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Neighborhoods and Cognitive
Development
JONDOU CHEN and JEANNE BROOKS-GUNN
Abstract
Research on neighborhoods and individual well-being has produced a substantive body of knowledge over the past quarter century. Neighborhood
conditions—especially socioeconomic status (SES), which is based on income and
education and to a lesser extent on residential stability—are predictive of cognitive
development. The strongest evidence controls for individual and family-level
characteristics or examines individuals clustered within neighborhoods in order to
obtain estimates of within- and between-neighborhood variance. Another line of
research has focused on housing mobility projects, which allow for the experimental
assignment of residents to more advantaged neighborhoods. Future research on
neighborhoods will continue to blend methods and data from an increasing number
of disciplines to better understand human development in context.
INTRODUCTION
In this review, we focus on cognitive development even as neighborhood
research has also examined health and behavior as outcomes. In some
studies, educational achievement and attainment are considered as an
outcome related to cognitive development even as educational outcomes
are influenced by both cognitive and noncognitive skills as well as by
school and neighborhood influences. This essay examines neighborhoods
but not schools, even though the two often overlap, especially during the
elementary school years. In addition, neighborhood research is heavily
weighted toward the study of urban rather than rural settings.
FOUNDATIONAL RESEARCH
That neighborhoods matter is not a new idea in social science (e.g., Shaw &
McKay, 1942), even though debate still continues on this premise (Sampson,
2011). Above the general issue of neighborhood effects, the primary questions
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
are “How?,” “How much?,” “For what outcomes?,” and “For what groups?”
These are challenging to answer, however, given two confounding questions.
“Do people not choose where they live?” renders neighborhood effects to
be individual selection effects. This, in turn, establishes what has been the
baseline for neighborhood research since, “Do neighborhoods matter above
and beyond individual-level factors?” A sizable body of research has been
produced in response, with a number of reviews condensing findings and
highlighting ongoing challenges (e.g., Chen & Brooks-Gunn, 2012; Leventhal & Brooks-Gunn, 2000). In a similar vein, we present here key research
findings from the past quarter century.
HOW ARE NEIGHBORHOODS STUDIED?
While some researchers use neighborhood data as a proxy for individual
data, only studies considering the unique contributions of neighborhoods
will be discussed here. Such data are considered “nested,” given that
individuals reside within neighborhoods. This requires both multilevel
modeling of the data and significantly larger sample sizes to provide
adequate statistical power. To find such numbers, large-scale existing data
sets from long-standing studies (e.g., the Panel Study of Income Dynamics
or the Infant Health and Development Program, Brooks-Gunn, Duncan,
Klebanov, & Sealand, 1993) or administrative sources (e.g., school grades,
Garner & Raudenbush, 1991) are used. Even better are studies that sample neighborhoods and then sample individuals, families, or households
within neighborhoods (e.g., the Project on Human Development in Chicago
Neighborhoods, Sampson, 2011).
It should be noted that by “neighborhoods,” we refer to the geographic
area in which a person lives rather than the social network to which a person belongs. In research, these geographic areas are administrative units such
as census tracts or school catchment areas for which data already exist and
have been collected, typically by government agencies (e.g., the US Census
Bureau). While research that incorporates novel neighborhood measures or
resident-defined geographic units exists, these studies are in the minority
and represent a limitation of neighborhood research that is discussed in our
section on future directions. Few studies have attempted to validate the designation of neighborhoods by census tracts with resident-defined units (e.g.,
Sampson, 2011).
Similarly, measures of cognitive development include traditional intelligence, achievement, and language tests (e.g., the Woodcock–Johnson
Achievement Tests in Sharkey & Elwert, 2011). A number of studies also
consider school-based assessments (e.g., the California Achievement Test
in Entwisle, Alexander, & Olson, 1994) and criteria including graduation
Neighborhoods and Cognitive Development
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and dropout (Brooks-Gunn et al., 1993). While a limited number of studies
researched neighborhood effects on adults (e.g., Sheffield & Peek, 2009),
most studies linking neighborhoods to cognition focus on the developmental
aspects experienced during childhood and through adolescence.
NEIGHBORHOOD ADVANTAGE/DISADVANTAGE
Neighborhood socioeconomic status (SES) consistently predicts cognitive
development. A strong qualitative and demographic research base exists
linking concentrated disadvantage and racial segregation to negative life
outcomes (Massey & Denton, 1993). These findings hold true even after
controlling for individual- and family-level characteristics, and indicate that
additional dynamics in the community shape individual development. For
instance, Crane (1991) found the percentage of neighborhood residents with
professional or managerial jobs to be a stronger predictor of high school
dropout than a number of negative indicators such as unemployment,
poverty, and middle school dropout. Such results are found in children as
early as preschool (Duncan, Brooks-Gunn, & Klebanov, 1994; Klebanov,
Brooks-Gunn, McCarton, & McCormick, 1998). And while earlier US studies
tended to focus on whites and African-Americans, more recent studies have
replicated previous findings in samples with larger immigrant populations
(Leventhal, Xue, & Brooks-Gunn, 2006), as well as in samples outside the
United States (Garner & Raudenbush, 1991; Kohen, Brooks-Gunn, Leventhal
& Hertzman, 2002).
A recurring question researchers have raised is whether SES should be
considered to be nonlinear and measured as affluence and poverty. For
example, in an analysis of two US data sets, poverty was linked to increased
internalizing and externalizing behavior in young children, but it was
affluence that predicted IQ scores at age 5 (Duncan et al., 1994). A more
recent study utilizing Head Start data found poverty rather than affluence to
significantly predict poorer cognitive performance on a picture vocabulary
test (Vaden-Kiernan et al., 2010). This finding should be interpreted with
caution, however, as children enrolled in Head Start by definition come from
poorer families, limiting generalizability.
Given the connections between neighborhoods and cognitive development, it is not surprising that neighborhood SES also predicts educational
attainment and attitudes. In an early study utilizing multilevel methods to
analyze Scottish high school data, neighborhood poverty (as well as unemployment, single-parent households, overcrowding, and chronic illness) was
linked to poorer educational attainment controlling for individual, family,
and school-level factors (Garner & Raudenbush, 1991). Returning to our
question regarding poverty versus affluence, another study using US census
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
data linked high school dropout rates more strongly to a single measure
of affluence (the number of neighborhood residents holding managerial or
professional jobs) than any measure of poverty (Crane, 1991). More recent
research continues to offer support for both affluence (e.g., Ainsworth,
2002) and poverty (South, Baumer, & Lutz, 2003) predicting educational
attainment.
Researchers have also studied why these links exist. Such research,
focusing more on behavioral and attitudinal data, has often been linked
to educational attainment as opposed to simply cognitive assessment.
Living in more affluent neighborhoods has been associated with more
exposure to more advanced maternal speech for developing children (Hoff,
2003). Additional high-status residents might also result in the collective
socialization of youth to spend more time on homework and attend schools
with better atmosphere (Ainsworth, 2002). Neighborhood affluence might
also encourage increased parental material investment in the home (Duncan
et al., 1994) or higher rates of participation in institutional resources such as
after-school lessons and summer camps (Dearing et al., 2009). These are more
positively nuanced perspectives than Crane’s (1991) contagion theory that
held that urban neighborhoods with the lowest concentrations of high-status
residents saw disproportionately large effect sizes. Other research utilizing
the National Survey of Children found that the neighborhood effects of
poverty could be explained by peer educational attitudes (South et al.,
2003). From a material perspective, individuals living in poverty might also
experience a psychological sense of relative economic deprivation with one
study finding that attainment is more strongly predicted by neighborhood
income inequality as opposed to only absolute poverty (Turley, 2002).
Gender differences have also emerged in several studies. In a sample of
elementary school students in Baltimore, neighborhood income and parent
education levels were stronger predictors for math scores for boys than
for girls (Entwisle et al., 1994). And as will be discussed later, a group of
adolescent girls moving out of highly disadvantaged public housing in
Baltimore saw some improvement in cognitive performance (Sanbonmatsu,
Kling, Duncan, & Brooks-Gunn, 2006). Similarly, neighborhood occupational
status was found to predict lower rates of high school dropout for boys but
not for girls in a 30-year study of poor Chicago youth (Ensminger, Lamkin,
& Jacobson, 1996).
With regard to race, it should be noted that it is difficult to interpret
different effects of SES by ethnicity/race as the two predictors remain highly
correlated (Clampet-Lundquist & Massey, 2008). Still, limited evidence of
SES by ethnicity/race interactions does exist. In two large US data sets,
neighborhood affluence was found to be a stronger protective factor for
white adolescents than for black adolescents in predicting high school
Neighborhoods and Cognitive Development
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dropout (Brooks-Gunn et al., 1993). Later analyses of these data found that
living in neighborhoods with higher concentrations of African-Americans
mitigated these racial differences (Turley, 2003). Similarly, immigrant status was associated with larger negative neighborhood effects for SES in
predicting school grades (Pong & Hao, 2007).
HOUSING MOBILITY PROGRAMS
Even as early neighborhood research consistently found links to cognitive
development controlling for individual-level data, criticism regarding
individuals selecting to live in certain neighborhoods remained. A unique
opportunity to address this arose, however, in the form of housing mobility
programs. Originally mandated through court order in cities such as Chicago
(Rosenbaum, 1995) and Yonkers (Briggs, 1998), these programs sought to
move residents out of highly disadvantaged neighborhoods. Despite the
lack of randomized assignment, early results from the Gautreaux housing
program in Chicago were promising, as black youth who moved to predominantly white suburbs did significantly better in school with regard to difficulty of classes taken, graduation, and college enrollment (Rosenbaum, 1995).
As a result of these findings, a large-scale housing experiment, the Moving to Opportunity (MTO) Program, was initiated by the US Department of
Housing and Urban Development. Taking place in five major US cities (Baltimore, Boston, Chicago, Los Angeles, and New York), 4600 families were
randomly assigned to receive housing vouchers, vouchers and additional
housing assistance, or no vouchers. Data from families were collected at baseline and then 2 years and 5 years following the move. After 2 years, initial results found benefits for moving to better neighborhoods for children
with regard to behavioral outcomes (Katz, Kling, & Liebman, 2001; Leventhal & Brooks-Gunn, 2003). Improved cognitive performance was seen in
youth from one MTO city (Baltimore) at 2 years, but these were found to have
faded by year 5 (Ludwig, Ladd, & Duncan, 2001). MTO effects in New York
City after 2 years were the opposite of Baltimore, however, with adolescent
movers reporting lower school grades and engagement (Leventhal, Fauth,
& Brooks-Gunn, 2005). After 5 years, no differences in cognitive functioning
using a standardized non-school-based assessment were found in analyses of
the pooled MTO sample except for African-American youth (Sanbonmatsu
et al., 2006). The only school-related benefits found were for girls and for
school-based behavior rather than cognitive performance (Kling, Liebman, &
Katz, 2007).
Possible reasons for poor findings from MTO include poor program uptake
by both the voucher only group (48%) and the voucher and assistance group
(60%) (Goering et al., 1999). Furthermore, a number of participants who
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
did not receive the voucher in the MTO study moved of their own accord
anyway, which should not be surprising given that all participants applied to
enter the voucher lottery at the beginning of the study (Clampet-Lundquist
& Massey, 2008). Even if families did move, the schools in MTO participants’
new neighborhoods were only marginally better, performing on average at
the 19th percentile compared to the 15th percentile for the control group,
indicating minimal, if any, improvement (Sanbonmatsu et al., 2006). A
similar argument can be made with regard to neighborhood SES as families
were moving into less poor but not affluent neighborhoods, supporting
the argument that it is affluence rather than poverty that predicts cognitive
performance. One final possibility is that school-level differences presented
a unique set of challenges, as children who moved had higher retention and
dropout rates (Sanbonmatsu et al., 2006) in more racially segregated schools
(Clampet-Lundquist & Massey, 2008).
The lack of lasting significant positive findings was also the case in
Yonkers, where court-ordered residential desegregation led to a public backlash against efforts to simultaneously desegregate schools (Briggs, 1998). As
a result, most Yonkers youth attended schools in their original neighborhoods, reducing the potential benefits of the new neighborhoods as students
felt less connected to their schools (Fauth, Leventhal, & Brooks-Gunn, 2007).
After the seemingly positive initial findings from Gautreaux, the nonsignificant and negative findings from Yonkers and MTO challenge researchers
and policymakers to identify adjustments or new solutions in the face of
continued neighborhood segregation and disadvantage (Sampson, 2012).
CUTTING-EDGE RESEARCH
Recent research on neighborhood effects has sought to broaden operationalization of neighborhood disadvantage and consider additional variables.
One study of black adolescents from Georgia and Iowa found that neighborhood physical disorder was a unique predictor of college aspirations
(Stewart, Stewart, & Simons, 2007). Another study of older adults in 65
Baltimore neighborhoods, linked data on social disorganization, physical
disorder, and public safety in addition to economic conditions to more rapid
cognitive impairment (Lee, Glass, James, Bandeen-Roche, & Schwartz, 2011).
In addition, individual aspects of neighborhood disadvantage are now being
considered apart from overall SES. For instance, the percentage of adults
in a neighborhood with at least a high school diploma has been associated
with higher cognitive functioning in adults, controlling for individual-level
educational attainment (Wight et al., 2006). Recent analyses of homicide
data in Chicago neighborhoods found that having a homicide occur in the
Neighborhoods and Cognitive Development
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same census block group in which one lives predicted lower performance
on standardized school tests (Sharkey, 2010).
Moving away from a deficit-based model of thinking, protective factors
such as residential stability have been found to have positive links to educational attainment (South et al., 2003), although such studies have tended
to find residential stability to be correlated with socioeconomic status. In a
Canadian sample of preschoolers, low social cohesion was found to operate uniquely from affluence in predicting child verbal ability (Kohen et al.,
2002). Another proxy for social cohesion has been increased neighborhood
concentration by ethnic group. In a sample of Mexican-Americans in the
southwestern United States, living in a neighborhood with a higher percentage of Mexican-Americans was associated with lower cognitive impairment
(Sheffield & Peek, 2009).
New longitudinal research has also taken advantage of intergenerational
data and new statistical methods. Independent of individual-level poverty,
children whose families have lived in high-poverty neighborhoods for more
than one generation were associated with even higher deficits in cognitive
ability (Sharkey & Elwert, 2011). These findings are especially troubling for
black children whose families are more likely to live in neighborhoods with
higher rates of multigenerational poverty. Researchers have also sought more
dynamic measures of neighborhood characteristics, which have been traditionally chronologically fixed in 10-year intervals because of the US Census.
By considering annual rather than decennial trends in neighborhood disadvantage, Wodtke, Harding, and Elwert (2011) were able to better control for
neighborhood selection, improving estimates of the negative consequences
of neighborhood disadvantage.
With US and global populations that are living longer on average, greater
consideration must be given to cognitive development in older populations.
In contrast to children, cognitive development in older adults represents
maintaining cognitive performance and avoiding deterioration. As already
alluded to, poor neighborhood conditions have been linked with more rapid
cognitive decline both in the United States (Espino, Lichtenstein, Palmer &
Hazuda, 2001) and abroad (Lang et al., 2008). Neighborhood disadvantage
has also been tied to the gene by environment interactions (Lee et al.,
2011) and neighborhood ethnic composition for ethnic minorities as well
(Sheffield & Peek, 2009).
KEY ISSUES FOR FUTURE RESEARCH
Ongoing neighborhood research will continue to wrestle with a number
of ongoing challenges even as new methods and data provide for creative
solutions.
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
SELECTION
Nonexperimental neighborhood research will continue to struggle with
the challenge of selection. New statistical and modeling methods must be
adopted into neighborhood research. Propensity score matching represents
one approach to controlling for neighborhood selection (Lee et al., 2011).
Such methods use variables typically used as controls or predictors of
neighborhood selection to match participants who share the same likelihood
of living in a certain type of neighborhood but in fact do not. Another study
utilizing Swedish data, used sibling data to control for home environment
and test for differences across neighborhoods as families moved or stayed
in the same neighborhood (Lindahl, 2011). The need to account for selection
is increasingly important as neighborhoods continue to become more
segregated (Sampson, 2012) with evidence of increasing residential selection
based on factors such as SES (Sawhill & McLanahan, 2006), race (Bayer,
Ferreira, & McMillan, 2007), immigrant status (Lauen, 2007), and school
quality (DeSena, 2006).
COLLINEARITY
Even as neighborhood researchers consider multiple and cross-classified levels of nesting, it is possible that factors existing at several levels (e.g., poverty)
are collinear across levels. That is, given that most measures of neighborhood poverty are simply the aggregation of individual-level poverty, these
two predictors are dependent on one another, which in turn will potentially
bias parameter estimates (Oakes, 2004). Others have argued, however, that
existing methods of modeling multilevel data provide more conservative
estimates of neighborhood effects by giving primacy (theoretically and statistically) to individual-level data (Duncan & Raudenbush, 1999).
OPERATIONALIZING NEIGHBORHOODS
Neighborhoods are a challenge to define and operationalize whether one
is a resident, a researcher, or a policymaker (Coulton, Korbin, Chan, & Su,
2001). Researchers have struggled to define neighborhood boundaries using
administrative units (Sampson, 2012), property lines (Clapp & Wang, 2006),
walking distances (Sturm, 2008), or even natural geographic boundaries
(Hoxby, 2000). Additional research has sought to understand shifting patterns within neighborhoods (Sampson & Sharkey, 2008), and the potential
for adjoining neighborhoods to influence one another (Morenoff, 2003). It
is also possible that definitions of neighborhoods and the size or type of
neighborhood needed to influence cognitive development might vary by
Neighborhoods and Cognitive Development
9
specific outcome, age, or even from region to region (e.g., Chase-Lansdale &
Gordon, 1996).
CROSS-CLASSIFICATION
People change residences from neighborhood to neighborhood across time,
and they also can move from neighborhood to neighborhood for employment and school. While early neighborhood studies could not account for
such movement (e.g., Garner & Raudenbush, 1991), researchers have begun
to utilize cross-classified models to consider student movement across
neighborhoods and between schools (Luo & Kwok, 2012). Furthermore, consideration of contextual factors must also distinguish between school-based
and neighborhood-based factors to correctly attribute variance to different
predictors beyond only family factors (Aikens & Barbarin, 2008; Whipple,
Evans, Barry, & Maxwell, 2010).
DEVELOPMENT ACROSS DOMAINS
Researchers have also found links between neighborhoods and a number
of other developmental domains including socioemotional functioning and
physical and sexual health. These associations represent potential indirect
pathways through which neighborhoods can influence cognitive development. For instance, teenage childbearing has been linked with not only high
school dropouts (Brooks-Gunn et al., 1993; Crane, 1991) but multigenerational
poverty that will be associated with cognitive deficits in the next generation
(Sharkey & Elwert, 2011). For younger children, neighborhood disadvantage is associated with higher levels of internalizing and externalizing behaviors (Xue, Leventhal, Brooks-Gunn, & Earls, 2005) and child dietary intake
(Florence, Asbridge, & Veugelers, 2008) and air pollution (Pastor, Sadd, &
Morello-Frosch, 2004), which, in turn, have been linked to lower cognitive
functioning for children.
MIXED METHODS RESEARCH ACROSS DISCIPLINES
Qualitative methods can help researchers identify future variables of interest
as well as anecdotal accounts for why existing relationships might be significant (Turney, Clampet-Lundquist, Edin, Kling & Duncan, 2006). This will
be especially critical in designing future neighborhood experiments. Similarly, opportunities for natural experiments, as was the case with Gautreaux,
should be sought out as policymakers continue to weigh the merit of highly
concentrated public housing for the poor (Currie & Yelowitz, 2000).
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
CONCLUSION
Taken together, neighborhood research finds that cognitive development
and performance are linked to neighborhood-level predictors. Altering
these trajectories through experiments and policies, however, remains
challenging. Future research will best be served by continued collaboration
between disciplines and replication of existing findings in new contexts
even as additional variables are considered.
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1464–1465.
Sampson, R. J., & Sharkey, P. (2008). Neighborhood selection and the social reproduction of concentrated racial inequality. Demography, 45, 1–29.
Sanbonmatsu, L., Kling, J. R., Duncan, G. J., & Brooks-Gunn, J. (2006). Neighborhoods
and academic achievement. Journal of Human Resources, 41, 649–691.
Sawhill, I., & McLanahan, S. (2006). Opportunity in America: Introducing the issue.
The Future of Children, 16, 3–17.
Sharkey, P. (2010). The acute effect of local homicides on children’s cognitive performance. Proceedings of the National Academies of Science, 107, 11733–11738.
Sharkey, P., & Elwert, F. (2011). The legacy of disadvantage: Multigenerational neighborhood effects on cognitive ability. American Journal of Sociology, 116, 1934–1981.
Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency and urban areas. Chicago, IL:
University of Chicago Press.
Sheffield, K. M., & Peek, M. K. (2009). Neighborhood context and cognitive decline
in older Mexican Americans: Results from the Hispanic established populations
for epidemiologic studies of the elderly. American Journal of Epidemiology, 169,
1092–1101.
Stewart, E. B., Stewart, E. A., & Simons, R. L. (2007). The effect of neighborhood
context on the college aspirations of African American adolescents. American Educational Research Journal, 44, 896–919.
South, S. J., Baumer, E. P., & Lutz, A. (2003). Interpreting community effects on youth
educational attainment. Youth & Society, 35, 3–36.
Sturm, R. (2008). Disparities in the food environment surrounding US middle and
high schools. Public Health, 122, 681–690.
Turley, R. N. L. (2002). Is relative deprivation beneficial? The effects of richer and
poorer neighbors on children’s outcomes. Journal of Community Psychology, 30,
671–686.
Turley, R. N. L. (2003). When do neighborhoods matter? The role of race and neighborhood peers. Social Science Research, 32, 61–79.
Turney, K., Clampet-Lundquist, S., Edin, K., Kling, J. R., & Duncan, G. J. (2006).
Neighborhood effects on barriers to employment: Results from a randomized
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Vaden-Kiernan, M., D’Elio, M. A., O’Brien, R. W., Tarullo, L. B., Zill, N., &
Hubbell-McKey, R. (2010). Neighborhood as a developmental context: A multilevel analysis of neighborhood effects on head start families and children. American Journal of Community Psychology, 45, 49–67.
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Wight, R. G., Aneshensel, C. S., Miller-Martinez, D., Botticello, A. L., Cummings, J. R.,
Karlamangla, A. S., & Seeman, T. E. (2006). Urban neighborhood context, educational attainment, and cognitive function among older adults. American Journal of
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JONDOU CHEN SHORT BIOGRAPHY
Jondou Chen, PhD, is a researcher and lecturer at Teachers College,
Columbia University. He currently coordinates the Mindset and Motivation
research program under Dr. Xiaodong Lin at Teachers College and Dr.
Carol Dweck at Stanford University. This research looks at how students’
perceptions of their brains, the nature of intelligence, and the history of
science shape their motivation to overcome life challenges and intellectual
struggles in learning. Before his current position, he served as a research
fellow under Dr. Jeanne Brooks-Gunn at the National Center for Children
and Families conducting policy-evaluation research on housing mobility
programs, early childhood education, and public schools in New York City.
JEANNE BROOKS-GUNN SHORT BIOGRAPHY
Jeanne Brooks-Gunn, PhD, is the Virginia and Leonard Marx Professor
of Child Development and Education at Teachers College and the College
of Physicians and Surgeons at Columbia University and she directs the
National Center for Children and Families (www.policyforchildren.org).
She is interested in factors that contribute to both positive and negative
outcomes across childhood, adolescence, and adulthood, with a particular
focus on key social and biological transitions over the life course. She
designs and evaluates intervention programs for children and parents (Early
Head Start, Infant Health and Development Program, Head Start Quality
Program). Other large-scale longitudinal studies include the Fragile Families
and Child Well-being Study and the Project on Human Development in
Chicago Neighborhoods (co-PI of both). She is the author of four books
including Consequences of Growing up Poor and Early Child Development
in the twenty-first century: Profiles of Current Research Initiatives. She has
been elected into both the Institute of Medicine of the National Academies
and the National Academy of Education, and she has received life-time
Neighborhoods and Cognitive Development
15
achievement awards from the Society for Research in Child Development,
American Academy of Political and Social Science, the American Psychological Society, American Psychological Association, and Society for Research
on Adolescence.
RELATED ESSAYS
Peers and Adolescent Risk Taking (Psychology), Jason Chein
Problems Attract Problems: A Network Perspective on Mental Disorders
(Psychology), Angélique Cramer and Denny Borsboom
Diversity in Groups (Sociology), Catarina R. Fernandes and Jeffrey T. Polzer
Micro-Cultures (Sociology), Gary Alan Fine
Participant Observation (Methods), Danny Jorgensen
Niche Construction: Implications for Human Sciences (Anthropology), Kevin
N. Laland and Michael O’Brien
Emotion and Intergroup Relations (Psychology), Diane M. Mackie et al.
Culture as Situated Cognition (Psychology), Daphna Oyserman
Bringing the Study of Street Gangs Back into the Mainstream (Sociology),
James F. Short, Jr. and Lorine A. Hughes
Social Neuroendocrine Approaches to Relationships (Anthropology), Sari M.
van Anders and Peter B. Gray
-
Neighborhoods and Cognitive
Development
JONDOU CHEN and JEANNE BROOKS-GUNN
Abstract
Research on neighborhoods and individual well-being has produced a substantive body of knowledge over the past quarter century. Neighborhood
conditions—especially socioeconomic status (SES), which is based on income and
education and to a lesser extent on residential stability—are predictive of cognitive
development. The strongest evidence controls for individual and family-level
characteristics or examines individuals clustered within neighborhoods in order to
obtain estimates of within- and between-neighborhood variance. Another line of
research has focused on housing mobility projects, which allow for the experimental
assignment of residents to more advantaged neighborhoods. Future research on
neighborhoods will continue to blend methods and data from an increasing number
of disciplines to better understand human development in context.
INTRODUCTION
In this review, we focus on cognitive development even as neighborhood
research has also examined health and behavior as outcomes. In some
studies, educational achievement and attainment are considered as an
outcome related to cognitive development even as educational outcomes
are influenced by both cognitive and noncognitive skills as well as by
school and neighborhood influences. This essay examines neighborhoods
but not schools, even though the two often overlap, especially during the
elementary school years. In addition, neighborhood research is heavily
weighted toward the study of urban rather than rural settings.
FOUNDATIONAL RESEARCH
That neighborhoods matter is not a new idea in social science (e.g., Shaw &
McKay, 1942), even though debate still continues on this premise (Sampson,
2011). Above the general issue of neighborhood effects, the primary questions
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.
1
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
are “How?,” “How much?,” “For what outcomes?,” and “For what groups?”
These are challenging to answer, however, given two confounding questions.
“Do people not choose where they live?” renders neighborhood effects to
be individual selection effects. This, in turn, establishes what has been the
baseline for neighborhood research since, “Do neighborhoods matter above
and beyond individual-level factors?” A sizable body of research has been
produced in response, with a number of reviews condensing findings and
highlighting ongoing challenges (e.g., Chen & Brooks-Gunn, 2012; Leventhal & Brooks-Gunn, 2000). In a similar vein, we present here key research
findings from the past quarter century.
HOW ARE NEIGHBORHOODS STUDIED?
While some researchers use neighborhood data as a proxy for individual
data, only studies considering the unique contributions of neighborhoods
will be discussed here. Such data are considered “nested,” given that
individuals reside within neighborhoods. This requires both multilevel
modeling of the data and significantly larger sample sizes to provide
adequate statistical power. To find such numbers, large-scale existing data
sets from long-standing studies (e.g., the Panel Study of Income Dynamics
or the Infant Health and Development Program, Brooks-Gunn, Duncan,
Klebanov, & Sealand, 1993) or administrative sources (e.g., school grades,
Garner & Raudenbush, 1991) are used. Even better are studies that sample neighborhoods and then sample individuals, families, or households
within neighborhoods (e.g., the Project on Human Development in Chicago
Neighborhoods, Sampson, 2011).
It should be noted that by “neighborhoods,” we refer to the geographic
area in which a person lives rather than the social network to which a person belongs. In research, these geographic areas are administrative units such
as census tracts or school catchment areas for which data already exist and
have been collected, typically by government agencies (e.g., the US Census
Bureau). While research that incorporates novel neighborhood measures or
resident-defined geographic units exists, these studies are in the minority
and represent a limitation of neighborhood research that is discussed in our
section on future directions. Few studies have attempted to validate the designation of neighborhoods by census tracts with resident-defined units (e.g.,
Sampson, 2011).
Similarly, measures of cognitive development include traditional intelligence, achievement, and language tests (e.g., the Woodcock–Johnson
Achievement Tests in Sharkey & Elwert, 2011). A number of studies also
consider school-based assessments (e.g., the California Achievement Test
in Entwisle, Alexander, & Olson, 1994) and criteria including graduation
Neighborhoods and Cognitive Development
3
and dropout (Brooks-Gunn et al., 1993). While a limited number of studies
researched neighborhood effects on adults (e.g., Sheffield & Peek, 2009),
most studies linking neighborhoods to cognition focus on the developmental
aspects experienced during childhood and through adolescence.
NEIGHBORHOOD ADVANTAGE/DISADVANTAGE
Neighborhood socioeconomic status (SES) consistently predicts cognitive
development. A strong qualitative and demographic research base exists
linking concentrated disadvantage and racial segregation to negative life
outcomes (Massey & Denton, 1993). These findings hold true even after
controlling for individual- and family-level characteristics, and indicate that
additional dynamics in the community shape individual development. For
instance, Crane (1991) found the percentage of neighborhood residents with
professional or managerial jobs to be a stronger predictor of high school
dropout than a number of negative indicators such as unemployment,
poverty, and middle school dropout. Such results are found in children as
early as preschool (Duncan, Brooks-Gunn, & Klebanov, 1994; Klebanov,
Brooks-Gunn, McCarton, & McCormick, 1998). And while earlier US studies
tended to focus on whites and African-Americans, more recent studies have
replicated previous findings in samples with larger immigrant populations
(Leventhal, Xue, & Brooks-Gunn, 2006), as well as in samples outside the
United States (Garner & Raudenbush, 1991; Kohen, Brooks-Gunn, Leventhal
& Hertzman, 2002).
A recurring question researchers have raised is whether SES should be
considered to be nonlinear and measured as affluence and poverty. For
example, in an analysis of two US data sets, poverty was linked to increased
internalizing and externalizing behavior in young children, but it was
affluence that predicted IQ scores at age 5 (Duncan et al., 1994). A more
recent study utilizing Head Start data found poverty rather than affluence to
significantly predict poorer cognitive performance on a picture vocabulary
test (Vaden-Kiernan et al., 2010). This finding should be interpreted with
caution, however, as children enrolled in Head Start by definition come from
poorer families, limiting generalizability.
Given the connections between neighborhoods and cognitive development, it is not surprising that neighborhood SES also predicts educational
attainment and attitudes. In an early study utilizing multilevel methods to
analyze Scottish high school data, neighborhood poverty (as well as unemployment, single-parent households, overcrowding, and chronic illness) was
linked to poorer educational attainment controlling for individual, family,
and school-level factors (Garner & Raudenbush, 1991). Returning to our
question regarding poverty versus affluence, another study using US census
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
data linked high school dropout rates more strongly to a single measure
of affluence (the number of neighborhood residents holding managerial or
professional jobs) than any measure of poverty (Crane, 1991). More recent
research continues to offer support for both affluence (e.g., Ainsworth,
2002) and poverty (South, Baumer, & Lutz, 2003) predicting educational
attainment.
Researchers have also studied why these links exist. Such research,
focusing more on behavioral and attitudinal data, has often been linked
to educational attainment as opposed to simply cognitive assessment.
Living in more affluent neighborhoods has been associated with more
exposure to more advanced maternal speech for developing children (Hoff,
2003). Additional high-status residents might also result in the collective
socialization of youth to spend more time on homework and attend schools
with better atmosphere (Ainsworth, 2002). Neighborhood affluence might
also encourage increased parental material investment in the home (Duncan
et al., 1994) or higher rates of participation in institutional resources such as
after-school lessons and summer camps (Dearing et al., 2009). These are more
positively nuanced perspectives than Crane’s (1991) contagion theory that
held that urban neighborhoods with the lowest concentrations of high-status
residents saw disproportionately large effect sizes. Other research utilizing
the National Survey of Children found that the neighborhood effects of
poverty could be explained by peer educational attitudes (South et al.,
2003). From a material perspective, individuals living in poverty might also
experience a psychological sense of relative economic deprivation with one
study finding that attainment is more strongly predicted by neighborhood
income inequality as opposed to only absolute poverty (Turley, 2002).
Gender differences have also emerged in several studies. In a sample of
elementary school students in Baltimore, neighborhood income and parent
education levels were stronger predictors for math scores for boys than
for girls (Entwisle et al., 1994). And as will be discussed later, a group of
adolescent girls moving out of highly disadvantaged public housing in
Baltimore saw some improvement in cognitive performance (Sanbonmatsu,
Kling, Duncan, & Brooks-Gunn, 2006). Similarly, neighborhood occupational
status was found to predict lower rates of high school dropout for boys but
not for girls in a 30-year study of poor Chicago youth (Ensminger, Lamkin,
& Jacobson, 1996).
With regard to race, it should be noted that it is difficult to interpret
different effects of SES by ethnicity/race as the two predictors remain highly
correlated (Clampet-Lundquist & Massey, 2008). Still, limited evidence of
SES by ethnicity/race interactions does exist. In two large US data sets,
neighborhood affluence was found to be a stronger protective factor for
white adolescents than for black adolescents in predicting high school
Neighborhoods and Cognitive Development
5
dropout (Brooks-Gunn et al., 1993). Later analyses of these data found that
living in neighborhoods with higher concentrations of African-Americans
mitigated these racial differences (Turley, 2003). Similarly, immigrant status was associated with larger negative neighborhood effects for SES in
predicting school grades (Pong & Hao, 2007).
HOUSING MOBILITY PROGRAMS
Even as early neighborhood research consistently found links to cognitive
development controlling for individual-level data, criticism regarding
individuals selecting to live in certain neighborhoods remained. A unique
opportunity to address this arose, however, in the form of housing mobility
programs. Originally mandated through court order in cities such as Chicago
(Rosenbaum, 1995) and Yonkers (Briggs, 1998), these programs sought to
move residents out of highly disadvantaged neighborhoods. Despite the
lack of randomized assignment, early results from the Gautreaux housing
program in Chicago were promising, as black youth who moved to predominantly white suburbs did significantly better in school with regard to difficulty of classes taken, graduation, and college enrollment (Rosenbaum, 1995).
As a result of these findings, a large-scale housing experiment, the Moving to Opportunity (MTO) Program, was initiated by the US Department of
Housing and Urban Development. Taking place in five major US cities (Baltimore, Boston, Chicago, Los Angeles, and New York), 4600 families were
randomly assigned to receive housing vouchers, vouchers and additional
housing assistance, or no vouchers. Data from families were collected at baseline and then 2 years and 5 years following the move. After 2 years, initial results found benefits for moving to better neighborhoods for children
with regard to behavioral outcomes (Katz, Kling, & Liebman, 2001; Leventhal & Brooks-Gunn, 2003). Improved cognitive performance was seen in
youth from one MTO city (Baltimore) at 2 years, but these were found to have
faded by year 5 (Ludwig, Ladd, & Duncan, 2001). MTO effects in New York
City after 2 years were the opposite of Baltimore, however, with adolescent
movers reporting lower school grades and engagement (Leventhal, Fauth,
& Brooks-Gunn, 2005). After 5 years, no differences in cognitive functioning
using a standardized non-school-based assessment were found in analyses of
the pooled MTO sample except for African-American youth (Sanbonmatsu
et al., 2006). The only school-related benefits found were for girls and for
school-based behavior rather than cognitive performance (Kling, Liebman, &
Katz, 2007).
Possible reasons for poor findings from MTO include poor program uptake
by both the voucher only group (48%) and the voucher and assistance group
(60%) (Goering et al., 1999). Furthermore, a number of participants who
6
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
did not receive the voucher in the MTO study moved of their own accord
anyway, which should not be surprising given that all participants applied to
enter the voucher lottery at the beginning of the study (Clampet-Lundquist
& Massey, 2008). Even if families did move, the schools in MTO participants’
new neighborhoods were only marginally better, performing on average at
the 19th percentile compared to the 15th percentile for the control group,
indicating minimal, if any, improvement (Sanbonmatsu et al., 2006). A
similar argument can be made with regard to neighborhood SES as families
were moving into less poor but not affluent neighborhoods, supporting
the argument that it is affluence rather than poverty that predicts cognitive
performance. One final possibility is that school-level differences presented
a unique set of challenges, as children who moved had higher retention and
dropout rates (Sanbonmatsu et al., 2006) in more racially segregated schools
(Clampet-Lundquist & Massey, 2008).
The lack of lasting significant positive findings was also the case in
Yonkers, where court-ordered residential desegregation led to a public backlash against efforts to simultaneously desegregate schools (Briggs, 1998). As
a result, most Yonkers youth attended schools in their original neighborhoods, reducing the potential benefits of the new neighborhoods as students
felt less connected to their schools (Fauth, Leventhal, & Brooks-Gunn, 2007).
After the seemingly positive initial findings from Gautreaux, the nonsignificant and negative findings from Yonkers and MTO challenge researchers
and policymakers to identify adjustments or new solutions in the face of
continued neighborhood segregation and disadvantage (Sampson, 2012).
CUTTING-EDGE RESEARCH
Recent research on neighborhood effects has sought to broaden operationalization of neighborhood disadvantage and consider additional variables.
One study of black adolescents from Georgia and Iowa found that neighborhood physical disorder was a unique predictor of college aspirations
(Stewart, Stewart, & Simons, 2007). Another study of older adults in 65
Baltimore neighborhoods, linked data on social disorganization, physical
disorder, and public safety in addition to economic conditions to more rapid
cognitive impairment (Lee, Glass, James, Bandeen-Roche, & Schwartz, 2011).
In addition, individual aspects of neighborhood disadvantage are now being
considered apart from overall SES. For instance, the percentage of adults
in a neighborhood with at least a high school diploma has been associated
with higher cognitive functioning in adults, controlling for individual-level
educational attainment (Wight et al., 2006). Recent analyses of homicide
data in Chicago neighborhoods found that having a homicide occur in the
Neighborhoods and Cognitive Development
7
same census block group in which one lives predicted lower performance
on standardized school tests (Sharkey, 2010).
Moving away from a deficit-based model of thinking, protective factors
such as residential stability have been found to have positive links to educational attainment (South et al., 2003), although such studies have tended
to find residential stability to be correlated with socioeconomic status. In a
Canadian sample of preschoolers, low social cohesion was found to operate uniquely from affluence in predicting child verbal ability (Kohen et al.,
2002). Another proxy for social cohesion has been increased neighborhood
concentration by ethnic group. In a sample of Mexican-Americans in the
southwestern United States, living in a neighborhood with a higher percentage of Mexican-Americans was associated with lower cognitive impairment
(Sheffield & Peek, 2009).
New longitudinal research has also taken advantage of intergenerational
data and new statistical methods. Independent of individual-level poverty,
children whose families have lived in high-poverty neighborhoods for more
than one generation were associated with even higher deficits in cognitive
ability (Sharkey & Elwert, 2011). These findings are especially troubling for
black children whose families are more likely to live in neighborhoods with
higher rates of multigenerational poverty. Researchers have also sought more
dynamic measures of neighborhood characteristics, which have been traditionally chronologically fixed in 10-year intervals because of the US Census.
By considering annual rather than decennial trends in neighborhood disadvantage, Wodtke, Harding, and Elwert (2011) were able to better control for
neighborhood selection, improving estimates of the negative consequences
of neighborhood disadvantage.
With US and global populations that are living longer on average, greater
consideration must be given to cognitive development in older populations.
In contrast to children, cognitive development in older adults represents
maintaining cognitive performance and avoiding deterioration. As already
alluded to, poor neighborhood conditions have been linked with more rapid
cognitive decline both in the United States (Espino, Lichtenstein, Palmer &
Hazuda, 2001) and abroad (Lang et al., 2008). Neighborhood disadvantage
has also been tied to the gene by environment interactions (Lee et al.,
2011) and neighborhood ethnic composition for ethnic minorities as well
(Sheffield & Peek, 2009).
KEY ISSUES FOR FUTURE RESEARCH
Ongoing neighborhood research will continue to wrestle with a number
of ongoing challenges even as new methods and data provide for creative
solutions.
8
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
SELECTION
Nonexperimental neighborhood research will continue to struggle with
the challenge of selection. New statistical and modeling methods must be
adopted into neighborhood research. Propensity score matching represents
one approach to controlling for neighborhood selection (Lee et al., 2011).
Such methods use variables typically used as controls or predictors of
neighborhood selection to match participants who share the same likelihood
of living in a certain type of neighborhood but in fact do not. Another study
utilizing Swedish data, used sibling data to control for home environment
and test for differences across neighborhoods as families moved or stayed
in the same neighborhood (Lindahl, 2011). The need to account for selection
is increasingly important as neighborhoods continue to become more
segregated (Sampson, 2012) with evidence of increasing residential selection
based on factors such as SES (Sawhill & McLanahan, 2006), race (Bayer,
Ferreira, & McMillan, 2007), immigrant status (Lauen, 2007), and school
quality (DeSena, 2006).
COLLINEARITY
Even as neighborhood researchers consider multiple and cross-classified levels of nesting, it is possible that factors existing at several levels (e.g., poverty)
are collinear across levels. That is, given that most measures of neighborhood poverty are simply the aggregation of individual-level poverty, these
two predictors are dependent on one another, which in turn will potentially
bias parameter estimates (Oakes, 2004). Others have argued, however, that
existing methods of modeling multilevel data provide more conservative
estimates of neighborhood effects by giving primacy (theoretically and statistically) to individual-level data (Duncan & Raudenbush, 1999).
OPERATIONALIZING NEIGHBORHOODS
Neighborhoods are a challenge to define and operationalize whether one
is a resident, a researcher, or a policymaker (Coulton, Korbin, Chan, & Su,
2001). Researchers have struggled to define neighborhood boundaries using
administrative units (Sampson, 2012), property lines (Clapp & Wang, 2006),
walking distances (Sturm, 2008), or even natural geographic boundaries
(Hoxby, 2000). Additional research has sought to understand shifting patterns within neighborhoods (Sampson & Sharkey, 2008), and the potential
for adjoining neighborhoods to influence one another (Morenoff, 2003). It
is also possible that definitions of neighborhoods and the size or type of
neighborhood needed to influence cognitive development might vary by
Neighborhoods and Cognitive Development
9
specific outcome, age, or even from region to region (e.g., Chase-Lansdale &
Gordon, 1996).
CROSS-CLASSIFICATION
People change residences from neighborhood to neighborhood across time,
and they also can move from neighborhood to neighborhood for employment and school. While early neighborhood studies could not account for
such movement (e.g., Garner & Raudenbush, 1991), researchers have begun
to utilize cross-classified models to consider student movement across
neighborhoods and between schools (Luo & Kwok, 2012). Furthermore, consideration of contextual factors must also distinguish between school-based
and neighborhood-based factors to correctly attribute variance to different
predictors beyond only family factors (Aikens & Barbarin, 2008; Whipple,
Evans, Barry, & Maxwell, 2010).
DEVELOPMENT ACROSS DOMAINS
Researchers have also found links between neighborhoods and a number
of other developmental domains including socioemotional functioning and
physical and sexual health. These associations represent potential indirect
pathways through which neighborhoods can influence cognitive development. For instance, teenage childbearing has been linked with not only high
school dropouts (Brooks-Gunn et al., 1993; Crane, 1991) but multigenerational
poverty that will be associated with cognitive deficits in the next generation
(Sharkey & Elwert, 2011). For younger children, neighborhood disadvantage is associated with higher levels of internalizing and externalizing behaviors (Xue, Leventhal, Brooks-Gunn, & Earls, 2005) and child dietary intake
(Florence, Asbridge, & Veugelers, 2008) and air pollution (Pastor, Sadd, &
Morello-Frosch, 2004), which, in turn, have been linked to lower cognitive
functioning for children.
MIXED METHODS RESEARCH ACROSS DISCIPLINES
Qualitative methods can help researchers identify future variables of interest
as well as anecdotal accounts for why existing relationships might be significant (Turney, Clampet-Lundquist, Edin, Kling & Duncan, 2006). This will
be especially critical in designing future neighborhood experiments. Similarly, opportunities for natural experiments, as was the case with Gautreaux,
should be sought out as policymakers continue to weigh the merit of highly
concentrated public housing for the poor (Currie & Yelowitz, 2000).
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
CONCLUSION
Taken together, neighborhood research finds that cognitive development
and performance are linked to neighborhood-level predictors. Altering
these trajectories through experiments and policies, however, remains
challenging. Future research will best be served by continued collaboration
between disciplines and replication of existing findings in new contexts
even as additional variables are considered.
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JONDOU CHEN SHORT BIOGRAPHY
Jondou Chen, PhD, is a researcher and lecturer at Teachers College,
Columbia University. He currently coordinates the Mindset and Motivation
research program under Dr. Xiaodong Lin at Teachers College and Dr.
Carol Dweck at Stanford University. This research looks at how students’
perceptions of their brains, the nature of intelligence, and the history of
science shape their motivation to overcome life challenges and intellectual
struggles in learning. Before his current position, he served as a research
fellow under Dr. Jeanne Brooks-Gunn at the National Center for Children
and Families conducting policy-evaluation research on housing mobility
programs, early childhood education, and public schools in New York City.
JEANNE BROOKS-GUNN SHORT BIOGRAPHY
Jeanne Brooks-Gunn, PhD, is the Virginia and Leonard Marx Professor
of Child Development and Education at Teachers College and the College
of Physicians and Surgeons at Columbia University and she directs the
National Center for Children and Families (www.policyforchildren.org).
She is interested in factors that contribute to both positive and negative
outcomes across childhood, adolescence, and adulthood, with a particular
focus on key social and biological transitions over the life course. She
designs and evaluates intervention programs for children and parents (Early
Head Start, Infant Health and Development Program, Head Start Quality
Program). Other large-scale longitudinal studies include the Fragile Families
and Child Well-being Study and the Project on Human Development in
Chicago Neighborhoods (co-PI of both). She is the author of four books
including Consequences of Growing up Poor and Early Child Development
in the twenty-first century: Profiles of Current Research Initiatives. She has
been elected into both the Institute of Medicine of the National Academies
and the National Academy of Education, and she has received life-time
Neighborhoods and Cognitive Development
15
achievement awards from the Society for Research in Child Development,
American Academy of Political and Social Science, the American Psychological Society, American Psychological Association, and Society for Research
on Adolescence.
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