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Matthew Pipes
Professor Cynthia Smith
SBS 318-12 Cultural Pluralism
December 12th, 2015
Pay Equity, Statistics, and the American Court System
American society can be hostile to women, and that includes the workplace. Women face
being excluded from certain professions because of outdated and antiquated ideas of what is an
appropriate career for a man or a woman. These barriers keep women in lower paying positions
than their male counterparts. In fact, the Institute for Women’s Policy Research (IWPR) has
concluded that women earn just 78.6% of what men earn (Hegewisch). This statistic is updated
yearly with new data, and inches closer to 100% every year. Previous research has stated the
number of 77%, and this has been the number that has stuck in ethos of American politics and
has driven the milieu to see this discrimination ended. To make it simple, the 77% statistic will
be used henceforth; just remember that this is something of a fluid number.
The 77% statistic is shocking and has been repeated by President Obama and numerous
other politicians and publications (Jacobson). Women should get paid the same as men for the
same work. There are laws in place as well that are supposed to prevent men and women being
paid differently for doing the same jobs. The Equal Pay act of 1963 bans employers from paying
different wages based on gender. Title VII of the Civil Rights Act of 1964 bans discrimination
in general in the work place based on gender. In fact, that statute was amended in 1991 to lay out
damages for those found to be discriminated against (Employment Discrimination). If women
are being paid 77% of what their male counterparts are being paid for the same work, they
should sue in federal court for damages under the Equal Pay Act 1963 and the Civil Rights Act

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of 1964. These lawsuits would be slam dunks, and lawyers would be competing with each other
to be able to represent these women. Lawyers would want to represent these women considering
what a slam dunk lawsuit those cases would, and how common these instances of obvious
discrimination are. If these women are in California, they have a leg up because on January 1st,
2016, the California Fair Pay Act will go into effect that will ban employers paying different
wages for “substantially similar” work (Mcgreevy).
Analysis of the Data and Methodology Behind the IWPR Report
So why aren’t there a flood of lawsuits for gender discrimination if women are earning
77% of what men earn? If these laws have been in place for 50 years now, why does this 77%
statistic still exist? A closer look at the 77% statistic reveals our answer, but first let us look at
the methodology used by the IWPR to come to their conclusion.
The United States Census Bureau puts out data that is publicly accessible on their website
to anyone with an internet connection. They put out a data set called “Historical Income Tables:
Full-Time, Year-Round Workers by Median Earnings and Sex.” This data set classifies all full-
time workers into two groups, men or women, and then finds the median of each group. If you
were fifteen years old or older and you worked full-time, you were included in each calculation.
What is a median? The median is the middle data point of that dataset. If there are eleven data
points, then the median will be six. The United States Census Bureau counted around 62,455,000
male full-time workers in 2014. The median income of that group is $50,383. That means that
31,227,500 men (almost exactly 50%) that worked full-time in 2014 made more than $50,383,
and an equal number make less. Using the median is a better way to extrapolate to usual
circumstances than an average. Averages can be distorted by single data points at either end of
the extreme. Included in a dataset is Bill Gates, and he pulls up the average income.

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Unfortunately, that does not mean that your lifestyle is in anyway related to that of Bill Gates. So
now that we know what is contained in the data set, and where the data comes from, what about
the analysis and the conclusion that women earn 77% of what men earn for the same work?
In the data set “Historical Income Tables: Full-Time, Year-Round Workers by Median
Earnings and Sex,” in 2014, men that work full-time earn a median income of $50,383. In the
same data set, women that worked full-time in 2014 earned a median income of $39,621. Once
these numbers are provided, simply divide the smaller number by the larger number. The answer
is .78639. Then multiply that number by 100 to turn it from a probability, which is based on a 0-
1 scale, to a percentage that works on a 0-100 scale, and the answer is 78.6%. Algebraically it
looks like this:
$39,621 ÷ $50,383 = .78639 x 100 = 78.639%
So the science is settled right? Women earn 77% of what men earn for the same work.
Wrong. The way we talk about statistics is equally important as the methodology behind those
statistics. Some have made the mistake of inferring from this statistic that if you show up at a job
that pays $15 an hour to their male workers, it will pay their female workers $11.55. That cannot
be conferred by the data provided, and, if based on the data provided, would be an incorrect
assumption. This interpretation assumes that all men and women worked at the same location,
with the same job, on the same project, for the same amount of time, and their output was exactly
the same. If that were the case, then this dataset could extrapolate that men and women are being
paid differently for the same work, but it is not the case, and thus such an extrapolation would be
incorrect. In addition, this data fails to take into account benefits offered by employers, which
can far expand the salary of an employee.

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Many people have made the mistake of thinking that the 77% statistic means women are
being paid less for the same work, including President Obama, which earned his comment a
rating of “mostly false” from Politifact.com and “Exaggerated” and a “false claim” from
Factcheck.org (Jacobson) (Jackson). The proper way to discuss the findings in the IWPR report
is to state that, “ throughout the nation the median annual income for women that work full-time
is 77% of men, across all job sectors and careers. Stating that the difference is from the same
work deviates from the empirical data.
This data is essentially a ratio and there have been previous academic warnings about
using simple ratios to discuss larger groups of people, most notably by British statistician
Richard Kronmal (Kronmal). A different data set that came from the Census Bureau that
accounted for all workers regardless of time worked, stated that there were 2 million more
female workers than men workers. That data is correct, so men should hire lawyers to sue for
gender discrimination too, right? No, because that number does not take into account very basic
variables such as income earned, population differences, and time worked. The jump from the
77% statistic to women are being paid less than men for the same work suffers from the same
deficiency: too many important variables are left out. To know that a woman is being paid less
than her male co-workers for the same work, more complicated statistical models must be
employed than a simple ratio, such as regression models.
Factors Causing Women to earn 77% of Men
This is not meant to be taken to mean that instances of women being paid less for the
same work as men are non-existent. Rather, it means we have to understand the root causes of
why women are bringing home 23% less earnings than men that work full time. Cases of women
being paid less for the same work have been documented before, most notably by the experience

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of Lilly Ledbetter. Lilly Ledbetter had been given poor evaluations based on her gender, and a
federal district court awarded her damages. That decision was reversed on appeal because it was
found that she filed her legal complaint after the statute of limitations had run out (Ledbetter v.
Good Year Tire and Rubber Co., Inc.). The overturning of the lower court’s decision did not
negate the fact that she had been directly discriminated against because of her sex.
Two variables have the highest impact on the wage gap. Those two variables are
discrimination, and work force segregations. Discrimination against women in the workplace has
been estimated by economists to account for about 40% of the 23% gender wage gap (Jackson).
There are many ways that an employer can discriminate against women in the work place
including firing, promotions, transfers, and hiring. In addition, careers that pay more may have
hostile environments to women, which could cause women to avoid, leave, or fail in those
professions. In these hostile environments, women can be sexually harassed or assaulted, and
held to a higher standard for success than their male co-workers. If all women were not being
discriminated against in the workplace, then the wage gap would still be 87.16%. So what is
causing the rest of that 12.84% of the wage gap?
One glaring variable is work force segregation. Men are more often lawyers, doctors, and
business executives. Women on the other hand, are more likely to be teachers, nurses, and office
assistants, all of which pay significantly less (Jackson). A study of MBA graduates applying for
some of the highest paying jobs in the country found that women applied less for Wall Street
finance jobs and consulting, while applying more often for lower paying jobs like accounting,
general management, and marketing. Three reasons effected what positions applicants applied to,
which were applicant’s preferences for pay and schedule flexibility, an applicant’s ability to
identify with the job, and the applicant’s expectations of getting the job. Women were more

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likely to prefer a job that afforded more schedule flexibility and personal time. In the same study,
women were hired into investment banking just as much as men, but they anticipated that they
would be discriminated against. This undoubtedly affected the number of women that applied.
The author of the study stated that investment banking is seen as a macho world, a perception
that is not true and perpetuated by the media, and these cultural perceptions need to change for
women to apply for these positions at the same rate as men. In addition, these companies can
reduce conflicts between work and family, and make job descriptions and structure gender
neutral. These steps should encourage more women to apply for higher paying careers (Quast).
How to Prove Gender Discrimination in Court
If someone is being paid less than their co-workers of the opposite sex, there is a way to
prove that which has been established by the American courts system. Under the Equal Pay Act
there is a two-step process to proving gender discrimination. The Equal Pay Act requires that two
people doing jobs that require similar skill, effort, and responsibility cannot be paid differently
because of sex. The first step to proving gender discrimination under the Equal Pay Act, is to
prove that the plaintiff (the person suing for gender discrimination) had a job that was
substantially equal in skill, effort, and responsibility to another employee. The second step is to
make prima facie argument, which is an argument that is considered to be true until refuted by
the defendant (the person being sued for gender discrimination) (Legal Dictionary - Law.com).
The Equal Pay Act allows for four affirmative defenses: a seniority system, a merit system,
measurements of quantity or quality of production, or a differential based on factors other than
sex (Luna, 196).
Title VII of the Civil Rights Act of 1964 is far more expansive than the Equal Pay Act
and includes workers of any suspect class, and has become the most frequently used law to

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challenge employment discrimination. This statute as well has two parts, the first of which makes
it illegal to discriminate against individuals of a suspect class in respect to compensation, terms,
conditions, privileges, or employment against a suspect group. The second part of Title VII
makes it unlawful for employers, “to limit, segregate, or classify his employees or applicants for
employment,” in a way that would adversely affect members of a suspect class, or deprive them
of opportunity (Luna, 197). The phrase “suspect group” is a legal term, which means that on its
face, to discrimination against that group is unreasonable, and the legal standard of strict scrutiny
is applied to those cases. An easy way to understand what a suspect class is, is whether or not it
is unreasonable to discriminate against people with long criminal records in hiring? No, but it is
unreasonable to discriminate against African Americans while hiring. On its face, without much
thought, the idea of discriminating against African Americans in hiring procedures is abhorrent,
while discriminating against people with long criminal records in hiring is understandable in
certain circumstances. In that example, African Americans would be a suspect group and people
with long criminal records would not be a suspect group. Suspect classes are race, religion, color,
sex, and national origin. To discriminate based on any of those reasons, the discriminatory policy
would have to overcome the legal standard of strict scrutiny. Strict scrutiny is intended to be
somewhat impossible to overcome. It requires that to discriminate based on a suspect class, the
government action must be in pursuit of a “compelling government interest,” and must be
“narrowly tailored” in order to achieve that government interest (Strict Scrutiny).
There are two ways to prove a case under Title VII, one of which is called the disparate
impact model. This covers practices that may seem fair in form, but are discriminatory in
operation. Discriminatory intent is not necessary to prove discrimination, just discriminatory
impact. Under this model, the plaintiff must show that a specific policy or practice discriminates

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against employees or prospective employees, and that the practice or policy is not justified by
business necessity and unrelated to job performance. Both requirements must be met to prove a
violation of Title VII (Luna, 197).
The second model is the disparate treatment model, which involves discriminatory intent.
This model has a three step process, the first of which is that the plaintiff must establish a
pattern, practice, or custom of discrimination by a preponderance of the evidence. Once done, the
defendant can challenge the plaintiff’s statistical evidence with their own. Or they must articulate
a legitimate, nondiscriminatory purpose for the challenged policy or practice. If the defendants
successfully meet this standard, the burden reverts to the plaintiff to prove the statistical proof is
inadequate or that the reasons provided were simply a pretext for discrimination (Luna, 198).
Statistics Accepted by the Federal American Court System
Statistics are useful to make a prima facia argument. If someone used the methodology
that the IWPR used, they would fail to make a prima facia argument, and their lawsuit would be
thrown out of court. Simply finding a median of all male and female workers working for a
specific company would fail to make prima facia argument. To start making a statistical model
that would be admissible in court, one has to find a sample to run statistical analysis on. The
Supreme Court of the United States has made clear that the sample must be a group closely
related to the lawsuit. One cannot have an analysis that shows teachers in general are
discriminated against, but must be far more specific to win their case. The sample must consist of
people that work similar amounts of time. If the lawsuit is about part-time teachers, the sample
cannot contain full-time teachers, and if it does the model must properly account for that (Luna,
204).

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There is an extremely important caveat in how one procures a dataset of their co-workers
salaries, and that is a lack of transparency on the part of employers. Many universities publish
the salaries of their professors, which does explain why there is so much case law in regards to
pay-equity in university settings. The same cannot be said for other industries. President Obama
has also signed a recent Executive Order that requires federal contractors to provide information
about how they compensate employees to the federal government. The data must include
variables like race and sex (Greenhut). These numbers can then be used in a dataset to make a
prima facia argument. This executive order though does not affect the entire economy, just
federally contracted employees. Receiving compensation data from a private employer can be
much more difficult. It is illegal for an employer to bar employees from discussing their own
salaries with each other, or from inquiring about other employee’s salaries. However, there is no
requirement for the company to disclose that data. Simply put, they just cannot retaliate for
seeking out that information, considering that the reason for seeking out that information is in the
pursuit of finding discrimination. This legal construction is criticized by those wishing to prove
discrimination because the data that would prove the discrimination is difficult to get. In
addition, business interest do not like this legal construction either. A poorly performing
employee that feels they are about to get fired could inquire about the pay of others as a way to
intimidate the employer not to fire them. The assumption is that if a poorly performing employee
is fired shortly after inquiring about the pay of others, they could file a frivolous lawsuit against
the company that would be expensive to defend against (Eilperin).
Once a representative sample is in hand, it is time to pick variables that affect the lawsuit.
The Supreme Court has been said that regression analyses do not have to contain all measurable
variables, but must only present a preponderance of the evidence (Luna, 207). One important

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variable the court wants to see in any regression model is rank of the employees included in the
sample. This comes with a caveat though; many employers wish to make rank an indicator of
performance. The higher the rank, the better the performance is the way they see it. The court has
found this logic insufficient and any regression model using rank as a proxy for performance
may be dismissed as evidence. An employee that was a manager, but has returned to the regular
workforce must be included and that study must also account for those employees higher
experience (Luna, 208). Merit is used on a case by case basis because of the unreliability of merit
systems used by employers (Luna, 219). Another important variable is market factors. In
universities, this could mean what discipline the professor teaches; in a business, it could be what
division one is in. A law professor or an MBA professor could make a lot more money outside a
university setting than a liberal studies professor, and someone who is the sales department of a
company has a much more crucial role to the business than someone in human relations. The
courts have made it clear that in any regression analysis, they want to see how the employees are
divided amongst themselves (Luna, 209).
To be admissible in court, a regression analysis must include most of the important
variables, but is not required to have all relevant variables. Variables that are used in promotion
and tenure are critical to have in a regression analysis in order for the analysis to be admissible
evidence. Rank is also highly recommended except when tainted by discrimination. Also it is
crucial to have a variable that depicts the discrepancies in pay amongst departments and
divisions. In addition, the analysis must break the employees down further to show differences
amongst their roles (Luna, 210). In addition, any comparisons of the plaintiff must be made in
regards to a real person and not a hypothetical person (Luna, 216). The court has also recognized
the importance of having the significance level of any analysis be at least be below .05 to be

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taken seriously (Luna, 211). If all of these points are included in a regression analysis, it is very
likely that your analysis will be accepted as prima facia by the court.
Conclusion
The findings of this paper show that the methodology used by IWPR does not show that
women are being paid less for the same work. The IWPR report shows that women that work
full-time make 77% of what men that work full-time make. These finding also should not be
taken to mean that every woman in the United States is paid fairly for the work that she does.
Discrimination in the workplace and work force segregation are the two main factors to why
women that work full-time make 77% of what men make that work full-time.
There are two federal laws that protect all women across the nation from being
discriminated against in the workplace. The Equal Pay Act of 1963 protects women from being
paid differently for the same work based on sex. A coffee shop may not pay one barista more
than another because they are a man or a woman, but they may pay those baristas differently
based on previous experience, years worked at the coffee shop, credentials, or productivity. Title
VII of the Civil Rights Act of 1964 bans general discrimination in the workplace against many
classes of people, for which sex is one.
In order to be properly compensated for being discriminated against, plaintiffs will have
to make prima facia argument, and the best way to do that is with statistics. The American courts
have made clear the variables are required in such a statistical analysis to be able to constitute a
prima facia argument. Those variables are the factors used in promotion, rank, market value, and
the divisions amongst the work of employees. Any comparison based on this kind of analysis
must also be to an actual person, and not of a hypothetical person derived from some form of
analysis.

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The 77% statistic is not misleading, but misunderstood. This is for a variety of reason but
two in particular. The public does not understand statistics, and the motivations of media outlets.
Regression analysis is a new word to the majority of the population, and they do not have the
understanding of mathematics and statistics to be able look into a claim to see if it stands up to
scrutiny or not. In addition, the norm for media outlets is sensationalism and corporate
ownership. Corporate ownership of media outlets creates the goal of increasing revenue, rather
than to properly inform the public. Controversial and sensational news keeps viewers and readers
returning to consume more media, which in turn drives up the revenue of the media outlet. The
media distributed does not have to be factual or representative, just sensational and controversial.
So even if media outlets did have a good enough understanding of statistics to know that many
factors influence the pay gap, it is in their financial interest to either directly change the
conclusions of the research to be more sensational, or to simply lead the audience to come to that
conclusion on their own without the broader conversation about why that gap exists. The average
media consumer in the United States does not know enough about statistics to be able to come to
any conclusions on their own, and also lacks any understanding of the motivations behind media
outlets.
Statistics and media are having an effect on every person in the country every day, yet
we do nothing to educate our children about these topics. We continue teaching them useless
skills like cursive and calculus, yet leave out topics that will affect them ever day of their lives
like media studies and statistics. The country needs to have an understanding about why women
are making 77% less than men for full-time work, and the research put out by IWPR can help
start that conversation, but it is up us to actually have that conversation.