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The Punishment Gap: School Suspension and Racial Disparities in Achievement


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While scholars have studied the racial “achievement gap” for several decades, the mechanisms that produce this gap remain unclear. In this article, we propose that school discipline is a crucial, but under-examined, factor in achievement differences by race. Using a large hierarchical and longitudinal data set comprised of student and school records, we examine the impact of student suspension rates on racial differences in reading and math achievement. This analysis—the first of its kind—reveals that school suspensions account for approximately one-fifth of black-white differences in school performance. The findings suggest that exclusionary school punishment hinders academic growth and contributes to racial disparities in achievement. We conclude by discussing the implications for racial inequality in education. Mientras que los eruditos han estudiado la "brecha racial educativa" desde hace varias décadas, los mecanismos que producen este vacío no están claros. En este trabajo, proponemos que la disciplina escolar es muy importante, pero poco examinada en el factor en las diferencias de rendimiento según la raza. Utilizando un conjunto de datos de jerárquica y datos longitudinales compuestos por registros escolares de estudiantes, examinamos el impacto de los tipos de suspensión de los estudiantes y las diferencias raciales en la lectura y el rendimiento en matemáticas. Este análisis es primero de su tipo y revela que las suspensiones escolares representan aproximadamente una quinta parte de las diferencias entre el rendimiento escolar entre negros-blancos. Los hallazgos sugieren que el castigo de la escuela de exclusión dificulta el crecimiento académico y contribuye a las disparidades raciales en el rendimiento académico. Concluimos con una discusión de las implicaciones para la desigualdad racial en la educación.
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The Punishment Gap: School Suspension
and Racial Disparities in Achievement
Edward W. Morris
and Brea L. Perry
University of Kentucky and
Indiana University
While scholars have studied the racial “achievement gap” for several decades, the mecha-
nisms that produce this gap remain unclear. In this article, we propose that school discipline
is a crucial, but under-examined, factor in achievement differences by race. Using a large hi-
erarchical and longitudinal data set comprised of student and school records, we examine
the impact of student suspension rates on racial differences in reading and math achieve-
ment. This analysis—the first of its kind—reveals that school suspensions account for ap-
proximately one-fifth of black-white differences in school performance. The findings suggest
that exclusionary school punishment hinders academic growth and contributes to racial dis-
parities in achievement. We conclude by discussing the implications for racial inequa lity in
KEYWORDS: achievement gap; school discipline; racial disparity; punishment; at-risk
Racial disparities in educational achievement are one of the most important sources of American in-
equality. Racial inequalities in adulthood in areas as diverse as employment, incarceration, and health
can be traced to unequal academic outcomes in childhood and adolescence (Belfield and Levin
2007). While the racial “achievement gap” has been consistently documented over several decades,
scholars are still working to understand the mechanisms that produce this gap (Jencks and Phillips
1998; Magnuson and Waldfogel 2008). In this article, we propose that school discipline is a crucial,
but under-examined, factor in achievement differences by race. Though large racial disparities in disci-
pline exist, this pattern has never been empirically examined as an explanation of racial gaps in school
performance. This article presents evidence that exclusionary school punishment may hinder aca-
demic growth and contribute to racial inequalities in achievement.
Using detailed data from school records and controlling for a host of school and non-school fac-
tors, we confirm that minority students are more likely to be suspended from school. Moreover, using
variance decomposition methods that isolate within-student trajectories, we show that suspension is
associated with significantly lower achievement growth across time. Finally, we conduct the first com-
prehensive analysis of suspension as an explanation for the racial gap in achievement. This analysis re-
veals that school suspensions account for approximately one-fifth of black-white differences in school
This research was supported by a grant from the Spencer Foundation. The authors wish to thank Rebecca DiLoretto and the
Children’s Law Center for their contributions to this project and for their commitment to equity and justice for all children in public
education. Direct correspondence to: Edward W. Morris, 1515 Patterson Office Tower, Department of Sociology, University of
Kentucky, 40506. E-mail:
The Author 2016. Published by Oxford University Press on behalf of the Society for the Study of Social Problems. All rights reserved.
For permissions, please e-mai l:
Social Problems, 2016, 63, 68–86
doi: 10.1093/socpro/spv026
Advance Access Publication Date: 8 January 2016
by guest on January 20, 2016 from
performance, demonstrating that exclusionary discipline may be a key driver of the racial achievement
gap. We suggest that the escalation of exclusionary discipline in schools can result in severe academic
consequences for minority students.
Racial differences in achievement between white and African American children have long been a
concern for researchers and policy makers. Today, this issue continues to present a complex and vex-
ing social problem. Data from the National Assessment of Educational Progress (NAEP) reveals that
although gaps in reading and mathematics achievement between black students and white students
have narrowed in the past 40 years, they remain significantly different (Hedges and Nowell 1999;
Jencks and Phillips 1998; Magnuson and Waldfogel 2008). In 2013, for example, African American
students, on average, scored 31 points below white students in eighth-grade math and 26 points be-
low in eighth-grade reading (NCES 2014).
Historically, black students made steady gains in closing
the gap after school desegregation in the 1960s; however, this progress leveled off in 1990. The gap
has fluctuated slightly since then, but has ultimately changed little over the past two decades. For
twelfth grade students, in fact, the gap in NAEP reading is wider now than it was in 1992 (NCES
Scholars have offered an array of explanations for these differences in academic performance.
Racial gaps in school readiness exist when children enter school, which suggests that inequalities out-
side of schools play an important role (Downey, von Hippel, and Broh 2004). Studies in this vein fo-
cus on family and neighborhood effects ranging from economic inequality (Berends, Lucas, and
Penaloza 2008; Magnuson and Waldfogel 2008) to non-cognitive skills (Grissmer and Eiseman
2008) to parental incarceration (Wildeman 2009). Such effects may be compounded when concen-
trated in specific schools and neighborhoods (Condron et al. 2012; see also Coleman et al. 1966).
Another proposed outside-of-school factor is student resistance to schooling. The widely debated op-
positional culture model asserts that minority students perceive schools as white dominated and this
prompts ambivalence toward achievement and disengagement from school.
Other explanations focus within education itself. Dennis Condron (2009) argued that outside-of-
school factors explain learning gaps by socioeconomic status, but not by race per se. Condron (2009)
and others point to de facto school segregation, which decreased through the 1980s before reversing
course and increasing beginning in the 1990s (Condron et al. 2012; Vigdor and Ludwig 2008).
Related research asserts that certain characteristics of predominately minority schools depress student
achievement, such as per pupil funding (Condron and Roscigno 2003), teaching experience
(Corcoran and Evans 2008), and school-level poverty (Rumberger and Palardy 2005). In addition to
across-school differences, research has examined processes within schools, especially ability grouping
or tracking (Berends et al. 2008; Tyson 2011). This work suggests that the learning opportunities of
minority students are restricted by instructional differentiation, which increases learning gaps over
Certainly, these explanations are not mutually exclusive, and racial inequality in achievement arises
from a complex interplay of school and non-school factors. However, we argue that this literature has
not adequately considered one indispensable piece of the puzzle: school punishment. Anne Gregory,
Russell Skiba, and Pedro Noguera (2010) have proposed that school discipline could be related to
achievement differences, but no empirical work has tested this claim. Yet, school punishment is a log-
ical explanation for achievement differences for several reasons. First, punishment varies widely by
race, meaning that it is potentially related to racial variation in achievement. Second, exclusionary
1. The data for fourth-grade and twelfth-grade students show similar patterns. The scale of the NAEP tests ranges from 0 to 500
points. The gap is equivalent to nearly one standard deviation, on average (Condron et al. 2012; NCES 2014).
2. It is impossible to summarize the extensive debate on oppositional culture in the limited space here. For key works, see
Ainsworth-Darnell and Downey (1998), Fordham and Ogbu (1986), and Harris (2011).
The Punishment Gap 69
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forms of school punishment, such as suspension, extract students from the learning environment,
which can threaten academic progress. Third, school suspensions increased markedly beginning in
the 1990s at the same time that progress on narrowing the achievement gap waned. This indicates
that overuse of exclusionary discipline may pose barriers in efforts to reduce racial inequalities in edu-
cation. The consideration of school punishment adds an important dimension to the argument that
school-level processes help reproduce the racial achievement gap.
Beginning in the 1990s, school discipline approaches became increasingly authoritarian and intrusive.
Several scholars have proposed that contemporary regimes of school discipline “criminalize” student
misbehavior in ways that mirror the criminal justice system (Hirschfield 2008; Kupchik 2010;
Kupchik and Monahan 2006; Wacquant 2001; Welch and Payne 2010). School resource officers
(uniformed police officers stationed in schools), security cameras, random searches, and “zero toler-
ance” policies requiring automatic suspension or expulsion for specified offenses all exemplify this
strict, encompassing approach. This shift in disciplinary mentality has resulted in a sharp increase in
school suspensions. Suspension rates in U.S. public schools have doubled since the 1970s, and in
2010, almost three million students were suspended from school (Losen and Gillespie 2012).
Zero tolerance policies in particular have markedly impacted school suspensions. Disciplinary re-
formers modeled these policies after “tough on crime” approaches to policing and sentencing that
grew in popularity in the late 1980s (Garland 2001; Simon 2007). According to the logic underpin-
ning these approaches, loose social control will allow deviance to flourish. Thus, even small transgres-
sions left unpunished can evolve into larger transgressions and eventually create a deviant normative
context. This logic dictated that early, “tough” punishments were critical to maintaining social order.
Hence, criminal sentencing guidelines such as “three strikes” laws emerged in the late 1980s. Zero tol-
erance policies in schools, which mandated automatic suspension or expulsion for serious or repeated
offenses, soon followed suit. Despite evidence that zero tolerance does not actually enhance school
climate or safety (see American Psychological Association 2008; Skiba and Peterson 1999), schools
across the country continue to be enamored with strict disciplinary policies (Hoffman 2014). Under
such policies, exclusionary school punishments such as suspension and expulsion have become wide-
spread, replacing milder repercussions such as detention or loss of privileges.
Although these new punitive policies intend to mete out discipline fairly, they disproportionately
impact minority students, especially African Americans. Since the publication of the Children’s
Defense Fund’s, School Suspensions: Are They Helping Children (1975), research has consistently re-
vealed that African American students are punished at higher rates, including classroom reprimands
(Ferguson 2000; Morris 2005), office referrals (Rocque 2010; Skiba et al. 2002), suspensions (Losen
2011; McCarthy and Hoge 1987; Wallace et al. 2008), and expulsions (Kewal Ramani et al. 2007 ;
Wallace et al. 2008). Black students are also more likely to experience severe punishment, such as
court action or notification of the police (Welch and Payne 2010). Research suggests that African
American students are approximately three times as likely as white students to be suspended
(Gregory et al. 2010; Wallace et al. 2008). A recent report found that nationwide, one out of six black
students has been suspended at least once (Losen and Gillespie 2012).
In addition, predominately
minority schools are most likely to rely on punitive forms of discipline such as out-of-school suspen-
sion or expulsion (Irwin, Davidson, and Hall-Sanchez 2013; Rocque and Paternoster 2011; Welch
and Payne 2010). While the discipline of minority students has long occurred at higher rates
3. For Latinos, the picture is more complex. Some research finds that the punishment of Latino students tends to be less extreme,
but still occurs at higher rates than whites (Losen and Gillespie 2012; Peguero and Shekarkhar 2011). Other research (including
our own analyses) finds that Latinos are not punished at higher rates after controlling for background factors such as free and re-
duced lunch eligibility. We focus on black-white gaps in this article, but future research should explore school discipline and
Latinos in more depth.
70 Morris and Perry
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compared to white students, this gap has widened as the prevalence of suspension has increased over-
all (Verdugo 2002). Using a natural experiment, Stephen Hoffman (2014) found that strict, punitive
discipline polices increase the racial gap in suspension and expulsion.
These alarming racial disparities in school discipline have prompted a response from the federal
government. In January 2014, the U.S. Department of Education issued a set of guiding principles
concerning discipline in public schools. Although the federal government cannot dictate local disci-
plinary policies, this document encourages schools to rely less on exclusionary forms of discipline and
reminds schools that they cannot discriminate in administering discipline (U.S. Department of
Education 2014). The missive cautions that punishments such as suspension, which remove students
from the learning environment, have been linked to ongoing educational problems. However, despite
the growing realization of negative consequences, there is surprisingly little research able to specify
the direct impact of suspension on outcomes such as academic achievement.
Suspension appears to have few behavioral or academic benefits for suspended students. Virginia
Contenbader and Samia Markson (1998) found that suspension does little to improve subsequent
student behavior, and may even exacerbate students’ anger or apathy. Exclusionary discipline can
weaken school bonds, which may actually increase the likelihood of further deviant behavior (Hirschi
1969; Jenkins 1997). Academically, school suspension has been correlated with low academic
performance (Davis and Jordan 1994) and higher risk of dropout (Ekstrom et al. 1986). A quasi-
experimental study by Emily Arcia (2006) followed two groups of similar students over time, the
only major difference between the groups was that one had been suspended and the other had not.
After two years, the suspended group was nearly five grade levels behind the non-suspended group,
which suggests that suspension greatly impedes academic progress. More recently, Brea Perry and
Morris (2014) found that high rates of suspension at the school level tend to depress student achieve-
ment, even for students who were not personally suspended.
However, research that traces the effects of suspension on achievement longitudinally for a large
and diverse group of students remains thin. While prior educational research has connected exclu-
sionary discipline to lower achievement, it is still unclear whether or to what extent suspension redu-
ces achievement. In addition, no empirical research to our knowledge has been able to link
suspension disparities by race to achievement disparities by race. In this article, we use detailed longi-
tudinal data from school district records and conservative, unbiased fixed-effects modeling to more
accurately specify the impact of suspension on achievement over time. Moreover, we extend this anal-
ysis directly to the racial achievement gap to determine the extent to which school discipline dispar-
ities explain this gap.
Our unique data and analysis provide the first comprehensive study of the impact of suspen-
sion on racial differences in achievement. Using advanced multilevel methods that capitalize on
the rich explanatory power of longitudinal and hierarchical data, we focus on the following ques-
tions: (1) Are racial and ethnic minorities at disproportionate risk for school suspension? (2)
Are racial-ethnic background and school suspension associated with academic achievement in
reading and math, controlling for other individual characteristics and all school-level heterogene-
ity? (3) Do racial differences in the likelihood of suspension explain a significant proportion of
the racial achievement gap?
This research uses data from the Kentucky School Discipline Study (KSDS) (Perry and Morris
2014). Data are comprised of existing, deidentified school records and supplementary data collected
routinely from parents in a large, urban public school district. All data on school discipline and test
scores come directly from school records, eliminating any selection bias and social desirability effects
that occur when students or parents report on their own behavior. For each student offense resulting
The Punishment Gap 71
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in any disciplinary action (office referral, detention, suspension, expulsion, etc.), school personnel are
required to complete an electronic form containing information about the offense, all students in-
volved, and any response by school officials. This information is stored for the purposes of monitor-
ing school safety and reporting discipline statistics to the state, and is well regulated. Only
information on family structure (i.e., single parent family, number of people living in the home) is
drawn from the parent survey.
Our sample includes students in grades 6 through 10 (middle and high school) who were enrolled
in a district public school over a three-year period beginning in August 2008 and ending in June
2011. The full sample includes 24,347 students. However, 8,089 students (33 percent of the full sam-
ple) are dropped due to missing data on end-of-year (spring) Measure of Academic Progress (MAP)
test scores. MAP testing by the school district was inconsistent prior to 2009 during the pilot phase.
By the 2009-2010 school year, full implementation of the testing was in place. Because the piloting
process was random, missing data are unlikely to lead to biases. An additional ten cases were dropped
due to missing data on other variables.
The analysis sample includes 16,248 students nested in 17 schools, providing a total of 25,221 ob-
servations over three years of data. At baseline, about 65 percent of students are in grades 6 to 8
(ages 11 to 13), and 35 percent in grades 9 to 10 (ages 14 to 16). Approximately 49 percent of stu-
dents in the sample are girls and 51 percent are boys. The majority of these students are white (59
percent) or black (25 percent). However, 10 percent are Latino, 4 percent are Asian, and 3 percent
classify themselves as some other race. Also, 48 percent of students qualify for free or reduced-price
meals. These data, which are drawn from one school system, are not nationally representative of all
public school children. Most notably, a smaller percentage of the U.S. student population is non-
Hispanic black (17 percent) compared to our sample, and a greater percentage is Latino (21 percent;
NCES 2014). However, black populations tend to be concentrated in the Southeast where this school
district is located. Consequently, these data may be reasonably representative of the Southeastern
United States.
With respect to patterns of exclusionary discipline, our sample is on par with national trends
(Aud, Fox, and KewalRamani 2010). Specifically, rates of out-of-school suspension in the KSDS and
nationally representative National Household Education Surveys (NHES 2007) (U.S. Department of
Education 2007) are the same (22 percent had ever been suspended). There are also similar patterns
of racial disparities in suspension, which is critical for this analysis in particular. In the KSDS, about
42 percent of black students had ever been suspended, compared to 43 percent in the NHES sample
(a non-significant difference). Among Latinos, 26 percent in the KSDS district had ever been sus-
pended compared to 22 percent nationally (p < .001). Also, Asians in both data sets were less likely
to be suspended, though this difference is larger in Kentucky (4 percent and 11 percent, respectively;
p < .001). Finally, 18 percent of girls and 26 percent of boys in the KSDS had been suspended com-
pared to 15 percent of girls and 28 percent of boys nationally. This indicates that boys in the general
population are slightly more likely to have been suspended than students in the Kentucky district.
Overall, these patterns are remarkably similar in magnitude and always in the same direction. These
results suggest that exclusionary discipline patterns in the data used for this analysis are representative
of national trends, supporting the use of cautious inference to students in other districts.
Several static characteristics of individual students are examined as independent variables in multivariate
models. Gender is coded as a binary variable (1 ¼ female; 0 ¼ male). Race is measured in five catego-
ries and coded as binary indicators: white, African American, Latino, Asian, and other. Family structure
is measured by a binary variable indicating whether two parents or guardians were listed on each stu-
dent’s parent information form (1 ¼ two parents; 0 ¼ one parent). Because this measure is available
only in the final wave of the study, missing values on 14 percent of observations are replaced via a
72 Morris and Perry
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logistic regression multiple imputation method. Ten imputations are computed, and Stata’s -mi- com-
mands are used for imputation and estimation of models that include the family structure variable.
Time is coded using academic year beginning with 0 at baseline in 2008-2009 and ending with 2
in 2010-2011. Time-squared and time-cubed are also calculated to assess the non-linearity of the
growth or decline in school suspensions and academic achievement over time. All other time-varying
measures are divided into their between-person and within-person information to differentiate the
degree to which outcomes are due to average differences between students across waves or differ-
ences over time in the characteristics of a student compared to him or herself at other waves
(Raudenbush and Bryk 2002). Between-person variance is reflected in the average score for the three
waves of the study, and is held constant across observations nested within the same individual.
Within-person variance is the average score subtracted from the score for the current wave of the
study, and measures how different a person is in a given wave from their own average. For binary var-
iables, the between-person measure is equivalent to the proportion of waves in which each student
had the characteristic in question. The within-person score is the difference between the binary indi-
cator for a given wave and the between-person proportion. It ranges from -.67 (having the character-
istic in every wave except the current wave) to .67 (having the characteristic only in the current
wave), with zero indicating no change across waves.
Socioeconomic status is measured using participation in the free or reduced meal program. For
this variable, between-person variance is the average of free/reduced lunch status (coded 1 ¼ yes;
0 ¼ no) across three waves of the study. This is also equal to the proportion of waves in which each
student participated in the free/reduced lunch program. The within-person measure is the difference
between the binary variable for the current wave and the proportion of waves in which the student
participated in the free/reduced lunch program. Receipt of special education services is also measured
using binary coding, and is decomposed into between- and within-person variation.
Out-of-school student suspension is measured as a dichotomous variable and is the dependent var-
iable in the first set of regressions. Information on student suspensions is drawn from official school
records. Though a small minority of students experienced multiple out-of-school suspensions in a
given school year, there are insufficient cases to employ a count variable. In subsequent regressions
predicting academic achievement, suspension is an independent variable and is split into between-
and within-person variation. The between-person measure of suspension is the proportion of waves
in which a student is suspended, while the within-person measure is suspension in the current wave
minus the proportion of years with suspensions.
Performance on tests in math and reading are used to assess achievement, and are also drawn
from official school records. Between 2008 and 2011 in the targeted school district, academic achieve-
ment was measured using MAP testing across the state. This is a computerized adaptive test that is
designed to help schools monitor academic growth in reading and math and make informed decisions
about placement and needed services. Scores are numeric and normally distributed. The tests are not
timed, and are administered multiple times per year. To reduce concerns about reverse causation
(i.e., low academic performance leading to suspension), scores from the end-of-year MAP testing are
used in this analysis, making it unlikely that any suspensions occurred following testing. In cases
where data from the end-of-year academic achievement tests are missing, the average scores from
MAP testing occurring earlier in the same school year are imputed. Currently, similar racial differ-
ences in the NAEP appear for both math and reading components of the test (NCES 2014).
Therefore, it is appropriate for us to use both math and reading outcomes here. MAP scores for read-
ing and math are examined separately to provide a strong overall assessment of achievement.
Analyses focus on identifying the association between race and ethnicity, suspension, and academic
achievement. Multivariate effects are modeled with multi-level mixed logistic and linear regression
The Punishment Gap 73
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models using Stata 13 (StataCorp 2013). These adjust for the hierarchical data structure and the
interdependence among observations resulting from having multiple observations over time for each
student and multiple students in schools. The models have a three-level structure where level-one ob-
servations (time points) are nested in level-two individual students, which are nested in level-three
Because these models focus on predicting an individual-level outcome using both time-invariant
and time-variant characteristics, the models include a random intercept at level two. To control for
unmeasured time- and student-invariant characteristics of schools, these models include level-three
fixed effects using dichotomous school indicators (estimates not shown in tables). This means that
mechanisms of suspension and achievement for students in a particular school are estimated relative
to other students in the same school. Variables such as the neighborhood in which the school is lo-
cated and other potential confounding school-level effects that are time invariant or which can rea-
sonably be expected to change very little over a three-year period are controlled since all
comparisons are between students within the same school. This strategy also eliminates the small n
problem at level three (i.e., 17 schools) because school-level variation is controlled in the fixed-effects
model rather than being used for prediction.
The basic mixed-effects model with three levels predicting test scores using two independent vari-
ables, for example, takes the following form:
¼ b
þ b
þ b
þ f
þ a
þ e
In this model, i corresponds to time (level one), j to student (level two), and k to school (level
three). The symbol f
represents the random intercept at the student level and a
is a fixed parameter
representing all differences between schools that are stable over time. The fixed parameter at the
school level is accomplished through binary school indicators, as noted above. Finally, e
is the level
one residual. Together, f
and e
represent the random parts of the model, while the other compo-
nents are fixed.
The first set of models examines the effects of race and ethnicity on the log odds of suspension. A
baseline model (1a) includes race and ethnicity as well as time, but does not include dichotomous
school indicators. This is the only model estimated without school-level fixed effects, and this is to
demonstrate that part of the increased susceptibility of minorities to exclusionary discipline is ex-
plained by racial and ethnic segregation into different schools. In addition, a supplemental regression
of school-level characteristics is computed to confirm that the partial confounding effect of dichoto-
mous school indicators is due to black students attending schools with higher suspension rates, con-
trolling for school size and socioeconomic status composition (results not shown). The second
model (2a) predicting student suspension includes race and ethnicity, time, and dichotomous school
indicators. The third model (3a) adds potential confounding factors, including sociodemographics
and special education placement. The fourth model (4a) adds a family structure variable and is esti-
mated using multiple imputation procedures due to missing data on that variable.
In the second set of analyses, quadratic growth curve models are estimated to determine how read-
ing and math achievement scores change over time in this school system. Baseline models include
time and race and ethnicity (1b and 1c). The second set of models (2b and 2c) add between- and
within-person measures of suspension to assess the degree to which group differences in exclusionary
discipline experiences explain the racial and ethnic academic achievement gap. Mediation of the rela-
tionship between race and ethnicity and academic achievement by suspension is formally tested using
the -sgmediation- command in Stata. The purpose of this analysis, following Michael E. Sobel (1986)
and Reuben M. Baron and David A. Kenny (1986), is to test whether a mediator carries the influence
of an independent variable (IV) to a dependent variable (DV). The -sgmediation- command tests all
four relationships required to meet criteria for mediation: (1) the IV significantly affects the mediator,
74 Morris and Perry
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(2) the IV significantly affects the DV in the absence of the mediator, (3) the mediator has a signifi-
cant unique effect on the DV, and (4) the effect of the IV on the DV is reduced when the mediator is
added to the model. The indirect effect of race on achievement through suspension is tested using a
conservative bootstrapped estimation procedure with case resampling (MacKinnon and Dwyer
1993). This method for testing the statistical significance of an indirect effect (i.e., mediation) has
been shown to produce less biased estimates than the Baron and Kenny (1986) and Sobel (1986)
methods in simulation studies (MacKinnon, Warsi, and Dwyer 1995).
The third set of models (3b and 3c) predicting test scores add student sociodemographic charac-
teristics that may confound the relationship between race, suspension, and academic achievement
(e.g., socioeconomic status). In these models, time invariant characteristics (i.e., gender and race and
ethnicity) are measured at level two, while time variant characteristics (i.e., suspension, socioeco-
nomic status, and special education status) are measured at level one. All level one variables are sepa-
rated into between-student effects (e.g., Why are students different from each other, on average?)
and within-student effects (e.g., Why are students different from themselves this year compared to
other years?). In addition, the family structure variable is added to the final models (4b and 4c),
which are estimated using multiple imputation procedures.
To demonstrate the long-term effects of suspension on academic achievement, we use the above
models to generate a graph of predicted values for test scores over time. These depict trajectories of aca-
demic achievement based on early and repeated suspensions in the academic career. Between-student
effects of suspended and never-suspended students are reflected in intercept differences between
groups, while within-student effects of suspension are depicted by changes in the angles of the lines
over time. This figure is based on a model containing all student- and school-level control variables.
Though between-school (i.e., time invariant) school-level characteristics are controlled by the
fixed-effects approach, we conduct supplemental analyses to assess the sensitivity of the models to
time-variant school-level variables that might be correlated with test scores and/or suspension. These
variables include within-school variation on percent racial/ethnic minority, percent free/reduced
lunch, percent special education, expenditures per student, school size, and total number of offenses
in a school in a given year. Estimates of these effects are for the most part unreliable because there is
little variation over three years in these indicators, with the exception of number of offenses.
However, including time-variant school-level indicators has very little impact on the coefficients for
race or suspension in models predicting test scores, and did not change the substantive conclusions
of this research. Consequently, these models are not included in tables of results.
A number of student- and school-level variables (e.g., race, socioeconomic status, and likelihood of
suspension) are correlated, introducing the possibility of multicollinearity. However, variance infla-
tion factors (VIFs) do not exceed 3.08 for any model. This reduces concerns about the degree to
which multicollinearity might lead to biased estimates.
Descriptive statistics in Table 1 suggest that 12 percent of public school students will receive an out-
of-school suspension in any given year. Academic achievement scores in reading (m ¼ 220.21;
s ¼ 17.49) and math (m ¼ 231.33; s ¼ 19.60) vary substantially across the sample, which includes stu-
dents in grades 6 through 10. However, scores within schools are less variable, ranging from a stan-
dard deviation of 10.80 to 23.28 when accounting for time invariant school-level heterogeneity. Also,
the interclass correlations for MAP reading and math scores are .71 and .81, respectively, suggesting
substantial correlation in academic achievement across time within each student.
The Racial and Ethnic Gap in Exclusionary Discipline
Table 2 contains the results from a mixed-effects logistic regression of suspension on race and ethnic-
ity. Findings in Model 1a do not include school-level fixed effects, permitting the relationship
The Punishment Gap 75
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between race and ethnicity and suspension to reflect group differences in the kinds of schools that
minority students are likely to attend. These indicate that black students are estimated to be 7.57
times as likely to be suspended as white students (p < .001), and Latinos are over twice as likely as
whites (OR ¼ 2.39; p < .001). Students of other races are predicted to be 2.61 times more likely to
be suspended than whites (p < .001), while Asians are less likely than whites (OR ¼ .20; p < .001).
Findings in Model 2a add school-level fixed effects, controlling for all observed and unobserved time
invariant heterogeneity in characteristics of schools. In other words, all estimates reflect differences
between students in the same school. These findings indicate that black students are still estimated to
be almost six times as likely to be suspended as white students (OR ¼ 5.91; p < .001), while Latinos
are nearly twice as likely (OR ¼ 1.87; p < .001). Students of other races are 2.47 times more likely to
be suspended than whites (p < .001), on average, while Asians are estimated to be suspended at
lower rates than whites (OR ¼ .23; p < .001). In all, racial segregation into different schools explains
about 12 percent of the effect of being black on the odds of suspension, and supplemental analyses
confirm that schools with larger concentrations of black students have significantly higher rates of
out-of-school suspension. Each additional percentage of the student body that is black is estimated to
increase the annual number of school suspensions by about ten, controlling for school size and socio-
economic composition (b ¼ 10.16; p < .01).
As shown in Model 3a of Table 2, the addition of sociodemographic covariates reduces the magnitude
of the impact of race and ethnicity on suspension, and this result is attributable almost entirely to racial
and ethnic differences in socioeconomic status (i.e., free/reduced lunch). Students who qualify for free/re-
duced lunch in all three waves of the study are predicted to be over six times as likely to be suspended as
those who never qualify (OR ¼ 6.36; p < .001). Students who receive special education serves are also es-
timatedtobemorelikelytobesuspended(OR¼ 3.19; p < .001), while girls are less likely to be sus-
pended than boys (OR ¼ .36; p < .001). However, even after controlling for socioeconomic status,
special education services, and gender, black students are predicted to have nearly three times the odds of
suspension compared to whites (OR ¼ 2.80; p < .001), and students of other races are 57 percent more
likely than white students to be suspended (p < .05). In contrast, the elevated risk of suspension associ-
ated with being Latino is entirely explained by this group’s lower levels of socioeconomic status.
Results in Model 4a of Table 2 include a variable measuring family structure, and are estimated
using multiple imputation procedures. Students with two parents are 56 percent less likely to be
, on average, than those with only one parent or guardian (p < .001). Family structure
Table 1. Descriptive Sample Characteristics
Students Proportion Mean SD Range
Female .49
Race and ethnicity
White .59
Black .25
Latino .10
Asian .04
Other .03
Free/reduced lunch .48
Special education .09
Suspended .12
Two-parent family .63
MAP reading score 220.21 17.49 141.00-280.00
MAP math score 231.33 19.60 143.00-300.00
N ¼ 16,248
76 Morris and Perry
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explains a small amount of the variation in the effect of being black on suspension, but black students
are still estimated to be nearly two and a half times as likely to be suspended as white students in
this model (OR ¼ 2.46; p < .001). The effect of being some other race or ethnicity becomes non-
significant in this model, suggesting that differences in suspension rates for this group are entirely
explained by socioeconomic status and family structure. Also, the effect of free/reduced lunch qualifi-
cation on odds of suspension is partially explained by family structure, but continues to have a large
significant effect in this full model (OR ¼ 4.81; p < .001).
Effects of Exclusionary Discipline on Academic Achievement
Table 3 displays the effects of race and ethnicity and suspension on academic achievement in reading.
There is evidence of significant curvilinear growth in academic achievement over the study period
such that the test scores grow more substantially early in the study period, but that growth begins to
taper off over time (p < .001). This is consistent with expectations for MAP growth, where gains are
Table 2. Mixed-Effects Logistic Regression of Suspension on Race and Ethnicity over Time
Model 1a
Model 2a Model 3a Model 4a
Within-student D
Time (years) 1.05
Free/reduced lunch .90
Special education .95
Race and ethnicity
Black 7.57
Latino 2.39
Asian .20
Other 2.61
Female .36
Free/reduced lunch 6.36
Special education 3.19
Two-parent family .44
N 16,284 16,284 16,284 16,284
Obs 25,221 25,221 25,221 25,221
q .63 .61 .57 .56
Wald X
/F 555.87*** 709.11*** 978.35*** 38.99***
Notes: Odds ratios are presented, confidence intervals in parentheses. Models 2 through 4 control for dichotomous school indicators.
Model 1 does not control for dichotomous school indictors (i.e., there is no school-level fixed effect).
Omitted category is white.
* p < .05 ** p < .01 *** p < .001 (two-tailed tests)
The Punishment Gap 77
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more substantial in earlier grades relative to later ones. As shown in Model 1b, students who are black
(b ¼ -10.87; p < .001), Latino (b ¼ -12.95; p < .001), Asian (b ¼ -2.04; p < .01), and some other race
(b ¼ -4.79; p < .001) are all predicted to have significantly lower scores on achievement in reading
compared to white students, controlling for school-level fixed effects.
Table 3. Mixed-Effects Linear Regression of Reading Achievement on Student Suspension
Over Time
Model 1b Model 2b Model 3b Model 4b
Within-student D
Time (years) 3.40
Time-squared 2.37
Suspended 1.01
Free/reduced lunch .55
Special education 1.73
Race and ethnicity
Black 10.87
Latino 12.95
Asian 2.04
Other 4.79
Suspended 15.05
Female 1.49
Free/reduced lunch 9.21
Special education 20.19
Two-parent family 1.39
Constant 215.42
N 16,284 16,284 16,284 16,284
Obs 25,221 25,221 25,221 25,221
q .78 .76 .71 .71
Wald X
/F 4,055.50*** 5,371.83*** 10,696.51*** 352.93***
Notes: Unstandardized coefficients, standard errors in parentheses; models control for dichotomous school indicators.
Omitted category is white.
* p < .05 ** p < .01 *** p < .001
(two-tailed tests)
78 Morris and Perry
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Model 2b adds between- and within-person variation in suspension over time, and demonstrates
that out-of-school suspension is significantly related to academic achievement. The proportion of
waves in which a student is suspended (i.e., propensity to be suspended) is associated with decreases
in reading such that those who have been suspended each year of the study are predicted to
have a MAP reading score that is over 15 points lower than those who have never been suspended
(b ¼ -15.05; p < .001). This is nearly a one-standard deviation decrease in academic achievement. In
other words, being suspended is a strong predictor of a student’s academic performance relative to
other students in the same school. Also, having a suspension in a given wave is associated with signifi-
cantly lower performance on reading evaluations (b ¼ -1.01; p < .001) at the end of that academic
year relative to other years, comparing each student to him or herself.
As seen in Models 3b and 4b of Table 3 , girls tend to score higher than boys in reading achieve-
ment (b ¼ 1.49; p < .001), on average. Between-person variation in proportion of waves spent in
free/reduced lunch status is associated with significant differences in reading achievement (b ¼ -9.21;
p < .001), as is between-student variation in special education placement (b ¼ -20.19; p < .001).
However, within-person changes in these statuses over time do not significantly affect reading or
math achievement. Model 4b includes a measure of family structure, suggesting that students in
two-parent families perform better in reading than those with one parent (b ¼ 1.39, p < .001). Most
importantly, the addition of these potential confounding factors only partially explains differential
academic achievement by race and ethnicity and by suspension.
Findings in Table 4 reflect the effects of race and ethnicity and suspension on math achievement.
Again, there is evidence of curvilinear growth in math scores over the study period (p < .001), as antici-
pated. As shown in Model 1c, students who are black (b ¼ -13.34; p < .001), Latino (b ¼ -12.57;
p < .001), and some other race (b ¼ -6.97; p < .001) are all predicted to have significantly lower scores
on achievement in math compared to white students, controlling for school-level fixed effects. In contrast,
Asian students are estimated to perform better in math than whites, on average (b ¼ 9.40; p < .001).
The effects of suspension on math achievement are included in Model 2c of Table 4. The propor-
tion of waves in which a student is suspended is associated with decreases in math performance such
that those who have been suspended each year of the study are predicted to have a MAP math score
that is 16.21 points lower than those who have never been suspended (p < .001; nearly a one stan-
dard deviation reduction). Also, having a suspension in a given wave is associated with significantly
lower math performance (
b ¼ -.56; p < .05)
at the end of that academic year relative to other years,
comparing each student to him or herself.
Effects of control variables on math achievement mirror those for reading achievement. Gender
differences are the exception (see Models 3c and 4c of Table 4), as girls score lower than boys, on av-
erage, in math achievement (b ¼ -2.09; p < .001). Also, between-person variation in free/reduced
lunch (b ¼ -11.14; p < .001) and special education status (b ¼ -24.21;p< .001) are associated with
lower math performance. Model 4c shows that students with two parents are estimated to score
higher in math achievement than those with one parent (b ¼ 1.68; p < .001). As with reading
achievement, the addition of these potential confounding factors only partially explains differential
math achievement by race and ethnicity and suspension.
Figure 1 depicts results from Model 3c in Table 4. Differences in math achievement between sus-
pended and never-suspended students (i.e., between-student effects) are reflected in baseline pre-
dicted values of math MAP performance (year 0). Within-student effects of suspension are depicted
by changes in predicted values over time. A student who is never suspended has a linear growth in
math performance that is reflected in a six-point increase across the three measures, as would be ex-
pected for students making normal academic progress. Suspended students have lower baseline
scores than never-suspended students, on average, possibly reflecting other unmeasured mechanisms
of student success that are correlated with suspension. However, suspension does have meaningful
and lasting adverse effects over time independent of early disparities between ever- and never-
suspended students. Though students experiencing one early suspension begin with only a three-
The Punishment Gap 79
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point deficit relative to those without a suspension, that deficit grows to nine points at the end of the
two-year study period. Students with an early suspension experience no significant growth in math
achievement. Students with two years of suspension do demonstrate modest growth (three points),
but they begin with a much larger eight-point deficit relative to never-suspended students. By the end
Table 4. Mixed-Effects Linear Regression of Math Achievement on Student Suspension
Over Time
Model 1c Model 2c Model 3c Model 4c
Within-student D
Time-squared 1.99
Suspended .56
Free/reduced lunch .16
Special education 1.14
Race and ethnicity
Black 13.34
Latino 12.57
Asian 9.40
Other 6.97
Suspended 16.21
Female 2.09
Free/reduced lunch 11.14
Special education 24.21
Two-parent family 1.68
Constant 228.01
N 16,284 16,284 16,284 16,284
obs 25,221 25,221 25,221 25,221
q .86 .85 .81 .81
Wald X
4,460.13*** 5,640.90*** 11,539.23*** 379.40***
Notes: Unstandardized coefficients, standard errors in parentheses; models control for dichotomous school indicators.
Omitted category is white.
* p < .05 ** p < .01 *** p < .001
(two-tailed tests)
80 Morris and Perry
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of the study period, that deficit has grown to 11 points. Importantly, this figure suggests that when stu-
dents who were initially at risk for low performance are suspended, this event places them at further risk of
academic decline.
Reproduction of Racial Inequality through Exclusionary Discipline
The first set of analyses demonstrates that racial and ethnic minorities are disproportionately suscep-
tible to suspension. This effect is particularly pronounced for black students, and this effect is only
partially explained by socioeconomic status, family structure, and other variables. The suspension dis-
parity operates at both the school and individual levels such that black students are more likely than
white students to attend schools that employ higher levels of exclusionary discipline, and black stu-
dents are also more likely to be suspended than their white peers within the same schools. In turn, ra-
cial and ethnic minorities underperform on reading and math achievement tests relative to white
students in this school system. As shown in Model 2 of Tables 3 and 4, adding between- and within-
person measures of suspension to the regression of academic achievement on race and ethnicity
reduces the effect of minority status. To assess the extent to which group differences in exclusionary
discipline experiences explain the racial and ethnic academic achievement gap, mediation analyses
with a bootstrapped estimation of the indirect effect are conducted. These findings suggest that 20
percent of the effect of being black on reading achievement (b ¼ -2.07; p < .001) and 17 percent on
math achievement (b ¼ -2.24; p < .001) works indirectly through inequalities in exclusionary disci-
pline experiences. In other words, the racial achievement gap for black students is reproduced in part
through disproportionate exposure to exclusionary discipline in public schools.
Our analysis provides evidence that school suspension contributes to racial inequalities in achieve-
ment. According to our results, African Americans and Latinos are disproportionately susceptible to
suspension. Because there are fixed-effects parameters at the school level, this result cannot be
Figure 1. Predicted Values of MAP Math Scores Over Time as a Function of Suspensions
Note: Based on Model 3c in Table 4.
The Punishment Gap
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explained by racial and ethnic segregation into different kinds of schools. In other words, African
Americans and Latinos are more likely to be suspended than whites and Asians within the same
school. For African Americans, this finding persists even after controlling for socioeconomic status
and other relevant individual-level variables.
Results indicate that suspension has important linkages to student academic achievement.
Students who have been suspended score substantially lower on end-of-year academic progress tests
than those who have not, and even students with a propensity to be suspended perform worse in
years where they are suspended relative to years when they are not. We find that the effects of sus-
pension are long lasting, setting into motion a trajectory of poor performance that continues in subse-
quent years, even if a student is not suspended again. Indeed, our results show that academic growth
drops precipitously after one early suspension (see Figure 1). In all, our analysis provides strong evi-
dence that suspension is harmful to academic achievement.
As hypothesized, the most striking finding from this research is the important association between
suspension and patterns of achievement disparity. Our study is the first to our knowledge to directly
examine the implications of racial differences in punishment for racial differences in achievement.
The results support the proposition that school discipline is a major source of the racial achievement
gap and educational reproduction of inequality (Gregory et al. 2010). Particularly for African
American students in our data, the unequal suspension rate is one of the most important factors hin-
dering academic progress and maintaining the racial gap in achievement. Consistent with previous re-
search, we find that family economic background and family structure explain much, but certainly not
all, of the achievement gap (Hedges and Nowell 1999) and the discipline gap (Skiba et al. 2002).
Our findings add a critical new dimension to the long-standing discussion of academic disparities
by race. Recent perspectives on the achievement gap emphasize a complex interplay of between-
school, within-school, and non-school factors, instead of an either-or view ( Berends et al. 2008;
Condron et al. 2012). We agree with this multifaceted approach, and our findings on school disci-
pline align with each set of factors. The discipline disparities we observe emanate at least partially
from the types of schools black students attend (Condron 2009). In addition, home-based inequal-
ities are undoubtedly an important part of why suspension reduces achievement (Downey et al.
2004), as schools send suspended students home, often with little academic guidance or oversight.
However, our findings on school punishment most directly add to the notion that practices within
schools contribute to the achievement gap.
Because we find that school discipline is related to racial differences in achievement, we cast our
findings as a possible example of hidden inequality embedded within routine educational practices.
Scholars of race assert that subtle, covert forms of discrimination are major drivers of racial inequality
in the post-civil rights “color-blind” era (Bonilla-Silva 2006; Pager and Shepherd 2008; Quillian
2006). Such inequality occurs indirectly, through the routine enactment of everyday institutional poli-
cies and procedures (Pager and Shepherd 2008). Similarly, education scholars from Pierre Bourdieu
and Jean-Claude Passeron (1977) to Karolyn Tyson (2011) have argued that seemingly neutral pro-
cesses in schools conceal certain biases and reproduce inequalities. Indeed, purportedly neutral disci-
pline policies that increase the overall use of suspension in schools (e.g., zero tolerance) have been
shown to exacerbate the racial gap in suspension (Hoffman 2014; Verdugo 2002).
Although we lack the data to test racial bias in discipline directly, we do show an alarming racial
gap in punishment even after controlling for a host of background variables. This racial difference in-
dicates that the enactment of discipline, while likely holding no discriminatory intent, nevertheless
generates de facto racial inequalities. While it is possible that black students simply misbehave more
than white students, previous studies have found racial discrepancies in how punishment is adminis-
tered, even for similar offenses (Ferguson 2000; Morris 2005; Skiba et al. 2002). Thus, our results
align with evidence of racial inequality (even if subtle and unrecognized) in school punishment. Our
analysis advances this research by linking such punishment to disparate academic outcomes. Future
82 Morris and Perry
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research could complement our study by fleshing out the micro-level processes of discipline and aca-
demic progress in greater detail.
Limitations and Future Directions
While our research reveals a strong relationship between school suspension and achievement, it also
has limitations. The primary limitation is that our data, while longitudinal, cannot prove a causal link
between suspension and achievement. In particular, unmeasured endogenous factors could be driving
the association between exclusionary discipline and achievement.
One of the most likely intervening factors is that black students could demonstrate worse behavior
on average, which would lead to more suspensions for black students. This same behavior could also
interfere with the learning process, resulting in lower achievement. We do not possess the data to di-
rectly examine differences between student behavior and the discipline they receive. However, we can
draw from previous studies, which have noted that minority students are disciplined more harshly
than white students for similar misbehavior. For example, in an analysis that controls for teacher-
reported behavior, Michael Rocque and Raymond Paternoster (2011) found that “(racial) dispropor-
tionality in discipline is not explained by differential behavior” (p. 662). Moreover, qualitative studies
by Ann Arnett Ferguson (2000) and Edward Morris (2005) have shown that black and Latino stu-
dents are more closely monitored and more often punished than white students for similar types of
infractions. According to Ferguson (2000:68), school officials tend to interpret behavior through a
“racialized key” that accentuates transgressions of minority students. Thus, while we cannot assert it
definitively based on our data, we can look to previous research to suggest that disciplinary polices
and interpretations within schools contribute to at least part of the racial disparity in discipline.
However, we think that the association between discipline and behavior is ultimately complex, and
begs further study. Student behavior, discipline, and achievement interact as students progress through
schooling. The challenge for future research is to plumb this relationship further to gain a deeper pic-
ture of the mechanisms producing differences in punishment. It would be especially fruitful for studies
to examine students progress over time, as they transition across various levels of schooling, and to ex-
amine the types of academic resources students have access to after a suspension. It would also be use-
ful to compare the effects of different types of discipline to ascertain whether any act of punishment is
associated with diminished achievement, or whether it is exclusionary discipline per se. Likewise, an im-
portant next step is assessing whether missed instruction is a mechanism of our findings. For instance,
future research should compare the influence of missed instruction due to suspension and other causes
(e.g., illness, truancy, etc.) to determine whether it is punishment per se or lost classroom time in gen-
eral that underlies the link between exclusionary discipline and achievement.
Another significant limitation is that we do not possess data on student perceptions of discipline
and relationships with school officials. Even when strict, if students perceive discipline as fair, this
may foster a positive relationship with school and result in higher achievement (Arum 2003; Kupchik
and Ellis 2008). For minority students in particular, developing supportive bonds with institutional
officials appears critical for academic success (Conchas 2006; Stanton-Salazar 1997). These bonds
may be enhanced by minority teachers, who tend to assess the behavior of minority students more
positively (Downey and Pribesh 2004; McLoughlin and Noltemeyer 2010; Quiocho and Rios 2000;
Rocha and Hawes 2009), but such teacher-student dynamics are complex (McGrady and Reynolds
2013). Future research should examine student perceptions of discipline and relationships with
school officials as potentially important factors in school punishment disparities.
This study adds a critical new piece to the puzzle over racial disparities in achievement. In particular,
it demonstrates how exclusionary forms of punishment such as suspension have important, racialized
academic consequences. Our study presents evidence that disparate suspension lowers school
The Punishment Gap 83
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performance and contributes to racial gaps in achievement. Discipline is a necessary condition for stu-
dent learning. However, unequal exclusionary discipline severely restricts opportunities for students
to learn and grow. For genuine progress to be made in closing the racial achievement gap, we must
also make progress in closing the racial punishment gap.
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... Several African American male students are embedded in a system of racism and punished at a higher rate with school suspension. Morris et al. (2016) [22] stated that the high rates of school suspensions endured by African American males "have hindered academic growth and contribute to racial inequalities in achievement" (p. 68). ...
... Several African American male students are embedded in a system of racism and punished at a higher rate with school suspension. Morris et al. (2016) [22] stated that the high rates of school suspensions endured by African American males "have hindered academic growth and contribute to racial inequalities in achievement" (p. 68). ...
... (2016) [22] documented a strong relationship exists between school suspension and achievement, but their "study could not prove a causal link between suspension and achievement" (p. 83). ...
... According to advocates, in addition to serving as a punishment, zero tolerance policies can serve as a deterrent and achieve harmony on campus by deterring violence or conflict on campus [4]. Nevertheless, past experiences with implementation suggest that zero-tolerance policies adhere to a social-level distribution of power and expose marginalized students to greater vulnerability [5,6]. Based on the historical implementation of zero tolerance policies, this paper analyzes and gathers four levels of drawbacks: ambiguous definitions, exaggerated claims, abuses, and harmfulness. ...
Full-text available
Scientific research is increasingly showing the drawbacks and potential ill effects of zero tolerance policies on students. This article presents criticisms of the zero tolerance policy in four main areas: ambiguous definitions, exaggerated claims, abuses, and harmfulness. There is also a serious disparity in the treatment of racially diverse students under zero tolerance policies. It is important that schools that are still using punitive policies such as zero tolerance policies pay attention to these voices of accusation and make some adjustments in the context of the school. These criticisms and recommendations are intended to create a healthy and harmonious campus environment and to promote the physical and mental development of students.
... School suspension has long-term negative consequences on students' academic achievement. These negative disciplinary consequences could account for some of the disparities between Black and White academic achievement because Black students are disproportionately susceptible to suspension (Morris & Perry, 2016). Colombi and Osher (2015) argue that instructional time deprivation, achievement decreases, and higher dropout rates all come from exclusionary, punitive discipline. ...
Full-text available
The relationship between academic tracking and exclusionary discipline actions has only been studied in a limited number of empirical studies. By placing students at the lower strata, schools deprive them of the educational opportunities, widening the educational opportunity gap in a process we define as “opportunity stratification.” Using a quantitative analysis of data from the Educational Longitudinal Study, we found students in low-track, non-college preparatory courses had higher odds of experiencing both in-school and out-of-school suspensions when compared to students in the high-track, college preparatory courses. Our findings support the intersecting role of exclusionary discipline and tracking in opportunity stratification.
... Intersectionality and CRT proclaim examining the intersection between student race and gender in teachers' discipline decisions is important to reduce racialized suspensions, but also students' own achievement related outcomes (Cobb-Clark et al. 2015 E.W. Morris and Perry 2016). Black students are often aware of the differential treatment in school discipline practices between Black and White students, shaping their social-emotional and behavioral outcomes (Howard 2008;Sheets 1996). ...
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Educators’ differential selection of Black and Latine students for office discipline referrals is a significant driver of inequity in exclusionary outcomes. Using demographic data and discipline records for all students in one large urban school district, we use descriptive statistics and multilevel regression models to consider whether referral reasons are racialized and if these patterns intersect with gender. Our analyses indicate that educators are consistently more likely to refer Black students than White students to the office for several subjective reasons, including habitual disruption, that are purportedly race-neutral but privilege Whiteness. They are less likely to make referrals for Black students in the objective category of drug and alcohol use or possession. Latine students are more likely than White youth to be referred for habitual disruption and substance use or possession. We draw on Critical Race Theory to interpret these findings and their implications.
Disproportionality in out-of-school suspensions (OSS) is a persistent social and racial justice issue. Available research indicates that Indigenous children are disproportionately represented in both OSS and the child protective services (CPS) system. This secondary data analysis followed a cohort of 3 rd grade students ( n = 60,025) in Minnesota public schools from 2008– 2014. It examined the relationship between CPS involvement, Indigenous heritage, and OSS. Results from a zero-inflated negative binomial regression indicated that Indigenous students had two times the odds of suspension compared to white students (OR = 2.06, p < .001). Furthermore, there was a significant interaction between CPS involvement and Indigeneity on frequency of OSS (OR = 0.88, p < .05). Indigenous students showed a much higher odds ratio of OSS compared to white students, but the distance between the odds ratios of the two groups decreased as the number of child maltreatment allegations increased. Indigenous students may experience relatively high levels of both CPS and OSS due to systematic racism. We discussed implications for practice and policy to reduce discipline disparities.
Limited emotional and behavioral disorders (EBD)–focused intervention mixed methods research (MMR) has been published, particularly in top-tier EBD journals (i.e., Behavior Disorders and Journal of Emotional and Behavioral Disorders). The lack of published MMR creates what could be perceived as a Catch-22 situation wherein those who conduct EBD intervention research are not encouraged to conduct MMR because they do not see published examples in the journals that they read. The purpose of this article is to demonstrate how the routine use of MMR should be considered and suggest why doing so is a worthwhile endeavor. An overview of potential barriers to conducting intervention MMR in the field of EBD is provided. Examples of the use of MMR with Functional Behavior Analysis and Multi-tiered Systems of Support for students with Tier 2 needs are discussed. Details regarding the use of a mixed methods profile analysis to understand treatment successes and challenges are provided. Our hope is that this article will inspire EBD researchers to consider MMR as they develop interventions for children with EBD.
The continual use of suspension and expulsion remains an unsettling concern for many educational stakeholders. With some guidance from the federal government, states have begun to address the issue of discipline disproportionality through policy reform that restrict the use of suspension and expulsion for certain student groups (e.g., Pre-Kindergarten through 2nd grade) and particular behaviors (e.g., dress code, tardiness, willful disobedience). This study is novel as it extends the research base by providing a preliminary assessment of restrictive discipline policies, or non-suspension and non-expulsion policies. Findings from this study found that 0 states had non-in-school-suspension policies, 20 states had non-OSS policies, and 18 states had non-expulsion policies. Further, descriptive statistics demonstrated that White, Hispanic, and Black students saw increases in academic achievement after non-out-of-school-suspension and non-expulsion policies were implemented with Black students experiencing lower rates of OSS but higher expulsions and Hispanic students experiencing both higher rates of OSS and expulsions compared to White students. Implications for policy, research, and practice are discussed.
The purpose of the current study was to determine the probability that a student with a disability not being served by Individuals with Disabilities Education Act (IDEA) would be expelled. Expulsion data were obtained from the Civil Rights Data Collection produced by the U.S. Office of Civil Rights. The latest data from all 50 states and the District of Columbia for the 2017 to 2018 school year were analyzed. Bayes’ Theorem was used to determine this probability based upon existing probabilities and conditional probabilities. Analyses were also conducted by state and ethnicity. Results indicated that 1 in 14 of expelled students is likely to have an unserved disability under IDEA but variability according to race/ethnicity nationwide and by state was observed. Students who were White were the least likely to be an unserved student with a disability under IDEA among those expelled. The findings encourage investigation into the intersection of variables, especially the importance of including disability status and ethnicity when explaining disparate and punitive discipline. Practitioners, especially school psychologists, work at this intersection and can influence both special education identification and discipline practices.
Using the most comprehensive data set on school dropouts that we have to date, the High School and Beyond study, Ruth Ekstrom, Margaret Goertz, Judith Pollack, and Donald Rock provide an analysis of the salient characteristics of the dropout population.
Seriousness of purpose in seeking to avert the tragedy of school violence does not necessarily demand rigid adherence to harsh and extreme measures. There are alternatives to politically facile get-tough strategies, the authors point out.
Disparities in educational outcomes between African Americans and whites declined steadily for most of the twentieth century, but this progress has halted or even reversed in recent years (Neal 2006). Understanding why the black-white test score gap narrowed over time, and why this progress stalled during the 1990s, is critical if we are to design policies capable of further reducing inequality in schooling outcomes in the United States. Given the widely documented association between educational outcomes and earnings, health and crime, successful efforts to further reduce the gap would undoubtedly have far-reaching consequences for society as a whole. This chapter considers the role of school and neighborhood segregation in explaining trends in the black-white schooling gap. As we will discuss, provocative time-series and cross-sectional evidence points to strong associations between segregation and achievement gaps. The break in trend toward narrower test score gaps coincided with another toward greater school integration. Cross-sectionally, states with more segregated schools also tend to have wider gaps. It is not clear, however, whether causal interpretations should be attached to these correlations. Social science has struggled mightily to produce definitive proof that segregation and racial disparities are causally linked, and the available evidence-backed by eminently plausible theoretical arguments and proposed mechanisms-has influenced policy debates for decades. Key to the Supreme Court's transformational decision fifty years ago in Brown v. Board of Education is the assumption that racial segregation within the public schools contributes to black-white inequality in schooling outcomes. Attending a disproportionately minority school might affect both the motivation of students and their perceptions of the larger opportunity structure they face in society and their exposure to high-quality school resources or even the academic climate in the school. It is possible that the race-ethnicity of one's schoolmates might simply be a stand-in for their academic achievement level, which could affect the way or rate at which teachers present material or the productivity of student study groups, or as a stand-in for their socioeconomic status (SES). In fact, the influential report by James Coleman in the mid-1960s argued that a school's SES composition was at least as important in explaining inequality in student achievement as is school racial composition (Coleman et al. 1966). Understanding the distinct influences of school racial versus social class composition is relevant because some policies focused on reducing school racial segregation may not have very large impacts on school socioeconomic composition, and vice versa. Measures of school segregation by either race or social class could matter in largest part because they are proxies for the racial or class composition of the local neighborhood. Because most children attend local public schools, in national data there will be a great deal of cross-sectional correlation between school and neighborhood measures of race or SES segregation. The neighborhood social environment could matter above and beyond the composition of the local public school by shaping the youth social norms that help shape children's behavior, particularly given that so much socializing and exposure to local role models occur outside of school (Wilson 1987). Understanding the distinct influences of school versus neighborhood environments is important for policy because policies such as public or private school choice have the potential to change school but not neighborhood social compositions, and some housing mobility interventions might generate larger changes in neighborhood than school characteristics (see, for example, Sanbonmatsu et al. 2006). Figure 5.1 presents a basic conceptual framework outlining the potential causal mechanisms linking segregation to test score gaps. Neighborhood segregation could affect these outcomes directly or through its influence on school segregation. Direct links between neighborhood and test scores might be through neighborhood-level deviant peer influence, role model effects, or impacts on parental income derived from spatial mismatch-type effects. Direct links between school segregation and test scores could be mediated by differences in school input quality, or by peer influence operating at the school or classroom level. Thinking carefully about the particular mechanism through which social environment affects children is also important in understanding patterns in black-white student outcomes because different measures of segregation have been following different trajectories in recent years. Neighborhood racial and economic segregation actually declined during the 1990s, though these national trends mask important differences by region and so do not necessarily imply that neighborhood segregation is not relevant for understanding the slowdown in narrowing of the blackwhite test score gap (Glaeser and Vigdor 2003; Jargowsky 2003). Controversy continues about whether school segregation has increased or decreased over this same period. Increases reported in some studies (for example, Clotfelter 2004; Orfield and Eaton 1996) confound the increasing diversity of the student body in American public schools with increases in the separation of blacks from students of other races (Logan 2004). Measures more commonly accepted in the sociological and economic literature on segregation indicate very slight decreases in the degree of segregation over the past two decades. Regardless of measure, it is clear that school segregation has declined more slowly than neighborhood segregation over the same time period. The failure of school segregation to track neighborhood segregation reflects a broad decline in governmental efforts to integrate public schools (Orfield and Eaton 1996; Clotfelter, Ladd, and Vigdor 2005). In our judgment, the evidence linking school segregation to student achievement is stronger than evidence supporting a direct causal role for neighborhood segregation. The current evidence implicating school race segregation certainly outweighs the available research on the effects of school segregation by social class, though the specific mechanisms through which racial composition affects student outcomes remain poorly understood. The best available evidence suggests that a 10 percent increase in the black share of a school's student body would reduce achievement test scores for black students by between 0.025 and around 0.08 standard deviations, and reduce test scores for whites by perhaps one-quarter to two-fifths as much. Thus, if school segregation had displayed a decrease in the 1990s commensurate with the observed decrease in neighborhood segregation, our best estimate is that the black-white test score gap would be roughly 0.01 to 0.02 standard deviation narrower.
Alarge body of research in social science has been directed toward addressing the causes of and solutions to continuing inequality of outcomes between black and white Americans (for recent summaries, see Neckerman 2004; Jencks and Phillips 1998). Historically, this research has created an expectation that such gaps would close over generations in a competitive economy if educational and labor market opportunities were equalized. The research, however, has been unable to account for several key aspects of the empirical data-specifically, to explain why progress is so slow, why there can be periods of either rapid or no progress, and why substantial gaps might remain. In this chapter, we examine three factors that might help explain these more complex dynamics in the empirical data: • Recognizing that early childhood environments create much of the achievement gap and may set limits on later achievement, educational attainment and labor force outcomes • Recognizing that the behavior and proximal processes that underlie early cognitive development may be different for racial-ethnic groups, and these differences may not be strongly linked to differences in commonly used surrogate variables (income, parent education), and may not change even as social and economic equality improves • Recognizing that a narrow focus on cognitive achievement measures rather than a broader focus on noncognitive skills such as behavioral, emotional, social and motor skills can leave persisting gaps because long-term educational attainment and labor force outcomes depend not only on cognitive skills, but also on noncognitive skills ■ some of these noncognitive skills may be implicit cognitive skills,1 that is, linked to the development or performance of cognitive skills The identification of these factors is done through reviews of recent literature and empirical estimations with the Early Childhood Longitudinal Survey of Kindergartners (ECLS-K). The major contributions from the literature are related to the interactive genetic and environmental mechanisms driving development from conception to kindergarten entrance, the emerging importance of noncognitive skills in explaining educational and labor market outcomes, and identifying implicit cognitive skills. Historically, researchers assumed that achievement gaps emerge during schooling through inequality of schools and family characteristics, and looked to the equalization of schooling opportunity as a major policy lever. However, research now shows that a substantial share of the gap is present at school entry, and that school equalization may therefore not fully close score gaps (Lee and Burkam 2002; Fryer and Levitt 2004, 2006).2 Research also suggests that closing these gaps may be more difficult at later ages (Heckman and Masterov 2007; Cunha et al. 2006) and that some intense early environments may limit later cognitive development even in the best of later circumstances (O'Connor, Rutter, and Beckett 2000; Beckett et al. 2006). Eliminating gaps before school entrance now appears to be critical to achieving social and economic equality in adulthood. This task may be even more challenging than equalizing schooling, given the numerous, diverse, and complex influences on child development in the early years. The behaviors and proximal processes that underlie the causative mechanisms in early cognitive development may not be adequately captured by the traditional surrogate variables focusing on family and neighborhood characteristics. Research suggests that interactions between genes and these behaviors and processes may account for much variance making their identification and measurement difficult (Rutter 2002; Shonkoff and Phillips 2000). Yet it will likely be in the discovery of these behaviors and processes and the possible differences across groups that will lead to the knowledge required for efficient early interventions to eliminate gaps. Finally, an implicit assumption in achieving social equality has been that cognitive development plays the dominant role in predicting gaps in later, long-term outcomes. However, noncognitive skills such as social, motor, emotional, and behavioral skills are now also known to affect educational attainment and other long-term outcomes and are receiving increasing attention in early development (Bowles, Gintis, and Osborne 2001, 2002; Heckman and Rubinstein 2001; Heckman, Stixrud, and Urzua 2006; Borghaus, Duckworth, and Heckman forthcoming). In fact, such noncognitive skills may directly affect more than longer-term outcomes. Developmental research and the evidence in this chapter suggest that some noncognitive skills may also be implicitly linked to development and cognitive skills at kindergarten entrance (Diamond 2000; Duncan et al. 2007; Raver, Garner, and Smith-Donald 2007; Snow 2007; Blair et al. 2007; Diamond et al. 2007). Historically, educational policy makers and researchers who have studied schooling effects have focused on cognitive skills as measured by achievement scores as the most important and often as their sole measure of interest. Such focus might help explain persisting score gaps if noncognitive skills also influence later, long-term outcomes. Developmental researchers studying early childhood have included both cognitive and noncognitive skills in their research, but the specific links between cognitive and noncognitive skills are not yet well understood, and persisting score gaps are possible until these links become better known.
Taken together, there are few observable teacher characteristics that have consistently shown evidence of large and systematic effects on student achievement on standardized tests. The metric offering the most compelling evidence-a teacher's academic aptitude-is seldom available to researchers, particularly those interested in drawing inferences about changes in teacher quality over time.13 Yet each of the attributes cited has been found in at least one rigorous empirical study to have an important effect-if sometimes small-on student achievement. Several observable qualifications or traits-such as teaching experience, content knowledge, NBPTS certification, and race-more often than not show systematic effects on student outcomes. Other qualifications such as advanced degrees may matter more for the academic achievement of black students than for white. Beyond the independent effects of individual teacher characteristics on student learning, there is only limited evidence on how teacher attributes interact or cumulate in the production of education. It may be that students exposed to teachers lacking along multiple dimensions fall behind more than those whose teachers fall short on only one qualification. Further, repeated exposure to inexperienced or underqualified teachers may have cumulative effects, where differences that appear small in any one year of achievement growth compound into a much larger inequalities in acquired skills over time (Sanders and Rivers 1996). In the following section, we consider how a wide range of teacher attributes varies across schools and classrooms of varying racial compositions, and how the distribution of teachers over students has changed over time.
Police officers, armed security guards, surveillance cameras, and metal detectors are common features of the disturbing new landscape at many of today's high schools. You will also find new and harsher disciplinary practices: zero-tolerance policies, random searches with drug-sniffing dogs, and mandatory suspensions, expulsions, and arrests, despite the fact that school crime and violence have been decreasing nationally for the past two decades. While most educators, students, and parents accept these harsh policing and punishment strategies based on the assumption that they keep children safe, Aaron Kupchik argues that we need to think more carefully about how we protect and punish students. In Homeroom Security, Kupchik shows that these policies lead schools to prioritize the rules instead of students, so that students' real problems-often the very reasons for their misbehavior-get ignored. Based on years of impressive field research, Kupchik demonstrates that the policies we have zealously adopted in schools across the country are the opposite of the strategies that are known to successfully reduce student misbehavior and violence. As a result, contemporary school discipline is often unhelpful, and can be hurtful to students in ways likely to make schools more violent places. Furthermore, those students who are most at-risk of problems in schools and dropping out are the ones who are most affected by these counterproductive policies. Our schools and our students can and should be safe, and Homeroom Security offers real strategies for making them so.