School-Based Resources as Protective Factors from the Influence of Parental Incarceration on Depressive Symptoms

Abstract

Although research on the deleterious effects of parental incarceration is extensive, few studies have examined factors that may attenuate these consequences. This study uses data from the National Longitudinal Study of Adolescent to Adult Health to examine the relationship between experiencing parental incarceration during childhood and depressive symptoms in young adulthood. Most importantly, it considers the degree to which this relationship is moderated by the availability of secondary-school-based resources. Results suggest that the consequences of parental incarceration on depressive symptoms are weaker for adolescents who attend schools that provide emotional counseling or nursing services on school premises or employ at least 50 percent of teachers with a master’s degree or higher. These findings suggest that the availability of school resources contributes to adolescents’ long-term resilience despite facing adverse circumstances.

Keywords

parental incarceration mental health resilience school resources Add Health

The majority of U.S. prisoners (52 percent in state prisons and 63 percent in federal prisons) are parents of minor children (i.e., under 18 years of age; Glaze and Maruschak 2008). As the incarceration rate increased between the mid-1970s and 2000s (Bronson and Carson 2019), the number of parents incarcerated in state and federal correctional facilities almost doubled between 1991 and 2007, and the estimated number of minor children with a parent in prison increased from 945,600 to 1.7 million between that time (Glaze and Maruschak 2008). Comparable national statistics for the number of children with a parent in jail are not available, but scholars suggest that millions more have had a parent incarcerated in a local jail (Davis and Shlafer 2017; Kemper and Rivara 1993). In total, estimates indicate that upward of 8 million children have experienced parental incarceration (PI; Haskins, Amorim, and Mingo 2018; Kjellstrand and Eddy 2011b; Wakefield and Wildeman 2018). Albeit widespread, disadvantaged, minority children are more likely to have a parent incarcerated than more affluent, non-minority children (Haskins et al. 2018; Wakefield and Wildeman 2014; Wildeman 2009). In light of this era of mass incarceration, researchers have begun examining how the consequences of incarceration “spillover” onto children, caregivers, and communities (Foster and Hagan 2015; Hagan and Foster 2012a, 2012b; Hatzenbuehler et al. 2015).

Research has documented a variety of deleterious consequences associated with experiencing PI. For instance, studies suggest that PI is associated with socioeconomic disadvantages across the life course (Geller and Franklin 2014; Hagan and Foster 2012a, 2012b; Wakefield and Uggen 2010; Wakefield and Wildeman 2014; Western and Pettit 2010). PI has also been linked to children’s externalizing behaviors from adolescence to adulthood, such as involvement in antisocial behavior (Geller et al. 2012; Mears and Siennick 2016; Muftić and Smith 2015; Murray, Farrington, and Sekol 2012; Roettger and Swisher 2011; Wildeman 2010) and criminal justice system contact (Burgess-Proctor, Huebner, and Durso 2016; Dallaire 2007; Roettger and Swisher 2011). Experiencing the incarceration of a parent is also associated with poorer physical health throughout the life course (Lee, Fang, and Luo 2013; Miller and Barnes 2015; Roettger and Boardman 2012). Furthermore, and most pertinent to the current study, research has documented the association between PI and worse mental health outcomes throughout adolescence and adulthood (Casey, Shlafer, and Masten 2015; Davis and Shlafer 2017; Foster and Hagan 2013; Gaston 2016; Lee et al. 2013; Mears and Siennick 2016; Swisher and Shaw-Smith 2015).

Although researchers have extensively examined the collateral consequences of incarceration, few studies have considered possible protective, or resilience factors, which may buffer children from the consequences of PI (Dallaire and Zeman 2013). Indeed, risk and resilience perspectives suggest that protective factors, such as a supportive family and non-family network, can facilitate positive adaptations and outcomes despite experiencing adverse events such as PI (Luthar, Cicchetti, and Becker 2000; Manglos-Weber and Avelis 2019; Parke and Clarke-Stewart 2003; Rutter 1999). Moreover, identifying factors that contribute to resiliency is particularly important for designing and implementing effective public policies (Jenson and Fraser 2016).

With this in mind, the current study used data from the National Longitudinal Study of Adolescent to Adult Health to examine the association between experiencing PI during childhood, protective factors during adolescence as measured by the availability of school-based resources (including emotional counseling, nursing services, and having at least 50 percent of teachers with a master’s degree on school premises), and symptoms of depression in adulthood. We examined whether the influence of PI varied by school-based resources to determine whether secondary school resources acted as protective factors and attenuated the influence of PI on depressive symptoms.

PI, Mental Health, and Exclusionary Regimes

Diminished mental health is a consequence of contact with the criminal justice system (Fernandes 2020). In addition, experiencing the incarceration of a parent has been linked to poor mental health outcomes across the life course (e.g., Arditti and Savla 2015; Casey et al. 2015; Lee et al. 2013; Swisher and Roettger 2012; Wakefield and Wildeman 2014; Zeman, Dallaire, and Borowski 2016). Murray and colleagues’ (2009) meta-analysis suggested that children who experience PI were more likely to display worse mental health outcomes, including general internalizing problems, depression, and anxiety. More recently, Davis and Shlafer (2017) found that adolescents of currently and formerly incarcerated parents had an increased risk for exhibiting mental health issues, including formal diagnosis, professional treatment, self-injurious behavior, and suicidal ideation/attempts than adolescents without incarcerated parents. Children who experience PI are also at a higher risk for developing depressive symptoms (e.g., Gaston 2016; Mears and Siennick 2016) and being diagnosed with depression (e.g., Lee et al. 2013) in adulthood. Furthermore, Murray and Farrington (2008) revealed that the influence of PI on boys’ anxiety and depression extended into their late 40s.

Conceptualizing the consequences of PI on children’s depressive symptoms is arguably best understood through Foster and Hagan’s (2015) integrated systemic social exclusion framework, which suggests that exclusionary practices at the macro- (i.e., state) and meso- (i.e., school and neighborhood) levels contribute to and exacerbate the micro- (i.e., individual and family) level consequences associated with PI. As incarceration rates rose in the United States, some states enacted less generous social programs and more punitive penal policies that solidified state-level exclusionary policy regimes (Beckett and Western 2001; Esping-Anderson 1990; Foster and Hagan 2015). Disadvantaged, minority communities are particularly susceptible to state-level regimes characterized by high corrections expenditures, the termination of parenting rights, disenfranchisement, labor market exclusion, and concentrated disadvantage (Beckett and Western 2001; Foster and Hagan 2015), which perpetuate intergenerational exposure to emotional and financial strains and traumas (e.g., Cho 2011; Kjellstrand and Eddy 2011a, 2011b; Morgan-Mullane 2017).

For adolescents in particular, the ramifications of experiencing the incarceration of a parent are further conditioned by exclusionary school-level regimes, which are characterized by concentrated school-level disadvantage, high levels of delinquency and PI, and punitive school policies (Foster and Hagan 2015). These exclusionary school contexts may further contribute to depressive symptoms among children of incarcerated parents through diminished counseling and social interventions and increased criminal justice interventions (Kupchik and Monahan 2006). Indeed, patterns of social stratification and segregation in the United States place disadvantaged, minority children at a greater risk for attending schools that are lower ranked (Phillips, Larsen, and Hausman 2015), have financial constraints (Sosina and Weathers 2019), and utilize school resource officers (Theriot 2009). Children of incarcerated parents also attend more disadvantaged and punitive schools than similarly situated children without any history of PI (Haskins 2017). Moreover, poor attendance and chronic absenteeism from school is more common among disadvantaged, at-risk children (Ready 2010; Romero and Lee 2008), and lower involvement in school contexts is more common among parents with a history of incarceration (Haskins and Jacobsen 2017). Thus, the consequences of PI may also be compounded by disengagement from school among children and parents (Cho 2011; Haskins and Jacobsen 2017).

Indeed, inequality and social exclusion, particularly among disadvantaged, minority populations, have deepened due to the concentrated use of incarceration among these communities (Foster and Hagan 2007; Western and Pettit 2010). Paralleling notions of cumulative disadvantage (Sampson and Laub 1997), PI therefore likely harms families by adding to the preexisting exclusionary regimes faced by disadvantaged children (Foster and Hagan 2007, 2015; Wakefield and Wildeman 2014). Moreover, some research suggests that the harms of PI are exacerbated among those most susceptible to exclusionary regimes—that is, disadvantaged, minority children (e.g., Foster and Hagan 2013; Swisher and Roettger 2012; Wildeman 2014).

Inclusionary Regimes and Resilience Despite PI

Whereas exclusionary practices at the macro- and meso-levels likely exacerbate the consequences of PI, Foster and Hagan (2015) contended that inclusionary practices, such as generous social welfare programs and family prison policies, may enhance families’ ability to navigate the incarceration of a parent. Indeed, risk and resilience theories contend that protective factors may buffer children from the consequences associated with exposure to stressful circumstances (Luthar et al. 2000; Manglos-Weber and Avelis 2019; Parke and Clarke-Stewart 2003; Rutter 1999). However, our understanding of potential protective factors is limited to select individual characteristics and social dynamics as opposed to macro- or meso-level inclusionary practices.

For instance, recent research analyzing data from 61 children (ages 6–13 years) of incarcerated mothers suggested that hopefulness (Hagen, Myers, and Mackintosh 2005) and emotional regulation skills (Lotze, Ravindran, and Myers 2010; Myers et al. 2013) acted as protective factors against internalizing and externalizing behaviors. Similarly, Dallaire and Zeman’s (2013) research with 210 elementary school children found that empathy acted as a protective factor between PI and aggressive peer relations. Positive co-parenting relationships between formerly incarcerated parents and other caregivers may also act as a protective factor for young children of incarcerated parents (McHale et al. 2013). Analyzing survey data from 8th-, 9th-, and 11-grade students in Minnesota, Davis and Shlafer (2017) found that parental closeness contributed to resilience against poor mental health outcomes. Research also indicates that social support from family members, teachers, and coaches, as well as school connectedness contribute to resilience among children, adolescents, and young adults exposed to PI (Hagen and Myers 2003; Luther 2015; Nichols, Loper, and Meyer 2016; Thurman et al. 2018).

Although existing research has made important contributions to our understanding of protective factors that may buffer the consequences of PI, most have used small, non-random samples. As a result, the generalizability of these promising findings to the broader population remains unknown. Moreover, the majority (e.g., Hagen and Myers 2003; Hagen et al. 2005; Lotze et al. 2010; Luther 2015; McHale et al. 2013; Myers et al. 2013; Thurman et al. 2018) utilized samples that did not include a comparison group of children who did not experience PI. Furthermore, despite the fact that macro- and meso-level inclusionary practices may promote positive adaptations (Foster and Hagan 2015), and that education is a core social institution that plays an important role in adolescents’ lives (Renzulli 2014; Shlafer and Poehlmann 2011), few studies consider whether/how school contexts promote or hinder positive outcomes among children of incarcerated parents (Foster and Hagan 2015; Haskins 2017). The present study therefore contributes to this research by examining the moderating effects of school-based resources during adolescence on the relationship between PI and depressive symptoms using a nationally representative U.S. sample.

Inclusionary School Practices as Protective Factors

Cochran, Siennick, and Mears (2018) concluded that school contexts likely play a critical role in facilitating positive outcomes among adolescents of incarcerated parents. We suspect that inclusionary school-level practices, particularly the availability of school-based resources, may buffer the commonly observed consequences of PI on children’s mental health outcomes. Indeed, secondary school resources appear to contribute to adolescents’ emotional well-being.

Estimates indicate that approximately 50 percent of secondary schools in the United States offer mental health counseling services on site (Slade 2003). The availability of school-based mental health resources presumably addresses logistical barriers to access, such as financial barriers and lack of transportation to services, and reduces stigma associated with receiving mental health services (Amaral et al. 2011; Nichols et al. 2016). As suspected, school-based health care centers increase adolescents’ use of mental health services (Slade 2002; Guo, Wade, and Keller 2008). In addition, among adolescents who use mental health services, schools are the primary source of service (Green et al. 2013). Recent research also indicates that school-based counselors take a multifaceted approach when working with children of incarcerated parents by providing counseling and crisis services, referring students/families to outside social services such as child protective services, and collaborating with the caregivers of children of incarcerated parents (Brown and Barrio Minton 2017).

School nurses are also important facilitators of mental health services for students because they are situated to identify those at risk and refer said students to mental health services (Bohnenkamp, Stephan, and Bobo 2015; Shannon, Bergren, and Matthews 2010; Stephan and Connors 2013). As Bains and Diallo (2015) noted, the first health care professionals that children and adolescents with mental health issues interact with are often school nurses. Furthermore, using a nationally representative survey of approximately 83,000 public schools in the United States, Foster and colleagues (2005) found that school nurses spent one-third of their time providing informal mental health services to students.

Finally, students see teachers as a potential resource when facing mental health concerns (Pinto-Foltz, Hines-Martin, and Logsdon 2010). Although some research suggests that teachers have limited knowledge regarding mental health issues (Frauenholtz, Mendenhall, and Moon 2017; Reinke et al. 2011; Rothì, Leavey, and Best 2008), other research suggests that teachers can effectively identify students in need of mental health services (Headley and Campbell 2011; Loades and Mastroyannopoulou 2010). Moreover, studies have found that higher educational attainments among teachers are associated with higher mental health literacy (Aghukwa 2009; Bella, Omigbodun, and Atilola 2011), which refers to the ability to recognize mental illnesses and risk factors, having knowledge about the availability of treatment, and possessing attitudes of acceptance that promote the use of mental health services (Jorm et al. 1997). Presumably then, teachers with master’s degrees may be better equipped to support students with mental health issues.

The Present Study

Seeing as adolescents with a history of PI is less likely to have access to health-related resources (e.g., Foster and Hagan 2007; Turney 2017), the availability of on-site school resources, including counseling and nursing services, as well as qualified teachers, may be a particularly important safety net for children of incarcerated parents due to the multilayered exclusion that they face. In addition, research showing a positive relationship between PI and depression (e.g., Davis and Shlafer 2017; Gaston 2016) suggests that these children conceivably have more to gain from school-based resources compared with those who have not experienced PI. However, the extent to which inclusive school-based resources facilitate resilience against the long-term mental health consequences associated with PI remains to be investigated.

With this in mind, the current study examined the association between PI, school-based resources, and depressive symptoms. First, we investigated how experiencing PI during childhood influenced depressive symptoms during adulthood. In light of the aforementioned literature, we hypothesized that PI would be associated with experiencing more severe adult depressive symptoms. Second, we examined whether the association between PI and later symptoms of depression varied by the availability of secondary school resources during adolescence. We hypothesized that school-based resources would act as a protective factor in the relationship between PI and depressive symptoms.

Data

We used data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), which utilized an in-school sampling frame to survey a nationally representative group of over 90,000 students in the 7th to 12th grades during the 1994–1995 school year. Administrators from the participating schools also completed a questionnaire that provided information on the schools’ characteristics. All of the students from the participating schools’ rosters were eligible for selection into the in-home interview sample. The first wave of in-home interviews (Wave I) consisted of a core of 12,105 adolescents plus additional oversampled groups, resulting in 20,745 participants. A parent/guardian of each adolescent respondent also completed a questionnaire for Wave I.

Add Health has conducted in-home interviews with the adolescent participants three times since Wave I. The initial follow-up (Wave II), conducted in 1996, included nearly 15,000 participants from the Wave I interview. The third round of in-home interviews (Wave III), conducted in 2001–2002 (when the sample was largely between the ages of 18 and 26 years), included over 15,000 respondents who participated in the Wave I in-home interview. Wave IV interviews were conducted in 2007–2008 (when the sample was largely between the ages of 24 and 32 years) and included approximately 80 percent of the eligible cases from Wave I. For each Wave of Add Health, community contextual data are also available.

We used in-school, school administrator, parent, contextual, and in-home interview data from 10,819 respondents present at Waves I, III, and IV. Respondents without valid data on the dependent variable (i.e., adult depressive symptoms), focal independent variable (i.e., PI during childhood), or valid sampling weights were excluded. Missing data on the majority of covariates were minimal (<4 percent). To address missing data, we used multiple imputation via the SAS PROC MI procedure across 30 imputations.

Measures

Dependent Variable

Depressive symptoms during adulthood, measured at Wave III, was created using indicators from the Center for Epidemiological Studies Depression Scale (Radloff 1977). Respondents reported how often during the week preceding the interview (ranging from 0 = never or rarely to 3 = most or all of the time) they (1) were bothered by things that usually did not bother them, (2) could not shake off the blues, (3) felt as good as other people (reverse coded), (4) had trouble concentrating, (5) felt depressed, (6) felt too tired to do things, (7) felt sad, (8) enjoyed life (reverse coded), and (9) felt that people disliked them. Similar to Gaston (2016), we summed these nine items to create a continuous measure of depressive symptoms that ranged from 0 to 27 (Cronbach’s α = .81).

Independent Variable

To measure PI, we utilized retrospective questions asked at Wave IV, similar to other scholars using Add Health (e.g., Burgess-Proctor et al. 2016; Miller and Barnes 2015). First, respondents reported whether their biological mother and/or father ever spent time in jail or prison. Among those who reported ever experiencing the incarceration of their mother and/or father, respondents also reported how many times their mother and/or father had been incarcerated. In addition, respondents reported how old they were when their mother and/or father was incarcerated and released (or “incarcerated for the first time” and “most recently” released for those whose mother and/or father was incarcerated more than once). To ensure temporal consistency, we used respondents’ age at Wave I and respondents’ age at the time their mother and/or father was incarcerated and released to determine whether either or both of respondents’ biological parents had been incarcerated before Wave I. Respondents who did not experience PI before Wave I but did experience PI after Wave I were excluded from the analyses. The final measure of PI is a dichotomous indicator coded as 1 for those who experienced maternal incarceration, paternal incarceration, or the incarceration of both biological parents one or more times prior to the Wave I interview, referencing those who never experienced PI at any point in their lives.¹

Moderators

We examined secondary school resources as possible protective factors. To gauge the resources available at students’ schools, we included dichotomous measures from the school administrator questionnaire to indicate whether emotional counseling (1 = emotional counseling available; 0 = otherwise) and nursing services (1 = nursing services available; 0 = otherwise) were available on school premises. Furthermore, a binary measure reflecting whether at least 50 percent of the full-time classroom teachers at students’ schools held a master’s degree or higher was included (1 = ≥50 percent of teachers with a master’s degree or higher, 0 = otherwise). We opted for the 50 percent cutoff because approximately 50 percent of all public secondary school teachers had a master’s degree in 1999 (National Center for Education Statistics 2018).

Controls

To account for measures suspected to be correlated with both PI and depressive symptoms, we controlled for a wide range of individual background characteristics (all from the Wave I in-home survey, unless otherwise noted), including indicators for demographics, socioeconomic status (SES), stressors, antisocial and prosocial behaviors, and psychological well-being. We also included measures for students’ secondary school setting characteristics (all from the school administrator data).

Demographics

Being male was created as a dichotomous variable (1 = male, 0 = female). Age in years was included as a continuous variable. Six dichotomous race/ethnicity variables were constructed to indicate non-Hispanic white (reference category), non-Hispanic black, Hispanic, non-Hispanic Asian, non-Hispanic Native American, and non-Hispanic other racial identities. A dichotomous variable was also included to denote whether respondents were foreign born (1 = foreign born, 0 = native born).

SES

Family SES was created following Ford, Bearman, and Moody’s (1999) operationalization, which was based on a combination of parents’ educational attainment and occupational status and ranges from 1 to 10, where higher values represent higher family SES. Parents’ welfare receipt was measured with information from the parent questionnaire and indicates whether respondents’ parents/guardians (or anyone in the household) received public assistance, welfare, or food stamps in the month preceding the Wave I interview (1 = received welfare, 0 = otherwise). To measure whether respondents had health insurance, we used information reported by parents in the parent questionnaire to create a dichotomous indicator (1 = had health insurance, 0 = no health insurance). To account for adolescents’ family structure, we constructed five binary indicators for whether respondents lived with both biological parents (reference category), a stepparent/stepfamily, a single mom, a single dad, or other household members.

We used Add Health’s community contextual measures at Wave I, which matched respondents’ home locations to corresponding Census tract data reported by the Census Bureau’s American Community Survey, to measure neighborhood disadvantage. This variable was created as the average of the following proportions: (1) families below the poverty level, (2) persons 16 years and over unemployed, (3) occupied housing units with a female householder with children under 18, and (4) households receiving public assistance income (Gaston 2016; Sampson, Raudenbush, and Earls 1997; α = .93). The final measure was multiplied by 100 so that a one-unit change equals a one-percentage change in neighborhood disadvantage. We also controlled for the county-level crime rate with the information from the Uniform Crime Report that was included in Add Health’s community contextual data.

Stressors

Following Feigelman et al. (2016), deceased parent reflected whether respondents experienced the death of a biological parent before Wave I (1 = parental death, 0 = otherwise). In addition, using information from the parent questionnaire, we created a dichotomous variable to indicate whether respondents had an alcoholic parent (1 = biological parent has alcoholism, 0 = otherwise). Following previous studies using Add Health (e.g., Gaston 2016; Swisher and Shaw-Smith 2015), we measured respondents’ experiences of childhood abuse with retrospective questions at Wave IV. Sexual abuse reflected whether respondents’ parents/caregivers ever touched them in a sexual way, forced them to touch him or her in a sexual way, or forced them to have sexual relations prior to Wave I (1 = sexual abuse, 0 = no sexual abuse). Physical abuse reflected whether respondents’ parents/caregivers ever hit, kicked, or threw respondents down to the floor, into a wall, or down stairs prior to Wave I (1 = physical abuse, 0 = no physical abuse). Emotional abuse reflected whether respondents’ parents/caregivers ever said things that really hurt their feelings or made them feel not wanted or loved prior to Wave I (1 = emotional abuse, 0 = no emotional abuse). Crime victimization was based on respondents’ report of how often during the year preceding the Wave I interview someone (1) pulled a knife or gun, (2) shot, (3) cut or stabbed, or (4) jumped them. Response options ranged from never (0) to more than once (2). We dichotomized the responses to indicate whether respondents experienced each of the four types of victimization (1 = experienced, 0 = otherwise) and then summed the values to create a count measure of victimization (ranging from 1 to 4).

Antisocial behavior

Given the co-occurrence of substance use disorders and mental health issues (Gaston 2016), we included a dichotomous control for drug use (1 = used marijuana, cocaine, inhalants, or any other illegal drug before Wave I, 0 = otherwise). Similar to others using Add Health (e.g., Gaston 2016; McGloin 2009), delinquency was based on nine self-reported indicators of how often respondents engaged in each of the following behaviors in the year preceding the Wave I interview: (1) damaged property, (2) stole something worth more than US$50, (3) went into a house/building to steal something, (4) used/threatened to use a weapon, (5) sold drugs, (6) stole something worth less than US$50, (7) took part in a physical fight, (8) got into a serious fight, and (9) hurt someone badly enough that they needed medical care. Each item was coded as 0 if the event never happened, 1 if the event happened one or two times, 2 if the event happened three or four times, and 3 if the event happened five or more times. We summed these nine items to create the final delinquency indicator that ranged from 0 to 27. A binary indicator for whether respondents ever skipped school without an excuse, measured with the in-school questionnaire, was also included (1 = skipped school, 0 = otherwise).

Prosocial behavior

Given the association between grade point average (GPA) and depression (e.g., Shippee and Owens 2011), we control for GPA. We operationalized GPA as the average of respondents’ self-reported grades in English, history, math, and science (Glass, Sutton, and Fitzgerald 2015). To gauge respondents’ closeness to school, we used reports of how much respondents agreed or disagreed with the following statements: they (1) feel/felt close to people at their school, (2) feel/felt like they are a part of their school, and (3) are/were happy to be at their school. Response options ranged from 1 = strongly disagree to 5 = strongly agree. We averaged these responses to create the feel close to school measure (α = .78).

Psychological well-being

Self-esteem (α = .78) was measured similarly to McPhie and Rawana (2012). Respondents reported how much they (1) have a lot to be proud of, (2) have lots of good qualities, (3) like themselves the way they are, (4) feel like they are doing everything right, (5) feel socially accepted, and (6) feel loved and wanted. Response options ranged from 1 = strongly disagree to 5 = strongly agree. We summed these six items to create a continuous measure of self-esteem that ranged from 6 to 30. As early experiences of depressive symptoms are a significant predictor of subsequent depression (e.g., Gaston 2016), we controlled for adolescent depressive symptoms, which was operationalized identically to our dependent variable. This measure can therefore be thought of as a lagged dependent measure accounting for stable differences in depressive symptoms between respondents.

Secondary school setting characteristics

Following Hagan and Foster (2012a, 2012b), we accounted for various school setting characteristics (obtained from the school administrator questionnaire), including measures for the number of full-time teachers and average daily attendance (coded as 1 = 75–79 percent, 2 = 80–84 percent, 3 = 85–89 percent, 4 = 90–94 percent, and 5 = 95 percent or more). We also included dichotomous controls indicating whether the school was located in an urban area (1 = urban, 0 = rural or suburban), a public institution (1 = public, 0 = private), and small (reference category), medium, or large in size.

Results

Bivariate Relationships

Table 1 displays the weighted means/percentages for all variables partitioned by PI. Consistent with our hypotheses, those who experienced PI during childhood reported more depressive symptoms in adulthood compared with those who did not experience PI. Differences in the averages of our SES and stressor variables were also in the expected directions. For instance, children who experienced PI had lower family SES and higher neighborhood disadvantage than children who did not experience PI. In addition, a higher percentage of children of incarcerated parents experienced all types of abuse than children without incarcerated parents. Turning to the secondary school resources suspected to moderate the relationship between PI and depression, a significantly smaller percentage of those who experienced PI attended schools with emotional counseling and nursing services available on site. In contrast, a larger proportion of adolescents with a history of PI attended schools with at least 50 percent of teachers with a master’s degree.

Table 1.

Means and Percentages by Parental Incarceration.

Variables	Parental incarceration	No parental incarceration	Total sample
Depressive symptoms (adulthood)	5.297***	4.316	4.432
Secondary school resources (%)
Emotional counseling	52.563*	55.015	54.724
Nursing services	53.215**	57.665	57.136
≥50% of teachers with a master’s degree or higher	54.898*	51.801	52.169
Individual (adolescent) characteristics
Demographics
Male (%)	48.508	49.269	49.179
Age	15.389^†	15.480	15.469
Race and ethnicity (%)
White (reference)	59.608***	70.885	69.545
Black	24.063***	13.247	14.533
Hispanic	13.921**	10.795	11.167
Native American	1.431**	0.512	0.621
Asian	0.626***	3.724	3.356
Other	0.351**	0.837	0.779
Foreign born (%)	2.563***	5.530	5.177
SES
Family SES	4.937***	6.380	6.209
Welfare receipt (%)	21.536***	5.851	7.715
Health insurance (%)	83.173***	88.834	88.161
Family structure (%)
Both biological parents (reference)	29.140***	65.938	61.565
Stepparent/stepfamily	22.933***	11.956	13.261
Single mom	32.422***	15.476	17.490
Single dad	4.160**	2.577	2.765
Other household members	11.344***	4.053	4.919
Neighborhood disadvantage	10.825***	8.166	8.482
County-level crime rate	57.614***	54.830	55.161
Stressors
Deceased parent (%)	7.910***	3.902	4.379
Alcoholic parent (%)	40.752***	12.158	15.557
Sexual abuse (%)	8.798***	3.414	4.054
Physical abuse (%)	28.202***	12.014	13.938
Emotional abuse (%)	55.337***	36.017	38.313
Crime victimization	0.467***	0.250	0.276
Antisocial behavior
Drug use (%)	44.783***	28.174	30.148
Delinquency	2.808***	1.652	1.789
Skipped school (%)	36.335***	27.068	28.170
Prosocial behavior
GPA	2.424***	2.811	2.765
Feel close to school	3.620***	3.766	3.749
Psychological well-being
Self-esteem	24.555	24.683	24.668
Depressive symptoms	6.509***	5.472	5.595
Secondary school setting characteristics
Number of full-time teachers	59.634	61.132	60.954
Average daily attendance	3.948***	4.212	4.181
Urban (%)	27.885*	25.158	25.482
Public (%)	94.322*	92.736	92.924
Size (%)
Small (reference)	18.890*	16.437	16.729
Medium	45.551	45.086	45.141
Large	35.559*	38.477	38.130
Sample size	1,331	9,488	10,819

Note. SES = socioeconomic status; GPA = grade point average.

†

p < .10. *p < .05. **p < .01. ***p < .001.

As a preliminary investigation, we examined the mean differences in depression by the availability of school-based resources and PI status, as shown in Figure 1. Among those who experienced PI, the average number of depressive symptoms is significantly lower for those in schools with any available resources. Among those who did not experience the incarceration of a parent, school resources appear to do little to alleviate depressive symptoms. This may be attributed to the fact that adolescents who do not experience PI already report, on average, lower levels of depressive symptoms than those with a history of PI.

Figure 1.

Depressive symptoms by parental incarceration and secondary school resources.

Multivariable Analysis

The patterns observed in the bivariate relationships suggest that school-based resources may attenuate symptoms of depression among those who experienced PI. Whether these patterns persist after accounting for demographics, SES, stressors, behavioral indicators, psychological well-being, and secondary school setting characteristics remains to be investigated. With this in mind, we employed multilevel linear regression via the “PROC MIXED” procedure in SAS and included sample weights in all models to adjust for the differential probabilities of sampling and retention. The 10,819 respondents in our analytic sample were nested within 132 schools at Wave I (an average of ~82 respondents per school). In Model 1 of Table 2, we regressed depressive symptoms in young adulthood on the indicator of PI as well as the controls. Models 2, 3, and 4 individually tested the interactions between PI and the availability of emotional counseling on school premises, the availability of nursing services on school premises, and having at least 50 percent of teachers with a master’s degree or higher, respectively. Model 5 included all interactions in the same model.

Table 2.

Adult Depressive Symptoms Regressed on PI, Secondary School Resources, Individual (Adolescent) Characteristics, and Secondary School Setting Characteristics.

Variables	Model 1		Model 2		Model 3		Model 4		Model 5
Variables	b	Standard error	b	Standard error	b	Standard error	b	Standard error	b	Standard error
Intercept	6.645	0.745***	6.528	0.746***	6.577	0.746***	6.604	0.747***	6.474	0.748***
PI	0.240	0.120*	0.640	0.170***	0.489	0.172**	0.577	0.171***	1.007	0.220***
Secondary school resources
Emotional counseling	−0.108	0.130	−0.018	0.132	−0.107	0.130	−0.113	0.130	−0.034	0.133
Nursing services	0.039	0.130	0.043	0.130	0.094	0.133	0.041	0.131	0.071	0.133
≥50% of teachers with a master’s degree or higher	0.173	0.121	0.168	0.121	0.174	0.121	0.245	0.125*	0.233	0.125^†
PI × School Resources
PI × Emotional Counseling			−0.753	0.228***					−0.650	0.236**
PI × Nursing Services					−0.458	0.225*			−0.229	0.234
PI × ≥50% Master’s Degree or Higher							−0.627	0.225**	−0.552	0.227*
Individual (adolescent) characteristics
Demographics
Male	−0.546	0.077***	−0.549	0.077***	−0.547	0.077***	−0.542	0.077***	−0.546	0.077***
Age	−0.154	0.026***	−0.154	0.026***	−0.152	0.026***	−0.154	0.026***	−0.153	0.026***
Race and ethnicity
Black	0.510	0.137***	0.521	0.137***	0.516	0.137***	0.509	0.138***	0.522	0.138***
Hispanic	0.420	0.143**	0.421	0.143**	0.424	0.143**	0.419	0.143**	0.422	0.143**
Native American	−0.149	0.463	−0.164	0.463	−0.156	0.463	−0.175	0.463	−0.188	0.463
Asian	0.523	0.230*	0.518	0.230*	0.524	0.230*	0.526	0.230*	0.522	0.230*
Other	0.046	0.407	0.048	0.407	0.048	0.407	0.046	0.407	0.048	0.407
Foreign born	0.094	0.185	0.086	0.185	0.088	0.185	0.089	0.185	0.079	0.185
SES
Family SES	−0.028	0.017	−0.029	0.017^†	−0.028	0.017	−0.028	0.017	−0.028	0.017
Welfare receipt	0.175	0.162	0.182	0.162	0.169	0.162	0.187	0.162	0.188	0.163
Health insurance	0.167	0.131	0.160	0.131	0.159	0.131	0.166	0.131	0.157	0.131
Family structure
Stepparent/stepfamily	−0.134	0.113	−0.141	0.113	−0.133	0.113	−0.137	0.113	−0.142	0.113
Single mom	0.015	0.108	0.013	0.108	0.020	0.108	0.013	0.108	0.014	0.108
Single dad	−0.080	0.222	−0.084	0.222	−0.071	0.222	−0.073	0.222	−0.072	0.222
Other household members	0.335	0.178^†	0.327	0.178^†	0.327	0.178^†	0.336	0.178^†	0.325	0.178^†
Neighborhood disadvantage	−0.014	0.008^†	−0.014	0.008^†	−0.014	0.008^†	−0.013	0.008	−0.013	0.008
County-level crime rate	0.005	0.003^†	0.005	0.003^†	0.005	0.003^†	0.005	0.003^†	0.005	0.003^†
Stressors
Deceased parent	0.225	0.195	0.211	0.195	0.219	0.195	0.234	0.195	0.217	0.195
Alcoholic parent	−0.005	0.123	−0.006	0.123	−0.004	0.123	−0.003	0.123	−0.004	0.123
Sexual abuse	0.526	0.188**	0.527	0.188**	0.530	0.188**	0.516	0.188**	0.519	0.188**
Physical abuse	0.375	0.120**	0.370	0.120**	0.371	0.120**	0.374	0.120**	0.368	0.120**
Emotional abuse	0.639	0.090***	0.645	0.090***	0.640	0.090***	0.644	0.090***	0.649	0.090***
Crime victimization	0.095	0.064	0.097	0.064	0.096	0.064	0.096	0.064	0.098	0.064
Antisocial behavior
Drug use	−0.107	0.093	−0.105	0.093	−0.109	0.093	−0.107	0.093	0.107	0.093
Delinquency	0.016	0.015	0.017	0.015	0.016	0.015	0.017	0.015	0.017	0.015
Skipped school	−0.072	0.116	−0.072	0.116	−0.074	0.116	−0.075	0.116	−0.076	0.116
Prosocial behavior
GPA	−0.229	0.050***	−0.225	0.050***	−0.227	0.050***	−0.227	0.050***	−0.223	0.050***
Feel close to school	−0.074	0.046	−0.075	0.046	−0.074	0.046	−0.075	0.046	−0.076	0.046
Psychological well-being
Self-esteem	−0.031	0.012*	−0.030	0.012*	−0.031	0.012*	−0.030	0.012*	−0.030	0.012*
Depressive symptoms	0.270	0.011***	0.271	0.011***	0.270	0.011***	0.270	0.011***	0.270	0.011***
Secondary school setting characteristics
Number of full-time teachers	<0.001	0.003	<0.001	0.003	<0.001	0.003	<0.001	0.003	<0.001	0.003
Average daily attendance	−0.010	0.074	−0.001	0.074	−0.005	0.074	−0.013	0.074	−0.003	0.074
Urban	−0.008	0.147	−0.003	0.147	−0.006	0.147	−0.008	0.148	−0.003	0.147
Public	0.015	0.224	0.020	0.224	0.015	0.224	0.012	0.225	0.017	0.225
Size
Medium	−0.071	0.173	−0.060	0.173	−0.067	0.173	−0.062	0.174	−0.052	0.173
Large	−0.009	0.255	0.010	0.255	−0.008	0.255	0.001	0.257	0.017	0.256

Note. N = 10,819; Schools = 132. PI = parental incarceration; SES = socioeconomic status; GPA = grade point average.

†

p < .10. *p < .05. **p < .01. ***p < .001.

Consistent with prior research, estimates in Model 1 suggest that experiencing PI is positively associated with depressive symptoms in adulthood. Although not necessarily a focus of the present study, several of the controls are also significantly associated with depression in the expected directions. For instance, all types of abuse are all positively related to symptoms of depression in adulthood. In contrast, higher levels of self-esteem are negatively related to depressive symptoms.

Central to our analysis are Models 2, 3, and 4, where we examined the extent to which secondary school resources moderated the relationship between PI and depressive symptoms. In Model 2, we added the interaction between PI and the availability of counseling services on school premises. By including this interaction term, the estimate associated with PI now represents the effect for respondents who attended a school without emotional counseling services available, while the interaction term represents the difference in that relationship for those who attended a school with emotional counseling services. Results suggest that PI is positively related to symptoms of depression in young adulthood for those without available emotional counseling at school (i.e., 0.640; p < .001). However, the positive estimate associated with PI, coupled with the significant negative interaction term (i.e., 0.640−0.753 = −0.113), suggests that PI is not significantly related to increases in depressive symptoms for those who attended schools with counseling available on site.

We examined the interaction between PI and the availability of nursing services on school premises in Model 3. Again, by including the interaction term, the estimate associated with PI (i.e., 0.489, p < .01) represents the effect for respondents who attended a school without nursing services available, while the interaction term (i.e., −0.458, p < .05) represents the difference for those with available nursing services. Consistent with the patterns observed in Model 2, PI is positively associated with depressive symptoms for those without nursing services on school premises. Yet, the significant, negative interaction indicates that the depressive consequences associated with PI are weaker for adolescents who attend schools that offer nursing services on site (i.e., 0.489 − 0.458 = 0.031). To determine whether the positive effect of PI for students with access to school-based nursing services (i.e., 0.031) was significant, in a supplemental analysis (available upon request), we reverse coded the nursing services measure so that 1 represented schools without available nursing services and 0 represented schools with nursing services. By doing so, the main estimate associated with PI in the interaction model reflected the effect for those with available nursing services, thus yielding a test of significance for this positive coefficient. The standard error associated with this estimate is 0.158 (p = .843), which confirms that PI is not significantly associated with symptoms of depression for those with available school nursing services.

In Model 4, we examined the interaction between PI and attending a school that employed at least 50 percent of teachers with a master’s degree or higher. Again, as indicated by the positive coefficient associated with PI (i.e., 0.577, p < .001), we found that PI is positively related to symptoms of depression in young adulthood for students attending schools that do not employ at least 50 percent of teachers with a master’s degree. Coupling this estimate with the significant negative interaction term (i.e., 0.577 − 0.627 = −0.050) indicates that the consequences associated with PI on symptoms of depression are weaker for students who attended schools where at least 50 percent of the teachers had a master’s degree.

In the final model (Model 5), we included all three interactions simultaneously to examine the extent to which school resources attenuated the positive association between PI and depressive symptoms in young adulthood. Although all three of the interaction terms are negative, it appears that the availability of emotional counseling and that having at least 50 percent of teachers with a master’s degree are most important for reducing the depressive consequences linked to PI. Collectively, results suggest that the consequences associated with PI are weaker, and at times statistically insignificant, for adolescents who attend schools with more on-site resources.

Discussion

The present study examined the relationship between PI during childhood and depressive symptoms in adulthood. It also considered how available school-based resources during adolescence moderated this association. Most broadly, and consistent with existing research (e.g., Gaston 2016; Mears and Siennick 2016; Murray and Farrington 2008), we found evidence that experiencing the incarceration of a parent during childhood was associated with more severe depressive symptoms in adulthood. Furthermore, we found that the availability of school-based resources were protective factors in the association between experiencing PI during childhood and depressive symptoms in adulthood.

Prior studies examining protective factors have suggested that individual characteristics, positive family dynamics, and social support from non-family members may buffer the effects of PI (e.g., Dallaire and Zeman 2013; Hagen et al. 2005; Lotze et al. 2010; Luther 2015; McHale et al. 2013; Myers et al. 2013). In light of the broader (i.e., macro- and meso-) exclusionary and inclusionary contexts surrounding exposure to PI (Foster and Hagan 2015; Haskins 2017), we considered whether school-based resources—namely, the availability of emotional and nursing services on site and employing at least 50 percent of teachers with a master’s degree—contributed to resiliency. Overall, the consequences of PI were particularly adverse among those attending exclusionary school regimes and were weaker among adolescents attending inclusionary school contexts.

As Haskins (2017) showed, adolescents with a history of PI generally attend more disadvantaged and punitive schools than those of similar backgrounds without incarcerated parents. Our results suggest that these exclusionary meso-level regimes further compound the inequalities that tend to accompany exposure to PI (Foster and Hagan 2007, 2015). At the same time, our results support the notion that schools can play a critical role in facilitating positive life trajectories among children exposed to PI (Cochran et al. 2018).

Our findings suggest that school-based mental health services are particularly beneficial for children exposed to PI. This is perhaps attributable to the fact that children of incarcerated parents generally face more severe disadvantage than children not exposed to PI and that school-based mental health services might combat logistical barriers to access that disadvantaged populations face (Amaral et al. 2011). Similar to previous estimates (e.g., Slade 2003), approximately 54 percent of secondary schools in our sample offered emotional counseling on school premises. Given that offering school-based mental health services was a protective factor for children facing adverse familial circumstances, increasing the availability of emotional counseling services in secondary schools is a clear policy implication of the current study.

At the same time, research indicates that school counselors who work with children of incarcerated parents face numerous barriers, such as lacking knowledge regarding the specific needs of these children as well as navigating resistance and tension from caregivers (Brown and Barrio Minton 2017). To ensure that school-based mental health services effectively facilitate positive outcomes despite PI, it is important that such barriers be addressed. In light of recent research suggesting that trauma-focused cognitive-behavioral therapy may be a successful therapeutic intervention for children exposed to PI (Morgan-Mullane 2017), it may be worthwhile to ensure that school-based mental health service providers are well versed in trauma-focused interventions. Furthermore, given that PI is concentrated among disadvantaged, minority children (Haskins et al. 2018; Wakefield and Wildeman 2014; Wildeman 2009), school-based providers of mental health services should also be aware of, and sensitive to, the demographic, socioeconomic, and cultural differences of children of incarcerated parents (Morgan-Mullane 2017).

Our results also suggested that on-site nursing services are useful for adolescents facing PI. Indeed, psychological distress is a common underlying factor to the physical complaints that school nurses encounter among children and adolescents (Shannon et al. 2010), and school nurses are equipped to identify emotionally distressed students and refer said students to counseling services (Bohnenkamp et al. 2015; Shannon et al. 2010; Stephan and Connors 2013). Yet, only 57 percent of the secondary schools in the current study offered nursing services on site. Thus, another implication derived from the current study indicates that offering school-based nursing services in more secondary schools would be beneficial.

Having a greater proportion of teachers with higher educational attainments also appeared to act as a protective factor in the association between PI and children’s subsequent depressive symptoms. Teachers are among the first to observe changes in students’ behavior and emotional well-being (Trudgen and Lawn 2011; Whitley, Smith, and Vaillancourt 2013), and students view teachers as a resource when facing emotional difficulties (Pinto-Foltz et al. 2010). Yet, research suggests that teachers feel unprepared to adequately support the needs of students with mental health illnesses (Frauenholtz et al. 2017; Reinke et al. 2011; Rothì et al. 2008). Further support for teachers to receive advanced education or specialized training that covers mental health issues is therefore an important policy avenue (Jorm et al. 2010; Trudgen and Lawn 2011; Weston, Anderson-Butcher, and Burke 2008; Whitley et al. 2013).

Broadly speaking, our research further illustrates the importance of ensuring that adequate funding is directed toward maintaining and increasing the availability of school-based resources. Utilizing funds to offer services on school premises, for example, would likely combat the deleterious effects of PI and curtail wider social exclusion and inequalities. However, despite the fact that research has documented how the removal of institutional resources (e.g., public housing and welfare) contributes to the collateral consequences of PI on children (Wildeman 2014), increasing rates of incarceration have forced communities to reallocate financial resources to corrections-related expenditures (Chung and McFadden 2010; Leventhal and Brooks-Gunn 2000). Moreover, financial reforms targeting public school funding have largely been unsuccessful; revenue for public schools primarily comes from local property taxes and, consequently, school funding is unequally distributed throughout the United States (Meanwell and Swando 2013). For schools with limited resources, our results suggest that offering emotional counseling services on site or employing more teachers with advanced degrees may be particularly important.

Still, the mere presence of school-based resources is only part of the solution to the broader issue at hand, as another fundamental issue facing disadvantaged communities is poor school attendance (Ready 2010; Romero and Lee 2008). Chronic absenteeism likely undermines any potential benefits associated with the availability of school-based resources. Thus, consistent school attendance among at-risk students might be particularly important for ensuring students’ access to, and utilization of, school-based resources, which may contribute to resiliency and combat wider social inequalities (Foster and Hagan 2007; Romero and Lee 2008).

Some limitations to this research are important to acknowledge. For instance, the measure of PI was retrospective in nature, and we were unable to examine the frequency or duration of PI (e.g., Swisher and Shaw-Smith 2015). In addition, Add Health is a school-based sample; adolescents who are most prone to PI may therefore not be included in the sample due to absence or dropout. Furthermore, although parental criminality may explain the association between PI and depressive symptoms (Giordano and Copp 2015; Murray et al. 2014; Swisher and Roettger 2012), measures of parental criminality were not available in these data. Finally, while we accounted for various background characteristics in an attempt to reduce confounding bias, issues of selection (e.g., parents have some choice in where their children attend school; Fong 2019; Phillips et al. 2015) and omitted variable bias (e.g., more proximate characteristics such as contact with the criminal justice system, victimization, employment status, or marital status in adulthood) remain. Future research should aim to address these limitations.

Conclusion

This study documented the long-term consequences of PI on children’s depressive symptoms during adulthood. Our results indicated that the availability of school-based resources during adolescence acted as protective factors that buffered the consequences associated with PI. These findings reinforce the critical role that schools play in adolescents’ lives and document the benefits of offering school-based resources, particularly for children facing PI. We hope that school administrators and policymakers will consider this empirical evidence to implement changes that contribute to children’s resilience despite facing adverse circumstances in this era of mass incarceration.

Footnotes

Acknowledgements

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. Information on how to obtain the Add Health data files is available on the Add Health website (). No direct support was received from grant P01-HD31921 for this analysis.

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) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jessica G. Finkeldey

Notes

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