This document summarizes a study examining the racial wage gap in the United States using data from the 2014 Current Population Survey. A regression analysis found that black and American Indian workers earned significantly less than white workers with the same education and experience levels, indicating a persisting racial wage gap. Specifically, the analysis found that black workers earned 21.6% less and American Indian workers earned 58% less than white workers on average. The study concludes that racial discrimination continues to negatively impact minorities' wages in the U.S. labor market.

1. Leonardo Sprintzin
The Racial Gap On Wages
Econometrics 4140 Project
Dr. Ryan Ruddy

2. 1) Introduction
Perhaps one of the most common questions about today’s labor market is if a
significant racial gap in wages exists in the United States, in other words, do White
workers earn more than minorities? This paper’s goal is to answer this question taking
into consideration the different levels of education, holding all else constant.
Consequently, this is a relevant question because if proven that there is a significant gap
in our collected data it opens room for a possible racial discrimination that employees are
subject to when firms are determining their wages. The data evaluated in this study is a
sub sample of the Current Population Survey 2014. The Analysis is based on a regression
via OLS, which accounts for factors used to determine wage based of different levels of
education, (high school, bachelor’s and masters), age and workers class and then factors
that shouldn’t be relevant upon determining wages such as gender and race. After
conducting this study and running several regressions it is safe to assume that the racial
gap on wages still exists in the American society, subsequently it is easy to illustrate my
statement by comparing the wages between Black and Whites workers.
2) Data
The data set consists of 1,199 observations of working individuals between the ages of 23
and 59
Table 1: Mean
Mean
Age 41.598
(10.75785)
Earnings per hour 22.65073
(13.89619)
Female .5029191

3. (.5002001)
White .8106756
(.3919296)
Black .1067556
(.3089312)
American Indian .0100083
(.0995813)
Asian .058382
(.2345622)
Mixed Races .0141785
(.1182756)
Hispanic .1351126
(.3419864)
High School .2710592
(.4446921)
Bachelor's Degree .2335279
(.4232518)
Master's Degree .0917431
(.2887834)
Part time .0075503
(.0866003)
Observations 1,199
Standard deviation in parentheses
The average age in the sample is 41.59 years old. On average, individuals made $
22.65 an hour with a standard deviation of $13.89. Approximately half of our sample was
female. The majority was White (81.06%). 10.67% of the individuals were Black, and
14.17% were mixed races, it is worth mentioning that 13.51% of individuals in our

4. sample were Hispanics. 27.1% of our sample holds a High School diploma, 23.35% have
a bachelor’s degree and 9.17% have completed a master’s degree. 0.755% of our sample
have a part time job.
Most of the data is located to the right side of the histogram and relies on the
milestone years. 13, 14, 16 and 18 areas represent high school diplomas, associates,
bachelors and master’s degrees. The gaps that exist in the graph represent areas where
there are no people in our sample that can be classified in those groups.
3) Empirical Model
To identify and compare racial differences in the returns on wages on different levels of
education I ran the following linear regression on log wages:

5. Log (wage) = β0 + β1 Age + β2 Age
2 + β3 Years of Education + β4 Black + β5 American Indian + β6
Asian/PacificIslands + β7 Other + β8 Hispanic + β9 female + β10 part-time + ɛi
I decided to use log wages since it makes it easier to read and interpret the results
of the regression. It is also important to remember that log wage are more normally
distributed and consequently it may increase the goodness of fit. Holding all else
constant, based on my previous research, I expect to see a positive, yet diminishing,
return to age. Therefore, I would expect β1 to be positive and β2 to be negative. I expect
β3 to be a positive coefficient because the more education you have more skills and
knowledge you will acquire and therefore increases your chances of having a higher
paying job position. To match my hypothesis I expect the coefficients β4, β5, β6, β7 and β8
to be negative. I also expect B9 to be negative due to income inequality between men and
women. Lastly I anticipate β10 to be negative since part time jobs pay less than full time
jobs.
Omitted bias variable may occur in this model, because there is a lack of
geographical data in the model affecting the race variable. It is worth mentioning that
there is a significant Black population living down South, also the economy in that part of
the country is less developed than the Northeastern part of the United States,
subsequently this factor could affect people’s wages. For example, an employee that
works for a law firm in New York City, New York may earn more than an employee in
that works for a law firm in Jackson, Mississippi since the cost of living in New York is
higher than the cost of living in Mississippi due to the economic development of these
two regions. Consequently, this would mean that the error term is negatively correlated

6. with wages causing a downward or upward biased in some of the estimate of the race
coefficient.
4) Result
Table 2: RegressionCoefficient
(1) (2) (3)
Model Log Wage Alternative Log
Wage - White
Alternative Log
Wage - Black
Education 0.0931*** 0.0969*** 0.0960***
(0.00674) (0.00648) (0.00647)
Age 0.0471*** 0.0495*** 0.0471***
(0.0142) (0.0142) (0.0142)
Age2 -0.000449*** -0.000479*** -0.000447***
(0.000172) (0.000173) (0.000172)
Female -0.133*** -0.137*** -0.136***
(0.0356) (0.0358) (0.0357)
Black -0.216*** -0.204***
(0.0596) (0.0590)
American Indian -0.580***
(0.206)
Asian -0.0424
(0.0720)
Mixed 0.244*
(0.140)
Hispanic -0.0607
(0.0540)
White 0.123***
(0.0454)
Part- Time -0.419** -0.421** -0.420**
(0.206) (0.207) (0.207)
Constant 0.618** 0.390 0.565*
(0.296) (0.298) (0.295)

7. Observations 810 810 810
R2 0.280 0.265 0.269
Adjusted R2 0.271 0.259 0.264
Standard errors in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
Table 2 presents 810 observations, the reason why it shows a lower number of
observations than Table 1 is due to the fact that among 1,199 people in our sample only
810 individuals had a job. The first column in our table (Model Log Wage) reveals that
the workers wage will increase on average by 9.31% for each additional year of
education, all else held constant. The effect of one additional year age is an average
increase in wages of 4.71% minus 0.0449% times current age, all else held constant. The
returns to ages are decreasing. In comparison to male, females earn on average 13.3%
less, all else held constant. In comparison to White workers, Black workers earn on
average 21.6% less, all else held constant. Also in comparison to White workers,
American Indian workers earn on average 58% less, all else held constant. Asian workers
earn on average 4.24% less than White workers, all else held constant. Mixed races
workers on average earn 24.4% more than White workers all else held constant.
Hispanics earn on average 6.07% less than white workers all else held constant. Part time
workers earn on average 41.9% less in comparison to full time workers. The R2 is 0.280
meaning 28% of the variation of the log wage is explained by the model. The variables
that are statistically significant at a 5% level are: Education, Age, Age2, Part -time,
Female, Black workers, and American Indian workers. Summing to the effect that if they
didn’t have an effect on the wage the chances that we get the data as extreme as we did it
will be less than 5%. I also conducted a T-test for the Black workers variable and I was to
reject the null hypothesis and prove that White workers earn more than Black workers.

8. I conducted an F test, I excluded race dummy variables to build s my restricted
model and I was able to reject the null, as the corresponding p-value is 0.0417. It can be
concluded that the majority of the race coefficients are jointly significant in determining
the log of wage.
The regression’s result predicts that a 40-year-old White male with 10 years
education working full time will get a log wage of 4.833. A Black male with the same
exact level of education, the same age and working on a full time job will get a log wage
of 4.617. Since the Black workers are earning less in comparison to the White workers
the result matches with my predictions.
To exemplify my thesis a little bit more I ran two other regressions with almost
same variables, (with an exception of Hispanic), that I used on the Model Log Wage
regression but I made some adjustments on the race dummy variables. In the Alternative
Log Wage – White regression I kept the White variable and excluded all the other race
dummy variables so I could have a better dimension of how much more White workers
earn in comparison to other races, and I concluded that White workers make on average
12.3% more than other races, all else held constant. The R2 is 0.265 meaning 26.5% of
the variation of the log wage is explained by the model. In the Alternative Log Wage –
Black regression I kept the Black variable and also excluded all the other race dummy
variables so I could have a better dimension of how much less Black workers earn in
comparison to other races and I concluded that Black workers make on average 20.4%
less than other races, all else held constant. The R2 is 0.269 meaning 26.9% of the
variation of the log wage is explained by the model. Taking into consideration both of the
R2 ‘s presented in the alternative models it is safe to assume that a model lacking any race

9. or ethnicity relatively does a poor job of explaining the variation in log wages when
compared to the model with all the races and ethnicities.
5) Conclusion
In today’s society higher education has a significant impact upon the
determination of wages, the more years of education a worker has the more likely he will
be compensated with a competitive salary. After all the highest paying jobs available in
the market require many skills that for the most part can be acquired through education.
Consequently it is understandable that a worker that has a master’s degree will be
receiving a higher salary that a worker that only has his/her high school diploma.
Although, according to my results, the racial gap is alive and well in the American
society in the year of 2014 despite all our best efforts to eradicate racial based
discrimination in the labor market. Thus minorities are still getting paid less than white
workers even if they have the same amount of education.