U.S. Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

U.S. Neighborhood Income 

Inequality in the 2005–2009 Period

American Community Survey Reports

By

Daniel H. Weinberg

ACS-16

Issued October 2011

INTRODUCTION

Income inequality is one measure of societal conditions. Some have argued that 

large (and recently increasing) income 

inequality is potentially harmful to social 

stability—letting “the rich get richer while 

the poor get poorer” is thought to be bad 

for the United States. Others have argued 

that income inequality is one of the 

engines that drives economic growth and 

innovation and taxing away the incomes 

of those at the top end of the income 

distribution can stifle creativity and lower 

the well-being of everyone. This paper 

will not take a position as to the benefits 

or costs of income inequality, or examine 

the changes in inequality over time at a 

local level, but rather it will examine the 

extent to which income inequality differs 

spatially inside the United States, focusing on small geographic areas.

Spatial income inequality is neither 

intrinsically bad nor good. One would not 

expect much income inequality in neighborhoods consisting of new high-priced 

houses. Nor would one expect much in 

neighborhoods consisting of low-rent 

private or public housing. Some would 

indeed argue sorting of households by 

income within a city or metropolitan area 

is natural as it is a rare neighborhood 

that has all kinds and prices of housing. 

On the other hand, others would say that 

diversity in incomes among neighbors 

can enhance the social environment. The 

idea that households will sort themselves 

into neighborhoods with a narrow distribution of income dates at least as early 

as Thurow (1971) if not indeed to Tiebout 

(1956). 

Income sorting might have negative 

consequences for the poor. Wheeler 

(2008) notes “The movement of highincome individuals away from the poor, 

for example, may leave the poor with 

relatively few jobs (e.g., Kain 1968) or 

reduce the extent to which the rich confer positive spillovers on the poor (e.g., 

Wilson 1987 and Benabou 1996).” Watson 

(2009) argues that “Income sorting 

affects the distribution of role models, 

peers, and social networks” and that “the 

characteristics of one’s neighbors and 

peers in school affect outcomes.” But, 

as Hardman and Ioannides (2004) state, 

“For the vast majority of US households, 

neighbors’ incomes and other characteristics are the market-driven outcome of 

individual choices. Households’ tastes 

for housing space, quality and access to 

jobs and amenities, together with their 

incomes and assets, define demand for 

housing types and locations. Prices set in 

the housing market determine what housing units and neighborhoods households 

can afford.” Among the implications, 

as Watson notes, is that “if residential 

choice is sensitive to the income distribution, economic policies that moderate or 

amplify income inequality may shape the 

cities in which we live.”

This paper will start at the national level 

to give the overall context for the level 

of U.S. income inequality and drill down 

through states and large cities to neighborhoods—here defined as census tracts 

(designed by local officials and experts 

to be relatively homogenous and to have 

an average of roughly 1,500 housing 

units and 4,000 people). The data to do 
2 U.S. Census Bureau

so became available in December 

2010—when the first-ever income 

inequality estimates were released 

for neighborhoods from the 

American Community Survey 

(ACS).1 Those period estimates 

cover the years 2005 through 2009 

and thus average together the final 

years of economic growth in the 

first decade of the 21st century 

with the first years of downturn.2

The ACS was designed as a replacement for the Census 2000 long 

form—the previous source of 

neighborhood characteristics such 

as median household income. As 

an annual survey of roughly 3 

million addresses, it provides an 

annual portrait of the demographic, 

social, economic, and housing characteristics of small towns, neighborhoods, and population groups. 

As part of their data dissemination, 

the Census Bureau publishes estimates on the Internet for median 

household income and for the Gini 

index of household income inequality (the Gini index ranges from 0.0, 

when all households have equal 

shares of income, to 1.0, when one 

household has all the income and 

the rest none).3

THE MEASURES OF 

INEQUALITY

The Gini measure is supplemented 

here by two additional measures 

of inequality calculated from the 

source data—the ratio of household income at the 90th percentile 

to that at the 10th (called here 

the P90/10 index), and the ratio 

1 Thus the census tracts boundaries are 

defined as of 2000, for use with that census. 

Data released in late 2011 from the ACS will 

use the 2010 Census tract boundaries. 2 Income is asked of respondents about 

the prior 12-month period, so interviews in 

January 2005 covered calendar year 2004 

income, and interviews in December 2009 

covered income for December 2008 through 

November 2009. The National Bureau of 

Economic Research dates the most recent 

recession as December 2007 to June 2009. 3 For more information on measurement 

of income in the ACS and on the Gini index of 

inequality, see Bishaw and Semega 2008.

of household income at the 95th 

percentile to that at the 20th (called 

here the P95/20 index). While the 

Gini index is available for all places 

and tracts (of sufficient size), the 

P90/10 and P95/20 indexes are 

not, as some areas have 10th and 

even 20th percentile incomes of 

$0. Nevertheless, it is hoped that 

the two ratio measures will provide 

a useful additional perspective.

Not all neighborhoods in the United 

States are included in this study, 

because many have too few households in the sample to allow for the 

computation of reliable statistics. 

An admittedly arbitrary decision 

was made to exclude all census 

tracts (and places) with fewer than 

50 interviews in the 5-year period 

under study from the tabulations 

presented below, though the computations for higher levels of geography include all households. All 

significance testing is performed at 

the 90 percent confidence level.

THE NATIONAL PICTURE

Each year, the Census Bureau 

publishes an estimate of national 

income inequality from the Current 

Population Survey Annual Social 

and Economic Supplement (CPS 

ASEC; the latest is DeNavas-Walt 

et al. 2010). According to analysis 

of that survey (which has been 

measuring income inequality since 

1947) by Jones and Weinberg 2000 

(see also Weinberg 1996), “between 

1967 (when income data for 

households first became available) 

and 1992, the shape of the household income distribution changed 

dramatically. This 25-year period 

was one of increasing household 

income inequality—as evidenced by 

several measures.” Subdividing that 

period, and using the Gini index as 

the measure, Jones and Weinberg 

observed that “household income 

inequality was generally stable 

between 1967 and 1980.” However, 

they go on to note that “In contrast 

to the . . . Gini measures,

 . . . percentile measures . . . 

suggest that household income 

inequality increased from 1967 to 

1980 . . . .The 95/20 ratio

 . . . increased from 1967 to 1980, 

while the 90/10 ratio . . . declined.” 

Despite the ambiguity in the 1967–

1980 period, they conclude “it is 

clear that the household income 

distribution became increasingly 

unequal beginning in 1981.” Their 

study ended with 1998 data, but 

the Census Bureau statistics continue to show increases in inequality since then. The Gini coefficient 

for household income in 2009 

was 0.468, higher than for 1998 

(0.456).4

The basic CPS Gini measure of 

income inequality is now supplemented with a parallel CPS measure that attempts to correct for 

household size in computing the 

indexes, since households with 

more people tend to have more 

workers and therefore higher 

incomes. “Household Equivalent 

Income” (HEI) is computed by using 

the three-parameter equivalence 

scale used to compute the poverty 

thresholds used in the Census 

Bureau’s series of supplemental 

poverty measures. As expected, 

Gini indexes for HEI are typically 

lower (show more equality) than 

for unadjusted household income; 

indeed the HEI Gini indexes were all 

statistically lower than the unadjusted Ginis for each year over the 

1977–2009 period (see DeNavasWalt et al. 2010, Table A-3).5 The 

HEI Gini was lower in 1977 than in 

2009 (0.369 versus 0.458, respectively) and like the unadjusted Gini 

the series of HEI Gini estimates 

4 The Gini index for 1998 was lower than 

in 2001, 2004–2006, 2008, and 2009 and not 

statistically different from that in 1999, 2000, 

2002, 2003, and 2007. 5 The earliest year for which the Gini 

index for HEI has been calculated is 1967; the 

full series was first published by the Census 

Bureau in DeNavas-Walt et al. (2010).
U.S. Census Bureau 3

demonstrates a growth in income 

inequality over the period. Because 

rankings based on the HEI Gini differ little from those based on the 

(unadjusted) household incomebased Gini, income inequality 

measures based on this alternative 

measure of income will not be pursued further in this paper.

The national P90/10 ratio based on 

data from the CPS ASEC stood at 

11.36 in 2009, was not statistically 

different from the ratios for 2003, 

2005, 2007, and 2008, but higher 

than for any other prior year (and 

up from 10.44 in 1998 and up from 

the lowest recorded since 1967—

8.53 in 1975).6 The CPS ASEC 

P95/20 ratio stood at 8.80 in 2009, 

higher than for 2004 or any year 

prior to that (and up from 8.20 in 

1998 and from the lowest recorded 

since 1967—5.97 in 1968).7

The Gini coefficient for household 

income as measured by the ACS 

rather than the CPS for 2009 was 

0.469, not statistically different 

from the CPS measure. The Gini 

measures from the CPS and the 

ACS are compared in Table 1.8 Note 

that there is no reason to expect 

the statistics to agree exactly—the 

surveys are very different.9

6 The P90/10 for 1975 was not statistically different from that ratio in 1968, 1974, 

1976, or 1977. 7 The P95/20 for 1968 was not statistically different from that ratio in 1969. 8 The CPS and ACS Gini estimates are 

not statistically different for 3 of the 5 years 

(2007 through 2009), but the CPS measure 

is higher in 2005 and 2006. The CPS Gini for 

2010 was 0.469 (standard error of 0.0027), 

which was not statistically different from the 

ACS Gini for 2010 of 0.469 (standard error 

of 0.0006), or from any of the CPS Gini estimates from 2005 to 2009. 9 See “Guidance About Income Sources” at 

<www.census.gov/hhes/www/income

/method/guidance/index.html> for guidance on using the income data from the two 

surveys. For example, “Each of these surveys 

differs from the others in some ways, such as 

the length and detail of its questionnaire, the 

number of households included (sample size), 

and the methodology used to collect and 

process the data . . . . The CPS ASEC is the 

preferred source for national analysis

 . . . . The ACS is preferred for subnational 

data on income.”

Table 2 shows the P90/10 and 

P95/20 income inequality measures for the ACS. These ratios 

show basically the same time series 

story as did the Gini coefficient—

an increase in income inequality 

from 2006 to 2009.10 Following the 

guidance from the Census Bureau 

income experts, ACS data will be 

used for subnational comparisons.

STATE AND MICROPOLITAN/

METROPOLITAN AREALEVEL INCOME INEQUALITY

Table 3 presents the Gini, P90/10, 

and P95/20 measures of income 

10 For the P90/10 ratio, neither of the 

changes from 2005 or 2006 to 2007 was significant. For the P95/20 ratio, the differences 

from 2005 to 2006 and 2007 to 2008 were 

not significant.

inequality for the United States 

as a whole and for the 50 states 

plus the District of Columbia for 

2005–2009.11 These measures are 

close substitutes—the Pearson 

rank correlation coefficients are 

0.930, 0.968, and 0.978 for the 

three measures taken two at a time. 

There are only eight states that had 

income inequality higher than for 

the United States as a whole on all

11 While the ACS samples size is large 

enough to provide reliable estimates for areas 

or groups of 65,000 population or more every 

year, the 5-year estimates are used exclusively hereafter to allow comparisons across 

geographic areas of all sizes. The Puerto Rico 

Community Survey is part of the ACS and 

provides similar estimates for that entity and 

its geographic components but is not discussed here. Puerto Rico’s income inequality 

is typically about the same as the District of 

Columbia but higher than for any state. 

Table 1. 

Comparison of Gini Household Income Inequality Measure 

Between the Current Population Survey (CPS) and the 

American Community Survey (ACS): 2005–2009

Year

Inequality measure

Gini-CPS Gini-ACS

Measure Standard error Measure Standard error

2009. . . . . . . . . . . . 0.468 0.0028 0.469 0.0020

2008. . . . . . . . . . . . 0.466 0.0027 0.469 0.0009

2007. . . . . . . . . . . . 0.463 0.0029 0.467 0.0001

2006. . . . . . . . . . . . 0.470 0.0029 0.464 0.0005

2005. . . . . . . . . . . . 0.469 0.0029 0.464 0.0005

Note: CPS and ACS estimates significantly different for 2005 and 2006 only.

Source: U.S. Census Bureau, Current Population Survey Annual Social and Economic Supplement 

and American Community Survey. For information on confidentiality protection, sampling error, 

nonsampling error, and definitions, see <www.census.gov/apsd/techdoc/cps/cpsmar10.pdf> for the 

CPS and <www.census.gov/acs/www/> for the ACS.

Table 2. 

American Community Survey P90/10 and P95/20 

Household Income Inequality Measures: 2005–2009 

Year

Inequality measure

P90/10 P95/20

Measure Standard error Measure Standard error

2009. . . . . . . . . . . . 11.452 0.030 8.714 0.038

2008. . . . . . . . . . . . 11.380 0.029 8.603 0.041

2007. . . . . . . . . . . . 11.111 0.038 8.542 0.029

2006. . . . . . . . . . . . 11.065 0.057 8.425 0.017

2005. . . . . . . . . . . . 11.198 0.049 8.406 0.034

Notes: P90/10 is the ratio of household income at the 90th percentile to that at the 10th percentile; 

P95/20 is a similar ratio at the 95th compared to the 20th percentile. 

Source: U.S. Census Bureau, American Community Survey. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see <www.census.gov/acs/www/>.
4 U.S. Census Bureau

Table 3. 

Household Income Inequality Measures for States Compared to U.S. Estimate: 2005–2009

Gini index P90/10 index P95/20 index

State Standard Standard Standard 

Value error Value error Value error

Higher than United States on all three measures of income inequality

District of Columbia ............................................ 0.540 0.0033 21.987 0.6687 13.722 0.2926

New York .................................................... 0.499 0.0007 13.507 0.0538 10.132 0.0384

Connecticut .................................................. 0.481 0.0015 11.571 0.1170 8.925 0.0677

Louisiana .................................................... 0.477 0.0017 12.653 0.1244 9.517 0.0692

Mississippi ................................................... 0.474 0.0018 12.423 0.1193 9.290 0.0781

Texas ....................................................... 0.474 0.0013 11.491 0.0502 8.904 0.0258

Alabama .................................................... 0.471 0.0015 11.871 0.1141 9.006 0.0608

California .................................................... 0.469 0.0012 11.400 0.0431 8.715 0.0231

Higher than United States on two measures of income inequality

Massachusetts ................................................ 0.465 0.0012 12.648 0.0978 8.813 0.0526

Does not differ from United States on any measure

Tennessee ................................................... 0.468 0.0014 11.228 0.0751 8.616 0.0571

 UNITED STATES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 .467 0 .0001 11 .251 0 .0168 8 .547 0 .0110

Differs from United States on two or three measures of income 

inequality with at least one above and one below

Florida ...................................................... 0.469 0.0010 10.208 0.0433 8.131 0.0291

Georgia ..................................................... 0.465 0.0012 11.607 0.0806 8.620 0.0505

Kentucky .................................................... 0.462 0.0015 11.749 0.1252 8.693 0.0726

New Jersey .................................................. 0.462 0.0009 11.495 0.0719 8.463 0.0473

Rhode Island ................................................. 0.452 0.0021 11.639 0.1629 8.412 0.1288

Lower than United States on one measure of income inequality

New Mexico .................................................. 0.458 0.0019 11.206 0.1534 8.454 0.0814

Lower than United States on two measures of income inequality

Illinois ....................................................... 0.465 0.0013 11.120 0.0490 8.394 0.0407

Arkansas .................................................... 0.459 0.0017 10.653 0.1037 8.432 0.0788

Virginia ...................................................... 0.455 0.0012 11.222 0.0757 8.194 0.0383

Lower than United States on all three measures of income inequality

North Carolina ................................................ 0.463 0.0014 10.969 0.0593 8.382 0.0416

South Carolina ................................................ 0.460 0.0016 11.056 0.1022 8.277 0.0479

Oklahoma ................................................... 0.459 0.0015 10.474 0.0779 8.005 0.0600

Pennsylvania ................................................. 0.457 0.0014 10.528 0.0444 8.188 0.0320

West Virginia ................................................. 0.453 0.0020 10.918 0.1223 8.175 0.0818

Colorado .................................................... 0.452 0.0015 10.604 0.1064 7.784 0.0555

Arizona ..................................................... 0.451 0.0015 9.926 0.0703 7.616 0.0446

Missouri ..................................................... 0.449 0.0012 10.138 0.0686 7.661 0.0327

Michigan .................................................... 0.448 0.0009 10.606 0.0599 7.825 0.0351

Ohio ........................................................ 0.447 0.0013 10.473 0.0567 7.736 0.0262

Oregon ...................................................... 0.447 0.0018 10.095 0.0768 7.635 0.0589

North Dakota ................................................. 0.443 0.0029 9.805 0.1648 7.304 0.0860

Kansas ...................................................... 0.442 0.0017 9.557 0.0710 7.383 0.0508

Washington .................................................. 0.442 0.0011 10.000 0.0859 7.360 0.0327

Maryland .................................................... 0.439 0.0015 9.976 0.0915 7.246 0.0426

South Dakota ................................................. 0.439 0.0035 9.133 0.1636 6.879 0.0856

Montana ..................................................... 0.438 0.0026 9.339 0.1428 7.306 0.1127

Delaware .................................................... 0.435 0.0029 9.303 0.1514 7.068 0.1069

Minnesota ................................................... 0.435 0.0012 9.546 0.0582 7.144 0.0402

Maine ....................................................... 0.434 0.0021 9.494 0.0611 7.356 0.0671

Indiana ...................................................... 0.433 0.0013 9.458 0.0538 7.046 0.0391

Nevada ..................................................... 0.433 0.0022 8.618 0.1003 6.661 0.0579

Nebraska .................................................... 0.430 0.0018 9.073 0.0798 6.925 0.0616

Vermont ..................................................... 0.430 0.0027 9.340 0.1135 7.050 0.0729

Hawaii ...................................................... 0.427 0.0021 9.904 0.1785 6.881 0.1151

Idaho ....................................................... 0.426 0.0024 8.519 0.0964 6.653 0.0714

Iowa ........................................................ 0.426 0.0018 8.864 0.0655 6.823 0.0487

Wisconsin ................................................... 0.426 0.0015 8.909 0.0463 6.744 0.0278

Wyoming .................................................... 0.424 0.0037 8.448 0.1504 6.452 0.1006

New Hampshire ............................................... 0.418 0.0021 8.862 0.1396 6.627 0.0756

Alaska ...................................................... 0.411 0.0036 8.576 0.1821 6.343 0.1106

Utah ........................................................ 0.411 0.0019 7.987 0.0810 6.065 0.0504

Notes: Sorted in order of Gini index within category. P90/10 is the ratio of household income at the 90th percentile to that at the 10th percentile; P95/20 is a 

similar ratio at the 95th compared to the 20th percentile. 

Source: U.S. Census Bureau, American Community Survey. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see 

<www.census.gov/acs/www/>.
U.S. Census Bureau 5

three of the measures examined 

here—the District of Columbia, 

New York, Connecticut, Louisiana, 

Mississippi, Texas, Alabama, and 

California—and one more had 

higher inequality on two of the 

three measures—Massachusetts. 

Another seven states had income 

inequality that did not differ from 

that of the United States as a 

whole.12 That means there were 

33 states that had lower income 

inequality on at least two of the 

three measures. Figure 1 illustrates 

the differences.

There was only one state where 

the different income inequality 

measures tell a confused story, as 

indicated by a difference in ranking 

of at least 10 places. Florida was 

ranked ninth by the Gini index and 

with greater income inequality than 

the U.S. Gini (0.469 versus 0.467); 

it was however ranked twentyseventh by the P90/10 index, and 

twenty-third by the P95/20 index, 

with both the latter two indexes 

showing lower income inequality than for the United States as 

a whole.13 This suggests that the 

extremes of income are not as 

prevalent in Florida as in the 

other states.

The District of Columbia had higher 

income inequality than any state, 

independent of which of the three 

measures of inequality is used. 

Excluding DC, among the states, 

New York had the highest income 

inequality (ranked second on all 

three measures), followed by

12 For five states, the indexes differed 

from the United States on two or three measures, with at least one above and one below. 

For Tennessee, there was no statistically 

significant difference on any of the three measures, and for New Mexico, only one measure 

showed a lower level of inequality with no 

difference on the other two. 13 The ranks are not shown. States with 

similar ranks may not have indexes that differed significantly (see footnote 14 for 

an example).

Connecticut.14 On the other end of 

the scale, the state with the lowest 

income inequality is Utah (ranked 

fifty-first on all measures), followed 

by Alaska.15

Given the relatively small variation 

in findings when using the percentile measures of inequality in place 

of the Gini measure, the remainder 

of the report will focus on the Gini 

measure alone.

As of January 1, 2010, there were 

940 Core Based Statistical Areas 

(CBSAs) outside of Puerto Rico, 

including both micropolitan and 

metropolitan areas.16 The CBSAs 

with the most unequal income 

distribution, according to the 

Gini index, are the Starkville MS, 

Raymondville TX, and Clarksdale 

MS micropolitan areas, with high 

Gini indexes that are not statistically different (measured at 0.562, 

0.559, and 0.524, respectively). 

The most unequal metropolitan 

area is Bridgeport-StamfordNorwalk CT with an index of 0.538. 

The most equal income distributions among CBSAs are found in the 

Brigham City UT, Gillette WY, Los 

Alamos NM, North Vernon IN, and 

14 Connecticut is not statistically different from California, Georgia, Kentucky, New 

Jersey, Rhode Island, or Texas on its P90/10 

index and is not statistically different from 

Alabama, Massachusetts, or Texas on its 

P95/20 index. 15 Utah and Alaska are not statistically 

different on their Gini indexes. Alaska is also 

not statistically different from Idaho, Iowa, 

Nevada, New Hampshire, or Wyoming on its 

P90/10 index and is not statistically different 

from Wyoming on its P95/20 index. 16 Metropolitan and micropolitan statistical 

areas are geographic entities defined by the 

U.S. Office of Management and Budget for use 

by federal statistical agencies in collecting, 

tabulating, and publishing federal statistics. 

The term “Core Based Statistical Area” (CBSA) 

is a collective term for both metro and micro 

areas. A metropolitan area contains a core 

urban area of 50,000 or more population, and 

a micropolitan area contains an urban core of 

at least 10,000 (but less than 50,000) population. Each metropolitan or micropolitan area 

consists of one or more counties and includes 

the counties containing the core urban area, 

as well as any adjacent counties that have a 

high degree of social and economic integration (as measured by commuting to work) 

with the urban core.

Van Wert OH micropolitan areas 

with 0.346, 0.350, 0.352, 0.358, 

and 0.362, respectively, not statistically different from each other, 

and in the Appleton WI, OgdenClearfield UT, and Sheboygan MI 

metropolitan areas (all with a Gini 

index of 0.389).17

Table 4 presents the income 

inequality measures for those 

CBSAs having more than 1 million 

in population in 2009. Of those 

51 metropolitan areas, there are 

8 with higher income inequality than in the United States as a 

whole, with the New York-Northern 

New Jersey-Long Island NY-NJ-PA 

CBSA having the highest income 

inequality, followed by Miami-Fort 

Lauderdale-Pompano Beach FL, then 

Los Angeles-Long Beach-Santa Ana 

CA, and then the triad of HoustonSugar Land-Baytown TX, Memphis 

TN-MS-AR, and New OrleansMetairie-Kenner LA.18 The large 

metropolitan areas with the lowest 

income inequality are Salt Lake 

City UT and Virginia Beach-NorfolkNewport News VA-NC, followed by 

Minneapolis-St. Paul-Bloomington 

MN-WI, Las Vegas-Paradise NV, 

Riverside-San Bernardino-Ontario 

CA, Sacramento—Arden-Arcade—

Roseville CA, Kansas City MO-KS, 

and Washington-ArlingtonAlexandria DC-VA-MD-WV.19 Figure 

2 illustrates the differences.

17 The Gini indexes for Appleton, OgdenClearfield, and Sheboygan metropolitan areas 

are not statistically different from those for 

several other metropolitan areas, including 

Monroe MI, York-Hanover PA, Lebanon PA, 

Hinesville-Fort Stewart GA, and Fairbanks AK. 18 The Gini indexes for the Memphis and 

New Orleans metropolitan areas are not 

statistically different from those for the 

San Francisco-Oakland-Fremont CA and 

Birmingham-Hoover AL metropolitan areas. 19 The Gini index for Salt Lake City metropolitan area is not statistically different from 

that for the Virginia Beach metropolitan 

area; the indexes for Minneapolis, Las Vegas, 

Riverside, Sacramento, Kansas City, and 

Washington are not different.
6 U.S. Census Bureau Source: U.S. Census Bureau, American Community Survey. For estimates, see Table 3. Compared to U.S. Estimate Higher on all three measures Higher on two measures At least one measure above and one below Does not differ on any measure Lower on one measure Lower on two measures Lower on all three measures The three inequality measures used for comparison are the Gini index, P90/10 ratio, and P95/20 ratio. AK MD UT NV CA MT AZ WY TX CO NM OK KS AR NE SD IA ND MN MOLA TN MS AL GA FL SC WI KY IL IN OH MI VA NC WV 

PA NY 

WA OR ID HI 

Figure 1. Income Inequality Measures for States Compared to U.S. Estimate: 
U.S. Census Bureau 7

Table 4. 

Gini Index of Household Income Inequality for Metropolitan Areas of Over 1 Million 

Population: 2005–2009

Metropolitan area Population Gini index Standard error

Higher income inequality than United States

New York-Northern New Jersey-Long Island, NY-NJ-PA. . . . . . . . . . . . . 18,912,644 0.502 0.0007

Miami-Fort Lauderdale-Pompano Beach, FL. . . . . . . . . . . . . . . . . . . . . . 5,484,777 0.493 0.0015

Los Angeles-Long Beach-Santa Ana, CA. . . . . . . . . . . . . . . . . . . . . . . . 12,762,126 0.484 0.0010

Houston-Sugar Land-Baytown, TX. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,595,262 0.478 0.0015

Memphis, TN-MS-AR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,287,231 0.478 0.0029

New Orleans-Metairie-Kenner, LA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,153,788 0.476 0.0027

San Francisco-Oakland-Fremont, CA. . . . . . . . . . . . . . . . . . . . . . . . . . . 4,218,534 0.473 0.0014

Birmingham-Hoover, AL. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,112,213 0.472 0.0027

Same income inequality as the United States

 UNITED STATES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301,461,533 0.467 0.0006

Chicago-Naperville-Joliet, IL-IN-WI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9,461,816 0.466 0.0010

Boston-Cambridge-Quincy, MA-NH. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,513,934 0.465 0.0015

Charlotte-Gastonia-Concord, NC-SC. . . . . . . . . . . . . . . . . . . . . . . . . . . 1,641,257 0.464 0.0049

Oklahoma City, OK. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,191,174 0.464 0.0046

San Antonio, TX. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,979,686 0.463 0.0041

Lower income inequality than United States

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD. . . . . . . . . . . . . . . . . 5,910,593 0.464 0.0021

Cleveland-Elyria-Mentor, OH. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,101,821 0.462 0.0049

Dallas-Fort Worth-Arlington, TX. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,144,234 0.461 0.0046

Nashville-Davidson—Murfreesboro—Franklin, TN. . . . . . . . . . . . . . . . . 1,520,649 0.460 0.0041

Tampa-St. Petersburg-Clearwater, FL. . . . . . . . . . . . . . . . . . . . . . . . . . . 2,702,390 0.459 0.0039

Pittsburgh, PA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,360,259 0.459 0.0026

Detroit-Warren-Livonia, MI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,452,548 0.454 0.0037

Austin-Round Rock, TX. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,589,393 0.453 0.0038

Buffalo-Niagara Falls, NY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,128,813 0.453 0.0029

Atlanta-Sandy Springs-Marietta, GA. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5,238,994 0.452 0.0024

San Diego-Carlsbad-San Marcos, CA. . . . . . . . . . . . . . . . . . . . . . . . . . . 2,987,543 0.451 0.0041

Denver-Aurora-Broomfield, CO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,451,038 0.450 0.0045

St. Louis, MO-IL. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,803,776 0.448 0.0023

San Jose-Sunnyvale-Santa Clara, CA. . . . . . . . . . . . . . . . . . . . . . . . . . . 1,784,130 0.448 0.0032

Milwaukee-Waukesha-West Allis, WI. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,546,312 0.448 0.0031

Phoenix-Mesa-Scottsdale, AZ. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4,151,634 0.447 0.0027

Cincinnati-Middletown, OH-KY-IN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,140,796 0.447 0.0029

Indianapolis-Carmel, IN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,695,807 0.447 0.0040

Providence-New Bedford-Fall River, RI-MA. . . . . . . . . . . . . . . . . . . . . . . 1,602,591 0.447 0.0022

Louisville-Jefferson County, KY-IN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,235,476 0.447 0.0032

Jacksonville, FL. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,294,684 0.446 0.0047

Baltimore-Towson, MD. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,669,987 0.445 0.0029

Columbus, OH. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,758,531 0.445 0.0036

Hartford-West Hartford-East Hartford, CT. . . . . . . . . . . . . . . . . . . . . . . . 1,186,939 0.443 0.0051

Orlando-Kissimmee, FL. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,023,605 0.442 0.0029

Seattle-Tacoma-Bellevue, WA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,306,836 0.440 0.0037

Portland-Vancouver-Beaverton, OR-WA. . . . . . . . . . . . . . . . . . . . . . . . . 2,163,436 0.440 0.0031

Richmond, VA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,209,484 0.437 0.0038

Raleigh-Cary, NC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,042,848 0.437 0.0027

Rochester, NY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,033,026 0.436 0.0036

Washington-Arlington-Alexandria, DC-VA-MD-WV. . . . . . . . . . . . . . . . . 5,332,297 0.433 0.0043

Kansas City, MO-KS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,013,797 0.433 0.0042

Sacramento—Arden-Arcade—Roseville, CA. . . . . . . . . . . . . . . . . . . . . 2,076,579 0.432 0.0043

Riverside-San Bernardino-Ontario, CA. . . . . . . . . . . . . . . . . . . . . . . . . . 4,022,939 0.431 0.0021

Las Vegas-Paradise, NV. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,821,507 0.431 0.0030

Minneapolis-St. Paul-Bloomington, MN-WI. . . . . . . . . . . . . . . . . . . . . . . 3,202,412 0.430 0.0031

Virginia Beach-Norfolk-Newport News, VA-NC. . . . . . . . . . . . . . . . . . . . 1,669,614 0.421 0.0028

Salt Lake City, UT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,090,416 0.417 0.0042

Source: U.S. Census Bureau, American Community Survey. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see 

<www.census.gov/acs/www/>.
8 U.S. Census Bureau Source: U.S. Census Bureau, American Community Survey. For estimates, see Table 4. Higher Gini index than U.S. estimate Same Gini index as U.S. estimate Lower Gini index than U.S. estimate Figure 2. Gini Index for Metropolitan Areas With Population Over One Million Compared to U.S. Estimate: 2005–2009
U.S. Census Bureau 9

COMPARING PLACES20

The Census Bureau definition of 

place is “A concentration of population either legally bounded as an 

incorporated place, or identified as 

a Census Designated Place (CDP). 

. . . Incorporated places have legal 

descriptions of borough (except in 

Alaska and New York), city, town 

(except in New England, New York, 

and Wisconsin), or village.” It is difficult to identify the places with the 

highest and lowest levels of income 

inequality, as small areas have large 

sampling variability. Of the 17,823 

places with at least 50 interviewed 

housing units in the sample, the 48 

places with the highest measured 

Gini index and the 46 places with 

the lowest measured Gini index are 

all under 10,000 people. 

The places with the numerically 

highest and lowest measured 

Gini indexes are listed in Table 

5. Compared to the U.S. Gini for 

2005–2009 of 0.467, all of these 

places have notably high (low) Gini 

indexes. Fountainhead-Orchard 

20 Counties (which are not places) are not 

analyzed here.

Hills CDP MD, just north of 

Hagerstown MD and part of the 

Hagerstown Urbanized Area (see 

Map 1), has the highest measured 

income inequality at 0.714, using 

the Gini index (though not different from the others in the table 

because of the small sample sizes), 

or 153 percent of the U.S. figure. It 

is important to note that this place 

is not necessarily the place in the 

United States that has the highest 

income inequality, but only the one 

with the highest measured income 

inequality. In particular, different estimators (e.g., a shrinkage 

estimator; see Paddock et al. 2006) 

could have substantial effects on 

such measures by taking account 

of additional information (direct 

survey-based estimates like those 

reported in the table by construction do not). Further discussion 

of this place is included here only 

because it is likely to be illustrative 

of similar places with high measured inequality.

Fountainhead-Orchard Hills is 

demographically heterogeneous. 

For example, in FountainheadOrchard Hills in 2005–2009, 53 

percent of households are marriedcouple households while 23 percent consisted of one person only; 

17 percent had moved during the 

past year from another residence; 

15 percent of people 25 years and 

over had less than a high school 

diploma while 13 percent had a 

graduate or professional degree; 

the median income of households 

was $52,321; and 25 percent of the 

population was under 18 years.

Country Knolls CDP NY, part of the 

Albany Urbanized Area about halfway between Albany and Saratoga 

Springs NY (see Map 2), has the 

lowest measured income inequality (though not different from the 

others in the table because of the 

small sample sizes)—0.214, or 46 

percent of the U.S. figure.21 In these 

small places with low inequality, sorting is likely the source 

of the homogeneity in income. 

For example, in Country Knolls 

in 2005–2009, 96 percent of the 

people were White non-Hispanic; 85 

percent of households are marriedcouple households; 97 percent 

of the people at least 1 year old 

were living in the same residence 

1 year earlier; 26 percent of people 

25 years and over had a graduate or professional degree; the 

median income of households was 

$107,589; and only 9 of 609 housing units were renter-occupied (1.5 

percent). 

LARGE PLACES

Table 6 presents the Gini income 

inequality measure for the 10 

places with the highest and lowest 

measured inequality among the 

269 places of 100,000 population 

or more. These large places have 

much more reliable estimates of the 

income inequality measures than 

do small places. The large place 

with the highest measured income 

21 Country Knolls is discussed only as 

illustrative of places with low measured 

income inequality.

Table 5. 

Places With Highest and Lowest Measured Household 

Income Inequality: 2005–2009

Geographic name Estimated 

population Gini index 

Standard 

error

HIGHEST INEQUALITY 

Fountainhead-Orchard Hills CDP, Maryland. . . . . . . 5,059 0.714 0.1498

Charlevoix city, Michigan. . . . . . . . . . . . . . . . . . . . . . 2,681 0.702 0.0714

Dover CDP, Florida. . . . . . . . . . . . . . . . . . . . . . . . . . 2,929 0.696 0.1201

Dierks city, Arkansas. . . . . . . . . . . . . . . . . . . . . . . . . 1,105 0.688 0.1357

Grafton city, Illinois. . . . . . . . . . . . . . . . . . . . . . . . . . . 847 0.674 0.0751

LOWEST INEQUALITY 

Shoreacres city, Texas. . . . . . . . . . . . . . . . . . . . . . . . 1,716 0.223 0.0165

Runnells city, Iowa. . . . . . . . . . . . . . . . . . . . . . . . . . . 499 0.222 0.0506

Buckner CDP, Kentucky. . . . . . . . . . . . . . . . . . . . . . . 4,279 0.220 0.0259

Spring Valley village, Ohio. . . . . . . . . . . . . . . . . . . . . 717 0.217 0.0434

Country Knolls CDP, New York. . . . . . . . . . . . . . . . . 1,767 0.214 0.0219

Notes: CDP=Census Designated Place. Gini for all places in the table not statistically different from 

that of all others in the table in its same category and from many other places not listed, due to small 

sample sizes. For example, the 90 percent confidence interval for the Gini index for Country Knolls CDP 

New York is 0.178 to 0.250. Egegik city, Alaska, with a Gini index of 0.156, with a 90 percent confidence 

interval of 0.024 to 0.288 (standard error of 0.0804) is excluded above because its estimated population is 

only 61.

Source: U.S. Census Bureau, American Community Survey. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see <www.census.gov/acs/www/>.
10 U.S. Census Bureau

PA

Hagerstown UA

VA

WV MD

DC

FountainheadOrchard Hills

Area Location
U.S. Census Bureau 11

NY

Albany UA

VT

MA

Country Knolls

Area Location
12 U.S. Census Bureau

inequality is Atlanta GA. All other 

places in the top ten have the same 

income inequality, except that the 

Gini for New Orleans is higher than 

that for Dallas TX and Baton Rouge 

LA. Note that Washington DC (the 

city) had a high level of inequality, 

while the Washington DC metropolitan area showed a low level of 

inequality.

In contrast, no very large city 

(500,000 or more population) is on 

the list of the ten large places with 

the lowest income inequality. The 

table shows that the large places 

with low income inequality include 

seven places in the West. The large 

place with the most homogeneous 

income distribution (lowest income 

inequality) is West Jordan UT, with a 

Gini index of 0.327, 70 percent of 

the U.S. figure (recall that Utah was 

one of the two states with the most 

equal income distribution).

Figure 3 displays the relationship 

between the level of median household income and the Gini measure 

of inequality. There appears to be 

a slight relationship, with the Gini 

index falling as median household 

income rises, at least for all places 

and for large places, with perhaps 

the opposite holding true for the 

34 very large places.

DRILLING DOWN TO 

NEIGHBORHOODS

This section focuses on the 61,358 

U.S. census tracts within which 

50 or more interviews were conducted between January 2005 and 

December 2009. These tracts average 4,825 population, 1,812 households, and 2,054 housing units; 

some individuals live in group 

quarters and their incomes are not 

reflected here.22 The census tract 

boundaries used here are the ones 

established for the Census 2000. 

States had the option to propose 

22 The number of households is the same 

as the number of occupied housing units.

revisions to tract boundaries to create more homogeneous tracts for 

2010 Census reporting so the ACS 

data issued for tracts for 2006–

2010 may show more homogeneity 

of characteristics.

Table 7 shows the ten census tracts 

with the highest and lowest income 

inequality, as measured by the Gini 

index.23 The ten with the highest 

income inequality are all urban 

except one, and four are in Texas. 

The ten with the lowest income 

inequality are also all urban except 

one, and three are in Colorado (all 

in the Denver-Aurora urbanized 

area). 

When compared to U.S. averages, 

certain characteristics stand out as 

23 As with places, the specific tracts 

discussed below are meant to be illustrative 

of similar tracts and are not necessarily those 

with the highest or lowest income inequality.

important for the tracts to fall into 

these two categories.24 For the ten 

with the highest income inequality, 

all have relatively low income levels—all have their 20th percentile 

of household income that is 0.67 of 

the U.S. level or less, 50th percentile at 0.76 or less, and 90th percentile at 0.75 or less. The fraction 

with no workers is all at 1.26 times 

the U.S. fraction or more (with the 

one exception of the Allegheny 

County tract at 0.99). These tracts 

all also have a higher vacancy rate 

than the U.S. figure. There appear 

to be no systematic differences 

along racial or ethnic lines. 

Census Tract 17 in Hamilton 

County Ohio, the tract with the 

most unequal measured income 

distribution, is part of downtown 

24 The demographic and housing characteristics examined can be seen in Table 8.

Table 6. 

Large Places With Highest and Lowest Measured Household 

Income Inequality: 2005–2009

(Large places have at least 100,000 population)

Geographic name Estimated 

population Gini index Standard error

HIGHEST INEQUALITY

Atlanta city, Georgia. . . . . . . . . . . . . . . . . . . 515,843 0.571 0.0046

New Orleans city, Louisiana. . . . . . . . . . . . 328,669 0.546 0.0055

Washington city, District of Columbia. . . . . 588,433 0.540 0.0054

Miami city, Florida. . . . . . . . . . . . . . . . . . . . 418,480 0.540 0.0033

Gainesville city, Florida. . . . . . . . . . . . . . . . 115,146 0.537 0.0071

Athens-Clarke County unified government 

(balance), Georgia1. . . . . . . . . . . . . . . . . . 111,814 0.537 0.0081

New York city, New York. . . . . . . . . . . . . . . . 8,302,659 0.536 0.0012

Fort Lauderdale city, Florida. . . . . . . . . . . . 183,374 0.534 0.0071

Dallas city, Texas. . . . . . . . . . . . . . . . . . . . . 1,269,204 0.532 0.0031

Baton Rouge city, Louisiana 2. . . . . . . . . . . 225,780 0.530 0.0056

LOWEST INEQUALITY

Elgin city, Illinois 2. . . . . . . . . . . . . . . . . . . . 102,590 0.371 0.0057

Port St. Lucie city, Florida. . . . . . . . . . . . . . 145,740 0.370 0.0061

Olathe city, Kansas. . . . . . . . . . . . . . . . . . . 117,116 0.366 0.0063

Gilbert town, Arizona. . . . . . . . . . . . . . . . . . 204,904 0.363 0.0062

West Valley City city, Utah. . . . . . . . . . . . . . 122,006 0.359 0.0058

North Las Vegas city, Nevada. . . . . . . . . . . 205,483 0.358 0.0048

Elk Grove city, California. . . . . . . . . . . . . . . 130,007 0.358 0.0069

Norwalk city, California. . . . . . . . . . . . . . . . 102,910 0.358 0.0059

Thornton city, Colorado. . . . . . . . . . . . . . . . 110,768 0.348 0.0060

West Jordan city, Utah. . . . . . . . . . . . . . . . . 101,727 0.327 0.0071

1

 Part of Bogart, GA, (the portion in Clarke County) and all of Winterville, GA, are included in the AthensClarke County unified government, but have retained legal status as municipalities, so estimates for them are 

computed separately, leaving the “balance” as a place as well.

2

 Gini for Baton Rouge and Elgin each not statistically different from that of several places in the table and 

other places not listed. 

Source: U.S. Census Bureau, American Community Survey. For information on confidentiality protection, 

sampling error, nonsampling error, and definitions, see <www.census.gov/acs/www/>.
U.S. Census Bureau 13

Regression line

Regression line

Regression line

Gini index

Median household income

Figure 3.

Gini Index by Median Household Income for Places: 2005–2009

Notes: Number of places are (a) 25,127, (b) 281, and (c) 34. 

Source: U.S. Census Bureau, American Community Survey.

0 $50,000 $100,000 $150,000 $200,000 $250,000 $300,000

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(a) All Places

0 $50,000 $100,000 $150,000 $200,000 $250,000 $300,000

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(b) Large Places—Population of 100,000 or More

0 $50,000 $100,000 $150,000 $200,000 $250,000 $300,000

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

(c) Very Large Places—Population of 500,000 or More
14 U.S. Census Bureau

Cincinnati (see Map 3).25 It is 

populated mostly by individuals living alone (58 percent of all 

households) and by female householders with no husband present 

(35 percent), about two-thirds of 

whom have children at home; 86 

percent of the tract’s population is 

female and 78 percent is Black or 

African-American. The population is 

relatively poor and uneducated—

67 percent have household income 

under $10,000 per year and 60 percent received Food Stamps in

25 As noted in the table, none of the Gini 

measures for the highest (lowest) inequality tracts are statistically different from any 

others in the list of highest (lowest) inequality 

tracts, or from that for numerous other tracts 

not listed.

the past 12 months, and 38 percent have less than a high school 

diploma, while only 6 percent have 

incomes above $100,000 per year. 

However, the tract’s population is 

roughly evenly split between those 

in the labor force and those not in 

the labor force (43 and 57 percent, 

respectively, not a statistically significant difference); the unemployment rate for the former is fairly 

low—5 percent. The housing stock 

is old, with 77 percent built before 

1939, and the vacancy rate is high, 

at 64 percent; 97 percent of the 

housing is renter-occupied.

In contrast, the ten tracts with the 

lowest income inequality appear to 

have relatively high incomes—all 

have their 10th percentile of 

household income that is 1.7 times 

the U.S. level or more, and all have 

their 20th percentile of household 

income that is 1.2 times the 

U.S. level or more. The fraction 

with no workers is 0.72 of the 

U.S. fraction or less. Finally, these 

low inequality tracts all have a 

lower fraction living in households 

with Supplemental Security Income, 

cash public assistance income, 

or Food Stamps in the past 12 

months—all at 0.44 or less, except 

for the tract in Salt Lake County 

at 0.85. 

Census Tract 601 in Cass County 

Missouri, the tract with the 

most equal measured income 

Table 7. 

Census Tracts With the Highest and Lowest Measured Household Income Inequality: 

2005–2009

Name of area Households Population

Housing 

units Gini index

Standard 

error Urbanized area1

Tracts with the highest income inequality

Census Tract 17, Hamilton County, Ohio. . . . . . . . . . . . . . 321 630 894 0.833 0.1024 Cincinnati

Census Tract 20, Kent County, Michigan. . . . . . . . . . . . . . 1,060 1,936 1,429 0.798 0.0430 Grand Rapids

Census Tract 207.01, Bell County, Texas. . . . . . . . . . . . . . 631 1,226 868 0.770 0.1504 Temple

Census Tract 7, Travis County, Texas. . . . . . . . . . . . . . . . . 592 964 830 0.764 0.1308 Austin

Census Tract 9506, Rusk County, Texas. . . . . . . . . . . . . . 754 1,846 1,052 0.759 0.0965 -noneCensus Tract 1018, Tarrant County, Texas. . . . . . . . . . . . . 941 1,165 1,092 0.755 0.0405 Fort Worth

Census Tract 1301, Allegheny County, Pennsylvania. . . . 983 2,113 1,293 0.728 0.1337 Pittsburgh

Census Tract 122.05, Boulder County, Colorado. . . . . . . . 1,603 2,894 1,876 0.724 0.0860 Boulder

Census Tract 126, Tuscaloosa County, Alabama. . . . . . . . 839 2,171 1,083 0.718 0.0405 Tuscaloosa

Census Tract 9706, Richmond County, North Carolina. . . 1,634 4,550 1,860 0.716 0.1055 Rockingham

United States census tract averages . . . . . . . . . . . . . . 1,812 4,825 2,054 0.467 (X) (X)

Tracts with the lowest income inequality

Census Tract 85.24, Adams County, Colorado. . . . . . . . . 2,292 7,419 2,435 0.231 0.0134 Denver-Aurora

Census Tract 98.29, Jefferson County, Colorado. . . . . . . . 732 2,249 732 0.230 0.0256 Denver-Aurora

Census Tract 115, Richland County, South Carolina. . . . . 958 13,063 1,481 0.229 0.0201 Columbia

Census Tract 32.03, Kern County, California. . . . . . . . . . . 750 2,542 789 0.228 0.0266 Bakersfield

Census Tract 70.82, Arapahoe County, Colorado. . . . . . . 1,341 4,686 1,341 0.227 0.0163 Denver-Aurora

Census Tract 5, Wayne County, North Carolina. . . . . . . . . 924 3,737 1,544 0.222 0.0247 Goldsboro

Census Tract 4064.02, Los Angeles County, California. . . 606 2,191 606 0.221 0.0243

Los Angeles-Long 

Beach-Santa Ana

Census Tract 1135.26, Salt Lake County, Utah. . . . . . . . . 1,262 4,977 1,279 0.215 0.0148 Salt Lake City

Census Tract 9702, Meade County, Kentucky. . . . . . . . . . 903 2,811 1,602 0.213 0.0295 -noneCensus Tract 601, Cass County, Missouri. . . . . . . . . . . . . 686 1,886 719 0.204 0.0191 Kansas City

(X) Not applicable. 

Notes: None of the Gini measures for the highest (lowest) inequality tracts are statistically different from any others in the list of highest (lowest) inequality tracts, 

or from that for numerous other tracts not listed. For example, the 90 percent confidence interval for the Gini index for Census Tract 17 Hamilton County Ohio is 

0.665 to 1.000, and the 90 percent confidence interval for the Gini index for Census Tract 601 Cass County Missouri is 0.173 to 0.236. Table excludes tracts with 

fewer than 50 interviews in the 2005–2009 period.

1

 Census Tract 9506, Rusk County, Texas is between Carthage and Henderson. Rockingham is an urban cluster, not an urbanized area. Census Tract 9702, 

Meade County, Kentucky is southwest of Louisville.

Source: U.S. Census Bureau, American Community Survey. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see 

<www.census.gov/acs/www/>.
U.S. Census Bureau 15

KY

Cincinnati UA

OH

IN

Census Tract 17

Area Location
16 U.S. Census Bureau

KS

Kansas City UA

NE

MO

Census Tract 601

Area Location
U.S. Census Bureau 17

distribution, is very different 

from Census Tract 17 in Hamilton 

County Ohio (see Map 4). While it 

is also part of an urbanized area 

(Kansas City in this case), it is 

suburban rather than center city, 

located in the northwest corner of 

Cass County near Belton MO and 

the former Richards-Gebaur Air 

Force Base, and south of the county 

in which most of Kansas City is 

found (Jackson County MO). Only 

17 percent of its households were 

ones in which the householder 

is female with no husband present; 28 percent of the households 

consist of an individual living alone 

and 46 percent are married-couple 

families. The tract is racially and 

ethnically mixed, with 72 percent 

of the tract’s population being 

Non-Hispanic White, 14 percent 

reporting themselves as Black or 

African-American, and 10 percent 

as Hispanic. There’s even a sizeable 

Samoan representation (3 percent). 

Unemployment there is low (3 

percent), and income is distributed 

over much of the range (only 2 

percent received under $10,000 

while 88 percent received between 

$25,000 and $74,999). None of the 

housing was built before 1939; 70 

percent was built between 1950 

and 1969 and the vacancy rate was 

5 percent.

This univariate analysis above suggests that perhaps those relatively 

well-off want to live mainly with 

those who are also relatively welloff, and those who are toward the 

lower end of the income spectrum 

don’t have that choice and live in 

areas with high inequality.

MULTIVARIATE ANALYSIS 

OF CENSUS TRACTS

This section will discuss the results 

of a multivariate analysis of the correlates of inequality as measured 

by the Gini index. It is not intended 

to be a causal analysis, but merely 

to point out the associations with 

income inequality. The importance 

of demographic and housing factors is indicated by the simple correlation matrix for state-level and 

tract-level measures presented in 

Table 8. Two factors have a positive correlation of 0.40 or more at 

the state level and 0.20 or more at 

the tract level—the fraction living 

in “assisted” households (receiving 

Supplemental Security Income, cash 

public assistance, or Food Stamps) 

and the fraction of households with 

no workers—and four factors have 

a negative correlation of the same 

magnitudes—the fraction of those 

16 or older with earnings, the 

fraction of households with two or 

more workers, and the fraction of 

housing units that are single-family, 

and the fraction of housing units 

that are owner-occupied. The two 

factors positively correlated with 

higher income inequality in a state 

or neighborhood may be thought 

of as indicators of a predominately 

low-income population, while the 

Table 8. 

Correlation of State-Level and Tract-Level Household Income Inequality (as Measured by 

the Gini Index) With Demographic and Housing Characteristics: 2005–2009

Demographic or housing characteristic State-level 

correlation

Tract-level 

correlation

Total population: Fraction White alone. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.5068 –0.0981

Total population: Fraction Hispanic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.2116 –0.0215

Total population: Fraction Black alone or in combination. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.7007 0.1533

Total population: Fraction American Indian or Alaska Native alone or in combination. . . . . . . . . . . –0.2705 0.0120

Total population: Fraction Asian alone or in combination. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.0928 –0.0242

Total population: Fraction Native Hawaiian or Pacific Islander alone or in combination. . . . . . . . . . –0.1737 –0.0306

Total population: Fraction 65 or older. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.0930 0.1662

Total population: Fraction under 18. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.2725 –0.1761

Total population: Fraction in family households. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.3750 –0.3068

Total population: Fraction in group quarters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.3994 0.0773

Total population: Fraction living in households with Supplemental Security Income (SSI), cash 

public assistance income, or Food Stamps/SNAP in the past 12 months. . . . . . . . . . . . . . . . . . . . 0.4872 0.2359

Total Persons 16 or older: Fraction with earnings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.5602 –0.2944

Total Persons 25 or older: Fraction with less than high school diploma . . . . . . . . . . . . . . . . . . . . . . 0.6077 0.1155

Total Persons 25 or older: Fraction with bachelor’s, graduate, or professional degree . . . . . . . . . . . 0.2695 0.1359

Total Persons Civilian 16+ Employed: Fraction in management, professional, or 

related occupations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.4055 0.0883

Total Households: Fraction with no workers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.4059 0.4164

Total Households: Fraction with two or more workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.7071 –0.4609

Total Housing Units: Fraction occupied. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.1434 –0.2263

Total Housing Units: Fraction as single-family units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.6317 –0.3347

Total Housing Units: Fraction owner-occupied. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.5276 –0.3138

Note: Table excludes tracts with fewer than 50 interviews in the 2005–2009 period.

Source: U.S. Census Bureau, American Community Survey. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see 

<www.census.gov/acs/www/>.
18 U.S. Census Bureau

four factors associated with lower 

income inequality are more indicators of a higher-income population. 

(Reflecting the scatter plots shown 

in Figure 3 for places, correlations 

of the Gini index with median 

household income are –0.12 at the 

state level and –0.22 at the tract 

level.)

The highest correlations of the 

Gini index at the state level are 

with the fraction Black alone or in 

combination (0.70) and the fraction 

with two or more workers (–0.71), 

followed by the fraction of housing 

units as single-family units (–0.63) 

and fraction with less than high 

school diploma (0.61). At the tract 

level, the highest correlations are 

the fraction with no workers (0.42) 

and the fraction with two or more 

workers (–0.46).

The first step of multivariate 

analysis of association is carried 

out using the Gini index as the 

dependent variable in an ordinary 

Table 9. 

Regression of Gini Index of Household Income Inequality on Tract Characteristics

Dependent variable:

Gini coefficient of household income

Unweighted ordinary least 

squares

(each tract weighted equally)

Weighted least squares

(tracts weighted by population)

Weighted least squares

(tracts weighted by square root of 

population)

Step1

Coefficient

Standard 

error t-stat Step1

Coefficient

Standard 

error t-stat Step1

Coefficient

Standard 

eror t-stat

Intercept. . . . . . . . . . . . . . . . . . . . . . . . . . 0 0.4302 0.0047 *** 0 0.4519 0.0048 *** 0 0.4406 0.0048 ***

10th percentile of household income 

(divided by 100,000). . . . . . . . . . . . . . . 3 –0.4104 0.0030 *** 3 –0.4119 0.0029 *** 3 –0.4112 0.0030 ***

90th percentile of household income 

(divided by 100,000). . . . . . . . . . . . . . . 2 0.0454 0.0004 *** 2 0.0479 0.0004 *** 2 0.0467 0.0004 ***

Fraction of persons Hispanic. . . . . . . . . . 14 –0.0108 0.0012 *** 13 –0.0122 0.0012 *** 14 –0.0115 0.0012 ***

Fraction of persons Black2. . . . . . . . . . . . 15 –0.0070 0.0009 *** 14 –0.0092 0.0009 *** 15 –0.0080 0.0009 ***

Fraction of persons American Indian or 

Alaska Native2. . . . . . . . . . . . . . . . . . . . (X) –0.0029 0.0032 (X) –0.0032 0.0038 (X) –0.0025 0.0035

Fraction of persons Asian2. . . . . . . . . . . . 11 –0.0394 0.0022 *** 10 –0.045 0.0022 *** 10 –0.0423 0.0022 ***

Fraction of persons Native Hawaiian or 

Pacific Islander2. . . . . . . . . . . . . . . . . . . 19 0.0434 0.0090 *** 17 0.0537 0.0092 *** 18 0.0490 0.0091 ***

Fraction of persons 65 or older. . . . . . . . 13 –0.0542 0.0042 *** 7 –0.0748 0.0042 *** 9 –0.0641 0.0042 ***

Fraction of persons under 18. . . . . . . . . . 20 –0.0067 0.0046 20 –0.0095 0.0046 ** 20 –0.0082 0.0046 *

Fraction of persons in family 

households. . . . . . . . . . . . . . . . . . . . . . 18 –0.0109 0.0032 *** 16 –0.0159 0.0032 *** 17 –0.0130 0.0032 ***

Fraction of persons in group quarters. . . 10 0.0252 0.0035 *** 11 0.0243 0.0038 *** 11 0.0251 0.0037 ***

Fraction of persons living in assisted 

households3. . . . . . . . . . . . . . . . . . . . . . 9 0.0437 0.0025 *** 12 0.0468 0.0025 *** 12 0.0050 0.0025 ***

Fraction of persons 16 or older with 

earnings. . . . . . . . . . . . . . . . . . . . . . . . 17 –0.0295 0.0039 *** 15 –0.0413 0.0040 *** 16 –0.0351 0.0040 ***

Fraction of persons 25 or older with 

less than a high school diploma . . . . . . 6 0.0697 0.0027 *** 5 0.0705 0.0028 *** 5 0.0701 0.0027 ***

Fraction of persons 25 or older with a 

bachelor’s degree or more. . . . . . . . . . 4 0.1015 0.0025 *** 4 0.1094 0.0025 *** 4 0.1054 0.0025 ***

Fraction of civilian persons 16 or older 

employed in management, 

professional, or related occupations. . . 12 0.0283 0.0028 *** 18 0.0132 0.0028 *** 13 0.0208 0.0028 ***

Fraction of households with 

no workers. . . . . . . . . . . . . . . . . . . . . . . 5 0.1348 0.0034 *** 6 0.1319 0.0036 *** 6 0.1336 0.0035 ***

Fraction of households with two or 

more workers. . . . . . . . . . . . . . . . . . . . . 1 –0.0069 0.0030 ** 1 –0.0075 0.0030 ** 1 –0.0075 0.0030 **

Fraction of housing units occupied. . . . . 8 –0.0420 0.0018 *** 8 –0.0497 0.0019 *** 8 –0.0459 0.0019 ***

Fraction of housing units that are 

single-family units . . . . . . . . . . . . . . . . . 16 –0.0071 0.0012 *** 19 –0.0030 0.0012 *** 19 –0.0052 0.0012 ***

Fraction of housing units owneroccupied. . . . . . . . . . . . . . . . . . . . . . . . 7 –0.0277 0.0016 *** 9 –0.0279 0.0016 *** 7 –0.0278 0.0016 ***

R-squared. . . . . . . . . . . . . . . . . . . . . . . . 0.611 0.637 0.623

Notes: Number of observations = 61,352. Table excludes tracts with fewer than 50 interviews in the 2005–2009 period.

1

Step entering stepwise regression; (X) indicates its addition does not add to explanatory power of the regression.

2

Alone or in combination with another race.

3

Assisted household defined as receiving Supplemental Security Income, cash public assistance income, or Food Stamps/SNAP in the past 12 months.

*/**/*** = Significant at the 0.10/0.05/0.01 level.

Source: U.S. Census Bureau, American Community Survey. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see 

<www.census.gov/acs/www/>.
U.S. Census Bureau 19

least squares (OLS) regression on 

all the demographic characteristics 

included in Table 8, and all tracts 

are weighted equally. Two further 

extensions are also reported: (1) a 

stepwise OLS regression, allowing 

the most influential independent 

variables to enter one by one; and 

(2) weighted least squares (WLS) 

regressions; stepwise WLS results 

are also reported. The weights for 

WLS regressions were designed to 

give more weight to larger tracts 

wherein the Gini coefficients are 

expected to be more reliable. One 

set of weights uses the population 

directly and the second set uses 

the square root of population; all 

weights are adjusted to sum to the 

number of tracts (that is, the same 

sum of the implicit weights used in 

the OLS regressions).

Table 9 shows the regression 

results for all six approaches. The 

fit (R2) is reasonably high for a 

cross-sectional regression; these 

independent variables explain 61 

percent of the variation in the Gini 

index using OLS, and slightly more 

(64 and 62 percent) using the two 

WLS approaches. 

In the OLS regression, there are 

only two variables that have no 

independent association with 

the Gini coefficient—the fraction 

American Indian or Alaska Native 

(AIAN), and the fraction of the 

population under 18.26 However, in 

the two WLS regressions, only the 

fraction AIAN does not contribute 

to the explanatory power of the 

independent variables. Below, the 

26 The latter is significant only at the 

0.13 level.

magnitude of the association of the 

independent variables with the Gini 

coefficient is discussed using the 

regression equation with the best 

fit—the WLS regression using the 

total population weights. 

The three most influential independent variables affecting the Gini 

coefficient—the ones entering the 

stepwise regression first—are the 

fraction with two or more workers, 

and the two income variables (the 

10th and the 90th percentiles of 

income). Together they explain 57 

percent of the variance in the Gini 

index by tract that is, almost 90 

percent of the 64 percent explained 

by the independent variables. 

All estimated coefficients are 

converted to percentage changes 

evaluated at the means of all the 

Table 10. 

Effects of Change in Tract Characteristics on Gini Index of Household Income Inequality 

of a One Standard Deviation Change in the Characteristic

Demographic or housing characteristic

Mean

Standard 

deviation

Implied 

change in 

Gini 

coefficient

10th percentile of household income (divided by 100,000). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.159 0.093 –0.038

90th percentile of household income (divided by 100,000). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.317 0.739 0.034

Fraction of persons Hispanic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.135 0.201 –0.002

Fraction of persons Black1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.137 0.222 –0.002

Fraction of persons American Indian or Alaska Native1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.016 0.050 –

Fraction of persons Asian1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.045 0.087 –0.003

Fraction of persons Native Hawaiian or Pacific Islander1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.003 0.019 0.001

Fraction of persons 65 or older. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.135 0.069 –0.004

Fraction of persons under 18. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.240 0.065 –

Fraction of persons in family households. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.809 0.125 –0.001

Fraction of persons in group quarters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.022 0.065 0.002

Fraction of persons living in assisted households2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.143 0.120 0.005

Fraction of persons 16 or older with earnings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.682 0.094 –0.003

Fraction of persons 25 or older with less than a high school diploma . . . . . . . . . . . . . . . . . . . . . . . . 0.160 0.118 0.008

Fraction of persons 25 or older with a bachelor’s degree or more. . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.263 0.178 0.018

Fraction of civilian persons 16 or older employed in management, professional, or 

related occupations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.332 0.143 0.004

Fraction of households with no workers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.268 0.105 0.014

Fraction of households with two or more workers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.345 0.105 –0.001

Fraction of housing units occupied. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.887 0.097 –0.004

Fraction of housing units that are single-family units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.685 0.236 –0.002

Fraction of housing units owner-occupied. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.666 0.218 –0.006

– Represents or rounds to zero. 

Notes: The effect is evaluated for a change from one-half a standard deviation below to one-half above, at the means for the other independent variables using 

the population-weighted least squares regression.

1

 Alone or in combination with another race.

2

 Assisted household defined as receiving Supplemental Security Income, cash public assistance income, or Food Stamps/SNAP in the past 12 months. 

Source: U.S. Census Bureau, American Community Survey. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see 

<www.census.gov/acs/www/>.
20 U.S. Census Bureau

other independent variables in 

Table 10. The only three variables 

with a notable effect on the Gini 

coefficient using this alternate 

approach include two of the three 

that entered the stepwise regression first—the two income variable 

(the 10th and the 90th percentiles 

of income)—one related to the third 

stepwise variable—the fraction with 

no workers—and one other—the 

fraction of persons 25 or older 

with a bachelor’s degree or more. 

However, none of them affect the 

Gini by as much as one standard 

deviation of the tract Gini index 

(0.062); the two income variables 

affect the Gini by about one-half 

as much (0.038 and 0.034 for the 

10th and 90th percentile variables, 

respectively), and the fraction with 

no workers and the fraction of persons 25 or older with a bachelor’s 

degree or more have only about 

one-quarter the effect (0.014 and 

0.018, respectively).

CONCLUDING REMARKS

High income inequality is sometimes viewed as an unalloyed 

problem. This paper has shown 

that low income inequality at the 

neighborhood level is most likely 

a result of income sorting. In other 

words, it may be that higherincome households, when they can, 

choose to live away from lowerincome ones, sometimes forming 

“enclaves” with little income variation. Alternatively, it may be that 

developers concentrate higher-end 

houses in certain tracts and those 

can be afforded only by households of higher incomes. The most 

income-mixed areas (the ones with 

the highest income inequality) tend 

to be found in cities, with older 

housing on average, while the most 

income-segregated areas (the ones 

with the lowest income inequality) 

tend to be found in suburbs, with 

younger housing on average. 

While this paper estimated a regression model describing the relationship between the Gini coefficient of 

income inequality and tract characteristics, it is not a causal model. 

The key characteristics associated 

with higher income inequality at 

the census tract level are a higher 

90th percentile of income in that 

tract, the fraction of persons 25 

or older with a bachelor’s degree 

or more, and the fraction with no 

workers. The key characteristic 

associated with lower income 

inequality at the census tract 

level is a higher 10th percentile 

of income. These characteristics 

are descriptive not determinative. 

Therefore, to the extent that individuals and households prefer one 

type of community to another, they 

can undertake to find an area with 

the ranges of household income 

that they would choose to increase 

their neighborhood satisfaction.

ACKNOWLEDGMENTS

The author wishes to thank 

Roderick Little, Charles Nelson, 

and Edward Welniak for their helpful comments and suggestions, 

Theodore Logothetti for his programming assistance, Ryan Kenma 

for thematic mapping, Andrew 

McIntire for mapmaking, and 

Stephen Clark and Michael Roebuck 

for statistical review. 
U.S. Census Bureau 21

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