Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Jul 13.
Published in final edited form as: Youth Violence Juv Justice. 2019 Oct 31;18(3):235–255. doi: 10.1177/1541204019880945

Exploring the School-to-Prison Pipeline: How School Suspensions Influence Incarceration During Young Adulthood

Paul Hemez 1, John J Brent 2, Thomas J Mowen 1
PMCID: PMC8277150  NIHMSID: NIHMS1706891  PMID: 34262407

Abstract

A growing body of research has evoked the life-course perspective to understand how experiences in school relate to a wide range of longer term life outcomes. This is perhaps best typified by the notion of the school-to-prison pipeline which refers to a process by which youth who experience punitive punishment in schools are increasingly enmeshed within the criminal justice system. While this metaphor is commonly accepted, few studies have examined the extent to which exclusionary school discipline significantly alters pathways toward incarceration as youth transition into young adulthood. Applying a life-course perspective and leveraging 15 waves of data from the National Longitudinal Survey of Youth 1997, this study examines how school suspensions influence the odds of imprisonment during young adulthood. Mixed-effects longitudinal models demonstrate that receiving a suspension serves as a key turning point toward increased odds of incarceration, even after accounting for key covariates including levels of criminal offending. However, results show that repeated suspensions do not appear to confer additional risk of incarceration. Results carry implications for the ways in which school punishment impacts youths’ life-course.

Keywords: suspension, incarceration, life-course, youth, school-to-prison

Although mounting scrutiny over school discipline has led to various reform initiatives (see Gregory, Clawson, Davis, & Gerewitz, 2016; Hirschfield, 2018a), the use of punitive and exclusionary punishment practices persists across the United States (Kupchik, 2016; Musu-Gillette, Zhang, Wang, Zhang, & Oudekerk, 2018). Recent reports from the Department of Education’s (2018) Office of Civil Rights reveal that approximately 2.7 million students experienced at least one out-of-school suspension during the 2015–2016 academic year. In fact, estimates suggest that about one third of all students in the United States will receive at least one suspension by the time they graduate from high school (Shollenberger, 2015). These trends, which have largely increased over the last few decades (Department of Education, 2018), become more salient when considering there have been significant decreases in offending and violence within schools since the late 1980s (Musu-Gillette et al., 2018). Furthermore, research has tied exclusionary practices to a host of negative outcomes including lower levels of attendance, self-esteem, academic performance, and graduation as well as higher levels of anxiety, dropout, delinquency, victimization, and arrest (for a thorough overview, see Welsh & Little, 2018).

To compound matters, the current landscape of school discipline extends beyond suspensions to include a variety of practices to prevent and punish delinquent behaviors (Hirschfield, 2008). The spread of security mechanisms in the form of surveillance systems, drug-sniffing dogs, metal detectors, and school resource officers, for example, have increased over the past few decades (Casella, 2006; Musu-Gillette et al., 2018). Furthermore, punitive policies have become increasingly procedural and standardized, preventing school officials from using their discretion when administering disciplinary sanctions (for an overview, see Kupchik, 2016). As such, the growing use of zero tolerance policies throughout the 1990s contributed to a significant increase in suspensions and expulsions across schools in the United States (Hirschfield, 2008). When taken in sum, this assemblage of punishment practices has been indicted with establishing a school-to-prison pipeline (Skiba, Arredondo, & Williams, 2014; Wald & Losen, 2003; for a thorough overview of the metaphor, see Crawley & Hirschfield, 2018). This pipeline refers to a process whereby youth who are punished under criminalized disciplinary practices find themselves in contact with the criminal justice system (Hirschfield, 2008; Wald & Losen, 2003; see also Simmons, 2017, p. 4, concept of the prison school).

Cast against research applying the life-course perspective, scholars have recently highlighted that school discipline can serve as a turning point that negatively affects individuals’ future outcomes (Mowen & Brent, 2016). The life-course perspective recognizes that pivotal life events such as criminal justice contact are aligned with life-course trajectories associated with other adverse outcomes including incarceration, arrest, and future offending (for an overview, see Sampson and Laub, 2005). Recent studies have demonstrated that school discipline can serve as a turning point both toward increased risk of arrest (Mowen & Brent, 2016) and increased levels of offending (Mowen, Brent, & Boman, 2019) as youth progress through school. Moreover, school discipline can contribute to increased turmoil within the family (e.g., Kupchik, 2016), sever student bonds to their family and school (Mowen et al., 2019), and place youth at greater risk of dropping out of school (Crawley & Hirschfield, 2018). Despite the understanding that school discipline can function as a turning point (e.g., Mowen & Brent, 2016) and that school suspensions are tied to a number of negative short-term outcomes (see Crawley & Hirschfield, 2018, for an overview), understanding the longer term outcomes and specific pathways through which suspensions promote the school-to-prison pipeline remains theoretically and empirically clouded.

Overall, despite the knowledge that school discipline contributes to deleterious outcomes for youth and young adults, few studies have examined how school discipline functions as a turning point across time that may function to promote incarceration as youth move into adulthood. Consequently, while the pipeline between school discipline and prison is a commonly accepted metaphor, few studies have directly examined this relationship. This oversight is particularly notable in light of the widespread use of exclusionary school sanctions, their association with well-established negative outcomes, and their potential to significantly alter life-course outcomes (e.g., Mowen & Brent, 2016). To address this gap in the literature, the current study adopts a life-course framework and leverages 15 waves of data from the National Longitudinal Survey of Youth 1997 (nLSY97) to examine the extent to which school suspensions experienced during adolescence are associated with the odds of incarceration in young adulthood.

Life Course and the Continuity of Negative Events

Starting in the late 1970s and 1980s, an intellectual resurgence took place within criminology focusing on understanding the longitudinal development of antisocial behavior, juvenile delinquency, and adult crime (Blumstein, Cohen, & Farrington, 1988; Blumstein, Cohen, Roth, & Visher, 1986; Caspi, 1987; Elder, 1975; Loeber, 1982). During this time, scholars began developing theoretical frameworks to explain the onset, persistence, and desistence of criminal conduct as youth moved into—and through—adulthood (Elder, 1975; Loeber, 1982; Moffitt, 1993; Sampson & Laub, 1993, 1997). As a result, research began focusing on criminogenic and prosocial events influencing criminal pathways over time (Elder, 1985; Laub & Sampson, 2003; Sampson & Laub, 1993). These pivotal life events would later be conceptualized as turning points by Sampson and Laub (1993), which marked events in one’s life that disconnected their past from their present. Serving as catalysts for social and behavioral transitions, turning points can be either prosocial or antisocial. Prosocial turning points, or life events promoting criminal desistance, often include a stable marriage, engaged parenthood, gainful employment, academic achievements, and successful military service. Antisocial turning points, or those events encouraging criminal persistence, frequently include divorce, family instability, unemployment, educational failure, and criminal justice involvement (for an overview, see Sampson & Laub, 2005).

To further explain criminal pathways across time, the life-course perspective borrows Caspi’s (1987) concepts of cumulative continuity and interactional continuity. Cumulative continuity refers to the accumulation of life consequences, while interactional continuity denotes repeatedly provoking reactions from others (Caspi, 1987). Within the realm of life-course criminology, these concepts suggest that negative turning points and maladaptive behaviors can evoke a durable sequence of reinforcing conditions that increasingly build onto one another as they hinder future outcomes (Sampson & Laub, 1997; see also Elder, 1998; Moffitt, 1993). For Sampson and Laub (1997), this represents a process of cumulative disadvantage in which the sustained consequences of criminal justice contact limit opportunities in conventional domains. Further, Sampson and Laub (1997) contend that the sustained continuity between negative outcomes is intimately linked to four institutions of social control—two of which being schools and state sanctions.

Schools, Discipline, and the School-to-Prison Pipeline Metaphor

A review of criminology’s theoretical infrastructure demonstrates that schools have long been central institution under examination (Cernkovich & Giordano, 1992; Rocque, Jennings, Piquero, Ozkan, & Farrington, 2017). As such, a sizable literature highlights the impact of schools and education on crime and criminal justice outcomes. Under the umbrella of life-course criminology, schools have received considerable attention given their potential to influence adolescent’s life trajectories. For instance, educational snags, or negative school experiences such as poor educational performance, lack of school attendance, and permanent disciplinary records, have been associated with lower levels of academic achievement, occupation stability, and economic mobility as well as amplified levels of juvenile delinquency, adult criminality, criminal justice contact, and incarceration (Bersani & Chappie, 2007; Elder, 1998; Hagan, MacMillan, & Wheaton, 1996; Jimerson, 1999; Moffitt, 1993; Pettit & Western, 2004; Laub & Sampson, 2003; Thornberry, Moore, & Christenson, 1985). These results indicate that school failure can act as a significant negative turning point within the life course of youth (Bersani & Chappie, 2007).

More recently, schools have become sites of intense examination given concerns over the negative consequences associated with intensified disciplinary assemblages (see Heitzeg, 2009). National reports and scholarly efforts consistently find that criminal justice–based mechanisms (i.e., surveillance systems, school resource officers, metal detectors, drug-sniffing dogs, and notification systems) have become commonplace within the school environment (Casella, 2006; Kupchik, 2010; Musu-Gillette et al., 2018; Nolan, 2011). Further, evidence suggests that more punitive sanctions associated with zero tolerance policies have structured schools’ responses to minor forms of student misconduct (Advancement Project, 2000; American Psychological Association Zero Tolerance Task Force, 2008; Curran, 2016; Curtis, 2014; Noltemeyer, Ward, & Mcloughlin, 2015; Phaneuf, 2009; Skiba & Peterson, 2000). More pertinent to this study, the escalation of exclusionary practices—such as in- and out-of-school suspensions and expulsions—have been shown to negatively impact the future outcomes of youth.

In perhaps the most recent systematic and comprehensive review on the subject, Welsh and Little (2018, p. 316) synthesize the existing evidence on how punitive school punishment practices affect students’ educational and life outcomes. In their review of 71 peer-reviewed articles published between 1990 and 2018, findings suggest that school suspensions are the most common form of punitive punishment used in schools across the United States (Welsh & Little, 2018, p. 335). When examining outcomes associated with exclusionary discipline, Welsh and Little (2018, p. 321) largely find that exclusionary practices are not only negatively related to short-term educational outcomes but also to more long-term life outcomes. More specifically, their review overwhelmingly indicates that current disciplinary trends are strongly tied to diminished educational achievements, lower scores on standardized tests, diminished graduation rates, decreased school attendance, and lower rates of educational matriculation. Further, exclusionary discipline has been found to be positively associated with higher dropout rates, greater levels of grade retention, missed instructional time, and delays in graduation. Perhaps more instructive to the current study, Welsh and Little’s (2018) review also highlights that sanctioned youth experience increased levels of contact with juvenile justice and arrest. Despite these amassed findings, Welsh and Little (2018, p. 335) conclude by stating that most research lacks a theoretical framework when interpreting disciplinary pathways leading to negative outcomes and therefore undertheorize the influence of school discipline. We echo Welsh and Little’s (2018) conclusion and, therefore, turn now to a discussion of school discipline from a life-course perspective.

The Life-Course Perspective on School Discipline

The life-course perspective posits that pivotal life experiences can serve as turning points and transitions that alter one’s life trajectory toward or away from crime as they move into and through adulthood (Elder, 1985; Farrington, 2003; Laub & Sampson, 1993; Sampson & Laub, 2003). Perhaps more importantly, these experiences have the ability to knife off (Moffitt, 1993) important opportunity structures and produce a cumulative effect (Sampson & Laub, 1997), compounding on one another as they shape criminal pathways. As we discuss below, a number of studies reveal that school suspensions may contribute to antisocial outcomes as youth progress through school, and emerging research has highlighted that school suspensions can function as a short-term turning point toward antisocial developmental outcomes.

At least two recent studies have conceptualized school discipline within the life-course context. For example, Mowen and Brent (2016) found that school suspensions increase odds of arrest and suggest that school discipline can function as a negative turning point that increases contact with the criminal justice system. In a follow-up study, Mowen, Brent, and Boman (2019) examined the effect of school suspensions on offending behaviors using four waves of data from the NLSY97. The authors found that school suspensions actually increased offending behaviors among youth who experienced school punishment. At the same time, the authors highlight an important limitation to their study, noting that although their findings demonstrate school suspensions are an important life event for youth in the short term, their results do not speak to longer term outcomes (Mowen et al., 2019).

Other studies have also considered how school suspensions may contribute to antisocial outcomes among adolescents. Although not specifically applying life-course theory, in an analysis of data encompassing 4,665 13- to 17-year-old youth in an urban school district, Cuellar and Markowitz (2015) found that youth who received a school suspension were far more likely to report increases in offending behaviors than youth who were not suspended. As a result, suspended youth were also more likely to have contact with the criminal justice system (e.g., arrest and incarceration). This finding supports the life-course notion that school suspensions may function as a turning point toward increased contact with the criminal justice system. In a related vein, Rosenbaum (2018) used propensity score matching to examine outcomes for 480 youth matched to 1,193 emerging adults. Findings revealed that youth who were suspended were less likely than youth who were not suspended to have graduated high school and were more likely to be arrested or on probation. Similarly, using the Add-Health data, Wolf and Kupchik (2017) show that suspended youth reported much greater levels of offending than nonsuspended youth in emerging adulthood. These latter two studies further demonstrate that school suspensions may serve as a turning point as youth progress through school.

Overall, the extant research has provided evidence that school suspensions can affect outcomes across time (e.g., Cuellar & Markowitz, 2015; Rosenbaum, 2018; Wolf & Kupchik, 2017), and emerging research has conceptualized school discipline as a key turning point toward short-term antisocial outcomes (e.g., Mowen & Brent, 2016). Yet, drawing upon the life-course perspective, a key question that remains unanswered is how school suspensions function to influence longer term outcomes. Given the widespread use of the school-to-prison pipeline (Crawley & Hirschfield, 2018), the lack of scientific scrutiny on the link between suspension and incarceration across ones’ life course is a startling limitation particularly given the acknowledgment within these extant studies that school discipline is both theoretical and empirically a turning point for adolescents.

Part of the limitation to existing studies is the reliance on only two waves of data (e.g., Rosenbaum, 2018; Wolf & Kupchik, 2017) or focusing exclusively on data during the time frame in which the majority of youth are enrolled in school (e.g., Mowen & Brent, 2016; Mowen et al., 2019). Yet, the school-to-prison pipeline describes a process of many years whereby youth are placed at a greater risk of incarceration even as they move into, and through, young adulthood. Thus, research is needed that situates exclusionary discipline within the life-course framework to examine its impact on trajectories as men and women move into adulthood while simultaneously documenting the specific mechanisms that drive this pipeline. This need raises attention to the goals of the current study.

Current Study

The primary aim of the current study is to examine how school suspensions experienced in middle and high school relate to incarceration as youth transition into young adulthood. To accomplish this, we establish three goals to guide the present investigation. The first goal of this study is to broadly examine the relationship between school suspension and incarceration during young adulthood. Specifically, we examine how the share of men and women who are incarcerated during their young adult years differs for those who were suspended during middle or high school compared to those who never experienced a suspension. Based on the negative outcomes associated with school punishments, we predict that:

Hypothesis 1: The share of individuals who experience incarceration during young adulthood will be greater for those who were suspended at least once in middle or high school than for those who were never suspended.

Next, we investigate the school-to-prison pipeline by moving into the multivariate framework to examine the extent to which suspension functions as turning point toward incarceration over time, net the effect of key covariates such as offending and race/ethnicity. Largely reflecting the literature reviewed above, we expect that:

Hypothesis 2: Young adults who experienced a suspension during grades 7 through 12 will be placed at significantly higher odds of incarceration, even after accounting for levels of delinquency and offending.

Finally, drawing from the concept of cumulative disadvantage, we then focus only on those who reported receiving a suspension to examine the extent to which the total number of suspensions received relates to incarceration throughout young adulthood. Within this subgroup, we expect that:

Hypothesis 3: A greater number of suspensions will relate to increasingly greater odds of incarceration across time, thus demonstrating a cumulative effect of suspension on incarceration.

Data and Methods

Data

To explore the relationship between school suspension and incarceration, we use the first 15 rounds of the NLSY97. Sponsored by the Bureau of Labor Statistics, the NLSY97 collects information on a variety of topics including the educational and employment outcomes of adolescents as they transitioned into adulthood. The initial sample consisted of 6,748 nationally representative respondents who were between the ages of 12 and 16 in 1997 (born between 1980 and 1984), as well as an oversample of 2,236 Black and Hispanic adolescents, resulting in an initial sample size of 8,984 respondents. Yearly interviews were conducted for the first 15 rounds (1997–2011), with the survey switching to a biennial design after 2011. Although the NLSY97 suffers from some attrition, more than 80% of the original sample is retained during the first 15 rounds of the survey. The NLSY97’s longitudinal nature allows us to observe and control for the within- and between-person characteristics and experiences throughout respondent’s teenage years and as they transition into adulthood. Being able to observe such indicators is crucial when studying the transition from adolescence to adulthood as one’s characteristics and experiences in adolescence can lead to varying outcomes in later life (Elder, 1998; Johnson, Crosnoe, & Elder, 2011; Macmillan & Hagan, 2004).

The data are converted into person-year intervals representing young adults between the ages of 18 and 26. The analyses are restricted to these ages to ensure that young adults have aged out of school and are therefore no longer eligible to be suspended. Within the analyses, this restriction establishes a sequence of events where suspension experiences occurred prior to incarceration.

Dependent Measure: Incarceration

The dependent measure in this study is incarceration. The NLSY97 provides information on respondent’s incarceration status during each wave of the survey. Using this information, we create a time-varying dichotomous measure representing whether the respondents experienced an incarceration during each year from ages 18–26. As shown in Table 1, about 1.5% of the sample reported being incarcerated at any given wave, though this does significantly vary within individuals across time (within-person standard deviation = 0.092).

Table 1.

Descriptive Statistics for Variables Used in Multivariate Analyses.

TI/TV Variable Mean SD Range Within SD
Dependent variable
 TV Incarcerated 0.015 0.112 0–1 0.092
Independent variables
 TI Ever suspended 0.345 0.475 0–1 —
 TI Number of grades suspended (given suspended during at least one grade) 1.527 0.803 1–6 —
Demographic controls
 TI Age (in years) at Round 1 14.80 1.44 12.17–18.25 —
 TI Male 0.498 0.500 0–1 —
 TI Non-Hispanic White 0.543 0.498 0–1 —
 TI Non-Hispanic Black 0.254 0.435 0–1 —
 TI Hispanic 0.203 0.403 0–1 —
 TV Married 0.160 0.367 0–1 0.244
 TV Number of biological children 0.441 0.824 0–8 0.428
Criminal and delinquent controls
 TV Crime 7.190 51.390 0–1,500 40.819
 TI Respondent teen gang participation 0.088 0.283 0–1 —
 TI Most peers belong in gang 1.580 0.971 1–5 —
 TI Delinquent peers 2.347 1.056 1–5 —
Socioeconomic controls
 TV Less than high school 0.220 0.415 0–1 0.108
 TV High school or equivalent 0.328 0.470 0–1 0.250
 TV Some college 0.306 0.461 0–1 0.323
 TV Bachelor’s degree or more 0.145 0.352 0–1 0.258
 TV Income (in 1997 dollars) $46,696.9 $49,560.6 $0.0–$417,074.3 $36,574.1
 TI Mother less than high school 0.230 0.421 0–1 —
 TI Mother high school or equivalent 0.358 0.479 0–1 —
 TI Mother some college 0.235 0.424 0–1 —
 TI Mother bachelor’s degree or more 0.178 0.382 0–1 —
Contextual controls
 TV Living in the south 0.398 0.489 0–1 0.132
 TI Family routines 15.002 4.280 0–28 —
 TI School bonds 12.295 1.961 4–16 —
Open in a new tab

Note. TI = time-invariant; TV = time-variant; SD = standard deviation.

Focal Independent Measure: School Suspensions

Suspension experience during the 7th through 12th grades serves as the independent variable. To capture this measure, we draw on data from two questions in the first round of the NLSY97 that asked: “Have you ever been suspended from school?” and “In what grade(s) did this happen?” Similar questions were asked during subsequent rounds: “Were you suspended from school since [the last interview]?” and “In what grades did this happen?” Using responses to these questions, we create two measures of suspension experiences. The first measure represents individuals who were ever suspended during Grades 7 through 12 as a dichotomous measure (1 = ever suspended, 0 = never suspended). Overall, about 34.5% of the sample reported receiving a suspension sometime during school. The second measure captures the total number of grades in which respondents reported receiving a suspension. Among those who were ever suspended, respondents experienced a suspension in 1.53 grades on average, with a standard deviation of 0.80, and a range from 1 (suspended in one grade) to 6 (suspended in all grades).

Control Measures

Demographic controls.

An array of time-variant and -invariant control measures are included in the multivariate analyses. We begin by including a variety of demographic indicators associated with suspension and incarceration. Age, closely linked to both offending and incarceration, is included as a time-invariant measure. During the first interview, respondents were 14.8 years old on average, with a standard deviation of about 1.44 years and range from 12.17 to 18.25 years. We create a measure representing the square of respondent’s age to capture the nonlinear nature of the age–crime relation (Hirschi & Gottfredson, 1983).1 The sample is about 49.8% female and 50.2% male. In the analyses, we withhold female as the contrast group. We also include race/ethnicity in the analysis as a series of binary variables. Overall, 25.4% of the sample was coded as Black, and 20.3% Hispanic, in contrast to 54.3% White. Due to a lack of variation in the number of “Other/Mixed” race/ethnic respondents who reported being incarcerated, this group is omitted from the analyses. Finally, to capture the influence of family formation as a turning point, we include measures representing marriage and parenthood. About 16.0% of the sample was married, and respondents reported 0.44 biological children on average, with a standard deviation of about 0.82 and a within-person standard deviation of 0.43.

Criminal and delinquent controls.

Measures that capture delinquency/offending as respondent’s offending should be the most significant predictor of both incarceration and suspension. We draw data from 6 items asking how many times the respondent: (1) carried a gun in the past 30 days, (2) destroyed property, (3) stole something worth more than $50, (4) stole something worth less than $50, (5) attacked or assaulted, and/or (6) sold illegal drugs in the past year. We sum responses to these variables such that greater scores represent larger amounts of delinquent behaviors. This measure has a mean of 7.19, and ranges from 0 (no offending) to 1,500 (a great deal of offending) with an overall standard deviation of 51.39. As a time-variant measure, delinquency/offending varies across time within persons (within standard deviation = 40.82). We transformed values of this measure using the natural log function to correct for the significant right skew.

In addition to offending, we also make use of a question asking whether respondents had been members of a gang during the first nine rounds of the survey. Responses are dichotomized to represent gang participation as an adolescent, with about 8.8% of respondents reporting gang participation and a standard deviation of 0.28. Peer gang participation is also captured and established through a question asking the percentage of the respondent’s peers who were part of a gang in 1997 (1 = almost none, 2 = about 25%, 3 = about half, 4 = about 75%, 5 = almost all). Responses averaged 1.58, with a standard deviation of 0.97, suggesting that on average less than 25% of respondent’s peers were in a gang. Finally, a measure of peer delinquency is based on five measures indicating the share of respondent’s peers who smoked, drank alcohol, used illegal drugs, skipped school, and had sex in 1997 (1 = almost none, 2 = about 25%, 3 = about half, 4 = about 75%, 5 = almost all). Responses are averaged and produce a mean of 2.35 with a standard deviation of 1.06.

Socioeconomic controls.

Socioeconomic controls are represented as educational attainment, household income, and mother’s educational attainment. Twenty-two percent of the sample reported less than a high school education, about one third reported high school and some college education (32.8% and 30.6%, respectively), and about 14.5% had a bachelor’s degree or more. The respondent’s total family income (in 1997 dollars) has a mean of $46,697, standard deviation of $49,561, and ranges from $0 to $417,074 a year. The modal educational attainment for respondent’s mothers is a high school degree (35.8% of the sample), whereas about 23.0% of respondents had mothers with less than a high school education, 23.5% had mothers with some college experience, and 17.8% had mothers with a bachelor’s degree or more.

Contextual controls.

Finally, a set of contextual controls are added to account for factors related to the respondent’s environment that may contribute to the odds of suspension and/or incarceration. To account for higher rates incarceration in the South (Carson, 2018), a dichotomous time-varying measure is included representing if respondents lived in a Southern state (the Census definition of the South is used). Slightly less than 40% of the sample lived in the South during the period of observation, with a within-person standard deviation of 0.13 as respondents moved into (or out of) the South. A time-invariant family routines scale capturing how frequently respondents participated in activities with their family in 1997 is also included. The scale ranges from 0 to 28, with higher scores indicating more family routine activities. The average sample score is 15.0 with a standard deviation of 4.28.

In addition to geographic location and family routines, we also include a scale representing bonds to the respondent’s school experiences in 1997. Factor loading was used to identify four school-related questions to create a scale with the following items: whether teachers are good, whether teachers are interested in students, whether students are graded fairly, and whether respondents feel safe at school. Responses to these items included 1 = strongly agree, 2 = agree, 3 = disagree, and 4 = strongly disagree and were reverse coded such that higher scores represent more positive school experiences. The final scale ranges from 4 to 16, with a mean of 12.30 and a standard deviation of 1.96.

Analytic Strategy

To address the research questions presented above, we conduct three analyses. As our first broad aim is to explore the bivariate relationship between suspension and incarceration throughout young adulthood, we first begin by plotting the share of respondents who reported being incarcerated between the ages of 18 and 26 by their suspension experience status. Creating this allows us to gain a visual understanding of the association between suspension and incarceration during young adulthood.

Next, we turn to multivariate analyses to gain a more comprehensive understanding of the relationship between school suspension and incarceration across time. Because the NLSY97 data are longitudinal panel data, a model must be used that accounts for this nested design as the data violate the assumption of independence made by ordinary least squares regression. To capture both within-person changes and between-person differences, we use a mixed-effects model (Rabe-Hesketh & Skrondal, 2012). A mixed-effects model nests time within the individual and, through the introduction of a random intercept, accounts for a lack of independence over time. In the case of the NLSY97, time is nested within the individual allowing each case to randomly vary across the 15 waves of data. To address our second research aim and test whether any suspension during middle or high school is associated with incarceration, the first set of longitudinal models uses a dichotomous measure of suspension experience (and an array of controls) to predict incarceration for the entire analytical sample (n = 7,623).

To address our third research aim, we then focus solely on students who ever received a suspension to examine whether a greater number of suspensions (e.g., being suspended in more grades) is significantly associated with an increased risk of incarceration later in life. Thus, for this final analysis, an interval-level measure of grades suspended is included in the model, and the sample is limited to those who experienced at least one suspension (n = 2,710).2

Results

We begin by examining the share of men and women who were incarcerated between ages 18 and 26 by their suspension status (Figure 1). Among those who never experienced a suspension during grades 7 through 12, less than 1% were incarcerated during any given year. The share who experienced an incarceration during each round of the NLSY97 was greater for those who reported at least one suspension. At 18, about 2.5% of the ever suspended sample reported being incarcerated, and this share peaked to 4.5% at age 26. The higher incarceration rates of the ever suspended sample throughout young adulthood provides evidence—at least at the bivariate level—of a positive association between suspension and the odds of experiencing an incarceration. To examine whether this relationship persists when delinquent, socioeconomic, demographic, and contextual characteristics are accounted for, we turn to our mixed-effects models.

Figure 1.

Figure 1.

Open in a new tab

Percentage of respondents who experienced an incarceration, by suspension status.

Table 2 presents the mixed-effects models examining the association between experiencing any suspension during grades 7–12 and the risk of incarceration. To ease interpretation of the generalized multilevel models, we report odds ratios from the multivariate models. Model 1 from Table 2 uses the dichotomous measure of suspension experience as the focal independent variable and the set of demographic controls. We first note that the significant Χ2 value (915.54, p < .001) indicates the model fits the data well, with about 66.4% of the variability in incarceration occurring within persons across time. Χ2 values remain significant in subsequent Table 2 models and are not discussed further. Turning to the substantive results, the model demonstrates that experiencing a suspension during grades 7–12 is significantly associated with greater odds of incarceration in young adulthood. Specifically, ever suspended youth report 878% greater logged odds of experiencing an incarceration than youth who were never suspended. Regarding demographic characteristics, the results show that males report higher logged odds of incarceration than females. Black respondents also report higher odds of incarceration than White respondents, while those who are currently married report a 75% reduction in the logged odds of incarceration. Finally, each additional child born is associated with a 49% increase in the logged odds of incarceration, a result likely due to the positive correlation between multipartner fertility and incarceration history (Carlson & Furstenberg, 2006).

Table 2.

Mixed-Effects Regression Models Predicting the Odds of Incarceration.

Model 1
 Model 2
 Model 3
β SE OR β SE OR β SE OR
Ever suspended 2.28 0.18*** 9.78 1.97 0.18*** 7.17 1.35 0.17*** 3.88
Demographic controls
 Age (in years) at Round 1 0.00 1.15 0.21 −1.92 1.15 0.15 −1.71 1.08 0.18
 Age (in years) at Round 1 squared 0.05 0.04 1.06 0.06 0.04 1.07 0.06 0.04 1.06
 Male 2.41 0.21*** 11.15 2.30 0.21*** 10.00 2.11 0.19*** 8.21
Race/ethnicity (ref. = non-Hispanic White)
 Non-Hispanic Black 0.63 0.18*** 1.88 0.61 0.18*** 1.83 0.42 0.18* 1.52
 Hispanic 0.28 0.21 1.32 0.27 0.21 1.31 0.09 0.21 1.10
Married −1.33 0.25*** 0.26 −1.28 0.25*** 0.28 −1.17 0.24*** 0.31
Number of biological children 0.40 0.07*** 1.49 0.38 0.07*** 1.46 0.25 0.07*** 1.29
Criminal and delinquent controls
 log(Crime) — — — 0.18 0.03*** 1.20 0.17 0.03*** 1.18
 Respondent teen gang participation — — — 0.95 0.20*** 2.58 0.70 0.19*** 2.01
 Most peers belong in gang — — — −0.03 0.08 0.97 −0.09 0.08 0.91
 Delinquent peers — — — 0.19 0.10* 1.21 0.14 0.09 1.14
Socioeconomic controls
 Educational attainment (ref. = high school)
  Less than high school — — — — — — 1.15 0.16*** 3.15
  Some college — — — — — — −0.67 0.21** 0.51
  Bachelor’s degree or more — — — — — — −1.67 0.49*** 0.19
 Income (in 1997 dollars) — — — — — — 0.00 0.00* 1.00
 Mother’s education (ref. = high school)
  Less than high school — — — — — — −0.13 0.18 0.87
  Some college — — — — — — −0.42 0.21* 0.66
  Bachelor’s degree or more — — — — — — −0.10 0.26 0.91
Contextual controls
 Living in the South — — — — — — −0.09 0.15 0.91
 Family routines — — — — — — 0.01 0.02 1.01
 School bonds — — — — — — −0.05 0.03 0.96
Random intercept 0.83 8.52 2.29 3.62 8.50 37.23 3.78 7.99 43.66
χ2 915.54*** 872.86*** 685.18***
Percent of within-person variation 66.44% 65.45% 59.80%
Open in a new tab

Note. n = 7,623; OR = Odds Ratio.

*

p < .05.

**

p < .01.

***

p < .001.

Controls for criminal and delinquent behaviors by both the respondent and their peers are introduced in Model 2. The addition of these measures reduces the strength of the association between any suspension experience and incarceration, although ever suspended individuals continue to experience logged odds of incarceration that are 617% greater than their never suspended peers. The Logged Crime Scale control is significantly associated with the odds of incarceration, as a one unit increase in the logged self-reported crime scale increases the logged odds of incarceration by 20%. Gang participation also increases the odds of incarceration during young adulthood by 158%, and a one unit increase on the measure of peer delinquency increases the odds of incarceration by 21%.

Experiencing any suspension during grades 7 through 12 significantly increases the logged odds of incarceration in young adulthood by 288% when socioeconomic and contextual controls are incorporated in the mixed-effects model (Model 3, Table 2). Results of the rest of the model echo recent work as Black individuals reported significantly elevated odds of incarceration relative to White individuals, males report higher odds in incarceration relative to females, self-reported crime is positively associated with incarceration, and social class (measured as educational attainment) is negatively related to incarceration.

Table 3 uses mixed-effects modeling to estimate the association between the number of grades in which respondents experienced a suspension and the risk of incarceration in adulthood. To accomplish this task, the models in Table 3 are restricted to only respondents who experienced at least one suspension during grades 7 through 12, and the focal independent variable is an interval-level measure representing the number of grades respondents were suspended. Model 1 includes this measure of suspension and demographic controls. The model fits the data well with a significant Χ2 value (684.51, p < .001) and about 64.54% of the variability in incarceration occurring within persons across time. Subsequent models in Table 3 also fit the data well as indicated by significant Χ2 values. Model 1 suggests that each additional grade a suspension was experienced increases the logged odds of incarceration by 34%. Furthermore, men who were suspended at least once experienced 1,329% greater logged odds of incarceration than their female counterparts, Blacks reported 63% greater logged odds of incarceration relative to Whites, married young adults experienced a 68% reduction in the logged odds of incarceration, and each additional biological born increased the odds of incarceration by 37%.

Table 3.

Mixed-Effects Regression Models Predicting the Odds of Incarceration.

Model 1
 Model 2
 Model 3
β SE OR β SE OR β SE OR
Number of grades suspended 0.29 0.11** 1.34 0.23 0.11* 1.26 0.18 0.11 1.20
Demographic controls
 Age (in years) at Round 1 −2.10 1.37 0.12 −2.53 1.37 0.08 −2.19 1.30 0.11
 Age (in years) at Round 1 squared 0.08 0.05 1.08 0.09 0.05 1.09 0.07 0.04 1.08
 Male 2.66 0.26*** 14.29 2.59 0.26*** 13.29 2.41 0.25*** 11.08
Race/ethnicity (ref. = non-Hispanic White)
 Non-Hispanic 0.49 0.21* 1.63 0.47 0.21* 1.60 0.37 0.21 1.44
 Black
 Hispanic 0.17 0.26 1.18 0.16 0.26 1.17 0.11 0.25 1.11
Married −1.14 0.28*** 0.32 −1.09 0.28*** 0.34 −1.01 0 28*** 0.37
Number of biological children 0.31 0.08*** 1.37 0.29 0.08*** 1.34 0.22 0.07** 1.25
Criminal and delinquent controls
 log(Crime) — — — 0.13 0.04*** 1.14 0.12 0.04*** 1.13
 Respondent teen gang participation — — — 0.78 0.22*** 2.18 0.62 0.21** 1.85
 Most peers belong in gang — — — 0.01 0.09 1.01 −0.05 0.09 0.95
 Delinquent peers — — — 0.20 0.11 1.22 0.16 0.11 1.18
Socioeconomic controls
 Educational attainment (ref. = high school)
  Less than high school — — — — — — 0.97 0.19*** 2.64
  Some college or morea — — — — — — −0.75 0.26* 0.47
 Income (in 1997 dollars) — — — — — — 0.00 0.00 1.00
 Mother’s education (ref. = high school)
  Less than high school — — — — — — −0.18 0.21 0.83
  Some college — — — — — — −0.58 0.26* 0.56
  Bachelor’s degree or more — — — — — — 0.15 0.32 1.17
Contextual controls
 Living in the South — — — — — — −0.20 0.18 0.82
 Family routines — — — — — — 0.04 0.02 1.04
 School bonds — — — — — — −0.05 0.04 0.95
Random intercept 6.16 10.09 474.44 9.28 10.11 10,706.57 7.73 9.66 2,279.45
χ2 684.51*** 651.49*** 533.00***
Percent of within-person variation 64.54 63.64 59.59
Open in a new tab

Note. n = 2,710; OR = Odds Ratio.

a

Educational attainment categories “some college” and “bachelor’s degree or more” collapsed in the Table 3 analyses due to small cell sizes.

*

p < .05.

**

p < .01.

***

p < .001.

The number of grades suspended remained significantly associated with the odds of incarceration for ever suspended young adults with the addition of crime and delinquent controls in Model 2. Specifically, each additional grade a respondent was suspended increased the logged odds of incarceration by 26%. Self-reported crime and gang participation as an adolescent also increased the odds of incarceration such that a one unit increase in the Logged Crime Scale heightened the logged odds of incarceration by 14% and adolescent gang participation amplified the logged odds of incarceration by 118%.

When introducing socioeconomic and contextual controls in the final model of Table 3, the association between the number of grades respondents was suspended and incarceration fails to reach statistical significance. Young adults who experienced any suspension and who did not complete a high school education reported logged odds of incarceration that were 164% greater than ever suspended young adults with a high school education. Those with at least some college experience, on the other hand, experienced a 53% reduction in the logged odds of incarceration than their high school educated peers and young adults whose mother had a college degree reported 44% lower odds of incarceration than those whose mothers had a high school education.

In additional analyses (Appendix), we compare versions of Model 3 from Table 3 with and without the measure of respondent’s educational attainment and find that education mediated away the significant relationship between number of suspensions and incarceration. This finding suggests that the positive and significant effect of repeated school suspension on incarceration are driven largely due to failure to graduate high school.

Discussion and Conclusion

Through the lens of the life-course perspective (Elder, 1985; Farrington, 2003; Laub & Sampson, 1993; Sampson & Laub, 2003), and situated alongside research investigating exclusionary school discipline and the school-to-prison pipeline metaphor (Wald & Losen, 2003; see also Crawley & Hirschfield, 2018; Hirschfield, 2018b; Mowen & Brent, 2016; Schollenberger, 2015; Rosenbaum, 2018), this study sought to examine the empirical relationship between school suspensions in adolescence and incarceration during young adulthood. Leveraging 15 waves of data from the NLSY97, results of mixed-effects models, overall, demonstrated a significant positive relationship between school suspensions experienced during adolescence and the odds of later imprisonment, net the effect of key controls such as levels of crime and delinquency. The following section discusses results from the study, outlines their contributions to the school discipline/life-course literature, and proposes policy implications for recent trends in school discipline and punishment.

Our first broad aim of the study was to examine the relationship between suspension and incarceration across time at the bivariate level. Mirroring prior work (e.g., Losen & Martinez, 2013; Shollenberger, 2015), results of a time-series plot demonstrated a strong link between suspension experience and incarceration between the ages of 18 and 26, supporting our first hypothesis. However, it is possible that this bivariate relationship could be due to selection. That is, suspended youth may be more delinquent as youth (and thus, suspended) and criminal into adulthood (and therefore, incarcerated). To account for this effect, we then moved into the multivariate context and hypothesized (Hypothesis 2) that having experienced a suspension between grades 7 and 12 would be positively associated with the odds of incarceration even after accounting for key covariates including levels of offending. Results from mixed-effects regression model found support for this second hypothesis. Specifically, our findings demonstrated that youth who experienced a suspension between grades 7 and 12 experienced significantly higher odds of incarceration as young adults, relative to youth who were never suspended. When placed within the life-course framework, this finding strongly suggests that school suspensions serve as a negative turning point that places youth at much greater risk of experiencing incarceration as they transition to adulthood. In short, this finding supports the notion of a school-to-prison pipeline whereby youth who experience exclusionary punishment in school are, in fact, put at significant risk of incarceration (Crawley & Hirschfield, 2018).

Finally, and largely drawing from the concept of cumulative disadvantage (Sampson & Laub, 1997), we then focused our analysis on only youth who reported receiving at least one suspension and hypothesized that a greater number of suspensions would relate to increasingly greater odds of incarceration. This third hypothesis was not supported suggesting that the frequency of suspension does matter as much as the difference between no suspension and at least one suspension. Thus, in our primary analysis, we find no evidence that repeated suspension experiences confer cumulative disadvantage. Because this finding stands in stark contrast to our life-course-informed theoretical expectations based on the concept of cumulative disadvantage, we engaged in a supplemental analysis which we turn to now.

To unpack this unexpected finding in greater detail, we examined whether this cumulative effect was partially mediated by school attainment. This subsequent analysis, shown in Appendix, demonstrated that educational attainment did, in fact, partially mediate the relationship between the number of suspensions experienced and odds of incarceration. When placed within the life-course framework, this finding suggests that school suspensions may present cumulative disadvantage to students who do not complete high school. In this manner, the cumulative effect of suspensions on incarceration exists among students who fail to graduate high school. Although our analysis demonstrates that school suspensions do not directly present cumulative disadvantage to adolescents as they age, we find support for the notion that school suspensions may indirectly promote incarceration across time through educational attainment. This finding becomes more salient considering that school suspensions have been identified as a key factor in students failing to graduate from high school (Crawley & Hirshfield, 2018). Though studies should aim to unpack this finding further, this result echoes Caspi (1987) and Sampson and Laub’s (1997) concepts of cumulative continuity and cumulative disadvantage. From this vantage point, experiencing exclusionary school sanctions may encourage additional negative outcomes—such as failure to complete high school—that progressively build on one another as they mortgage future conventional opportunities and reinforce life trajectories heading toward imprisonment (Sampson & Laub, 1997). Taken together, our results suggest that the effect of suspension on incarceration may be partially mediated by educational attrition highlighting the need for future research to explore additional mediating mechanisms through which the school-to-prison pipeline may operate.

Overall, within the life-course literature, educational snags in the form of missed educational time, grade retention, and dropping out are linked to a host of adverse consequences for youth as they move into and through adulthood (Bersani & Chappie, 2007; Elder, 1998; Hagan et al., 1996; Jimerson, 1999; Moffitt, 1993; Pettit & Western, 2004; Sampson & Laub, 2003; Thornberry et al., 1985). Mounting research is converging on the idea that current punitive disciplinary strategies not only increase the likelihood of these snags but—perhaps more importantly—serve as antisocial turning points themselves (Mowen & Brent, 2016). However, research in this area has been limited to examining how exclusionary discipline impacts short-term effects on youths’ academic and personal outcomes including arrest and juvenile justice contact (see Mowen & Brent, 2016; Welsh & Little, 2018). This study extends prior scholarship by locating school discipline within the longer life course process by showing that suspensions—a disciplinary mechanism within a larger assemblage of punitive punishment practices—function as a negative turning point increasing the odds of incarceration as an adult.

Certainly, these results stack alongside others challenging the effectiveness of exclusionary punishment practices in their current form (see Welsh & Little, 2018). From a policy standpoint, these findings bolster recent calls for disciplinary reform, alternative strategies, and remedial practices (American Psychological Association Zero Tolerance Task Force, 2008; Gregory et al., 2016; Hirschfield, 2018b). Perhaps the most commonly cited includes the behavioral management system known as Positive Behavioral Interventions and Supports. This approach seeks to enhance schools’ response to student misconduct and the school climate through the use of effective, efficient, and equitable practices (see Sugai & Horner, 2002). Restorative justice principles have also been proposed which would address the damages and needs of all parties involved to remedy harms, address underlying issues, and prevent future misconduct (González, Sattler, & Buth, 2018; Zehr, 2015). Others have outlined specific changes to how schools respond to student misconduct; these recommendations include supporting educators though professional training, ongoing data collection and analysis, collaborating with communities, working with families, and increasing the presence of mental health supports (Kupchik & Catlaw, 2015; Skiba & Losen, 2015; Winkler, Walsh, de Blois, Maré, & Carvajal, 2017). While each strategy addresses the immediate outcomes of school discipline, it is likely they will also curb the findings here. However, it is important to note that there are likely to be ideological, financial, personnel, political, and institutional barriers that hinder such reform initiatives (Brent, 2019; Lohrmann, Forman, Martin, & Palmieri, 2008).

Outside of the contributions of this research, there are several notable limitations. First is that the results presented under this study are limited to a sample of men and women who were born between 1980 and 1984 and attended school during the late 1990s. As a result, the associations identified in the current study may differ for more recent generations of young men and women. Specifically, the positive relationship between suspension and incarceration may be stronger among current cohorts of students as schools have continued to expand techniques intended to capture and punish delinquent behaviors. Whether this association is stronger for contemporary cohorts should be explored by subsequent literature. Second is that the data only examine respondents into early adulthood. Future work should explore whether school suspension continues to be associated with greater odds of incarceration as men and women age through their adult lives.

Third, the Logged Crime Scale used in the multivariate analyses does not completely account for the possibility of spuriousness within the documented relationship between suspension and incarceration. Preexisting behaviors or disorders that were not captured could increase the odds of both suspension and incarceration. Finally, we note the measure representing respondents’ race/ethnicity is limited in that Hispanic ethnicity and indicators of race are combined into mutually exclusive categories. We are therefore unable to distinguish respondents who identify as both Hispanic ethnicity and as members of a particular racial group. Likewise, we do not explore the relationship between suspension and incarceration for race and ethnic groups beyond White, Blacks, and Hispanics due to an insufficient sample size of other race/ethnic group members. This is problematic given the high suspension rates experienced by some minority groups such as Native Americans (U.S. Department of Education Office for Civil Rights, 2014). Future research should test whether school sanctions are associated with greater odds of criminal justice contact for members of minority racial and ethnic groups who are not captured in this study.

An important contribution the present study offers to the literature on school sanctions as a turning point is the lasting influence that suspensions can have throughout young adulthood. However, we do not explore the specific mechanisms by which suspensions are positively associated with incarceration. Studies using much smaller time frames of the NLSY97 have shown that school suspensions can promote offending (Mowen et al., 2019). Although we account for offending, with the limitation in this measure above, it could be that school suspensions promote offending far beyond adolescence, and future research should examine this possibility. Wiley, Slocum, and Esbensen (2013) suggest, for example, that school discipline could also introduce teenagers to the criminal justice system through the growing presence of school resource officers. An understanding of the exact mechanisms by which suspended youth are more likely to experience contact with the criminal justice system would provide researchers and policy makers with a better understanding of ways to implement school sanctions that act as less of a negative turning point in the lives of young men and women.

Overall, this study builds on work documenting the negative effects associated with school discipline by situating their effects within youths’ life course. Although existing studies have highlighted that school discipline may function as a turning point toward short-term antisocial life outcomes such as arrest (e.g., Mowen & Brent, 2016), this study uncovers that suspensions may incite adverse long-term outcomes extending into adulthood. When interpreted through the life-course perspective, these findings suggest that suspensions may serve as important antisocial turning points that reshape trajectories and usher youth toward incarceration later in life. This study also provides empirical evidence documenting the widely employed school-to-prison pipeline metaphor used within the literature. In a similar vein, findings uncover that suspensions serve as a significant disciplinary conduit within schools through which the school-to-prison pipeline operates.

Acknowledgments

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported in part by the Center for Family and Demographic Research, Bowling Green State University, which has core funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD050959).

Author Biographies

Paul Hemez’s research interests focus on the influence of youth and young adult experiences on later life outcomes.

John J. Brent’s interests focus on the cultural and structural dynamics of crime and crime control, how institutions create and perpetuate inequalities, building a theoretical foundation for criminal justice theory, and how individuals are disciplined and punished. Among other projects, his recent work includes a series of publications examining the intersections of institutional discipline and punishment, cultural dispositions, and inequality.

Thomas J. Mowen’s research explores the impact of punishment on families and youth. His recent work has appeared in Criminology, Justice Quarterly, and Journal of Research in Crime and Delinquency.

Appendix

Table A1.

Role of Educational Attainment on Relationship Between Number of Grades Suspended and Odds of Incarceration.

Model 1
 Model 2
β SE OR β SE OR
Number of grades suspended 0.23 0.11* 1.26 0.18 0.11 1.20
Demographic controls
 Age (in years) at Round 1 −2.33 1.35 0.10 −2.19 1.30 0.11
 Age (in years) at Round 1 squared 0.08 0.05 1.08 0.07 0.04 1.08
 Male 2.59 0.26*** 13.29 2.41 0.25*** 11.08
Race/ethnicity (ref. = non-Hispanic White)
 Non-Hispanic Black 0.34 0.22 1.41 0.37 0.21 1.44
 Hispanic 0.08 0.26 1.08 0.11 0.25 1.11
Married −1.10 0.28*** 0.33 −1.01 0.28*** 0.37
Number of biological children 0.27 0.08*** 1.31 0.22 0.07** 1.25
Criminal and delinquent controls
 Log(crime) 0.13 0.04*** 1.14 0.12 0.04*** 1.13
 Respondent teen gang participation 0.76 0.22*** 2.13 0.62 0.21** 1.85
 Most peers belong in gang −0.01 0.09 0.99 −0.05 0.09 0.95
 Delinquent peers 0.18 0.11 1.20 0.16 0.11 1.18
Socioeconomic controls
Educational attainment (ref. = high school)
 Less than high school - - - 0.97 0.19*** 2.64
 Some college or morea - - - −0.75 0.26* 0.47
Income (in 1997 dollars) 0.00 0.00* 1.00 0.00 0.00 1.00
Mother’s education (ref. = high school)
 Less than high school 0.03 0.22 1.03 −0.18 0.21 0.83
 Some college −0.69 0.27* 0.50 −0.58 0.26* 0.56
 Bachelor’s degree or more −0.15 0.32 0.86 0.15 0.32 1.17
Contextual controls
 Living in the South −0.11 0.18 0.89 −0.20 0.18 0.82
 Family routines 0.04 0.02 1.04 0.04 0.02 1.04
 School bonds −0.08 0.05 0.93 −0.05 0.04 0.95
Random intercept 8.71 9.99 6,059.13 7.73 9.66 2,279.45
χ2 602.39*** 533.00***
Percent of within-person variation 62.26 59.59
Open in a new tab

Note. n = 2,710. OR = Odds Ratio. Educational attainment categories “some college” and “bachelor’s degree or more” collapsed due to small cell sizes.

*

p < .05.

**

p < .01.

***

p < .001.

Notes

1

Similar conclusions are drawn from multivariate analyses when a linear or quadratic term is used to model age.

2

We performed an attrition analysis to examine how missing data affected the results of the study. Results of a series of t tests (not shown, but available) demonstrated no significant patterns of sample attrition, suggesting that patterns of missing data are missing at random.

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.

References

  1. Advancement Project. (2000). Opportunities suspended: The devastating consequences of zero tolerance and school discipline. Cambridge, MA: The Civil Right Project. [Google Scholar]
  2. American Psychological Association Zero Tolerance Task Force. (2008). Are zero tolerance policies effective in the schools? American Psychologist, 63, 852–862. [DOI] [PubMed] [Google Scholar]
  3. Bersani BE, & Chappie CL (2007). School failure as an adolescent turning point. Sociological Focus, 40, 370–391. [Google Scholar]
  4. Blumstein A, Cohen J, & Farrington DP (1988). Criminal career research: Its value for criminology. Criminology, 26, 1–35. [Google Scholar]
  5. Blumstein A, Cohen J, Roth JA, & Visher CA (1986). Criminal careers and “career criminals.” Washington, DC: National Academy Press. [Google Scholar]
  6. Brent J (2019). Enduring dispositions: Examining punitive logics in the context of disciplinary reform. Theoretical Criminology, 23, 96–116. [Google Scholar]
  7. Carlson MJ, & Furstenberg FF (2006). The prevalence and correlates of multipartnered fertility among urban U.S. parents. Journal of Marriage and Family, 68, 718–732. [Google Scholar]
  8. Carson EA (2018). Prisoners in 2016. U.S. Department of Justice, Bureau of Justice Statistics (nCJ 251149). Washington, DC: Bureau of Justice Statistics. [Google Scholar]
  9. Casella R (2006). Selling us the fortress: The promotion of techno-security equipment for schools. New York, NY: Routledge. [Google Scholar]
  10. Caspi A (1987). Personality in the life course. Journal of Personality and Social Psychology, 53, 1203–1213. [DOI] [PubMed] [Google Scholar]
  11. Cernkovich SA, & Giordano PC (1992). School bonding, race, and delinquency. Criminology, 30, 261–291. [Google Scholar]
  12. Crawley K, & Hirschfield P (2018). Examining the school-to-prison pipeline metaphor. In Oxford research encyclopedia of criminology. Retrieved from http://oxfordre.com/criminology/view/10.1093/acrefore/9780190264079.001.0001/acrefore-9780190264079-e-346?print=pdf [Google Scholar]
  13. Cuellar AE, & Markowitz S (2015). School suspension and the school-to-prison pipeline. International Review of Law and Economics, 43, 98–106. [Google Scholar]
  14. Curran FC (2016). Estimating the effect of state zero tolerance laws on exclusionary discipline, racial discipline gaps, and student behavior. Educational Evaluation and Policy Analysis, 38, 647–668. [Google Scholar]
  15. Curtis AJ (2014). Tracing the school-to-prison pipeline from zero-tolerance policies to juvenile justice dispositions. Georgetown Law Journal, 102, 1251–1277. [Google Scholar]
  16. Department of Education. (2018). 2015–2016 civil rights data collection: School climate and safety. Washington, DC: U.S. Department of Education Office for Civil Rights. [Google Scholar]
  17. Elder GH (1975). Age differentiation and the life course. Annual Review of Sociology, 1, 165–190. [Google Scholar]
  18. Elder GH (1985). Perspectives on the life course. In Elder G (Ed.), Life course dynamics: Trajectories and transitions, 1968–1980 (pp. 23–49). Ithaca, NY: Cornell University Press. [Google Scholar]
  19. Elder GH (1998). The life course as developmental theory. Child Development, 69, 1–12. [PubMed] [Google Scholar]
  20. Farrington DP (2003). Developmental and life-course criminology: Key theoretical and empirical issues—The 2002 Sutherland Award address. Criminology, 4, 221–225. [Google Scholar]
  21. Gregory A, Clawson K, Davis A, & Gerewitz J (2016). The promise of restorative practices to transform teacher-student relationships and achieve equity in school discipline. Journal of Educational and Psychological Consultation, 26, 325–353. [Google Scholar]
  22. González T, Sattler H, & Buth AJ (2018). New directions in whole-school restorative justice implementation. Conflict Resolution Quarterly, 36(3), 207–220. doi: 10.1002/crq.21236. [DOI] [Google Scholar]
  23. Hagan J, MacMillan R, & Wheaton B (1996). New kid in town: Social capital and the life course effects of family migration on children. American Sociological Review, 61, 368–385. [Google Scholar]
  24. Heitzeg N (2009). Education or incarceration: Zero tolerance and the school to prison pipeline. Forum on Public Policy, 2, 1–12. [Google Scholar]
  25. Hirschfield PJ (2008). Preparing for prison? The criminalization of school discipline in the USA. Theoretical Criminology, 12, 79–101. [Google Scholar]
  26. Hirschfield PJ (2018a). Trends in school social control in the United States: Explaining patterns of decriminalization. In Deakin J, Taylor E & Kupchik A (Eds.), The Palgrave international handbook of school discipline, surveillance, and social control (pp. 43–64). Cham, Switzerland: Palgrave Macmillan. [Google Scholar]
  27. Hirschfield PJ (2018b). Schools and crime. Annual Review of Criminology, 1, 149–169. [Google Scholar]
  28. Hirschi T, & Gottfredson M (1983). Age and the explanation of crime. American Journal of Sociology, 89, 552–584. [Google Scholar]
  29. Jimerson SR (1999). On the failure of failure: Examining the association between early grade retention and education and employment outcomes during late adolescence. Journal of School Psychology, 37, 243–272. [Google Scholar]
  30. Johnson MK, Crosnoe R, & Elder GH (2011). Insights on adolescence from a life course perspective. Journal of Research on Adolescence, 21, 273–280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Kupchik A (2010). Homeroom security: School discipline in the age of fear. New York, NY: New York University Press. [Google Scholar]
  32. Kupchik A (2016). The real school safety problem: The long-term consequences of harsh school punishment. Oakland, CA: University of California Press. [Google Scholar]
  33. Kupchik A, & Catlaw TJ (2015). Discipline and participation: The long-term effects of suspension and school security on the political and civic engagement of youth. Youth & Society, 47, 95–124. [Google Scholar]
  34. Laub JH, & Sampson RJ (1993). Turning points in the life course: Why change matters to the study of crime. Criminology, 31, 301–325. [Google Scholar]
  35. Laub JH, & Sampson RJ (2003). Shared beginnings, divergent lives: Delinquent boys to age 70. Cambridge, MA: Harvard University Press. [Google Scholar]
  36. Loeber R (1982). The stability of antisocial and delinquent child behavior: A review. Child Development, 53, 1431–1446. [PubMed] [Google Scholar]
  37. Lohrmann S, Forman S, Martin S, & Palmieri M (2008). Understanding school personnel’s resistance to adopting schoolwide positive behavior support at a universal level of intervention. Journal of Positive Behavior Interventions, 10, 256–269. [Google Scholar]
  38. Losen D, & Martinez T (2013). Out of school and off track: The overuse of suspensions in American middle and high schools. Los Angeles, CA: The UCLA Center for Civil Rights Remedies at the Civil Rights Project. [Google Scholar]
  39. Macmillan R, & Hagan J (2004). Violence in the transition to adulthood: Adolescent victimization, education, and socioeconomic attainment in later life. Journal of Research on Adolescence, 14, 127–158. [Google Scholar]
  40. Moffitt T (1993). Adolescent-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review, 100, 674–701. [PubMed] [Google Scholar]
  41. Mowen T, & Brent J (2016). School discipline as a turning point. Journal of Research in Crime & Delinquency, 53, 628–653. [Google Scholar]
  42. Mowen T, Brent J, & Boman J (2019). The effect of school discipline on offending across time. Justice Quarterly. Advance Online Publication. doi: 10.1080/07418825.2019.1625428 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Musu-Gillette L, Zhang A, Wang K, Zhang J, & Oudekerk BA (2018). Indicators of school crime and safety: 2017 (nCES 2017– 064/NCJ 250650). Washington, DC: U.S. Department of Justice. [Google Scholar]
  44. Nolan K (2011). Police in the hallways: Discipline in an urban high school. Minneapolis: University of Minnesota Press. [Google Scholar]
  45. Noltemeyer AL, Ward RM, & Mcloughlin C (2015). Relationship between school suspension and student outcomes: A meta-analysis. School Psychology Review, 44, 224–240. [Google Scholar]
  46. Pettit B, & Western B (2004). Mass imprisonment and the life course: Race and class inequality in US incarceration. American Sociological Review, 69, 151–169. [Google Scholar]
  47. Phaneuf SW (2009). Security in schools: Its effect on students. El Paso, TX: LFB Scholarly Publishing. [Google Scholar]
  48. Rabe-Hesketh S, & Skrondal A (2012). Multilevel and longitudinal modeling using STATA (3rd ed.). College Station, TX: Stata Press. [Google Scholar]
  49. Rocque M, Jennings WG, Piquero AR, Ozkan T, & Farrington DP (2017). The importance of school attendance: Findings from the Cambridge Study in Delinquent Development on the life-course effects of truancy. Crime & Delinquency, 63, 592–612. [Google Scholar]
  50. Rosenbaum J (2018). Educational and criminal justice outcomes 12 years after school suspension. Youth & Society, 52(4), 515–547. doi: 10.1177/0044118X17752208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Sampson RJ, & Laub JH (1993). Crime in the making: Pathways and turning points throughout life. Cambridge, MA: Harvard University Press. [Google Scholar]
  52. Sampson RJ, & Laub JH (1997). A life-course theory of cumulative disadvantage and the stability of delinquency. In Thornberry T (Ed.), Developmental theories of crime and delinquency (pp. 133–161). New Brunswick, NJ: Transaction Publishers. [Google Scholar]
  53. Sampson RJ, & Laub JH (2005). A general age-graded theory of crime: Lessons learned and the future of life-course criminology. In Farrington D (Ed.), Advances in criminological theory: Testing integrated development/life course theories of offending (pp. 165–182). New York, NY: Routledge. [Google Scholar]
  54. Shollenberger TL (2015). Racial disparities in school suspension and subsequent outcomes: Evidence from the national longitudinal survey of youth. In Losen DJ (Ed.), Closing the school discipline gap: Equitable remedies for excessive exclusion (pp. 31–43). New York, NY: Teachers College Press. [Google Scholar]
  55. Simmons L (2017). The prison school: Educational inequality and school discipline in the age of mass incarceration. Oakland, CA: University of California Press. [Google Scholar]
  56. Skiba RJ, Arredondo MI, & Williams NT (2014). More than a metaphor: The contribution of exclusionary discipline to a school-to-prison pipeline. Equity & Excellence in Education, 47, 546–564. [Google Scholar]
  57. Skiba RJ, & Losen DJ (2015). From reaction to prevention: Turning the page on school discipline. American Educator, 39, 4–11. [Google Scholar]
  58. Skiba RJ, & Peterson RL (2000). School discipline at a crossroads: From zero tolerance to early response. Exceptional Children, 66, 335–346. [Google Scholar]
  59. Sugai G, & Horner R (2002). The evolution of discipline practices: School-wide positive behavior supports. Child & Family Behavior Therapy, 24, 23–50. [Google Scholar]
  60. Thornberry TP, Moore M, & Christenson RL (1985). The effect of dropping out of high school on subsequent criminal behavior. Criminology, 23, 3–18. [Google Scholar]
  61. U.S. Department of Education Office for Civil Rights. (2014). Civil rights data collection: Data snapshot (school discipline). Washington, DC: U.S. Department of Education Office for Civil Rights. [Google Scholar]
  62. Wald J, & Losen D (2003). Defining and redirecting a school-to-prison pipeline. Framing Paper for the School-to-Prison Pipeline Research Conference. The Civil Rights Project, Harvard University, Cambridge, MA. [Google Scholar]
  63. Welsh RO, & Little S (2018). Caste and control in schools: A systematic review of the pathways, rates and correlates of exclusion due to school discipline. Children and Youth Services Review, 94, 315–339. [Google Scholar]
  64. Wiley SA, Slocum LA, & Esbensen FA (2013). The unintended consequences of being stopped or arrested: An exploration of the labeling mechanisms through which police contact leads to subsequent delinquency. Criminology, 51, 927–966. [Google Scholar]
  65. Winkler JL, Walsh ME, de Blois M, Maré J, & Carvajal SC (2017). Kind discipline: Developing a conceptual model of a promising school discipline approach. Evaluation and Program Planning, 62, 15–24. [DOI] [PubMed] [Google Scholar]
  66. Wolf KC, & Kupchik A (2017). School suspensions and adverse experiences in adulthood. Justice Quarterly, 34, 407–430. [Google Scholar]
  67. Zehr H (2015). The little book of restorative justice: Revised and updated. New York, NY: Simon and Schuster. [Google Scholar]

RESOURCES