Organized activities and youth development

1
Organized Activities as
Developmental Contexts for Children
and Adolescents
Joseph L.Mahoney
Yale University
Reed W.Larson
University of Illinois at Urbana-Champaign
Jacquelynne S.Eccles
University of Michigan
Heather Lord
Yale University
School-age children in the United States and other Western
nations spend almost half of their

waking hours in leisure activities (Larson & verma, 1999). How
young persons can best use this
discretionary time has been a source of controversy. For some,
out-of-school time is perceived as
inconsequential or even counterproductive to the health and
well-being of young persons.
Consistent with this view, the past 100 years of scientific
research has tended either to ignore this
time or to focus selectively on the risks present during the out-
of-school hours (Kleiber &
Powell, chap.2, this volume). More recently, however, there is
increased interest in viewing out-
of-school time as an opportunity for young persons to learn and
develop competencies that are
largely neglected by schools. Researchers are beginning to
recognize that along with family,
peers, and school, the organized activities in which some youth
participate during these hours are
important contexts of emotional, social, and civic development.
At the same time, communities
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and the federal government in the United States are now
channeling considerable resources into

creating organized activities for young people’s out-of-school
time (Pittman et al., chap.17, this
volume). The primary aim of this volume is to bring scientific
research to bear on how this time
can be used constructively.
In this chapter, we overview central issues in the field of
research on organized activities to
provide a background and framework for the chapters that
follow. Four main areas are addressed.
First, we discuss definitional issues in the field and clarify what
is meant by organized activities
within this volume. Second, we outline the available research
indicating that participation in
these activities affects shortand long-term development. Third,
we consider the features of
organized activities thought to account for their developmental
impact and, lastly, we review
evidence on factors that Influence participation in these
activities and whether youth benefit from
their developmental potential.
WHAT ARE ORGANIZED ACTIVITIES?

This volume focuses principally on formal activities for
children 6 to 18 years of age that are not
part of the school curriculum. By “organized,” we refer to
activities that are characterized by
structure, adult-supervision, and an emphasis on skill-building
(e.g., Eccles & gootman, 2002;
Larson, 2000; Roth & Brooks-gunn, 2003). These activities are
generally voluntary, have regular
and scheduled meetings, maintain developmentally based
expectations and rules for participants
in the activity setting (and sometimes beyond it), involve
several participants, offer supervision
and guidance from adults, and are organized around developing
particular skills and achieving
goals. These activities are often characterized by challenge and
complexity that increase as
participants’ abilities develop (e.g., Csikszentmihalyi, 1990;
Larson, 1994). In general, organized
activities share the broad goal of promoting positive
development for the participants.
A variety of labels have been used to describe organized
activities for young persons. They
usually denote the who (school-age, child, adolescent, youth),
where (school-based, community-

based), what (activities, programs, organizations), and when
(after-school, extracurricular,
summer, nonschool, out-of-school) elements of participation.
These descriptors are meaningful
and do clarify the phenomenon of interest. Accordingly, we use
the term organized activities to
refer to these variations collectively. The word organized is also
used to make clear that so-
called unstructured activities (e.g., watching television,
listening to music, “hanging out” with
peers, and “cruising” in cars) and other forms of passive leisure
(e.g., eating, resting, and
personal care) are not the focus of this volume, except as a
backdrop of what else youth might be
doing during their after-school hours (Kleiber & Powell, chap.2,
this volume; Osgood et al.,
chap.3, this volume).
Breadth and Diversity of Activities
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The range of organized activities available to children and
adolescents in the United States and
other Western nations is substantial (Carnegie Council, 1992;
Eccles & gootman, 2002; Quinn,
1999). They include nationally sponsored youth organizations
and federally funded after-school
programs (e.g., Boys and girls Clubs, YMCA, YWCA, 21st
Century Community Learning
Centers, 4-H, Boy/girl Scouts, Camp Fire). They involve
community, school, and locally
organized programs: autonomous grassroots youth
developmental organizations, faith-based
youth organizations, and activities provided by parks and
recreation services, museums, libraries,
youth centers, youth sports organizations and amateur leagues
(e.g., little league), school-
sponsored extracurricular and after-school activities, and
community service programs. They
also include specifc types of activities (e.g., sports, music,
hobby clubs, social clubs, religious,
service activities) that can be differentiated on the basis of
activity-related goals, atmosphere,

and content (Roth & Brooks-gunn, 2003).
This volume considers organized activities across this diverse
range. Methodological and
logistical challenges make it diffcult to study national
organizations and little research is
available at this level of assessment. By contrast, considerable
research has been conducted on
community-sponsored programs and activities (e.g., Eccles &
gootman, 2002; Kirshner et al.,
chap.7, this volume; McIntosh et al., chap.15, this volume;
Stattin et al., chap.10, this volume),
and school-based extracurricular activities (e.g., Barber et al.,
chap.9, this volume; Eccles &
Templeton, 2002; Pedersen & Seidman, chap.5, this volume).
Recently, school-sponsored after-
school programs for elementary and middle-school youth have
been increasing dramatically and
research is beginning to be conducted on these activities (Casey
et al., chap.4, this volume;
vandell et al., chap.20, this volume; Weisman et al., chap.21,
this volume). Finally,
developmental research that compares different types of
activities is relatively new and featured
in several chapters (e.g., Barber et al., chap.9, this volume;

Eccles & Barber, 1999; Hansen,
Larson, & Dworkin, 2003; Jacobs et al., chap.11, this volume;
Pedersen & Seidman, chap.5, this
volume). Studies of specifc activities have been common in the
fields of leisure studies and
sports psychology and are also considered here (e.g., Duda &
Ntoumanis, chap.4, this volume;
O’Neill, chap.12, this volume; Scanlan et al., chap.13, this
volume).
ORGANIZED ACTIVITIES AND SALIENT
DEVELOPMENTAL TASKS FOR YOUNG PERSONS
In order to evaluate how these organized activities contribute to
development, scholars are
examining whether they help children and adolescents address
the developmental tasks
associated with their age periods—how they help youth achieve
age-appropriate competencies.
During childhood, key developmental tasks in our society
include (a) acquiring habits of physical
and psychological health, (b) forming a positive orientation
toward school and achievement, (c)
getting along with others including peers and adults, and (d)

acquiring appropriate value systems
about rules and conduct across different contexts. These issues
remain important during
adolescence, but are renegotiated in the light of interdependent
changes in the bio-psycho-social
system (Eccles, Barber, Stone, & Temple ton, 2003; Mahoney &
Bergman, 2002). In addition,
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new tasks such as identity formation, personal mastery/efficacy,
intimacy with peers, and
preparation for the transition to adulthood and postsecondary
education or work become
increasingly important across adolescence (Brown, Clausen, &
Richer, 1986; Collins, 2002;
Levesque, 1993). In the global world of the 21st century, the
development of competencies to
move between diverse contexts defned by ethnicity, religion,
sexual orientation, and other forms
of difference is increasingly important (Larson, Wilson, Brown,
Furstenberg, & verma, 2002).
Achieving competency at these tasks allows an individual to
take advantage of personal and
environmental resources that promote positive functioning in
the present, reduce the risk for
developing problem behaviors, and increase the likelihood for
healthy adjustment in the future
(Eccles et al, 2003; Mahoney & Bergman, 2002; Masten &
Coatsworth, 1998). Research shows
that participation in organized activities can have a range of

positive Influence on children and
adolescence. We now highlight some of this evidence for
school-sponsored extracurricular
activities, and community-based and after-school programs.
Extracurricular Activities and Community Programs
Involvement in organized activities such as sports teams,
lessons, and clubs is relatively common
during adolescence. For example, among youth ages 12 to 17
from the National Survey of
Families (NASF; 1997), 57% participated on a sports team, 29%
participated in lessons, and
60% participated in clubs or organizations after school or on
weekends during the last year.
Recent reviews support the conclusion that participation in
organized activities helps young
persons negotiate the salient developmental tasks of childhood
and adolescence (e.g., Eccles &
gootman, 2002; Eccles & Templeton, 2002).
Increased Educational Attainment and Achievement. A
longstanding finding from quasi-
experimental and experimental studies is that participation in
extracurricular activities and

community programs promotes education attainment. This
includes low rates of school failure
and dropout (e.g., Hahn, Leavitt, & Aaron, 1994; Mahoney &
Cairns, 1997; McNeal, 1995), high
rates of postsecondary school education, and good school
achievement (e.g., Eccles & Barber,
1999; Mahoney, Cairns, & Farmer, 2003; Marsh, 1992; Otto,
1975, 1976). Explanations for these
education gains include the association of participation in
organized activities with heightened
school engagement and attendance, better academic performance
and interpersonal competence,
and higher aspirations for the future (e.g., Barber, Eccles &
Stone, 2001; grossman & Tierney,
1998; Lamborn, Brown, Mounts, & Steinberg, 1992; Mahoney et
al., 2003; Newman, Wehlage,
& Lamborn, 1992).
Reduced Problem Behaviors. A number of studies indicate that
participation in organized
activities is associated with reduced problem behaviors across
adolescence and into young
adulthood. For instance, earlier work in sociology shows that
activity participation is related to
low rates of delinquency (e.g., Elliott & voss, 1974; Hanks &

Eckland, 1976). More recent
developmental research shows that involvement in organized
activities reduces the likelihood of
developing problems with alcohol and drugs (grossman &
Tierney, 1998; Youniss, Yates, & Su,
1997, 1999), aggression, antisocial behavior, and crime (e.g.,
Jones & Offord, 1989; Mahoney,
2000; Rhodes & Spencer, chap.19, this volume), or becoming a
teenage parent (Allen, Philliber,
Herrling, & gabriel, 1997). Activity-related affliations with
nondeviant peers, mentoring from
adult activity leaders, and the fact that organized activities
represent a conventional endeavor that
is highly valued, challenging, and exciting represent the main
explanations why organized
activities protect against problem behaviors (e.g., Barber et al.,
chap.9, this volume; Fletcher,
Elder, & Mekos, 2000; Larson, 2000; Rhodes & Spencer,
Heightened Psychosocial Competencies. Organized activity
participation is positively
associated with psychosocial adjustment in a number of areas
(Eccles & gootman, 2002). For

instance, participation is related to low levels of negative
emotions such as depressed mood and
anxiety during adolescence (Barber et al., 2001; Brustad,
Babkes, & Smith, 2001; Larson, 1994;
Mahoney, Schweder, & Stattin, 2002). Motivation for learning
and high self-efficacy is linked
with participation (Catalano, Berglund, Ryan, Lonczak, &
Hawkins, 1999; Duda & Ntoumanis,
chap.14, this volume). These contexts also appear ideal for
promotion of a more general
psychological capacity—initiative—which involves the
application of extended effort to reach
long-term goals (e.g., Larson, 2000; Larson et al., chap.8, this
volume). Finally, maintaining or
increasing self-esteem (e.g., McLaughlin, 2000; Rhodes &
Spencer, chap.19, this volume) and
developing a clear and civic-minded identity (McIntosh et al.,
chap.15, this volume; Youniss,
McLellan, Su, & Yates, 1999) appear to be positively
Influenced by activity participation. The
unique combination of psychological features and opportunities
for social relationships and
belonging are main factors thought to impact these psychosocial
processes.

Extracurricular Activities During Childhood. Although most
investigations of organized
activities have been conducted with adolescents, available
research suggests that children benefit
from participation as well. For example, consistent participation
in extracurricular activities
during kindergarten and first grade is related to high reading
and math achievement (National
Institute of Child Health and Human Development [NICHD]
Early Child Care Research
Network, 2004). A moderate level of participation during the
first grade has also been associated
with high levels of social competence several years later (Pettit,
Laird, Bates, & Dodge, 1997).
Similarly, participation in extracurricular activities during
middle childhood is indicative of
positive achievement and emotional adjustment (McHale,
Crouter, & Tucker, 2001; Posner &
vandell, 1999), and predicts perceived competence and values
during adolescence (Jacobs,
Lanza, Osgood, Eccles, & Wigfield, 2002; chap.11, this
volume).
After-School Programs

Owing in large part to increases in maternal employment, after-
school programs now provide a
common form of child care and adult supervision for over 7
million American children with
working parents (Capizzano, Tout, & Adams, 2000). In
addition, many after-school programs are
implemented with the goal of providing safe environments and
alternatives to self-care, as well
to take advantage of opportunities for social and academic
enrichment during the nonschool
hours (vandell et al., chap.20, this volume). These programs are
oriented to children in the
elementary and middle-school years.
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Relative to children in other after-school arrangements, quasi-
experimental longitudinal studies
show that consistent participation in after-school programs
promotes positive academic
performance and reduces behavior problems such as aggression
(Pettit, Laird, Bates, & Dodge,
1997; vandell et al., chap.20, this volume; Weisman et al.,
chap.21, this volume). Similarly,
formal evaluations of after-school programs comparing
participants and nonparticipants over
time have found fewer school absences, higher school
achievement, and improved work and
study habits for participants.
1
Parents of participants also report that after-school programs
support their work schedules and that they worry less about
their children’s safety. These
benefits are frequently stronger for disadvantaged children,
those with social, academic, or
language defcits, and families residing high-risk neighborhoods.
Overall, after-school program participation appears to promote

competence in several key
developmental tasks during middle childhood including
academic performance, school
engagement, and social behaviors and relationships. The
likelihood for benefcial outcomes
appears greatest for: (a) after-school programs of higher quality
and those in later stages of
development, (b) students who show greater consistency in their
program participation, and (c)
programs serving low-income and low-achieving students at
high risk for developing social-
academic problems. However, many after-school programs
focus on academic achievement
rather than aiming to promote competence in personal and
interpersonal domains (vandell et al.,
chap.20, this volume). Future research will need to examine
what types of programs best serve
the needs of young persons in the short and long term (Quinn,
The 21st-century Community Learning Centers. Funding that
supports the 21st-century
Community Learning Centers (21st CCLCs) provides a major
source of after-school programs in
the United States. A national evaluation of the 21st CCLCs was

recently undertaken by
Mathematica Policy Research, Inc. A report describing the first-
year findings of the evaluation
purports that the 21st CCLCs had little impact on the academic
or social behavior of the
participating elementary and middle-school students (U.S.
Department of Education, Offce of
Under Secretary, 2003). This provided the basis for a proposed
40% budget reduction for the
21st CCLCs in 2004 (Education Budget Summary and
Background, 2003). However, there are
several limitations with the evaluation. For instance, the
elementary school sample was not
representative of the larger population of elementary schools
receiving 21st CCLC funds.
Comparison groups in the middle-school sample were not
equivalent; the after-school
participants were at much higher risk at the beginning of the
study. The absence of certain
baseline data, treatment and comparison group contamination,
and issues surrounding the
evaluation’s timing and measurement were also methodological
concerns in the National
Evaluation (Bissell et al., 2003; Mahoney, 2003), Finally, the

quality of the programs included in
the evaluation was not systematically considered. As already
noted, the quality of after-school
programs is very important for their effectiveness. Due to these
several limitations, making
generalizations about the effectiveness of the 21st CCLCs based
on the reported first-year
findings do not seem warranted. Extrapolation to after-school
programs in general is not possible
on the basis of the 21st CCLC evaluation, particularly in light
of the several carefully controlled
intervention studies that provide solid evidence of the
effectiveness of high quality after-school
programs.
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Summary
Organized activities are important contexts that help young
persons build competencies and
successfully negotiate the salient developmental tasks of
childhood and adolescence.
Participation is associated with academic success, mental
health, positive social relationships and
behaviors, identity development, and civic engagement. These
benefits, in turn, pave the way for
long-term educational success and help prepare young persons
for the transition to adulthood.
However, although the research findings are generally positive,
variations across the types of
programs and the participants suggest the need for researchers
to differentiate the features of
programs that facilitate development and the conditions under
which the benefits is most likely
to occur.
FEATURES OF ORGANIZED ACTIVITIES THAT
PROMOTE DEVELOPMENT
The preceding section makes it clear that organized activities
are contexts in which children and

adolescents develop a range of important competencies. But
why is this so? Why should
activities such as playing hockey for the school team, singing in
a youth community chorus,
participating in school government, or spending afternoons in
after-school program activities
matter?
To address this question, a committee of scholars appointed by
the National Research Council
and Institute of Medicine recently evaluated what features of
contexts promote positive
development (Eccles & gootman, 2002). By looking at research
on development in contexts such
as families and schools, they derived the list of eight key
features in Table 1.1 that are proven to
facilitate positive growth (see also Blum, 2003; Lerner, Fisher,
& Weinberg, 2000; Roth &
Brooks-gunn, 2003). The panel cautioned that no single feature
from this list is sufficient to
ensure positive development, but also that few contexts are
likely to provide optimal experiences
in all of these areas. They also cautioned that future research
can be expected to add to or refine

this list. Nonetheless these eight features represent the state of
the art for thinking about what
might make organized activities such as hockey, student
government, or an after-school program
an effective context of development.
TABLE 1.1Features of Contexts That Promote Positive
Development
These features can be considered as explanatory mechanisms or
mediators by which participation
in organized activities affect the development process. The
available research evaluating these
features for organized activities is limited but generally
indicates, first, that many organized
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activities are high on several of these features and, second, that
these features are linked to the
positive outcomes described. On the first point, organized
activities typically offer a context of
safety during the after-school hours (e.g., McLaughlin, 2000),
often provide opportunities for

skill building and efficacy (Larson, 2000), and are frequently
important contexts of supportive
relationships with adults and peers (e.g., Barber et al., chap.9,
this volume; Hansen et al., 2003;
Mahoney et al., 2002; Rhodes & Spencer, chap.19, this volume).
Although research specifcally linking these features to
outcomes is rare, the National Research
Council committee concluded that successful youth programs
are characterized by many of these
features. The most successful programs provide integration
between a youth’s family, school,
and community experiences, engage youth in relationships with
caring adults, and provide many
of the other positive features (Eccles & gootman, 2002; Pittman
et al., chap.17, this volume;
Walker et al., chap.18, this volume). Targeted research that
begins to critically test the specifc
linkage between some of these features and positive
development is only beginning. As one
exemplar, Mahoney et al. (2003) used longitudinal data to show
that the fostering of
interpersonal skills in youth programs is a mediator of high
aspirations for the future in

adolescence and high educational attainment—including college
attendance—at young
adulthood.
Furthermore, we are only beginning to understand how the
different combinations of features in
organized activities interact to promote positive development.
For instance, although
participation in organized activities appears to affect complex
processes such as identity
formation and the development of initiative, this seems to
depend on an appropriate balance of
many of the features already summarized (e.g., Barber et al.,
chap.9, this volume; Larson et al.,
chap.8, this volume; McIntosh et al., chap.15, this volume).
Although expert youth workers have
developed a fund of practitioner wisdom for understanding these
balancing processes (Pittman et
al., chap.17, this volume; Walker et al., chap.18, this volume),
researchers are far behind in
subjecting this wisdom to critical test. Precisely which features
are involved and how they co-act
to produce specifc developmental changes have not yet been
evaluated. Thus, one task for
researchers is to understand better the interplay between these

features, which patterns are most
critical for promoting different competencies, and how these
relations may change over
development. A second and related task will be to assess how
and why some organized activity
contexts are more effective at providing these positive features
than others (e.g., Stattin et al.,
chap.20, this volume; vandell et al., chap.20, this volume). Both
tasks will require researchers to
conduct process-oriented studies focused on individual change
that are explicitly designed to
assess a broad array of structural and process quality parameters
in the activity context.
2
FACTORS THAT INFLUENCE ACTIVITY
PARTICIPATION AND EFFECT DEVELOPMENTAL
OUTCOMES
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The preceding sections have described different benefits and
positive features associated with
organized activity participation. However, the extent to which
organized activities Influence
development can vary across individual youth, programs, and
community contexts. To derive the
greatest benefit from organized activities, a youth must
participate.
3
The selection processes
affecting whether an individual participates and continues to
participate is complex. To begin

with, the degree to which a youth can actually “select” to
participate in organized activities
depends on the individual considered, his or her family, and the
community in which he or she
resides (Caldwell & Baldwin, in press; Elder & Conger, 2000;
Furstenberg, Cook, Eccles, Elder,
& Sameroff, 1999). Activity selection involves a reciprocal
process between contextual
constraints and opportunities for participation, and the
individual’s motivation and ability to
perceive and act on them.
Demographic and Familial Influences
Availability and affordability of activities are the most basic
factors affecting participation. The
presence of resources such as parks, community centers, playing
fields, and the availability of
willing and competent adults to provide the activities are
requisites. The provision of programs
for youth is generally less in poor urban neighborhoods and
isolated rural areas (Carnegie
Council, 1992; Pedersen & Seidman, chap.5, this volume).
Beyond availability, factors such as

transportation and a family’s economic means to pay the costs
of activities, as well as cultural
and ethnic factors, have considerable Influence on participation
rates (Elder & Conger, 2000;
Furstenberg et al., 1999; villarruel et al, chap.7, this volume).
These factors—availability,
economy, and culture—are often interrelated and likely account
for the relatively low-
participation rates among economically disadvantaged children
and adolescents and those from
traditionally defned minority groups (e.g., Hultsman, 1992;
Jackson & Rucks, 1993; Pedersen &
Seidman, chap.5, this volume). It may also explain why income
support programs and the
provision of culturally appropriate activities appear to increase
participation rates (Casey et al.,
chap.4, this volume; villarruel et al., chap.6, this volume).
Parents’ desire to enroll their children in organized activities
and their ability to manage their
children’s participation in such activities differ. If parents work
full time and children need
transportation to get to organized activities, then it may be quite
diffcult for a child to get and
remain involved in such activities. Similarly, if parents fear the

types of children and adolescents
likely to participate in the organized activities, they may prefer
to keep their children at home
(Furstenberg et al., 1999; Jarrett, 1997; Stattin et al., chap.10,
this volume). Finally, if parents
rely on their children for help at home, they may not encourage
their children to participate in
organized activities (Elder & Conger, 2000; Fletcher et al.,
2000).
Individual Characteristics
An individual’s competence, age, and developmental status can
constrain participation in
organized activities. For instance, because skill level can
determine access to some organized
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activities, particularly in adolescence, early activity
involvement may be required for some forms
of later activity participation (McNeal, 1998). Therefore,
children who do not (or cannot)
become competent in the skills developed through organized
activities early on are likely to fnd
that opportunities for involvement in such activities diminish
across childhood and adolescence.
It is no small irony that organized activities are particularly
effective at building the very
competencies that would facilitate participation (Larson et al.,
The maturity of the youth may also lead some adolescents to
drop out of organized activities. In
general, participation in many out-of-school organized activities
declines as children move into
and through adolescence (Eccles & gootman, 2002).
4
This refects (a) a decline in some organized
programs for older children, s an increase in competition for
membership in available activities
that excludes some youth from participating, (c) the fact that
programs do not always provide the

kinds of activities likely to be of interest to adolescents, (d)
diminished school budgets that fund
extracurricular activities, and (e) the increase in adolescent
employment during the nonschool
hours. Programs that are successful at retaining their adolescent
members offer increasing
opportunities for leadership, decision making, and meaningful
service (Kirshner et al., chap.7,
this volume; McLaughlin, 2000; Pittman et al., chap.17, this
volume; Walker et al., chap.18, this
volume); in other words, they offer opportunities that ft the
maturing adolescents’ sense of self
and expertise.
In addition, individual and social-contextual factors ordinarily
interact to affect opportunities for
participation. Individuals participate in organized activities for
different reasons. Sometimes they
participate of their own volition because they want to
participate (they select to be involved). In
other cases, they are recruited by peers, parents, and/or activity
leaders to participate based on
personal characteristics, ability, or social connection (they are
socialized to participate). Often,

both of these processes—selection and socialization—are at
work in the decision to participate.
Furthermore, because participation itself is a socializing
experience, experiences while involved
can both increase and decrease the likelihood of continued
participation. Thus, continued
participation refects both of these processes.
Finally, greater benefits tend to be evident for students who
show consistent participation in
organized activities. For instance, children’s rate of attendance
in after-school programs is
positively associated with gains in school achievement and work
habits (Cosden, Morrison,
Albanese, & Macias, 2001; Marshall et al., 1997; NICHD Early
Child Care Research Network,
in press). Similarly, Barber et al. (chap.9, this volume) and
Mahoney et al. (2003) fnd that
whether participation in school-based extracurricular activities
is transient or stable affects the
developmental process and related outcomes. These findings are
consistent with the broader
literature on effective school and community interventions (e.g.,
Catalano et al., 1999; Durlak &
Wells, 1997; Eccles & gootman, 2002). Building relationships,

forming new behavior patterns,
and acquiring competencies take time. However, the fact that
selection and socialization
processes interact to initiate and maintain participation makes it
diffcult to evaluate the
effectiveness of long-term participation in organized activities.
Program Resources and Content
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As with all programs for children and adolescents, the extent to
which organized activities are
benefcial depends on their quality and content. Quality is partly
defned by whether a program
offers the eight features shown in Table 1.1. But, it is also
affected by the material and human

resources within a program. Indeed, these are likely to be vital
to the program’s ability to provide
the eight features. After-school programs for children provide a
good example. Considerable
variation exists in program resources, as evident in child-to-
staff ratios, staff education, and staff
turnover. Research shows, in turn, that these are related to
developmental outcomes for children
in the expected direction (e.g., vandell et al., chap.20, this
volume). Children attending programs
that are low on these factors may either fail to benefit or may
develop increased rates of problem
behaviors relative to children in alternative and high-quality
after-school arrangements. In this
volume, chapter 17 by Pittman et al. provides a valuable
discussion of what communities need to
do to build a robust system of high-quality programs to meet the
needs of youth.
The content of activities within programs is also likely to affect
a youth’s developmental
experiences. Although there is great variability in how adult
leaders organize a given activity,
preliminary research suggests that differing activities may
provide distinct developmental

opportunities and liabilities. Associated changes in substance
use, antisocial behavior, school
achievement, and self-esteem vary across different activities
(Barber et al. 2001, chap.9, this
volume; Pedersen & Seidman, chap.5, this volume; Stattin et
al., chap.10, this volume). For
instance, in a survey in one community, Hansen et al. (2002)
found that high-school youth
reported more experiences related to identity exploration and
emotional learning in sports
compared to other organized activities, but also more negative
peer and adult interactions.
Consistent with this, a controlled longitudinal study found that
sports participation was
associated with increases in both academic achievement and
alcohol consumption (Eccles &
Barber, 1999).
It is important to caution that these differences are not likely to
inhere in the activity itself. They
could, for example, refect how the current generation of coaches
or music directors construct the
culture, values, and goals youth experience in that specifc
activity. They could also refect the
current peer culture associated with the activity. Finally, they

may also refect the selection of
youth to activities that provide a culture and experiences in line
with their current values and
desires. Much future research is needed to begin to separate out
how developmental
opportunities are shaped by the type of activity and numerous
interrelated factors.
Problematic Activities. To be sure, not all youth activities and
programs are benefcial and some
are quite limited in the number of positive features they
possess. Some structured activities are
organized in ways that do not facilitate positive development
and may be harmful. One example
is provided by mentoring programs. volunteer mentors are often
a valuable resource in
remedying the decreased availability of adult guidance for
youth and facilitate perceived self-
esteem and school achievement. However, the program may
pose a risk if the mentoring
relationship is short-lived or fails (Rhodes & Spencer, chap.19,
this volume). A second example
involves participation in youth recreation centers that provide
relatively low structure and lack
skill-building aims. Regular involvement in these settings

appears to facilitate deviant peer
relationships during adolescence and persistent criminal
behavior into adulthood (e.g., Mahoney,
Stattin, & Magnussen, 2001; Stattin et al., chap.10, this
volume). This is likely to be particularly
true if such centers attract youth who are already involved in
problematic behaviors and activities
_6.html#tab1.1
_27.html
_24.html
_15.html
_10.html
_16.html
_26.html
_16.html
(Dishion & McMahon, 1998; Dishion, McCord, & Poulin, 1999;
Dishion, Poulin, & Burraston,
2001; Marshall et al., 1997; Pettit, Bates, Dodge, & Meece,
1999; Pettit et al., 1997; Reid &
Patterson, 1989). These examples serve as a powerful reminder

that organizing out-of school
activities appropriately is critical and essential.
Other Factors Infuencing Program Impact
There are numerous other factors that may affect the impact of
organized activities. Studies have
suggested that high-risk youth may benefit more from organized
activities than other youth
(Eccles & Temple ton, 2002; Mahoney, 2000), The likely
explanation is that these youth have
less access in other parts of their lives to the types of resources
and developmental experiences
that these activities provide, thus the impact is likely to be
greater (Elder & Conger, 2000). Little
research has been done to evaluate how gender and ethnicity
may Influence a child’s likelihood
of gaining from a program (Pedersen & Seidman, chap.5, this
volume). It should not be assumed
that a single program will be equally benefcial to youth with
differing backgrounds. An
important horizon of future research is to understand how the ft
between a youth program and the
characteristics of individuals shapes development (Eccles &
Templeton, 2002).

OVERVIEW OF THE CONTENTS
This book presents conceptual, empirical, and policy-relevant
advances in research on children’s
and adolescent’s participation in the developmental contexts
represented by extracurricular
activities and after-school and community programs. Many of
the issues brought to light in this
chapter are taken up in greater detail in the chapters to follow.
The volume is organized into three main sections. Part I
discusses social and cultural
perspectives on organized activity participation. It begins with
the historical evolution of leisure
activities in the United States and the associated risks inherent
in a leisure experience that is
unstructured and lacks involvement in organized activities.
Next, new perspectives on the role of
organized activity involvement in the development of youth
from low-income families and those
from traditionally defned minority groups are provided. Finally,
the involvement of youth as
participants in the research process itself is considered. Part II
provides a collection of new

empirical studies on how participation in organized activities
affects developmental processes
and outcomes. Across the chapters, particular attention is given
to the developmental experiences
provided through participation in difference types of organized
activities, and how the
experiences translate into psychosocial adjustment and
competence. It concludes with a
commentary by Jacquelynne Eccles that discusses chapters in
Parts I and II of this volume. Part
III links the conceptual and research knowledge base on
organized activities to practice and
policy issues surrounding out-of-school time for young persons.
This includes empirically based
and practical guidance for developing effective organized
activities in general, and specifc
_10.html
insights into optimal practices for community and after-school
programs. This section concludes
with a commentary on out-of-school practices and policy
considerations by Jane Quinn.

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1 See, for example, after-school evaluations of the YS-CARE
After School Program http://
www.gse.uci.edu/asp/aspeval/resources/YSCARE13.pdf, LA’s
Best http://www.lasbest.
org/learn/eval.html, The After-School Corporation
http://www.tascorp.org/pages/promising_es1.pdf

Summary
2 In contrast to the variety of measures for assessing program
quality during early childhood,
there are few established instruments to evaluate out-of-school
programs for school-age children
or adolescents (e.g., Harms, Jacobs, & White, 1996). The
diversity of program content and goals
that characterize activities and programs for older children and
youth may account for this
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http://www.lasbest/
http://www.tascorp.org/pages/
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discrepancy. One possibility is to defne the process quality of
out- of-school activities in terms of
the eight features of positive youth contexts summarized in this
chapter.
3 Intervention research shows that a treatment can be benefcial
for persons who do not directly
receive the treatment. This phenomenon—known as spread of
effect—may also be true for
organized activities. For example, whether or not youth

participate in organized activities,
problem behaviors are lower if their peer group (Mahoney,
2000) or their parents (Mahoney &
Magnusson, 2001) are participants. Similarly, making organized
activities available to
adolescents has been linked to decreased levels of juvenile
antisocial behavior in the community
(Jones & Offord, 1989).
4 Participation in school-based extracurricular activities has
been found to increase, rather than
decline, across adolescence in some studies (e.g., Kinney, 1993;
Mahoney & Cairns, 1997). The
increase in participation may refect the relatively small number
of extracurricular activities
available to students prior to middle school and the rapid
expansion of extracurricular activities
in high school.
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The Impacts of Neighborhoods on Intergenerational Mobility II:
County-Level Estimates∗
Raj Chetty, Stanford University and NBER
Nathaniel Hendren, Harvard University and NBER
December 2017
Abstract
We estimate the causal effect of each county in the U.S. on
children’s incomes in adulthood.
We first estimate a fixed effects model that is identified by
analyzing families who move across
counties with children of different ages. We then use these fixed
effect estimates to (a) quantify
how much places matter for intergenerational mobility, (b)
construct forecasts of the causal effect
of growing up in each county that can be used to guide families
seeking to move to opportunity,
and (c) characterize which types of areas produce better
outcomes. For children growing up in
low-income families, each year of childhood exposure to a one
standard deviation (SD) better
county increases income in adulthood by 0.5%. There is
substantial variation in counties’ causal
effects even within metro areas. Counties with less concentrated
poverty, less income inequality,
better schools, a larger share of two-parent families, and lower
crime rates tend to produce
better outcomes for children in poor families. Boys’ outcomes
vary more across areas than girls’
outcomes, and boys have especially negative outcomes in highly
segregated areas. Areas that
generate better outcomes have higher house prices on average,

but our approach uncovers many
“opportunity bargains” – places that generate good outcomes
but are not very expensive.
∗ An earlier version of this paper was circulated as the second
part of the paper, “The Impacts of Neighborhoods
on Intergenerational Mobility: Childhood Exposure Effects and
County Level Estimates.” The opinions expressed in
this paper are those of the authors alone and do not necessarily
reflect the views of the Internal Revenue Service or the
U.S. Treasury Department. This work is a component of a larger
project examining the effects of tax expenditures
on the budget deficit and economic activity. All results based on
tax data in this paper are constructed using
statistics originally reported in the SOI Working Paper “The
Economic Impacts of Tax Expenditures: Evidence from
Spatial Variation across the U.S.,” approved under IRS contract
TIRNO-12-P-00374. We thank Gary Chamberlain,
Maximilian Kasy, Lawrence Katz, Jesse Shapiro, and numerous
seminar participants for helpful comments and
discussions. Sarah Abraham, Alex Bell, Augustin Bergeron,
Michael Droste, Niklas Flamang, Jamie Fogel, Robert
Fluegge, Nikolaus Hildebrand, Alex Olssen, Jordan Richmond,
Benjamin Scuderi, Priyanka Shende, and our other
pre-doctoral fellows provided outstanding research assistance.
This research was funded by the National Science
Foundation, the Lab for Economic Applications and Policy at
Harvard, Stanford University, and Laura and John
Arnold Foundation.
I INTRODUCTION
How are children’s economic opportunities shaped by the
neighborhoods in which they grow up?

In the first paper in this series (Chetty and Hendren 2017), we
showed that neighborhoods have
significant childhood exposure effects on children’s life
outcomes. Although those results establish
that place matters for intergenerational mobility, they do not
tell us which areas produce the best
outcomes, nor do they identify the characteristics of
neighborhoods that generate good outcomes
– two key inputs necessary for developing place-focused
policies to improve children’s outcomes.
In this paper, we build on the exposure-time design developed
in our first paper to estimate the
causal effect of each county in the U.S. on children’s incomes
in adulthood. Formally, our first paper
identified one treatment effect – the average impact of exposure
to an area where children have
better outcomes – while this paper pursues the more ambitious
goal of identifying (approximately)
3,000 treatment effects, one for each county in the country.1
We estimate counties’ causal effects on children’s ranks in the
income distribution at age 26
using data from de-identified tax returns for all children born
between 1980 and 1986.2 We estimate

each county’s effect using a fixed effects regression model
identified by analyzing families who move
across counties, exploiting variation in children’s ages when
families move. To understand how the
model is identified, consider families in the New York area. If
children who move from Manhattan to
Queens at younger ages earn more as adults, we can infer that
growing up in Queens has a positive
causal effect relative to growing up in Manhattan under the
assumption that other determinants of
children’s outcomes are unrelated to the age at which they
move. Building on this logic, we use our
sample of cross-county movers to regress children’s income
ranks at age 26 on fixed effects for each
county interacted with the fraction of childhood spent in that
county. We estimate the county fixed
effects separately by parent income level, permitting the effects
of each area to vary with parent
income. We include origin-by-destination fixed effects when
estimating this model, so that each
county’s effect is identified purely from variation in the age of
children when families make a given
move rather than variation in where families move.
1To maximize statistical precision, we characterize

neighborhood (or “place”) effects at the county level. We
recognize that counties are much larger than the typical
geographic units used to define “neighborhoods.” In the
presence of heterogeneity across local areas within counties, the
county-level effects we estimate can be interpreted as
weighted averages of the local area effects. In future work, the
methods we develop here could be applied to estimate
place effects at smaller geographies, such as Census tracts.
2We measure incomes at age 26 because children’s mean ranks
in each area tend to stabilize by age 26. For
example, the (population-weighted) correlation between mean
income ranks at age 26 and age 32 across commuting
zones is 0.93 for children growing up in low-income (25th
percentile) families and 0.77 for children growing up in
high-income (75th percentile) families.
1
http://www.equality-of-opportunity.org
The key assumption required to identify counties’ causal effects
using this research design is
that children’s potential outcomes are orthogonal to the age at
which they move to a given county.
This assumption is motivated by the evidence in our first paper
showing that the age at which
children move to an area where permanent residents (non-
movers) have better or worse outcomes
on average is orthogonal to their potential outcomes. However,
it is a stronger requirement than

the condition required to identify average exposure effects in
our first paper because it imposes
3,000 orthogonality conditions – one for each county – rather
than a single orthogonality condition
that must hold on average.
We assess the validity of this stronger identification assumption
using two approaches. First, we
show that controlling for parental income levels and marital
status in the years before and after the
move – which are strong predictors of children’s outcomes –
does not affect the estimates, supporting
the view that our estimates are not confounded by selection on
other determinants of children’s
outcomes. Second, we implement placebo tests by (a) estimating
each area’s fixed effect on teenage
labor force participation rates at age 16 (a strong predictor of
incomes in adulthood), using the
subsample of families who move after age 16 and (b) estimating
each area’s effect on income at
age 26 using parents who move after their children turn 23, the
point at which neighborhood
exposure no longer appears to affect children’s outcomes based
on the evidence in our first paper.

These placebo fixed effect estimates are uncorrelated with our
baseline estimates, supporting the
assumption that the time at which parents move to a given
county is orthogonal to their children’s
potential outcomes.
We use the estimates of counties’ causal effects for three
purposes. First, we quantify how much
neighborhoods matter for children’s incomes. We model the
estimated county effects as the sum of
a latent causal effect and noise due to sampling error, and
estimate the signal variance of the latent
causal effects. For a child with parents at the 25th percentile of
the national income distribution, we
find that spending one additional year of childhood in a one SD
better county (population-weighted)
increases household income at age 26 by 0.17 percentile points,
equivalent to an increase in mean
income of approximately 0.5%. Extrapolating over 20 years of
childhood, growing up in a 1 SD
better county from birth would increase a child’s household
income in adulthood by approximately
10%.3
3We focus primarily on estimates of place effects on household
income (including spousal income), but also report

estimates using individual income below. The household income
estimates are highly correlated with the individual
income estimates for males, whose outcomes are typically used
as a measure of economic opportunities in the literature
on intergenerational mobility because the variance in earnings
due to differences in labor force participation rates is
smaller for men than women (e.g., Solon 1999).
2
Neighborhoods have similar effects in percentile rank or dollar
terms for children of higher-
income parents, but matter less in percentage terms because
children in high-income families have
higher mean incomes. For children with parents at the 75th
percentile of the income distribution,
the signal SD of annual exposure effects across counties is 0.16
percentiles, which is approximately
0.3% of mean income. Importantly, areas that generate better
outcomes for children in low-income
families generate slightly better outcomes on average for
children in high-income families as well.
This result suggests that the success of the poor does not have
to come at the expense of the rich.
In the second part of the paper, we construct forecasts of the
causal effect of growing up in each

county that can be used to guide families seeking to move to
better areas. Formally, we construct
forecasts that minimize the mean-squared-error (MSE) of the
predicted impact of growing up in
a given neighborhood relative to the true impact. Although the
raw county fixed effects provide
unbiased estimates of counties’ causal effects, they do not
themselves provide good forecasts because
many of the estimates have substantial noise, leading to high
MSE. In highly populated counties,
such as Cook County (the city of Chicago), nearly 75% of the
variance in the fixed effect estimates
is signal; however, in most counties, more than half of the
variance in the fixed effect estimates is
due to noise from sampling variation.
To obtain forecasts that have lower MSE, we use a shrinkage
estimator that incorporates data
on the permanent residents’ (non-movers) outcomes in each
area.4 The permanent residents’ mean
outcomes have very little sampling error, but are imperfect
forecasts of a county’s causal effect
because they combine causal effects with sorting. The best
MSE-minimizing linear forecast of each
county’s causal effect is therefore a weighted average of the

fixed effect estimate based on the movers
and a prediction based on permanent residents’ outcomes, with
greater weight on the fixed effect
estimate when it is more precisely estimated (i.e., in large
counties).
Among the 100 most populated counties in the country, DuPage
County, IL is forecasted to
generate the highest incomes for children growing up in low-
income (25th percentile) families. Each
additional year that a child in a low-income family spends in
DuPage County, IL instead of the
average county in the U.S. raises his or her household income in
adulthood by 0.80%. Growing
4Our methodology contributes to a recent literature that builds
on empirical Bayes methods dating to Robbins
(1956) by using shrinkage estimators to reduce mean-squared-
error (risk) when estimating a large number of param-
eters. For instance, Angrist et al. (2017) combine experimental
and observational estimates to improve forecasts of
school value-added. Our methodology differs from theirs
because we have unbiased (quasi-experimental) estimates
of causal effects for every area, whereas Angrist et al. have
unbiased (experimental) estimates of causal effects for a
subset of schools. Hull (2017) develops methods to forecast
hospital quality, permitting nonlinear and heterogeneous
causal effects. Abadie and Kasy (2017) show how machine
learning methods can be used to reduce risk, using the
fixed effect estimates constructed in this paper as an
application.

3
up in DuPage County from birth – i.e., having about 20 years of
exposure to that environment –
would raise such a child’s income by 16%. In contrast, growing
up in Cook County – one of the
lowest-ranking counties in the U.S. – from birth reduces a
child’s income by approximately 13%.
Hence, moving from Cook County (the city of Chicago) to
DuPage County (the western suburbs)
at birth would increase a child’s income by about 30% on
average.5
We find that neighborhoods matter more for boys than girls: the
signal standard deviation of
county-level effects is roughly 60% larger for boys than girls in
low-income (25th percentile) families.
The distribution has an especially thick lower tail for boys, as
counties with high concentrations
of urban poverty such as Baltimore City and Wayne County
(Detroit) produce especially negative
outcomes for boys.6
Our estimates of the causal effects of counties and commuting
zones (CZs) are highly correlated

with the observational statistics on intergenerational mobility
reported in Chetty et al. (2014) –
as expected given the findings in Chetty and Hendren (2017) –
but there are many significant
differences. For example, children who grow up in low-income
families in New York City have
outcomes comparable to the national mean, but the causal effect
of growing up in New York City
– as revealed by analyzing individuals who move into and out of
New York – is well below the
national mean. One potential explanation for this pattern is that
New York has a large share
of immigrants, who tend to have high rates of upward mobility
(Hilger 2016). More generally,
this example illustrates the importance of estimating the causal
effect of each area directly using
movers (as we do in this paper) rather than predicting
neighborhood effects purely from permanent
residents’ outcomes.
In the third part of this paper, we characterize the properties of
CZs and counties that produce
good outcomes for low-income children (i.e., generate high
rates of upward mobility). Prior work has

shown that in observational data, upward mobility is highly
correlated with area characteristics
such as residential segregation, income inequality, social
capital, and school quality as well as
demographic characteristics such as the fraction of children
being raised by single mothers and racial
shares (Wilson 1987; Sampson et al. 2002; Chetty et al. 2014).
However, it is unclear whether these
correlations are driven by the causal effects of place or
selection effects. For instance, is growing
5Interestingly, many families involved in the well-known
Gautreaux housing desegregation project moved from
Cook County to DuPage County. Our results support the view
that much of the gains experienced by the children of
the families who moved as part of Gautreaux (Rosenbaum 1995)
was due to the causal effect of exposure to better
neighborhoods.
6These gender differences are partly related to differences in
rates of marriage. For example, the San Francisco
area generates high individual incomes but relatively low
household incomes for girls because growing up in San
Francisco reduces the probability that a child gets married.
4
up in a less segregated area beneficial for a given child or do
families who choose to live in less

segregated areas simply have better unobservable
characteristics?
We decompose the correlations documented in prior work into
causal vs. sorting components
by correlating each characteristic with both our causal effect
estimates and permanent residents’
outcomes, which combine both causal and selection effects. We
find that many of the correlations
between area-level characteristics and upward mobility are
driven almost entirely by causal effects
of place. For example, 80% of the association between
segregation and upward mobility across CZs
in observational data is driven by the causal effect of place;
only 20% is due to sorting. Growing
up in a CZ with a one standard deviation higher level of
segregation from birth reduces the income
of a child in a low-income (25th percentile) family by 5.2%.7
Urban areas, particularly those
with concentrated poverty, generate particularly negative
outcomes for low-income children. These
findings support the view that growing up in an urban “ghetto”
reduces children’s prospects for
upward mobility (Massey and Denton 1993; Cutler and Glaeser
1997).

Areas with greater income inequality – as measured by the Gini
coefficient or top 1% income
shares – also generate significantly worse outcomes for children
in low-income families. Hence, the
negative correlation between inequality and intergenerational
mobility documented in prior work
– coined the “Great Gatsby curve” by Krueger (2012) – is not
simply driven by differences in
genetics or other characteristics of populations in areas with
different levels of inequality. Rather,
putting a given child in an area with higher levels of inequality
makes that child less likely to rise
up in the income distribution. The negative correlation between
the causal effects and top 1%
shares contrasts with the findings of Chetty et al. (2014), who
find no correlation between top 1%
shares (upper-tail inequality) and rates of upward mobility in
observational data. Our analysis of
movers reveals that low-income families who live in areas with
large top 1% shares (such as New
York City) are positively selected, masking the negative
association between top 1% shares and the
causal effect of places of upward mobility in observational data.
We find strong correlations between areas’ causal effects and

output-based measures of school
quality, such as test scores adjusted for parent income levels.
We also find strong correlations
between the causal effects and proxies for social capital, such as
crime rates and Rupasingha and
Goetz’s (2008) summary index.
Selection plays a bigger role in explaining correlations between
demographic characteristics and
7This result does not necessarily imply that reducing
segregation in a given area will improve children’s outcomes.
Other factors associated with less segregation (e.g., better
schools) could potentially be responsible for the gain a
child obtains from moving to a less segregated area.
5
upward mobility in observational data. For example, the
fraction of single mothers is the single
strongest predictor of differences in upward mobility for
permanent residents across areas. However,
the fraction of single mothers – although still a significant
predictor – is less highly correlated with
CZs’ causal effects on upward mobility than other factors such
as segregation. This is because nearly
half of the association between permanent residents’ outcomes

and the fraction of single mothers is
due to selection. Similarly, areas with a larger African-
American population have significantly lower
rates of upward mobility in observational data. Roughly half of
this association is also unrelated to
causal effect of place, consistent with Rothbaum (2016).
Nevertheless, the correlation between the
causal effects of place and the African-American share remains
substantial (-0.51 across CZs and
-0.37 across counties within CZs). Place effects therefore
amplify racial inequality: black children
have worse economic outcomes because they grow up in worse
neighborhoods.
Finally, we examine how much more one has to pay for housing
to live in an area that generates
better outcomes for one’s children. Within CZs, counties that
produce better outcomes for children
have slightly higher rents, especially in highly segregated cities.
However, rents explain less than
5% of the variance in counties’ causal effects for families at the
25th percentile. This result suggests
that current “small area” fair market rent proposals in housing
voucher programs – which condition
voucher payments on local neighborhood rents – may not

maximize vouchers’ effects on upward
mobility, as many areas that are more expensive do not produce
better outcomes. Moreover, it
shows that some areas are “opportunity bargains” – counties
within a labor market that offer good
outcomes for children without higher rents.8 For example, in
the New York metro area, Hudson
County, NJ offers much higher levels of upward mobility than
Manhattan or Queens despite having
comparable rents during the period we study.
To understand the source of these opportunity bargains, we
divide our causal county effects
into the component that projects onto observable area-level
factors, such as poverty rates and
school expenditures, and a residual “unobservable” component.
We find that only the observable
component is capitalized in rents, suggesting that the
opportunity bargains may partly exist because
families do not know which neighborhoods have the highest
value-added. This result underscores
the importance of measuring neighborhood quality directly
using children’s observed outcomes
instead of using traditional proxies such as poverty rates.

The rest of this paper is organized as follows. In Section II, we
summarize the data, focusing on
8Of course, the areas that are “opportunity bargains” in rents
may come with other disamenities – such as longer
commutes to work – that might make them less desirable. Our
point is simply that housing costs themselves are not
necessarily a barrier to moving to opportunity.
6
differences relative to the sample used in our first paper. In
Section III, we formalize our empirical
objectives using a statistical model. Section IV reports the
baseline fixed effect estimates and
evaluates the validity of the key identification assumptions.
Section V quantifies the magnitude of
place effects, Section VI presents the CZ- and county-level
MSE-minimizing forecasts, and Section
VII examines the characteristics of places that generate better
outcomes. Section VIII presents the
results on housing costs and opportunity bargains. Section IX
concludes. Supplementary results
and details on estimation methodology are provided in an online
appendix. Estimates of CZs’ and
counties’ causal effects are available on the Equality of
Opportunity Project website.

II DATA AND SAMPLE DEFINITIONS
We use data from federal income tax records spanning 1996-
2012. Our primary analysis sample is
the same as the sample of movers in Chetty and Hendren (2017,
Section II), with three exceptions.
First, we limit the sample to children in the 1980-86 birth
cohorts because we measure children’s
incomes at age 26. Measuring children’s incomes at age 26
strikes a balance between the competing
goals of minimizing lifecycle bias by measuring income at a
sufficiently old age and having an
adequate number of birth cohorts to implement our research
design. Among permanent residents
(parents who stay in the same CZ from 1996-2012) at the 25th
percentile of the income distribution,
the population-weighted correlation between children’s mean
ranks at age 26 and age 32 across CZs
is 0.93.9 This suggests that measuring children’s incomes at
later ages would not affect our estimates
of places’ causal effects substantially.
Second, we focus on the subset of families who move across
CZs or counties when their child is
23 or younger, motivated by our finding that childhood

exposure effects persist until age 23. To
simplify estimation, we focus on families who move exactly
once during the sample period, dropping
those who move multiple times. We divide the sample of one-
time movers into two groups – those
who move across CZs and those who move across counties
within CZs – and analyze these two sets
of movers separately.
Third, to maximize precision, we include all movers – not just
those who move between large
CZs – in our analysis sample. However, we only report
estimates of causal effects for CZs with
populations above 25,000 and counties with populations above
10,000 in the 2000 Census (excluding
9The key point here is that children’s average ranks in each area
stabilize by age 26. At the individual level,
children’s incomes stabilize later, around age 32 (Haider and
Solon 2006, Figure 2a). Intuitively, children’s ranks
change rapidly in their late 20s, but these idiosyncratic
individual-level changes average out at the area level, so that
areas where children have high ranks at 26 tend to have high
ranks at age 32 as well.
7
http://www.equality-of-opportunity.org/data/index.html#movers

0.36% of the population).10
We measure children’s and parents’ incomes at the household
level using data from 1040 forms
(for those who file tax returns) and W-2 forms (for non-filers),
which we label family (or household)
income. We identify individuals’ locations in each year using
the ZIP code from which they filed
their tax returns or to which their W-2 forms were mailed. We
also measure a set of additional
outcomes for children, such as individual income, college
attendance, and marriage. All of these
variables are defined in the same way as described in Section II
of Chetty and Hendren (2017).
Table I presents summary statistics for children in our primary
sample who move across CZs
(Panel A) and counties within CZs (Panel B). There are
1,397,260 children whose parents move
once across CZs and 931,138 children whose parents move
across counties within CZs in our primary
sample. The sample characteristics are generally very similar to
those reported in Table I of Chetty
and Hendren (2017), with a median family income of $24,253 at
age 26 for children in the CZ
movers sample and $24,993 in the county-within-CZ movers

sample (in 2012 dollars).
III EMPIRICAL FRAMEWORK
In this section, we first define the estimands we seek to identify
using a statistical model of neighbor-
hood effects. We then describe the research design we use to
identify these parameters and the key
identification assumptions underlying our analysis. Finally, we
discuss the empirical specification
and estimation procedures we use to implement this research
design.
III.A Statistical Model
We estimate place effects using a statistical model motivated by
the childhood exposure effects
documented in Chetty and Hendren (2017). Let yi denote a
child’s income (or other outcome) in
adulthood, measured at age T . We model yi as a function of
three factors: the neighborhoods
in which the child grows up, disruption costs of moving across
neighborhoods, and all other non-
neighborhood inputs, such as family environment and genetics.
Let c (i, a) denote the place in which child i lives at age a = 1,
..., A of his childhood, where
A < T . Let µc denote the causal effect of one additional year of

exposure to place c on the child’s
10In Chetty and Hendren (2017), we limited our primary sample
to CZs with populations above 250,000 to minimize
attenuation bias in exposure effect estimates resulting from
noise in permanent residents’ outcomes. This attenuation
bias does not arise here because we identify causal effects
purely from the sample of movers, without projecting their
outcomes onto permanent residents. This is why we impose
lower population restrictions here, providing estimates
for a larger set of CZs. Because our goal is to provide estimates
of place effects at the county (rather than CZ) level,
we also do not impose any restrictions on the distance of moves
we examine.
8
outcome yi.
11 Given the linear childhood exposure effects documented in
Chetty and Hendren
(2017, Figure IV), we assume that the exposure effect µc is
constant for ages a ≤ A and is zero
thereafter. Let κ denote the cost of moving from one
neighborhood to another during childhood
(e.g., due to a loss of connections to friends or other fixed costs
of moving). Finally, let θi denote
the impact of other factors, such as family inputs. The
parameter θi captures both time-invariant
inputs, such as genetic endowments, and the total amount of

time-varying inputs, such as parental
investments during childhood.
Combining the effects of neighborhoods, disruption effects of
moving, and other factors, the
child’s outcome is given by
yi =
A∑
a=1
[
µc(i,a) − κ1 {c (i, a) 6= c (i, a− 1)}
]
+ θi (1)
The production function for yi in (1) imposes three substantive
restrictions that are relevant for
our empirical analysis. First, it assumes that neighborhood
effects µc do not vary across children
(conditional on parent income).12 Second, it assumes that place
effects are additive and constant
across ages, i.e., that there are no complementarities between
neighborhood effects across years.
Third, it assumes that the disruption costs of moving κ do not
vary across neighborhoods or the age
of the child at the time of the move.13 We believe these
restrictions are reasonable approximations

given the findings of our first paper, which show that childhood
exposure effects are constant
throughout childhood and symmetric when children move to
areas with better or worse permanent
resident outcomes on average (conditional on parent income).
Nevertheless, we view estimating
models that permit richer forms of heterogeneity in place effects
as an important direction for
future research.14
11In our empirical application, we permit place effects µpc to
vary with parental income rank p(i), but we suppress
the parental income index in this section to simplify notation.
12This constant treatment effects assumption is a common
simplification in the literature. For instance, in work
on firm effects and teacher effects (Abowd et al. 1999, Chetty,
Friedman, and Rockoff 2014a), analogous restrictions
rule out worker-firm or teacher-student match effects. Our fixed
effect estimates can be interpreted as mean place
effects in the presence of heterogeneous treatment effects if
such heterogeneity is orthogonal to individuals’ transition
rates across areas, i.e. as long as there is no “essential”
heterogeneity. Understanding how the present estimates of
µpc can be interpreted in the presence of essential heterogeneity
and estimating models that permit richer forms of
heterogeneity are important directions for further work.
13The model can be extended to allow the disruption cost to
vary with the neighborhood to which the child moves,
or to allow the disruption cost to vary with the age of the child

at the time of the move. Neither of these extensions
would affect our estimates. The key requirement for approach to
identifying {µc} is that the disruption costs do not
vary in an age-dependent manner across neighborhoods.
14We do not estimate such models here primarily because of a
lack of adequate statistical power. As we will see
below, obtaining precise estimates of 6,000 treatment effects
(one per county, interacted with parent income) with
our sample of approximately 3 million movers is itself
challenging. Estimating higher-dimensional models may be
feasible as additional years of tax data become available in the
U.S. or using longer administrative panels in other
countries.
9
Our objective in this paper is to identify ~µ = {µc}, the causal
exposure effect of spending a year
of one’s childhood in a given area (CZ or county) of the U.S.
One way to identify ~µ would be to
randomly assign children of different ages to different places
and compare their outcomes, as in the
Moving to Opportunity experiment (Chetty, Hendren, and Katz
2016). Since conducting such an
experiment in all areas of the country is infeasible, we develop
methods of identifying place effects
in observational data.

III.B Identifying Place Effects in Observational Data
Building upon the approach in Chetty and Hendren (2017), we
identify ~µ by exploiting variation
in the timing of when children move across areas. To
understand the intuition underlying our
approach, consider a set of children who move from a given
origin o (e.g., New York) to a given
destination d (e.g., Boston). Suppose that children who make
this move at different ages have
comparable other inputs, θi. Then one can infer the causal effect
of growing up in Boston relative
to New York (µd − µo) by comparing the outcomes of children
who move at different ages; for
instance, if those who move at younger ages have better
outcomes, we learn that µd > µo.
Under the model in (1), we can combine information from all
such pairwise comparisons to
estimate each place’s causal effect using the following fixed
effects specification:
yi = αod + ~ei ~·µ+ �i, (2)
where αod denotes an origin-by-destination fixed effect and ~ei
= {eic} is a vector whose entries
denote the number of years of exposure that child i has to place
c before age A. In a sample of

children who move exactly once before age A, eic is given by
eic =
A−mi if d (i) = c
mi if o (i) = c
0 otherwise
where mi denotes the age of the child at the time of the move.
Equation (2) is a reduced form of the model in (1) for one-time
movers, where αod = θ̄od − κ
captures the sum of the disruption effect and the mean value of
other inputs θi for children who
move from o to d, while �i = θi− θ̄od captures idiosyncratic
variation in other inputs. By including
origin-by-destination (αod) fixed effects in (2), we identify ~µ
purely from variation in the timing
of moves, rather than comparing outcomes across families that
moved to or from different areas.15
15The fixed effects ~µ are identified up to the normalization
that the average place effect is zero, E [µc] = 0, because
the matrix E = {~ei}i does not have full rank. Intuitively, using
movers to identify place effects allows us to identify
the effect of each place relative to the national average.
10

The identification assumption required to obtain consistent
estimates of ~µ when estimating (2)
using OLS is the following standard orthogonality condition.
Assumption 2. Conditional on αod, exposure time to each place,
~ei, is orthogonal to other
determinants of children’s outcomes:
Cov(eic, �i) = 0 ∀ c. (3)
Assumption 2 requires that children with different exposure
times to a set of places do not system-
atically differ in other inputs, �i = θi − θ̄od, conditional on
origin-by-destination fixed effects. This
assumption is a stronger version of Assumption 1 in Chetty and
Hendren (2017), which required
that exposure to better places – as measured by the outcomes of
permanent residents – is not cor-
related with �i on average. Assumption 2 extends that
assumption to require that the amount of
exposure to every place satisfies such an orthogonality
condition. This stronger assumption allows
us to go beyond establishing that neighborhoods have causal
effects on average and characterize
precisely which areas produce the best outcomes. We provide

evidence supporting Assumption 2
in Section IV.B after presenting our baseline results.
III.C Empirical Implementation
In our empirical analysis, we generalize (2) to account for two
features of the data that we omitted
from the stylized model in Section III.A for simplicity.
First, we allow for the possibility that places may have different
effects across parent income
levels, as suggested by the maps of permanent residents’
outcomes in Chetty and Hendren (2017,
Figure II). To do so, we first measure the percentile rank of the
parents of child i, p(i), based on their
positions in the national distribution of parental household
income for child i’s birth cohort. Chetty
et al. (2014) show that within each area, children’s expected
income ranks are well approximated
by a linear function of their parents’ income rank p(i). We
therefore generalize (2) to allow place
effects µc to vary linearly with parent income rank, p (i). We
denote the causal effect of place c at
parent rank p by
µpc = µ
0

c + µ
1
cp (4)
where µ0c , the intercept, represents the causal effect of the
place for children in the lowest-income
families and µ1c , the slope, captures how the causal effect
varies with parent rank. For symmetry,
we also allow the origin-by-destination fixed effect αod in (2)
to vary linearly with parent rank p in
order to capture potential heterogeneity in selection effects by
parent income.
11
Second, because we measure children’s incomes at a fixed age,
we measure their incomes in
different calendar years. In particular, incomes at age 26 are
measured between 2006-2012 for
children in our primary sample (the 1980-86 birth cohorts), a
period with rapidly changing labor
market conditions. We account for these fluctuations – allowing
for differential shocks across areas
and income groups – by including a control function
god(p, s) = ψ

0
ods+ ψ
1
ods
2 + ψ2odsp+ ψ
3
ods
2p (5)
when estimating (2), where s denotes the child’s birth cohort
(or, equivalently, the year in which
the child’s income is measured). We show in Online Appendix
A that alternative parameterizations
of the g(p, s) control function yield very similar results.
Incorporating these two extensions of (2), our baseline
estimating equation is
yi = αod + α
P
odp+ ~ei ~·µp + god(pi, si) + εi, (6)
where αod is an origin-by-destination fixed effect, α
P
odp is an origin-by-destination fixed effect inter-
acted with parent rank, and ~µp = {µpc} denotes the vector of
causal place effects parameterized as
in (4).

Estimation: Hierarchical Structure. Our goal is to use (6) to
identify the causal effects of places
at two geographic levels: commuting zones – aggregations of
counties that represent local labor
markets – and counties. Directly estimating the 741 CZ-level
and 3,138 county-level fixed effects
along with the incidental parameters in (6) is not feasible
because of computational constraints.
We therefore estimate ~µp using a hierarchical structure,
separately estimating the causal effects of
CZs and counties within CZs.
We begin by estimating CZ effects using our sample of cross-
CZ movers. For computational
tractability, we use a two-step estimator, described in detail in
Online Appendix A. In the first
step, we estimate (6) separately for each origin-destination (o,
d) pair, which yields an estimate of
the exposure effect for each origin relative to each destination,
µpod = µpd − µpo, for each level of
parental income, p.16 We then consider a fixed parental income
level (e.g. p = 25) and regress
the pairwise effects {µpod} on a design matrix that consists of
positive and negative indicators for
each CZ to obtain an estimate of each CZ’s fixed effect at

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