adolescent exposure to community and family in
TRANSCRIPT
Adolescent Exposure to Community and Family in Neighborhoods with High Intergenerational Mobility
Jocelyn S. Wikle
Department of Economics Brigham Young University
Abstract
While certain neighborhoods stand out above others for improving future outcomes for low-
income children, little is known about how neighborhood effects are transmitted or what day-to-
day mechanisms are at play. Do neighborhoods primarily influence children through exposure to
quality community programs, mentors, and resources, or do they influence children through
improved parenting practices and family processes? This research addresses this question by
combining data from the American Time Use Survey and the Equality of Opportunity Project to
compare daily time use in areas with high and low economic mobility. I find differences in
parenting practices; parents living in areas of high intergenerational mobility more often spend
time obtaining government services, spend more time at home, more time with household
members, and more time in high quality care of children. This suggests that social norms
governing parenting practices may contribute to improved opportunity for children more than
previously realized. In contrast, I find no differences between areas with high and low economic
mobility in the amount of community exposure time among adolescents, suggesting that
community exposure may be operating through undetected differences in the quality of
interactions.
Keywords: inequality, intergenerational mobility, neighborhood effects, family processes, time use
JEL: I32, I38, J13, J62
I acknowledge helpful comments from Sandra Black, Dan Hamermesh, and Lars Lefgren. I thanks seminar participants for suggestions and comments.
Introduction
While certain neighborhoods are better than others at improving future outcomes for low-
income children, we don’t know exactly how neighborhood effects are transmitted day-to-day to
young people. Because children and adolescents learn through observing their world, spending
time with role models, and imitating those around them (Bandura & Walters, 1959), it is natural
to suspect that heterogeneous community environments lead to different long-run outcomes.
However, the set of heterogeneous community factors governing the mobility process are largely
unknown.
Researchers have made progress on understanding connections between neighborhoods
and long-run outcomes of children. The recent work of Chetty and Hendren (2017a)
demonstrates that there is a spatial component of opportunity in the United States. Using tax
records to follow the income and locations of families of adolescents and young adults for
sixteen years, they compare siblings who moved at different ages during adolescence to estimate
the causal effect of living in various neighborhoods on income later in life, propensity to attend
college, and marriage patterns. Other research—dealing with the random assignment of refugees
(Damm & Dustmann, 2014) and housing demolitions (Chyn, 2016)—also finds that
neighborhoods impact long-run outcomes for adolescents. These scholars argue that these
patterns are causal, persistent over time, and independent of selection patterns or other
characteristics of people living in neighborhoods. In addition, neighborhood effects matter
through childhood and are not explained by differences in labor market conditions. “Moving to a
better area just before entering the labor market has little impact on individual’s outcomes,
suggesting that place-conscious policies to promote upward mobility should focus primarily on
improving the local childhood environment” (Chetty & Hendren, 2017a). Each year of
childhood exposure matters roughly equally, and there is no age threshold at which the returns on
neighborhood exposure spike or evaporate. Thus, the constant and cumulative effect of
neighborhoods year after year into early adulthood can be quite large and have roots in
mechanisms spanning childhood and adolescence.
While researchers know that neighborhoods affect long-run outcomes in a gradual and
cumulative fashion, people can say little about the day-to-day mechanisms that contribute to
mobility. Do neighborhoods primarily change outcomes through adolescent exposure to the
community in the form of programs, mentors, policy, and quality schools? Or perhaps do
neighborhoods impact child outcomes through their effects on parenting practices and family
processes that then lead to long-run opportunity for children? These questions remain
unanswered to date.
The purpose of this paper is to determine whether low-income adolescents and parents in
high-opportunity neighborhoods show differences from low-income adolescents and parents in
low-opportunity neighborhoods in regard to daily community exposure and family practices.
The analysis is motivated by the gap in research surrounding how neighborhood effects shape
adolescent outcomes. There is great interest among parents and policymakers alike in
understanding what factors at home and in communities help or hinder children and adolescents.
Past empirical research has faced methodological challenges that have made measuring the
mechanisms of neighborhood exposure difficult, which explains why the existing literature
remains inconclusive about how neighborhood effects transmit. In addition, it is difficult to
separate the effects of neighborhood environment from systemic differences in the types of
people living in neighborhoods.
I use refined measures of time use among adolescents and parents from the American
Time Use Survey (ATUS) to construct measures of community and family exposure not
previously used by researchers. To uncover potential mechanisms and processes behind
economic opportunity, I combine mobility data and time-use data to study differential patterns in
family and community exposure. While I do not address selection concerns, the research
nonetheless contributes to a better understanding of how neighborhoods may influence outcomes
for poor families and provides suggestions for future research based on the empirical links found.
The research yields two lessons. First, the quality of adolescent community exposure
likely matters more than the quantity of community exposure. The amount of time that
adolescents spend interacting directly with their neighborhoods and communities remained equal
on all measures regardless of whether these adolescents lived in a high-opportunity area or a
low-opportunity area, suggesting that any community-to-child interactions which improve
mobility likely operate through the quality of the interaction. Second, parents’ daily practices in
interacting with their communities and families differed depending on where they lived,
suggesting that neighborhood effects may operate through parents more than previous thought.
Background
Adolescents and Community Interactions
In appealing to theory to give insight into how community exposure shapes adolescents, I
focus on a taxonomy of theoretical models linking neighborhoods and child development that
were put forth by Jencks and Mayer (1990) and Wikström and Sampson (2006). Most relevant
to this research are theories of institutional inputs, collective socialization, and epidemic spread.
Institutional input models posit that the quality and quantity of policy, institutions, and
people outside of neighborhoods help or hinder positive outcomes for the children and
adolescents within a neighborhood. This includes both publicly provided amenities and
nonprofit and community-based civic organizations. While these explanations commonly
attribute geographic differences in opportunity to institutional differences in educational
investments (Solon, 2004; see Black & Devereux, 2011, for a review), other institutional
mechanisms such as tax policy may also be at work (Lefgren, McIntyre, & Sims, 2015).
Empirical work evaluating state-level educational funding and tax policies finds a relatively
unimportant role for state-level education funding and tax policy in explaining geographic
differences in opportunity at the neighborhood level (Lefgren, Pope, & Sims, 2016). Moreover,
policy candidates put forth as mechanisms in empirical work often operate at a state level and do
little to explain differences at a local level.
Collective socialization models theorize that social capital is spread as adults in a
community influence young people and that the collective presence of involved adults likely
contributes to child development. Adult–child interaction and exposure to neighborhood role
models contributes to adolescent socialization (Ellen & Turner, 1997), adolescent well-being
(Hoagland & Wikle, 2018), and economic opportunity (Erickson, McDonald, & Elder, 2009).
On the other hand, communities with high levels of disorganization provide fewer models of
successful adults and offer fewer opportunities for youth (Hoffmann, 2006; Wilson, 1996). The
dissemination of social capital from adults to adolescents likely depends on both the quality of
interactions and the frequency of social interactions among neighbors, as well as other
“neighboring” patterns (Coleman, 1986).
Epidemic theories suggest that neighborhoods affect the development of young people
through providing opportunities for peer interactions, supposing that peer behavior is contagious
and that peer connections among children spread positive or negative behaviors. High
concentrations of poverty would hinder mobility by limiting exposure to peers who model pro-
mobile behaviors. In addition, neighborhoods likely play a role in an individual’s choice of peer
group. However, given the evidence that peer influences change throughout childhood (Berndt,
1996), models of epidemic spread predict non-constant effects through adolescence, which is
inconsistent with data observations of gradual constant neighborhood effects on mobility through
adolescence. Nonetheless, as part of the study I explore peer interactions as a potential
mechanism linked to opportunity.
Adolescents and Family Interactions
Theoretical models of adolescent exposure to a community assume that the quantity or
quality of community-to-child investments affect outcomes. Likewise, empirical work has
generally focused on community-to-child investments. Absent from these hypotheses is the
possibility that geographies may in fact impact parenting practices and family processes (Burton
& Robin, 2000), or that families may be the unit absorbing neighborhood effects. Theoretical
and empirical literature to date has remained primarily silent on the question about the role of
neighborhoods on parents, and “relatively few studies have considered families and
neighborhoods concurrently to investigate how they may interact and conjointly affect
individuals” (Noah, 2015). The failure to consider neighborhoods and parents together when
studying opportunity is surprising considering the large body of research on the role of parents in
child outcomes. Notable exceptions include work on the role of neighborhoods in parenting that
suggest connections between neighborhoods and family structure (Hoffmann, 2006), maternal
mental health (Klebanov, Brooks-Gunn, & Duncan, 1994; Kotchick, Dorsey, & Heller, 2005),
and premarital pregnancy and childbearing (Billy & Moore, 1992; Brooks-Gunn, Duncan,
Klebanov, & Sealand, 1993; Crane, 1991; Sucoff & Upchurch, 1998), all of which affect child
outcomes. If in reality neighborhoods impact short-run day-to-day parenting processes that span
childhood and adolescence, neighborhoods could generate spatial differences in opportunity
through their influence on parenting practices.
A variety of theoretical models connect parent practices and child outcomes, with a
general consensus that both nature and environment contribute to life outcomes. Becker and
Tomes (1976) broadly theorized that parents transmit human capital to children through
investments in time. These time investments could bring about social learning among children
(Bandura & Walters, 1959), greater ability of parents to respond to their child’s needs (Maccoby
& Martin, 1983), an improved context of a child’s environment (Bronfenbrenner & Morris,
2006), or healthy attachment to parents (Ainsworth & Bowlby, 1991), thus laying the foundation
for development and learning. Thus, theoretically, parental time investments in children lead to
the development of capable adults.
Empirical research connects parent practices with a wide variety of child outcomes. Of
note is empirical research connecting parental inputs with child outcomes in terms of time
parents spend with children (Price, 2008), time mothers spend at home (Datcher-Loury, 1988),
level of father’s involvement (Pleck, 1997), frequency of reading and playing (Zick, Bryant, &
Österbacka, 2001), and frequency of eating dinner as a family (Eisenberg, Olson, Neumark-
Sztainer, Story, & Bearinger, 2004). Additionally, parents often set expectations about child
involvement in home duties, and adolescents performing home duties likely benefit from the
work. Participation in home duties builds practical knowledge, soft skills, self-confidence,
responsibility, and dependability, traits that are difficult to measure and that likely impact future
achievement and success in college and employment (Beach, 1997; Call, Mortimer, & Shanahan,
1995; Kuperminc, Jurkovic, & Casey, 2009).
A growing body of research recognizes distinct differences in the way parents treat time
spent with their children compared to time spent on leisure and home production (Aguiar &
Hurst, 2007; Guryan, Hurst, & Kearney, 2008; Kimmell & Connelly, 2007), suggesting that
parents believe time with children accomplishes different objectives. There seems to be broad
theoretical and empirical consensus that parent practices shape child outcomes. If in fact,
neighborhoods influence parents, that influence would likely spill into child outcomes. Thus, it
seems natural to evaluate links between neighborhoods and daily family practices.
Data and Measures
The empirical analysis of economic opportunity and family and community exposure
utilizes individual-level time-use diaries from the American Time Use Survey (ATUS; see
Hofferth, Flood, & Sobeck, 2015). The Bureau of Labor Statistics administered the ATUS in
connection with the Current Population Survey (CPS). Households were sampled from the
outgoing rotation of the CPS, and one household member age 15 or older was randomly selected
to complete the time-use module. A phone interview lasting about thirty minutes documented
time use over a 24-hour period, from 4 a.m. of the previous day to 4 a.m. of the interview day.
Respondents accounted for all the time composing the day (Hamermesh, Frazis, & Stewart,
2005). Interviewers used the Day Reconstruction Method and computer assistance to elicit high-
quality recall and accuracy (Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004). Given the
stability of area mobility over time, I pooled data spanning from 2003 through 2016. The data
included geographic information for respondents residing in densely populated areas only, and as
a result, all respondents without geographic information were dropped. The final analysis
includes two samples. I used a parent sample of 36,838 adults who had a child age 18 or
younger living in the household; Table 1 provides a demographic description of the parent
sample. I also used an adolescent sample of 4,351 adolescents between the ages of 15 and 18
who were not married or parents themselves. I used sampling weights provided by the ATUS to
ensure that the sample was representative of the United States’ national population. Participants
came from every state within the United States and Washington, D.C.
Dependent Variables
A central component of this research was my ability to measure daily patterns of time use
among parents and adolescents. Of particular interest is the survey’s refined measures of a
respondent’s activity, a respondent’s location, and who was with a respondent. I utilized the
location and companion information for daily activities to construct measures of community and
family exposure. I present neighborhood exposure using several measures of daily interaction
with a community. A community’s institutional infrastructure contributes to but does not
determine civic participation and community exposure. While most prior research studied the
presence of neighborhood institutions (Elliott et al., 1996; Coulton, Korbin, & Su, 1999), it is
important to focus on participation or time spent in community institutions as a measure of
community exposure when possible (Wikström & Sampson, 2006). I measured the time an
individual spends with non-household members, such as neighbors, friends, and community
mentors. In addition, I measured time spent at various places away from home and on activities
with community interactions such as educational activities, religious activities, working, and
volunteering. Finally, for parents, I measured the prevalence of obtaining government services.
Table 1. Description of Parents in the Poorest Quartile by County Quality County intergenerational mobility
Highest mobility quartile (N1
= 742)
Lowest mobility
quartile (N2 = 2,155)
Variables M SD Range M SD Range
Age 32.90 13.20 15–80 32.0 13.40 15–85
Percent female 0.57 0.50 0–1 0.61 0.49 0–1
Percent Black 0.11 0.31 0–1 0.22*** 0.41 0–1
Percent Hispanic 0.39 0.49 0–1 0.59*** 0.49 0–1
Percent White 0.44 0.50 0–1 0.16*** 0.36 0–1
Percent married 0.43 0.50 0–1 0.39 0.49 0–1
Number of household children 2.00 1.05 1–7 2.17*** 1.22 1–10
Percent single-parent home 0.57 0.50 0–1 0.61 0.49 0–1
Percent with a high school diploma 0.35 0.48 0–1 0.28*** 0.45 0–1
Percent employed 0.56 0.50 0–1 0.46*** 0.46 0–1
Average household income $17,254 6,375 5K–27K $16,481** 6,347 5K–27K
Note: * p≤0.10; ** p≤0.05; *** p≤0.01
I measured interactions with family members throughout a day, including time spent with
family members, time spent at home, and time devoted to positive development of children.
Developmental care is characterized by a high level of interaction involving play, talking,
teaching, eating together, and other developmentally healthy activities that include interaction
(Folbre & Yoon, 2007; Wikle, Jensen, & Hoagland, 2017).
Table 2. Summary Statistics of Parents’ Family and Community Exposure Time
Activity description Fraction with
time > 0
Mean of those with time > 0
Mean across sample
Community exposure
Time with non-household adults 37.3% 200.4 85.46
Time in public places 75.0% 190.3 164.06
Time with friends 36.2% 242.2 89.96
Time in religious activities 8.0% 100.8 10.98
Time in public or community institutions 12.4% 108.3 13.44
Time obtaining government services
0.1% 68.3 5.69
Family exposure
Time with household members 92.5% 390.7 412.77
Time with young household children 36.7% 394.2 169.50
Time at home (excluding sleep) 97.7% 426.0 443.98
Quality time with children 76.0% 104.5 92.19
Time in developmental care of children 29.0% 85.3 28.09
Time completing home duties 73.4% 138.2 118.52
Time eating with family members 76.0% 53.9 47.39
Time in leisure and sports 68.2% 142.0 105.08
Media and computer time 81.2% 188.3 153.90
Time alone 91.5% 247.7 219.26
Note: N = 36,838
I measure quality time with children, a broader measure meant to capture shared time with a
reasonable amount of interaction, such as eating and attending events with children (Price, 2008).
Table A1 in Appendix A provides a detailed description of the activities included in the
developmental care measure and quality time measure. Table 2 provides averages of the time
parents spent interacting with family and the community, and Table 3 provides averages of the
time adolescents spent in these interactions.
Table 3. Summary Statistics of Adolescents’ Family and Community Exposure Time
Activity description Fraction with
time > 0
Mean of those with time > 0
Mean across sample
Community exposure
Collective socialization:
Time with neighbors 5.4% 137.3 7.36
Time with non-household adults 4.7% 205.6 107.95
Time in public places 75.9% 227.5 197.74
Epidemic spread:
Time with friends 71.6% 293.9 226.02
Institutional inputs:
Time volunteering 6.8% 133.0 10.07
Time in religious activities 7.0% 112.0 10.93
Time in educational activities 55.0% 373.5 161.63
Time in public or community institutions 12.1% 132.8 19.16
Family exposure
Time with household members 86.8% 251.2 231.42
Time with parents 75.7% 182.0 153.22
Time at home (excluding sleep) 96.4% 348.6 348.87
Time completing home duties 50.1% 76.1 41.00
Time eating with family members 64.7% 43.7 29.63
Time in leisure and sports 75.1% 169.0 135.42
Media and computer time 86.5% 226.5 207.93
Time alone 92.3% 237.7 222.07
Note: N = 4,351
Explanatory Variables
Given the focused interest on mobility for low-income children, I identified families at or
below the 25th percentile of the United States’ income distribution. This was done by comparing
a household’s income in 2016 dollars to the income distribution of the full 2016 Current
Population Survey (Flood, King, Ruggles, & Warren, 2017). I measured respondents’ sex as a
dichotomous variable. To account for cultural components of time-use patterns, I analyzed race
and ethnicity (Hispanic; Black, non-Hispanic; other). Education levels may influence daily time-
use patterns, and I included indicators for the highest level of education achieved up to a
bachelor’s degree. I measured whether a person reported holding a job as a dichotomous
variable. Family composition may correlate with time-use patterns, and I included marital status
and a count of the number of household children age 18 and younger to account for these
connections.
In addition to ATUS data, I used estimates of economic mobility by county as estimated
by the Equality of Opportunity Project (Chetty & Hendren, 2017b). Neighborhoods are treated
as a site—a geographic boundary—rather than a culturally determined or perceived area. I used
the reported mobility estimates following shrinkage to eliminate concerns over measurement
error bias. I focused on county mobility estimates for children with parents’ income at the 25th
percentile of the national income distribution to give insight into combating poverty persistence.
Analytic Approach
I combined time-use measures and economic mobility data to analyze associations
between economic opportunity and daily patterns among parents and adolescents. Analyses
were conducted at the person level to preserve accountability for neighborhood effects on
individuals. I used ordinary least squares to regress each time-use variable on a set of
explanatory variables with the goal of predicting time-use profiles. An alternative equivalent
approach is to predict time-use profiles using various systems of seemingly unrelated regression
equations with a restriction that totals the sum of time to one day. The models include binary
indicators for each quartile of income, each quartile of county quality, and county-by-income
interactions. The models also control for family characteristics, education, employment, and
other demographics. Estimates for the adolescent sample and parent sample were performed
separately. The regressions take the following form:
𝑇𝑇𝑗𝑗 = 𝛼𝛼𝑗𝑗 + 𝛽𝛽𝑗𝑗 ∙ (𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖) + 𝛾𝛾𝑗𝑗 ∙ (𝑖𝑖𝑖𝑖𝑚𝑚𝑖𝑖𝑚𝑚𝑖𝑖𝑚𝑚𝑚𝑚) + 𝛿𝛿𝑗𝑗 ∙ (𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 × 𝑖𝑖𝑖𝑖𝑚𝑚𝑖𝑖𝑚𝑚𝑖𝑖𝑚𝑚𝑚𝑚) + 𝜃𝜃𝑗𝑗 ∙ 𝑋𝑋 + 𝜖𝜖𝑡𝑡
where 𝑇𝑇𝑗𝑗 is a measure of the time spent in activity j. The vector “income” includes indicators for
family income quartile, and the vector “mobility” includes indicators for the economic mobility
quartiles of the respondent’s residence. The vector “income x mobility” includes interactions of
all income and mobility indicators. The vector X contains control variables (including
educational attainment, employment status, marital status, race, ethnicity, sex, and number of
children in the household). I estimated predicted values of the activity-time variables for all
income by mobility quartiles.
Results
Adolescents
The results for the adolescent sample show differences in family processes depending on the
opportunity of the adolescent’s neighborhood, as shown in Table 4. Of note is the finding that
among adolescents from low-income homes, mobility correlates with the time adolescents spend
performing home duties. Low-income adolescents in neighborhoods with high opportunity
spend 15 more minutes per day performing home duties compared to adolescents in
neighborhoods with low opportunity. In contrast, I find no significant differences in the amount
of community-to-child exposure time for adolescents in low- and high-opportunity areas. The
amount of time adolescents interact with individuals in the community does not correlate with
opportunity.
Parents
The results show differences in parent practices when comparing low-income parents in
areas of high and low opportunity, as seen in the estimated time-use profiles found in Table 4.
Low-income parents in high-opportunity areas spend a predicted 14 minutes more per day with
household members compared to their counterparts in low-opportunity areas. These parents also
spend about three minutes more each day in developmental care of their children, which is
equivalent to an additional 20 hours per year in developmental care. Parents in high-opportunity
areas compensate for the additional time spent with family members by spending less time alone;
they spend 13 minutes less time alone per day compared to parents in low-opportunity areas.
In addition to differences observed at home, I find differences in the way that parents
interact with institutions, as seen in Table 5. The incidence of using government services on a
given day was more than 200% higher (.07 percentage points higher) among low-income parents
in high-opportunity areas compared to low-income parents in low-opportunity neighborhoods.
The amount of time spent, conditional on using government services, was similar across
geographies, suggesting differences in the prevalence of using government services for low-
income families.
Table 4: Predicted Family Interactions Among the Poorest Quartile, by County Quality County intergenerational mobility Highest quartile Lowest quartile Difference Variables Parents (36,838): Time with household members 360.0 346.0 14.0 ** (13.6) (7.3) Time with young household children 148.1 153.9 -5.7 (13.1) (9.5) Quality time with children 74.3 72.0 2.3 (14.3) (2.7) Time in developmental care of children 30.2 26.8 3.3 ** (3.2) (1.9) Time at home (excluding sleep) 451.8 438.9 13.0 (12.5) (10.3) Time completing home duties 99.9 101.1 -1.2 (6.6) (4.1) Time eating with family members 40.1 38.5 1.6 (1.7) (1.3) Time alone 233.6 247.0 -13.3 ** (10.1) (6.9) Time in leisure and sports 98.4 94.3 4.1 (6.3) (3.7) Media, computer time 175.9 176.0 -0.1 (7.7) (4.9) Adolescents (N = 4,351): Time with household members 247.5 201.4 46.1 (46.1) (32.1) Time with parents 110.4 94.2 16.1 (31.4) (18.6) Time at home (excluding sleep) 376.7 331.8 44.9 (42.0) (34.2) Time in home duties 45.2 30.3 14.9 *** (14.2) (7.7) Time eating with family members 30.6 30.6 0.0 (8.1) (7.0) Time alone 301.4 307.7 -6.4 (38.1) (30.5) Time in leisure and sports 144.1 139.7 4.4 (36.6) (30.9) Media, computer time 207.1 211.6 -4.5 (33.7) (28.1) *p < .10. **p < .05. ***p < .01, Controls: education, employment, marital status, race, ethnicity, sex, and hh child
Table 5: Predicted Neighborhood Exposure Patterns Among the Poorest Quartile, by County Quality County intergenerational mobility Highest quartile Lowest quartile Difference
Variables Parents (36,838): Time with non-household members 81.1 79.6 1.4 (7.1) (3.6) Time in public places 127.2 131.0 -3.8 (6.7) (4.3) Time with friends 76.6 77.5 -0.9 (7.8) (4.7) Time in religious activities 6.5 6.4 0.1 (1.43) (1.1) Time in public or community institutions 8.8 8.9 0.0 (1.8) (1.4) Prevalence of obtaining gov’t. services 0.01 0.003 0.01 *** (0.006) (0.001) Adolescents (N = 4,351): Institutional inputs Time in religious activities 6.8 9.1 -2.4 (5.5) (6.0) Time in educational activities 172.9 174.8 -2.0 (55.5) (46.3) Time in public or community institutions 10.5 16.1 -5.6 (7.7) (8.1) Collective socialization Time with neighbors 15.3 19.9 -4.6 (9.3) (9.8) Time with non-household adults 150.0 143.7 6.3 (43.8) (35.9) Time in public places 191.4 207.5 -16.1 (45.9) (39.4) Epidemic spread Time with friends 120.6 119.8 0.8 (42.8) (33.7) *p < .10. **p < .05. ***p < .01
Controls: educational attainment, employment, marital status, race, ethnicity, sex, and number of household children
Discussion
This study underscores the importance of daily interactions as a potential mechanism of
intergenerational mobility. Little is known about how neighborhood effects are transmitted day-
to-day to young people. Thus, the broad aim of the study was to use nationally representative
data to determine whether low-income adolescents and parents who live in high-opportunity
neighborhoods exhibited differences in daily community exposure patterns and family practices
compared to low-income adolescents and parents who live in low-opportunity neighborhoods.
Adolescents
Opportunity is linked with adolescent contributions to family processes. Adolescents in
high-opportunity areas more often helped with home duties, and in the process they likely
developed their abilities for future achievement and success in college and employment (Beach,
1997; Call et al., 1995; Kuperminc et al., 2009). Home may be an effective setting for positive
mentoring, modeling, experimentation, and imperfection as adolescents develop new skills.
Additionally, parents instilling a value of order at home likely spills into youths’ abilities to
effectively organize responsibilities at home, at school, and at work. This finding linking home
duties with opportunity suggests that the benefits of adolescents performing home duties may be
more important than previously realized.
I found no differences between low-opportunity and high-opportunity neighborhoods in
the amount of time that adolescents interacted in their communities. This finding suggests that
the quality of adolescent–community exposure likely matters more than the quantity of
community exposure for adolescents. Institutional influence models suggest a role for quality in
the delivery of educational inputs and after-school extracurricular activities (Solon, 2004; Black
& Devereux, 2011). Similarly, collective socialization models and epidemic spread models
suggest the importance of quality as youths look to adults and peers in their neighborhoods as
role models, rely on their advice, and utilize their networks for advantageous connections (Ellen
& Turner, 1997; Erickson et al., 2009). Any community-to-child interactions that improve
mobility likely operate through the quality of the interaction.
Parents
Parents’ daily practices in interacting with their families differed depending on where
they lived. Low-income parents in high-opportunity areas spent more time with household
members, more time in developmental care of children, and less time alone than their
counterparts in low-opportunity areas. Opportunity for children being linked with parental time
investment is consistent with theoretical models that posit that parents transmit human and social
capital by spending time with children (Bandura & Walters, 1959; Becker & Tomes, 1976), and
the investment in children likely translates into increased abilities later in life (Price, 2008).
These patterns are descriptive, and more work needs to be done to understand how much of the
link stems from confounding factors. Nonetheless, the findings regarding parental time
investment suggest potential mechanisms of opportunity that warrant future study.
Parents in areas with high opportunity interacted with community institutions differently.
Low-income parents in high-opportunity areas were much more likely to obtain government
services on a given day compared to low-income parents in low-opportunity areas. There were
no differences in time spent, conditional on seeking services, suggesting that wait times and
service times were similar across geographies. Information about participation in specific
programs or program eligibility was unavailable, but it is likely that low-income families were
eligible for programs in low- and high-opportunity areas. Linking intergenerational mobility
with parents’ access to government services provides important hints about the role that public
programs may play in lifting children out of poverty in the long term. Government spending on
adult parents and families may be as important for a child’s opportunity as government spending
directly on children (Mayer & Lopoo, 2008), and neighborhood effects may operate by providing
support to parents through public programs more than previously realized.
Policy Implications
The research results lend themselves to important and perhaps novel policy implications.
First, our findings suggest that the quantity of interactions between adolescents and
neighborhood institutions, mentors, and peers do not link with opportunity and leave open the
possibility that the quality of interactions matters more. While more research is needed,
policymakers can consider investing in institutions with demonstrable commitments to high-
quality programming for youths in who live in low-opportunity areas.
Policymakers could also consider policy aimed at supporting parents, considering that
investments in adults in low-opportunity areas likely have important spillover effects for their
children. The support could include financial support, unemployment services, and access to
medical care for their children. Additionally, policy could aim to support adults in their roles as
parents. Social programs could aim to provide education on parenting that could teach parents
the importance of spending time with children. The training could provide tools for cultivating
child development and involving children in home duties.
Limitations and Conclusion
The ATUS data allow us to make progress in understanding day-to-day patterns in
community and family interactions as potential mechanisms of opportunity. In this study,
intergenerational mobility is linked with family patterns and processes. Parents invest more time
in children and youths spend more time in home duties in high-opportunity areas. Mobility is
also linked with parents spending time obtaining government services. On the other hand,
intergenerational mobility is not linked with the time that youths spend directly interacting with
their communities. These findings suggest that families, rather than adolescents, may be more
important than previously thought in absorbing neighborhood effects. Despite the progress
made, limitations of this research remain. The ATUS allowed for new measures of community
and neighborhood interactions; however, the measures were not adequate in measuring the
quality of interactions, and I hope to see future work addressing relationship quality or other
quality measures. Due to the sampling method used in the ATUS, respondents were asked about
their time use for only one day; longitudinal data would allow researchers to follow variations in
time use within neighborhoods to better link family processes with outcomes later in life.
Finally, this survey uncovers potential mechanisms in daily family patterns, but selection into
neighborhoods remains a primary concern. Future work should explore these mechanisms using
research designs that are able to isolate cause from confounds. Despite these limitations, the
study contributes to the current literature by finding that daily family and parent practices link
with neighborhood intergenerational mobility in interesting ways.
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Table A1. Summary Statistics of Specific Types of Parent–Child Interactions
Activity description Fraction with
time > 0
Mean of those with time > 0
Mean across sample
Included in developmental care
Reading to/with 9.2% 30.2 2.8
Playing with (sports and non-sports) 18.9% 104.8 19.8
Arts and crafts 0.4% 57.2 0.2
Talking with/listening 9.2% 34.7 3.19
Helping or teaching 0.1% 38.9 .05
Helping with homework 11.7% 56.4 6.6
School conferences 1.1% 53.6 0.6
Providing medical care 1.5% 1.6 0.0
Included in quality time
Developmental care activities 38.7% 88.0 28.09
Eating with 73.9% 53.6 39.6
Playing games with 2.4% 90.5 2.2
Doing hobbies with 0.4% 65.5 0.3
Reading or writing with 3.9% 57.7 2.2
Attending performing arts with 0.2% 132.9 0.2
Attending museums with 0.2% 165.5 0.4
Exercise and recreating with 5.6% 97.4 5.5
Attending religious activities 5.0% 96.9 4.8
Note: N = 36,838