In my last post, I briefly looked at differences between average wage earnings between Males and Females for 2015. I use the Census Population Survey (CPS) for March 2015 . Again, the average annual wages between males and females is shown in the table below. On average males make $50,344 and females make $42,671.

In this post we are first going to look at __racial & gender__ differences in average annual wages.

We first begin by looking at females among races. The table above shows that Nonwhite females in this sample on average make 82 cents compared to white females.

Now, let us look at the case of white males and nonwhite males.

From the table above, we see roughly a 10% difference between white males and on white males. Nonwhite males earn $46,198 whereas white males earn $51,151.45.

All these average comparison tells a story about wage differentials. The most important thing to question is, does a person’s endowment plays a role in his/her earnings potential. Answering that question requires more empirical investigation. I can employ a multivariate regression analysis to try estimate a model of earnings. If you would like to learn the statistics behind it, click Here!

I construct two models.

Model 1: Is trying to estimate the average effect of Experience, and Experience Squared **ON**** Wages.**

Model 2= everything in Model 1 **+** Female, and Nonwhite **ON**** Wages.**

A brief summary of these variables would give you a better idea of what they represent.

**Experience**is basically ( Age – Education – 6), we assume that people are not working during education and everyone started school at age 6. So computing experience was suggested by the CPS.**Experience Squared**is basically Experience x Experience. Why? Well, human capital theory suggests that experience diminishes. Meaning that after a certain point of years of experience the incremental gain from that experience is smaller than the previous. For example, after 35 years of experience you are probably going to get old and your skills and knowledge will become less significant and obsolete. The inclusion of experience squared is important. Since experience can not be forever linear. As the graph below shows. Experience has a nonlinear effect on wages.

**Education**is years of schooling. For example, 12 years = high school, 16 years= Bachelors, etc..**Females**are just a binary variable that takes the value of 1 if female or 0 if male. For example, if an individual in the data is a female, the female variable will take the value of 1.**Nonwhite**are also a binary variable that takes the value of 1 if nonwhite or 0 if white.

The table below is the output from the regression.

**Model 1: Education and Experience (Column 1)**

- 1 more year of experience
**increases**wages by 3.53% - 1 more year of education
**increases**wages by 10.9%

**Model 2: Model 1+ Female and Nonwhite (Column 2)**

- 1 more year of experience
**increases**wages by 3.43% - 1 more year of education
**increases**wages by 11.3$ - Being a female
**reduces**wages by 20.8% - Being a nonwhite
**reduces**wages by 7.47%

These findings suggest that there other factors that affect earnings other than the conventional Education and Experience. The implication here is serious, that genetic endowment at birth might play a significant role for determining wages. In other words, your productive capacity given by your education and experience is not enough to determine your wage. Other factors such as gender and race might have a substantial role.