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JOURNAL OF URBAN ECONOMICS 7,155-W (1980)
Are Blacks Making It in the Suburbs? Some New Evidence on lntrametropolitan Spatial
Segmentation
JOHN VROOMAN*
Universi@ of Montana
AND
STUART GREENFIELD
State of Texas
Received August 18, 1977; revised June 6, 1978
INTRODUCTION
From his work with Meyer and Wohl, Kain has fostered the thesis that, in the face of the rapid suburbanization of ‘tjobs traditionally held by Negroes,” the token suburbanization of black households has trapped central city blacks in spatially isolated unemployment [17- 19, 251. Subse- quent criticism of Kain’s thesis has followed two primary avenues. The first such avenue has concerned the appropriate remedial strategy once the thesis has been accepted [3, 6, 8, 20-221, while the second has sought to demonstrate the invalidity of the underlying spatial segmentation premise [5, 9, 27, 281.
As understanding of ghetto labor markets has evolved since Kain’s initial proposition, a third avenue has emerged. Labor economists now maintain that ghetto poverty derives not from the unavailability of em- ployment, but from the inferior quality of the existing inner city employ- ment opportunities. Research of metropolitan labor markets has demon- strated that the quality of the potential suburban employment opportuni- ties for blacks is not significantly better than the quality of chances experienced by them in the central city [13-15, 16, 23, 241. The compara- tive availability of jobs for central city blacks in suburban areas is an important issue, but unless those jobs are of different quality than availa- ble central city jobs, the question of their quantity becomes moot.
We have been presented conflicting evidence about the numbers of jobs available to blacks from ghetto dispersal, and we are left wondering whether the number of jobs alone is a solution to the complex problem of black poverty in the metropolitan central city. The purpose of this study is
‘John Vrooman is llow a visiting scholar at the University of Texas.
155 00941190/80/020155-13$02.00/0 copyrilhf 0 1980 by Academic I’rcs, Inc.
All rights Of t.qmxtuction in any form -cd.
156 VROOMAN AND GREENFIELD
to analyze the earnings of blacks residing in American metropolitan central cities in comparison with the earnings of blacks residing in the suburbs. In the light of recent developments in the dispersal controversy it seems quite possible that Kain’s original position, empirical evidence, and the policy prescriptions that have followed from them have been mislead- ing and indicative of a misconception of the intrametropolitan spatial distribution of the economic welfare of American blacks.
SOME PROBLEMS WITH THE EXISTING EVIDENCE
A review of evidence offered in support of ghetto dispersal strategy points out two important deficiencies. First, because most studies have dealt with the employment gains (losses) of black males, little has been said of the impact of residential suburbanization on the occupational status and earnings potential of black females. Second, little has been said of the comparative occupational status and earnings of transplanted central city blacks once they are employed in suburban markets.
Ghetto dispersal strategy rests upon the premise that metropolitan labor market segmentation is spatially coincident with residential segregation. If this is true, then the elimination of such spatial segregation mechanisms as housing discrimination would imply elimination of such labor market segmentation mechanisms as overt and institutional earnings discrimina- tion. A ghetto dispersal strategy would seem appropriate if central city blacks were the only metropolitan blacks who were underemployed, and if their physical location was the cause of that underemployment. Dispersing blacks to the suburbs could free them from the market segmentation mechanisms peculiar to the urban core. Conversely, ghetto dispersal should be seriously questioned as an appropriate strategy if labor market seg- mentation mechanisms are not spatially coincident with the central city-suburban dichotomy, and the nature of black male and female suburban employment is not different from black male and female central city employment. The spatial coincidence of labor market segmentation mechanisms is a necessary condition for acceptance of dispersal strategy, and proof of such coincidence has yet to be advanced.
Investigation of segmentation coincidence within the metropolis can be conducted through a comparative analysis of occupational mobility and earnings of black males and females in labor markets characterized by central city and suburban residence. Analysis of these two spatially defined markets should indicate the extent to which present suburban black residents have been able to escape labor market segmentation char- acterized by central city residence. Specifically, the following questions are posed: (1) To what extent does schooling affect the occupational mobility and earnings potential of central city blacks vis-a-vis suburban blacks? (2) If schooling does affect the occupations and earnings of blacks, how does it do so for central city blacks vis-a-vis suburban blacks? (3) If schooling
ARE BLACKS MAKING IT IN THE SUBURBS? 157
does not matter in either or both areas, what factors do influence earnings? (4) To what extent are spatially isolated segmentation mechanisms race and sex specific?
The answers to these questions require that intrametropolitan residential location be treated as a discriminatory labor market segmentation mechanism that affects racial earnings disparity in the same manner as other institutional and occupational mechanisms. This allows a synthesis of the third avenue of the dispersal debate with the received evidence of segmented labor market theory.
THE SCHOOLING MODEL
As the segmented market approach to the study of earnings discrimina- tion has developed, racial segmentation of labor markets and their char- acteristic earnings have been analyzed through the use of reduced form earnings models such as
where Yi/ is a measure of earnings and Xiik is a vector of K “human capital” characteristics for the members of jth market segment. The manifestation of earnings segmentation has been viewed as the interaction of two phenomena: the disparate allocation of the human capital compo- nents X&, and the disparate market returns & to accumulations of human capital among the j segments. The former has often served as evidence of an element of institutional earnings discrimination, while the latter has been attributed to a residual element of overt market discrimination.
The precise roles of schooling, occupational status, and intrametropoli- tan location in the labor market segmentation process are unclear. If schooling serves as a mechanism of segmentation, we are not certain how it performs its discriminatory function. Similarly, we suspect that occupa- tional position should affect one’s earnings, but the precise nature of the discriminatory effects of occupational status on earnings remains the subject of debate.
Implicit in the specification of a reduced form earnings model (1) is a structural form that posits the endogeneity of a cognitive linkage between schooling and earnings. The cognitive model suggests that schooling im- parts cognitive skills which are sought and rewarded by prospective em- ployers. In this case prospective employees acquire marketable skills institutionally and independent of the job. Alternatively, it can be argued that schooling works a noncognitive linkage with earnings, and that the labor market rewards schooling regardless of its impact on cognitive skills. As an alternative to the cognitive model Gintis [ 121 proposes an “affective” model, wherein the theoretical schooling process circumvents the cognitive
158 VROGMAN AND GREENFIELD
linkage and creates efficient and desirable workers by repressing unstable personality characteristics.
As a test of the comparative strengths of the two models Gintis suggests an expanded earnings argument such as (1) that includes both schooling and achievement measures as regressors:
where A, and S, are specific achievement and schooling measures of the general x$ Vector Of human Capital arguments for members Of the jth
market segment. The magnitude of the schooling coefficient & relative to the magnitude of the achievement coefficient pj, should serve as a test of the relative strengths of the affective and cognitive schooling mechanisms. This technique will be used to isolate the discriminatory function of the schooling mechanism as it varies between labor markets defined by central city and suburban residence.
THE COINCIDENCE OF OCCUPATIONAL AND SPATIAL SEGMENTATION
Mincer [26] argues that interracial differences in returns to similar investments in institutional forms of human investment over the life cycle may reflect hidden effects of differential investments in postinstitutional forms of human capital. Groups who suffer earnings disparity in spite of their accumulations of institutionally acquired human capital typically exhibit shallow experience-earnings profiles. The shallow experience-eam- ings profiles of certain black workers are possible reflections of the low-skilled occupations in which they are concentrated.
Thurow [29] poses a job competition model, in which the allocation of differentially skilled occupations among a homogeneous, institutionally trained work force creates earnings differentials among the members of that work force. The segmented market theory implies that unequal amounts of postschool human investments can segment a homogeneously schooled population, and hence, contribute to an earnings disparity that lies beyond the explanatory power of a simple schooling model. If the occupational segmentation theory is accurate, then it can be hypothesized that postschool job-specific experience is a manifestation of occupational discrimination. Those groups most favorably treated in their occupational distribution are expected to exhibit high and late-peaking experience-eam- ings profiles, while those trapped in unskilled occupational segments should demonstrate shallow and insignificant postschool-earnings profiles.
If this experience component is taken as a manifestation of occupational segmentation, then a test of the coincidence of occupational and in-
ARE BLACKS MAKING IT IN THE SUBURBS? 159
trametropolitan spatial segmentation would involve the comparative signi- ficance of intraoccupational returns to experience between the two spa- tially defined market segments. If there is a significant improvement in black returns to experience in the suburbs then we should judge the move to the suburbs to be equivalent to a penetration of the higher-skilled occupations available in suburban markets. If, on the other hand, there emerges no significant difference between the experience profiles of central city and suburban blacks, we would conclude that such spatially defined markets are subject to cross-segmentation and that they are structurally equivalent in their treatment of metropolitan blacks. Acceptance of spatial-occupational segmentation coincidence would yield support for the dispersal position, while its rejection would bring the efficacy of dispersal strategy under serious question.
THE SURVEY AND THE DATA
The data are derived from a study conducted by Opinion Research Corporation for the Adult Performance Level (APL) project at the Univer- sity of Texas at Austin [ 11. These data are unique in that the purpose of the survey was the administration of an examination developed by the APL project to measure the functional competence of U.S. adults. Available from the survey are a number of relevant economic and demographic characteristics such as: years of formal and vocational schooling, age, residential location, occupation, employment status, race, sex, and weekly earnings of the respondents.
TABLE 1
Mean Values and Standard Deviations by Race, Sex, and Intrametropolitan Lwatiod
Years Full YeUS vocational Years employment Occupation Weekly
schooling training experience APL dummy index earnings N
central city 250,000
white males
Black malea
Black females
Suburban areas White males
Black male8
Black females
12.35
(3.44) 12.18
(0.W 11.22
(1.W
12.38 (3.05) 11.82 (0.58) 11.71
(2.W
0.62 (1.45) 0.07
(0.26) 0.13
(0.48)
0.39 (0.95) 0.69
(1.32) 0.12
(0.47)
22.98 (14.45)
10.23
(6.87) 21.44
(13.23)
23.42 (13.86)
13.44 (5.76) 22.93
(15.70)
2.53 0.84
(0.67) (0.37) 1.75 0.71
(0.62) (04 1.16 0.49
(0.77) (O.so)
2.54 0.89 (0.67) (0.32)
1.79, 0.91 (0.75) (0.29) 2.17 0.69
(0.70) (0.47)
202.68 (105.94)
149.22 (94.93) 96.20
(93.89)
216.29 (99.49) 156.38 (71.55) 159.51
(111.12)
253.10 610 (352.59)
131.45 74 (96.88)
71.76 106 (73.79)
309.66 966 (352.56) 228.85 48
(116.40) 111.10 65 (62.43)
160 WUXMAN AND GREENFIELD
The universe for the survey was defined as the coterminous U.S. popula- tion aged 18 through 65 years, who were living in households and were physically able to read and write. The APL examination was developed to test the competency of adults within each of five knowledge areas: occupa- tional knowledge, consumer economics, health, community resources, government and law; and four skill areas: reading, writing, computation, and problem solving. The scores were coded and the sample was trichoto- mized into levels of adult functional competence: APL 1, functionally incompetent; APL 2, marginally competent; and APL 3, functionally proficient. Testing was conducted by the Opinion Research Corporation from December 1973 to January 1974 on a master sample of the popula- tion universe. From this sample we have selected six subsets: white males, black males, and black females residing in central cities of metropolitan areas with populations greater than 250,000 and those residing in the metropolitan suburbs. Descriptive statistics for the data are recorded in Table 1.
A STRUCTURAL MODEL
Empirical inquiries into earnings discrimination processes are usually limited to the comparative analysis of the regression estimates of such reduced form models as (1). Isolation of mechanisms implicit in the reduced form argument requires the estimation of a structural earnings model of the specification
0, = pi0 + &Au + ,O$$ + &Vu + /3,,Xu + ,6$,X; + uij, (3)
+ yi& + virOti + vu (4)
for each of the six ORC sample groups, where
Oti = an index of the earnings potential of an occupation estimated by the mean weekly earnings of all chief wage earners in the 10 reported occupational categories
Yti = reported weekly earnings S, = years of formal schooling y? = years of vocational education xii = residual experience estimate (age, - Si - 5) Eii = employment binary: fully employed = 1, part-time or unem-
ployed = 0 A, = adult performance level (1, 2 or 3) for the ith member of thejth
group
TABL
E 2
Regr
essio
n Re
sults
by
In
tram
etro
polita
n Lo
catio
d
Inte
rcep
t S
A V
X X2
0
E R*
white
m
aled
ce
ntra
l cit
y oc
cupa
tion
(2.1
) -
12.6
0 14
.28’.
-
19.4
0**
2.04
9.
489.
-
1.79
.. 0.
274
(23.
60)
(1.W
(6
.47)
(2
.62)
(1
.03)
(0
.W
Earn
ing3
(2.2
) -
556.
95’.
10.7
3.
112.
29’.
- 19
.78.
10
.68*
* -0
.134
* 0.
381’
217.8
0**
0.18
2 (9
1.30
) (5
.18)
(2
3.80
) (9
.34)
(3
.96)
(0
.078
) (0
.168
) (4
6.70
) Su
burb
an
area
s oc
cupa
tion
(2.1
) 27
.38
6.29
.. 1a
.aa*
* 9.
01’.
7.35
.. -0
.152
** 0.
208
(17.
10)
(1.1
4)
(4.8
4)
(3.1
1)
(0.W
(0
.016
)
Earn
ings
(2.2
) -3
66x&
** 19
.94*
* -
7.97
-4
1.09
** 7.
6899
-o
.mY
1.11
0**
134.
64
0.23
2 (7
3.30
) (4
.06)
(1
5.94
) (1
0.81
) (3
.07)
(0
.059
) (0
.132
) (4
2.08
) Bl
ack
mal
es
cmtd
cit
y O
ccup
ation
(2
.1)
-568
.41*
* 57
.04*
* -5
8.51
15
8.17
’* 16
.16.
-0
.341
0.
384
(145
.75)
(1
2.14
) (3
0.79
) (5
4.51
) (8
.98)
(0
.379
)
E==h
(2
.2)
130.
45
- 9.
95
4.16
28
.03
- 1.
08
0.22
8 -
0.32
4.
195.5
4**
0.80
3 (1
08.7
0)
(9.9
5)
(20.
80)
(35.
04)
(5.4
0)
(0.2
53)
(0.1
91)
(39.
91)
Subu
rban
ar
eas
Occ
upat
ion
(2.1
) -6
59.2
9’.
63.7
5..
3.58
18
.99”
2.
26
0.05
8 0.
826
(106
.34)
(1
0.81
) (8
.95)
(7
.03)
(5
.65)
(0
.145
)
Earn
ings
(2.2
) -
667.
07-
85.1
3’ -
60.3
4”
50.5
4..
- 11
.62
0.15
6 0.
547
b 0.
603
(370
.56)
(3
7.01
) (2
3.21
) (1
8.72
) (1
2.91
) (0
.312
) (0
.391
) Bl
ack
fam
lcs
cent
ral
city
Occ
upat
ion
(2.1
) -
165.
92..
6.81
33
.78.
. 56
.68.
. 7.
90**
- 0.
079
0.34
6 (5
9.26
) (4
.26)
(1
0.90
) (1
7.71
) (2
.72)
(0
.066
) Ea
rning
s (2
.2)
-4l.W
-
0.47
18
.39*
* 55
.38”
3.
06**
-0.0
80**
0.58
8*’
14.5
5 0.
867
(21.
63)
(1.5
7)
(4.W
(7
.42)
(1
.W
(0.0
24)
(0.0
43)
(8.5
6)
Subu
rban
ar
eas
occu
patio
n (2
.1)
-91.
07
42.5
5.’
- 15
0.53*
* -3
9.14
’ 13
.72*
* -0
.301
** 0.
574
(101
.19)
(7
.60)
(2
0.07
) (2
1.74
) (3
.19)
(0
.065
)
-gs
(2.2
) -
143.
73..
9.28
’ 4.
05
26.4
8’.
8.75
’. -0
.167
** 0.
019
86.1
9..
0.79
0 (4
2.27
) (4
.64)
(1
3.50
) (9
.8
1)
(1.5
1)
(0.0
32)
(0.0
63)
(12.
14)
‘Sta
ndar
d er
rors
ar
c lis
ted
in pa
rent
hese
s. %
suffic
ient
toler
ance
. *S
ignific
ant
at
0.05
. **S
ignific
ant
at
0.01
.
162 VRGGMAN AND GREENFIELD
The estimation of experience as a residual may result in an upward bias for those with discontinuous work histories, i.e., females. We have selected only chief wage earners (CWEs) to minimize the discontinuity bias within the limitations of the data. Ordinary least-squares estimates of (3) and (4) are shown in Table 2.
SPATIAL DIFFERENCES IN THE ROLES OF SCHOOLING
Isolation of the relative nature of the school earnings relationship among the six selected segments can be accomplished by a comparative path analysis of the normalized estimates of (3) and (4) [7, 30, 311. The structural model (3) and (4) posits four possible linkages between schooling and earnings: (i) a cognitive interoccupational component (school-APL- occupation-earnings), (ii) a cognitive intraoccupational component (school-APL-earnings), (iii) a noncognitive interoccupational component (school-occupation-earnings) and (iv) a noncognitive intraoccupational component (school-earnings).
Path analysis of these school-earnings linkages would typically require the ordinary least-squares estimation of the three recursive arguments:
A, = ai0 + aj,Su + eg, (5)
Oii=&,+~,Au+&jS~+-~ +uu, (3)
Y, = yj~ + ~j,Aii + ~2jS, + Y3jOij + . . . +V~. (4
In this case the achievement variable in question (A$ is a trichotomous ordinal measure limited to the value 1, 2, or 3. Therefore, its use as a dependent variable as the estimation of (5) would yield estimates of (Y that were inefficient. This problem can be avoided by the use of a x2 con- tingency test of the schooling-achievement relationship hypothesized in (5). For intergroup comparisons the x2 statistics are corrected for sample size by the calculation of Cramer’s V contingency coefficients for each of the six groups. The contingency coefficients of (5) and the normalized regression coefficients of (3) and (4) are recorded in Fig. 1.
These results indicate that the function of schooling in the earnings processes of both black males and black females varies between central city and suburban markets. The suburban labor market relies on schooling to perform a noncognitive function, whereas the central city market relies on the cognitive nature of schooling. A dispersed black male would find his earnings sensitive to his schooling only if it did not impart APL measured skills. Indeed, functional literacy is actually associated with lower earnings for black males in suburban markets. Similarly, a trans- planted black female would leave a central city market which rewarded her
ARE BLACKS MAKING IT IN THE SUBURBS? 163
CENTRAL CITY SUBURBAN AREAS
White Males:
sci~+~"gs sc;<+~"gs
Occupation
Black Males:
Black Females: APL
.784** / ,
School a Ear 'ninas
.A\.L” Occupation
- --......z-
A\,/
Occupation
FIG. 1. Comparative path analysis of the school-earnings nexus by intrametropolitan location, race and sex. The school-APL components are Cramer’s V contingency coefficients; all other components are normalized /3 coefficients calculated from Tables 1 and 2. (*) Significant at 0.05. (**) Significant at 0.01.
functional literacy to encounter a suburban market that sought only the noncognitive effects of the schooling process. If a dispersal strategy is concerned only with the end result of suburbanization on the earnings of blacks, this evidence is not sufficient to demonstrate its invalidity. But if the means of the school-earnings relationship are considered, these results suggest that a dispersal strategy favors a market which rewards schooling over one which rewards education.
SPATIAL EARNINGS DISCRIMINATION
By performing selective substitutions into the regression estimates of (3) and (4) we can estimate the impact on the occupations and earnings of central city blacks of three types of discrimination: (i) institutional, (ii) overt and (iii) spatial. The institutional element can be estimated by creating the hypothetical situation in which black males and females continue to receive their market prices for characteristics equivalent to those of the white male population in their respective areas. The substitu- tion of the mean characteristics of the white males into the respective black
164 VROOMAN AND GREENFIELD
male and black female estimates of (3) and (4) yields the variable switch projections recorded in Table 3. Similarly, the “overt” discrimination element can be measured by allowing each spatially defined market to reward the existing characteristics of its blacks in the same manner that it does for its respective population of white males. The substitution of the respective mean values for the characteristics of black males and black females into the estimated model (3) and (4) for the white males in their residential location yields the equation switch estimates recorded in Table 3. Finally, the spatial discrimination component can be estimated by substituting the mean values for central city black males and females into the respective estimates of their suburban equations. These estimates are recorded in Table 3 as “location switch.” From these projections of hypothetical occupational positions and intraoccupational earnings we can estimate the proportions of the existing interracial gaps in occupational status and earnings that could be absorbed by antidiscrimination policies designed to treat each of the institutional, overt, and spatial elements.
TABLE 3 Decomposition of Occupation and Earnings Gaps into
Institutional, Overt, and Spatial Components
Institutional Overt Spatial Respective Variable Equation Location Actual component component component estimate9 switch* switchc switchd gape (96 gap) @ PaPI (% pap)
Central city Black males
Occupation -rigs
Black females occupation Earnings
Suburban areas Black males
OCWpati0n Earnings
Black females occupation Earning.9
138.90 306.02 189.09 126.91 194.12 187.35
113.05 121.52 220.94 97.10 128.97 196.51
143.97 226.28 233.08 119.83 68.7 74.4
232.48 273.15 284.36 141.38 28.8 34.6
227.67 237.97 255.11 36.13 28.5 75.9
153.65 185.15 334.33 220.21 14.3 82.0
127.71 99.59 195.10 173.40
320.70 125.44 175.49 203.27
167.8 so.4 (11.2) 38.7 34.8 39.3
6.8 86.0 165.6 15.7 48.9 (10.6)
ERespcctive wimatca involve the substitution of black male and female mean values into their respective estimates of (3),
(4). bVariable switch involves the substitution of mean values of white male variables in (Table 1) to e~timatw of (3). (4) for
black males and femalea pable 2). cBquation switch involvea tbe substitution of mean values of black male and female variables (Table 1) into estimates of
(3). (4) for white males (Table 2). ‘%ocation switch involves the substitution of mean values of central city black males and females into e&mates of (3), (4)
for suburban black males end females. Because white malea would also gain from the move the recorded are net of 26.12 ocfvpatimal and 56.67 earnin@ sains of white males.
‘Actual gap is the amount by which white male respative estimates exceed black male and female estimates. Central city white male occup~tioa index - 238.49; camin@ - 300.37, whenap for suburban white males: occupation index - 263.80, eamingr - 373.86.
Brent (%) gap institutional is the portion of tbe gap absorbed by variable switch, X gap overt is the portion absorbed by equation switch, and % gap spatial is the portion of the gap absorbed by location switch.
ABE BLACKS MAKING IT IN THE SUBURBS? 165
These estimates have been recorded in Table 3 as institutional, overt, and spatial gap components.
Based primarily on the impact of suburbanization on the convertibility of occupation to earnings (u,), the discrimination estimates of Table 3 indicate that the earnings disparity between black males and white males can be attributed equally to institutional, overt, and spatial elements. Specifically, dispersal has the potential of reducing the earnings gap by 40%. In contrast, the estimates for the hypothetically dispersed black females indicate discrimination in favor of the central city resident. The earnings gaps of these black females (CWEs) are widened by the suburban move. This result is consistent with the findings of Bell [2].
The occupational and earnings discrimination detected against the black females of this sample emerge as market phenomena throughout the metropolitan area. This leads to two conclusions. First, policy programs designed to rearrange the institutional distribution of human capital to these black females would have little effect in reducing their occupational and earnings disparities. Second, a dispersal strategy which draws validity from estimates of potential black male earnings gains alone could be damaging to the welfare of the central city black female wage earners.
ARE SUBURBAN BLACKS BETTER OFF? THE IMPLICATIONS OF OCCUPATIONAL
CROSS-SEGMENTATION
The antidiscrimination policy simulations in the preceding section lead to the conclusion that dispersal could possibly result in improved occupa- tional status for black females, but that their earnings could be adversely affected in two respects. First, the suburban black female demonstrates an inability to convert her occupational status into earnings. Second, the suburban black female leaves a market that rewards her cognitive skills and encounters a market uninterested in those skills. This leads to the conclusion that central city black females would not benefit from dispersal to suburban labor markets that are characterized by low occupational convertibility and a noncognitive school-earnings nexus. Indeed, they could lose from such a move. Instead, these results point to the severity of sex discrimination in both central city and suburban markets, and lead to a recommendation for policies aimed at such discrimination.
In contrast, our policy simulations indicate that dispersal of central city black males into suburban markets could reduce their earnings disparity with white males by an amount commensurate with overt and institutional policies. The suburban market advantage apparently lies in the improved convertibility of occupational status into earnings.
However, certain reservations about the benefits of a dispersal strategy for black males stem from the structural similarity of the central city and
166 VROOMAN AND GREENFIELD
suburban markets as it is manifested in the experience profiles of black males. In both spatially defined markets black male profiles are statisti- cally insignificant, and in both markets they are a major source of black male earnings disparity. These nonexistent intruoccupational profiles are peculiar to the black male occupations regardless of where in the metro- politan areas they reside (see Table 2).
A criterion for the acceptance of dispersal strategy should be some indication that spatial segmentation contributes to occupational segmenta- tion, or at least a demonstration that residential and labor market segmen- tation are spatially coincident. The prospect of suburbanization should promise some improvement in intraoccupational returns to experience. These regression results lead to the conclusion that suburban labor markets are as occupationally segmented as those of the inner city. Movement of black males between spatially defined labor markets cannot be expected to remedy an earnings disparity that derives from a racial segmentation mechanism that transcends those markets. Unless antidis- crimination policies address the mechanisms of labor market segmentation throughout the metropolis, they hold little promise for success.
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Occupational and Adult Education, Office of Education, USDHEW (1975). 2. D. Bell, Residential location, economic performance and public employment (von Fur-
stcnberg et al., Eds.), Putterns, I 55-76 (1974). 3. California Governor’s Commission on Los Angeles Riots, “Violence in the City-An End
of a Beginning?” Los Angeles (1%5). 4. B. Chiswick, “Income Inequality, Regional Analyses within a Human Capital Frame-
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