effects of adolescent neighbour hood poverty on adult employment (holloway & mulherin 2004)

8/9/2019 Effects of Adolescent Neighbour Hood Poverty on Adult Employment (Holloway & Mulherin 2004)

1/30

THE EFFECT OF ADOLESCENT NEIGHBORHOODPOVERTY ON ADULT EMPLOYMENT

STEVEN R. HOLLOWAYUniversity of Georgia

STEPHEN MULHERINCalifornia State University, Los Angeles

ABSTRACT: Neighborhood environments affect the long-term labor market success of Amer-

icas urban youth. Urban poverty grew more spatially concentrated during the 1970s and 1980s

as industrial economies dramatically restructured. Some policies attempted to address the

problems of impoverished neighborhoods by stimulating in-situ economic development, while

others sought to geographically disperse the poor. Poverty grew less concentrated during the

1990s because of robust national economic growth and dispersal-oriented federal policies.

Before celebrating, however, the long term effects of growing up in poor neighborhoods need

to be considered. We used National Longitudinal Survey of Youth (NLSY) data, geocoded to

census tracts, to examine the effects of neighborhood poverty rates encountered during adoles-

cence on adult employment. Living in poor neighborhoods during adolescence carries a long-

term labor market disadvantage, caused at least in part by the limited ability to accumulate

early work experience. Males appear to be more sensitive to these neighborhood effects than

females.

The geographic concentration of poverty in distressed urban neighborhoods increaseddramatically during the 1970s and 1980s, especially in the manufacturing-based cities of

the Midwest and Northeast. These remarkable changes took place at the same time that

metropolitan populations rapidly decentralized, spawning widespread suburbanization.

These geographic shifts stimulated popular concern and motivated policy efforts to reduce

the spatial concentration of poverty. Increased concentration marked the poverty of both

black and white populationsthe rate of increase was greater among blacks during the

1970s and greater among whites during the 1980s. Many observers came to hold a deep

suspicion that growing up in poor, especially extremely poor, neighborhoods imposes anegative impact, perhaps even a lasting impact. Social science research has demonstrated

growing interest in the impacts of neighborhood contexts on residents social and eco-

nomic experiences. Despite the growing cottage industry that now examines these issues,

however, there is much that we still do not know (Duncan & Raudenbush, 2001).

*Direct correspondence to: Steven R. Holloway, Department of Geography, University of Georgia, 204 GGBuilding, Athens, GA 30602. E-mail: [email protected]

JOURNAL OF URBAN AFFAIRS, Volume 26, Number 4, pages 427454.

Copyright#

2004 Urban Affairs AssociationAll rights of reproduction in any form reserved.

ISSN: 0735-2166.


2/30

Census 2000 data documenting trends in poverty concentration during the 1990s are

being analyzed as we write this article. Early results indicate that the rapid economic

growth of the 1990s produced dramatic reductions in the number of high poverty areas

and in the proportion of the poor that live in these areas (Jargowsky, 2003). These reports

have been accompanied by optimistic assessments of federal policys efficacy: The decline

in concentrated poverty represents, in part, the triumph of smart federal policies thatdemolished failed public housing, rewarded work and overhauled welfare (Bruce Katz,

director of the Center on Urban and Metropolitan Policy at the Brookings Institution,

quoted in Pear, 2003). Before dismissing spatially concentrated urban poverty as no longer

problematic, however, close inspection reveals dark linings to the bright clouds. Note that

the 2000 Census was taken at the peak of the economic expansion before the stock market

bubble burst. Even in the context of expanding prosperity, Jargowsky (2003) reports that

reductions in poverty concentration marked the South and Midwest regions, but not the

Northeast. Moreover, urban areas in the West experienced, on average, increases in

poverty concentration. Los Angeles, Bakersfield, Fresno, Riverside, and Washington,

DC, were among the large metropolitan regions that witnessed increased numbers of

poor people living in concentrated poverty. Jargowsky (2003) also notes that suburban

areas across the country, especially older inner-ring suburbs, did not experience the same

reduction in concentrated poverty that central cities experienced. Overall, approximately

2,500 very poor neighborhoods remain home to almost 8 million residents.

An additional issue plaguing the current debate centers on the impacts that concen-

trated poverty has on residents of impoverished neighborhoods. A robust neighborhood

effects literature has shown the impact of neighborhood contexts on a variety of social and

behavioral outcomes. One of the main deficiencies in the current literature, however, is the

issue of how long neighborhood effects might last: most research explores concurrent links

between neighborhood contexts and individual social and economic outcomes. Indeed,

most policy prescriptions seem to rest on simplistic and perhaps problematic assumptionsthat relocation out of, or in-situ improvement in impoverished neighborhoods will alle-

viate the main troubles. We explore the possibility that disadvantages imposed by living

and growing up in impoverished neighborhoods may persist into adulthood, even for

individuals who are able to move out. We use longitudinal data to examine the temporally

lagged effects of living in areas of geographically concentrated urban poverty during

adolescence on adult labor market attachment. Our focus on the persistence of neighbor-

hood effects on labor market disadvantages seems especially relevant given the recent

declines in concentrated poverty. Before celebrating too vigorously, we need to consider

the possibility that adverse neighborhood effects imposed during previous decades may

linger into the present.The empirical analysis presented in this article addresses several weaknesses in the

current research literature. First, with the exception of the spatial mismatch literature

discussed below, not enough research has focused on the complex linkages between

impoverished neighborhoods and the labor market experiences of children and adoles-

cents who grow up and live in them. While the impacts of neighborhood contexts on labor

market experiences are of considerable conceptual and policy concern (e.g., Galster &

Killen, 1995), research is just beginning to address their importance empirically (see

ORegan & Quigley, 1996). The research that highlights the importance of labor markets

for the social, political, and economic fate of inner-city communities (notably the work of

Wilson 1987, 1996) focuses inadequate empirical attention on adolescents. Empirical

research on neighborhood contextual effects has emphasized an array of cognitive andsocial development measures, schooling decisions, and sexual behavior (e.g., Brewster,

428 | JOURNAL OF URBAN AFFAIRS | Vol. 26/No. 4/2004


3/30

1994a, 1994b; Brewster, Billy, & Grady, 1993; Crane, 1991; Duncan, 1994; Orfield, 1992;

see especially the edited collection of papers in Brooks-Gunn, Duncan & Aber, 1997a,

1997b). Given the importance of employment in alleviating poverty at individual and

family levels, we argue for greater attention to neighborhood effects in adolescent employ-

ment relations.

Second, the research reported here explores long-term effects. Most of the neighbor-hood-effects literature examines the cross-sectional impacts of neighborhood location on

concurrent behaviors and outcomes. Very little of the existing literature has addressed the

lasting impacts of neighborhood contexts, even though we are often motivated by such

suspicions. We focus on whether and to what extent living in poor neighborhoods during

adolescence may impose labor market disadvantages that last into adulthood, perhaps

even for individuals who leave poverty neighborhoods. By focusing on accumulated work

experience as a potential link between adolescent neighborhood contexts and adult labor

market outcomes, this analysis also informs policy on potential ways to minimize the long-

term impacts of geographically concentrated urban poverty.

BACKGROUNDGEOGRAPHICALLY CONCENTRATED URBAN POVERTY

The project reported in this article is motivated broadly by the stream of research

flowing from Wilsons (1987, 1996) arguments that deteriorating neighborhoods are

occupied by the most disadvantaged minorities and that urban poverty is becoming

spatially concentrated. This research has provocatively focused more on describing and

explaining urban povertys increasing concentration than it has on examining its effects.

Moreover, while sometimes only implicit, much of this research has been concerned with

the growing geographic concentration of black poverty. Jargowsky and Bane (1990, 1991),

for example, found large increases in concentrated urban poverty during the 1970s in the

largest metropolitan areas. More recent work addressed race more explicitly and foundthat while black poverty is much more concentrated than white poverty in absolute terms

and increased rapidly during the 1970s (Jargowsky, 1994), the concentration of white

poverty increased more rapidly during the 1980s (Galster & Mincy, 1993; Mulherin 2000).

Indeed, Galster and Mincy (1993) found that the greatest increases in poverty concentra-

tion during the 1980s took place in smaller metropolitan areas and that the relative growth

of poverty among whites during the 1980s was almost three times the rate of growth

among blacks. Massey, Gross, and Shibuya (1994) examined relations between the con-

centration of poverty and racial residential segregation. Housing market discrimination

limits blacks residential mobility and spatially concentrates their poverty. Poor whites

increasingly tend to live in racially mixed poor areas. Jargowsky (1997) linked increasedpoverty concentration to metropolitan-scale economic restructuring: poverty is spatially

concentrating within metropolitan areas because metropolitan labor demand has shifted

in ways that disadvantage the low end of the labor market (see Strait, 2001).

We recognize that our examination of impoverished neighborhoods potential impact

on labor market outcomes highlights only one aspect of concentrated povertys potentially

multi-faceted impacts. Yet we contend that labor market outcomes deserve greater atten-

tion. To develop our argument, we focus our subsequent discussion on (1) reviewing

literature that maps out linkages between neighborhood contexts and adolescent labor

market outcomes, and (2) critically developing one possible mechanism that may link

adolescent neighborhood poverty with adult labor market outcomes. Note that neighbor-

hood contextual processes that generate initial impacts on adolescents may differ from theprocesses that sustain or transform these impacts into adult years.

| Neighborhood Poverty and Adult Employment | 429


4/30


5/30

Spatial Accessibility to Employment

The predominant strand of thinking that links residential location with adolescent labor

market experiences has focused on issues of spatial accessibility. The familiar spatial

mismatch hypothesis (Kain, 1968) continues to enjoy considerable popularity among

scholars and policy makers and receives ample attention in the many review papers already

in the literature (e.g., Holzer, 1991; Jencks & Mayer, 1990a; Preston & McLafferty, 1999).Spatial accessibility to blue-collar employment decreased dramatically in central city neigh-

borhoods as manufacturing shifted from older sites to the suburban fringe of metropolitan

areas. As the 1970s and 1980s unfolded, the literature broadened its conception to reflect severe

overall manufacturing job losses throughout North America, in addition to geographic shifts at

other scales (i.e., assembly was shifted overseas or to non-metropolitan areas). The literature

also came to address the increasing suburban dominance of most sectors (manufacturing and

non-manufacturing) within the urban economic structure.

Minorities historically located in central-city neighborhoods were unable to take advant-

age of the increasingly suburban-biased labor demand because of racially segmented hous-

ing markets marked by prejudice and institutional discrimination (Kain, 1968; Massey &Denton, 1993; Yinger, 1995). Spatial inaccessibility to job opportunities increases job search

costs (direct and indirect), limits the informal flow of useful job information, and raises

applicants reservation wages in anticipation of longer and costlier commutes (Holzer,

Ihlanfeldt, & Sjoquist, 1994). To the extent that poor neighborhoods are more likely to be

close to the center of cities, spatial inaccessibility to employment may account for labor

market problems. A significant segment of the spatial mismatch literature has focused on

youth labor market outcomes, in part motivated to avoid sample selection bias that arises

for adults (see Ellwood, 1986). Empirical work by several scholars (typified by Ihlanfeldt,

1992) supports the basic spatial mismatch argument that accessibility to employment

matters, even though Holloway (1996) found that the positive employment benefit of job

proximity appeared to have weakened for male youths through the 1980s.

The spatial mismatch hypothesis as typically portrayed is fairly limited in scope. None-

theless, it retains relevance as a component of a larger story; there are additional ways that

youth employment may be impacted adversely by the deepening spatial segmentation of

urban labor and housing markets. As suburban labor markets developed and diversified in

the post World War II decades, complex patterns emerged. For example, large-box

suburban retailers often have difficulty finding workers in part because surrounding

residential areas do not house the desired kind of labor supply. At the same time, youths

living in central city areas may face increased supply-side competition from immigration

and additional opportunity costs associated with place-based illicit or informal economic

activities.

Neighborhood Social Context

The spatial mismatch hypothesis reduces the importance of a neighborhood to its

geographic location vis-a` -vis the broader metropolitan space economy thus ignoring

other neighborhood characteristics as well as social and cultural mechanisms. The area

of research that has paid the greatest attention to neighborhood effects examines links

between neighborhoods and youth behavior by prioritizing social networks (i.e., the

impacts of the social relations that take place within the neighborhood). This branch of

research was invigorated by Wilsons (1987) arguments that neighborhood deteriorationcaused by economic restructuring was partly responsible for the emergences of an urban



6/30


7/30

of our arguments, neighborhoods seem to exert their greatest impact during early child-

hood and adolescence. Affluent neighbors seem to exert a greater positive influence than

poor neighbors exert a negative influence. Most of the neighborhood effects examined

show complex variations by race and gender.

ORegan and Quigley (1996) compared the relative strength of spatial mismatch factors

with neighborhood social factors in a cross-sectional study of youth labor market experi-ences in New Jersey metropolitan areas. They found that neighborhood social character-

istics had a greater impact than accessibility measures, concluding quite clearly that the

constellation of factors that distinguish good from bad neighborhoods affects teenage

employment in profound ways (p. 52). Even so, they could not definitively distinguish

between the spatial economic and the social effects of neighborhood on employment.

Neighborhood Stigma

In addition to spatial accessibility factors and neighborhood social context, a third

hypothesis also links residential location with adolescent labor market outcomes.Wacquant (1993) and Kirschenman and Neckerman (1991) argued that residents of

poor neighborhoods, especially those disproportionately occupied by minorities, suffer

from a form of territorial, or spatial, statistical discrimination. Job applicants listing

addresses in the worst neighborhoods are less likely to find a job because employers

screen them out based on perceptions that they will not be productive employees or will

introduce problems to the work place. This territorial stigma may be a form of geo-

graphically contingent racial prejudice or may function as spatial statistical discrimination

because residence is correlated with race due to high levels of residential racial segregation

(Massey & Denton, 1993). Either way, it can be an important link between neighborhood

residence and labor market outcomes.

Bauder (2001, 2002) extends this notion by arguing that places take on cultural mean-

ings through complex interactions between residents, institutions and other stakeholders

in local communities. These cultural meanings shape values and influence behaviors,

including those that affect labor market outcomes. These cultural meanings also play

into the ways that employers and other institutional actors (i.e., social workers, teachers,

job training counselors, etc.) behave towards neighborhood residents. In his recent

critique, Bauder (2002) suggests that typical neighborhood effects research grossly under-

estimates the impact of, and thus contributes to the further extension of, territorial stigma.

We have discussed various ways that neighborhoods may impact adolescent employ-

ment prospects as if they operate independently of one another. Of course, there are many

ways these factors interact and intersect. For example, employers that practice spatialstatistical discrimination may make illicit economic activities more attractive and rational

by denying employment opportunities to youths from poor neighborhoods. Stereotypes

leading to stigma and spatially targeted discrimination may be strengthened by such

activities. The social and economic characteristics of neighborhood peers and adults will

also be influenced by employers practicing spatial statistical discrimination.

Institutional Effects

Finally, geographic variations in the institutional landscape may also play an important

role. Most obviously, school quality exhibits wide variability based on the racial, ethnic,and poverty status of surrounding neighborhoods. In addition, there may be wide



8/30

variations across neighborhoods in recreational and informal educational opportunities,

which may factor into youth behavior (see Bauder, 2001).

Adolescent Neighborhood Context and Adult Labor Market Outcomes

The research literature that attempts to link neighborhood context with adolescentlabor market outcomes over time is limited yet provocative. Rosenbaums (1995) analysis

of Chicagos Gatreaux program (see also Rosenbaum & Popkin, 1991) perhaps gives us

our best evidence to date. As court-required redress for discriminatory public housing

policies, residents were quasi-randomly assigned to new housing in two different loca-

tionsone in central city neighborhoods much like those they left, and the other in

suburban neighborhoods. The suburban neighborhoods were much less segregated and

disadvantaged; they largely offered the stereotypical middle-class suburban ideal. Rosenbaums

research followed the families that were relocated over a period of time. Focusing on

the longer-term patterns for children in these families, Rosenbaum reports that sub-

urban movers are much more likely than central city movers to be employed (75 versus

41%), earn higher wages (21 versus 5% earned more than $6.50 an hour), and to work

in jobs with benefits (55 versus 23%). Suburban movers were also less likely to drop out

of school and were more likely to be in a college track or in college.

Moving-to-Opportunity (MTO) is a relatively recent federal program modeled on the

presumed success of the Gatreaux program (Popkin, Buron, Levy, & Cunningham, 2000).

Implemented in 1993, MTO offers Section 8 housing vouchers and housing counseling to

public and assisted housing residents currently living in high poverty neighborhoods

(greater than 40% poor). Participants must meet basic eligibility requirements (e.g.,

must have children) and agree to relocate to a low-poverty neighborhood (less than

10% poor) for at least a year. Program evaluation was a major component of the

programs design. Comparisons are made between three groups. In addition to theMTO treatment group, some participating families received Section 8 housing vouchers

without the requirement that they relocate to a low-poverty neighborhood, and some

families received no change in benefits. Assignment to the three groups was made ran-

domly from among the families that qualified and were willing to participate in the

program. Goering, Feins and Richardson (2002) summarized some of the still emerging

evaluation studies. Significant improvements due to program participation were experi-

enced by children and adults, though the benefits did not generally extend to labor market

outcomes.

While the quasi- and fully-experimental nature of the Gatreaux and MTO programs

provide evidence for the benefits of relocation to residents of impoverished neighbor-hoods, they do not fully address the problem explored in this article. First, there are

methodological weaknesses in studies of both programs that prevent conclusive interpre-

tations of their findings (Popkin et al., 2000). Second, they have not yet followed parti-

cipants from adolescence through to adulthood in the same way that we do. Third, these

experiments focus by design only on residents of public housing and recipients of govern-

mental housing subsidies. In order to address the temporal lag questions we pose, we need

(1) to compare respondents who lived during adolescence in high poverty neighborhoods

with respondents who lived during adolescence in low poverty neighborhoods, (2) to

include respondents who did not live in public or assisted housing, and (3) to follow

respondents for a longer period into adulthood.

In addition to summarizing existing wisdom regarding neighborhood effects on currentadolescent residents, Figure 1 also illustrates possible ways that former residential context



9/30

can impact adult outcomes. It serves as a visual guide to the conceptual framework that

informs the exploratory empirical work we present. Note first that we do not seek to

present an exhaustive framework that identifies all of the possible links between adoles-

cent residence and adult labor market outcomes; we present and explore the potential of a

cumulative labor market process to account for the basic patterns we observe. We depict

these linkages with thicker lines in the diagram. Accordingly, a central feature of ourthinking is that the impacts of impoverished neighborhood contexts on labor market

experiences during adolescence affect labor market outcomes later in life. Specifically,

problematic and limited exposure to work as a youth, shaped initially by adolescent

neighborhood contexts according to the mechanisms outlined above (i.e., spatial mis-

match, neighborhood social impacts, and territorial stigma), will accumulate in the form

of limited work experience. Since work experience is a central form of human capital

(Becker, 1975), forces that limit its accumulation will adversely affect downstream labor

market outcomes. In other words, we explore the extent to which adolescent neighbor-

hood poverty is important because it prevents the early accumulation of work experience,

which then limits adult labor market success.

We focus our attention on accumulated work experience as a potential mechanism

linking adolescent residence with adult labor market outcomes for several reasons. It is

perhaps the most commonsense place to start given that little research has yet addressed

the temporally lagged effect of impoverished neighborhood contexts. Human capital

theory stills dominates academic and policy discourse surrounding labor markets. More-

over, human capital deficiencies may be more effectively addressed by policy intervention

than other mediating mechanisms.

Theoretically, we do not argue that accumulated work experience necessarily is the only

or even most important factor producing persistent labor market disadvantages. Potential

links between living in impoverished neighborhoods during adolescence and adult labor

market problems include limited acquisition of other forms of human capital, includingeducational attainment. More troubling, however, are the potential links that do not

function through human capital. For example, youths living in impoverished neighbor-

hoods may adopt oppositional attitudes towards work and employers in response to early

difficulties in finding work (or possibly due to observing adults having difficulty finding

employment): these attitudes, if not altered by subsequent experiences, may persist and

limit later labor market success even in the face of a residential relocation to a better

neighborhood. Similarly, early involvement in criminal activity associated with living in

impoverished neighborhoods during adolescence may impose a lingering effect on adult

labor market outcomes, regardless of the kind of neighborhood inhabited during adult-

hood. The policy implications of alternative linkages are quite important. According toLehman and Smeeding (1997), we realize that it is not enough to know, as a general

proposition, that neighborhoods matter. It matters how neighborhoods matter (p. 259).

Nonetheless, at this early exploratory stage of research we do not directly investigate

alternative mediating mechanisms. We anticipate that future research will explore the

relative importance of multiple pathways that connect early residential contexts with adult

experiences.

DATA AND METHODOLOGY

The research reported here takes advantage of a unique and confidential data set that

matches individuals to their neighborhoods and follows the respondents over many years.The National Longitudinal Survey of Youth (NLSY) selected a nationally representative



10/30

sample of young people between ages 14 and 22 in 1979, and has conducted follow-up

interviews on a regular basis. The questions asked of these individuals reveal information

about ascriptive characteristics such as race, ethnicity, and gender, as well as a wide range

of other personal information such as family structure, education levels, occupational

history, and poverty status. These data have proved especially useful in studies of labor

market behavior and employment experiences. The research we report takes advantage ofa recent initiative that matched NLSY respondents to their geographic (census tract)

locations. The Social Science Research Council (SSRC)-sponsored initiative to study

neighborhood effects reported in Brooks-Gunn, Duncan, and Aber (1997a, 1997b) used

a single year of address-matched information for the children of the NLSY sample. Our

analysis required address-matching all possible respondents for all survey years. We used

census tract data for 1980 to represent each respondents adolescent neighborhood context

based on their 1979 address, and census tract data for 1990 to represent each respondents

adult neighborhood context based on their 1990 address. Given the increasing geographic

concentration of poverty within cities that motivates this project, we restricted our atten-

tion to the subset of NLSY respondents who lived in a metropolitan area of the US in

1980. Individual-level attributes for the sample used in subsequent analyses are shown in

Table 1.

The geocoding process was conducted in several stages. The first stage recovered

residential addresses provided by NLSY respondents at each interview from archived

storage and transformed them into an appropriate digital format. The second stage

identified the census tract location of each address via address matching (i.e., comparing

each address against TIGER [Topologically Integrated Geographic Encoding and

Referencing] files created by the U.S. Census Bureau] that contain the majority of streets

and address ranges in the country) (see Clarke, 1990). Initial matches were handled easily.

Addresses that could not be matched due to typographical errors, inconsistent use of

abbreviations, or road and place name changes were hand checked using detailed printatlases. The final successful match rate for the entire set of addresses was approximately

90%. We chose to exclude respondents who reported a zip code but no address, despite the

possibility of using the geographic centroid of zip code areas: zip codes are too large to

adequately represent neighborhoods. The third stage of the data preparation process

merged 1980 and 1990 census tract data with the individual-level NLSY data using the

census tract identifier.

Several problems impacted the preparation of the address-matched data. First, only half

of the addresses for the initial (1979) NLSY respondents were readable due to physical

degradation of the archived computer tape. We could not recover this information even

from the National Opinion Research Center (NORC), which conducted the initial surveyand archived the original paper survey forms. Second, because the NLSY project was not

designed initially to provide the level of geographic detail needed for the type of study we

conducted, address information was not asked of every respondent at each interview.

Addresses were typically recorded only for respondents who moved since the previous

survey, or incidentally at the discretion of the interviewer.

These two problems, combined with natural sample atrophy, limited the number of

usable respondents. We expanded the number of respondents with usable address

information by taking advantage of other questions in the survey that asked whether

respondents had moved since the previous survey. For example, for a respondent with a

valid address in 1981, but not for 1980 or 1979 (due the degradation of the tape), we

imputed the 1981 address back to 1979 if the surveys for 1981 and 1980 indicated that therespondent had not moved during the previous year. Other respondents were matched to



11/30

their parents address (parental addresses were kept in a separate file) if the respondentindicated in the 1979 or 1980 survey that they lived with their parents. Addresses in 1990

could be identified for respondents without a reported address if they had a valid address

at some point since 1979 and survey questions indicated that they had not moved during

any of the intervening years. For example, the 1987 address was imputed to 1990 if the

respondent reported a new address in the 1987 survey, no address in the 1988 through

1990 surveys, and that they had not moved during the previous years in each of the 1988

through 1990 surveys.

The sample used in subsequent analysis includes 5,978 respondents (58.3% of the 10,253

respondents to the 1990 survey) who lived in a metropolitan area in 1980 and did not live

on a military base in 1980. The full NLSY sample includes 12,686 respondents. We

compared the sample used in this analysis with the sample we would have used had webeen able to successfully identify the census tract location of all respondents in order to

TABLE 1

Characteristics of the NLSY sample

1980 Tract Poverty Rate

Variable Geocoded Sample 40%

Age at first interview 17.37 17.40 17.33 17.24

(2.23) (2.25) (2.19) (2.18)

Highest grade completed 12.97 13.29 12.25 12.14

(2.41) (2.44) (2.18) (2.23)

No earned degree? .16 .11 .23 .32

High school degree? .56 .55 .58 .53

Accumulated years of work experience 7.48 7.96 6.53 5.70

(3.16) (2.93) (3.35) (3.43)

Ever held government job? .17 .12 .29 .32

Ever received vocational training? .38 .40 .36 .29

High school curriculum vocational? .20 .19 .23 .27

High school curriculum college prep.? .41 .43 .37 .36

Work-limiting disability? .02 .02 .03 .02

Union member? .11 .11 .12 .13

Non-Hispanic black? .27 .15 .48 .68

Hispanic? .18 .14 .30 .26

Female? .50 .50 .49 .51

Independent household in 1979? .13 .15 .09 .09

Lived with both parents until 18? .60 .65 .52 .38

Family below poverty line in 1979? .20 .13 .32 .50

Family poverty not reported in 1979? .08 .07 .10 .09

Anyone in household received welfare in 1979? .16 .09 .27 .46

In jail in 1979? .00 .00 .01 .00

In jail in 1990? .01 .01 .01 .03

Lived with parents in 1990? .15 .12 .20 .25Never married? .37 .33 .44 .57

Children younger than 6 at home? .40 .39 .42 .40

Children older than 6 at home? .09 .08 .13 .13

1980 Neighborhood poverty rate for 1979 residence .16

(.13)

.08

(.05)

.29

(.06)

.50

(.08)

N 5978 4165 1464 349

Note. Values are means, with standard deviations in parentheses for continuously measured variables. All nominalvariables are coded 1 for the positive response and 0 otherwise.



12/30

examine the impact of sample selection procedures (Table 2). Our sample is generally very

similar to the full sampledifferences noted should not substantively affect our analysis.

The geocoded sample is slightly more likely to be male, minority, and living at home in a

non-poor family than the full sample. Respondents in our sub-sample are also slightly

more likely to have never married and less likely to have children at home.

To explore the extent to which adolescent neighborhood poverty affects adult employ-ment, we combine descriptive tabular and graphic analysis with multivariate statistical

modeling. To address the basic question of the lasting impact of adolescent neighborhood

poverty on adult labor market activities, we grouped adult activities into four mutually

exclusive categories: employed, unemployed, out of the labor force, and in the military.

Our descriptive measures focus on the percentage of respondents in each activity state

cross-classified by adolescent neighborhood poverty rate category. We utilize three pov-

erty rate categories: non-poor census tracts with poverty rates less than 20%, poor census

tracts with poverty rates between 20 and 40%, and extremely poor census tracts with

poverty rates greater than 40%. This scheme has been used in many studies of the

increasing concentration of poverty (Jargowsky, 1994, 1997; Jargowsky & Bane, 1990).

We also examine the differential impact of adolescent neighborhood poverty rate on adult

activities for respondents who moved out of their initial neighborhood versus respondents

TABLE 2

Characteristics of Full and Geocoded NLSY Samples

Variable Full Sample Geocoded Sample

Age at first interview 17.61 17.37

Highest grade completed 12.79 12.97

No earned degree? .18 .16High school degree? .55 .56

Accumulated years of work experience 7.38 7.48

Ever held government job? .17 .17

Ever received vocational training? .37 .38

High school curriculum vocational? .20 .20

High school curriculum college prep.? .37 .41

Work-limiting disability? .03 .02

Union member? .10 .11

Non-Hispanic black? .25 .27

Hispanic? .16 .18

Female? .58 .50

Independent household in 1979? .21 .13Lived with both parents until 18? .58 .60

Family below poverty line in 1979? .22 .20

Family poverty not reported in 1979? .08 .08

Anyone in household received welfare in 1979? .16 .16

In jail in 1979? .00 .00

In jail in 1990? .01 .01

Lived with parents in 1990? .12 .15

Never married? .33 .37

Children younger than 6 at home? .42 .40

Children older than 6 at home? .11 .09

1980 Neighborhood poverty rate for 1979 residence N/A .16

N 10,253 5,978

Note. The full sample excludes NLSY respondents living on military bases and in non-metropolitan areas in 1980.



13/30

who did not move, and for respondents based on both their 1980 and 1990 poverty rate

categories.

In order to assess more rigorously the relationship between adolescent neighborhood

poverty and adult labor market activities, we estimated a series of multivariate statistical

models (linear and logistic regression) based on the individual-level information in the

data. These models predict adult activities and labor market outcomes with standard setsof predictive variables augmented by the poverty rate of the neighborhood respondents

lived in during adolescence. By controlling for individual- and family-level labor market

predictors, we are able to isolate the unique and lasting impact of adolescent neighbor-

hood poverty on adult labor market outcomes.

Does Adolescent Neighborhood Poverty Affect Adult Employment Outcomes?

The basic question posed by this research is whether living in poor neighborhoods

during adolescence implies long-term consequences for adult labor market activity.

Figure 2 illustrates that for the sample of NLSY respondents included in the analysis,individuals living in non-poor census tracts in 1980 (poverty rate < 20%) were much

more likely to be employed and less likely to be either unemployed or out of the labor

force in 1990 than individuals that lived in either moderately poor (20 to 40% poor)

or extremely poor neighborhoods (poverty rate exceeded 40%) in 1980. Individuals

living in moderately poor neighborhoods in 1980 were worse off than individuals from

non-poor neighborhoods, but better off than individuals living in extremely poor

neighborhoods in 1980.

Figures 3 and 4 suggest that some of the effect of adolescent neighborhood poverty on adult

labor market experiences is conditioned by whether individuals moved out of the neighbor-

81.0371.65

60.46

3.51

8.06

10.89

14.1218.37

27.51

0%

20%

40%

60%

80%

100%

Tract40% Poor

1980 Census Tract Poverty Rate

Employed Unemployed Not in Labor Force Military

FIGURE 2

1990 Labor Force Activity by Census Tract Poverty Rate in 1980



14/30

hood. Individuals who remained in extremely poor neighborhoods had a substantially lower

adult employment rate than individuals who moved out of such neighborhoods (Figure 3).

There is no substantial difference between movers and stayers living in non-poor and moder-ately poor neighborhoods in 1980. Whether this implies residential self-selection on the part of

0% 20% 40% 60% 80% 100%

>40%

20%40%

40% (1980)

20%40% (1980)

40% (1990)

> 40% (1990)

>40% (1990)

20%40% (1990)

20%40% (1990)

20%40% (1990)

< 20% (1990)

< 20% (1990)


15/30

the residents of extremely poor neighborhoods is not known. Individuals who moved to better

neighborhoods were more likely to be employed (and less likely to be unemployed)

than individuals who moved to neighborhoods with poverty rates similar to their

adolescent neighborhoods (or actually did not move out) or individuals who moved

to neighborhoods with higher poverty rates (Figure 4). Figure 4 thus provides support

for the argument that living in poor neighborhoods during adolescence has lastingimpacts; individuals starting out in the poorest neighborhoods always have the lowest

adult employment rates. Even among respondents living in non-poor neighborhoods

in 1990, employment rates are lowest (and unemployment rates highest) among those

who started out in extremely poor neighborhoods, followed by those who started out

in moderately poor neighborhoods.

These descriptive patterns do not allow us to directly assess the reasons that living

in poor neighborhoods during adolescence seems to predict adult labor market

difficulties. Any or several of the theories we reviewed above may be operative. If

adolescent residents of poor neighborhoods find available jobs inaccessible due to

broader geographic patterns of urban economic expansion, they will have difficulties

in securing employment. As Hughes (1995) reminds us, however, the problem of

accessibility is one of labor demand as well as labor supply. In addition, theories

that accentuate accessibility often imply that improving accessibility will solve

employment problems. Our findings suggest that early disadvantages do not com-

pletely disappear even for individuals who move to much better neighborhoods that

presumably enjoy better access to jobs. Other neighborhood factors accounting for

the patterns we observe may be related to human capital and insufficient quantities

and/or poor quality of schooling, for example. Poor neighborhoods may also sur-

round adolescents with social contexts that adversely affect their labor market

experiences. Some suggest that youths internalize values and attitudes that limit

their labor market success because of peer networks, inadequate role modeling,and/or inadequate supervision. Others suggest that youths do not internalize poor

values and attitudes; rather they suffer from the weak and ineffective social capital

contained in poor neighborhoods. Some observers argue that we should focus more

attention on the values, attitudes, and behavior of employers. Demand for the labor

of youth who live in or come from poor neighborhoods may be limited because

employers hold racial stereotypes that stigmatize potential workers based on where

they live. Each of the notions provides plausible mechanisms that our empirical

analysis cannot directly distinguish. Yet our analysis can determine the degree to

which the patterns depicted in Figures 2 through 4 result from the respondents

measurable characteristics, including several important human capital attributes. Inthe following sections, we present the results of multivariate models that include a

broad array of individual- and family-level measures that have been shown in

previous research to affect labor market outcomes.

Multivariate Models

The evidence presented so far does not take into consideration differences between

individuals in characteristics that might predict adult employment outcomesindividuals

living in poor neighborhoods during adolescence may show lower employment rates as

adults because they completed fewer years of education, for example. We also do not

know if the effect of adolescent neighborhood poverty is distinct from that of adolescentfamily poverty. The logistic regression models presented in Tables 3 and 4 address these



16/30

concerns by statistically controlling for a wide range of human capital and other factors

that have been found in previous research to affect employment outcomes. We estimate

two sets of individual-level logistic regression models predicting adult labor marketactivities (see Tables 3 and 4 for estimates).

TABLE 3

Logistic Regression Models Predicting the Probability of Being Employed or in the Military in 1990

Males Females

w/o Experience w/Experience w/o Experience w/Experience

Variable bk ebk bk e

bk bk ebk bk e

bk

Age at first interview .036 1.037 .115** .891 .005 .995 .167** .846Highest grade completed .007 .993 .016 1.016 .066* 1.068 .018 1.019No earned degree? .765** .465 .627* .534 .885** .413 .172 .842High school degree? .275 .760 .278 .757 .318** .727 .320 .726Accumulated years of

work experience

.338** 1.402 .467** 1.595

Ever held government job? .227 .797 .178 .837 .165 .848 .237* .789Ever received vocational

training?

.039 1.039 .120 1.128 .168* 1.183 .077 .926

High school curriculum

vocational?

.298* 1.347 .216 1.241 .089 1.093 .075 1.078


college prep.?

.424** 1.528 .375** 1.456 .126 1.134 .061 1.062

Work-limiting disability? 1.252** .286 .769** .464 .072 1.074 .601 1.823Union member? .855** 2.351 .480** 1.617 1.536** 4.647 1.289** 3.631

Non-Hispanic black? .682** .506 .435** .647 .230* 1.259 .565** 1.759Hispanic? .076 .927 .050 .952 .185 1.203 .500** 1.648Independent household

in 1979?

.233 .792 .227 .797 .012 1.012 .403** 1.46

In jail in 1979? .551 .576 .345 1.411Lived with both parents

until 18?

.115 1.122 .015 .986 .008 .992 .146 .864

Family below povertyline in 1979?

.502** .605 .232 .793 .272** .762 .007 .993

Family poverty not reported

in 1979?

.552** .576 .518** .596 .380** .684 .132 .877

Welfare receipt in 1979? .138 1.148 .406** 1.501 .420** .657 .007 .993In jail in 1990? 3.958** .019 3.638** .026Lived with parents in 1990? .831* .436 .710** .492 .008 1.008 .153 1.165Never married? .638** .528 .463** .629 .287** .750 .008 .992Children younger than 6 at

home?

.110 .895 .306* .737 1.239** .290 .954** .385

Children older than 6 at

home?

.148 .862 .134 .874 .485** .616 .098 1.102

1980 Neighborhood

poverty rate

1.494** .224 1.275** .279 .882** .414 .140 1.151

Constant 2.861** 2.583** 1.299** 1.048

2LL 1777.20 1586.36 3109.87 2439.432 versus Intercept-only

Model

525.71** 716.55** 477.97** 1148.42**

Note. ebk is the exponentiated parameter estimate and represents the factor change in the odds of the outcome producedby a one unit increase in the value of the independent variable.*p< .10. **p< .05.



17/30

lnPA1

PA0

b0 bHCHC bFF bPRPRj ei Equation 1

In each of the models reported in Tables 2 and 3, HC is a vector of human capital

characteristics (bHC is a row vector of parameter estimates), and F is a vector of additionalindividual- and family-level attributes (bF is a row vector of parameter estimates) attached

TABLE 4

Logistic Regression Models Predicting the Probability of Being Out of the Labor Force in 1990

Males Females

w/o Experience w/Experience w/o Experience w/Experience

Variable bk ebk bk e

bk bk ebk bk e

bk

Age at first interview .038 .962 .127** 1.135 .010 .990 .126** 1.135Highest grade completed .052 1.053 .035 1.036 .063* .939 .013 .987No earned degree? .955** 2.599 .836** 2.308 .718** 2.050 .015 1.015

High school degree? .499* 1.647 .557 1.746 .209 1.232 .161 1.174

Accumulated years of

work experience

.391** .677 .438** .645

Ever held government job? .061 1.062 .011 1.011 .119 1.127 .161 1.174

Ever received vocational

training?

.250 .779 .360* .697 .130 .878 .135* 1.145


vocational?

.327 .721 .186 .831 .087 .917 .077 .926

High school curriculum college

prep.?

.331* .718 .263 .769 .133 .876 .085 .919

Work-limiting disability? 1.499** 4.478 .889** 2.432 .207 1.230 .220 .802

Union member? 1.381** .251 .878** .416 1.717** .180 1.439** .237Non-Hispanic black? .305 1.357 .027 .974 .527** .590 .889** .411Hispanic? .097 1.102 .121 1.128 .224* .799 .536** .585Independent household in 1979? .438 1.550 .422 1.526 .118 1.125 .202 .817In jail in 1979? 1.238 3.448 .427 1.532

Lived with both parents

until 18?

.319* .727 .206** .814 .090 1.094 .229** 1.257

Family below poverty line

in 1979?

.129 1.138 .243 .784 .282** 1.326 .045 1.046

Family poverty not reported

in 1979?

.382 1.465 .315** 1.371 .567** 1.762 .381** 1.464

Welfare receipt in 1979? .148 1.159 .143 .867 .374** 1.453 .028 .973In jail in 1990? 4.567** 96.232 4.353** 77.692

Lived with parents in 1990? .614** 1.849 .437** 1.548 .094 .910 .244 .783Never married? .745** 2.106 .491** 1.633 .292** 1.339 .011 1.011

Children younger than 6

at home?

.009 1.009 .233 1.263 1.353** 3.869 1.079** 2.941

Children older than 6 at

home?

.338 1.402 .369 1.446 .440** 1.553 .142 .867

1980 Neighborhood poverty

rate

1.205* 3.337 .826 2.284 .816* 2.261 .225 .798

Constant 3.829** 3.540** 1.320* .9032LL 1204.57 1044.05 2852.93 2304.082 versus Intercept-only Model 443.82* 604.35** 430.25** 979.10**

Note. ebk is the exponentiated parameter estimate and represents the factor change in the odds of the outcome producedby a one unit increase in the value of the independent variable.*p< .10. **p< .05.



18/30

to individual-level records (i indexes individuals in the sample, j indexes census tracts). See

Table 1 for a description of these variables. We chose not to include current (adult) school

enrollment as a variable in our models as only 5% of the full sample was enrolled in school

at the time of the 1990 survey, and only 10% (including most of the 5% already

mentioned) had been enrolled in school at any time during 1989 or 1990. Educational

attainment is already controlled for in the models. We measure neighborhood poverty(PRj) continuously as the 1980 poverty rate of census tract j associated with the respond-

ents 1979 residence. To explore the impact of accumulated work experience on the

neighborhood poverty-adult activity relationship, we display estimates of the models

both including and excluding the work experience variable.

In the models reported in Table 3, A 1 for respondents who are employed or in themilitary in 1990 (versus A 0 for respondents out of the labor force or unemployed). Inthe models reported in Table 4, A 1 for respondents who are out of the labor force(versus A 0 for respondents who are employed, unemployed, or in the military). Thesecond set of models rests on the well-recognized difference between unemployment and

non-employment. Unemployed workers are considered to be active members of the labor

force due to the pursuit of employment required for benefits eligibility; survey respondents

not employed, not in the military, and not classified as unemployed may be discouraged

workers. Respondents who voluntarily withhold their labor from the labor market (e.g.,

for child-rearing) are controlled for with additional variables in the model. Note that we

expect the sign of the parameter estimates in the second set of models to be opposite those

in the first set of model. We estimate each set of models separately for males and females.

To aid in the interpretation of results, we report exponentiated parameter estimates (ebk)

as a measure of the factor change in the odds of the outcome produced by a one unit

increase in the value of the independent variable. Percentage changes in the odds of the

outcome are easily computed ([(ebk 1)*100]). Recall that the units implied by the raw

coefficients of a logistic regression model (log-odds) are not intuitively interpretable (seeLong, 1997). The factor change measure of effect on odds has the additional benefit of

being independent from the settings of the independent variables, unlike predicted prob-

abilities and most marginal effects measures.

The basic patterns depicted by the models reported in Tables 3 and 4 follow findings of

common labor market research. The probability of being employed or in the military

(Table 3) is higher for respondents who have more work experience (in the models that

include the variable), attended a college-preparatory high school (males only), are mem-

bers of a union, and have lived in an independent household at an early age (females only).

Employment (or military) probabilities are reduced by age at first interview in the models

that control for work experience, not having a high school degree (for males: relative tohaving an advanced degree) and having a high school degree (for females: relative to

having an advanced degree), and having children younger than six years old at home.

There are some interesting differences between males and females, including the opposite

impact of race (relative to white women, black women are more likely to be employed

while black men are less likely to be employed than white men). Also, living with parents

as an adult and having never married are associated with lower employment probabilities

for men but not for women (though the causal ordering of these variables is difficult to

determine). Results of the models predicting the probability of being out of the labor force

(Table 4) largely mirror those for employment (with reversed signs as expected) with a few

differences.

The parameter estimates that most concern us here, given our conceptual framework,are those associated with adolescent neighborhood poverty rate and accumulated work



19/30

experience. In both Table 3 and Table 4, the models that exclude the work experience

variable represent reduced form estimates of the total effect (direct effects plus indirect

effects as mediated through work experience) of adolescent neighborhood poverty. Put

another way, excluding work experience is appropriate if we view accumulated work

experience and adult labor market outcomes as jointly determined by neighborhood

poverty. Toward that end, we explore the effect of neighborhood poverty on accumulatedwork experience later in this article. Despite the set of control variables included in the

models (including family poverty status), adolescent neighborhood poverty rates have a

statistically significant impact on adult labor market outcomes (though only marginally

for the out-of-the labor-force models). Based on exponentiated parameter estimates, a

female respondent coming from an extremely poor neighborhood with a 50% poverty rate

has 16% lower odds of being employed or in the military than a respondent from a

moderately poor neighborhood with a 30% poverty rate, and 30% lower odds than a

respondent from a non-poor neighborhood with a 10% poverty rate, net of human capital

and other individual- and family-level predictors. Females from moderately poor neigh-

borhoods (30% poor) have 16% lower odds of employment than females from non-poor

neighborhoods (10% poor). Adolescent neighborhood poverty has even greater impact on

males. A male respondent from an extremely poor neighborhood (50% poor) has 26%

lower odds of being employed or in the military than a respondent from a moderately

poor neighborhood (30% poor), and 45% lower odds than a respondent from a non-poor

neighborhood (10% poor), net of the other variables in the model. Males from moderately

poor neighborhoods (30% poor) have 26% lower odds of being employed or in the

military than males from non-poor neighborhoods (10% poor).

The relationship between a continuous variable and the probability of an outcome, as

captured by logistic regression, is inherently non-linear and depends on the settings of the

independent variables (Liao, 1994, Long, 1997). To aid in interpreting the relationship

between adolescent neighborhood poverty and adult employment probabilities, we con-structed predicted probability plots (Figure 5) using the parameter estimates reported in

Table 3 (model for males that excludes the work experience variable). We set the values of

the independent variables to represent four hypothetical respondents: a black male and a

white male from non-poor families, and a black male and a white male from poor families.

Each of these hypothetical males was 17 years old at the beginning of the survey,

completed 13 years of schooling, obtained a high school degree in a school with a

college-preparatory curriculum, lived with both parents until they were 18 years old,

had never married, and did not have children living with them in 1990. Amongst males,

black respondents from poor families are the most sensitive to adolescent neighborhood

poverty, indicated by the steepness of the predicted probability line.

How Important is Accumulated Work Experience?

The second set of models (second and fourth pairs of columns in Tables 3 and 4) add the

accumulated work experience variable. The parameter estimate associated with this vari-

able represents the direct effect of past work experience on adult labor market activity, net

of neighborhood context. This model specification is appropriate if we view accumulated

work experience primarily as a human capital attribute not jointly determined by adoles-

cent neighborhood poverty. Accumulated work experience has a strong effect on labor

market outcomes, slightly more pronounced for women than men. Each additional year ofwork experience increases the odds of employment by almost 60% for women and 40%



20/30

for men. Slightly smaller effects are noted for the out-of-the-labor-force model (odds are

reduced by 35% for females and 32% for males).

The two model specifications that we present are based on alternative conceptions ofthe role of accumulated work experience; the models excluding the variable treat past

experience and current activity as jointly determined by adolescent neighborhood context.

The total reduced-form effect of adolescent neighborhood poverty is captured by its

coefficient in the first pair of models in Tables 3 and 4. In contrast, the direct, non-

reduced form, effect of adolescent neighborhood poverty is captured by its coefficient in

the second pair of models in Tables 3 and 4. The second set of models (including work

experience) allow us (1) to assess the degree to which work experience functions as an

intermediate mechanism by examining how the neighborhood poverty coefficient changes

between the two models, and (2) to assess the degree to which adolescent neighborhood

poverty exerts influence over adult labor market outcomes net of its impact on work

experience. As suspected, the effect of neighborhood poverty depends strongly on whether

the model includes the accumulated work experience variable. The magnitude of the

estimate is reduced in each model and remains statistically significant only for males in

the employment model. Note that no other variable included in the models affected the

estimate of the neighborhood poverty variable to this extent. For males, this second set of

models suggests that adolescent neighborhood poverty has important effects on adult

activities not transmitted through the attenuation of work experience. This finding sug-

gests that for males, the social and institutional explanations we reviewed earlier in the

article are important.

Work experiences effect on the parameter estimates associated with adolescent neigh-

borhood poverty in the adult activity models suggests that adolescent neighborhoodcontext may have important effects on accumulated work experience. We explore this

0.6

0.7

0.8

0.9

1

0 20 40 60 80 100

1980 Census Tract Poverty Rate

PredictedEmployment(o

rMilitary)

Probabilityin1990

Non-Poor White Non-Poor Black Poor White Poor Black

FIGURE 5

Effect of 1980 Tract Poverty Rate on the Predicted Probability of Being Employed or in the Military in

1990, for Black and White Males, by Race and Family Poverty Level



21/30

possibility by regressing accumulated work experience on a set of individual- and family-

level variables in addition to adolescent neighborhood poverty (Table 5). For males and

females, living in poor neighborhoods during adolescence statistically associates with

attenuated work experience, net of other important predictors such as living in a poor

family and educational attainment. The relationship is stronger for females than males.

We see this as consistent with our finding that neighborhood poverty remains significantin the male employment model that controls for work experience. The association between

adolescent neighborhood poverty and work experience, while statistically significant and

substantively meaningful, is not large enough to create problematic multicollinearity in the

logistic regression models (the maximum bivariate correlation between neighborhood

poverty and work experience is .57, which is a value not typically considered problema-

tically large).

Our segmented models reflect our understanding of labor market process as deeply

gendered: substantial research documents that men and women have fundamentally

different experiences in the labor market. While some argue that supply-side differences

(most notably, child-bearing) create these differences, structural and feminist research

argues that women are segmented into gender-typed occupations and industries (Hanson

& Pratt, 1995; Nelson, 1986) and experience lower returns on human capital investments.

Estimating our models separately for male and female respondents allowed us to account

for gender differences in sensitivity to the poverty of adolescent neighborhood contexts.

On one hand, some literature suggests that more disadvantaged groups are more sensitive

TABLE 5

Ordinary Least Squares Regression Models of Accumulated Years of Work Experience

Females Males

Variable bk bk bk b

k

Age at first interview .349** .236 .506** .383

Highest grade completed .089* .061 .133** .115No earned degree? 2.249 .226 .876** .115High school degree? .216 .032 .086 .015Ever held government job? .028 .003 .266* .034Ever received vocational training? .556** .083 .029 .005

High school curriculum vocational? .135 .016 .255* .036

High school curriculum college prep.? .165 .024 .096 .016

Work-limiting disability? .770* .038 1.495** .074Union member? .858** .076 .922** .108

Non-Hispanic black? .710** .095 1.140** .171Hispanic? .464** .054 .111 .015Independent household in 1979? .743** .084 .271 .026In jail in 1979? 3.937** .098Lived with both parents until 18? .292** .043 .316** .053

Family below poverty line in 1979? .711** .087 .966** .131Family poverty not reported in 1979? .743** .059 .366* .033Welfare receipt in 1979? 1.331** .148 .758** .0941980 Neighborhood poverty rate 2.858** .117 1.372** .062Constant .878** 1.712**

R2 .321 .313

F versus Intercept-only Model 78.03** 70.81**

Note. bkis the standardized parameter estimate.*p< .05. **p< .01.



22/30

to their contexts. If we view females as disadvantaged in the labor market because of

gender discrimination, we might expect them to be more sensitive than males to their

neighborhood contexts. Women face complex life-course decisions (e.g., child-bearing and

care giving) that sometimes have a greater labor supply effect than for men. On the other

hand, neighborhoods effects on youth employment prospects may be stronger for males,

perhaps because of greater peer-group orientation. Our models suggest that the effect ofadolescent neighborhood poverty adult labor market activities is weaker overall for

females than males. Moreover, accumulated work experience is a stronger mediating

intervening mechanism between adolescent neighborhood and adult labor market out-

comes for women than men. This finding probably reflects a combination of greater

weight that employers may place on work experience as a signal of on-going commitment

to work and productivity for women because of the complex decisions they face and the

self-selection of women out of the labor market for reasons not reflected completely in the

models. The stronger neighborhood poverty effect on males, combined with the weaker

role of accumulated work experience as an intervening factor, lead us to consider their

position more vulnerable in ways that we discuss in greater detail in the next section.

DISCUSSION AND CONCLUSIONS

The purpose of this article was to explore the degree to which the adverse labor market

effects of living in impoverished neighborhoods during adolescence are still present years

later and expressed in subsequent adult employment difficulties. Using longitudinal data

from the NLSY augmented with 1980 and 1990 census tract information, we found that

living in poor neighborhoods during adolescence reduces adults chances of being

employed (or in the military) and increases their chances of being out of the labor force.

Our analysis suggests that this neighborhood effect is present even for individuals who

moved into more affluent neighborhoods. Moreover, we observed the effect of adolescentneighborhood poverty in a series of multivariate statistical models that controlled for a

broad range of individual- and family-level factors, including the poverty status of the

adolescents family. The adolescent neighborhood poverty effect is larger for males than

females, especially for black males in poor families. Accumulated years of work experience

was the only variable that substantially reduced the estimated magnitude of the adolescent

neighborhood poverty effect in the logistic regression models. Living in a poor neighbor-

hood during adolescence appears to limit the degree to which respondents, especially

females, can accumulate work experience over the life course. To summarize, adolescent

neighborhood poverty appears to have an independent and lasting effect on adult labor mar-

ket experiencesan effect that is greater for males than females, yet one that appears to betransmitted through limited accumulation of work experience more for females than for males.

We turn in our last section of this article to a discussion of the implications of our

findings. We do so at two scales. We first discuss the potential of interventions to improve

the chances of adult labor market success for adolescents that grow up in poor neighbor-

hood contexts. Second, we consider broader debates about poverty and place in Americas

cities. In both discussions, we consider policy and research issues.

Part of our motivation for considering the role of accumulated work experience in

mediating long-lasting neighborhood effects stems from the possibility that interventions

designed to strengthen adolescent workforce connections can effectively enhance adult

labor market experiences. Such interventions will be effective only to the extent that

neighborhood poverty exerts its lasting effects through the attenuated accumulation ofwork experience. Several strategies might enhance and strengthen youths attachments to



23/30

the labor force, thus providing the work experience needed to mitigate the adverse effects

of poor neighborhoods. General efforts that focus on youth labor supply might include

job training programs designed to provide both the skills needed to accomplish work tasks

and the more intangible soft skills needed for effective relationships with employers, other

employees, and perhaps clients and/or customers. Social mentoring might stem the gap in

role models. Job information programs might redress weaknesses in job informationcircuits that often follow personal contacts.

Other efforts to facilitate early and sustained labor market success might focus more

broadly on the demand side of labor markets. These efforts can enhance adolescents labor

market experiences and may aid in achieving a positive downstream employment outcome

even if the programs are not targeted for inner-city youth. For example, the promotion of

a living wage through minimum wage legislation might entice at-risk youths into the labor

market as teens, drawing them away from alternatives such as criminal activity. Expand-

ing the Earned Income Tax Credit may help as well, but the significant lag time between

initial earnings and the reward of an increased tax return in the following year might limit

the effectiveness of this option. Union organization may help draw youths into the

workforce and strengthen their connection to the labor market, but its effectiveness

could vary widely by geographic area. In an era of decreasing union jobs and increasing

part time and temporary work, the impact of union organization could be minimal.

Our analysis, while suggesting the effectiveness of work-enhancing interventions, also

leaves us with a certain degree of caution. First, males are more sensitive than females to

the poverty of their adolescent neighborhood contexts at the same time that work

experience accounts for a smaller share of that effect. Thus, interventions designed to

enhance adolescent workforce attachments hold less promise for males than females.

Second, even to the degree that enhancing workforce attachments can successfully miti-

gate the negative consequences of living in poor neighborhoods during adolescence, there

may be important interactions with the mechanisms through which early labor marketdisadvantages are transmitted. For example, if the main culprit is the spatial mismatch

between poor inner-city neighborhoods and suburban employment opportunities, then we

need policies and programs that address this geographic imbalance. Hughes (1995) sug-

gests that three program elements are needed: facilitating the flow of information between

suburban employers and inner-city job seekers, facilitating the mobility of workers

through targeted commutes, and strengthening support services for suburban workers

living in inner-city neighborhoods. The main culprit, however, may involve employers

using racial/spatial stereotypes about values, attitudes, and behaviors to limit employment

opportunities for minority youth living in poor neighborhoods. In such cases, reverse

commute and job information program will have little impact. Stronger anti-discrimina-tionpolicy may be warranted.

We shift our discussion now to consider broader debates about poverty, its geographies,

and the city. Crafting appropriate policy responses to concentrated urban poverty has

long been a point of considerable debate, both in and out of the academy. Some have

stressed the development of in-situ new economic opportunities for the urban poor.

Examples of the development strategy include enterprise and empowerment zones and

spatially targeted job creation programs. Critics suggest that developing jobs in the ghetto

deepens racial segregation and ultimately will fail to counter the seemingly inexorable

decentralization of metropolitan space economies. The result will be to anchor the urban

poor to inner city neighborhoods that ultimately are not economically viable. Hughes

(1995) mobility strategy is an approach that attempts to focus attention on linking theurban poor to suburban job opportunities. This strategy does not attempt to change the



24/30

economic landscape by bringing jobs back into the center, or the social landscape by

facilitating the movement of poor minorities into the suburbs.

The current dominant alternative to the development strategy is dispersala strategy

designed to enable relocation of the urban poor to parts of the city where they can take

advantage of better economic and social opportunities. Some policies focus on overcom-

ing barriers to housing the poor in suburban communities, including anti-discriminationefforts. Other policies attempt to increase the supply of affordable housing in the suburbs

by removing land use barriers and by developing incentives and requirements that devel-

opers provide affordable housing. Finally, some policies focus directly on relocating the

poor by subsidizing private market housing (e.g., Section 8 vouchers) or requiring that

new public housing be built in suburban or scattered sites. Over the last decade, the focus

of the dispersal strategy has locked onto redeveloping the worst existing public housing

projects (through HOPE VI), which have been shown to spatially anchor urban poverty

(Holloway, Bryan, Chabot, Rogers, & Rulli, 1998; Massey & Kanaiaupuni, 1993), and

increasing the ability of public housing tenants to relocate to the suburbs through the

Gautreaux-inspired Moving-To-Opportunity (MTO) program. Goetz (2002) contends

that HOPE VI constitutes a forced relocation program, while MTO constitutes a program

of voluntary mobility. Critics argue that dispersal undermines minority political power

and in the current urban space economy serves to remove the poor from land now

valuable and attractive to middle- and upper class whites (Crump, 2002).

Emerging analyses of Census 2000 data suggest that the dispersal strategy has finally

workedthe spatial concentration of poverty has declined. According to the assumptions

of the dispersal strategy, this signals better days for the urban poor as they enjoy better

social contexts and better accessibility to employment opportunities. Before celebrating,

however, we should recall the implications of existing studies, including the results we

present in this article. The generally optimistic reports on the impacts of dispersal through

the Gautreaux and MTO programs (Goering, Feins, & Richardson, 2002; Rosenbaum,1995; Rosenbaum & Popkin, 1991) do not extend as fully to employmentresidents that

relocated to the suburbs showed somewhat fewer employment improvements (see also

Galster & Zobel, 1998, which provides a slightly more optimistic assessment of employ-

ment outcomes). Goetz (2002) reports few employment benefits associated with dispersal

from an inner-city public housing site. Our results suggest that the labor market problems

initiated by residence in a poor neighborhood during adolescence do not disappear upon

relocating to a better neighborhood. Moreover, firming up workforce attachments will not

remove the effect of neighborhood poverty for males.

Our focus on the links between poverty concentration and the labor market are not

unique. Recall Katz claim that it was the combination of public housing policy with newwork requirements instituted as part of welfare reform that deconcentrated urban poverty

in the 1990s. Moreover, Crump (2003) reminds us that federal policy explicitly linked the

deconcentration of poverty (through public housing redevelopment) and work through

the Quality Housing and Work Responsibility Act of 1998 (QHWRA). He argues that

public housing redevelopment and restructuring of the low end of the labor market were

linked goals of the Clinton administrations neo-liberal agenda to spatially restructure

urban labor markets. The structure of urban labor markets has clearly changed over the

last 20 years with increased polarization between high- and low-end service jobs and the

continued loss of manufacturing employment overlain by welfare reform and renewed

immigration. We do not know how a new sample of adolescents living in poor neighbor-

hoods in the late 1990s would fare in a future study similar to the one we report here, but



25/30

we are not optimistic that their adult labor market experiences will be much better than in

the NLSY respondents that constitute our sample.

This conclusion echoes the cautionary note sounded by Popkin et al. (2000) in their

evaluation of Chicago Housing Authoritys (CHA) Henry Horner Homes redevelopment

and efforts to relocate distressed public housing residents into better neighborhoods

through Section 8 vouchers. They conclude

The legacy of the landmark Gautreaux case . . . has meant that mixed-income and dis-

persal strategies now dominate federal housing policy . . . as our preliminary studies of

public housing transformation in Chicago show, these new strategies seem to offer

benefits for distressed public housing communities but also bring with them the risk

of leaving the most vulnerable current tenants worse off than they were (Popkin et al.,

2000, p. 937).

Similarly pessimistic themes are sounded by Goetz (2002) and Galster and Zobel (1998).

Thus as we reflect on the emerging analysis of Census 2000 data (Jargowsky, 2003), our

evaluation remains cautious. While the reduction in poverty concentration undoubtedlyreflects improvements in the neighborhood conditions faced by many residents, there is no

guarantee that better neighborhoods will result in better lives, especially for those resi-

dents who are relocated out of poor neighborhoods.

The research presented in this article is exploratory; there are several areas that future

work should consider. First, our analysis has not considered fully enough the role of racial

segregation and racial discrimination. The territorial stigma hypothesized by Wacquant

(1993) and Kirschenman and Neckerman (1991) appears to be generally problematic for

youths who grow up in poor neighborhoods; yet this stigma may be magnified by racial

segregation. We might ask, for example, whether equally poor black, white or Hispanic

neighborhoods produce different levels of labor market disadvantage. Adding a neighbor-hood segregation index and a measure of neighborhood racial and ethnic composition to

the analysis could aid in the further understanding of territorial stigma as it impacts a

youths adult labor market experience. We might also productively investigate interactions

between the racial/ethnic identities of NLSY respondents and the racial/ethnic composi-

tion of their adolescent neighborhoods.

Second, we have not yet adequately considered the role that criminal activity and

interactions with the justice system might play in transmitting the adolescent neighbor-

hood poverty effect into adulthood. For example, black male youth have been targets of

racial profiling by law enforcement officialstheir incarceration rates are unusually high.

We also know that a criminal record affects future employment prospects regardless of the

neighborhood of current residence. The impact may well last through the life course: acriminal record remains a hinderance to employment even if the individual no longer lives

in the neighborhood. We suspect, therefore, that multiple forces increase the probability

that black male youth living in poor neighborhoods encounter the criminal justice system

and that these encounters will adversely affect adult labor market experiences even for

those who successfully move to less impoverished neighborhoods. Future research is

needed to explore the degree to which a criminal record functions as the link between

adolescent neighborhood context and adult labor market outcomes.

Finally, empirical analysis needs to utilize more of the longitudinal information avail-

able in the NLSY data. Given that detailed information on work histories is available,

future research can examine the timing of events related to neighborhood residence andwork experience using techniques such as event history analysis. The problem with this



26/30


27/30

Ellwood, D. T. (1986). The spatial mismatch hypothesis: Are there teenage jobs missing in the ghetto? In

R. B. Freeman & H. J. Holzer (Eds.), The black youth employment crisis (pp. 147185). Chicago:

The University of Chicago Press.

Freeman, R. B. (1986). Who escapes? The relation of churchgoing and other background factors to the socio-

economic performance of black male youths from inner-city tracts. In R. B. Freeman & H. J. Holzer

(Eds.), The black youth employment crisis (pp. 353376). Chicago: The University of Chicago Press.

Freeman, R. B. (1991). Employment and earnings of disadvantaged young men in a labor shortageeconomy. In C. Jencks & P. E. Peterson (Eds.), The urban underclass (pp. 103121). Washington,

DC: The Brookings Institute.

Freeman, R. B., & Holzer, H. J. (Eds.). (1986). The black youth employment crisis. Chicago: The

University of Chicago Press.

Galster, G. C., & Killen, S. P. (1995). The geography of opportunity: A reconnaissance and conceptual

framework. Housing Policy Debate, 6(1), 743.

Galster, G., & Mincy, R. (1993). Understanding the changing fortunes of metropolitan neighborhoods:

1980 to 1990. Housing Policy Debate, 4(2), 303352.

Galster, G., & Zobel, A. (1998). Will dispersed housing programmes reduce social problems in the US?

Housing Studies, 13, 605622.

Goering, J., Feins, J. D., & Richardson, T. M. (2002). A cross-site analysis of initial moving to oppor-tunity demonstration results. Journal of Housing Research, 13(1), 130.

Goetz, E. G. (2002). Forced relocation vs. voluntary mobility: The effects of dispersal programmes on

households. Housing Studies, 17, 107123.

Green, G. P., Tigges, L. M., & Brown, I. (1995). Social resources, job search, and poverty in Atlanta.

Research in Community Sociology, 5, 161182.

Hanson, S. G., & Pratt, G. (1995). Gender, work, and space. London and New York: Routledge.

Holloway, S. R. (1996). Job accessibility and male teenage employment, 19801990: The declining

significance of space? The Professional Geographer, 48(4), 44558.

Holloway, S. R. (1998). Male youth activities and metropolitan context. Environment and Planning A,

30(3), 385399.

Hol

effects of adolescent neighbour hood poverty on adult employment (holloway & mulherin 2004)

Documents