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GEOGRAPHIC CONTEXT ANDCONCENTRATED URBAN POVERTY WITHINTHE UNITED STATESThomas J. Cooke aa Department of Geography, University of Connecticut, Storrs,Connecticut 06269-2148 Tel: 860-486-1769 Fax: 860-486-1348tcooke@uconn.eduPublished online: 15 May 2013.

To cite this article: Thomas J. Cooke (1999) GEOGRAPHIC CONTEXT AND CONCENTRATEDURBAN POVERTY WITHIN THE UNITED STATES, Urban Geography, 20:6, 552-566, DOI:10.2747/0272-3638.20.6.552

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GEOGRAPHIC CONTEXT AND CONCENTRATED URBAN POVERTY WITHIN THE UNITED STATES1

Thomas J. Cooke Department of Geography University of Connecticut

Storrs, Connecticut 06269-2148 Tel: 860-486-1769 Fax: 860-486-1348 tcooke@uconn.edu

Abstract: Responding to previous analyses that assume that places are passive recipients of the various macro-level social phenomena associated with concentrated urban poverty, I hypoth­esize that concentrated urban poverty takes on different forms in different places as a result of how macro-level social phenomena are mediated by locally specific structures. To investigate how concentrated urban poverty takes on different forms in different places, I first decompose the poverty rates of all high-poverty urban neighborhoods in the United States into their race-specific rate and composition effects, and classify high-poverty neighborhoods based on these decomposition values. The results of the analysis demonstrate that poverty in a majority of the high-poverty neighborhoods in the United States is undoubtedly affected by geographically spe­cific processes. For example, within one set of high-poverty neighborhoods, poverty is associ­ated with both the lack of economic opportunity and high rates of class-based residential segregation within mixed-race immigrant ethnic/immigrant enclaves in large gateway cities. A second set of high-poverty neighborhoods, located in the metropolitan areas of the southern United States, has high rates of poverty because of the residential segregation and geographic concentration of poverty-prone African Americans. And lastly, among a third set of tracts, poverty experiences in African American ghettos are linked to declining economic and social opportunities and class-based residential segregation within large manufacturing cities. A set of recommendations for additional research includes addressing how one-size-fits-all anti-poverty public policies should be modified for the specific needs of each type of high-poverty neighborhood. [Key words: context, poverty, segregation, employment, race, ethnicity.]

Between 1970 and 1990, the number of residents of high-poverty urban neighbor­hoods (defined as metropolitan census tracts with a poverty rate of at least 40%) increased by 95%—from 4.1 to 8.0 million (Jargowsky, 1996). This dramatic increase in the con­centration of urban poverty has attracted the attention of academics, journalists, and pol­iticians, who have continued to debate both its causes and consequences over the course of the past two decades. With respect to the causes of concentrated urban poverty, discus­sion has largely focused on the relative importance of economic and social restructuring versus the continuing legacy of residential segregation. The former argument usually is framed within the context of Wilson's (1987) The Truly Disadvantaged, while the latter argument is identified most closely with Massey and Denton's (1993) American Apart­heid. Recent cross-sectional analysis of all high-poverty urban neighborhoods in the United States confirms that both the level of economic inequality within a metropolitan

552 Urban Geography, 1999, 20, 6, pp. 552-566. Copyright © 1999 by V. H. Winston & Son, Inc. All rights reserved.

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GEOGRAPHIC CONTEXT AND CONCENTRATED URBAN POVERTY 553

area and the level of class-based residential segregation are related most strongly to the concentration of urban poverty (Jargowsky, 1996).

While the methodological approaches used in this academic debate are varied, running from highly localized ethnographic studies to cross-sectional analyses of all census tracts within the United States, the intellectual focus is clearly deductive and emphasizes the development of a generalized explanation for the concentration of urban poverty through the empirical testing of alternative hypotheses. In contrast to previous research, the approach used in this paper is descriptive and inductive. I explicitly assume that places are not passive recipients of macro-level social phenomena (e.g., economic restructuring and residential segregation) but rather that macro-level social phenomena are mediated by locally specific structures (Massey, 1984, 1994; Pred, 1984). Therefore, I hypothesize that concentrated urban poverty takes on different forms in different places (e.g., Kodras, 1997).

To observe how concentrated urban poverty takes on different forms in different places, I first decompose the poverty rates of all high-poverty urban neighborhoods in the United States into their race-specific rate and composition effects. Composition effects reflect the degree to which neighborhood poverty rates are influenced by the racial com­position of the population, while rate effects reflect the degree to which neighborhood poverty rates are influenced by forces that are independent of the neighborhood popula­tion. Following the decomposition of neighborhood poverty rates, I classify high-poverty census tracts into groups, based on their respective decomposition values, and identify a typology of high-poverty urban neighborhoods. The typology demonstrates that poverty experiences vary dramatically with regional and local geographic context and suggests that generalized explanations and solutions to concentrated urban poverty must likewise consider and incorporate the mediating effect of place.

THE CONCENTRATION OF URBAN POVERTY

While there is a large body of research on the concentration of neighborhood poverty, the essential concepts relied on in this paper are embodied in the research of Wilson (1987, 1996), Kasarda (1989,1990), Massey and Denton (1993), and Jargowsky (1996).2

Wilson (1987, 1996) argued that the growing concentration of urban poverty is a reflec­tion of the declining economic and social health of predominantly minority neighbor­hoods because of the effects of contemporary economic restructuring and the improved residential mobility of middle-class minorities. Wilson (1987, 1996) and Kasarda (1989, 1990) outlined how the combined forces of deindustrialization, the suburbanization of job opportunities, and occupational bifurcation have economically isolated poorly educated working-class minorities. Compounding the economic problems of poor minorities is an increase in social isolation caused by continuing residential segregation and an increase in class-based residential segregation among minorities. As a result, poorly educated minorities have become economically and socially isolated, causing an increase in the concentration of urban poverty along with a host of interconnected social phenomena that label the residents of high-poverty neighborhoods as members of the "urban underclass."

In contrast, Massey and Denton (1993) argued that the increasing concentration of urban poverty is a reflection of the economic and social composition of the residents of high-poverty neighborhoods because of racial and, to a lesser degree, economic segrega-

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554 THOMAS J. COOKE

tion in the presence of a more general increase in poverty and economic inequality (e.g., Danziger and Gottschalk, 1995). Therefore, while Massey and Denton (1993) basically agreed with Wilson (1987) regarding the wide range of possible causes responsible for the formation of the urban underclass, they view his explanations as unnecessarily com­plex and reduce the explanation for a rise in concentrated poverty to a few key processes:

Given a high and rising level of urbanization, growing income inequality, and rising class segregation, an increase in the geographic concentration of affluence and pov­erty is all but inevitable. These spatial processes are magnified, however, when they occur in a group that is also segregated on the basis of an ascribed characteristic such as race; and no feature of our national life has proved to be as enduring as the residential color line separating black from white America. (Massey, 1996, p. 403)

More recent research by Jargowsky (1996) provided a more comprehensive view. In Poverty and Place, Jargowsky recognized that the processes described by Massey and Denton (1993) and Wilson (1987) are not mutually exclusive and devised a methodology to identify which factors have the greatest explanatory power. In his analysis of all high-poverty urban census tracts in the United States, Jargowsky (1996) concluded that both the level of economic inequality within a metropolitan area (i.e., Massey and Denton, 1993) and the level of class-based residential segregation (i.e., Wilson, 1987) are most strongly related to the concentration of poverty. Yet, Jargowsky (1996) found little evi­dence linking the concentration of urban poverty to either race-based residential segrega­tion (i.e., Massey and Denton, 1993) or the decentralization of employment opportunities (i.e., Wilson, 1987). Jargowsky (1996) concluded that

...the primary factors behind the increasing concentration of poverty are metropol­itan economic growth and the general processes that create and sustain segregation by race and class. Metropolitan-level variables for economic opportunity and seg­regation can explain about four-fifths of the variation among metropolitan areas and about the same proportion of the changes in neighborhood poverty over time. (Jar­gowsky, 1996, p.186)

One critically important similarity in the research of Wilson (1987,1996), Massey and Denton (1993), and Jargowsky (1996) is that they all emphasize how various macro-level social and economic processes are involved in the creation of a distinct geographic pattern of urban poverty; yet they neglect to fully consider how these processes might be affected by regionally and locally specific processes. For example, Jargowsky is prepared to accept as valid all of the various factors implicated by Wilson (1987) and Massey and Denton (1993), but he then set out to determine, using cross-sectional statistical methods, which of those factors best explain the concentration of urban poverty across all of the metropolitan areas in the United States. As a result of his methodological approach, Jar­gowsky (1996, p. 186) is led to make the rather general claim that".. .although such fac­tors as spatial location, neighborhood culture, and social policy may play a role, they are secondary to income generation and neighborhood sorting, which together explain most of the observed variations in ghetto poverty."

I am motivated to write this paper by what I see as an overreliance on macro-level explanations for concentrated urban poverty. Such generalizations regarding the causes of

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GEOGRAPHIC CONTEXT AND CONCENTRATED URBAN POVERTY 555

concentrated urban poverty may not be valid in every metropolitan area and, perhaps even more important, may result in the formation of public policies that may not be appropriate for specific metropolitan areas. Therefore, my purpose is simple—to contend that the concentration of urban poverty is a locally contingent process. My basic assumption is that places are not passive recipients of macro-level social and economic processes but rather, macro-level social and economic processes are expected to have different out­comes in different places because of how local social and economic structures react to— and transform—larger social and economic processes (Massey, 1984, 1994; Pred, 1984; Sayer, 1984).

A singular example of such an approach as applied to the geography of poverty is a recent paper by Janet Kodras (1997), which explores county-level poverty patterns across the United States between 1970 and 1990. She wrote that

Beyond the evident point that inequality is a systemic feature of capitalism, the role of the market and state in affecting poverty cannot be studied in the abstract, as these take concrete forms and perform specific functions according to the particular place and time in which they are inserted. The interaction of the market and the state is geographically specific, because each locality consists of a distinct amal­gam of economic, political, and cultural relations—a historically accumulated social order—that influences how the market and state affect poverty. (Kodras, 1997, p. 68)

To demonstrate her point, Kodras presented five vignettes of different places (i.e., Detroit, Silicon Valley, Eastern Kentucky, the Mississippi Delta, and the Dakota Bad­lands) and described how global and national economic and political transformations are translated into different poverty experiences in particular locales as a result of local cul­tural, economic, and social structures.

Similarly, I argue that such macro-level processes as economic restructuring, occupa­tional bifurcation, employment decentralization, race-based residential segregation, and class-based segregation do not have the same effects on concentrated urban poverty in every metropolitan area. In contrast, Jargowsky (1996) implicitly assumed, through the use of a cross-sectional model of concentrated urban poverty, that the causes of concen­trated urban poverty are universally the same, and who treats differences in how social phenomena are manifest in particular metropolitan areas as error disturbances from the norm (see the previous quote from Jargowsky, 1996, p.186). In contrast, viewing the effects of the aforementioned macro-level social and economic processes as contingent on the characteristics of a particular metropolitan area means that the causes of concen­trated urban poverty vary across metropolitan areas (Sayer, 1984).

For example, Jargowsky (1996) found little evidence to support the hypothesis that the decentralization of employment opportunities is linked to the growth of concentrated urban poverty (the so-called "spatial-mismatch hypothesis"). Yet, in my own comparative analysis of the spatial mismatch hypothesis (Cooke, 1996), I found evidence in support of the hypothesis in large, low-density cities, with long average commutes (e.g., Dallas, TX; Los Angeles, CA; New York, NY; Washington, DC), while I found no support for the hypothesis in small, high-density cities, with short average commutes (e.g., Cleveland, OH; Jackson, MS; Memphis, TN; Newark, NJ). My point is that, while employment

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556 THOMAS J. COOKE

decentralization is a universal phenomena, it may only have an impact on concentrated urban poverty in large, sparsely settled metropolitan areas. Similar positions can be taken with respect to the other hypothesized causes of concentrated urban poverty. For example, economic restructuring is likely to be a cause of concentrated urban poverty only in met­ropolitan areas with a large segregated minority workforce dependent on traditional blue-collar industries (e.g., Kasarda, 1989, 1990). I therefore propose that concentrated urban poverty must be viewed as a local phenomena, which is influenced as much by local and regional factors as by macro-level social and economic processes. The purpose of this paper, therefore, is to make this point by investigating how the characteristics associated with the causes of concentrated urban poverty vary substantially across all of the high-poverty neighborhoods of the United States. I argue that these differences are significant enough to demonstrate that such macro-scale processes as the decentralization of employ­ment, race- and class-based segregation, and economic restructuring are mediated by locally contingent processes.

DATA AND METHODS

To investigate how the characteristics associated with the causes of concentrated urban poverty vary with geographic context, this study draws data from the Urban Institute's Under Class Data Base (Tobin, 1993). The Under Class Data Base (UDB) contains census tract-level data from the 1970, 1980, and 1990 U.S. Censuses, as well as a number of derived variables. To be consistent with Jargowsky's (1996) analysis, data are restricted to the 1990 UDB file and to all census tracts within metropolitan areas with a poverty rate of at least 40%. The resulting sample has 2,858 tracts, compared with 2,866 for Jar-gowsky.3 Observations with missing values for the components of census tract poverty rates, and with greater than 10% of the population living in dormitories (to eliminate census tracts with large numbers of college students) further reduces the final sample to 2,473 high-poverty urban census tracts.

The census tract poverty rates are decomposed into two major components: (1) a race-adjusted rate effect and (2) a race-adjusted composition effect (Odland and Ellis, 1998). First, define Fti as a racial group's (i) share of a census tract's (t) total population and Qti as the census tract poverty rate for the same racial group. The census tract poverty rate (PRt) is then given as:

PRt = Σ[F t i*Q t i] (1)

Second, define Fni as the proportion of racial group (i) in the national population (n) and Qni as the national poverty rate for the same racial group. Then the expression for PRt can be written in deviation form as

PRt = SUM [Fni + (Fti - Fni)][Qni + (Qti - Qni)]. (2)

Lastly, performing the multiplication on the right-hand side and distributing the sum­mation operation yields

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GEOGRAPHIC CONTEXT AND CONCENTRATED URBAN POVERTY 557

PRt = Σ [Fni * Qni] + Σ [Qni (Fti - Fni)] + Σ [Fni (Qti - Qni)] + Σ [(Fti - Fni)(Qti - Qni)]. (3)

To calculate the components of the total poverty rate, a dichotomous classification scheme is used to define the proportion of the national population and census tract popu­lations by race and ethnicity (i.e., Fni and Fti). The "minority" population consists of His-panics and non-Hispanic African Americans, while the "majority" population consists of the balance of the population. The use of this simple dichotomous racial and ethnic clas­sification scheme is driven entirely by the available data since national and tract-level poverty rates are not cross-classified by race and ethnicity. To calculate national and cen­sus tract "minority" and "majority" poverty rates (i.e., Qni and Qti), the White poverty rate is substituted for the "majority" poverty rate and the population weighted average of His­panic and African American poverty rates are substituted for the "minority" poverty rates.

Applying these data to equation 1 decomposes census tract poverty rates into four parts. The first term on the right-hand side (RHS) is merely the national poverty rate. The second term on the RHS is the difference between national and census tract poverty rates owing to differences in racial composition, holding race-specific poverty rates to the national average. In other words, this is the race-adjusted composition effect and if posi­tive (negative) states that the poverty rate in a census tract is higher (lower) than the national average because the census tract is populated by racial groups that have high (low) rates of poverty regardless of residential location. The third term on the RHS is the difference between national and census tract poverty rates owing to differences in race-specific poverty rates, holding racial composition at the national average. In other words, this is the race-adjusted rate effect and if positive (negative) states that the poverty rate in a census tract is higher than the national average because of forces that are independent of the racial composition of the census tract (e.g., the lack of economic opportunity and/ or a high degree of class-based residential segregation).4 The last term on the RHS, termed the race-adjusted place-specific effect by Odland and Ellis (1998), measures the covariation between the rate effect and composition effect and emphasizes the joint impact of both effects on tract-level poverty rates. Beyond its algebraic derivation, this term is necessary for the following reasons: (1) the rate-effect term equates the census tract population composition with the national population composition while allowing race-specific poverty rates to vary from the national race-specific means, while (2) the composition-effect term equates the census tract race-specific poverty rates with the national race-specific rates while allowing racial composition to vary from the national racial composition. In most cases, the place-specific term is small but there are two cases where it can be large: (1) when the poverty rate for the majority population is very large relative to the minority population the place effect will be negative because the rate effect will be overstated and (2) when the poverty rate for the minority population is large rela­tive to the majority population the place effect will be positive because the rate effect will be understated.

I present the decomposition values for four hypothetical census tracts in Table 1. Col­umn 1 estimates the decomposition values for a hypothetical tract with a population com­position similar to that of the United States population. Of course, the total poverty rate is equal to the United States poverty rate with no rate, composition, or place effects. Column

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558 THOMAS J. COOKE

TABLE 1.—DECOMPOSITION EXAMPLES

Tract composition

Percent majority Percent minority Majority poverty rate Minority poverty rate Decomposition values

National poverty rate Rate effect Composition effect Place effect

Total poverty rate

National (%)

79 21 11 32

15 0 0 0

15

Scenario 1 (%)

40 60 40 60

15 29

8 0

52

Scenario 2 (%)

10 90 40 60

15 29 15 -1 58

Scenario 3 (%)

10 90 50 70

15 39 15 -1 68

2 presents what Jargowsky (1996) identified as a "typical" high-poverty census tract: Racially mixed and with both population groups experiencing poverty rates about 30% higher than their respective national averages. In this case, the total poverty rate of 52% is decomposed into the national component (15%), rate-effect component (29%), and the composition-effect component (8%). Rate effects predominate because both population groups have higher than average poverty rates; suggesting either a lack of economic opportunity and/or a high degree of class-based segregation. In this hypothetical tract, composition effects are relatively small but nonetheless reflect the fact that the tract has a higher-than-average share of minorities, who have high rates of poverty wherever they live. The third example (Column 3) keeps the race-specific poverty rates the same as in the previous example, but increases the minority population composition upward. As expected, the total poverty rate is higher than in the previous example (58% versus 52%), the rate effect has not changed (because the race-specific poverty rates have not changed), and the composition effect has increased from 8% to 15%. The increase in the composi­tion effect reflects the increasing concentration of poverty-prone minorities in the hypo­thetical census tract. The effect of an increase in the share of poverty-prone minorities on the total poverty rate, as reflected in the composition effect, is the same effect of race-based segregation on poverty presented by Massey and Denton (1993). Lastly, Column 4 demonstrates how an increase in poverty rates among both minorities and nonminorities cause an increase in the total poverty rate resulting from an increase in the rate effect. Again, this reflects a general increase in the risk of living in poverty (either because of the lack of economic opportunity or because of class-based segregation) for members of both groups.

Preliminary results of the decomposition of poverty rates among the sample of high-poverty census tracts demonstrate that there is great variation in the importance of rate, composition, and place-specific effects across the tracts in this sample (total poverty rate:

= .51, σ = .11; rate effect: = .30, a = .21; composition effect: = .11, σ = .06; place-specific effect: = -.05, a = .19). In words, the average high-poverty census tract has a poverty rate 30 points higher than the national average because of either the lack of eco-

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GEOGRAPHIC CONTEXT AND CONCENTRATED URBAN POVERTY 559

nomic opportunity among the residents of those tracts and/or a high degree of class-based segregation. In contrast, composition effects increase the average poverty rate in high-poverty tracts by 11 points, while place-specific effects reduce the difference between high-poverty tracts and the national average by only 5 points. Most importantly, however, these results indicate great variation in the decomposition values across the high-poverty census tracts in the sample—suggesting that the locale plays an important role in deter­mining which macro-scale phenomena induce concentrated urban poverty.

To make more meaningful generalizations of the data, a cluster analysis is conducted. Cluster analysis is a relatively simple technique for grouping observations into clusters based on their degree of similarity/dissimilarity as measured by a set of clustering criteria. In this case, all 2,473 tracts are clustered according to their rate-effect, compositional-effect, and place-specific—effect values. The particular clustering technique employed is the SAS FASTCLUS algorithm (SAS Institute, 1990). FASTCLUS engages a disjoint clustering algorithm of the data based on the following iterative procedure: (1) the researcher chooses the number of clusters (n) to be identified in the data; (2) the values for the first n observations are chosen as initial cluster seeds, with the values for these seeds serving as cluster means; (3) observations are incrementally assigned to the nearest cluster and each cluster mean is adjusted accordingly; (4) after all observations are assigned to one of the n clusters, the new cluster means are used to reassign all observa­tions according to the logic of step 3; and (5) step 4 is repeated until it fails to alter the composition of the clusters. One advantage of the FASTCLUS procedure is that outliers frequently are assigned to clusters with few members and most of the observations are assigned to a limited number of clusters. The primary decision left to the researcher is to determine how many clusters for FASTCLUS to identify. One way to make the process more objective is to conduct multiple cluster analyses covering a range of predefined clusters and then to identify the best clustering solution based on the number of large clusters identified and the total amount of the variance accounted for by the clusters. In this case, separate cluster analyses were conducted with a pre-specified number of clus­ters ranging from two through 10.

RESULTS

Table 2 shows the size of each cluster for each of the nine clustering alternatives, along with a measure of the approximate R2 explained by those clusters. The scenario with nine clusters is chosen as the best alternative based on its relatively high approximate R2 and the presence of four relatively large clusters. Summary poverty rate, rate-effect, composi­tion-effect, and place-specific—effect values for the 9-cluster solution are listed in Table 3 (the five small clusters are not discussed). To get a better picture of what is occurring among the tracts belonging to each cluster, the geographic distribution and concentration of each cluster's tracts, as well as the characteristics of the population residing in each cluster's tracts are listed in Tables 4 and 5. More specifically, Table 4 lists the 10 metro­politan areas that have the largest proportion of their high-poverty tracts in a particular cluster (metropolitan areas with less than five high-poverty census tracts are excluded). Table 5 includes a set of descriptive variables, including indicators of social and eco­nomic dislocation, and following Jargowsky's (1996) classification scheme, the percent

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560

THO

MA

S J.

CO

OK

E

TAB

LE 2

.—A

LTER

NA

TIV

E C

LUST

ERIN

G S

CEN

AR

IOS

Pre-

defin

ed

num

ber o

f clu

sters

2 3 4 5 6 7 8 9 10

1

2,47

2

2,47

2

2,46

9

1,886

1,530

1,549

1,545

1,149

833

2 2 2

582

659

586

335

714

613

3 1 1 2

281

332 4

399

448

Num

ber o

f

4 1 1 1 3 1

203

371

high

-pov

erty

5 1 1 1 1 4

200

tract

s ass

igne

d

6 1 1 1 1 4

to e

ach

clus

ter

7 1 1 1 1

8 1 1

9 1 1

10

1

R2

(%)

38.5

65.1

73.2

78.1

81.3

83.7

85.4

86.8

87.9

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GEOGRAPHIC CONTEXT AND CONCENTRATED URBAN POVERTY 561

TABLE 3.—CLUSTER MEANS

Cluster

1 2 3 4 Entire sample

Rate effect

X

0.25 0.42 0.03 0.71 0.30

σ

0.07 0.10 0.08 0.09 0.21

Composition effect

X

0.09 0.13 0.15 0.15 0.11

σ

0.06 0.05 0.03 0.04 0.06

Place effect

X

-0.01 -0.14 0.19

-0.43 -O.05

a

0.14 0.09 0.10 0.14 0.19

N

1,149 714 399 203

2,473

of tracts classified as a ghetto (>2/3 non-Hispanic African American), barrio (>2/3 His­panic), slum (>2/3 non-Hispanic White), and mixed (all others).5

Cluster 1

Cluster 1 is the largest (1,149 tracts or 46% of the entire sample) and the least defined of the four clusters. The average poverty rate for this group is 48% and the decomposition rates indicate that most of the differences between the national poverty rate and tract-level poverty rates are caused by rate effects, with small compositional effects, and negligible place-specific effects. Racially, this group of tracts is diverse: 30% of these tracts are categorized as ghettos, 9% as barrios, 18% as slums, and fully 43% as mixed slums. In all, this large group of tracts is most similar to the aggregate characteristics of the entire sam­ple—both in terms of the decomposition values but also with respect to the characteristics of their residents (of course, this may be largely because this cluster comprises 46% of the entire sample). Furthermore, Table 4 demonstrates that Cluster 1 tracts do not have a well-defined geographic distribution and are not highly concentrated in any specific type of areas. In general, Cluster 1 tracts fit with the findings of Jargowsky (1996), and others, that most high-poverty tracts are racially mixed, with moderately high poverty primarily associated with the quality of economic opportunities and degree of class-based residen­tial segregation in a metropolitan area, rather than race-based residential segregation.

Cluster 2

Cluster 2 consists of 714 tracts (29% of all tracts in the sample). In contrast with Clus­ter 1 tracts, the average poverty rate of Cluster 2 tracts is relatively high (55%). The decomposition values attribute most of the difference between census tract and national poverty rates to high rate effects, moderate composition effects, and negative place-spe­cific effects (although the large negative place-specific effects suggest that the high rate effect may be overstated because of the particular population composition and race-spe­cific poverty rates of these tracts). Racially, Cluster 2 tracts are as mixed as Cluster 1 tracts: 34% of Cluster 2 tracts are classified as ghettos, 29% as barrios, 7% as slums, and 30% as mixed slums. The key to understanding the poverty experiences of Cluster 2 tracts is to note their geographic concentration. Clearly, all of the metropolitan areas listed in Table 4 are either immigrant gateway cities or border towns with large Hispanic and/or

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562

THOM

AS J

. CO

OKE

TABLE 4

.—METROPOLITAN AR

EAS W

ITH G

REATEST C

ONCENTRATION OF

EAC

H TYPE O

F CLUSTER

AS A PE

RCENT OF TO

TAL N

UMBE

R OF T

RACTS

Clu

ster

1

Met

ro a

reas

Pueb

lo, C

O

Spok

ane,

WA

Erie

, PA

Col

lege

Sta

tion,

TX

Lafa

yette

, LA

Am

arill

o, T

X

Wic

hita

Fal

ls, T

X

Salt

Lake

City

, UT

Peor

ia, I

L

Den

ver,

CO

N 5 6 5 5 6 6 5 5 5 9

%

100

100

100

100 86

86

83

83

83

82

Clu

ster

2

Met

ro a

reas

Lare

do, T

X

El P

aso,

TX

McA

llen,

TX

San

Ant

onio

, TX

Bro

wns

ville

, TX

Corp

us C

hris

ti, T

X

New

Yor

k, N

Y

Tam

pa, F

L

Los A

ngel

es, C

A

Mia

mi,

FL

N 12

17

30

23

22 6 151 7 28

17

%

92

85

81

74

73

60

50

50

47

47

Clu

ster

3

Met

ro a

reas

Atla

nta,

GA

Jack

son,

MS

St. L

ouis

, MO

Mon

tgom

ery,

AL

Shre

vepo

rt, L

A

Mem

phis

, TN

Milw

auke

e, W

I

New

Orle

ans,

LA

Bal

timor

e, M

D

Mob

ile, A

L

N 17 7 16 5 6 16

20

20

11 6

%

55

54

52

50

43

38

36

35

34

32

Clu

ster

4

Met

ro a

reas

Kan

sas

City

, MO

Chi

cago

, IL

Mem

phis

, TN

Bal

timor

e, M

D

New

Orle

ans,

LA

Phila

delp

hia,

PA

New

Yor

k, N

Y

Milw

auke

e, W

I

Cle

vela

nd, O

H

Det

roit,

MI

N 6 29 8 5 7 7 30 5 6 12

%

25

25

19

16

12

10

10 9 8 8

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GEOGRAPHIC CONTEXT AND CONCENTRATED URBAN POVERTY 563

TABLE 5.—CHARACTERISTICS OF EACH CLUSTER

Variable

Decomposition values Rate effect Composition effect Place-specific effect Total poverty rate

Area characteristics Ghetto (%) Barrio (%) Slum (%) Mixed slum (%) % foreign born % adult males employed < 26 weeks per year % of 16- to 19-year-olds not enrolled in school or not high-school graduates % of household receiving public assistance % of households with children headed by females % of workers commuting > 45 minutes % of workers using public transit % of households without a car

Total metropolitan area population

Cluster 1

0.250 0.085

-0.008 0.479

29.5 9.1

17.9 43.4 9.8

54.8

10.8 27.9

54.7 11.6 17.1 43.1

3,411,158

Cluster 2

0.415 0.128

-0.143 0.552

34.2 29.4 6.7

29.7 14.0 59.6

11.3 33.9

58.9 17.2 27.8 51.5

5,781,058

Cluster 3

0.027 0.149 0.185 0.514

90.0 1.3 0.0 8.8 3.1

59.2

11.3 34.6

69.8 15.3 25.2 50.8

3,639,471

Cluster 4

0.707 0.151

-0.429 0.580

85.7 3.0 1.5 9.9 4.2

66.2

11.0 39.5

72.8 20.0 33.3 58.0

5,380,526

Total sample

0.306 0.113

-0.056 0.514

45.1 13.2 10.5 31.2 9.5

57.8

11.0 31.6

59.8 14.5 22.7 47.9

4,286,243

immigrant populations. As well, these 10 metropolitan areas include 44% of Cluster 2 tracts (note that over one-half of New York City's high-poverty census tracts belong to Cluster 2). The immigrant status of Cluster 2 tracts is also supported by the fact that 14% of the population of Cluster 2 tracts include large numbers of foreign born (as compared with only 9% of the entire sample). Cluster 2 tracts are consistent with mixed-race immigrant ethnic/immigrant enclaves in large gateway cities, where poverty is associated with both the lack of economic opportunity and high rates of class-based residential segregation.

Cluster 3

Cluster 3 consists of 399 tracts (16% of all tracts in the sample). The average poverty rate is equivalent to the average for the entire sample (51%). The decomposition values attribute most of the difference between census tract and national poverty rates to compo­sition effects, with only moderate place-specific effects, and negligible rate effects. Racially, Cluster 3 tracts are predominantly African American: 90% of Cluster 3 tracts are classified as ghettos, 1 % as barrios, 0% as slums, and 9% as mixed slums. Cluster 3 tracts therefore have high rates of poverty because race-based residential segregation con­centrates large numbers of poverty-prone minorities (in this case, African Americans)

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564 THOMAS J. COOKE

into isolated ghettos (even though the composition effect among Cluster 3 tracts is similar to that of the other clusters, in these tracts composition effects predominate in the pres­ence of relatively low rate effects). The positive place-effect values indicate rate effects may be understated because of the particular combination of a predominately minority population and low rates of poverty among nonminorities. Table 4 indicates that the 10 metropolitan areas with the largest concentration of Cluster 3 tracts are predominantly medium-sized southern cities, which together contain 31% of all Cluster 3 tracts. This result suggests that concentrated urban poverty in many southern metropolitan areas is strongly associated with the race-based segregation of African Americans, rather than some phenomena that are separate from the racial composition of the population (e.g., the structure of economic opportunity and class-based segregation). Table 4 also demon­strates higher values for the social and economic dislocation variables than in the larger sample and reflects the social and economic isolation of these tracts.

Cluster 4

Cluster 4 consists of 203 tracts (8% of all tracts in the sample). The average poverty rate is the highest of all clusters (58%). The decomposition values attribute nearly all of the very large difference between census tract and national poverty rates to rate effects, a small share to composition effects, and a very large negative share to place-specific effects. Racially, Cluster 4 tracts are predominantly African American: 86% of Cluster 4 tracts are classified as ghettos, 3% as barrios, 2% as slums, and 10% as mixed slums. Cluster 4 tracts therefore have high rates of poverty because local economic and social processes limit economic opportunity and/or class-based residential segregation concen­trates the poor into these tracts. In fact, across most of the variables in Table 5 that mea­sure social and economic dislocation, Cluster 4 values are dramatically higher than in the larger sample. Also, Table 5 demonstrates that the metropolitan areas with the greatest concentration of Cluster 4 tracts generally are large manufacturing cities that, as a group, contain 57% of all tracts in Cluster 4. These areas conform to the stereotypical image of an industrial city ghetto with very high rates of poverty caused by the lack of local eco­nomic opportunity, economic restructuring, and deindustrialization, along with an addi­tional effect caused by residential segregation.

CONCLUSION

This analysis establishes that the causes of concentrated urban poverty clearly depend on regional and local geographic context. While a plurality of high-poverty neighbor­hoods in the United States (46%) do not exhibit any clear geographic component and conform to the generalization that poverty is the result of more general social and eco­nomic phenomena, poverty in the remainder of the high-poverty neighborhoods in the United States is undoubtedly affected by geographically specific processes. Within one set of high-poverty neighborhoods (Cluster 2), high rates of poverty are associated with a lack of social and economic opportunity and/or a high degree of class-based residential segregation within Hispanic and mixed-race immigrant enclaves in gateway cities and border towns. A second set of high-poverty neighborhoods (Cluster 3), located in the metropolitan areas of the southern United States, has high rates of poverty because of the

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GEOGRAPHIC CONTEXT AND CONCENTRATED URBAN POVERTY 565

residential segregation and geographic concentration of poverty-prone African Ameri­cans. And lastly, among a third set of tracts (Cluster 4) poverty experiences in African American ghettos are linked to declining economic and social opportunities within large manufacturing cities.

This diversity of poverty experiences demonstrates that previous analyses of the con­centration of urban poverty have focused too much on macro-level social processes and have not considered the mediating effect of geographic context. Clearly, the emerging view that the concentration of urban poverty is most strongly associated with economic opportunity at the metropolitan level is supported by those census tracts belonging to Cluster 1. Yet, this analysis presents solid evidence that in over half of the high-poverty neighborhoods in the United States poverty experiences differ markedly from the norm as a result of particular regional and local geographic processes. While this analysis has not delved very deeply into why poverty experiences differ so greatly by type and location of metropolitan area—rather the purpose of this research has been to uncover the diversity of poverty experiences—further analysis is needed to uncover why, for example, the con­centration of urban poverty in the African American ghettos of medium-to-large metro­politan areas in the South is associated with residential segregation, while in contrast, the concentration of urban poverty in the African American ghettos of large northern indus­trial cities is associated with metropolitan social and economic context.

Clearly, there are more questions raised by these results than can be addressed in this paper. Therefore, continued research is warranted on the following issues. First, the empirical results of this analysis must be verified. In particular, additional research is needed to improve the decomposition procedure and identify the most important contex­tual variables associated with each cluster. Second, this analysis has merely found that there is great diversity in poverty experiences from place to place and has described in the most general terms how those experiences differ. The more important question is why poverty experiences differ from place to place. As discussed earlier in this paper, future theoretical work on the causes of concentrated urban poverty should attempt to elevate the role of the locale to the same status as such macro-level social phenomena as race-and class-based residential segregation and economic restructuring. Third, and perhaps most important, the implications for this analysis on public policy must be evaluated. If the causes of concentrated urban poverty vary with geographic context, then it is important to evaluate how and why it does, and then to modify existing "one-size-fits-all" anti-poverty policies to the needs of specific places.

NOTES

1 The author thanks Mark Ellis, Janet Kodras, John Odland, and Richard Wright for their useful comments on previous drafts of this manuscript. 2 See Chapter 5 of Jargowsky (1996) for an excellent review of the literature. 3 The source of this discrepancy is not known. 4 Because the rate effect assumes that each tract has a racial composition equal to the national aver­age, the rate effect is more sensitive to changes in the majority poverty rate than to changes in the minority poverty rate. 5 See Wacquant (1997) for a historical, theoretical, and empirical criticism of Jargowsky's (1996) classification scheme.

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566 THOMAS J. COOKE

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