"We seldom get into trouble when we speak softly. It is only when we raise our voices that the sparks fly and tiny molehills become great mountains of contention."

It
is really difficult to sit and watch the television these days. So
many of the contemporary shows have so much vulgarity that I do not
enjoy them. That leaves me to turn to the news channels to see what
people are talking about.

And
what do I see? Our President talking about the pay disparity between
men and … that that is how far I got before I went to see if
there was actually history being shown on the History Channel (it
should actually be called the Pawn Shop Channel).

Why,
why, why, are we fourteen years into the 21^{st} Century
talking about the disparity between the pay between men and women?
Even the The Washington Post thinks what the President was
asserting was not accurate.

Here
are the two sides of this issue. The President says that the average
women’s salary is 77% of the average man’s salary. This
is a fact. The research done on the issue (you know, the science the
President likes to say he endorses) indicates that once life
differences (chosen profession, taking time off to have babies, etc.)
are taken into account, then the difference is supposed to be 4%, not
23%.

People
hear that last statement and kind of understand what it is saying,
but what does it really mean? How do the economists know?

This
article is going to, at a very high level, try to explain what
economists do to understand the pay differences between men and
women. Just a quick warning, this article is going to get very nerdy
in a hurry.

Economist
rarely talk about averages. They mean nothing. For example, if I
told you that the average pay for someone in New York City was 300%
higher than someone in Cozad, Nebraska (actual place we always stayed
at when travelling across the country) would you be surprised? Not
likely. Why, because you would immediately think of several rather
important reasons for the disparity in wages.

So
what do economists do? They do something they call econometrics.
Using regression models, they try to find relationships between a
dependent variable and several independent variables.

Reading
the word “regression” is not a reason for your eyes to
glaze over. All regression is doing is seeing how the dependent
variable changes over time as the independent variables change.

In
our example between New York and Cozad, the salary of the people is
the dependent variable. What the different people get paid is
determined by other independent variables. What would those
independent variables be? Let’s just pick two: level of
education and chosen profession.

So
our model (this is a terrible model but I am trying to make it easy
to understand the concept not make the reader capable of doing
econometrics) would look like the following:

Amount of Pay (Y) =
(X_{1})level of education + (X_{2})chosen profession
+ e

We
would start building our data by putting the information about
individuals from New York and Cozad into the model. After the data
is built, then we run our regression.

The
regression is going to tell us a couple of important things. It will
tell us whether our independent variables (education and profession)
help explain differences in our dependent variable (wages).

When
the independent variable is statistically significant then the answer
is yes, the independent variable does help explain some of the change
in the dependent variable. If the independent variable is not
statistically significant, then it is not explanatory.

We
learn more from the regression. The coefficients X_{1} and X_{2
}tell us how much the independent variable changes the dependent
variable. So the coefficient X_{1} would tell us how much
pay changes as the level of education changes, and so forth.

The
e on the end is the amount of change in pay not explained by our
independent variables.

Let’s
get back the President’s statement. An average women’s
salary is 77% of a man’s salary. Based on our now expanded
understanding of econometrics, what do we know about this statement?

The
word “average” is a red flag. When the word is used, it
usually means somebody is telling you something that is entirely
meaningless.

The
statement does not hold up under the scrutiny of econometrics.
Using the pay differential as the dependent variable, economists
have used independent variables that have been shown to be
statistically significant to help them determine what causes the pay
differential.

Some
of these independent variables that explain the pay difference are
level of education, types of degrees earned, hours worked per week
and time taken from careers for child rearing.

Since
the pay gap can be explained by other influences, the charge of wide
spread, systematic discrimination is not true.

There
was a statement made by someone wondering why they always trotted out
men to make the claim that there were other items influencing the pay
differential. Where were the women economists? This is hard for some
people to understand, but not all academic disciplines are about
feelings.

The
computer running the regression to determine what causes the pay
difference does not care whether it is a woman or a man.

My assumption is that women economists are working on questions far more interesting than an issue to which 96% of the answer has already been determined.

Adam Smith is obviously not the actual name of the author of this column. The real author has
worked for two Fortune 500 companies, one privately held company, and a public accounting
firm. His undergraduate degree was in accounting, and he earned an MBA for his graduate
degree. He also has completed coursework for a PhD. in finance. He continues to be employed
by one of the Fortune 500 companies.

The author grew up in the Washington D.C. area but also lived for several years in Arizona. He
currently resides with his family on the East Coast.

The author has held various callings in The Church of Jesus Christ of Latter-day Saints.