This study examines factors that contribute to differences in wages across professions using data from the 2006 and 2011 Current Population Survey. The dependent variable is salary. Independent variables are education, experience (measured by age), occupation, geography, gender, and race. Descriptive statistics show average salary was $40,591 in 2006 and $44,449 in 2011, with average education being some college for both years. Regression analysis will determine how these independent variables impact salary and if their effects differed before and after the recession.

1. Rebecca Rohr rrohr2@jhu.edu
Do people with more education across various professions really earn more, how does that vary by
experience, gender, and race, and did that change pre/post-recession?
Rebecca Rohr
Introduction:
The objective of this study is to examine what factors or variables (if any) contribute to differences in
wages across various professions. Earnings are usually defined by a combination of salary, bonuses,
and/or company perks, but this study will define earnings using salary of individuals who worked. This
study will utilize an OLS model to examine if various factors contribute to differences in earnings with
salary being measured as the dependent variable. The research question is: Do people with more
education across various professions really earn more, how does that vary by experience, gender, and
race, and did that change pre/post-recession?
There have been a lot of studies on differences in earnings involving various factors. These studies focus
on the variables that contribute to wage differences examining fairness and equality in the labor market.
Findings have suggested that the gender pay gap is attributable to wage inequality because women face
discrimination in the labor market.1
There have also been findings that have suggested that race-wage
differentials are highly attributable to discrimination of various kinds.2
This study is interesting because
it will bring some clarity to discrimination in the labor market. If we can understand that the differences
in earnings are attributable to something that is uncontrollable like gender and race, maybe change can
start to happen. Looking at how these variables change pre/post-recession can help shed some light on
how these variables are changing and whether or not the gender wage gap or the race wage gap is
converging or diverging.
The study of labor economics is generally designed to answer questions about how healthy our labor
market actually is via unemployment. There are also studies centered around the human capital model.
Human capital is the accumulation of resources by individuals to perform labor. Human capital
investment can vary in its definition, but generally consists of education, experience, knowledge, and
skills.3
Generally speaking, the more one invests in their education, skills, and knowledge, with all things
being equal, the more money they would make in their individual line of work. This is not always the
case due to experience/inexperience and/or discrimination in the workplace. Chance (luck) and ability
are also sources of income inequality.4
Another interesting phenomenon is how the labor market
responds to a recession. Young graduates just entering the labor market in a recession tend to suffer
1
National Bureau of Economic Research, “Gender Differences in Pay,” 2000,
http://www.nber.org/papers/w7732.pdf.
2
The Journal of Human Resources, “Wage Discrimination: Reduced Form and Structural Estimates,” 1973,
http://www.jstor.org/stable/144855?seq=1#page_scan_tab_contents.
3
Journal of Business Venturing, “Human Capital and Entrepreneurial Success: A Meta-Analytical Review,” 2009,
http://strathprints.strath.ac.uk/35466/1/Unger_Rauch_Frese_Rosenbusch_2011.pdf.
4
American Economic Association, “The Distribution of Labor Incomes: A Survey With Special Reference to the
Human Capital Approach,” 1970, http://www.jstor.org/stable/2720384?seq=1#page_scan_tab_contents.

2. Rebecca Rohr rrohr2@jhu.edu
significant and lasting earning losses that fade after 8 to 10 years.5
This paper will also examine how
gender, experience, and race variables change pre/post-recession.
Data:
The data on pay inequality was consolidated from the Current Population Survey (CPS) data. The CPS
data is a monthly survey that is used to measure national unemployment. This CPS data is the primary
source of labor force statistics for the population of the United States. The CPS data is a source used for
high-profile economic statistics such as the national unemployment rate and is also used for relating
employment and earnings. The CPS data also collects extensive demographic data that complement and
enhance our understanding of the labor market conditions in the nation overall among many different
population groups.6
The CPS data I am using for this analysis consists of the March Demographic
Supplement for 2006 and 2011. I only wanted to focus on actual wage earners, so for the March 2006
and March 2011 survey data, I summed the male and female groups together, dropping anyone in the
survey data that had a salary of zero or less than zero. This dropped the number of observations down
by 25% for the March 2006 data, resulting in 75% left to work with. For the March 2011 data, I dropped
28% of the observations (salary <= 0), resulting in 72% of the data left to work with. Income based on
salary and earnings is what I use for my dependent variable. I also dropped anyone with an occupation
code that had a value of -1 (meaning that they did not have an occupation code that was recognizable
within this study). This drop for occupation code in both the 2006 study and the 2011 study did not
have a significant impact on the overall number of observations. This only cut out roughly 5% of the
data.
In addition to my dependent variable, I use data that might have some kind of effect on salary and
earnings that can explain for differences in pay based on years of experience (I use age as my
independent variable for experience), along with education, occupation, and geography. The
independent variables I include that might explain for discrimination in pay are gender and race.
Table 1 shows the descriptive statistics of each variable for the 2006 data set. In the first row we see
that the average of the variable Salary & Earnings is $40,591.06. The average age is 40 years old. The
minimum age for this survey was 18, with the max age of 65 years old. The average education level is
High School graduate, some college. Education is coded as a dummy variable with education levels
represented with numbers from 0 to 1 and variables created for each level of education with one
variable omitted to control for collinearity. (The chart shows that Education is from 0 to 5 to provide a
one snap-shot summary, instead of listing every variable out. All variables are listed in the regression
model.) Occupation is coded as a dummy variable with different occupations represented with numbers
from 0 to 1 and variables created for each occupation with one variable omitted to control for
collinearity. (The chart shows that Occupation is from 0 to 10 to provide a one snap-shot summary,
instead of listing every variable out. All variables are listed in the regression model.) Geography is
coded as a dummy variable with different geographic locations in the United States represented with
5
American Economic Journal Applied Economics, “The Short- and Long-Term Career Effects of Graduating in a
Recession,” 2012, http://www.researchgate.net/profile/Philip_Oreopoulos/publication/227349860_The_Short-
_and_Long-Term_Career_Effects_of_Graduating_in_a_Recession/links/0deec51926c882745f000000.pdf.
6
Bureau of Labor Statistics, “Labor Force Statistics from the Current Population Survey,” 2015,
http://www.bls.gov/cps/.

3. Rebecca Rohr rrohr2@jhu.edu
numbers from 0 to 1 and variables created for each geographic location with one variable omitted to
control for collinearity. (The chart shows that Geography is from 0 to 8 to provide a one snap-shot
summary, instead of listing every variable out. All variables are listed in the regression model.) Gender
is represented also by a dummy variable with 0 representing male and 1 representing female. Race is
coded as a dummy variable with different races represented with numbers from 0 to 1 and variables
created for each race with one variable omitted to control for collinearity. (The chart shows that Race is
from 0 to 4 to provide a one snap-shot summary, instead of listing every variable out. All variables are
listed in the regression model.)
Table 4 shows the descriptive statistics of each variable for the 2011 data set. In the first row we see
that the average of the variable Salary & Earnings is $44,449.08. The average age is 41 years old. The
minimum age for this survey was 18, with the max age of 65 years old. The average education level is
High School graduate, some college. Education is coded as a dummy variable with education levels
represented with numbers from 0 to 1 and variables created for each level of education with one
variable omitted to control for collinearity. (The chart shows that Education is from 0 to 5 to provide a
one snap-shot summary, instead of listing every variable out. All variables are listed in the regression
model.) Occupation is coded as a dummy variable with different occupations represented with numbers
from 0 to 1 and variables created for each occupation with one variable omitted to control for
collinearity. (The chart shows that Occupation is from 0 to 10 to provide a one snap-shot summary,
instead of listing every variable out. All variables are listed in the regression model.) Geography is
coded as a dummy variable with different geographic locations in the United States represented with
numbers from 0 to 1 and variables created for each geographic location with one variable omitted to
control for collinearity. (The chart shows that Geography is from 0 to 8 to provide a one snap-shot
summary, instead of listing every variable out. All variables are listed in the regression model.) Gender
is represented also by a dummy variable with 0 representing male and 1 representing female. Race is
coded as a dummy variable with different races represented with numbers from 0 to 1 and variables
created for each race with one variable omitted to control for collinearity. (The chart shows that Race is
from 0 to 4 to provide a one snap-shot summary, instead of listing every variable out. All variables are
listed in the regression model.)
Table 1: 2006 CPS data set:
2006 data obs mean median std. dev min max
Salary & Earnings 89,687 40,591.06 30,000 46,832.29 1 607,643
Age (Experience) 89,687 39.82 40 11.88 18 65
Education 89,687 2.28 2 1.56 0 5
Occupation 89,687 4.21 3 2.74 0 10
Geography 89,687 4.46 5 2.42 0 8
Female 89,687 0.48 0 0.50 0 1
Race 89,687 0.61 0 1.04 0 4

4. Rebecca Rohr rrohr2@jhu.edu
Table 2: 2011 data set:
Empirical Results:
I estimate six different specifications for the 2006 data set. The dependent variable in each specification
is Salary & Earnings, as measured by an actual dollar figure. Table 3 shows the empirical results for the
2006 CPS data set. Age (Experience), Education, and Occupation are the independent variables in each
regression. In regression (6) Geography is included. In regressions (1), (2), (3), (4), (5), and (6) Gender is
included as an independent variable. In regressions (5) and (6) Race is included as an independent
variable. The adjusted R-squared value gets higher as more variables are added to the model but also
because the variables added add more significance to the overall reason or explanation for salary and
earnings. In the first specification, I regress salary and earnings on gender. The coefficients are all
statistically significant. The intercept indicates that there is a negative effect on salary when one is
female. This indicates that males have a higher salary compared to females with no other variables
included in the model. In regression (2), age is added as experience and all of the variables are
statistically significant. The intercept term is lower, but the result for female is still negative. In
regression (3), education is added with all of the variables being statistically significant. Wage earners
are higher for males and higher education. Regression (4), occupation is added. Some of the
occupations aren’t statistically significant. If one is male, salary is higher if their occupation is in
business for example. Regression (5), race is added. A lot more of the variables are not statistically
significant, but a white male in a business occupation earns more than a female does. Regression (6),
geographic location is added. Some of the variables are not statistically significant.
A female earns less across all of the regression estimates. For example, a female (controlling for other
factors) wage earner aged 18 to 65, white with a bachelor’s degree in a business occupation in the 2006
study earned: $6,916.40 - $19,941.69(female) + $23,776.82(bachelor_degree) +
$22,785.04(business_occupation) + $4,766.36(white) = $38,302.93; however, a white male in this study
with the same occupation and education would have earned: $6,916.40 + $23,776.82(bachelor_degree)
+ $22,785.04(business_occupation) + $4,766.36(white) = $58,244.62. A male on average for this specific
education, occupation, and race earns 52.06% more salary than a female.
I estimate six different specifications for the 2011 data set. The dependent variable in each specification
is Salary & Earnings, as measured by an actual dollar figure. Table 4 shows the empirical results for the
2011 CPS data set. Age (Experience), Education, and Occupation are the independent variables in each
2011 data obs mean median std. dev min max
Salary & Earnings 85,431 44,449.08 33,250 52,825.80 2 1,699,999
Age (Experience) 85,431 40.66 41 12.18 18 65
Education 85,431 2.43 2 1.57 0 5
Occupation 85,431 4.05 3 2.70 0 10
Geography 85,431 4.50 5 2.42 0 8
Female 85,431 0.49 0 0.50 0 1
Race 85,431 0.66 0 1.06 0 4

5. Rebecca Rohr rrohr2@jhu.edu
regression. In regression (6) Geography is included. In regressions (1), (2), (3), (4), (5), and (6) Gender is
included as an independent variable. In regressions (5) and (6) Race is included as an independent
variable. The adjusted R-squared value gets higher as more variables are added to the model but also
because the variables added add more significance to the overall reason or explanation for salary and
earnings. In the first specification, I regress salary and earnings on gender. The coefficients are all
statistically significant. The intercept indicates that there is a negative effect on salary when one is
female. This indicates that males have a higher salary compared to females with no other variables
included in the model. In regression (2), age is added as experience and all of the variables are
statistically significant. The intercept term is lower, but the result for female is still negative. In
regression (3), education is added with all of the variables being statistically significant. Wage earners
are higher for males and higher education. Regression (4), occupation is added. Some of the
occupations aren’t statistically significant. If one is male, salary is higher if their occupation is in
business for example. Regression (5), race is added. A lot more of the variables are not statistically
significant, but a white male in a business occupation earns more than a female does. Regression (6),
geographic location is added. Some of the variables are not statistically significant.
A female earns less across all of the regression estimates. For example, a female (controlling for other
factors) wage earner aged 18 to 65, white with a bachelor’s degree in a business occupation in the 2011
study earned: $7,171.88 - $19,753.81(female) + $24,129.91(bachelor_degree) +
$23,171.42(business_occupation) + $4,403.92(white) = $39,123.32; however, a white male in this study
with the same occupation and education would have earned: $7,171.88 + $24,129.91(bachelor_degree)
+ $23,171.42(business_occupation) + $4,403.92(white) = $58,877.13;. A male on average for this
specific education, occupation, and race earns 50.49% more salary than a female.

6. Rebecca Rohr rrohr2@jhu.edu
TABLE 3: Regression Results for 2006 data set:
2006 CPS Data Set
Dependent variable: Salary & Earnings (in $)
(1) (2) (3) (4) (5) (6)
Intercept 49,606.44 16,368.14 5,210.06 9,427.42 6,916.40 4,546.84
(212.29)** (541.86)** (620.36)** (760.54)** (1088.57)** (1266.11)**
Female -18,812.95 -18,954.77 -19,960.75 -20,027.96 -19,941.69 -19,884.46
(303.66)** (299.40)** (279.67)** (302.56)** (302.68)** (302.27)**
Experience 836.45 618.07 560.57 551.98 545.43
(12.60)** (11.91)** (11.76)** (11.81)** (11.80)**
Education HS, No College 9,603.64 7,142.58 6,287.75 6,289.10
(498.06)** (495.10)** (509.93)** (509.34)**
Education HS, Some College 14,078.82 9,469.96 8,556.48 8,596.55
(529.81)** (537.20)** (553.50)** (553.31)**
Education Associate Degree 19,230.70 13,715.44 12,633.09 12,757.51
(615.65)** (625.72)** (640.95)** (640.40)**
Education Bachelor's Degree 35,033.65 24,932.77 23,776.82 23,718.74
(532.63)** (576.44)** (594.88)** (594.28)**
Education Above Bachelor's Degree 59,133.89 48,207.02 47,062.77 46,832.23
(621.63)** (690.07)** (705.60)** (705.01)**
Occupation Business 22,989.01 22,785.04 22,740.76
(538.12)** (538.10)** (537.39)**
Occupation Professional 5,568.52 5,461.95 5,433.41
(510.57)** (510.34)** (509.62)**
Occupation Service -5,965.81 -5,728.11 -5,742.56
(504.31)** (504.50)** (503.83)**
Occupation Sales 5,347.66 5,160.56 5,175.70
(559.46)** (559.35)** (558.54)**
Occupation Military -6,579.69 -351.59 -7,106.86
(12323.74) (12315.49) (12296.93)
Occupation Farming -12,234.45 -11,971.38 -11,830.36
(1618.47)** (1618.84)** (1617.49)**
Occupation Construction -2,054.02 -1,953.26 -1,807.25
(699.87)** (700.47)** (699.81)**
Occupation Maintenance -105.56 -351.59 -292.14
(828.40) (828.15) (826.94)
Occupation Production -1,891.01 -1756.74 -1,309.06
(651.89)** (651.76)** (652.13)*
Occupation Transit -4,340.25 -4178.09 -4,064.89
(695.40)** (695.19)** (6944.25)**
Race White 4,766.36 5,425.50
(793.14)** (800.97)**

7. Rebecca Rohr rrohr2@jhu.edu
Race Hispanic 1,572.70 1,157.59
(857.42) (858.75)
Race Black 1,076.35 1,089.36
(886.97) (902.48)**
Race Asian 3,624.63 2,516.26
(1013.18)** (1014.22)*
Geography New England 2,367.28
(771.64)**
Geography Middle Atlantic 5,729.44
(787.22)**
Geography East North Central 1,192.33
(758.69)
Geography West North Central -2,017.36
(759.13)**
Geography South Atlantic 3,410.75
(722.66)**
Geography West South Central 1,496.51
(811.09)
Geography Mountain 389.45
(774.53)
Geography Pacific 5,203.92
(752.97)**
R-squared 0.0403 0.0852 0.2086 0.2393 0.2403 0.2428
Adj. R-squared 0.0403 0.0852 0.2086 0.2391 0.2402 0.2425
Number of observations is 89,687
Standard errors are in parentheses.
**significant at 1%, *%significant at 5%

8. Rebecca Rohr rrohr2@jhu.edu
TABLE 4: Regression Results for 2011 data set:
2011 CPS Data Set
Dependent variable: Salary & Earnings (in $)
(1) (2) (3) (4) (5) (6)
Intercept 53,150.64 18,367.33 3,772.51 8,585.33 7,171.88 3,797.61
(248.56)** (632.06)** (765.48)** (930.60)** (1317.56)** (1530.01)*
Female -17,887.84 -17,819.98 -19,650.23 -19,881.04 -19,753.81 -19,703.84
(356.38)** (349.19)** (327.97)** (351.46)** (351.41)** (351.14)**
Experience 854.73 673.96 616.11 607.26 600.60
(14.33)** (13.56)** (13.44)** (13.48)** (13.48)**
Education HS, No College 9,446.99 6,716.93 5,644.15 5,501.35
(627.52)** (626.00)** (643.62)** (643.45)**
Education HS, Some College 14,306.25 9,393.13 8,227.27 8,126.37
(661.18)** (670.04)** (690.02)** (689.66)**
Education Associate Degree 20,502.95 14,095.98 12,750.42 12,754.69
(741.20)** (755.02)** (775.21)** (774.96)**
Education Bachelor's Degree 37,096.45 25,595.72 24,129.91 23,888.68
(649.73)** (702.07)** (726.69)** (726.54)**
Education Above Bachelor's Degree 61,904.29 48,636.74 47,178.58 46,848.23
(729.96)** (811.55)** (834.03)** (834.36)**
Occupation Business 23,506.11 23,171.42 23,089.77
(634.26)** (634.29)** (633.77)**
Occupation Professional 8,378.56 8,166.48 8,136.22
(599.09)** (598.81)** (598.27)**
Occupation Service -6,421.76 -6,143.12 -6,119.05
(594.03)** (593.97)** (593.56)**
Occupation Sales 4,441.06 4,198.86 4,146.56
(676.50)** (676.20)** (675.57)**
Occupation Military -9,347.23 -10,135.71 -10,427.08
(13027.30) (13016.02) (13003.55)
Occupation Farming -8,338.55 -8,081.54 -7,869.13
(1876.42)** (1876.69)** (1876.45)**
Occupation Construction -1,788.73 -1,915.62 -1,805.60
(874.04)* (874.16)* (873.43)*
Occupation Maintenance 1,483.23 1,124.45 1,240.73
(1005.97) (1005.50) (1004.63)
Occupation Production -1,505.13 -1,412.72 -889.65
(801.96) (801.61) (802.48)
Occupation Transit -6,418.77 -6,150.88 -6,020.70
(825.96)** (825.61)** (824.98)**
Race White 4,403.92 5,012.69
(956.15)** (966.03)**

9. Rebecca Rohr rrohr2@jhu.edu
Race Hispanic 499.18 82.91
(1021.25) (1023.15)
Race Black -1,060.84 -1,112.44
(1059.61) (1078.63)
Race Asian 2,020.99 1,384.12
(1157.88) (1158.65)
Geography New England 4,294.60
(915.39)**
Geography Middle Atlantic 6,731.00
(935.82)**
Geography East North Central 1,264.54
(904.63)
Geography West North Central -122.99
(899.77)
Geography South Atlantic 4,987.90
(857.87)**
Geography West South Central 3,532.83
(948.75)**
Geography Mountain 2,207.26
(921.12)*
Geography Pacific 5,318.78
(890.72)**
R-squared 0.0286 0.0675 0.1848 0.2109 0.2123 0.2140
Adj. R-squared 0.0286 0.0674 0.1847 0.2107 0.2121 0.2137
Number of observations is 85,431
Standard errors are in parentheses.
**significant at 1%, *%significant at 5%
Conclusion
The analysis in this paper shows that pay inequality has a negative effect on salary and earnings. The
effect remains statistically significant even after controlling for experience, education, and occupation.
All of the regression estimates show that males earn a higher salary than females. Whites also earn
higher wages than other races.
Pre/post-recession only shows a slight difference in wages from the 2006 study to the 2011 study. A
white male with a bachelor’s degree in a business occupation in 2006 earned 52.06% on average more
than a female. A white male with a bachelor’s degree in a business occupation in 2011 earned 50.49%
on average more than a female. That is a difference of only 1.57%. More tests and analyses need to be
done for ruling out the null hypothesis that recessions have an effect on salary.

10. Rebecca Rohr rrohr2@jhu.edu
The fact that pay inequality leads to lower salaries implies that workers should fight for their rights and
demand higher pay and possibly turn down jobs that aren’t paying what they know is the standard for
their specific occupation. Given these results, it is surprising that hiring managers don’t offer equal pay.
If managers offer a candidate a job and give them a salary based on their previous salary they earned in
a previous job, they need to add more to the offer if it is not within industry standard. This would help
solve the gap in pay amongst race and gender.
The conclusions above are subject to limitations. It is unclear why people are offered lower salaries and
how their salary started low in the first place. It is also unclear as to why they aren’t offered more when
hiring decisions and offers are made. Therefore, the estimation procedure needs to correct for this.
Finally, there may be other variables that affect salary and earnings not looked at in this paper like
ability, chance, and luck.

11. Rebecca Rohr rrohr2@jhu.edu
REFERENCES:
National Bureau of Economic Research, “Gender Differences in Pay,” 2000,
http://www.nber.org/papers/w7732.pdf.
The Journal of Human Resources, “Wage Discrimination: Reduced Form and Structural Estimates,” 1973,
http://www.jstor.org/stable/144855?seq=1#page_scan_tab_contents.
Journal of Business Venturing, “Human Capital and Entrepreneurial Success: A Meta-Analytical Review,”
2009, http://strathprints.strath.ac.uk/35466/1/Unger_Rauch_Frese_Rosenbusch_2011.pdf.
American Economic Association, “The Distribution of Labor Incomes: A Survey With Special Reference to
the Human Capital Approach,” 1970,
http://www.jstor.org/stable/2720384?seq=1#page_scan_tab_contents.
American Economic Journal Applied Economics, “The Short- and Long-Term Career Effects of Graduating
in a Recession,” 2012,
http://www.researchgate.net/profile/Philip_Oreopoulos/publication/227349860_The_Short-
_and_Long-
Term_Career_Effects_of_Graduating_in_a_Recession/links/0deec51926c882745f000000.pdf.
Bureau of Labor Statistics, “Labor Force Statistics from the Current Population Survey,” 2015,
http://www.bls.gov/cps/.