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Chapter
Residential Segregation by
Education in the U.S., 2016-2020
SamanthaFriedman and ThaliaTom
Abstract
While there is much research on income segregation, we know less about the
factors that contribute to the uneven distribution of households across neighborhoods
by educational attainment. Although globalization is thought to influence segrega-
tion, its association with socioeconomic segregation is debated. Using data from
the 2016–2020 American Community Survey, the Globalization and World Cities
Research Network, and the MIT Election Data + Science Lab, we investigate the corre-
lates of educational segregation within large core-based statistical areas in the United
States, focusing on globalization, income inequality, and political preferences in the
2016 presidential election. Multivariate results reveal that globalization and income
inequality are the most significant correlates of educational segregation. Political
preferences are only significantly associated with residential dissimilarity between
those with a master’s degree or higher and those with some college. We discuss the
implications of these results for understanding residential inequality on the basis of
education in metropolitan America.
Keywords: residential segregation, education, globalization, political preferences,
metropolitan area
. Introduction
Residential segregation by socioeconomic status is an important topic studied by
urban scholars because cities around the world continue to experience significant
spatially-based divisions [1–4]. Although globalization is a salient force shaping
inequalities in cities [5], the role it plays in influencing socioeconomic residential
segregation is subject to debate [6, 7]. Recent research has increasingly suggested
that global forces are not solely responsible for the spatial polarization observed in
cities, which has motivated researchers to investigate additional factors such as social,
cultural, and historical factors [1, 4, 6–8].
Although a substantial body of scholarship examines socioeconomic segregation,
it is limited in at least three ways. First, the focus of this research has largely been
on income-based residential segregation [3, 9, 10]. Only a handful of studies have
investigated residential segregation by educational status [2, 11–13]. Second, much of
the research on socioeconomic segregation is largely descriptive in nature [4–8, 14].
An increasing body of literature systematically examines variation in socioeconomic
Recent Trends in Demographic Data
segregation across cities or other geographic areas through empirical analyses [2, 3, 9,
10, 12, 15–17].
Third, little attention has been paid to the association between political prefer-
ences and socioeconomic residential segregation, and particularly residential seg-
regation by educational status [15]. In the U.S., the significant growth in animosity
between political parties as well as the geographic separation between people of
different political parties necessitates an examination of the association between
political preferences and educational segregation [18–23]. Examining the association
between these factors is particularly important because trends in political polarization
have occurred during a period when educational segregation increased [11, 13].
This study’s primary objectives are to document residential segregation by educa-
tional attainment in metropolitan core-based statistical areas (CBSAs) in the U.S. and
examine the factors associated with such segregation, including globalization, income
inequality, and political preferences. In doing so, we aim to fill the three gaps in the
socioeconomic segregation literature just discussed. First, we document educational
segregation in the U.S. Second, we examine the association between globalization and
educational segregation in the U.S. Finally, we explore the association between politi-
cal preferences and residential segregation by educational status, an intersection that
has largely been overlooked in the literature.
. Literature review
Although limited research on educational segregation in the U.S. context may
reflect the assumption that educational attainment does not contribute much addi-
tional variance to existing studies of income segregation, we argue that it is important
to assess the extent of educational segregation during a period characterized by the
college-for-all ethos [24]. Moreover, trends in educational and economic segregation
do not perfectly correspond; the limited research on educational segregation in the
U.S. indicates that the level of educational dissimilarity nearly doubled between 1970
and 2000, while economic segregation has not exhibited changes of the same mag-
nitude [11, 13]. Critically, as the U.S. population has grown more educated—37.9%
of adults aged 25 and older held at least a bachelor’s degree in 2021 [25] compared
to 20.3% in 1990 [26], it has also come to reside in increasingly unequally resourced
and politically polarized contexts, with consequences for social stratification and the
prospects of intergroup cooperation that enhance collective goods [18, 27]. Below, we
provide a discussion of how key economic factors, such as globalization and income
inequality, as well as political preferences may be associated with variation in educa-
tional segregation by reviewing the literature on the correlates of residential segrega-
tion by socioeconomic status.
. Globalization, income inequality, and socioeconomic residential segregation
Globalization has been the starting point in much of the comparative literature
examining residential segregation by socioeconomic status [4, 6, 7]. Sassen [5]
advanced a global city thesis with important implications for the study of socio-
spatial inequality. According to Sassen [5], cities that are global are characterized as
“command points” in the world economy and contain headquarters of multinational,
financial and high-order service companies as well as producers of innovation. These
industries have largely replaced manufacturing firms. At the same time, global cities
Residential Segregation by Education in the U.S., 2016-2020
DOI: http://dx.doi.org/10.5772/intechopen.1001900
function as key markets for products and innovations produced by these high-end
firms. The occupational structure present in global cities is a bifurcated one that
simultaneously experiences growth in the high- and low-income classes of workers,
resulting in “increased asymmetry” or polarization [5]. This economic polarization
results in spatial polarization. Sassen [5] compares the gentrification that occurs in
global cities to the simultaneous concentration of poverty as evidence of this socio-
spatial inequality.
Sassen’s [5] global city thesis has been the subject of much empirical testing, but
the evidence is mixed as to whether spatial polarization is more present in global cities
as compared to cities not demarcated as “global” [1, 4, 6]. For example, Hong Kong
and Tokyo are considered to sit atop the world city hierarchy as “alpha+ countries,”
according to the highly regarded ranking by the Globalization and World Cities
Research Network [28]. Yet, they are surprisingly low in their levels of income-based
levels of segregation [6]. On the other hand, places like Copenhagen, Budapest, and
Tallinn rank lower in the world city hierarchy, but their levels of segregation by social
class are much higher than those found in Hong Kong, Tokyo, and Prague [1, 6].
Part of the reason for these contradictory findings likely relates to another
important predictor of socioeconomic residential segregation identified in the
literature -- income inequality. Studies document a significant, positive relation-
ship between income inequality and socioeconomic segregation [2, 10, 14]. Income
inequality is particularly important to examine in the U.S. In 2017, among all G7
countries (i.e., those with the most advanced economies), the U.S. had the highest
level of income inequality, as gauged by the Gini coefficient of inequality, with a value
of .434 [29]. However, studies that examine the association between income inequal-
ity and socioeconomic residential segregation rarely examine globalization [9, 10]. If
globalization increases income inequality, as suggested by Sassen [5], the main effect
of globalization may be weaker when controlling for income inequality. It remains
to be seen whether globalization is associated with segregation by educational status
after accounting for the correlation between income inequality and educational
segregation.
. Political preferences and socioeconomic residential segregation
The findings that globalization does not always lead to high levels of socioeconomic
segregation have also led scholars to suggest that global forces, alone, are not respon-
sible for spatial polarization in cities. Other studies find that structural- and insti-
tutional-level factors are associated with socioeconomic residential segregation and
can modify the effect of globalization [4, 6–8]. The main reason why income-based
segregation is lower in some countries compared to others is because of the strong
social safety net present in these societies, particularly in Western European coun-
tries [1, 4, 7, 8]. Welfare and housing benefits buffer the negative economic impact
of social inequality created by globalization that is faced by lower-income groups,
thereby reducing residential segregation by socioeconomic status [1, 4]. However,
because the safety net is not as strong in the United States as in Western Europe, we
expect that other factors could explain the variation in educational segregation.
A factor that has largely been ignored in the socioeconomic segregation literature
is political preferences, and we believe this is particularly salient for any contem-
porary study of residential segregation by educational status. The United States
exhibits extraordinarily high levels of political polarization [30], which is perhaps no
better encapsulated than by the violent insurrection that roiled the U.S. Capitol on
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January 6th, 2021 in an attempt to disrupt the symbolic transfer of power from one
administration to the next. In recent decades, scholars have noticed an increase in the
geographic separation of people of different political parties [18, 19, 21, 23]. Recent
evidence finds a political divide among those with a college degree and those with-
out a college degree; in 2020, 56% of voters with a high school degree or less voted
Republican while 56% of those with a college degree voted Democrat [31].
According to the social structural sorting perspective [32], residential segregation
reflects an aggregation of the residential preferences and mobility of individuals that
depends in part on the information that people have about neighborhoods intheir
housing search field. In general, when people consider neighborhoods in which to
live, they want to share communities with others that share a similar culture and
political ideology [33]. While other factors like housing affordability, crime, and
school quality are main considerations of movers, similarities in political ideology
and cultural values play a role, once accounting for the main factors [34].
If political preferences are related to educational attainment, this begs the question
of whether political preferences in the aggregate could be associated with educational
segregation. This was found to be the case in Turkey, which is even more politically
polarized than the U.S. [15, 30]. Across Turkish provinces, the percentage voting for
the liberal political party, which is the political out-group in the society, was positively
associated with greater levels of educational segregation across all measures of educa-
tional residential segregation [15]. Similar to the U.S., those who voted more liberally
tended to be more educated, and areas with greater shares of votes cast towards the
outgroup wanted to live among each other, thereby raising educational residential
segregation. Across Turkish provinces, the percentage voting for the conservative
political party, the party that has been in power, was negatively associated with
educational residential segregation [15]. We expect similar findings for the U.S. – the
percentage of votes for Clinton in the 2016 election will be positively associated with
more educational segregation from 2016 to 2020; the percentage of votes for Trump in
the 2016 election will be negatively associated with educational segregation from 2016
to 2020.
. Data and methods
. Data and measures
Data come from the 2016–2020 American Community Survey (ACS), the
Globalization and World Cities Research Network (GaWC), and the MIT Election
Data + Science Lab (MEDSL). We obtain educational attainment counts from the
2016–2020 ACS in order to estimate our primary dependent variable—the dissimilar-
ity index (D) or D-score, which captures the evenness with which two groups are
distributed across geographic units. These data are among the most recent data avail-
able and coincide with the period after the 2016 presidential election but include only
one year of the COVID-19 pandemic, making these data ideal for our study. As noted
previously, we estimate educational dissimilarity at the census-tract level within
metropolitan CBSAs. Consistent with methodological recommendations and prior
scholarship, we limit our analyses to metropolitan areas with populations of 500,000
or more [3] and at least 1000 people within each educational attainment category to
ensure that we get accurate estimates of segregation [35]. D-scores, which are one of
Residential Segregation by Education in the U.S., 2016-2020
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the most commonly used measures of segregation, typically range from a minimum
of 0 (indicating no segregation) to a maximum of 1 (indicating complete segrega-
tion), but we multiply their values by 100 for ease of interpretation.
Within this context, D-scores may be interpreted as the percentage of individuals
within one of two defined social groups who would have to move neighborhoods in
order to achieve an even distribution of educational attainment within a particular
CBSA. Indices above 60 are classified as high levels of segregation; scores between 30
and 60 are classified as moderate levels of segregation; and scores below 30 are clas-
sified as low levels of segregation [36]. Because the dissimilarity index is a pairwise
measure of segregation, we obtain estimates for the following educational attainment
dyads: 1) bachelor’s degree vs. high school diploma; 2) bachelor’s degree or higher
vs. high school diploma; 3) master’s degree or higher vs. high school diploma; and
4) master’s degree or higher vs. some college. We focus on these particular categories
because we want to evaluate the nature of residential segregation between dyads with
high and low levels of education. Additionally, whereas past research has examined
dissimilarity between high school and college graduates and between high school
diploma and master’s degree recipients [11, 13], to our knowledge, limited attention
has been paid to the residential sorting patterns of those with some college education.
In order to examine the association between globalization and residential segrega-
tion, we include an indicator of whether each CBSA contains a global city as defined by
the classification of global cities in 2016 by GaWC [28]. Cities with advanced pro-
ducer services that are integrated with the world city network are identified as global
cities by this methodology [28]. This classification has been used by many researchers
[15, 37, 38].
As discussed above, past research has also implicated income inequality as a robust
correlate of residential segregation [10], so we evaluate the association between the
Gini index of inequality and educational dissimilarity. The data for the Gini index of
inequality at the CBSA level come from the 2016–2020 ACS. The values range from 0
to 1, with 1 indicating high levels of income inequality in the CBSA. We multiply their
values by 100 for ease of interpretation and so that the variable is on the same scale as
the index of dissimilarity.
To investigate the relationship between educational segregation and political pref-
erences, we use MEDSL data to calculate the percentage of votes cast for Clinton and
Trump in the 2016 presidential election within each CBSA. MEDSL data are available
at the county level [39]. We aggregated the counts to the CBSA level and calculated
the percentages of votes cast for Clinton and Trump. This source of data is beneficial
to our study because it allows us to obtain county-level voting data with national
coverage. MEDSL has been widely used by scholars in recent research [40–42].
Beyond these key independent variables, we include the following CBSA-level
control variables, also obtained from the 2016–2020 ACS data, in our multivari-
ate analyses: 1) percentage with a bachelor’s degree (in the models relevant for this
population); 2) percentage with a master’s degree or higher (in the relevant models);
3) percentage employed in manufacturing; 4) log of the total population of the CBSA;
and 5) dummy variables indicating the region where the CBSA is located. Past research
indicates that educational attainment and manufacturing employment are salient
correlates of socioeconomic segregation [10, 17]. Population size could increase the
extent of opportunities for residential sorting by educational attainment and may be
positively correlated with educational residential segregation [9, 12, 17]. Alternatively,
it may have little to no association with educational residential segregation [15].
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. Analytical plan
Our analysis proceeds as follows. First, we present descriptive statistics for the
dissimilarity index for each educational attainment dyad, and for our key indepen-
dent and control variables. Then, we report the results from a series of ordinary least
squares (OLS) regression analyses that model educational segregation as a function of
globalization, income inequality, and political preferences while controlling for other
characteristics.
. Results
Table reports our descriptive results. Across the 108 CBSAs in our analytical
dataset, the average level of educational dissimilarity is in the moderate range. The
minimum values of the D-scores for all educational dyads fall in the low range of
segregation and do not exceed 27. The maximum values fall in the moderate range,
generally falling in the middle of the moderate range. The standard deviations for all
four sets of dissimilarity scores fall between 4.30 and 5.19, indicating that across the
largest metropolitan CBSAs there is similar variation in residential segregation by
educational status across the examined dyads. With respect to the D-score values for
specific educational dyads, across CBSAs, the average residential segregation between
those with a bachelor’s degree and those with a high school diploma is 33.9, and the
average D-score between those with a bachelor’s degree or higher and those with a
high school diploma is 36.3, with both scores falling at the lower end of the moderate
range. The average D-score between those with at least a master’s degree and those
with a high school degree is 42.3, and the maximum value for this set of D-scores is
the highest at 53.2. The average level of residential segregation between those with at
least a master’s degree and those with some college education is 33.4.
Our findings that the average levels of residential segregation by educational status
are in the moderate range are similar to the findings of Quillian and Lagrange [2] who
examine average levels of both educational and income segregation in the largest met-
ropolitan CBSAs in the U.S. using 2006–2010 ACS data. With respect to educational
segregation, they find that in the largest 51 CBSAs, the average D-score between those
with at least an associate’s degree and those with a high school diploma or less is 32.9.
They find that dissimilarity scores gauging residential segregation between income
groups falling at or below the income percentile and those falling above the income
percentile fall in the moderate range of segregation, regardless of the income percen-
tile being examined (see Figure 1b in [2]). Even when they examine specific CBSAs,
like New York, their income segregation D-scores are still within the moderate range
(see Figure 2b in [2]).
The second part of Tabl e contains descriptive statistics for our key independent
and control variables. With respect to the former, just under half (46.3%) of our 108
CBSAs contain a city categorized as global by the GaWC [28]. The mean Gini index
across CBSAs is 46.3, with a range of values between 38.9 and 54.0 and a standard
deviation of 2, indicating little variation in the Gini index across these large met-
ropolitan areas. This value aligns with the magnitude of average income inequality
reported in previous studies of populous CBSAs [9]. With respect to political prefer-
ences, Table shows that in the 2016 presidential election, on average, 48.40% of the
population in our 108 CBSAs voted for Clinton, but values ranged from a minimum of
14.08% to a maximum of 77.07%, which indicates significant CBSA-based variation in
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DOI: http://dx.doi.org/10.5772/intechopen.1001900
residents’ political preferences. Tabl e shows that on average, 45.31% of the popula-
tion voted for Trump, with a similar range of variation; the minimum percentage
voting for Trump is 16.63% and the maximum value is 64.88%.
With respect to our control variables, the data in Table show that across CBSAs
an average of 20.87% of the population obtained a bachelor’s degree and 13.03% had
obtained at least a master’s degree. The range of educational attainment across CBSAs
reveals that the minimum percentage of the population with a bachelor’s degree is
11.38% and with a master’s degree is 5.35%; the maximum values, respectively, for
these educational categories are 30.08% and 25.58%. Tab le reveals that on average,
9.86% of the population is employed in the manufacturing industry. The average
log of the total population is 14.12. Finally, the majority of the CBSAs in our ana-
lytic sample were located in the South (40.7%), followed by the West (22.2%), the
Northeast (18.5%), and the Midwest (18.5%).
Tables and present the results of our multivariate models examining the
factors associated with residential segregation by education. Table uses the per-
centage voting for Clinton as the measure for political preferences. Tab le uses the
Mean SD Min Max
Residential Segregation Scores
Bachelor’s Degree/High
School
33.9 4.82 22.1 4 5.4
Bachelor’s Degree or Higher/High School 36.3 5.01 2 3.6 4 7. 2
Master’s Degree or Higher/High School 42.3 5.19 26.8 53.2
Master’s Degree or Higher/Some College 33.4 4.30 2 2.1 43.6
Key Independent Variables
Global City .463 .501 0 1
Gini Index of Inequality 46.3 2.00 38.9 54.0
% of Votes for Clinton 48.4 0 10.51 14.08 7 7. 07
% of Votes for Trump 45.31 9.76 16.63 64.88
Control Variables
% with a Bachelor’s Degree 20.87 3.88 11.38 30.08
% with a Master’s Degree or Higher 13.03 3.73 5.35 25.58
% Employed in Manufacturing 9.86 3.60 2.87 20.4 4
Log Population 14.12 .826 13.15 16.77
Region:
Northeast .185 .39 0 1
Midwest .185 .39 0 1
South .407 .494 0 1
Wes t .222 .418 0 1
Source: Data come from the – American Community Survey (ACS), the MIT Election Data + Science Lab
(MEDSL), and the Globalization and World Cities Research Network (GaWC).
Note: Our unit of analysis is at the core-based statistical area (CBSA) level and includes the CBSAs with K+
population.
Tab le 1 .
Descriptive statistics for dependent, key independent, and control variables.
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percentage voting for Trump. Each table reports coefficients and standard errors from
four OLS regression models predicting dissimilarity scores for each of the follow-
ing educational dyads: 1) bachelor’s degree vs. high school diploma; 2) bachelor’s
degree or higher vs. high school diploma; 3) master’s degree or higher vs. high school
diploma; and 4) master’s degree or higher vs. some college, respectively. Each model
includes the key independent variables discussed above and the control variables.
The association between global cities and residential segregation by educational
status is significant and positive in all models in Tables and , with the exception
of the models for the educational dyad of those with a master’s degree or higher and
() () () ()
BA/ HS BA+/HS MA+/HS MA+/SC
Global City 2.457 * 2.349* 2.486* 1.552
(1.066) (1.103) (1.098) (0.901)
Gini Index of Inequality 0.720*** 0.810*** 0.790*** 0.689***
(0.206) (0.213) (0.215) (0.176)
% Votes for Clinton −0.030 0.021 0.057 0.099**
(0.039) (0.040) (0.044) (0.036)
% with a Bachelor’s Degree 0.230* 0.289**
(0.103) (0.106)
% with a Master’s Degree or Higher 0.269* 0.283**
(0.117) (0.096)
% Employed in Manufacturing 0.083 0.111 0.121 0.147
(0.11 2) (0.116) (0.11 9) (0.098)
Log Population 0.639 0.532 0.451 0.303
(0.666) (0.689) (0.708) (0.581)
Midwest 5.071*** 5.015*** 6.218*** 4.156***
(1.180) (1.220) (1.289) (1.058)
South 5.603*** 5.163*** 5.903*** 3.450***
(0.980) (1.014) (1.076) (0.883)
Wes t 5.233*** 5.226*** 6.681*** 3.149**
(1.087) (1.124) (1.197) (0.983)
Constant −18.113 −22.087 −14.2 58 −16.274
(10.965) (11.341) (11.326) (9.297)
Observations 108 108 108 108
R-squared 0.536 0.542 0.550 0.558
Source: Data come from the – ACS, MEDSL, and GaWC.
Notes: Standard errors are in parentheses; *** p<., ** p<., * p<..
BA=Bachelor’s degree; BA+=Bachelor’s degree or higher; HS=High school diploma; MA+=Master’s degree or higher;
SC=Some college education.
Tab le 2 .
OLS regression models of educational residential segregation for key educational dyads using percent votes for
Clinton in the largest CBSAs in the US, 2016–2020.
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DOI: http://dx.doi.org/10.5772/intechopen.1001900
those with some college (column 4 of Tables and ), even after controlling for the
Gini index of inequality. The results in model 1 of Tabl e indicate that the dissimilar-
ity index between those with a bachelor’s degree and those with a high school diploma
is 2.457units higher in CBSAs with a global city than in CBSAs without a global city,
controlling for other factors. Similarly, the coefficients for global city in models 2 and
3 of Table indicate that 1) educational segregation between those with a bachelor’s
degree or higher and those with a high school diploma is 2.349units higher in CBSAs
with a global city than in those without a global city; and 2) the D-score is 2.486units
() () () ()
BA/ HS BA+/HS MA+/HS MA+/SC
Global City 2.430* 2.371* 2.501* 1.549
(1.068) (1.104) (1.094) (0.902)
Gini Index of Inequality 0.696*** 0.825*** 0.839*** 0.783***
(0.198) (0.205) (0.206) (0.170)
% Votes for Trump 0.034 −0.026 −0.077 −0.111**
(0.043) (0.044) (0.049) (0.041)
% with a Bachelor’s Degree 0.243* 0.278*
(0.107) (0 .110)
% with an Master’s Degree
or Higher
0.235 0.251*
(0.12 3) (0.101)
% Employed in
Manufacturing
0.083 0.112 0 .12 5 0.144
(0.11 2) (0.115) (0.1 18) (0.098)
Log Population 0.632 0.528 0.419 0.311
(0.664) (0.687) (0.705) (0.581)
Midwest 5.074*** 5.016*** 6.152*** 4.077 ***
(1.180) (1.220) (1.286) (1.060)
South 5.601*** 5.180*** 5.908*** 3.396***
(0.981) (1.014) (1.069) (0.881)
Wes t 5.390*** 5.108*** 6.276*** 2.5 71*
(1.089) (1.126) (1.224) (1.009)
Constant −20.226 −20.255 −9.3 7 1 −10.349
(11.957) (12.363) (12.250) (10.097)
Observations 108 108 108 108
R-squared 0.536 0.542 0.553 0.558
Source: Data come from the – ACS, MEDSL, and GaWC.
Notes: Standard errors are in parentheses; *** p<., ** p<., * p<..
BA=Bachelor’s degree; BA+=Bachelor’s degree or higher; HS=High school diploma; MA+=Master’s degree or higher;
SC=Some college education.
Table 3.
OLS regression models of educational residential segregation for key educational dyads using percent votes for
Trump in the largest CBSAs in the US, 2016–2020.
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higher between those with a master’s degree or higher and those with a high school
diploma, respectively, controlling for other factors. The results for the global city
coefficients in models 1 through 3 of Tabl e are similar in magnitude and signifi-
cance as the results in Table .
The coefficients for the Gini index reveal that the variable is a highly significant
and positive correlate of educational dissimilarity across all models in Tables and ,
controlling for other factors. Model 1 of Tabl e demonstrates that a one-unit increase
in the Gini index is associated with a .72-unit increase in the D-score between those
with a bachelor’s degree and those with a high school diploma, controlling for other
factors. The magnitude of the association between the Gini index and educational
dissimilarity is similar across all educational dyads, and the results are similar in the
models that use the votes cast for Trump as the independent variable gauging political
preferences (see models 1 to 4 in Tabl e ).
How do political preferences relate to educational segregation? Tables and
show that the percentage of votes cast for Clinton and the percentage of votes cast for
Trump, respectively, are only significantly associated with educational dissimilarity
between those with a master’s degree or higher and those with some college, control-
ling for other factors. The results in model 4 of Table indicate that a one percentage-
point increase in votes cast for Clinton is associated with a .099-unit-increase in the
educational dissimilarity index between those with a master’s degree or higher and
those with some college. Conversely, model 4 of Table shows that a one percentage-
point increase in Trump votes is associated with a .111-unit decrease in segregation
between those with a master’s degree or higher and those with some college, control-
ling for other factors.
Turning to the results of our control variables, we find that the percentage of the
population with a bachelor’s degree is significantly and positively associated with
residential segregation of educational dyads involving those with at least a bachelor’s
degree (see models 1 and 2 of Tables and ). Similarly, in general, the percentage of
the population with at least a master’s degree is significantly and positively associated
with educational segregation of those with at least a master’s degree (see models 3 and
4 of Table ; and model 4 of Table ). Controlling for other factors, the percentage
employed in manufacturing and the log of the population size are not significantly
associated with residential segregation by educational status, regardless of the educa-
tional dyad examined (see Tables and ). Across all educational dyads, educational
segregation is significantly higher in the Midwest, South, and West, relative to the
Northeast, controlling for other factors (see models 1 through 4 of Tables and ).
. Discussion
This study makes three contributions to the literature on residential segregation.
Our first contribution lies in our focus on segregation by educational attainment,
which is relatively novel within the U.S. context. To date, the vast majority of research
on socioeconomic segregation in the United States has focused on income segregation
[10, 43, 44]. While there is a burgeoning line of research on educational segregation in
international contexts such as Turkey [15] and South Korea [45], scholarship on this
phenomenon in the United States remains limited [2, 11]. This study expands upon
past research by examining residential segregation between four educational dyads:
1) bachelor’s degree vs. high school diploma; 2) bachelor’s degree or higher vs. high
school diploma; 3) master’s degree or higher vs. high school diploma; and 4) master’s
Residential Segregation by Education in the U.S., 2016-2020
DOI: http://dx.doi.org/10.5772/intechopen.1001900
degree or higher vs. some college. Our descriptive results reveal moderate levels of
bachelor’s degree/high school diploma dissimilarity compared to previously reported
2000 county-level indices that fell into the low range of dissimilarity [11]. Per our
estimates, over one-third (33.9%) of either bachelor’s degree or high school diploma
recipients would have to move in order to achieve an even distribution of both groups
throughout a CBSA, which is similar to the level found by Quillian and LaGrange [2].
The segregation of all other educational dyads also falls into the moderate range of
segregation, although there is variation in the level of segregation depending upon
the educational groups compared, thereby moving beyond the one-dyad measure
examined by Quillian and LaGrange [2]. Our results are similar to those examining
residential segregation by educational status in South Korea [45] and Turkey [15] but
differ from those for France, which fall in the low range of segregation [2].
Our second contribution to the literature is our examination of the association
between educational segregation and globalization, which has remained absent from
much of the U.S.-based literature. We test Sassen’s global city thesis [5] by assessing
whether globalization is associated with segregation, which has been subject to debate
in the existing literature [6, 7]. With the exception of the master’s degree or higher/
some college educational dyad, we find that educational segregation is significantly
higher in CBSAs that contain a global city relative to those that do not have such a city.
As Sassen’s [5] thesis would suggest, spatial polarization between most educational
attainment dyads is greater in places with global cities, which are theorized to have a
bifurcated occupational structure. These results echo those in previous comparative
and international research on socioeconomic segregation that find globalization to be
a force tied to growing inequality [4, 6, 7, 46]. The fact that globalization is not sig-
nificantly associated with the educational segregation of those with at least a master’s
degree and those with some college may be because those educational levels are not
reflective of the occupational bifurcation that is found between those at further ends
of the educational spectrum.
It is notable that the association between globalization and educational residential
segregation is significant even after controlling for the Gini index of income inequal-
ity. Past research has found income inequality to be a highly significant predictor
of residential segregation by income [10, 44]. Our models also show that income
inequality is a positive and highly significant predictor of educational dissimilarity,
and this result holds across all educational attainment dyads. Globalization, however,
also remains significant, suggesting that future research on socioeconomic segrega-
tion should include globalization as a correlate.
Our final contribution is our focus on the association between political preferences
and segregation. To our knowledge, there is little research examining the relationship
between political preferences and residential segregation (for an exception, see [15]).
Our results indicate that the percentage of votes cast for Clinton within a given CBSA
are positively and significantly associated with segregation between the most highly
educated (i.e., those with a master’s degree or higher) and one of the least highly edu-
cated (i.e., those with some college) groups in our sample. While our results cannot
speak to why this association exists, it suggests that specific educational groups value
different lifestyles, which plays out in a separation within geographic space.
We envision several fruitful avenues for research to build upon this work in order
to elaborate on other aspects of the segregation regime that experts have identified as
increasingly important in today’s divided cities [1, 6, 7, 47]. More work should be done
examining the causal linkages between globalization, income inequality, and educa-
tional segregation. How do the relationships form over time? Or does globalization
Recent Trends in Demographic Data
tend to thrive in areas that are already stratified? Characterizing the trends in educa-
tional segregation patterns and income segregation would also be a worthy pursuit in
order to see how the two aspects of socioeconomic segregation converge and diverge
across different metropolitan areas. The fact that political preferences are signifi-
cantly associated with educational residential segregation for at least one educational
dyad necessitates further study of individuals’ residential mobility behavior as it
relates to their political preferences and their educational level. The social structural
sorting perspective is focused on explaining racial and ethnic residential segregation
[32]. However, it would be worth examining how people’s perceptions of neighbor-
hoods are shaped by their political and educational values and preferences and by
other factors like their peer networks and social media. Social distance between
political parties, as is already prevalent in the U.S., imperils democracy because it
threatens mutual cooperation across parties [18, 22]. More work needs to be done to
explore how this social distance is translating into spatial distance and the implica-
tions of such residential segregation for our democracy. Moreover, attention should be
paid to the association between political preferences and educational segregation for
smaller metropolitan and micropolitan areas, which are areas that may be even more
politically polarized than larger metropolitan areas.
. Conclusions
Socioeconomic segregation remains a pressing social problem insofar as it rein-
forces stratification via access to opportunities and the distribution of life chances.
This study broadens the traditional focus of scholarship on socioeconomic segrega-
tion beyond income to incorporate educational attainment, an understudied compo-
nent of SES with ramifications for residential sorting. Within the 108 most populous
CBSAs in ACS 2016–2020, we find moderate levels of segregation by educational
attainment, with the most distance between those with the highest level of education
in our sample (i.e., master’s degree or higher) and those with the lowest level (i.e.,
high school diploma). Thus, educational attainment appears to be a salient character-
istic shaping the contemporary sorting of households across metropolitan America.
Additionally, this analysis contributes to ongoing scholarly debates surrounding the
importance of globalization for socioeconomic segregation [6, 7]. Consistent with
Sassen’s global city thesis [5], among most educational attainment dyads, spatial
polarization is most acute in places characterized by globalization. Finally, our
consideration of the relationship between residential segregation and voting behavior
demonstrates that political preferences may align with educational cleavages in values
and lifestyles that manifest in residential separation between those with a master’s
degree or higher and those with some college. In an era marked by educational expan-
sion and heightened political polarization in the U.S., it is increasingly important for
scholars to identify factors shaping residential stratification along these lines.
Acknowledgements
Support for this research was provided by a grant to the Center for Social and
Demographic Analysis at the University at Albany, SUNY from NICHD (R24
HD044943). This work was also supported by a 2014-2015 Fulbright U.S. Scholar
Research Fellowship.
Residential Segregation by Education in the U.S., 2016-2020
DOI: http://dx.doi.org/10.5772/intechopen.1001900
Author details
SamanthaFriedman1*† and ThaliaTom2†
1 Department of Sociology, University at Albany, SUNY, Albany,NY, USA
2 Department of Sociology, University of Southern California, LosAngeles,CA, USA
*Address all correspondence to: samfriedman@albany.edu
†Authors contributed equally to this manuscript
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of
the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided
the original work is properly cited.
Recent Trends in Demographic Data
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