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RESEARCH ARTICLE
Diverging trajectories of neighborhood
disadvantage by race and birth cohort from
childhood through young adulthood
Jennifer CandipanID
1
*, Robert J. Sampson
2
1Department of Sociology, Brown University, Providence, RI, United States of America, 2Department of
Sociology, Harvard University, Cambridge, MA, United States of America
*jennifer_candipan@brown.edu
Abstract
Prior research has established the greater exposure of African Americans from all income
groups to disadvantaged environments compared to whites, but the traditional focus in stud-
ies of neighborhood stratification obscures heterogeneity within racial/ethnic groups in resi-
dential attainment over time. Also obscured are the moderating influences of broader social
changes on the life-course and the experiences of Latinos, a large and growing presence in
American cities. We address these issues by examining group-based trajectory models of
residential neighborhood disadvantage among white, Black, and Latino individuals in a
multi-cohort longitudinal research design of over 1,000 children from Chicago as they transi-
tioned to adulthood over the last quarter century. We find considerable temporal consistency
among white individuals compared to dynamic heterogeneity among nonwhite individuals in
exposure to residential disadvantage, especially Black individuals and those born in the
1980s compared to the 1990s. Racial and cohort differences are not accounted for by early-
life characteristics that predict long-term attainment. Inequalities by race in trajectories of
neighborhood disadvantage are thus at once more stable and more dynamic than previous
research suggests, and they are modified by broader social changes. These findings offer
insights on the changing pathways by which neighborhood racial inequality is produced.
Introduction
Two related facts have been established in the neighborhood demography of racial stratifica-
tion. One, exposure to disadvantaged residential contexts is strongly associated with a host of
quality-of-life indicators. Although disagreements exist on the causal status of neighborhood
effects, there is little doubt about the strength or consistency of associations and there is a plau-
sible case for the consequential effects of exposure to neighborhood disadvantage on well-
being [1–4]. Second, there is longstanding inequality by race in such residential environments.
Black and white households reside in substantially different neighborhoods in the U.S., and
the odds of being born into high-poverty neighborhoods is much higher for Black children rel-
ative to white children [5]. Taken together, these facts underscore that the neighborhood
demography of racial stratification is crucial for understanding inequality in life chances.
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Citation: Candipan J, Sampson RJ (2023)
Diverging trajectories of neighborhood
disadvantage by race and birth cohort from
childhood through young adulthood. PLoS ONE
18(4): e0283641. https://doi.org/10.1371/journal.
pone.0283641
Editor: Thiago Machado Ardenghi, Federal
University of Santa Maria: Universidade Federal de
Santa Maria, BRAZIL
Received: August 25, 2022
Accepted: March 1, 2023
Published: April 19, 2023
Copyright: ©2023 Candipan, Sampson. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The authors used
restricted data from the Project from Human
Development in Chicago Neighborhoods and its
additions (PHDCN+) for this study and publicly
available census tract data from the Census and
American Community Survey (ACS). The authors
obtained the Census and ACS data directly from the
Social Explorer website (https://www.
socialexplorer.com/) for all study years. The
individual longitudinal cohort data cannot be
shared publicly because of the use of restricted
However, the traditional focus in neighborhood stratification research is on average resi-
dential attainment—the typical residential environments by which individuals are surrounded
over extended periods—which we argue obscures important dynamic heterogeneity within
and between racial/ethnic groups over time (see also Bader and Warkentien 2016) [6]. Moti-
vated by findings of differential class permeability by race in exposure to disadvantage, the
present study goes further by testing for heterogeneity by race in the neighborhood residential
trajectories of individuals over time. Building on a body of research on the processes of neigh-
borhood attainment and residential mobility, we view cumulative neighborhood exposure as a
sequence over the life course and highlight the differentiated residential trajectories that chil-
dren follow as they grow up, come of age, and enter young adulthood. We depart from past
work in a number of other ways, as well.
First, we focus on dynamic heterogeneity in the residential pathways of exposure to neigh-
borhood poverty among individuals from multiple birth cohorts in Chicago under the influ-
ences of different periods of social change. In most neighborhood-focused work, macrolevel
influences are largely relegated to the background of empirical focus. However, secular factors
(i.e., period contexts) or discrete events specific to a particular period may influence the
dynamics of residential moves and set children on different neighborhood trajectories. Our
study sheds light on potentially important differences by race between cohorts, drawing on
rich, longitudinal data with a multi-cohort design.
Second, our study benefits from a racially and socioeconomically diverse sample in which
white, Black, and Latino children are all well represented. Prior research has focused largely on
comparing African American and white individuals despite the fact that the Latino share of
the population has grown dramatically in recent decades. By following children that lived in
Chicago at the start of our study, we also effectively account for city- and state-level character-
istics that may vary between places and could influence neighborhood attainment and residen-
tial moves, thus allowing us to observe potentially divergent trajectories from children that
began in similar urban settings. In particular, our residential history data moves beyond the
Black-white binary and allows us to simultaneously examine the neighborhoods of white,
Black, and Hispanic children over a sustained period. Note that throughout this paper, we rely
on census designations for racial/ethnic groups.
Third, our study period spans nearly three decades almost to the present—our data on resi-
dential moves and context begins in the early 1990s and continues through 2018. This nearly
three-decade period coincided with a great deal of urban change in Chicago, as well as sociode-
mographic change on a national level. Since our study treats variation between historical peri-
ods as important theoretical terms to be foregrounded in models, this study frame supports
our goal of analyzing differential trajectories by birth cohort. Moreover, we are able to follow
children through an important period of the life course while also observing how broader
structural influences shape residential pathways.
Using these multi-cohort data, we incorporate group-based trajectory methods that allow
us to test both the consistency of between-racial group inequality, as well as within-group het-
erogeneity. Overall, we find remarkable consistency of white advantage and dynamic heteroge-
neity of nonwhite disadvantage, but these patterns are conditioned by the historical change
that differentiates the experience of successive birth cohorts.
Theoretical motivation
Neighborhood stratification processes
A large body of research aims to understand processes of neighborhood stratification. One line
of inquiry focuses on the residential mobility of people into unequal neighborhood
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geographic identifiers. Non-restricted PHDCN data
for waves 1-3 are available from a third party, the
University of Michigan, through its Inter-university
Consortium for Political and Social Research
(ICPSR). Data documentation can be found on the
ICPSR website (https://www.icpsr.umich.edu/web/
pages/NACJD/guides/phdcn/index.html). For
information on waves 4 and other data collection,
see https://sites.harvard.edu/phdcn/. Other
investigators wishing to obtain access to the
geographic variables contained in the restricted
data can visit the ICPSR website for more
information or contact the authors.
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
environments by racial and economic status. These studies focus on conditions predicting a
residential move, often relying on logistic regression or discrete choice models, where the out-
come is a move to specific type of neighborhood. For example, past work examining the move-
ment of individuals between different types of neighborhoods over time has largely focused on
the probability of moves, for a single cohort, between poor and nonpoor neighborhoods at dis-
crete points [7–9]. Such studies yield important insights about how individual, family, and
extra-household (e.g., neighborhood- or metropolitan area-level) factors interact to induce
individual moves [9–14].
Another stream of research focuses on the neighborhood attainment of households which,
like the residential mobility literature on choice, examines origin and destination neighbor-
hoods [15–18]. But unlike residential choice models, the neighborhood attainment scholarship
focuses more squarely on the characteristics of neighborhoods as outcomes rather than the
decision-making process or conditions under which families move—e.g., whether a residential
move results in a change in exposure to neighborhood racial/ethnic or socioeconomic
composition.
Regardless of approach, research on residential sorting and neighborhood attainment typi-
cally describes an average experience, by race or economic status. In this paper, we argue that
the focus on average neighborhood attainment obscures potential heterogeneity within- and
between racial/ethnic groups in any particular year and over time. There is limited empirical
evidence on this issue, but reason to hypothesize that residential trajectories mask considerable
differences. For example, two studies use Panel Study of Income Dynamics (PSID) data to
examine change in residential environments with a focus on disparities between Black and
white individuals. Sharkey (2012) finds that young adults that move out of segregated metro-
politan areas experience less economic inequality in their neighborhoods than earlier years,
though the trend only lasts through their late 20s [19]. Wagmiller (2013) observes different tra-
jectories between white and Black individuals in their exposure to neighborhood racial compo-
sition, with black households experiencing more racially diverse neighborhoods in early
adulthood [20]. Both studies suggest that residential environments of Black young adults
change over time and that these changes in their residential trajectories may attenuate racial
inequality in neighborhood contexts.
There are distinct theoretical reasons supporting our argument on neighborhood trajecto-
ries. Although coming from a different tradition, Wilson (1978) theorized about the origins of
class stratification, arguing that macrohistorical forces combined to generate heterogeneous
groups within Black America. Wilson asserts that while discrimination by race played the pri-
mary role in shaping Black Americans’ social lives prior to World War II, the macrohistorical
forces in the period thereafter increased the importance of economic factors [21]. Postwar
industrial expansion, coinciding with the civil rights movement and newly legislated anti-dis-
crimination laws, made possible economic opportunities previously unavailable to Black
households. It is behind this historical backdrop that class stratification among Black Ameri-
cans began to take shape. A stable Black middle-class emerged as those with greater human
capital and formal education were able to take advantage of the economic opportunities during
this period, distancing themselves from the spot on the proverbial ladder that their lower-
resourced counterparts occupied [21]. The relatively recent development of a non-uniform
“Black class structure” that Wilson describes since the postwar period, in which affluent and
poor Black individuals are increasingly and meaningfully stratified, carries empirical implica-
tions in that it necessitates inquires beyond examinations of a monolithic Black experience
[22]. This point is undertaken further in subsequent sociological work demonstrating the dif-
ferentiated and nuanced experiences of the Black middle class [23–27].
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Nevertheless, the intersection of class and race matter; past work also shows that, despite
their advances, the rising economic fortunes of the Black middle class does not necessarily dis-
sipate existing disparities with White households. This includes the neighborhoods in which
they live, as Black middle-class households are not always able to translate their higher eco-
nomic status into lower poverty residential environments at the same rate as their White coun-
terparts [25,28,29]. Sharkey (2014) also finds that there has been no change over time in the
degree to which majority-Black neighborhoods are surrounded by spatial disadvantage—pre-
dominantly Black neighborhoods, regardless of their own socioeconomic composition, con-
tinue to be spatially linked with areas of severe disadvantage [30].
Past work attempting to explain how residential outcomes vary between members of differ-
ent racial/ethnic groups has largely relied on two theories of residential sorting. Place stratifica-
tion theory, typically used to explain the residential location outcomes of Black households,
centers race in explaining why households with higher incomes or socioeconomic states (SES)
are unable to actualize their individual economic position into lower-poverty neighborhoods
[31]. Place stratification is typically used to explain the residential location outcomes of Black
households, but there is some evidence to suggest that there is more heterogeneity in this asso-
ciation between race, class, and residence [32,33]. For example, Freeman (2008) finds that, for
Black households, higher socioeconomic status is generally associated with lower poverty resi-
dential environments, though their ability to translate higher SES to greater locational attain-
ment changes little between 1970 and 2000 [32]. Spatial assimilation theory, on the other
hand, is typically used to explain the residential outcomes of Hispanics and asserts that non-
white households are able to translate their increasing socioeconomic status into lower-poverty
neighborhoods [31]. While this would suggest that class plays a role in differentiating loca-
tional outcomes between Hispanic and Black households, there is mixed evidence as to
whether this has changed over time [33].
The racial demography of neighborhood stratification through the lens of
the life course
A few recent studies apply a life-course perspective to neighborhood attainment research by
asserting that early residential environments are associated with later-life neighborhood con-
texts. This line of work examines how the sociodemographic contexts of one’s residential
neighborhood in childhood [34] and adolescence [35] predict the type of neighborhoods that
one attains in later life. Huang et al. (2020) find that children that spend more time in high-
poverty neighborhoods are less likely to move from a nonpoor to poor neighborhood and
more likely to move from a nonpoor to poor neighborhood [36]. Further, the duration of time
spent in poor neighborhoods explains most of the differences in the probability of moves
between white and Black adults. Using growth curve models, South et al. (2016) find that
neighborhood economic conditions improve in later life more for white, relative to Black, chil-
dren and that the more economically advantaged adolescent neighborhood environments pre-
dicted neighborhood advantages for white individuals in adulthood [35]. The authors of both
studies draw on the life course principle of time and space, which emphasizes how individual
lives are linked to macrolevel events and trends [35,37].
Viewing the racial demography of neighborhood stratification through the lens of the life
course is useful for thinking about when and, under what conditions, individuals’ residential
environments change or perpetuate [4]. Although the past work described provides important
insights about how residential environments unfold as individuals age from childhood to mid-
dle age [10,35,36], they do not disaggregate how broader trends and events during different
periods might shape residential processes between children that experience life stages in
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different years [37–40]. Moreover, these studies observe the average residential trajectories
[35] or exposures [36] of children over time; we hypothesize that there is more diversity within
racial/ethnic groups and between individuals that came of age during different periods.
Experiencing a change in residential environments may result from change at either the
individual household or the neighborhood level, or both [5,19,34,41]. Neighborhoods may
change in economic status because the incumbent resident population experiences increasing
socioeconomic status (SES) or, alternatively, when the in-moving population differs from
existing residents along racial/ethnic and SES lines—e.g., during gentrification and neighbor-
hood ascent or as affluent neighborhoods further consolidate their wealth [42–45].
A key underexplored factor, we argue, is that the trajectories of both neighborhoods and
individuals, at any given stage, may be influenced by macro-structural conditions tied to that
particular period. For example, the disadvantageous impact of the housing crisis of the 2000s
(and its fallout) was racially patterned with Black and Hispanic households affected the most
by subprime lending practices of that period [46,47]. Hall et al. (2018) find that experiencing a
foreclosure during the Great Recession was associated with moving to more disadvantaged
neighborhoods, particularly for Hispanics [48]. Such foreclosure-induced migration at the
individual level aggregates to reconfigure the sociodemographic makeup of neighborhoods.
On a broader secular trend level, other scholars have documented the shifting spatial organiza-
tion and changing demography of neighborhood poverty since the 1990s, with the proportion
of poor neighborhoods in suburbs increasing and, in the case of Chicago, a deconcentration of
neighborhood poverty [49–51]. Such period “shocks” and trends likely redirect the residential
pathways differently between racial/ethnic groups, and between individuals at different life
stages.
Most prior studies also focus on the neighborhood environments of Black and white house-
holds [7]. Since the 1990s, however, cities across the U.S. have seen an increasing number of
mixed-race and “global neighborhoods” as a greater share of Hispanic and Asian households
migrate to urban areas [52–54] under the backdrop of broader sociodemographic change at
the local and national level in which white individuals represent a decreasing share of the pop-
ulation and in which income inequality between households and neighborhoods has risen
[45,55]. These changes are reshaping neighborhoods and cities, and the question remains as to
how these demographic trends affect neighborhood attainment for all racial/ethnic groups, but
especially Hispanics. Indeed, some scholars have argued that immigration from Latin America
has reshaped the Black-white framework of prior research [56]. We therefore highlight the
neighborhood economic trajectories of Hispanic-American individuals compared to African-
American and white individuals.
Finally, past work largely captures cumulative exposure to neighborhood poverty over the
life course as the proportion of time that individuals spend in poor and nonpoor neighbor-
hoods or the average level of neighborhood poverty of some extended period of time (often
childhood) [8,14,36]. While capturing a temporal dimension largely overlooked in early neigh-
borhood research (i.e., duration of exposure) [4], such summary measures may mask dynamic
heterogeneity in the sequence and timing of exposure to residential environments between
subgroups. Timberlake (2007) uses period life tables to predict transitions into neighborhood
poverty during childhood, showing important change over time in Black and white children’s
predicted exposure to neighborhood poverty (or affluence) at different stages of childhood and
between periods [57]. This study relies on data prior to 1997, however, after which broader
sociodemographic change has occurred, and it focuses on the individual’s predicted duration
of time in neighborhood poverty rather than focusing on identifying the trajectories of neigh-
borhood poverty themselves.
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Advancing prior work, the present study tests for potential differences in neighborhood tra-
jectories between historical periods and by race, using data that spans from the early 1990s to
2018. To do so, we examine the overall sequence of neighborhood exposure to poverty that
individuals from different birth cohorts and racial/ethnic groups experience across stages of
the adolescent and early adult life course.
Data, analysis plan, and measures
Data
To answer our questions, we draw on data from the Project on Human Development in Chi-
cago Neighborhoods (PHDCN), a representative longitudinal cohort study of 6,207 children
and their caregivers that began in the 1995. Our study was approved by the Harvard University
Committee on the Use of Human Subjects in Research.
PHDCN employed a two-stage sampling procedure to first identify 343 neighborhood clus-
ters (NCs) in the city of Chicago, then draw a random, stratified sample of 80 NCs, and second,
sampling within these 80 NCs from randomly selected and screened households for children in
seven age cohorts (zero [birth], three, six, nine, twelve, fifteen, and eighteen). The resulting
cohort populations from initial collection were representative of the diversity of children in Chi-
cago in 1995 [58]. This multi-cohort design, which includes children born up to 17 years apart,
and stratified by neighborhood socioeconomic status and race/ethnic composition, makes the
PHDCN an ideal data source for analyzing intra- and inter-cohort differentiation in individual’s
neighborhood trajectories [59]. See Sampson et al. (2022) for further details about the design,
implementation, and unique features of the PHDCN’s longitudinal multi-cohort design [58].
Three waves of extensive in-home interviews and assessments were collected over a span of
seven years—wave 1 in 1995–1997, wave 2 in 1997–1999, and wave 3 in 1999–2002—with rela-
tively high retention (approximately 75%) as children were followed, regardless of whether
they remained in Chicago. These interviews generated important information about the loca-
tion of current and past residences (including locations for inter-wave years), which could be
used to construct annual residential histories for each child over time.
In 2012 and early 2013, the Mixed-Income Project (MIP) randomly sampled from the origi-
nal birth (0 y/o) and 9- to 15-year-old cohorts that were last interviewed in wave 3, then relo-
cated and re-interviewed sampled children via in-person (~60 percent), phone, and electronic
interviews. For ease, we refer to this follow-up with a subset of baseline respondents as
PHDCN wave 4. During wave 4 collection efforts, investigators collected additional informa-
tion about respondents’ current residence, as well as previous residential moves in the preced-
ing years. The follow-up efforts resulted in a 63 percent response rate of eligible cases and
similar racial/ethnic composition of PHDCN at baseline (roughly 19 percent white; 37 percent
Black; 40 percent Hispanic; 4 percent other race). The birth cohort comprised nearly 36 per-
cent of the wave 4 sample (n = 378), while “older” respondents were relatively evenly distrib-
uted among the 9- (n = 226), 12- (n = 236), and 15-year-old (n = 217) cohorts. These four
groups in wave 4 were selected, in part, to enable cross-cohort analysis between respondents
that varied in their life course timing and experiences.
Location efforts resumed in 2018 and 2019. NORC at the University of Chicago attempted
to follow up with all PHDCN respondents that were located during wave 4 and were able to
gather detailed information on respondents’ current residential address (in 2018/19) for about
83 percent of respondents. We appended this information to the residential history data that
we constructed for wave 4 respondents from 1995 to 2013. Additionally, for respondents in
the 9-, 12-, and 15-year-old cohorts, we were able to retroactively gather residential data for
4–5 years prior to baseline, essentially providing a file with annual residential location
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information from as early as 1990 to as late as 2019. After geocoding respondents’ addresses in
each year, we then joined them to tract-level decennial census and American Community Sur-
vey (ACS) data, interpolating values during intercensal years prior to the introduction of the
ACS in 2005. The resulting file contained roughly three decades of information, for each
cohort, on when and where respondents moved, and on the sociodemographic contexts of
their residential neighborhoods in each year (N = 1057). Respondents ages ranged from 20 to
37-years-old during the final year of data collection (see Table 1). Our cohorts in our file
closely mirrored distribution from wave 1 in 1995 (33 vs. 35 for the 0-year-old birth cohort; 67
vs. 65 for the combined 9-, 12-, 15-year-old birth cohort).
Analysis plan
Our analysis proceeds in several stages with each part building sequentially on previous steps.
We first aim to understand the average sociodemographic contexts of children’s residential
neighborhoods, and whether they vary by race and period cohort. We analyze descriptive
trends in sample members exposure to neighborhood poverty by race and cohort by year,
from 1995 to 2018. For these analyses, we combine the 9-, 12-, and 15-year-olds into a single
cohort (“older” cohort) that we compare to the birth cohort (“younger” cohort) so that we can
make meaningful inter-cohort inferences about children that experience stages of the life
course in substantively different periods. Results from these unadjusted analyses indicate
potential within- and between-group differences in residential trajectories, motivating our
subsequent analyses. From there, we place our question in a multivariable framework.
Table 1. Overview of analysis file by cohort.
Younger Cohort Older Cohort
0 9 12 15
32.5% 21.0% 25.3% 21.2%
Year
at Age 5 1999–2001 1990–92 1987–89 1984–86
at Age 9 2003–05 1994–96 1991–93 1988–90
at Age 23 2017–19 2008–10 2005–07 2002–04
Age
in 1995 0–1*8–10 11–13 14–16
in 1998 2–4 11–13 14–16 17–19
in 2001 5–7 14–16 17–19 20–22
in 2008 12–14 21–23 24–26 26–28
in 2011 15–17 24–26 27–29 30–32
in 2013 17–19 26–28 29–31 32–34
in 2018 22–24 32–34 35–37 38–40
Age Range,1995–2018
0–1 to 22–24 y/o 8–10 to 32–24 y/o 11–13 to 35–37 y/o 14–16 to 38–40 y/o
Race (percent)
White 36.0 19.9 24.8 19.2
Black 31.5 20.3 29.3 19.0
Hispanic 30.6 23.1 21.3 24.9
Other 42.4 12.1 30.3 15.2
Note: Residential information for pre-baseline (1995) years were collected from in-depth interviews with primary caregivers and/or sample members. Sample
percentages refer to the analysis sample (N = 838).
*Roughly 96 percent of the birth cohort (0) was born in 1995 and 1996 (59 and 37 percent, respectively), but a small percentage was born in 1994 (~4 percent).
https://doi.org/10.1371/journal.pone.0283641.t001
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Model: Group-based trajectory analysis
Past work on this topic often takes analytic approaches such as period life tables, logistic regres-
sion, or transition matrices [8,10,57]. Often, these studies employ growth curve models (South
et al. 2016), which are effective for empirically describing individual-level variability in trajecto-
ries of exposure to high-poverty neighborhoods over time, but are limited in that they allow the
residuals to vary around the estimate for a single trajectory. By estimating mean trends, these
methods assume that all individuals in the population follow a similar functional form in their
residential pathways—i.e., one trajectory shape that is assumed to “fit all” (Nagin and Odgers
2013:115)—thereby potentially overlooking important within-group differences [60].
Our theoretical motivation centers on testing whether this heterogeneity exists in the resi-
dential pathways themselves that children from different birth cohorts experience, which
requires a different methodological approach than used in prior work in this area, namely one
that is group-based and that does not carry these stated assumptions. Thus, to answer funda-
mentally different questions about uncovering potential heterogeneity in neighborhood pov-
erty trajectories among all sample members, we rely on group-based trajectory models
(GBTM), a method that allows for errors to cluster around several trajectories. This technique
is particularly useful for identifying unexpected or latent trajectories and provides a more
refined classification of exposure based on temporal order [6]. GBTM offers a meaningful
alternative to prior work in that these models classify individuals into different residential tra-
jectories based on similarity in their actual propensity to live over time in high-poverty
neighborhoods.
GBTM is a specialized application of finite mixture modeling that categorizes clusters of
individuals that experience similar trajectories over time into classes, identifying statistically
similar patterns of residential trajectories by subgroups [61]. For our analysis, we perform
group-based trajectory prevalence models (also called “risk” models) to identify high-poverty
neighborhood pathways among individuals over a 2.5-decade period (1995–2018), focusing on
the pathways that emerge from childhood to young adulthood (i.e., ages 9 to 27). We per-
formed our group-based trajectory modeling in Stata (version 16) using the traj command.
Essentially, GBTM uses maximum likelihood estimation to jointly estimate the shape of trajec-
tories and the proportion of the sample in each trajectory. Each respondent is assigned a probabil-
ity of belonging to each group. GBTM fit statistics help guide researchers to the optimal number
of groups by showing if the addition of one more trajectory improves the fit of the model.
GBTM can fit any number of groups and functional forms. Following Nagin (2005), we
specified the final number of groups and functional form based on model fit (BIC) and group
size (smallest group >5 percent of full analysis sample) after empirically testing 1 to 7 groups
with various functional forms (linear, quadratic, cubic, and combinations of the three forms),
as well as the interpretability of the model for explaining the data [62]. All models had an aver-
age predicted probability of group membership (APP) greater than 0.9, which was well above
the recommended threshold (>= 0.7) for best-fit model consideration [62]. Appendix
Figure S1 in S1 Appendix further illustrates this point, displaying kernel density plots of the
predicted probability of membership into the observed trajectory for all children included in
group-based trajectory prevalence models for residence in high-poverty neighborhoods from
1995 to 2018, demonstrating that our GBTM model predicts membership well for both cohorts
and for white, Black, and Hispanic sample members. Importantly, Nagin and Odgers (2013)
suggest that researchers pair these fit statistics with their theoretical understanding of the pro-
cess under focus [60]. Our model selections represent the most parsimonious summary of the
data after accounting for these recommendations. Guided by these recommendations, we
selected 5-group models for neighborhood poverty trajectories, relying on cubic age terms.
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We emphasize Nagin and Odgers’s (2013:118) view that “trajectory groups are just approxima-
tions of a more complex reality” and that researchers should “move away from interpretations
of trajectory groups as literally distinct entities” [60]. See Appendix Table S1 in S1 Appendix
for details on diagnostics and model selection.
Our group-based prevalence analysis relies on several measures to identify trajectories of
neighborhood poverty. Using decennial census and 5-year ACS data, we construct sociodemo-
graphic measures of neighborhood context, which we capture annually from 1991 to 2013, and
again in 2018. We perform a prevalence GBT model, which uses the logit link function and
relies on a binary outcome for neighborhood poverty for each sample member and at each
time point (in our analysis, we draw on data from 1991 to 2018, but use an individual’s age as
our time variable). To do this, we construct a dichotomous measure of high-poverty neighbor-
hoods denoting whether the poverty rate of a sample member’s residential neighborhood is
above 20 percent, following past work [36,57].
In our GBT models, we further account for several time-invariant individual measures and
baseline family characteristics captured at wave 1 (in 1995), drawing on the PHDCN. We
include three individual time-invariant binary measures: race,cohort membership, and gender.
Race of the child is reported by the primary caregiver during the initial interview and consists of
four categories: White, Black, Hispanic, and other race. As described above, for our dichoto-
mized measure of birth cohort membership, we classified those in cohort 0 (infancy) as mem-
bers of the younger cohort (= 0) and those in the 9-, 12-, and 15-year-old birth cohorts as the
older cohort (= 1). Gender categorizes a child as female or male, as reported by the primary care-
giver in wave 1. We further include baseline family factors (drawn from PHDCN) known to be
related to later economic attainment, such as binary indicators for homeownership, whether the
primary caregiver (PCG) earned a college degree,employment status of the household head
(employed or not employed), and family size (whether a household has five or more members).
We also construct a dichotomous baseline measure denoting whether the child’s family is high-
income. Family income designations are drawn from PHDCN using a 7-category household
income measure observed at baseline (1995). The top category captures families with incomes
greater than 50,000 in 1995, which we define as high-income. Based on the distribution of the
baseline family income variable, we categorize sample members in the highest income category
(>50,000 or more) as “high income.” We further include measures capturing exposure to vio-
lence and family personal and institutional troubles. We used factor analysis to calculate a base-
line family composite score, which summarizes answers to six correlated interview questions
from the PHCDN about family personal and institutional troubles—number of family members
currently in jail or prison (0–3); that have had trouble with the law (0–7); that have had trouble
with their job, fights or school (0–6); treated for drug use or emotional problems (0–6); that
have alcohol use (0–6) or drug use (0–7) that led to trouble with family, jobs, health, or the law
—into a single continuous measure, with higher values indicating greater troubles (mean = 0,
sd = 1). Exposure to violence is a dichotomous measure indicating exposure at baseline. For fur-
ther details on measurement see Neil and Sampson (2021, Table 1) [59].
Model: Multinomial logistic regression
Finally, after reducing the heterogeneity in the individual-level neighborhood poverty trajecto-
ries to a set of distinct group trajectories, we perform multinomial logistic regression models
to analyze how membership in the different trajectory groups varies as a function of time-
invariant and baseline predictors measured prior to the start of the trajectories (Nagin 2005).
For our multinomial logistic regression, we construct a categorical measure denoting the five
neighborhood poverty trajectory groups (identified via GBTM) as our outcome variable. In
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these models, we include all time-invariant and baseline measures from GBTM as predictors
(described above). Additionally, we account for whether a child was residing in a high-poverty
neighborhood at baseline (0 = not high poverty; 1 = high-poverty).
All coefficients from multinomial logistic regression models are reported as log-odds. This
approach allows us to simultaneously estimate both the relationship between baseline time-
invariant covariates with the probability of trajectory group membership and the parameters of
the trajectories themselves [62]. This enables us to examine whether key childhood factors prior
to the start of the observation period for the trajectory analysis are associated with the probabil-
ity of membership in each residential trajectory group. Note that while the information derived
from these time-invariant covariates shows which of the neighborhood trajectories a child is
most likely to follow, it does not define the specific form of that trajectory over time.
Analysis sample characteristics
Table 1 presents an overview of our sample, displaying the age of each cohort member in select
years. Our baseline year is 1995, though we collect data on residential history beginning in
1990 (for older birth-cohort members). We are thus able to register neighborhood context for
all but the oldest sample members during their childhood years and can track the youngest
sample members through their mid-20s.
Missing data
Similar to other longitudinal modeling techniques, group-based trajectory models retain
respondents even if they are missing from some waves, so missing data at a given age should
not affect trajectory development [62,63]—i.e., an individual remains in the sample even with
a single wave of non-missing data on the outcome of interest. In our analysis, the sample mem-
ber with the most non-missing neighborhood poverty data contributed data points for four
time points. (Recall that the GBT model uses age of the individual as its time variable and only
observes respondents when they are between the ages of 9 and 27). Roughly 97 percent of
respondents had neighborhood poverty data for at least nine time points and the median per-
centage of missing neighborhood poverty for respondents was zero.
On the other hand, missing covariate data that is not random may introduce bias in multi-
nomial regression models that analyze the association of these risk factors with the identified
trajectory groups. For our analysis, we eliminated sample members that had missing data on
the baseline family characteristics included in our models. This restriction reduced the full
geocoded sample (N = 1057) to 838 sample members. Appendix Table S2 in S1 Appendix com-
pares the characteristics of the full geocoded sample to our final analysis sample and shows
that the two samples have very similar racial composition (overall and by cohort). The analysis
sample has a slightly higher proportion of children from the youngest birth cohort, but other-
wise has a comparable distribution of birth cohorts as the full sample. Additionally, sample
means for all measures included in our GBTM and multinomial regression models are
strongly aligned between the analysis and full geocoded files which alleviate concerns about
potential bias (see Appendix Table S2 in S1 Appendix).
Findings
Descriptive analyses: Unadjusted mean poverty trajectories over time,
1995–2018
We begin by descriptively analyzing trends in neighborhood sociodemographic contexts
among sample members by race and period cohort from 1995, the baseline of the PHDCN, to
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2018. Fig 1 plots the average neighborhood poverty rate, by race, for the younger (0-year-old
[birth] cohort) and older (9/12/15-year-old) cohorts. As expected, we observe differences in
mean neighborhood poverty race between white, Black, and Hispanic children at baseline that
continues over the course of the study period. We also observe differences within racial/ethnic
groups between cohorts. The disparity in exposure to neighborhood poverty rate between
cohorts widens beginning in the mid-1990s and continues until around 2008, coinciding with
the period of the Great Recession and its aftermath. After 2010, the period cohort trend lines
for white and Hispanic individuals converge, while they begin widening again for Black
individuals.
Group-based trajectory models
Does childhood neighborhood context follow people from childhood through young adult-
hood? Our unadjusted descriptive analyses provide suggestive evidence of meaningful rela-
tionships between race and cohort on children’s exposure to neighborhood poverty in each
year and over time. Black and Hispanic sample members seem to experience more heterogene-
ity in their trajectories of neighborhood exposure to poverty.
Our next set of analyses relies on group-based trajectory models (GBTM) to identify poten-
tial latent subgroups experiencing similar trajectories of exposure to residential contexts dur-
ing the early life course. It could be the case that there are other unifying characteristics aside
from race and cohort that group sample members into similar trajectory classes.
To illustrate findings, we generated figures that display the trajectory subgroups identified
in our GBT models using the trajplot command in Stata (version 16). Fig 2 shows results from
a prevalence GBTM analysis identifying trajectories in the risk of exposure to high-poverty
Fig 1. Mean neighborhood poverty rate by cohort and race, 1995–2018. Note: Proportion poverty denotes the share
of poor residents that live in a census tract (measured in each year). Tract-level neighborhood data come from
decennial census in 1990 and 2000, and American Community Survey (ACS) 5-year estimates from 2005–09 to 2014–
18. We use the midpoint of ACS estimates for year (i.e., we use ACS 2005–09 for the year 2007) except for ACS 2014–
18, which uses the endpoint as year (i.e., 2018).
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(>20 percent) residential contexts from childhood through young adulthood (age 9 to 27).
Our models rely on an analysis sample that includes the oldest cohort members (15-y/o birth
cohort) for which we have less information during early childhood years, thereby taking
advantage of GBTM’s ability to use all of the information to draw out distinct subgroups in the
data. To ensure our results were not driven by noise in our data, we performed as a robustness
check models excluding the 15-year-old members of the older cohort, which produced the
same trajectories. Because the final wave of data (2018) contained a substantially higher share
of missing data than previous years, we also performed models that used 2013 as the end year.
Trajectories mirrored those observed when using the sample that included all years. Note also
that we report results based on unweighted data because they are more efficient than the corre-
sponding weighted estimates and our main quantities of interest are the longitudinal trajecto-
ries within our sample. We also condition on race and baseline economic status, two key
factors related to the original design stratification. As a check, however, we plotted the mean
neighborhood poverty level across all study years using the weighted sample, then compared
results to those drawn from our analysis sample (Appendix Figure S2 in S1 Appendix).
We identified five poverty trajectories. Two poverty trajectories are characterized by stabil-
ity while three feature changing exposure to neighborhood high-poverty contexts at different
stages of the early life course. Children in the largest group, the durably advantaged (DA) tra-
jectory (27.6 percent), experience effectively zero risk of residing in a neighborhood with
greater than 20 percent poverty as they age through their 20s. Conversely, the persistent poverty
(PP) trajectory is characterized by increasing propensity of exposure to high-poverty neighbor-
hoods in late childhood followed by continuous, severely elevated propensity of residence in
Fig 2. Residential trajectories in the risk of exposure to high-poverty neighborhoods. Note: 1) PP = persistent
poverty (18.5%); 2) IP = increasing poverty (18.7%); 3) MP = marginal poverty (17.1%); 4) DP = decreasing poverty
(18.9%); 5) DA = durably advantaged (26.8%). Observed residential trajectories drawn from a group-based trajectory
model (cubic age term) predicting the risk of exposure to high-poverty (>.20) neighborhoods, by age. Percentages in
legend correspond to the posterior probability of each trajectory. This is calculated by aggregating each person-year
observation’s predicted probability of membership in each trajectory group.
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high-poverty neighborhoods. Three trajectories are characterized by changing propensity of
high-poverty neighborhood exposure over the life course. For children in the increasing pov-
erty (IP) trajectory, the low propensity of residing in high-poverty contexts increases sharply
from adolescence to adulthood. On the other hand, those in decreasing poverty (DP) trajecto-
ries experience the opposite trend as their substantially elevated risk of high-poverty neighbor-
hood exposure decreases dramatically beginning in adolescence. The probability of exposure
fluctuates moderately during late childhood and early adulthood for individuals in the mar-
ginal poverty (MP) trajectory, though the probability remains lower at nearly all ages compared
to the IP and DP trajectories.
To alleviate concerns that our group-based prevalence models were sensitive to our thresh-
old definition for high-poverty neighborhoods (>.20), we also plotted the actual neighborhood
poverty rate for each of the classes identified via GBTM (Appendix Figure S3 in S1 Appendix).
The shape of the trajectories mirrored what we found in our group-based prevalence models,
which gave us confidence that our results were not sensitive to individuals hovering just above
or below the neighborhood high-poverty threshold.
Descriptive characteristics of high-poverty trajectory groups
Extending our GBTM analysis, Table 2 displays baseline descriptive characteristics of the five
high-poverty residential trajectories. Sample members in the DA trajectory are disproportion-
ately white and the most advantaged with respect to exposure to high-SES, low-poverty neigh-
borhood contexts, an advantage that persists from childhood through young adulthood.
Conversely, the PP trajectory is predominantly comprised of Black, lower-SES, and older
cohort sample members. A majority of children’s parents had a college degree at baseline for
all trajectory groups, though the proportion of parents with a college degree is highest for the
DA group. Note that baseline family SES is similar for IP and DP trajectories, with the propor-
tion of high-income in the IP group slightly higher than DP. As with PP trajectories, a moder-
ately larger share of Black sample members (relative to their overall representation) is grouped
in IP trajectories. Most Hispanic sample members are classified in the MP trajectory. While
the risk of high-poverty neighborhood exposure is lower for MP groups relative to IP and DP,
so, too, is the average baseline parental income and proportion of homeowners (though the
share of MP households with a college degree is slightly higher).
Drawing on descriptive statistics presented in Table 2, Figs 3and 4illustrate the disparities
in how groups are represented in high-poverty residential trajectories.
Fig 3 displays the extent to which white sample members concentrate into advantaged tra-
jectory groups relative to what we would expect if all racial/ethnic groups were distributed
equally, given their representation in our sample. In terms of racial/ethnic composition in
high-poverty neighborhoods, although white sample members represent just over 19 percent
of the analytic sample, they comprise nearly half of the durably advantaged trajectory type
(48.9 percent). This is most evident when we display these findings as observed-expected ratios
(see Appendix Figure S4 in S1 Appendix). White sample members are represented in the dura-
bly advantaged class at a rate of 2.5 times more than we would expect if racial/ethnic groups
were distributed into each class proportional to their overall representation in the sample. On
the other hand, we observe that Black sample members are overwhelmingly overrepresented
in the persistent poverty trajectory relative to what we would expect if all groups were distrib-
uted in equal measure (65.4 to 37.1). Notably, however, Black sample members are also slightly
overrepresented in poverty trajectories experiencing both increasing (IP) and decreasing pov-
erty exposure (DP). This suggests more dynamic permeability and heterogeneity in the trajec-
tories of Black sample members relative to the trajectories of white sample members.
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Interestingly, Hispanic sample members are overrepresented in the trajectory group char-
acterized by marginal poverty—an overall low probability of high-poverty neighborhood resi-
dence, but with modestly increasing probability during the transition to young adulthood
(MP). Hispanic sample members are even more clustered in the group experiencing decreas-
ing poverty exposure (DP). The changing fortunes of Hispanic sample members could be the
result of intergenerational differences within families as they migrate into lower-poverty con-
texts after leaving their childhood and adolescent neighborhoods. It could also indicate impor-
tant differences from unobserved macrolevel influences between Hispanic respondents that
experienced the transition to adulthood during different periods. Finally, it could also be the
case that higher-SES residents are moving into Hispanic sample members’ existing neighbor-
hoods, thus changing the poverty contexts while sample members age in place. Gentrification
is an example of this type of residential sorting process, and this explanation is consistent with
prior research on gentrification of Chicago’s Hispanic neighborhoods since the 1990s (Hwang
and Sampson 2014). This is also consistent with earlier descriptive findings (Fig 1) which pro-
vided suggestive evidence of cohort differences between younger and older Hispanics in their
mean neighborhood poverty contexts since 1995.
Table 2. Descriptive statistics of residential (high-poverty) trajectories by race and cohort.
PP IP MP DP DA Overall
mean sd mean sd mean sd mean sd mean sd mean sd
Gender distribution
Male 0.47 0.50 0.50 0.50 0.49 0.50 0.41 0.49 0.55 0.50 0.49 0.50
Female 0.53 0.50 0.50 0.50 0.51 0.50 0.59 0.49 0.45 0.50 0.51 0.50
Race/Ethnicity
White 0.03 0.16 0.11 0.32 0.13 0.34 0.05 0.22 0.49 0.50 0.19 0.39
Black 0.65 0.48 0.49 0.50 0.30 0.46 0.42 0.50 0.11 0.31 0.37 0.48
Hispanic 0.31 0.47 0.35 0.48 0.57 0.50 0.49 0.50 0.32 0.47 0.40 0.49
Other 0.01 0.08 0.05 0.22 0.00 0.00 0.03 0.18 0.08 0.28 0.04 0.19
Birth Cohort
Younger 0.12 0.32 0.45 0.50 0.20 0.40 0.34 0.47 0.44 0.50 0.32 0.47
Older 0.88 0.32 0.55 0.50 0.80 0.40 0.66 0.47 0.56 0.50 0.68 0.47
Baseline family factors
Owns home 0.33 0.47 0.42 0.49 0.46 0.50 0.34 0.47 0.69 0.46 0.47 0.50
Employed 0.58 0.50 0.67 0.47 0.63 0.49 0.69 0.46 0.75 0.43 0.67 0.47
High-income 0.10 0.31 0.23 0.42 0.21 0.41 0.13 0.33 0.56 0.50 0.28 0.45
Parent BA 0.04 0.19 0.08 0.27 0.12 0.33 0.08 0.27 0.35 0.48 0.15 0.36
Composite family score 0.08 1.19 -0.04 0.88 0.14 1.23 -0.09 0.84 -0.04 0.85 0.00 0.99
Neighborhood factors (in 1995)
Neighborhood % poverty 30.76 12.93 17.76 9.69 19.06 8.79 29.91 11.25 9.16 5.49 20.27 12.88
Neighborhood % White 11.66 14.74 23.67 24.27 27.08 26.31 16.91 17.82 56.10 25.96 29.70 28.26
N 153 159 137 158 231 838
Note: High-poverty (>20 percent) neighborhood trajectories drawn from group-based trajectory (logit) models using all sample members with sufficient data across
waves (N = 838). We assigned sample members into the trajectory group with their highest predicted probability of membership. Most predicted probabilities were
greater than .90 (see Appendix Figure S1 in S1 Appendix). All baseline family factors were observed at wave 1 of the PCHDN (1995). We used factor analysis to calculate
the baseline family composite score, which summarizes answers to six correlated interview questions about family trouble into a single measure with higher values
indicating greater family exposure to drug and alcohol use, punitive systems, and legal trouble (mean = 0, sd = 1). High-income is a dichotomous indicator denoting
household income greater than 50,000. Family size is a binary measure denoting a household size with five or more members. Younger cohort was born between 1994
and 1996; older cohort consists of children that were generally 9, 12, and 15 from 1994–96 (see Table 1).
DA = durably advantaged; PP = persistent poverty; IP = increasing poverty; MP = marginal poverty; DP = decreasing poverty.
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The striking imbalance in white sample members’ representation in the trajectory groups
(they are substantively absent from the three residential pathways characterized by higher pro-
pensities to reside in high-poverty neighborhoods) underscores white children’s seemingly
intractable advantage–their privileged starting point generally follows them through the early
life course.
Fig 4 displays observed and expected cohort membership into high-poverty trajectory
groups. (Alternatively, Appendix Figure S5 in S1 Appendix displays these results as observed-
expected cohort composition ratios.) Here, we observe both between-cohort differences in pre-
dicted group membership and within-cohort differences in the shape and direction of high-
poverty trajectories. While older cohort members represent 67.5 percent of the GBTM sample,
they make up over 88 percent of the PP group and nearly 80 percent of the MP group. Just
Fig 3. Observed vs. expected racial composition in high-poverty trajectory groups. Note:Overall refers to the racial
composition of the analytic sample used for group-based trajectory models. If sample members were evenly distributed
by race into the trajectory groups, we would expect the distribution for each of the five trajectory types to mirror the
Overall distribution.
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Fig 4. Equally distributed vs. observed cohort composition in high-poverty trajectory groups. Note:Overall refers
to the cohort (period) composition of the analytic sample used for group-based trajectory models. If sample members
were evenly distributed by cohort into the trajectory groups, we would expect the distribution for each of the five
trajectory types to mirror the Overall distribution.
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under a third of sample members were born into the younger cohort, but roughly 4 out of 9
experience IP and DA trajectories.
Summarizing our key findings, Black children have the highest amount of within-race vari-
ation. Hispanic children have some variation but differ from Black and white children in that
they are far more likely to be in the “middle” trajectory. White children have little internal vari-
ation and are overwhelmingly durably advantaged with respect to their residential environ-
ments. Children coming of age more recently (younger cohort) tend to experience more
advantaged neighborhood trajectories relative to those born earlier (older cohort).
Multinomial logit regression predicting trajectory groups
Finally, we further explore descriptive characteristics of trajectory groups, but from a multivar-
iate framework. Table 3 displays results from multinomial logistic regression models (MNL)
that simultaneously predict membership in high-poverty neighborhood trajectories based on
time-invariant covariates and baseline family characteristics, as well as the parameters of the
five group trajectories [62]. This analytic approach allows us to examine which factors in early
childhood are associated with the residential pathways that individuals will follow through the
early life course. The coefficients are reported as log odds, with the durable advantage (DA)
trajectory, characterized by persistently low propensity of exposure to high-poverty neighbor-
hood contexts, as the reference group.
The relative likelihood of following any non-DA trajectory compared with following the
durably advantaged trajectory is substantially higher for Black individuals relative to whites
(reference category), as indicated by the significant negative main effects across all trajectories.
Hispanic children are also more likely to follow a non-DA trajectory relative to white children,
though the relative risk is substantially lower compared to Black children, and the likelihood of
membership is fairly similar for all non-DA groups. While Black sample members, compared
to whites, are associated with a higher risk of membership in all high-poverty trajectory types
relative to those in durably advantaged low-poverty trajectories, the odds of following the per-
sistently high-poverty neighborhood trajectory are substantially and significantly greater. Put
together, and mirroring the descriptive results from Fig 3, we observe the sheer persistence of
advantaged residential contexts in which white sample children find themselves. On the other
hand, there tends to be far greater heterogeneity and change in the residential pathways that
Black and Hispanic children follow.
Parental employment in childhood significantly lowers the risk of membership in the PP and
MP trajectories, relative to membership in the DA trajectory. On the other hand, having a par-
ent that attained a college degree attenuates the risk for membership all non-DA groups. Child-
hood family income does not significantly predict membership in any of the trajectories,
demonstrating that not all baseline family SES factors lower the risk of membership in non-DA
groups, relative to the DA group. However, in models that separately interact race with cohort
and family income, the risk of following the IP, MP, and DP (trend level significance) trajecto-
ries, relative to following the durably advantaged trajectory, is significantly lowered for Black
children from high-earning childhood households (see Appendix Table S3 in S1 Appendix).
Finally, we also observe evidence of period effects that predict membership in poverty tra-
jectory groups. The relative risk of following the persistently poor (PP) or marginal poverty
(MP) trajectories is significantly lower for the younger birth cohort relative to the older cohort
across similar ages, and slightly and marginally lower for following decreasing poverty trajec-
tories. This underscores, on a conceptual and empirical level, the importance of disaggregating
between children growing up during different periods since specific macrolevel historical con-
text may shape their trajectories differently depending on when they came of age [37,40].
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The consistency of white advantage and the heterogeneity of
nonwhite disadvantage
Our trajectory analyses uncover both stability and change in exposure to neighborhood pov-
erty by race and birth cohort. Advancing beyond prior neighborhood-focused research that
typically evaluates an average neighborhood context, our study provides a nuanced portrait of
individuals’ cumulative residential environments that incorporates the sequence and timing of
exposure. Over time, and between children coming of age during different periods, we find
strikingly stable homogeneity of white advantage and dynamic heterogeneity of nonwhite dis-
advantage in their residential environments. An important finding from our study is that het-
erogeneity in neighborhood poverty impacts Black children the most, and, to a somewhat
Table 3. Multinomial logistic regression of risk factors on high-poverty neighborhood trajectories (baseline outcome = durably advantaged (DA))
a
.
PP IP MP DP
Log odds [95% CI] Log odds [95% CI] Log odds [95% CI] Log odds [95% CI]
Ref.category:DA
Individual time-invariant factors
White (ref) (ref) (ref) (ref)
Black 3.95*** [2.70, 5.20] 2.85*** [2.08, 3.63] 2.21*** [1.40, 3.02] 2.60*** [1.55, 3.66]
Hispanic 1.34*[0.13, 2.54] 0.86*[0.19, 1.54] 1.24*** [0.56, 1.91] 1.01*[0.04, 1.98]
Older (9/12/15) Cohort (ref) (ref) (ref) (ref)
Younger (0) Cohort -2.02*** [-2.76, -1.28] 0.07 [-0.46, 0.60] -1.17*** [-1.76, -0.57] -0.57+ [-1.20, 0.06]
Female (ref) (ref) (ref) (ref)
Male 0.1 [-0.50, 0.69] 0.09 [-0.41, 0.60] 0.05 [-0.47, 0.56] -0.37 [-0.95, 0.21]
Baseline measures
Composite family score 0.04 [-0.27, 0.35] 0.03 [-0.25, 0.31] 0.22 [-0.04, 0.47] -0.13 [-0.46, 0.20]
Parent college degree (BA) -1.59** -2.71, -0.48] -1.71*** [-2.52, -0.89] -0.74+ [-1.50, 0.02] -0.94+ [-1.89, 0.02]
Family high-income -0.41 [-1.25, 0.44] -0.47 [-1.10, 0.16] -0.44 [-1.09, 0.22] -0.64 [-1.45, 0.17]
PCG employed -0.87** -1.53, -0.21] -0.50+ [-1.07, 0.07] -0.75*[-1.33, -0.17] -0.43 [-1.08, 0.22]
Family owns home -0.39 [-1.03, 0.25] -0.38 [-0.92, 0.17] -0.34 [-0.90, 0.22] -0.59+ [-1.22, 0.03]
Exposure to violence 0.83 [-0.31, 1.97] 0.64 [-0.40, 1.67] 0.35 [-0.75, 1.46] 0.83 [-0.30, 1.96]
Large family size 0.12 [-0.50, 0.74] 0.04 [-0.49, 0.56] -0.17 [-0.71, 0.36] -0.04 [-0.64, 0.56]
High-poverty neighborhood = 0 -4.47*** [-5.37, -3.58] -1.78*** [-0.41, 0.60] -2.64*** [-3.45, -1.83] -4.62*** [-5.48, -3.75]
Constant -1.07*[-2.14, 0.47] 0.98 [-0.22, 2.18] 2.12*** [0.95, 3.29] 2.70*** [1.36, 4.04]
N 805 805 805 805
log likelihood -936.21 -936.21 -936.21 -936.21
Pseudo R2 0.27 0.27 0.27 0.27
Note: 95% confidence intervals in brackets; High-poverty neighborhoods are census tracts with >20 percent poverty rate. Younger cohort was born between 1994 and
1996; older cohort consists of children that were generally 9, 12, and 15 from 1994–96 (see Table 1). Due to small cell sizes, and because they are not the focus of this
analysis, we exclude "other race" (n = 33) in multinomial regression models predicting membership in observed trajectory groups. We used factor analysis to calculate
the baseline family composite score, which summarizes answers to six correlated interview questions about family personal and institutional troubles into a single
measure with higher values indicating greater troubles (mean = 0, sd = 1). Exposure to violence is a dichotomous measure indicating exposure at baseline. High-income is
a dichotomous indicator denoting household income greater than 50,000 dollars in 1995. PCG refers to parental caregiver. Large family size is a binary measure
denoting a household size with five or more members.
a
DA = durably advantaged; PP = persistent poverty; IP = increasing poverty; MP = marginal poverty; DP = decreasing poverty.
+ p<0.1
*p<0.05
** p<0.01
*** p<0.001.
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lesser degree, Hispanic children. Our results imply that researchers and practitioners should
dig deeper into the correlates and explanations for the differences within Black and Hispanic
children. Our group-based trajectory method allows us to conclude that typical methodologi-
cal approaches that capture of an average effect, such as growth curve models or transition
matrices, miss this important distinction of within-group heterogeneity.
Our study first examined the different neighborhood poverty trajectories that individuals
follow as they age from childhood to young adulthood and asked whether they vary within and
between racial/ethnic groups. Does childhood neighborhood context follow individuals as
they age through different life stages? For white children, our results suggest that it does. Find-
ings from our group-based trajectory analyses further reveal that most white children travel
along the same advantaged path regardless of when they came of age.
These findings arguably demonstrate another way in which white advantage persists in con-
temporary society. While there are differences in their absolute levels of exposure to neighbor-
hood sociodemographic contexts, white sample members from both cohorts, younger and
older, overwhelmingly follow durably low-poverty residential pathways that continue through
adulthood, regardless of baseline family factors during childhood. Thus, while prior work
examining differences in neighborhood contexts often observes from the perspective of non-
white children’s disadvantaged residential contexts, our results suggest that another useful
frame for understanding longstanding disparities in residential environments is through a lens
that focuses on the persistence and resiliency of white advantage.
On the other hand, Black and Hispanic trajectories are characterized by considerable het-
erogeneity. Both within and between racial/ethnic groups, Black and Hispanic children follow
multiple residential pathways. Interestingly, Hispanics are overrepresented in the residential
trajectory with a relatively lower risk of poverty than all groups except for the durably advan-
taged trajectory. Hispanics from the younger cohort tend to follow lower-poverty trajectories
than older Hispanics, offering some evidence of period effects.
While Black sample members are overrepresented in the residential trajectory characterized
by increasing propensity of residing in high-poverty neighborhoods from childhood to adult-
hood, they are also overrepresented in the trajectory that experiences decreasing risk of poverty
exposure. Moreover, both Black and Hispanic sample members are represented in all trajec-
tory groups. These collective results suggest important differentiation in the residential experi-
ences of nonwhite children over the life course that past work has overlooked. Our findings
call for future work to view the residential pathways of nonwhite children as more nuanced or
heterogeneous than prior research acknowledges, beyond the persistent poverty pathways that
confirm durable racial inequality compared to white individuals.
Our study further explored potential cohort differentiation in residential trajectories of
exposure to neighborhood disadvantage. By incorporating an inter-cohort analysis, our study
foregrounds how macrolevel forces differentially influence residential poverty trajectories of
children that grew up and came of age during different periods, a form of cohort differentia-
tion due to social change. Our results indicate differences in the risk of neighborhood poverty
exposure between cohorts, with the younger birth cohort generally experiencing the most
advantaged residential pathways. Identifying empirically the precise social changes that
account for cohort differentials in trajectories is beyond the scope of this paper, but they
deserve attention in future research. For example, did the Great Recession and its ensuing fore-
closure crisis, which coincided with early adulthood for the older cohort, result in an economic
fallout that was worse and more enduring for older cohort members relative to the younger
cohort members who were experiencing early adolescence during this same period? Did the
deconcentration of poverty in Chicago neighborhoods since the 2000s result in younger cohort
members generally being more exposed to lower poverty neighborhoods overall? Can the
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dynamic residential trajectories for Hispanic and Black children be explained by more wide-
spread gentrification in Chicago neighborhoods since the mid-2000s? In the case of gentrifica-
tion, an individual’s exposure to neighborhood poverty could change as the result of higher-
SES households moving into their existing neighborhoods or nonwhite gentrification that sees
Black and Hispanic households moving to gentrifying neighborhoods. Although these are
important questions, by conditioning our analyses on classic background factors of childhood
exposure, we can nonetheless reasonably conclude that broader social changes rather than
cohort compositional features are the main driver of differences in poverty trajectory groups
across younger versus older cohorts.
Our study leaves open several other questions as well, which we hope will motivate future
work. Our study focuses on individuals’ varied sequence of exposure to one economic dimen-
sion of residential context: neighborhood poverty. Future research should examine the various
pathways of exposure that children follow in terms of other sociodemographic and ecological
features, such as racial isolation or environmental toxins like lead or airborne pollution. Future
research could also use the identified neighborhood trajectories as predictors and examine the
effects of following various residential pathways on later-life outcomes [64]. Another line of
inquiry might investigate when exposure to various trajectories matter most for predicting
individual outcomes in later adulthood and whether this has changed over time. Due to data
limitations, our study is limited to a study frame that follows children through early adulthood.
Researchers with information on residential histories might examine trajectories over a longer
period of time or during different life stages. Moreover, we rely on data that follows children
from Chicago in the 1990s. Other work should examine whether residential patterns hold in
other cities within and outside the U.S.
In sum, that we find both durability (of white advantage) and dynamism (in nonwhite path-
ways) suggests the need to change the way we frame residential processes of stratification over
time. That we find differences between cohorts, controlling for their baseline composition,
suggests that we pay greater attention to the ways in which individual pathways intersect with
broader population dynamics and macrolevel change, a classic theme of demography and yet
one which is often unrealized in longitudinal studies of neighborhood stratification. While
much past work highlights overall disparities between racial/ethnic groups in their exposure to
neighborhood environments, our findings highlight the theoretical and empirical need to also
consider the dynamic heterogeneity that exists within racial/ethnic groups and between
periods.
Supporting information
S1 Appendix. Contains all supporting appendix tables and figures.
(DOCX)
Author Contributions
Conceptualization: Jennifer Candipan, Robert J. Sampson.
Data curation: Jennifer Candipan, Robert J. Sampson.
Formal analysis: Jennifer Candipan, Robert J. Sampson.
Investigation: Jennifer Candipan, Robert J. Sampson.
Methodology: Jennifer Candipan, Robert J. Sampson.
Project administration: Jennifer Candipan, Robert J. Sampson.
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Diverging trajectories of neighborhood disadvantage by race and birth cohort
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Software: Jennifer Candipan.
Writing – original draft: Jennifer Candipan.
Writing – review & editing: Jennifer Candipan, Robert J. Sampson.
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