N E IG H B O R H OO D S, CIT IE S, AN D M E TR O S
June 2015

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Copyright © June 2015. Urban Institute. Permission is granted for reproduction of this file, with attribution to the
Urban Institute. Cover image by Tim Meko.

Inequality among Households Affects Neighborhoods
Key Findings: Inequality in the CZs in 2010 and Changes Since 1990
How the 2010 Top and Bottom Tracts Changed from 1990
Conclusions and Implications
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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.
I V A C K N O W L E D G M EN TS

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.
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
W O R L D S A P A R T 1

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.
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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.)
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).
W O R L D S A P A R T 3

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
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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.
The largest commuting zones in the United States have higher incomes, housing values, and collegeeducation 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.
W O R L D S A P A R T 5

2
1
0
-1
FIGURE 2
Top Neighborhoods Are More Advantaged in Large Commuting Zones Than in Small Ones
Neighborhood advantage score, top neighborhoods
4
3
-2
10,000 100,000 1,000,000
Commuting zone population, 2010
10,000,000
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.
100,000,000
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.
6 W O R L D S A P A R T

FIGURE 3
Bottom Neighborhoods Are No Better Off in Large Commuting Zones Than in Small Ones
Neighborhood advantage score, top neighborhoods
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-2.5
-3.0
-3.5
10,000 100,000 1,000,000
Commuting zone population, 2010
10,000,000
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.
100,000,000
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.
W O R L D S A P A R T 7

5
4
3
FIGURE 4
Top-Bottom Inequality Peaks in Commuting Zones with 5 – 10 Million Residents
But inequality grows slowly from 1 million to 5 million
Average neighborhood inequality index
6
2
1
0
<0.1
0.1–0.5 0.5–1 1–2 2–5
Commuting zone population, 2010 (millions)
5–10
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.
10+
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
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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 mediumsized 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.
W O R L D S A P A R T 9

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.
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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.
W O R L D S A P A R T 1 1

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.)
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.
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
1 2 W O R L D S A P A R T

populations, and another 36 percent lost between 10 and 50 percent of their population from 1990 to
2010.
80
60
40
20
0
FIGURE 6
Top Tracts Were More Likely to Be Locked in Than Bottom Tracts from 1990 to 2010
Number of commuting zones
Bottom Top
200
180
160
140
120
100
0%–33% 34%–50% 50%–67% 68%–80%
Percent of tracts locked in at the bottom or the top from 1990 to 2010
>80%
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
W O R L D S A P A R T 1 3

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.
1 4 W O R L D S A P A R T

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.
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.
W O R L D S A P A R T 1 5

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.
1 6 W O R L D S A P A R T

TABLE A.1
Neighborhood Advantage Scores and Neighborhood Inequality Indices
Commuting zones over 250,000 in 2010 only
Commuting zone
Albany, NY
Albuquerque, NM
Allentown, PA
Alton, IL
Altoona, PA
Amarillo, TX
Anchorage, AK
Anniston, AL
Appleton, WI
Asheville, NC
Atlanta, GA
Austin, TX
Bakersfield, CA
Baltimore, MD
Bangor, ME
Baton Rouge, LA
Beaumont, TX
Biloxi, MS
Binghamton, NY
Birmingham (rural), AL
Bloomington, IN
Boise, ID
Boston, MA
Bridgeport, CT
Brownsville, TX
Buffalo, NY
Burlington, VT
Canton, OH
Cape Coral, FL
Carbondale, IL
Cedar Rapids, IA
Champaign-Urbana, IL
Charleston, SC
Charleston, WV
Charlotte, NC
Charlottesville, VA
Chattanooga, TN-GA
Population, 2010
285,954
1,088,803
282,211
611,901
5,007,957
3,493,705
1,195,300
2,305,658
321,946
705,864
789,759
250,336
270,924
368,318
659,112
340,597
1,652,100
302,208
531,995
1,084,209
814,995
694,950
391,274
395,517
254,597
400,941
525,977
566,270
446,989
4,483,974
1,556,120
727,683
2,581,940
294,555
887,074
484,067
461,924
Neighborhood Advantage
Score, 2006–10
Bottom Top
-1.10
-2.65
-2.12
-2.28
-2.06
-2.67
-2.40
-2.37
-2.70
-2.67
-2.07
-2.07
-1.24
-1.88
-2.77
-2.85
-2.51
-1.00
-2.53
-2.11
-2.13
-2.38
-2.44
-1.80
-2.45
-2.65
-2.16
-2.11
-2.10
-2.39
-2.15
-2.59
-2.70
-1.17
-2.49
-1.83
-1.49
2.33
1.30
2.67
0.63
1.87
1.94
2.76
1.64
1.08
2.82
1.58
2.72
3.48
3.43
0.65
2.02
2.92
3.02
2.02
3.20
3.25
2.01
3.19
1.29
2.22
0.78
1.23
2.20
2.54
2.30
1.29
0.31
1.64
2.88
1.26
1.36
2.03
Neighborhood inequality index
3.43
3.95
4.79
2.91
3.93
4.62
5.17
4.01
3.78
5.49
3.65
4.79
4.71
5.32
3.42
4.87
5.43
4.02
4.55
5.31
5.38
4.39
5.63
3.09
4.67
3.43
3.38
4.30
4.64
4.69
3.44
2.90
4.34
4.05
3.75
3.19
3.52
A P P E N D I X A 1 7

TABLE A.1 CONTINUED
Commuting zone
Chicago, IL
Chico, CA
Cincinnati, OH-KY-IN
Claremont, NH
Clarksville, TN-KY
Cleveland, OH
Colorado Springs, CO
Columbia, MO
Columbia, SC
Columbus, GA-AL
Columbus, OH
Corpus Christi, TX
Dallas, TX
Davenport, IA-IL
Dayton, OH
Daytona Beach, FL
Denver, CO
Des Moines, IA
Detroit, MI
Dover, DE
Duluth, MN
Eau Claire, WI
El Paso, TX
Elkhart, IN
Elmira, NY
Erie, NY
Eugene, OR
Evansville, IN
Fayetteville, AR
Fayetteville, NC
Flagstaff, AZ
Florence, SC
Fort Collins, CO
Fort Smith, AR-OK
Fort Wayne, IN
Fort Worth, TX
Fredericksburg, VA
Fresno, CA
Gadsden, AL
Gainesville, FL
Gainesville, GA
Gary, IN
Gastonia, NC
Grand Rapids, MI
Population, 2010
8,256,699
1,160,755
580,084
2,470,111
638,050
5,147,510
508,772
284,375
317,662
868,663
389,295
335,784
639,579
1,000,267
403,141
467,234
675,779
322,024
578,595
480,855
354,295
580,921
2,044,131
299,392
1,547,103
318,357
343,736
348,773
703,792
443,053
1,333,746
421,451
2,028,227
277,076
271,189
2,575,732
584,858
326,663
776,924
286,472
1,791,321
513,811
3,676,828
382,822
Neighborhood Advantage
Score, 2006–10
Bottom
-1.93
Top
3.32
-2.82
-2.24
-2.40
-2.72
-1.73
-2.62
-2.00
-2.64
-2.67
-2.06
-1.76
-2.21
-2.68
-1.47
-2.36
-1.60
-1.91
-2.51
-2.01
-2.57
-2.61
-2.55
-0.16
-2.23
-2.59
-2.02
-1.90
-2.76
-2.41
-2.37
-1.92
-2.56
-0.96
-2.65
-2.79
-1.91
-2.04
-2.29
-3.20
-2.62
-2.75
-2.60
-2.59
1.67
0.65
1.71
0.78
1.99
1.13
1.76
1.38
1.82
1.73
3.32
2.27
2.74
1.89
1.74
1.06
2.07
1.39
2.39
0.59
1.61
2.38
2.29
1.91
0.51
2.51
1.39
1.73
1.64
2.08
1.70
2.60
2.57
1.09
2.47
2.85
2.01
2.32
1.86
2.91
1.51
3.16
1.87
Neighborhood inequality index
5.25
4.49
2.88
4.12
3.50
3.72
3.76
3.76
4.03
4.48
3.79
5.07
4.49
5.42
3.36
4.10
2.66
3.98
3.90
4.40
3.16
4.22
4.93
2.46
4.14
3.10
4.53
3.30
4.49
4.05
4.45
3.62
5.17
3.53
3.74
5.26
4.76
4.05
4.61
5.06
5.54
4.26
5.77
4.46
1 8 A P P E N D I X A

TABLE A.1 CONTINUED
Commuting zone
Green Bay, WI
Greensboro, NC
Greenville, NC
Greenville, SC
Hagerstown, MD
Harrisburg, PA
Hickory, NC
Honolulu, HI
Houma, LA
Houston, TX
Huntington, WV-KY-OH
Huntsville, AL
Indianapolis, IN
Jackson, MI
Jackson, MS
Jacksonville, FL
Johnson City, TN-VA
Joplin, MO
Kalamazoo, MI
Kansas City, MO-KS
Kennewick, WA
Killeen, TX
Knoxville, TN
Lafayette, IN
Lafayette, LA
Lake Charles, LA
Lake Jackson, TX
Lakeland, FL
Lansing, MI
Las Vegas, NV-AZ
Lexington-Fayette, KY
Lima, OH
Lincoln, NE
Little Rock, AR
Longview, TX
Lorain, OH
Los Angeles, CA
Louisville, KY-IN
Lubbock, TX
Macon, GA
Madison, WI
Manchester, NH
Mansfield, OH
Medford, OR
Population, 2010
515,030
1,803,439
358,663
351,653
793,240
352,334
577,844
329,714
356,817
681,572
445,403
1,263,977
539,702
254,359
320,046
690,731
329,499
1,135,322
557,482
968,999
471,360
1,127,642
423,721
827,276
284,394
5,250,614
343,611
584,074
1,687,048
299,923
570,826
1,319,058
602,423
280,605
309,050
436,991
17,018,144
1,184,626
295,282
422,827
635,617
1,259,013
314,176
282,759
Neighborhood Advantage
Score, 2006–10
Bottom Top
-2.04
-2.42
-2.07
-2.46
-2.22
-2.77
-2.34
-2.55
-2.52
-2.66
-1.97
-2.67
-2.38
-2.48
-2.58
-2.40
-2.50
-2.49
-1.57
-2.69
-2.61
-2.82
-1.24
-1.03
-2.71
-1.90
-2.61
-2.59
-2.66
-2.61
-2.78
-2.29
-2.32
-2.50
-2.29
-2.59
-2.62
-2.64
-1.87
-2.51
-2.29
-0.64
-2.21
-2.68
1.91
1.52
2.13
1.73
2.41
0.71
2.42
2.03
1.71
2.73
2.15
1.16
2.59
1.10
1.50
1.18
0.69
1.63
3.05
2.59
1.72
1.57
2.91
2.64
0.34
2.02
0.39
2.65
2.64
1.00
2.34
2.65
1.02
0.26
1.48
2.15
1.74
2.02
1.67
1.88
2.24
2.88
1.12
2.83
Neighborhood inequality index
3.95
3.95
4.20
4.19
4.63
3.48
4.76
4.58
4.24
5.39
4.13
3.83
4.97
3.58
4.08
3.58
3.18
4.12
4.61
5.28
4.33
4.40
4.15
3.67
3.05
3.92
2.99
5.24
5.30
3.61
5.11
4.93
3.34
2.77
3.77
4.74
4.36
4.67
3.53
4.39
4.53
3.52
3.32
5.51
A P P E N D I X A 1 9

TABLE A.1 CONTINUED
Commuting zone
Memphis, TN-AR-MS
Miami, FL
Milwaukee, WI
Minneapolis, MN-WI
Mobile, AL
Modesto, CA
Monmouth-Ocean, NJ
Monroe, LA
Montgomery, AL
Morgantown, WV
Morristown, TN
Muncie, IN
Nashville, TN
New Orleans, LA
New York, NY
Newark, NJ
Norfolk, VA-NC
Ocala, FL
Odessa, TX
Oklahoma City, OK
Omaha, NE
Orlando, FL
Palm Bay, FL
Pensacola, FL
Peoria, IL
Philadelphia, PA
Phoenix, AZ
Pittsburgh, PA
Pocatello, ID
Portland, ME
Portland, OR-WA
Poughkeepsie, NY
Providence, MA-NH
Provo, UT
Racine, WI
Raleigh, NC
Reading, PA
Reno, NV
Richmond, VA
Roanoke, VA
Rockford, IL
Rome, GA
Sacramento, CA
Saginaw, MI
Population, 2010
291,156
1,252,348
808,631
2,039,792
647,590
648,194
535,667
5,744,102
3,080,664
2,447,001
310,433
720,044
2,010,851
888,473
1,571,680
405,694
1,217,927
3,884,047
1,696,983
3,082,302
628,163
811,261
1,190,960
254,952
371,419
268,583
252,734
398,334
1,414,346
1,124,952
11,822,001
5,921,312
573,991
446,440
619,212
1,636,592
1,187,177
471,763
1,122,093
471,844
655,677
476,682
2,581,359
526,866
Neighborhood Advantage
Score, 2006–10
Bottom Top
-2.35
-2.52
-1.98
-1.76
-1.33
-1.30
-1.80
-1.41
-2.57
-2.70
-2.47
-2.01
-1.97
-2.26
-2.42
-2.52
-2.23
-2.13
-2.67
-2.29
-2.44
-2.20
-2.69
-2.30
-1.65
-2.65
-2.36
-2.68
-2.23
-2.47
-1.49
-1.55
-2.07
-2.11
-2.88
-2.08
-2.63
-1.37
-2.69
-2.08
-1.11
-2.81
-2.74
-2.37
2.75
2.31
1.57
2.19
2.98
2.36
2.65
2.41
1.55
2.26
2.28
2.58
2.44
2.15
1.57
3.13
1.62
3.16
1.58
2.80
3.03
1.92
1.32
0.58
2.69
1.27
0.14
0.53
3.11
2.42
3.29
3.43
2.47
1.09
2.40
2.80
2.63
3.00
1.59
1.60
3.12
1.30
2.19
1.35
Neighborhood inequality index
5.09
4.83
3.55
3.95
4.32
3.66
4.45
3.81
4.12
4.96
4.75
4.59
4.41
4.41
3.99
5.64
3.85
5.28
4.25
5.10
5.47
4.11
4.00
2.89
4.34
3.92
2.51
3.21
5.34
4.90
4.77
4.98
4.54
3.20
5.28
4.88
5.26
4.37
4.28
3.68
4.23
4.11
4.93
3.72
2 0 A P P E N D I X A

TABLE A.1 CONTINUED
Commuting zone
Salt Lake City, UT
San Antonio, TX
San Diego, CA
San Francisco, CA
San Jose, CA
Santa Barbara, CA
Santa Rosa, CA
Sarasota, FL
Savannah, GA
Scranton, PA
Seattle, WA
Shreveport, LA
South Augusta, GA
South Bend, IN
Spartanburg, SC
Spokane, WA
Springfield, IL
Springfield, MA
Springfield, MO
St. Cloud, MN
St. Louis, MO-IL
State College, PA
Syracuse, NY
Tallahassee, FL
Tampa, FL
Terre Haute, IN
Toledo, MI
Topeka, KS
Tucson, AZ
Tulsa, OK
Tuscaloosa, AL
Tyler, TX
Valdosta, GA
Virginia Beach, VA
Waco, TX
Washington, DC-MD-VA-
WV
Wausau, WI
West Palm Beach, FL
Wichita, KS
Wilmington, DE-MD
Wilmington, NC
Winston-Salem, NC
Population, 2010
499,429
294,369
2,367,449
305,969
1,050,896
451,201
2,514,502
265,646
824,111
330,107
1,037,896
961,265
276,294
471,886
259,970
1,159,338
296,185
1,553,455
1,882,938
2,797,753
4,623,748
2,359,835
639,449
609,576
831,714
457,956
883,792
4,100,215
514,749
561,570
641,123
378,732
675,106
262,880
669,265
4,891,082
381,910
1,637,935
562,893
629,126
420,291
531,707
Neighborhood Advantage
Score, 2006–10
Bottom Top
-2.79
-2.63
-2.39
-2.63
-2.64
-2.49
-2.70
-2.01
-2.72
-2.45
-1.26
-2.59
-2.11
-2.74
-2.28
-2.13
-2.63
-1.26
-2.90
-2.63
-2.62
-2.75
-1.85
-2.59
-2.34
-1.80
-2.67
-1.59
-0.89
-0.76
-0.97
-1.16
-2.08
-2.40
-2.24
1.78
1.98
2.68
2.14
1.86
1.30
0.80
2.62
0.93
1.58
1.66
2.88
2.20
1.83
2.64
2.29
0.39
3.15
1.60
2.13
1.80
1.33
2.09
1.80
2.41
2.82
2.38
3.22
3.55
3.59
2.92
2.55
2.42
2.86
1.35
-0.85
-1.87
-2.08
-2.72
-1.83
-2.24
-2.52
3.75
0.85
2.86
2.09
2.96
2.53
2.44
Neighborhood inequality index
4.56
4.61
5.07
4.77
4.50
3.80
3.49
4.62
3.65
4.03
2.93
5.46
4.31
4.57
4.92
4.42
3.02
4.41
4.50
4.76
4.42
4.08
3.94
4.39
4.75
4.62
5.05
4.81
4.44
4.35
3.89
3.71
4.51
5.25
3.60
4.60
2.73
4.94
4.81
4.80
4.77
4.96
A P P E N D I X A 2 1

TABLE A.1 CONTINUED
Commuting zone
Yakima, WA
Youngstown, OH
Yuma, AZ
Population, 2010
266,342
763,779
294,803
Neighborhood Advantage
Score, 2006–10
Bottom Top
-2.61
-2.79
-2.54
1.26
0.73
1.14
Neighborhood inequality index
3.87
3.52
3.68
2 2 A P P E N D I X A

Commuting zone
Albany, NY
Albuquerque, NM
Allentown, PA
Alton, IL
Altoona, PA
Amarillo, TX
Anchorage, AK
Anniston, AL
Appleton, WI
Asheville, NC
Atlanta, GA
Austin, TX
Bakersfield, CA
Baltimore, MD
Bangor, ME
Baton Rouge, LA
Beaumont, TX
Biloxi, MS
Binghamton, NY
Birmingham (rural), AL
Bloomington, IN
Boise, ID
Boston, MA
Bridgeport, CT
Brownsville, TX
Buffalo, NY
Burlington, VT
Canton, OH
Cape Coral, FL
Carbondale, IL
Cedar Rapids, IA
Champaign-Urbana, IL
Charleston, SC
Charleston, WV
Charlotte, NC
Charlottesville, VA
Chattanooga, TN-GA
Chicago, IL
Chico, CA
Cincinnati, OH-KY-IN
Claremont, NH
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
Bottom Top
$39,430
$38,139
$35,697
$42,498
$33,226
$30,710
$52,840
$30,977
$43,189
$41,964
$36,196
$39,489
$34,357
$35,725
$40,648
$31,544
$34,116
$36,287
$31,101
$30,035
$40,662
$39,717
$43,086
$38,180
$26,646
$29,724
$46,130
$32,299
$37,916
$34,838
$40,079
$33,051
$35,327
$34,697
$33,596
$45,583
$32,601
$37,325
$38,272
$32,705
$52,711
$115,005
$120,289
$120,115
$95,043
$74,676
$112,180
$164,869
$91,692
$100,376
$102,803
$166,130
$168,418
$126,059
$166,278
$77,561
$114,643
$91,328
$93,724
$91,652
$149,801
$78,307
$128,925
$183,778
$240,088
$83,739
$112,241
$115,288
$97,249
$147,015
$76,589
$112,815
$101,563
$124,426
$106,256
$155,774
$141,346
$115,646
$181,287
$89,960
$144,053
$114,538
Change Tract
Household Income
1990–2010
Bottom Top
-$1,393 $10,174
$4,240 $10,611
-$4,575 $2,131
$2,160 $13,872
$2,629
$1,405
$5,685
$1,929
-$5,003 $16,420
-$2,190 $9,448
$149 $14,500
$5,800 $15,292
$381 $16,075
$3,534 $38,395
-$1,226 $13,214
-$1,244 $16,154
-$2,591 $6,766
$1,492 $18,162
$5,863 $6,356
$3,386 $18,991
-$1,511 -$265
$945 $22,006
$1,093 -$3,689
$2,625 $37,583
-$753 $32,891
-$4,879 $35,434
$199 $4,754
-$2,259 $1,226
-$27 $22,189
-$1,491 $7,048
-$398 $13,178
-$490 $11,419
$2,187 $14,245
-$1,868 $7,607
$2,283 $26,095
$1,390 $15,137
-$3,611 $32,872
$3,073 $27,382
$3,079 $17,408
$2,093 $20,063
$720 $8,382
-$90 $19,139
$2,646 $24,546
Top:Bottom Income Ratio
1990 2010 Change
4.19
3.62
3.17
4.06
1.64
3.21
3.01
2.27
2.57
3.24
2.93
2.01
2.25
3.76
2.57
2.48
2.00
2.42
2.82
4.39
2.07
2.46
3.44
4.75
2.99
3.47
2.02
2.67
2.68
3.33
4.58
2.17
3.81
1.80
3.49
1.84
2.60
2.69
2.98
2.74
3.30
4.59
4.26
3.67
4.65
1.91
3.63
2.68
2.58
2.92
3.15
3.36
2.24
2.25
3.65
3.12
2.96
2.32
2.45
2.95
4.99
1.93
3.25
4.27
6.29
3.14
3.78
2.50
3.01
3.10
3.55
4.86
2.35
4.40
2.17
3.88
2.20
2.81
3.07
3.52
3.06
4.64
0.40
0.65
0.50
0.59
0.27
0.42
-0.33
0.31
0.35
-0.08
0.44
0.22
-0.01
-0.11
0.55
0.48
0.33
0.03
0.13
0.59
-0.15
0.78
0.82
1.54
0.16
0.31
0.48
0.34
0.42
0.22
0.28
0.18
0.60
0.38
0.38
0.35
0.21
0.38
0.55
0.33
1.33
A P P E N D I X A 2 3

TABLE A.2 CONTINUED
Commuting zone
Clarksville, TN-KY
Cleveland, OH
Colorado Springs, CO
Columbia, MO
Columbia, SC
Columbus, GA-AL
Columbus, OH
Corpus Christi, TX
Dallas, TX
Davenport, IA-IL
Dayton, OH
Daytona Beach, FL
Denver, CO
Des Moines, IA
Detroit, MI
Dover, DE
Duluth, MN
Eau Claire, WI
El Paso, TX
Elkhart, IN
Elmira, NY
Erie, NY
Eugene, OR
Evansville, IN
Fayetteville, AR
Fayetteville, NC
Flagstaff, AZ
Florence, SC
Fort Collins, CO
Fort Smith, AR-OK
Fort Wayne, IN
Fort Worth, TX
Fredericksburg, VA
Fresno, CA
Gadsden, AL
Gainesville, FL
Gainesville, GA
Gary, IN
Gastonia, NC
Grand Rapids, MI
Green Bay, WI
Greensboro, NC
Greenville, NC
Greenville, SC
2 4
Average Tract
Household Income
2006–10
Bottom Top
$24,576
$39,876
$37,594
$31,657
$40,414
$33,368
$40,440
$30,662
$43,907
$33,128
$38,322
$34,917
$33,075
$36,076
$62,101
$34,220
$31,270
$35,068
$34,287
$28,284
$37,573
$34,234
$35,033
$23,374
$31,084
$31,316
$34,155
$31,572
$32,633
$35,443
$40,571
$39,835
$29,905
$44,749
$32,917
$39,917
$44,379
$29,558
$35,245
$35,760
$37,814
$31,371
$30,169
$30,976
$101,233
$83,465
$89,135
$83,539
$91,944
$93,697
$108,687
$87,526
$92,362
$95,251
$112,571
$83,421
$107,707
$141,715
$132,770
$112,398
$82,281
$117,150
$88,902
$135,569
$128,802
$94,785
$111,927
$118,524
$147,148
$102,560
$195,201
$112,359
$108,077
$99,418
$173,545
$123,935
$142,924
$97,959
$95,666
$83,951
$94,306
$105,861
$104,068
$113,787
$99,636
$114,881
$88,990
$108,507
Change Tract
Household Income
1990–2010
Bottom Top
Top:Bottom Income Ratio
1990 2010 Change
$1,639
$79
$9,553 2.43
-$98 4.81
$1,264 $18,236 3.05
-$3,154 $10,483 2.25
-$1,425 $13,938 2.69
-$542 $5,138 4.74
-$3,020 $13,630 3.92
$3,721 $4,105 3.57
-$3,149 $31,769 4.38
$39 $16,451 3.04
-$1,954 -$4,300 3.25
-$184 $16,561 2.33
$2,648 $32,188 3.73
$611 $3,850 3.06
-$2,416 -$7,846 4.66
-$758 $17,663 1.76
$1,597 $15,508 2.56
-$2,171 $12,684 1.69
-$3,198 $10,422 3.27
-$365 -$5,412 2.21
-$523 $5,864 2.18
$692 -$2,863 2.79
-$203
-$599
$5,830
-$515
2.12
2.77
$2,761 $36,590 1.91
-$3,314 $4,623 2.44
$11,011 $15,407 2.34
-$2,005 $13,236 2.33
$2,402 $17,728 2.64
-$242 $2,857 2.29
-$4,953 -$24,981 3.49
$839 $14,554 3.61
$6,927 $37,590 1.73
-$1,095 -$199 3.19
-$1,313 $7,582 2.29
-$3,454 $16,135 2.62
-$739 $13,311 1.80
$1,282 $724 3.72
-$5,661 $16,289 2.15
-$1,656
$1,546
$3,879 2.94
$5,432 2.60
-$5,375 $10,535 2.84
-$2,140 $7,406 2.53
-$3,403 $14,999 2.72
2.10
2.88
2.94
2.39
3.26
3.93
2.14
3.28
2.63
3.34
4.12
2.09
2.37
2.64
2.28
2.81
2.69
2.85
2.13
3.58
2.95
3.18
2.63
3.66
2.95
3.50
3.31
2.80
4.28
3.11
4.78
2.19
2.91
2.10
2.59
4.79
3.43
2.77
3.19
5.07
4.73
3.28
5.72
3.56
-0.24
0.54
0.30
0.10
-0.23
0.32
0.41
0.10
0.34
0.72
0.85
-0.12
0.19
-0.15
0.15
0.03
0.77
0.41
0.33
-0.14
0.81
0.24
0.04
0.82
0.42
0.78
0.06
0.48
0.55
0.05
0.11
0.42
0.35
0.41
0.16
-0.02
0.38
0.51
0.51
0.33
0.82
-0.29
1.33
0.52
A P P E N D I X A

TABLE A.2 CONTINUED
Commuting zone
Hagerstown, MD
Harrisburg, PA
Hickory, NC
Honolulu, HI
Houma, LA
Houston, TX
Huntington, WV-KY-OH
Huntsville, AL
Indianapolis, IN
Jackson, MI
Jackson, MS
Jacksonville, FL
Johnson City, TN-VA
Joplin, MO
Kalamazoo, MI
Kansas City, MO-KS
Kennewick, WA
Killeen, TX
Knoxville, TN
Lafayette, IN
Lafayette, LA
Lake Charles, LA
Lake Jackson, TX
Lakeland, FL
Lansing, MI
Las Vegas, NV-AZ
Lexington-Fayette, KY
Lima, OH
Lincoln, NE
Little Rock, AR
Longview, TX
Lorain, OH
Los Angeles, CA
Louisville, KY-IN
Lubbock, TX
Macon, GA
Madison, WI
Manchester, NH
Mansfield, OH
Medford, OR
Memphis, TN-AR-MS
Miami, FL
Milwaukee, WI
Minneapolis, MN-WI
Average Tract
Household Income
2006–10
Bottom Top
$32,823
$34,026
$30,301
$38,594
$47,540
$35,604
$39,827
$37,966
$37,822
$31,587
$34,943
$31,822
$37,039
$34,734
$40,731
$29,913
$29,659
$28,076
$38,467
$35,833
$37,172
$49,235
$41,718
$34,401
$31,128
$31,166
$32,341
$29,721
$27,274
$35,209
$35,125
$34,277
$32,105
$31,220
$41,028
$35,358
$49,340
$49,638
$32,109
$36,714
$26,398
$32,320
$31,696
$41,987
$130,415
$82,040
$102,149
$97,629
$114,655
$104,506
$101,714
$121,431
$125,331
$90,241
$131,528
$113,923
$95,776
$103,711
$176,745
$133,327
$123,417
$102,857
$99,593
$109,101
$127,874
$153,412
$102,350
$176,695
$76,573
$128,083
$142,539
$85,576
$120,492
$146,762
$82,322
$76,226
$99,798
$141,400
$117,962
$101,089
$135,155
$129,881
$72,413
$86,953
$133,989
$153,897
$138,866
$151,471
Change Tract
Household Income
1990–2010
Bottom Top
-$586 $25,128
-$4,097 $10,849
-$4,318 $39,813
-$2,311 -$4,975
$7,478 $20,450
$711 $24,803
-$1,403 -$1,913
-$9,142 $13,644
-$2,927
-$4,327
$9,418
$1,136
$1,264 $12,934
$610 $25,236
$1,040 $838
$2,314 $11,716
-$2,944
-$1,082
-$551
$4,736
$3,331 $22,176
$3,560 $23,502
$2,409 $16,684
-$6,483 -$1,246
$2,037 $13,083
$9,529 $18,483
$5,626 $15,835
$262 $13,342
-$1,796 -$2,248
$763 $5,981
$2,455 $18,990
-$3,395 $1,024
-$1,323 $25,280
-$840 $14,434
$3,112 $16,940
-$2,928 $14,357
-$386
$662
$5,905
$8,031
$829 $21,488
-$3,088 -$3,143
$2,869 $24,789
-$219 $12,363
-$3,176 -$5,838
$1,377
$2,085
$8,081
$544
-$977 $11,924
$1,982 $11,995
$1,993 $20,323
Top:Bottom Income Ratio
1990 2010 Change
3.01
2.55
2.93
3.05
2.32
2.37
4.15
4.28
3.54
3.40
3.74
2.06
3.15
2.72
2.36
2.58
2.50
3.10
2.37
2.36
2.22
2.23
5.49
4.26
4.27
3.28
4.14
3.51
2.39
2.02
2.86
4.23
2.54
2.44
1.91
2.46
2.12
3.07
2.39
4.51
2.41
2.84
3.77
2.48
3.31
2.86
3.76
3.58
2.59
2.99
4.34
4.46
4.16
3.66
3.97
2.41
3.37
2.53
2.41
2.94
2.55
3.20
2.74
2.62
2.26
2.37
5.08
4.76
4.38
3.61
4.42
4.17
2.34
2.22
3.11
4.53
2.88
2.86
2.59
3.04
3.44
3.12
2.45
5.14
2.46
4.11
4.41
2.88
0.31
0.31
0.83
0.53
0.26
0.61
0.18
0.17
0.63
0.26
0.23
0.36
0.22
-0.19
0.05
0.36
0.06
0.10
0.36
0.26
0.04
0.14
-0.41
0.50
0.11
0.33
0.28
0.66
-0.05
0.21
0.25
0.30
0.33
0.42
0.68
0.58
1.32
0.04
0.06
0.63
0.05
1.27
0.63
0.40
A P P E N D I X A 2 5

TABLE A.2 CONTINUED
Commuting zone
Mobile, AL
Modesto, CA
Monmouth-Ocean, NJ
Monroe, LA
Montgomery, AL
Morgantown, WV
Morristown, TN
Muncie, IN
Nashville, TN
New Orleans, LA
New York City, NY
Newark, NJ
Norfolk, VA-NC
Ocala, FL
Odessa, TX
Oklahoma City, OK
Omaha, NE
Orlando, FL
Palm Bay, FL
Pensacola, FL
Peoria, IL
Philadelphia, PA
Phoenix, AZ
Pittsburgh, PA
Pocatello, ID
Portland, ME
Portland, OR-WA
Poughkeepsie, NY
Providence, MA-NH
Provo, UT
Racine, WI
Raleigh, NC
Reading, PA
Reno, NV
Richmond, VA
Roanoke, VA
Rockford, IL
Rome, GA
Sacramento, CA
Saginaw, MI
Salt Lake City, UT
San Antonio, TX
San Diego, CA
San Francisco, CA
2 6
Average Tract
Household Income
2006–10
Bottom Top
$37,202
$35,625
$37,501
$33,526
$35,461
$33,909
$37,327
$40,457
$44,740
$45,284
$34,917
$37,518
$37,232
$37,502
$33,817
$36,731
$34,352
$36,971
$26,305
$36,051
$44,127
$27,928
$26,419
$31,872
$34,715
$30,551
$35,716
$30,801
$39,144
$41,402
$38,985
$35,916
$37,316
$32,706
$33,947
$39,226
$30,946
$36,559
$41,382
$30,546
$41,535
$32,111
$41,565
$47,499
$128,707
$113,732
$109,151
$169,109
$147,413
$127,395
$100,834
$105,749
$135,878
$125,950
$126,257
$122,924
$100,274
$152,422
$102,971
$149,718
$152,391
$97,012
$103,443
$105,829
$184,241
$100,036
$115,189
$91,188
$71,043
$79,920
$163,149
$122,622
$210,061
$210,698
$117,547
$90,226
$133,120
$132,569
$134,742
$136,829
$99,024
$79,685
$130,579
$95,060
$140,864
$140,307
$165,219
$203,027
Change Tract
Household Income
1990–2010
Bottom Top
Top:Bottom Income Ratio
1990 2010 Change
$2,285 $8,011 3.97
-$4,022 $13,304 2.31
-$923 $22,620 3.59
$3,369 $10,245 3.66
$1,471 $4,142 4.45
-$977 $27,503 1.94
$1,997 $6,248 1.98
-$2,584 -$6,510 2.61
$1,457 $26,695 3.98
$6,406 $14,237 4.44
$2,080 $30,730 4.84
-$4,527 $25,118 4.04
$183 $20,327 2.51
-$497 $9,846 2.21
$6,535 $14,003 3.87
$1,507 $18,631 3.65
$764 $6,863 3.85
-$1,669 $12,186 3.05
-$5,036 $6,733 2.89
$3,602 $28,640 2.66
$5,191 $15,607 2.90
-$2,929 $16,806 4.18
$590 $10,256 3.93
$880 $11,454 3.51
$2,348 $19,818 2.32
-$417 $13,203 2.26
$4,274 $18,257 2.91
-$520 $16,204 2.40
-$1,735 $19,442 2.91
-$1,504 $36,843 2.21
-$2,302 $9,705 2.29
-$1,778 $28,730 3.15
-$4,172 $6,761 2.53
-$5,479 $19,534 3.08
$713 $13,786 4.12
$826 $11,330 2.37
-$5,081 -$3,212 2.84
-$2,097 -$2,165 2.12
$769 $24,095 2.62
-$1,149 -$2,713 3.08
$4,949 $22,848 3.23
$3,143 $22,442 4.07
$2,379 $18,551 3.74
$3,606 $35,803 3.81
3.04
2.78
3.62
3.28
2.69
4.06
3.04
4.08
4.44
2.62
3.46
3.19
2.91
5.04
4.16
3.76
2.70
2.61
3.20
2.18
3.16
3.11
3.39
4.37
3.97
4.27
5.37
5.09
3.02
2.51
3.57
4.05
3.97
3.49
3.93
2.94
4.18
3.58
4.36
2.86
2.05
2.62
4.57
3.98
0.13
0.39
0.70
1.07
0.40
0.92
0.51
0.99
0.32
0.25
0.57
0.54
0.02
0.87
0.22
0.25
0.39
0.35
0.36
0.06
0.53
0.03
0.17
0.30
0.23
0.46
0.53
1.05
0.51
0.30
-0.30
0.40
0.12
0.44
-0.04
0.63
0.59
-0.07
-0.09
0.92
0.07
0.01
0.58
-0.46
A P P E N D I X A

TABLE A.2 CONTINUED
Commuting zone
San Jose, CA
Santa Barbara, CA
Santa Rosa, CA
Sarasota, FL
Savannah, GA
Scranton, PA
Seattle, WA
Shreveport, LA
South Augusta, GA
South Bend, IN
Spartanburg, SC
Spokane, WA
Springfield, IL
Springfield, MA
Springfield, MO
St. Cloud, MN
St. Louis, MO-IL
State College, PA
Syracuse, NY
Tallahassee, FL
Tampa, FL
Terre Haute, IN
Toledo, MI
Topeka, KS
Tucson, AZ
Tulsa, OK
Tuscaloosa, AL
Tyler, TX
Valdosta, GA
Virginia Beach, VA
Waco, TX
Washington, DC-MD-VA-
WV
Wausau, WI
West Palm Beach, FL
Wichita, KS
Wilmington, DE-MD
Wilmington, NC
Winston-Salem, NC
Yakima, WA
Youngstown, OH
Yuma, AZ
$52,689
$42,777
$35,741
$31,424
$39,723
$36,774
$30,936
$34,684
$26,972
$34,809
Average Tract
Household Income
2006–10
Bottom Top
$32,160
$28,855
$36,839
$28,564
$27,468
$32,562
$33,210
$32,834
$27,620
$34,369
$31,758
$36,032
$27,384
$53,441
$48,572
$44,481
$38,099
$34,035
$38,343
$46,853
$28,596
$30,335
$28,918
$29,647
$39,221
$35,573
$30,392
$32,171
$46,557
$31,295
$41,221
$109,776
$126,685
$118,820
$78,215
$108,930
$105,750
$121,348
$131,713
$107,085
$104,090
$78,055
$132,480
$87,721
$202,579
$155,330
$125,358
$127,584
$128,078
$95,822
$150,477
$113,394
$115,628
$103,928
$93,928
$106,294
$110,747
$109,854
$102,339
$106,491
$149,729
$101,607
$223,056
$86,356
$188,186
$131,538
$164,382
$118,042
$130,064
$88,471
$82,386
$94,933
Change Tract
Household Income
1990–2010
Bottom Top
Top:Bottom Income Ratio
1990 2010 Change
-$23 $31,489 3.20
$554 $4,088 3.15
$2,356 $18,193 2.54
-$761 $18,754 2.80
$6,014
-$559
$6,321 4.35
$7,506 2.27
$2,896 $32,307 2.69
$4,305 $14,371 4.08
$1,404 $15,208 3.47
-$3,347 -$1,750 3.28
-$5,437 $10,216 2.39
$3,640 $9,602 2.72
$2,108 -$1,743 3.36
-$2,373 $9,415 3.07
$2,541 $1,898 3.39
$2,463 $24,350 1.86
-$944 $15,947 4.15
$829 $20,448 2.01
-$1,486 $11,817 2.91
-$1,815 $16,479 3.59
$2,922 $17,201 3.00
-$4,288 $1,581 2.33
-$4,138 -$2,276 3.52
-$3,704 $13,509 2.54
$3,668 $13,146 3.66
-$994 $1,833 3.84
$297 $17,874 3.27
$926 $20,446 2.50
-$80 -$1,270 2.49
$3,631 $25,399 3.30
$806 -$1,659 3.36
3.41
4.39
3.23
2.74
3.97
3.25
3.65
4.01
3.88
3.03
2.46
3.68
3.20
3.17
2.71
3.11
3.61
3.18
2.29
4.78
2.46
3.79
3.20
2.82
3.35
3.76
2.50
3.21
3.97
3.81
3.59
0.50
0.80
0.23
0.41
0.45
0.70
-0.01
0.17
0.61
0.53
-0.03
0.37
-0.16
0.78
-0.01
-0.25
0.55
-0.21
0.42
0.63
0.46
0.59
0.05
0.27
0.55
-0.58
0.23
0.52
-0.11
0.34
0.32
$3,097 $43,418 3.62
$284 $9,009 1.82
-$2,979 $25,684 4.20
-$3,334 $5,094 3.64
-$5,213 $11,419 3.40
$4,411 $29,925 2.72
-$4,724 $5,731 3.49
$4,407
-$2,104
-$33
$9,473 2.61
-$386 2.85
$5,381 2.57
4.23
2.02
5.27
4.19
4.14
3.21
4.20
2.55
3.05
2.73
0.61
0.20
1.07
0.55
0.73
0.49
0.72
-0.06
0.21
0.16
A P P E N D I X A 2 7

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).
2 8 N O TE S

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/lessequal-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.
R E F E R E N C E S 2 9

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.
3 0 A B O U T TH E A U TH O R S

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