separate when equal? racial inequality and residential...
TRANSCRIPT
Separate When Equal? Racial Inequality and
Residential Segregation�
Patrick Bayer Hanming Fang Robert McMillan
January 13, 2005
Abstract
Conventional wisdom suggests that residential segregation will fall when racial di¤erences
in education and other sociodemographics decline. This paper argues that this prediction may
not be accurate. We identify, both theoretically and empirically, a forceful mechanism that may
lead to persistent and even increasing residential segregation as racial di¤erences in sociodemo-
graphics decline. The key feature of the mechanism relates to the emergence of middle-class
black neighborhoods as the sociodemographics of blacks improve. We show that, middle-class
black neighborhoods are in short supply given the current black sociodemographics in many
U.S. metropolitan areas, forcing wealthy blacks either to live in white neighborhoods with high
levels of neighborhood amenities or in more black neighborhoods with lower amenity levels.
Increases in black sociodemographics in the metropolitan areas will lead to the emergence of
new middle-class black neighborhoods, relieving the prior neighborhood supply constraint and
leading to increases in residential segregation. We present cross-MSA evidence from the 2000
Census indicating that this mechanism does in fact operate: as the proportion of highly educated
blacks in an MSA increases, the segregation of educated blacks, and all blacks more generally,
goes up. We also present evidence against leading alternative hypotheses.
Keywords: Segregation, Racial Sorting, Racial Inequality, Neighborhood Formation.
JEL Classi�cation Numbers: H0, J7, R0, R2.
�We are grateful to Caroline Hoxby, Kim Rueben, and Jacob Vigdor as well as conference/seminar participants
at the NBER and Yale for helpful comments and suggestions. We are responsible for all remaining errors. Contact
Addresses: Bayer and Fang are at the Department of Economics, Yale University, P.O. Box 208264, New Haven, CT
06520-8264; McMillan is at the Department of Economics, University of Toronto, 150 St. George Street, Toronto,
Ontario M5S 3G7, Canada.
1 Introduction
There is a large literature on the causes and consequences of racial segregation (Massey and
Denton [22], Cutler and Glaeser [9], Cutler, Glaeser and Vigdor [10]). Many researchers have
attempted to evaluate the contributions of various socioeconomic variables to the racial segrega-
tion. For example, Bayer, McMillan and Reubin [2] used restricted-access 2000 Census microdata
to show that a set of sociodemographic variables including education, income and language can
explain 30 percent of Black segregation and 93 percent of Hispanic segregation in the Bay Area
housing market.1 The basic idea in this literature is simple. Given that demand for housing and
neighborhood quality increases with income and that whites earn more than blacks in general,
neighborhood with higher quality houses will be primarily occupied by white households. More
generally, racial di¤erences in socioeconomic characteristics a¤ect households�residential choices,
some racial segregation would emerge even in the absence of any sorting on the basis of race. The
implicit conclusion from this literature is that reductions in racial di¤erences in income and other
important sociodemographics would decrease the level of residential segregation.
In this paper, we argue that the conventional wisdom may not be accurate. We argue that
improvements in the blacks�socioeconomic characteristics (proxied by their level of education) in
an MSA may lead more segregation of educated blacks and all blacks more generally. Our analysis
is motivated by three empirical observations about the current state of racial segregation in the U.S.
(see Section 2). First, in almost every metropolitan-area, there are few, if any, neighborhoods with
both high fractions of blacks and highly-educated households. The shortage of such middle-class
(i.e., with high average education) black neighborhoods forces highly-educated black households to
choose between predominantly black neighborhoods with low average education and predominantly
white neighborhoods with high levels of education. Second, faced with limited choice set of neigh-
borhood alternatives, highly-educated blacks do live in a very diverse set of neighborhoods: while
a fraction live in neighborhoods with very few other black households and many college-educated
neighbors, many live in neighborhoods that have a high fraction of black households and very few
other college-educated households. Third, most of the middle-class black neighborhoods are located
in metropolitan areas with a signi�cant proportion of highly-educated blacks.
The above empirical observations suggest the following. First, neither race nor average ed-
ucation of the neighborhood is all-important in the location decisions of highly-educated blacks
1See Miller and Qingley [24] for an earlier study using similar methodology. Sethi and Somanathan [29] proposed
a di¤erent method for the decomposition of segregation measures into one component that can be attributed to the
e¤ect of racial income disparaties alone, and another component that combines the e¤ects of neighborhood preferences
and discrimination.
1
(we present more evidence of preferences over race and neighborhood characteristics in Section
3). This further implies that highly-educated blacks may have preferred to live in highly-educated
majority-black neighborhoods � alternatives most of the metropolitan areas in the U.S. do not
currently. Second, middle-class majority-black neighborhoods, currently only existing in a handful
of metropolitan areas, are more likely to emerge when the number of highly-educated blacks in-
creases in the metropolitan area. The emergence of middle-class black neighborhoods will then lead
to more residential segregation because these neighborhoods are highly attractive to middle-class
black households. Section 3 presents a simple and stylized model of household residential choice
and neighborhood formation that captures the essence of the above mechanism.2
Our empirical analysis takes seriously the mechanism of decentralized residential sorting and
neighborhood formation, by examining how changes in the structure of the population within a
metropolitan area a¤ect the way that households sort on the basis of race and education.3 Our
central hypothesis relates to whether the relative exposure of highly educated black households to
blacks is an increasing or decreasing function of the education level of blacks in the metro area.4
Using 2000 Census data from 277 US metropolitan-areas (MSA or CMSAs) and summarized at the
tract level, the empirical analysis involves a series of regressions that relate the racial-educational
composition of an individual�s tract to an individual�s own race-education category, a set of MSA
�xed e¤ects, and interactions of individual and MSA race-education characteristics. The results
reveal that relative to other households in the MSA, highly-educated blacks are increasingly exposed
to other blacks as the education level of blacks in the metropolitan area increase. This change is
driven primarily by a large relative increase in exposure to other highly-educated blacks and is
more than completely o¤set by a decrease in exposure to highly-educated whites. These changes
are likely to result in a slight decrease in the average level of education in the neighborhoods in
which highly-educated blacks reside. At the same time, highly-educated blacks are also increasingly
exposed to less-educated blacks and vice-versa. This e¤ect is consistent with the comparative-statics
prediction of our model when highly-educated blacks in an MSA increase from moving from a low
to a moderate proportion in an MSA.
In a nutshell, Section 4 established a positive correlation at the MSA level between the edu-
cational attainment of blacks and the segregation of highly-educated blacks.5 Section 5 attempts
2A recent paper by Sethi and Somanathan [30] presents a di¤erent model in which they show that racial segregation
and income inequality does not exhibit a monotonic relationship. Further discussions of this paper is in Section 3.3 In our analysis, we use education as a proxy for socioeconomic characteristics more generally.4Note that the inclusion of MSA �xed e¤ects in the regression absorbs any mechanical increase in same-race
exposure due to the changing population composition.5Our empirical analysis exploits cross-MSA variations in the proportion of highly-educated blacks; as such, our
2
to empirically distinguish our �ndings from several other plausible mechanisms. We �rst rule out
reverse causality by appealing to the �ndings from Cutler and Glaeser [9].6 They found that blacks
aged 20-30 in more segregated areas have signi�cantly worse outcomes, including educational out-
comes, than blacks of the same age group in less segregated areas. It is worth emphasizing that our
�ndings do not necessarily contradict Cutler and Glaeser [9]. First, they focused on the educational
outcomes of blacks aged 20-30 while we focus on the educational attainment of all blacks in the
MSA; second, we use individual-speci�c exposure rates as our measure of segregation while they
use MSA-level dissimilarity index.7 We can also rule out statistical discrimination in the hous-
ing market or mortgage lending as the explanation to our empirical �ndings. Standard models of
statistical discrimination generally predict that, as the average characteristics of blacks (average
educational attainment, for example) in the MSA improve, highly-educated blacks would less likely
be discriminated against in both the housing market and in mortgage applications. To the extent
that statistical discrimination in the housing market and in mortgage application still exist, the
mechanism we identify may in fact be operating more forcefully than our estimates would imply. A
third alternative explanation is the selection bias where the primary concern is that highly-educated
blacks that select into MSAs with a higher fraction of educated blacks have a stronger taste for
segregation. We address this concern by decomposing the current metropolitan sociodemographic
composition for each household into a lagged measure corresponding to the MSA where the house-
hold lived �ve years ago and the di¤erence between the current and lagged measure. Including
both the lagged and di¤erence measures in an analogous set of regressions reveals that the active
selection related to movement over the past �ve years actually weakens rather than strengthens our
main �ndings, implying that selection across MSAs is not likely to be a signi�cant factor driving
results are not directly related to time-series segregation measures conducted by Cutler, Glaeser and Vigdor [10] and
Glaeser and Vigdor [15]. However, our �nding is consistent with Glaeser and Vigdor�s [15] �nding that �Segregation
decline most sharply in places that ... blacks made up a small portion of the population in 1990. Segregation remains
extreme in the largest metropolitan areas."6Earlier related literature in this line include Ihlanfeldt and Sjoquist [17] and O�Regan and Quigley [25].7The dissimilarity index was proposed by Duncan and Duncan [?] measures the fraction of blacks that would
have to switch areas to achieve an even racial distribution citywide (see Cutler, Glaeser and Vigdor [10] for more
discussions). It is an aggregate-level measure. The exposure rate has the advantage over dissimilarity index in that
it can be speci�c to individuals. It construction can be illustrated simply as follows (see Bayer, McMillan and Ruben
[2]). Let rij be a set of indicator variables that take value 1 if household i is of race j and 0 otherwise. Let Rik be the
fraction of households of race k in household i0s neighborhood (the Census tract, for example). The average exposure
of households of race j to households of race k; denoted by Ejk; is
Ejk =
Pi r
ijR
ikP
i rij
:
3
our results. The �nal alternative explanation relates to omitted variable bias and selection bias,
the concern being that historic patterns of black settlement, migration, and segregation in the U.S.
might give rise to a spurious correlation between metropolitan sociodemographic composition and
segregation. While this explanation can not be addressed to complete satisfaction due to data
limitations, we show that our main �ndings are completely robust to the inclusion of a full set of
interactions with metropolitan size and region.
Our results have several important implications. First, they suggest caution in the conventional
wisdom that racial segregation will necessarily decrease with the decline of the racial di¤erences
in socioeconomic characteristics.8 The mechanism uncovered in our analysis in fact suggests that,
overall increase in blacks�educational attainment may lead to a decrease in residential segregation
if highly-educated blacks are dispersed in many, instead of concentrated in few, metropolitan areas.
This echoes Glaeser and Vigdor�s [15] �nding that segregation is lowest among growing cities in
the West where there is not yet a high concentration of highly-educated blacks. Our �nding is also
related to Wilson [34]. He argues that reductions in institutional discrimination in the housing
market in the middle of the 20th century led to large-scale reductions in the exposure of less-
educated to more-educated blacks, as more-educated blacks left the inner city neighborhoods to
where they were formerly restricted. Our �ndings suggest that this trend may not have been severe
in cities in which the black population was more educated initially and may partially reverse itself
as the black population becomes relatively more educated over time.
The remainder of the paper is structured as follows. Section 2 empirically documents the neigh-
borhood choice sets in U.S. metropolitan areas; Section 3 presents a simple model of neighborhood
formation that highlights the key features of the mechanism underlying our empirical results; Sec-
tion 4 presents our main empirical �nding that the exposure of highly-educated blacks and all
blacks more generally to other blacks increases as the proportion of highly-educated blacks in the
metropolitan area increases; Section 5 evaluates leading alternative hypotheses; �nally, Section 6
concludes.
2 Neighborhood Choice Sets in US Metropolitan Areas
In this section we present some empirical facts about the neighborhood choice sets in U.S.
metropolitan areas using Census Tract Summary Files from 2000 Census. In our analysis, a �neigh-
borhood�corresponds to a �census tract,�which typically contains 3,000 to 5,000 individuals. The
Census Tract Summary Files provides information on the distribution of education by race for each
8This conventional view was embraced by many scholars in this literature (see Durlauf [12], Wilson [34] and Mayer
[23]).
4
Census tract. We characterize the race and educational attainment of households as that of the
head of household and focus speci�cally on non-Hispanic black and non-Hispanic white households
throughout our analysis.9 Educational attainment of the head of the household is used to proxy
the socioeconomic status of the household more generally. We characterize each neighborhood
in a metropolitan area in two dimensions: the fraction of black households, and the fraction of
highly-educated (i.e. educational attainment is college or above) households.
We establish four empirical facts about neighborhood choices sets:
� Highly-educated black households constitute a small fraction of the population living in typicalmetropolitan areas;
� Neighborhoods that combine high fractions of both college-educated and black householdsare in extremely short supply in almost every metropolitan area;
� College-educated blacks choose to live in a very diverse set of neighborhoods in each metropol-itan area;
� Middle-class black neighborhoods are concentrated in few metropolitan areas with sizeable
highly-educated black households.
[Table 1 About Here]
Table 1 describes the joint distribution of education and race for black and white households.
Based on our race de�nitions, black and white households respectively constitute 11.1 and 69.5
percent of the U.S. population that reside in metropolitan areas. Among black households, 15.4
percent are headed by an individual with a college degree, while the comparable number for white
households is 32.5 percent, and for all U.S. households, 27.7 percent.
[Table 2 About Here]
Table 2 documents the number of tracts in the U.S. by the percentage of households with
a college degree and the percentage of households that are black and white, respectively. Panel
1 describes the number of tracts in which more than 0, 20, 40, 60, and 80 percent of head of
households are college-educated, respectively. Panel 2 reports the number of tracts in each of these
categories that contain a minimum fraction of black households equal to 20, 40, 60, and 80 percent,
9The vast majority of households that checked two races can be characterized as either Hispanic or non-Hispanic
Asian or Paci�c Islander. Other households that checked two or more races - a very small fraction overall - were
dropped from this analysis.
5
respectively. As the corresponding numbers show, a much smaller fraction of the tracts with a high
fraction of black households have a high fraction of households with a college degree. For example,
while 23 percent of all tracts are at least 40 percent college educated (a number comparable to the
fraction of U.S. households with a college degree), only 2.5 percent of tracts that are at least 40
percent black are at least 40 percent college educated, and only 1.1 percent of tracts that are at
least 60 percent black are at least 40 percent college educated. Panel 2 of Table 2 shows analogous
numbers for white households, reporting the number of tracts in the U.S. that meet the education
criterion described in each column heading subject to a minimum fraction of white households equal
to 20, 40, 60, and 80 percent, respectively. The corresponding numbers show a markedly di¤erent
pattern of neighborhood choices for white households, with a greater fraction of neighborhoods
with at least 40, 60, and 80 percent white households meeting each education criterion.
[Table 3 About Here]
While Table 2 revealed a paucity of neighborhoods with high fractions of both black and college-
educated households in the U.S. as a whole, Table 3 further shows that such tracts, to the extent
that they exist, are in fact concentrated in only a handful of metropolitan areas, most notably
Washington, DC. This implies that the supply of such neighborhoods in most metropolitan areas is
even more limited. Table 3 illustrates, for example, that of the 44 tracts (less than 0.1 percent of all
tracts) that are at least 60 percent black and 40 percent college-educated, 13 are in the Washington
DC PMSA, 8 in Detroit, 6 in Los Angeles, and 5 in Atlanta. Almost 75 percent of these tracts can
thus be found in one of only four PMSAs. Of the 142 tracts that are at least 40 percent black and
40 percent college-educated, almost two-thirds are in the PMSAs listed above along with Chicago
and New York.
Tables 2-3 taken together show clearly that while neighborhoods that combine high fractions of
both college-educated and white households are amply supplied in all metropolitan areas, neighbor-
hoods that combine high fractions of both college-educated and black households are in extremely
short supply. This suggests that college-educated black households in most metropolitan areas may
face a trade-o¤ between living with other blacks versus other college-educated neighbors.
[Figure 1 About Here]
To graphically demonstrates the potential trade-o¤ faced by highly-educated black households,
Figure 1 shows the scatterplots of neighborhoods in four metropolitan areas: Boston, Dallas,
Philadelphia, and St. Louis. In each scatterplot, a circle represents a Census tract and its co-
ordinates represent the fraction of highly-educated households (vertical axis) and the fraction of
6
black households (horizontal axis) in the tract. The diameter of the circle is proportional to the
number of highly-educated black households in the tract, thus the largest circles correspond to the
tracts where highly-educated blacks are most likely to live.10 For these four metropolitan areas, the
scatterplots demonstrate the short supply of neighborhoods that combine high fractions of both
highly-educated and black households, neighborhoods that would have appeared in the north-east
corner of the plot. Consequently, highly-educated black households may face a trade-o¤ when
making their residential choices.
Figure 1 also demonstrates that, facing the constrained choice set, highly-educated black house-
holds do choose to live in a diverse set of neighborhoods: while a sizeable fraction of highly-educated
blacks in each of the �ve MSAs chooses neighborhoods with few black and many highly-educated
neighbors �neighborhoods in the north-western corner of the plots, another sizeable fraction choose
neighborhoods with many black and few highly-educated neighbors �neighborhoods in the south-
western corner of the plots.
[Table 4 About Here]
Panel A of Table 4 summarizes the characteristics of neighborhoods chosen by highly-educated
blacks in metropolitan areas throughout the U.S. We �rst rank highly-educated black households
in each metropolitan area by the fraction of blacks in their Census tract and assign households
to their corresponding quintile of this distribution. This corresponds to drawing four vertical
lines in the scatterplot for each metropolitan area such that an equal number of highly-educated
black households are in each of the quintiles. Panel A of Table 4 then summarizes the average
fractions of black and Highly-educated households in the tract corresponding to the quintiles of
this distribution, averaged over all U.S. metropolitan areas. The �rst column of Panel A, for
example, the 20 percent college-educated black households with the smallest fraction of other
black households in their metropolitan are (the bottom quintile) live in neighborhoods that are
on average 5.7 percent black and 38.0 percent highly-educated. The numbers corresponding to
di¤erent quintiles show a clear trade-o¤ for highly-educated black households between the fraction
of their neighbors that are black and the fraction that are highly-educated: the average fraction of
highly-educated neighbors falls from 38.0 percent for those college-educated blacks living with the
smallest fraction of black neighbors to 13.8 percent for those living with the largest fraction. Panel
B of Table 4 reports analogous numbers for college-educated white households. The comparison
of Panels A and B reveal that, college-educated blacks in each metropolitan area who live in the
bottom quintile of tracts in terms of the smallest fraction of other blacks have roughly the same
10Note that tracts that do not contain any highly-educated blacks do not show up in these scatterplots.
7
fraction of college-educated neighbors as college-educated whites do on average; however, college-
educated blacks living in the top quintile of tracts with the greatest fraction of other blacks have
only about one-third of that fraction of highly-educated neighbors.
We argue that such a high fraction of college-educated black households in U.S. metropolitan
areas choose segregated neighborhoods with relatively low average education attainments suggest
that race remains an important factor in the location decisions of a large number college-educated
black households. In Section 3 we present more evidence for race preferences and propose a simple
mechanism based on the emergence of highly-educated black neighborhoods to argue that such
racial preferences may lead to more segregation of highly-educated black households as the average
education of the block population in a metropolitan area increases. Speci�cally, we argue that
when the number of highly-educated black households in the metropolitan area increase, a greater
supply of neighborhoods that combine high fractions of both black and college-educated neighbors
may form. Figure 2 depicts the scatterplots of neighborhoods in Atlanta, Chicago, Detroit and
Washington, D.C. As we showed in Table 3, these metropolitan areas contain a sizeable number of
college-educated black households. Figure 2 shows that they supply substantially greater number
of neighborhoods combining relatively high fractions of both black and highly-educated house-
holds, and thus relax the constraint of neighborhood choice set for highly-educated blacks. As the
neighborhood supply constraint relaxes for highly-educated blacks, highly-educated blacks may be
increasingly exposed to other black households.
3 A Model
In this section, we present a simple model of residential choice and endogenous neighborhood
emergence. This simple model formalizes our idea that the set of neighborhood available to highly-
educated blacks will expand as the fraction of highly-educated blacks increase in a metropolitan
area, leading to more segregation. Sethi and Somanathan [30] presented an alternative model
in which they show that low racial inequality is consistent with extreme and even rising levels
of segregation in cities where the minority population is large. Their model does not explicitly
emphasize the idea of neighborhood emergence since they �x the total number of neighborhoods
exogenously, where our model emphasize the emergence of new middle-class neighborhoods, a fact
consistent with the empirical facts documented in Section 2.
Basic Ingredients. Before describing the details we highlight three key ingredients of the model
that drive our results. The �rst key ingredient is that black households� preferences are such
8
that, on net, they prefer to live near others of the same race and education level.11 There is
ample empirical evidence that individuals prefer to live in neighborhoods where they own race is in
majority.12 For example, in the Multi-City Survey of Urban Inequality (MCSUI) respondents were
shown a card representing a neighborhood with �fteen houses (in three parallel rows of �ve houses
each), and were asked to illustrate the racial composition of their �ideal� neighborhoods where
they were presumed to live in the house located at the center of the middle row. Using MCSUI
conducted between 1993-1994 in Atlanta, Detroit, and Los Angeles metropolitan areas, Ihlanfeldt
and Sca�di [17] found that, 35-43 percent of blacks designates the all-black neighborhood or the
mostly black neighborhood (eleven blacks and four whites) as their top choice; and 81-92 percent
of the blacks chose all black or mostly black neighborhoods as one of their top two choices.13
Our assumption that black households prefer neighbors with the same educational attainment
needs more explanation. Indeed intuitive all households may prefer to live with highly-educated
neighbors, due to the positive externality in human capital production (see Benabou [5] and Cutler
and Glaeser [9] for example). Our assumption is sensible only because housing prices �which we
abstract from �would have most likely capitalized such positive externalities. Many sorting models
would generate the prediction that, ceteris paribus, less-educated households are not willing to pay
as much as highly-educated households to live with more educated neighbors. Thus we consider our
assumption on the preference to live with neighbors of same educational attainment as a convenient
reduced-form, which allows us to place the role of housing prices in the background of the analysis.
The second key ingredient of our model is the notion of critical neighborhood size. In the
model below we capture the notion of critical neighborhood size by assuming that each resident
in a neighborhood has to incur a cost that decreases with its total number of residents. Multiple
interpretations can be given for this formulation. One interpretation is that the average cost of
providing both formal and informal local public goods decreases in neighborhood size, at least over
some range; alternatively, it can capture the idea that a larger population can sustain more local
options in retail, television, restaurants, newspaper, and the internet, both in quantity and quality
(see, e.g., Berry and Waldfogel [4] and reference cited therein).
The third ingredient of our model is some degree of idiosyncratic location preferences, unrelated
11Schelling [27][28] is the �rst to note that individual racial preferences can lead to segregation.12Cornell and Hartmann [8], Farley et al [13], O�Flaherty [21] and Lundberg and Startz [20] provide various
theoretical arguments about why individuals might care about the racial composition of their neighborhoods.13Also see Vigdor [32] and Charles [6][7] for related evidence. It is important to emphasize that such evidence has
to be at best considered as suggestive, as the MCSUI survey questions makes no mentions of neighborhood amenties,
housing prices, or other factors that might in�uence residential choices. Thus such evidence does not necessarily
reveal fundamental racial preferences. King and Mieszkowski [19], Yinger [35] and Galster [14] found evidence of
segregation preferences based on housing prices or rents.
9
to sorting on the basis of education or race, in the households�residential choices. We capture this
heterogeneity by assuming that households have employment locations distributed in space and
would prefer to commute shorter distances. Such assumption is standard in the �spatial mismatch�
literature (see Kain [18], Ross [26] and Weinberg [33]).
Model. Consider a metropolitan area located on a straight line with length 2, represented by
the interval [�1; 1]. The population density in the metropolitan area is given by N > 0, thus
its total population is 2N: There are two racial groups r 2 fb; wg and a proportion �w 2 (0; 1)is white and the remaining proportion �b = 1 � �w is black. Moreover, individuals within eachracial group di¤er in their educational attainment: a fraction �r 2 (0; 1) of race-r individuals arehighly-educated (denoted by type-h) and the remaining fraction 1 � �r are of the low-educationtype (denoted by type-l).14 Cross-race inequality in socioeconomic characteristics is re�ected by
the di¤erence �w � �b: Clearly for all metropolitan areas in the U.S., the relevant case is �w > �b.Thus a narrowing in the racial gap in educational attainment can be represented by an increase in
�b while keeping �w �xed.
For simplicity, we assume that whites�residential locations are �xed: at each endpoint of the
line, there are two communities, one for highly-educated whites (called communities HW and HW�)
and one for less-educated whites (called communities LW and LW�). We focus our analysis on the
residential location choices of black households and the emergence of black neighborhoods.
We model idiosyncratic locational preferences of the blacks by assuming that their job locations
are uniformly distributed on the straight line. Commuters experience a cost of � > 0 per unit
distance between their work and place of residence.
There is a cost of maintaining a community, and the average per-resident cost is given by c(n)
where n is the number of residents in the community.15 Naturally we assume that c decreases in n:
We now describe black households� preferences. Consider a black household with education
e 2 (l; h) whose job location is at point z 2 [�1; 1] on the straight-line. Its utility from living in a
community j 2 J where J is the set of available communities to be determined in equilibrium, isgiven by:
u(j; z; e) = � [pb(j) + 1pw(j)] + � [pe(j) + 2pe0(j)]� �D(j; z)� c (n(j)) ; (1)
where e0 6= e is the other education category; pr(j) is the proportion of residents in community j ofrace r; pe(j) is proportion of residents in j with education attainment e; D(j; z) is the commuting
distance between community j and z�s job location; n(j) is the number of residents in community
14The heterogeneity could equally be in terms of income.15Technically this rules out tiny enclaves of individuals claiming to form a neighborhood of their own.
10
j; � > 0; � > 0; 1 2 (0; 1) ; 2 2 (0; 1) are constants. In utility function (1), the �rst term
�[pb(j) + 1pw(j)] captures the utility from interacting with people of di¤erent races in the same
community, where 1 < 1 captures the same-race preference discussed earlier. The interpretation
of the second term �[pe(j) + 2pe0(j)] is more subtle. As we explained previously, it is meant to
capture, in a reduced form, the idea that highly-educated workers will on net (taking into account
both human capital externality and housing price) prefer to live in more expensive neighborhoods
with many other highly-educated residents, while less-educated workers will prefer on net to live in
cheaper neighborhoods with other less-educated residents.
An equilibrium of this simple model is characterized by the set of neighborhoods J� (including
the existing neighborhoods HW, HW�, LW, LW�) and the residential choices of all black households
such that: (1) given J�; all black households�residential choices is utility-maximizing; (2) there is
a positive measure of residents in all neighborhoods j 2 J�: Note that we do not need to directlyimpose a threshold neighborhood size in our model. The existence of the four white neighborhoods,
together with the assumption that c (n) is decreasing in n; endogenously ensures that small enclaves
of black households will not claim to form their own neighborhood.
The equilibrium set of neighborhoods J� depends on the parameters of the model. We are
particularly interested in how J� is a¤ected by an increase in �b �the fraction of highly-educated
black households in the metropolitan area. Consider an equilibrium in which a single black com-
munity, community B, emerges at point 0. Clearly community B, were it to emerge, will consist
of blacks whose job locations are close to point 0. Thus given J� = {HW, HW�, LW, LW�, B},
black households�optimal residential choices can be characterized by a pair fx�h; x�l g such that allhighly-educated (less-educated, respectively) black households will choose to live in community B
if and only if their job location z satis�es jzj � x�h (jzj � x�l ; respectively). The marginal types
fx�h; x�l g can be determined from the indi¤erence conditions (see Appendix A for details). Figure 3
depicts this type of equilibrium when �b is small.16
[Figure 3 About Here]
Imagine that we increase the fraction of highly-educated blacks �b from an initial low level.
First, note that as �b increases, the proportion of highly-educated blacks in community B, ph (B) ;
will increase even if the thresholds fx�h; x�l g were hypothetically unchanged. As ph (B) increases,
16 If such an equilibrium exists with a su¢ ciently small �b, one can show that xl� > xh�. The reason is simple:
when �b is small, community B is necessarily a predominantly lowly-educated all-black community. Because 2 < 1,
the utility for a lowly-educated black from community B is always higher than that for a highly-educated black at
any job location. Thus lowly-educated blacks are more willing to commute to community B. This is not important
for the analysis but explains the ranking of x�l and x�h in Figure 3.
11
community B becomes more attractive vis-à-vis community HW for highly-educated blacks. As a
result, the marginal type of highly-educated black who commutes to community B, x�h; will increase.
Thus the probability of a highly-educated blacks living in all-black community B with less-educated
blacks will initially increase in �b. The results for less-educated blacks are more ambiguous. On
the one hand, community B becomes more educated. On the other hand, the increase of the total
population in community B drives down the average community cost c. Thus, whether or not
community B becomes more desirable for less-educated blacks is indeterminate. It is possible that
exposure of highly- and less-educated blacks to one another may increase. Moreover, the increase
in exposure of highly-educated blacks to other highly-educated blacks comes at the expense of
exposure to highly-educated whites (see Figure 4b for the graphical illustration).
[Figure 4 About Here]
When �b is su¢ ciently high, however, a point may be reached when it becomes pro�table for
highly-educated blacks in community B to form their own community at point 0, called BH, and
leaving behind a less-educated black community BL (see Figure 4c). The exact point at which the
highly-educated black neighborhood BH emerges is determined by the balance of two forces. First,
by separating from the less-educated blacks living in community BL, highly-educated blacks have
to incur a higher per capita cost c as a result of smaller population size; second, because community
BH consists only of highly-educated blacks, the utility component ph (BH) = 1 > ph (B)+ 2pl (B)
because 2 < 1:17 This is the key insight of our simple model: a highly-educated black community
BH emerges only when the proportion of highly-educated blacks �b is su¢ ciently high. Of course,
the emergence of such communities also depends positively on the population density N , the overall
proportion of blacks in the metropolitan area �b. It also indirectly depends on the commuting cost
� and the community cost function c (n) via their e¤ects on xh�.
It is also worth pointing out that the emergence of community BH is likely to induce an ac-
celerated emigration of highly-educated blacks from community WH and WH�to community BH,
resulting in greater racial segregation in residential locations.
4 Empirical Results
We now present the main empirical analysis of this paper. We begin this section by charac-
terizing the pattern of segregation broken out by race and education in U.S. metropolitan areas.
17We abstract from the coordination problem among highly-educated blacks in their decision to form their own
neighborhood.
12
We then explore how this pattern varies with the sociodemographic composition of the metropol-
itan area, focusing especially on how the segregation of highly-educated blacks (and blacks more
generally) is a¤ected by the fraction of highly-educated blacks in the metropolitan area.
4.1 Segregation Patterns in US Metro Areas
We begin by describing the general pattern of segregation in the United States as a whole. Panel
A of Table 5 shows the average tract-level cross exposure for households in four race-education
categories (black/white; college/non-college educated) for U.S. metropolitan areas. The �rst entry
in Panel A, for example, implies that the average black household without a college degree lives in
a tract where 33.2 percent of the households are black and without a college degree. This compares
to the national average exposure to less-educated blacks of 9.4 percent. Panel B reports the average
cross exposure of households by race-education categories relative to the MSA average. The �rst
row of Panel B states that relative to an average household in the same metropolitan area, blacks
without a college degree are exposed to 19.6 percentage points more blacks without a college degree
and 2.1 percentage points more highly-educated blacks, etc. Table 5 illustrates a clear pattern of
racial segregation in U.S. metropolitan areas for highly-educated blacks as well as those with lower
levels of educational attainment.
[Table 5 About Here]
Table 6 reports segregation patterns in a manner analogous to Panel B of Table 5, but sepa-
rately for metropolitan areas with above and below the median fraction of college-educated black
households (1.23 percent). Table 6 provides some initial evidence on how segregation patterns
vary with the sociodemographic composition of the metropolitan area. Table 6 shows that, the
relative exposure of blacks in each education category to both highly- and less-educated blacks is
signi�cantly greater in metropolitan areas with above-median fractions of highly-educated black
households. For both highly- and less-educated blacks, the average tract-level exposure to blacks
relative to the fraction of blacks in the MSAs above the median more than doubles that for MSAs
below the median.
[Table 6 About Here]
4.2 A Regression-Based Approach
To more formally control for the sociodemographic structure of the metropolitan area, Table 7
reports the results of a series of regressions of various tract-level exposure measures on individual
13
and MSA characteristics.18 The dependent variable is listed in the head of each column. For
example, column 1 corresponds to a regression whose dependent variable is individuals�tract-level
exposure rate to highly-educated blacks. Each regression includes a complete set of controls for
individual race-education categories and MSA �xed e¤ects. The inclusion of the MSA �xed e¤ects
ensures that all of the other parameters characterize tract-level exposure relative to the MSA
average for each set of individuals. In addition, the regressions also include individual characteristics
interacted with MSA characteristics.19 The coe¢ cients on the interaction terms characterize how
tract-level exposure for various sets of individuals varies with MSA characteristics.
[Table 7 About Here]
Due to the various interaction terms in these regressions, coe¢ cient estimates are di¢ cult to
interpret in isolation. In what follows we use these coe¢ cient estimates to present two statistical
tests. The �rst test is about whether an increase in the fraction of highly-educated blacks, holding
constant the fraction of black households, changes the relative tract-level exposure of less- and more-
educated blacks, respectively, to households in the given race-education category. This corresponds
to examining the impact of an increase in the average education level of black population. The
second test is about whether an increase in the fraction of highly-educated blacks, holding constant
the fraction of highly-educated individuals in the metropolitan area, changes the relative tract-level
exposure of less- and more-educated blacks, respectively, to households in the given race-education
category. This corresponds to increasing the fraction of the educated population that is black.
[Table 8 About Here]
Table 8 summarizes these e¤ects for a 1 percentage point change in the fraction of college-
educated blacks in the MSA population. Panel A shows that when the fraction of college-educated
blacks in a metropolitan area increase by 1 percent at the expense of less-educated blacks, the
relative exposure of highly-educated blacks to other highly-educated blacks increase by 1 percentage
point and it is statistically signi�cant; the relative exposure of less-educated blacks to highly-
educated blacks also increase by 1.1 percentage points. Overall, the relative exposures of both
highly- and less-educated blacks to blacks increase by 4 and 6.1 percentage points respectively. This
empirical �nding is consistent with our model�s prediction when �b is in intermediate range (Figure
18We include individual level characteristics to the extent that they are available in the Census Tract Summary
Files. In practice this is equivalent to running weighted OLS where the weight is given by the number of individuals
in each race/education cell.19 If we do not include these interactions, the coe¢ cients on the individual characteristics in these regressions would
return the estimates reported in Panel B of Table 5.
14
4b), which we think is the plausible scenario for most of the U.S. metropolitan areas in the U.S.
Panel B shows that similar results holds when the fraction of highly-educated blacks is increased
by reducing the fraction of highly-educated whites (i.e., increasing the fraction of the educated
population that is black, Panel B). For both highly- and less-educated blacks, the increased relative
exposure to blacks is driven by increased exposure to blacks in both education categories. These
relative increases are o¤set by a decrease in the exposure to (especially highly-educated) whites. On
net, an increase in the average education of the black population has a slightly negative (although
marginally statistically insigni�cant) e¤ect on the average education level in the neighborhoods
that blacks reside in relative to the metropolitan area average.20
[Table 9 About Here]
Table 9 reports results analogous to those reported in Table 8 with the exception that the
underlying measure of highly-educated is changed to include those having at least attended college.
With this de�nition, the fraction of households in U.S. metro areas that are highly-educated is
54 percent, the fraction that are both highly-educated and black is 5 percent, and the fraction of
black households that are highly educated is 45 percent. Our primary objective in examining an
alternative de�nition is to explore a de�nition of highly-educated that includes a larger fraction
of households and especially black households. A comparison of the results reported in Table 9
to those reported in Table 8 reveals a qualitatively similar pattern. With the expanded highly-
educated category, the relative increase in exposure of both highly- and less-educated blacks to
other blacks is more evenly split between highly- and less-educated blacks.
Both de�nitions of �highly-educated� reveal a pattern of increased relative exposure of both
highly- and less- educated blacks to blacks in each education category when the fraction of highly-
educated blacks in the metropolitan area increases. This pattern is consistent with the predictions
of our model corresponding to an increase in �b from small to moderate levels. With an increase
in the average education level of the black population, highly-educated blacks move on net into
20To ensure that the results of Table 7 are not driven by the form of the dependent variable that we employ, we also
conducted a series of regressions analogous to those reported in Table 7 de�ning the dependent variable as the fraction
of households in a given category in an individual�s tract divided by the fraction in the metropolitan area as a whole.
In this way, an increase in tract-level exposure to households in a given category from 6 to 12 percent following an
increase in the proportion of these households in the metro area from 3 to 6 percent would not result in an increase
in the dependent variable in this case, while it would result in a 3 percentage point increase in the dependent variable
used in the regressions reported in Table 7. The resulting parameter estimates led to a qualitatively identical set of
conclusion, thereby ensuring that our initial results are not driven by the functional form of the dependent variable.
Throughout the remainder of the paper, we present the results of regressions analogous to those reported in Table 7.
15
more segregated neighborhoods, increasing the average education level in some of the most segre-
gated neighborhoods. In terms of the scatterplots, this pattern is consistent with the formation of
segregated, black neighborhoods with mixed education levels � that is, with shift of the implicit
neighborhood supply constraint that is more upward than outward.
5 Robustness to Alternative Explanations
Our main empirical �nding can be summarized as the discovery of positive relationship at the
MSA level between the educational attainment of blacks and the segregation of highly educated
blacks. We suggested a forceful mechanism that can explain this positive correlation based on
the idea that middle-class black neighborhoods may emerge as the sociodemographics of blacks
improve.
In this section, we present arguments and robustness tests to cast doubt on alternative expla-
nations for our empirical �ndings. The �rst possible story is reverse causality. Cutler and Glaeser
[9], however, cast doubt on this explanation. They studied the impact of segregation at the MSA
level on educational outcomes for blacks aged 20-30, and �nding a large negative e¤ect.21 The
second possible story is statistical discrimination, both in housing market and in mortgage market.
However, statistical discrimination story would predict a negative correlation: as the fraction of
highly educated blacks increases in a metropolitan area, blacks in general would be less likely to
be discriminated against thus leading to less not more segregation. To the extent that statistical
discrimination exists in reality, the actual mechanism that we identify in our paper may in fact be
stronger than what our main estimates would imply. Below we consider in detail the possibility of
omitted variables and selection biases.
Omitted Variable Bias. The concern over omitted variables is that the fraction of the metropol-
itan area that is highly-educated and black might be correlated with other variables that are as-
sociated with di¤erent levels of segregation. This is a di¢ cult problem to address satisfactorily,
especially since we use cross-MSA data. Here we address this problem by adding metropolitan size
and region, two most prominent factors because of historic patterns of black settlement, migration,
and segregation in the U.S., into our regression. Table 10 reports the results of a set of regressions
analogous to those reported in Table 9 with the addition of interactions between each individual�s
race-education category and a measure of metropolitan size and four dummies for Census region
21There are other di¤erences between their paper and ours: for example, they use dissimilarity index as the
segregation measure, and the data set they used is the 1990 Census.
16
(Northeast, Midwest, South, West).22
[Table 10 About Here]
A comparison of the results presented in Table 10 to those in Table 9 reveals a qualitatively
similar pattern in terms of both the magnitude and the statistical signi�cance of the results. In
particular, with the additional controls, the increase in the relative exposure of both highly- and
less-educated blacks to other blacks declines by 15-20 percent in magnitude, but remains highly
signi�cant. Changes in relative exposure to highly-educated neighbors also decline and remains
insigni�cant in each case. Taken together, these results give us con�dence that the main conclusions
of the paper are not driven by obvious omitted variable biases.
While we added metropolitan size and interactions in the results reported in Table 10, we still
assumed that the e¤ects of the fraction of highly-educated blacks on segregation does not depend
on metropolitan size. The critical mass story implicit in our model implies that not only the
fraction but the number of highly-educated blacks in the metropolitan area may be important for
the formation of more-educated and segregated black neighborhoods. Given the same fraction of
highly-educated black households, highly-educated black neighborhoods might more easily form in
large (in population) rather than small metropolitan areas.
[Table 11 About Here]
Table 11 estimates separate regressions, including the additional 16 control variables added
in Table 10, for small (0-200k), medium (200-600k), and large (600k+) metropolitan areas. For
brevity, we only report results related to exposure to black households. A clear pattern emerges
in the table � the increased relative exposure of both highly- and less-educated blacks to other
blacks following an increase in average education level of the black population is much greater in
large versus small metropolitan areas. For highly-educated blacks, the magnitude of the e¤ect rises
from 0.002 in small, to 0.025 in medium-sized, and 0.40 in large metro areas. The results tend
to have higher statistical signi�cance in larger metropolitan areas. A similar pattern emerges for
less-educated black households. Interestingly, in large metropolitan areas, the increased exposure
of highly-educated blacks to other blacks is dominated by an increased exposure to other highly-
educated blacks. Thus, for this subsample, an increase in the average education of the black
population might be associated with the formation of predominantly highly-educated, segregated
black neighborhoods �i.e., the prediction of the model corresponding to an increase in the fraction
of highly-educated blacks from a moderate to large number.
22A total of 16 interaction terms are added to the regression.
17
Selection Bias. The concern about selection bias is that highly-educated blacks that select into
MSAs with a higher fraction of educated blacks may have a stronger taste for segregation. To
address this possibility, we make use of an alternative organization of the 2000 Census �the Public
Use Microdata Sample (PUMS). PUMS has the advantage over the Tract Summary Files used in
the above analysis in that observations are at the individual level, but has the disadvantage that
a less detailed level of geographic speci�city is provided. In PUMS individuals are assigned to
PUMAs, which contain greater than 100,000 households. From our perspective the key additional
variable contained in the PUMS data is the metropolitan area in which each individual resided
�ver years ago (i.e., in 1995). This variable allows us to explore whether the pattern of active
selection across metropolitan areas over this �ve-year period is in the direction of causing an over-
or under-statement in the coe¢ cients estimated in our main speci�cations.
Before exploring the selection bias issue with these data, we �rst replicate our main speci�cations
reported in Table 9 for this organization of the Census data. Table 12 shows, as one may expect
as a result of the increased geographic aggregation in this data set, a similar pattern of relative
exposures to those seen in Table 9 but at rates that are smaller in magnitude.
[Table 12 About Here]
[Table 13 About Here]
Table 13 explore the likely direction of selection bias in our main speci�cation by estimating the
following speci�cation. Using the metropolitan area in which each individual resided �ve years prior
to the 2000 Census, we decompose the sociodemographic composition of each individual�s current
metropolitan area into two components: the �rst component is called the �lagged measure�, which
is the composition of the metropolitan area in which that person live �ve years ago; the second
component is called the �di¤erenced measure,� which is the di¤erence between the composition
of current metropolitan area the lagged measure. For about 90 percent of the population that
did not move, the di¤erenced measure is zero; while for movers this di¤erence re�ects the change
in metropolitan area sociodemographics associated with their move. We then include distinct
interaction terms with both measures in the same speci�cation. The estimated coe¢ cients on the
lagged versus di¤erenced measures indicate the direction of the selection bias. Table 13 indicates
that the estimated coe¢ cients on the di¤erenced measures are smaller in magnitude than those
on the lagged measures for all three categories of exposure. This indicates that the active across-
metropolitan selection observed over the past �ve years leads to an understatement of the main
coe¢ cients in our main speci�cation. To the extent that selection in previous periods in time was
qualitatively similar to that over the past �ve years, we would generally expect, then, that our
18
main speci�cation understates the impact of the average education of the black population on the
segregation of both highly- and less-educated blacks.
6 Conclusion
This paper explores the relationship between metropolitan level sociodemographic composition
(particularly racial inequality in education) and residential segregation. We present theoretical
argument and empirical evidence that the conventional wisdom �which suggests that residential
segregation will fall when racial di¤erences in education and other sociodemographics decline �may
not be accurate. We show that, middle-class black neighborhoods are in short supply given the cur-
rent black sociodemographics in many U.S. metropolitan areas, forcing wealthy blacks either to live
in white neighborhoods with high levels of neighborhood amenities or in more black neighborhoods
with lower amenity levels. Increases in black sociodemographics in the metropolitan areas will lead
to the emergence of new middle-class black neighborhoods, relieving the prior neighborhood sup-
ply constraint and leading to increases in residential segregation. We present cross-MSA evidence
from the 2000 Census indicating that this mechanism does in fact operate: as the proportion of
highly educated blacks in an MSA increases, the segregation of educated blacks, and all blacks
more generally, goes up. We also present evidence against leading alternative hypotheses.
Our primary empirical analysis examines how changes in the structure of the population within
a metropolitan area a¤ect the way that households sort on the basis of race and education. The
results reveal that relative to other households in the MSA, highly-educated blacks are increasingly
exposed to other blacks as the education level of blacks in the metropolitan area increase. This
change is driven primarily by a large relative increase in exposure to other highly-educated blacks
and is more than completely o¤set by a decrease in exposure to highly-educated whites. At the
same time, highly-educated blacks are also increasingly exposed to less-educated blacks and vice-
versa. This e¤ect is consistent with the predictions of our theoretical model when moving from a
low to a moderate proportion of highly educated blacks. We also show, to the extent possible, that
our results are robust to concerns related to omitted variable and selection biases.
Our results have a number of important implications. First, they imply that racial segregation
is unlikely to disappear with convergence in racial di¤erences in socioeconomic characteristics. The
results also have implications concerning the impact of racial sorting in the housing market on the
long-run convergence of educational attainment across race. In particular, the results indicate that
given the current sociodemographic structure of U.S. metropolitan areas, increases in the average
education level of black households may result in a slight decrease in the relative exposure of both
highly- and less-educated blacks to educated neighbors. A third implication relates to the literature
19
following Wilson (1987), which demonstrates that reductions in institutional discrimination in the
housing market in the middle of the 20th century led to large-scale reductions in the exposure of
less educated to more educated blacks as more educated blacks left the inner city neighborhoods
to which they were formerly restricted. The evidence we present here suggests that this trend may
not have been severe in cities in which the black population was more educated initially and may
partially reverse itself as the black population becomes relatively more educated over time.
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A Appendix
In this appendix, we provide details about how the marginal types of black households fx�h; x�l gare determined. We �rst restriction to equilibrium in which if a highly-educated (less-educated,
respectively) black household is to choose not to live in community B, she will choose commu-
nity HW or HW�(community LW or LW�respectively) depending on proximity. Given a pair of
thresholds fxl; xhg ; the total measure of less- and highly-educated black households in community
22
B are respectively 2N�b(1 � �b)xl and 2N�b�bxh. Thus the total population in community B is2N�b[�bxh + (1� �b)xl]. Moreover, the relevant proportions for community B are
pb(B) = 1; pw(B) = 0; ph(B) =�bxh
�bxh + (1� �b)xl; pl(B) =
(1� �b)xl�bxh + (1� �b)xl
:
Thus, the utilities for a highly- and less-educated black household with job location z 2 [0; 1] fromliving in community B are respectively:
V hB (z;xh; xl) = �+ �
��bxh
�bxh + (1� �b)xl+ 2
(1� �b)xl�bxh + (1� �b)xl
�� �z � c (2N�b [�bxh + (1� �b)xl]) ;
V lB(z;xh; xl) = �+ �
�(1� �b)xl
�bxh + (1� �b)xl+ 2
�bxh�bxh + (1� �b)xl
�� �z � c (2N�b [�bxh + (1� �b)xl]) :
We can also calculate the utilities from living in communities HW for a highly-educated black with
job location z 2 [0; 1] : Given the postulated threshold xh; the measure of highly-educated blacksin community HW is N�b�b(1�xh): Taking into account of the measure of highly-educated whitesin community HW, we have the following proportions:
pb(HW ) =�b�b(1� xh)
�b�b(1� xh) + �w�w; pw(HW ) =
�w�w�b�b(1� xh) + �w�w
; ph(HW ) = 1; pl(HW ) = 0:
Thus the utility for a highly-educated black from living in community HW is:
V hHW (z;xh) = �
��b�b(1� xh)
�b�b(1� xh) + �w�w+ 1
�w�w�b�b(1� xh) + �w�w
�+���(1�z)�c (N [�b�b(1� xh) + �w�w]) :
Finally, the utility for a less-educated black with job location z 2 [0; 1] from living in community
LW is:
V lLW (z;xl) = �
��b(1� �b)(1� xl)
�b(1� �b)(1� xl) + �w(1� �w)+ 1
�w�w�b(1� �b)(1� xl) + �w(1� �w)
�+� � �(1� z)� c (N [�b(1� �b)(1� xl) + �w(1� �w)]) :
The equilibrium pair of thresholds (x�l ; x�h)must satisfy:
V hB (x�h;x
�h; x
�l ) = V hHW (x
�h;x
�h); (2)
V lB(x�l ;x
�h; x
�l ) = V lLW (x
�l ;x
�l ): (3)
Equation (2) requires that the marginal type for highly-educated blacks xh� is indi¤erent between
living in community B (an all-black mixed-education community) and community HW (a highly-
educated community with a white majority). Equation (3) requires that the marginal type for
less-educated blacks xl� is indi¤erent between living in community B and community LW (a less-
educated community with white majority). We assume that the parameters of the model are such
that equation system (2) and (3) have solutions.
23
Boston
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Percent Black
Perc
ent H
ighl
y-Ed
ucat
ed
Dallas
0
0.2
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Figure 1: Neighborhood Choice Sets in Boston, Dallas, New York and St. Louis.
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Figure 2: Neighborhood Choice Sets in Atlanta, Chicago, Detroit and Washington DC-Baltimore.
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Table 1: Make-Up of Population Living in US Metropolitan Areas(1) (2)
Percentage of Percentage Race Education Overall Population by RaceBlack Less than HS 0.029 0.258Non-Hispanic HS 0.032 0.291
Some College 0.033 0.297College Degree 0.011 0.102Advanced Degree 0.006 0.052
White Less than HS 0.091 0.132Non-Hispanic HS 0.185 0.266
Some College 0.192 0.277College Degree 0.124 0.178Advanced Degree 0.102 0.147
Note: The first column reports percentages with respect to all households residing in USmetropolitan areas. The second column reports percentages relative to all such households inthe corresponding race category. Race and educational attainment of head of household is usedin the analysis.
Table 2: Number of Tracts in United States in 2000 by Race and Education
0% 20% 40% 60%
Number 49,021 26,351 11,094 3,005Fraction of tracts at least 0% black 100.0% 53.8% 22.6% 6.1%
at least 20% BlackNumber 9,149 2,567 641 59Fraction of tracts at least 20% black 100.0% 28.1% 7.0% 0.6%
at least 40% BlackNumber 5,657 1,164 142 14Fraction of tracts at least 40% black 100.0% 20.6% 2.5% 0.2%
at least 60% BlackNumber 3,921 623 44 5Fraction of tracts at least 60% black 100.0% 15.9% 1.1% 0.1%
at least 80% BlackNumber 2,559 271 21 1Fraction of tracts at least 80% black 100.0% 10.6% 0.8% 0.0%
at least 20% WhiteNumber 43,179 25,178 11,041 2,999Fraction of tracts at least 20% black 100.0% 58.3% 25.6% 6.9%
at least 40% WhiteNumber 39,602 24,566 10,839 2,967Fraction of tracts at least 40% black 100.0% 62.0% 27.4% 7.5%
at least 60% WhiteNumber 35,154 22,543 10,214 2,870Fraction of tracts at least 60% black 100.0% 64.1% 29.1% 8.2%
at least 80% WhiteNumber 26,910 17,539 8,102 2,339Fraction of tracts at least 80% black 100.0% 65.2% 30.1% 8.7%
Percent College Degree or Moreat least
Note: Tracts considered have a minimum of 800 households (the average tract in the US has almost 3,000 households).Analysis is based on households and race and educational attainment of head of household is used.
Panel A: All Tracts
Panel B: Tracts by Percent Black
Panel C: Tracts by Percent White
Table 3: Locations of Tracts with High Fractions of Both Black and College-Educated Households
Percentage black >80% >60% >40% Metro Size Percent % of Black HhldsPercentage with college degree >40% >40% >40% (millions) Black College-Educated
Baltimore-Washington 5 14 33 5.06 0.24 0.21Detroit 5 8 19 3.51 0.19 0.13Chicago 3 16 6.11 0.16 0.15New York 4 15 14.88 0.15 0.17Los Angeles 4 6 10 11.50 0.06 0.18Atlanta 5 5 8 2.65 0.26 0.22Cleveland 1 6 1.96 0.15 0.11Philadelphia 1 5 4.12 0.17 0.13San Francisco-Oakland 5 4.95 0.06 0.19Raleigh-Durham 1 3 0.65 0.12 0.22Indianapolis 3 1.05 0.12 0.14Jackson, MS 1 1 2 0.44 0.25 0.17Houston 1 1 2 3.10 0.15 0.18Columbia, SC 2 0.59 0.17 0.17New Orleans 2 0.85 0.33 0.13
Total 21 44 142 0.11 0.15
Notes: Tracts considered have a minimum of 800 households (the average tract in the US has almost 3,000 households). Analysis is based on households and race and educational attainment of head of household is used.
Average for US Metro Areas
Table 4: Neighborhood Patterns for College-Educated Households in the United States
Panel A: Neighborhood Patterns for College-Educated Black Households Households are first ranked by percent black in Census tract within its MSAMeasures reported by household's corresponding quintile within its MSA
Quintile 1 2 3 4 5 TotalPercent Black 5.7 14.4 28.3 54.6 78.9 32.0Percent Highly Educated 38.0 31.6 26.2 18.4 13.8 27.2Percent Black Low-Ed 4.4 11.1 22.1 46.6 69.0 26.8Percent Black High-Ed 1.3 3.3 6.2 8.0 10.0 5.2Percent White Low-Ed 48.3 44.6 33.7 18.2 7.4 32.9Percent White High-Ed 32.9 24.1 16.1 8.2 2.7 18.8
Panel B: Neighborhood Patterns for College-Educated White Households Households are first ranked by percent white in Census tract within its MSAMeasures reported by household's corresponding quintile within its MSA
Quintile 1 2 3 4 5 TotalPercent White 55.0 77.9 86.6 90.4 94.5 77.4Percent Highly Educated 27.0 36.2 40.7 39.3 39.2 35.3Percent Black Low-Ed 21.1 5.6 2.6 1.8 1.2 8.4Percent Black High-Ed 3.3 1.6 0.9 0.6 0.3 1.6Percent White Low-Ed 34.9 47.5 50.4 54.3 57.1 46.9Percent White High-Ed 20.1 30.4 36.2 36.1 37.4 30.4
Note: The panels of the table summarize the average distribution of neighborhoods in which college-educatedblacks and whites in US metro areas reside, respectively. To construct the numbers in the upper panel, college-educated blacks in each metro area are ranked by the fraction of black households in their tract and assigned to oneof five quintiles . Average neighborhood sociodemographic characteristics are then reported for each quintile,averaging across all metro areas. The lower panel reports analogous figures for college-educated whites, firstranking by their tract-level exposure to whites within each MSA.
Table 5: Average Tract-Level Exposure by Race and Education
Household Black-Low Ed Black - High Ed White - Low Ed White High-Ed High Ed BlackBlack Low-Ed 0.332 0.046 0.315 0.150 0.220 0.378Black High-Ed 0.268 0.052 0.329 0.188 0.272 0.320White Low-Ed 0.075 0.013 0.564 0.222 0.259 0.088White High-Ed 0.084 0.016 0.469 0.304 0.353 0.100
Household Black-Low Ed Black - High Ed White - Low Ed White High-Ed High Ed BlackBlack Low-Ed 0.196 0.021 -0.136 -0.077 -0.063 0.217Black High-Ed 0.133 0.026 -0.103 -0.044 -0.021 0.159White Low-Ed -0.014 -0.003 0.048 -0.003 -0.009 -0.017White High-Ed -0.010 -0.001 0.002 0.043 0.044 -0.011
Panel A: Average Tract-Level Exposure
Panel B: Average Tract-Level Exposure Relative to MSA Average
Note: Table reports average tract characteristics for households in the race-education category shown in row heading. The lower panel reports average tractcharacteristics relative to the average characteristics of the household's metropolitan area.
Table 6: Average Tract-Level Exposure by Race and Education
Panel A: Metropolitan Areas Below Median Fraction of Colleg-Educated Blacks (<1.23 percent)
Individual Black-Low Ed Black - High Ed White - Low Ed White High-Ed High Ed BlackBlack Low-Ed 0.111 0.013 -0.107 -0.049 -0.038 0.124Black High-Ed 0.078 0.016 -0.078 -0.014 0.005 0.094White Low-Ed -0.006 -0.001 0.030 -0.003 -0.004 -0.007White High-Ed -0.007 0.000 0.002 0.042 0.045 -0.007
Panel B: Metropolitan Areas Above Median Fraction of College-Educated Blacks (>1.23 percent)
Individual Black-Low Ed Black - High Ed White - Low Ed White High-Ed High Ed BlackBlack Low-Ed 0.220 0.024 -0.144 -0.085 -0.070 0.244Black High-Ed 0.147 0.028 -0.110 -0.052 -0.027 0.175White Low-Ed -0.024 -0.005 0.067 -0.004 -0.014 -0.029White High-Ed -0.012 -0.002 0.002 0.044 0.043 -0.014
Average Tract-Level Exposure Relative to MSA Average
Average Tract-Level Exposure Relative to MSA Average
Note: This table reports average tract characteristics relative to the average characteristics of the household's metropolitan area. The upper panel reportsaverages for households residing in metro areas in which college-educated black households constitute less than 1.23 percent of the metropolitan area. The lowerpanel reports averages for metro areas in whcih black college-educated households constitute more than 1.23 percent of the population.
Table 7: Segregation and Metropolitan Area Sociodemographics - Average Tract-Level Exposure
Dependent Variable:
% Black % Black % White % White % High Ed % Black& High Ed & Low Ed & High Ed & Low Ed
(1) (2) (3) (4) (5) (6)
I_BlackHighEd* 0.968 3.040 -2.110 -2.492 -1.261 4.008M_BlackHighEd (0.201) (1.575) (0.842) (0.902) (0.861) (1.720)
I_BlackHighEd* -0.065 0.069 0.200 0.111 0.128 0.004M_BlackLowEd (0.062) (0.360) (0.135) (0.264) (0.124) (0.413)
I_BlackHighEd* -0.058 -0.257 0.114 0.239 0.070 -0.315M_WhiteHighEd (0.018) (0.091) (0.043) (0.063) (0.042) (0.104)
I_BlackHighEd* 0.000 0.148 0.055 -0.166 0.066 0.149M_WhiteLowEd (0.024) (0.091) (0.036) (0.088) (0.033) (0.113)
I_BlackLowEd* 1.022 4.911 -3.097 -2.663 -2.340 5.933M_BlackHighEd (0.119) (1.983) (1.150) (0.898) (1.295) (2.030)
I_BlackLowEd* -0.086 -0.062 0.365 0.171 0.319 -0.148M_BlackLowEd (0.040) (0.399) (0.179) (0.255) (0.185) (0.427)
I_BlackLowEd* -0.039 -0.325 0.186 0.301 0.191 -0.364M_WhiteHighEd (0.014) (0.094) (0.053) (0.077) (0.045) (0.105)
I_BlackLowEd* 0.014 0.218 0.026 -0.149 0.079 0.232M_WhiteLowEd (0.017) (0.124) (0.055) (0.105) (0.044) (0.140)
I_BlackHighEd 0.023 0.030 -0.019 0.030 -0.007 0.053(0.015) (0.060) (0.022) (0.046) (0.024) (0.073)
I_BlackLowEd 0.011 0.049 -0.057 -0.025 -0.086 0.059(0.011) (0.078) (0.036) (0.061) (0.029) (0.088)
I_WhiteHighEd -0.004 -0.038 0.100 0.084 0.094 -0.041(0.002) (0.011) (0.013) (0.014) (0.014) (0.011)
I_WhiteLowEd -0.005 -0.037 0.048 0.130 0.035 -0.041(0.002) (0.010) (0.011) (0.015) (0.012) (0.012)
Tract-Level Exposure
Note: All regressions include metropolitan area fixed effects. The I_ prefix indicates an individual's own race-educated category. The M_ prefix indicates the fraction of individuals in the corrresponding race-education categorythat reside in the individual's metro area. Standard errors adjusted for clustering at the metropolitan area level arereported in parentheses; p-values are reported for tests.
Highly-Educated Black 0.010 0.030 -0.023 -0.026 -0.014 0.040
Less-Educated Black 0.011 0.050 -0.035 -0.028 -0.027 0.061
Highly-Educated Black 0.010 0.033 -0.022 -0.027 -0.013 0.043
Less-Educated Black 0.011 0.052 -0.033 -0.030 -0.025 0.063
Note: This table summarizes the key coefficient differences for which test statistics were reported at the bottom of Table 7. The upper panel reports theresults of increasing the fraction of highly-educated blacks in the metro area and decreasing the fraction of less-educated blacks by 1 percent,respectively. The lower panel reports the results of increasing the fraction of highly-educated blacks in the metro area and decreasing the fraction ofhighly-educated whites by 1 percent, respectively. In both cases, the corresponding change in the relative exposure of both highly- and less-educatedblacks to each of the racial-education groups shown in the column head is shown. The highly-educated category is defined as having a college-degree ormore. P-values are reported.
Panel A: Increasing Fraction of Highly-Educated Blacks by 1 Percent at Expense of Less-Educated Blacks
Highly-Educated Black 0.027 0.017 -0.028 -0.013 -0.003 0.044
Less-Educated Black 0.027 0.031 -0.037 -0.015 -0.013 0.058
Panel B: Increasing Fraction of Highly-Educated Blacks by 1 Percent at Expense of Highly-Educated Whites
Highly-Educated Black 0.020 0.016 -0.022 -0.011 -0.003 0.036
Less-Educated Black 0.020 0.027 -0.028 -0.012 -0.011 0.047
Note: This table is analogous to the specification reported in Table 8 with the exception that the underlying regressions (analogous to those reported inTable 7) re-define the highly-educated category is defined as having at least attended college for some time rather than having received a four-yeardegree. P-values are reported in italics.
Panel A: Increasing Fraction of Highly-Educated Blacks by 1 Percent at Expense of Less-Educated Blacks
Highly-Educated Black 0.023 0.014 -0.033 -0.012 -0.002 0.038
Less-Educated Black 0.021 0.025 -0.039 -0.012 -0.011 0.046
Panel B: Increasing Fraction of Highly-Educated Blacks by 1 Percent at Expense of Highly-Educated Whites
Highly-Educated Black 0.018 0.013 -0.024 -0.008 -0.004 0.030
Less-Educated Black 0.016 0.022 -0.029 -0.008 -0.011 0.038
Note: This table is analogous to the specification reported in Table 9 (highly-educated category is defined as having at least attended college for sometime) with the exception that the underlying regressions also include controls for the interaction of the four individual race-education variables with thetotal population of the metropolitan area as well as four region dummies corresponding to the Census-defined regions (Northeast, Midwest, South, andWest). P-values are reported in italics.
Table 11: Segregation and Metro Area Sociodemographics - By Metropolitan Size
% Black % Black % Black % Black % Black % Black % Black % Black % Black& High Ed & Low Ed & High Ed & Low Ed & High Ed & Low Ed
Panel A: Increasing Fraction of Highly-Educated Blacks by 1 Percent at Expense of Less-Educated Blacks
Highly-Educated Black 0.001 0.001 0.002 0.012 0.013 0.025 0.027 0.012 0.0400.644 0.892 0.810 0.015 0.063 0.022 0.000 0.431 0.067
Less-Educated Black 0.003 0.011 0.014 0.012 0.016 0.028 0.024 0.025 0.0490.311 0.104 0.119 0.001 0.046 0.012 0.001 0.204 0.055
Panel B: Increasing Fraction of Highly-Educated Blacks by 1 Percent at Expense of Highly-Educated Whites
Highly-Educated Black 0.002 0.004 0.006 0.009 0.013 0.022 0.020 0.012 0.0320.124 0.269 0.184 0.002 0.008 0.002 0.000 0.204 0.012
Less-Educated Black 0.003 0.012 0.015 0.009 0.017 0.026 0.018 0.021 0.0390.093 0.005 0.007 0.000 0.005 0.001 0.000 0.063 0.010
Note: This table is analogous to the specification reported in Table 10 (highly-educated category is defined as having at least attended college for some time; controls for theinteraction of the four individual race-education variables with the total population of the metropolitan area as well as four region dummies) run separately for three categoriesof Metro areas by population size. 147 metro areas have less than 200k individuals; 75 metro areas have between 200-600k individuals; 54 metro areas have more than 600kindividuals; P-values are reported in italics.
Change in Relative Tract-Level ExposureMSA Population (0-200K) MSA Population (200-600K) MSA Population (600K+)
% Black % Black % White % White % High Ed % Black& High Ed & Low Ed & High Ed & Low Ed
Panel A: Increasing Fraction of Highly-Educated Blacks by 1 Percent at Expense of Less-Educated Blacks
Highly-Educated Black 0.023 0.010 -0.022 -0.008 -0.001 0.033
Less-Educated Black 0.024 0.022 -0.028 -0.012 -0.007 0.046
Panel B: Increasing Fraction of Highly-Educated Blacks by 1 Percent at Expense of Highly-Educated Whites
Highly-Educated Black 0.017 0.011 -0.016 -0.008 -0.001 0.028
Less-Educated Black 0.017 0.018 -0.017 -0.009 -0.004 0.035
Note: This table is analogous to the specification reported in Table 9 (highly-educated category is defined as having at least attended college for sometime) with the exception that the dependent variables in the underlying regressions are measured at the Census PUMA rather than Census tract level. P-values are reported in italics.
Table 13: PUMA-Level Analysis with Lagged and Differenced Metropolitan Area Sociodemographics
Dependent Variable:
Actual Lagged Differenced Actual Lagged Differenced Actual Lagged Differenced
Panel A: Increasing Fraction of Highly-Educated Blacks by 1 Percent at Expense of Less-Educated Blacks
Highly-Educated Black 0.023 0.024 0.017 0.010 0.011 0.014 0.033 0.035 0.0200.000 0.000 0.000 0.092 0.085 0.722 0.001 0.001 0.076
Less-Educated Black 0.024 0.024 0.020 0.022 0.023 0.011 0.046 0.047 0.0310.000 0.000 0.000 0.008 0.010 0.226 0.000 0.001 0.010
Panel B: Increasing Fraction of Highly-Educated Blacks by 1 Percent at Expense of Highly-Educated Whites
Highly-Educated Black 0.017 0.018 0.013 0.011 0.012 0.014 0.028 0.030 0.0190.000 0.000 0.000 0.006 0.005 0.233 0.000 0.000 0.007
Less-Educated Black 0.017 0.017 0.015 0.018 0.018 0.011 0.035 0.036 0.0260.000 0.000 0.000 0.002 0.002 0.073 0.000 0.000 0.002
Note: This first column of each panel repeats the corresponding results from Table 12 based on Census PUMA level relative exposure rates. The second and third columns reportthe corresponding coefficients when 2000 MSA sociodemographics are decomposed into a lagged measure based on where the householded lived in 1995 and the differencebetween the 2000 and 1995 measure. For these results, a complete set of interaction terms were included in the underlying regressions for both the lagged and differenced terms. P-values are reported in italics.
Percent Less-Educated Black
Single Regression
PUMA-Level ExposurePUMA-Level Exposure PUMA-Level ExposurePercent Black
Single Regression
Percent Highly-Educated Black
Single Regression