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Impacts of adolescent and young adult civic engagement on health and socioeconomic status in adulthood

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CIVIC ENGAGEMENT, HEALTH, AND SES
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Impacts of adolescent and young adult civic engagement on health and socioeconomic
status in adulthood
Parissa J. Ballard
Wake Forest School of Medicine
Lindsay Till Hoyt
Fordham University
Mark C. Pachucki
University of Massachusetts
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Acknowledgements
This research was supported in part by the Maternal and Child Health Bureau (MCHB), Health
Resources and Services Administration (HRSA) of the U.S. Department of Health and Human
Services (HHS) under a Cooperative Agreement UA6MC27378 for the Adolescent and Young
Adult Health Research Network. We would like to thank the Robert Wood Johnson Foundation
for their financial support. We would like to thank Kara Rudolph for fielding analytical questions
and sharing R code for generating Figure 1. This research uses data from Add Health, a program
project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman,
and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by
grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and
Human Development, with cooperative funding from 23 other federal agencies and foundations.
Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the
original design. Information on how to obtain the Add Health data files is available on the Add
Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant
P01-HD31921 for this analysis.
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Accepted 9/15/17 for publication at Child Development. Cite as:
Ballard, Parissa J, Lindsay T. Hoyt, Mark C. Pachucki. 2017. “Impacts of adolescent and young
adult civic engagement on health and socioeconomic status in adulthood.” Child Development. In
press.
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Abstract
The present study examines links between civic engagement (voting, volunteering, and activism)
during late adolescence and early adulthood and socioeconomic status and mental and physical
health in adulthood. Using nationally representative data from the National Longitudinal Study
of Adolescent to Adult Health, a propensity score matching approach is used to rigorously
estimate how civic engagement is associated with outcomes among 9,471 adolescents and young
adults (baseline mean age = 15.9). All forms of civic engagement are positively associated with
subsequent income and education level. Volunteering and voting are favorably associated with
subsequent mental health and health behaviors while activism is associated with more health risk
behaviors and not associated with mental health. Civic engagement is not associated with
physical health.
Keywords: civic engagement, volunteering, activism, health, positive youth development,
socioeconomic status, longitudinal methods
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Many adolescents and young adults participate in civic life by joining with others to
address social issues, caring for others in their communities, and fighting for social change.
Voting, volunteering, and activism are forms of civic engagement, which can be defined as
“individual and collective actions designed to identify and address issues of public concern
(American Psychological Association). Civic engagement, a multi-dimensional construct that
includes attitudes, beliefs, and behaviors, is critical to the positive development of individuals,
communities, and democracies (e.g., Levine, 2013). However, the impact of civic engagement on
positive developmental trajectories across adulthood is not clear. In particular, young people who
participate in civic activities may already be on positive developmental trajectories (Hershberg,
Johnson, DeSouza, Hunter, & Zaff, 2015), perhaps especially those who participate in “non-
conflictual” forms of civic engagement such as voting (e.g. Flavin & Keane, 2012). This makes
it difficult to isolate the contribution of civic experiences to positive outcomes. In this paper, we
examine the longitudinal association between voting, volunteering, and activism (examples of
the behavioral dimension of civic engagement) and key developmental outcomes using analytical
methods that account for self-selection into civic engagement.
In line with current thinking in developmental science, we view adolescence and the
transition into adulthood as a time of social changes and developmental opportunity (Dahl, 2004)
with special formative significance across domains such as identity, work, peer and romantic
relationships and health, as well as moral, political, and civic concerns. Below, we review
relevant theory and evidence that underscore the importance of understanding how civic
engagement during this transitional period affects health and socioeconomic outcomes into
adulthood.
Civic Engagement and Health
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Many scholars and practitioners argue that civic engagement plays an important role in
healthy development (Ballard & Syme, 2015; Christens & Peterson, 2012; Hershberg et al.,
2015; Hope & Spencer, 2017). Given that the empirical and theoretical literature linking civic
engagement with health among adolescents and young adults is emergent, we draw on evidence
and theory that are relevant for understanding how civic participation relates to health (e.g.,
physical, mental health and health behaviors) as well as well-being (e.g., self-esteem, self-
confidence). We define the constructs of health and well-being as operationalized by authors
wherever possible.
The most relevant theoretical frameworks come from positive youth development theory,
sociopolitical development theory, and empowerment theory (Ballard & Ozer, 2015). From the
perspective of positive youth development, civic engagement is often considered to be a marker
of healthy development (Hershberg et al., 2015). For instance, positive developmental contexts
are said to give young people opportunities to develop competence and connection, allowing
youth to thrive and thus to contribute to their communities (Hershberg et al., 2015; Lerner,
Johnson, Wang, Ferris, & Hershberg, 2015). In the contexts of oppression and disadvantage, the
sociopolitical development framework examines how individuals develop a critical
understanding of how society works (Watts & Guessous, 2006) and become involved in activism
and resistance forms of civic engagement (Ginwright & James, 2002; Watts, Diemer, & Voight,
2011; Watts, Williams, & Jagers, 2003). According to this framework, activist forms of civic
engagement in the face of systemic disadvantage may have a role in both an individuals’ healthy
development, as well as positive systems change (Ballard & Ozer, 2016; Hope & Spencer, 2017).
Specifically, this might work through empowering young people (Christens, 2012; Zimmerman,
1995). According to empowerment theory, psychological empowerment is the process through
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which people gain greater control over their lives, take a proactive approach in their communities,
and develop critical understandings of their sociopolitical environments (Zimmerman, 1995);
this process is suggested to facilitate well-being (Christens, 2012; Wallerstein, 1992).
Civic engagement comes in many forms. Developmental psychologists often argue for a
broad inclusion of diverse behavioral forms of civic engagement given that young people lack
access to many forms of civic engagement available to adults, such as voting (Flanagan, 2009)
and that young people, particularly those who are not college-bound, face fewer opportunities for
civic engagement as they transition from adolescence to young adulthood (Flanagan & Levine,
2010). In addition, there are disparities in the types of civic opportunities available to young
people from different sociodemographic backgrounds (Levinson, 2010), making it important to
attend to the diverse ways that individuals participate in their communities. However, three key
forms of civic engagement (i.e., volunteering, voting, and activism) have different predictors and
consequences (Ballard, 2014, Obradović & Masten, 2007; Sánchez-Jankowski, 2002; Wray-Lake
& Sloper, 2015; Zaff et al., 2011). For example, volunteering is generally supported by society
while political civic engagement, such as activism, is more controversial; volunteering involves
private activities directed at helping people or groups while activism often involves publically
voicing controversial opinions; and volunteering is often motivated by wanting to help or “give
back” while activism is often motivated by a desire to create change and right perceived
injustices (Ballard et al., 2015; Ginwright & James, 2002; Walker, 2000; Youniss & Levine,
2009). These very different activities, while both examples of civic engagement, likely have
different implications for individual development. Therefore, it is important to clarify,
theoretically and empirically, the potentially different roles that these forms of civic engagement
play in healthy development.
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Volunteering and health. In cross-sectional studies, volunteering is positively linked
with many aspects of health and well-being. Volunteering might affect health by allowing people
to feel good about themselves, to feel like they matter, to experience social connection and
decreased loneliness, and feel satisfaction from contributing to others (Ballard & Syme, 2015;
Konrath, Fuhrel-Forbis, Lou, & Brown, 2012; Poulin, Brown, Dillard, & Smith, 2013). While
there is a robust literature linking volunteerism to positive outcomes, including health (e.g.
Musick & Wilson, 2007), the vast majority of evidence linking volunteering to health relies on
cross-sectional data and older adult samples. In a review of 73 published papers linking
volunteerism and health among older adults, Anderson and colleagues (2014) found that
volunteerism is correlated with reduced depressive symptoms, better self-reported health, fewer
functional limitations, and lower mortality. One notable recent study used a randomized control
trial design to examine the effects of volunteering on physical health among late adolescents.
High school students were randomly assigned to volunteer at an after-school program for
elementary school children weekly for 2 months in the Fall (intervention group) or Spring
(control group). After two months, intervention group participants had lowered cardiovascular
risk, as measured through inflammatory markers, and lower cholesterol and body mass index
compared to the control group (Schreier, Schonert-Reichl, & Chen, 2013). However, given that
adolescents and young adults generally experience relatively good health compared to older
adults, the effects of volunteerism on health during the teenage and young adult years are
difficult to document whereas potential long-term cumulative health effects into adulthood are
more straightforward to observe. In a recent longitudinal study drawing on data from the
National Study of Adolescent to Young Adult Health, volunteerism in late adolescence predicted
fewer depressive symptoms in adulthood among those who participated voluntarily (Kim &
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Morgül, 2017). Although this study did not account for various factors that predict volunteerism
(i.e. selection effects), findings suggest an important role of motivations in links between
volunteering and health. Using the same dataset, Wray-Lake and colleagues (in press) also found
links between community engagement (including volunteer service) and fewer depressive
symptoms.
Activism and health. The hypothesized association between activism and health is less
clear since very few studies have tested links directly. On the one hand, activism might
positively affect health and well-being, similar to other forms of civic engagement. Participating
in activism can present young people with opportunities for coping with stress, generating
empowerment, developing a positive sense of purpose and identity, forming connections and
building social capital, and effecting systemic change (Ballard & Ozer, 2016; Christens, 2012).
On the other hand, activism exposes young people to difficult social problems and barriers to
social change. Thus, activism might undermine health because it can be stressful, can make
people vulnerable, and can place undue burden on individuals to address systemic problems
(Ballard & Ozer, 2016; Kahne & Westheimer, 2006).
One longitudinal study found that among Germans who were concerned about one
particular social issue – nuclear threat – activism predicted better mental health across the life
course (Boehnke & Wong, 2011). In contrast, a study of Israeli activists found self-reported
well-being was significantly higher among less experienced community activists in Israel
compared to more experienced activists, perhaps pointing to a developed sense of
disillusionment among long-time activists (Itzhaky & York, 2003). In Wray-Lake and colleagues
study (in press), high-cost political behaviors such as attending a protest predicted more
depressive symptoms over time. Although there is limited direct evidence linking activism and
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health, indirect evidence supports both positive and negative theorized pathways from youth
activism to well-being. For example, activism has been linked with self-esteem, empowerment,
and self-confidence (Ginwright & James, 2002; Itzhaky & York, 2003), which are important
predictors of mental health (Christens & Peterson, 2012; Zimmerman, Ramirez-Valles, & Maton,
1999). In contrast, activism often arises in response to feeling marginalized or discriminated
against (Ballard, 2014; Swank & Fahs, 2016), experiences that are linked to poor health.
Voting and health. From a theoretical standpoint, voting presents an opportunity to
exert voice, perhaps establishing a path to health through empowerment. However, there is little
evidence of links between voting and health. Poor health is related to lower voting behavior at
the state level (Blakely, Kennedy, & Kawachi, 2001) and at the individual level, some evidence
from England suggests that less healthy people are less likely to vote (Denny & Doyle, 2007).
One study found that young adults with depressive symptoms were less likely to vote and that
voting predicts less depression over time (Wray-Lake, Shubert, Lin, & Starr, in press). There is
also evidence that voting leads to physiological changes in the short-term, such as elevated levels
of cortisol (Waismel-Manor, Ifergane, & Cohen, 2011), perhaps especially for those who vote
for the losing candidate (Stanton, LaBar, Saini, Kuhn, & Beehner, 2010). These studies provide
some indication of a connection between voting and biological functioning, which can be
considered indicators or precursors of health. However, very little is known about the impact, if
any, of casting a vote on subsequent health over the long-term.
Civic Engagement and Socioeconomic Status
Participation in civic activities in adolescence and young adulthood might also affect
social well-being later in life. While links between various types of civic participation and
socioeconomic status (SES) are established, the role that civic activities play in social mobility
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across different developmental stages is not known. Civic engagement is often stratified by SES
background. Individuals from high SES backgrounds are typically more involved in traditional
forms of civic engagement (e.g., voting, campaigning, and volunteering; Levinson, 2010)
compared to those from low SES backgrounds. Some research has found comparable or higher
levels of issue-based participation in activism and local community organizing among
immigrants and people of color, who tend to be from lower SES backgrounds (Ballard, Malin,
Porter, Colby, & Damon, 2015; Jensen, 2010; Marcelo, Lopez, & Kirby, 2007; Stepick, Stepick,
& Labissiere, 2008). However, even if individuals from lower SES backgrounds participate (at
equal or higher levels) in some forms of civic engagement, the clear inequality in access to civic
power by SES is problematic for American democracy, which is predicated on citizen
participation and equal rights under the law (American Political Science Association, 2004;
Bartels, 2008).
Cross-sectional disparities in civic engagement by SES are clear, but less is known about
longitudinal links between civic engagement in late adolescence and adult social class. In Kim
and Morgül’s (2017) study, volunteerism in late adolescence predicted educational attainment
and personal earnings in adulthood. Importantly, this was true regardless of whether the
volunteerism was voluntary or involuntary, which suggests that selection effects do not fully
explain positive links between volunteerism and SES. Participating in civic life might place
youth on positive socioeconomic trajectories for a variety of reasons. First, all three forms of
civic engagement can serve an instrumental function by connecting young people to social
networks. Exposure to non-familial adults might provide professional models and opportunities
for youth (Jarrett, Sullivan, & Watkins, 2005; Zeldin et al., 2003; Zeldin et al., 2005). Second, all
three forms of civic activities might serve a social function by putting young people in contact
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with adult mentors and like-minded peers who can provide psychological support and shape
expectations, aspirations and goals (Diemer, 2009; Malin, Ballard, & Damon, 2015; Zeldin,
Larson, Camino, & O'Connor, 2005). Third, meaningful engagement in civic life might engage
young people in their education. At a time when some young people are at risk for disengaging
from school, civic opportunities can provide a context for them to derive purpose and find
meaning in their lives (Malin et al., 2015), increase their future orientation and a tendency to
plan for the future (Robbins & Bryan, 2004), and redirect effort towards attaining goals, all of
which might lead to more academic engagement and better academic performance. However, it
could also be the case that certain forms of civic engagement might funnel people toward career
paths in helping or advocacy professions, which may result in lower SES in adulthood.
The Present Study
The present study adds to existing work linking civic engagement and developmental
outcomes in three important ways. First, we provide empirical evidence for links between civic
engagement and subsequent health and SES using statistical methods that improve estimation of
causality. Second, we examine the potential differential impact of civic engagement on multiple,
developmentally relevant aspects of health (i.e., depressive symptoms, risky health behaviors,
metabolic markers) and SES (i.e., educational attainment, personal earnings, and household
income). Finally, we examine the potential differential effects of three distinct forms of civic
engagement: voting, volunteering, and activism. Thus, the research questions in the present study
are: 1) Given similar health and SES backgrounds, does civic engagement during late
adolescence and early adulthood predict health and SES outcomes later in adulthood? 2) Do the
effects of civic engagement differ across three types of health outcomes (mental health,
metabolic risk, and health behaviors) and two types of SES outcomes (income and education)? 3)
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Do outcomes differ depending on the form of civic engagement (voting, volunteering, and
activism)?
Method
The data were drawn from Waves 1, 3, and 4 of the National Longitudinal Study of
Adolescent to Adult Health (Add Health), a nationally representative sample of students in
grades 7 through 12 in the United States in 1994-1995. The study used a school-based design to
select a stratified sample of 80 high schools and feeder middle schools with selection probability
proportional to the size of the school. The survey design has been described extensively
elsewhere (Harris et al., 2009). Wave 1 (1994-1995) included 20,745 adolescents (aged 11-20).
All of the original Wave 1 participants were eligible to participate in Wave 3 (n = 15,197, aged
18-27) and Wave 4 (n = 14,800; aged 24-32). Data from Wave 2 was omitted because it
comprised only a subset of the original Wave 1 population. In the current study, we used survey
weights designed by Add Health to account for sampling design and to ensure that the estimates
were nationally representative.
Participants
The final analytic sample for the present study included 13,014 respondents. Detailed
descriptive statistics are reported in Table 1 for the subsample of participants (N = 9,471) who
contained no missing data on Wave 1 covariates, contained data for at least one of the Wave 3
civic variables, and contained data for at least one of the Wave 4 outcome variables. The final
sample size for each analytical model varies due to missing data on the outcome variables or
civic variables in a given model. At baseline (Wave 1), participants identified as 49.4% female,
and had a mean age of 15.71 (SD = 1.78) years. Participants identified as 68.9% White; 15.7%
Black; 3.2% Asian; 10.6% Hispanic; and 2.4% reported another race.
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Measures
All questions in Add Health were constructed for the goals of the Add Health study and
were not drawn from any existing measures (Harris et al., 2009). For the scales that follow, we
provide indices of internal reliability where applicable and details about scale construction in the
case of new measures.
Civic engagement. The key predictors in our analyses were three distinct forms of civic
engagement measured at Wave 3: vote, volunteer, and activism. Each variable was
dichotomously measured as 0 (non-participation) or 1 (participation) based on one Wave 3
variable. Whereas previous research has operationalized civic engagement using different ways
of combining civic behaviors, for example through a composite score of 20 civic activities
measured by Add Health surveys (e.g., Duke et al., 2009) and composite of community
engagement and voting (Wray-Lake et al., in press), the present study separates three forms of
civic behaviors to specify unique links between civic behaviors and health and SES outcomes.
Vote was measured by the question: if eligible, did you vote in the most recent presidential
election? There were 47 people in our analytic sample who were not eligible to vote at Wave 3,
so they were not included in analyses. Volunteer was measured by the question: During the last
12 months did you perform any unpaid volunteer or community service work? Activism was
measured by the question: “Which of the following things have you done during the last 12
months (check all that apply): attended a political rally or march.
SES and Health outcomes in Young Adulthood. All outcomes were measured at Wave
4 (see Table 2 for descriptive statistics) and standardized (M= 0, SD= 1) for final analyses.
Education. To assess educational attainment at Wave 4, we used participants’ responses
to the question: “What is the highest level of education that you have achieved to date?” Answer
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options ranged from “8th grade or less” to “graduate school.” In order to make this a continuous
outcome, we estimated total years of education, which ranged from 6 (8th grade or less) to 20
(graduate school; M = 14.412, SD = 2.18); 5% of data were missing on this variable.
Household Income. Household income at Wave 4 was measured by one question:
“Thinking about your income and the income of everyone who lives in your household and
contributes to the household budget, what was the total household income before taxes and
deductions in (2006/2007/2008)? Include all sources of income, including non-legal sources.
The options for household yearly income ranged from “less than $5,000” to “$150,000 or more:
(M = $63,794, SD = $38,041) and 6.4% of data were missing on this variable.
Personal Earnings. Personal earnings at Wave 4 was measured by one question: “What
is your best guess of your personal earnings before taxes?” The options for personal yearly
income ranged from “less than $5,000” to “$150,000 or more: (M = $36,2623, SD = $27,224)
2.0% of data were missing on this variable.
Depressive symptoms. A depressive symptoms scale was created by taking the mean of
10 items from the (Center for Epidemiological Studies Depression Scale; (Radloff, 1977).
Participants answered “How often was the following true during the past 7 days?” from 0 (never
or rarely) to 3 (most of the time or all of the time): felt blue; bothered by things that do not
usually bother you; felt depressed, had trouble keeping mind on things; did not enjoy life; did not
feel happy; did not feel just as good as other people; felt disliked by people; felt sad; and felt too
tired to do things (never/rarely; sometimes; a lot of the time; most/all of the time; alpha = .84.
Scores on the depressive symptom scale ranged from 0 to 3 (M = .57, SD = .44) and .01% of
data were missing on this variable.
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Risky health behavior index. To measure health-compromising behaviors, we created a
risky health behavior index using young adults’ responses to six categories. Responses in the
lowest quartile for physical activity (one or fewer activities per week) and the highest quartiles
for screen time (29 hours or more per week of tv/video/screen games), fast food consumption
(four or more meals per week), cigarette smoking (more than 20 days per month), binge drinking
(more than once a month) and one or more uses of marijuana in the past 30 days (22% of
sample) each counted as a score of one towards this risky health behavior index. The final risky
health behavior index ranged from 0 (low risk) to 6 (high risk; M = .23, SD = .21) and .2% of
data were missing on this variable. Multiple measures of these subcomponents have been
analyzed in detail elsewhere (Hoyt et al., 2012).
Metabolic risk index. Wave 4 metabolic risk included four components: waist
circumference, systolic and diastolic blood pressure, and hemoglobin A1c (HbA1c). Each
metabolic risk marker was standardized within the sample, with the exception of waist
circumference, which was standardized within gender due to gender differences in body
composition. Participants were categorized into quartiles for each marker, and participants in the
top quartile were considered to be at high risk (Ehrlich, Hoyt, Sumner, McDade, & Adam, 2015).
Then, we summed across markers to create a metabolic risk composite. Scores ranged from 0
(not high risk on any marker) to 4 (high risk on all four markers; M = .95, SD = 1.11) and 6.7%
of data were missing on this variable.
Covariates. There is an extensive list of possible covariates available in Add Health.
Following recommendations for Propensity Score Matching (PSM) approaches, we included a
rich set of covariates in analyses that are theoretically important predictors of health and SES,
and civic engagement (Caliendo & Kopeinig, 2008; Starks & Garrido, 2004). We included 37
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Wave 1 variables that index: demographic characteristics, health variables, social connections,
and school performance and extracurricular activities (see Table 1 for the list of variables and
Appendix A for detailed description of covariates).
There was minimal missing data on the baseline (Wave 1) covariates: 71.14% of
participants were not missing any baseline variables and only 4.75% of the sample were missing
data on three or more covariates. There is a not broad agreement about how to handle missing
data with propensity score matching but mean replacement is suggested (Harding, 2015;
Haviland et al., 2007). In this approach, a missing data dummy is created for each variable and
the propensity score is estimated using the imputed mean values. Missing data on the treatment
and outcome variables were not imputed (Harding, 2015).
Analytic Strategy
A key concern when testing the relations between civic engagement and future health and
SES is addressing a number of selection issues that may bias the results. Propensity score
approaches are an increasingly popular approach to mitigate sources of selection bias by
matching youth based on observable characteristics and ensuring balance on these observed
potential confounders (Caliendo & Kopeinig, 2008; Rosenbaum & Rubin, 1983; Rubin, 2004).
Importantly, using this approach, we compare two extremely similar groups of youth (i.e.,
matched on demographic characteristics, health characteristics, social connections, and grades in
school). The difference is that the “treatment group” participated in the civic activity (e.g., voted
in last presidential election) and the “treated group” did not (e.g., did not vote in the last
presidential election).
In the first step of analysis, we ran three separate probit regressions to predict the
probability of voting, volunteering, and activism, based on the full set of covariates (Imbens,
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2000). A single propensity score was then created using the predicted values (pscore) for each
civic variable (voting, volunteering, activism), given a set of measured characteristics. Following
guidelines (Starks & Garrido, 2014), we next tested balance on the propensity score across
treatment and control blocks and then on covariates across treatment and control groups within
blocks of the propensity scores.
Next, we chose our analytical approach for comparing groups. As recommended (e.g.,
Starks & Garrido, 2014) we tried three approaches: nearest neighbor matching, radius matching,
and inverse propensity score weighting (IPSW). We selected IPSW matching for two important
reasons: (1) we achieved the best balance using IPSW and, (2) IPSW allowed us to integrate
survey weights, which is best practice for generalizing conclusions about the target population
and obtaining unbiased estimates of population parameters and standard errors (Tourangeau &
Shin, 1999). The IPSW approach uses the inverse of the propensity score as the weight for each
participant in the treatment group, and the inverse of one minus the propensity score as the
weight for each participant in the control group. IPSW is increasingly recognized as a preferred
matching technique (Imbens, 2000; Murnane & Willett, 2010). Past work has demonstrated that
using parametric estimates of the propensity score, rather than the true propensity score, is more
efficient in adjusting for differences in observable covariates (Hirano, Imbens, & Ridder, 2003;
Rubin & Thomas, 1996; Wooldridge, 1999).
We ran our main models to examine the relations between the three forms of adolescent
civic engagement and the six health and SES indicators in young adulthood. One major
advantage of matching approaches over OLS regression is comparing observably similar
individuals and eliminating observations without an appropriate comparison. Therefore, we
restricted the sample to the region of “common support,” which allowed us to eliminate
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observations for whom no appropriate matched control observation exists by trimming models at
5% for each of the three types of civic engagement (see Bassok, 2010 for more detail), Finally,
we examined the balance of our sample after inverse propensity score weighting to ensure that
covariate balance was optimized.
Results
Preliminary analyses
All survey-weighted descriptive statistics for covariates for the treatment and control
matched samples on voting, volunteering, and activism are shown in Table 1. Descriptive
statistics for Wave 4 outcomes are shown in Table 2. In terms of frequency of participating in the
civic activities, 45.21% of eligible participants reported voting, 30.03% reported volunteering
and 3.62% reported attending a rally or march. Civic activity variables were weakly correlated (r
=.133 for activism and vote; r = .148 for activism and volunteer; r = .173 for vote and
volunteer).
Weighting. A propensity score for each participant for each type of civic engagement
was estimated from the full set of covariates (see Table 1) using probit models. These analyses
indicated that 17 out of the 37 covariates predicted likelihood of voting (p < .05), 18 predicted
likelihood of volunteering, and 11 variables predicted likelihood of activism. Empirically-based
methods were used to establish the optimal number of blocks needed so that mean propensity
scores were not different for treatment and controls.
Establishing Balance. As an initial step for assessing balance, we tested for balance on
all covariates across treatment and control groups within each block of the propensity scores
(Starks & Garrido, 2014). We conducted this analysis for each of the three civic engagement
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variables. Although some covariate imbalance can be expected (Starks & Garrido, 2014), we saw
a small amount of imbalance. Only 12 covariates were not balanced.
As the final balance check, it was necessary to ensure balance on covariates across
treatment and control groups in the weighted sample (Starts & Garrido, 2014). Significant
imbalance existed on several variables for each of the three forms of civic participation before
weighting. After weighting by propensity scores, all standardized mean differences (the
difference in means in units of the pooled standard deviation; Austin, 2011) were less than 10%
for voting and volunteering and less than 15% for activism (Figure 1) with the majority reduced
to less than 5% (Austin, 2011; Caliendo & Kopeinig, 2008; Rudolph et al., 2014). Thus,
covariate balance between the voters and non-voters, volunteers and non-volunteers, and
activists and non-activists was achieved in the final weighted sample.
Main analyses
Next, we ran our main analyses to test the effects of adolescent civic engagement on SES
and health in adulthood. We ran 18 models total testing the “treated” and “control” groups on
three civic activities (voting, volunteering, and activism) on six outcomes: household income,
personal earnings, education, depressive symptoms, risky health behaviors, and metabolic risk in
adulthood. Final propensity score models included the full set of covariates. These conservative,
“doubly robust” estimators are the gold standard in propensity score matching (Ho et al., 2007;
Rudolph et al., 2014). Final models also controlled for the other two civic activities in order to
isolate the links between each type of civic activity and the outcomes. Below we report the
average treatment effect as effect size (ES), standard error, and p-value. See Table 3 for full
results with confidence intervals and sample size for each model. The results for our main
analyses are organized below by form of civic engagement.
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Voting. Voting was associated with higher SES in adulthood. Voting was associated with
more years of education (ES = .22, SE = .016, p < .001), higher household income (ES = .13, SE
= .019, p = .000) and higher personal earnings (ES = .14, SE = .019, p < .001) in adulthood. In
terms of health, voting was associated with decreased risky health behaviors (ES = -.12, SE
= .018, p < .001), and fewer depressive symptoms (ES = -.055, SE = .019, p = .003). There was
no association between voting and metabolic risk in adulthood.
Volunteering. Volunteering was associated more years of education (ES = .28, SE = .019,
p < .001), household income (ES = .092, SE = .022, < .001) and higher personal earnings (ES
= .095, SE = .021, p < .001) in adulthood. In terms of adult health, volunteering was associated
with decreased risky health behaviors (ES = -.19, SE = .021, p < .001) and decreased depressive
symptoms (ES = -.115, SE = .021, p < .001). There was no association between volunteering and
metabolic risk.
Activism. Activism was associated with significantly more years of education (ES = .32,
SE = .059, p < .001) and higher personal earnings (ES = .13, SE = .056, p = .025) but not
household income in adulthood. In terms of adult health, activism was associated with an
increase in risky health behaviors (ES = .13, SE = .059, p = .027) There were no significant
associations between activism and depressive symptoms or metabolic risk.
Robustness checks
We ran two sets of additional analyses to check the robustness of our findings. In the first
set, we ran models including the relevant W3 variable in each model (i.e. controlling for W3
education level in model predicting W4 education). We ran these models for each of the
outcomes except for metabolic syndrome because it was not collected at W3. Including the W3
controls is the most conservative estimate of W3 civic engagement on change in outcomes
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between W3 and W4. However, since the civic engagement variables measure activities in the
past 12 months, these models might be overly conservative. This is because W3 civic
engagement might plausibly affect W3 outcomes; controlling for the W3 outcome can mask this
through the variance accounted for in the link between the W3 and W4 outcome.!!
Nonetheless, with one exception, we find consistent findings with those reported in our
main analyses. As expected, most of the effects are weaker in this set of analyses. Voting was
associated with more years of education (ES = .12, SE = .013, p < .001), higher household
income (ES = .087, SE = .045, p = .050) and higher personal earnings (ES = .14, SE = .021, p
< .001) in adulthood. In terms of health, voting was associated with decreased risky health
behaviors (ES = -.070, SE = .018, p < .001). The one difference in this set of sensitivity analyses
was that the association between voting and depressive symptoms drops to non-significance (ES
= -.023, SE = .018, p = .186). Volunteering was associated with more years of education (ES
= .28, SE = .019, p < .001), household income (ES = .099, SE = .022, p < .001) and higher
personal earnings (ES = .097, SE = .023, p < .001) in adulthood. In terms of adult health,
volunteering was associated with decreased risky health behaviors (ES = -.11, SE = .020, p
< .001) and decreased depressive symptoms (ES = -.11, SE = .020, p < .001). Activism during
adolescence was associated with significantly more years of education (ES = .27, SE = .059, p
< .000) and higher personal earnings (ES = .13, SE = .056, p = .016) but not household income
in adulthood. Activism was associated with increased risky health behaviors (ES = .14, SE
= .059, p = .019).
We ran another set of models using a civic composite variable. These models compared
participants who had done any of the three civic activities compared to those who had not done
any civic activity. After achieving balance, we then ran models looking at main effects of “any
CIVIC ENGAGEMENT, HEALTH, AND SES
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civic vs. no civic activity” on the six outcomes. Those who had done no civic activity (compared
to those who had participated in any of the three civic activities had higher depression (ES = .11,
SE = .019, p < .001), higher risky health behaviors (ES = .181, SE = .019, p < .001), less
education (ES = -.34, SE = .016, p < .001) and lower household income (ES = -.16, SE = .019, p
< .001) and personal earnings (ES = -.16 SE = .019, p < .001). The composite “no civic activity”
variable was not linked with metabolic risk.
Discussion
In this study, we examined links between three forms of civic engagement during the
transition to adulthood and socioeconomic status and mental and physical health in adulthood.
Our propensity score matching approach accounted for differential selection into civic
engagement to more rigorously estimate effects of civic engagement on developmental outcomes.
Civic engagement can take multiple forms, and developmental theory led us to predict slightly
different roles of each civic activity on later outcomes. Overall, we found evidence for strong
positive associations between all three forms of civic engagement during the transition to
adulthood and adult SES. In terms of physical and mental health in adulthood, volunteering and
voting were robust predictors of better mental health and fewer risky health behaviors and
activism predicted more risky health behaviors.
Civic Engagement and Adult SES
All three forms of civic engagement in late adolescence and early adulthood were
associated with higher educational attainment and income in adulthood. The effect sizes for these
links were strong and suggest that civic engagement may have an important function in social
mobility. Our findings add to previous literature documenting associations, although often weak,
between various forms of prosocial adolescent activity, including volunteering, and subsequent
CIVIC ENGAGEMENT, HEALTH, AND SES
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SES (Barber, Eccles, & Stone, 2001; Kim & Morgül, 2017), which may be accounted for by
selection effects (Pilivian & Siegl, 2014). These findings are among the first to assess the
function of voting and activism on social mobility. Thus, the present study advances our
knowledge considerably, given that results were found after accounting for selection effects of
key cofounders like parental education levels and academic performance that predict civic
engagement.
Civic engagement might operate on SES in several ways. Civic engagement might serve
an instrumental or social function by helping young people develop greater social capital,
professional skills that support academic and job performance, connecting them to social
networks, and helping them develop occupational expectations (Diemer, 2009; Jarrett, Sullivan,
& Watkins, 2005; La Due Lake & Huckfeldt, 1998; Malin et al., 2015). Civic activities can also
allow young people to connect with important “real life” issues, especially through active school-
based civic programs (e.g., Levinson, 2010; Ballard, Cohen & Littenberg-Tobias, 2016) perhaps
re-invigorating a sense of their own potential or inspiring them put more effort into school and
career development. In sum, civic participation in its various forms might affect SES in
adulthood by altering education and professional trajectories.
It is noteworthy that the links between activism and education level and personal earnings
are among the strongest (albeit, the least common) of the three forms of civic engagement. While
relatively little is known about the role activism plays in development, it is theorized to facilitate
positive development especially among marginalized youth (Hope & Spencer, 2017; Watts et al.,
2011) and some evidence finds links between activist attitudes and participation and higher SES
among marginalized youth (Diemer, 2009) as measured by occupation and income. Activism is a
unique and powerful context for youth, especially from low-income backgrounds, to join with
CIVIC ENGAGEMENT, HEALTH, AND SES
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like-minded peers and mentors to focus a critical eye on real-world problems. Studies of young
people involved in one specific form of activism, youth organizing, report that these youth
develop important skills and identities (Conner, 2011) and show higher academic attainment
(Rogers & Terriquez, 2016). Thus, our findings lead us to believe that becoming involved in
activism during the transition to adulthood, an uncommon civic activity that often involves deep
commitment to a cause, might offer an especially powerful civic experience that can influence
educational and personal earnings trajectories.
However, although activism predicted higher personal SES (higher education level and
personal earnings), it did not predict household income in adulthood while volunteering and
voting did Activism might affect SES through individual-level pathways, for example, helping
people build skills and get jobs that can affect educational attainment and higher personal
earnings, whereas volunteerism and voting might additionally affect SES through social
pathways such as plugging people into to new, perhaps high achieving or higher SES social
networks, which can influence mate selection and thus higher household income. Indeed, Kim
and Morgül’s (2017) finding that volunteerism predicted higher educational attainment and
personal earnings whether it was voluntary or involuntary supports the idea that volunteerism
might operate via helping youth build social skills and social ties. It will be interesting for future
work to understand mechanisms involved in each form of civic engagement and social mobility
attending to how activism might differentially predict SES indicators such as income and
education.
It is well-known that high SES is linked with positive functioning and that there are
widespread disparities in life trajectories by SES (Adler & Ostrove, 1999). Therefore, it is
exciting to consider the possibility that civic engagement might be a formative experience with a
CIVIC ENGAGEMENT, HEALTH, AND SES
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role in shaping educational attainment and income. The current study provides solid grounding
for future work, which should aim to understand how exactly each form of civic engagement
shapes SES. Our study focused on civic engagement at the transition to adulthood but given the
formative potential of civic engagement to development across adolescence we believe it is
fruitful to expand civic opportunities earlier in adolescence. For example, we propose that
schools, and other youth organizations might serve youth well by facilitating activism
opportunities at rates comparable to opportunities for volunteerism. In terms of policy, many
schools currently require community service hours (Farkas & Duffett, 2010) with the goals of
both contributing to youth development as well as to community projects. It may be beneficial to
students if activism also counts toward such requirements. This aligns with both current calls to
broaden the definition and opportunities for civic participation to include youth from diverse
backgrounds (Jensen & Flanagan, 2008; Stepick et al., 2008) as well as goals of community
service requirements.
Civic Engagement and Adult Health
Voting and volunteering at the transition to adulthood were associated with fewer risky
health behaviors in adulthood while activism predicted more risky health behaviors. We interpret
the positive findings regarding voting and volunteering in light of psychological and social
resources potentially provided by these activities that might decrease health risk-taking behavior.
These resources include positive future orientation (Robbins & Bryan, 2004), positive affect,
optimism, perceived social support (Hoyt, Chase-Lansdale, McDade, & Adam, 2012), stronger
sense of community membership (Zeldin, 2004), and sense of perceived control about one’s own
life outcomes (McDade et al., 2011; Wills, 1994); each is associated with fewer health risk
behaviors among adolescents. Embeddedness in social networks may also reinforce positive
CIVIC ENGAGEMENT, HEALTH, AND SES
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behavioral trajectories, for example through mechanisms of social comparison and social norms
(Pachucki & Goodman, 2015; Thoits, 2011). In the case of volunteering, it is also possible that
spending time in volunteer activities diverts away from spending time on riskier activities
(Pilivian & Seigl, 2014).
It was notable that activism (measured as involvement in a march/rally) was associated
with an increase in risky health behaviors and was unrelated to the other forms of health in
adulthood. Activism differs from voting and volunteering in a few key ways that might explain
the divergent findings. Volunteerism is primarily about helping others and alleviating suffering
(Walker, 2000) and voting is about exercising voice whereas activism is most often aimed at
social change (Ginwright & James, 2002). Perhaps it is easier to feel satisfied that one “made a
difference” given the more straightforward goals of voting and volunteerism compared to
activism. Thus, feelings of accomplishment or goal achievement might accompany voting and
volunteerism while activism might be accompanied by frustration with a slow pace of social
change. This frustration might lead to risky health behaviors sometimes used to cope with
negative feelings, such as drinking or smoking. The experiences of voting, volunteering, and
activism are also different. Whereas voting and volunteerism are non-controversial activities that
exist within formal structures, activism often involves publically voicing opinions that are
controversial (Ballard & Ozer, 2016) and is a more oppositional activity that often exists outside
of a formal structure, perhaps connecting individuals into social networks where risk-taking
behaviors are the norm. Given that activism predicted more risky health behaviors, but positively
predicted SES, it is well worth future research exploring potential mechanisms by which
activism affects development as well as potential moderators to clarify whether there are specific
groups of youth for whom activism might impact health in negative and positive ways.
CIVIC ENGAGEMENT, HEALTH, AND SES
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Voting and volunteering were associated with fewer depressive symptoms in adulthood.
This extends evidence, mostly from adult samples (Thoits & Hewitt, 2001), and suggests that
engaging in community as a volunteer or a voter can boost mental health among younger
samples (Wray-Lake et al., in press). In addition to potentially operating on the same
psychological resources reviewed above, in the adult literature, it is specifically proposed that
helping others can boost healthy functioning through the psychological benefits of giving support
to others and “mattering.” The experience of volunteering and voting may be more likely to lead
to positive emotions like making people feel good about themselves, perhaps providing the boost
to mental health, whereas experiences in activism are likely more emotionally complicated,
perhaps explaining why activism was not associated with change in depressive symptoms.
Others suggest biological mechanisms based on hormones like oxytocin (Poulin & Holman,
2013), buffering stress (Poulin et al., 2013) and improving immune functioning (Schreier et al.,
2013). Voting might be indicative of a general sense of connectedness with society,
empowerment to be civically involved, or a belief in civic responsiveness. Each of these might
serve promotive functions for mental health.
Given considerable evidence that chronic stress predicts poor mental health and risky
health behaviors in adolescence and young adulthood (Adam et al., 2011; Romer, 2010), it is
exciting to consider voting and volunteering as a potential disruption to these processes. Offering
youth a chance to exert voice and exercise control (Zimmerman & Rappaport, 1988) and
providing the opportunity for role-fulfillment and an emotional outlet might lead youth to fewer
health risk behaviors and better mental health. It is important for future work to closely examine
a potentially risky role of activism for health behaviors, and to understand how best to scaffold
activism to promote healthy behaviors.
CIVIC ENGAGEMENT, HEALTH, AND SES
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None of the three forms of civic engagement in the present study were associated with
metabolic risk in adulthood. Future work should focus on the time course by which civic
engagement might affect biological processes. Biological and physiological effects show up
closer in time to civic engagement, especially in the form of helping as with volunteerism
(Poulin & Holman, 2013; Schreier et al., 2013), but long term cumulative physical effects are not
yet understood among adolescents and young adults.
It is important to note that in our models comparing any civic engagement to no civic
engagement, we found main effects of civic engagement on five out of the six outcomes in this
study. This underscores the need for research to differentiate between forms of civic engagement
in predicting developmental outcomes; combining the forms of civic engagement may obscure
the unique role each forms plays in development.
Limitations and Future Work
Despite the methodological and theoretical contributions of the present study, some
limitations must be noted. First, our analytic approach reduced selection bias but does not
establish causality. Using propensity scores to move toward causal inference adds considerably
to past work investigating the effects of civic engagement on developmental outcomes that
typically relies on traditional linear regression techniques. However, the utility of this approach
depends on thoroughly accounting for the earlier variables that affect selection into civic
engagement and potentially affect the outcomes of interest. In the present analyses, endogenous
unobservable characteristics could still account for the results (Foster, 2010; Shadish, Cook, &
Campbell, 2002). For example, due to data limitations, in the present study we used parental
education to account for family SES and we were not able to capture family political
CIVIC ENGAGEMENT, HEALTH, AND SES
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socialization processes, which affect selection into civic engagement and potentially affect
outcomes such as education level.
Second, the civic engagement measures used here were not able to capture the vast
differences in the quality of civic experiences young people have. Experiences in volunteerism
and activism can be categorized on many descriptive spectra. More finely grained measures of
types of civic engagement, as well as the quality of engagement (Ozer, Ritterman, & Wanis,
2010) would add considerably to understanding how civic engagement might promote or
undermine health. Not only are the qualities and quantity of civic engagement likely to affect
health trajectories, but the nature of civic experiences likely moderate the links between civic
engagement and health. As an example, reflection is a critical component of civic engagement
that moderates links between community service participation and civic outcomes (van Goethem,
Hoof, Orobio de Castro, van Aken, & Hart, 2014); the same might be true for effects of each
type of civic engagement on health and SES outcomes.
Additionally, we faced limitations common to using longitudinal, nationally
representative datasets like Add Health, including missing data and large temporal gaps between
survey waves. To address potential bias from attrition, we used sampling weights that were
created by Add Health, which adjust for participant non-response. Data were self-reported, so
estimation results are sensitive to mismeasurement or misreporting. Finally, biomarker outcomes
were not added to the study protocol until Wave 4, so we were only able to control for self-report
health measures in our analyses.
In the present study, we were primarily concerned with testing main effects of civic
engagement on health and SES outcomes accounting for selection effects. It will be exciting for
future work to understand mechanisms explaining links between civic engagement and health
CIVIC ENGAGEMENT, HEALTH, AND SES
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and SES in adulthood. For example, activism might promote health for marginalized youth when
it involves the development of certain attitudes like critical consciousness (Diemer & Li, 2011;
Christens, 2012) and volunteerism might operate through beneficial effects of helping others
(Brown & Okun, 2013). Future work should also attend to demographic and contextual factors
that might moderate links between civic engagement and health and SES. For example, civic
engagement during adolescence and young adulthood might operate in different ways for males
and females because of differences in civic socialization and participation across gender (e.g.
Jenkins, 2005) and a historic legacy of excluding women from voting practices and elected office.
Further, the positive effects of civic engagement might be amplified when the social networks
youth become embedded in have qualities such as being large and tightly-knit as opposed to
sparse, or diverse rather than homogenous (La Due Lake & Huckfeldt, 1998). In addition, future
work can expand our understanding of the non-behavioral (e.g., cognitive and emotional) aspects
of civic engagement on health and SES.
Conclusion
This study documents how engaging in civic life at the transition into adulthood affects
developmental trajectories. All forms of civic engagement have robust positive associations with
SES in adulthood over and above family SES and known background characteristics predicting
selection into civic engagement. Volunteering and voting predict improved mental health and
health behaviors, but not physical health, while activism predicted more risky health behaviors.
Overall, civic engagement seems to be a powerful experience in adolescence and young
adulthood with long-term implications for development.
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Table 1
Survey-weighted descriptive statistics for treatment and control sample Wave 1 covariates
Voting
Volunteering
Activism
Treatment
Control
Treatment
Control
Treatment
Control
N = 4,154
N = 4,880
N = 2,798
N = 6,271
N = 334
N = 8,751
Mean (SD)/%
Mean (SD)/%
Mean (SD)/%
Mean (SD)/%
Mean (SD)/%
Mean (SD)/%
Demographics
Male
0.51
0.51
0.49
0.51
0.54
0.50
Age
15.90
1.77
15.58
1.77
15.54
1.75
15.79
1.78
15.53
1.70
15.72
1.78
Agesq
255.91
56.40
245.95
55.84
244.47
55.28
252.45
56.76
244.05
53.37
250.24
56.51
White
0.71
0.67
0.74
0.67
0.73
0.69
Black
0.19
0.13
0.12
0.17
0.14
0.16
Hispanic
0.07
0.13
0.09
0.12
0.07
0.11
Asian
0.02
0.04
0.04
0.03
0.02
0.03
Native
0.01
0.01
0.00
0.01
0.02
0.01
Other
0.01
0.01
0.01
0.01
0.02
0.01
Foreign born
0.02
0.15
0.07
0.25
0.04
0.20
0.05
0.22
0.03
0.18
0.05
0.22
Parental education
13.65
2.54
12.57
2.57
13.87
2.57
12.68
2.55
14.74
2.32
12.98
2.60
Region (West)
0.15
0.35
0.16
0.37
0.15
0.36
0.15
0.36
0.12
0.33
0.15
0.36
Region (Midwest)
0.31
0.46
0.31
0.47
0.32
0.47
0.31
0.46
0.34
0.48
0.31
0.46
Region (Northeast)
0.13
0.34
0.14
0.35
0.15
0.36
0.13
0.34
0.14
0.35
0.14
0.35
College aspirations
4.44
0.96
4.06
1.12
4.49
0.92
4.11
1.15
4.67
0.72
4.21
1.11
Health
General health
1.99
0.87
2.15
0.91
1.93
0.83
2.15
0.92
1.89
0.84
2.09
0.90
Symptoms
6.94
5.97
7.28
6.57
6.91
5.80
7.21
6.51
7.57
6.23
7.11
6.30
Physical limitations
0.02
0.15
0.02
0.16
0.03
0.16
0.02
0.15
0.05
0.23
0.02
0.15
Depression
0.55
0.44
0.64
0.46
0.53
0.41
0.63
0.46
0.52
0.42
0.60
0.45
BMI
22.34
4.42
22.36
4.58
22.12
4.38
22.45
4.58
22.08
4.31
22.36
4.53
Physical activity
3.87
2.12
3.88
2.16
4.22
2.13
3.72
2.13
4.33
2.15
3.86
2.14
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Screen time
2.35
0.88
2.38
0.86
2.34
0.89
2.38
0.86
2.26
0.94
2.37
0.87
Marijuana use
1.08
5.33
1.32
6.16
0.57
3.69
1.49
6.49
1.06
5.07
1.22
5.81
Binge drinking
0.53
1.15
0.64
1.32
0.44
1.06
0.66
1.32
0.43
1.06
0.60
1.25
Smoking regularly
0.15
0.35
0.22
0.42
0.12
0.32
0.22
0.41
0.12
0.33
0.19
0.39
Social connections
Neighbors look out
0.24
0.43
0.28
0.45
0.22
0.42
0.28
0.45
0.21
0.41
0.27
0.44
Neighborhood not safe
0.08
0.27
0.11
0.01
0.07
0.01
0.11
0.01
0.08
0.02
0.10
0.01
Feel safe in school
3.88
0.99
3.77
1.04
3.93
0.95
3.77
1.04
3.90
1.02
3.82
1.01
Teachers care
3.64
0.94
3.48
1.01
3.74
0.90
3.47
1.01
3.66
0.85
3.55
0.98
Parents care
4.84
0.46
4.80
0.53
4.86
0.41
4.80
0.53
4.87
0.41
4.82
0.50
Friends care
4.31
0.74
4.23
0.79
4.36
0.68
4.23
0.80
4.35
0.69
4.27
0.77
Family understands
3.64
0.98
3.58
1.01
3.69
0.94
3.57
1.02
3.69
0.94
3.61
1.00
Religiosity
0.17
0.85
0.03
0.91
0.24
0.81
0.03
0.91
0.18
0.87
0.05
0.89
School Performance
Civic extracurric.
0.38
0.49
0.37
0.49
0.39
0.49
0.36
0.48
0.40
0.49
0.37
0.49
Grade in English
2.03
0.90
1.77
0.98
2.16
0.86
1.76
0.96
2.25
0.84
1.87
0.95
Grade in math
1.84
1.01
1.63
1.06
1.95
0.99
1.62
1.05
1.96
1.02
1.71
1.04
Grade in soc. studies
2.11
0.92
1.80
1.02
2.23
0.88
1.81
1.01
2.37
0.84
1.92
0.99
CIVIC ENGAGEMENT, HEALTH, AND SES
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Table 2
Descriptive statistics for Wave 4 outcome variables
Mean
SD
Min
Max
Health
Depressive symptoms
0.57
0.44
0.00
3.00
Risky health behaviors
0.23
0.21
0.00
1.00
# Cigarettes/month
3.58
8.17
0
100
Alcoholic beverages/month
16.48
36.14
0
504
Marijuana use/past 30 days
0.64
1.63
0
6
Fast food/last week
2.31
3.73
0
99
Physical activity/last week
3.73
3.07
0
21
Hours screen time/last week
22.03
20.58
0
270
Metabolic risk
0.95
1.11
0.00
4.00
Waist circumference/cm
97.96
17.11
50
195
Systolic blood pressure
124.85
13.52
77
215
Diastolic blood pressure
79.31
10.23
30
147
Hemoglobin A1c
5.57
0.81
3.8
23.1
SES
Education
14.41
2.18
6.00
20.00
Household income
63.79
38.04
2.50
150.00
Personal income
36.262
27.224
0
150
CIVIC ENGAGEMENT, HEALTH, AND SES
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Table 3
Relations between adolescent and young adult civic engagement and SES and health in adulthood
Depressive
Symptoms
Risky health
behaviors
Metabolic
risk
Education
Household
income
Personal
income
!!
!
β (se)
β (se)
β (se)
β (se)
β (se)
β (se)
[95% CI]
[95% CI]
[95% CI]
[95% CI]
[95% CI]
[95% CI]
Voting
-.056 (.018)
**
-.121 (.018)
***
.002 (.018)
.220 (.016)
***
.131 (.019)
***
0.138 (.019)
***
[-.092, -.019]
[-.158, -.084]
['-.037, .038]
[.189, .250]
[.092, .169]
[.101, .174]
N = 10, 835
N = 10,818
N = 10,737
N = 10,832
N = 10,145
N = 10,628
Volunteering
-.115(.021)
***
-.186 (.021)
***
.021 (.021)
.282 (.019)
***
.092 (.022)
***
0.095 (.021)
***
[-.157, -.072]
[-.228, -.145)
[-.063, .021]
[.245, .319)
[.049, .035]
[.054, .136]
N = 10,878
N = 10,862
N = 10,776
N = 10,876
N = 10,194
N = 10,663
Activism
-.061 (.051)
.132 (.060)
*
.077 (.068)
.316 (.059)
***
.046 (.059)
0.125 (.056)
*
[-.162, .040]
[.015, .250]
[-.056, .211]
[.201, .431]
[-.069, .162]
[.016, .235]
N =10,714
N = 10,697
N =10,615
N =10,712
N = 10,046
N = 10,512
Note. * p< .05; ** p< .01; *** p< .001.
CIVIC ENGAGEMENT, HEALTH, AND SES
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Figure 1. Standardized differences scores on all covariates before and after weighing for (a) voting (b) volunteering (c) and activism.
Plan to attend college
Parental education
Grade in Social Studies
Religiosity
Grade in English
Grade in Math
Age
Age squared
Black
Friends care
Parents care
Feel safe in school
Teachers care
Physical activity
Region
Family understands
Male
BMI
Civic extracurriculars
Screen time
Native
Physical limitations
Other
Binge drinking
Marijuana use
Symptoms
Asian
Neighbors look out
Neighborhood not safe
Foreign born
Hispanic
Missing 3+ covariates
Depression
Smoking regularly
General health
Survey weight
20 10 0 10 20 30
Standardized Difference (%)
Preweighting
Postweighting
a) Voting
Grade in Social Studies
Plan to attend college
Grade in English
Parental education
Religiosity
Grade in Math
Physical activity
Feel safe in school
Friends care
Parents care
Family understands
Teachers care
Region
Asian
Physical limitations
Other
Male
Civic extracurriculars
Foreign born
BMI
Native
Screen time
Survey weight
Black
Symptoms
Hispanic
Neighbors look out
Missing 3+ covariates
Neighborhood not safe
Marijuana use
Age squared
Age
Binge drinking
General health
Depression
Smoking regularly
20 0 10 20 30 40
Standardized Difference (%)
Preweighting
Postweighting
b) Volunteering
Parental education
Plan to attend college
Grade in Social Studies
Grade in English
Grade in Math
Physical activity
Religiosity
Physical limitations
Feel safe in school
Native
Friends care
Parents care
Other
Family understands
Male
Teachers care
Symptoms
Region
Marijuana use
Civic extracurriculars
Asian
Neighborhood not safe
Missing 3+ covariates
Neighbors look out
Foreign born
Survey weight
Black
Hispanic
BMI
Screen time
Age squared
Age
Depression
Smoking regularly
Binge drinking
General health
20 0 20 40 60
Standardized Difference (%)
Preweighting
Postweighting
c) Activism
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Many scholars posit that engaging in civic actions enhances mental health, yet existing evidence is limited. To address gaps in the literature and advance understanding of benefits of civic engagement from adolescence to young adulthood, we estimated a longitudinal structural model to test bidirectional associations between civic engagement (i.e., voting, community engagement, political behaviors) and depressive symptoms using nationally representative longitudinal U.S. data. Adolescent and early young adulthood (EYA) depressive symptoms predicted decreases in later voting. Adolescent and EYA community engagement predicted decreases in later depressive symptoms. Analyses suggested that findings generalized across gender, age, socioeconomic status, race/ethnicity, and EYA social roles. Despite key limitations in measurement of civic engagement over time, results provide some support the notion that community engagement may have mental health benefits for youth, and that depression may reduce later civic engagement.
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