Interdisciplinary Studies
Title
Is credit score influenced by race?
Abstract
In this paper, how race influences credit score will be studied. The main motivation
for this work is that there is a general belief that implies that some races are not
equally treated. In this research race will be the independent and categorical variable
while credit score will be the continuous and dependent variable. Furthermore, there
will be some other variables included as controls, to ensure that the effect found it is
purely due to race. The controls will be income, education, and equity.
Introduction
In this work, the relationship between credit score and race will be analyzed. The
main hypothesis is that being of some races, in particular for this study African
American, is negatively correlated with the credit score of a person. In simpler words,
if everything else is held constant, just for being black, a person would have a lower
credit score than if s/he is white.
To complete this research, a data base of demographics, gender, race, and credit score
will be used. The tools employed will be a comparison in means and a linear
regression or a probit/logit model.
The Main point of this paper is to supply the word with new information about racial
discrimination. One of the biggest concerns is that this creates an endless circle of
poverty for a certain race with no valid base. This study, if proven true, will help
mitigate that issue.
Summary of Article Critiques
In a recent study (Gerardi et al, 2020) The authors explain that black borrowers paid
more than 40 basis points higher mortgage interest rates than non-Hispanic white
borrowers. The main inferential statistics used was how likely each race group is to
get a loan and principally to refinancing their loans. They found that this is the main
reason why these ethnical groups have worse credits scores (regardless of how their
skin look) their interpretation is that is that Hispanics and black people have less
equity and income, the reason why they can’t refinance.
On the other hand, the main idea on Henderson et al (2015) is that Net of credit-
relevant factors, the credit scores of new business startups owned by African
Americans are lower than those owned by Whites, and they found that to be true,
same for Latino, and Chinese. The main result is that some ethnic groups receive
lower than expected credits, based on their status and condition, solely due to the
owner having another race that is not white.
With a completely different methodology I found the work of Dymski,
Hernandez, and Mohanty (2013) that addresses 2 big questions that haven’t been
answered until this work. The first is why were minority applicants over included in
subprime lends, like some races or genders, who had been excluded from equal access
to mortgage, while before that event they hardly get a loan. And the second one is
why the over lending of mortgage credit in the 2000s housing boom reduced the
proportion of minority and women borrowers burdened with unpayable subprime
mortgages.
In Yass (2020) that the price of the home insurance is always higher for black people
as he called them, or African American people as we are used to. While I liked the
idea, and I found this to be a very good approach to show that the credit score
is discriminatory, I concluded that this is simply a starting point. The reason is that he
just assumed, as something that it’s known that African American people pay more,
but he never proves it with numbers.
Finally in Ashlyn (2010) they start from the thesis that housing decisions are affected
by race indirectly. The middle variable is credit score. They say that some races are
given worse options for credits, therefore they must choose cheaper options for
housing. They found the thesis to be true. I agree quite a lot with the authors, at first, I
believed that people alike, like to live close to each other, but in reality is just what
they can afford.
Anticipated Results
The expected result is a negative correlation between race (going from white to black,
in a dummy variable where 0 is white and 1 is black). Or that on average, given
everything else constant, the credit score of someone whose race is African American,
will have a lower credit score than someone whose race is White.
What is more interesting is to what degree this is true, is just marginal, or there is a
clear disadvantage for some race?
Anticipated Conclusions
Apart from the anticipated results, further studies are expected to be required. The
reason is that in the phrase “held everything else constant” there is a myriad of data
needed in order to achieve that.
In this first exploratory and expansionary of previous research study, it’s not likely
that all the data required will be available, which will make a replication of this work
very likely in the future in order to fully unravel the true.
Anticipated Recommendations
The first anticipated recommendation is made in the previous section, to gather
enough primary data to be able to fully explore the relationship between race and
credit score.
The second recommendation is to expand the study to other ethics and races like
Asian, or Latinos, since following the same approach; they are also likely to
be discriminated.
Finally, the third recommendation is to take action to prevent this type of
discrimination if the study shows that it actually happens.
Resources
Dymski, Hernandez, J., & Mohanty, L. (2013). Race, Gender, Power, and the US
Subprime Mortgage and Foreclosure Crisis: A Meso Analysis. Feminist
Economics, 19(3), 124–151. https://doi.org/10.1080/13545701.2013.791401
Gerardi, Kristopher S. and Willen, Paul S. and Zhang, David Hao, Mortgage
Prepayment, Race, and Monetary Policy (December 1, 2020). Available at
SSRN: https://ssrn.com/abstract=3697625 http://dx.doi.org/10.2139/ssrn.3697625
Henderson, L., Herring, C., Horton, H. D., & Thomas, M. (2015). Credit where credit
is due? Race, gender, and discrimination in the credit scores of Business
Startups. The Review of Black Political Economy, 42(4), 459–479.
https://doi.org/10.1007/s12114-015-9215-4
Nelson. (2010). Credit scores, race, and residential sorting. Journal of Policy Analysis
and Management, 29(1), 39–68. https://doi.org/10.1002/pam.20478
Yass. (2020). HOMEOWNER’S INSURANCE AND CREDIT SCORE: A
CRITICAL RACE THEORY PERSPECTIVE. Connecticut Insurance Law
Journal, 27(1), 286–.
