1. Wage Gaps and Occupational Segregation
An Analysis of Wage Gaps for Minority Races in the US across States and
Occupations
In this paper we use data from the American Community Survey in the US for 2014 to
analyze the presence of wage and occupational gap for minority races in the US economy.
We tested for wage differences across two dimensions: states and occupations, and attempted
to determine if the difference in wage levels arose due to occupational segregation or if they
could solely be explained by other factors such as education. We found that wage levels for
non-Hispanic whites were consistently higher than all minority races across most states.
However, these gaps are reduced when controlled for occupation. Although whites still have
higher wages, this are explained by the type of job they are doing. Our findings show
evidence of occupational segregation; there is a high proportion of minorities working in in
low paying jobs, and a low proportion in high paying jobs (compared to whites). This is true
for Hispanics and Blacks, but for Asians there is no strong evidence that supports it.
Aishath Zara Nizar
Jose Diaz Barriga Ocampo
Byungchul Yea
Special Project required for the completion of Masters of Arts in Economics
New York University
December 2016
2. 2
I. Introduction
The United States (US) has had a decreasing trend of non-Hispanic whites in its population as
diverse races or ethnicity groups have immigrated into the country. The proportion of non-
Hispanic whites has decreased from 76% in 1990 to 65% in 2010 (U.S Census Bureau),
which has had a large impact on the labor dynamics in the US. As more people of color
immigrate into the country, there have been specific trends observed about the type of jobs a
particular ethnic or racial group are more inclined towards, determined by a considerable
number of factors such as geography, immigration status (legal or not), ability and fluency in
speaking English and education. Moreover, a large amount of research has recognized that
minority groups have faced consistent wage discrimination in the labor market even up until
today. Thus, we recognize the pivotal interplay between wage differences and occupational
segregation of minority races in the US.
Many studies have documented the relationship between wages, race, education, gender and
occupation. Some of them, such as Catanzarite (2000), show that occupational segregation
exists at local levels such as metropolitan areas or selected cities. Also, Farley (1989, 1990)
investigated economic performance based upon racial identification. He found that virtually
all of the racially self-declared white minorities have economic profiles at or above the
national mean and all of them had higher per capita incomes than black.
However, as far as we know, there has been no study that has analyzed wage differences in
the US at the state and occupation levels. We use data from the Integrated Public Use
Microdata Series (IPUMS), which includes raw data originally collected from the American
Community Survey, for 2014, with a final sample of more than one million observations. We
essentially build upon Becker’s (1957) framework as a theoretical model, which is given by:
3. 3
where Y is an (n x 1) vector of observed wages, X is an (n x k) vector of exogenous
explanatory variables and Z is an (n x j) vector indicating membership in a minority group.
Our research aims to compare the interaction between wage and race in all racial groups
against non-Hispanic whites using recent data by considering state and occupations.
Furthermore, we want to see if there is evidence of wage discrimination across races, and
how much of that can be explained through state-specific and occupation-specific
characteristics. We also believe that there may exist occupational segregation within races,
which may help to explain these differences in wages. We further delve into possible wage
differences within occupations, which may give stronger evidence of wage discrimination
(after controlling for basic wage determinants as education, age and gender).
Our paper is structured as follows: Section 2 lays out the literature review which we based
our research on while Section 3 summarizes the data used in our analysis. Section 4 explains
in detail our basic model; and discusses the regression results in which we attempt to
compare wage gaps across states and occupations, determine factors which may explain the
magnitude of these gaps, and link these results to the extent of occupational segregation we
find in our data. We conclude with a brief look at the limitations in our analysis and avenues
for future research.
4. 4
II. Literature Review
The primary paper we based our research on is a study by Carlos Gradin at el. (2011) titled
“Occupational Segregation by Race and Ethnicity in the US: Differences across States”. This
paper analyzed occupational segregation in the US, conditional and non-conditional at the
state level. The empirical model uses cross sectional data from 2005 to 2007 from the Public
Use Microdata Samples (PUMS) files of the American Community Survey.
The unconditional analysis showed that District of Columbia, New Jersey, Hawaii, and
Southwestern states have a high degree of segregation by race and ethnicity. It hypothesized
that the main reasons for this disparity are an uneven distribution of workers and a diversity
in industrial structures across states. The key result is that the wage segregation is
significantly reduced after conditioning by racial composition in the states (controlling for
races). Moreover, the states with the biggest negative gap were the ones located in the East
Central region such as Kentucky, Alabama, Tennessee, and Indiana.
The second paper is titled “Workplace Segregation in the United States: Race, Ethnicity, and
Skill” (Judith K. Hellerstein and David Neumark, 2008). This paper studies measurements of
workplace segregation by education, language, race, and ethnicity in the US and skill
differences based on race and ethnic segregation. It uses Decennial Employer-Employee
Database for 1990, using a Monte Carlo simulation for random occurring segregation.
The analysis was based on the percentage of workers in an individual’s establishment or
workplace in different demographic groups that average the percentages separately for each
group in the sample. The primary result is that the differences in wages can be explained by
education and skills for white workers (17%), but for Hispanic workers, language is more
significant in explaining the wage gap (29%). Additionally, the magnitude explained by each
5. 5
race is almost the same size as the one explained by education (14%), Hispanic having the
biggest negative impact (20%). This paper gives a good insight into the idea that education,
skill, and race affects workplace segregation.
The third paper is entitled “Explaining Differences in Economic Performance Among Racial
and Ethnic Groups in the USA: The Data Examined” (William Darity Jr. et al, 1996). The
authors begin by measuring the effects of races in wages in both men and women. Later, they
introduce control variables such as English fluency, foreign or domestic site of birth, and
indicator of the extent to which a person is assimilated. The conclusion was that there is no
systemic evidence of discriminatory differentials affecting the income between ethnic groups
on women. Japanese, Chinese, and Korean men show strong evidence of a flip from negative
to positive gap on their behalf. This paper provides a basic guideline for thinking about the
necessary factors to control for race as well as gender.
III. Data
The data used in our study is from the IPUMS database. We used annual data for 2014
throughout the study, for our analysis to reflect the most recent data available. Table 1 below
summarizes the variables used.
In all our analyses, the dependent variable is the natural log of wage. Our main variable of
interest is race; in the original IPUMS dataset, only the following five races are explicitly
recorded as dummy variables for race: white, Black, Asian, Pacific Islander, and American
Indian or Alaska Native. We combined the last two variables (Pacific Islander, American
Indian or Alaska Native) due to the small number of observations in each.
6. 6
Table 1: Data details
Variable Type Description Values
wage continuous Each individual’s total pre-tax wage and
salary income from an employer;
measured in current U.S. dollars
Ranges from 10,000 to
642,000
age Factor with
14 levels
Individual’s age, grouped by 5-year
terms1
16-20 years, 21-25 years, ….
76-80 years, 81-100 years
education Factor with 4
levels
Level of education, transformed to a
factor variable based on the years of
education of each individual
Less than high school, high
school, incomplete college,
college or more
sex Factor with 2
levels
Gender of the individual Male, female
race Factor with 6
levels
Race of the individual White, Black, Hispanic,
Asian, Pacific Islander and
Native American, Mixed
race
state Factor with
51 levels
State where the individual lives All 50 states and District of
Columbia
minor_
occupation
Factor with
97 levels
Occupation of each individual based on
the 2010 Standard Occupational
Classification (SOC)’s minor occupation
groups
major_
occupation
Factor with
23 levels
Occupation of each individual based on
the 2010 SOC’s major occupation groups
Hispanic (as a variable), is recorded separately (since it is an ethnicity and not a race),
classing people according to their country of origin.2
We manipulated this variable to also
follow a dummy variable classification with 1 being Hispanic and 0 being not Hispanic.
Naturally, there was an overlap between people who identified themselves as Hispanic and of
a particular race. Hence, we further coded the new Hispanic variable such that, if a person is
Hispanic and of another single race, he will be coded as a Hispanic. If a person is Hispanic
and of two or more races, or any other two race combination, we created a new variable,
1
Due to a very small number of observations after 80 years, we made a 20-year term (81-100) as the last age
group.
2
In the IPUMS database, “Hispanics” identifies persons of Hispanic/Spanish/Latino origin where “origin” is
defined by the Census Bureau as ancestry, lineage, heritage, nationality group, or country of birth. This variable
has factors: not Hispanic; Mexican; Puerto Rican; Cuban; other; and not reported.
7. 7
“mixed race”, to account for this. Finally, we combined all these variables to create the “race”
variable, with each race and Hispanic, being a level of this factor variable, where any single
person is only identified as one race or ethnicity..
The minor_occupation and major_occupation variables were created using the occupation
classification code for each individual in the dataset. This is a 6-digit code classifying the
person’s primary occupation, based on the 2010 Standard Occupation Classification system.
Of this 6-digit code, the first two digits represent the major occupation group while the third
digit (along with the first two) represents the minor occupation group.3
We used this
information to create each person’s minor and major occupation groups.
We cleaned the data by removing all individuals who were unemployed or did not specify
their jobs. We also removed individuals who reported less than $10,000 as their yearly
income, which can be attributed to being employed only for a short time during the year. Our
final dataset consisted of 1.2 million observations.
3
For instance, if a person’s 6-digit code was 29-1062 (“Family and General Practitioner”), his major occupation
code would be 29-000 (“Healthcare Practitioners and Technical Occupations”) and his minor occupation code
would be 29-1000 (“Health Diagnosing and Treating Practitioners”).
8. 8
IV. Model and Estimation Method
We used the OLS model to run the regressions for this paper. Using log of wages as the
dependent variable, we controlled for sociodemographic factors in our dataset to determine
the effect of race on wages.
∑ (1)
where i refers to each individual and X is a matrix including the constant and basic
sociodemographic controls (age, gender and education). After this step, we test this equation
by controlling for the state that the individual resides in.
∑ ∑ (2)
Alternatively, we added occupation controls to Equation 1. We did so by adding controls in
two variations: major and minor occupations.4
Major occupations consist of 23 levels of
occupation; these are further broadened into 97 levels to make up minor occupation levels.
∑ ∑ (3)
∑ ∑ (4)
We further enhanced the model by adding both state and occupation controls. This
strengthens our overall evaluation by enabling us to see wage gaps across two dimensions:
states and occupations. The results for all the equations are shown below in Table 2.
4
As explained in Section 3: Data, we followed the 2010 Standard Occupational Classification (SOC) published
and made available by the Bureau of Labor Statistics. Please refer to the appendix or to http://www.bls.gov/soc/
for more details on the occupation levels.
9. 9
∑
∑ ∑ (5)
∑
∑ ∑ (6)
Table 2: Wage gap of each race relative to non-Hispanic whites, pooled
Hispanic Black Asian Native American
/ Pacific Islander
Mixed race
(1) Basic controls -0.1287
***
-0.1539
***
-0.0067
**
-0.1401
***
-0.0543
***
(2) State controls -0.1811
***
-0.1711
***
-0.0664
***
-0.1422
***
-0.0827
***
(3) Major occupations -0.0864
***
-0.1092
***
-0.0203
***
-0.0999
***
-0.0421
***
(4) Minor occupations -0.0679
***
-0.0816
***
-0.0043
.
-0.0844
***
-0.0318
***
(5) State and major
occupations
-0.1329
***
-0.1215
***
-0.0773
***
-0.1033
***
-0.0691
***
(6) State and minor
occupations
-0.1117
***
-0.0920
***
-0.0611
***
-0.0863
***
-0.0581
***
*** p-value <0.001, ** p-value <0.01, “.” p-value <0.1
10. 10
Table 2 reports the wage gap estimates for the reference category (males of age 16-20 years
who have not completed high school). Each estimate represents the wage gap of that
particular race against non-Hispanic whites. The negative sign of all coefficients that hold
throughout imply that all minority groups receive lower wages than their non-Hispanic white
counterparts. It can be observed that as we add more stringent occupation controls, the
regression coefficients, with the exception of Asians, are all becoming smaller in absolute
terms, while still remaining significant (i.e. the wage gap is becoming smaller as we move
from Equation 1, to 3 and 4). This implies that specifying more details about the type of job
helps to explain the wage gaps between races.
After adjusting for the minor group of occupations (Equation 4), we see that Native
Americans and Pacific Islanders, and Blacks have the largest wage gap, receiving 8.4% and
8.2%, less than whites for the reference category, respectively. Asians have the smallest wage
gap, and further, shows the smallest reduction in the wage gap after adding the occupation
controls.
It is also interesting to note from the results above, that adding state controls cause the
regression coefficients to become larger (by moving in the opposite direction than what we
expected). The estimates from Equation (2), (5) and (6) indicate that specifying each
individual’s state causes the wage gap to become larger, possibly pointing to the fact that
there is a bigger concentration of minorities (particularly Hispanics and Blacks) in poorer
states.
Looking at the other control variables, we noted that the estimates for the gender variable (a
dummy which took 1 for female) was consistently negative throughout; the gender wage gap
11. 11
as per our analysis ranged from -11% to -36% (after controlling for minor occupations)5
with
the smallest wage gap observed in D.C. As for education, the returns on schooling was
positive at all levels and for all states, as expected, with individuals having completed 4 years
of college or more recording the highest level of wages. After controlling for minor
occupations, the highest “college effect” was observed for California and New York.
How do wage gaps compare across states?
To further explore the details of the wage gaps, we then ran Equations (1) and (4) for each of
the 51 states (including D.C.) separately. This enabled us to find out which state had the
largest (and smallest) gaps for each race, and observe the significance for the wage gaps for
each race in specific states. By adding in the occupation controls,6
we were able to tease out
the “occupation” effects in each state. Figure 1 below shows us the wage differences across
states for each race group.
5
The values represent the reference category (a female of 16-20 years who did not complete high school).
6
In this section, all “occupation controls” refer to controlling for the minor level of occupations (and not the
major level of occupations) even though it is not explicitly mentioned.
12. 12
Figure 1: Wage differences by state, without controlling for occupation
Figure 1 shows the results of Equation 1 run separately for each state, obtaining wage gaps for each race, across all states.
These wage gaps are graphed at the same scale across all five maps, where blue colors indicate a negative wage gap ( non-
Hispanic whites have a higher wage than the particular race in that state) and red colors indicate a positive wage gap (the
particular race receive higher wages than non-Hispanic whites in that state). White indicates zero i.e. the wage gap between
the reference group and the minority race is essentially zero. Alaska and Hawaii are only omitted in the graphical
representation. Estimates for all states can be found in the appendix.
In the figure above, the blue colors indicate that non-Hispanic whites have a higher wage than
the given race; red colors illustrate the opposite, that the specific race has an average wage
higher than non-Hispanic whites within the state. The way the scale is constructed makes
these maps comparable with each other. For instance, the biggest difference in wages across
all races is for Blacks in North Dakota. It is clear that non-Hispanic whites have better wages
across all states and across all races. When we control for occupations, the results are as
follows, shown in Figure 2.
13. 13
Figure 2: Wage differences by state, including controls for occupation
Figure 2 shows the results of Equation 4 run separately for each state, obtaining wage gaps for each race
across all states, controlling for the minor level of occupations. These graphs have been constructed at the same
scale as in Figure 1, which means that the magnitude of the gaps (as per the shade of red and blue) in each state
can be compared with all the maps in Figure 2 as well as those in Figure 1. Similar to Figure 1, Alaska and
Hawaii are only omitted in the graphical representation. Estimates for all states can be found in the appendix.
After controlling for occupations we observe some interesting results. In general, the wage
differences are smaller or remain about the same, as we saw for the results in Table 2. States
that have big wage differences still have those differences after controlling for occupation,
although at a smaller scale. This can be seen with the lighter colors in the maps after
controlling for occupation.
There are few states that change color between the two sets of maps; the change in colors
mean that before controlling for occupation there was a positive or negative wage difference,
14. 14
and that it changed when controlling for occupation. For instance, in Oregon, Kansas and
West Virginia, Blacks received lower wages than their non-Hispanic white counterparts
before controlling for occupation. After adding in these controls, theses states became
“red”— Blacks are shown to have a higher wage level than the reference group. This tells us
that the wage gap between whites and Blacks in these states is mainly because of the type of
job they are involved in.
An example of the opposite scenario is given by New Hampshire in the Hispanics graph.
Before controlling for occupation, it is seen that Hispanics receive a higher wage than whites.
After controlling for occupation however, the gap becomes negative, i.e. Hispanics receive a
lower wage than whites within the state. This may point to wage discrimination for Hispanics
in New Hampshire, although the population of Hispanics in New Hampshire may not be big
enough for the results to be interesting.
States that have a big mixture of races such as California, Florida and New York remain
about the same relative to other states, although after controlling for occupations the scale
(and therefore the wage differences) is smaller. Even when wage gaps are reduced after
controlling for occupation, relative to the gaps in other states, the difference remains about
the same. Hispanics, Blacks and Asians have almost the same wage difference in these states.
How do wage gaps compare across occupations?
To obtain a more in-depth picture on the wage gaps, we then proceeded to figure out how
these wage differences fared when compared by occupation. While forgoing the use of “state
controls” since it caused wage gaps to diverge rather than converge, we ran Equation (1) on
two sets of data: on each of the 23 major occupations separately, and on each of the 97 minor
occupations separately. Similar to the “state wage gap” analysis done previously, we were
15. 15
then able to compare how the wage gaps persisted based on the type of the occupation. For
our analysis, we limited ourselves to the results for the major occupations set rather than the
minor occupations (although they provided more reliable estimates in the pooled equations)
due to reduced sample size.7
We show the wage gap by major occupations for Hispanics, Blacks and Asians, ordered by
average wage, below in Figure 3. One of the trends that stand out most in the graph is that
while the wage gap for Hispanics and Blacks are relatively equal, the wage gap for Asians
follows a markedly different course, particularly for higher-paid occupations. Moreover, we
see that Asians receive a higher wage than non-Hispanic whites for higher-paid occupations
(average wage greater than $58,000). Among these three races, the largest positive gap is for
Asians, who receive 15% more than the reference group, in “Healthcare Practitioners and
Technical Occupations”. The trend in where Asians are better paid than most other races can
be attributed to a higher concentration of Asians in these jobs, as well as a larger portion of
Asians being well-educated. The largest negative wage difference is for Hispanics in
“Farming, Fishing, and Forestry Occupations”, receiving almost 25% less on average than
non-Hispanic whites.
7
This was because once the equation was run for each 97 occupation separately, some races had a handful of
people in some occupations, leading to substantially biased estimates. For instance, only two Asians, five Native
American and Pacific Islanders and five persons of mixed race worked in the occupation “Helpers, construction
trades”.
16. 16
Figure 3: Wage differences by occupation
What drives state and occupation gaps?
From the state-level and occupation-level wage differences we found in our earlier analyses,
we then proceeded to find out whether the level of the wage gaps across states could be
explained by certain characteristics of the state, and whether the level of the wage differences
by occupation could be explained by similar job-specific explanatory variables. To test the
state gaps, we used the coefficients obtained from Equation 4 (run separately for each state)
to be the dependent variables and tested for the following:
a. Do wage gaps across states differ based on geographic regions?
b. Do richer states have higher wage gaps between races?
c. Are wage gaps lower in states where minorities are a larger fraction of the population?
-0.30
-0.20
-0.10
0.00
0.10
0.20 FoodPreparationandServingRelatedOccupations
PersonalCareandServiceOccupations
HealthcareSupportOccupations
BuildingandGroundsCleaningandMaintenance…
Farming,Fishing,andForestryOccupations
OfficeandAdministrativeSupportOccupations
TransportationandMaterialMovingOccupations
ProductionOccupations
CommunityandSocialServiceOccupations
MilitarySpecificOccupations
ConstructionandExtractionOccupations
Education,Training,andLibraryOccupations
Installation,Maintenance,andRepairOccupations
ProtectiveServiceOccupations
SalesandRelatedOccupations
Arts,Design,Entertainment,Sports,andMedia…
Life,Physical,andSocialScienceOccupations
BusinessandFinancialOperationsOccupations
HealthcarePractitionersandTechnicalOccupations
ArchitectureandEngineeringOccupations
ComputerandMathematicalOccupations
ManagementOccupations
LegalOccupations
Hispanic
Black
Asian
In Figure 3, the x-axis shows all
the 23 major occupation
groups, arranged in ascending
order based on the average
way. The left-most occupation is
Food Preparation and Serving
Related Occupations, with an
average wage of about $22,500
and the highest-paid occupation
is Legal Occupations, with an
average wage of about
$117,000. It is important to note
that, although the occupations
on the x-axis are placed equally
apart, the average wage gap
between any two occupations is
not uniform. The curves show
the wage gap for each of the
three major races, for each
occupation separately
17. 17
It is not obvious from the state maps, but we wanted to test whether the wage gaps were
statistically different in the different regions. For instance, do minority races receive higher or
lower wages compared to whites, based on which geographic region they live in? We
hypothesize that the wage gap may be more different in southern and midwestern and
northeastern states, perhaps due to the differences in work culture and industries.
We also hypothesize that for richer states, which potentially have more competitive job
markets, the wage differences between races may be smaller compared to poorer and more
rural states, where wage discrimination can be more ingrained. As for the third conjecture, we
believe that there could be a “diversity effect” — in states with a higher share of minorities,
there is a more level playing field in the job market due to a greater participation in the
workforce by non-white races, therefore leading to lower wage gaps between races. For
example, for Hispanic men in California the average share of co-workers who are Hispanic is
51.1%, whereas in Florida it is over 6 percent points higher, at 57.5% (Judith Hellerstein et.
al, 42). The results of these hypotheses are shown in Table 3, where each row represents a
tested hypothesis.
Table 3: Hypothesis testing for state gaps
Hispanic Black Asian Native American /
Pacific Islander
Mixed race
(1) By geography
- Northeast
- South
- West
-0.0089
-0.0306 *
-0.0285 .
0.0619
0.0146
0.0484
-0.0236
-0.0080
-0.0199
0.1248 ***
0.0373
0.0058
-0.0186
0.0126
0.435 .
(2) By income
- log (GDP per
capita)
-0.0320 -0.0894 . -0.0739 . -0.0292 0.0482
(3) By minority
concentration
- 80% white
0.0297 ** 0.0249 0.0404 . 0.0162 -0.0413 *
*** p-value <0.001, ** p-value <0.01, * p-value <0.05, “.” p-value <0.1
18. 18
By geography: when testing for geographic regions, we followed the geographical
classification used by the US Census Bureau which adopts a system of four specific regions:
northeast, midwest, south and west. We ran the regression using each region as a dummy
variable and the midwest to be the reference category. It is interesting that the coefficient
estimates for Hispanics and Asian is negative (although for Asians, the values are not
significant). These imply that for Hispanics, the wage gap is significantly wider in the
southern and western states compared to the midwest. This could be due to a combination of
the concentration of Hispanics in these areas (or the lack of) and the types of jobs with a large
(or small) Hispanic population in these specific states. For Blacks, the estimates suggest the
highest wage gap to be in the midwest itself, with all other regions having lower wage gaps,
but these values are not significant.
By income: the explanatory variable is taken to be the log of the GDP per capita in each state
in 2014. For all races except for the mixed race category, it can be seen that the wage gap is
larger in richer states. This could possibly be due to more competitive labor markets in these
states, or the difference in concentration of minority races in richer states, specifically in
states such as North Dakota and Alaska, whose economy are dominated by few industries.
However, the wage gap is only significant for Blacks and Asians.
By minority concentration: to create a dichotomous variable indicating states of a high level
of minorities, we looked at the median percent of whites in each state (80% in the sample)
and created “low white” cities and “high white” cities (latter being equal to 1). It is
interesting that almost all the coefficients are positive, contrary to our expectation of a
“diversity effect”. Indeed, our results show that racial integration possibly has no effect or a
negative effect on the magnitude of state-level wage gaps. The coefficient estimates are
19. 19
significant for Hispanics and Asians, indicating that the wage gap is smaller for these races in
states where 80% or more of the population is white.
The rationale for testing occupation gaps, was in effect, trying to find out how much of a
wage gap there exists within each occupation— and in what kind of occupations these are the
largest and the smallest. We attempted to find out whether these occupation gaps could be
explained by occupation characteristics. Naturally, there were fewer possible explanatory
variables that we could come up with to explain occupation gaps. We looked at the average
wage of each occupation, dividing the jobs into three categories based on its wage tercile, and
regressed the occupation gaps on these categorical variables.8
Table 4: Hypothesis testing for occupation gaps
Hispanic Black Asian Native American
/ Pacific Islander
Mixed race
(1) By wage level of
occupation
- Quartile 2
- Quartile 3
-0.0226
-0.0568***
-0.0394
-0.1003***
-0.0264
-0.0612
-0.0243
-0.1375***
-0.0024
-0.0051
*** p-value <0.001, ** p-value <0.01, * p-value <0.05, “.” p-value <0.1
We find that for occupations that are paid higher, the wage gaps for minority races become
even bigger (as indicated by the negative signs on all the coefficients shown in Table 4).
Interestingly, these effects are significant only on the most highly paid jobs, and are not
observed for the occupations that are in the middle tercile. Further, our regressions show that
such wage gaps by income is not faced by Asians at all. Collectively, this could suggest that
minority races are not able to participate in higher paid jobs as much as whites, essentially,
8
Although earlier in Section 4, we used major occupations to graphically compare occupation gaps, we used the
coefficients obtained from running Equation (1) on each of the minor ooccupations separately in this hypothesis
testing stage. The reason for this was, using minor occupation gaps gave us 97 observations while using major
occupations gave us 23. This meant that the estimates from using only 23 observations would were likely to be
substantially biased due to the small sample size.
20. 20
being segregated in certain types of occupations, and that this occupational segregation is
creating such stark differences in wage gaps. Moreover, it could mean that even when
minority races are able to participate in higher paid jobs, they are discriminated against in
terms of pay.
A big part of the wage differences is due to the type of job the average individual of each race
performs. We believe that although there exists some wage differences after controlling for
occupation, these differences are small, and the big determinant on a subject’s wage is the
type of job he is involved in.
Occupational segregation
It is evident that the wage gap is closely linked to dynamics in the occupation. Not all
occupations have uniform participation by race; certain occupations have a high
concentration of Hispanics, Blacks and whites— determined by a number of factors including
education and language. To explore this further, we map out the occupational distribution of
each race to see whether this could be used to explain the wage differences in specific states.
Each of the graphs below shows, as a percent, how much more of each race participates or is
engaged in an occupation. The blue line in each indicates this gap for those with a college
degree or more, while the dotted red line represents those without a college degree.
Essentially, each downward spike below zero points to a larger number of minorities (relative
to their whole population) working in a particular occupation compared to whites; each
upward spike points to a larger number of whites in an occupation (relative to their whole
population) versus the minority race. The occupations on the x-axis have been arranged in
ascending order, based on the average wage of each occupation. Overall, what we are
attempting to show from the figure is the occupational segregation of each race based on the
21. 21
average wage of each. We did not include the graphs for Native American and Pacific
Islanders, and mixed races, as they did not reveal any significant findings.
Figure 3: Occupation segregation gap of each race
-4
-3
-2
-1
0
1
2
3
OtherFoodPreparationandServing…
Textile,Apparel,andFurnishingsWorkers
GroundsMaintenanceWorkers
FoodProcessingWorkers
MaterialMovingWorkers
Forest,Conservation,andLoggingWorkers
PrintingWorkers
MilitaryEnlistedTacticalOperationsand…
ReligiousWorkers
Counselors,SocialWorkers,andOther…
Preschool,Primary,Secondary,and…
FuneralServiceWorkers
ArtandDesignWorkers
First-LineEnlistedMilitarySupervisors
PostsecondaryTeachers
SupervisorsofConstructionand…
SocialScientistsandRelatedWorkers
OtherManagementOccupations
MathematicalScienceOccupations
TopExecutives
Hispanics
With BA Without BA
-4
-3
-2
-1
0
1
2
3
OtherFoodPreparationandServing…
Textile,Apparel,andFurnishingsWorkers
GroundsMaintenanceWorkers
FoodProcessingWorkers
MaterialMovingWorkers
Forest,Conservation,andLoggingWorkers
PrintingWorkers
MilitaryEnlistedTacticalOperationsand…
ReligiousWorkers
Counselors,SocialWorkers,andOther…
Preschool,Primary,Secondary,and…
FuneralServiceWorkers
ArtandDesignWorkers
First-LineEnlistedMilitarySupervisors
PostsecondaryTeachers
SupervisorsofConstructionand…
SocialScientistsandRelatedWorkers
OtherManagementOccupations
MathematicalScienceOccupations
TopExecutives
Blacks
With BA Without BA
-11
-9
-7
-5
-3
-1
1
3
5
7
9
OtherFoodPreparationand…
Textile,Apparel,andFurnishings…
GroundsMaintenanceWorkers
FoodProcessingWorkers
MaterialMovingWorkers
Forest,Conservation,andLogging…
PrintingWorkers
MilitaryEnlistedTactical…
ReligiousWorkers
Counselors,SocialWorkers,and…
Preschool,Primary,Secondary,…
FuneralServiceWorkers
ArtandDesignWorkers
First-LineEnlistedMilitary…
PostsecondaryTeachers
SupervisorsofConstructionand…
SocialScientistsandRelated…
OtherManagementOccupations
MathematicalScienceOccupations
TopExecutives
Asians
With BA Without BA
This graph first identifies two
proportions: (1) from the total
proportion of whites, the proportion of
whites in a particular occupation, and
(2) from the total proportion of each
race (eg: Hispanics), the proportion of
that race (Hispanics) in a particular
occupation. The graph shows the first
proportion minus the second. This has
been classified for two groups: those
with a college degree (blue) and those
without (red)
22. 22
In general, it can be seen that for both the Hispanics and Blacks graph, there are more red
downward spikes in the left half of each graph. This means that for a number of occupations
at the lower spectrum of wages, we see a higher participation of Hispanics and Blacks than
whites, even after controlling for education. For Hispanics, significant red downward spikes
are seen for cooks and food preparation workers, agricultural workers, building and pest
control workers, and construction workers. Anecdotally, these are occupations with a large
presence of Hispanic workers. For Blacks, we observe large red downward spikes for nursing
and home health aides, and building and pest control workers.
Upward red spikes indicate that for those without a college degree, a larger participation of
the minority race compared to whites (relative to each of their populations) within an
occupation. It is interesting that while construction workers had a large downward spike for
Hispanics (discussed in the previous paragraph), for Blacks, we observe an upward tick—
indicating that more whites than Blacks are engaged in that occupation. This could potentially
be due to cultural factors, or the concentration of more whites and Hispanics in construction-
centered states. Next, we also see that for secretaries and administrative assistants, financial
clerks, and other management occupations, even without a college degree, more whites than
Hispanics or Blacks are in these occupations. This could be an evidence for recruitment
biases, creating artificial segregation by occupations.
Looking at the population with a college degree (the blue lines), more noticeably there are
more downward spikes in the left half of the graph and more upward ones on the right half.
The biggest downward spikes on the blue lines are for the counselors, social workers and
other community specialists category, both for Hispanics and Blacks. These may be driven by
cultural biases of what is seen as acceptable or reputable occupations; on the other hand, it
could even be driven by less competition and less taxing barriers to entry within such
23. 23
occupations. On the higher spectrum of wages, it can be seen that Blacks have a lower
presence in the field of engineers and top engineers. Again, this could possibly be due to
ingrained cultural biases against minority races being manifested in certain occupations being
more less desirable or even difficult to work in.
While interesting trends are seen for both Hispanics and Blacks, the findings for Asians in the
third graph, are less pronounced. Although there are red spikes in the left half of the graph
(for lower paid occupations), there are none in the second half, implying that for higher paid
jobs, Asians without a college degree have a relatively equal participation as whites. Looking
at red downward significant spikes, we observe a higher concentration of Asians in
occupations such as personal appearance workers, cooks and food preparation workers, and
retail sales workers. On the other hand, we also see a lower proportion of Asians as
construction trade workers.
For Asians who do have a college degree, there is a significantly large proportion of Asians
who work in computer occupations and in health and diagnosing practitioners, both on the
higher-paid half of the graph. This seems to evidence cultural factors that place great
emphasis on Asians on achieving in certain types of reputable occupations. Similarly, for the
same group, there is a substantially low proportion of Asians who work in the education field
as schoolteachers.
24. 24
V. Results and Limitations
Our main finding is that all minority groups studied had negative wage gaps compared to
non-Hispanic whites. Using “state” as a control variable seemed to add more noise into our
estimates; hence we only used occupation as controls, in which adding more stringent levels
of occupation caused the wage differences to become smaller. Running our regression
separately for each state, we found that in a large number of states, whites received higher
wages than minority groups— although Asians received higher wages than their non-
Hispanic white counterparts in a number of states. Despite the wage gaps being small, as seen
in the appendix, there were significant, both at the overall level and for a large number of
states when tested separately.
We tried to explain the differences in the wage gap with three hypotheses including:
geographic areas, GDP per capita of states, and percentage of minority population in states.
Testing for geographic regions, Hispanics that live in the Midwest were found to have a
smaller gap compared to Southern and Western states. For most races, geography did not
produce significant results in explaining wage gaps across states. Testing for minority
concentration, we expected a “diversity effect” — that a higher concentration of minorities in
a state would be correlated with a lower wage gap. This proved to be untrue based on our
analysis; on the contrary, we found that a higher level of racial integration has a significant
and negative effect on state-level wage gaps for Hispanics and Asians. In testing if there is
higher wage gap in poorer states, it was observed that wage gap is actually larger in richer
states, which reversed our expectation that richer states have smaller gaps because of
competitive job markets. These results were only significant for Blacks and Asians, at a 10%
confidence level.
25. 25
When testing for wage gaps by the level of occupation, we found that the wage gap for
Hispanics and Blacks were larger for higher-paid jobs, while Asians had a more pronounced
positive wage gap in higher-paid occupations. We analysed the occupational distribution of
each race to link to our previous analyses, and found that most of the wage gaps are
consistent with the patterns found in the occupational distribution of each race. This is
especially true for the data on Hispanics and Blacks, which suggests that they are highly
concentrated in low-paying jobs. We also studied the difference in the occupational
distribution based on education, and discovered different trends, confirming that education is
a very important determinant, too, as expected, to help balance out occupation gaps compared
to whites.
We also found strong wage discrimination against females. While we did explore some of the
factors that may explain the wage differences, a natural next step in research would be to link
our findings to gender segregation, and see if there are common factors (mainly cultural) that
help explain occupational and gender segregation.
In terms of our dataset, one possible limitation of our study is that we only use data for 2014
(cross sectional). We do not believe that results may be significantly different using panel
data, but it is a consideration. We also intended to use other control variables to figure out
patterns in the wage gaps. We considered citizenship status and English language ability;
however, we were unable to do so because we did not have consistent data. We think that
having a variable such as English proficiency would be very significant. As our analysis
points out to an occupational segregation rather than wage discrimination, the ability to
speak, read and understand English may be important, especially for high paying jobs. We
expect US citizenship to be a significant factor as well, due to legal barriers.
26. 26
Occupational segregation may occur due to a range of factors, which we did not study in this
paper. A natural continuing research topic may be to explain these gaps, and the factors that
cause them. We hypothesize that some factors may be cultural factors, barriers to entry for
some occupations (such as language and legal barriers), and the tradeoff that immigrants face
of getting quickly a job or wait for a better job (search costs).
VI. Conclusion
The primary result of our study is that while there is a negative association when controlling
for age, education and gender between wage level and race, this gap in wages is reduced
significantly when controlling for occupation. This may be an indicator that while non-
Hispanic white people have lower wages, this difference is explained more by the type of job
they are involved in, rather than the wage discrimination across occupations.
Generally, across all states, non-Hispanic whites have better wages against other races even
after controlling for occupations. When we control for occupation, the wage gaps were
reduced significantly, although the relative differences within states remained about the same.
Finally, we observed that in high paying jobs, there exists occupational segregation. This
segregation is reduced when controlling for education. The opposite happens for low paying
jobs, where the proportion of minorities that are engaged in this jobs is higher than the
proportion of whites.
27. 27
References
Becker, G.S. (1957). The Economics of Discrimination, Chicago: University of Chicago Press 1971,
original edition 1957.
Catanzarite, L. (2000). Brown-Collar Jobs: Occupational Segregation and Earnings of Recent-
Immigrant Latinos, Sociological Perspectives, 43(1), 45-75.
Cotter, D. A., Hermsen, J. M. & Vanneman, R. (2003). The Effects of Occupational Gender
Segregation across Race. The Sociological Quarterly, 44(1), 17-36.
Darity, W., Guilkey, D. K. & Winfrey, W. (1996). Explaining differences in economics performance
among racial and ethnic group in the USA. The American Journal of Economics and Sociology,
55(4), 411-425.
de Walque, D. (2008). Race, Immigration and the US Labor Market: Contrasting the Outcome of
Foreign Born and Native Blacks. Policy Research Working Paper, No 4737, World Bank.
Farley, R. (1990). Black, Hispanics and White Ethnic Groups: Are Blacks Uniquely Disadvantaged?,
American Economic Review, 80(2), 237-241.
Farley, R. (1989). Race and Ethnicity in the U.S. Census: An Evaluation of the 1980 Ancestry
Question, Population Studies Center, University of Michigan at Ann Arbor.
Gradin, C., del Rio, C. & Alonso-Villar, O. (2011). Occupational Segregation by Race and Ethnicity
in the US: Differences across States. Universidade de Vigo, Campus Lagoas-Marcosende; 36310
Vigo. 1-27.
Hellerstein, J. K. & Neumark, D. (2007). Workplace segregation in the United States: Race, Ethnicity
and Skill. The Review of Economics and Statistics, 90(3), 459-477.
Hellerstein, J. K. & Neumark, D. (2002). Ethnicity, Language, and Workspace Segregation: Evidence
from a New Matched Employer-Employee Data Set. NBER Working Paper No. 9037, 1-66.
Kamara, J. (2015). Decomposing the Wage Gap: Analysis of the Wage Gap Between Racial and
Ethnic Minorities and Whites. Pepperdine Policy Review, 8(1)
28. Appendix
Figure 1: Plots of basic variables
Table 1: Descriptive statistics
N Mean Median St. Dev Min Max
Age 1,211,781 45.36 46 13.50 17 97
Income 1,211,781 54,743.36 40,000 58,126.51 10,000 642,000
Log (income) 1,211,781 10.60 10.60 0.75 9.21 13.37
Table 2: Descriptive statistics for education
Original education levels Frequency Modified levels
N/A or no schooling 9,606
Less than High
School
Nursery school to grade 4 4,091
Grade 5, 6, 7, or 8 18,881
Grade 9 9,989
Grade 10 11,474
Grade 11 15,343
Grade 12 389,552 High School
1 year of college 183,697 Incomplete
College2 years of college 117,515
29. 3 years of college 0
4 years of college 276,209
College or more
5+ years of college 175,424
Figure 2a: Distribution of regression coefficients for Female, by state (Equation 4 run for each state separately)
Figure 2b: Distribution of regression coefficients, by occupation (Equation 1 run for each occupation
separately)
30. Table 3: Tabulation of Minor Occupations (Ordered by Average Wage) and Race
Total
Average
Wage
Other Food Preparation and Serving Related
Workers 3663 19,432.7
Cooks and Food Preparation Workers 17886 20,598.2
Food and Beverage Serving Workers 17433 21,854.4
Other Personal Care and Service Workers 16153 23,838.9
Other Education, Training, and Library Occupations 9926 25,886.9
Textile, Apparel, and Furnishings Workers 4343 25,898.8
Personal Appearance Workers 5886 26,041.6
Tour and Travel Guides 308 26,106.2
Nursing, Psychiatric, and Home Health Aides 14971 26,315.8
Agricultural Workers 7251 26,373.3
Grounds Maintenance Workers 7257 26,915.3
Building Cleaning and Pest Control Workers 25914 27,735.1
Animal Care and Service Workers 1358 28,155.2
Helpers, Construction Trades 365 29,673.7
Baggage Porters, Bellhops, and Concierges 688 29,769.0
Food Processing Workers 5416 29,860.0
Other Healthcare Support Occupations 10458 29,939.9
Retail Sales Workers 38779 30,690.8
Supervisors of Food Preparation and Serving
Workers 6939 30,949.4
Entertainment Attendants and Related Workers 1894 31,299.3
Material Moving Workers 28989 31,786.2
Woodworkers 1216 32,902.5
Assemblers and Fabricators 10300 33,499.4
Information and Record Clerks 39683 34,024.4
Other Transportation Workers 1980 34,543.0
Forest, Conservation, and Logging Workers 627 34,741.8
Communications Equipment Operators 644 34,853.0
Other Protective Service Workers 9707 35,564.3
Secretaries and Administrative Assistants 31855 35,721.4
Other Office and Administrative Support Workers 26881 36,375.9
Printing Workers 2045 36,429.0
Financial Clerks 23355 36,652.2
Material Recording, Scheduling, Dispatching, and
Distributing Workers 29096 36,732.1
Other Production Occupations 25972 38,780.3
Motor Vehicle Operators 36245 39,211.9
Military Enlisted Tactical Operations and
Air/Weapons Specialists and Crew Members 3695 39,632.7
Occupational Therapy and Physical Therapist
Assistants and Aides 922 40,125.9
Fishing and Hunting Workers 253 40,466.8
Construction Trades Workers 43972 40,649.6
Supervisors of Building and Grounds Cleaning and
Maintenance Workers 2720 41,769.4
Religious Workers 5932 41,867.4
Metal Workers and Plastic Workers 17126 41,912.9
Supervisors of Personal Care and Service Workers 729 42,889.8
Vehicle and Mobile Equipment Mechanics,
Installers, and Repairers 15090 43,224.4
Other Teachers and Instructors 5428 43,841.5
Counselors, Social Workers, and Other Community
and Social Service Specialists 17508 44,091.6
Health Technologists and Technicians 26264 44,153.5
Librarians, Curators, and Archivists 2551 44,950.8
Other Construction and Related Workers 3028 46,545.0
31. Total
Average
Wage
Supervisors of Farming, Fishing, and Forestry
Workers 568 47,111.8
Preschool, Primary, Secondary, and Special
Education School Teachers 51629 47,406.6
Other Installation, Maintenance, and Repair
Occupations 18977 47,857.1
Life, Physical, and Social Science Technicians 3223 48,849.6
Electrical and Electronic Equipment Mechanics,
Installers, and Repairers 4647 49,455.8
Media and Communication Equipment Workers 2072 50,497.8
Funeral Service Workers 477 51,028.5
Legal Support Workers 5091 51,997.6
Supervisors of Transportation and Material Moving
Workers 2010 53,800.1
Drafters, Engineering Technicians, and Mapping
Technicians 6080 54,524.8
Supervisors of Office and Administrative Support
Workers 13058 55,248.8
Art and Design Workers 7322 55,450.6
Law Enforcement Workers 12613 58,427.0
Other Healthcare Practitioners and Technical
Occupations 967 58,435.6
Entertainers and Performers, Sports and Related
Workers 4412 58,901.8
Supervisors of Production Workers 9094 59,118.7
First-Line Enlisted Military Supervisors 860 59,222.3
Supervisors of Sales Workers 36453 59,391.9
Extraction Workers 2515 60,943.9
Plant and System Operators 2796 61,081.6
Supervisors of Installation, Maintenance, and
Repair Workers 2882 61,832.8
Postsecondary Teachers 14202 63,190.7
Water Transportation Workers 798 63,977.3
Fire Fighting and Prevention Workers 3149 64,738.0
Media and Communication Workers 6274 65,066.6
Military Officer Special and Tactical Operations
Leaders 307 65,243.3
Supervisors of Construction and Extraction
Workers 6943 65,764.8
Other Sales and Related Workers 8857 65,819.0
Rail Transportation Workers 1132 69,707.5
Business Operations Specialists 35356 69,955.1
Supervisors of Protective Service Workers 3068 69,982.5
Social Scientists and Related Workers 2565 72,604.3
Life Scientists 2555 77,515.0
Sales Representatives, Wholesale and
Manufacturing 12878 78,978.8
Architects, Surveyors, and Cartographers 1979 79,106.0
Financial Specialists 31022 79,424.9
Other Management Occupations 80295 80,729.7
Computer Occupations 36418 81,691.5
Physical Scientists 3822 83,410.7
Sales Representatives, Services 14450 84,612.2
Air Transportation Workers 2776 89,035.1
Mathematical Science Occupations 2130 89,483.4
Health Diagnosing and Treating Practitioners 55530 90,845.6
Engineers 18079 90,981.3
Operations Specialties Managers 29439 91,174.0
Advertising, Marketing, Promotions, Public
Relations, and Sales Managers 9081 98,516.1
Top Executives 20090 129,338.3
Lawyers, Judges, and Related Workers 10139 149,270.4
32. Table 4: Tabulation of major occupations and race
Total
Average
Wage
Food Preparation and Serving Related Occupations 45921 22546.3
Personal Care and Service Occupations 27493 26188.3
Healthcare Support Occupations 26351 28237.3
Building and Grounds Cleaning and Maintenance Occupations 35891 28632.9
Farming, Fishing, and Forestry Occupations 8699 28740.5
Office and Administrative Support Occupations 164572 37275.9
Transportation and Material Moving Occupations 73930 39176.8
Production Occupations 78308 40444.9
Community and Social Service Occupations 23440 43528.7
Military Specific Occupations 4862 44714.9
Construction and Extraction Occupations 56823 44860.2
Education, Training, and Library Occupations 83736 47226.8
Installation, Maintenance, and Repair Occupations 41596 47323.4
Protective Service Occupations 28537 52588.9
Sales and Related Occupations 111417 55448.1
Arts, Design, Entertainment, Sports, and Media Occupations 20080 58702.4
Life, Physical, and Social Science Occupations 12165 70737.3
Business and Financial Operations Occupations 66378 74380.8
Healthcare Practitioners and Technical Occupations 82761 75649.3
Architecture and Engineering Occupations 26138 81602.0
Computer and Mathematical Occupations 38548 82122.1
Management Occupations 138905 91136.4
Legal Occupations 15230 116754.6
Table 5: Tabulation of States and Race
White Hispanic Black Asian
Native
American/
Pacific
Islander
Mixed
Race Total
Alabama 12452 434 3444 183 71 160 16744
Alaska 1668 95 47 163 614 129 2716
Arizona 15014 5220 824 815 1152 423 23448
Arkansas 8045 484 1172 116 64 119 10000
California 62353 43876 6014 23047 1045 3593 139928
Colorado 17495 3090 649 565 147 409 22355
Connecticut 11818 1345 1147 676 23 199 15208
Delaware 2658 223 556 137 16 45 3635
District of Columbia 1649 268 1065 131 3 78 3194
Florida 44699 14750 8821 2096 164 911 71441
Georgia 23329 2407 8607 1371 76 395 36185
Hawaii 1449 326 116 2291 582 1159 5923
Idaho 4690 498 25 57 86 73 5429
Illinois 38299 5868 4224 2562 52 500 51505
Indiana 22738 1009 1430 403 46 223 25849
