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Residential Segregation by Education in the U.S: 2016-2020

Written By

Samantha Friedman and Thalia Tom

Submitted: 24 January 2023 Reviewed: 11 May 2023 Published: 06 June 2023

DOI: 10.5772/intechopen.1001900

Recent Trends in Demographic Data IntechOpen
Recent Trends in Demographic Data Edited by Parfait M Eloundou-Enyegue

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Recent Trends in Demographic Data [Working Title]

Prof. Parfait M Eloundou-Enyegue

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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 segregation, 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 correlates 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

1. 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, 2, 3, 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, 7, 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, 12, 13]. Second, much of the research on socioeconomic segregation is largely descriptive in nature [4, 5, 6, 7, 8, 14]. An increasing body of literature systematically examines variation in socioeconomic segregation across cities or other geographic areas through empirical analyses [2, 3, 9, 10, 12, 15, 16, 17].

Third, little attention has been paid to the association between political preferences and socioeconomic residential segregation, and particularly residential segregation 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, 19, 20, 21, 22, 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 educational 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 political preferences and residential segregation by educational status, an intersection that has largely been overlooked in the literature.

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2. Literature review

Although limited research on educational segregation in the U.S. context may reflect the assumption that educational attainment does not contribute much additional 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 magnitude [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 educational segregation by reviewing the literature on the correlates of residential segregation by socioeconomic status.

2.1 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 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 relationship 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 inequality 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.

2.2 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 responsible for spatial polarization in cities. Other studies find that structural- and institutional-level factors are associated with socioeconomic residential segregation and can modify the effect of globalization [4, 6, 7, 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 countries [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 contemporary 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 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 without 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 in their 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 educational 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.

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3. Data and methods

3.1 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 dissimilarity 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 available 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 the most commonly used measures of segregation, typically range from a minimum of 0 (indicating no segregation) to a maximum of 1 (indicating complete segregation), 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 classified 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 segregation, 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 producer 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 preferences, 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, 41, 42].

Beyond these key independent variables, we include the following CBSA-level control variables, also obtained from the 2016–2020 ACS data, in our multivariate 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].

3.2 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 independent 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.

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4. Results

Table 1 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.

MeanSDMinMax
Residential Segregation Scores
Bachelor’s Degree/High
School
33.94.8222.145.4
Bachelor’s Degree or Higher/High School36.35.0123.647.2
Master’s Degree or Higher/High School42.35.1926.853.2
Master’s Degree or Higher/Some College33.44.3022.143.6
Key Independent Variables
Global City.463.50101
Gini Index of Inequality46.32.0038.954.0
% of Votes for Clinton48.4010.5114.0877.07
% of Votes for Trump45.319.7616.6364.88
Control Variables
% with a Bachelor’s Degree20.873.8811.3830.08
% with a Master’s Degree or Higher13.033.735.3525.58
% Employed in Manufacturing9.863.602.8720.44
Log Population14.12.82613.1516.77
Region:
Northeast.185.3901
Midwest.185.3901
South.407.49401
West.222.41801

Table 1.

Descriptive statistics for dependent, key independent, and control variables.

Source: Data come from the 2016–2020 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 108 CBSAs with 500K+ population.

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 metropolitan 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 percentile 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 Table 1 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 metropolitan areas. This value aligns with the magnitude of average income inequality reported in previous studies of populous CBSAs [9]. With respect to political preferences, Table 1 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 residents’ political preferences. Table 1 shows that on average, 45.31% of the population 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 1 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%. Table 1 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 analytic sample were located in the South (40.7%), followed by the West (22.2%), the Northeast (18.5%), and the Midwest (18.5%).

Tables 2 and 3 present the results of our multivariate models examining the factors associated with residential segregation by education. Table 2 uses the percentage voting for Clinton as the measure for political preferences. Table 3 uses the percentage voting for Trump. Each table reports coefficients and standard errors from four OLS regression models predicting dissimilarity scores for each of the following 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.

(1)(2)(3)(4)
BA/HSBA+/HSMA+/HSMA+/SC
Global City2.457*2.349*2.486*1.552
(1.066)(1.103)(1.098)(0.901)
Gini Index of Inequality0.720***0.810***0.790***0.689***
(0.206)(0.213)(0.215)(0.176)
% Votes for Clinton−0.0300.0210.0570.099**
(0.039)(0.040)(0.044)(0.036)
% with a Bachelor’s Degree0.230*0.289**
(0.103)(0.106)
% with a Master’s Degree or Higher0.269*0.283**
(0.117)(0.096)
% Employed in Manufacturing0.0830.1110.1210.147
(0.112)(0.116)(0.119)(0.098)
Log Population0.6390.5320.4510.303
(0.666)(0.689)(0.708)(0.581)
Midwest5.071***5.015***6.218***4.156***
(1.180)(1.220)(1.289)(1.058)
South5.603***5.163***5.903***3.450***
(0.980)(1.014)(1.076)(0.883)
West5.233***5.226***6.681***3.149**
(1.087)(1.124)(1.197)(0.983)
Constant−18.113−22.087−14.258−16.274
(10.965)(11.341)(11.326)(9.297)
Observations108108108108
R-squared0.5360.5420.5500.558

Table 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.

Source: Data come from the 2016–2020 ACS, MEDSL, and GaWC.

Notes: Standard errors are in parentheses; *** p < 0.001, ** p < 0.01, * p < 0.05.

BA = Bachelor’s degree; BA+ = Bachelor’s degree or higher; HS = High school diploma; MA+ = Master’s degree or higher; SC = Some college education.

(1)(2)(3)(4)
BA/HSBA+/HSMA+/HSMA+/SC
Global City2.430*2.371*2.501*1.549
(1.068)(1.104)(1.094)(0.902)
Gini Index of Inequality0.696***0.825***0.839***0.783***
(0.198)(0.205)(0.206)(0.170)
% Votes for Trump0.034−0.026−0.077−0.111**
(0.043)(0.044)(0.049)(0.041)
% with a Bachelor’s Degree0.243*0.278*
(0.107)(0.110)
% with an Master’s Degree or Higher0.2350.251*
(0.123)(0.101)
% Employed in Manufacturing0.0830.1120.1250.144
(0.112)(0.115)(0.118)(0.098)
Log Population0.6320.5280.4190.311
(0.664)(0.687)(0.705)(0.581)
Midwest5.074***5.016***6.152***4.077***
(1.180)(1.220)(1.286)(1.060)
South5.601***5.180***5.908***3.396***
(0.981)(1.014)(1.069)(0.881)
West5.390***5.108***6.276***2.571*
(1.089)(1.126)(1.224)(1.009)
Constant−20.226−20.255−9.371−10.349
(11.957)(12.363)(12.250)(10.097)
Observations108108108108
R-squared0.5360.5420.5530.558

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.

Source: Data come from the 2016–2020 ACS, MEDSL, and GaWC.

Notes: Standard errors are in parentheses; *** p < 0.001, ** p < 0.01, * p < 0.05.

BA = Bachelor’s degree; BA+ = Bachelor’s degree or higher; HS = High school diploma; MA+ = Master’s degree or higher; SC = Some college education.

The association between global cities and residential segregation by educational status is significant and positive in all models in Tables 2 and 3, with the exception of the models for the educational dyad of those with a master’s degree or higher and those with some college (column 4 of Tables 2 and 3), even after controlling for the Gini index of inequality. The results in model 1 of Table 2 indicate that the dissimilarity index between those with a bachelor’s degree and those with a high school diploma is 2.457 units 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 2 indicate that 1) educational segregation between those with a bachelor’s degree or higher and those with a high school diploma is 2.349 units higher in CBSAs with a global city than in those without a global city; and 2) the D-score is 2.486 units 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 Table 3 are similar in magnitude and significance as the results in Table 2.

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 2 and 3, controlling for other factors. Model 1 of Table 2 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 Table 3).

How do political preferences relate to educational segregation? Tables 2 and 3 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, controlling for other factors. The results in model 4 of Table 2 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 3 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, controlling 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 2 and 3). 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 2; and model 4 of Table 3). 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 educational dyad examined (see Tables 2 and 3). 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 2 and 3).

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5. 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 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 significantly 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 inequality. 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 segregation 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 educated (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 educational segregation. How do the relationships form over time? Or does globalization tend to thrive in areas that are already stratified? Characterizing the trends in educational 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 significantly 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 neighborhoods 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 implications 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.

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6. Conclusions

Socioeconomic segregation remains a pressing social problem insofar as it reinforces stratification via access to opportunities and the distribution of life chances. This study broadens the traditional focus of scholarship on socioeconomic segregation beyond income to incorporate educational attainment, an understudied component 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 characteristic 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 expansion and heightened political polarization in the U.S., it is increasingly important for scholars to identify factors shaping residential stratification along these lines.

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Acknowledgments

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.

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Written By

Samantha Friedman and Thalia Tom

Submitted: 24 January 2023 Reviewed: 11 May 2023 Published: 06 June 2023