriminate less, and whether new entrants
are on average less discriminatory. As shown in Section 4.3, discriminating
banks do discriminate less in the face of competitive pressures, but there is
weak evidence that the entering banks are even less discriminatory than the
reformed incumbents.
4.1
Empirical Strategy
The objective is to identify which banks are more discriminatory. To that end,
the paper runs the following regression each year:
accepti = βbt × Bank b × Racei
+ ΓA Xi + ΓC Xc + ΓZ Xz + γs + γb + i
(5)
For each bank b and each year y regression (5) recovers βbt , which represents
how much the bank considers race in making its lending decision. A more
negative βbt means the bank tends to lend less to black applicants. These
βbt estimates then become left-hand side and right-hand side variables in the
analysis to follow.
18
4.2
Changes in Market Share
The first objective is to test whether banks pay for discrimination in the face
of more competition. To do so, the paper considers the following scenario.
Consider bank D and N D in a given market; D discriminates, N D does not.
Suppose that lending competition in the market increases. The expectation
is that both D and N D will lose market share, but if D is engaged in costly
discrimination, D will lose more market share than N D because with more
competition, it is more difficult for D to pay for its discriminatory practices.
To test this hypothesis, the paper regresses:
market sharesbt = γ1 βst + γD βbst × Compst + ηs + ηt + sbt
Where Compst is either State-Level Herfindahl (OLS), Post IBBEA (RED),
or the first stage of State-Level Herfindahl on IBBEA (IV). γD is the coefficient of interest. For the OLS and IV, a negative γD means that the loss of
market share in high-competition environments is large for banks banks with
more negative βbst —those who discriminate more. For the reduced form, a
positive γD means that after IBBEA, banks that discriminate more lost more
market share. Table 5 shows the results. The sign of the coefficients are
consistent with discriminating banks losing market share in high competition
environments. This confirms the hypothesis that discriminating banks pay for
their discrimination in the face of more competition. Note further that while
all types of incumbents lose market share when new banks enter, the results
above imply that the incumbent banks who do not discriminate grow relative
to the incumbent banks who do discriminate. Thus, the overall change in discrimination is partly driven by a within-incumbent composition change, where
the share of loans made by non-discriminators grows relative to the share of
loans made by discriminators.
An alternate explanation is that it is not discriminatory banks that lose
market share, but rather high-standards banks that lose market share. That
is, to the extent that race proxies for unobserved but economically relevant
variables, banks that are more conservative with respect to these variables
19
Table 5: Does a banks’ coefficient on applicant race impact their market share
loss following more competition or the relaxation of the IBBEA? The top rows
are the test; the bottom rows are the placebo test on income coefficient. See
16 for full table.
Dependent variable:
Market Share
(OLS)
(RED)
(IV)
−0.135
(0.053)
-
0.008∗∗∗
(0.002)
−0.270∗∗∗
(0.010)
-
State, Year FE
State × Post Clusters
0.003
(0.014)
Y
Y
0.001
(0.0005)
Y
Y
0.037∗
(0.020)
Y
Y
Observations
R2
10,514
0.633
10,514
0.634
10,514
0.622
Herf × Race Coeff
Post × Race Coeff
Herf × Income Coeff
Post × Income Coeff
Note:
∗∗
∗
20
p<0.1;
∗∗
p<0.05;
∗∗∗
p<0.01
lose market share because when new banks enter, the low-standards banks
respond by lending to lower-quality applicants. To rule out this explanation,
the bottom half of Table 5 tests how banks’ coefficient on income predicts their
loss in market share. That is, do banks that are relatively more conservative
with respect to the applicant’s income also lose market share in the face of
increased competition? As before, a well-estimated zero rules out this story.
Indeed, both the OLS and reduced form estimates are precisely close to zero,
and the IV estimator, though it has a somewhat larger standard error, is not
significant at the 95% confidence level. This test suggests that the changes
in market share do not simply reflect more aggressive banks gaining market
share relative to less aggressive banks; rather, banks that discriminate do lose
market share relative to those that do not.
4.3
Changes in Behavior
Having shown that discriminatory banks pay for their discrimination by losing
market share, the paper next tests whether increases in competition induce
incumbent banks to modify their behavior. The behavior change when the
decision-maker has a preference for discrimination could arise from a simple agency problem: Suppose that the bank’s management is purely profitmaximizing, but that the bank’s loan officers making day-to-day lending decisions do have a preference for discrimination. The bank’s management, understanding that they will pay for their employees’ discriminatory practices, may
choose to implement tighter controls—more automated screening techniques
or auditing—in order to rein in the employees’ discriminatory tastes. Before
competition increased, it may not have been worthwhile for the bank mangers
to implement these controls, but facing potential market share losses when
competition does increase, the policies become worthwhile.
While HMDA does not have loan officer-level data, the paper attempts
to test for changes at the bank level by testing whether the bank-specific
coefficients on race obtained above change when competition increases. To
21
that end, the regression is:
βbst = γB Compst + ηsb + bst
Where ηsb is a state-bank fixed effect. The coefficient of interest is γB . The
regression compares how a bank within a state changes its behavior when
the state becomes more competitive or the IBBEA is implemented in the
state. A negative coefficient for γB for (OLS) and (IV) means that higher
competition leads to less discriminatory behavior; a positive coefficient for γB
for (RED) means that after IBBEA, banks discriminate less. Table 6 confirms
the prediction that increases in competition reduce bank discrimination.
Table 6: How do banks’ coefficients on applicant race change with competition
or passage of the IBBEA? See Table 18 for full table.
Dependent variable:
Race Coeff
Herf
Post
State × Bank FE
State × Post Cluster
Observations
R2
Note:
(OLS)
(RED)
(IV)
−0.338
(0.274)
Y
Y
0.019∗∗∗
(0.006)
Y
Y
−0.847∗∗
(0.354)
Y
Y
4,753
0.515
4,753
0.517
4,753
0.514
∗
p<0.1;
∗∗
p<0.05;
∗∗∗
p<0.01
The regression identifies how an incumbent bank in a given state takes
race into account in the low- and high-competition periods. The large and
significant coefficients on the reduced form and IV indicate that increases in
competition cause discriminating banks to modify their behaviors, and that
the overall change identified in Section 3.1 is not merely the result of less
discriminatory banks entering.
To show that banks do not adjust their lending behavior along other
margins such as income, Table 7 shows how banks modify their coefficient
22
on income using the same specification as above with the income coefficient
replacing the race coefficient on the left-hand side. A negative and significant
value for the reduced form would indicate that banks lend to lower-income
individuals after the implementation of the IBBEA. On the contrary, the table
shows that there is almost no change along this margin.
Table 7: Placebo test: How do banks’ coefficient on income change with
competition and the relaxation of the IBBEA. See Table 19 for full table.
Dependent variable:
Income Coeff
Herf
Post
State × Bank FE
State × Post Cluster
Observations
R2
Note:
(OLS)
(RED)
(IV)
−0.447
(0.385)
Y
Y
0.003
(0.012)
Y
Y
−0.140
(0.523)
Y
Y
4,753
0.513
4,753
0.513
4,753
0.513
∗
p<0.1;
∗∗
p<0.05;
∗∗∗
p<0.01
Finally, the paper attempts to measure how the overall reduction in discrimination in an area comes from incumbents altering their behavior or new
entrants entering who have different policies regarding discrimination. To that
end, the paper regresses bank coefficients on (1) whether the deregulation has
taken place, and (2) whether the bank in question is an incumbent in the
market. The regression is as follows:
βbst = γP P ostst + γE Entrantb + ηs + bst
γP measures how an incumbent bank’s behavior changes. An incumbent
bank’s coefficient on race is greater by γP relative to before. γE measures how
an entrant in the post period differs by an incumbent in the post period—that
is, an entrant’s coefficient on race in the post period, is γE greater than an
incumbent’s coefficient on race in the post period; relative to an incumbent in
23
the pre-period, an entrant’s coefficient on race is greater by γP + γE . Table 8
shows the results for the race coefficient and the income coefficient.
Table 8: How does the coefficient on applicant race vary pre and post between
incumbents and entrants in the market? Column (1) is the test, column (2) is
a placebo on the income on coefficient. See Table 20 for full table.
Dependent variable:
Race Coeff
Income Coeff
(1)
(2)
∗∗∗
Post
State FE
State Clust.
0.021
(0.004)
0.004
(0.006)
Y
Y
0.008
(0.007)
0.005
(0.009)
Y
Y
Observations
R2
10,288
0.009
10,288
0.003
Entrant
Note:
∗
p<0.1;
∗∗
p<0.05;
∗∗∗
p<0.01
To interpret the numbers, the 0.021 coefficient on post means that relative
to before, after IBBEA, incumbent banks are 2.1% more likely to lend to black
applicants. The 0.004 coefficient on entrants, though not significant, means
that in addition to the 2.1% change after post, new entrants are additionally
more likely to lend to black applicants by 0.4%, meaning that overall, a new
entrant after IBBEA is 2.5% more likely to lend to a black borrower than an
incumbent in the same market would have been before IBBEA. The result
of this test is that (1) both incumbents and entrants discriminate less after
the passage of the law, and (2) entrants are slightly but not significantly less
discriminatory than incumbents after the law.
This section explored what happens at the bank level when competition
increases. The results support the predictions of the theory that discrimination is expensive and that competition causes discriminatory banks to lose
market share. Moreover, the results show that competition causes discriminating banks to discriminate less. This change in behavior could be caused, for
example, by tighter controls or accountability standards given to low-level dis24
criminatory loan officers from high-level non-discriminatory managers. With
the empirical findings presented, the paper now discusses their implications.
5
Discussion
This paper established (1) racial discrimination occurs in mortgage lending, (2)
competition decreases it, (3) discriminating banks pay for their practices when
competition increases, and (4) discriminating banks change their behaviors to
become less discriminatory when competition increases. This section discusses
the legal implications of these findings. First, it highlights some reasons why,
despite racial discrimination being illegal, it stills exists. Second, it discusses
how policies promoting competition can be both complements and substitutes
for direct legal sanctions. Third, it flags lessons for addressing discrimination
in other markets and along other demographic characteristics.
5.1
Why Discrimination Still Happens
Racial discrimination is illegal, yet this paper finds evidence of significant
differences in outcomes between otherwise similar white and black applicants.
Borrowers who have been discriminated against have a cause of action, so
what is allowing banks to discriminate? Besides the typical problems related
to costs of bringing cases to low-income borrowers, the methodology of this
paper illustrates a systematic proof problem in individual discrimination cases
seeking to use statistical evidence to prove discrimination. In short, this paper
can tease out subtle differences in treatment of black and white borrowers
because it has a large “N ”—a large number of observations—an advantage
that an individual plaintiff does not have.
In particular, this paper finds discrimination against black borrowers on a
nationwide level—a systemic problem. To do this, it uses millions of mortgage
applications made to the entire US banking system. A discriminated plaintiff,
however, cannot sue the banking system but rather must sue a particular bank.
While statistical evidence using HMDA is sufficient to prove a bank’s intention
25
to discriminate in an FHA action,16 as a statistical matter it is more difficult
to establish with high confidence that a particular bank treats black and white
applicants differently because estimates of differences are noisier when there
are far fewer observations. That is, the confidence with which one can state
that based on mortgage acceptance patterns that a bank is discriminating is
much lower than the confidence with which this paper can state that discrimination occurs on a systemic level. Even if all banks discriminate based on
race, such discrimination may not be observable on the individual bank level
with enough confidence for a successful FHA action.
5.2
Competition as a Policy Tool
Direct legal action may fail to completely eliminate systemic discrimination for
the reasons discussed above. This paper has shown, however, that increases
in competition have the effect reducing systemic discrimination without the
need for direct legal action against a particular bank. While it does not offer
an ex-post remedy to a harmed borrower, competition does provide ex-ante
incentives for banks to correct their discriminatory behavior.
A direct lawsuit requires proof and funding for the lawsuit. Both of
these may be difficult for a low-income borrower to produce. Knowing this,
banks have little incentive to correct their behaviors. Reducing discrimination
through competition, on the other hand, relies only on the threat of entry by a
large player. So long as there are business opportunities in a market and banks
willing and able to enter the market, entry and the threat of entry serves to reduce discrimination both by pressuring incumbents and by the direct entry of
less discriminatory banks. The benefit of the competition-induced punishment
of discrimination is that it puts the onus of enforcement on likely well-funded
agents—banks—with strong incentives to take action. The entering banks’
entry (or entry threats) rely only on a profit motive, and as a side effect of
16
The elements necessary to se under the Fair Housing Act are (1) the property must be
covered, (2) the transaction must be covered, (3) there must be an illegal basis of discrimination (e.g., race), and (4) the denial must be because of the protected basis. The fourth
element is usually the element in dispute; evidence that the bank treats otherwise equal
white and black applicants differently is sufficient to establish it. See Schwemm (1994).
26
their entry (or threats), racial discrimination is reduced.
Of course, particularly undeserved areas where lending opportunities for
new entrants are small may not see the benefits of hands-off policies that allow
for more lending competition. For these areas, regulations that provide incentives for encourage bank expansion into under-served neighborhoods. Regulations that encourage bank entry into traditionally minority areas, therefore,
have a doubly-beneficial impact: there is the direct effect of increasing credit
supply in those areas, and further there is the indirect effect of increasing
competition in those areas, leading to less discrimination by the incumbent
lenders. Cases such as this suggest that regulation and competition can be
complements.
5.3
Lessons for Other Areas
Lessons regarding competition and discrimination in lending markets suggest
problems and solutions that arise in other types of discrimination, for instance,
regarding the gender wage gap. As discrimination taking place at a single
bank may be difficult to prove due to sample size problems, and a case alleging
discrimination against an individual may be expensive to prosecute, so too may
be employment discrimination cases against particular companies. Indeed,
studies finding a gender wage gap at a national level have the same luxury
that this paper has in a large sample size; an individual plaintiff, however,
must sue an individual employer, and not the United States labor market writ
large. Policies seeking to address the gender wage gap in the United States,
therefore, in addition to direct legal action, would be well-served to consider
policies that increase competition for hiring. Another example is the case of
ride sharing. Minorities have often been denied access to Taxis, but with new
ride sharing technology that improves competition for passengers, one would
expect to see improved access, especially among minorities.
27
6
Conclusion
This paper tested whether competition in the lending market decreases racial
discrimination and found that it does. During the relaxation of bank branching
laws during the 1990s, banks’ lending decisions on FHA loans became less biased against black applicants. While at the start of the period, observationally
equivalent black applicants were roughly 8% less likely than white applicants to
have their applications approved, after the passage of the law and subsequent
increase in competition, the gap between white and black applicants decreased
by 25%. This result confirms predictions from the economics of discrimination
literature that a preference for discrimination is costly and more difficult to
sustain when there is more competition. Next, the paper showed that discriminating banks indeed lost more market share than non-discriminating banks
as competition increased, but that they tended to modify their behavior to
discriminate less. Both changing behavior by incumbents and entry of less
discriminatory banks from across state lines contributed to the decrease in
competition.
The paper’s results have important policy and legal implications and leave
open many interesting avenues for further research. The paper provides evidence on how strong market forces can be in reducing discrimination. The
paper shows that competition and market liberalization does in fact reduce
discrimination, but that even with increased competition and market liberalization, discrimination does not disappear. Market-based reductions through
competition can fill in holes where direct sanctions would be difficult to enforce, particularly due to proof problems and litigation costs. This suggests
that market-based and direct approaches are complementary in addressing
discrimination.
As to future extensions, while the paper provides evidence on the costs
associated with discrimination coming through a loss in market share, it does
not explore the role of other—often legal—incentives to change behavior. Further research might test whether, for example, the passage of IBBEA created
more regulatory carrots for good behavior by making the rewards for good
28
Community Reinvestment Act examinations greater through the increased opportunities to branch across state lines.
29
References
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Tootell, G. M. (1996). Redlining in boston: Do mortgage lenders discriminate
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7
Appendix
The appendix provides information on FHA Insurance in Section 7.1; robustness tests
on the main result in Section 7.2; regression tables with the full sets of covariates in
Section 7.3.
30
7.1
Federal Housing Authority Insurance Institutional
Details
As of 2016, FHA loans had the following requirements:17
• Minimum down payment of 3.5%.
• Borrowers must have a minimum FICO credit score of 580 for financing with
a the minimum 3.5% downpayment.
• Borrowers with a a FICO credit score between 500 and 579 must have a loanto-value ration of less than 90% and a minimum down payment of 10%.
• Loan must be for primary residence.
• Back-end ratio (mortgage payment plus monthly debt) must be less than 43%
of monthly gross income.
• Borrowers must have a steady income.
• Borrowers must have a valid US Social Security number.
• The property must meet certain minimum standards of appraisal.
A borrower failing to meet these requirements can often overcome them if the
lender believes the loan should be approved due to a “compensating” factor. This
provides another way that a lender can discriminate—by “going to bat” for a borderline white borrower but not for a borderline black borrower. In both cases, the
FHA will insure the loan, so the bank bears the same credit risk.
7.2
Robustness Tests
This section provides several robustness checks on the main results. First, Section
7.2.1 attempts to place plausible bounds on the size of any omitted variable bias
impacting the results. Second, Section 7.2.2 performs a very conservative check by
matching applications and considering only those applications made by a borrower
who eventually had a successful FHA application. The results in these sections
support the main results.
7.2.1
Plausible Bounds on Omitted Variable Bias
Consider omitted variable zi . The estimator of the βRACE , β̂RACE gives
β̂RACE = βRACE + βz βz,r
17
http://www.zillow.com/mortgage-learning/fha-loan/
31
Where βz is the coefficient of the outcome of interest on zi and βz,r is the regression
coefficient (orthogonalized to other observables). For the omitted variable bias to be
large both βz,r and βz must be large. Though one does not directly observe βz and
βz,r for the omitted variables of concern—primarily the applicant’s credit score—
one can calculate how large the effect would be if the regression omitted a similar
variable—the applicant’s income. Table 9 shows how the estimate for race would
change omitting this variable.
Table 9: Changes with omitted variable. See Table 14 for full covariates.
Dependent variable:
Application Accepted
(Full)
(Omitted)
−0.070
(0.006)
−0.074∗∗∗
(0.006)
0.054∗∗∗
(0.006)
-
Y
Y
Y
Y
Y
Y
Y
Y
Observations
R2
4,367,944
0.102
4,367,944
0.099
Note:
∗
Black
log(Income)
∗∗∗
State-County FE
Year FE
Bank FE
State × Year Clusters
p<0.1;
∗∗
p<0.05;
∗∗∗
p<0.01
The coefficient decreases from −0.070 to −0.074 when omitting income—the direction is as expected because black applicants tend to have lower incomes and lower
incomes predict a less successful application. The important difference, however, is
the magnitude, which in this case is quite small.
7.2.2
Accepted Borrowers Subsample
This section performs the most conservative robustness check on the results regarding
discrimination and discrimination changes by considering a very special subsample
of borrowers: FHA applicants who made at least two applications and had at least
one application accepted. By conditioning on those applicants who had at least one
successful FHA application, the inference is that they are indeed FHA-compliant
applicants. A rejection of such an applicant must therefore have occurred at the
lender level rather than at the FHA level. Lenders making such rejections would bear
no credit risk, and thus when comparing a white applicant and a black applicant in
32
this pool, with certainty the comparision is of loans with equal expected returns to
the lender—an ideal experiment.
HMDA does not identify borrowers, even anonymously across loans for which
a single borrower applies, and therefore the paper must infer the identities of borrowers from their observables. To match, the paper looks for applications where the
applicant’s race, sex, income, and loan amount exactly match in a given census tract
and year. The sample of matched FHA loans is substantially smaller than the full
sample because (1) the matching methodology is conservative, and (2) the frequency
of applicants making multiple FHA loan applications is low. The restricted sample
contains 543,561 matched loans. Summary statistics are given the in Table 10.
Table 10: Summary statistics for matched FHA loans.
Statistic
loanamt
appinc
accepted
applicantBlack
napps
applicantAccepted
post
dereg_index
state_herf
income_to_loan
N
Mean
St. Dev.
Min
Max
569,180
569,180
569,180
569,180
569,180
569,180
565,763
565,763
543,514
569,180
96.640
42.433
0.871
0.139
2.135
1.000
0.686
3.076
0.044
−0.821
38.164
23.832
0.335
0.346
0.450
0.000
0.464
1.744
0.025
0.340
1
1
0
0
2
1
0
0
0.018
−4.654
1,295
2,300
1
1
16
1
1
5
0.873
4.489
Table 11 gives the results for the matched sample of loans. As in the main
results, black borrowers are significantly less likely to receive loans, but this effect is smaller when there is more competition and after the implementation of the
IBBEA. The magnitudes of the coefficients are smaller both on the absolute amount
of discrimination and on the change that competition causes. In the case of the
reduced-form regression, black applications in this selected sample are roughly 5%
less likely to get a loan as compared to 8% in the whole sample. Further, the relaxation of the IBBEA reduced this amount by roughly 12.5% as opposed to the larger
change in the whole sample.
This reduction in effect is to be expected because the regression are conditioning
on a very special subset of the population that made multiple loans and eventually
was accepted. These borrowers may be savvier in locating less discriminatory lenders,
for example. Moreover, these borrowers must have been in areas where there were
non-discriminatory lenders offering loans to black borrowers, which means that these
are areas where there is, due to selection, less scope for entry by non-discriminating
banks and changes in behavior. Nevertheless, the fact the directions are consistent
and significant bolsters the main results. Because many observations are lost in this
procedure, the remainder of this paper uses the full sample.
33
Table 11: Estimate of impact of competition/IBBEA on the subset of FHA
loans conditional on at least one of the applicant’s loans being accepted. See
Table 21 for full table.
Dependent variable:
Loan Accepted
(OLS)
(RED)
∗∗∗
Black
−0.033
(0.003)
Herf × Black
−0.238∗∗∗
(0.076)
Observations
R2
Adjusted R2
Note:
−0.048
(0.002)
(IV)
−0.023∗∗∗
(0.009)
−0.496∗∗
(0.213)
0.006∗∗
(0.003)
Post × Black
State FE
Year FE
∗∗∗
Y
Y
Y
Y
Y
Y
543,561
0.009
0.009
565,810
0.009
0.009
540,422
0.004
0.003
∗
p<0.1;
34
∗∗
p<0.05;
∗∗∗
p<0.01
7.3
Full Regression Tables
Table 12: Summary Statistics
Statistic
loanMade
applicantBlack
census.pct_black
census.pct_edu_lt_9
census.pct_edu_coll
census.inc_mhh
state_county
year
censustr
census.pct_poverty
appinc
loanamt
post
dereg_index
share_sq_state_county
state_herf
bank_weighted_herf_state_county
state
ZIP
FRAC_BELOW_620
FRAC_BELOW_640
FRAC_BELOW_660
FRAC_TOT_DEF_NO
FRAC_MOR_DEF_NO
PCT_MOR
PCT_HEQ
PCT_CAR
PCT_AUT
census.pct_non_black
black_x_census_non_black
share_sq_x_black
share_sq_x_census_non_black
share_sq_x_black_x_census_non_black
share_sq_bank_x_black
N
Mean
St. Dev.
Min
Max
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
4,367,944
0.787
0.161
0.128
0.055
0.115
34,298.860
29,239.510
1,997.136
2,362.303
0.113
46.305
81.175
0.529
3.288
0.050
0.052
0.047
29.150
46,881.170
0.242
0.291
0.345
0.072
0.043
0.323
0.127
3.940
0.310
0.872
0.090
0.007
0.045
0.004
0.007
0.409
0.367
0.229
0.037
0.081
12,002.160
14,911.230
3.952
3,352.771
0.088
145.944
300.825
0.499
1.949
0.044
0.038
0.037
14.890
26,545.210
0.113
0.122
0.128
0.043
0.060
0.147
0.067
0.604
0.144
0.229
0.247
0.021
0.037
0.014
0.021
0
0
0.000
0.000
0.000
2,499
1,001
1,991
1.000
0.000
1
1
0
0
0.013
0.018
0.013
1
1,002
0.000
0.000
0.000
0.000
0.000
0.004
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
1
1
1.000
1.147
1.667
200,001
56,045
2,005
9,989.000
1.389
9,999
97,850
1
5
1.000
0.873
1.002
56
99,403
1.000
1.000
1.000
1.000
1.000
3.143
3.000
23.000
3.000
1.000
1.000
0.873
0.872
0.863
0.801
35
Table 13: Main regression table
Dependent variable:
Application Accepted
Black
(OLS)
(OLS)
(RED)
(IV)
−0.070∗∗∗
(0.006)
−0.047∗∗∗
(0.007)
−0.081∗∗∗
(0.009)
−0.019
(0.017)
0.015∗∗∗
(0.003)
Herf
0.423
(1.945)
−0.003
(0.003)
Post
−0.488∗∗∗
(0.085)
Herf × Black
−1.076∗∗∗
(0.394)
0.022∗∗
(0.010)
Post × Black
Census Pct Non Black
log(Income)
log(Loan Amt)
log(Census Med. Inc.)
Census % Less Than HS
Census % College
Census % Poverty
Zip Credit % < 620
Zip Credit % < 640
Zip Credit % < 660
Zip % Total in Default
Zip % Mort in Default
Zip % With Mortgages
Zip % With HEQ
Zip % With CC
Zip % With Auto
State-County FE
Year FE
Bank FE
State × Year Clusters
Observations
R2
Adjusted R2
0.040∗∗∗
(0.006)
0.054∗∗∗
(0.006)
0.016∗∗∗
(0.005)
−0.030∗∗∗
(0.007)
−0.235∗∗∗
(0.030)
−0.011
(0.015)
−0.195∗∗∗
(0.018)
−0.004
(0.011)
0.002
(0.016)
−0.001
(0.011)
0.024
(0.016)
0.003
(0.005)
0.012∗∗
(0.006)
−0.020
(0.013)
0.002∗
(0.001)
−0.007
(0.008)
Y
Y
Y
Y
0.039∗∗∗
(0.006)
0.054∗∗∗
(0.006)
0.016∗∗∗
(0.005)
−0.030∗∗∗
(0.006)
−0.232∗∗∗
(0.029)
−0.010
(0.014)
−0.192∗∗∗
(0.018)
−0.007
(0.011)
0.003
(0.016)
−0.001
(0.011)
0.030∗
(0.017)
0.003
(0.005)
0.012∗∗
(0.005)
−0.017
(0.013)
0.002∗
(0.001)
−0.006
(0.008)
Y
Y
Y
Y
0.039∗∗∗
(0.006)
0.054∗∗∗
(0.006)
0.016∗∗∗
(0.005)
−0.030∗∗∗
(0.007)
−0.233∗∗∗
(0.029)
−0.011
(0.015)
−0.194∗∗∗
(0.018)
−0.009
(0.010)
0.003
(0.016)
0.00003
(0.011)
0.029∗
(0.017)
0.002
(0.005)
0.012∗∗
(0.005)
−0.016
(0.012)
0.002
(0.001)
−0.006
(0.008)
Y
Y
Y
Y
0.039∗∗∗
(0.005)
0.054∗∗∗
(0.006)
0.016∗∗∗
(0.005)
−0.029∗∗∗
(0.006)
−0.232∗∗∗
(0.035)
−0.012
(0.019)
−0.190∗∗∗
(0.019)
−0.008
(0.018)
0.002
(0.018)
−0.003
(0.014)
0.041
(0.029)
0.001
(0.007)
0.010
(0.017)
−0.015
(0.023)
0.003
(0.003)
−0.005
(0.011)
Y
Y
Y
Y
4,367,944
0.102
0.100
4,367,944
0.102
0.100
4,367,944
0.102
0.100
4,367,944
0.102
0.100
∗
Note:
36
p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01
Table 14: Changes with omitted variable
Dependent variable:
loanMade
Black
log(Income)
log(Loan Amt)
Census % Non Black
log(Census Med. Inc.)
Census % Less Than HS
Census % College
Census % Poverty
Zip Credit % < 620
Zip Credit % < 640
Zip Credit % < 660
Zip % Total in Default
Zip % Mort in Default
Zip % With Mortgages
Zip % With HEQ
Zip % With CC
Zip % With Auto
State-County FE
Year FE
Bank FE
State × Year Clusters
Observations
R2
Note:
(Full)
(Omitted)
−0.070∗∗∗
(0.006)
−0.074∗∗∗
(0.006)
0.054∗∗∗
(0.006)
-
0.016∗∗∗
(0.005)
0.040∗∗∗
(0.006)
−0.030∗∗∗
(0.007)
−0.235∗∗∗
(0.030)
−0.011
(0.015)
−0.195∗∗∗
(0.018)
−0.004
(0.011)
0.002
(0.016)
−0.001
(0.011)
0.024
(0.016)
0.003
(0.005)
0.012∗∗
(0.006)
−0.020
(0.013)
0.002∗
(0.001)
−0.007
(0.008)
Y
Y
Y
Y
0.038∗∗∗
(0.004)
0.040∗∗∗
(0.006)
−0.025∗∗∗
(0.007)
−0.239∗∗∗
(0.030)
0.010
(0.014)
−0.193∗∗∗
(0.019)
−0.007
(0.010)
0.003
(0.016)
−0.001
(0.011)
0.022
(0.016)
0.002
(0.005)
0.011∗
(0.006)
−0.023∗
(0.013)
0.002∗
(0.001)
−0.004
(0.008)
Y
Y
Y
Y
4,367,944
0.102
4,367,944
0.099
∗
p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01
37
Table 15: Placebo tests on income
Dependent variable:
loanMade
(Baseline)
(Placebo 1)
(Placebo 2)
−0.081∗∗∗
(0.009)
−0.082∗∗∗
(0.009)
−0.070∗∗∗
(0.006)
0.054∗∗∗
(0.006)
0.052∗∗∗
(0.008)
0.052∗∗∗
(0.008)
Post
−0.003
(0.003)
−0.018
(0.028)
−0.009
(0.027)
Post × Black
0.022∗∗
(0.010)
0.022∗∗
(0.010)
-
-
0.004
(0.008)
0.003
(0.008)
0.039∗∗∗
(0.006)
0.016∗∗∗
(0.005)
−0.030∗∗∗
(0.007)
−0.233∗∗∗
(0.029)
−0.011
(0.015)
−0.194∗∗∗
(0.018)
−0.009
(0.010)
0.003
(0.016)
0.00003
(0.011)
0.029∗
(0.017)
0.002
(0.005)
0.012∗∗
(0.005)
−0.016
(0.012)
0.002
(0.001)
−0.006
(0.008)
Y
Y
Y
Y
0.039∗∗∗
(0.006)
0.016∗∗∗
(0.005)
−0.030∗∗∗
(0.007)
−0.233∗∗∗
(0.030)
−0.011
(0.015)
−0.194∗∗∗
(0.018)
−0.008
(0.010)
0.003
(0.016)
−0.0002
(0.011)
0.030∗
(0.017)
0.002
(0.005)
0.012∗∗
(0.005)
−0.016
(0.013)
0.002
(0.001)
−0.006
(0.008)
Y
Y
Y
Y
0.040∗∗∗
(0.006)
0.016∗∗∗
(0.005)
−0.030∗∗∗
(0.007)
−0.235∗∗∗
(0.030)
−0.011
(0.015)
−0.195∗∗∗
(0.018)
−0.003
(0.011)
0.002
(0.016)
−0.001
(0.011)
0.025
(0.017)
0.003
(0.005)
0.012∗∗
(0.006)
−0.020
(0.013)
0.002∗
(0.001)
−0.007
(0.008)
Y
Y
Y
Y
4,367,944
0.102
4,367,944
0.102
4,367,944
0.102
Black
log(Income)
Post × log(Income)
Census % Non Black
log(Loan Amt)
log(Census Med. Inc.)
Census % Less Than HS
Census % College
Census % Poverty
Zip Credit % < 620
Zip Credit % < 640
Zip Credit % < 660
Zip % Total in Default
Zip % Mort In Default
Zip % With Mortgages
Zip % With HEQ
Zip % with CC
Zip % with Auto
State-County FE
Year FE
Bank FE
State × Year Clusters
Observations
R2
∗
Note:
38
p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01
Table 16
Dependent variable:
Market Share
(OLS)
Herf
(RED)
(IV)
∗∗∗
1.093∗∗∗
(0.104)
0.810
(0.067)
−0.024∗∗∗
(0.001)
Post
0.006∗∗
(0.003)
−0.005
(0.004)
Inc Coeff
−0.00004
(0.0003)
−0.001∗∗
(0.0003)
Herf × Race Coeff
−0.135∗∗
(0.053)
−0.270∗∗∗
(0.010)
0.003
(0.014)
0.037∗
(0.020)
Herf
Herf × Inc Coeff
Post × Race Coeff
0.008∗∗∗
(0.002)
Post × Inc Coeff
0.001
(0.0005)
Observations
R2
Adjusted R2
Residual Std. Error
10,514
0.633
0.529
0.118 (df = 8177)
0.013∗∗∗
(0.003)
10,514
0.634
0.530
0.118 (df = 8177)
∗
Note:
39
p<0.1;
10,514
0.622
0.515
0.120 (df = 8178)
∗∗
p<0.05;
∗∗∗
p<0.01
Table 17
Dependent variable:
White Market Share
(OLS)
Herf
(RED)
1.083∗∗∗
(0.029)
0.807
(0.021)
−0.024∗∗∗
(0.001)
Post
Race Coeff
0.006∗∗
(0.003)
−0.005∗∗∗
(0.002)
Inc Coeff
−0.001
(0.001)
−0.001
(0.001)
Herf × Race Coeff
Herf × Inc Coeff
0.014∗∗∗
(0.005)
−0.125∗∗∗
(0.046)
−0.273∗∗∗
(0.097)
0.013
(0.028)
0.034
(0.039)
Post × Race Coeff
0.008∗∗∗
(0.002)
Post × Inc Coeff
0.0004
(0.001)
Observations
R2
Adjusted R2
Residual Std. Error
(IV)
∗∗∗
10,514
0.642
0.539
0.121 (df = 8177)
10,514
0.642
0.539
0.121 (df = 8177)
∗
Note:
40
p<0.1;
10,514
0.632
0.527
0.122 (df = 8178)
∗∗
p<0.05;
∗∗∗
p<0.01
Table 18
Dependent variable:
coeffs
(1)
(2)
(3)
−0.338
(0.274)
state_herf
0.019∗∗∗
(0.006)
post
−0.847∗∗
(0.354)
‘state_herf(fit)‘
Observations
R2
Adjusted R2
Residual Std. Error (df = 1242)
4,753
0.515
−0.854
1.671
∗
Note:
4,753
0.517
−0.849
1.668
p<0.1;
∗∗
4,753
0.514
−0.859
1.673
p<0.05;
∗∗∗
p<0.01
Table 19
Dependent variable:
incCoeffs
(1)
(2)
(3)
−0.447
(0.385)
state_herf
post
0.003
(0.012)
−0.140
(0.523)
‘state_herf(fit)‘
Observations
R2
Adjusted R2
Residual Std. Error (df = 1242)
4,753
0.513
−0.863
3.119
Note:
∗
41
p<0.1;
4,753
0.513
−0.864
3.120
∗∗
p<0.05;
4,753
0.513
−0.863
3.120
∗∗∗
p<0.01
Table 20
Dependent variable:
coeffs
incCoeffs
(1)
(2)
post
∗∗∗
0.021
(0.004)
0.008
(0.007)
entrant
0.004
(0.006)
0.005
(0.009)
Observations
R2
Adjusted R2
Residual Std. Error (df = 10237)
10,288
0.009
0.004
1.459
10,288
0.003
−0.002
2.051
∗
Note:
42
p<0.1;
∗∗
p<0.05;
∗∗∗
p<0.01
Table 21
Dependent variable:
accepted
(OLS)
Herf
(RED)
(IV)
∗∗∗
−2.061∗∗
(0.986)
−0.289
(0.041)
0.033∗∗∗
(0.003)
Post
−0.033∗∗∗
(0.003)
−0.048∗∗∗
(0.002)
−0.023∗∗∗
(0.009)
log(Inc/Amt)
0.011∗∗∗
(0.003)
−0.020∗∗∗
(0.002)
0.029
(0.023)
Herf × Black
−0.238∗∗∗
(0.076)
−0.496∗∗
(0.213)
Herf × log(Inc/Amt)
−0.321∗∗∗
(0.052)
−0.660
(0.539)
Black
Post × Black
0.006∗∗
(0.003)
Post × log(Inc/Amt)
0.026∗∗∗
(0.003)
Observations
R2
Adjusted R2
Residual Std. Error
543,561
0.009
0.009
0.332 (df = 543479)
565,810
0.009
0.009
0.334 (df = 565733)
∗
Note:
43
p<0.1;
540,422
0.004
0.003
0.333 (df = 540345)
∗∗
p<0.05;
∗∗∗
p<0.01