RESEARCH REPORT Worlds Apart - urban.org...Key Findings: Inequality in the Commuting Zones in 2010...

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.


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).


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


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.


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


FIGURE 3

Bottom Neighborhoods Are No Better Off in Large Commuting Zones Than in Small Ones


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


FIGURE 4

Top-Bottom Inequality Peaks in Commuting Zones with 5–10 Million Residents

But inequality grows slowly from 1 million to 5 million


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


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.


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


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


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.


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.


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


TABLE A.1 CONTINUED


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


TABLE A.1 CONTINUED



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


TABLE A.1 CONTINUED



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


TABLE A.1 CONTINUED



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


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


TABLE A.2 CONTINUED


2006–10




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


TABLE A.2 CONTINUED


2006–10




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


TABLE A.2 CONTINUED


2006–10




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


TABLE A.2 CONTINUED


2006–10




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).

http://www.census.gov/hhes/www/income/data/historical/household/2013/h03AR.xls

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.

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