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Volume 11 Issue 4, October-December 2023 

ISSN: 2836-9416 

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77 | P a g e  

INVESTIGATING RACIAL/ETHNIC DISPARITIES IN LOAN 
APPROVAL: A MISSISSIPPI PERSPECTIVE 

 

Dr. James A. Smith, and Dr. Gail G. Fulgham, 
Professor of Economics, Jackson State University 

 
Abstract: Discrimination in homeownership access has long hindered minority populations, 
particularly African Americans, from accumulating wealth through housing investments. This study 
delves into the pervasive issue of discrimination in American society, which denies certain social 
groups the privileges enjoyed by others. This discrimination, rooted in various factors such as race, 
ethnicity, religion, gender, and more, permeates multiple facets of American life, including education, 
political participation, employment, and housing. As a consequence, the quintessential "American 
Dream" remains elusive for many citizens through no fault of their own, making the prospect of 
purchasing a home in a desirable location or neighborhood an uphill battle. This research investigates 
how institutions controlled by privileged groups perpetuate this discriminatory practice and its far-
reaching implications on wealth accumulation. 
Keywords: Discrimination, Homeownership, Wealth Inequality, Minority Populations, Access to 
Housing  
 
Introduction   
Homeownership is one of the major sources of wealth for American families. However, the lack of 
access for minority populations and especially African Americas to loan approvals for housing 
purchases have limited their ability to accumulate wealth from the housing stock. The issue of 
discrimination has been a prevalent and highly debated topic in American socio-economic literature. 
Discrimination occurs when there is evidence that certain social groups are denied benefits naturally 
extended to other privileged groups.  It may be based on religion, gender, sexuality, race, ethnicity or 
other factors.  The practice of discrimination affects numerous aspects of American life, ranging from 
education to political participation, to employment and housing. As a result, the pursuit and 
achievement of the “American Dream” may be out of reach for many American citizens through no fault 
of their own. For many individuals, the simple notion of purchasing a home in a desirable location or 
neighborhood can be an uphill task.  Institutions controlled by the privileged class are usually 
manipulated to deny access to the other groups.   
In the housing industry, in particular, banking laws and processes are sometimes violated to deny 
prospective buyers access to funds necessary to acquire desirable properties. Historically, the southern 
region of the United States has been plagued by numerous incidents of discrimination.  The state of 
Mississippi has often been cited as one of those southern states where the practice of discrimination is 
still rampant.  Thus, several Mississippi banks have been investigated and sanctioned by the United 
States Department of Justice for violations of the Community Reinvestment Act (CRA) and for unfair 
banking practices. Generally, eligibility for loan financing requires that borrowers meet requirements 
of credit worthiness and ability to pay, regardless of race, ethnicity, gender and religion.  According to 
the U.S. Census Bureau, between 1994 and 2013, home ownership among U.S. families rose from 63.9% 

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Volume 11 Issue 4, October-December 2023 

ISSN: 2836-9416 

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78 | P a g e  

in 1994 to 65.1% in 2013.  However, when taking race into account, the rates of increase in home 
ownership revealed some distinct disparities. Home ownership rates among black families rose by 
1.89%, from 42.3% to 43.1% between 1994 and 2013. Hispanic families saw an 11.89% increase, from 
41.2% to 46.1% over the same period. While white families experienced an increase of 4.8%, from 70% 
to 73.35%.  In absolute terms, the 2013 census data reveal ownership rates of 73.35% for white 
households, 43.1% for blacks or African Americans, and 46.1% for Hispanics.  The appreciable increase 
in home ownership for Hispanics over that period may be attributed to the significant increase in the 
Hispanic population.  Comparably, black households experienced the lowest rate of homeownership as 
well as the lowest ownership growth (U.S. Census Bureau, 2013).  
  
 The process of lending discrimination or loan denial based on location is generally referred to as 
redlining.  In an effort to address this issue and to promote transparency and accountability, the United 
States Congress passed the Home Mortgage Disclosure Act (HMDA) of 1975, requiring lending 
institutions to disclose to the public, annually, detailed information about their home lending activities.  
Additionally, Congress passed the Community Reinvestment Act of 1977. The Act requires that 
commercial banks demonstrate through their activities that they meet the credit needs of their 
community, including low and moderate-income neighborhoods. As indicated previously, 
discrimination is evident when mortgage lending is denied to certain population groups based on race, 
gender, religion or other non-economic factors.  Over the years, disparities in home ownership rates 
and in ownership growth have attracted the attention of economists, social scientists and government 
institutions, seeking to investigate the possible causes of this troubling phenomenon.  As possible 
causes, some assertions refer to the concept of redlining, the propensity of financial institutions toward 
differential or unequal use of credit risk instruments for different racial and ethnic groups, the use of 
statistical discrimination, and the profit motive of banks and mortgage lenders. One of the early 
analyses on race and mortgage lending is attributed to John McKnight, a sociologist and community 
activist who, in the 1960s, brought special attention to the concept of redlining in residential mortgages.  
Thus this study examines the loan denial rate in the three counties within Jackson metropolitan 
statistical area (MSA). The three counties studied are Hinds, Madison, and Rankin counties. This study 
will examine loan approval rates and loan denial rates among the different racial and ethnic groups in 
three adjacent Mississippi counties with different ethnic and racial population distributions.  The study 
examines the role of location, minority population of location, and the ratio of loan amount to income 
on loan denial rate.     
Literature Review  
Anyamele (2015) established that the recent financial crisis impacted minorities more than whites. The 
study used 2001, 2004, 2007, and 2010 Surveys of Consumer Finances (SCF) data. Ezeala-Harrison et. 
al (2008) found consistent high denial rates in housing loan decisions against minorities in Metro-
Jackson, Southern Mississippi Corridor, and the Northern district of Mississippi. Their study used a 
combination of data from Western Economic Services (WES) and Home Mortgage Disclosure Act 
(HMDA) that covered 1993 to 2003 period. One of the major contributions of their study is that 
redlining still exist in Mississippi. Redlining is the uniquely American phenomenon where large areas 
of center city neighborhoods are deemed unsafe for home mortgage investments (Greer 2012).   The 

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79 | P a g e  

word, redlining, is particularly fitting, because historically these agencies would draw red lines around 
the inner city areas and neighborhoods that were deemed “hazardous” or “definitely declining,” which 
led to the creation of “Residential Security Maps” (Greer 2012).  More importantly, these areas were 
predominantly occupied by non-whites and non-white Hispanics, which suggested some form of racial 
profiling.  Redlining was not only a concept but also an action that was practiced on a regular basis as 
the interests of both public and private institutions merged. Furthermore, redlining was formulated 
greatly in response to the Great Migration of rural African Americans from the South to Northern cities 
(Greer 2012).  This transition of people was inevitable, because it was believed that the redlined areas, 
or people living in those areas, would degrade the land and the neighboring areas.    
According to Greer (2012), redlining emphasizes four main factors: the decline of inner city areas, the 
inability of non-whites to take on loans, the racial beliefs and actions of federal agencies, and the co-
dependence of the financial and real-estate markets.  As a result, the urban and rural dichotomy was 
forcefully formed not only through association but also through governmental pressures. In other 
words, the U.S. government implemented discriminatory acts that were carried out by the people.  
James Greer (2012), proposed that the racialization of the American real estate market is not wrong, 
but instead incomplete.  He points out the fact that race is only one explanatory variable on the 
disinvestment of mortgage lending in the U.S. economy and although it may be a contributing factor, it 
is certainly not the sole factor that redlining rhetoric seems to suggest. Phillips-Patrick and Rossi (1995) 
observed evidence of red-lining in their analysis of mortgage loans approval and denials in the 
Washington, DC metropolitan area.  Examining mortgage loan approvals within and across census 
tracks, they discovered that the ratio of mortgage originations in black neighborhoods was significantly 
lower than in non-black areas. They also found that as the percentage of black households in a 
neighborhood rises, the ratio of mortgage applications to units rises and that originations drop in 
neighborhoods as the percentage of black residents rises. The results could indicate redlining or it could 
reflect the omitted variables such as creditworthiness. 
On the other hand, some researchers questioned the red-lining phenomenon in mortgage lending.  For 
example, in challenging findings of redlining by other researchers, Carr and Megbolugbe (1993) cited 
the work of Benston and Horsky which found no differences between households in allegedly redlined 
areas and those in control areas in terms of their ability to secure mortgage financing in Cincinnati, 
Indianapolis, and Nashville.  Carr and Megbolugbe also asserted that the Boston Fed study found no 
evidence that lenders in Boston denied loans to an area because it has a large proportion of minority 
residents.  They further contended that a limitation of research which attempts to show redlining is 
that important information which contributes to the borrower’s creditworthiness is disregarded. In an 
investigation of the determining factors to differential lending rates, Ferguson and Peters (1995) found 
that some commercial banks and mortgage lenders applied different credit standards to different 
population groups.  Therefore, since credit risks were unequally assigned to different population 
segments, they concluded that “color blind” lending would not result in equal denial or default rates 
across different segments of the population.  
Other definitions of lending discrimination centered on the basis of the concept of statistical 
discrimination (Phelps, 1972; Han, 2004). According to Phelps, statistical discrimination is a practice 
in which a lender, lacking full information on a borrower’s creditworthiness, applies group stereotypes 

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80 | P a g e  

to individual borrowers in evaluating loan applications. In Han’s study, the reiteration of this type of 
discrimination was investigated and it was determined that since statistical discrimination implied 
higher rejection rates for minority applicants, rejection rates would not be a reliable measure of 
discrimination in lending.  Han concluded that statistical discrimination would imply that loans to 
minority borrowers have smaller sizes than those to majority borrowers with the same characteristics 
observed by lenders at the time of loan originations. Also, Han (2004) found that loans to minority 
borrowers carried higher interest rates.   
Ladd (1998) attributed a profit motive for financial institutions in discriminating against minority 
borrowers.  Ladd asserted that, despite the efforts of the federal government through actions such as 
the Fair Housing Act of 1968 and Equal Credit Opportunity Act of 1974, to promote fairness and to 
combat discrimination in lending, lending institutions could still have a profit-oriented motive towards 
discriminating against minorities. In her study, if institutions expected minorities on average to have 
higher default rates than whites, then lenders might believe that discrimination against minorities in 
the labor market could make the income of minorities more volatile on average over the economic cycle 
than that of whites and hence making minorities more likely to default. The notion speculated by these 
lenders would be cheaper screening device than other ways which would be used to distinguish between 
the quality of similar applicants.  
Although much of the research conducted concerning lending discrimination was based on denial and 
default rates, discrimination can also exist by lenders refusing to service a particular area which is 
within their servicing area typically known as redlining. Numerous studies have indicated evidence that 
discrimination may have been committed through the use of this tool as well. In a comprehensive study 
of lending behavior, Schafer and Ladd (1981) analyzed lending data on commercial banks, Mutual 
Savings banks, and Savings and Loan companies in California and New York, and tested for 
discrimination against a wide variety of groups based on race, gender, and marital status.   
From their investigation, they found evidence of discrimination based on races.  In 18 of the 32 
California areas and six of the ten New York areas, black applicants had significantly higher chances of 
loan denials than similar white applicants. Furthermore, they found that black applicants were 1.58 to 
7.82 times as likely to be denied as whites.  Ladd (1998) found that the issue with loan denials stemmed 
around setting higher cutoffs in terms of creditworthiness for minorities than for whites so that the 
minorities who received loans would be more creditworthy than the whites who received loans.   The 
situation places a standard higher than the requirements set forth by the lending company.     
The Model   
Similar to previous studies, we posit that loan denial rates will be higher for minority populations in 
the three JacksonMetropolitan counties of Hinds, Madison, and Rankin in Mississippi. Also, we expect 
higher loan denial rates for individuals with lower income, high loan amount to income ratio, and 
individuals who live in high minority population. The analysis will rely primarily on (HMDA) data.  
Furthermore, this study is the first attempt to examine the loan denial rate in these counties post the 
2008 financial crisis. Thus, it makes a significant contribution in appraising and documenting the loan 
denial rate in Jackson (MSA) post the great recession. The results derived from this analysis will then 
be compared with other national studies previously conducted in other regions of the country. The 
analysis will rely primarily on census data and banking data for the three adjacent counties.   

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The loan denial rate equation can be written as follows:   
  Yit = βXit + µit                   (1)  
Where Yit is a binary variable that takes the value of 1 or 0. If the ith loan application is denied, Yit is 1; 
otherwise, it is 0. X is a vector of independent variables, and β is the vector coefficients to be estimated, 
while µ is the error term. Thus, we can write the loan denial equation as follows: D=1 if the loan 
application is denied, or D=0 otherwise. P (Denied = 1 |x) =F (x, β) or P (Denied = 0|x) = 1-F (x, β)                                     
(2)  
Where x represents a vector of economic and demographic characteristics, β represents a vector of the 
estimated coefficients, and F is the cumulative distribution function.  
Definition of Variables  
The variables for the logistic regression are applicant’s race (White, African American, Hispanics, and 
Asians); the ratio of loan amount to applicant’s income; this is transformed into a categorical variable 
of normal or high. It is normal if it is <= 3, it is high if it is > 3. Income level, which has six categories < 
$45,000, 1, $45,000 to $75,000 = 2, $75,001 to $85,000 = 3, $85,000 to $95,000 = 4, and > $95,000 
= 5; loan total variable has six categories < $100,000 = 1, $100,00 to $200,000 = 2, $200,000 to 
$300,000 = 3, $300,000 to $400,000 = 4, $400,001 to $500,000 = 5 and>$500,000 = 6.    
Other variables are loan purpose which has three categories namely home purchase, home 
improvement, and home refinance. Loan type has four categories conventional loan, Veterans 
Administration loan, Federal Housing Administration loan, and Farm Service Agency or Rural Housing 
Service loan. Therefore, the variable ratio of minority population in a census tract is divided into two 
categories: low minority population in the census tract or high minority population in the census tract. 
This is ratio is considered high if it is >= 50 percent and low minority if it is < 50 percent. Location is 
represented by the three counties. The survey years of HMDA data represents the environment.  
Model Results  
Table 1present the results of the logistic regression for loan denial in three Mississippi counties of 
Hinds, Madison, and Rankin from the years 2007 to 2013. Compared with Asian Americans, African 
Americans are 1.54 times more likely to be denied loan. Whites are 32.06% less likely to be denied loans 
than Asian Americans while Hispanics are 1.55 times more likely to be denied loans when compared to 
Asian Americans. Living in a high minority population area has a 1.34 times likelihood of being denied 
loans. Moreover, having a high loan ratio to income increases the likelihood of denial. The probability 
of loan denial is inversely related to the level of income and appears to have a gradient. Loan denial is 
3.62 times higher for loans for home improvement compared to home purchase. The figure is also high 
for loans on home refinance. Compared to loans for home purchases, loans for home refinances is 1.88 
times higher. On the basis of county, loans from Madison County are 14.18% less likely to be denied 
when compared to Hinds County while loans from Rankin County are 1.03 times likely to be denied as 
compared to Hinds.   
Loans that range from $200,000 to $500,000 are more likely to be approved while loans that are 
greater than $500,000 are 1.33 times more likely to be denied compared to loans of $100,000 or less. 
Loan type compared to conventional loan, such as FHA loans, VA loans, and FSA/RHS loans are more 
likely to be approved.  
Table 1: Logistic Regression on Loan Denial 2007-2013  

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Independent  Combined 
All Races  

White  African 
Americans  

Asian 
Americas  

Hispanics  

Variables  Model 1  Model 2  Model 3  Model 4  Model 5  
No Race  -0.107          
  (-1.50)          
Asian Americans  Referent0          
  1.00          
African Americas  0.434***          
  (6.16)          
White  -0.386***          
  (-5.57)          
Hispanics  0.436***          
  (6.26)          
High Minority Pop  0.297***  0.415***  0.184***  0.0876  0.266  
  (15.66)  (13.00)  (6.52)  (0.49)  (1.24)  
HLV to income   0.0695***  -  -  -  -  
  (3.37)          
Income <$45,000  Referent  Referent  Referent  Referent  Referent  
  1.00  1.00  1.00  1.00  1.00  
$45,001<=$75,000  -0.421***  -0.497***  -0.388***  -0.419*  -0.382  
  (-21.79)  (-16.63)  (-13.03)  (-2.34)  (-1.96)  
$75,001<=$85,000  -0.639***  -0.676***  -0.565***  -0.405  -0.556  
  (-18.59)  (-13.84)  (-9.58)  (-1.45)  (-1.66)  
$85,001<=$95,000  -0.679***  -0.770***  -0.625***  -0.572  -0.507  
  (-17.46)  (-14.15)  (-8.83)  (-1.65)  (-1.40)  
>$95,000  -0.930***  -0.999***  -0.775***  -0.944***  -0.584*  
  (-34.27)  (-25.64)  (-16.50)  (-3.82)  (-2.40)  
Home Purchase  Referent  Referent  Referent  Referent  Referent  
  1.00  1.00  1.00  1.00  1.00  
Home Improvement  1.287***  0.989***  1.108***  1.232***  1.874***  
  (46.82)  (22.33)  (27.80)  (4.94)  (6.50)  
Home Refinance  0.630***  0.537***  0.528***  0.401*  0.920***  
  (35.15)  (20.22)  (18.84)  (2.45)  (5.65)  
Hinds County  Referent  Referent  Referent  Referent  Referent  
  1.00  1.00  1.00  1.00  1.00  
Madison County  -0.153***  -0.180***  -0.0562  -0.0890  -0.132  
  (-7.41)  (-5.84)  (-1.64)  (-0.46)  (-0.72)  
Rankin County  0.0342  0.0992***  0.0293  0.101  -0.173  
  (1.66)  (3.53)  (0.69)  (0.51)  (-0.87)  
Loan <$100,000  Referent  Referent  Referent  Referent  Referent  

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  1.00  1.00  1.00  1.00  1.00  
$100,001<=200,000  -0.348***  -0.460***  -0.253***  -0.693***  -0.549**  
  (-18.14)  (-16.42)  (-7.93)  (-3.89)  (-2.65)  
$200,001<=300,000  -0.300***  -0.358***  -0.236***  -0.0486  -0.379  
  (-9.93)  (-8.60)  (-4.25)  (-0.18)  (-1.36)  
$300,001<=400,000  -0.217***  -0.327***  -0.112  -1.072  -0.0306  
  (-4.63)  (-5.12)  (-1.24)  (-1.95)  (-0.08)  
$400,001<=500,000  -0.362***  -0.518***  -0.102  -0.0735  -0.0421  
  (-5.36)  (-5.80)  (-0.71)  (-0.10)  (-0.09)  
>$500,000  0.315***  0.180  0.480**  0.335  -0.680  
  (3.99)  (1.81)  (2.63)  (0.53)  (-0.82)  
Conventional Loan  Referent  Referent  Referent  Referent  Referent  
  1.00  1.00  1.00  1.00  1.00  
FHA Loan  -0.303***  -0.0155  -0.694***  -0.615**  0.157  
  (-14.13)  (-0.46)  (-21.53)  (-2.90)  (0.71)  
VA Loan  -0.0625  0.178*  -0.487***  -0.175  1.078  
  (-1.24)  (2.31)  (-6.34)  (-0.41)  (1.59)  
FSA/RHS Loan  -0.939***  -0.527***  -1.543***  -0.577  -0.662  
  (-13.67)  (-5.69)  (-14.45)  (-1.00)  (-0.64)  
Year 2007  Referent  Referent  Referent  Referent  Referent  
  1.00  1.00  1.00  1.00  1.00  
2008  0.0533*  0.0911*  0.134***  0.382  -0.00623  
  (2.36)  (2.55)  (3.79)  (1.83)  (-0.02)  
2009  -0.273***  -0.317***  -0.0367  0.0525  -0.348  
  (-10.30)  (-7.35)  (-0.89)  (0.21)  (-1.32)  
2010  0.0237  0.00812  -0.0139  0.269  -0.264  
  (0.73)  (0.16)  (-0.30)  (0.83)  (-0.69)  
2011  0.00317  -0.162***  0.0509  0.216  -0.324  
  (0.12)  (-4.09)  (1.15)  (0.85)  (-1.24)  
2012  -0.0566*  -0.201***  -0.0152  -0.00278  -0.394  
  (-2.19)  (-5.31)  (-0.36)  (-0.01)  (-1.52)  
2013  -0.00951  -0.0934*  0.00370  0.0889  -0.435  
  (-0.37)  (-2.48)  (0.09)  (0.37)  (-1.72)  
NLV to Income  -  -0.0180  -0.128***  -0.138  -0.801***  
    (-0.57)  (-3.89)  (-0.69)  (-4.07)  
Constant  -1.237***  -1.424***  -0.567***  -0.734**  -0.707*  
  (-16.84)  (-31.28)  (-12.72)  (-2.74)  (-2.37)  
N  138802  78775  36965  1638  1333  

Thus, the Environment can be captured in the different survey years. From Table 1, we see that loan 
denial was more likely to occur in 2008 compared to 2007 and less likely to occur in 2009, 2010, 2011, 

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84 | P a g e  

2012, and 2013. Models1, 2, 3, 4, and 5in Table 25 is the logistic regression of loan denial rates for all 
races, White, African Americans, Hispanics, and Asians from 2007 to 2013. Model 1, suggeststhat 
Whites who apply for loans in high minority areas are 1.51 times more likely to be denied loans. The 
higher your income the less likely you will be denied loan. Compared with home purchase applications, 
home improvement loans, and home refinance loans are more likely to be denied for White applicants 
over the study period. Model 2 shows that Whites who live in Madison are less likely to be denied loans 
compared to Whites who live in Rankin County and more likely to be denied loan compared to Whites 
in Hinds County. These results are significant both in magnitude and coefficients on loan total or 
amounts. The results are consistent with the results from model 1 that shows that loan amounts greater 
than $500,000 are more likely to be denied. However, Whites who apply for VA loans are more likely 
to be denied loans.  
This result is not consistent with the result frommodel 1 that shows that VA loans are more likely to be 
approved compared to conventional loans. Further, the results on environment in model 2 is similar to 
the results frommodel1. Model 3 represents the results for African Americans. African Americans who 
live in high minority tract population are 1.2 times more likely to be denied loans. It is interesting to 
note that African Americans who have loan to income ratio that is three times or less are 11.98% more 
likely to be approved for a loan compared with those with loan to income ratio that is more than 3 times 
the loan amount. The result on income is consistent and appears to show that there is a gradient in loan 
denial with income. The result on loan purpose is consistent with both Models 2 and 3 that show that 
loan applications for home improvement and home refinance are more likely to be denied compared to 
home purchase. Although not significant at 5% significant level, African Americans who live in Madison 
are less likely to be denied loans compared to Hinds County and African Americans who live in Rankin 
County are more likely to be denied loans compared to Hinds County. The results on loan amount are 
consistent with both the results from models 2 and 3, respectively. 
However, the results on types of loans in model 3 is similar to the results frommodel 1 and different 
fromModel 2 which showed VA loans to be more likely to be denied for Whites. Environment is similar 
for both Models 1and 2 with the exception of 2008, which is significant in loan denial for African 
Americans. Model 4 shows the results for Hispanics on loan denial. The result seems to follow a similar 
pattern, however some of the variables are insignificant although the signs are consistent with results 
from Models1, 2, 3, 4, and 5. Further, Model4 shows that income is consistent in determining loan 
denial. Model4 also shows that loans for home improvement and home refinance are more likely to be 
denied compared to loans for home purchase for Hispanics. Model 5 which represent the logistic 
regression for Asian Americans also show that income, loan to income ratio, and purpose for the loan 
are significant in determining loan denial over the study period. These results are similar to the findings 
of Anyamele (2015) and Weller (2009) that showed that African Americans are more likely to be credit 
constrained. To further understand the impact of location or county on loan denial in Mississippi, a 
logistic regression is ran for each county. Table 2, Models 6, 7, 8 and 9 represent results for all three 
counties combined, Hinds, Madison, and Rankin counties, respectively.  
 Table 2: Logistic Regression on Loan Denial in Jackson MSA 2007-2013  
  

Independent  Model 6  Model 7  Model 8  Model9  

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Variables  Combined  Hinds  Madison  Rankin  
No Race  -0.0145  0.0143  -0.0230  -0.0181  
  (-1.52)  (0.77)  (-1.68)  (-1.07)  
Asian Americas  Referent  Referent  Referent  Referent  
  1.00  1.00  1.00  1.00  
African Americans  0.0820***  0.0940***  0.103***  0.111***  
  (8.68)  (5.12)  (7.55)  (6.45)  
White  -0.0473***  -0.0451*  -0.0519***  -0.0198  
  (-5.12)  (-2.46)  (-3.98)  (-1.21)  
Hispanics  0.0634***  0.0398*  0.0690***  0.0792***  
  (6.23)  (2.21)  (4.08)  (4.64)  
High Minority Pop  0.0557***  0.0737***  0.0320***  0.0747  
  (20.42)  (18.93)  (7.33)  (0.71)  
NLV to income  0.00378  0.00291  0.00134  0.0314***  
  (1.31)  (0.60)  (0.26)  (6.32)  
Income <$45,000  Referent  Referent  Referent  Referent  
  1.00  1.00  1.00  1.00  
$45,001<=$75,000  -0.0747***  -0.0713***  -0.0768***  -

0.0682***  
  (-26.49)  (-15.95)  (-13.40)  (-14.14)  
$75,001<=$85,000  -0.106***  -0.114***  -0.0941***  -

0.0949***  
  (-23.23)  (-14.72)  (-11.24)  (-12.40)  
$85,001<=$95,000  -0.111***  -0.130***  -0.0952***  -

0.0926***  
  (-22.14)  (-14.93)  (-10.62)  (-10.89)  
>$95,000  -0.141***  -0.157***  -0.120***  -0.131***  
  (-38.66)  (-27.09)  (-16.96)  (-20.39)  
Home Purchase  Referent        
  1.00        
Home Improvement  0.234***        
  (55.23)        
Home Refinance  0.0797***        
  (35.77)        
Loan <$100,000  Referent  Referent  Referent  Referent  
  1.00  1.00  1.00  1.00  
$100,001<=$200,000  -0.0474***  -0.0522***  -0.0869***  -

0.0859***  
  (-17.65)  (-12.22)  (-16.49)  (-19.32)  

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$200,001<=$300,000  -0.0314***  -0.0436***  -0.0745***  -
0.0649***  

  (-8.15)  (-6.39)  (-11.58)  (-9.65)  
$300,001<=$400,000  -0.0228***  -0.0209  -0.0684***  -

0.0627***  
  (-3.99)  (-1.83)  (-8.31)  (-5.49)  
$400,001<=$500,000  -0.0347***  -0.0630***  -0.0649***  -

0.0742***  
  (-4.69)  (-4.17)  (-6.64)  (-4.30)  
>$500,000  0.0352**  0.00942  0.00803  0.0256  
  (3.26)  (0.42)  (0.60)  (0.91)  
Hinds County  Referent        
  1.00        
Madison County  -0.0176***        
  (-6.50)        
Rankin County  0.00545*        
  (2.00)        
Year 2007  Referent  Referent  Referent  Referent  
  1.00  1.00  1.00  1.00  
2008  0.00732*  0.0353***  -0.000118  0.0200***  
  (2.29)  (6.71)  (-0.02)  (3.72)  
2009  -0.0332***  0.00164  -0.0298***  -

0.0626***  
  (-9.48)  (0.29)  (-5.32)  (-8.09)  
2010  0.00193  0.0137*    0.112***  
  (0.42)  (2.27)    (11.97)  
2011  -0.00170  0.0325***  0.00466  0.0171**  
  (-0.46)  (5.01)  (0.74)  (2.90)  
2012  -0.00887*  0.0105  -0.000757  0.0213***  
  (-2.54)  (1.70)  (-0.12)  (3.82)  
2013  -0.00144  0.0125*  0.000346  0.0213***  
  (-0.41)  (2.01)  (0.06)  (3.73)  
Conventional Loan  Referent  Referent  Referent  Referent  
  1.00  1.00  1.00  1.00  
FHA Loan  -0.0433***  -0.120***  -0.0128*  -

0.0489***  
  (-15.67)  (-27.62)  (-2.37)  (-10.79)  
VA Loan  -0.0117  -0.0688***  -0.0388***  -0.0241*  
  (-1.83)  (-5.94)  (-3.32)  (-2.49)  
FSA/RHS Loan  -0.0997***  -0.227***  -0.120***  -0.116***  

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  (-15.31)  (-17.71)  (-11.39)  (-12.12)  
Constant  0.241***  0.301***  0.305***  0.260***  
  (24.24)  (15.88)  (20.88)  (15.08)  
N  138802  61556  35642  41604  

t statistics in parentheses  
*p< 0.05, **p< 0.01, ***p< 0.001  
FromModel 6 in Table 2, African Americans and Hispanics are more likely to be denied loans compared 
to Asian Americans while Whites are less likely to be denied loans compared to Asian Americans over 
the study period. FromModel 7, we see that census tracts with high minority populations are more likely 
to be denied loans. Income, purpose of loan, loan to income ratio, loan amount and environment all are 
significant and consistent with results from earlier tables. Model 8 is the logistic regression result for 
Madison County. The result is similar and consistent with the results fromModels 6 and 7 in Table 26. 
Model 9 presents the logistic regression results for Rankin County. With the exception of 2010 loan 
denial, all the other results are similar. Although high minority population tract is not significant, the 
sign is consistent with models 6, 7, and 8. The result is consistent with the results of Ezeala-Harrison 
et al (2008).  
Blinder-Oaxaca Decomposition and Loan Denial Discrimination   
To further understand the burden imposed by discrimination on loan denial to different ethnic groups 
we employ the Blender-Oaxaca decomposition method to analyze the loan denial rates in the counties 
of Hinds, Madison and Rankin from 2007 to 2013. Oaxaca and Ranson (1994) concluded that the 
pooled method of decomposition provides the best estimate of the combined effects of pure nepotism 
and pure discrimination.   
Nielson (1988) found discrimination to be responsible for 26% of the greater difference in formal sector 
employment in Zambia. Anyamele (2015) found that 32.17% of the differences in loan delinquency rates 
are attributable to discrimination either legally or illegally. Blinder (1973) concluded that 40% of age 
differences between Whites and African Americans came from discrimination of different sorts. Jann 
(2008) and Sinning et al (2008) showed how to interpret the results of both linear and non-Oaxaca 
decomposition regression models. Fairlie (2005) extended the Blinder-Oaxaca decomposition into a 
non-linear model. This study employs the Blinder-Oaxaca decomposition to measure the difference 
between the Whites/African Americans andWhites/Hispanics loan denial rate. We express the average 
value of the dependent variables denial rate, Y, is expressed such that   
Y ̅W - Y̅B = [(X ̅W - X ̅B) β̂W] + [X̅B (β̂W - β̂B)]            (3)  
Where X ̅j is a row vector for of average values of the independent variables and β̂j is a vector of 
coefficient estimates for race j. The decomposition for a non-linear loan denial rate equation, Y = F 
(Xβ̂), may be written as:   

 Y ̅W - Y̅B = [(∑_(i=1)^NW▒〖F (X〗iWβ̂W)/NW - ∑_(i=1)^NB▒〖F (X〗iBβ̂W)/NB] +  

[∑_(i=1)^NB▒〖F (〗 Xiββ̂W)/NB -∑_(i=1)^NB▒〖F (〗 Xiββ̂B)/NB]               (4)  
Where  Nj is the sample size for race j. Both equations 3 and 4 show that the first term in brackets 
represents the part of the racial loan denial difference that isdue to group differences from the 
independent variables. The second term is the group differences from unobserved endowments or 
unexplained difference in loan denial rates among the different racial groups. This is the part that some 

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researchers have attributed some part of it as discrimination. The data shows that the group mean for 
non-Whites loan denial rates is 0.272 while the group mean for Whites is 0.129, yielding a loan denial 
differences of 0.143.  Further,  31.18% of the differences in loan denial between Whites and non-
Whitescomes from differences in endowments. Thus, 56.15% is the change in loan denial rate if non-
White characteristics apply to Whites and12.31% is the simultaneous effect of the loan denial 
differences from both the endowments and non-White characteristics. Similarly, for African 
Americans, the group mean for the loan denial for Whites is 0.141 while the group mean for loan denial 
of African Americans is 0.329 yielding a loan denial rate of 0.188.  Furthermore, 28.19% of the 
differences in loan denial comes from endowments difference between African Americans and Whites,  
while  63.30% is the change in loan denial rate that will occur if White characteristics apply to African 
Americans and8.51% is from both endowments and non-African American characteristics.   
For Asians, the group mean for loan denial for Whites is 0.191 and the group mean of loan denial 
difference of 112.8% of the differences in loan denial rate comes from endowments between Asians and 
Whites;  while 54.91% is the change in loan denial rates that will occur if White characteristics apply to 
Asians. Moreover, 42.08% of the loan denial rate is from endowments and non-Asian characteristics. 
For Hispanics, the group mean for loan denial of Whites is 0.1904 and the group mean for the loan 
denial for Hispanics is 0.2431 resulting in a denial difference of8.02% of the difference is loan denial 
come from endowments between Hispanics and Whiteswhile 92.41% is the change in loan denial rate 
that will occur if White characteristics apply to Hispanics.   
  
Table 3 is the pooled, Blinder-Oaxaca decomposition that show the contribution of the independent 
variables used. First, for all the races, income explains the differences in loan denial rates with the 
exception of Hispanics, high minority population explains the differences in loan denial rate in the three 
counties between the years 2007 to 2013. This result is consistent with both the descriptive statistics 
and the logistic regression that shows that loans from high minority tract population are more likely to 
be denied. Furthermore, it also gives credence to the findings of previous studies that redlining exists 
in the housing market in Mississippi (Ezeala-Harrison et al.2008).  
   
  
  
  
  
  
  
  
  
  

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                                                             Journal of Economics and Development Studies, Vol. 5(3), 
September 2017 
Table 3: Blinder-Oaxaca Decomposition of Loan Denial in Jackson MSA 2007-2013  
  
  White  

Loan  
Denied  

  African  
Americ
an  
Loan  
Denied  

  Asian  
Loan  
Denied  

  Hispan
i 
c  
Loan  
Denied  

  
  

Differenti
al  

                

Prediction
_1  

0.272***    0.141***    0.191***    0.190***    

  (149.67)    (129.19)    (180.07)    (179.80)    
Prediction
_2  

0.129***    0.329***    0.166***    0.243***    

  (108.15)    (134.60)    (18.06)    (20.69)    
Difference  0.142***    -0.188***    0.0251**    -

0.0527**

*  

  

  (65.55)    (-70.30)    (2.72)    (-4.46)    
Explained    %  

Explaine
d  

  %  
Explaine
d  

  %  
Explaine
d  

  %  
Explaine
d  

HM Pop  0.0231***  16.24  -
0.0240***  

12.75  0.0148**

*  
58.79  -

0.0007
68  

1.46  

  (27.78)    (-23.82)    (15.54)    (-0.59)    
NLV to 
income  

-
0.00012
4  

-0.09  0.00000
41 4  

-0.02  0.0000
83 7  

0.33  0.0000
17 6  

-0.03  

  (-1.05)    (0.06)    (0.58)    (0.49)    
Post Crisis  0.00136*

**  
0.96  -

0.00109*

**  

0.58  0.00253
***  

10.05  -
0.0004
59  

0.87  

  (4.48)    (-7.36)    (7.20)    (-1.58)    
Income  0.0244**

*  
17.14  -

0.0271***  
14.41  0.0127**

*  
50.33  - 

0.00558
***  

10.59  

  (36.03)    (-35.01)    (8.42)    (-3.53)    
Loan  
Amount  

0.00388
***  

2.72  -
0.00470*

**  

2.50  0.0032
9***  

13.09  - 
0.00118
***  

2.24  

  (8.60)    (-8.78)    (7.24)    (-3.60)    

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Conventio
na 
l  

-
0.0118***  

-8.28  0.0124***  -6.60  -
0.0116**

*  

-46.01  0.0001
08  

-0.20  

  (-19.78)    (19.98)    (-11.08)    (0.09)    
FHA loan  0.00567

***  
3.98  -

0.00566*

**  

3.01  0.00501
***  

19.93  0.00013
0  

-0.25  

  (11.36)    (-12.48)    (7.76)    (0.19)    
VA loan  0.00073

6** 
*  

0.52  - 
0.00089
7***  

0.48  0.00169
***  

6.71  -
0.00116
*  

2.20  

  (7.44)    (-7.43)    (6.29)    (-2.21)    
Home 
improvem
en 
t  

0.0122**

*  
8.58  -

0.0192***  
10.22  0.00742

***  
29.50  0.0005

91  
-1.12  

  (26.97)    (-31.03)    (5.18)    (0.31)  -2.67  
Home  
refinance  

-
0.00508
***  

-3.56  0.00627*

**  
-3.33  0.0029

9**  
11.88  0.00140    

  (-19.80)    (21.11)    (3.02)    (1.29)    
Total  0.0544**

*  
38.19  -

0.0640***  
34.01  0.0389*

**  
154.61  -

0.0068
9*  

13.09  

  (47.94)    (-47.98)    (14.92)    (-2.16)    
Unexplain
ed  

  Unexplai
ne d  

  Unexplai
ne d  

  Unexplai
ne d  

  Unexplai
ne d  

HM  
Populatio
n  

0.00552
***  

  0.0112***    0.00510    0.0173*    

  (3.95)    (4.71)    (1.32)    (2.30)    
NLV to 
income  

-0.0106*    0.0224***    0.0800*

**  
  0.00554    

  (-2.42)    (4.30)    (4.32)    (0.23)    
Post Crisis  0.0182**

*  
  -0.00267    0.0124    -

0.0054
6  

  

  (7.08)    (-0.87)    (0.84)    (-0.40)    
Income  -

0.0370**

*  

  0.0237***    -
0.0723**  

  0.00325    

  (-7.93)    (4.41)    (-3.27)    (0.14)    
Loan  
Amount  

-
0.00196  

  0.00899    -0.0197    0.0111    

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  (-0.37)    (1.34)    (-0.89)    (0.39)    
Conventio
na 
l  

0.0744**

*  
  -0.119***    0.0105    0.00861    

  (10.35)    (-15.41)    (0.18)    (0.14)    
FHA loan  0.00616

**  
  -

0.00994*

**  

  -
0.00375  

  0.0180    

  (2.96)    (-3.97)    (-0.42)    (1.12)    
VA loan  0.00124*

*  
  -

0.00148*

*  

  -
0.00118  

  0.0023
6  

  

  (3.22)    (-3.12)    (-0.94)    (0.67)    
Home 
improvem
en 
t  

0.0133***    -
0.00261*  

  -
0.00411  

  -
0.00245  

  

  (16.71)    (-2.35)    (-1.46)    (-0.61)    
Home  
refinance  

0.0312***    -
0.0138***  

  -0.0141    0.0085
3  

  

  (12.84)    (-4.78)    (-1.52)    (0.66)    
Constant  -0.0123    -0.0404**    -

0.0066
4  

  -0.112    

  (-1.14)    (-3.15)    (-0.10)    (-1.38)    
Total  0.0881**

*  
61.81  -0.124***  65.99  -0.0137  -54.61  -

0.0458*

**  

86.91  

  (38.73)    (-43.77)    (-1.52)    (-4.02)    
N  78775    36965    1638    1333    

t statistics in parentheses  
*p< 0.05, **p< 0.01, ***p< 0.001  
Conclusion   
Income has a significant impact in denial rate in the three counties. This finding suggest that this 
increases the likelihood of one obtaining a loan approval in the state of Mississippi. Anyamele (2015) 
found income to be significant in explaining loan delinquency rate. Previous studies have found that 
African Americans and Hispanics tend to have higher credit constraints than Whites (Crook 1996, 
2001; and Weller 2009). Rugh and Massey (2010) concluded that housing segregation was an 
important predictor of the number and rate of foreclosures across US metropolitan areas. Their study 
found that Hispanics and African Americans bore the brunt of the recent financial crisis. Philips (2010) 
concluded that the housing and the related economic crisis that disproportionally affected African 
American communities are inextricably linked to the persuasive forces of inequality and uneven 
investment in African American communities.As shown from the results of decomposition, high 
minority population tract contributes more to loan denial rates in the three counties studied. This result 
points to the existence of redlining in loan denial in the counties and which is similar to previous 

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findings on this subject (Rugh and Massey, 2010). Moreover, this paper has investigated the loan denial 
rates in three counties of Mississippi. This study used HMDA data from 2007 to 2013. The study found 
that African Americans and Hispanics are more likely to be denied loans compared to Asians, while 
Whites are less likely to be denied loans compared to Asians. Also important, loans from high minority 
population tracts are more likely to be denied compared to loans from low minority population tracts.  
This result is consistent in Hinds, Rankin, and Madison counties. This finding is a strong indication 
that redlining exists in all three counties. This finding may lead to policymakers and the regulatory 
agencies to quickly act to redress the situation. As noted earlier, loan denial due to discrimination 
reduces the ability of African Americans and Hispanics to acquire wealth through the housing stock 
which has been a historical investment for many in America, especially minorities.   
References  

Anyamele, Okechukwu.  2015.  Racial/Ethnic Differences in Household Loan Delinquency  Rates.  
Review of Black Political Economy 42 (4): 415-442.  

Carr, James H. & Issac F. Megbolugbe. 1993. "The Federal Reserve Bank of Boston study on mortgage 
lending revisited." Fannie Mae Office of Housing Policy Research.  

Crook J. 1996. Credit constraints and US households.Applied Financial Economics6:477-85.  

Crook J.  2001. The demand for household debt in the USA: Evidence from the 1995 Survey of 
Consumer Finances. Applied Financial Economics.  11:83–91.  

Ezeala-Harrison, Fidel. 2008. Determinants of Housing Loan Patterns Towards Minority Borrowers in 
Mississippi.  Journal of Economic Issues 35 (1): 43-54.  

Ferguson, Michael F. & Peters, Stephen R. 1995. What Constitutes Evidence of Discrimination in 
Lending? The Journal of Finance 50 (2): 739-748.  

Greer, James. 2012. Race and Mortgage Redlining in the United States. Western Political Science 
Association Meetings.  Portland, Oregon.  March 22 -24, 2012.  

Han, Song. 2004. Discrimination in Lending: Theory and Evidence.  Journal of Real Estate Finance 
and Economics 29 (1): 5-46.  

Home Mortgage Discloure Act of 1975.  Section 301 of title III of the Act of December 31, 1975 (Pub. L. 
No. 94-200; 89 Stat. 1125), effective June 28, 1976.  

Housing and Community Development Act of 1977.  Section 801 of title VIII of the Act of October 12, 
1977 (Pub. L. No. 95--128; 91 Stat. 1147), effective October 12, 1977.  

Jann B. 2008. The Blinder-Oaxaca Decomposition for Linear Regression Models. Stata Journal 
(4):453–79.  

Ladd, Helen. 1998. Evidence of Discrimination in Mortgage Lending.  Journal of Economic Perspective 
12 (2): 41-62.  

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Anyamele, Fulgham & Claude Assad                                                                                                                          93  

   

  

American Research Journal of Economics, Finance and Management 
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93 | P a g e  

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