The Relationship Between Financial Hardships and the Development of Risk-Taking Behaviors: The Protective Role of Extracurricular Activities

Abstract

Past research has demonstrated an association between financial hardships and risk-taking behaviors (defined as delinquent or transgressive acts). However, this effect may differ based upon the provision of protective spaces, like those offered in extracurricular activities. In the current study, we examined the longitudinal effect of financial hardships on risk-taking in a sample of Australian adolescents (N = 3,852). We found experiences of financial hardship at the age of 12/13 predicted higher risk-taking 2 years later. Further, a longitudinal indirect effect was observed, where financial hardships were associated with higher risk-taking 4 years later via increased exposure to risk-taking peers. Non-sporting activity participation (e.g., debating, dance) was found to offset the risks associated with experiencing financial hardships. Participation in sports was not significantly related to any of our outcomes. Our data highlights how the provision of non-sporting activities may improve behavioral outcomes, especially among more financially disadvantaged adolescents.

Keywords

risk-taking peers social norms agency delinquency extracurricular activities organized activities sport non-sport

Adolescence is an important developmental period for social relations. Social networks begin to widen in adolescence as most young people place greater importance on their friendships and romantic interests (Fabes et al., 1999). With a greater emphasis on social relationships, adolescents continue to grow their social competencies and socially interact with a wider range of people (Van der Graaff et al., 2018). These interactions come with both rewards and risks. A key risk is that social processes may lead adolescents to engage in risk-taking behaviors, comprised of acts that harm others or their property, are transgressive, or viewed as delinquent by the wider community (Carlo et al., 2011; Van der Graaff et al., 2018; Wiesner & Windle, 2004). While risk-taking is a normative by-product of social experimentation in adolescence and can be adaptive (e.g., Windle, 2016), persistently high rates of risk-taking during adolescence can contribute to less optimal outcomes across the lifespan (Makarios et al., 2017; Moffitt et al., 2002). For example, having a criminal record can greatly constrain young people’s education and career options. In large part because of these risks, adolescence is seen as an important space for policy interventions, largely aimed at young people who are economically disadvantaged or otherwise marginalized.

The purpose of the current study is to inform debate on an established protective factor—participation in extracurricular activities—that may help address risk-taking behavior among more disadvantaged adolescents. Unlike policing and criminal justice interventions, extracurricular activities such as organized sports, music and youth clubs are not generally seen as stigmatizing by policy makers and offer a community-based approach to promote positive behaviors. In the following section, this paper articulates the mechanisms linking financial hardships to risk-taking before arguing how sporting and non-sporting extracurricular activities may offset these risks and promote positive, developmental outcomes.

Economic Disadvantage and Risky Behaviors

Where families and communities have adequate resources and adolescents can see desirable and feasible pathways from school to post-school education and careers, there is considerable implicit pressure to conform to modes of behavior that are seen as acceptable in wider society. Taking a sociological perspective on agency in the context of poverty, Lister (2016) argued that in economically disadvantaged families and communities, pressures associated with poverty can elicit a wide range of responses. These include socially approved means of “getting out” of poverty (seeking employment; upskilling through training or education); “getting by”—surviving day-to-day on available resources without expecting to move out of poverty; and “getting (back) at,” forms of resistance that can involve transgressions such as welfare fraud, or violence as a response to perceptions of daily humiliation associated with poverty (Tyler, 2013). It is in this latter category of agency that the participation by young people in risk-taking activities can be placed.

General Strain Theory (Agnew, 2019) provides a complementary criminological perspective, positing that where social structures inhibit economic achievement and positively valued outcomes, young people experience negative emotional states, including anger. Although many young people will deploy legitimate coping strategies, criminal conduct and risk-taking can increase when coping strategies are either unavailable or ineffective (Broidy, 2001). Under these circumstances young people will often challenge perceived injustices and display frustrations at their predicament (e.g., aggression) or engage in behaviors that work toward traditional economic goals (e.g., theft, Agnew, 1992, 1999). By emphasizing the affective role of anger and the presence of coping strategies, the General Strain Theory has provided a framework to explore under what psychological and social conditions strain can lead to risk-taking. This corpus of evidence suggests associations with risk-taking others may be an important condition for risk-taking (e.g., Piquero & Sealock, 2000; Song, 2020), congruent with other theoretical frameworks.

The Social Development Model of crime provides another perspective, highlighting the importance to some young people of the social rewards that they associate with participation in risk-taking behaviors (Catalano & Hawkins, 1996). Engagement in risk-taking behaviors with peers or seeking validation from peers can reinforce bonds with others and form a feedback loop that promotes participation in future risky activities (Haynie, 2001; Warr & Stafford, 1991). In this framework, the link between risk-taking behaviors and risk-taking peers is dynamic and reciprocal. While adolescents are drawn to peer groups with shared interests and values, frequently occurring behaviors in a peer network also become normalized (Granovetter, 1985). Accordingly, the attributes of peers shape the perspectives of young people and influence their future behaviors and outlook to align with the peer group (Choukas-Bradley et al., 2015). Longitudinal research supports the bi-directional association between risk-taking and associations with risk-taking peers (Thornberry, 1987; Thornberry et al., 1994), especially within the context of economic deprivation where less safe community spaces can provide greater exposure to risk-taking others. However, the Social Development Model also proposes that engagement with alternative social environments and peer groups can contribute to discontinuity in risk-taking behaviors (Hawkins & Weis, 1985).

Each of these three frameworks offer a different perspective to explain the emergence of risk-taking among individuals who experience financial hardships but can complement each other. Lister (2016) argues that “getting (back) at” is a personal response to poverty, although her theory does not offer a perspective on getting (back) at as an individual or as a group activity. In contrast, the Social Development Model acknowledges the importance of social groups during adolescence and how peers and social norms can guide individual behaviors. However, each perspective converges to argue that where young people can see the possibility of using socially approved means to advance their life chances, they may reduce their participation in risk-taking activities. We argue here that participation in extracurricular activities, while not fundamentally changing contexts of disadvantage in which many young people live, may offer opportunities to advance their self-interests and simultaneously offer opportunities to expand social networks and socially approved opportunities for their development.

Extracurricular Activities and Risk-Taking

Participation in organized extracurricular activities provides developmentally enriching experiences that can promote positive outcomes for adolescents. Youth who engage in such activities are provided with opportunities to build their skills and competencies in specialized areas (Mahoney et al., 2005) and can learn to cope with stressors in an adaptive way (Heaslip et al., 2021). Activities are also important social contexts in the lives of adolescents. Social relations with extrafamilial adults and likeminded peers become regular and scheduled occurrences for adolescent activity participants, leading to the formation of new friendships and widening social networks (Schaefer et al., 2011). These social relations can serve to model and reinforce socially approved behaviors when co-participants are more focused on academic achievement and other prosocial behaviors (Eccles et al., 2003). Thus, participation in extracurricular activities can reduce participation in risk-taking behaviors both directly (reducing the length of time adolescents spend unsupervised) and indirectly (by broadening young people’s social landscapes). Evidence supports this conclusion, with research demonstrating that participation in organized activities is associated with lower levels of behaviors such as aggression, school conduct issues, delinquency, and non-conformity (Mahoney & Stattin, 2000). However, it is also important to acknowledge the diverse and varied cultures and contexts that produce heterogenous outcomes associated with activity participation. In the current study, we focus on young people living in contexts of economic disadvantage.

Economic Deprivation and Activity Participation

Adolescents who engage in extracurricular activities have diverse and varied experiences prior to their participation that can serve to amplify or diminish the effects of participation (Heath et al., 2022). As noted above, many adolescents will participate against a backdrop of structural inequalities and disadvantages that may simultaneously constrain conventional routes toward “getting out” and contribute to the formation of peer groups who engage in risk-taking. Given greater exposure to these risk factors, extracurricular activities may be especially important for economically disadvantaged young people and compensate for limited developmental resources (Heath et al., 2022; Morris, 2015; O’Donnell & Barber, 2021). In contrast, adolescents with greater financial security often have access to a plethora of developmental assets, including school resources and social capital, that may provide experiences comparable to those embedded within activities that naturally reduce the likelihood individuals will be exposed to risk-taking norms in social settings (Leventhal et al., 2009). The benefits of extracurricular activity participation are therefore argued to be less essential for advantaged adolescents who are likely to have better developmental outcomes regardless of participation (Morris, 2015).

A growing literature is now demonstrating that the effects of extracurricular activities on improved interpersonal, psychological, and academic outcomes are stronger for economically disadvantaged youth (Agasisti & Longobardi, 2017; Blomfield & Barber, 2011; Crosnoe et al., 2015; Fredricks & Eccles, 2008; O’Donnell et al., 2020; Ren et al., 2021). However, these outcomes have been found to vary by type of extracurricular activity. For example, O’Donnell and Barber (2021) found continued participation in non-sporting activities predicted lower levels of risk-taking behaviors when participants attended schools in low socioeconomic status communities. This issue is important in the context of the present study.

Type of Extracurricular Activity

Scholars often compare the developmental outcomes associated with participating in a diverse range of activities (e.g., Blomfield & Barber, 2009; Eccles & Barber, 1999; Kort-Butler & Hagewen, 2011; Larson et al., 2006) as it is generally acknowledged that distinct activities can expose adolescents to divergent risk and protective factors (Hansen et al., 2003). Sporting activities typically promote physical activity, teamwork and goal setting, and can build coping skills following losses in competition. However, sport participation is not consistently associated with positive outcomes during adolescence. While some studies have reported that sport participation predicts lower risky behaviors (Fredricks & Eccles, 2006), other studies have found no relationship (Jaf et al., 2021; Spruit et al., 2016), or that some sports may actually increase the propensity to engage in risk-taking behaviors (Fauth et al., 2007; Gardner et al., 2009; Kreager, 2007; Maume & Parrish, 2021). Further, most adolescents report positive relations with their peers and coaches (Fry & Gano-Overway, 2010), but competition can reduce prosocial behaviors toward others (Kavussanu & Stanger, 2017) and expose adolescents to risk-taking teammates (O’Donnell & Barber, 2018). These divergent findings may be attributed to heterogeneity in the characteristics of sporting contexts, where rule breaking, risk-taking, and competitive relationships can emerge in some instances (Rutten et al., 2011).

Sporting activities remain one of the most popular forms of organized extracurricular pursuits. However, many adolescents engage in other activities (Oberle et al., 2019). Non-sporting activities allow adolescents to pursue their interests in artistic, community, service, religious, and academic avenues. Much like young athletes, adolescents who engage in non-sporting activities develop skills, interact with prosocial peers at scheduled intervals, develop goal setting, and engage in teamwork (Mahoney et al., 2005). However, non-sporting activities are almost always protective spaces and are less likely to expose adolescents to negative peer and adult influence (Hansen et al., 2003). Accordingly, research has found a consistent negative relationship between participation in non-sporting activity and participation in risk-taking behaviors (Fleming et al., 2008; O’Donnell & Barber, 2021). In line with a broader literature, we will consider the independent association between both sporting and non-sporting activities and risky behaviors in the current study (e.g., Blomfield & Barber, 2009).

The Current Study

Our study builds on existing theoretical frameworks and decades of research that largely supports the notion that financial disadvantage can be related to the emergence and continuation of delinquent behaviors during adolescence. Consistent with agentic explanations of risk-taking, Lister (2016) argues many transgressive behaviors are a form of resistance against inequities. General Strain Theory (Agnew, 2019) offers a complimentary explanation, suggesting negative affect is the conduit linking financial hardships to adolescent risk-taking, especially in social settings where risk-taking is normalized. Congruent with this latter argument, the Social Development Model of crime (Catalano & Hawkins, 1996) postulates positive peer feedback following risk-taking acts can provide validation that propagates the continuation of behaviors. Despite their differences, each theoretical model argues that risk-taking will decrease if young people in disadvantaged contexts are provided with developmentally and socially enriching opportunities. Whether these opportunities include using socially approved means of advancing life outcomes (i.e., “getting out,” Lister, 2016), deploy appropriate coping strategies to attenuate negative affect (General Strain Theory; Agnew, 2019), or recalibrating social networks to encourage prosocial behaviors (Social Development Model, Catalano & Hawkins, 1996).

In the current study, we propose participation in extracurricular activities may be one avenue to attenuate the long-term effect of financial hardships on adolescent risk-taking. Past research has established that activity participation is associated with lower levels of risk-taking (Mahoney & Stattin, 2000), and the effect may be stronger in contexts of socioeconomic disadvantage (O’Donnell & Barber, 2021). However, whether extracurricular activities buffer against the deleterious effects of financial hardships via social or psychological mechanisms remains an open question that the current study will explore.

We tested the longitudinal effects of financial hardship, extracurricular activities, exposure to risk-taking peers, and risk-taking behaviors in a national sample of Australian adolescents across 4 years (12- and 13-year-olds through to 16- and 17-year of age). Consistent with various theoretical models, we hypothesize the presence of bi-directional relationships between risk-taking behaviors and associations with risk-taking peers across time. Importantly, we will also test for interaction effects between financial hardships and participation in different types of extracurricular activities at the first timepoint. We hypothesize that extracurricular activities will moderate the direct association between financial hardships and risk-taking. Activities can increase young peoples’ expectations for success (Lipscomb, 2007) leading to a visible pathway of “getting out” while simultaneously teaching emotional regulation skills (Heaslip et al., 2021) that can reduce adverse responses to experiences of strain. Juxtaposed against these intra-psychological mechanisms, we will also test if the association between financial hardships and associations with risk-taking peers is moderated by extracurricular activity participation. It is equally plausible that the social interactions within activity contexts protect financially disadvantaged young people against elevated rates of risk-taking via increased exposure to prosocial others consistent with the Social Development Model.

The effects of sporting and non-sporting activity participation will be independently evaluated. While we anticipated clear benefits stemming from non-sporting activities, the role of sports participation is more exploratory given the inconsistent evidence linking sports to increased (Gardner et al., 2009) and decreased (Fredricks & Eccles, 2006) risk-taking over time. The inclusion of both activity types will provide an opportunity to compare the potential benefits of the two activity types.

Disentangling the psychological and social mechanisms of adolescent risk-taking is important. Extracurricular activities are seen as a vehicle to promote better outcomes among young people (Mahoney et al., 2005), but it is widely accepted that universal strategies and interventions are ineffective at inducing change (e.g., Kuyken et al., 2022). Accordingly, it is vital we understand how and for whom activity participation may be beneficial and the potential barriers inhibiting participation among those who may benefit most.

Method

Participants and Procedure

In 2003, the Longitudinal Study of Australian Children (K-cohort) commenced with the recruitment of 4,983 children between the ages of 4 and 5. Participants were initially recruited from a national database of medical records using a stratified sampling method to ensure the sample was nationally representative (Gray & Smart, 2008; Sanson et al., 2002). The current study uses responses during adolescence starting with the responses of 3,852 12- and 13-years-olds (51.1% Male, M_age = 12.41, SD = 0.49). Data collection occurred every second year, leading to the retention of 3,317 14- and 15-years-olds and 2,954 16- and 17-years-olds. The adolescents and their primary caregiver (99.1% biological parent; 94.3% Female) completed a combination of in-home interviews and computer-assisted self-interviews over the years. All study resources including questionnaires, technical reports, and data can be accessed online (https://growingupinaustralia.gov.au/).

Materials

Risk-taking behaviors

Participants were provided a list of 12 behaviors that are often classified as “anti-social” or transgressive, and asked to indicate how often they had engaged in those behaviors during the previous 12 months (0 = Not at all to 5 = Five or more times). Responses were then recoded to indicate whether the behavior had occurred (0 = No, 1 = Yes) and summed to create an index that represents the breadth of antisocial behaviors participants engaged in. The behaviors included aggression (e.g., Got into physical fights in public) and more serious activities (e.g., Broken into a house, flat or vehicle). The measure was internally consistent (Table 1).

Table 1.

Descriptive Statistics, Cronbach’s Alpha, and Bivariate Correlations Between Extracurricular Activity Participation, Socioeconomic Status, and Social Behaviors Over Time.

	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.	12.	13.
1. Risk-taking (T1)	—
2. Risk-taking (T2)	.12**	—
3. Risk-taking (T3)	.15**	.38**	—
4. Risk-taking peers (T1)	.20**	.24**	.16**	—
5. Risk-taking peers (T2)	.14**	.41**	.32**	.40**	—
6. Risk-taking peers (T3)	.11**	.31**	.43**	.27**	.54**	—
7. Sport participation	−.02	.01	.01	−.03	.01	.07**	—
8. Non-sporting participation	−.03	−.04*	−.01	−.08**	−.04*	−.04*	.01	—
9. Socioeconomic status	−0.03	−.03	.01	−.11**	−.05*	−.02	.10**	.09**	—
10. Financial hardship	.07**	.09**	.07**	.08**	.08**	.06**	−.10**	−.05*	−.14**	—
11. Gender^a	−.09**	−.11**	−.14**	−.11**	−.03	−.09**	−.04*	.15**	−.01	−0.03*	—
12. Region^b	−.01	−.01	−.02	.04*	.04*	.05*	−.04*	−.08**	−.35**	.03	−.01	—
13. Age	.03	.01	.01	.03	.06**	.07**	.03	−.04*	−.02	.01	.01	.03	—
Mean (SD)	0.60 (1.94)	0.36 (1.09)	0.57 (1.58)	1.22 (0.35)	1.31 (0.45)	1.58 (0.54)	—	—	1.01 (0.07)	0.28 (0.70)	—	—	12.41 (0.49)
Range	0–12	0–12	0–12	1–5	1–5	1–5	—	—	0.72–1.15	0–4	—	—	12–13
α	.93	.77	.86	.79	.81	.76	—	—	—	—	—	—	—

1 = Female, 0 = Male; ^b1 = Metro, 0 = Non-Metro; *p <.05; ** p < .001.

Risk-taking peer exposure

Participants were asked to think of the young people they spend time with in their school, neighborhood, and other places, before responding to seven questions asking what proportion of these young people engaged in transgressive behaviors (e.g., “They get into fights”; “They drink alcohol”; “They have broken the law”). The items were taken from the “What my Friends are Like” Scale (Gray et al., 2017) and responded to on a 5-point scale (1 = None of them to 5 = All of them). A mean was computed, with higher scores indicating participants were aware of risk-taking behaviors among their peers in different contexts.

Extracurricular activity participation

At the age of 12 or 13, participants were asked whether they had engaged in two types of sporting activities (team sport, individual sport) and 5 types of non-sporting (e.g., community group, art music or performance, religious services, etc.) activities outside of their standard school time during the previous 12 months. Responses were coded into two dichotomous variables that measured whether participants had participated in any form of sporting (76.4% yes) or non-sporting activities (57.1%).

Financial hardship

Participants’ primary caregiver were asked whether they had experienced six different types of financial hardships in the previous 12-months (e.g., Pawned or sold something because you needed cash; 1 = Yes, 0 = No). Items were summed to form a composite scale. The scale has previously been used in longitudinal research and has demonstrated predictive validity (i.e., mental health outcomes; O’Donnell, Stuart, & O’Donnell, 2020). In the current study, few participants reported five (N = 8) or six (N = 3) financial hardships. As such, responses were winsorized to have a maximum score of 4.

Covariates

The current study controlled for participants’ age in years, gender, and region of residence. Further, we controlled for an index of community socioeconomic status after participant’s postcodes were matched with area-level information from the Australian Bureau of Statistics (the Index of Relative Socio-economic Disadvantage, IRSD; Australian Bureau of Statistics, 2016). The Australian Census is used to identify the percentage of households within each community that experience a range of socioeconomic disadvantages (e.g., occupied households without cars, unemployment, children who live with unemployed parents). The measure is normed so that the average Australian community has a score of 1,000 (SD = 100) and higher scores indicate the community has fewer disadvantages. In the final model, the measure is rescaled to help with model convergence (M = 1.00, SD = 0.10).

Analytical Strategy

A random-intercept cross-lagged panel model (RI-CLPM; Hamaker et al., 2015) was used in the current study to explore the dynamic relationship between risk-taking peer associations and behaviors. Initially, a standard cross-lagged panel model was conducted. The subsequent inclusion of random intercepts significantly improved the model fit (Δχ²(3) = 31.74, p < .001), suggesting there are trait-like, between person differences in the current study (i.e., individual differences across the duration of the entire study). By explicating between-person variances, the remaining cross-lagged and autoregressive coefficients denote within-person effects, or the extent to which individuals vary over time. Activity participation, financial hardships, and the covariates were then added to the model as predictors of risk-taking peers and behaviors alongside two interaction terms: (1) sport participation × financial hardships and (2) non-sport activity participation × financial hardships.

The inclusion of the interaction terms provided an opportunity to explore indirect and conditional indirect effects. If a dynamic, bi-directional relationship between peer attributes and individual behaviors emerge over time, then a proximal effect of financial hardships (T1) on either outcome (T2), should then have an indirect effect on the alternative construct (T3). The significance of the indirect effect were evaluated with bias corrected confidence intervals for 1,000 bootstrapped samples. As a final step of analyses, significant interactions between financial hardships and activity participation were then evaluated using (1) Hayes’ index of moderated mediation (Hayes, 2015), and (2) simple slopes that plotted the strength of the indirect effect of financial hardships (T1) on future outcomes outcome (T3) depending upon activity participation status (Igartua & Hayes, 2021). Syntax was derived from instructions provided by Stride et al. (2015).

An analysis of missing data determined the data were not missing completely at random, χ²(299) = 495.03, p < .001; Little (1988). Compared to participants who were retained in the study, the participants who ceased participating were more likely to live in disadvantaged communities, t(1699.39) = −4.973, p < .001, associate with more risk-taking peers, t(1547.59) = 3.74, p < .001, engage in greater risk-taking behaviors, t(1605.12) = 2.25, p = .025, and were less likely to participate in either sporting, χ² (1) = 27.78, p < .001, or non-sporting activities at T1, χ²(1) = 8.39, p = .004. Thus, attrition and missing data were addressed using full information maximum likelihood (FIML). In this approach, all available datapoints are used to estimate the coefficients and standard errors and does not impute data. FIML is argued to be an appropriate and unbiased approach when missingness is dependent on other variables in the model (Enders & Bandalos, 2001). All analyses were conducted in Mplus (v8.7, Muthén & Muthén, 1998–2017) using maximum likelihood estimation.

We then conducted a series of sensitivity analyses as a final stage of our analytical strategy. We largely replicated our findings using (1) the unaltered financial hardship measure that was not windsorized, (2) a standard CLPM declaring the financial hardship and risk-taking variables as count, (3) a RI-CLPM using the maximum likelihood estimator with robust standard errors which corrects standard errors for non-normality, (4) a multigroup RI-CLPM (see Mulder & Hamaker, 2021) that did not find any empirical support to analyze boys and girls separately (Δχ²(17) = 12.74, p = .753), (5) multiple imputation as an alternative approach to address missing data, (6) a RI-CLPM using continuous indicators of adolescent risk-taking, and (7) a model exploring within-person change in financial hardship over three timepoints. All models except (7) replicated our findings and so we opted to report the bootstrapped RI-CLPM with sex as a control variable and financial hardships only included at T1. Controlling for temporally invariant, between-person differences in financial hardship conceptually and statistically removes the effects of living with persistent poverty from our analyses. In contrast, within-person deviations in financial hardships are more congruent with transitory experiences of poverty. Disaggregating these facets of adolescent poverty reduced the effect sizes in the model. However, the inclusion of a single indicator of financial hardships at T1 explores how financial strain is associated with developmental outcomes regardless of temporal trends. The syntax and output for the sensitivity analyses are provided online (http://doi.org/10.17605/OSF.IO/TNXMB).

Results

Bivariate Correlations

The bivariate correlations (Table 1) indicated that exposure to risk-taking peers and engagement in risk-taking behaviors were significantly associated across all timepoints, suggesting individuals who engaged in antisocial behaviors were likely to know youth who engage in similar behaviors. Both sporting and non-sporting extracurricular activity participation did not predict risk-taking behaviors across time (with the exception of non-sporting activity participation and risk-taking at T2). However, the relationships with risk-taking peer exposure were more variable. Participation in non-sporting activities was associated reduced exposure to risk-taking others across time, but sport participants at the age of 12 and 13 had marginally more exposure to risk-taking peers 4 years later. Financial hardship was clearly important, being related to more risk-taking behaviors and peer exposure across time and a reduced likelihood of participating in both sporting and non-sporting activities. Activity participation was also lower in regional and more disadvantaged communities and substantial gender differences emerged, whereby females were more likely to engage in non-sporting activities and males were more likely to engage in sporting activities.

The Full Model

In the full model (Figure 1, Table 2), there was evidence for a bi-directional relationship between risk-taking behaviors and exposure to risk-taking peers. Engagement in risk-taking behaviors predicted future increases in risk-taking peer exposure across both time intervals. Similarly, a greater proportion of risk-taking peers predicted increased risk-taking over time.

Figure 1.

Caption: A random-intercept cross-lagged panel model depicting the relationship between risk-taking peers and risk-taking behaviors from the ages of 12 to 13 (T1) to the ages of 16 to 17 (T3). Extracurricular activity participation, financial hardship, and the interaction terms are also included. Bolded lines denote statistically significant pathways (p < .05). For parsimony, covariates and covariances are omitted from the figure.

Table 2.

Regression Coefficients Exploring the Longitudinal Relationship Between Extracurricular Activity Participation, School Belonging, Peer Connectedness, and Prosocial Peers.

	T1 → T2				T2 → T3
	Risk-taking		Risk-taking peers		Risk-taking		Risk-taking peers
	b [CI_95%]	β	b [CI_95%]	β	b [CI_95%]	β	b [CI_95%]	β
Risk-taking	−0.02 [−0.08 to 0.04]	−0.04	0.02 [0.01 to 0.04]	.09*	0.28 [0.03 to 0.51]	.18*	0.07 [0.02 to 0.11]	.12*
Risk-taking peers	0.50 [−0.29 to 1.28]	.18	0.34 [0.06 to 0.54]	.26*	0.94 [0.64 to 1.31]	.27**	0.51 [0.44 to 0.62]	.44**
Sport participation	0.05 [−0.06 to 0.14]	.05	0.03 [−0.01 to 0.07]	.07	0.05 [−0.10 to 0.16]	.03	0.08 [0.04 to 0.12]	.16**
Non-sporting participation	−0.03 [−0.11 to 0.05]	−.03	−0.01 [−0.04 to 0.02]	−.03	0.06 [−0.04 to 0.17]	.04	−0.01 [−0.04 to 0.03]	−0.01
Financial hardship	0.13 [0.04 to 0.23]	.14*	0.04 [0.01 to 0.07]	.09*	0.11 [0.01 to 0.20]	.07*	0.03 [−0.02 to 0.04]	.03
Socioeconomic status (SES)	−0.13 [−0.91 to 0.62]	−.13	0.003 [−0.24 to 0.28]	.01	0.34 [−0.45 to 1.13]	.23	0.21 [−0.07 to 0.48]	.41
Gender^a	−0.18 [−0.24 to −0.11]	−.19*	0.01 [−0.02 to 0.05]	.03	−0.32 [−0.43–−0.22]	−.21**	−0.07 [−0.10 to −0.03]	−.13**
Region^b	−0.04 [−0.13 to 0.04]	−.05	0.02 [−0.02 to 0.06]	.05	−0.06 [−0.17 to 0.06]	−.04	0.03 [−0.01 to 0.07]	.07
Age	−0.01 [−0.08 to 0.07]	−.01	0.04 [0.01 to 0.07]	.09*	−0.01 [−0.13 to 0.09]	−.01	0.06 [0.03 to 0.09]	.11**
Sport intensity × Financial hardship	0.15 [−0.01 to 0.31]	.15	0.03 [−0.03 to 0.09]	.07	0.07 [−0.15 to 0.28]	.04	−0.02 [−0.09 to 0.05]	.01
Non-sporting intensity × Financial hardship	−0.04 [−0.23 to 0.16]	−.04	−0.07 [−0.14–−0.01]	−.17*	−0.03 [−0.23 to 0.15]	−.02	0.01 [−0.06 to 0.06]	−.03

1 = Female, 0 = Male; ^b1 = Metro, 0 = Non-Metro; *p <.05; **p < .001.

Model Fit: χ²(19) =171.12, p < .001; RMSEA = .05; SRMR = .03; CFI = .96.

The results of the current study suggest that financial hardships were positively related to changes in risk-taking behavior and exposure to risk-taking peers 2 years later. In line with the hypotheses, we subsequently explored the long-term effects of financial hardship by modeling indirect effects. Our analysis revealed two significant indirect effects. First, financial hardships (T1) predicted greater exposure to risk-taking peers (T2), which subsequently was associated with increased actual risk-taking behaviors (T3), b = 0.04, CI_95% [0.01 to 0.08], p < .05. Second, financial hardships (T1) predicted an increase in risk-taking behaviors (T2), which culminated in greater exposure to others who engage in risk-taking (T3), b = 0.01, CI_95% [0.003 to 0.02], p < .05.

Neither non-sporting nor sporting activity participation directly predicted risk-taking behaviors or peers. However, the main effect of financial hardships on risk-taking peers was qualified by a significant interaction with non-sporting activity participation. No other moderation terms, including those with sport participation, were significant. As a final step of the analyses, the index of moderated mediation was used to determine that the indirect effect between financial hardships (T1) and risk-taking behaviors (T3) via risk-taking peers (T2) was conditional on non-sporting activity participation, b = −0.07, CI_95% [−0.16 to −0.01], p < .05. Subsequent simple slopes revealed that the specific indirect effect was only significant for participants who did not participate in non-sporting activities, b = 0.08 CI_95% [0.03 to 0.16], p < .05. Thus, non-sporting activity participation offered some protection and buffered adolescents who experience financial hardships from increased risk-taking behaviors via peer exposure, b = 0.01, CI_95% [−0.02 to 0.05], p > .05. To further illustrate the different effects by non-sporting activity participation, the indirect effect of financial hardships (T1) on risk-taking behaviors (T3) was modeled for participants who did, and did not, engage in non-sporting activities (Figure 2).

Figure 2.

Caption: The Indirect Effect of financial hardships (12–13 years of age) on risk-taking behaviors (16–17 years of age) via risk-taking peer exposure (14–15 years of Age) for participants who engaged in non-sporting activities and those who did not. Scores represent within-person deviations, thus, positive scores denote increased risk-taking and negative scores denote decreased risk-taking.

Discussion

Discovering and developing how to expand social networks and behave within social conventions are essential developmental tasks. While most adolescents do not engage in behaviors that involve significant risk to themselves and others (Wiesner & Windle, 2004), policy and research attention has tended to focus on “treating” the minority who do (Gillies, 2000). In the current study, we used evidence suggesting the likelihood of engaging in risk-taking behaviors is disproportionately higher among adolescents who experience economic deprivation as a starting point for our analysis of publicly available data. In line with this established literature, we found that financial hardship at the age of 12 and 13 was associated with increased risk-taking behaviors during adolescence. While our data could not explain the motivational dynamics behind these associations, it is argued by Agnew et al. (2008) that participation in risky, transgressive and potentially illegal behaviors can, for at least some young people, be motivated by perceptions of powerlessness and frustration in instances where more socially approved behaviors do not produce positive benefits. Such arguments place transgressive behaviors in deprived contexts as individual agency in the context of social and economic constraints (hence “getting(back) at”; Lister, 2016). However, individual and agentic explanations for the development of risk-taking do not fully take into account the role of social norms and expectations within peer groups that are known determinants of the uptake and escalation of such behaviors across adolescence.

Congruent with an established literature (Thornberry et al., 1994), we found the relationship between exposure to risk-taking peers and individual risk-taking was largely dynamic, and bi-directional over time. Exposure to risk-taking peers could bolster beliefs that risk-taking behaviors are both normative and adaptive, thereby reinforcing the acceptability of future risk-taking activities, in part, perhaps as a means of strengthening bonds with adolescents who engage in similar behaviors (Catalano & Hawkins, 1996), or in contexts where socially approved means of “getting out” of poverty may not seem possible (Agnew, 2019; Lister, 2016). The reciprocal effects between peer attributes and individual behaviors provided another mechanism leading to increased risk-taking among financially disadvantaged adolescents: Financial hardships (T1) predicted increased exposure to risk-taking peers (T2), which subsequently had a cascading effect on actual risk-taking (T3). Provision of safe and supportive spaces, like extracurricular activities, may offset these risk factors. Our findings, discussed in more depth below, support this conclusion.

The Moderating Role of Extracurricular Activity Participation: Findings and Implications

A large corpus of data now suggests extracurricular activities (especially non-sporting ones) promote positive peer groups and reduce risk-taking (e.g., Fredricks & Eccles, 2006; Gardner et al., 2009; O’Donnell & Barber, 2021). However, the results in the current study were not consistent with this evidence-base. While we tested whether participation in both sporting and non-sporting activity is associated with reduced participation in risk-taking, even the bivariate correlations between activity participation and risk-taking outcomes were weak and mostly not significant over time. These null findings may be due to the low propensity for most adolescents in our nationally representative sample to engage in risk-taking activities either individually or with peers. Alternatively, it may attest to the importance of structural barriers and macro-level contexts that alter the effects of extracurricular activity participation.

Scholars are increasingly pointing to the diverse experiences and contextual constraints that may serve to influence the effects of activity participation in adolescence. Young people in contexts of poverty often have fewer opportunities to expand prosocial social networks among peers and adults (Leventhal & Brooks-Gunn, 2000; Leventhal et al., 2009). In these circumstances, the enriching social experiences inherent in activities are argued to be especially salient, and potentially compensatory, for young people (e.g., Ren et al., 2021). Consistent with this approach, we found a protective effect of non-sporting activities among adolescents with experiences of financial hardship.

Most forms of extracurricular activity participation are characterized by experiences of teamwork, co-operation, and community-orientated tasks which may reinforce the benefits of helping others within social groups (Eccles & Barber, 1999; Fredricks & Eccles, 2005). However, in comparison to sporting activities, non-sporting organized activity contexts more readily expose adolescents to positive peer and adult influences (Hansen et al., 2003). Thus, non-sporting activities may more readily provide opportunities for adolescents to interact with socially conforming peers (Mahoney et al., 2005). Adolescents with economic advantages that can promote a broader milieu of prosocial others may benefit less from additional immersion in social contexts characterized by prosociality (O’Donnell & Barber, 2021). In contrast, exposure to prosocial others may be especially salient in contexts of disadvantage where structural barriers inhibiting more socially acceptable forms of advancement produce a social environment where transgressions are more prevalent.

The results from the current study highlight the importance of peer groups in shaping outcomes for adolescents and the importance of leisure time pursuits in shaping peer groups. Inconsistent with our hypothesis, the direct relationship between financial hardships and risk-taking was not moderated by activity participation. Thus, the effects of non-sporting activity stemmed from activities’ role as a social conduit that shaped the attributes of participants’ peer associations among adolescents who experienced financial hardships. These findings are important for the management and co-ordination of non-sporting activities and attests to the potential impact of wide-spread activity uptake.

Rates of Extracurricular Activity Participation and Financial Hardships

Our data demonstrate that participation in non-sporting activities indirectly reduced the likelihood of economically disadvantaged youth engaging in risk-taking behavior. However, despite prominent calls to further promote the inclusion of economically disadvantaged youth in organized activities over several decades (McLaughlin et al., 1994), rates of participation among this group remains low (Meier et al., 2018). So, although the benefits for economically disadvantaged youth (and society as a whole) of participation in non-sporting extracurricular activities are clear and have an indirect association with reduced participation in risk-taking activities, economic disadvantage is itself a barrier to their participation in extracurricular activities—especially in contexts where families are expected the shoulder the costs of participation.

There is a strong relationship between experiences of family poverty or hardship and participation in fee-paying activities (Barber et al., 2010; Damelang & Kloß, 2013), particularly when young people are required to pay for uniforms or equipment (Skattebol & Redmond, 2019). While the reduction of fees or financial subsidies are clearly one mechanism to increase participation (e.g., Foley et al., 2021), the relationship between experiences of poverty and extracurricular activity participation is complex and requires a more comprehensive approach that addresses structural barriers.

Poor families often live in poorly resourced neighborhoods. This can amplify inequalities via community-wide constraints that emerge over and beyond family-level income (Weininger et al., 2015). Notably, a key determinant of extracurricular activity participation among economically disadvantaged youth is feelings of safety within their own community spaces (Coulton & Irwin, 2009). Further, many young people report apprehension when occupying spaces in more privileged communities where they may be accused of wrongdoing or otherwise harassed (Skattebol & Redmond, 2019). Disadvantaged communities are also documented to offer fewer activity options to young people (Cohen et al., 2007; Stearns & Glennie, 2010). Decisions to participate in extracurricular activities are often driven by interests in specific pursuits (Fredricks et al., 2002). Without a broad array of activities on offer that are safe and affordable, there is a greater likelihood that adolescents may not see a satisfactory fit between their interests and the activities on offer that their family can afford, subsequently inhibiting the uptake and continuation of extracurricular activities (O’Donnell & Barber, 2021). Efforts to increase activity participation among economically deprived families should therefore evaluate whether the activities on offer are appropriate based upon the needs and wants of young people within specific communities and include a broad range of subsidized non-sporting options.

Strengths, Limitations, and Future Research

Our analysis of a high-quality, nationally representative dataset is a clear strength of the paper. Similarly, our longitudinal analysis provided an opportunity to examine the dynamic interrelation of social behaviors over time. Our inclusion of the random intercepts accounts for potentially confounding between-person effects and accounts for a range of potentially confounding factors. Despite these notable methodological and analytical strengths, the current study is not without limitations.

Contrary to an established literature, we found little evidence that participation in extracurricular activities had a direct impact on young people’s transgressive behaviors. It is possible the divergent effects stem from the measurement of extracurricular activities in the current study. Scholars have previously suggested that activities can be either beneficial or detrimental depending upon the climate produced by peers, adults, and the activity tasks (Guest & McRee, 2009). Further, the nature of participation is also important. Intensity (total number of hours) and breadth (total number of activity-types) are common indicators of activity participation that provide information on how immersed young people are in activity settings (Bohnert et al., 2010). It is widely argued that the personal and relational assets developed via participation require time to develop. Similarly, effects of activity participation can compound over time, suggesting those who have participated for longer may derive more beneficial effects (Bohnert et al., 2010). Unfortunately, the methodology of the current study did not allow a nuanced understanding of the role of activities in reducing risk-taking behaviors. Information on the attributes or qualities of the activity settings were not collected, precluding any exploration of how prolonged immersion in distinct activity contexts may predict social behaviors.

Finally, self-selection biases are widely documented in extracurricular activity participation research (Barber et al., 2010). In the current study, it may be that individuals who are less predisposed to engage in risk-taking may be attracted to non-sporting activities. The partition of within- and between-person differences in the current study minimize potential biases stemming from self-selection, but longer studies that account for prior participation and individual differences may be warranted.

Conclusion

Positive social behaviors are essential in advancing community interests, are valued by others, and can promote greater subjective well-being and future life-chances (Baumsteiger, 2019). Thus, efforts to inhibit risk-taking behaviors are important for the welfare of adolescents and the wider community. The current study provided information that could inform these efforts. Non-sporting activities were found to promote a decrease in exposure to risk-taking others which subsequently decreased antisocial behaviors 4 years later, but only when participants’ families experienced financial hardships. In contrast, sporting activities were not predictive of risk-taking in the current study. Although past research has documented clear psychosocial benefits derived from sporting activities (e.g., Eccles & Barber, 1999), the current study adds to a divergent literature that suggests sporting activities may increase, decrease, or otherwise not impact risk-taking. Taken together, our findings illustrate how providing access to protective spaces, like non-sporting activities, for economically disadvantaged youth can provide protections and assets to improve their outcomes.

Footnotes

Acknowledgements

We thank all of those involved, including participants and their families, for developing the study.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research undertaken for this paper was supported by the Australian Research Council (Project DP190100247). The Longitudinal Study of Australian is conducted by the Australian Institute of Family Studies with the Department of Social Services. The findings and views reported in this paper, however, are those of the author and should not be attributed to the Australian Government, DSS, or any of DSS’ contractors or partners.

ORCID iD

Alexander W. O’Donnell

Author Biographies

Alexander W. O’Donnell is an applied researcher whose work sits at the nexus of developmental, community, and social psychology. He aims to exploit the benefits of longitudinal data to identify mechanisms that can help overcome structural and social disadvantages disproportionally present in specific social groups. He is currently employed as a Lecturer at the University of Tasmania.

Gerry Redmond is a Professor of Public Policy at the College of Business, Government and Law, Flinders University. His research aims to privilege the perspectives of marginalised children and young people, their experiences of material deprivation, how this impacts their wellbeing and development, and how policy can work to improve their life chances.

Jennifer Skattebol, Dip Ed (EC), B.Ed., PhD, explores issues of economic inequality and how they are experienced by children and their families. She is primarily a qualitative and mixed method researcher grounded in the disciplines of education, sociology and social policy. Her methodological expertise is designing research with children, young people and their families in contexts of disadvantage. She has worked extensively with government and non-government bodies on issues related to early years education, child and youth poverty and how service systems might better meet the needs of these young Australians and their families. She is interested in how constructions of family are lived and experienced and how the nexus of the family is a site where inequalities and power focus, operate, are resisted and transformed. This work includes consideration of how young people manage tensions between family and school (and other services) and how services might be more responsive to the needs of disadvantaged citizen. Current research projects include equitable access to early childhood education and care, of child wellbeing in the middle years and an investigation of the lifecourses of young people using multiple service systems.

Jacqueline Allen is a lecturer in the School of Criminology and Criminal Justice, Griffith University. Jacqueline’s research is about the development of antisocial behaviour and mental health problems from childhood to young adulthood. Jacqueline is committed to translating research findings in these areas into preventative strategies, particularly through building the capabilities of frontline professionals and community agencies to make better use of measures, tools, and methods based on solid science. Also of special interest is the statistical modelling of complex longitudinal processes, and innovative use of administrative data.

Cathy Thomson is a Research Fellow at the Social Policy Research Centre, UNSW, Sydney. Cathy specialises in academic and applied research based on mixed methods and quasi experimental design focusing on the characteristics and processes of complex systems that assist or prevent vulnerable population groups accessing services and resources that support health and well-being.

Colin MacDougall is Emeritus Professor of Public Health at Flinders University in South Australia and holds honorary positions at the University of Melbourne and in Nepal and PNG. He has international teaching and research experience with participatory and rights-based approaches to research and practice with children.

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