research report worlds apart - urban.org...key findings: inequality in the commuting zones in 2010...
TRANSCRIPT
RESEARCH REPORT
Worlds Apart Inequality between America’s Most and Least Affluent Neighborhoods
Rolf Pendall
with Carl Hedman
June 2015
N E I G H B O R H O O D S , C I T I E S , A N D M E T R O S
ABOUT THE URBAN INSTITUTE The nonprofit Urban Institute is dedicated to elevating the debate on social and economic policy. For nearly five
decades, Urban scholars have conducted research and offered evidence-based solutions that improve lives and
strengthen communities across a rapidly urbanizing world. Their objective research helps expand opportunities for
all, reduce hardship among the most vulnerable, and strengthen the effectiveness of the public sector.
Copyright © June 2015. Urban Institute. Permission is granted for reproduction of this file, with attribution to the
Urban Institute. Cover image by Tim Meko.
Contents Acknowledgments iv
Worlds Apart: Inequality between America’s Most and Least Affluent Neighborhoods 1
Inequality among Households Affects Neighborhoods 1
Key Findings: Inequality in the CZs in 2010 and Changes Since 1990 3
How the 2010 Top and Bottom Tracts Changed from 1990 12
Conclusions and Implications 15
Appendix A Summary Statistics 17
Notes 28
References 29
About the Authors 30
Statement of Independence 31
I V A C K N O W L E D G M E N T S
Acknowledgments This report was funded by the Rockefeller Foundation as part of its generous support for the
preparation of the 2010 Neighborhood Change Database. We are grateful to them and to all our
funders, who make it possible for Urban to advance its mission. Funders do not, however, determine our
research findings or the insights and recommendations of our experts. The views expressed are those of
the authors and should not be attributed to the Urban Institute, its trustees, or its funders.
The dedicated efforts of the National Neighborhood Change Database (NCDB) team, led by Peter
Tatian and including Chris Hayes and Rob Pitingolo, made this analysis possible. The NCDB is a product
developed in a partnership between the Urban Institute and GeoLytics, which is responsible for
producing the NCDB 2010 CD-ROM.
Comments on drafts of this document were provided by Peter Tatian, Brett Theodos, Karen
Chapple, and Margery Austin Turner. Serena Lei, Elizabeth Forney, and Tim Meko provided editorial
and design support. Erwin de Leon managed the production of this and other research products out of
NCDB 2010.
W O R L D S A P A R T 1
Worlds Apart: Inequality between
America’s Most and Least Affluent
Neighborhoods Since 1990, inequality among households has grown significantly in the United States. At the top,
incomes and wealth rose steadily, with the top 20 percent of households gaining an average of $35,000
in 2013 dollars from 1990 to 2013 and the top 5 percent gaining over $80,000. Meanwhile, the incomes
of the bottom 20 percent of households grew a little between 1990 and 2000 but then dropped again
between 2000 and 2013.1 Wealth inequality also increased during this period; the top quintile (20
percent) of American households held over 80 times the net worth of the second-lowest quintile in
2011 (Vornovitsky, Gottschalck, and Smith n.d.). Recent studies by Urban Institute researchers show
that inequality of income and wealth also plays out between people of different races (McKernan et al.
2013). The average white household has five times the wealth of the average Hispanic household and
six times that of the average black household.
Inequality among Households Affects Neighborhoods
Since households with higher income and wealth can live in more expensive houses, neighborhoods,
cities, and metropolitan areas, one would expect that inequality among neighborhoods has increased in
parallel with income and wealth inequality among households. This report uses the newly produced
national Neighborhood Change Database (NCDB) to understand more about the magnitude of current
inequality and inequality growth across the entire United States between 1990 and 2010. The NCDB
reconciles both the changing boundaries of neighborhoods—defined as census tracts per their
boundaries in 2010—and the changing definitions of the variables collected in successive US Census
Bureau surveys of households so that researchers can study the same phenomenon over time in
neighborhoods with fixed boundaries.
Usually, inequality among neighborhoods is based on income among households. But other aspects
of inequality are also important and pervasive, like the distribution of wealth and human capital, which
vary among neighborhoods just as they do among households. To get a more complete picture of
2 W O R L D S A P A R T
geographic inequality, therefore, the Urban Institute used factor analysis to extract a single composite
score from four of the NCDB’s indicators of advantage and disadvantage:2
Average household income, an indicator of purchasing power of households within the census
tract
Share with a college degree, an indicator of the “human capital” of the tract’s residents
Homeownership rate, an indicator of the extent to which the tract’s residents have access to
this source of wealth
Median housing value, an indicator of the wealth of the tract’s homeowners—who generally
have more wealth than renters
In 2010, the highest neighborhood advantage score was just over 4.30. The three most advantaged
tracts in the United States are just outside Washington, DC, with neighborhood advantage scores of
4.31, 4.20, and 4.19. The first tract in these areas, in Chevy Chase, Maryland, has an average household
income of over $466,000 a year, and the other two, in neighboring Bethesda, have average annual
incomes of $270,000 and $290,000. Their median housing values exceed $900,000. At least 9 out of
every 10 adult residents have college degrees, and over 90 percent of the neighborhoods’ homes are
owner occupied. The populations and housing stock in these top neighborhoods were stable or grew
between 1990 and 2010, and few homes were vacant in 2010.
Two tracts at the other end of the scale, with neighborhood advantage scores less than –3.40, are in
Columbus, Ohio, and Memphis, Tennessee. Both have average annual household incomes of less than
$16,000, median home values less than $40,000, and owner-occupancy rates lower than 10 percent.
Practically none of their adults have a college education. Both tracts lost population between 1990 and
2010, and over a fifth of their remaining houses were vacant in 2010.
Neighborhoods close to the top or the bottom of the advantage index are among the nation’s most
advantaged or disadvantaged on all four components of the index. Therefore, the index’s meaning is
easy to interpret at the extremes: either all factors are very high, or all of them are very low. In the
middle, however, the advantage index can yield similar scores for neighborhoods that are distinct from
one another. A neighborhood with a high level of one characteristic but a low level of another could
have a score similar to another neighborhood with the reverse situation.3 For this reason, we limit our
analysis here to neighborhoods at the top or the bottom of the advantage index.
W O R L D S A P A R T 3
Inequality within Commuting Zones: Comparing Neighborhoods
To understand the differences between neighborhoods that share the same housing and labor markets,
we used commuting zones (CZs), county-based regions defined in the 1990s. Unlike metropolitan areas,
commuting zones cover the entirety of the United States, and their definitions are constant over time.
We ranked every CZ’s tracts from lowest to highest neighborhood advantage score. Then we identified
the top 10 percent and the bottom 10 percent of tracts—the most advantaged and least advantaged
neighborhoods in each CZ—for further exploration. We call these top and bottom tracts. In CZs like
New York City or DC, which have many high-income households, expensive housing, and high rates of
college education, the top tracts have higher average scores than those in poorer CZs like Brownsville,
Texas, or Bakersfield, California. We analyze only the 570 CZs that had at least 10 census tracts in 2010
(there are 740 CZs in the United States).
To show how large neighborhood inequality is in each CZ, we developed a final index—the
neighborhood inequality score—by subtracting the average neighborhood advantage score of the CZ’s
bottom tracts from the average of its top tracts. Baltimore, Columbus, Dallas, Houston, and Philadelphia
were the large CZs with the highest inequality scores; all exceeded 5.5. Most of the CZs with low
inequality scores were small, with fewer than 30 census tracts. Appleton, Wisconsin, had the lowest
score among CZs with at least 500,000 residents: 3.19. (Table A.1 in the appendix lists the
neighborhood advantage indices and neighborhood inequality indices for all CZs with at least 250,000
residents in 2010.)
Key Findings:
Inequality in the Commuting Zones in 2010
and Changes since 1990
Top and Bottom Tracts Are Worlds Apart
The differences between top and bottom tracts go deeper than their residents’ incomes, wealth, and
education level. These neighborhoods are physically separate from one another too—often by fairly
large distances. The Northeast Corridor, running from Boston to DC, provides some vivid examples of
this separation (figure 1).
4 W O R L D S A P A R T
FIGURE 1
Worlds Apart on the Northeast Corridor
Top neighborhoods in suburbs, except in New York and DC; bottom neighborhoods mostly in central cities
Source: Neighborhood Change Database 2010; underlying data, American Community Survey 2006–10.
Notes: All maps at same scale. Cities with over 100,000 residents are outlined in gray.
Almost always, the central city accounts for the majority of its CZ’s bottom tracts. For instance, in
large CZs like Boston, Newark, and Philadelphia, some of the bottom tracts are in their small, former
industrial cities (Lawrence, Paterson, Camden, and Chester). In DC, distress has spread beyond the
W O R L D S A P A R T 5
district boundaries and into suburban tracts in Prince George’s County, but in the other Northeast
Corridor cities this impoverishment of suburbia is not the main story. Our interactive map
(http://datatools.urban.org/features/ncdb/top-bottom/index.html) shows that, though poverty may
have grown in suburban areas, distressed tracts are still predominantly either dense urban
neighborhoods or low-density rural areas.
Most top tracts, on the other hand, are located well outside the limits of a central city. Often they
have low housing and population densities and occupy wide swathes of suburban, exurban, and rural
countryside. However, some central cities have a “favored quarter” where some of the commuting
zone’s most affluent households live. Some examples include Boston’s Back Bay, Baltimore’s Roland
Park, and neighborhoods in DC west of Rock Creek Park. Like bottom tracts in the same cities, these
high-income, central-city areas often form part of much larger zones that extend into the suburbs. Some
of these areas began as “suburbs within the city” in the 1800s and early 1900s and have maintained
their positions atop the neighborhood hierarchy thanks to their appeal to a subset of high-income
households.
New York stands out from most other cities in that so many of its top households live in the middle
of the central city. These super-rich neighborhoods—which spread into previously affordable areas,
giving rise to much recent debate on gentrification—form the densest concentration of affluence in the
United States, eclipsing the smaller privileged areas that have recently gentrified in Boston and DC.
Populous CZs Have More Advantaged Top Tracts than Less-Populous CZs
The largest commuting zones in the United States have higher incomes, housing values, and college-
education levels in their top tracts than smaller ones (figure 2). Large CZs are more economically
productive than small CZs (Combes et al. 2012). This means that people with a certain level of
education employed in a particular job in a big CZ like Chicago or New York will earn more than those
with the same education and the same job in smaller CZs like Toledo or Syracuse.
Second, large CZs have more potential for the formation of homogeneous neighborhoods than
small CZs. Tracts have about 4,000 residents on average. The largest CZs have hundreds of tracts,
offering many opportunities for the development and evolution of neighborhoods that are internally
homogeneous. When these CZs have large numbers of exceptionally privileged people, builders and
local governments have many incentives to accommodate clustering of the affluent into their own
neighborhoods.
6 W O R L D S A P A R T
FIGURE 2
Top Neighborhoods Are More Advantaged in Large Commuting Zones Than in Small Ones
Source: Neighborhood Change Database 2010; underlying data from American Community Survey 2006–10.
Notes: Includes only 570 commuting zones with at least 10 census tracts. Population is on a logarithmic scale.
High Wealth and Income in Big CZs Do Not Trickle Down
The productivity and higher costs of big CZs do not translate to higher incomes, education levels, and
housing values in their bottom tracts (figure 3). If anything, conditions in the bottom tracts are a little
better on average in the smaller CZs—measured on this national scale—than in larger ones. The
processes that add up to clustering by affluent households in privileged neighborhoods may therefore
be leaving disadvantaged people behind in distressed neighborhoods.
-2
-1
0
1
2
3
4
10,000 100,000 1,000,000 10,000,000 100,000,000
Commuting zone population, 2010
Neighborhood advantage score, top neighborhoods
W O R L D S A P A R T 7
FIGURE 3
Bottom Neighborhoods Are No Better Off in Large Commuting Zones Than in Small Ones
Source: Neighborhood Change Database 2010; underlying data from American Community Survey 2006–10.
Notes: Includes only 570 commuting zones with at least 10 census tracts. Population is on a logarithmic scale.
Inequality Peaks in CZs with 5–10 Million Residents
Since large CZs feature substantial advantages in their top neighborhoods over small ones, and there is
no difference in disadvantage at the bottom, large CZs have markedly higher levels of inequality in
income, housing value, education, and homeownership than small ones (figure 4). There may be a
threshold in the relationship between CZ size and inequality, however. For populations between about
1 and 5 million residents, CZ size is not a good predictor of inequality between the top and bottom
neighborhoods (figure 4). Inequality is highest for CZs, between 5 and 10 million residents. Los Angeles
and New York, the only two CZs with more than 10 million residents, have inequality levels a little lower
than the average of the 57 CZs with populations between 1 million and 1.5 million.
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
10,000 100,000 1,000,000 10,000,000 100,000,000
Commuting zone population, 2010
Neighborhood advantage score, top neighborhoods
8 W O R L D S A P A R T
FIGURE 4
Top-Bottom Inequality Peaks in Commuting Zones with 5–10 Million Residents
But inequality grows slowly from 1 million to 5 million
Source: Neighborhood Change Database 2010; underlying data from American Community Survey 2006–10.
Note: Includes only 570 commuting zones with at least 10 census tracts.
Inequality between Top and Bottom Grew Substantially from 1990 to 2010
Between 1990 and 2010, inequality between top and bottom tracts grew. This change, however, is
more complicated than it seems. First, the 2010 neighborhood inequality index would not be
comparable with a 1990 version of the same index because these indices compare each CZ to one
another. But the distributions of income, housing value, education, and homeownership within CZs
changed in different ways between 1990 and 2010, so growing inequality might not be captured or
might be overstated. Second, shifts in inequality result from changes in both the top and the bottom.
Inequality may increase even when the bottom grows, and it may decrease even when the top grows. To
reduce the complexity and reveal the trends, therefore, we use only one of our four neighborhood
0
1
2
3
4
5
6
<0.1 0.1–0.5 0.5–1 1–2 2–5 5–10 10+
Commuting zone population, 2010 (millions)
Average neighborhood inequality index
W O R L D S A P A R T 9
advantage variables—average household income—and discuss changes for the top and bottom tracts
from 1990 to 2010 before showing what happened to income inequality over these two decades.4 Table
A.2 in the appendix contains household income data for top and bottom tracts of all CZs with at least
250,000 residents in 2010.
CHANGES IN INCOME AT THE TOP AND BOTTOM FROM 1990 TO 2010
Following the national trend toward rising incomes among top-earning households, the average income
(in 2012 US dollars) of top tracts grew from $123,000 to $138,300, over 12 percent. But top
neighborhoods’ income rose spectacularly in some CZs. Washington, DC, led the largest CZs, with
average income of top neighborhoods surging from $180,000 in 1990 to $223,000 in 2012. Boston,
Bridgeport (all of Connecticut), Dallas, Denver, New York, San Francisco, San Jose, and Seattle also all
had increases of over $30,000 at the top from 1990 to 2010. (Figure 5 shows income change by CZ for
top tracts.)
Not every CZ had incomes at the top that grew, however. Detroit’s top tracts lost almost $8,000 on
average, falling from over $150,000 to just under $142,000 from 1990 to 2010. Several other medium-
sized to large auto-manufacturing CZs had income decline at the top: Fort Wayne declined by $25,000,
Muncie by $6,500, and Dayton by $4,300. In all, income fell for top neighborhoods in 76 CZs.
Annual income in bottom tracts, meanwhile, grew from $36,800 to $37,150—less than 1 percent
(figure 5). The average income of bottom tracts declined in 209 of the 570 CZs. The most severe losses
at the bottom among large CZs occurred in Bridgeport (all of Connecticut), Newark, and Dallas, which
dropped by $4,900, $4,500, and $3,150, respectively. But more CZs experienced gains at the bottom;
large CZs that stand out for gains include Portland, Oregon, ($4,200), San Francisco ($3,600), and DC
($3,100). Chicago and New York also had gains at the bottom, while Los Angeles dropped by a few
hundred dollars.
1 0 W O R L D S A P A R T
FIGURE 5
Income Changes in the Top and Bottom Tracts Contributed to Changes in Inequality
1990–2010
Source: Urban Institute analysis of Neighborhood Change Database. CPI-U used to convert incomes to 2012 dollars.
Notes: Only commuting zones with at least 250,000 residents as of 2010 depicted. Honolulu and Anchorage both lost income at
the bottom and grew less than 0.75 in the ratio between top and bottom. Honolulu lost income at the top, while Anchorage gained
income at the top.
W O R L D S A P A R T 1 1
CHANGES IN INEQUALITY FROM 1990 TO 2010
As a result of these changes at the top and bottom, income inequality between top and bottom tracts
grew from 1990 to 2010 in 433 of the 570 commuting zones. In 237 CZs, income inequality grew
because of rapid increases at the top coupled with modest increases at the bottom. Outstanding among
the larger CZs in this category is Washington, DC, where the purchasing power in the most advantaged
tracts rose over $43,000 while that in the most disadvantaged tracts grew only $3,100. In 166 other
CZs—the largest of which included Boston, Los Angeles, Newark, and Philadelphia—incomes fell in the
bottom tracts but rose in top ones.
The fastest growth in inequality did not always occur in places where the incomes of the top and
bottom tracts moved in opposite directions. San Francisco and Los Angeles offer an instructive contrast.
The real average income of San Francisco’s bottom tracts rose over these two decades by about $3,600,
while top tracts rose by $35,800, leading to a sharp rise in inequality. In Los Angeles, inequality
increased less, because, while the average income of the bottom neighborhoods fell by about $400, top
tracts rose by only $5,900.
In 30 commuting zones, inequality rose as income declined in both top and bottom tracts. In
Detroit, for example, the average income of the most advantaged neighborhoods dropped from just
over $150,000 in 1990 to about $143,000 in 2010, while that of the least advantaged neighborhoods
fell from $32,300 to $29,900. Other CZs in this group included Dayton, Fresno, and several other CZs
around the Great Lakes.
Among the 135 CZs with falling inequality, about two-thirds might be viewed in a positive light,
because real income grew some in the top neighborhoods but even more in the least advantaged ones.
In Memphis, for example, the average income of the bottom tracts grew by about $2,100 while that in
top neighborhoods grew by $550. This pattern also held for Albuquerque, Gary, Mobile, New Orleans,
Spokane, and Tucson. But in 46 CZs, inequality fell because of declines in top neighborhoods and either
stagnation or slight increases in the bottom ones. These CZs, like others with declining incomes at both
ends of the spectrum, concentrated in the Great Lakes area, including Cleveland and Fort Wayne.
Though our data do not reveal the drivers of decline at the top, it is likely a product of a combination of
out-migration to more economically robust parts of the United States, retirement, mortality, and
declines in wages from economic restructuring and the Great Recession.
1 2 W O R L D S A P A R T
How the 2010 Top and Bottom Tracts Changed from
1990
A unique advantage of the Neighborhood Change Database is that it allows analysis of social and
economic conditions in tracts over time. A look back at where the top and bottom tracts in 2010 came
from reveals important things about the nature of concentrated advantage and disadvantage today.
(Again, we define these top and bottom tracts as those with the highest and lowest neighborhood
advantage scores in their CZs.)
Top Tracts Were More Likely to Be “Locked In” Than Bottom Ones
Policymakers and academics have been concerned recently about the extent to which distressed
neighborhoods stay that way over time—that is, they get “locked in” to a distressed status by a cycle in
which investment lags, crime grows, and households and businesses flee when they have a chance to
find a better location. This is demonstrated in the majority of bottom tracts in 2010—62 percent—that
were already bottom tracts in 1990. A closer look shows that lock-in is even more pronounced in top
tracts. An even larger majority of 2010 top tracts—67 percent—also stood atop their CZs’
neighborhood advantage score in 1990. In 108 CZs, over 80 percent of the top tracts in 2010 were also
top tracts in 1990 (figure 6). Only 75 CZs had this level of lock-in for bottom tracts.
Among the large CZs, lock-in at the bottom was most pronounced in slow-growth, racially
segregated CZs. In Baltimore, Boston, Bridgeport, Buffalo, Detroit, Milwaukee, Philadelphia, and St.
Louis, between 70 and 80 percent of the bottom tracts in 2010 were also bottom tracts in 1990. All
these CZs also had high levels of lock-in at the top, but a few other high-income CZs— Los Angeles, New
York, San Jose, Seattle, and Washington, DC, for example—were also among those in which over 70
percent of the top neighborhoods in 2010 had already become top neighborhoods by 1990.
Bottom Tracts Often Lost Population from 1990 to 2010
The tracts in the 570 CZs we analyzed gained almost 50 million residents from 1990 to 2010. But the
bottom tracts grew by fewer than 30,000 people over that period. While many of these distressed
neighborhoods grew over these two decades, many others lost population and housing units and
experienced rising vacancy rates. About 3 percent of the bottom tracts lost at least half their
W O R L D S A P A R T 1 3
populations, and another 36 percent lost between 10 and 50 percent of their population from 1990 to
2010.
FIGURE 6
Top Tracts Were More Likely to Be Locked in Than Bottom Tracts from 1990 to 2010
Source: Neighborhood Change Database 2010; underlying data from 1990 Census STF3A and American Community Survey 2006–10.
Note: Includes only 570 commuting zones with at least 10 census tracts.
The CZs where the bottom tracts lost population were largely located in the older cities of the
Midwest and Northeast where population grew slowly or declined (figure 7). Chicago’s bottom tracts
cumulatively lost about 125,000 residents from 1990 to 2010, and Detroit’s lost over 160,000.
Baltimore, Cleveland, New Orleans, Philadelphia, Pittsburgh, and St. Louis each lost at least 50,000
residents in their bottom neighborhoods from 1990 to 2010.
Faster-growing CZs and immigration gateways gained more residents in their bottom
neighborhoods than other CZs, including the large CZs in the Rocky Mountain and Pacific Coast states,
the larger Texas metros apart from San Antonio, Florida, and the mid-South (Atlanta, Nashville, and
0
20
40
60
80
100
120
140
160
180
200
0%–33% 34%–50% 50%–67% 68%–80% >80%
Percent of tracts locked in at the bottom or the top from 1990 to 2010
Bottom TopNumber of commuting zones
1 4 W O R L D S A P A R T
Raleigh). CZs where urban expansion is made difficult by infrastructure constraints, topography, and
regulations were especially likely to gain population in their bottom tracts. The population of Los
Angeles’s bottom tracts grew by over 180,000 from 1990 to 2010, eclipsing New York, whose bottom
tracts grew by just over 90,000. Austin, Denver, Phoenix, Portland (Oregon), and Seattle also had
population growth of over 50,000 residents in their bottom tracts.
FIGURE 7
Bottom Tracts Emptied in the East and South but Grew in the West and Florida,
and Top Tracts Grew Everywhere
1990–2010
Source: Neighborhood Change Database 2010; underlying data from 1990 Census STF3A and American Community Survey
2006–10.
Notes: Only commuting zones with at least 250,000 residents depicted. Top and bottom tracts in Anchorage and Honolulu grew
by less than 50,000 residents between 1990 and 2010.
W O R L D S A P A R T 1 5
Almost All CZs Had Population Growth in Their Top Tracts
The top tracts, by contrast, almost always grew fast (see figure 7). Only three CZs with over 250,000
residents lost population in their top tracts. Many CZs whose top tracts grew the most were those
where the population grew large amounts, such as Atlanta, Dallas, Houston, Los Angeles, and Orlando.
But even some CZs that lost people overall still gained people in their top tracts. Chicago, Detroit, and
Philadelphia, for example, lost more residents from their bottom tracts than any other cities from 1990
to 2010, but each also gained over 100,000 in its top tracts at the same time. These old industrial
commuting zones with persistent black-white segregation grew fast at their privileged edges while
people fled their oldest and most distressed neighborhoods.
Conclusions and Implications
Rising inequality among households in the United States since 1990 has played out across the urban,
suburban, and rural landscape. This changing inequality reminds us that, in many respects, the most
affluent and impoverished neighborhoods are worlds apart.
All over the United States since 1990, affluent households have moved into new areas at the urban
fringe of major cities. Over the past two decades, these top and bottom tracts have grown far apart both
physically and economically. As early as the 1990s, Robert Reich raised concerns about the “secession
of the successful” into communities far away from low- to middle-income Americans (Reich 1991). Since
then, incomes have risen even further and many more affluent households have relocated to tracts in
the distant suburbs.
Affluent households have also rediscovered the advantages of urban living as crime has fallen and
real estate values have stabilized and increased. This discovery partly parallels a demographic
transition, with increasing numbers of well-off empty nesters and their twenty-something children at a
stage in their life in which suburbia appeals less than it does to families with young children. Though not
as far apart in space from the bottom neighborhoods as affluent suburban and exurban enclaves, top
neighborhoods in central cities still are separate worlds from those of the nation’s lowest-income
residents. And even the modest physical distance between top and bottom neighborhoods is often
interrupted by physical barriers like Washington’s Anacostia River, the San Francisco Bay, and
Interstate 35 in Austin.
1 6 W O R L D S A P A R T
Despite the retention or increase of affluent inner-city neighborhoods in some central cities, most
central cities have few or no top neighborhoods; they accommodate instead most of their commuting
zones’ bottom neighborhoods. Bridgeport, which includes the entirety of Connecticut, already was one
of the most unequal commuting zones in 1990. Its top and bottom neighborhoods pulled further apart
in income between 1990 and 2010; practically all its top neighborhoods are still in the suburbs, and
practically all its bottom neighborhoods are in central cities. Newark and Philadelphia followed a similar
trend, as well as most cities of the old industrial heartland from Syracuse and Buffalo to Milwaukee.
While poverty has grown in suburbia, distress as severe as that of the bottom neighborhoods is usually
confined to central cities.
Finally, spatial inequality is not limited to the top and bottom neighborhoods of individual
commuting zones. In a subset of large, high-income commuting zones that increasingly dominate the
national economy, the top neighborhoods have residential environments in which elites live
increasingly a world apart from even the top neighborhoods of smaller commuting zones, as well as
from middle and bottom neighborhoods across the United States. At the other end of the spectrum, and
as far removed in every conceivable way from the elite neighborhoods of Washington and New York,
are the poor rural neighborhoods that dominate the landscape of Appalachia, the Mississippi Delta, and
many tribal lands. More than 50 years after the publication of Michael Harrington’s The Other America,
these disadvantaged neighborhoods not only remain isolated but also appear to be slipping behind the
rest of the nation in heath, life expectancy, and economic prospects (Avendano and Kawachi 2014; King,
Morenoff, and House 2011).
Two great demographic transitions are now under way: the retirement of the baby boomers and the
passage into independent households and homeownership of the millennials. Both these trends will see
us through the next 20 years. As long as income and wealth inequality are high, the gap between top and
bottom neighborhoods will likely also remain high. The federal, state, and local policies that have helped
build these separate worlds over the past several decades could also harness the energy of
demographic growth to bring the top and bottom closer together. Such policies would encourage the
development of mixed-income neighborhoods and districts in central cities and suburbs; limit the
creation of new isolated enclaves of privilege, especially in regions whose populations are declining; and
invest more heavily in the very small proportion of the nation’s land mass contained in its bottom
neighborhoods.
A P P E N D I X A 1 7
Appendix A. Summary Statistics TABLE A.1
Neighborhood Advantage Scores and Neighborhood Inequality Indices
Commuting zones over 250,000 in 2010 only
Commuting zone Population, 2010
Neighborhood Advantage Score, 2006–10 Neighborhood
inequality index Bottom Top
Albany, NY 1,084,209 -2.11 2.20 4.30
Albuquerque, NM 814,995 -2.10 2.54 4.64
Allentown, PA 694,950 -2.39 2.30 4.69
Alton, IL 391,274 -2.15 1.29 3.44
Altoona, PA 395,517 -2.59 0.31 2.90
Amarillo, TX 254,597 -2.70 1.64 4.34
Anchorage, AK 400,941 -1.17 2.88 4.05
Anniston, AL 525,977 -2.49 1.26 3.75
Appleton, WI 566,270 -1.83 1.36 3.19
Asheville, NC 446,989 -1.49 2.03 3.52
Atlanta, GA 4,483,974 -2.11 3.20 5.31
Austin, TX 1,556,120 -2.13 3.25 5.38
Bakersfield, CA 727,683 -2.38 2.01 4.39
Baltimore, MD 2,581,940 -2.44 3.19 5.63
Bangor, ME 294,555 -1.80 1.29 3.09
Baton Rouge, LA 887,074 -2.45 2.22 4.67
Beaumont, TX 484,067 -2.65 0.78 3.43
Biloxi, MS 461,924 -2.16 1.23 3.38
Binghamton, NY 285,954 -2.70 1.08 3.78
Birmingham (rural), AL 1,088,803 -2.67 2.82 5.49
Bloomington, IN 282,211 -2.07 1.58 3.65
Boise, ID 611,901 -2.07 2.72 4.79
Boston, MA 5,007,957 -1.24 3.48 4.71
Bridgeport, CT 3,493,705 -1.88 3.43 5.32
Brownsville, TX 1,195,300 -2.77 0.65 3.42
Buffalo, NY 2,305,658 -2.85 2.02 4.87
Burlington, VT 321,946 -1.10 2.33 3.43
Canton, OH 705,864 -2.65 1.30 3.95
Cape Coral, FL 789,759 -2.12 2.67 4.79
Carbondale, IL 250,336 -2.28 0.63 2.91
Cedar Rapids, IA 270,924 -2.06 1.87 3.93
Champaign-Urbana, IL 368,318 -2.67 1.94 4.62
Charleston, SC 659,112 -2.40 2.76 5.17
Charleston, WV 340,597 -2.37 1.64 4.01
Charlotte, NC 1,652,100 -2.51 2.92 5.43
Charlottesville, VA 302,208 -1.00 3.02 4.02
Chattanooga, TN-GA 531,995 -2.53 2.02 4.55
1 8 A P P E N D I X A
TABLE A.1 CONTINUED
Neighborhood Advantage
Score, 2006–10 Neighborhood inequality index Commuting zone Population, 2010 Bottom Top
Chicago, IL 8,256,699 -1.93 3.32 5.25
Chico, CA 421,451 -1.92 1.70 3.62
Cincinnati, OH-KY-IN 2,028,227 -2.56 2.60 5.17
Claremont, NH 277,076 -0.96 2.57 3.53
Clarksville, TN-KY 271,189 -2.65 1.09 3.74
Cleveland, OH 2,575,732 -2.79 2.47 5.26
Colorado Springs, CO 584,858 -1.91 2.85 4.76
Columbia, MO 326,663 -2.04 2.01 4.05
Columbia, SC 776,924 -2.29 2.32 4.61
Columbus, GA-AL 286,472 -3.20 1.86 5.06
Columbus, OH 1,791,321 -2.62 2.91 5.54
Corpus Christi, TX 513,811 -2.75 1.51 4.26
Dallas, TX 3,676,828 -2.60 3.16 5.77
Davenport, IA-IL 382,822 -2.59 1.87 4.46
Dayton, OH 1,160,755 -2.67 1.82 4.48
Daytona Beach, FL 580,084 -2.06 1.73 3.79
Denver, CO 2,470,111 -1.76 3.32 5.07
Des Moines, IA 638,050 -2.21 2.27 4.49
Detroit, MI 5,147,510 -2.68 2.74 5.42
Dover, DE 508,772 -1.47 1.89 3.36
Duluth, MN 284,375 -2.36 1.74 4.10
Eau Claire, WI 317,662 -1.60 1.06 2.66
El Paso, TX 868,663 -2.82 1.67 4.49
Elkhart, IN 389,295 -2.24 0.65 2.88
Elmira, NY 335,784 -2.40 1.71 4.12
Erie, NY 639,579 -2.72 0.78 3.50
Eugene, OR 1,000,267 -1.73 1.99 3.72
Evansville, IN 403,141 -2.62 1.13 3.76
Fayetteville, AR 467,234 -2.00 1.76 3.76
Fayetteville, NC 675,779 -2.64 1.38 4.03
Flagstaff, AZ 322,024 -1.91 2.07 3.98
Florence, SC 578,595 -2.51 1.39 3.90
Fort Collins, CO 480,855 -2.01 2.39 4.40
Fort Smith, AR-OK 354,295 -2.57 0.59 3.16
Fort Wayne, IN 580,921 -2.61 1.61 4.22
Fort Worth, TX 2,044,131 -2.55 2.38 4.93
Fredericksburg, VA 299,392 -0.16 2.29 2.46
Fresno, CA 1,547,103 -2.23 1.91 4.14
Gadsden, AL 318,357 -2.59 0.51 3.10
Gainesville, FL 343,736 -2.02 2.51 4.53
Gainesville, GA 348,773 -1.90 1.39 3.30
Gary, IN 703,792 -2.76 1.73 4.49
Gastonia, NC 443,053 -2.41 1.64 4.05
Grand Rapids, MI 1,333,746 -2.37 2.08 4.45
A P P E N D I X A 1 9
TABLE A.1 CONTINUED
Neighborhood Advantage Score, 2006–10 Neighborhood
inequality index Commuting zone Population, 2010 Bottom Top
Green Bay, WI 329,499 -2.29 1.48 3.77
Greensboro, NC 1,135,322 -2.59 2.15 4.74
Greenville, NC 557,482 -2.62 1.74 4.36
Greenville, SC 968,999 -2.64 2.02 4.67
Hagerstown, MD 471,360 -1.87 1.67 3.53
Harrisburg, PA 1,127,642 -2.51 1.88 4.39
Hickory, NC 423,721 -2.29 2.24 4.53
Honolulu, HI 827,276 -0.64 2.88 3.52
Houma, LA 284,394 -2.21 1.12 3.32
Houston, TX 5,250,614 -2.68 2.83 5.51
Huntington, WV-KY-OH 343,611 -2.61 0.39 2.99
Huntsville, AL 584,074 -2.59 2.65 5.24
Indianapolis, IN 1,687,048 -2.66 2.64 5.30
Jackson, MI 299,923 -2.61 1.00 3.61
Jackson, MS 570,826 -2.78 2.34 5.11
Jacksonville, FL 1,319,058 -2.29 2.65 4.93
Johnson City, TN-VA 602,423 -2.32 1.02 3.34
Joplin, MO 280,605 -2.50 0.26 2.77
Kalamazoo, MI 515,030 -2.52 1.71 4.24
Kansas City, MO-KS 1,803,439 -2.66 2.73 5.39
Kennewick, WA 358,663 -1.97 2.15 4.13
Killeen, TX 351,653 -2.67 1.16 3.83
Knoxville, TN 793,240 -2.38 2.59 4.97
Lafayette, IN 352,334 -2.48 1.10 3.58
Lafayette, LA 577,844 -2.58 1.50 4.08
Lake Charles, LA 329,714 -2.40 1.18 3.58
Lake Jackson, TX 356,817 -2.04 1.91 3.95
Lakeland, FL 681,572 -2.42 1.52 3.95
Lansing, MI 445,403 -2.07 2.13 4.20
Las Vegas, NV-AZ 1,263,977 -2.46 1.73 4.19
Lexington-Fayette, KY 539,702 -2.22 2.41 4.63
Lima, OH 254,359 -2.77 0.71 3.48
Lincoln, NE 320,046 -2.34 2.42 4.76
Little Rock, AR 690,731 -2.55 2.03 4.58
Longview, TX 309,050 -2.50 0.69 3.18
Lorain, OH 436,991 -2.49 1.63 4.12
Los Angeles, CA 17,018,144 -1.57 3.05 4.61
Louisville, KY-IN 1,184,626 -2.69 2.59 5.28
Lubbock, TX 295,282 -2.61 1.72 4.33
Macon, GA 422,827 -2.82 1.57 4.40
Madison, WI 635,617 -1.24 2.91 4.15
Manchester, NH 1,259,013 -1.03 2.64 3.67
Mansfield, OH 314,176 -2.71 0.34 3.05
Medford, OR 282,759 -1.90 2.02 3.92
2 0 A P P E N D I X A
TABLE A.1 CONTINUED
Neighborhood Advantage Score, 2006–10 Neighborhood
inequality index Commuting zone Population, 2010 Bottom Top
Memphis, TN-AR-MS 1,217,927 -2.88 2.40 5.28
Miami, FL 3,884,047 -2.08 2.80 4.88
Milwaukee, WI 1,696,983 -2.63 2.63 5.26
Minneapolis, MN-WI 3,082,302 -1.37 3.00 4.37
Mobile, AL 628,163 -2.69 1.59 4.28
Modesto, CA 811,261 -2.08 1.60 3.68
Monmouth-Ocean, NJ 1,190,960 -1.11 3.12 4.23
Monroe, LA 254,952 -2.81 1.30 4.11
Montgomery, AL 371,419 -2.74 2.19 4.93
Morgantown, WV 268,583 -2.37 1.35 3.72
Morristown, TN 252,734 -2.36 0.14 2.51
Muncie, IN 398,334 -2.68 0.53 3.21
Nashville, TN 1,414,346 -2.23 3.11 5.34
New Orleans, LA 1,124,952 -2.47 2.42 4.90
New York, NY 11,822,001 -1.49 3.29 4.77
Newark, NJ 5,921,312 -1.55 3.43 4.98
Norfolk, VA-NC 573,991 -2.07 2.47 4.54
Ocala, FL 446,440 -2.11 1.09 3.20
Odessa, TX 291,156 -2.57 1.55 4.12
Oklahoma City, OK 1,252,348 -2.70 2.26 4.96
Omaha, NE 808,631 -2.47 2.28 4.75
Orlando, FL 2,039,792 -2.01 2.58 4.59
Palm Bay, FL 647,590 -1.97 2.44 4.41
Pensacola, FL 648,194 -2.26 2.15 4.41
Peoria, IL 535,667 -2.42 1.57 3.99
Philadelphia, PA 5,744,102 -2.52 3.13 5.64
Phoenix, AZ 3,080,664 -2.35 2.75 5.09
Pittsburgh, PA 2,447,001 -2.52 2.31 4.83
Pocatello, ID 310,433 -1.98 1.57 3.55
Portland, ME 720,044 -1.76 2.19 3.95
Portland, OR-WA 2,010,851 -1.33 2.98 4.32
Poughkeepsie, NY 888,473 -1.30 2.36 3.66
Providence, MA-NH 1,571,680 -1.80 2.65 4.45
Provo, UT 405,694 -1.41 2.41 3.81
Racine, WI 619,212 -2.23 1.62 3.85
Raleigh, NC 1,636,592 -2.13 3.16 5.28
Reading, PA 1,187,177 -2.67 1.58 4.25
Reno, NV 471,763 -2.29 2.80 5.10
Richmond, VA 1,122,093 -2.44 3.03 5.47
Roanoke, VA 471,844 -2.20 1.92 4.11
Rockford, IL 655,677 -2.69 1.32 4.00
Rome, GA 476,682 -2.30 0.58 2.89
Sacramento, CA 2,581,359 -1.65 2.69 4.34
Saginaw, MI 526,866 -2.65 1.27 3.92
A P P E N D I X A 2 1
TABLE A.1 CONTINUED
Neighborhood Advantage Score, 2006–10 Neighborhood
inequality index Commuting zone Population, 2010 Bottom Top
Salt Lake City, UT 1,553,455 -1.80 2.82 4.62
San Antonio, TX 1,882,938 -2.67 2.38 5.05
San Diego, CA 2,797,753 -1.59 3.22 4.81
San Francisco, CA 4,623,748 -0.89 3.55 4.44
San Jose, CA 2,359,835 -0.76 3.59 4.35
Santa Barbara, CA 639,449 -0.97 2.92 3.89
Santa Rosa, CA 609,576 -1.16 2.55 3.71
Sarasota, FL 831,714 -2.08 2.42 4.51
Savannah, GA 457,956 -2.40 2.86 5.25
Scranton, PA 883,792 -2.24 1.35 3.60
Seattle, WA 4,100,215 -1.26 3.15 4.41
Shreveport, LA 514,749 -2.90 1.60 4.50
South Augusta, GA 561,570 -2.63 2.13 4.76
South Bend, IN 641,123 -2.62 1.80 4.42
Spartanburg, SC 378,732 -2.75 1.33 4.08
Spokane, WA 675,106 -1.85 2.09 3.94
Springfield, IL 262,880 -2.59 1.80 4.39
Springfield, MA 669,265 -2.34 2.41 4.75
Springfield, MO 499,429 -2.45 1.58 4.03
St. Cloud, MN 294,369 -1.26 1.66 2.93
St. Louis, MO-IL 2,367,449 -2.59 2.88 5.46
State College, PA 305,969 -2.11 2.20 4.31
Syracuse, NY 1,050,896 -2.74 1.83 4.57
Tallahassee, FL 451,201 -2.28 2.64 4.92
Tampa, FL 2,514,502 -2.13 2.29 4.42
Terre Haute, IN 265,646 -2.63 0.39 3.02
Toledo, MI 824,111 -2.79 1.78 4.56
Topeka, KS 330,107 -2.63 1.98 4.61
Tucson, AZ 1,037,896 -2.39 2.68 5.07
Tulsa, OK 961,265 -2.63 2.14 4.77
Tuscaloosa, AL 276,294 -2.64 1.86 4.50
Tyler, TX 471,886 -2.49 1.30 3.80
Valdosta, GA 259,970 -2.70 0.80 3.49
Virginia Beach, VA 1,159,338 -2.01 2.62 4.62
Waco, TX 296,185 -2.72 0.93 3.65
Washington, DC-MD-VA-WV 4,891,082 -0.85 3.75 4.60
Wausau, WI 381,910 -1.87 0.85 2.73
West Palm Beach, FL 1,637,935 -2.08 2.86 4.94
Wichita, KS 562,893 -2.72 2.09 4.81
Wilmington, DE-MD 629,126 -1.83 2.96 4.80
Wilmington, NC 420,291 -2.24 2.53 4.77
Winston-Salem, NC 531,707 -2.52 2.44 4.96
2 2 A P P E N D I X A
TABLE A.1 CONTINUED
Neighborhood Advantage Score, 2006–10 Neighborhood
inequality index Commuting zone Population, 2010 Bottom Top
Yakima, WA 266,342 -2.61 1.26 3.87
Youngstown, OH 763,779 -2.79 0.73 3.52
Yuma, AZ 294,803 -2.54 1.14 3.68
A P P E N D I X A 2 3
TABLE A.2
Average Tract Household Incomes, Top and Bottom Tracts, and Top-to-Bottom Income Ratios
Commuting zones over 250,000 in 2010 only
Average Tract Household Income
2006–10
Change Tract Household Income
1990–2010 Top:Bottom Income Ratio
Commuting zone Bottom Top Bottom Top 1990 2010 Change
Albany, NY $39,430 $115,005 -$1,393 $10,174 2.57 2.92 0.35
Albuquerque, NM $38,139 $120,289 $4,240 $10,611 3.24 3.15 -0.08
Allentown, PA $35,697 $120,115 -$4,575 $2,131 2.93 3.36 0.44
Alton, IL $42,498 $95,043 $2,160 $13,872 2.01 2.24 0.22
Altoona, PA $33,226 $74,676 $2,629 $5,685 2.25 2.25 -0.01
Amarillo, TX $30,710 $112,180 $1,405 $1,929 3.76 3.65 -0.11
Anchorage, AK $52,840 $164,869 -$5,003 $16,420 2.57 3.12 0.55
Anniston, AL $30,977 $91,692 -$2,190 $9,448 2.48 2.96 0.48
Appleton, WI $43,189 $100,376 $149 $14,500 2.00 2.32 0.33
Asheville, NC $41,964 $102,803 $5,800 $15,292 2.42 2.45 0.03
Atlanta, GA $36,196 $166,130 $381 $16,075 4.19 4.59 0.40
Austin, TX $39,489 $168,418 $3,534 $38,395 3.62 4.26 0.65
Bakersfield, CA $34,357 $126,059 -$1,226 $13,214 3.17 3.67 0.50
Baltimore, MD $35,725 $166,278 -$1,244 $16,154 4.06 4.65 0.59
Bangor, ME $40,648 $77,561 -$2,591 $6,766 1.64 1.91 0.27
Baton Rouge, LA $31,544 $114,643 $1,492 $18,162 3.21 3.63 0.42
Beaumont, TX $34,116 $91,328 $5,863 $6,356 3.01 2.68 -0.33
Biloxi, MS $36,287 $93,724 $3,386 $18,991 2.27 2.58 0.31
Binghamton, NY $31,101 $91,652 -$1,511 -$265 2.82 2.95 0.13
Birmingham (rural), AL $30,035 $149,801 $945 $22,006 4.39 4.99 0.59
Bloomington, IN $40,662 $78,307 $1,093 -$3,689 2.07 1.93 -0.15
Boise, ID $39,717 $128,925 $2,625 $37,583 2.46 3.25 0.78
Boston, MA $43,086 $183,778 -$753 $32,891 3.44 4.27 0.82
Bridgeport, CT $38,180 $240,088 -$4,879 $35,434 4.75 6.29 1.54
Brownsville, TX $26,646 $83,739 $199 $4,754 2.99 3.14 0.16
Buffalo, NY $29,724 $112,241 -$2,259 $1,226 3.47 3.78 0.31
Burlington, VT $46,130 $115,288 -$27 $22,189 2.02 2.50 0.48
Canton, OH $32,299 $97,249 -$1,491 $7,048 2.67 3.01 0.34
Cape Coral, FL $37,916 $147,015 -$398 $13,178 3.49 3.88 0.38
Carbondale, IL $34,838 $76,589 -$490 $11,419 1.84 2.20 0.35
Cedar Rapids, IA $40,079 $112,815 $2,187 $14,245 2.60 2.81 0.21
Champaign-Urbana, IL $33,051 $101,563 -$1,868 $7,607 2.69 3.07 0.38
Charleston, SC $35,327 $124,426 $2,283 $26,095 2.98 3.52 0.55
Charleston, WV $34,697 $106,256 $1,390 $15,137 2.74 3.06 0.33
Charlotte, NC $33,596 $155,774 -$3,611 $32,872 3.30 4.64 1.33
Charlottesville, VA $45,583 $141,346 $3,073 $27,382 2.68 3.10 0.42
Chattanooga, TN-GA $32,601 $115,646 $3,079 $17,408 3.33 3.55 0.22
Chicago, IL $37,325 $181,287 $2,093 $20,063 4.58 4.86 0.28
Chico, CA $38,272 $89,960 $720 $8,382 2.17 2.35 0.18
Cincinnati, OH-KY-IN $32,705 $144,053 -$90 $19,139 3.81 4.40 0.60
Claremont, NH $52,711 $114,538 $2,646 $24,546 1.80 2.17 0.38
2 4 A P P E N D I X A
TABLE A.2 CONTINUED
Average Tract Household Income
2006–10
Change Tract Household Income
1990–2010 Top:Bottom Income Ratio
Commuting zone Bottom Top Bottom Top 1990 2010 Change
Clarksville, TN-KY $34,287 $88,902 $1,639 $9,553 2.43 2.59 0.16
Cleveland, OH $28,284 $135,569 $79 -$98 4.81 4.79 -0.02
Colorado Springs, CO $37,573 $128,802 $1,264 $18,236 3.05 3.43 0.38
Columbia, MO $34,234 $94,785 -$3,154 $10,483 2.25 2.77 0.51
Columbia, SC $35,033 $111,927 -$1,425 $13,938 2.69 3.19 0.51
Columbus, GA-AL $23,374 $118,524 -$542 $5,138 4.74 5.07 0.33
Columbus, OH $31,084 $147,148 -$3,020 $13,630 3.92 4.73 0.82
Corpus Christi, TX $31,316 $102,560 $3,721 $4,105 3.57 3.28 -0.29
Dallas, TX $34,155 $195,201 -$3,149 $31,769 4.38 5.72 1.33
Davenport, IA-IL $31,572 $112,359 $39 $16,451 3.04 3.56 0.52
Dayton, OH $32,633 $108,077 -$1,954 -$4,300 3.25 3.31 0.06
Daytona Beach, FL $35,443 $99,418 -$184 $16,561 2.33 2.80 0.48
Denver, CO $40,571 $173,545 $2,648 $32,188 3.73 4.28 0.55
Des Moines, IA $39,835 $123,935 $611 $3,850 3.06 3.11 0.05
Detroit, MI $29,905 $142,924 -$2,416 -$7,846 4.66 4.78 0.11
Dover, DE $44,749 $97,959 -$758 $17,663 1.76 2.19 0.42
Duluth, MN $32,917 $95,666 $1,597 $15,508 2.56 2.91 0.35
Eau Claire, WI $39,917 $83,951 -$2,171 $12,684 1.69 2.10 0.41
El Paso, TX $24,576 $101,233 -$3,198 $10,422 3.27 4.12 0.85
Elkhart, IN $39,876 $83,465 -$365 -$5,412 2.21 2.09 -0.12
Elmira, NY $37,594 $89,135 -$523 $5,864 2.18 2.37 0.19
Erie, NY $31,657 $83,539 $692 -$2,863 2.79 2.64 -0.15
Eugene, OR $40,414 $91,944 -$203 $5,830 2.12 2.28 0.15
Evansville, IN $33,368 $93,697 -$599 -$515 2.77 2.81 0.03
Fayetteville, AR $40,440 $108,687 $2,761 $36,590 1.91 2.69 0.77
Fayetteville, NC $30,662 $87,526 -$3,314 $4,623 2.44 2.85 0.41
Flagstaff, AZ $43,907 $92,362 $11,011 $15,407 2.34 2.10 -0.24
Florence, SC $33,128 $95,251 -$2,005 $13,236 2.33 2.88 0.54
Fort Collins, CO $38,322 $112,571 $2,402 $17,728 2.64 2.94 0.30
Fort Smith, AR-OK $34,917 $83,421 -$242 $2,857 2.29 2.39 0.10
Fort Wayne, IN $33,075 $107,707 -$4,953 -$24,981 3.49 3.26 -0.23
Fort Worth, TX $36,076 $141,715 $839 $14,554 3.61 3.93 0.32
Fredericksburg, VA $62,101 $132,770 $6,927 $37,590 1.73 2.14 0.41
Fresno, CA $34,220 $112,398 -$1,095 -$199 3.19 3.28 0.10
Gadsden, AL $31,270 $82,281 -$1,313 $7,582 2.29 2.63 0.34
Gainesville, FL $35,068 $117,150 -$3,454 $16,135 2.62 3.34 0.72
Gainesville, GA $44,379 $94,306 -$739 $13,311 1.80 2.13 0.33
Gary, IN $29,558 $105,861 $1,282 $724 3.72 3.58 -0.14
Gastonia, NC $35,245 $104,068 -$5,661 $16,289 2.15 2.95 0.81
Grand Rapids, MI $35,760 $113,787 -$1,656 $3,879 2.94 3.18 0.24
Green Bay, WI $37,814 $99,636 $1,546 $5,432 2.60 2.63 0.04
Greensboro, NC $31,371 $114,881 -$5,375 $10,535 2.84 3.66 0.82
Greenville, NC $30,169 $88,990 -$2,140 $7,406 2.53 2.95 0.42
Greenville, SC $30,976 $108,507 -$3,403 $14,999 2.72 3.50 0.78
A P P E N D I X A 2 5
TABLE A.2 CONTINUED
Average Tract Household Income
2006–10
Change Tract Household Income
1990–2010 Top:Bottom Income Ratio
Commuting zone Bottom Top Bottom Top 1990 2010 Change
Hagerstown, MD $38,467 $99,593 -$586 $25,128 1.91 2.59 0.68
Harrisburg, PA $35,833 $109,101 -$4,097 $10,849 2.46 3.04 0.58
Hickory, NC $37,172 $127,874 -$4,318 $39,813 2.12 3.44 1.32
Honolulu, HI $49,235 $153,412 -$2,311 -$4,975 3.07 3.12 0.04
Houma, LA $41,718 $102,350 $7,478 $20,450 2.39 2.45 0.06
Houston, TX $34,401 $176,695 $711 $24,803 4.51 5.14 0.63
Huntington, WV-KY-OH $31,128 $76,573 -$1,403 -$1,913 2.41 2.46 0.05
Huntsville, AL $31,166 $128,083 -$9,142 $13,644 2.84 4.11 1.27
Indianapolis, IN $32,341 $142,539 -$2,927 $9,418 3.77 4.41 0.63
Jackson, MI $29,721 $85,576 -$4,327 $1,136 2.48 2.88 0.40
Jackson, MS $27,274 $120,492 $1,264 $12,934 4.14 4.42 0.28
Jacksonville, FL $35,209 $146,762 $610 $25,236 3.51 4.17 0.66
Johnson City, TN-VA $35,125 $82,322 $1,040 $838 2.39 2.34 -0.05
Joplin, MO $34,277 $76,226 $2,314 $11,716 2.02 2.22 0.21
Kalamazoo, MI $32,105 $99,798 -$2,944 -$551 2.86 3.11 0.25
Kansas City, MO-KS $31,220 $141,400 -$1,082 $4,736 4.23 4.53 0.30
Kennewick, WA $41,028 $117,962 $3,331 $22,176 2.54 2.88 0.33
Killeen, TX $35,358 $101,089 $3,560 $23,502 2.44 2.86 0.42
Knoxville, TN $32,823 $130,415 $2,409 $16,684 3.74 3.97 0.23
Lafayette, IN $34,026 $82,040 -$6,483 -$1,246 2.06 2.41 0.36
Lafayette, LA $30,301 $102,149 $2,037 $13,083 3.15 3.37 0.22
Lake Charles, LA $38,594 $97,629 $9,529 $18,483 2.72 2.53 -0.19
Lake Jackson, TX $47,540 $114,655 $5,626 $15,835 2.36 2.41 0.05
Lakeland, FL $35,604 $104,506 $262 $13,342 2.58 2.94 0.36
Lansing, MI $39,827 $101,714 -$1,796 -$2,248 2.50 2.55 0.06
Las Vegas, NV-AZ $37,966 $121,431 $763 $5,981 3.10 3.20 0.10
Lexington-Fayette, KY $37,822 $125,331 $2,455 $18,990 3.01 3.31 0.31
Lima, OH $31,587 $90,241 -$3,395 $1,024 2.55 2.86 0.31
Lincoln, NE $34,943 $131,528 -$1,323 $25,280 2.93 3.76 0.83
Little Rock, AR $31,822 $113,923 -$840 $14,434 3.05 3.58 0.53
Longview, TX $37,039 $95,776 $3,112 $16,940 2.32 2.59 0.26
Lorain, OH $34,734 $103,711 -$2,928 $14,357 2.37 2.99 0.61
Los Angeles, CA $40,731 $176,745 -$386 $5,905 4.15 4.34 0.18
Louisville, KY-IN $29,913 $133,327 $662 $8,031 4.28 4.46 0.17
Lubbock, TX $29,659 $123,417 $829 $21,488 3.54 4.16 0.63
Macon, GA $28,076 $102,857 -$3,088 -$3,143 3.40 3.66 0.26
Madison, WI $49,340 $135,155 $2,869 $24,789 2.37 2.74 0.36
Manchester, NH $49,638 $129,881 -$219 $12,363 2.36 2.62 0.26
Mansfield, OH $32,109 $72,413 -$3,176 -$5,838 2.22 2.26 0.04
Medford, OR $36,714 $86,953 $1,377 $8,081 2.23 2.37 0.14
Memphis, TN-AR-MS $26,398 $133,989 $2,085 $544 5.49 5.08 -0.41
Miami, FL $32,320 $153,897 -$977 $11,924 4.26 4.76 0.50
Milwaukee, WI $31,696 $138,866 $1,982 $11,995 4.27 4.38 0.11
Minneapolis, MN-WI $41,987 $151,471 $1,993 $20,323 3.28 3.61 0.33
2 6 A P P E N D I X A
TABLE A.2 CONTINUED
Average Tract Household Income
2006–10
Change Tract Household Income
1990–2010 Top:Bottom Income Ratio
Commuting zone Bottom Top Bottom Top 1990 2010 Change
Mobile, AL $26,305 $103,443 $2,285 $8,011 3.97 3.93 -0.04
Modesto, CA $36,051 $105,829 -$4,022 $13,304 2.31 2.94 0.63
Monmouth-Ocean, NJ $44,127 $184,241 -$923 $22,620 3.59 4.18 0.59
Monroe, LA $27,928 $100,036 $3,369 $10,245 3.66 3.58 -0.07
Montgomery, AL $26,419 $115,189 $1,471 $4,142 4.45 4.36 -0.09
Morgantown, WV $31,872 $91,188 -$977 $27,503 1.94 2.86 0.92
Morristown, TN $34,715 $71,043 $1,997 $6,248 1.98 2.05 0.07
Muncie, IN $30,551 $79,920 -$2,584 -$6,510 2.61 2.62 0.01
Nashville, TN $35,716 $163,149 $1,457 $26,695 3.98 4.57 0.58
New Orleans, LA $30,801 $122,622 $6,406 $14,237 4.44 3.98 -0.46
New York City, NY $39,144 $210,061 $2,080 $30,730 4.84 5.37 0.53
Newark, NJ $41,402 $210,698 -$4,527 $25,118 4.04 5.09 1.05
Norfolk, VA-NC $38,985 $117,547 $183 $20,327 2.51 3.02 0.51
Ocala, FL $35,916 $90,226 -$497 $9,846 2.21 2.51 0.30
Odessa, TX $37,316 $133,120 $6,535 $14,003 3.87 3.57 -0.30
Oklahoma City, OK $32,706 $132,569 $1,507 $18,631 3.65 4.05 0.40
Omaha, NE $33,947 $134,742 $764 $6,863 3.85 3.97 0.12
Orlando, FL $39,226 $136,829 -$1,669 $12,186 3.05 3.49 0.44
Palm Bay, FL $37,202 $128,707 -$5,036 $6,733 2.89 3.46 0.57
Pensacola, FL $35,625 $113,732 $3,602 $28,640 2.66 3.19 0.54
Peoria, IL $37,501 $109,151 $5,191 $15,607 2.90 2.91 0.02
Philadelphia, PA $33,526 $169,109 -$2,929 $16,806 4.18 5.04 0.87
Phoenix, AZ $35,461 $147,413 $590 $10,256 3.93 4.16 0.22
Pittsburgh, PA $33,909 $127,395 $880 $11,454 3.51 3.76 0.25
Pocatello, ID $37,327 $100,834 $2,348 $19,818 2.32 2.70 0.39
Portland, ME $40,457 $105,749 -$417 $13,203 2.26 2.61 0.35
Portland, OR-WA $44,740 $135,878 $4,274 $18,257 2.91 3.04 0.13
Poughkeepsie, NY $45,284 $125,950 -$520 $16,204 2.40 2.78 0.39
Providence, MA-NH $34,917 $126,257 -$1,735 $19,442 2.91 3.62 0.70
Provo, UT $37,518 $122,924 -$1,504 $36,843 2.21 3.28 1.07
Racine, WI $37,232 $100,274 -$2,302 $9,705 2.29 2.69 0.40
Raleigh, NC $37,502 $152,422 -$1,778 $28,730 3.15 4.06 0.92
Reading, PA $33,817 $102,971 -$4,172 $6,761 2.53 3.04 0.51
Reno, NV $36,731 $149,718 -$5,479 $19,534 3.08 4.08 0.99
Richmond, VA $34,352 $152,391 $713 $13,786 4.12 4.44 0.32
Roanoke, VA $36,971 $97,012 $826 $11,330 2.37 2.62 0.25
Rockford, IL $30,946 $99,024 -$5,081 -$3,212 2.84 3.20 0.36
Rome, GA $36,559 $79,685 -$2,097 -$2,165 2.12 2.18 0.06
Sacramento, CA $41,382 $130,579 $769 $24,095 2.62 3.16 0.53
Saginaw, MI $30,546 $95,060 -$1,149 -$2,713 3.08 3.11 0.03
Salt Lake City, UT $41,535 $140,864 $4,949 $22,848 3.23 3.39 0.17
San Antonio, TX $32,111 $140,307 $3,143 $22,442 4.07 4.37 0.30
San Diego, CA $41,565 $165,219 $2,379 $18,551 3.74 3.97 0.23
San Francisco, CA $47,499 $203,027 $3,606 $35,803 3.81 4.27 0.46
A P P E N D I X A 2 7
TABLE A.2 CONTINUED
Average Tract Household Income
2006–10
Change Tract Household Income
1990–2010 Top:Bottom Income Ratio
Commuting zone Bottom Top Bottom Top 1990 2010 Change
San Jose, CA $53,441 $202,579 -$23 $31,489 3.20 3.79 0.59
Santa Barbara, CA $48,572 $155,330 $554 $4,088 3.15 3.20 0.05
Santa Rosa, CA $44,481 $125,358 $2,356 $18,193 2.54 2.82 0.27
Sarasota, FL $38,099 $127,584 -$761 $18,754 2.80 3.35 0.55
Savannah, GA $34,035 $128,078 $6,014 $6,321 4.35 3.76 -0.58
Scranton, PA $38,343 $95,822 -$559 $7,506 2.27 2.50 0.23
Seattle, WA $46,853 $150,477 $2,896 $32,307 2.69 3.21 0.52
Shreveport, LA $28,596 $113,394 $4,305 $14,371 4.08 3.97 -0.11
South Augusta, GA $30,335 $115,628 $1,404 $15,208 3.47 3.81 0.34
South Bend, IN $28,918 $103,928 -$3,347 -$1,750 3.28 3.59 0.32
Spartanburg, SC $29,647 $93,928 -$5,437 $10,216 2.39 3.17 0.78
Spokane, WA $39,221 $106,294 $3,640 $9,602 2.72 2.71 -0.01
Springfield, IL $35,573 $110,747 $2,108 -$1,743 3.36 3.11 -0.25
Springfield, MA $30,392 $109,854 -$2,373 $9,415 3.07 3.61 0.55
Springfield, MO $32,171 $102,339 $2,541 $1,898 3.39 3.18 -0.21
St. Cloud, MN $46,557 $106,491 $2,463 $24,350 1.86 2.29 0.42
St. Louis, MO-IL $31,295 $149,729 -$944 $15,947 4.15 4.78 0.63
State College, PA $41,221 $101,607 $829 $20,448 2.01 2.46 0.46
Syracuse, NY $32,160 $109,776 -$1,486 $11,817 2.91 3.41 0.50
Tallahassee, FL $28,855 $126,685 -$1,815 $16,479 3.59 4.39 0.80
Tampa, FL $36,839 $118,820 $2,922 $17,201 3.00 3.23 0.23
Terre Haute, IN $28,564 $78,215 -$4,288 $1,581 2.33 2.74 0.41
Toledo, MI $27,468 $108,930 -$4,138 -$2,276 3.52 3.97 0.45
Topeka, KS $32,562 $105,750 -$3,704 $13,509 2.54 3.25 0.70
Tucson, AZ $33,210 $121,348 $3,668 $13,146 3.66 3.65 -0.01
Tulsa, OK $32,834 $131,713 -$994 $1,833 3.84 4.01 0.17
Tuscaloosa, AL $27,620 $107,085 $297 $17,874 3.27 3.88 0.61
Tyler, TX $34,369 $104,090 $926 $20,446 2.50 3.03 0.53
Valdosta, GA $31,758 $78,055 -$80 -$1,270 2.49 2.46 -0.03
Virginia Beach, VA $36,032 $132,480 $3,631 $25,399 3.30 3.68 0.37
Waco, TX $27,384 $87,721 $806 -$1,659 3.36 3.20 -0.16
Washington, DC-MD-VA-WV $52,689 $223,056 $3,097 $43,418 3.62 4.23 0.61
Wausau, WI $42,777 $86,356 $284 $9,009 1.82 2.02 0.20
West Palm Beach, FL $35,741 $188,186 -$2,979 $25,684 4.20 5.27 1.07
Wichita, KS $31,424 $131,538 -$3,334 $5,094 3.64 4.19 0.55
Wilmington, DE-MD $39,723 $164,382 -$5,213 $11,419 3.40 4.14 0.73
Wilmington, NC $36,774 $118,042 $4,411 $29,925 2.72 3.21 0.49
Winston-Salem, NC $30,936 $130,064 -$4,724 $5,731 3.49 4.20 0.72
Yakima, WA $34,684 $88,471 $4,407 $9,473 2.61 2.55 -0.06
Youngstown, OH $26,972 $82,386 -$2,104 -$386 2.85 3.05 0.21
Yuma, AZ $34,809 $94,933 -$33 $5,381 2.57 2.73 0.16
2 8 N O T E S
Notes 1. Table H-3, “Mean Household Income Received by Each Fifth and Top 5 Percent, All Races: 1967 to 2013,” US
Census Bureau, http://www.census.gov/hhes/www/income/data/historical/household/2013/h03AR.xls.
2. All these variables are collected by the US Census Bureau, in 1990 and 2000 in the “long form” of the US decennial census and in 2010 as part of the American Community Survey, which is averaged over 2006 to 2010 to provide precise-enough estimates to report at the census tract level. We excluded neighborhoods where most of the residents lived in “group quarters” (mainly jails, prisons, dormitories, barracks, and nursing homes) and those with fewer than 500 residents in 1990. When considering how particular neighborhoods changed over time, we report data only for neighborhoods that met these criteria in both years. For more information on factor analysis, see, for example, Kim and Mueller (1978).
3. For example, close to an average level of advantage, with a score of just about zero, was a tract in Fife, Washington, a small city just east of Tacoma (Seattle commuting zone [CZ]). Its average household income was $45,350, and 21 percent of its adults had college degrees. The median housing value was $285,000, but only 38 percent of the housing was owner occupied. Neighborhoods with nearly identical scores in Pomona, California (Los Angeles CZ), and Independence, Missouri (Kansas City CZ), had homeownership rates of 46 percent and 85 percent, respectively, housing values of $408,000 and $134,500, average household incomes of $42,400 and $56,500, and college-degree rates of 14 percent and 19 percent.
4. Some of these tracts were the same, but some changed between 1990 and 2010 (see “Changes in Inequality from 1990 to 2010” on page 11).
R E F E R E N C E S 2 9
References Avendano, Mauricio, and Ichiro Kawachi. 2014. “Why Do Americans Have Shorter Life Expectancy and Worse
Health Than People in Other High-Income Countries?” Annual Review of Public Health 35: 307–25.
Combes, Pierre‐Philippe, Gilles Duranton, Laurent Gobillon, Diego Puga, and Sébastien Roux. 2012. “The Productivity Advantages of Large Cities: Distinguishing Agglomeration from Firm Selection.” Econometrica 80 (6): 2543–94.
Harrington, Michael. 1962. The Other America: Poverty in America. New York: Scribner.
Kim, Jae-On, and Charles W. Mueller. 1978. Introduction to Factor Analysis: What It Is and How to Do It. Beverly Hills, CA: SAGE Publications.
King, Katherine E., Jeffrey D. Morenoff, and James S. House. 2011. “Neighborhood Context and Social Disparities in Cumulative Biological Risk Factors.” Psychosomatic Medicine 73 (7): 572–9.
McKernan, Signe-Mary, Caroline Ratcliffe, Eugene Steuerle, and Sisi Zhang. 2013. Less Than Equal: Racial Disparities in Wealth Accumulation. Washington, DC: Urban Institute. http://www.urban.org/research/publication/less-equal-racial-disparities-wealth-accumulation/view/full_report.
Reich, Robert B. 1991. The Work of Nations: Preparing Ourselves for Twenty-First Century Capitalism. New York: Alfred Knopf.
Vornovitsky, Marina, Alfred Gottschalk, and Adam Smith. n.d. “Distribution of Household Wealth in the U.S.: 2000 to 2011.” Washington, DC: US Census Bureau. http://www.census.gov/people/wealth/files/Wealth%20distribution%202000%20to%202011.pdf.
3 0 A B O U T T H E A U T H O R S
About the Authors Rolf Pendall is director of the Metropolitan Housing and Communities Policy Center at
the Urban Institute. In this role, he leads a team of over 40 experts on a broad array of
housing, community development, and economic development topics, consistent with
Urban’s nonpartisan, evidence-based approach to economic and social policy.
Carl Hedman is a research assistant at the Urban Institute’s Metropolitan Housing and
Communities Policy Center. His work focuses on examining policy issues surrounding
residential economic and racial segregation, neighborhood change, and housing
affordability. He received his BA in economics from Reed College.
ST A T E M E N T O F I N D E P E N D E N C E
The Urban Institute strives to meet the highest standards of integrity and quality in its research and analyses and in
the evidence-based policy recommendations offered by its researchers and experts. We believe that operating
consistent with the values of independence, rigor, and transparency is essential to maintaining those standards. As
an organization, the Urban Institute does not take positions on issues, but it does empower and support its experts
in sharing their own evidence-based views and policy recommendations that have been shaped by scholarship.
Funders do not determine our research findings or the insights and recommendations of our experts. Urban
scholars and experts are expected to be objective and follow the evidence wherever it may lead.
2100 M Street NW
Washington, DC 20037
www.urban.org