center on urban and metropolitan policy stunning progress, … · 2016-07-21 · i. introduction f...
TRANSCRIPT
I. Introduction
For many years, the conditions of lifein the poorest of poor neighborhoodshave attracted the attention of film-makers, journalists, and academic
researchers. Each in their own way, these witnesses provide stark evidence about the
devastating effects impoverished environ-ments can have on those unfortunate enoughto dwell within them, and about how theseeffects spill over into society at large.
Poverty, in government statistics, is definedon the basis of a family’s income relative to afixed poverty line, a standard meant to reflectthe cost of basic necessities. This narrow,
May 2003 • The Brookings Institution The Living Cities Census Series 1
“This report docu-
ments a dramatic
decline in the
1990s in the
number of
high-poverty
neighborhoods,
their population,
and the concen-
tration of the
poor in these
neighborhoods.”
Center on Urban and Metropolitan Policy
■ The number of people living in high-poverty neighborhoods—where thepoverty rate is 40 percent orhigher—declined by a dramatic 24percent, or 2.5 million people, inthe 1990s. This improvement markeda significant turnaround from the1970–1990 period, during which thepopulation in high-poverty neighbor-hoods doubled.
■ The steepest declines in high-poverty neighborhoods occurred inmetropolitan areas in the Midwestand South. In Detroit, for instance,the number of people living in high-poverty neighborhoods dropped nearly75 percent over the decade.
■ Concentrated poverty—the share ofthe poor living in high-povertyneighborhoods—declined among allracial and ethnic groups, especiallyAfrican Americans. The share of poor
black individuals living in high-povertyneighborhoods declined from 30 per-cent in 1990 to 19 percent in 2000.
■ The number of high-poverty neigh-borhoods declined in rural areas andcentral cities, but suburbs experi-enced almost no change. A number ofolder, inner-ring suburbs around majormetropolitan areas actually experiencedincreases in poverty over the decade,though poverty rates there generallyremain well below 40 percent.
While the 1990s brought a landmarkreversal of decades of increasingly con-centrated poverty, the recent economicdownturn and the weakening state ofmany older suburbs underscore that thetrend may reverse once again withoutcontinued efforts to promote economicand residential opportunity for low-income families.
FindingsA national analysis of high-poverty neighborhoods, and the concentration of poor individu-als in those neighborhoods, in 1990 and 2000 indicates that:
Stunning Progress, Hidden Problems:The Dramatic Decline of Concentrated Poverty in the 1990sPaul A. Jargowsky1
bookkeeper’s conception of poverty,however, fails to capture the multipleways in which poverty acts to degradethe quality of life and limit the oppor-tunities of those in its grip. One of themost important aspects of poverty notcaptured in the official statistics is itsspatial dimension. In theory, poor fam-ilies and their children could be widelydispersed throughout the population.In fact, they often tend to live nearother poor people in neighborhoodswith high poverty rates. The problemis particularly acute for the minoritypoor, who are segregated by both raceand income.
Why should we be concerned withthe spatial organization of poverty?The concentration of poor familiesand children in high-poverty ghettos,barrios, and slums magnifies the problems faced by the poor. Concen-trations of poor people lead to a con-centration of the social ills that causeor are caused by poverty. Poor childrenin these neighborhoods not only lackbasic necessities in their own homes,but also they must contend with a hos-tile environment that holds manytemptations and few positive role mod-els. Equally important, school districtsand attendance zones are generallyorganized geographically, so that theresidential concentration of the poorfrequently results in low-performingschools. The concentration of povertyin central cities also may exacerbatethe flight of middle-income andhigher-income families to the suburbs,driving a wedge between social needsand the fiscal base required to addressthem.
Between 1970 and 1990, the spatialconcentration of the poor rose dramat-ically in many U.S. metropolitanareas.2 The number of people living inhigh-poverty areas doubled; thechance that a poor black child residedin a high-poverty neighborhoodincreased from roughly one-in-four toone-in-three; and the physical size ofthe blighted sections of many centralcities increased even more dramati-
cally. By contrast, poverty—measuredat the family level—did not increaseduring this period. Thus, there was anot a change in poverty per se, but afundamental change in the spatialorganization of poverty. The poorbecame more physically isolated fromthe social and economic mainstreamof society.
Two key factors contributed to theincreasing concentration of povertyduring the 1970s and 1980s. First,weaknesses in local or regionaleconomies tended to disproportion-ately impact central cities. And sec-ondly, exclusionary suburbandevelopment patterns contributed toincreasing economic segregation.
Policymakers have been anxious toknow how the spatial organization ofpoverty may have changed in the1990s. For most metropolitan areasand the country as a whole, thedecade was a period of unparalleledeconomic growth. However, rapid sub-urban development continued andperhaps even accelerated during thisperiod. The net effect of these trendson the concentration of poverty in the1990s is therefore ambiguous.
Only the decennial Census providessufficient detail at the neighborhoodlevel to examine the concentration ofpoverty. With the release of Census2000, we are now able to assess thenet impact of the economy, suburbandevelopment, and other forces on thespatial dimension of poverty over thelast decade.
Based on the trend of prior decades,one might have reasonably assumedthat high-poverty neighborhoods werean unavoidable aspect of urban lifeand would continue to grow inexorablyin size and population. The latest evi-dence contradicts this gloomy assess-ment. This report documents adramatic decline in the 1990s in thenumber of high-poverty neighbor-hoods, their population, and the con-centration of the poor in theseneighborhoods. It also finds, however,several indications that poverty rose in
the older suburbs of many metropoli-tan areas, even during a decade of eco-nomic expansion. The paper concludeswith a discussion of the meaning ofthese trends, and the more recentdecline in economic conditions, forpoor families and communities in thecurrent decade.
II. Methodology
This report examines thechanges in the concentrationof poverty in the 1990s usingsample data (the “long form”)
from the 1990 and 2000 decennialcensuses.
For the purpose of this study,poverty is defined using official U.S.poverty guidelines. An individual isconsidered poor if he lives in a familywhose income is less than a specificthreshold that varies by family size andcomposition. While the official defini-tion suffers from a number of knownflaws and limitations, it is neverthelesswidely accepted.3 More importantly,the Census Bureau provides data onpoverty status based on the long formof the census.
In everyday usage, one can talkabout a neighborhood in general termswithout specifying exact boundaries.For tabulation purposes, however,every household in the nation mustreside in one and only one geographi-cally specific neighborhood. In thisstudy, we use census tracts as proxiesfor neighborhoods. Census tracts aresmall, relatively homogeneous areasdevised by the Census Bureau andlocal planning agencies, making use of bounding features such as majorroads, railroad tracks, and rivers when-ever possible. On average, they con-tain 4,000 persons, but in practicethey vary widely in population. Theyalso vary widely in geographic size dueto differences in population density.When initially delineated, censustracts are meant to be relatively homo-geneous with respect to social andeconomic characteristics and housing
May 2003 • The Brookings Institution The Living Cities Census Series2
stock considerations. While they maynot always capture the mental map ofneighborhoods that city residentshave, they do divide the nation alonggeographic lines. In less dense ruralareas, one census tract may representall or a substantial portion of a county.
As populations grow and change,census tracts may be split, merged, ormodified in other ways. In thisresearch, contemporaneous tracts areused. That is, 1990 census tractboundaries are used to interpret 1990data, and 2000 census tract bound-aries are used for the 2000 figures.Using contemporaneous boundaries isimportant, because to do otherwisewould invite a systematic bias into theanalysis. For example, if the 2000 cen-sus tract grid were superimposed on1970 data, average neighborhood pop-ulation would be far smaller in 1970than in 2000. Defining neighborhoodsdifferently over time would systemati-cally bias the results of any analysisthat is sensitive to the size of theneighborhood units.4
Combining the poverty dimensionand the spatial dimension, a censustract is considered a high-povertyneighborhood if 40 percent or more ofits residents are classified as poorusing the federal poverty standard.While any specific threshold is inher-ently arbitrary, the 40 percent level hasbecome the standard in the literature
and has even been incorporated intofederal data analysis and programrules.5 In addition to tabulating thenumber of high-poverty neighborhoodsand the number and characteristics oftheir residents, this paper examinesthe concentration of poverty—definedas the percentage of the poor in somecity or region that resides in high-poverty neighborhoods.
These two concepts—the incidenceof high-poverty neighborhoods, andthe concentration of poverty—are notunrelated. In general, the greater thenumber of high-poverty neighborhoodsin a city or metropolitan area is, themore likely poor residents of that placewill be “concentrated” in those neigh-borhoods. However, each measureanswers a different question. The for-mer relates to the geographic footprintof very-low-income districts within acity or metropolitan area, which hasimportant implications for economicdevelopment efforts and city planning.The latter captures the percentage ofpoor individuals who not only mustcope with their own low incomes, butalso with the economic and socialeffects of the poverty that surroundsthem.
The figures presented below includeall census tracts in the United States,including both metropolitan and non-metropolitan areas, except as noted. Ametropolitan area usually consists of
one or more population centers, orcentral cities, and the nearby countiesthat have close economic and com-muting ties to the central cities.6 TheCensus Bureau defines several typesof metropolitan areas. There arestand-alone Metropolitan StatisticalAreas (MSAs) and Primary Metropoli-tan Statistical Areas (PMSAs). PMSAsare part of larger constructions calledConsolidated Metropolitan StatisticalAreas (CMSAs). In this analysis, met-ropolitan areas are defined to includeMSAs and PMSAs, not CMSAs.CMSAs are so large that they do notrepresent unified housing and labormarkets, and so they are not consid-ered in this analysis.7
Like census tracts, the boundariesof metropolitan areas are adjusted overtime. New counties are added, andexisting counties are deleted or movedto different metropolitan areas if thereare changes in their demographics, inthe commuting patterns of their resi-dents, or if the Census Bureauchanges the rules for allocating coun-ties to metropolitan areas. In thisanalysis, the definitions of metropoli-tan areas (including MSAs andPMSAs) in effect for Census 2000 areapplied to both 1990 and 2000 data.In keeping with this, any changes inthe figures for metropolitan areasshown below reflect actual changes inpopulation demographics and notchanges in boundaries or definitions.
To examine variation among racialand ethnic groups, population isdivided first by Hispanic origin, andthen non-Hispanics are further dividedby racial group—black, white, Ameri-can Indian, Asian, and people whoindicated more than one race (in2000) or “other race.” Thus, a refer-ence to whites refers to non-Hispanicpersons who indicated “White or Cau-casian” as their sole racial group onthe census form, a reference to blacksindicates non-Hispanic persons whochose “Black or African-American” astheir sole race, and so on.
A final methodological note: A por-
May 2003 • The Brookings Institution The Living Cities Census Series 3
The Federal Poverty Standard
Developed by Molly Orshansky of the Social Security Administration in the1960s for use in the War on Poverty, the federal poverty standard has beencriticized from every conceivable angle. Despite its imperfections, it hasendured as both an administrative tool to determine program eligibility andas a research tool. Persons are considered poor if they live in families whosetotal family income is less than a threshold meant to represent the cost ofbasic necessities. The thresholds vary by family size, and are adjusted eachyear for inflation. For example, in 2002, the poverty level was $15,260 for atypical family of three and $18,400 for a typical family of four. For moreinformation, see Orshansky (1965), Fisher (1992), and the HHS poverty website: aspe.hhs.gov/poverty/poverty.shtml.
tion of this study analyzes levels andchanges in high-poverty neighbor-hoods based on their location in cen-tral cities, suburbs, or rural areas. Inpractice, census tracts are subdivisionsof counties, and thus often do notrespect the municipal borders thatdefine central cities.8 In such cases,the tract’s poverty status is classifiedby the poverty rate for the entire tract.That is, there is only one poverty ratefor each whole census tract, no matterhow many ways the tract is split overcity or metropolitan boundaries. Inthis way, the count of persons residingin high-poverty areas is consistent, andsystematic biases that would arisefrom the splitting of census tracts areavoided.
III. Findings
A. The number of people living inhigh-poverty neighborhoods—wherethe poverty rate is 40 percent orhigher—declined by a dramatic 24percent, or 2.5 million people, inthe 1990s.The strong economic conditions thatprevailed throughout most of the1990s appear to have dramaticallyaltered long-term trends in the spatialorganization of poverty. The number ofhigh-poverty neighborhoods—censustracts with poverty rates of 40 percentor more—declined by more than one-fourth, from 3,417 in 1990 to 2,510 in2000 nationwide. This is a stunningreversal of the trend between 1970and 1990, as shown in Figure 1.9
More importantly, the total numberof residents of high-poverty areasdeclined by 24 percent, from 10.4 mil-lion in 1990 to 7.9 million in 2000.The sharp decline does not merelyreflect declines in overall poverty. Infact, despite the strong economy, thenumber of persons classified as poorin the United States actually rosebetween 1990 and 2000, from 31.7million to 33.9 million. The overallpoverty rate did decline over thedecade (from 13.1 percent to 12.4 per-
cent), but by a much smaller degreethan did the number of high-povertyneighborhoods. The implication is thatthere was a substantial change in thespatial organization of poverty duringthe 1990s. Poor neighborhoods, or atleast the residents of high-povertyneighborhoods in 1990, benefited dis-proportionately from the boom.
Virtually the whole spectrum ofracial and ethnic groups benefitedfrom the decline in the number of per-sons residing in high-poverty neighbor-hoods. The number of white residentsof these areas declined by 29 percent(from 2.7 to 1.9 million), and thenumber of black residents declined byan even faster 36 percent (from 4.8million to 3.1 million). Despite thisdecline, however, blacks remained thesingle largest racial/ethnic group livingin high-poverty neighborhoods.
The major exception to the patternwas Hispanics, whose numbers inhigh-poverty neighborhoods actuallyincreased slightly, by 1.6 percent. Atthe same time, the number of Hispan-ics in the U.S. overall increased dra-matically in the 1990s—by 57.9
percent, compared to only 3.4 percentgrowth for whites and 16.2 percent forblacks. In the context of this rapidpopulation growth, fueled by theimmigration of many low-income per-sons from Central and South America,as well as births to immigrant families,a growth rate of only 1.6 percent inthe number of Hispanics in high-poverty neighborhoods could beviewed as a positive outcome.
Given that different racial and eth-nic groups were growing at differentrates, the composition of high-povertyzones changed over the period. Figure2 shows how the population in high-poverty neighborhoods changedbetween 1990 and 2000 by race andethnicity. Hispanic and Asian sharesincreased, while those for whites andblacks declined. Most notably, Hispan-ics now comprise a larger share ofhigh-poverty neighborhood residentsthan whites.
B. The steepest declines in high-poverty neighborhoods occurred inmetropolitan areas in the Midwestand South.
May 2003 • The Brookings Institution The Living Cities Census Series4
3,500
3,000
2,500
2,000
1,500
1,000
500
0
9,000
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
Hig
h Po
vert
y N
eigh
borh
oods
High Poverty N
eighborhood Population (1000s)
1970 1980 1990 2000
■ High-Poverty Neighborhoods High-Poverty Neighborhood Population (thousands)
Figure 1. High-Poverty Neighborhoods and High-PovertyNeighborhood Population, U.S. Metropolitan Areas, 1970–2000
Based on metropolitan areas as defined in year of census.
Earlier research indicated that theexpansion of high-poverty ghettos andbarrios was particularly acute in theMidwest, especially in central cityneighborhoods. Now, the Midwest hasexhibited the most rapid turnaroundduring the boom of the 1990s.
As shown in Figure 3, populationchanges in high-poverty areas varieddramatically across regions of thecountry. In general, places with thelargest declines in the number of high-poverty neighborhoods also expe-rienced the steepest drops in the num-ber of people living in such areas.10
The decline was largest in the Mid-west, where the population of high-poverty neighborhoods was nearlyhalved over the decade. There was alsoa substantial decline in the South,which nonetheless remained home tothe largest number of high-povertyneighborhoods in 2000.
At the same time, the number ofhigh-poverty neighborhoods in theNortheast remained virtually the samein 2000 as in 1990, and the Westactually saw a substantial 26 percentincrease in the population of these
neighborhoods, albeit from a smallbase. In 1990, the population of high-poverty neighborhoods in the Westwas half that in the Midwest; by 2000,nearly 300,000 more people lived in
high-poverty neighborhoods in theWest than in the Midwest. Thisincrease is explained almost entirely byan increase in the size and populationof Hispanic barrios; the number of
May 2003 • The Brookings Institution The Living Cities Census Series 5
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
West(+25.9%)
South(-34.7%)
Midwest(-45.6%)
Northeast(-0.3%)
■ 1990■ 2000
1,839 1,823
2,526
1,374
4,712
3,077
1,3281,672
Figure 3. Population of High-Poverty Neighborhoods by Region,1990–2000
Black 47%
White 26%
Asian 2%
Hispanic 22%
Other 3%
Black 39%
Hispanic 29%
Asian 4%
White 24%
Other 4%
1990 2000
Figure 2. Racial/Ethnic Composition of High-Poverty U.S. Neighborhoods, 1990–2000
Popu
lati
on (
thou
sand
s)
non-Hispanic persons in high-povertyareas in the West declined slightly.
While only two out of four regionsshowed significant declines in theaggregate, the view at the state level ismore positive. Figure 4 maps the per-centage change in high-poverty neigh-borhood population by state. Fully 40states had declines, with an averagedecline of 78,000 persons residing inhigh-poverty neighborhoods. Tenstates, as well as the District ofColumbia, had increases averaging61,000 persons. Trends in the West asa whole are clearly driven by Califor-nia, which had an 87 percent increasein the population of high-povertyneighborhoods.
Table 1 shows the 15 metro areaswith the largest decreases in high-poverty-area population. The regionalflavor is readily apparent. Withoutexception, the metropolitan areaslisted are located in the Midwest or inthe South. Detroit’s decline in thepopulation of high-poverty neighbor-hoods was substantially larger than inany other metropolitan area. Chicago,however, experienced a comparabledecrease in the number of high-poverty census tracts. All told, 200 outof 331 metropolitan areas (MSAs andPMSAs) saw declines in the numberof people living in high-poverty neigh-borhoods (Appendix A shows relevantdata for all U.S. metropolitan areas,and non-metropolitan areas by state).
In most metropolitan areas, high-poverty neighborhoods tend to be clus-tered in one or two mainagglomerations located in the centralcity. In this way, the United States dif-fers markedly from most other nationsof the world, in which poor neighbor-hoods are typically located on theperiphery of urban areas. As thesezones of concentrated povertyincreased in size between 1970 and1990, they contributed to a generalprocess of population deconcentrationthat generated “donut cities”—depop-ulating and impoverished urban coressurrounded by prosperous and growing
suburbs. But then came the 1990s anda boon for central cities. Just as cen-tral cities bore the brunt of the fiscal,social, and economic burden of con-centrating poverty in prior decades,they became prime beneficiaries of its
reduction in the 1990s.A case in point is the Detroit, MI
metro area. Figure 5 shows the high-poverty zones in Detroit over threedecades. From 1970 to 1990, there isa rapid growth in the number of neigh-
May 2003 • The Brookings Institution The Living Cities Census Series6
Table 1. Top 15 Metropolitan Areas by Decline in Population ofHigh-Poverty Neighborhoods, 1990–2000
Decline in % Decline in Decline in Metropolitan Area Population Population Census TractsDetroit, MI 313,217 74.4 97Chicago, IL 177,908 43.1 73San Antonio, TX 107,272 70.1 18Houston, TX 77,662 47.8 27Milwaukee-Waukesha, WI 63,357 45.0 16Memphis, TN-AR-MS 61,924 43.6 11New Orleans, LA 57,332 34.6 18Brownsville-Harlingen-San Benito, TX 50,559 37.1 4Columbus, OH 48,020 55.4 11El Paso, TX 44,489 40.2 4Dallas, TX 41,805 45.3 19St. Louis, MO-IL 38,866 35.5 13Lafayette, LA 33,978 54.8 10Minneapolis-St. Paul, MN-WI 32,005 40.5 18Flint, MI 31,631 61.2 6
Figure 4. Percentage Change in Population of High-PovertyNeighborhoods by State, 1990–2000
Change, 1990–2000
May 2003 • The Brookings Institution The Living Cities Census Series 7
Figure 5. High-Poverty Neighborhoods in Detroit, 1970–2000
1970
1990
1980
2000
Figure 6. High-Poverty Neighborhoods in Dallas, 1970–2000
1970
1990
1980
2000
borhoods with poverty rates of 40 per-cent or more. By 1990, nearly half theland area of the City of Detroit, theboundary of which is shown in yellow,had become a high-poverty zone. Thistrend is reversed between 1990 and2000. The change is so dramatic, itstrains credulity. To some extent, thevivid map colors may overstate thechange, since many of Detroit’s censustracts had all but emptied out by1990. Thus, a movement or change inpoverty status of just a few familiescould serve to change the color of anentire census tract on the map. Evenso, the Detroit metro area underwentan astonishing 74.4 percent reductionin the number of people residing inhigh-poverty zones between 1990 and 2000.
The growth of high-poverty zonesbetween 1970 and 1990 and their sub-sequent declines were by no meanslimited to the Midwest. Figure 6shows the trend in the Dallas metro-
politan area over the last threedecades. The poverty areas of Dallasexperienced their greatest expansionbetween 1980 and 1990, after the col-lapse of the OPEC oil cartel led tosharply lower oil prices. At the sametime, Dallas was also experiencingrapid suburban development. Plano,TX, a “Boomburb” just north of Dallas,was for years the fastest growing cityin the nation.12 After 1990, however,there was substantial redevelopment
of the downtown area, including con-dominium and apartment develop-ments just north of downtown andalong Interstate 45. The overalldecline in the population of Dallas’shigh-poverty areas was 45 percentbetween 1990 and 2000.
The regional picture is quite differ-ent when we examine the metropolitanareas with the largest increases inhigh-poverty area population. Seven ofthe 15 metropolitan areas in Table 2
May 2003 • The Brookings Institution The Living Cities Census Series8
Mapping Poor Neighborhoods
The maps shown in these figures were produced using an interactive website.By visiting the web site, users can easily produce maps such as these for anymetropolitan area in the United States. The address for the web site iswww.urbanpoverty.net. Construction of the web site was funded by theBrookings Institution Center on Urban and Metropolitan Policy and the Bru-ton Center for Development Studies at the University of Texas at Dallas.Comments and suggestions about the web site are welcome.11
Figure 7. High-Poverty Neighborhoods in Los Angeles, 1970–2000
1970
1990
1980
2000
are located in one state—California—and six of those lie in either southernCalifornia or the state’s agriculturalhub, the San Joaquin Valley. Othermetro areas include a handful in theNortheast (Providence, Wilmington,Rochester, and two suburban New Jer-sey metros) and two smaller metros inTexas. Altogether, a total of 91 out of331 metropolitan areas had at least anominal increase in persons living inhigh-poverty neighborhoods.
It is worth noting that the size ofthe population increase in high-poverty zones falls off rapidly as weread down the list. The fifteenth met-ropolitan area, Monmouth-Ocean, NJ,had a 9,000-person increase in itshigh-poverty neighborhood population,whereas the fifteenth metropolitanarea in Table 1 (Flint, MI) had a32,000-person decline in its poverty-area population.
Figure 7 illustrates the process in LosAngeles. The expansion of high-povertyneighborhoods, indicated in red, is quiteapparent. Also apparent is a consider-able increase in the number of neigh-
borhoods with moderate poverty rates(between 20 and 40 percent).
Los Angeles is notable for three fac-tors that may explain its divergencefrom the national trend. First, the cityexperienced a deadly and destructiveriot after the Rodney King verdict in1992, and further heightening ofracial tension due to the trial of O.J.Simpson in 1995. The riot and itsaftermath almost certainly acceleratedmiddle-class flight from the centralcity area, and the trial emphasizedracial divisions in the region. Second,the Los Angeles region experiencedtremendous immigration from Mexicoand other Central and South Americancountries.13 Riverside/San Bernadino,Fresno, and (to a lesser extent) SanDiego also experienced a significantincrease in low-income Hispanic pop-ulation; the population of high-povertyneighborhoods increased in theseareas as well. Third, the recession ofthe early 1990s was particularly severein Southern California, and the eco-nomic recovery there was not as rapidas in other parts of California (such as
the San Francisco/Silicon Valley area)that benefited from the Internet boom.
The other major exception to thetrend was the Washington, D.C. metroarea. The number of high-povertyneighborhoods in the nation’s capitalmore than doubled over the decade.The major factor at work here waslikely the devastating fiscal crisis thatplagued the District during the earlyand mid-1990s. The crisis underminedpublic confidence in the governance ofthe District and led to serious cut-backs in public services, includingpublic safety. For this and other rea-sons, there was a rapid out-migrationof moderate- and middle-income blackfamilies, particularly into suburbanMaryland counties to the east of thecentral city. The poor were left behindin economically isolated neighbor-hoods with increasing poverty rates.The late 1990s real estate boom inWashington seems not to haveimproved conditions in these neigh-borhoods.
Of course, these metros and othersin Table 2 represent the exceptions toan overall decline in the number ofhigh-poverty neighborhoods, and pop-ulation of high-poverty neighborhoods,in the 1990s. Most areas of the U.S.saw improvements over the decadethat were much greater in magnitudethan the deterioration that occurred ina minority of metro areas.
C. Concentrated poverty—the shareof the poor living in high-povertyneighborhoods—declined among allracial and ethnic groups, especiallyAfrican Americans.In the 1990s, consonant with thedecline in high-poverty neighbor-hoods, the concentration of poverty—defined as the proportion of the poor ina given area that resides in high-poverty zones—dropped across most ofthe nation. The number of poor per-sons living in high-poverty areasdeclined 27 percent, from 4.8 millionto 3.5 million. In 1990, the share ofpoor individuals nationwide who lived
May 2003 • The Brookings Institution The Living Cities Census Series 9
Table 2. Top 15 Metropolitan Areas by Increase in Population ofHigh-Poverty Neighborhoods, 1990–2000
Increase in % Increase Increase in Metropolitan Area Population in Population Census TractsLos Angeles-Long Beach, CA 292,359 109.2 81Fresno, CA 60,005 68.7 12Riverside-San Bernardino, CA 58,669 260.5 12Washington, DC-MD-VA-WV 56,954 276.4 14Bakersfield, CA 42,622 190.8 9San Diego, CA 33,274 86.1 10McAllen-Edinburg-Mission, TX 28,117 12.0 (1)Providence-Fall River-Warwick, RI-MA 22,186 235.4 3Chico-Paradise, CA 16,675 103.3 4Middlesex-Somerset-Hunterdon, NJ 14,020 n/a* 3Wilmington-Newark, DE-MD 12,349 276.3 2Bryan-College Station, TX 11,746 29.4 4Visalia-Tulare-Porterville, CA 11,176 60.1 2Rochester, NY 9,989 29.8 0Monmouth-Ocean, NJ 9,114 318.3 (1)*Middlesex-Somerset-Hunterdon, NJ, had no census tracts with poverty rates of 40 percent or higher
in 1990.
in high-poverty areas (the concentratedpoverty rate) was 15 percent. By 2000,that figure had declined to 10 percent.
These declines are both striking andgratifying. Between 1970 and 1990,the concentration of poverty grewsteadily worse, especially for blacks.About one-fourth of the black poorlived in high-poverty areas in 1970; by1990, the proportion had increased toone-third. The rate was even higherfor black children, especially those insingle-parent families. The economicand social isolation of these familiesand children prompted great concernamong researchers investigating theopportunities and constraints facinglow-income families in economicallyimpoverished neighborhoods.14
Some have argued that poor personsmay benefit from having poor neigh-bors. For example, they may share coping strategies and draw on geo-graphically-based support networks.Yet most researchers, and most of thegeneral public, assume that the bene-fits of poor persons living in high-poverty neighborhoods are outweighedby the extra hardships that such neigh-borhoods impose, including their dele-terious effects on child developmentand the ability of poor adults toachieve self-sufficiency.
For those reasons, it is good newsindeed that all racial and ethnicgroups shared in the deconcentrationof poverty of the 1990s, as shown inFigure 8. The decline was most signifi-cant for poor blacks; the percentageliving in high-poverty neighborhoodsdeclined from 30.4 percent in 1990 to18.6 percent in 2000. American Indi-ans experienced a similarly largedecrease. Yet despite these substantialdeclines, blacks and American Indiansstill suffer the highest concentratedpoverty rates, the former in highly seg-regated urban ghettos and the latter inremote rural reservations. The concen-tration of poverty among non-Hispanicwhites, low to start with, dropped byroughly one-sixth. The chances that apoor Hispanic lives in a high-poverty
neighborhood dropped from morethan one in five (21.2 percent) in1990 to less than one in seven (13.8percent) in 2000.15
The declines in concentratedpoverty were not driven by a few largeor unrepresentative metropolitanareas. Indeed, substantial declineswere the national norm. Of the 331metropolitan areas in the UnitedStates in 2000, 227 (69 percent) sawthe concentration of poor blacksdecrease between 1990 and 2000;another 49 (15 percent) had nochange; and only 55 (17 percent) hadincreases (Appendix A). The story wassimilar for non-metropolitan areas:The concentration of poor blacks inrural areas declined in 29 of 49 states,and remained the same in another 11states.16
The numbers were similar, if notquite as positive, for Hispanics. Morethan half of all metropolitan areas haddecreases in concentrated Hispanicpoverty, 87 (26 percent) had increases,and the remainder experienced nochange.
The deconcentration of poverty for
racial and ethnic minorities spreadwidely across the nation’s largest met-ropolitan areas. Table 3 reports con-centrated poverty rates among blacksand Hispanics in the 20 largest met-ros, sorted by change in the concen-trated black poverty rate between1990 and 2000. Most of these areasexperienced declines in the concentra-tion of poverty for both groups. Thelargest declines for blacks were inDetroit (37.5 percentage points), Min-neapolis-St. Paul (20.3) and Chicago(18.8). Four metropolitan areas haddouble-digit percentage point declinesin the concentrated poverty rate forHispanics: Detroit (29.1 percentagepoints), Minneapolis-St. Paul (12.3),Philadelphia (12.1), and Houston(10.3).
To be sure, the percentage-pointdeclines were generally largest in areasthat had high rates of concentratedpoverty to begin with; while the shareof blacks living in high-poverty neigh-borhoods in the Seattle metro areawas halved in the 1990s, this repre-sented a decline of only 3.3 percent-age points. Still, the extent of the
May 2003 • The Brookings Institution The Living Cities Census Series10
-15
-10
-5
0
5
10
15
20
25
30
35
AsianAmericanIndian
HispanicBlackWhite
■ 1990 ■ 2000 ■ Percentage Point Change
Perc
enta
ge o
f Po
or in
Hig
h-Po
vert
y N
eigh
borh
oods
7.1 5.9
-1.2
30.4
18.6
-11.8
21.5
13.8
-7.4-11.2
-2.9
9.8
12.7
19.5
30.6
Figure 8. Concentration of U.S. Poor by Race/Ethnicity,1990–2000
decline in places like Detroit and Min-neapolis-St. Paul is remarkable com-pared to an area like New York, whichdespite modest declines still has veryhigh concentrated poverty rates forboth groups.
Consistent with the data on the pop-ulation of high-poverty areas, two areasof the country cut against the nationaltrend. In Los Angeles-Long Beach andRiverside-San Bernardino, concen-trated poverty increased among boththe black and Hispanic poor; in SanDiego, Hispanic concentrated povertyrose. In Washington, D.C., poor blacksbecame more spatially concentrated,but poor Hispanics did not.
One additional note on Westernhigh-poverty neighborhoods: In thatregion, the increase in high-povertyneighborhoods owed almost entirely to
an increase in the number of barrios—predominantly Hispanic high-povertycommunities. While increasing con-centrated poverty among Hispanics insouthern California is certainly causefor concern, researchers haveexpended considerably greater effortstudying the deleterious effects thathigh-poverty neighborhoods in theMidwest and Northeast have on thelife chances of their residents, who arepredominantly black. With their sub-stantial immigrant populations, West-ern inner-city barrios could representmore of a “gateway” to residential andeconomic mobility than inner-cityghettos in other areas of the country.Regardless, the rise in concentratedHispanic poverty in California duringthe 1990s highlights a need to betterunderstand how the opportunity struc-
ture in these communities may differfrom that in other types of high-poverty neighborhoods. 17
D. The number of high-povertyneighborhoods declined in ruralareas and central cities, but suburbsexperienced almost no change.So far, this paper has considered sta-tistics on changes in high-povertyneighborhoods and the concentrationof poverty at the national and metro-politan levels. These statistics obscurean important aspect of the trend in the1990s that the maps help illuminate:Central cities, rather than suburbs,reaped the benefits of the decline. Noteven the maps, however, reveal whattranspired in rural America. This sec-tion examines changes within metro-politan areas, and outside them, in
May 2003 • The Brookings Institution The Living Cities Census Series 1 1
Table 3. Concentration of Black and Hispanic Poverty in the 20 Largest Metro Areas, 1990–2000
Black HispanicMetro Area 1990 2000 Change 1990 2000 ChangeDetroit, MI 53.9 16.4 -37.5 36.1 6.9 -26.1Minneapolis-St. Paul, MN-WI 33.3 13.0 -20.4 18.2 5.9 -12.3Chicago, IL 45.3 26.4 -18.8 12.4 4.7 -7.7St. Louis, MO-IL 39.1 23.8 -15.3 12.8 5.2 -7.6Baltimore, MD 34.7 21.5 -13.2 9.7 3.5 -6.2Dallas, TX 25.4 13.8 -11.6 12.8 3.5 -9.3Tampa-St. Petersburg-Clearwater, FL 29.1 17.8 -11.4 7.3 4.7 -2.6Houston, TX 28.0 17.1 -10.9 13.1 2.8 -10.3Phoenix-Mesa, AZ 25.7 15.4 -10.3 21.3 12.2 -9.1New York, NY 40.1 32.5 -7.6 40.9 32.2 -8.7Philadelphia, PA-NJ 31.0 23.6 -7.5 61.6 49.5 -12.1Boston, MA-NH 12.5 6.2 -6.3 10.7 8.1 -2.6Atlanta, GA 26.6 20.5 -6.1 6.8 2.5 -4.2Seattle-Bellevue-Everett, WA 6.8 3.4 -3.3 8.1 1.3 -6.8San Diego, CA 15.4 13.0 -2.4 10.2 12.5 2.3Nassau-Suffolk, NY 0.5 0.0 -0.5 0.0 0.0 0.0Orange County, CA 0.0 0.0 0.0 0.0 0.1 0.1Los Angeles-Long Beach, CA 17.3 21.3 4.1 9.1 16.9 7.8Riverside-San Bernardino, CA 5.7 12.3 6.6 4.4 8.9 4.5Washington, DC-MD-VA-WV 6.3 15.0 8.7 1.0 0.4 -0.6
Figures represent percentage of metro-wide poor individuals in each racial/ethnic group living in census tracts with poverty rates of 40 percent or higher.
Increases shown in bold.
neighborhood poverty over the decade. All neighborhoods—census tracts—
nationwide can be classified as lyingwithin the central cities of metropoli-tan areas, the suburbs—defined as thebalance of metropolitan areas—ornon-metropolitan areas, which consistof rural areas and cities and towns toosmall or detached to be consideredpart of a metropolitan area. As shownin Figure 9, the decline in high-poverty area population was actuallylargest in non-metropolitan areas,where the decline was nearly 50 per-cent. Central city areas, as indicatedby the maps, also experienced a largedecline of 21 percent.
It was the suburbs that had theslowest overall decline in poverty arearesidents—only 4.4 percent. As aresult of these differing declines, by2000 suburban America was actuallyhome to more neighborhoods of con-centrated poverty than rural America.While the suburbs have more thantwice the number of residents as non-metropolitan areas, this finding isnonetheless striking given that theoverall poverty rate outside metropoli-tan areas (14.6 percent) was consider-ably higher in 2000 than the povertyrate in suburbs (8.4 percent).
Moreover, a careful inspection oftrends in the geography of suburbanpoverty over the 1990s reveals somedisturbing trends. Not only did thenumber of neighborhoods of highpoverty decline slowly in the suburbs.Also, poverty rates actually increasedalong the outer edges of central citiesand in the inner-ring suburbs of manymetropolitan areas, including those thatsaw dramatic declines in poverty con-centration. In short, poverty trends inthese areas moved in the oppositedirection from those in inner-cityneighborhoods and booming suburbs atthe metropolitan fringe.
Several metropolitan areas illustratethe case. Figure 10 shows the changein the poverty rate by census tractbetween 1990 and 2000 for four—Detroit, Chicago, Cleveland, and Dal-
las. Neighborhoods shaded in greenhad decreases in their poverty rates,while red indicates census tracts withincreases in their poverty rates. InDetroit, central city tracts experienceddramatic decreases in their povertyrates in the 1990s, dropping many ofthem below the 40-percent threshold.However, a ring of neighborhoods justbeyond the border of the central city—located in the area’s older suburbs—saw increases in their poverty rates.Many of these neighborhoods stillhave poverty rates of below 20 per-cent, and so cannot be consideredhigh-poverty. Yet it is notable that in adecade of widespread economicgrowth, the poverty rates in theseolder suburban neighborhoods wererising. The maps of Chicago, Cleve-land, and Dallas also exhibit the dis-tinctive “bull’s-eye” pattern ofimprovements in the central city andincreasing poverty in the inner ring ofsuburbs. This pattern is repeated inmetro areas across the nation.
The economic decline of inner-ringsuburbs, already evident in earlier
decades, continued in the 1990s evenas conditions were improving dramati-cally in most central cities.18 The factthat inner-ring suburbs declined dur-ing this period is really quite astonish-ing. Census 2000 was conducted inApril of 2000, coinciding with thepeak of a long economic boom. Unem-ployment rates nationwide were 4 per-cent, and lower in some of thesemetropolitan areas. The economy, inall likelihood, will never be strongerthan it was during this period, at leastnot for any extended period of time.
A vigorous debate is underway con-cerning the role of suburban develop-ment in central city and oldersuburban decline and the concentra-tion of poverty. There is, as yet, noconsensus that rapid suburban devel-opment, characterized as “sprawl” byits opponents, exacerbates economicdecline in the core. In fact, someargue the contrary, and contend thatthese development patterns are a con-sequence of the economic and socialdisorder of the inner cities. Thesequestions will continue to engender
May 2003 • The Brookings Institution The Living Cities Census Series12
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000 11,000
Non-Metropolitan
(-47%)
Suburbs(-4%)
Central City(-21%)
Total U.S (-24%)
■ 1990■ 2000
10,394
7,946
7,560
5,988
1,089
1,041
1,745
917
Population (thousands)
Figure 9. Population of High-Poverty Neighborhoods byLocation, 1990–2000
vigorous debate in the years ahead.However, it is clear from the data andmaps presented here that there is rea-son to be concerned about theprospects for inner-ring suburbs. Ifpoverty in these areas rose during thestrongest economy we can reasonablyexpect to enjoy, then they may wellhave a bleak future and develop manyof the same fiscal and social concernsthat plagued central cities in earlierperiods.
IV. Conclusion
The concentration of poverty isan important public policyconcern because it hasdynamic effects on income
distribution, because it underminesthe political and social fabric of thenation’s major metropolitan areas,and—most importantly—because itrestricts opportunity for some. Fortu-nately, the excellent economy of the
1990s reversed several decades ofincreasing concentration of povertyand central city decline. With fewexceptions, metropolitan and ruralareas across the U.S. saw a drop inconcentrated poverty for all racial andethnic groups.
The extent to which some of thesegains have already been erased by thedownturn since the date of the Censusis not known. However, even at theheight of the boom, troubling signscould be found that the pattern ofmetropolitan development, with rapidgrowth at the periphery, might beundermining other parts of metropoli-tan areas, particularly the inner ring ofsuburbs. This quiet erosion, largelyunnoticed during the good times ofthe 1990s, leaves metropolitan areasin a weaker state and reduces theirability to cope with the less robusteconomic conditions that prevailtoday.
While the reductions in concentra-
tion of poverty in the 1990s are cer-tainly welcome news, the long-run pic-ture is far from sanguine. Thesnapshot of progress as of April 2000may be as misleading as the level ofthe NASDAQ on that date. If theinner-ring suburbs provide any indica-tion, then the underlying developmentpattern that leads to greater neighbor-hood stratification was still at work inthe 1990s, and is likely to have contin-ued in the considerably weaker eco-nomic climate of the last three years.If so, greater concentration of povertyand more geographically stratifiedmetropolitan areas could exacerbatesocial problems in a host of areas,from public safety to education totransportation. We should celebratethe gains made during the 1990s, tothe extent that they haven’t alreadyerased, but we should not ignore thewarning signs that our society is stillvulnerable to increasing concentrationof poverty.
May 2003 • The Brookings Institution The Living Cities Census Series 13
Figure 10. Poverty Rate Changes in Selected Metro Areas, 1990–2000
Detroit
Cleveland
Chicago
Dallas
May 2003 • The Brookings Institution The Living Cities Census Series14
App
endi
x A
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701
10
-12,
067
0-2
,067
5.5
0.0
-5.5
11.2
0.0
-11.
28.
90.
0-8
.9C
ham
paig
n-U
rban
a, I
L17
9,66
94
40
24,5
3622
,482
-2,0
5437
.140
.73.
629
.011
.4-1
7.5
40.1
48.2
8.1
May 2003 • The Brookings Institution The Living Cities Census Series 15
Tota
l Are
aHi
gh-P
over
ty
Popu
latio
n in
Co
ncen
trat
ed
Conc
entr
ated
Co
ncen
trat
ed
Popu
latio
nCe
nsus
Trac
tsHi
gh-P
over
ty C
ensu
s Tr
acts
Pove
rty
Rate
: Tot
alPo
vert
y Ra
te: B
lack
sPo
vert
y Ra
te: H
ispa
nics
MSA
/PM
SA/B
alan
ce o
f Sta
te N
ame
2000
1990
2000
Chan
ge19
9020
00Ch
ange
1990
2000
Chan
ge19
9020
00Ch
ange
1990
2000
Chan
geC
harl
esto
n, W
V25
1,66
22
1-1
3,84
71,
426
-2,4
215.
02.
2-2
.816
.85.
6-1
1.2
14.0
9.5
-4.5
Cha
rles
ton-
Nor
th C
harl
esto
n, S
C54
9,03
313
11-2
27,6
0927
,194
-415
17.8
15.5
-2.3
23.8
17.9
-5.9
3.2
10.7
7.5
Cha
rlot
te-G
asto
nia-
Roc
k H
ill, N
C-S
C1,
499,
293
104
-627
,102
7,42
4-1
9,67
810
.72.
1-8
.621
.35.
0-1
6.3
5.9
0.0
-5.9
Cha
rlot
tesv
ille,
VA
159,
576
33
010
,560
8,86
1-1
,699
29.2
25.8
-3.4
19.6
11.4
-8.3
25.1
18.4
-6.7
Cha
ttan
ooga
, TN
-GA
465,
161
74
-313
,963
11,3
89-2
,574
14.4
11.7
-2.7
41.8
32.8
-8.9
5.0
9.6
4.6
Che
yenn
e, W
Y81
,607
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Chi
cago
, IL
8,27
2,76
818
711
4-7
341
2,85
323
4,94
5-1
77,9
0826
.413
.7-1
2.8
45.3
26.4
-18.
812
.44.
7-7
.7C
hico
-Par
adis
e, C
A20
3,17
13
74
16,1
4032
,815
16,6
7518
.735
.016
.311
.846
.835
.016
.128
.312
.2C
inci
nnat
i, O
H-K
Y-IN
1,64
6,39
531
23-8
74,3
8751
,120
-23,
267
24.4
16.1
-8.2
52.1
33.1
-19.
018
.117
.2-0
.9C
lark
svill
e-H
opki
nsvi
lle, T
N-K
Y20
7,03
31
0-1
3,05
00
-3,0
504.
10.
0-4
.16.
50.
0-6
.50.
00.
00.
0C
leve
land
-Lor
ain-
Ely
ria,
OH
2,25
0,87
171
52-1
910
2,49
476
,146
-26,
348
21.7
15.3
-6.4
38.2
26.5
-11.
723
.715
.7-8
.0C
olor
ado
Spr
ings
, CO
516,
929
21
-11,
640
1,73
999
1.7
1.6
-0.1
3.0
1.2
-1.8
1.7
2.0
0.4
Col
umbi
a, M
O13
5,45
46
5-1
21,0
4917
,149
-3,9
0037
.134
.4-2
.748
.328
.0-2
0.2
47.2
23.8
-23.
4C
olum
bia,
SC
536,
691
118
-324
,702
18,2
88-6
,414
18.2
9.8
-8.5
26.4
13.2
-13.
118
.82.
7-1
6.0
Col
umbu
s, G
A-A
L27
4,62
413
10-3
28,2
9418
,986
-9,3
0833
.724
.0-9
.743
.430
.3-1
3.1
9.0
13.3
4.3
Col
umbu
s, O
H1,
540,
157
2413
-11
86,6
5738
,637
-48,
020
25.3
13.0
-12.
342
.716
.3-2
6.4
22.1
10.8
-11.
3C
orpu
s C
hris
ti, T
X38
0,78
310
7-3
41,0
6622
,462
-18,
604
27.1
14.9
-12.
243
.131
.7-1
1.3
31.6
16.4
-15.
1C
orva
llis,
OR
78,1
532
20
16,3
587,
149
-9,2
0943
.821
.3-2
2.6
86.3
48.0
-38.
438
.615
.4-2
3.2
Cum
berl
and,
MD
-WV
102,
008
10
-153
40
-534
1.4
0.0
-1.4
0.7
0.0
-0.7
0.0
0.0
0.0
Dal
las,
TX
3,51
9,17
636
17-1
992
,275
50,4
70-4
1,80
513
.85.
4-8
.425
.413
.8-1
1.6
12.8
3.5
-9.3
Dan
bury
, CT
217,
980
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Dan
ville
, VA
110,
156
10
-12,
383
0-2
,383
5.7
0.0
-5.7
10.0
0.0
-10.
00.
00.
00.
0D
aven
port
-Mol
ine-
Roc
k Is
land
, IA
-IL
359,
062
72
-510
,790
4,20
2-6
,588
11.7
5.4
-6.3
32.3
15.1
-17.
29.
83.
3-6
.5D
ayto
n-S
prin
gfie
ld, O
H95
0,55
818
8-1
051
,835
23,3
35-2
8,50
021
.57.
2-1
4.3
46.3
13.7
-32.
617
.92.
2-1
5.6
Day
tona
Bea
ch, F
L49
3,17
53
2-1
11,6
435,
873
-5,7
7010
.23.
7-6
.440
.214
.9-2
5.3
1.2
0.5
-0.7
Dec
atur
, AL
145,
867
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Dec
atur
, IL
114,
706
24
22,
324
4,93
82,
614
8.3
15.7
7.3
18.9
28.0
9.1
0.0
25.4
25.4
Den
ver,
CO
2,10
9,28
211
2-9
24,1
024,
518
-19,
584
7.6
1.5
-6.1
17.8
2.1
-15.
712
.42.
4-1
0.0
Des
Moi
nes,
IA
456,
022
20
-27,
773
0-7
,773
9.2
0.0
-9.2
22.9
0.0
-22.
98.
80.
0-8
.8D
etro
it, M
I4,
441,
551
150
53-9
742
0,73
910
7,52
2-3
13,2
1736
.010
.4-2
5.6
53.9
16.4
-37.
536
.16.
9-2
9.1
Dot
han,
AL
137,
916
30
-38,
546
0-8
,546
18.0
0.0
-18.
029
.50.
0-2
9.5
3.1
0.0
-3.1
Dov
er, D
E12
6,69
71
0-1
1,55
60
-1,5
562.
00.
0-2
.02.
50.
0-2
.50.
00.
00.
0D
ubuq
ue, I
A89
,143
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Dul
uth-
Sup
erio
r, M
N-W
I24
3,81
56
5-1
9,09
06,
948
-2,1
4211
.910
.9-1
.032
.542
.09.
48.
615
.36.
8D
utch
ess
Cou
nty,
NY
280,
150
01
10
1,96
31,
963
0.0
4.1
4.1
0.0
6.7
6.7
0.0
9.4
9.4
Eau
Cla
ire,
WI
148,
337
21
-17,
021
5,32
2-1
,699
18.9
16.3
-2.5
2.9
22.1
19.2
7.6
6.4
-1.2
El P
aso,
TX
679,
622
2016
-411
0,73
566
,246
-44,
489
35.9
20.4
-15.
516
.69.
2-7
.338
.621
.4-1
7.2
Elk
hart
-Gos
hen,
IN
182,
791
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Elm
ira,
NY
91,0
701
21
2,44
52,
713
268
11.8
8.8
-2.9
44.9
17.4
-27.
512
.012
.40.
5E
nid,
OK
57,8
130
00
00
00.
00.
00.
00.
00.
00.
00.
00.
00.
0E
rie,
PA
280,
843
52
-313
,082
4,31
1-8
,771
17.8
4.5
-13.
350
.110
.5-3
9.6
35.0
9.2
-25.
8E
ugen
e-S
prin
gfie
ld, O
R32
2,95
93
2-1
11,9
009,
509
-2,3
9113
.210
.0-3
.116
.612
.0-4
.619
.35.
1-1
4.3
Eva
nsvi
lle-H
ende
rson
, IN
-KY
296,
195
10
-11,
318
0-1
,318
2.0
0.0
-2.0
9.1
0.0
-9.1
0.0
0.0
0.0
Farg
o-M
oorh
ead,
ND
-MN
174,
367
21
-19,
237
5,21
0-4
,027
9.0
5.2
-3.8
6.3
1.1
-5.1
12.1
15.1
2.9
Faye
ttev
ille,
NC
302,
963
54
-18,
952
7,19
6-1
,756
12.8
8.8
-4.0
20.5
13.4
-7.1
4.8
4.7
0.0
Faye
ttev
ille-
Spr
ingd
ale-
Rog
ers,
AR
311,
121
11
05,
254
3,21
4-2
,040
7.8
0.9
-7.0
13.4
1.5
-11.
812
.00.
7-1
1.3
Fit
chbu
rg-L
eom
inst
er, M
A14
2,28
40
00
00
00.
00.
00.
00.
00.
00.
00.
00.
00.
0F
lags
taff
, AZ-
UT
122,
366
62
-422
,622
3,08
5-1
9,53
742
.26.
8-3
5.4
53.9
40.5
-13.
413
.211
.1-2
.1F
lint,
MI
436,
141
159
-651
,714
20,0
83-3
1,63
133
.915
.8-1
8.1
56.2
26.0
-30.
136
.210
.1-2
6.1
Flo
renc
e, A
L14
2,95
02
20
4,48
73,
695
-792
11.7
9.5
-2.2
28.9
22.2
-6.7
12.0
9.0
-3.1
Flo
renc
e, S
C12
5,76
12
1-1
10,5
673,
686
-6,8
8120
.07.
9-1
2.1
25.8
11.2
-14.
716
.50.
0-1
6.5
Fort
Col
lins-
Lov
elan
d, C
O25
1,49
41
10
5,29
77,
819
2,52
23.
914
.810
.96.
117
.010
.91.
710
.79.
0Fo
rt L
aude
rdal
e, F
L1,
623,
018
44
013
,473
17,3
473,
874
4.8
4.2
-0.5
11.5
10.4
-1.1
0.4
0.8
0.5
Fort
Mye
rs-C
ape
Cor
al, F
L44
0,88
81
10
4,30
74,
843
536
6.9
6.6
-0.3
26.1
27.8
1.7
2.5
3.8
1.3
Fort
Pie
rce-
Port
St.
Luc
ie, F
L31
9,42
63
30
13,4
5911
,423
-2,0
3625
.516
.7-8
.757
.947
.5-1
0.5
9.6
7.6
-2.0
May 2003 • The Brookings Institution The Living Cities Census Series16
Tota
l Are
aHi
gh-P
over
ty
Popu
latio
n in
Co
ncen
trat
ed
Conc
entr
ated
Co
ncen
trat
ed
Popu
latio
nCe
nsus
Trac
tsHi
gh-P
over
ty C
ensu
s Tr
acts
Pove
rty
Rate
: Tot
alPo
vert
y Ra
te: B
lack
sPo
vert
y Ra
te: H
ispa
nics
MSA
/PM
SA/B
alan
ce o
f Sta
te N
ame
2000
1990
2000
Chan
ge19
9020
00Ch
ange
1990
2000
Chan
ge19
9020
00Ch
ange
1990
2000
Chan
geFo
rt S
mit
h, A
R-O
K20
7,29
02
0-2
1,85
30
-1,8
532.
60.
0-2
.611
.50.
0-1
1.5
0.0
0.0
0.0
Fort
Wal
ton
Bea
ch, F
L17
0,49
80
00
00
00.
00.
00.
00.
00.
00.
00.
00.
00.
0Fo
rt W
ayne
, IN
502,
141
42
-25,
080
2,37
1-2
,709
7.1
3.2
-3.9
25.9
10.5
-15.
52.
41.
8-0
.6Fo
rt W
orth
-Arl
ingt
on, T
X1,
702,
625
138
-534
,385
17,9
97-1
6,38
810
.34.
2-6
.025
.313
.2-1
2.1
8.4
2.3
-6.1
Fre
sno,
CA
922,
516
1527
1287
,293
147,
298
60,0
0525
.133
.68.
436
.543
.36.
823
.135
.712
.6G
adsd
en, A
L10
3,45
91
21
1,48
24,
271
2,78
94.
211
.47.
25.
626
.020
.40.
02.
02.
0G
aine
svill
e, F
L21
7,95
57
70
45,5
8344
,894
-689
44.0
43.0
-1.0
32.8
26.2
-6.5
58.5
52.4
-6.1
Gal
vest
on-T
exas
Cit
y, T
X25
0,15
84
2-2
6,89
04,
466
-2,4
2411
.77.
4-4
.325
.317
.3-8
.06.
55.
3-1
.2G
ary,
IN
631,
362
119
-221
,408
18,6
31-2
,777
16.2
12.2
-4.0
25.5
22.2
-3.3
16.5
7.9
-8.6
Gle
ns F
alls
, NY
124,
345
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Gol
dsbo
ro, N
C11
3,32
91
10
495
560
651.
11.
20.
11.
31.
1-0
.37.
70.
0-7
.7G
rand
For
ks, N
D-M
N97
,478
01
10
5,00
45,
004
0.0
8.3
8.3
0.0
3.8
3.8
0.0
6.8
6.8
Gra
nd J
unct
ion,
CO
116,
255
20
-21,
423
0-1
,423
4.0
0.0
-4.0
0.0
0.0
0.0
13.7
0.0
-13.
7G
rand
Rap
ids-
Mus
kego
n-H
olla
nd, M
I1,
088,
514
93
-619
,281
5,82
6-1
3,45
510
.42.
8-7
.632
.57.
2-2
5.3
3.2
3.3
0.1
Gre
at F
alls
, MT
80,3
572
1-1
2,54
072
1-1
,819
10.0
3.1
-6.9
15.0
6.0
-9.0
11.1
6.1
-5.0
Gre
eley
, CO
180,
936
31
-28,
031
3,46
2-4
,569
12.4
6.1
-6.3
11.0
6.9
-4.0
10.7
1.9
-8.8
Gre
en B
ay, W
I22
6,77
80
11
01,
949
1,94
90.
00.
10.
10.
00.
00.
00.
00.
00.
0G
reen
sbor
o-W
inst
on-S
alem
-Hig
h Po
int,
NC
1,25
1,50
96
60
18,0
2618
,314
288
7.4
5.7
-1.7
17.2
11.8
-5.4
2.3
5.2
2.9
Gre
envi
lle, N
C13
3,79
81
32
8,56
417
,290
8,72
615
.928
.712
.726
.023
.0-3
.00.
08.
98.
9G
reen
ville
-Spa
rtan
burg
-And
erso
n, S
C96
2,44
19
8-1
18,5
2019
,883
1,36
38.
87.
5-1
.213
.712
.2-1
.52.
01.
2-0
.8H
ager
stow
n, M
D13
1,92
30
00
00
00.
00.
00.
00.
00.
00.
00.
00.
00.
0H
amilt
on-M
iddl
etow
n, O
H33
2,80
74
2-2
20,7
0412
,681
-8,0
2324
.915
.9-9
.044
.21.
4-4
2.8
28.7
13.2
-15.
5H
arri
sbur
g-L
eban
on-C
arlis
le, P
A62
9,40
11
21
5,65
86,
519
861
6.4
5.9
-0.4
19.9
16.9
-3.0
15.5
15.8
0.3
Har
tfor
d, C
T1,
183,
110
118
-327
,515
19,7
66-7
,749
18.2
10.0
-8.2
31.3
16.5
-14.
836
.018
.7-1
7.4
Hat
ties
burg
, MS
111,
674
54
-117
,205
14,4
34-2
,771
33.5
23.1
-10.
557
.138
.4-1
8.7
36.1
23.6
-12.
5H
icko
ry-M
orga
nton
-Len
oir,
NC
341,
851
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Hon
olul
u, H
I87
6,15
65
61
6,91
17,
715
804
6.3
4.8
-1.5
2.2
7.1
4.9
5.7
2.0
-3.7
Hou
ma,
LA
194,
477
21
-19,
521
2,49
3-7
,028
9.4
3.2
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21.6
7.8
-13.
84.
00.
0-4
.0H
oust
on, T
X4,
177,
646
5124
-27
162,
487
84,8
25-7
7,66
215
.16.
2-8
.928
.017
.1-1
0.9
13.1
2.8
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3H
unti
ngto
n-A
shla
nd, W
V-K
Y-O
H31
5,53
85
3-2
12,0
635,
437
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267.
73.
7-4
.04.
62.
3-2
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.34.
3-9
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unts
ville
, AL
342,
376
44
010
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8,49
0-2
,082
16.8
11.8
-5.0
30.1
21.5
-8.6
14.1
7.9
-6.1
Indi
anap
olis
, IN
1,60
7,48
610
3-7
24,3
845,
341
-19,
043
7.3
1.6
-5.7
18.6
4.2
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50.
40.
3-0
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wa
Cit
y, I
A11
1,00
64
3-1
14,6
3814
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36.6
39.4
2.8
27.4
8.8
-18.
630
.026
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ckso
n, M
I15
8,42
24
2-2
8,43
83,
850
-4,5
8824
.212
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2.2
59.4
33.9
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522
.016
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.0Ja
ckso
n, M
S44
0,80
117
8-9
51,9
6521
,368
-30,
597
33.4
12.9
-20.
540
.916
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4.8
24.9
4.6
-20.
2Ja
ckso
n, T
N10
7,37
74
3-1
9,82
97,
721
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0828
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2.0
50.7
0.0
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7Ja
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nvill
e, F
L1,
100,
491
127
-527
,005
18,6
03-8
,402
11.8
6.8
-4.9
21.1
13.2
-7.8
6.8
1.0
-5.7
Jack
sonv
ille,
NC
150,
355
01
10
1,25
51,
255
0.0
2.8
2.8
0.0
8.0
8.0
0.0
1.4
1.4
Jam
esto
wn,
NY
139,
750
30
-39,
086
0-9
,086
14.7
0.0
-14.
726
.20.
0-2
6.2
7.7
0.0
-7.7
Jane
svill
e-B
eloi
t, W
I15
2,30
71
0-1
557
0-5
571.
70.
0-1
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00.
0-3
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10.
0-4
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rsey
Cit
y, N
J60
8,97
52
20
8,44
56,
496
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494.
83.
3-1
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.513
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8-1
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n C
ity-
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gspo
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N-V
A48
0,09
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10
532,
042
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10.
10.
00.
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00.
00.
00.
0Jo
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own,
PA
232,
621
20
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948
0-3
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5.7
0.0
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31.1
0.0
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119
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0-1
9.3
Jone
sbor
o, A
R82
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01
10
5,62
95,
629
0.0
16.1
16.1
0.0
36.1
36.1
0.0
44.7
44.7
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in, M
O15
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20
00
00
00.
00.
00.
00.
00.
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7519
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858
21.4
11.2
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237
.58.
1-2
9.4
17.5
11.6
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kake
e, I
L10
3,83
32
0-2
5,97
90
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7924
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0-2
4.6
49.6
0.0
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69.
40.
0-9
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ansa
s C
ity,
MO
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1,77
6,06
224
13-1
131
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16,5
76-1
5,32
09.
94.
7-5
.219
.310
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1-1
6.8
Ken
osha
, WI
149,
577
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Kill
een-
Tem
ple,
TX
312,
952
21
-15,
396
1,73
0-3
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6.4
2.2
-4.2
15.9
2.2
-13.
76.
22.
5-3
.7K
noxv
ille,
TN
687,
249
810
224
,959
26,5
731,
614
11.5
12.0
0.5
37.0
26.2
-10.
98.
216
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6K
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o, I
N10
1,54
11
0-1
309
0-3
091.
40.
0-1
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80.
0-0
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00.
0L
a C
ross
e, W
I-M
N12
6,83
82
20
10,3
0510
,057
-248
26.5
23.8
-2.7
15.9
10.9
-5.0
12.0
28.2
16.3
Laf
ayet
te, I
N18
2,82
16
4-2
26,3
5418
,128
-8,2
2638
.233
.4-4
.832
.210
.9-2
1.3
43.1
11.8
-31.
3L
afay
ette
, LA
385,
647
166
-10
62,0
2728
,049
-33,
978
30.2
15.4
-14.
845
.524
.5-2
1.0
11.9
6.0
-5.9
May 2003 • The Brookings Institution The Living Cities Census Series 17
Tota
l Are
aHi
gh-P
over
ty
Popu
latio
n in
Co
ncen
trat
ed
Conc
entr
ated
Co
ncen
trat
ed
Popu
latio
nCe
nsus
Trac
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gh-P
over
ty C
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s Tr
acts
Pove
rty
Rate
: Tot
alPo
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y Ra
te: B
lack
sPo
vert
y Ra
te: H
ispa
nics
MSA
/PM
SA/B
alan
ce o
f Sta
te N
ame
2000
1990
2000
Chan
ge19
9020
00Ch
ange
1990
2000
Chan
ge19
9020
00Ch
ange
1990
2000
Chan
geL
ake
Cha
rles
, LA
183,
577
51
-411
,988
1,82
0-1
0,16
819
.13.
2-1
5.9
38.2
6.7
-31.
516
.70.
0-1
6.7
Lak
elan
d-W
inte
r H
aven
, FL
483,
924
12
12,
650
2,83
318
32.
52.
5-0
.17.
95.
1-2
.70.
51.
91.
5L
anca
ster
, PA
470,
658
21
-15,
424
3,55
5-1
,869
7.4
4.0
-3.3
31.0
14.3
-16.
723
.610
.4-1
3.2
Lan
sing
-Eas
t L
ansi
ng, M
I44
7,72
88
5-3
19,3
6818
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-692
19.2
12.9
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22.1
5.2
-16.
916
.45.
0-1
1.3
Lar
edo,
TX
193,
117
139
-466
,005
41,9
78-2
4,02
764
.834
.1-3
0.7
43.9
58.7
14.8
65.2
34.5
-30.
7L
as C
ruce
s, N
M17
4,68
24
3-1
24,9
4518
,551
-6,3
9428
.116
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1.2
11.5
20.2
8.7
32.6
16.4
-16.
3L
as V
egas
, NV-
AZ
1,56
3,28
25
2-3
12,8
513,
536
-9,3
157.
00.
8-6
.220
.33.
6-1
6.7
0.9
0.5
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Law
renc
e, K
S99
,962
22
011
,941
12,6
9875
725
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.4-1
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.829
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3L
awre
nce,
MA
-NH
396,
230
51
-412
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1,81
8-1
0,83
818
.22.
4-1
5.8
25.4
5.9
-19.
532
.33.
7-2
8.6
Law
ton,
OK
114,
996
12
12,
410
2,51
010
08.
75.
8-3
.024
.29.
1-1
5.0
3.8
5.1
1.2
Lew
isto
n-A
ubur
n, M
E90
,830
11
01,
613
1,32
1-2
927.
36.
1-1
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.90.
0-1
6.9
25.0
0.0
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0L
exin
gton
, KY
479,
198
57
218
,214
17,7
49-4
6510
.312
.32.
121
.217
.8-3
.43.
46.
12.
7L
ima,
OH
155,
084
41
-36,
771
1,64
8-5
,123
18.7
4.5
-14.
344
.88.
1-3
6.6
0.0
6.4
6.4
Lin
coln
, NE
250,
291
32
-13,
674
5,25
71,
583
8.8
0.2
-8.5
13.1
0.0
-13.
17.
30.
0-7
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ittl
e R
ock-
Nor
th L
ittl
e R
ock,
AR
583,
845
43
-113
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4,80
8-8
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8.8
3.3
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18.3
6.4
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93.
61.
9-1
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iew
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shal
l, T
X20
8,78
03
0-3
3,48
00
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804.
30.
0-4
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70.
0-6
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0-2
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os A
ngel
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ong
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ch, C
A9,
519,
338
5613
781
267,
666
560,
025
292,
359
9.0
14.9
5.9
17.3
21.3
4.1
9.1
16.9
7.8
Lou
isvi
lle, K
Y-IN
1,02
5,59
811
121
35,2
7738
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2,88
317
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635
.840
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27.
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1L
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l, M
A-N
H30
1,68
65
1-4
12,7
682,
754
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014
22.4
5.8
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521
.810
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1.0
29.0
13.2
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8L
ubbo
ck, T
X24
2,62
88
2-6
19,4
285,
625
-13,
803
21.6
5.6
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140
.45.
9-3
4.5
26.1
6.0
-20.
1Ly
nchb
urg,
VA
214,
911
32
-15,
133
1,28
2-3
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7.9
0.8
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17.6
0.8
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80.
00.
00.
0M
acon
, GA
322,
549
119
-222
,380
16,4
47-5
,933
20.7
14.9
-5.8
25.3
19.1
-6.2
3.7
7.2
3.5
Mad
ison
, WI
426,
526
43
-127
,744
23,0
22-4
,722
29.7
25.1
-4.6
8.6
5.5
-3.1
20.4
10.6
-9.8
Man
ches
ter,
NH
198,
378
01
10
2,38
52,
385
0.0
7.1
7.1
0.0
5.7
5.7
0.0
13.6
13.6
Man
sfie
ld, O
H17
5,81
83
0-3
3,47
40
-3,4
747.
10.
0-7
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.60.
0-2
2.6
0.0
0.0
0.0
McA
llen-
Edi
nbur
g-M
issi
on, T
X56
9,46
337
36-1
234,
467
262,
584
28,1
1774
.560
.6-1
3.9
70.9
47.5
-23.
475
.861
.4-1
4.4
Med
ford
-Ash
land
, OR
181,
269
10
-11,
904
0-1
,904
4.0
0.0
-4.0
0.0
0.0
0.0
5.9
0.0
-5.9
Mel
bour
ne-T
itus
ville
-Pal
m B
ay, F
L47
6,23
01
10
4,26
83,
530
-738
5.8
3.4
-2.4
20.3
12.6
-7.7
6.0
1.6
-4.5
Mem
phis
, TN
-AR
-MS
1,13
5,61
451
40-1
114
2,06
080
,136
-61,
924
39.3
21.5
-17.
848
.928
.0-2
0.9
22.0
2.4
-19.
6M
erce
d, C
A21
0,55
43
41
17,2
5515
,578
-1,6
7721
.116
.6-4
.527
.423
.5-3
.919
.015
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iam
i, F
L2,
253,
362
3331
-214
8,08
312
2,27
4-2
5,80
920
.714
.2-6
.640
.730
.6-1
0.0
11.4
7.0
-4.4
Mid
dles
ex-S
omer
set-
Hun
terd
on, N
J1,
169,
641
03
30
14,0
2014
,020
0.0
6.9
6.9
0.0
2.3
2.3
0.0
2.4
2.4
Milw
auke
e-W
auke
sha,
WI
1,50
0,74
159
43-1
614
0,82
577
,468
-63,
357
43.3
21.9
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464
.638
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5.9
54.9
5.3
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6M
inne
apol
is-S
t. P
aul,
MN
-WI
2,96
8,80
633
15-1
879
,048
47,0
43-3
2,00
517
.38.
6-8
.733
.313
.0-2
0.3
18.2
5.9
-12.
3M
isso
ula,
MT
95,8
021
10
2,26
82,
083
-185
6.2
6.5
0.3
40.3
0.0
-40.
37.
94.
2-3
.7M
obile
, AL
540,
258
2616
-10
59,4
3835
,798
-23,
640
33.9
21.1
-12.
854
.736
.4-1
8.3
6.1
10.0
3.9
Mod
esto
, CA
446,
997
32
-13,
327
9,37
06,
043
3.2
5.8
2.6
4.8
9.4
4.6
3.4
7.1
3.7
Mon
mou
th-O
cean
, NJ
1,12
6,21
74
3-1
2,86
311
,977
9,11
41.
47.
56.
13.
17.
44.
33.
26.
73.
5M
onro
e, L
A14
7,25
012
11-1
34,7
8226
,612
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7054
.542
.7-1
1.9
76.6
62.0
-14.
616
.819
.62.
8M
ontg
omer
y, A
L33
3,05
510
6-4
33,3
4717
,779
-15,
568
34.1
19.7
-14.
443
.224
.8-1
8.4
18.5
11.9
-6.6
Mun
cie,
IN
118,
769
44
011
,175
13,8
932,
718
27.8
26.7
-1.2
13.7
10.5
-3.2
21.5
39.1
17.6
Myr
tle
Bea
ch, S
C19
6,62
90
00
00
00.
00.
00.
00.
00.
00.
00.
00.
00.
0N
aple
s, F
L25
1,37
72
20
7,95
68,
087
131
27.8
14.2
-13.
684
.741
.8-4
2.9
37.5
18.2
-19.
3N
ashu
a, N
H19
0,94
90
00
00
00.
00.
00.
00.
00.
00.
00.
00.
00.
0N
ashv
ille,
TN
1,23
1,31
111
7-4
32,8
3422
,064
-10,
770
14.9
7.8
-7.1
34.7
19.8
-14.
917
.11.
2-1
5.9
Nas
sau-
Suf
folk
, NY
2,75
3,91
32
1-1
8,81
418
4-8
,630
0.5
0.0
-0.5
0.5
0.0
-0.5
0.0
0.0
0.0
New
Bed
ford
, MA
175,
198
02
20
4,34
44,
344
0.0
8.5
8.5
0.0
15.4
15.4
0.0
15.7
15.7
New
Hav
en-M
erid
en, C
T54
2,14
93
30
5,05
011
,796
6,74
65.
58.
73.
28.
511
.73.
211
.214
.93.
7N
ew L
ondo
n-N
orw
ich,
CT-
RI
293,
566
20
-232
80
-328
0.7
0.0
-0.7
2.2
0.0
-2.2
3.4
0.0
-3.4
New
Orl
eans
, LA
1,33
7,72
667
49-1
816
5,75
110
8,41
9-5
7,33
233
.722
.1-1
1.6
47.1
31.2
-15.
910
.86.
7-4
.0N
ew Y
ork,
NY
9,31
4,23
527
925
3-2
696
0,29
294
5,25
5-1
5,03
731
.324
.9-6
.440
.132
.5-7
.640
.932
.2-8
.7N
ewar
k, N
J2,
032,
989
2124
349
,189
55,9
846,
795
13.2
12.4
-0.8
21.2
20.3
-0.8
12.3
11.5
-0.8
New
burg
h, N
Y-PA
387,
669
32
-113
,853
14,3
6751
424
.621
.6-3
.033
.90.
1-3
3.7
18.3
0.0
-18.
3N
orfo
lk-V
irgi
nia
Bea
ch-N
ewpo
rt N
ews,
VA
-NC
1,56
9,54
124
18-6
61,2
7445
,544
-15,
730
20.1
14.1
-6.1
29.9
22.3
-7.6
6.2
5.0
-1.2
Oak
land
, CA
2,39
2,55
79
123
29,6
1033
,725
4,11
56.
06.
40.
310
.29.
1-1
.16.
42.
6-3
.8
May 2003 • The Brookings Institution The Living Cities Census Series18
Tota
l Are
aHi
gh-P
over
ty
Popu
latio
n in
Co
ncen
trat
ed
Conc
entr
ated
Co
ncen
trat
ed
Popu
latio
nCe
nsus
Trac
tsHi
gh-P
over
ty C
ensu
s Tr
acts
Pove
rty
Rate
: Tot
alPo
vert
y Ra
te: B
lack
sPo
vert
y Ra
te: H
ispa
nics
MSA
/PM
SA/B
alan
ce o
f Sta
te N
ame
2000
1990
2000
Chan
ge19
9020
00Ch
ange
1990
2000
Chan
ge19
9020
00Ch
ange
1990
2000
Chan
geO
cala
, FL
258,
916
21
-16,
522
2,07
9-4
,443
11.0
2.6
-8.3
32.7
9.2
-23.
52.
95.
02.
1O
dess
a-M
idla
nd, T
X23
7,13
27
0-7
22,3
200
-22,
320
25.8
0.0
-25.
844
.60.
0-4
4.6
33.2
0.0
-33.
2O
klah
oma
Cit
y, O
K1,
083,
346
2421
-339
,420
35,0
85-4
,335
13.1
9.5
-3.7
22.3
9.6
-12.
719
.418
.5-1
.0O
lym
pia,
WA
207,
355
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Om
aha,
NE
-IA
716,
998
83
-516
,825
7,40
0-9
,425
12.2
4.2
-7.9
34.6
12.7
-21.
810
.90.
7-1
0.3
Ora
nge
Cou
nty,
CA
2,84
6,28
91
21
952,
974
2,87
90.
00.
70.
70.
00.
00.
00.
10.
10.
1O
rlan
do, F
L1,
644,
561
55
016
,773
19,7
402,
967
6.5
5.3
-1.2
18.4
11.3
-7.0
1.0
1.6
0.6
Ow
ensb
oro,
KY
91,5
451
0-1
3,28
20
-3,2
8210
.60.
0-1
0.6
43.8
0.0
-43.
810
.00.
0-1
0.0
Pana
ma
Cit
y, F
L14
8,21
72
1-1
4,60
01,
680
-2,9
209.
53.
7-5
.828
.65.
1-2
3.4
1.9
3.2
1.3
Park
ersb
urg-
Mar
iett
a, W
V-O
H15
1,23
70
11
072
472
40.
01.
41.
40.
07.
87.
80.
00.
00.
0Pe
nsac
ola,
FL
412,
153
72
-514
,132
6,52
5-7
,607
12.2
5.2
-7.0
26.9
11.8
-15.
11.
93.
81.
9Pe
oria
-Pek
in, I
L34
7,38
76
71
10,7
7913
,658
2,87
915
.919
.03.
043
.841
.7-2
.126
.532
.45.
9P
hila
delp
hia,
PA
-NJ
5,10
0,93
170
67-3
241,
863
240,
926
-937
23.0
19.6
-3.4
31.0
23.6
-7.5
61.6
49.5
-12.
1P
hoen
ix-M
esa,
AZ
3,25
1,87
627
303
92,6
7391
,844
-829
15.2
10.5
-4.7
25.7
15.4
-10.
321
.312
.2-9
.1P
ine
Blu
ff, A
R84
,278
52
-313
,513
6,42
1-7
,092
30.0
14.4
-15.
638
.018
.3-1
9.7
45.7
2.1
-43.
7P
itts
burg
h, P
A2,
358,
695
4226
-16
74,8
9848
,076
-26,
822
13.3
8.5
-4.7
45.3
24.6
-20.
712
.09.
3-2
.7P
itts
fiel
d, M
A84
,699
10
-132
0-3
20.
40.
0-0
.40.
00.
00.
00.
00.
00.
0Po
cate
llo, I
D75
,565
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Port
land
, ME
243,
537
20
-22,
366
0-2
,366
5.9
0.0
-5.9
2.7
0.0
-2.7
3.2
0.0
-3.2
Port
land
-Van
couv
er, O
R-W
A1,
918,
009
104
-615
,304
8,18
9-7
,115
4.4
1.7
-2.7
23.4
1.9
-21.
54.
60.
6-4
.0Po
rtsm
outh
-Roc
hest
er, N
H-M
E24
0,69
80
11
07,
564
7,56
40.
08.
28.
20.
05.
35.
30.
06.
66.
6P
rovi
denc
e-Fa
ll R
iver
-War
wic
k, R
I-M
A1,
188,
613
58
39,
425
31,6
1122
,186
3.5
9.6
6.1
19.7
22.1
2.4
7.5
21.0
13.5
Pro
vo-O
rem
, UT
368,
536
35
228
,148
26,3
01-1
,847
30.0
28.9
-1.2
38.2
28.5
-9.8
27.9
13.3
-14.
7P
uebl
o, C
O14
1,47
25
1-4
8,05
11,
419
-6,6
3214
.10.
5-1
3.6
17.1
2.6
-14.
515
.80.
1-1
5.7
Pun
ta G
orda
, FL
141,
627
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Rac
ine,
WI
188,
831
21
-14,
602
623
-3,9
7913
.01.
9-1
1.1
22.0
1.5
-20.
512
.90.
0-1
2.9
Ral
eigh
-Dur
ham
-Cha
pel H
ill, N
C1,
187,
941
86
-223
,369
20,6
21-2
,748
7.3
5.3
-2.0
12.3
8.4
-3.9
3.2
0.5
-2.8
Rap
id C
ity,
SD
88,5
650
00
00
00.
00.
00.
00.
00.
00.
00.
00.
00.
0R
eadi
ng, P
A37
3,63
82
42
7,07
614
,023
6,94
712
.418
.86.
420
.225
.14.
934
.134
.50.
4R
eddi
ng, C
A16
3,25
60
00
00
00.
00.
00.
00.
00.
00.
00.
00.
00.
0R
eno,
NV
339,
486
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Ric
hlan
d-K
enne
wic
k-Pa
sco,
WA
191,
822
10
-13,
877
0-3
,877
8.1
0.0
-8.1
21.8
0.0
-21.
817
.50.
0-1
7.5
Ric
hmon
d-Pe
ters
burg
, VA
996,
512
106
-424
,415
19,3
08-5
,107
15.5
10.4
-5.1
22.7
14.6
-8.2
3.0
3.9
0.8
Riv
ersi
de-S
an B
erna
rdin
o, C
A3,
254,
821
618
1222
,523
81,1
9258
,669
3.2
7.7
4.5
5.7
12.3
6.6
4.4
8.9
4.5
Roa
noke
, VA
235,
932
23
14,
929
4,05
3-8
768.
96.
0-2
.823
.89.
7-1
4.1
22.0
16.8
-5.2
Roc
hest
er, M
N12
4,27
71
0-1
850
0-8
500.
10.
0-0
.10.
00.
00.
00.
00.
00.
0R
oche
ster
, NY
1,09
8,20
120
200
33,5
1043
,499
9,98
915
.416
.61.
233
.333
.80.
538
.731
.9-6
.8R
ockf
ord,
IL
371,
236
32
-19,
676
6,46
3-3
,213
16.2
8.6
-7.6
41.3
20.2
-21.
17.
18.
41.
3R
ocky
Mou
nt, N
C14
3,02
61
21
331
743
412
0.6
1.5
0.9
0.8
2.0
1.2
0.0
0.3
0.3
Sac
ram
ento
, CA
1,62
8,19
75
61
18,2
9426
,602
8,30
85.
35.
40.
18.
68.
0-0
.66.
97.
81.
0S
agin
aw-B
ay C
ity-
Mid
land
, MI
403,
070
106
-425
,492
16,3
45-9
,147
22.6
16.1
-6.6
63.9
44.2
-19.
730
.017
.0-1
3.0
Sal
em, O
R34
7,21
40
11
03,
388
3,38
80.
00.
50.
50.
00.
00.
00.
01.
41.
4S
alin
as, C
A40
1,76
20
00
00
00.
00.
00.
00.
00.
00.
00.
00.
00.
0S
alt
Lak
e C
ity-
Ogd
en, U
T1,
333,
914
64
-27,
456
5,86
4-1
,592
3.8
2.7
-1.2
22.9
11.7
-11.
29.
44.
2-5
.1S
an A
ngel
o, T
X10
4,01
02
1-1
2,77
81,
579
-1,1
998.
74.
9-3
.742
.78.
2-3
4.6
7.7
6.8
-0.8
San
Ant
onio
, TX
1,59
2,38
331
13-1
815
2,93
645
,664
-107
,272
28.9
8.0
-20.
928
.414
.4-1
4.0
35.3
8.9
-26.
4S
an D
iego
, CA
2,81
3,83
38
1810
38,6
4471
,918
33,2
746.
39.
12.
715
.413
.0-2
.410
.212
.52.
3S
an F
ranc
isco
, CA
1,73
1,18
34
1-3
12,1
274,
649
-7,4
783.
71.
7-2
.017
.110
.2-6
.90.
60.
1-0
.5S
an J
ose,
CA
1,68
2,58
50
00
00
00.
00.
00.
00.
00.
00.
00.
00.
00.
0S
an L
uis
Obi
spo-
Ata
scad
ero-
Paso
Rob
les,
CA
246,
681
13
29,
292
9,59
830
613
.012
.1-0
.810
.28.
0-2
.37.
35.
4-2
.0S
anta
Bar
bara
-San
ta M
aria
-Lom
poc,
CA
399,
347
44
019
,857
20,6
7181
418
.717
.2-1
.511
.810
.7-1
.05.
15.
60.
5S
anta
Cru
z-W
atso
nvill
e, C
A25
5,60
20
00
00
00.
00.
00.
00.
00.
00.
00.
00.
00.
0S
anta
Fe,
NM
147,
635
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
San
ta R
osa,
CA
458,
614
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Sar
asot
a-B
rade
nton
, FL
589,
959
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Sav
anna
h, G
A29
3,00
011
8-3
15,0
6010
,121
-4,9
3920
.913
.0-7
.928
.818
.3-1
0.5
3.5
5.1
1.6
May 2003 • The Brookings Institution The Living Cities Census Series 19
Tota
l Are
aHi
gh-P
over
ty
Popu
latio
n in
Co
ncen
trat
ed
Conc
entr
ated
Co
ncen
trat
ed
Popu
latio
nCe
nsus
Trac
tsHi
gh-P
over
ty C
ensu
s Tr
acts
Pove
rty
Rate
: Tot
alPo
vert
y Ra
te: B
lack
sPo
vert
y Ra
te: H
ispa
nics
MSA
/PM
SA/B
alan
ce o
f Sta
te N
ame
2000
1990
2000
Chan
ge19
9020
00Ch
ange
1990
2000
Chan
ge19
9020
00Ch
ange
1990
2000
Chan
geS
cran
ton—
Wilk
es-B
arre
—H
azle
ton,
PA
624,
776
22
03,
483
8,56
85,
085
2.2
3.7
1.5
13.1
6.6
-6.5
0.0
5.9
5.9
Sea
ttle
-Bel
levu
e-E
vere
tt, W
A2,
414,
616
94
-524
,775
14,6
46-1
0,12
95.
02.
4-2
.66.
83.
4-3
.38.
11.
3-6
.8S
haro
n, P
A12
0,29
33
2-1
4,36
32,
407
-1,9
5613
.58.
7-4
.761
.831
.6-3
0.2
21.3
47.1
25.8
She
boyg
an, W
I11
2,64
60
00
00
00.
00.
00.
00.
00.
00.
00.
00.
00.
0S
herm
an-D
enis
on, T
X11
0,59
51
0-1
325
0-3
251.
20.
0-1
.22.
80.
0-2
.84.
00.
0-4
.0S
hrev
epor
t-B
ossi
er C
ity,
LA
392,
302
1818
059
,130
40,7
41-1
8,38
935
.425
.2-1
0.2
47.0
32.0
-15.
015
.217
.11.
9S
ioux
Cit
y, I
A-N
E12
4,13
02
1-1
4,51
751
-4,4
6613
.60.
2-1
3.3
27.6
0.0
-27.
625
.00.
6-2
4.4
Sio
ux F
alls
, SD
172,
412
10
-11,
252
0-1
,252
5.2
0.0
-5.2
4.7
0.0
-4.7
0.0
0.0
0.0
Sou
th B
end,
IN
265,
559
10
-11,
525
0-1
,525
3.0
0.0
-3.0
7.1
0.0
-7.1
3.1
0.0
-3.1
Spo
kane
, WA
417,
939
61
-56,
766
2,20
3-4
,563
5.9
2.0
-3.8
5.9
4.3
-1.5
4.9
1.7
-3.1
Spr
ingf
ield
, IL
201,
437
21
-14,
000
1,81
8-2
,182
10.8
5.9
-5.0
28.4
13.9
-14.
60.
00.
00.
0S
prin
gfie
ld, M
A59
1,93
211
110
43,8
1434
,957
-8,8
5728
.721
.7-7
.039
.921
.6-1
8.3
59.1
43.2
-15.
9S
prin
gfie
ld, M
O32
5,72
14
2-2
13,1
636,
046
-7,1
1711
.23.
6-7
.631
.54.
6-2
6.9
10.9
7.1
-3.8
St.
Clo
ud, M
N16
7,39
23
1-2
12,1
721,
102
-11,
070
22.9
1.1
-21.
86.
53.
2-3
.219
.81.
0-1
8.8
St.
Jos
eph,
MO
102,
490
20
-24,
132
0-4
,132
12.7
0.0
-12.
739
.50.
0-3
9.5
3.5
0.0
-3.5
St.
Lou
is, M
O-I
L2,
603,
607
3926
-13
109,
516
70,6
50-3
8,86
620
.513
.0-7
.539
.123
.8-1
5.3
12.8
5.2
-7.6
Sta
mfo
rd-N
orw
alk,
CT
353,
556
00
00
00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Sta
te C
olle
ge, P
A13
5,75
85
61
23,6
7928
,955
5,27
652
.149
.5-2
.654
.536
.6-1
7.9
28.6
55.1
26.5
Ste
uben
ville
-Wei
rton
, OH
-WV
132,
008
42
-25,
756
3,33
0-2
,426
12.5
8.2
-4.4
57.6
33.9
-23.
77.
813
.96.
1S
tock
ton-
Lod
i, C
A56
3,59
85
72
25,8
5834
,504
8,64
615
.215
.50.
322
.324
.42.
118
.115
.7-2
.4S
umte
r, S
C10
4,64
63
0-3
10,5
450
-10,
545
22.1
0.0
-22.
127
.30.
0-2
7.3
0.0
0.0
0.0
Syr
acus
e, N
Y73
2,11
714
12-2
38,1
5034
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700,
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545,
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381,
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00
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0.0
0.0
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May 2003 • The Brookings Institution The Living Cities Census Series20
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Non
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Non
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098,
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4320
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Non
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Non
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Rho
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Non
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as3,
159,
940
5126
-25
210,
746
109,
885
-100
,861
16.0
9.0
-7.0
7.0
6.5
-0.5
32.6
16.5
-16.
1N
on-M
etro
polit
an A
reas
in U
tah
524,
673
14
33,
495
17,7
0914
,214
4.1
11.9
7.7
0.0
12.5
12.5
0.8
2.1
1.3
Non
-Met
ropo
litan
Are
as in
Ver
mon
t43
9,43
61
0-1
70
-70.
00.
00.
00.
00.
00.
00.
00.
00.
0N
on-M
etro
polit
an A
reas
in V
irgi
nia
1,55
0,44
74
3-1
22,2
5822
,778
520
2.9
6.2
3.3
0.2
1.0
0.8
11.0
4.3
-6.7
Non
-Met
ropo
litan
Are
as in
Was
hing
ton
994,
967
54
-117
,111
18,0
5194
04.
75.
20.
523
.415
.3-8
.14.
51.
6-2
.9N
on-M
etro
polit
an A
reas
in W
est
Vir
gini
a1,
042,
776
106
-433
,300
19,6
36-1
3,66
45.
54.
0-1
.410
.33.
6-6
.77.
25.
0-2
.2N
on-M
etro
polit
an A
reas
in W
isco
nsin
1,72
3,36
74
0-4
12,6
760
-12,
676
2.9
0.0
-2.9
1.0
0.0
-1.0
1.4
0.0
-1.4
Non
-Met
ropo
litan
Are
as in
Wyo
min
g34
5,64
22
1-1
7,41
23,
345
-4,0
678.
11.
5-6
.60.
016
.616
.62.
00.
3-1
.6
U.S
. Tot
al28
1,42
1,90
63,
417
2,51
0-9
0710
,393
,954
7,94
6,29
1-2
,447
,663
15.1
10.3
-4.8
30.4
18.6
-11.
821
.213
.8-7
.4
May 2003 • The Brookings Institution The Living Cities Census Series 21
References
Danziger, Sheldon H. and PeterGottschalk. 1987. “Earnings Inequal-ity, the Spatial Concentration ofPoverty, and the Underclass.” Ameri-can Economic Review 77: 211–15.
Fisher, Gordon M. 1992. “The Devel-opment and History of the PovertyThresholds,” Social Security Bulletin55: 3–14.
Jargowsky, Paul A. 1997. Poverty andPlace: Ghettos, Barrios, and the Ameri-can City. New York: Russell SageFoundation.
Jargowsky, Paul A. and Mary Jo Bane.1990. “Ghetto Poverty: Basic Ques-tions.” In Inner-City Poverty in theUnited States, edited by L. E. Lynnand M. G. H. McGeary. Washington:National Academy Press.
Kasarda, John D. 1993. “Inner-CityPoverty and Economic Access.” In J.Sommer and D. A. Hicks, eds., Redis-covering Urban America: Perspectiveson the 1980s. U.S. Department ofHousing and Urban Development.
Orfield, Myron. 1996. Metropolitics: A Regional Agenda for Community and Stability. Washington: BrookingsInstitution Press.
——— . 2002. American Metropoli-tics: The New Suburban Reality. Wash-ington: Brookings Institution Press.
Orshansky, Mollie. 1965. “Countingthe Poor: Another Look at the PovertyProfile,” Social Security Bulletin 28:3–29.
Ruggles, Patricia. 1990. Drawing theLine: Alternative Poverty Measures andTheir Implications for Public Policy.Washington: Urban Institute Press.
Wilson, William Julius. 1987. TheTruly Disadvantaged: The Inner-City,the Underclass and Public Policy.Chicago: University of Chicago Press.
Endnotes
1. Paul Jargowsky directs the Bruton Centerfor Development Studies at the Universityof Texas at Dallas, and is associate profes-sor of political economy there. He is also aSenior Research Affiliate at the NationalPoverty Center at the University of Michi-gan. He is the author of Poverty and Place:Ghettos, Barrios, and the American City(New York: Russell Sage Foundation,1997).
2. For a thorough discussion of the trends inconcentrated poverty between 1970 and1990, see Jargowsky (1997), especiallychapter 2.
3. Patricia Ruggles, Drawing the Line: Alter-native Poverty Measures and Their Implica-tions for Public Policy (Washington: UrbanInstitute Press, 1990).
4. This is known as the Modifiable Areal UnitProblem, and has been studied extensivelyby geographers. See S. Openshaw and P.J.Taylor, “The Modifiable Areal Unit Prob-lem.” In N. Wrigley and R.J. Bennett, eds.,Quantitative Geography: A British View(London: Routledge, 1981).
5. Sheldon H. Danziger and Peter Gottschalk,“Earnings Inequality, the Spatial Concen-tration of Poverty, and the Underclass,”American Economic Review 77 (1987):211–15; Paul A. Jargowsky and Mary JoBane, “Ghetto Poverty: Basic Questions.”In L. E. Lynn and M. G. H. McGeary, eds.,Inner-City Poverty in the United States(Washington: National Academy Press,1991); John D. Kasarda, “Inner-CityPoverty and Economic Access.” In J. Som-mer and D. A. Hicks, eds., RediscoveringUrban America: Perspectives on the 1980s(U.S. Department of Housing and UrbanDevelopment, 1993).
6. In New England, metropolitan areas arebuilt up from subdivisions of countiesrather than whole counties. The CensusBureau also defines New England CountyMetropolitan Areas (NECMAs), which arecomposed of whole counties.
7. New England County Metropolitan Areas(NECMAs) are not considered for thesame reasons. Non-metropolitan areaswere first completely divided into censustracts in 1990, so Census 2000 providesthe first opportunity to conduct a trulynationwide study of the trends in concen-trated poverty. For presentation purposes,non-metropolitan neighborhoods aregrouped by state, but it should be notedthat these areas are residuals and may ormay not be contiguous.
8. In New England, census tracts can evencross metropolitan area boundaries.
9. The data in Figure 1 are for metropolitanareas only, as they existed at the time ofeach census. Nationwide data on neighbor-hood poverty are not available prior to1990, because census tracts in non-metro-politan areas were defined for the first timewith the release of the 1990 census.
10. Exceptions included metro areas such asAkron, Cleveland, Pittsburgh, andYoungstown, which despite double-digitdecreases in the number of high-povertyneighborhoods had declines of less than30,000 people living in such neighbor-hoods.
11. A technical note regarding the maps: Formapping purposes, a consistent set of cen-sus tract boundaries is employed. That way,it is possible to show how different areaschanged over time. However, for calculat-ing statistics, it is important to have a con-sistent neighborhood size over time. This isbest achieved by using contemporaneoustracts. In view of that, there is not an exactcorrespondence between the data used forthe maps and the data used for the tablesand figures presented in the text. For 1990and earlier years, these maps use datainterpolated to the 2000 census tract gridby the Urban Institute and Geolytics, Inc.
12. Robert E. Lang and Patrick A. Simmons,“Boomburbs: The Emergence of Large,Fast-Growing Suburban Cities.” In BruceKatz and Robert E. Lang, eds., RedefiningUrban and Suburban America: Evidencefrom Census 2000 (Washington: BrookingsInstitution Press, 2003).
13. During the 1990s, Los Angeles County lostsignificant white population, at the sametime that its Hispanic population grew bynearly 900,000.
14. William Julius Wilson, The Truly Disadvan-taged: The Inner-City, the Underclass andPublic Policy (University of Chicago Press,1987); Ronald B. Mincy and Susan J.Weiner, The Under Class in the 1980s:Changing Concepts, Constant Reality(Washington: The Urban Institute Press);Danziger and Gottschalk 1987.
15. Unfortunately, due to limitations in thecollection of detailed ethnicity for Hispanicsubgroups in Census 2000, it is not possi-ble to examine the concentration of povertyamong people of Cuban, Puerto Rican,Mexican, etc., ancestry.
16. The 50th state is New Jersey, which doesnot have any non-metropolitan areas,according to the official census definitions.
17. For recent evidence exploring differencesin job seeking in predominantly Hispanicversus predominantly black high-povertycommunities, see James R. Elliott andMario Sims, “Ghettos and Barrios: TheImpact of Neighborhood Poverty and Raceon Job Matching Among Blacks and Lati-nos,” Social Problems 48(3)(2001):341–361.
18. See Myron Orfield, Metropolitics: ARegional Agenda for Community and Sta-bility (Washington: Brookings InstitutionPress, 1996), and Myron Orfield, AmericanMetropolitics: The New Suburban Reality(Washington: Brookings Institution Press,2002).
May 2003 • The Brookings Institution The Living Cities Census Series22
May 2003 • The Brookings Institution The Living Cities Census Series 23
For More Information:
Paul JargowskySchool of Social Sciences GR 31University of Texas at Dallas2601 N. Floyd RdRichardson, TX [email protected]
Acknowledgments:
The author would like to thank J.D. Kim, Sonia Monga, and Karl Ho forresearch assistance and technical support. He would also like to acknowl-edge participants in the Social Science Workshop at the University of Texasat Dallas, including Brian Berry, Don Hicks, and Dan O’Brien and otherswho asked questions and made helpful suggestions at an early stage of thisresearch. Also helpful were reviewers from the Brookings Institution.
The Brookings Institution Center on Urban and Metropolitan Policy wouldlike to thank Living Cities for their support of its work on Census 2000.Brookings would also like to thank the Fannie Mae Foundation for theirfounding support of the center and their continued commitment to our work.
The Brookings Institution
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Center on Urban and Metropolitan Policy
About the Living Cities Census Series
The 2000 census provides a unique opportunity to define the shape of urban and metro-politan policy for the coming decade. With support from Living Cities: The NationalCommunity Development Initiative, the Brookings Institution Center on Urban andMetropolitan Policy has launched the Living Cities Census Series, a major three-yeareffort to illustrate how urban and suburban America has changed in the last two decades.As a part of this effort, Brookings is conducting comparative analyses of the major social,economic, and demographic trends for U.S. metropolitan areas, as well as a special effortto provide census information and analysis in a manner that is tailored to the citiesinvolved in the Living Cities initiative.
Living Cities: The National Community Development Initiative is a partnership of lead-ing foundations, financial institutions, nonprofit organizations, and the federal govern-ment that is committed to improving the vitality of cities and urban communities. LivingCities funds the work of community development corporations in 23 cities and uses thelessons of that work to engage in national research and policy development. Visit LivingCities on the web at www.livingcities.org
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